Baseline study Fish, fry and commercial fishery Nysted Offshore ...

March 2003 

Baseline study 

Fish, fry and commercial fishery 

Nysted Offshore Wind Farm 

at Rødsand

Baseline study 

Fish, fry and commercial fishery 

Nysted offshore Wind Farm at Rødsand 

Status report

Prepared for: 

SEAS Distribution A.m.b.A., Slagterivej 25 4690 Haslev 

Performed by: 

Bio/consult as, Johs. Ewalds Vej 42-44, 8230 Åbyhøj 

Text: Artwork: Editing: 

Christian B. Hvidt Kirsten Nygaard Gitte Spanggaard 

Kirsten Engell-Sørensen Christian B. Hvidt Michael Bech 

Maks Klaustrup 

Project manager: 

Christian B. Hvidt 

31.03.2003 

SEAS. Baseline study – Fish, fry and commercial fishery Dok. nr. 2148-03-001-rev3 2P.doc


Table of contents 

Summary .......................................................................................................................I 

Baseline study of fish and fry ....................................................................................I 

Baseline study of commercial fishery .....................................................................IV 

Resumé (In Danish) ...................................................................................................VII 

Baselinestudie af fisk og fiskeyngel.......................................................................VII 

Baselineundersøgelse af erhvervsfiskeri .................................................................. X 

1. Introduction and objectives .......................................................................................1 

2. Background...............................................................................................................3 

2.1. Baseline program...............................................................................................3 

2.2. Location and description of Nysted Offshore Wind Farm at Rødsand ................4 

2.3. The importance of the area as a probable migration route and spawning 

ground for species of commercial importance...................................................7 

2.4. Previous fish studies ..........................................................................................9 

2.4.1. Preliminary fish study .................................................................................9 

2.4.2. Preliminary study on commercial fishery................................................... 10 

2.4.3. Trail fishery using pound netting and fry trawl.......................................... 10 

3. Fish and Fry............................................................................................................ 11 

3.1. Materials and methods..................................................................................... 11 

3.2. Field studies .................................................................................................... 11 

3.3. Applied gears................................................................................................... 15 

3.3.1. Fish study.................................................................................................. 16 

3.3.2. Fry Study .................................................................................................. 16 

3.4. Scope............................................................................................................... 16 

3.4.1. Fish study.................................................................................................. 16 

3.4.2. Fry study................................................................................................... 17 

3.5. Information recorded ....................................................................................... 18 

3.6. Data processing ............................................................................................... 19 

3.6.1. Statistical analyses .................................................................................... 20 

3.6.2. Power analysis .......................................................................................... 21 

3.7. Margin of error in the method.......................................................................... 22 

3.8. Results - Fish study.......................................................................................... 23 

3.8.1. Observations ............................................................................................. 23 

3.8.2. Overview of catches.................................................................................. 23 

3.8.3. Selection of indicator species .................................................................... 27 

3.8.4. Individual selected species ........................................................................ 28 

3.8.4.1 Baltic herring (Clupea harengus) ........................................................... 28 

3.8.4.2 Brisling (Sprattus sprattus) .................................................................... 30 

3.8.4.3 Atlantic cod (Gadus morhua)................................................................. 32 

3.8.4.4 Small sandeel (Ammodytes tobianus) .................................................... 34 

3.8.4.5 Great sandeel (Hyperoplus lanceolatus) ................................................. 36 

3.8.4.6 Eelpout (Zoarces viviparous)................................................................. 38 

3.8.4.7 Short-spined sea scorpion (Myoxocephalus scorpius) ............................ 40 

3.8.4.8 Turbot (Psetta maxima) ......................................................................... 42 

3.8.4.9 Flounder (Platichthys flesus) ................................................................. 44 

3.8.5. Comments on the remaining species.......................................................... 46 

3.8.6. Statistical comparison of areas. ................................................................. 46 


3.9. Results - Fry study........................................................................................... 53 

3.9.1. Observations ............................................................................................. 53 

3.9.2. Overview of catches.................................................................................. 53 

3.9.3. Selection of indicator species .................................................................... 57 

3.9.4. Individual selected species ........................................................................ 59 

3.9.4.1 Fifteen-spined stickleback (Spinachia spinachia) ................................... 59 

3.9.4.2 Two-spotted goby (Gobiusculus flavescens).......................................... 61 

3.9.4.3 Sand goby (Pomatoschistus minutus)..................................................... 63 

3.9.4.4 Eelpout (Zoarces viviparus)................................................................... 65 

3.9.4.5 Short-spined sea scorpion (Myoxocephalus scorpius) ............................ 67 

3.9.4.6 Longspined bullhead (Taurulus bubalis) ................................................ 69 

3.9.4.7 Flounder (Platichthys flesus) ................................................................. 71 

3.9.5. Comments on some the remaining species................................................. 73 

3.9.6. Statistical comparison of sampling areas. .................................................. 73 

3.10. Discussion ..................................................................................................... 79 

3.10.1. Evaluation of methods............................................................................. 79 

3.10.2. Evaluation of statistics............................................................................. 79 

3.10.2.1 Parametric vs. non-parametric ............................................................. 80 

3.10.2.2 Univariate or multivariate analysis?..................................................... 86 

3.10.2.3 Examples from the actual investigation................................................ 88 

3.10.3. Biological evaluation............................................................................... 92 

3.10.3.1 Fish study............................................................................................ 92 

3.10.3.2 Fry study ............................................................................................. 93 

3.10.4. Evaluation and development of the monitoring program.......................... 93 

3.10.4.1 Recommendations in relation to parametric univariate analysis............ 93 

3.10.4.2 Recommendations in relation to non-parametric multivariate analysis . 95 

4. Commercial fishery................................................................................................. 97 

4.1. Methods........................................................................................................... 97 

4.1.1. Interviews with local commercial fishermen.............................................. 97 

4.1.2. Quality assurance ...................................................................................... 98 

4.1.3. Review of existing information ................................................................. 98 

4.2. Results............................................................................................................. 98 

4.2.1. The scope of the fishery ............................................................................ 99 

4.2.2. Fishing grounds....................................................................................... 102 

4.2.3. Catches recorded in the Danish Directorate for Fisheries logbook 

records from 1982 to 2001 ..................................................................... 104 

4.2.4. Expectations for the occurrence of commercially important species ........ 106 

4.2.5. Comparison between information on catches from the interview and 

data from the Directorate of Fisheries from area 38G1. .......................... 107 

4.2.6. Reported catches in area 38G1 from 1982 to 2001................................... 109 

4.2.7. Estimate of the catch within the wind farm site alone .............................. 110 

4.3. Discussion..................................................................................................... 111 

5. Conclusion............................................................................................................ 113 

5.1. Fish study ...................................................................................................... 113 

5.2. Fry Study....................................................................................................... 114 

5.3. Commercial fishery ....................................................................................... 114 

6. References ............................................................................................................ 115 

Appendix 1. Review of the biology of fish species .................................................... 117 

Appendix 2. List of research positions (stations), WGS-84........................................ 128 

Appendix 3. Levene’s test and Uni-ANOVA analysis of abundance.......................... 129 


Fish study............................................................................................................. 129 

Fry study.............................................................................................................. 136 

Appendix 4. Levene’s test and Uni-ANOVA analysis of weight................................ 144 


Fry study.............................................................................................................. 151 

Appendix 5. Non-parametric Kolmogov/Smirnoff analysis of distribution of length 

159 


Fry study .............................................................................................................. 161 

Appendix 6. Mann-Whitney U .................................................................................. 163 

Appendix 7. Power analysis part 1. ........................................................................... 169 

Appendix 8. Power analysis part 2. ........................................................................... 171 

Appendix 9. Commercial Fishery.............................................................................. 178 

Appendix 9.1. Inquiry form used for questioning the fishermen............................ 178 

Appendix 9.2. Division of Danish Waters according to ICES areas....................... 180 

Appendix 9.3. Data regarding landings in area 38G1 obtained from the 

Directorate of Fisheries, Ministry of Food and Fisheries............................... 181 

Appendix 9.4. Map of all fishermen’s recorded fishing locations within the wind 

farm area and reference area south of Rødsand for the years 1997 to 2001.... 189 

Appendix 9.5. Seasons for the fishery of commercially important species............. 199 

Appendix 9.6. Presentation of the fishermen’s statements..................................... 201 

Appendix 9.7. Announcements on seasonal protections of fish and shellfish in 

saltwater. A map of the baseline around Sjælland and Lolland-Falster is 

included. ...................................................................................................... 211 

Appendix 9.8. Announcement no. 91 of Jan. 22 1991 regarding logbooks etc....... 217 

Appendix 10. Note concerning statistical analysis of data from the fisheries 

survey. ......................................................................................................... 218 



Bio/consult as Page I 

Summary 

The planned 150 MW offshore wind farm at Rødsand will be placed in the Femern Belt 

approximately 10 kilometres south of Nysted on Lolland. The wind farm will cover an 

area of approximately 24 km 2 . The wind farm will consist of 72 turbines each of 2.2 

MW placed in 8 north-south orientated rows separated by a distance of 850m. 

The objective of the baseline study ”Fish, Fry and commercial fishery – Nysted 

Offshore Wind Farm at Rødsand” was to establish a basis for the following monitoring 

programme by selecting the optimal investigations and parameters to assess the basic 

condition of the area and to collect an adequate amount of baseline data to distinguish 

the natural variation from the environmental impact caused by the operation of the 

offshore wind farm. This is especially with regard to changes caused by noise, 

vibrations, and changes in the food basis, sediment and magnetic fields, including other 

unexpected effects. 

Furthermore, the objective of this baseline study was to document and, so far as 

possible, quantify the expected and unexpected effects on the commercial fishery, inside 

and around Nysted Offshore Wind Farm at Rødsand. 

The baseline study consisted of three partly separate studies; baseline study of fish, 

baseline study of fry and baseline study of commercial fishery. 

Baseline study of fish and fry 

Special emphasis was placed on statistical comparisons between the wind farm area and 

the reference area to verify the hypothesis that there are no differences in number, 

weight and mean length of selected fish species with respect to the two areas and that 

possible changes in number and weight (even if the differs between the two areas) 

exhibit parallel changes. 

A preliminary fishing investigation (screening) from the area was used to clarify the 

variation of the area and to estimate an adequate number of samples. An overall power 

analysis on the results, showed that the wind farm area and the reference area as a 

minimum should be fished with 22 samples from 11 stations in each area to achieve a 

criteria which, with 80% certainty, detects a significant (p

Bio/consult as Page II 

In the baseline study of fish, a total of 785 individuals divided among 23 species with a 

total weight of 47.4 kg were caught in the wind farm area. In the reference area, the 

corresponding total catch was 685 individuals with a total weight of 67.8 kg divided 

among 24 different species. Only a few adult turbot were caught, despite the use of an 

extra type of net especially aimed at catching turbot. 

In all different 26 species was recorded of which Baltic herring, brisling, Atlantic cod, 

small sandeel, great sandeel, eelpout, short spined sea scorpion, turbot and flounder 

were selected according to their indicator characters. 

The statistical results were to a high degree defective due to lack of variance 

homogeneity. Most of the selected species did not fulfil this test assumption. 

Only short-spined sea scorpion and flounder fulfilled the statistical criteria of tests 

concerning number, weight, length distribution and mean length between the two areas 

with respect to space and time. Other species was only partly fulfilling the test 

assumption. 

The abundance of turbot and flounders appear to be equally distributed throughout the 

wind farm and reference areas where as eelpout and short-spined sea scorpion showed 

differences between the to areas. 

Similarities in weight between the two areas appeared in brisling, short-spined sea 

scorpion and flounder. 

The length distribution was only found to be different between the Wind farm and 

reference areas for of for the selected indicator species; Atlantic cod, great sandeel and 

flounder. The mean length of Atlantic cod, small sandeel, great sandeel, turbot and 

flounder was larger in the reference area compared to the wind farm area. Mean length 

varied over time for more than half of the species. 

Both short-spined sea scorpion and flounder showed higher numbers and weight in May 

relative to June whereas the abundance of eelpout and turbot did not change. 

In the baseline study of fry, each sample consisted of one standard fyke net and one fry 

fyke net. In both investigations of the baseline studies, the total number of samples 

obtained during the spring and autumn programme was 192 biological survey gill nets, 

96 turbot gill nets, 236 standard fyke nets and 236 fry fyke nets. The sampling and 

observation of the baseline study of fry in spring were conducted in the periods 16 th – 

27 th of May and 16 th –20 th of June as a part of the baseline study of fish. In the autumn 

sampling of the baseline study of fry were conducted in the periods 24 th – 27 th of 

September 25 th - 28 th of October and 24 th - 26 th of November 2001. 

In the baseline study of fry a total of 960 individuals divided among 28 species with a 

total weight of 20.5 kg were caught in the wind farm area. In the reference area, the 

corresponding total catch was 1234 individuals with a total weight of 19.1 kg divided 

among 24 different species. In all 30 species was recorded of which fifteen-spined 

stickleback, two-spotted goby, sand goby, eelpout, short-spined sea scorpion, 


Bio/consult as Page III 

longspined bullhead and flounder were selected for further analysis according to their 

indicator characters and abundance in the catch. 

Abundances of fry and small fry during spring were generally lower than catches in 

autumn. 

The catch data of all selected indicator species in the baseline study of fry suffer from 

lack of variance homogeneity both in consideration to number and biomass and by that, 

it was not possible to show effects of interaction to established temporal and/or spatial 

differences. Though, especially eelpout but also two spotted goby indicated effect of 

interaction. 

A power analysis for species caught in at least 50 % of the samples revealed that, only 

the eelpout fulfilled the 50% criterion. For this species, statistical reliable results can be 

achieved if a minimum of 16 stations per area is applied. 

The classic analytical techniques used in both the baseline study of fish and baseline 

study of fry could not deal with the many empty samples recorded. Furthermore, high 

variation was recorded in many samples, which posed a problem concerning the use of 

the classic statistical methods in the BACI design, which require homogeneity of the 

variance. 

Depending on the purpose and taking into account the properties of the data at hand, 

both univariate and multivariate statistical analysis can be carried out according to an 

either a parametric or a non-parametric test. 

One of the main problems of the investigation was to verify if the reference area could 

be used as control area in a future monitoring programme of the effects from the wind 

farm. The answer to that question requires a selection of the optimal statistical model, 

which depends on various factors such as the characteristics of the variance and the 

distribution of the data. 

In relation to the classic parametric univariate design used hitherto it was realized that 

the problem with obtaining normal distribution could be solved by dividing the fish 

species into functional groups. The division of species into groups of stationary, prey 

and reef species shows good statistical strength. The grouping will reduce the 

occurrence of empty samples and improve the possibility to fulfil the conditions of the 

analysis. The grouping should be chosen a priori and not based on the results of the 

investigation. 

A power of 80% can be achieved if the number of sampling stations is increased from 

12 to 26 in each area without increasing the effort significantly because, the replicates 

can be redistributed into new samplings stations. 

The main improvement of the statistics was the application of non-parametric 

multivariate techniques mainly because the data could not be fitted to a normal 

distribution. 


Bio/consult as Page IV 

The non-parametric multivariate analysis of variance (NPMANOVA) showed that the 

reference area and the wind farm area was not significantly different with the present 

abundance of fish and number of species. 

The suggested strategies can only be carried out on the level of numbers since the 

morphological features, of the grouped species differ greatly in weight, length 

distribution and mean length. 

It may be biological relevant to time the sampling occasion according to a yearly 

recurrent occasion which can be established by supervision instead of using fixed dates. 

Hereby, the problem of reduced catch efficiency due to high occurrence of drifting 

filamentous algae can to be minimized. 

It cannot be recommended to establish a monitoring program on a statistical basis, in 

association to the fry study according to the spatial distribution of fry and small fry. 

However, the fry study can be used to qualitatively evaluate the importance of the area 

as spawning and nursery ground. 

Baseline study of commercial fishery 

The baseline study of commercial fishery was based on interviews and logbooks from 

local fishermen located in Kramnitse, Gedser, Rødbyhavn and Hesnæs and material 

from the Directorate of Fisheries regarding area 38G1 (ICES division of Danish coastal 

water). 

At least ten commercial fishermen have fished the area at Rødsand form Hyllekrog to 

Gedser including the area of the planned wind farm in the period from 1997 to 2001. 

These consist of 3 trawl fishermen, six gill net fishermen and one pound net fisherman. 

The fish were mainly landed at Gedser and Kramnitse, Rødbyhavn and Hesnæs. 

The fishermen agreed that the fishing was quite good in the area mainly because of the 

abundance sand eel and shrimp, which serves especially as food for cod and turbot. 

Three trawl fishermen at Rødsand fished especially for cod, in the area south of the base 

line study, from April to June and from September to November. Net fishing for cod 

takes place mainly from December to May effectively in the whole area south of 

Rødsand. 

Turbot is mainly caught in the spring and summer up to spawning. There is agreement 

that the area serves as a spawning ground for turbot. Small numbers of plaice, flounder 

and dab are part of the by-catch for cod and turbot fishermen. Silver eel is easiest caught 

in pound nets from September to November because of their migration towards the 

Sargasso Sea. 

The annual catch of all species except turbot from 1997 to 1999 and silver eel amounts 

to a small part of the total recorded in logbooks from area 38G1 (Division of Danish 

Waters according to ICES areas). The total catches of turbot apparently equalled 

approximately 1 /3 of the turbot catch in the official statistics for area 38G1 from 1997 to 

1999. The difference can partly be explained by the fact that the fishermen who catch 

turbot sail under the coastal waters regulations, and so their figures are not included in 

the official catch statistics for area 38G1. 


Bio/consult as Page V 

Towards the end of the 1980s and the start of the 1990s, higher catches of dab and 

flounder were recorded, although the catch decreased again from 1992 until 1994. From 

the early 1990s, there has been an increase in the annual catch of herring, flounder, 

plaice and cod. The catch of turbot peaked in 1995. In the years 1997 to 2001, the 

annual net fishery catch in the wind farm site itself can be estimated as amounting to 

between 1,6 and 11.1 tons of cod, between 0.00 and 0.41 tons of turbot and between 

0.20 and 0.68 tons of silver eel. 

As expected, cod, flounder, dab, turbot and plaice are fished at Rødsand. In addition, 

silver eel are caught from September to November. In the years 1997 to 2001, the 

annual net fishery catch in the wind farm site itself can be estimated as amounting to 

between 1,6 and 11.1 tons of cod, between 0.00 and 0.41 tons of turbot and between 

0.20 and 0.68 tons of silver eel. There is no trawling within the proposed site itself. 


Bio/consult as Page VI 


Bio/consult as Page VII 

Resumé (In Danish) 

Den projekterede 150 MW vindmøllepark vil blive placeret i Femern Bælt omkring 10 

kilometer syd for Nysted på Lolland. Vindmølleparken vil dække et område på 24 km 2 

og bestå af 72 turbiner på hver 2,2 MW. Møllerne vil blive placeret i 8 nord-syd 

orienterede rækker med 850 meter mellem rækkerne. 

Formålet med baselineundersøgelsen “Fisk, yngel og kommerciel fiskeri – Nysted 

vindmøllepark ved Rødsand” var at etablere basis for det følgende 

monitoreringsprogram ved at udvælge de optimale undersøgelser og parametre til at 

bestemme de basale forhold for området og samtidig indsamle en tilstrækkelig mængde 

data til at skelne den naturlige variation fra en mulig miljømæssige påvirkning fra 

vindmølleparken. Miljømæssige påvirkninger kan skyldes øget niveau af støj og 

vibrationer, ændringer i fødemængde, sediment og elektromagnetiske felter. 

Ydermere er formålet af dette baselinestudie at dokumentere og så vidt muligt 

kvantificere effekterne på det kommercielle fiskeri i området omkring vindmølleparken. 

Baselineundersøgelsen bestod af tre separate studier af henholdsvis fisk, yngel og 

erhvervsfiskeriet i området. 

Baselinestudie af fisk og fiskeyngel 

I undersøgelsen blev der lagt særlig statistisk vægt på sammenligningen mellem 

mølleparkens område og et referenceområde med henblik på at påvise, at der ikke var 

signifikant forskel mellem antal, vægt og længde af udvalgte arter fra de to respektive 

områder, og om eventuelle ændringer udvikles parallelt i begge områder. 

En foreløbig fiskeundersøgelse fra området blev foretaget for at belyse variansen på 

data og for at få et estimat for, hvor mange prøver der skulle til for, at undersøgelsen var 

statistisk forsvarlig. En power-analyse viste, at der krævedes et minimum på 22 prøver 

fordelt på 11 stationer med 2 replikater i såvel mølleområdet som referenceområdet for 

at opnå en statistisk forsvarlig undersøgelse med 80% sikkerhed for at detektere en 

ændring på 50% (p

Bio/consult as Page VIII 

I alt blev der registreret 26 forskellige arter, hvoraf sild, brisling, torsk, kysttobis, plettet 

tobiskonge, ålekvabbe, almindelig ulk, pighvar og skrubbe blev udvalgt med baggrund i 

deres indikatorkarakterer til videre statistisk analyse. 

Resultatet af de statistiske analyser var i vid udstrækning uanvendelige som følge af 

manglende varianshomogenitet. De fleste af de udvalgte indikatorarter kunne således 

ikke opfylde forudsætningerne for de statistiske modeller og analyser. 

Kun almindelig ulk og skrubbe opfyldte forudsætningerne for at gennemføre de 

statistiske analyser af tidslige og rumlige sammenligninger mellem mølleområdet og 

referenceområdet hvad angår antal, vægt, længdefordeling og gennemsnitslængde. De 

andre arter opfyldte ikke eller kun delvist forudsætningerne for disse analyser. 

Forekomsten af pighvar og skrubbe var således ikke forskellig i mølleområdet og 

referenceområdet, hvorimod forekomsten var hvad angår ålekvabbe og almindelig ulk. 

Biomassen af brisling, almindelig ulk og skrubbe var ligeledes ens i de to områder. 

Der blev kun fundet forskelle i længdefordelingerne mellem mølleområdet og 

referenceområdet hos følgende udvalgte indikatorarter: Torsk, plettet tobiskonge og 

skrubbe. For mere end halvdelen af arterne varierede middellængden med tiden. 

Gennemsnitslængden hos torsk, kysttobis, plettet tobiskonge, pighvar og skrubbe var 

større i referenceområdet end i mølleområdet. 

Forekomst og biomasse af almindelig ulk og skrubbe var større i maj end i juni, 

hvorimod der ikke var forskelle i forekomsten af ålekvabbe og pighvar i de to måneder. 

Hver prøve i baselinestudiet af yngel bestod af fangsten fra en almindelig kasteruse og 

en yngelruse. Den totale mængde fangstdata fra begge baselineundersøgelser i 

henholdsvis forårs- og efterårsundersøgelsen bestod af 192 befiskninger med biologiske 

overvågningsgarn, 96 med pighvargarn, 236 med kasteruser og 236 med yngelruser. 

Baselineundersøgelsen af yngel blev foretaget både i foråret i perioderne 16. – 27. maj 

og 16. – 20. juni og i efteråret i perioderne 24. – 27. september, 25. – 28. oktober og 24. 

– 27. november i 2001. 

I studiet af yngel i mølleområdet blev der fanget 28 forskellige arter fordelt på 960 

individer med en samlet vægt på 20,5 kg. I referenceområdet blev der tilsvarende fanget 

24 arter fordelt på 1.234 individer med en samlet vægt på 19,1 kg. 

I alt blev 30 forskellige arter registreret. Til nærmere statistisk analyse blev toplettet 

kutling, sand kutling, ålekvabbe, almindelig ulk og langtornet ulk og skrubbe udvalgt 

som indikatorarter på baggrund af deres talrighed og adfærdsmæssige karakterer. 

Forekomsten af fiskeyngel og småfisk var generelt mindre i foråret end i efteråret. 

Det var ikke muligt at udføre statistiske sammenligninger mellem antal eller biomasse 

og derved påvise vekselvirkninger i tid og/eller mellem områder som følge af 

manglende varianshomogenitet. Dog syntes der specielt for ålekvabbe, men også 

toplettet kutling, at være en indikation af vekselvirkning. 


Bio/consult as Page IX 

En power-analyse for de arter som er fanget i minimum 50% af prøverne viste at kun 

ålekvabbe tilfredsstiller sandsynligheden for med 80% sikkerhed at kunne spore en 50% 

ændring. For denne art vil det statistisk grundlag kunne opnås med et minimum af 16 

stationer for hvert af de to områder. 

Der var afgørende problemer med den klassiske statistiske analyseteknik, som blev 

anvendt i både baselineundersøgelsen af fisk og baselineundersøgelsen af yngel, fordi 

metoden ikke kan håndtere en stor frekvens af tomme prøver. Endvidere var der stor 

variation i mange prøver, hvorved den statistiske forudsætning om varianshomogenitet 

ikke kunne opfyldes i forbindelse med de statistiske analyser i et klassisk BACI design. 

Alt efter formålet og med udgangspunkt i de indsamlede datas egenskaber kan både den 

univariable og den multivariable analyse gennemføres i en parametriseret model eller 

med anvendelse af forskellige ikke-parametriske test, hvoraf de sidst udviklede hører til 

gruppen af beregningstunge permutationstest. 

Et væsentligt formål med undersøgelsen var at efterprøve om referenceområdet kan 

anvendes som kontrolområde i et kommende moniteringsprogram til overvågning af 

mulige effekter fra mølleparken. For at kunne verificere dette skal der anvendes en 

optimal statistisk model, hvor valget afhænger af forskellige faktorer som eksempelvis 

karakter af datas fordeling og varians. 

Ved anvendelse af det klassiske parametriske univariate design kan det observerede 

problem med at opnå varianshomogenitet løses ved at inddele de registrerede arter i 

funktionelle grupper. En sådan gruppedeling af indikatorarterne i stationære arter, bytte 

fisk og hårbundsarter viste god statistisk styrke. En gruppering vil reducere frekvensen 

af tomme prøver og forbedre grundlaget for forudsætningerne for analysen. Inddeling af 

grupper bør prædefineres og ikke udføres på grundlag af undersøgelsens resultater. 

En power på 80% kan opnås ved at øge antallet af stationer fra 12 til 24 i både 

reference- og mølleområde. Det behøver ikke medføre en væsentligt øget indsats, 

eftersom det vil være forsvarligt at omfordele replikaterne til nye stationer. 

Et væsentligt fremskridt for statistikken ved nærværende type opgave blev opnået ved at 

anvende non-parametrisk multivariat teknik, ikke mindst fordi data herved blev 

normalfordelt. 

Den non-parametriske multivariate analyse af varians (NPMANOVA) viste, at der ikke 

var forskelle i antal arter og forekomsten af fisk mellem referenceområdet og 

mølleområdet. 

Disse statistiske strategier kan dog kun udføres på niveauet af antal og arter af fisk, 

eftersom de grupperede arter vil kunne variere betydeligt med hensyn til vægt, 

længdefordeling og gennemsnitslængde. 

Det vil være biologisk relevant at planlægge prøvetagningerne ud fra en årligt 

tilbagevendende begivenhed, som kan bestemmes ud fra tilsyn, i stedet for en 


Bio/consult as Page X 

planlægning på bestemte datoer. Herved kan eksempelvis problemer med en reducerede 

fangsteffektivitet som følge af drivende trådalger minimeres. 

Det kan ikke anbefales at i værksætte et moniteringsprogram for fiskeyngel og småfisk 

med udgangspunkt i en statistisk vurdering som følge af en spredt forekomst af 

fiskeyngel og småfisk. Derimod kan en undersøgelse rettet med fiskeyngel give en 

kvalitativ vurdering af mølleparkens betydning som gyde- og opvækstområde for 

fiskeyngel. 

Baselineundersøgelse af erhvervsfiskeri 

Baselineundersøgelsen af erhvervsfiskeri tog udgangspunkt i interviews med lokale 

erhvervsfisker, deres logbog samt materiale fra Fiskeridirektoratet. 

Området ved Rødsand blev i perioden 1997-2001 benyttet af mindst ti erhvervsfiskere. 

Heraf er tre trawlfiskere, seks garnfiskere og en bundgarnsfisker. Fangsterne er 

hovedsagelig landet i Gedser, Kramnitse, Rødbyhavn og Hesnæs. 

Der var blandt fiskerne enighed om at fiskeriet i området var godt, hvilket blev 

begrundet med tilstedeværelsen af en store mængde tobis og rejer, der er vigtige 

fødeemner specielt for torsk og pighvar. 

De tre trawlfiskere ved Rødsand fisker specielt efter torsk syd for 

baselineundersøgelsesområdet, i perioden april til juni og fra september til november. 

Garnfiskeri efter torsk forgår i hele området syd for Rødsand og hovedsagelig i perioden 

fra december til maj. 

Pighvar bliver primær fanget i forår- og sommerperioden, frem til starten af 

gydesæsonen. Der er enighed om, at området benyttes som gydeområde for pighvar. Et 

mindre antal rødspætte, skrubbe og ising udgør en del at bifangsterne ved fiskeriet efter 

pighvar og torsk. Blankål er hovedsageligt fanget i bundgarn i perioden september til 

november i forbindelse med deres migration mod Sargassohavet. 

De årlige fangster af alle arter, bortset fra pighvar og blankål, udgør en lille del af den 

totale registrering i logbøgerne fra område 38G1 (inddelingen af de danske kystnære 

områder til ICES områder) i perioden 1997 til 1999. Den totale fangst af pighvar udgør 

cirka 1 /3 af de pighvar, der optræder i de officielle fangstrapporter for område 38G1 fra 

1997 til 1999. Forskellen kan forklares ved det faktum, at fiskere, som fanger pighvar, 

høre ind under regulativet for kystnære områder, og deres tal bliver derved ikke 

medtaget i den officielle statistik for område 38G1. 

I slutningen af 1980´erne og i begyndelsen af 1990´erne blev der registreret større 

fangster af ising og skrubbe. Fangsterne faldt dog igen fra 1992 til 1994. Fra 

begyndelsen af 1990érne øgedes de årlige fangster af sild, rødspætte og torsk. 

Fangsterne af pighvar toppede i 1995. I årene fra 1997 til 2001 kan de årlige fangster i 

selve mølleområdet estimeres til: Torsk 1,6 - 11,1 tons, pighvar 0,00 - 0,41 tons samt 

blankål 0,20 - 0,68 tons. 


Bio/consult as Page XI 

Torsk, skrubbe, ising, pighvar og rødspætte bliver som forventet fanget ved Rødsand. 

Ligeledes bliver der fanget blankål i perioden september til november. Der foregår 

ingen trawling i selve undersøgelsesområdet. 


Bio/consult as Page XII 


Bio/consult as Page 1 

1. Introduction and objectives 

The establishment of a demonstration offshore wind farm in the waters at Rødsand, 

south of Nysted in the eastern part of Femer Belt, se Figure 2.1.1, has been granted by 

the Ministry of Environment and Energy in compliance with the conditions of 

environmental impact as outlined in the EIA study (SEAS 2000). 

Establishing an offshore wind farm at Rødsand can have an impact on the local marine 

fish population during the construction and the operational phases. Effects are expected 

during the operational phase, partly due to the physical appearance of the offshore wind 

farm, including the effect of the artificial reefs created by the foundations of the wind 

turbines, and partly due to the cable trace to land. 

The objective of the baseline study was to establish a basis for the following monitoring 

programme by selecting the optimal investigations and parameters to assess the basic 

condition of the area and to collect an adequate amount of baseline data to distinguish 

the natural variation from the possible environmental impact caused by the operation of 

the offshore wind farm. Eventual environmental impact caused by offshore wind farms 

might be due to increased level of noise and vibrations, changes in the food basis, 

sediment and magnetic fields and other unexpected effects. 

Furthermore, the objective of this baseline study was to establish a basis for later 

documentation and quantifying the effects on the commercial fishery, inside and around 

Nysted Offshore Wind Farm at Rødsand. 

This report presents the results of the baseline study on fish and the baseline study of fry 

at the Nysted Offshore Wind Farm at Rødsand and an adjacent reference area besides 

study on the effects on the commercial fishery in the area. The baseline study on fish 

and fry was performed from May to November 2001. 





2. Background 

2.1. Baseline program 

The Baseline study of adult fish and fry (young-of-the-year) and juvenile fish at Nysted 

Offshore Wind Farm at Rødsand was prepared and undertaken according to evaluations 

and recommendations described in “Monitoring programme for fish and fisheries for the 

offshore wind farm at Rødsand“ (Bio/consult 2001a) and “Baseline programme for fish 

and fisheries offshore wind farm at Rødsand.” (Bio/consult 2001b). 

According to the baseline programme the baseline study is expected to enable the 

verification of the possible changes in the fish population in the wind farm area during 

its operational phase. Therefore, a series of issues have been suggested for the following 

monitoring programme. It is expected to verify these issues by testing of the following 

0-effect hypotheses: 

1. The offshore wind farm will not cause a change in the density and biomass of 

selected fish species in the wind farm area. 

2. The offshore wind farm will not cause a change in the length distribution of 

indicator fish species in the wind farm area. 

3. The offshore wind farm will not cause a change in the behaviour of reproduction 

patterns of indicator fish species in the wind farm area. 

4. The offshore wind farm will not cause a change in the density and biomass of fish 

fry in the wind farm area. 

5. The offshore wind farm will not cause a change in the commercial catch in the wind 

farm area. 

6. The offshore wind farm will not cause changes in the fishery pattern in the wind 

farm area. 

7. The cable trace will not cause obstacles for the migration of indicator fish species. 

This baseline study consists of three partly separate studies: 

• Baseline study of fish (basically adult fish) 

• Baseline study of fry (includes fry, juveniles and small fish species) 

• Baseline study of commercial fishery 

The 0-effect hypothesis number 7 is discussed in a separate baseline study “Baseline 

study - Fish at the cable trace, Nysted Offshore Wind Farm at Rødsand” (Bio/consult 

2003a). 



2.2. Location and description of Nysted Offshore Wind Farm at 

Rødsand 

The planned 150MW offshore wind farm at Rødsand is placed in the Femern Belt 

approximately 10 kilometres south of Nysted on Lolland (Figure 2.2.1). 

The wind farm will cover an area of approximately 24 km 2 . A 200m wide zone with 

limited admittance will be established around the wind farm during the construction 

phase, resulting in an overall area of approximately 28 km 2 . Fishing will be allowed 

during the operational phase with regard to executive orders concerning fishery near sea 

cables. 

The wind farm will consist of 72 turbines each of 2.2MW placed in 8 north-south 

orientated rows separated by a distance of 850m. 

The turbine foundations will be gravity foundations in concrete. The bottom plate of the 

foundations will be covered with stones to protect against erosion and just above the 

water surface a special collar will protect against ice. The foundations will take up an 

area of about 45.000m 2 , corresponding to 0.2% of the total area of the wind farm. 

The wind farm is located on a gently sloping seabed with depths between 6m and 9.5m. 

The majority of the seabed area consists of glacier deposits covered by thin layers of 

sand. The largest part of the area consists of sand with larger and smaller ridges. In 

certain areas at the base of these ridges, there is loose material that easily becomes 

suspended. In other areas, there are pebbles, gravel or shell. Stones >10 cm do occur 

sporadically but the overall coverage is

SEAS. Baseline study – Fish, fry and commercial fishery Dok. nr. 2148-03-001-rev3 2P.doc 

Figure 2.1.1. Map of Nysted Offshore Wind Farm at Rødsand with associated transformer platform 

and planned cable trace. 

Nysted Offshore Wind Farm at Rødsand 

Cable trace 

q Mills 

Tranformer platform 

Nysted 

q q 

q q 

qqq q q 

q q q q 

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q q q q 

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Vantore Strand 

Bio/consult as Page 5




The turbines will be interconnected with a 33 kV sea-cable of a total length of about 48 

km. From a 33/132 kV transformer platform, placed on a foundation similar to the wind 

turbine foundation 200m north of the northernmost row of turbines, a 10 km long 132 

kV cable will lead to land at Vantore Strandhuse east of Nysted. All cables are planned 

to be Alternating Current (AC) cables and will be sluiced/buried at a depth of one metre. 

2.3. The importance of the area as a probable migration route and 

spawning ground for species of commercial importance 

The western part of the Baltic lies within the distribution areas for cod, flounder, dab, 

plaice, turbot, eel and herring. 

Thin-shelled shellfish as well as bristle worms form an important part of the diet of flat 

fish. Cod have a wide food spectrum and eat all kinds of crustaceans, bristle worms and 

shellfish. Larger cod also eat fish. Herring, like sandeel, eat zooplankton, especially 

copepods. In general, most fish do not eat in the spawning period, and their need for 

food decreases in the winter. 

The western Baltic as a whole is probably a spawning ground for cod, flounder, dab, 

turbot, plaice and herring (Krog, 1993). Flounder, dab, turbot, and to some extent cod, 

for which spawning depends on the water temperature, can spawn in waters of depths 

similar to those at Rødsand, while plaice probably spawn in deeper waters (20 – 40 

metres). 

Plaice live on sandy or mixed sea bottoms from the coast out to depths of about 200 

metres. Most adult plaice occupy waters of 10–50 metres depth, while the younger fish 

stay in shallower waters. Plaice spawn in the Baltic over a long period (from November 

to March) because of the relatively low, constant temperatures in the deeper waters. 

(Muus & Nielsen, 1998). 

Flounder inhabit waters from the tidal zone out to depths of about 100 metres, the 

youngest ones in the shallowest waters. Spawning is from March to May. 

Dab live on sandy or soft bottoms at depths of 5–150 metres. The youngest can be 

found to depths of approximately 70 metres. Dab spawn in the Baltic from April to 

August. 

Turbot inhabit water depths of 20–70 metres and is found on sandy, stony or mixed 

bottoms apart from the spawning period. During spawning, they can be found in 

somewhat shallower waters (according to the literature, turbot spawn in depths of 10–40 

metres) from April to August. 

Cod are found from the coast out to a depth of 500 metres. Spawning cod seek out water 

temperatures of 4–6 °C. The fishermen believe the cod at Rødsand spawn from March 

to June. The cod in the western part of the Baltic are considered to spawn earlier than 

the cod around Bornholm. Cod in the Baltic have a very long spawning period (April– 

September) compared with other cod populations (Tomkiewiecz pers. Comm. 2001). In 

recent years, the spawning period of cod has extended even more, possibly because of 

the effects of hormone-like substances. 



Herrings are pelagic. The herring strains in the western Baltic probably spawn in an area 

off the German coast in spring. 

Figure 2.3.1 The silver eel migrate along the southern coast of the island of Lolland Falster in the 

autumn. Commercial fishery for silver eel is common in the area. 

Eel migrate through the area from September to November on their way to the Sargasso 

Sea. 

As a result of the fish studies at Rødsand, the probable migration and spawning 

conditions in the area can be stated. These are shown in Table 2.3.1. 

Commercial 

species 

Species 

Catch 1999 Behaviour 

Spring Autumn Stationary Migratory 

(April, June) (Sept. Oct.) 

Probable 

spawning in 

wind farm 

area and/or 

reference 

area 

Herring 4 28 X (X) 4 

Brisling 9 73 X (X) 4 

Cod 295 463 X X (X) 2) 

Whiting 0 428 X 

Flounder 18 10 X X (X) 3) 

Plaice 0 0 

Turbot 1 1 X X 

Short-spined sea scorpion 40 19 X X 

Fifteen-spined stickleback 0 116 X X 

Small sandeel 308 115 X (X) 1) Great sandeel 40 602 X (X) 

X 

1) Additional 

species 

caught in 

larger 

numbers Two spotted goby 17 12 X 

X 

Not studied 

Sand goby 0 99 X Not studied 

Eelpout 4 129 X X 

1) Daily migrations 

2) Only intermediary, juvenile and spent (reference area) cod were recorded. The absence of any sexually mature cod is attributed 

to the time of fishing (spawning season app. January to April) 

3) Only intermediary, sexually differentiated, juvenile and spent flounder were recorded. The absence of any sexually mature 

flounder is attributed to the time of fishing (spawning season April/early May). 

4) Only few individuals were caught in the spring. A few were mature or had already spent. 

Table 2.3.1. List of some important species recorded at Rødsand and location of their habitat as 

either stationary or temporary (visitor). It is also shown whether the species probably 

uses the area as a spawning area. (After bio/consult 2000a) 



2.4. Previous fish studies 

In connection to the planning of an offshore wind farm at Rødsand, preliminary fish 

studies (screenings) of the fish population in the area (Bio/consult, 2000a) and the 

existing fishery pattern for the commercial fishery (Bio/consult 2000b) have been 

carried out as part of the EIA study conducted in 1999. 

The studies have provided fundamental background knowledge about existing fish 

populations and commercial fishing in the area. Furthermore, the studies have provided 

experience of methods and gear, which can be applied to the baseline and monitoring 

program of fish at The Rødsand Offshore Wind Farm at Nysted. 

The studies form the basis for planning the program for the baseline study before the 

construction work starts in June of 2002, and for planning a subsequent monitoring 

programme for the operational phase of the wind farm, from 2003 to 2006. 

2.4.1. Preliminary fish study 

The preliminary fish study included fishing with gill and fyke nets at five stations in the 

wind farm area and an adjacent reference area (Bio/consult 2000a). Sampling was 

carried out as repeated fishings (fishing at the same station within a few days), where 

the number of replicates at each fishing was 10. Furthermore, trawling (TV3 Baltic 

Surveying Trawl) was undertaken at four stations in the wind farm area and at four 

stations in the adjacent reference area. The sampling regime was conducted with 

identical programmes in both the spring (April) and autumn (September). 

Results showed that it was acceptable to divide the sampling effort within a station over 

several days as the variation between the repeated-samplings was low and thus 

considered negligible as it only constituted up to 20% of the variation observed between 

replicates (the spatial variation). Hence, the spatial variation was much greater than the 

variation from local sampling over several days. 

An overall power analysis on the results of the fishing with gill and fyke nets, showed 

that the wind farm area and the reference area should be fished with a minimum of 22 

replicates. This is necessary in order to achieve a criteria which, with 80% certainty, 

detects a significant (p


2.4.2. Preliminary study on commercial fishery 

The study concerning the commercial fishery included interviews with the local 

fishermen, who have interests in the area south of Rødsand (Bio/consult 2000b). The 

interviews were concentrated on the fishing practice (important species, gears, seasons 

etc.) and collection and processing of catch data from the area. 

The catches by the commercial fishermen have been calculated for the years 1997-1999. 

This calculation has been partly made on the basis of data from log books, records of 

landed fish, yearly statements from the Fishery Department and private notes, and partly 

on official data from the Directorate of Fisheries, on the ICES area 38 G1 (the coastal 

area surrounding Lolland and Falster). 

The commercial fishery in the area south of Rødsand is primarily for Atlantic cod and 

turbot using gillnets and trawling, as well as pound netting for silver eel during autumn. 

There is no trawling inside the wind farm area. 

2.4.3. Trail fishery using pound netting and fry trawl 

In the autumn of 2000, a study was carried out to evaluate the possibility of using bycatches 

from pound netting in a future baseline and monitoring programme at Rødsand, 

and the possibility of using fry trawl and pound nets as a substitute or additional fishing 

gear (Bio/consult 2001e). 

There was a small degree of overlap between the catches from pound nets and the 

catches from fry trawls. A much greater number of species were caught in the pound 

nets compared to the fry trawls. The fry trawl was, however, more effective at catching 

small fish (< about 10 cm) whereas the pound net was more effective at catching larger 

fish (> about 20 cm). 

Even though the pound net fishery in the area is primarily aimed at catching silver eel, 

the catches in the pound nets also included a broad selection of other fish species 

(herring, sprat, Atlantic cod, short-spined sea scorpion, hooknose, flounder and plaice). 



3. Fish and Fry 

3.1. Materials and methods 

The two baseline studies, baseline study of fish and baseline study of fry, were carried 

out in the year of 2001 according to the recommendations in “Baseline programme for 

fish and fishery. Offshore wind farm at Rødsand” (Bio/consult 2001b). In spring, the 

program for baseline study of fish was undertaken with two identical sampling sessions 

in May and June, respectively. In autumn, the sampling program baseline study of fry 

was executed with three identical sampling sessions in September, October and 

November. 

Apart from the use of gill nets in the baseline study of fish and sampling seasons and 

number of samplings, the execution of the two baseline studies was identical. 

The chosen strategy for the studies was selected for its ability to describe the correlation 

of variations in time and place (corresponding to the principles in a BACI test). The 

baseline study of fish was also designed to catch spawning flatfish (e.g. turbot and 

flounder). The baseline study of fry was expected to catch the young-of-the-year of a 

number of species that spawn in early and mid summer as well as small fish species. 

With one exception, the baseline study of fry differed from the baseline program 

described in “Baseline programme for fish and fishery. Offshore wind farm at Rødsand” 

(Bio/consult 2001b). Data collected at the baseline study of fish was to some degree 

used in the baseline study of fry, as far as concerns identical stations and fishing gear. It 

was expected, that this alteration would strengthen the baseline study of fry. 

3.2. Field studies 

All sampling concerning the baseline study of fish and the baseline study of fry was 

conducted at the same 12 permanent stations in the area of the wind farm and 12 

permanent stations in the adjacent reference area (Figure 3.2.1). The research stations 

were selected in accordance to eight transects used in connection with studies of marine 

biological conditions (DHI, 2000). Four transects were selected in the wind farm area 

and four transects in the corresponding reference area. 

Repeated sampling at 12 stations in the area of the wind farm and corresponding 12 

stations in the reference area took place simultaneously. Sampling at all 24 stations were 

done within a period of three to four days, depending on the weather conditions. 

Gill and fyke nets were set from two high-speed rubber zodiacs (Humber Dive Pro 6) at 

geographical coordinates, established by GIS, within the selected stations. These 

coordinates were exported to the D-GPS system used on the rubber zodiacs and allowed 

for a better degree of repeated positioning of the fishing gear during sampling. 

The gill and fyke nets were set in the afternoon and retrieved the next morning in the 

same order to ensure that active sampling time was approximately the same for each 

sample. 





Figure 3.2.1. The area of the studies and the distribution of sampling stations. Stations were identical 

spring and autumn. 


Cable trace 

q Mills 

Tranformer platform 

Sampling stations in the reference area 

Sampling stations in the wind farm area 

13 

14 

15 

16 

17 

18 

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19 

20 

21 

22 

23 

24 





Figure 3.2.1 Seaworthy high-speed rubber zodiacs (Humber Dive Pro 6) were used in the field 

studies of both baseline studies because of their exceptional steadiness. 

3.3. Applied gears 

Only passive (stationary) fishing gear was used for sampling as recommended (SEAS 

2000 and SEAS 2001). The catch efficiency of passive fishing gear such as gill and fyke 

nets depends on fish behaviour such as migration. Migration can be seasonal (e.g. while 

spawning) or daily (e.g. while foraging in shallower and deeper waters within the local 

area). The catch depends on factors such as the mesh size, time of day, oceanographic 

conditions and weather conditions. 

Biological sampling gill nets 

The biological survey gill net (Lundgren gill net) is 42 metres long and 1.5 metres high. 

It can be placed in a pelagic or benthic position. Pelagic nets are positioned 0.5-1 m 

under the surface to reduce the risk of rolling and tangling. The biological survey net is 

divided into 14 sections with different mesh sizes ranging from 6.25 mm to 60 mm (half 

meshes). 

Turbot net 

The benthic biological survey nets used in this study was equipped with two additional 

4 m long sections with mesh sizes of 90 and 110 mm, respectively, suitable for catching 

larger flatfish like turbot. 

Standard fyke net 

The standard fyke net is a double-threaded cast net with a trap opening of 60 cm in 

diameter. In each fyke net, there are three funnels with a mesh size of 17 mm in the 

outer funnel, 14 mm in the middle funnel, and 11 mm in the innermost funnel. The 

leader between the two traps is eight metres long and has a mesh size of 18 mm. The 

leader has extra ballast to reduce rolling in bad weather. 



Fry fyke nets 

The fry fyke net is a double-threaded cast net with a trap opening of 60 cm. The three 

funnels in each trap have diameters of 55 cm, 45 cm and 35 cm, with mesh sizes of 8 

mm, 8 mm and 5 mm, respectively. The leader between the two traps is eight metres 

long and 55 cm in height, and has a mesh size of 18 mm. The leader has extra ballast to 

reduce rolling in bad weather. 

Figure 3.3.1. Due to its small mesh size fry fyke net was capable of catching gobies and other small 

fish to a size less than two centimetres. Left; sand gobies and two-spotted gobies. Right; 

Just before spawning – a black goby female. 

3.3.1. Fish study 

The baseline study of fish included the use of biological survey gill nets set in the 

pelagic, and benthos biological survey gill nets with additional sections of large mesh 

size, standard and fry fyke nets. 

3.3.2. Fry Study 

The baseline study of fry, juveniles and small fish only included the use of standard 

fyke nets and fry fyke nets, in both spring and autumn. 

3.4. Scope 

The baseline study of fish was undertaken in the spring and including sampling 

expeditions in May and June, respectively. The baseline study of fry was undertaken in 

autumn and consisted of expeditions in September, October and November, 

respectively. All sampling was repeated (replicates) at 12 stations in the wind farm area 

and 12 stations in the reference area, a total of 24 replicates in both areas. 

3.4.1. Fish study 

Sampling and observations were conducted in the periods 16 th –27 th of May and 16 th – 

20 th of June 2001. As mentioned above, for each sampling period 24 samples were 

taken in the wind farm area and 24 samples in the reference area giving a total of 96 

samples in all. 



Each sample consisted of fish from one pelagic and one benthic biological survey gill 

net, one turbot gill net, one standard fyke net and one fry fyke net. 

Figure 3.4.1. Tending of a biological survey net on a calm morning in May. 

3.4.2. Fry study 

Samples and observations where conducted in the periods 24 th – 27 th of September, 25 th 

- 28 th of October and 24 th - 26 th of November, 2001. Each sample consisted of one 

standard fyke net and one fry fyke net. 

In both September and November 24 samples were taken in the wind farm and reference 

areas while in October, only 22 samples were gathered in the wind farm and the 

reference area for a total of 140 samples. 

The fry study also includes data of catch results from fyke nets obtained in connection 

with the fish study. 

Figure 3.4.2 Samples from fyke nets and fry fyke nets were handled on board and stored in plastic 

bags together with all necessary information. 



3.5. Information recorded 

In all, the total number of samples obtained during the Spring and Autumn programme 

was 236, consisting of 192 biological survey gill nets, 96 turbot gill nets, 236 standard 

fyke nets and 236 fry fyke nets. 

The following information was recorded for each net: 

• Station 

• Date 

• Net 

• Time of activity 

• Position 

• Depth 

• Species of fish 

• Number of each species 

• Total weight of each species 

• Length (total length) of each individual rounded down to the nearest half centimetre 

• Corresponding values of length (mm) and weight (1/10 g) for each individual of the 

important species 

In the fish study, the gonad index was also determined for important species and was 

classified on the basis of five developmental levels: 

1. Juvenile gonads undeveloped and impossible to tell the sex of the fish 

2. Sexually differentiated – the sex of the fish could be determined from the gonads 

3. Intermediary – gonads in development with differentiation of roe cells and milt 

4. Mature – roe and milt flow freely when the abdominal cavity is pressed 

5. Spent – gonads empty or partly empty, slack and bloodshot 

Figure 3.5.1 From each sample species was identified and sorted. Each individual was measured and 

weighted and notes were carefully taken down. 

The following meteorological data for May, June, September, October and November 

was obtained from Danish Meteorological Institute and included for station 06149 

Gedser Odde: 



• Wind direction 

• Wind velocity 

• Current direction 

• Current velocity 

3.6. Data processing 

Information about the nets and related parameters along with catch results were entered 

into Bio/consult’s database, FISKSYS. All parameters and results were geographically 

oriented in relation to the Geographical Information System (GIS) Map Info. 

Figure 3.6.1. Identification of some species were time consuming because of less morphological 

differences between close related species. Pictures show identification of a longspined 

bullhead (Left) and a short-spined sea scorpion (Right). 

Catch Per Unit Effort (CPUE) for number and weight was calculated as the average 

catch per sample for a given species at a given station. 

In the fish study, one sample consisted of one pelagic gill net, one benthic gill net, one 

turbot gill net, one standard fyke net and one fry fyke net. 

In the fry study, one sample consisted of one standard fyke net and one fry fyke net. 

The unit of effort was one night of fishing with the sampling units mentioned above. 

Average values were calculated with regards to length, weight and condition factor. The 

condition factor (CF) was calculated for every fish as: 

CF = 100 · W/L 3 

where W = the weight of the fish 

L = the length of the fish 

In addition, the average weight of gonads was recorded along with their stage of 

development, broken down into five levels (the gonad index). 



With regard to uncertainty, exported data from FISKSYS was subjected to statistical 

comparisons in SPSS, as well as the power analyser in Sample Power from SPSS. 

The statistical analyses were carried out in preparation for use in a subsequent 

monitoring programme. 

Figure 3.6.2. Left: Different year classes of flounders. Right: Gonads of a flounder is removed for 

weighting and identification of gonad index. 

3.6.1. Statistical analyses 

Special emphasis was placed on statistical comparisons between the wind farm area and 

the reference area to verify if the fish-fauna in the two areas is similar with respect to 

both space and time. 

The hypothesis determining analysis was as follows: There are no differences in the 

number and weight of the fish-fauna with respect to area, and that possible changes in 

time exhibit parallel changes for the two areas. 

Two-way analysis of Variance was carried out using the variables number and weight 

(logarithmical transformed), as an ordinary dual variable analysis with the factors of 

Area and Time. The special aspect of the Two-way Anova analysis is that the 

interesting source of variation here is the interaction between Area and Time. This must 

not be significantly greater than the variation between the stations within a given area 

and for a given time, also known as the effect of interactions. 

One of the preconditions of using the Two-way Anova is that the sample variances are 

similar to each other i.e. the individual cells fulfil the criteria of homogeneity. This was 

tested using Levene’s test. 

Areas for both the fish and fry study are defined as the wind farm area and the 

corresponding reference area. Time is the sampling period defined as May and June for 

the fish study and May, June, September, October and November for the fry study. 



Differences between length distributions of the different fish species from the two areas 

(the wind farm and the reference area) were compared between studies and at different 

months. 

As the data for the distribution of fish length is often insufficient for a Two-way 

analysis of variance to be used, a Kolmogorov-Smirnov Z test was used instead. The 

Kolmogorov-Smirnov Z test is a non-parametric test for the difference in the 

distribution of frequency, where time and place are fixed. This test does not allow the 

interactions between time and place to be assessed. 

To determine if mean lengths of the separate fish species are similar in both the wind 

farm and the reference area a non-parametric Mann Whitney test was used. Length 

distribution results and length frequency for each species is to be included in evaluation 

of the non-parametric Mann Whitney test. 

3.6.2. Power analysis 

A power analysis was carried out after the statistical analysis to reveal the degree of 

certainty achieved in the analysis, in other words the extent to which there is a 

probability that the test will be able to show a real difference from a given amount of 

data. 

To run a power analysis at least three conditions that must be fulfilled: 

1) The starting point is a particular method of statistical analysis, for example 

the ANOVA test, in connection with the BACI design. 

2) The variance structure of the data must be known, including the number of 

replicates used. 

3) The selection of a difference in values that the analysis must be able to 

detect. This is normally called the size of effect. In this case, the size of 

effect was selected as 50%. 

The selection of the size of effect requires the balance of two contradictory factors. For 

certain biological processes, smaller sizes of effect can be of relevance, but selecting a 

smaller size of effect will mean a given study is less powerful. This creates a need for a 

larger number of samples in planning the subsequent sampling using the actual power 

analysis as the basis. 

If a power analysis returns a power of 10% for a given statistical test with a size of 

effect of 50%, results in only a 10% probability that the test will give a significant 

result, even though there is a real difference in the data of 50% for the variable selected 

between two studies of either time or space. 

A power analysis can also be used to calculate how many replicates are required to 

achieve the desired power (for example 80%) for a given test with a selected size of 

effect (for example 50%). 

Power analyses were used in this study for the dual variable analyses (BACI) for 

determining the difference in the number and total weight of the various species of fish. 



The power analysis was carried out at a size of effect of 50% difference between the 

two areas. 

3.7. Margin of error in the method 

The physical design of the gill net can cause a selective distribution of sizes in the catch. 

A given mesh size is most effective for fish of a particular size, shape and morphology. 

An attempt was made to counter this degree of uncertainty by using a range of different 

mesh sizes. The biological survey gill nets can, become “saturated” to some extent, in 

particular sections, after which it will no longer work efficiently in catching fish of 

certain sizes. 

The methods used for setting the biological survey gill nets, without gaps between the 

individual sections, could also introduce selectivity (and thus cause an overestimation of 

CPUE values) for larger fish. 

The efficiency of the gill nets can also depend on the species of fish and the nature of 

the water. Some fish, such as sculpins and sticklebacks, have a rough exterior 

morphology with extruding appendages, which means they are easily caught in the nets. 

Other species are smooth and slippery, such as eel, sandeel and eelpout, and are only 

caught in the gill nets if an operculum is trapped in the mesh or the fish becomes 

entangled. A species such as eel is therefore only rarely caught in the type of sampling 

nets used in this study. 

Biological sampling nets and fyke nets are passive gear (stationary), the catch therefore 

depends on the behaviour of the fish. This behaviour in turn depends on many factors, 

including the weather conditions, disturbances, etc. 

Because of the above-mentioned biases in catching efficiency due to the gear and 

sampling methods, it must be emphasised that the catch does not necessarily reflect the 

actual fish population with regard to species composition and size. 

Another possible source of uncertainty in the BACI design is the possible lack of 

homogeneity between the wind farm area and the reference area. The nature of the 

reference area, composed of four sub-areas, calls for greater variation in the catches, 

compared to the wind farm area when regarded as one complete area. 



3.8. Results - Fish study 

3.8.1. Observations 

The samplings in May and June 2001 were made difficult by the large occurrence of 

algae (Ectocarpus siliculosus / Pilayella littoralis). In addition, shore crabs (Carcinus 

maenas) were caught in large numbers. Algae and shore crabs entangled in the fishing 

gears are expected clearly to decline the catch efficiency of both gill nets and fyke nets. 

3.8.2. Overview of catches 

In all 26 species were registered during the sampling in May and June in the wind farm 

area and the reference area. Table 3.8.1 show the total catch of each species with respect 

to area, time and type of fishing gear. 

A total of 785 individuals divided among 23 species with a total weight of 47.4 kg were 

caught in the wind farm area. In the reference area, the corresponding total catch was 

685 individuals with a total weight of 67.8 kg divided among 24 different species. 

The largest number of a single category was 157 great sandeel caught in gill nets in the 

reference area in May. In addition, there were relatively large catches of cod, small 

sandeel, short-spined seas scorpion and flounder in gill nets and eelpout as regards fyke 

nets. 

There were relatively few individual catches of some species, which limits the 

possibility for statistical assessment. This problem has been thoroughly investigated in 

the section 3.10.2. 





Table 3.8.1. The total catche in fish study distributed with respect to time, area and 

applied gears. 

Gill net 

Wind farm area 

Fyke net 

May 

Gill net 

Reference area 

Species Number Weight (g) Number Weight (g) Number Weight (g) Number Weight (g) Number Weight (g) Number Weight (g) Number Weight (g) Number Weight (g) 

Baltic herring 

1 68 0 0 9 524 0 0 3 272 0 0 10 425 0 0 

Brisling 

3 75 0 0 5 61 0 0 15 99 0 0 8 95 0 0 

Common eel 

0 0 4 381 0 0 0 0 0 0 1 108 0 0 2 566 

Hornfish 

27 11340 0 0 49 20917 0 0 2 312 0 0 5 1125 0 0 

Snake pipefish 

0 0 0 0 0 0 0 0 0 0 1 11 0 0 0 0 

Straightnose pipefish 

0 0 1 1 0 0 0 0 2 2 1 1 0 0 1 0 

Fifteen-spined stickleback 2 18 15 113 2 12 5 32 0 0 6 49 0 0 3 19 

Whiting 

0 0 0 0 1 200 0 0 0 0 0 0 0 0 0 0 

Atlantic cod 

16 2051 6 630 61 16670 9 920 4 536 2 117 6 694 12 1960 

Small sandeel 

4 55 0 0 12 122 1 9 71 985 0 0 35 1065 0 0 

Great sandeel 

30 388 0 0 157 2076 0 0 44 506 0 0 49 737 0 0 

Two-spotted goby 

0 0 22 15 0 0 10 9 0 0 21 19 0 0 23 13 

Sand goby 

8 13 2 3 7 11 7 13 5 19 13 16 5 5 7 7 

Black goby 

4 26 1 4 1 4 0 0 0 0 0 0 0 0 2 17 

Transparent goby 

0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 

Goby.sp. 

0 0 0 0 1 2 1 1 0 0 0 0 0 0 0 0 

Butterfish 

1 18 2 27 0 0 0 0 0 0 0 0 0 0 2 25 

Eelpout 

8 208 100 3532 2 140 17 938 3 32 63 848 0 0 17 297 

Short-spined sea scorpion 127 13823 40 3528 42 4788 18 1858 15 1071 3 258 8 777 5 417 

Longspined bullhead 

9 322 5 177 2 107 2 36 4 144 3 72 3 98 3 84 

Hooknose 

1 14 0 0 0 0 1 16 0 0 0 0 0 0 0 0 

Lumpsucker 

1 214 0 0 1 1074 0 0 0 0 0 0 0 0 0 0 

Turbot 

14 1539 2 370 4 1305 1 196 1 58 0 0 6 1896 1 82 

Flounder 

12 843 17 1117 20 2414 13 1528 12 681 3 220 6 427 6 363 

European plaice 

1 27 0 0 0 0 0 0 0 0 0 0 0 0 2 20 

Sea trout 

0 0 0 0 0 0 0 0 0 0 0 0 1 634 0 0 

Total 

269 31041 217 9899 376 50427 85 5555 181 4716 118 1720 142 7977 86 3870 

Fyke net 

Gill net 


Fyke net 

June 

Gill net 


Fyke net 





3.8.3. Selection of indicator species 

On the basis of the Baseline programme (Bio/consult 2001b) a number of fish species, 

which are more important due to their high occurrence, commercial interest, indicatorcharacter, 

specific behaviour etc., have been proposed as indicator selected species in 

the fish study. The following species is proposed: Atlantic cod, turbot, flounder, dab, 

brisling and herring. Other selected species will be discussed in the baseline study of fry 

(this report) and Baseline study of fish at the cable trace (Bio/consult 2003a). 

These species and groups should form a part of the test of the 0-effect hypotheses with 

regard to the possible effects caused by reflections, noise and vibrations (brisling and 

herring) (Bio/consult 2001c), changes in the food basis (Atlantic cod, turbot, flounder, 

small fish and fish fry), sediment (brisling and herring) (Bio/consult 2001d), 

electromagnetic fields (eel) (Bio/consult 2002; 2003a) and possible unexpected effects 

(small fish such as goby and fish fry). The abundance of a species is also an important 

parameter with respect to its selection as indicator species. 

On the bases of the results in the present baseline study the list of selected species might 

be reconsidered compared to the list given in the baseline program. The criteria for 

selection is based on evaluation the following conditions: 

• The total number caught in the study 

• The ecological aspects of the species 

• Behaviour pattern of the fish, whether it is stationary or it is migratory 

• Commercial interest 

• Proposed selected species by the “Baseline Program for fish and fishery” . 

A total of 26 different species was caught. Most of the species were caught in relatively 

modest numbers. This fact limits the possibility for making statistic assessment in 

concern to the general abundance of these species. 

Including the selected species by the baseline programme a total of nine species are 

selected for further analysis. Table 3.8.2 Shows the selected species and outline roughly 

their preferred habitat. 

3.8.3.1.1 Species 

Behaviour Preferred habitat 

Stationary 

Migratory 

Boulder 

reef 

Gravel Sand Soft 

Table 3.8.2. Overview of the selected species and their spatial behaviour and choice of habitat. 


Vegetation 

Remarks 

Baltic herring X Pelagic 

Brisling X Pelagic 

Atlantic cod X X X X X X X Costal600m 

Small sandeel X (X) X X Costal 

Great sandeel X (X) X Costal 

Eelpout XX X X X Costal. 

Short-spined sea scorpion X X X X X Costal200 m. 

Turbot X X X X Depths >5m. 

Flounder X X X X Costal


3.8.4. Individual selected species 

In the following sections concerning the individual selected species, figures showing 

thematic maps of CPUE number and CPUE weight are for the sake of clearness 

expressed as CPUE values with an effort of one of each of the four transects in the wind 

farm area (from west transect 1 to 4) and reference area (from west transect 5 to 8), 

respectively. Be aware of that CPUE values used in statistics are representing an effort 

of each of the 24 stations. 

3.8.4.1 Baltic herring (Clupea harengus) 

The Baltic herring was caught in relatively modest numbers. Highest CPUE values were 

obtained at transects eight in the Gedser reef area. 

q 

Cabletrace 



Wind mills 

q q 

q q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

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q q 

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q q q 

q q 

q q 

q q q q 

q q 

q q q 

q q 

q q 

q q q 

q q 

q q q 

q q 

q q q 

q q 

q q 


CPUE number 

1 

CPUE weight 

60 

Figure 3.8.1. Thematic map showing CPUE number and CPUE weight of Baltic herring grouped in 

eight transects, four within the area of the wind farm and four within the reference area. 


May 

June 

May 

June


Figure 3.8.2. Length distribution of Baltic herring in the wind farm area and reference area in May 

and June. 

Most of the Baltic herring caught were regenerating their gonads at the time of 

sampling, and only one male had spent recently. 

Gonadeindeks N mean 

May Juv. Juvenile gonads 

June 

Number 

Number 

Male 

Female 

Juv. 

4 

3 

2 

1 

4 

3 

2 

1 

Male 

Female 

May 

sexual diff. 

sexual diff. 

intermediary 

sexual diff. 

Juvenile gonads 

Spent 

sexual diff. 

intermediary 

Længde 

(cm) 

K. F. 

mean 



Wind farm area Reference area 

May 

June June 

0 10 20 30 40 50 

Total length (cm) 

Gonadevægt (g) 

mean stdv 

% Gonadevægt 

mean stdv 

N mean 

1 20,0 ,70 ,0 ,0 

2 24,5 ,57 ,9 ,5 ,9 ,06 

1 20,5 ,70 ,6 1,0 

1 21,5 ,68 9,2 13,5 2 19,5 ,67 6,0 2,4 11,8 3,47 

3 18,8 ,67 ,4 ,3 ,8 ,51 

3 10,5 ,67 ,0 ,0 ,0 ,00 

1 25,0 ,68 1,8 1,7 

2 26,3 ,75 1,0 ,2 ,7 ,20 5 17,2 ,63 ,3 ,4 ,5 ,18 

1 24,6 ,67 ,6 ,6 

Table 3.8.3. Average of length, condition factor, weight and percentage of gonads as a function of 

the gonad index for Baltic herring. 


Længde 

(cm) 

K. F. 

mean 


Gill net 

0 10 20 30 40 50 



mean stdv 

% Gonadevægt 

mean stdv


3.8.4.2 Brisling (Sprattus sprattus) 

The catch of brisling were low but relative even of distributed throughout all transects. 

The pelagic species exhibit great spatial distribution often resulting in huge catches or 

no catch at all. The limited catch unevenly distributed in space and time could reflect 

their shoaling behaviour. 

q 

Cabletrace 



Wind mills 

q q 

q q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q q 

qq 

q q 

q q 

q q q 

q q 

q q q 

q q 

q q 

q q q q 

q q 

q q q 

q q 

q q 

q q q 

q q 

q q q 

q q 

q q q 

q q 

q q 

Brisling 

CPUE number 

1 

CPUE weight 

10 

Figure 3.8.1.3. Thematic map showing CPUE number and CPUE weight of Brisling grouped in eight 

transects four within the area of the wind farm and four within the reference area. 


May 

June 

May 

June


Number 

Number 

6 

4 

2 

0 

6 

4 

2 

Brisling 


May May 

June June 

0 

0 5 10 15 20 25 



Gill net 

0 5 10 15 20 25 


Figure 3.8.4. Length distribution of brisling in the wind farm area and reference area in May and 

June. 

Most of the brislings were regenerating their gonads. A single individual had mature 

genitals and a few were in the intermediary stage, which correlates with a diffuse 

spawning season for this species. 



June 

Male 

Female 

Juv. 

Male 

Female 

sexual diff. 

intermediary 

Mature 


sexual diff. 

sexual diff. 

Længde 

(cm) 

K. F. 

mean 



mean stdv 

1 13,6 ,64 ,1 ,6 

% Gonadevægt 

mean stdv 

1 10,6 ,58 ,0 ,0 

2 15,3 ,48 2,3 ,3 16,8 12,33 2 15,3 ,56 1,4 1,0 6,7 4,00 

6 10,6 ,63 ,1 ,0 1,3 ,15 

Længde 

(cm) 

N mean 

1 11,3 ,85 1,6 13,0 

2 9,3 1,24 ,0 ,0 ,0 ,00 

3 9,5 ,61 ,1 ,0 2,0 ,79 4 13,3 ,66 ,1 ,1 ,9 ,45 

K. F. 

mean 



mean stdv 

% Gonadevægt 

mean stdv 


the gonad index for brisling.


3.8.4.3 Atlantic cod (Gadus morhua) 

High CPUE values for Atlantic cod are found at transect 5, in the Hyllekrog area. The 

CPUE values at transect 5 is nearly three times the values of the second highest CPUE 

located in the wind farm area. 

13 

13 

14 14 14 14 

14 14 

15 

15 

q 

Cabletrace 



Wind mills 

16 

16 

17 

17 17 

18 

18 18 

q q 

q q qqq 

q 

q 

q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q q 

qq 

q q 

q q 

q11 

q q 

q q qqq 4 

q q 7 

q q 

10 10 10 

10 

q 

q q q 

q22 

q q 

q 

q 

q q5 

q q 8 

q q q 

q q 11 

11 

q q q 

q q 

q q3 

q 

q q 

q6 

q99 

12 12 12 

12 12 


19 

19 19 

20 20 20 

20 20 

21 

21 21 

Atlantic cod 

CPUE number 

3 

CPUE weight 

1.000 

Figure 3.8.5. Thematic map showing CPUE number and CPUE weight of Atlantic cod grouped in 


At least three cohorts (size classes) of Atlantic cod were caught in the reference area in 

May (Figure 3.8.6). The juvenile cod was primarily caught in fyke nets in both wind 

farm and the reference area. The Atlantic cods caught in transect 8 consist of mainly 

lager individuals. A likely explanation to the high CPUE weight relative to CPUE 

number, is that a 60 cm cod was caught here, resulting in an over estimate of the general 

size of the Atlantic cods caught in transect 8. 

May 

June 

22 22 22 22 

22 22 

23 23 23 23 

23 23 

24 24 24 

24 24 

May 

June


Number 

Number 

6 

4 

2 

0 

6 

4 

2 

Atlantic cod 


May May 

June June 

0 

0 15 30 45 60 75 



Gill net 

Fyke net 

0 15 30 45 60 75 


Figure 3.8.6. Length distribution of Atlantic cod in the wind farm area and reference area respectively 

in May and June. 

The main part of the Atlantic cod caught in both areas was less than 30 cm (Figure 

3.8.6). According to Table 3.8.5, only few fish processed intermediary gonads or had 

spent recently. 

Længde 

(cm) 

K. F. 



% Gonadevægt 

Gonadeindeks N mean mean mean stdv mean stdv N mean mean mean stdv mean stdv 

May Juv. Juvenile gonads 6 23,0 ,87 ,0 ,0 ,0 ,00 27 22,4 ,90 ,0 ,0 ,0 ,00 

Male sexual diff. 2 20,9 ,98 ,3 ,1 ,3 ,03 7 31,1 ,96 1,7 1,7 ,6 ,56 

intermediary 

1 38,0 ,94 7,3 1,4 

Female sexual diff. 12 23,5 ,93 ,8 1,3 ,5 ,30 19 28,7 1,00 2,0 2,1 ,8 1,30 

intermediary 

2 40,5 ,88 6,4 ,5 1,2 ,74 

Spent 

1 31,0 1,12 6,7 2,0 

June Juv. Juvenile gonads 

6 22,6 ,93 ,0 ,0 ,0 ,00 

Male sexual diff. 1 30,1 ,94 ,2 ,1 3 20,2 ,91 ,1 ,1 ,2 ,16 

Female sexual diff. 5 20,1 ,92 ,3 ,1 ,4 ,07 7 23,9 ,93 ,8 1,1 ,4 ,12 

Spent 

1 23,0 ,89 ,4 ,4 

Længde 

(cm) 

K. F. 



% Gonadevægt 

Table 3.8.5. Average of length, condition factor, weight, percentage of gonads as a function of the 

gonad index for Atlantic cod.


3.8.4.4 Small sandeel (Ammodytes tobianus) 

The small sandeel was mainly caught in June and close to the wind farm area. All 

except one of the small sandeels were caught in gill nets. 

q 

Cabletrace 



Wind mills 

13 

14 

15 

16 

17 17 

18 

q q 

q q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q qq 

q 

qq 

q q 

q q 

q1 

q 

q q 

q 

4 

q q q7 

q q q 10 

q 

q q q q 

q2q 

q 

q 

q 

q q5 

q q8 

q q q 

q q 11 

q q q 

q q 

q q3 

q 

q 6q 

q q9 

12 


19 

19 

20 20 

21 21 

P2148\mapinfo\Small_sandeel_fish.WOR 


CPUE number 

2 

May 

June 

22 

23 

24 24 24 24 24 24 

CPUE weight 

100 

Figure 3.8.7. Thematic map showing CPUE number and CPUE weight of small sandeel grouped in 


Similar to other pelagic species it was very difficult to evaluate the population size and 

structure of the small sandeel due to the spatial variation in the catch. The distribution 

of length within the population was narrow, indicating that the catches came from the 

same shoal. 

May 

June


Number 

Number 

20 

15 

10 

5 

0 

20 

15 

10 

5 



May May 

June June 

0 

0 5 10 15 20 25 



Gill net 

Fyke net 

0 5 10 15 20 25 


Figure 3.8.8. Length distribution of small sandeel in the wind farm area and reference area in May 


The gonad weight of the small sandeels was highest in June, and the major part caught 

in the wind farm area had already spent, whereas only a few from the reference area had 

spent. 



June 

Male 

Female 

Juv. 

Male 

Female 

sexual diff. 

sexual diff. 


sexual diff. 

intermediary 

Spent 

sexual diff. 

intermediary 

Spent 

Længde 

(cm) 

K. F. 

mean 



mean stdv 

% Gonadevægt 

mean stdv 

Længde 

(cm) 

N mean 

1 12,1 ,30 ,1 1,9 

1 14,7 ,31 ,1 1,0 5 15,1 ,29 ,1 ,0 1,2 ,44 

6 15,2 ,30 ,1 ,0 1,0 ,41 

2 14,8 ,38 ,0 ,0 ,0 ,00 1 14,7 ,35 ,0 ,0 

3 14,1 ,36 ,1 ,1 1,4 ,62 8 15,6 ,37 ,1 ,1 ,9 ,49 

4 16,2 ,37 ,8 ,4 4,7 1,97 1 16,2 ,38 ,7 4,4 

3 15,2 ,35 ,8 1,1 6,7 9,64 1 18,3 ,26 ,1 ,6 

10 15,6 ,34 ,2 ,1 1,4 ,33 11 16,0 ,33 ,1 ,1 1,0 ,48 

2 16,5 ,38 ,9 ,1 4,9 ,75 2 17,4 ,34 ,5 ,0 2,8 ,21 

7 15,9 ,39 ,1 ,0 ,8 ,28 2 16,1 ,34 ,1 ,0 ,7 ,12 

K. F. 

mean 



mean stdv 

% Gonadevægt 

mean stdv 

Table 3.8.6. Average of length, condition factor, weight and percentage of gonads as a function of the 

gonad index for small sandeel.


3.8.4.5 Great sandeel (Hyperoplus lanceolatus) 

The great sandeel had a complementary distribution in relation to the small sandeel, 

with larger catches in the reference areas compared to the of the wind farm area. 

Similar to the small sandeel the variation of the number caught was highly variable. 

q 

Cabletrace 



Wind mills 

13 

14 

15 

16 

17 

18 18 18 18 18 18 18 

q q 

q q q 

q q 

q 

q q 

q 

q q 

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q q 

q 

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q 

q qq 

q 

qq 

q q 

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q1 

q 

q q 

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4 

q q q7777777 

q q q 10 

q 

q q q q 

q2q 

q 

q 

q 

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q q q 

q q 

q q3 

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q 6q 

q q9 

12 


19 

19 

20 20 

21 21 

P2148\mapinfo\Great_sandeel_fish.WOR 


CPUE number 

5 

May 

June 

CPUE weight 

140 

Figure 3.8.9. Thematic map showing CPUE number and CPUE weight of great sandeel grouped in 


22 22 22 22 22 22 22 

23 

24 24 24 24 24 24 

May 

June


Number 

Number 

25 

20 

15 

10 

5 

25 

20 

15 

10 

5 

May May 

June 



0 10 20 30 40 50 



June 

Gill net 

0 10 20 30 40 50 


Figure 3.8.10. Length distribution of great sandeel in the wind farm area and reference area in May 


Most of the great sandeels were in the intermediary stage with respect to their gonads, 

and only a minority has spent. 



June 

Male 

Female 

Juv. 

Male 

Female 

sexual diff. 

intermediary 

Mature 

Spent 

sexual diff. 

intermediary 

Mature 


sexual diff. 

intermediary 

Mature 

Spent 

sexual diff. 

intermediary 

Mature 

Spent 

Længde 

(cm) 

K. F. 

mean 



mean stdv 

13 15,5 ,29 ,0 ,0 ,0 ,00 

1 23,3 ,19 ,2 ,8 19 17,4 ,45 ,0 ,1 ,3 ,98 

4 16,8 ,32 2,0 1,1 11,5 3,71 16 17,3 ,29 1,5 ,7 10,1 4,02 

4 16,9 ,30 2,2 1,2 13,8 2,59 

2 17,0 ,26 ,1 ,0 ,8 ,08 

2 13,6 ,26 ,1 ,0 1,6 ,52 12 16,8 ,40 ,1 ,3 1,3 2,40 

3 17,7 ,24 ,8 ,6 5,6 1,50 15 18,8 ,27 1,8 1,2 9,6 4,60 

5 17,9 ,30 2,8 1,3 16,5 7,96 

3 14,1 ,27 ,0 ,0 ,0 ,00 

8 14,1 ,29 ,1 ,1 1,9 1,03 3 14,7 ,29 ,5 ,6 5,2 6,24 

7 17,5 ,29 1,5 ,9 8,9 1,65 10 18,5 ,27 1,7 1,0 9,5 3,58 

1 18,3 ,32 ,1 ,5 

% Gonadevægt 

mean stdv 

1 16,7 ,29 1,5 11,1 

7 15,6 ,26 ,2 ,1 1,7 1,04 11 15,3 ,29 ,1 ,1 1,3 ,76 

6 17,4 ,27 1,0 ,9 6,1 2,74 9 21,4 ,26 2,0 1,2 7,3 1,43 

1 15,8 ,29 ,7 6,2 1 17,0 ,30 2,5 16,9 

2 15,3 ,31 ,2 ,1 1,3 ,33 

Længde 

(cm) 

N mean 

K. F. 

mean 



mean stdv 

% Gonadevægt 

mean stdv 


the gonad index for great sandeel.


3.8.4.6 Eelpout (Zoarces viviparous) 

The catches of the eelpout was not surprisingly greatest in the fyke nets, and especially 

the wind farm area contributed with high CPUE values. 

q 

Cabletrace 



Wind mills 

13 

13 

14 

14 

15 15 

15 

16 

16 

17 

17 

18 

18 

q q 

q q qqq 

q 

q 

q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q q 

qq 

q q 

q q 

q11 

q q 

q q qqq 4 

q q777 

q q 

10 10 10 

10 

q 

q q q 

q22 

q q 

q 

q 

q q5 

q q 8 

q q q 

q q 11 

11 

q q q 

q q 

q q3 

q 

q q 

q6 

q99 

12 12 12 

12 12 


19 

19 19 

20 

20 20 

21 

21 21 

Eelpout 

CPUE number 

4 

May 

June 

CPUE weight 

200 

Figure 3.8.11. Thematic map showing CPUE number and CPUE weight of eelpout grouped in eight 

transects four within the area of the wind farm and four within the reference area. 

Most of the eelpout caught in May were large mature individuals whereas mainly small 

individuals were caught in June. 

22 

22 

23 23 23 

23 23 

24 24 

24 24 

May 

June


Number 

Number 

15 

10 

5 

0 

15 

10 

5 

May May 

June 

Eelpout 


0 

0 10 20 30 40 50 


Gill net 

Fyke net 


June 

0 10 20 30 40 50 


Figure 3.8.12. Length distribution of eelpout in the wind farm area and reference area respectively 

May and June. 

The fact that the eelpout is viviparous complicated the field examinations by making it 

difficult to distinguish a spent individual from an individual in the early development of 

the gonads before the stage referred to as intermediate. 

Længde 

(cm) 

K. F. 



% Gonadevægt 


May Juv. Juvenile gonads 5 13,1 ,42 ,0 ,0 ,0 ,00 6 22,3 ,49 ,0 ,0 ,0 ,00 

Male sexual diff. 28 17,8 ,49 ,4 ,4 1,3 ,66 3 16,4 ,42 ,2 ,2 ,8 ,67 

intermediary 7 21,3 ,49 1,2 ,9 2,3 ,63 3 23,1 ,44 1,2 ,5 2,1 ,55 

Female sexual diff. 19 19,2 ,49 ,4 ,3 1,0 ,46 2 21,2 ,49 ,5 ,4 ,9 ,20 

intermediary 10 21,1 ,45 ,6 ,5 1,0 ,42 6 22,4 ,49 ,5 ,3 1,0 ,32 

Mature 

1 16,0 ,26 1,9 17,8 

June Juv. Juvenile gonads 4 8,7 ,39 ,0 ,0 ,0 ,00 4 9,5 ,42 ,0 ,0 ,0 ,00 

Male sexual diff. 4 16,0 ,48 ,4 ,1 1,9 ,83 1 22,1 ,40 1,0 2,3 

intermediary 6 19,5 ,46 ,6 ,5 2,0 1,22 1 16,0 ,46 ,5 2,6 

Female sexual diff. 7 20,0 ,44 ,3 ,3 ,8 ,24 2 22,8 ,50 ,6 ,2 ,9 ,30 

Spent 

6 19,1 ,49 ,4 ,1 1,1 ,14 1 20,7 ,28 ,1 ,4 

Længde 

(cm) 

K. F. 



% Gonadevægt 


the gonad index for eelpout.


3.8.4.7 Short-spined sea scorpion (Myoxocephalus scorpius) 

The short-spined sea scorpion is represented with high CPUE values in the wind farm 

area mainly in May. In transects 5 and 6 low CPUE values were found in the western 

part of the area. In June low values were obtained from all transects. 

q 

Cabletrace 



Wind mills 

13 

13 

14 

14 

15 15 

15 

16 

16 

17 

17 

18 

18 

q q 

q q qqq 

q 

q 

q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q q 

qq 

q q 

q q 

q11 

q q 

q q qqq 4 

q q777 

q q 

10 10 10 

10 

q 

q q q 

q22 

q q 

q 

q 

q q5 

q q 8 

q q q 

q q 11 

11 

q q q 

q q 

q q3 

q 

q q 

q6 

q99 

12 12 12 

12 12 


19 

19 19 

20 

20 20 

21 

21 21 

Short-spined sea scorpion 

CPUE number 

5 

May 

June 

22 

22 

23 23 23 

23 23 

24 24 

24 24 

CPUE weight 

1.000 

Figure 3.8.13. Thematic map showing CPUE number and CPUE weight of Short-spined sea scorpion 

grouped in eight transects, four within the area of the wind farm and four within the 

reference area. 

May 

June


Number 

Number 

15 

10 

5 

0 

15 

10 

5 



May May 

June June 

0 

0 10 20 30 40 50 



Gill net 

Fyke net 

0 10 20 30 40 50 


Figure 3.8.14. Length distribution of short-spined sea scorpions in the wind farm area and reference 

area in May and June. 

Most of the individuals caught were sexually differentiated and probably in the process 

of regenerating their gonads, because only five had spent recently. 

Længde 

(cm) 

K. F. 



% Gonadevægt 


May Juv. Juvenile gonads 7 16,6 1,50 ,0 ,0 ,0 ,00 11 17,0 1,51 ,0 ,0 ,0 ,00 

Male sexual diff. 12 17,1 1,40 ,4 ,4 ,6 ,64 8 17,7 1,52 ,3 ,5 ,3 ,33 

Spent 

1 18,2 1,53 ,1 ,1 

Female sexual diff. 14 20,4 1,43 1,5 1,2 1,1 ,83 8 19,8 1,47 1,1 ,7 1,0 ,49 

intermediary 9 19,8 1,50 1,5 ,6 1,2 ,36 6 21,8 1,43 2,2 1,1 1,4 ,33 

Spent 

2 21,1 1,42 1,6 ,4 1,2 ,35 7 20,7 1,63 ,7 ,7 ,5 ,53 

June Male sexual diff. 7 18,0 1,40 ,2 ,1 ,2 ,12 1 16,8 1,45 ,2 ,3 

Spent 

2 17,5 ,85 ,2 ,1 ,3 ,01 5 17,8 1,38 ,1 ,0 ,2 ,04 

Female sexual diff. 7 16,9 1,33 ,6 ,4 ,8 ,27 4 19,4 1,40 ,9 ,7 ,8 ,42 

Spent 

2 18,4 1,26 ,4 ,4 ,4 ,40 3 19,1 1,50 ,7 ,6 ,6 ,42 

Længde 

(cm) 

K. F. 



% Gonadevægt 


the gonad index for short-spined sea scorpion.


3.8.4.8 Turbot (Psetta maxima) 

Few individuals of turbot were caught, despite the use of special designed turbot net. 

q 

Cabletrace 



Wind mills 

q q 

q q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q q 

qq 

q q 

q q 

q q q 

q q 

q q q 

q q 

q q 

q q q q 

q q 

q q q 

q q 

q q 

q q q 

q q 

q q q 

q q 

q q q 

q q 

q q 

Turbot 

CPUE number 

2 

CPUe weight 

260 

Figure 3.8.15. Thematic map showing CPUE number and CPUE weight of turbot grouped in eight 

transects four within the wind farm area and four within the reference area. 


May 

June 

May 

June


Number 

Number 

4 

3 

2 

1 

4 

3 

2 

1 

Turbot 


May May 

June June 

0 10 20 30 40 50 


Gill net 

Fyke net 

0 10 20 30 40 50 


Figure 3.8.16. Length distribution of turbot in the wind farm area and reference area in May and June. 

According to the examination of the gonads of the turbots, most of them were found to 

be in the intermediary stage, though the biomass of the gonads was relatively large. 

Længde 

(cm) 

K. F. 



% Gonadevægt 


May Male sexual diff. 13 19,3 1,50 2,1 ,8 2,0 ,82 

intermediary 2 21,1 1,67 8,0 5,2 4,7 ,86 3 24,5 1,45 5,9 3,6 2,7 ,46 

Female intermediary 1 20,2 1,58 4,4 3,4 1 33,0 1,94 29,1 4,2 

June Male sexual diff. 1 15,4 1,60 ,4 ,7 4 20,9 1,52 1,2 ,8 ,8 ,13 

intermediary 

2 26,8 1,54 3,6 ,2 1,2 ,07 

Female intermediary 

1 35,5 1,77 79,7 10,0 


Længde 

(cm) 

K. F. 



% Gonadevægt 


the gonad index for Turbot.


3.8.4.9 Flounder (Platichthys flesus) 

The flounder was caught in reasonable numbers with highest values in May. The 

catches were distributed relatively equal throughout the transects. 

q 

Cabletrace 



Wind mills 

13 

13 

14 

14 

15 15 

15 

16 

16 

17 

17 

18 

18 

q q 

q q qqq 

q 

q 

q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q q 

qq 

q q 

q q 

q11 

q q 

q q qqq 4 

q q777 

q q 

10 10 10 

10 

q 

q q q 

q22 

q q 

q 

q 

q q5 

q q 8 

q q q 

q q 11 

11 

q q q 

q q 

q q3 

q 

q q 

q6 

q99 

12 12 12 

12 12 


19 

19 19 

20 

20 20 

21 

21 21 

Flounder 

CPUE number 

2 

May 

June 

CPUE weight 

200 

Figure 3.8.17. Thematic map showing CPUE number and CPUE weight of Flounder grouped in eight 


The catch in fyke net was fairly similar to the catch in gill net for this species. 

22 

22 

23 23 23 

23 23 

24 24 

24 24 

May 

June


Number 

Number 

6 

4 

2 

0 

6 

4 

2 

Flounder 


May May 

June June 

0 

0 10 20 30 40 50 



Gill net 

Fyke net 

0 10 20 30 40 50 


Figure 3.8.18. Length distribution of flounders in the wind farm area and reference area in May and 

June. 

Practically all the flounders found were in the phase of regenerating their sexual glands. 

Only one of the flounders spent even though, it is quite late in the spawning season. 

Længde 

(cm) 

K. F. 



% Gonadevægt 


May Juv. Juvenile gonads 3 17,0 1,19 ,0 ,0 ,0 ,00 13 18,6 1,17 ,0 ,0 ,0 ,06 


Spent 

1 33,5 1,07 ,1 ,0 

Female sexual diff. 13 18,2 1,09 ,5 ,3 ,8 ,34 12 19,9 1,31 2,3 3,4 2,2 2,94 

June Juv. Juvenile gonads 2 19,1 1,22 ,0 ,0 ,0 ,00 1 12,2 1,31 ,0 ,0 


Female sexual diff. 9 16,7 1,08 ,3 ,3 ,6 ,40 6 18,1 1,19 ,2 ,1 ,3 ,11 

intermediary 

1 21,1 1,21 1,4 1,2 

Længde 

(cm) 

K. F. 



% Gonadevægt 

Table 3.8.10. Average of length, condition factor, weight, percentage of gonads as a function of the 

gonad index for flounder.


3.8.5. Comments on the remaining species 

No particularly rare species were caught in the present survey. 

The hornfish was not included as selected indicator species because of their extremely 

migratory behaviour, and because no specific stocks are strongly associated with Danish 

coastal waters in comparison to the Baltic herring. They do however, warrant 

mentioning due to the large abundance in the samples. 

The length of hornfish ranged from 50–80 cm. Most individuals caught during May 

were females with well-developed gonads whereas individuals caught in June primarily 

consisted of mature males. Due to the developmental stage of gonads, it is most 

probable that spawning occurs in both areas. Concerning reflection and shadowing from 

the mills the hornfish may be a potential indicator species because they reside near the 

water surface, are abundant and cover large areas. 

Only seven individuals of the common eel were caught, four in May three in June. All 

eels were small averaging 151 gram in weight. 

Figure 3.8.19. No particular rare species were caught during the execution of the baseline studies. 

Though, many species rarely seen by the public where caught in the area of Rødsand 

(Left; longspined bullhead: Right; Hooknose) 

3.8.6. Statistical comparison of areas. 

Figures 3.8.19. and 3.9.20 visualises the development of abundance and weight 

respectively over time between the wind farm area and reference area. This visualising 

can only be used as guidelines for most of the species because of the low catch 

numbers. 

There seems to be higher abundance of Baltic herring and Atlantic cod in the reference 

area, whereas eelpout seems to occur in higher number in the Wind farm area. As the 

only species, great sandeel and turbot indicate effect of interference for both number 

and weight, but according to the test results this seems not to be the case, probably 

caused by the low number caught. 

Effect of interference concerning number only occurs for the species short-spined sea 

scorpion and great sandeel, even though short-spined sea scorpion doesn’t indicate this 



fact according to the plot. Great sandeel on the other hand shows effect of interference 

in both the plot and the test. 

Estimated Marginal Means LN (number) 



,3 

,2 

,1 

0,0 


1,0 

,8 

,6 

,4 

,2 

0,0 


1,2 

1,0 

,8 

,6 

,4 

,2 

0,0 



(Clupea harengus) 

Atlantic cod 

(Gadus morhua) 


(Ammodytes tobianus) 

Month 

Reference 

Reference 

May 

June 

Month 

May 

June 

Month 

Reference 

May 

June 

0,0 


Brisling 

(Sprattus sprattus) 

June 

Reference 





,4 

,3 

,2 

,1 

1,6 

1,4 

1,2 

1,0 

,8 

,6 

,4 

,2 


2,0 

1,5 

1,0 

,5 

0,0 


Eelpout 

(Zoarces viviparus) 


(Myoxocephalus scorpius) 

Month 

May 

Month 

May 

June 

Reference 

Month 

Reference 

Figure 3.8.19. The marginal means of LN (number) of the catches visualised to see eventual parallel 

development in time and space (See appendix 3). To be continued on next page. 

May 

June




1,6 

1,4 

1,2 

1,0 


,7 

,6 

,5 

,4 

,3 

,2 

,8 

,6 

,4 



(Hyperoplus lanceolatus) 

Flounder 

(Platichthys flesus) 

Month 

Reference 

Month 

Reference 

May 

June 

May 

June 


Turbot 

(Psetta maxima) 

Reference 

Figure 3.8.19. The marginal means of LN (number) of the catches visualised to see eventual parallel 

development in time and space (See appendix 3). 



,3 

,2 

,1 

0,0 

Month 

May 

June


Estimated Marginal Means LN (weight) 

1,4 

1,2 

1,0 

,8 

,6 

,4 

,2 

0,0 





5 

4 

3 

2 

1 

0 

3,5 

3,0 

2,5 

2,0 

1,5 

1,0 



(Clupea harengus) 

Atlantic cod 

(Gadus morhua) 



Month 

Reference 

Reference 

May 

June 

Month 

Month 

May 

May 

June 

June 

Reference 

,3 


Brisling 

(Sprattus sprattus) 

June 

Reference 





,8 

,7 

,6 

,5 

,4 

5,0 

4,5 

4,0 

3,5 

3,0 

2,5 

2,0 

1,5 

1,0 


7 

6 

5 

4 

3 

2 

1 


Eelpout 




Month 

May 

Month 

May 

June 

Reference 

Month 

Reference 

Figure 3.8.20. The marginal means of LN (weight) of the catches visualised to see eventual parallel 

development in time and space (See appendix 4). To be continued on next page. 

May 

June





3,5 

3,0 

2,5 

2,0 

1,5 

1,0 

3,5 

3,0 

2,5 

2,0 

1,5 

1,0 

,5 




Flounder 


Month 

Reference 

May 

June 

Month 

Reference 

May 

June 


Turbot 

(Psetta maxima) 

Reference 

Figure 3.8.20. The marginal means of LN (weight) of the catches visualised to see eventual parallel 

development in time and space (See appendix 4). 

The statistical results are summarized in tables 3.8.11 and 3.8.12. Most species do not 

fulfilling the test assumption of variance homogeneity (marked 1) ) and should only be 

evaluated as a guidance of a trend. 


wind farm and reference areas. The eelpout and short-spined sea scorpion showed 

differences between the to areas, with highest catches in the wind farm area. Similarities 

in weight between the two areas appeared in brisling, small sandeel, short-spined sea 

scorpion, turbot and flounder, however the species small sandeel and turbot did not 

fulfil the test assumption of variance homogeneity. 

The length distribution of Atlantic cod, great sandeel and flounder was found to be 

different between the Wind farm and Reference areas. The mean length of Atlantic cod, 

small sandeel, great sandeel, turbot and flounder was larger in the reference area relative 

to the wind farm area. 



1,4 

1,2 

1,0 

,8 

,6 

,4 

,2 

0,0 

Month 

May 

June


Differences in the measured parameters over time occurred in some species. Both shortspined 

sea scorpion and flounder showed higher numbers and weight in May relative to 

June whereas the abundance of eelpout and turbot did not change. 

Differences in the length distribution over time only occurred with the Baltic herring, 

brisling and the eelpout. With respect to the eelpout, this is probably a result of a new 

cohort, which is also supported by the concurrent decline in the mean length. 

The mean length of the small sandeel increased between May and June, whereas the 

opposite was observed for short-spined sea scorpion and flounder. 

Species Number Weight 

Length 

distribution. 

Mean length. 

Baltic herring Ref > Wind 1) Ref > Wind 1) Ref = Wind Ref = Wind 

Brisling Ref = Wind 1) Ref = Wind Ref = Wind Ref = Wind 

Atlantic cod Ref > Wind Ref > Wind 1) Ref ≠ Wind Ref > wind 

Small sandeel Ref = Wind 1) Ref = Wind 1) Ref = Wind Ref > wind 

Great sandeel Ref > Wind 1) Ref > Wind 1) Ref ≠ Wind Ref > wind 

Eelpout Ref < Wind Ref < Wind 1) Ref = Wind Ref = Wind 

Short-spined sea scorpion Ref < Wind Ref = Wind Ref = Wind Ref = Wind 

Turbot Ref = Wind Ref = Wind 1) Ref = Wind Ref > wind 

Flounder Ref = Wind Ref = Wind Ref ≠ Wind Ref > wind 

1) No variance homogeneity. 

Red indicates rejection of the hypothesis of similarities. 

Table 3.8.11. The results of the statistical tests between areas at the 5 % significance level. (Appendix 

3 to appendix 6). 


Length 

distribution. 

Mean length. 

Baltic herring May = June 1) May = June 1) May ≠ June May = June 

Brisling May = June 1) May = June May ≠ June May > June 

Atlantic cod May > June May > June 1) May = June May = June 

Small sandeel May < June 1) May < June 1) May = June May < June 

Great sandeel May = June 1), 2) May = June 1) May = June May = June 

Eelpout May = June May > June 1) May ≠ June May > June 

Short-spined sea scorpion May > June 2) May > June May =June May >June 

Turbot May = June May = June 1) May = June May = June 

Flounder May > June May > June May = June May > June 


2) Effect of interaction. 


Table 3.8.12. The results of the statistical tests over time at the 5 % significance level. (Appendix 3 to 

appendix 6). 

Table 3.8.13 shows the results of the power analysis split into month for species caught 

in at least 50 % of the samples (one pelagic gill net, one benthic gill net, one standard 

fyke net and one fry fyke net). According to the fish study, only eelpout shows catches 

in 50% of the samples in both June and May. The sand lances show catches in 50% of 

the samples in June, whereas short-spined sea scorpion and flounder show catches in 



50% of the samples in May. The power analysis shows that a sample size of 12 stations 

in May is required to attain reliable results for flounder. 

Species Period 

# Stations per. 

Area 

80% power 

% Stations 

Number > 0 

% Stations 

Number= 0 

Small sandeel June 15 54.2 45.8 

Great sandeel June 15 60.4 39.6 

Eel pout May 14 70.8 29.2 

Eel pout June 10 64.6 35.4 

Short-spined sea scorpion May 24 83.3 17.7 

Flounder May 12 60.4 39.6 

Table 3.8.13. Results of the power-analysis giving 80% validity in detecting 50% change. 



3.9. Results - Fry study 

3.9.1. Observations 

During the spring sampling in May and June, nets were occasionally fouled by large 

quantities of drifting macro algae. 

In October, only 20 stations each with two replicates were sampled due to unfavourable 

weather conditions. The 20 stations were however, equally distributed between the 

study and reference areas. 

In September, a large number of jellyfish were caught together with the algae 

(Ectocarpus silicolosus/Pilayella littoralis). These two factors resulted in a reduction of 

the capture efficiency of the fishing gear during these periods. 

3.9.2. Overview of catches 

In all, 31 fish species were registered during the five sampling periods, in the wind farm 

area and the reference area. Table 3.9.1 shows the total catch of each species with 

respect to area, time and type of fishing gear. 

In the wind farm area, a total of 960 individuals distributed among 28 species with a 

total weight of 20.5 kg were caught. In the reference area, the corresponding total catch 

was 1234 individuals with a total weight of 19.1 kg distributed among 24 different 

species. 

Numerically, the sand goby was the fish species with the greatest overall abundance 

totalling 615 individuals in the reference area. 





Table 3.9.1. The total catches in fry study, separated in area and month. 


Common eel 

Snake pipefish 

Straightnose pipefish 

Great pipefish 

Lesser pipefish 

Broad-nosed pipefish 

Fifteen-spined stickleback 

Whiting 

Atlantic cod 


Sandeel sp. 




Painted goby 

Black goby 

Transparent goby 

Goby.sp. 

Butterfish 

Eelpout 



Hooknose 

Striped seasnail 

Turbot 

Dab 

Flounder 

European plaice 

Common sole 

Total 


Fyke net 

May 


Fyke net 


Fyke net 

June 


Fyke net 


Species Number Weight (g) Number Weight (g) Number Weight (g) Number Weight (g) Number Weight (g) Number Weight (g) Number Weight (g) Number Weight (g) Number Weight (g) Number Weight (g) 

Fyke net 

September 


0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 45 0 0 

4 381 0 0 1 108 2 566 1 37 5 1611 1 22 0 0 0 0 0 0 

0 0 0 0 1 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

1 1 0 0 1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 

0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 1 2 0 0 0 0 1 0 0 0 0 0 

0 0 0 0 0 0 0 0 1 1 4 5 3 5 1 2 1 0 5 4 

15 113 5 32 6 49 3 19 48 140 59 206 23 81 16 64 21 98 15 55 

0 0 0 0 0 0 0 0 0 0 0 0 1 24 3 123 0 0 0 0 

6 630 9 920 2 117 12 1960 3 395 2 625 5 126 5 469 4 118 2 34 

0 0 1 9 0 0 0 0 0 0 2 25 0 0 0 0 1 14 0 0 

0 0 0 0 0 0 0 0 0 0 0 0 1 19 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 0 0 0 0 1 17 0 0 0 0 1 4 

22 15 10 9 21 19 23 13 11 7 123 61 9 5 17 8 4 3 3 5 

2 3 7 13 13 16 7 7 12 15 36 29 261 258 76 85 35 40 489 370 

0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 

1 4 0 0 0 0 2 17 4 26 10 82 1 11 4 24 1 5 0 0 

0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 

0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

2 27 0 0 0 0 2 25 1 15 1 11 0 0 0 0 0 0 0 0 

100 3532 17 938 63 848 17 297 15 206 64 933 72 541 29 324 10 196 5 50 

40 3528 18 1858 3 258 5 417 11 1155 13 874 11 643 7 647 5 395 8 1039 

5 177 2 36 3 72 3 84 8 153 22 232 9 186 13 238 2 12 5 44 

0 0 1 16 0 0 0 0 0 0 0 0 1 27 3 61 0 0 2 45 

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 14 0 0 

2 370 1 196 0 0 1 82 1 91 0 0 1 144 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 51 1 173 

17 1117 13 1528 3 220 6 363 3 231 1 37 5 488 2 219 9 2843 7 867 

0 0 0 0 0 0 2 20 0 0 0 0 0 0 0 0 0 0 0 0 

0 0 0 0 0 0 0 0 1 61 0 0 0 0 0 0 0 0 0 0 

217 9899 85 5555 118 1720 86 3870 122 2535 342 4731 406 2596 178 2265 97 3834 543 2691 

Fyke net 


Fyke net 

October 


Fyke net 


Fyke net 

November 


Fyke net 





3.9.3. Selection of indicator species 

Most of the 31 species were caught in relatively modest numbers limiting the possibility 

of making statistical assessments in relation to the general abundance of these species. 

Seven species were selected from the data for further analysis, and are suggested as 

indicator species in the monitoring programme. 

The selection of species was based on: 

• The total number caught in the study 

• The ecological importance of the species 

• Behaviour pattern of the fish (whether the species is stationary or migratory) 

• The economical interests of the species. . 

Species represented with at least 65 individuals were selected. Only one species of 

flatfish, the flounder, fulfilled this criterion, and is therefore the only flatfish 

representing the Order (pleuronectiformes). 

The majority of the selected species in the fry study are stationary. Only two-spotted 

goby and flounder appear to exhibit more or less migratory behaviours. Water currents 

mostly determine migration by the two-spotted goby, whereas the migration of flounder 

among others is driven by its search for food. 

The flounder was the only selected species of economical interests. Atlantic cod and 

turbot were not selected due to the low abundances. 

The fifteen-spined stickleback is interesting because of their territorial behaviour, which 

makes them relatively stationary. The fifteen-spined sticklebacks also have an annual 

lifespan, and therefore respond quickly to eventual disturbances possibly connected to 

the wind farm. 

The sand and two-spotted goby were selected because they are placed in the lower part 

of the food web. 

The eelpout is chosen because of its stationary behaviour and it is easily sampled with 

fyke nets. Their stationary behaviour can be so strong that different races within narrow 

locations are formed. 

According to the baseline program, Atlantic cod among others were suggested as a 

possible indicator species. In this study however relatively few individuals of this 

species were caught. 

Silver eel were not represented in sufficient numbers even though more intensives 

sampling strategy relative to the preliminary study has been made. 



3.9.3.1.1 Species 

Behaviour Preferred habitat 

Stationary 

Migratory 

Boulder 

reef 

Gravel Sand Soft 

Table 3.9.2. Short overview of the spatial behaviour and choice of habitat for the selected species. 


Vegetation 

Remarks 

Fifteen-spined stickleback. X X X X X Costal200m 

Sand goby X X X Costal200m 

Two-spotted goby X (X) X X X (X) Costal200m 

Eelpout X X X X X Costal 

Short-spined sea scorpion X X X X Costal200m 

Longspined bullhead X X X Costal100m 

Flounder X X X X Costal 

Fifteen-spined stickleback. X X X X X Costal200m


3.9.4. Individual selected species 

In the following sections concerning the individual selected species, figures showing 

thematic maps of CPUE number and CPUE weight are for the sake of clearness 

expressed as CPUE values with an effort of one of each of the four transects in the wind 

farm area (from west transect 1 to 4) and reference area (from west transect 5 to 8), 

respectively. Be aware of that CPUE values used in statistics are representing an effort 

of each of the 24 stations. 

3.9.4.1 Fifteen-spined stickleback (Spinachia spinachia) 

The CPUE values for fifteen-spined sticklebacks at transect 5 tended to be lower, in 

comparison to the easterly transects. 

q 

Cabletrace 



Wind mills 

13 

13 

14 

14 

15 15 

15 

16 

16 

17 

17 

18 

18 

q q 

q q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q q 

qq 

q q 

q q 

q11 

q q 

q q 

q q q 7 

q q q44 

10 

10 10 

q 

q q q q 

q22 

q q 

q 

q 

q q5 

q q 8 

q q q 

q q 11 11 11 11 

11 11 

q q q 

q q 

q q33 

q 

q q 

q6 

q9 

12 12 12 

12 


CPUE number 

2 

May 

June 

September 

October 

November 


19 19 

19 

20 20 

20 

21 21 

21 

CPUE weight 

14 

May 

June 

September 

October 

November 

Figure 3.9.1. Thematic map showing CPUE number and CPUE weight of Fifteen-spined stickleback 

grouped in eight transects, four within the wind farm area and four within the reference 

area. 

According to their life history traits, the individuals caught in the Spring sampling have 

over-wintered, and during the period of sampling the males are assumed to be guarding 

newly spawned eggs, whereas the females die shortly after spawning. 

The abundance of fifteen-spined sticklebacks is higher in September due to the presence 

of a new cohort originating from the spring spawning. In Figure 3.9.2, it can be seen 

that the abundance of the population declines towards the onset of winter. 

22 22 

22 

23 

23 

24 

24


Number 

Number 

Number 

Number 

Number 

8 

6 

4 

2 

8 

6 

4 

2 

8 

6 

4 

2 

8 

6 

4 

2 

8 

6 

4 

2 

May 

June 

Fyke net 


May 

June 

September September 

October 



October 

November November 

0 5 10 15 20 25 


0 5 10 15 20 25 


Figure 3.9.2. The length distribution of the fifteen-spined stickleback by area and month.


3.9.4.2 Two-spotted goby (Gobiusculus flavescens) 

Overall, The CPUE number of two-spotted goby is slightly lower in the wind farm area 

in comparison to the reference area. 

q 

Cabletrace 



Wind mills 

13 

14 

15 

16 

17 17 17 17 17 

18 

q q 

q q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q qq 

q 

qq 

q q 

q q 

q1 

q q 

q q 

q q 

q 4 q7777777 

q q 10 

q 

q q q q 

q2q 

q 

q 

q 

q q5 

q q8 

q q q 

q q 11 

q q q 

q q 

q q3 

q 

q 6q 

q q9 

12 


19 19 

19 

20 20 20 20 

21 21 21 

Two spotted goby 

CPUE number 

8 

May 

June 

September 

October 

November 

CPUE weight 

2,5 

May 

June 

September 

October 

November 

Figure 3.9.3. Thematic map showing CPUE number and CPUE weight of two-spotted goby grouped 

in eight transects, four within the wind farm area and four within the reference area. 

This was particularly evident in September when a large number was caught in the 

reference area in comparison to the wind farm area. The number of captures in 

November was very low. The varying distribution in time and space is probably 

reflecting the semi-pelagic behavior of this species. 

22 

23 

24


Number 

Number 

Number 

Number 

Number 

60 

40 

20 

0 

60 

40 

20 

0 

60 

40 

20 

0 

60 

40 

20 

0 

60 

40 

20 



May May 

June June 

Fyke net 


October October 


0 

0 5 10 15 20 25 


0 5 10 15 20 25 


Figure 3.9.4. The length distribution of the two-spotted goby by area and month. 



3.9.4.3 Sand goby (Pomatoschistus minutus) 

Transects located farthest from the wind farm area exhibited lower CPUE values. This 

was especially true at transect 5 near Hyllekrog in the western part, which showed very 

low values during the entire sampling season (Figure 3.9.5). 

q 

Cabletrace 



Wind mills 

13 

13 

14 

14 

15 

15 

16 16 

16 

17 

17 

18 

18 

q q 

q q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q q 

qq 

q q 

q q 

q11 

q q 

q q 

q q q 7 

q q q44 

10 

10 

q 

q q q q 

q22 

q q 

q 

q 

q q5 

q q 8 

q q q 

q q 11 

11 

q q q 

q q 

q q33 

q 

q q 

q6 

q9 

12 12 12 

12 


19 19 19 19 

19 

20 20 

20 

21 21 

21 


CPUE number 

20 

May 

June 

September 

October 

November 

CPUE weight 

40 

May 

June 

September 

October 

November 

Figure 3.9.5. Thematic map showing CPUE number and CPUE weight of sand goby grouped in eight 

transects, four within the wind farm area and four within the reference area. 

Sand goby were predominantly caught in late autumn sampling. This is probably due to 

the seasonal distribution pattern originating from an annual lifecycle in which new 

cohorts become more apparent in the population in October and November (Figure 

3.9.6). 

22 22 

22 

23 

23 

24 24 24 

24


Number 

Number 

Number 

Number 

Number 

100 

75 

50 

25 

0 

100 

75 

50 

25 

0 

100 

75 

50 

25 

0 

100 

75 

50 

25 

0 

100 

75 

50 

25 



May May 

June June 



Fyke net 


0 

0 5 10 15 20 25 


0 5 10 15 20 25 


Figure 3.9.6. The length distribution of the sand goby by area and month. 



3.9.4.4 Eelpout (Zoarces viviparus) 

CPUE values for the eelpout tended to be relatively higher in May and June, particularly 

in the wind farm area. A relatively high abundance was also observed in October. In 

May, sexual mature individuals were the most abundant together with a few juveniles, 

whereas juvenile individuals dominated in the rest of the samples. In general, the 

eelpouts caught in May consisted primarily of well-fed or pregnant individuals, which 

could be determined by comparing the CPUE number with the CPUE weight for each 

month (Figure 3.9.7), and by looking at the length distribution (Figure 3.9.8). 

q 

Cabletrace 



Wind mills 

13 

13 

14 

14 

15 15 15 

15 

16 

16 

17 17 17 

17 

18 18 18 

18 18 

q q 

q q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q q 

qq 

q q 

q q 

q11 

q q 

q q 

q q q 7 

q q q44 

10 

10 10 

q 

q q q q 

q22 

q q 

q 

q 

q q5 

q q 8 

q q q 

q q 11 

11 11 

q q q 

q q 

q q33 

q 

q q 

q6 

q99 

12 

12 


19 19 19 

19 

20 20 

20 

21 21 

21 

Eelpout 

CPUE number 

3 

May 

June 

September 

October 

November 

CPUE weight 

180 

May 

June 

September 

October 

November 

Figure 3.9.7. Thematic map showing CPUE number and CPUE weight of eelpout grouped in eight 


The new cohort from spring spawning in May/June is clearly visible in the length 

distribution of the eelpout. This new cohort was also present in the September and 

October samples, whereas in November just a few individuals were left in the area as 

the population migrated to deeper areas nearby to spend the winter. 

22 22 

22 

23 

23 23 

24 24 24 

24


Number 

Number 

Number 

Number 

Number 

15 

10 

5 

0 

15 

10 

5 

0 

15 

10 

5 

0 

15 

10 

5 

0 

15 

10 

5 

Eelpout 


May May 

June June 




0 

0 10 20 30 40 50 


Fyke net 

0 10 20 30 40 50 


Figure 3.9.8. The length distribution of the eelpout by area and month. 



3.9.4.5 Short-spined sea scorpion (Myoxocephalus scorpius) 

In May, a relatively greater abundance of short-spined sea scorpions were caught, 

especially in transect 4 in comparison to the other sampling periods. 

The length distribution of the fish caught in May ranged from 15–25 cm. The shortspined 

sea scorpion was not observed in transect 5 in any of the sampling periods, and 

occurred only with minor CPUE in transect 6. In autumn juvenile fish were present in 

modest numbers. 

q 

Cabletrace 



Wind mills 

13 

13 

14 

14 

15 

15 

16 

16 

17 

17 

18 

18 

q q 

q q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q q 

qq 

q q 

q q 

q11 

q q 

q q 

q q q 7 

q q q44 

10 

10 10 

q 

q q q q 

q22 

q q 

q 

q 

q q5 

q q 8 

q q q 

q q 11 11 11 11 

11 11 

q q q 

q q 

q q33 

q 

q q 

q6 

q9 

12 12 12 

12 


19 19 

19 

20 20 

20 

21 21 

21 


CPUE number 

2 

May 

June 

September 

October 

November 

CPUE weight 

250 

22 22 

22 

23 

23 

24 

24 

May 

June 

September 

October 

November 

Figure 3.9.9. Thematic map showing CPUE number and CPUE weight of short-spined sea scorpion 


area.


Number 

Number 

Number 

Number 

Number 

5 

4 

3 

2 

1 

5 

4 

3 

2 

1 

5 

4 

3 

2 

1 

5 

4 

3 

2 

1 

5 

4 

3 

2 

1 

May 



May 

June June 




0 10 20 30 40 50 


Fyke net 

0 10 20 30 40 50 


Figure 3.9.10. The length distribution of the short-spined sea scorpion by area and month. 



3.9.4.6 Longspined bullhead (Taurulus bubalis) 

In the spring sampling (May and June) longspined bullhead was only present in minor 

numbers, and it was not observed in transect 5 during these periods. A large abundance 

of longspined bullhead was observed in autumn sampling, especially at transect 8 in 

September. 

q 

Cabletrace 



Wind mills 

13 

13 

14 

14 

15 

15 

16 16 

16 

17 

17 

18 

18 

q q 

q q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q q 

qq 

q q 

q q 

q11 

q q 

q q 

q q q 7 

q q q44 

10 

10 

q 

q q q q 

q22 

q q 

q 

q 

q q5 

q q 8 

q q q 

q q 11 

11 

q q q 

q q 

q q33 

q 

q q 

q6 

q9 

12 12 12 

12 


CPUE number 

1 

May 

June 

September 

October 

November 


19 19 19 19 

19 

20 20 

20 

21 21 

21 

CPUE weight 

20 

May 

June 

September 

October 

November 

Figure 3.9.11. Thematic map showing CPUE number and CPUE weight of longspined bullhead 


area. 

The length distribution ranged from 5-15 cm suggesting the presence of two cohorts. 

22 22 

22 

23 

23 

24 24 24 

24


Number 

Number 

Number 

Number 

Number 

4 

3 

2 

1 

4 

3 

2 

1 

4 

3 

2 

1 

4 

3 

2 

1 

4 

3 

2 

1 



May May 

June June 




0 5 10 15 20 25 


Fyke net 

0 5 10 15 20 25 


Figure 3.9.12. The length distribution of the longspined bullhead by area and month. 



3.9.4.7 Flounder (Platichthys flesus) 

q 

Cabletrace 



Wind mills 

13 

13 

14 

14 

15 

15 

16 

16 16 

17 

17 

18 

18 

q q 

q q q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q 

q q 

q q 

qq 

q q 

q q 

q11 

q q 

q q 

q q q 7 

q q q44 

10 10 10 

10 

q 

q q q q 

q22 

q q 

q 

q 

q q5 

q q 8 

q q q 

q q 11 

11 

q q q 

q q 

q q33 

q 

q q 

q6 

q9 

12 12 12 

12 


19 

19 

20 

20 

21 21 

21 

Flounder 

CPUE number 

1 

May 

June 

September 

October 

November 

CPUE weight 

200 

May 

June 

September 

October 

November 

Figure 3.9.13. Thematic map showing CPUE number and CPUE weight of Fifteen-spined stickleback 


area. 

The population consisted largely of adult individuals ranging from 15-30 cm. Only one 

juvenile smaller than 10 cm of length was caught in this investigation. Thirty 

individuals were caught during sampling in May, whereas only four individuals were 

caught during sampling in September. 

22 

22 

23 

23 

24 

24


Number 

Number 

Number 

Number 

Number 

5 

4 

3 

2 

1 

5 

4 

3 

2 

1 

5 

4 

3 

2 

1 

5 

4 

3 

2 

1 

5 

4 

3 

2 

1 

Flounder 


May May 

June June 




0 10 20 30 40 50 


Fyke net 

0 10 20 30 40 50 


Figure 3.9.14. The length distribution of the flounder by area and month. 



3.9.5. Comments on some the remaining species. 

Only 50 individuals of Atlantic cod were caught, which was lower compared to 

abundances observed during the initial screening. Only a few of the individuals were 

considered 0-group juveniles. 

The presence of five species from the order pipefish contributed to increase the overall 

fish diversity, however none of them were found in large numbers. 

Four other species of flatfish were caught besides flounder, including one individual of 

the common sole (Solea solea), which was not represented in the preliminary screening 

investigation. 

3.9.6. Statistical comparison of sampling areas. 

The catch data of all species suffer from lack of variance homogeneity, in consideration 

to ln(number) and ln(weight) and by that, it was not possible to show effects of 

interaction, which makes it possible to establish temporal and/or spatial differences 

(Table 3.9.3). Though, especially eelpout but also two spotted goby indicated effect of 

interaction, which is deduced by the plots (Figure 3.9.15. and 3.9.16). 


wind farm and reference areas. The eelpout and short-spined sea scorpion showed 

differences between the to areas, with highest catches in the wind farm area. Similarities 

in weight between the two areas appeared in brisling, small sandeel, short-spined sea 

scorpion, turbot and flounder, however the species small sandeel and turbot did not 

fulfil the test assumption of variance homogeneity. 

The length distribution of Atlantic cod, great sandeel and eelpout was found to be 

different between the Wind farm and Reference areas. The mean length of Atlantic cod 

and sand goby was larger in the wind farm area relative to the reference area. 





Estimated Marginal Means LN(number) 

1,2 

1,0 

,8 

,6 

,4 

,2 

0,0 


1,2 

1,0 

,8 

,6 

,4 

,2 

0,0 


0,0 


Estimated Marginal Means LN(number) 

1,6 

1,4 

1,2 

1,0 

,8 

,6 

,4 

,2 

,5 

,4 

,3 

,2 

,1 

0,0 


Fifteen spined stickleback 

(Spinachia spinachia) 


(Gobiusculcus flavescence) 


(Pomatoschistus minutus) 

Flounder 


Month 

Reference 

Reference 

May 

June 

Month 

Month 

May 

June 

September 

October 

November 

May 

June 

September 

October 

November 

November 

Reference 

Month 

Reference 

September 

October 

May 

June 

September 

October 

November 


Eelpout 

(Zoraces viviparus) 

Reference 





1,6 

1,4 

1,2 

1,0 

,8 

,6 

,4 

,2 

0,0 

1,0 

,8 

,6 

,4 

,2 

0,0 


,5 

,4 

,3 

,2 

,1 

0,0 





(Taurulus babulis) 

Month 

Reference 

May 

June 

September 

October 

November 

Month 

Reference 

Figure 3.9.15. The marginal means of LN (number) of the catches are displayed to help visualise 

eventual parallel development in time and space. 

May 

June 

Month 

September 

October 

November 

May 

June 

September 

October 

November


Estimated Marginal Means LN(weight) 



2,0 

1,5 

1,0 

,5 

0,0 


,8 

,6 

,4 

,2 

0,0 


0,0 


Estimated Marginal Means LN(weight) 

1,6 

1,4 

1,2 

1,0 

,8 

,6 

,4 

,2 

2,5 

2,0 

1,5 

1,0 

,5 

0,0 



(Spinachia spinachia) 


(Gobiusculus flavescence) 


(Pomatoschistus minutus) 

Flounder 


Month 

Reference 

Reference 

May 

June 

Month 

Month 

May 

June 

September 

October 

November 

May 

June 

September 

October 

November 

November 

Reference 

Month 

September 

October 

May 

June 

September 

October 

November 

Reference 


Eelpout 


Reference 





5 

4 

3 

2 

1 

0 

3,5 

3,0 

2,5 

2,0 

1,5 

1,0 

,5 

0,0 


1,4 

1,2 

1,0 

,8 

,6 

,4 

,2 

0,0 





(Taurulus babulis) 

Month 

Reference 

Reference 

May 

June 

September 

October 

November 

Month 

Month 

May 

June 

September 

October 

November 

May 

June 

September 

October 

November 

Figure 3.9.16. The marginal means of LN (weight) of the catches are displayed to help visualize 

eventual parallel development in time and space.


The length distributions were not significantly different (P < 0,05) in the wind farm and 

reference areas, except for two-spotted goby, sand goby and eelpout. The mean lengths 

of the majority of species between the two areas were also similar, except for twospotted 

goby and sand goby, which were larger in the wind farm area. 

Species 

Number Weight Length 

distribution 

Mean length. 

Fifteen-spined stickleback Ref = Wind 1) Ref = Wind 1) Ref = Wind Ref = Wind 

Two-spotted goby Ref = Wind 1) 2). Ref = Wind 1) 2). Ref ≠ Wind Ref < Wind 

Sand goby Ref = Wind 1) Ref = Wind 1) Ref ≠ Wind Ref < Wind 

Eelpout Ref < Wind 1) 2). Ref < Wind 1) 2). Ref ≠ Wind Ref = Wind 

Short-spined sea scorpion Ref = Wind 1) Ref = Wind 1) Ref = Wind Ref = Wind 

Longspined bullhead Ref = Wind 1) Ref = Wind 1) Ref = Wind Ref = Wind 

Flounder Ref = Wind 1) Ref = Wind 1) Ref = Wind Ref = Wind 


2) Effect of interactions between area and time. 


Table 3.9.3. Statistical tests results between areas at the 5 % significance level (Note, No variance 

homogeneity). (Appendix 3 to appendix 6). 

Table 3.9.4 show the results of the power analysis for species caught in at least 50 % of 

the samples (one standard fyke net and one fry fyke net). In this fry study, only the 

eelpout in June and September fulfils the 50% criterion. From Table 3.9.5 it appears that 

a minimum of 16 stations per. area should be applied to attain reliable results for 

eelpout, if the criterion of variance homogeneity is fulfilled. 


Fifteen-spined stickleback P < 0.05 P < 0.05 

Two-spotted goby P < 0.05** P < 0.05** 

Sand goby P < 0.05. P < 0.05 

Eelpout P < 0.05** P < 0.05** 

Short-spined sea scorpion P < 0.05 P < 0.05 

Longspined bullhead P < 0.05 P < 0.05 

Flounder P < 0.05 P < 0.05 

All species lack variance homogeneity. 

(**) Effect of interactions between area and time. 

Table 3.9.4. Statistical tests results in time at the 5 % significance level. (Note, No variance 

homogeneity) (Appendix 3 to appendix 6). 



Species Period 

# stations per. Area % Stations Number 

80% power 

> 0 

% Stations 

Number= 0 

Eelpout June 13 64.58 35.42 

Eelpout September 16 52.08 47.92 

Table 3.9.5. Results of the power-analysis giving 80% validity in detecting 50% change. At least 

50% of the samples have caught the species. 





3.10. Discussion 

3.10.1. Evaluation of methods 

Only a few adult turbot were caught, despite the use of an extra type of net especially 

aimed at catching turbot. According to the fishermen (Se section 4) the catches of turbot 

was abnormally low in 2001. Despite the lack of turbot in 2001, it is recommended to 

continue using these turbot nets because 2001 have been an abnormal year. 

Figure 3.10.1. Despite the use of specially designed net sections aimed to catch turbot (left) only a few 

were caught. Flounder (right) were caught in greater numbers due to higher abundance 

in the area of investigation. 

Overall, abundances in the fry study during spring were generally lower than catches in 

autumn. This would also be expected since fry from spring spawning species have 

reached a catchable size in the autumn. As would be expected the high occurrence of 

drifting filamentous algae (Ectocarpus siliculosus/Pilayella littoralis) in spring 

probably had a large impact on the efficiency of the fyke nets. This trend was observed 

in practically all species caught in the two spring sampling periods in May and June. In 

light of these observations it would be recommended to undertake a baseline study 

using the described method in periods without high abundance of drifting algae, which 

normally occurs from late May to August, depending on light, temperature, nutrient 

level and current conditions. Early spring or autumn would be periods that are more 

appropriate. 

3.10.2. Evaluation of statistics 

The statistical evaluation was made in two steps. The initial evaluation identified a 

number of problems with the data set. 

The classic analytical techniques used could not deal with empty samples. Furthermore, 

high variation was recorded in many samples, which posed a problem concerning the 

use of the classic statistical methods in the BACI design, which require homogeneity of 

the variance. 



After these problems were identified, the statistical part of the assignment was 

scrutinised and the possibility of other statistical tools and techniques were investigated 

(See Appendix 10). 

Based on this, two alternative statistical designs were tested and found reliable to 

answer one of the main questions of the baseline study; is the wind farm area and the 

reference area similar and are they of parallel progress? 


both univariate and multivariate statistical analysis can be carried out according to an 

either a parametric or a non-parametric test. The latter contains the group of the most 

recent developed extensive permutation tests. 

In relation to the classic parametric design used hitherto it was realized that the problem 

with obtaining normal distribution could be solved by dividing the fish species into 

functional groups (see section 3.10.4.1). 

A new technique using non-parametric multivariate analysis is discussed in the next 

sections with the objective to improve the utilization and level of information from the 

data, and to improve the sampling regime. 

3.10.2.1 Parametric vs. non-parametric 

3.10.2.1.1 Parametric tests 

To be used in this group of tests, the data are assumed to follow known statistical 

distribution with parameters to be estimated such as average or variance. The linear 

normal distributed models are the most commonly used. Among these is the classic 

analysis of variance known as ANOVA or MANOVA when data are either univariate or 

multivariate. It is assumed that the data follow a normal distribution, are independent 

and have equality of variances in a set of samples. It is assumed in the models that the 

effects from the various factors such as time and place affects the observed data linearly 

and in the BACI model as a sum of the effect from time and place and their interaction. 

In its most simple form, the BACI analysis is carried out as a two-way analysis of 

variance with the test for cross effects between the two main groups as the significance 

test of interest. 

One of the advantages by the use of a linear normal model is that also the distribution of 

test parameter is known. This knowledge provides the basis to estimate the power of the 

test, to calculate how good the test is to detect an effect if there is one in “the real 

world”. The power is expressed as the probability to catch an effect of a certain, 

predetermined size according to a predetermined level of significance. The power or 

precision of the test increases with an increasing number of replicates. It is possible 

either to calculate the power of the test in relation to a certain effect size such as 50% 

change or to calculate the number of replications necessary if the structure of variance is 

known from a pilot study. It is therefore usually helpful to make a series of preliminary 

univariate analyses with selected variables to obtain an overview of the necessary 

number of samples for the final programme of biological surveys. 



However, data from biological field programs such as abundance and biomass are very 

rarely normal distributed. In that case it is essential to use an appropriate transformation 

of data to bring them on a normal form but also to secure a homogenous structure of 

variance between the set of replicates (the cells) of the analysis of variance which are 

formed in the cross between the factors of the analysis. There are usually a relation 

between average and variance in that kind of data where increasing abundance results in 

increasing variation between the replicates. The most commonly used transformation is 

the logarithm, but because it is not defined for zero, empty samples cause problems 

which normally is solved by adding a small number such as adding the figure 1 to the 

raw data of abundance data. It can be difficult to fulfil the requirements for normality if 

many of the samples are empty. 

It is possible to design models, which are analogue to the univariate linear normally 

distributed models in the event that data are not normally distributed. The analysis can 

be carried out according to one of the “generalizing linear models” if they follow e.g. a 

Poisson or Binomial distribution. This method will also be influenced by the presence 

of empty samples resulting in difficulties in relation to an agreement with one of the 

distributions in that group. During the last ten years, a number of statistical models have 

been developed to treat data with univariate counting data with a surplus of zeros. A 

number of special editions of the Poisson distribution are often used because it, with an 

additional parameter, can treat the presence of empty samples. According to our 

knowledge, none of these models is useful in the present sampling programme. 

Moreover, it doesn’t affect the fact that an increasing number of empty samples 

gradually reduce the amount of information of the chosen variable. 

3.10.2.1.2 Non-parametric test. 

A number of non-parametric tests can be useful for carrying statistical tests within the 

overall BACI-frame. 

Bootstrap 

The bootstrap simulation is the obvious choice in the univariate instance, at least as a 

control of the parametric test, in case of difficulties with fulfilling the conditions for 

normality and homogeneity. The bootstrap simulation is a permutation technique, which 

function by extracting a number of random samples from the original data, of a similar 

size as the original. The selection can be made with or without replacement of the 

chosen figures. A full analysis is then carried out on the chosen data set. By repeating 

this process, a large number of times it is possible to construct an empirical distribution 

of the parameter used for testing. In that present case, it would be the test parameter for 

the interaction in a two-sided analysis of variance, which would be the parameter of 

choice. 

The advantage of that procedure is that the distribution for the data will not be estimated 

mathematically as in the case of a normal distribution, but reconstructed empirically 

based on the properties of the original data. As a result, the bootstrap estimation can be 

used in these cases when the data are not normally distributed but in contrarily have 

typical signs of deviations such as skew distributions, upper or lower truncations, 

outliers etc. 



As a starting point, one should always attempt to use a parametric model such as a 

linear normal. If the assumptions are fulfilled, then the use of the parametric statistical 

analysis will result in a model of explanation, which can model the interactions and 

relations from the real world where the data originally came from. The use of a 

parametric analysis can provide an overview of how the participating factors interact 

opposed to a permutation test, which only provide information about the structure of the 

data in hand. 

Ordination 

In case of multivariate biological data like the ones in the present study, a number of 

tools or methods have been developed. These methods are basically built on the 

assumption, that the variables, e.g. abundances of a number of species, are mutually 

correlated which make it possible to reduce the number of variables to a lower number 

of “synthetic” variables without the loss of information. As an example: species which 

are always co-occurring in proportional similar amounts, or species which might have a 

negative effect on the abundance of each other can be reduced to one pseudo-species 

without the loss of information. 

A number of these ordination methods are solely geometric calculations in the pdimensional 

space in between the p species of the investigation. The use of these 

geometric methods reduces the space to a manageable number of dimensions. The 

purpose of these reductions could be to continue the analysis using the new synthetic 

species or the new “species” (with the correct name: ordination axes) could be the 

product for final interpretation. A Principal Component Analysis (PCA) belongs to that 

group. However, experience shows that it is often very difficult to interpret the results, 

when data comes from biological fieldwork, typically abundance and biomass of a 

number of species. The analysis rarely makes it possible to reduce the number of 

dimensions sufficiently without loosing to much information from the original data set. 

On the other hand, would a PCA-analysis be the obvious choice if the data consists of 

measurements of chemical or physical variables (nutrients, temperature etc.), because 

this method can extract the essential information, and translate it to intuitively 

intelligible environmental gradients. 

Ordination based on distance 

Another class of ordination techniques is designed as an analysis based on calculations 

of a “distance” between every pair of samples (Fig. 3.10.1). 



Figur.3.10.1. Steps in a multivariate analysis based on calculations of a “distance” between every pair 

of samples (dis-similarity). 

The first step is to calculate “distance” between the samples based on an appropriate 

metric. There are a number of different measures of distance available with each their 

qualities. A commonly used measure for distance in between samples in investigations 

of benthic fauna is the Bray-Curtis similarity, which principally function as an index of 

the ratio between the number of common species and the total number of species. The 

ratio is typically weighed with one of parameters attached to a single species, e.g. 

biomass or abundance. If that index of similarity is scaled as percentage the similarities 

will be in the range from 0 to 100 representing from totally different to identical. Using 

a proper transformation of the raw data, it is possible to increase or decrease the weight 

of different parameters, such as the presence of rare species, in the calculation of the 

similarity index. The final product is an N*N distance matrix, which provides all the 

mutual distances between the studies N samples. 

The second step is a more complex set of single steps. 

• A graphic presentation of the similarities as a Multi-Dimensional Scaling plot or 

MDS-plot. MDS is a complex mathematical method to construct a map of the 

samples in a certain number of dimensions. The purpose of the map is to place the 

samples on the map in accordance with the calculated distances in similarity. If 

sample A is more like sample B than C then A should be closer to B than to 

sample C. 

• Test of the similarity between two different distance matrixes. This test is part of 

the detective work to identify the responsible factors affecting the composition of 

species and their abundance in the samples. To identify the cause similar MDSplots 

of the chemical parameters can be made and compared to the MDS-plot of 

the biological parameters. A match between the set of maps would indicate that 

the chemical parameters was responsible for or affected the biological data. 

• Test of independence in the matrix of similarities to factors grouping the data 

material. The tests are similar to the test used in an analysis of variance. In the 

actual study, time and place are the factors and the tests are performed as 



permutation tests of each criterion independent of the other. The tests are by 

nature not subjected to the assumptions of multivariate normality, which limits the 

use of the usual MANOVA on this kind of data, but still the data must be 

independent and with a similar statistical distribution. The permutation test is in 

general based on a large number of exchange of replicates between the tested 

factor groups, for each permutations calculation of a test statistic, and finally, a 

comparison of the test statistics based on the original data with all the randombased 

statistics. 

The distance based ordination techniques have within the last ten years got a lot of 

attention as a method to analyse this type of data, and in other fields such as analysis 

benthic fauna as a tool to investigate and explain the patterns of similarities. 

However, in the BACI framework, for theoretical reasons these methods cannot, be 

used to test effects of interaction in an ANOVA design. As mentioned it is possible to 

use permutation tests to test the main effects in the BACI-model while a permutation 

test for the interaction between time and place can not be done as permutations between 

the original replicates. It requires a 

model based linear such as 

ANOVA but the main problem is 

the requirements to the measure of 

distance, which are not fulfilled by 

the Bray-Curtis index. The main 

question is whether the similarity 

measurement is metric or not (se 

box at right). 

A measuremnt of distance, D, is metric, if the measurement full fills 

the following: 

• Positivity: Dij has to be 0 or positive 

• Symmetri: Dij = Dji : The distance between A and B is the 

same as the distance between B and A. 

• Identity: Djj = 0 : a sample is identical with it self. 

• Triangle inequality: Dik < = Dij + Djk : The distance 

between two samples can not be bigger than the sum of 

distances between two other pairs. The distances can be 

constructed as a triangle. For instance: Dik = 10 og Dij = 3 

og Djk = 4 . If it isn’t possible to construct a triangle then 

the measurement of distance is not metric. 

If the area in questioning in a 

BACI-design is not significantly different from the reference and effect area then the 

effect of interaction can be tested by proving a difference between the areas at a later 

time. However, this is rarely the case, and furthermore surveys will often consist of a 

number of measurements in time, which only can be analysed for trends using the entire 

data set. 

Db-RDA and NPMANOVA 

There is a need for a permutation-based test for the interaction term. In recent years, the 

theoretical basis for this test have been further developed and even fitted to the 

situation, where the fieldwork is established in concordance with the BACI-design. At 

present there are at least two suitable procedures, which can be used on the present data 

material. These techniques are not yet included in the standard statistical packages. 

The first one is called db-RDA (distance-based redundans analysis - Legendre and 

Anderson, 1999). The method calculates the distance between replicates based on a 

metric of your own choice followed by a principal coordinates analysis and a 

redundancy analysis (Fig. 3.10.2). 



Figure: 3.10.2. Graphic outline of the procedures in a multivariate distance based test of interaction 

between criteria of sectioning. (Legendre and Anderson, 1999). 

The other method NPMANOVA (Non-parametric multivariate analysis of variance) 

(Anderson, 2001) provides a method based on distance-metric of your own choice and 

the product is an analysis of a variance table similar to one from the well-known 

analysis of variance. In the variance analytical table, the total sums of squares of 

distances are divided into sums of squares belonging to each of the factors of the 

analysis such as time, place, and the interaction between them. Again, as in the common 

ANOVA, these sums of squares are the basis for the F ratios, which again can be tested 

for significance using permutations tests. If the distances are calculated as Bray-Curtis 

dissimilarities then they also have to be treated in different ways before they can be 

used in the NPMANOVA procedure. 

It is important to note that both methods can only be used if certain assumptions are 

fulfilled just like the requirements to a parametric ANOVA/MANOVA. In principle, the 

requirements are the same except that the data do not have to be normal distributed. The 

data should still be independent, the effects in the model (time and place) should be 

additive and finally the variation should be uniform between the groups. The 



distribution of the similarities should be the same regardless of time and place. The 

requirement of homogenous variance is important because it is possible to get a 

significant result in the test solely based on differences in variance. Whether that 

requirement has been meet can only be tested in a visual estimation of a graphical 

illustration of the plot for example such as a non-metric MDS-plot. In the actual study, 

it is necessary to estimate if the similarities between replicates in the chosen areas and 

periods seem to be of similar magnitude. If not then a suitable transformation of raw 

data can often solve the problem. 

3.10.2.2 Univariate or multivariate analysis? 

As it appears from the previous, there is a fundamental choice to make regarding the 

analytical method for calculations the test statistics within the BACI design. Should the 

method be uni- or multivariate? It is crucial with respect to that choice whether the 

variables of the results are correlated or not. In relation to the data in the present study, 

it is important to consider if the occurrence of the fish species are independent of each 

other or if the presence of one species increase the probability to encounter certain 

species in the same area. Dependent variables are the rule of thumb in this kind of field 

study and accordingly so in the present investigation. The dependency was tested based 

on the null hypothesis of “no correlation different from zero among the p species” using 

Bartlett’s test for spericity. Intuitively it makes sense that certain species occur as 

functional groups attached to certain bottom conditions, food, water quality etc. The 

presence of correlation is the first indication to use a multivariate analysis to get the 

most information from the data. 

3.10.2.2.1 Univariate analysis 

If the data set is tested using a univariate BACI-analysis of the data from each 

individual species, then there are a number of conditions, which should be noted. 

• It is difficult to fulfil the requirements of normality and homogeneity of variance 

for most of the species even after transformation These demands must be meet 

to complete an ANOVA in its ordinary linear form. The main reason is the 

presence of empty samples when the data are considered separately for each 

species. 

• Furthermore, it is important to note the danger of deriving conclusions 

concerning overall significance based on what can be called mass tests. It is 

implicit to the choice of level of significance that one out of 20 analysis should 

show significance even if no interaction-effect in the BACI model exists in the 

real world. If the results from each of the individual species are gathered to a an 

overall level of significance then it is essential to adjust the level of significance 

for each test according to number of species. If five independent species are 

analysed using an univariate ANOVA then the level of significance should be 

adjusted as follows: 1-(1-0.05) 1/5 = 1.02%. Or if the level is fixated at 5 % in 

each sub-test, then there is a probability of 22.6 % for significance in at least one 

of the five sub-tests. 



3.10.2.2.2 Multivariate analysis 

When the variables in question (species) are correlated as in the present baseline study, 

then the natural choice of method would be a multivariate analysis. If not for the test of 

significance, the ordination techniques can be used to explore the structures of the data 

and therefore be guidance to the final test of significance. 

The choice is between: 

• The usual multivariate analysis in a parametric, normal and linear model. 

Different aspect about this analysis have been mentioned, but one essential 

problem is the requirement for normality and homogeneity of variance now 

sharpened as these requirements apply to all variables at the same time. It is a 

precondition of the MANOVA test that the number of observations minus one 

subtracted from the number of groups should be bigger than the number of 

variables (species). If the numbers of observations are at least 2 to 3 times bigger 

than the number of variables, then the test is fairly robust to deviations from the 

conditions about normality and homogeneity of variance. That is the case in the 

present baseline study from Rødsand where approximately 20 different species 

were caught. 

• The reduction of variables using the ordination technique. A PCA analysis can 

extract information from a large number of variables and represent that 

information using a smaller number of “synthetic” variables. These variables are 

by nature uncorrelated and can be used in univariate analysis. The catch is that it 

can be difficult to explain the real picture of the old variables using the new 

variables. 

• The reduction of number of variables by the use of scientific criteria to create 

functional groups of species which abundance and biomass can be tested in uni- or 

multivariate models. The obvious method is to make functional groups according 

to the expected biological effect in the actual investigation. As an example, it 

would be obvious to create a group of species with special attachment to the reef 

habitat. One of the effects of such a strategy would be fewer empty samples, 

making it easier to fulfil the preconditions to use the parametric 

ANOVA/MANOVA. 

• Extract various indices or statistics from the original data and use them in uni- or 

multivariate analysis. The two fundamental statistics are the total number of 

individuals and the total number of species per sample, but there are a number of 

diversity indexes etc. available. In general, this method does not provide useful 

results because of the difficulties involved in explaining a multidimensional real 

world using a model with only one dimension. 

• Use the full set of data in a non-parametric multi-step analysis based on measures 

of distance. Use permutation analysis to test for significant effects and use tools 

based on the calculated distances, in case of significance, to refer the explanation 

of the significance back to the particular species. The latter type of investigation 

can provide a grouping of species, which might fit the grouping in functional 

species. 



3.10.2.3 Examples from the actual investigation. 

The data of each species in the present baseline investigation does not represent a 

normal distribution and it was only possible in a few cases to transform the data to a 

normal distribution. Furthermore, there are problems with the preconditions of 

homogeneity of variance between the cells in the model of variance analysis, which is 

formed in the cross between place (wind farm area versus reference area) and time (five 

sampling occasions). 

Figure 10.3.3 provides a typical example of the distribution of number of eelpout per 

sample. 

6 

4 

2 

0 

6 

4 

2 

0 

Eelpout 

5 6 9 10 11 

5 10 15 20 25 

5 10 15 20 25 

Figure 3.10.3. Distribution of number of eelpout per sample. 

5 10 15 20 25 5 10 15 20 25 5 10 15 20 25 

To illustrate how data from a survey programme can be analysed with a distance based 

ordination analysis, there have been made calculations of similarities (Bray-Curtis 

index) between all the samples from the five months of the survey in 2001. All fish 

species have been included regardless of a biological evaluation if they were suitable. 

The similarities have been calculated based on square root transformed number of 

individuals, which result in a reasonable strong emphasis on abundance and less on rare 

species. 

The calculated similarities have since been illustrated in a map using a non-metric 

MDS, which is a MDS based on the rank of the distances instead of the calculation of 

the exact distances. 

Using the permutation technique, the differences between catch periods in the different 

areas and the difference between areas at the different sampling occasions. Furthermore 

the difference between areas seen isolated in each period were tested because the 

difference between areas in a BACI analysis is crucial for the later choice of analytical 

technique to test the effect of interaction between time and place. 


Windfarm Reference


All the catch periods gives different results, which was confirmed by the test results, 

unless in month 10 and 11 which were coinciding (Figure 10.3.4). The contribution 

from each species to these differences is not described in the present note. 

Abundance, MDS square root transformed , signature=months 

Stress: 0.24 

Figure 10.3.4. MDS-map of Bray-Curtis distances between all stations calculated as square root 

transformed data. 

Figure 3.1.0.5 shows the result of the same ordination with signatures marking the two 

areas. To facilitate the interpretation of the graph the samples have been separated into 

each of the five sampling occasions. If the graphs are placed on top of each other they 

will represent figure 3.10.4. A global permutation test shows significant areas if not 

considering the times. If each sampling occasion is tested isolated in each period only 

month 5 and 9 gives a significant difference between the areas. 


5 

6 

9 

10 

11


Month: 11 

Month: 9 Month: 10 


Reference 

Windfarm 

Figure 3.10.5. MDS-map of Bray-Curtis distances between all stations calculated as square root 

transformed data. The differences between the areas have been illustrated with different 

colours. Data from each month have been placed in each their sub plot but they 

originate from the same graph. The areas have been tested significant different in month 

5 and 9 using permutation test. 

To illustrate a significance test of the interaction effect of the BACI-model two sets of 

artificial data, which could have come from a baseline survey, were constructed. The 

sampling periods May and June were chosen and the number of individuals for 

stationary fish species in the wind farm area were increased with a factor 2 and 4 

respectively but only in the month of June. This could have been two data sets from a 

before and after situation where a change have occurred in the impact area and not in 

the reference area. 

The analysis was made using Bray-Curtis distances between all samples calculated on 

square root transformed data to reduce the effect of abundant species and to secure a 

uniform distribution of distances between the replicates from each sampling occasion. A 

NPMANOVA-test of significance was made using the same data set. 

The Figure 10.3.6 show the three nMDS plots from each their data set. Note that the 

points in the dense swarm moves away with an increase in number of individuals of the 

stationary species. 

This effect can also be found using the NPMANOVA analysis illustrated in the Table 

10.3.1. The results have been based on 5000 permutations per analysis. 

The results shows, that there are no interaction between area and time in the original 

data. There is an obvious difference between the areas and between the two sampling 

periods, but the change from May to June is parallel. 



After a twofold increase of the abundance of stationary species the analysis gives a test 

probability of 5.9 %, which is close to a significant interaction. 

The interaction is highly significant (0.04 %) after a fourfold increase of the abundance 

of the stationary species. 

nMDS on abundance in wind farm area in June. 

Stress: 0.24 

Windfarm 

5 

Windfarm 

6 

Reference 

5 

Reference 

6 

nMDS on abundance in wind farm area in June. Abundance increased by a factor 2 

Stress: 0.25 


W6 

W6 

Windfarm 

5 

Windfarm 

6 

Reference 

5 

Reference 

6 


Stress: 0.24 

W6 

Windfarm 

5 

Windfarm 

6 

Reference 

5 

Reference 

6 

Figure 3.10.6. nMDS plots with increasing number of individuals of stationary species in the wind 

farm area in June (W6).


Original data Non-parametric Multivariate Analysis of Variance 

Possible Denom. 

Source df SS MS F P No.perm. MS 

----------------------------------------------------------------------------- 

Omraa 1 7075.6513 7075.6513 4.6886 0.0004 >1.0E+10 Res 

maane 1 14002.8873 14002.8873 9.2789 0.0002 

The 

abundance 

of stationary 

species 

doubled in 

June. 

The 

abundance 

of stationary 

species 

increased 

fourfold in 

June. 

Res 

Omxma 1 1636.8030 1636.8030 1.0846 0.3842 >1.0E+10 Res 

Resid 44 66400.9367 1509.1122 

Total 47 89116.2784 

----------------------------------------------------------------------------- 

Non-parametric Multivariate Analysis of Variance 



----------------------------------------------------------------------------- 

omraa 1 8109.9132 8109.9132 5.3740 0.0002 >1.0E+10 Res 

maane 1 12625.0607 12625.0607 8.3659 0.0002 

Res 

omxma 1 2889.3945 2889.3945 1.9146 0.0586 >1.0E+10 Res 

Resid 44 66400.9367 1509.1122 

Total 47 90025.3051 

----------------------------------------------------------------------------- 




----------------------------------------------------------------------------- 

omraa 1 10159.6715 10159.6715 6.7322 0.0002 >1.0E+10 Res 

maane 1 12271.9657 12271.9657 8.1319 0.0002 

Res 

omxma 1 5587.7770 5587.7770 3.7027 0.0004 >1.0E+10 Res 

Resid 44 66400.9367 1509.1122 

Total 47 94420.3510 

----------------------------------------------------------------------------- 

Table 10.3.1. Results from a NPMANOVA test on the spring sampling of two artificial data sets with 

a built-in effect in the samples from June at the windmill park. 

Based on the above-mentioned techniques, improvement and recommendations to the 

sampling regime are given in section 3.10.4. 

3.10.3. Biological evaluation 

3.10.3.1 Fish study 

Pelagic species was found in both areas. The pelagic species is of less interest caused by 

their way of life, with great differences in time and space. Therefore, in an eventual 

future monitoring program, pelagic species can at first glance only be used as a 

qualitative measure coupled to the experience achieved from costal areas generally. 

However, pelagic species e.g. the hornfish may be a potential indicator species 

concerning impact caused by reflections and shadowing from the mills because they 

reside near the water surface, are abundant and cover large areas. 

Of the pelagic species, it is recommended to focus on the Baltic herring in the future, 

because of its documented ability to sense noise (Bio/consult 2001c), though it 

according to present baseline only can be assessed qualitatively. 

Of stationary species, eelpout and short-spined sea scorpion serve as representative 

caught in relatively high numbers. Both of these species was caught in higher numbers 



in the wind farm area than in the references area. Both species are recommended to be 

included in the monitoring program. 

The catch of the small sandeel and the great sandeel was very different in time and 

space, probably because of their pelagic behaviour in the dark hours. The measured 

biological parameters; such as length and gonads could be used to evaluate the areas as 

potential spawning and/or nursery grounds on a qualitative basis. 

Flounder and turbot are optimal species from a statistical point of view and they could 

become useful in the monitoring program, though they do not display a normal 

distribution. The turbot was caught in low numbers, and therefore many of the samples, 

which makes this species less reliable than flounder as indicator species. 

3.10.3.2 Fry study 

In the present design and scope, this study do not have basis for statistical verification of 

the 0-effect hypothesis because, data for all of the species lack variance homogeneity 

and for most of the species contain missing values in more than 50% of the samples 

(standard fyke net and fry fyke net). However, the study contributes to the knowledge of 

the fish fauna in the area of Rødsand as concern fry and small fry. 

The study areas clearly function as a spawning and nursery area for eelpout. This can be 

seen from the length distributions between the seasons, where it is possible to follow a 

cohort from birth and throughout the rest of the period for the fry study, and by 

comparing the number and weight over time. 

The numbers of the three species of gobies caught seems stochastic, and not a reflection 

of the actually population size. 

3.10.4. Evaluation and development of the monitoring program 

It is realized, that the use of parametric statistical test on individual fish species does not 

return significant answers to the main question of the baseline study of a BACI design; 

is the wind farm area and the reference area similar and are they of parallel progress? 

However, dividing of fish species into functional groups and use of classic parametric 

analysis or the use of a non-parametric multivariate analysis of the fish community 

return a positive answer to that question. 

3.10.4.1 Recommendations in relation to parametric univariate analysis 

In an attempt to escape the biases applied by the large amounts of zeroes, grouping 

species according to their biology and/or trophic level must be considered. 

Alternatively, grouping of species can be executed according to their expected 

sensitivity to possible environmental effects of offshore wind farms e.g. changes in type 

of habitat (construction of reef structures and seize of existing habitats), vibration 

(noise), reflections of light and electromagnetic fields. The grouping should be chosen a 

priori and not based on the results of the investigation. It is only possible to create 

functional groups if it is considered biological relevant. 



In biology similar grouping are most often used in examine plankton communities as 

well as benthic invertebrate communities. In relation to fish, grouping has been used in 

measuring energy flow through a system, by grouping into feeding guilds. 

The species caught in this study have been grouped into six different descriptive groups 

(Table 3.10.2). Some of the fish species occur in two groups if they fulfil the criterions 

for both groups. The groups are made by viewing the biology of the species and to a 

minor degree with respect to their trophic level. These groups have been used in both 

the fish study and fry study. 

Stationary species: Eelpout (Zoarces viviparous) 

Short-spined sea scorpion (Myoxocephalus scorpius) 

Longspined bullhead (Taurulus bubalis) 

Hooknose (Agonus cataprachtus) 

Sand lances: Small sandeel (Ammodytes tobianus) 

Great sandeel (Hyperoplus lanceolatus) 

Prey species: Sand goby (Pomatoschistus minutus) 

Two-spotted goby (Gobiusculus flavescens) 

Fifteen-spined stickleback (Spinachia spinachia) 

Black goby (Gobius niger) 

Pelagic species: Baltic herring (Clupea harengus) 

Brisling (Sprattus sprattus) 

Hornfish (Belone belone) 

Commercial species: Turbot (Psetta maxima) 

Atlantic cod (Gadus morhua) 

Flounder (Platichthys flesus) 

Reef species: Eelpout (Zoarces viviparous) 

Common eel (Anguilla anguilla) 


Turbot (Psetta maxima) 

Short-spined sea scorpius (Myoxocephalus scorpius) 

Table 3.10.2. Fish species caught in the baseline studies classified into descriptive groups. Rare 

occurring species are not included. 

To evaluate the groups a power analysis was used. Results of the power analysis within 

the groups are presented in Table 3.10.3. At least one individual from each group was 

caught in at least 50% of sample efforts (One effort being: one pelagic, one benthic, one 

fyke net and one fry fyke net.). The lowest values were obtained for the groups of sand 

lances and the commercial species. 

According to table 3.10.3 a power of 80% can be achieved if the number of sampling 

stations are increased from 12 to 26 in each area. Since the variance between replicates 

differ only slightly compared to differences between stations it is recommended to 

redistribute the replicates into 12 new samplings stations and adding at least 2 new 

sampling stations in each areas. 



Groups Period 

Stationary species 

Sand lances 

Prey species 

# Stations per area. 

80% power 

% Stations 

Number > 0 

% Stations 

Number= 0 

May 20 89.6 10.4 

June 14 77.1 22.9 

May 25 56.3 43.7 

June 19 77.1 22.9 

May 15 62.5 37.5 

June 11 68.8 31.2 

Pelagic species May 19 62.5 37.5 

Commercial species 

Reef species 

May 26 81.3 18.7 

June 14 50.0 50.0 

May 16 97.9 2.1 

June 15 83.3 16.7 

Table 3.10.3. Results of the power-analysis giving 80% validity in detecting 50% change, within 

groups. At least 50 % of the efforts made in the fish study, caught species within the 

group. 

The suggested strategy of grouping can only be carried out on the level of numbers 

since the morphological features, of the grouped species differ greatly in weight, length 

distribution and mean length. 

The species from the fry study were grouped into: stationary species, prey species and 

commercial species according to the groups mentioned previous, se Table 3.10.2. 

Grouping of species did not lead to any progress in the perquisites needed for unbiased 

result, since neither normal distribution nor variance homogeneity was achieved for any 

of the groups. Therefore, with the statistical technique used it is not to be recommended 

to continue the fry study, in an attempt to obtain quantitatively measure of fry and small 

fry (juvenile) in the area. Qualitatively, the fry study gives good ideas about usage of 

the area as a spawning and nursing ground. 

3.10.4.2 Recommendations in relation to non-parametric multivariate analysis 

Adjust the sampling programme with a focus to concentrate the effort at as many 

stations as possible. Multiple replicates are not necessary on each station because they 

will be considered as one sample in the final analysis because station is the basic unit in 

the comparison of areas. Adjust the effort pr. station to allow for a (biological) 

reasonable CPUE. 

Use eventually the liberated effort from avoiding replicates to include an additional 

reference area. Concentrate the effort on the species, which will be used in the test. Is 

there any reason to record the number of crabs and scrimps? On the other hand, to use 

de multivariate ordination techniques it is essential, that any species of biological 

relevance be accounted for. 

As a starting point for the size of the sampling program, use the result from the power 

analysis conducted in univariate BACI-models. The example illustrated in section 

3.10.2.3 of a multivariate analysis carried out on the data at hand shows, that plausible 

effects can be traced using these methods. Exact power calculations for the multivariate 



ordination techniques demands a very extensive simulation study, which is outside the 

scope of the programme of this baseline study. 

Consider the time of sampling thorough. A significant difference was found between 

most of the sampling occasions and the wind farm area and reference area at certain 

times It may be more biological relevant to time the sampling occasion according to a 

yearly recurrent occasion which can established by supervision instead of using fixed 

dates. 

Use the distance based multivariate ordination technique to analyse the data and the 

effects in the BACI–model. Refer the results to the explanations, which are based on the 

specific contribution of each individual species. Do not include species that are not 

directly or indirectly sensitive to expected impact from the wind farm. 



4. Commercial fishery 

4.1. Methods 

SEAS put forward the following hypothesis before the studies was conducted: 

The establishment of the wind farm will reduce the opportunities for fishing 

in and around the immediate area (i.e. the site of the wind farm) as a result 

of the mere physical structure and laying of cables. 

The fishermen’s logbooks, which form the basis for reports to the Directorate of 

Fisheries, show the landings for various species caught within a certain area (for 

example 38 G1, which includes Rødsand, see appendix 9.3.). As the catch reports to the 

Ministry cover a wider area than the area studied in connection with the proposed wind 

farm (see appendix 9.2.), it is only the fishermen themselves, who can assess the 

proportion of the reported catch that came from the area south of Rødsand It was 

therefore essential to make direct contact with these fishermen. 

4.1.1. Interviews with local commercial fishermen 

Advertisements were placed in 3 Danish publications addressed to commercial or semicommercial 

fishermen. In 1999, attempts were made to arrange a meeting with each of 

the fishermen who worked in the area south of Rødsand. Through personal meetings, 

the catch of each fisherman was established and questions concerning fish biology were 

discussed. In 1999, agreements were successfully made with nine fishermen who work 

in the area under study (wind mill area and reference area). 

It was agreed to have one or two meetings in the fishing harbours of Kramnitse, Gedser, 

Rødbyhavn and Hesnæs. Contact was made to other relevant harbours as Guldborg, 

Nakskov and Nysted. In most cases, the chairman of the local association was 

contacted. The response was that there were no fishermen with special interest in that 

area. 

The fishermen brought various types of evidence of catches to the meetings. Data for 

the catches are thus based on the fishermen’s own statements and reports in the form of 

logbooks, lot-sheets and annual statements from the Directorate of Fisheries 

supplemented with the fishermen’s own personal information about fishing sites. All 

interviews and records were treated as confidential. 

In 2002 a supplementary interview with the fishermen were done by phone, and the 

fishermen were asked to send relevant data to Bio/consult. 

Subjects for interview: 

All the fishermen interviewed were asked about the following (see appendix 9.1) 

• The basis of their catch reports (lot sheets, logbooks, annual statements from the 

Directorate for Fisheries, etc.). 

• Catch areas (a description of which part of the area they fished) 

• Type of nets 



• Approximate number of nets used in the area of study 

• Landing harbour 

• Approximate number of fishing days per year spent in the area of study 

• Landings of various species in the years 1997 to 2001. 

• Season and the reason for choosing this season 

• Other remarks 

4.1.2. Quality assurance 

The quality assurance system (QA) used in completing this task is built on the 

principles of the quality assurance system used by Carl Bro as. 

In relation to collecting and reporting data, a project manual was drawn up containing a 

description of the guidelines for carrying out each section of the task. 

It was not possible to assure the quality of the data collected from the fishermen. The 

collected data, however, were compared with official catch statistics from the Ministry 

of Food, Agriculture and Fisheries, although these figures are much less detailed. 

4.1.3. Review of existing information 

Material was obtained from the Directorate for Fisheries regarding the recorded 

landings in area 38G1 (ICES division of Danish coastal waters, see appendix 9.2.) 

The data recorded in logbooks do not include catches of fish under the coastal water 

regulation, as these catches were only recorded as having been made in ICES area IIIC 

(see appendix 9.2.). Including data for the whole area IIIC in the evaluation of the fish 

population at Rødsand would not make sense, as this information covers the whole of 

the Belt area and the Baltic. 

4.2. Results 

This section examines the extent of the fishery from the data collected for 1997 to 2001, 

including: 

• Types of nets used at Rødsand 

• Intensity of fishing 

• Total annual catch of commercially important species 

• Total annual catch from trawling 

• Total annual catch with nets and hooks 

• The fishermen’s usual areas for using nets and hooks 

• Recorded trawling routes 

• The season for commercially important fish 

• Other information gathered from the fishermen including the reason for the 

seasons 

In addition: 



• Catch data reported to the Directorate for Fisheries for area 38G1 from 1982 to 

2001 

4.2.1. The scope of the fishery 

A complete presentation of the fishermen’s statements is attached as appendix 9.6. 

Provision has been made for missing results from individual fishermen (some fishermen 

did not want to report their data). 

At least ten commercial fishermen fished at Rødsand from 1997 to 2001. These 

consisted of three trawl fisherman, six gill net fishermen and one pound net fisherman. 

The fish was mainly landed at Gedser and Kramnitse (transferred to Esbjerg), 

Rødbyhavn and Hesnæs. 

Tables 4.2.1 and 4.2.2 show the type of nets and the intensity of the fishing. 

Type of fishing tackle 1997 1998 1999 2000 2001 

Turbot net, 220 mm mesh 430 430 430 350 350 

Cod net, 110–120 mm mesh 185 185 185 120 120 

Pound net 6 8 18 5 7 

Cod net sp. 375 375 375 ? ? 

Trawl 3 3 3 2 2 

Table 4.2.1. Approximate number of nets used at Rødsand in 1997-2001 by type. The number of 

nets is an average. 

Fishing intensity (number of fishing 

tackle * number of fishing days 

1997 1998 1999 2000 2001 

Turbot net, 220 mm mesh 26,860 22,380 25,500 5,950 10150 

Cod net, 110–120 mm mesh 17,700 12,100 16,000 2,040 3480 

Pound net 540 720 1,620 450 630 

Cod net sp. 22,500 22,500 22,500 ? ? 

Trawl 81 81 79 135 55 

Table 4.2.2. Approximate fishing intensity (number of nets used * number of fishing days per net) at 

Rødsand in 1997-2001 by type. 

Table 4.2.3 and Figure 4.2.1 show the total catch with a breakdown of species at 

Rødsand in 1997-2001. Tables 4.2.4 and 4.2.5 and Figures 4.2.2 and 4.2.3 show the 

catch with a breakdown of trawling and net fishing. 



Total annual catch (tons) Cod Turbot Flounder Plaice Dab Eel 

1997 136,5 1,8 1,3 0,2 0,7 2,2 

1998 106,5 1,3 0,8 0,2 0,6 1,3 

1999 139,1 3,0 0,8 0,2 0,6 4,5 

2000 107,2 0,1 0,3 0,0 0,8 1,4 

2001 36,4 0,2 2,2 0,0 1,5 2,7 

Table 4.2.3. The total catch in the area of the wind farm and the reference area south of Rødsand in 

1997-2001, divided into the species cod, turbot, flounder, plaice, dab and silver eel, 

which are the only species of commercial importance. The data was gathered through 

interviewing the fishermen. 

Trawling. Total annual catch (tons) Cod Turbot Flounder Plaice Dab Eel 

1997 72,6 0,3 0,8 0,2 0,6 0,0 

1998 52,4 0,3 0,8 0,2 0,6 0,0 

1999 64,9 0,3 0,8 0,2 0,6 0,0 

2000 87,1 0,1 0,3 0,0 0,8 0,0 

2001 25,8 0,2 2,2 0,0 1,4 0,0 

Table 4.2.4. The total number of fish caught by trawling in the wind farm area and the reference area 

south of Rødsand in 1997-2001. Data is only given for species of commercial 

importance. The data was gathered through interviewing the fishermen. 

Net fishing. Total annual catch (tons) Cod Turbot Flounder Plaice Dab Eel 

1997 63,9 1,5 0,6 0,0 0,0 2,2 

1998 54,2 1,1 0,0 0,0 0,0 1,3 

1999 74,1 2,7 0,0 0,0 0,0 4,5 

2000 20,1 0,0 0,0 0,0 0,0 1,4 

2001 10,6 0,0 0,0 0,0 0,2 2,7 

Table 4.2.5. The total number of fish caught with nets and hooks in the wind farm area and the 

reference area south of Rødsand in 1997-2001. Data is only given for cod, turbot, 

flounder, plaice, dab and silver eel. The data was gathered through interviewing the 

fishermen. 



Annual catch (ton) . 

140 

120 

100 

80 

60 

40 

20 

0 

Total catch at Rødsand 1997-2001 

Figure 4.2.1. The total annual catch by commercial fishermen of various species within the wind farm 

area and the reference area south of Rødsand from 1997 to 2001. 


90 

80 

70 

60 

50 

40 

30 

20 

10 

0 

Cod Turbot Flounder Dab Eel 

Trawl catch at Rødsand 1997 - 2001 


Figure 4.2.2. The total annual catch by trawl fishermen of various species within the wind farm area 

and the reference area south of Rødsand from 1997 to 2001. 


1997 

1998 

1999 

2000 

2001 

1997 

1998 

1999 

2000 

2001



80 

70 

60 

50 

40 

30 

20 

10 

0 

Catch with fishing nets/hooks at Rødsand 1997 - 2001 


Figure 4.2.3. The total annual catch with fishing nets and hooks of various species within the wind 

farm area and the reference area south of Rødsand from 1997 to 2001. 

The above tables and figures show that virtually the same amount of cod was caught in 

and around the wind farm area by trawling and with nets from 1997 to 1999. In 2000 to 

2001 the picture was different. The net fishery was reduced and net fishermen caught 

less cod and turbot than was caught by trawling. Silver eels were only caught by net 

fishermen all five years. Flounder, plaice and dab were a by-catch in both cod nets and 

turbot nets. Silver eels were caught with pound nets. It is evident that 1999 was a good 

year for turbot and silver eels for the net fishermen. In 2000 and 2001 only a few turbot 

was caught. 

4.2.2. Fishing grounds 

The number of fishermen working in the area of the windmills and in the reference area 

varied from year to year (se table 4.2.6.) 

Year Number of net fishermen Number of trawl fishermen Total number of fishermen 

1997 6 3 9 

1998 6 3 9 

1999 6 3 9 

2000 3 2 5 

2001 2 2 4 

Table 4.2.6. Number of fishermen working in the area of study divided between trawling and net 

fishing. 

Nine commercial fishermen were recorded to work in the area concerned from 1997 to 

1999. Of these, seven were net fishermen and three trawl fishermen. From 2000 to 2001 

the number of net fishermen recorded to work in the mill area and reference area were 

reduced to 3 net fishermen in year 2000 and 2 in 2001. 3 fishermen from Kramnitze 


1997 

1998 

1999 

2000 

2001


whose catch were registered tin 1997 to 1999 claimed they were still fishing in the area 

but refused to take part of the investigation in 2000 and 2001, and are therefore not 

registered from 2000 and 2001. Du to declining catch one fisherman from Gedser 

stopped in 1999 and another only reported his catch form 2000. 

The number of trawl fishermen operating in the mill area and reference area were 

reduced from 3 fishermen in 1997-1999 to 2 fishermen i year 2000 and 2001. 

The fishermen agreed that the area is good for fishing and that the food chain includes 

sandeel and shrimp, which are eaten particularly by cod and turbot. 

Appendix 9.4. show the parts of the wind farm and the reference area that are fished by 

trawling and with nets. The boundaries should be taken as rough sketches of the fishing 

grounds of the individual fishermen. 

Effectively, the whole area south of Rødsand is fished with cod and turbot nets (see 

appendix 9.4.). Fishing takes place in both the wind farm area and the reference area. 

Gill netters 1 and 5 are based in the same harbour and report that they fish the same 

areas. The same is true for gill net fishermen 2, 3 and 4. 

Below the 9–10 metre bathymetric contour south of Rødsand, trawling takes place, 

mainly for cod. Seasons for the commercially important species and other information 

gathered from the fishermen, including the reason for the season. 

Fishing is impeded in the summer by filamentous annual algal species (Ectocarpus spp. 

and Pilayella spp). In the deeper waters, fishing is also impeded in summer by the 

presence of crabs. The severe south-western and western storms make fishing difficult 

in the winter. 

The fishermen say that sandeels bury themselves in the sand in the evening and come up 

again in the morning, when they are prey for cod and turbot in particular. “If the sun 

shines early in the spring so the sandeels come out, there are lots of cod and turbot.” 

According to the fishermen, there is roe in the cod from March until June. The roe starts 

being released by the cod in April. There is no closed season for cod fishing (see 

appendix 9.7.), but cod quotas apply. 

Three trawl fisherman have been working at Rødsand from 1997 to 2001. They fish 

from April to June and from September to November (see appendix 9.5). It is said that 

“the area along the southern part of Rødsand below the 9–10 metre zone offers good 

fishing for trawling, especially in warm weather, as the cod gather over the stones there. 

As it becomes colder, they move out into deeper water.” 

Pound net fishing is done in September, October and November (see appendix 9.5), 

mainly for silver eel, which command a high price. 

The gill net fishery for cod takes place from December to May (see appendix 9.5). A 

single fisherman catches cod at Rødsand all year round, but the cod in general is mainly 

caught during winter and spring. 



Turbot is mainly caught in the spring and summer up to the spawning season. It is 

generally agreed that the area serves as spawning ground for turbot. Turbot is protected 

below the base line from 1 June to 31 July. The base line is north of the trawling route 

(appendix 9.7.). This means that trawling for turbot is limited by seasonal restrictions 

but gill netting is not. 

4.2.3. Catches recorded in the Danish Directorate for Fisheries logbook records 

from 1982 to 2001 

Table 4.2.7, 4.2.8 and 4.2.9 and Figures 4.2.41 and 4.2.5 show catches for the 

commercially important species from 1982 until 2001 in area 38G1, taken from the 

logbook records of the Directorate for Fisheries. 

As shown in Table 4.2.7, the most important species for fishery in area 38G1 are 

brisling, dab, herring and cod. Turbot is also important as it fetches a high price. 

38G1 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 

Brisling 0 9 0 0 0 0 45 88 113 80 

Whiting 0 1 0 0 0 0 44 1 3 6 

Dab 9 2 1 1 1 11 27 32 12 15 

Turbot 0 0 0 0 0 0 2 1 1 1 

Plaice 0 0 1 0 0 2 0 0 0 0 

Baltic herring 0 169 0 0 10 6 8 420 114 180 

Flounder 18 2 1 0 1 1 3 8 11 6 

Atlantic cod 77 154 133 6 2 39 88 25 12 25 

38G1 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 

Brisling 933 796 921 286 353 256 309 230 285 1209 

Whiting 11 11 5 4 1 3 1 2 6 3 

Dab 16 9 62 52 34 19 19 35 28 29 

Turbot 1 1 7 13 10 3 3 4 6 3 

Plaice 0 0 0 2 5 3 12 21 23 22 


Flounder 2 1 2 4 9 12 18 13 69 85 

Atlantic cod 18 52 104 415 585 790 452 648 934 625 

Table 4.2.7. Total catches of most important commercial species in area 38G1 (source: Danish 

Directorate for Fisheries logbook records). 



38G1 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 

Eel 0 0 0 0 0 0 0 0 0 0 

Scrimp 0 0 0 0 0 0 0 0 0 0 

Brisling 0 0 0 0 0 0 0 0 0 0 

Whiting 0 0 0 0 0 0 0 0 0 0 

Dab 0 0 0 0 0 0 0 0 0 0 

Turbot 0 0 0 0 0 0 0 0 0 0 

Plaice 0 0 0 0 0 0 0 0 0 0 


Flounder 0 0 0 0 0 0 0 0 0 0 

Atlantic cod 0 0 47 0 0 3 0 1 0 7 

38G1 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 

Eel 0 0 1 1 4 0 0 0 0 0 

Scrimp 0 0 1 0 0 0 0 0 0 0 

Brisling 0 0 0 0 0 0 0 0 0 1 

Whiting 0 0 0 0 0 0 0 0 0 0 

Dab 0 0 1 6 3 2 1 3 8 5 

Turbot 0 0 6 0 7 1 0 2 3 2 

Plaice 0 0 0 8 1 2 1 5 8 12 


Flounder 0 0 0 1 3 3 3 2 28 43 

Atlantic cod 0 0 47 168 165 69 58 86 80 114 

Table 4.2.8. Net and hook catches of most important commercial species in tons in area 38G1 

(source: Danish Directorate for Fisheries logbook records). 

ton 

250 

200 

150 

100 

50 

0 

1982 

1983 

1984 

1985 

1986 

1987 

Net and hook catches in area 38G1 

1988 

1989 

1990 

1991 

1992 

1993 

Year 

1994 

1995 

1996 

1997 

1998 

1999 

2000 

2001 

Figure 4.2.4. Net and hook catches in area 38G1 from year 1982 to year 2001. 

Atlantic cod 

Flounder 


Plaice 

Turbot 


Dab 

Whiting 

Brisling


38G1 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 

Eel 0 0 0 0 0 0 0 0 0 0 

Scrimp 0 0 0 0 0 0 0 0 0 0 

Brisling 0 1 0 0 0 0 45 88 113 80 

Whiting 0 2 0 0 0 0 44 1 3 6 

Dab 9 0 1 1 0 10 27 32 12 15 

Turbot 0 0 0 0 0 0 2 1 1 1 

Plaice 0 0 1 0 0 1 0 0 0 0 


Flounder 89 2 1 0 0 1 3 8 11 6 

Atlantic cod 124 154 86 6 2 26 88 25 12 17 

38G1 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 

Eel 0 0 0 0 0 0 0 0 0 0 

Scrimp 0 0 0 0 0 0 0 0 0 0 

Brisling 933 796 921 286 353 256 309 230 285 1209 

Whiting 11 11 5 4 1 3 1 2 6 4 

Dab 16 9 61 46 31 17 18 32 20 25 

Turbot 1 1 1 4 3 2 3 1 3 1 

Plaice 0 0 0 2 3 1 10 16 14 10 


Flounder 2 1 2 2 7 10 15 11 42 41 

Atlantic cod 18 52 58 248 420 722 394 562 854 512 

Table 4.2.9. Trawl and not catches (tons) of most important commercial species in area 38G1 

(source: Danish Directorate for Fisheries logbook records). 

ton 

3000 

2500 

2000 

1500 

1000 

500 

0 

Trawl and not catches in area 38G1 

1982 

1983 

1984 

1985 

1986 

1987 

1988 

1989 

1990 

1991 

1992 

1993 

1994 

1995 

1996 

1997 

1998 

1999 

2000 

2001 

Year 

Figure 4.2.5. Trawl and not catches in area 38G1 from year 1982 to year 2001 

Atlantic cod 

Flounder 

Baltic 

herring 

Plaice 

Turbot 


Dab 

Whiting 

4.2.4. Expectations for the occurrence of commercially important species 

On the basis of sections 4.2.1., 4.2.2. and 4.2.3. it can be anticipated that the 

commercially important species (cod, whiting, herring, brisling, whiting, flounder, dab, 

turbot and plaice) will be found in catches in the area around Rødsand.


4.2.5. Comparison between information on catches from the interview and data 

from the Directorate of Fisheries from area 38G1. 

The total catches from Rødsand recorded in this study (Table 4.2.3) can be compared 

with data from the Directorate for Fisheries (logbook data from area 38G1 (Table 

4.2.10. and appendix 9.3.). 

Figures 4.2.6 and 4.2.7 compares table 4.2.3 and table 4.2.10. 

Area 38G1 

Total annual catch (tons) 

Cod Turbot Flounder Plaice Dab Silver eel 

1997 790 3 12 3 19 0 

1998 452 3 18 12 19 0 

1999 822 3 11 20 34 0 

2000 934 6 69 23 28 0 

2001 625 3 85 22 29 0 

Table 4.2.10. Total catch in area 38G1 in 1997 to 2001 for the species cod, turbot, flounder, plaice, 

dab and silver eel. The data is from the Directorate for Fisheries’ logbook records. 

Total aanual catch (tons) . 

3000 

2500 

2000 

1500 

1000 

500 

Comparison between the reported catches in area 38G1 and the total 

annual catches as recorded in the interview survey 

0 

38G1 

1997 1998 1999 2000 2001 

Atlantic cod 


Brisling 

interview 

38G1 

interview 

38G1 

Figure 4.2.6. Comparison between the reported catches in area 38G1 and the total annual catches as 

recorded in the interview survey. 


interview 

38G1 

Year recorded and source of data 

interview 

38G1 

interview


Total annual catch (tons) . 

160 

140 

120 

100 

80 

60 

40 

20 

0 

Comparison between the reported catches in area 38G1 and the total 

annual catches as recorded in the interview survey 

1997 1998 1999 2000 2001 

38G1 

Flounder 

Plaice 

Turbot 

Whiting 

Eel 

Dab 

interview 

38G1 

interview 

38G1 

Figure 4.2.7. Comparison between the reported catches in area 38G1 and the total annual catches as 

recorded in the interview survey. 

The catch of cod in the area south of Rødsand was amount to between 1 /8 and 1 /5 of the 

total data from logbook records for area 38G1 (Figure 4.2.6). The catches of flounder, 

plaice and dab in the area amount to just a small part of the catch in area 38G1 (figure 

4.2.7). 

The interviews did not provide information about catches of herring, whiting and 

brisling at Rødsand (see figures 4.2.6 and 4.2.7). 

There are no records for silver eel in these logbooks (figure 4.2.7), as the fishermen who 

catch silver eel around Rødsand sail under coastal waters regulations. This means that 

data from these fishermen is not included in the logbook data for area 38G1. 

Vessels of less than 10 metres, or of under 12 metres but with trips of less than 24 

hours, or vessels that for other reasons are exempted from the Ministry of Agriculture 

and Fisheries and also sail under coastal waters regulations are exempted under 

Statutory Order no. 91 from the obligation to complete logbooks (appendix 9.8.) 

There can also be uncertainty in recording the fish on a landing as being from a single 

area, as the fishermen fish several areas within the same day. 

As discussed earlier, it is only the fishermen themselves who are able to assess how 

large a part of their catch originates from Rødsand. The accuracy of their assessment 

inevitably depends on their memories. 

An interview survey from Øresund (Danish Institute for Fisheries Research, 1994) 

suggested that general problems with the official fishery statistics are: 


interview 

38G1 

Year recorded and source of data 

interview 

38G1 

interview


1) Unreported catches, especially of cod, eel and herring. 

2) Reports of catches from areas other than those actually fished. 

3) Reports of species subjected to restrictions as valuable species without 

restrictions. 

Any assessment as to how much this affects the official fishery statistics for area 38G1 

would be little mere speculation. 

However, the turbot fishery in the area under examination, including the area of the 

wind farm in 1999, appears to correspond to the total catch reported from area 38G1 

(figure 4.2.7). The turbot fishery in the area in 1997 and 1998 amounts to about 1 /3 of 

the catch of turbot in area 38G1. In 2000 and 2001, only a small part of the turbot 

caught in area 38G1 appears to be caught in the area of the wind farm and in the 

reference area. 

Six out of seven gill net fishermen come under the coastal waters regulations. These 

catches were not included in the data for area 38G1. All trawl fishermen kept a logbook 

and data are included in the data for area 38G1. 

There may well be more turbot caught in area 38G1 than shown in the official statistics 

for the area. As the fishermen consider Rødsand to be a spawning ground for turbot, it is 

also probable that a large part of the catch of turbot in area 38G1 in 1997-1999 is from 


4.2.6. Reported catches in area 38G1 from 1982 to 2001 

Figures 4.2.8 and 4.2.9 (as well as table 4.2.7) show the catches for commercially 

important species from 1982 until 1999 in area 38G1, taken from the logbook records of 

the Directorate for Fisheries. 

Catch, kg 

90.000 

80.000 

70.000 

60.000 

50.000 

40.000 

30.000 

20.000 

10.000 

0 

1982 

Catch of whiting, dab, turbot, plaice and flounder in area 38G1 from 

1982-1999 

1983 

Whiting 

Dab 

Turbot 

Plaice 

Flounder 

1984 

1985 

1986 

1987 

1988 

1989 

1990 

Figure 4.2.8. Catch of whiting, dab, turbot, plaice and flounder in area 38G1 from 1982 to 2001. 


1991 

1992 

Year 

1993 

1994 

1995 

1996 

1997 

1998 

1999 

2000 

2001


Very low catches were recorded in area 38G1 in the early 1980s. Towards the end of the 

1980s and the start of the 1990s, higher catches of dab and flounder were recorded, 

although the catch decreased again in 1992 and until 1994. Since the early 1990s, there 

has been an increase in the annual catch of herring, flounder, plaice and cod. The catch 

of turbot peaked in 1995. 

Catch, kg 

1.400.000 

1.200.000 

1.000.000 

800.000 

600.000 

400.000 

200.000 

Figure 4.2.9. Catch of brisling, herring and cod in area 38G1 from 1982 to 2001. 

4.2.7. Estimate of the catch within the wind farm site alone 

It was not possible to ascertain the precise catch within the wind farm site alone, 

because the fishermen’s daily records only show the catch for the whole area south of 

Rødsand. However, an approximate estimate of the amounts caught in the wind farm 

area can be made for the gill net fishery. The estimate is based on the proportion the 

wind farm occupies in relation to the entire area fished. It is assumed that: 

1) Net fishing is conducted with the same effort in the area of the wind farm as in the 

rest of the area south of Rødsand (appendix 9.4.). 

2) The wind farm site amounts to approximately 15% of the total area for which 

catches are recorded). 

It is thus possible to find the annual gill net catch within the wind farm site itself from 

1997 until 2001 (see table 4.2.11). There is no trawling within the proposed site. 

Estimate of fish catches with nets 

in the area of the wind farm 

0 

1982 

Catch of brisling, herring and cod in area 38G1 from 

1982-2001 

Brisling 

Herring 

Cod 

1984 

1986 

1988 

Cod Turbot Flounder Plaice Dab Silver eel 

1997 9.6 t .23 t .10 t 0 t 0 t .33 t 

1998 8.1 t .16 t 0 t 0 t 0 t .20 t 

1999 11.1 t .41 t 0 t 0 t 0 t .68 t 

2000 3.0 t .00 t 0 t 0 t 0 t .21 t 

2001 1,6 t .00 t 0 t 0 t .03 t .41 t 

Table 4.2.11. Approximation of the total annual catch with nets in the wind farm site itself. 

1990 


1992 

Year 

1994 

1996 

1998 

2000


4.3. Discussion 

As expected, cod, flounder, dab, turbot and plaice are fished at Rødsand. In addition, 

silver eel are caught from September to November. The interviews did not produce any 

information about catches of whiting, herring or brisling. 

At least ten commercial fishermen have fished at Rødsand from 1997 to 2001. These 

consist of three trawl fishermen, six gill net fishermen and one pound net fisherman. 

The fish were mainly landed at Gedser and Kramnitse, Rødbyhavn and Hesnæs. 

The fishermen agreed that the area is good for fishing mainly because of the abundance 

sandeel and shrimp, which serves especially as food for cod and turbot. 

Three trawl fishermen working at Rødsand have been recorded. They fish, especially for 

cod, in the area south of the base line, from April to June and from September to 

November. Net fishing for cod takes place mainly from December to May effectively in 

the whole area south of Rødsand. 

Turbot is mainly caught in the spring and summer up to spawning. There is agreement 

that the area serves as a spawning ground for turbot. 

Small numbers of plaice, flounder and dab are part of the by-catch for cod and turbot 

fishermen. 

Silver eel is easiest caught in pound nets from September to November because of their 

migration towards the Sargasso Sea. 

The annual catch of all species except turbot from 1997 to 1999 and silver eel amounts 

to a small part of the total recorded in logbooks from area 38G1. 

There are no records for silver eel in the logbooks, as the fishermen who catch silver eel 

around Rødsand operate under coastal waters regulations. This means that data from 

these fishermen is not included in the logbook data for area 38G1. 

The total catches of turbot apparently equalled approximately 1 /3 of the turbot catch in 

the official statistics for area 38G1 from 1997 to 1999. The difference can partly be 

explained by the fact that the fishermen who catch turbot sail under the coastal waters 

regulations, and so their figures are not included in the official catch statistics for area 

38G1. In the years 2000 and 2001 only little turbot were recorded top be caught in the 

area of the wind farm and in the reference area (some fishermen claimed they were 

fishing in the area but refused to take part of the investigation these two years). 

The official catch statistics show very low catches in area 38G1 in the early1980s. 

Towards the end of the 1980s and the start of the 1990s, higher catches of dab and 

flounder were recorded, although the catch decreased again from 1992 until 1994. From 

the early 1990s, there has been an increase in the annual catch of herring, flounder, 

plaice and cod. The catch of turbot peaked in 1995. 



In the years 1997 to 2001, the annual net fishery catch in the wind farm site itself can be 

estimated as amounting to between 1,6 and 11.1 tons of cod, between 0.00 and 0.41 tons 

of turbot and between 0.20 and 0.68 tons of silver eel. There is no trawling within the 

proposed site itself. 



5. Conclusion 

5.1. Fish study 

• In the fish study, selected indicator species showed no effect of interaction in time 

and space. Eelpout, short-spined sea scorpion, turbot and flounder show 

homogeneity in variance. Turbot and flounder however do not fulfil the criterion 

of normal distributed data. 

• The sampling programme should be adjusted with a focus to concentrate the effort 

at as many stations as possible. Multiple replicates are not necessary on each 

station because they will be considered as one sample in the final analysis because 

station is the basic unit in the comparison of the wind farm area and the reference 

area. 

• The fish study should be restructured directing replicates into new stations and 

adding stations to achieve at least 26 stations in each area. The period should as 

well be reconsidered in an attempt to avoided filamentous algae blooming. 

• A significant difference was found between most of the sampling occasions in the 

wind farm area and reference area at certain times. It may be more biological 

relevant to time the sampling occasions according to a yearly recurrent occasion, 

which can be established by supervision instead of using, fixed dates. The period 

should as well be reconsidered in an attempt to avoided filamentous algae 

blooming. 

• Univariate and multivariate tests in the normal linear model can also be made 

using groupings of the species. The grouping will reduce the occurrence of empty 

samples and improve the possibility to fulfil the conditions of the analysis. The 

grouping should be chosen a priori and not based on the results of the 

investigation. It is only possible to create functional groups if it is considered 

biological relevant. Especially the grouping in pelagic, stationary and reef species 

shows statistical potential. 

• Grouping fish shows potential to improve the statistical analysis, especially the 

groups of stationary, prey and reef species shows good statistical strength. Sand 

lances, pelagic and commercial species show no statistical improvement due to 

grouping. 

• One of the main problems of the investigation was to verify if the reference area 

can be used as control area in a future monitoring programme of the effects from 

the wind farm. The answer to that question requires a selection of the optimal 

statistical model, which depends on various factors such as the characteristics of 

the variance and the distribution of the data. The first statistical analysis of the 

data using ANOVA among others revealed problems, which resulted in a 

refinement of the statistical techniques. The classic BACI analysis does not take 

into account a high frequency of empty samples, which can result in erroneous 

interpretation of the data. The main improvement of the statistics was the 

application of parametric univariate or non-parametric multivariate techniques. 



• The parametric univariate analysis of variance showed that the reference area and 

the wind farm area was not significantly different within the present abundance of 

fish in fish groups of ? 

• The non parametric multivariate analysis of variance (NPMANOVA) showed that 

the reference area and the wind farm area was not significantly different with the 

present abundance of fish and number of species. 

5.2. Fry Study 

• It cannot be recommended to establish a monitoring program on a statistical basis, 

in association to the fry study according to the spatial distribution of fry and small 

fry. This distribution results in large sampling efforts, in relation to the amount of 

success. However, the fry study can be used to qualitatively evaluate the 

importance of the area as spawning and nursery ground. 

5.3. Commercial fishery 

• As expected, cod, flounder, dab, turbot and plaice are fished at Rødsand. In 

addition, silver eel are caught from September to November. In the years 1997 to 

2001, the annual net fishery catch in the wind farm site itself can be estimated as 

amounting to between 1,6 and 11.1 tons of cod, between 0.00 and 0.41 tons of 

turbot and between 0.20 and 0.68 tons of silver eel. There is no trawling within 

the proposed site itself. 

• The trawl fishermen especially catch cod in the area south of the wind farm area 

from April to June and from September to November. Net fishing for cod takes 

place mainly from December to May in effectively the whole area south of 




6. References 

Anderson, M.J. 2001. A new method for non-parametric multivariate analysis of 

variance. Austral Ecology 26: 32-46. 

Bio/consult 2000a. EIA study of the proposed offshore wind farm at Rødsand. 

Technical background report concerning fish. Report prepared by Bio/consult as. 

SEAS Distributions A.m.b.A., July 2000. 

Bio/consult 2000b. EIA study of the proposed offshore wind farm at Rødsand. 

Technical background report concerning fishery. Report prepared by Bio/consult 

as. SEAS Distributions A.m.b.A., July 2000. 

Bio/consult 2001a. Monitoring programme for fish and fishery for the offshore wind 

farm at Rødsand. Report prepared by Bio/consult as. SEAS Distributions 

A.m.b.A., November 2001. 

Bio/consult 2001b. Baseline programme for fish and fishery. Offshore wind farm at 

Rødsand. Report prepared by Bio/consult as. SEAS Distributions A.m.b.A., 

December 2001. 

Bio/consult 2001c. Evaluation of the Effect of Noise from Offshore Pile-Driving on 

Marine Fish. Technical report prepared by Bio/consult as. SEAS Distributions 

A.m.b.A., January 2001. 

Bio/consult 2001d. Evaluation of the Effect of Sediment Spill from Offshore Wind 

Farm Construction on Marine Fish. Technical report prepared by Bio/consult as. 

SEAS, January 2001. 

Bio/consult 2001e. Rødsand. Fish and Fishery. Pilot study: Evaluation of the possibility 

of using by-catches from pound netting in a future monitoring programme for 

Rødsand, including the possibility for using fry trawl as a complementary gear for 

the pound nets. Note prepared by Bio/consult as, Doc. no. 1919-02-03- 

003.rev1doc. SEAS Distributions A.m.b.A., April 2001. 

Bio/consult 2002. Baseline study Fish at the cable trace – Nysted Offshore Wind Farm 

at Rødsand. Report prepared by Bio/consult as. SEAS Distributions A.m.b.A., 

May 2002. 

Bio/consult 2003a. Baseline study Fish at the cable trace – Nysted Offshore Wind Farm 

at Rødsand. Report prepared by Bio/consult as. SEAS Distributions A.m.b.A., 

February 2003. 

Clarke, K.R. 1993. Non-parametric multivariate analyses of changes in community 

structure. Australian Journal of Ecology 18: 117-143. 

Clarke, K.R.; Green, R.H. 1988 Statistical design and analysis for a ’biological effects’ 

study. Marine Ecology Progress Series 46: 213-226. 



DHI 2000. EIA of an offshore wind park at Rødsand. Technical report concerning 

Marine Biological Conditions (bottom vegetation and bottom fauna) in the wind 

farm area. Report prepared by DHI Water and Environment. SEAS Distributions 

A.m.b.A., May 2000. 

Green, R. H. 1979. Sampling design and statistical methods for environmental 

biologists. New York: Wiley. 

Manly, Bryan F. J: 1997 Randomization, Bootstrap and Monte Carlo Methods in 

Biology, Second Edition. CRC Press; 

Podlich, H.M.; Faddy, M.J.; Smyth, G.K. (in press): A general approach to modelling 

and analysis of species abundance data with extra zeros. 

Legendre, P.; Anderson, M.J. 1999: Distance-based Redundancy Analysis: Testing 

multispecies responces in multifactorial ecological experiments. Ecol. Mon. 

69(1): 1-24. 

Krog, Karsten. Fiskeri og havmiljø G.E.C. Gads forlag 1993. 

Muus, B . Nielsen, J.G. Dahlstrøn, P & Nyström, B.O. Havfisk og foskeri Gads forlag 

1998. 

SEAS 2000. Havmøllepark ved Rødsand. Vurdering af Virkningern på Miljøet – VVMredegørelse. 

Rapport udarbejdet for Energi E2 A/S af SEAS Distrubution A.m.b.a. 

Energi E2 A/S juli 2000. 

Westerberg, H. 1994. Fiskeriundersökninger vid havbaseret vindkraftverk1990-1993. 



Appendix 1. Review of the biology of fish species 

Baltic herring (Clupea harengus) 

The herring occurs throughout northern Europe, and in all Danish waters, from brackish 

fjords to the more saline North Sea. There are several different strains of herring. At an 

early stage, approx. 1 year old, the herring fry begin to move out to open sea. They form 

shoals, which can include herring of different strains. When they approach sexual 

maturity (approx. 3–9 years old, depending on the strain, earliest in the Baltic) they 

migrate inshore. 

Each female produces between 20,000 and 50,000 eggs, which is a moderate amount 

compared with other pelagic fish. The herring spawns in free bodies of water, and the 

eggs quickly precipitate to the bottom. They attach to stones, macroalgae and other 

fixed objects. The fry hatch within around 14 days, depending on the temperature of the 

water (the higher the temperature, the sooner they hatch). 

When the yolk sac has been eaten, the fry start to forage for microscopic zooplankton. 

Over time, the diet becomes more varied and consists of opossum shrimps, krill, 

copepods, pteropods, fish fry and pelagic crustacean larvae. The herring fry themselves 

are often eaten by cod (Gadus morhua), mackerel (Scomber scombrus) and whiting 

(Merlangius merlangus). In addition, they are important food items for several sea 

birds, including gulls and terns, as well as marine mammals such as seals and porpoises. 

In general, growth is slower in the Baltic than in the North Sea, and Baltic herring 

mature earlier. 

Herring are mainly caught with lampara nets, trawl nets or driftnets, and are of 

considerable commercial importance. 

Brisling (Sprattus sprattus) 

Brisling belongs to the same family as the herring and, in many respects, is very similar, 

although it is somewhat smaller, and rarely grows to longer than 15–17 cm. Brisling is 

found all over Europe and in all Danish waters. It is pelagic in habit, and, like the 

herring, it forms shoals. In the winter, the shoals move out into deeper water, up to 150 

metres deep. During the day, the fish stay close together at the bottom, whereas at night, 

they rise and spread out. The diet of the brisling is very similar to that of the herring, 

and it forages for copepods in particular. They are themselves an important food item 

for other fish and are often attacked by parasites, especially crustaceans. 

The fish spawn some distance offshore in approx. 10–15 metres of water. They spawn 

6–14,000 pelagic eggs, which follow the currents and are thus moved away from the 

spawning ground. Spawning is in late spring (April–May). The fry hatch after about 

eight days. They can easily be confused with herring fry, although they are somewhat 

smaller. About ten days later, they have eaten the umbilical sac and begin to feed on 

diatoms and the larvae of benthic fauna. The brisling first spawns at two years and 

rarely lives more than six. Brisling are fished with lampara nets, and benthic and pelagic 

trawling. The fish caught are typically between 1 and 2 years old. 



Brisling are often known as Norwegian sardines, and are marinated and eaten as 

“anchovies”. 

Common eel (Anguilla anguilla) 

The European eel is found throughout Europe, and occurs all over Denmark. It has no 

lower limitation for salinity and so lives in fresh water too. It also tolerates high 

temperatures, although it has been observed that the sexual organs develop more rapidly 

at high temperatures. The eel is most numerous in the eelgrass belt, which is also an 

important foraging area. It completely avoids sandy or fine silt beds. 

The eel probably spawns in the Sargasso Sea. Then the larvae migrate with the sea 

current to European coasts, where they arrive as elvers. On arrival, they settle in the 

vegetation zones, where they develop a black pigment. Some then move up into fresh 

water (most probably mainly females) while others remain in salt water (most probably 

mainly males). At first they are characterised as yellow eels, but when they are between 

8 and 16 years old they change appearance and become silver eels. It is these silver eels 

that migrate back to the Sargasso Sea. They do not feed while migrating, which means 

that the stomach sac contracts and the anus constricts. If the eel does not migrate, it 

begins to feed again, and can achieve an age of more than 50. 

Eels mainly hunt in the twilight. Their diet is principally made up of benthic fauna, such 

as worms, crayfish, gammarus, crabs, shrimp, insect larvae and smaller fish. The eel 

normally grows slowly once it has passed 4–5 years. Before then, growth depends on 

the food supply and the water temperature. 

Attempts to track and catch eels in the Sargasso Sea have not proved possible. This 

means that the number of eels reaching the sea, or the number of eggs each female 

spawns are unknown. In recent years, eel-farming has begun, but the eels have not 

spawned in captivity to date. This has made it necessary to catch elvers and raise them. 

Elvers are typically caught in the English Channel, but in recent years, there has been a 

dramatic reduction in the number of elvers, and a corresponding decrease in eel fishery. 

Hornfish (Belone belone) 

The hornfish occurs throughout Europe, and is found in all Danish waters. This species 

has a pelagic habit and forms shoals close to the surface. Each spring, in April-May, the 

shoals arrive in the Danish coastal areas from the northern Atlantic. Hornfish are 

observed both in salt and freshwater, most often being found in saltwater. The hornfish 

is a long slender fish, reaching 90 cm in length and weighing up to 1.3 kg. They are 

perfect swimmers, and jump out of the water when chased by predators such as 

porpoises and tuna fish. 

Hornfish spawn in vegetated areas, mainly in shallow water. The eggs attach to 

seaweeds, stones and boulders via sticky adhesive filaments. Each female spawns 

1,000-35,000 eggs, depending on the size of the female. Eggs hatch after 3-5 weeks of 

incubation, the energing larvae being 12mm long and without hornlike jaws. At the 

length of 12 cm, the fish has developed its characteristic jaws. 



The fry remain near the coast feeding on copepods and other zooplankton. They grow 

very fast, becoming 20-25 cm by the first year. After the second year, they reach the 

length of approximately 45 cm, and are sexual mature. The adults spend the post 

spawning time roaming about in the coastal waters off Denmark, feeding on herring, 

brisling, sandeels and other shoaling fish. Hornfish hunt primarily by vision during the 

daytime. In the autumn, the fish return to the areas west and south of the British Isles. 

So far, there are no commercial fisheries for hornfish, but they are caught as by-catch in 

pound nets and with hooks, though only in small quantities. Hornfish consist of low fat 

meat, but owing to having green bones it is not a popular culinary species, even though 

the pigment is harmless. 

Fifteen-spined stickleback (Spinachia spinacia) 

This is a member of the same family as the three-spined stickleback. The fifteen-spined 

stickleback is found in the whole northern part of Europe. It typically lives in 

microalgae zones, in both brackish and salt water. It feeds on small crustaceans, 

opossum shrimps and shrimp fry. It is not a good swimmer, so its hunting strategy is to 

lie in wait. The adult fifteen-spined stickleback has few enemies. The spines become 

erect when the fish is threatened. It is also well camouflaged, as its pigmentation makes 

it easily mistaken for a piece of eelgrass (Zostera marina). 

It spawns in May–June, when the male builds a nest in which the female lays around 

100 eggs. The nest is built of macrophytes (usually green and brown algae). After 

fertilisation the male stays and guards the nest. Once the eggs hatch, the male dies and 

the fry have to fend for themselves. They grow very rapidly and are mature as early as 

one year old. 

Whiting (Merlangius merlangus) 

Whiting is common throughout Europe, although it does not occur in certain places in 

the Mediterranean. It is semi-pelagic, as it sometimes remains at the bottom. The largest 

numbers, however, are found at the bottom of deeper water. Juveniles stay in shallow 

water near the coast. 

The diet is very wide, a natural result of their habits. They eat anything from benthic 

crustaceans (shrimp) to pelagic fish, and can be cannibalistic. 

The whiting is migratory by nature. Soon after the fry hatch in the northern part of the 

North Sea, the spawning area, they are carried by the current into Danish waters. On the 

journey they survive on jellyfish and horse mackerel fry. When they arrive in Danish 

coastal waters, they are 10–20 mm long. They spend the first year inshore at the bottom. 

At the age of 2–4 they become sexually mature and begin a more active migration to the 

spawning grounds. Both migrations involve a large number of individuals. 

The eggs, like those of the cod, are pelagic, and spawning takes place between January 

and May. The females spawn anything between 100,000 and 1 million eggs. In 



Denmark, they are not considered a table fish. They are, however, used as an industrial 

fish for processing for meal and oil. 


The cod is common in northern Europe and occurs in all Danish waters. They live from 

the coast out to a depth of 500 m. They are very tolerant of different temperatures. In 

summer, they can be observed in water at 20 degrees, and in winter, in water close to 

freezing point. The cod has very variable habits, although they prefer to stay near the 

bottom where they can use their barbels to guide them. They are also sometimes 

pelagic. Cod includes a number of strains, even in an area as small as Danish waters. 

There are at least two strains that share the Skagerrak and Kattegat, and two special 

Baltic strains, one being the Bornholm strain, and the other, a less populous strain found 

to the west of Bornholm. 

The diet is very varied. As fry, some strains forage for the copepod (Calanus 

finmarchicus), especially the Icelandic strain (one of the North Sea strains). Juvenile 

cod eat anything of the right size, including crustacea, fan worms and molluscs. When 

they grow bigger, they begin to forage for fish such as whiting (Merlangius marlangus), 

herring (Clupea harengus) and sandeels (Ammodytes sp./Hyperoplus sp.), as well as 

larger crustaceans including the green crab (Cercinus maenas). The cod fry are 

important food elements for larger planktonic animals such as jellyfish, crustaceans and 

arrow worms. When they become bigger, they have other predators, including their own 

species and other predatory fish. Birds such as gulls and terns also take their share of 

small cod. 

In winter and early spring, the sexually mature cod gather in bodies of water of approx. 

4–6 degrees. Typically, this is at a depth of 30–60 m. These temperatures occur earlier 

in the North Sea than in the Baltic, which means the cod spawn earlier there. The egg 

and milt are released freely in the water, while the fish stay in large shoals. Each female 

releases 0.5–5 million eggs, which, as may be expected, are relatively small. The eggs 

have to remain in the head of water all the time, as they are dependent on the 

availability of dissolved oxygen. 

This is probably why the reproductive success of the Baltic strain is doubtful in some 

years. The fry hatch in 2–4 weeks, depending on the temperature of the water. They 

feed on the yolk sacs to start with, and once these have been consumed, they eat 

microscopic plankton organisms. 

The age of sexual maturity varies according to the strain. The large migratory North Sea 

strain attains maturity later than the more stationary coastal strain. A high pressure of 

fishing can also mean that the fish attain maturity earlier and at a shorter length. 

Cod is fished with many types of gear, including hooks, bottom nets, set gillnets, 

bottom trawl and Danish seine. It is the most popular table fish, and thus the most 

important commercially. 



Broad-nosed pipefish (Syngnathus typhle) 

Max. length 35 cm. 

This lives in the seaweed belts as a stationary fish inshore and in brackish waters. It 

occurs all along the coastline of Europe from northern Norway to the Mediterranean, 

except in the Gulf of Bothnia. 

Spawning takes place in the summer. The eggs are placed in the male’s brood pouch by 

the female to be fertilised. The brood pouch is an oblong space formed by two folds of 

skin on the ventral side, and can hold 50–100 eggs. The eggs are supplied with nutrients 

from the mucous membranes of the pouch. The eggs hatch after 4 weeks, but the fry, 

which resemble the adults in appearance, remain in the pouch for a short time. The fry 

mature as juveniles and live for 2–3 years. 

They feed on small crustaceans and fish fry. 

Great sandeel (Hyperoplus lanceolatus) 

The great sandeel is found throughout Europe, except in the Mediterranean. Like the 

small sandeel, it buries itself in the sea bed during the day, only coming out when it is 

dark or just before dawn. Until it reaches a length of approx. 15 cm, it lives on plankton, 

after which it feeds mainly on the fry of herring and small sandeel. 

The great sandeeel is itself an important food item for other species, including salmon 

(Salmo salar), sea trout (Salmo trutta) and cod (Gadus morhua). 

The fish spawns at the age of 2 years. In the North Sea, it spawns at a depth of between 

20 and 100 metres, from April to August, when it deposits up to 35,000 eggs in a cluster 

on the sand. It takes around three weeks for the eggs to hatch. 

It is mainly fished in the North Sea, with a fine meshed bottom trawl, but is also 

sporadically caught at certain locations in the Skagerrak and Kattegat. The main catch is 

juveniles. This means that if reproduction is not successful in a given year, there are 

significant consequent effects both for commerce and the food chain in the following 

year. 

Small sandeel (Ammodytes tobianus) 

The small sandeel is widespread throughout Europe, except in the Mediterranean. They 

prefer a bed with mixed coarse sand and shell breccia on a reef or the slope of a bank. 

The small sandeel buries itself down in the sand when the light intensity falls below 10 

lux, which means at night and in the winter. When it moves off the bed, the small 

sandeel moves in large shoals looking for food. The diet consists of phytoplankton 

filtered out of the water. The sandeel itself is eaten by predatory fish, especially salmon 

(Salmo salmo), sea trout (Salmo trutta) and cod (Gadus morhua). 

It becomes sexually mature around the age of two. There are two strains, one of which 

spawns in spring and the other in autumn. The eggs, around 4–25,000, are very adhesive 

and released on the sand in batches. Sandeels are often used as bait by anglers. They are 



also often caught in the North Sea along with Raitt’s sandeel, and used as an industrial 

fish. 

Sand goby (Pomatoschistus minutus) 

The sand goby is one of the best-known fish around sandy beaches all along the Danish 

coasts. It lives in very shallow waters out to a depth of a few metres. Spawning takes 

place in the spring and early summer. The male finds a shell with the aperture facing 

down, and digs underneath it. The male then attracts a female and guides her into the 

cavity, where she lays her eggs. The male then fertilises the eggs and guards them for 

10–14 days, until they hatch. While the male is protecting the eggs, he ensures that the 

water circulates so that the eggs do not become covered in sand. 

Once the fry hatch, they start a brief pelagic phase of around two weeks. Then they seek 

the bottom and remain there for the rest of the two years of their lives. 

The diet consists principally of crustaceans, shellfish and young snails. This diet means 

they compete with many of the fry of commercially important species. Several table fish 

feed on them, such as cod (Gadus morhua), turbot (Psetta maxima) and eel (Anguilla 

anguilla). 

Two-spotted goby (Gobiusculus flavescens) 

The two-spotted goby occurs throughout Europe, except in the inner Baltic. It forms 

shoals in depths of 0–5 metres. They are not especially benthic, and prefer habitats 

where there is vegetation. They also avoid water with low salinity. Spawning is in 

relatively deep water, and the eggs are released onto macrophytes, in April–May. By the 

approach of winter, the fry have achieved an adult size, and they attain maturity the 

following year. Like the sand goby, they form an important part of the food chain, 

firstly as a competitor with table fish, but also as food. 

Black goby (Gobius niger) 

The black goby occurs throughout Europe, except in the inner Baltic. It is very tolerant 

of low salinity and can thus also be found in the lower reaches of larger streams and 

rivers. It mainly stays in shallow waters, but can be found out to depths of about 50 

metres. 

It matures at the age of two, and spawns between 1,000 and 6,000 eggs, which attach to 

stationary surfaces such as stones and net stakes. The male remains near the eggs until 

they hatch. 

The diet consists principally of worms and crustacea. The black goby is an important 

food source for predatory fish, and is also used for bait by anglers. 

Transparent goby (Aphya minuta) 

As its name implies, this fish is virtually transparent when alive. When dead, it turns 

white and is therefore easier to spot. It is widespread throughout Europe, mainly in 



shoals in shallow waters. It lives near the bottom, except at night, when it moves up to 

forage. Its main diet consists of crustacea, small shellfish and other creatures of suitable 

size. 

Its reproductive behaviour is very similar to that of the sand goby. It lives for a year, 

and the adults die soon after spawning. 

Butterfish (Pholis gunnellus) 

The butterfish is found all over northern Europe, down to the French coast. They live in 

tidal waters out to a depth of 30 metres. They are extremely tolerant of cold, and do not 

therefore move out to deeper waters in the winter. They inhabit the seaweed on reefs 

and slowly seek food in these areas. Sometimes they hide under shells to lie in wait for 

food. Spawning takes place when they are 3 years old in January–July, and the female 

releases 100–200 eggs. The eggs rest on the sea bed in the form of a cone. Both sexes 

look after the eggs until they hatch. The fry are pelagic initially, until they are 3 cm 

long. At this point they seek the bottom. Their diet consists of crustacea and other small 

animals. 

Eelpout (Zoarces viviparus) 

Eelpout are widespread in Europe from the English Channel north. They live near the 

bottom of eelgrass zones, and are distinctly stationary fish. They stay in shallow water 

in the summer and move out to deeper waters in the winter. Their diet consists 

principally of gammarus, shellfish, snails and small crustaceans. In brackish waters they 

sometimes also take insect larvae. They move slowly around amongst the eelgrass, 

examining one clump of macroalgae after another. 

The eelpout is viviparous. The number of young varies, but is between 50 and 300. Like 

their parents, the young are benthic, and they very quickly begin to forage for 

themselves after birth. They grow very rapidly and are mature as early as their second 

year. 

They are treated as part of the by-catch in commercial fishery, and only occasionally 

used as a table fish. Many anglers use them as bait. 

Short-spined sea scorpion (Myoxocephalus scorpius) 

The short-spined sea scorpion occurs throughout northern Europe. It lives amongst 

stones and seaweed at depths from 1 to 200 metres. It tolerates low temperatures, which 

means it can also be found in the arctic region. It is a distinctly stationary fish, which 

means there are local varieties. 

Breeding takes place in December–January. Fertilised eggs have been found in females, 

indicating that fertilisation is internal. Once the female has spawned, the male looks 

after the eggs until they hatch 5 weeks later. The fry are pelagic in habit, until they 

achieve a length of approximately 15 mm, at which point they seek the bottom. 



Despite its somewhat somnolent behaviour, the short-spined sea scorpion is, in fact, 

predatory, and it eats anything that enters its vicinity. 

Longspined bullhead (Taurulus bubalis) occurs throughout the whole of Europe, and 

thus extends further south than the short-spined sea scorpion. In other respects, it is very 

similar to the short-spined sea scorpion (see above) in behaviour, reproduction and diet. 

Hooknose (Agonus cataphractus) 

The hooknose is distributed throughout the northern part of Europe, except the 

innermost parts of the Baltic. It is a typically benthic fish, preferring a soft bed. It lives a 

rather anonymous life, as it is not sought-after prey for other fish, and is only rarely 

found in nets. It breeds in February–April, when 2,500–3,000 eggs are attached to 

microalgae. The development from egg to fry is unusually long, taking around 11 

months. The fry are initially pelagic, but then they become benthic. 

The diet consists mainly of small crustaceans. The fish has virtually no importance as 

food for commercial fish. 

Lumpsucker (Cyclopterus lumpus) 

The lumpsucker is widespread in the northern part of Europe and is also found in the 

arctic. In Denmark, it is typically a seasonal fish and exists in two strains. The North 

Sea strain grows longer, up to 50 cm. The Baltic strain is a miniature form, only 

reaching around 20 cm. The lumpsucker lives on reefs, where it attaches itself to stones 

with its specially developed ventral fins. There is a large difference between the sexes, 

with regard to both colour and size. The male is the more colourful (shades of red), but 

is also the smaller. The female is greenish and can be around a third longer than the 

male. 

Spawning is in early spring in relatively shallow waters. The female releases around 

350,000 eggs in a single cluster. Then she abandons them, and the male remains to look 

after the eggs. 

The eggs hatch after approximately 7–8 weeks, at which point the male also leaves. The 

newly hatched fry are already equipped with suckers, which they use to attach 

themselves to large objects. When winter approaches, the fry migrate out of the shallow 

waters and do not return until they are sexually mature and ready to spawn. 

Their initial diet consists of small planktonic animals. Later they start to eat krill, small 

jellyfish and parts of larger jellyfish. 

Lumpsucker are fished in considerable quantities in Danish waters. It is the meat of the 

male that is particularly prized. The roe are also used, however, and are sold 

unprocessed (often as “caviar”). 

Striped seasnail (Liparis liparis) 



The striped seasnail is closely related to the lumpsucker, both being representatives of 

the Cyclopteridae family. It is distributed along the coastlines of northern Europe, from 

the English Channel to the Polar Circle. The striped seasnail reaches a maximum length 

of 15 cm. The fish uses a suction disc (larger than the eye diameter and being modified 

pectoral fins), to attach to stones. Smaller fish are also found attached to seaweed, 

especially brown algae of the family Laminaria spp.. 

It is mostly found in near coastal waters, associated with stony vegetated areas, but is 

also found in shallow offshore reef areas with relatively calm water. 

The striped seasnail spawns in the wintertime. The transparent and spherical eggs are 

1.5 mm in size, and are placed on the seabed at the time of spawning. The eggs incubate 

for 6 to 8 weeks before they hatch. The larvae, which are pelagic, migrate with the 

water current. The fish are carnivores and feed primarily on small crustaceans. 

No commercial exploitation of this species occurs, properly due to its moderate size. 

Turbot (Psetta maxima) 

The turbot is a sinistral flat fish, found from northern Norway to North Africa. It is not 

especially numerous anywhere in Danish waters. It prefers a hard bed with gravel or 

stone. It is mainly a stationary benthic fish, and can be hard to see because of its 

camouflage. It lies in wait for its prey, which mainly consists of other benthic fish, 

shellfish and larger crustaceans. It will, however, also take pelagic fish. It matures at the 

age of 3–5 years. The spawning grounds are not particularly defined, as it spawns where 

it lives, normally between April and August. Each female releases between 5 and 15 

million eggs, which hatch after 7–9 days. Once the fry reach a length of around 2.5 mm, 

they seek the bottom. As the spawning period approaches, they become more active. 

They prefer to spawn in waters between 10 and 40 m deep. The turbot moves out to 

deeper, warmer, waters in the winter, and moves back towards the coast in summer. 

There is an imbalance between the sexes. For reasons currently unknown, there are 

more males than females in the population, especially in the Baltic. 

As they are not numerous, they are not fished to any great extent. Despite this, they are 

highly valued as table fish, and thus a welcome by-catch. They are normally caught in 

nets in the period when they move around. In recent years, the catch of turbot has been 

increasing. 

Dab (Limanda limanda) 

The dab is widespread along the coasts of northern Europe, and is found in all Danish 

waters. It lives on sandy and soft beds at depths between 6 and 70 m. It is a very 

stationary fish and spawns where it lives. It has sharp pointed teeth and mainly lives on 

fan worms, crustacea, smaller echinoderms, thin-shelled shellfish and small fish. It is 

often used as a substitute for plaice as it is rare that both species are numerous in the 

same area. 

Growth is slow, and it therefore matures when it is not especially big, at the age of 2 

years. Their size means that they spawn before they are of a size to be caught in nets. As 



a result, the population is not affected to the same extent as species that mature after 

attaining a size where they can be caught. 

The dab spawns in April–June in the North Sea, and April–August in the Baltic. The 

female lays 50–150,000 eggs. Once the fry hatch, they begin a short pelagic period in 

relatively deep water (6–12 m). When they seek the bottom, they stay at these depths, 

which means they do not come into conflict with the fry of other species of flat fish, 

which mainly stay and feed in shallower waters. 

Flounder (Platichthys flesus) 

Flounder is common throughout Europe, although it does not occur in the southern 

Mediterranean. It is very common along the Danish coasts, although the population in 

the North Sea is not especially large, as the flounder prefers lower salinity. It can also 

stay for long periods in fresh water. It lives in the tidal zone, the younger fish close to 

the shore and the older fish further out. They like places where they can bury 

themselves, either pure sand or mud. The flounder can be sinistral or dextral, although 

around two thirds are dextral. 

Flounder fry mainly eat microscopic planktonic animals. After metamorphosis, the fry 

move towards the bottom and change their diet to microscopic crustacea. As an adult, 

the flounder forages at night, mainly for benthic invertebrates such as shellfish, 

mosquito larvae, bristleworms and crustacea. Juvenile flounder form small shoals. In 

winter they feed only to a limited extent and move out to deeper waters. 

Spawning is from February to May, and requires a certain level of salinity so the pelagic 

eggs do not sink to the bottom and perish due to lack of oxygen. Spawnings are around 

1.5–2 million eggs per female. In the Baltic, this takes place in the Bornholm deep, 

which has a salinity of approximately 10 promille. Flounder can cross-breed with plaice. 

Flounder is a valuable table fish, caught with gill and fyke nets. 

European plaice (Pleuronectes platessa) 

The European plaice is distributed throughout the north eastern Atlantic, from 

Greenland and the Bering Strait south to the coastlines of Morocco. This fish lives on 

mixed substrates at depths down to 200m. The adult is usually found in the deeper 

areas, whereas the fry is often seen around shallow bathing beaches. Plaice can reach 

100 cm in length and achieve a weight of 7 kg. 

This species lives both in brackish and marine water, though requires a salinity higher 

than 12 per mille for successful reproduction. The plaice is considered relatively 

sedentary, but tagging experiments show that they perform long migrations, up to 18-30 

km each day. 

The plaice is sexually mature at the age of 2-6 years depending on sex (earliest for the 

male) and location. Spawning occurs at a water temperature of approximately 6ºC, the 

timing differing from place to place depending on latitude, though in Danish waters they 

spawn in January-April. In the Baltic Sea the period of spawning is extended and take 

places from November to March, owing to the fact that temperatures in the deeper high 



saline water bodies stay colder for a longer period. European plaice spawn 50,000- 

500,000 transparent eggs, each measuring 1.6 mm in diameter and being pelagic. The 

eggs hatch after 10-20 days of incubation. As soon as the yolk sac is used, the plaice 

larva begin to feed on planktonic organisms. During this life stage, a high mortality 

occurs due to numerous jellyfish, and/or shortage in food supply. At the length of 12- 

14mm, plaice abandon the pelagic habit and search towards the sea bottom, and now 

exhibiting a benthic life habit. The Wadden sea has been shown to be of major 

importance as a nursery area in relation to the North Sea population. 

The plaice is most active during the night, the daytime being spent buried in sand. Its 

diet consists primarily of thin-shelled molluscs and polychaetes. Plaice are thus easily 

affected by nutrient enrichment of water and associated deoxygenation, which has 

resulted in a lowering of the quality of off shore nursery areas. 

The fishery on this species is highly commercial, and plaice are probably the most 

important species of flatfish in Europe. Fishing involves a variety of gear, including gill 

nets, bottom trawl, Danish seine and beam trawl. 

Common sole (Solea solea) 

The common sole is distributed from Trondheim Fjord in Norway southward, it is found 

in the Mediterranean, and as far south as the coasts of Senegal. In the inner Danish 

waters, the sole is only distributed in the Kattegat and the western Baltic Sea. The 

maximum length for this species is in Danish waters 60 cm, but rarely exceeds 50cm. 

The age of the sole at this length is more than 20 years. 

The sole lives at various depths from shallow water down to 150m. Favouring sandy 

and fine silt beds rich in detritus. 

In the North Sea, the common sole exists near its most northerly limit, and is thereby 

vulnerable to cold winters. The Kattegat contains a strain distinct from the population 

occurring in the North Sea, this particular strain being more resistant to cold waters. 

The common sole reaches maturity at a length of 25-35 cm and an age of 3-5 years. The 

preferred spawning areas are at 20-25 meters depth, with water temperatures of 6-12° C. 

In Danish areas, the spawning period is during April-June. At this time they spawn 

100,000-150,000 pelagic eggs, each measuring 3-4 mm and hatching after 10 days of 

incubation. The larvae search for the seabed after 4-6 weeks in the pelagic environment, 

this primarily taking place in shallow water near the coast. 

The Wadden Sea is an important nursery area for the North Sea population. The 

recruitment of common sole is very variable, but since the 1960’s the population growth 

rate of this species has improved, apparently due to the rise in nutrient levels in the 

nursery areas. 

The common sole is primarily nocturnal, searching the seabed for food items by using 

sensitive papilla on the head. Food consists of molluscs, polychaetes and small 

crustaceans, and thin-shelled mussels are particularly favoured. 

The common sole is a highly valued fish and is a by-catch in the fisheries for European 

plaice and lobsters. It is mainly caught in bottom trawl, Danish seine and beam trawl. 



Appendix 2. List of research positions (stations), WGS-84. 

EASTING NORTHING X Y 

STATION TRANSEKT 

Degree-min-dec. Degree-min-dec. Degree-decimal Degree-decimal 

1 1 11,40817 54,33947 11,68028 54,56578 

2 1 11,40819 54,33175 11,68032 54,55291 

3 1 11,40818 54,32404 11,68030 54,54007 

4 2 11,42410 54,33559 11,70684 54,55932 

5 2 11,42409 54,32756 11,70682 54,54593 

6 2 11,42399 54,32002 11,70665 54,53337 

7 3 11,43200 54,33714 11,71999 54,56191 

8 3 11,43212 54,32936 11,72020 54,54893 

9 3 11,43197 54,32149 11,71995 54,53582 

10 4 11,44789 54,33304 11,74648 54,55507 

11 4 11,44788 54,32509 11,74647 54,54182 

12 4 11,44765 54,31734 11,74609 54,52890 

13 5 11,29936 54,34998 11,49893 54,58331 

14 5 11,29936 54,34377 11,49893 54,57295 

15 5 11,29921 54,33673 11,49868 54,56122 

16 6 11,37425 54,34006 11,62376 54,56677 

17 6 11,37427 54,33077 11,62378 54,55128 

18 6 11,37435 54,32286 11,62391 54,53810 

19 7 11,48945 54,33243 11,81575 54,55405 

20 7 11,48921 54,32197 11,81536 54,53662 

21 7 11,48923 54,31228 11,81538 54,52046 

22 8 11,54433 54,33012 11,90722 54,55020 

23 8 11,54452 54,32215 11,90754 54,53692 

24 8 11,54431 54,31389 11,90719 54,52315 



Appendix 3. Levene’s test and Uni-ANOVA analysis of 

abundance. 

Fish study 

Levene's Test of Equality of Error Variances LN (number) 

Species F df1 df2 Sig. 

Baltic herring 8,262239742 3 44 0,000 

Brisling 5,2193355 3 44 0,004 

Common eel 11,21300138 3 44 0,000 

Hornfish 22,24999645 3 44 0,000 

Snake pipefish 4,84 3 44 0,005 

Straightnose pipefish 4,599371916 3 44 0,007 

Great pipefish , 3 44 , 

Lesser pipefish , 3 44 , 

Broad-nosed pipefish , 3 44 , 

Fifteen-spined stickleback 1,310710315 3 44 0,283 

Whiting 4,84 3 44 0,005 

Atlantic cod 2,557290842 3 44 0,067 

Small sandeel 2,945414261 3 44 0,043 

Sandeel sp. , 3 44 , 

Great sandeel 6,320867596 3 44 0,001 

Two-spotted goby 0,211588419 3 44 0,888 

Sand goby 1,161056066 3 44 0,335 

Painted goby , 3 44 , 

Black goby 5,942175034 3 44 0,002 

Transparent goby 4,84 3 44 0,005 

Goby.sp. 13,75 3 44 0,000 

Butterfish 15,02486679 3 44 0,000 

Eelpout 0,920565718 3 44 0,439 

Short-spined sea scorpion 2,446645861 3 44 0,076 

Longspined bullhead 1,116911036 3 44 0,352 

Hooknose 3,226666667 3 44 0,031 

Lumpsucker 3,226666667 3 44 0,031 

Striped seasnail , 3 44 , 

Turbot 2,673437415 3 44 0,059 

Dab , 3 44 , 

Flounder 0,462443956 3 44 0,710 

European plaice 6,920448179 3 44 0,001 

Common sole , 3 44 , 

Sea trout 4,84 3 44 0,005 



Species 

Unianova tests of Between-Subjects Effects LN (number) 

Type III df Mean F Sig. 

Sum of Squares Square 

Baltic herring Source Corrected Model 0,4475 3 0,1492 2,1966 0,102 

Intercept 1,0352 1 1,0352 15,2461 0,000 

AREA 0,4340 1 0,4340 6,3919 0,015 

MONTH 0,0100 1 0,0100 0,1474 0,703 

AREA * MONTH 0,0034 1 0,0034 0,0504 0,823 

Error 2,9877 44 0,0679 

Total 4,4704 48 

Corrected Total 3,4352 47 

Brisling Source Corrected Model 0,2928 3 0,0976 1,6625 0,189 

Intercept 1,5335 1 1,5335 26,1247 0,000 

AREA 0,0063 1 0,0063 0,1071 0,745 

MONTH 0,2299 1 0,2299 3,9166 0,054 

AREA * MONTH 0,0566 1 0,0566 0,9639 0,332 

Error 2,5827 44 0,0587 

Total 4,4089 48 


Common eel Source Corrected Model 0,0876 3 0,0292 2,0370 0,122 

Intercept 0,1226 1 0,1226 8,5556 0,005 

AREA 0,0225 1 0,0225 1,5714 0,217 

MONTH 0,0025 1 0,0025 0,1746 0,678 

AREA * MONTH 0,0626 1 0,0626 4,3651 0,042 

Error 0,6306 44 0,0143 

Total 0,8408 48 


Hornfish Source Corrected Model 4,4144 3 1,4715 5,9097 0,002 

Intercept 6,8359 1 6,8359 27,4543 0,000 

AREA 0,3099 1 0,3099 1,2448 0,271 

MONTH 3,9891 1 3,9891 16,0210 0,000 

AREA * MONTH 0,1153 1 0,1153 0,4633 0,500 

Error 10,9557 44 0,2490 

Total 22,2061 48 


Snake pipefish Source Corrected Model 0,0075 3 0,0025 1,0000 0,402 

Intercept 0,0025 1 0,0025 1,0000 0,323 

AREA 0,0025 1 0,0025 1,0000 0,323 

MONTH 0,0025 1 0,0025 1,0000 0,323 

AREA * MONTH 0,0025 1 0,0025 1,0000 0,323 

Error 0,1101 44 0,0025 

Total 0,1201 48 


Straightnose 

pipefish Source Corrected Model 0,0475 3 0,0158 0,9543 0,423 

Intercept 0,0626 1 0,0626 3,7671 0,059 

AREA 0,0225 1 0,0225 1,3562 0,250 

MONTH 0,0225 1 0,0225 1,3562 0,250 

AREA * MONTH 0,0025 1 0,0025 0,1507 0,700 



Error 0,7307 44 0,0166 

Total 0,8408 48 


Great pipefish Source Corrected Model 0,0000 3 0,0000 , , 

Intercept 0,0000 1 0,0000 , , 

AREA 0,0000 1 0,0000 , , 

MONTH 0,0000 1 0,0000 , , 

AREA * MONTH 0,0000 1 0,0000 , , 

Error 0,0000 44 0,0000 

Total 0,0000 48 


Lesser pipefish Source Corrected Model 0,0000 3 0,0000 , , 

Intercept 0,0000 1 0,0000 , , 

AREA 0,0000 1 0,0000 , , 

MONTH 0,0000 1 0,0000 , , 

AREA * MONTH 0,0000 1 0,0000 , , 

Error 0,0000 44 0,0000 

Total 0,0000 48 


Broad-nosed 

pipefish Source Corrected Model 0,0000 3 0,0000 , , 

Intercept 0,0000 1 0,0000 , , 

AREA 0,0000 1 0,0000 , , 

MONTH 0,0000 1 0,0000 , , 

AREA * MONTH 0,0000 1 0,0000 , , 

Error 0,0000 44 0,0000 

Total 0,0000 48 


Fifteen-spined 

stickleback Source Corrected Model 0,8210 3 0,2737 3,9385 0,014 

Intercept 2,1183 1 2,1183 30,4850 0,000 

AREA 0,3358 1 0,3358 4,8333 0,033 

MONTH 0,4071 1 0,4071 5,8588 0,020 

AREA * MONTH 0,0781 1 0,0781 1,1233 0,295 

Error 3,0573 44 0,0695 

Total 5,9966 48 


Whiting Source Corrected Model 0,0075 3 0,0025 1,0000 0,402 

Intercept 0,0025 1 0,0025 1,0000 0,323 

AREA 0,0025 1 0,0025 1,0000 0,323 

MONTH 0,0025 1 0,0025 1,0000 0,323 

AREA * MONTH 0,0025 1 0,0025 1,0000 0,323 

Error 0,1101 44 0,0025 

Total 0,1201 48 


Atlantic cod Source Corrected Model 3,9256 3 1,3085 3,5768 0,021 

Intercept 9,4917 1 9,4917 25,9450 0,000 

AREA 1,6818 1 1,6818 4,5972 0,038 

MONTH 1,9991 1 1,9991 5,4644 0,024 

AREA * MONTH 0,2447 1 0,2447 0,6689 0,418 



Error 16,0970 44 0,3658 

Total 29,5143 48 


Small sandeel Source Corrected Model 6,0779 3 2,0260 8,1829 0,000 

Intercept 10,2080 1 10,2080 41,2302 0,000 

AREA 0,1944 1 0,1944 0,7851 0,380 

MONTH 5,0152 1 5,0152 20,2566 0,000 

AREA * MONTH 0,8683 1 0,8683 3,5070 0,068 

Error 10,8938 44 0,2476 

Total 27,1797 48 


Sandeel sp. Source Corrected Model 0,0000 3 0,0000 , , 

Intercept 0,0000 1 0,0000 , , 

AREA 0,0000 1 0,0000 , , 

MONTH 0,0000 1 0,0000 , , 

AREA * MONTH 0,0000 1 0,0000 , , 

Error 0,0000 44 0,0000 

Total 0,0000 48 


Great sandeel Source Corrected Model 6,7503 3 2,2501 5,2179 0,004 

Intercept 38,4167 1 38,4167 89,0864 0,000 

AREA 3,8415 1 3,8415 8,9082 0,005 

MONTH 0,4292 1 0,4292 0,9952 0,324 

AREA * MONTH 2,4796 1 2,4796 5,7502 0,021 

Error 18,9741 44 0,4312 

Total 64,1411 48 


Two-spotted goby Source Corrected Model 0,7009 3 0,2336 0,9432 0,428 

Intercept 6,8216 1 6,8216 27,5404 0,000 

AREA 0,0346 1 0,0346 0,1397 0,710 

MONTH 0,5601 1 0,5601 2,2614 0,140 

AREA * MONTH 0,1061 1 0,1061 0,4285 0,516 

Error 10,8986 44 0,2477 

Total 18,4211 48 


Sand goby Source Corrected Model 0,1685 3 0,0562 0,4574 0,713 

Intercept 4,7707 1 4,7707 38,8526 0,000 

AREA 0,0034 1 0,0034 0,0279 0,868 

MONTH 0,0901 1 0,0901 0,7336 0,396 

AREA * MONTH 0,0750 1 0,0750 0,6106 0,439 

Error 5,4028 44 0,1228 

Total 10,3420 48 


Painted goby Source Corrected Model 0,0000 3 0,0000 , , 

Intercept 0,0000 1 0,0000 , , 

AREA 0,0000 1 0,0000 , , 

MONTH 0,0000 1 0,0000 , , 

AREA * MONTH 0,0000 1 0,0000 , , 

Error 0,0000 44 0,0000 

Total 0,0000 48 




Black goby Source Corrected Model 0,0868 3 0,0289 1,3440 0,272 

Intercept 0,1085 1 0,1085 5,0403 0,030 

AREA 0,0050 1 0,0050 0,2327 0,632 

MONTH 0,0292 1 0,0292 1,3556 0,251 

AREA * MONTH 0,0526 1 0,0526 2,4435 0,125 

Error 0,9472 44 0,0215 

Total 1,1425 48 


Transparent goby Source Corrected Model 0,0075 3 0,0025 1,0000 0,402 

Intercept 0,0025 1 0,0025 1,0000 0,323 

AREA 0,0025 1 0,0025 1,0000 0,323 

MONTH 0,0025 1 0,0025 1,0000 0,323 

AREA * MONTH 0,0025 1 0,0025 1,0000 0,323 

Error 0,1101 44 0,0025 

Total 0,1201 48 


Goby.sp. Source Corrected Model 0,0300 3 0,0100 2,2000 0,101 

Intercept 0,0100 1 0,0100 2,2000 0,145 

AREA 0,0100 1 0,0100 2,2000 0,145 

MONTH 0,0100 1 0,0100 2,2000 0,145 

AREA * MONTH 0,0100 1 0,0100 2,2000 0,145 

Error 0,2002 44 0,0045 

Total 0,2402 48 


Butterfish Source Corrected Model 0,0676 3 0,0225 2,1064 0,113 

Intercept 0,0626 1 0,0626 5,8511 0,020 

AREA 0,0025 1 0,0025 0,2340 0,631 

MONTH 0,0025 1 0,0025 0,2340 0,631 

AREA * MONTH 0,0626 1 0,0626 5,8511 0,020 

Error 0,4704 44 0,0107 

Total 0,6006 48 


Eelpout Source Corrected Model 8,7917 3 2,9306 11,3129 0,000 

Intercept 34,8403 1 34,8403 134,4934 0,000 

AREA 8,1663 1 8,1663 31,5242 0,000 

MONTH 0,4097 1 0,4097 1,5816 0,215 

AREA * MONTH 0,2158 1 0,2158 0,8329 0,366 

Error 11,3981 44 0,2590 

Total 55,0301 48 


Short-spined sea 

scorpion Source Corrected Model 16,5746 3 5,5249 15,6370 0,000 

Intercept 34,0469 1 34,0469 96,3631 0,000 

AREA 2,3099 1 2,3099 6,5378 0,014 

MONTH 12,6739 1 12,6739 35,8710 0,000 

AREA * MONTH 1,5907 1 1,5907 4,5022 0,040 

Error 15,5461 44 0,3533 

Total 66,1675 48 




Longspined 

bullhead Source Corrected Model 0,3427 3 0,1142 1,4197 0,250 

Intercept 1,8938 1 1,8938 23,5343 0,000 

AREA 0,1740 1 0,1740 2,1629 0,148 

MONTH 0,0400 1 0,0400 0,4976 0,484 

AREA * MONTH 0,1286 1 0,1286 1,5987 0,213 

Error 3,5406 44 0,0805 

Total 5,7771 48 


Hooknose Source Corrected Model 0,0100 3 0,0033 0,6667 0,577 

Intercept 0,0100 1 0,0100 2,0000 0,164 

AREA 0,0000 1 0,0000 0,0000 1,000 

MONTH 0,0100 1 0,0100 2,0000 0,164 

AREA * MONTH 0,0000 1 0,0000 0,0000 1,000 

Error 0,2202 44 0,0050 

Total 0,2402 48 


Lumpsucker Source Corrected Model 0,0100 3 0,0033 0,6667 0,577 

Intercept 0,0100 1 0,0100 2,0000 0,164 

AREA 0,0000 1 0,0000 0,0000 1,000 

MONTH 0,0100 1 0,0100 2,0000 0,164 

AREA * MONTH 0,0000 1 0,0000 0,0000 1,000 

Error 0,2202 44 0,0050 

Total 0,2402 48 


Striped seasnail Source Corrected Model 0,0000 3 0,0000 , , 

Intercept 0,0000 1 0,0000 , , 

AREA 0,0000 1 0,0000 , , 

MONTH 0,0000 1 0,0000 , , 

AREA * MONTH 0,0000 1 0,0000 , , 

Error 0,0000 44 0,0000 

Total 0,0000 48 


Turbot Source Corrected Model 0,3706 3 0,1235 1,1519 0,339 

Intercept 1,1215 1 1,1215 10,4582 0,002 

AREA 0,0000 1 0,0000 0,0000 1,000 

MONTH 0,1602 1 0,1602 1,4935 0,228 

AREA * MONTH 0,2104 1 0,2104 1,9622 0,168 

Error 4,7183 44 0,1072 

Total 6,2103 48 


Dab Source Corrected Model 0,0000 3 0,0000 , , 

Intercept 0,0000 1 0,0000 , , 

AREA 0,0000 1 0,0000 , , 

MONTH 0,0000 1 0,0000 , , 

AREA * MONTH 0,0000 1 0,0000 , , 

Error 0,0000 44 0,0000 

Total 0,0000 48 


Flounder Source Corrected Model 1,4672 3 0,4891 2,3710 0,083 



Intercept 10,1834 1 10,1834 49,3671 0,000 

AREA 0,0103 1 0,0103 0,0497 0,825 

MONTH 1,3983 1 1,3983 6,7789 0,013 

AREA * MONTH 0,0587 1 0,0587 0,2844 0,597 

Error 9,0763 44 0,2063 

Total 20,7269 48 


European plaice Source Corrected Model 0,0275 3 0,0092 1,3011 0,286 

Intercept 0,0225 1 0,0225 3,1935 0,081 

AREA 0,0025 1 0,0025 0,3548 0,554 

MONTH 0,0025 1 0,0025 0,3548 0,554 

AREA * MONTH 0,0225 1 0,0225 3,1935 0,081 

Error 0,3103 44 0,0071 

Total 0,3603 48 


Common sole Source Corrected Model 0,0000 3 0,0000 , , 

Intercept 0,0000 1 0,0000 , , 

AREA 0,0000 1 0,0000 , , 

MONTH 0,0000 1 0,0000 , , 

AREA * MONTH 0,0000 1 0,0000 , , 

Error 0,0000 44 0,0000 

Total 0,0000 48 


Sea trout Source Corrected Model 0,0075 3 0,0025 1,0000 0,402 

Intercept 0,0025 1 0,0025 1,0000 0,323 

AREA 0,0025 1 0,0025 1,0000 0,323 

MONTH 0,0025 1 0,0025 1,0000 0,323 

AREA * MONTH 0,0025 1 0,0025 1,0000 0,323 

Error 0,1101 44 0,0025 

Total 0,1201 48 




Fry study 

Levene's Test of Equality of Error Variances LN (number) 


Baltic herring , 115 116 , 

Brisling , 115 116 , 

Common eel , 115 116 , 

Hornfish , 115 116 , 

Snake pipefish , 115 116 , 

Straightnose pipefish , 115 116 , 




Fifteen-spined stickleback , 115 116 , 

Whiting , 115 116 , 

Atlantic cod , 115 116 , 

Small sandeel , 115 116 , 


Great sandeel , 115 116 , 

Two-spotted goby , 115 116 , 

Sand goby , 115 116 , 


Black goby , 115 116 , 

Transparent goby , 115 116 , 

Goby.sp. , 115 116 , 

Butterfish , 115 116 , 

Eelpout , 115 116 , 

Short-spined sea scorpion , 115 116 , 

Longspined bullhead , 115 116 , 

Hooknose , 115 116 , 

Lumpsucker , 115 116 , 


Turbot , 115 116 , 

Dab , 115 116 , 

Flounder , 115 116 , 

European plaice , 115 116 , 


Sea trout , 115 116 , 



Unianova- tests of Between-Subjects Effects LN (number) 

Species Type III df Mean F Sig. 

Baltic herring Intercept Hypothesis 0,001929667 1 0,00193 0,92887 0,3373 

Error 0,220207631 106 0,00208 

REF Hypothesis 0,001938344 1 0,00194 0,93305 0,3363 

Error 0,220207631 106 0,00208 

MM Hypothesis 0,00793852 4 0,00198 0,95533 0,4353 

Error 0,220207631 106 0,00208 

REF * MM Hypothesis 0,00793852 4 0,00198 0,95533 0,4353 

Error 0,220207631 106 0,00208 

SEKTION(REF * MM) Hypothesis 0,220207631 106 0,00208 1,00314 0,4923 

Error 0,240226507 116 0,00207 

Brisling Intercept Hypothesis 0 1 0 , , 

Error 0 106 0 

REF Hypothesis 0 1 0 , , 

Error 0 106 0 

MM Hypothesis 0 4 0 , , 

Error 0 106 0 

REF * MM Hypothesis 0 4 0 , , 

Error 0 106 0 

SEKTION(REF * MM) Hypothesis 0 106 0 , , 

Error 0 116 0 

Common eel Intercept Hypothesis 0,366342241 1 0,36634 11,5489 0,001 

Error 3,362420238 106 0,03172 


Error 3,362420238 106 0,03172 

MM Hypothesis 0,192269268 4 0,04807 1,51532 0,203 

Error 3,362420238 106 0,03172 


Error 3,362420238 106 0,03172 


Error 2,724692553 116 0,02349 

Hornfish Intercept Hypothesis 0 1 0 , , 

Error 0 106 0 


Error 0 106 0 


Error 0 106 0 


Error 0 106 0 


Error 0 116 0 

Snake pipefish Intercept Hypothesis 0,001929667 1 0,00193 0,92887 0,3373 

Error 0,220207631 106 0,00208 


Error 0,220207631 106 0,00208 

MM Hypothesis 0,00793852 4 0,00198 0,95533 0,4353 

Error 0,220207631 106 0,00208 




Error 0,220207631 106 0,00208 


Error 0,240226507 116 0,00207 

Straightnose pipefish Intercept Hypothesis 0,033934835 1 0,03393 4,1024 0,0453 

Error 0,87682675 106 0,00827 

REF Hypothesis 6,40775E-05 1 6,4E-05 0,00775 0,93 

Error 0,87682675 106 0,00827 

MM Hypothesis 0,028923824 4 0,00723 0,87415 0,4821 

Error 0,87682675 106 0,00827 


Error 0,87682675 106 0,00827 


Error 0,960906028 116 0,00828 

Great pipefish Intercept Hypothesis 0,002748922 1 0,00275 1,34774 0,2483 

Error 0,216203856 106 0,00204 


Error 0,216203856 106 0,00204 

MM Hypothesis 0,009940407 4 0,00249 1,21839 0,3074 

Error 0,216203856 106 0,00204 


Error 0,216203856 106 0,00204 


Error 0,240226507 116 0,00207 

Lesser pipefish Intercept Hypothesis 0,001929667 1 0,00193 0,92887 0,3373 

Error 0,220207631 106 0,00208 


Error 0,220207631 106 0,00208 

MM Hypothesis 0,00793852 4 0,00198 0,95533 0,4353 

Error 0,220207631 106 0,00208 


Error 0,220207631 106 0,00208 


Error 0,240226507 116 0,00207 

Broad-nosed pipefish Intercept Hypothesis 0,364857975 1 0,36486 14,4063 0,0002 

Error 2,68457671 106 0,02533 


Error 2,68457671 106 0,02533 

MM Hypothesis 0,266676986 4 0,06667 2,63242 0,0383 

Error 2,68457671 106 0,02533 


Error 2,68457671 106 0,02533 


Error 3,60921403 116 0,03111 

Fifteen-spined 

stickleback Intercept Hypothesis 44,4706371 1 44,4706 145,509 1E-21 

Error 32,39581264 106 0,30562 


Error 32,39581264 106 0,30562 

MM Hypothesis 19,43185559 4 4,85796 15,8954 3E-10 

Error 32,39581264 106 0,30562 




Error 32,39581264 106 0,30562 


Error 25,54607562 116 0,22022 

Whiting Intercept Hypothesis 0,043982759 1 0,04398 6,46913 0,0124 

Error 0,720679521 106 0,0068 


Error 0,720679521 106 0,0068 

MM Hypothesis 0,159046515 4 0,03976 5,84828 0,0003 

Error 0,720679521 106 0,0068 


Error 0,720679521 106 0,0068 


Error 0,960906028 116 0,00828 

Atlantic cod Intercept Hypothesis 4,101285264 1 4,10129 28,7032 5E-07 

Error 15,14591064 106 0,14289 


Error 15,14591064 106 0,14289 

MM Hypothesis 0,53583041 4 0,13396 0,93751 0,4453 

Error 15,14591064 106 0,14289 


Error 15,14591064 106 0,14289 

SEKTION(REF * MM) Hypothesis 15,14591064 106 0,14289 2,63969 2E-07 

Error 6,279064349 116 0,05413 

Small sandeel Intercept Hypothesis 0,024799999 1 0,0248 2,64573 0,1068 

Error 0,993600203 106 0,00937 


Error 0,993600203 106 0,00937 

MM Hypothesis 0,018548296 4 0,00464 0,4947 0,7396 

Error 0,993600203 106 0,00937 


Error 0,993600203 106 0,00937 


Error 1,083927494 116 0,00934 

Sandeel sp. Intercept Hypothesis 0,002748922 1 0,00275 1,34774 0,2483 

Error 0,216203856 106 0,00204 


Error 0,216203856 106 0,00204 

MM Hypothesis 0,009940407 4 0,00249 1,21839 0,3074 

Error 0,216203856 106 0,00204 


Error 0,216203856 106 0,00204 


Error 0,240226507 116 0,00207 

Great sandeel Intercept Hypothesis 0,009284892 1 0,00928 2,25521 0,1361 

Error 0,436411488 106 0,00412 

REF Hypothesis 6,40775E-05 1 6,4E-05 0,01556 0,901 

Error 0,436411488 106 0,00412 

MM Hypothesis 0,01373709 4 0,00343 0,83415 0,5064 

Error 0,436411488 106 0,00412 




Error 0,436411488 106 0,00412 


Error 0,480453014 116 0,00414 

Two-spotted goby Intercept Hypothesis 27,65265544 1 27,6527 72,8819 1E-13 

Error 40,21824162 106 0,37942 


Error 40,21824162 106 0,37942 

MM Hypothesis 9,306211434 4 2,32655 6,13191 0,0002 

Error 40,21824162 106 0,37942 


Error 40,21824162 106 0,37942 


Error 33,87900351 116 0,29206 

Sand goby Intercept Hypothesis 58,47688134 1 58,4769 54,3105 4E-11 

Error 114,1317593 106 1,07671 


Error 114,1317593 106 1,07671 

MM Hypothesis 23,14432758 4 5,78608 5,37383 0,0006 

Error 114,1317593 106 1,07671 


Error 114,1317593 106 1,07671 


Error 70,84156512 116 0,6107 

Painted goby Intercept Hypothesis 0,001929667 1 0,00193 0,92887 0,3373 

Error 0,220207631 106 0,00208 


Error 0,220207631 106 0,00208 

MM Hypothesis 0,00793852 4 0,00198 0,95533 0,4353 

Error 0,220207631 106 0,00208 


Error 0,220207631 106 0,00208 


Error 0,240226507 116 0,00207 

Black goby Intercept Hypothesis 0,765742389 1 0,76574 15,0613 0,0002 

Error 5,3892356 106 0,05084 


Error 5,3892356 106 0,05084 

MM Hypothesis 1,00708743 4 0,25177 4,95206 0,0011 

Error 5,3892356 106 0,05084 


Error 5,3892356 106 0,05084 


Error 3,533389477 116 0,03046 

Transparent goby Intercept Hypothesis 0,007718669 1 0,00772 1,85774 0,1758 

Error 0,440415263 106 0,00415 


Error 0,440415263 106 0,00415 

MM Hypothesis 0,011735203 4 0,00293 0,70611 0,5895 

Error 0,440415263 106 0,00415 




Error 0,440415263 106 0,00415 


Error 0,480453014 116 0,00414 

Goby.sp. Intercept Hypothesis 0,001929667 1 0,00193 0,92887 0,3373 

Error 0,220207631 106 0,00208 


Error 0,220207631 106 0,00208 

MM Hypothesis 0,00793852 4 0,00198 0,95533 0,4353 

Error 0,220207631 106 0,00208 


Error 0,220207631 106 0,00208 


Error 0,240226507 116 0,00207 

Butterfish Intercept Hypothesis 0,069468022 1 0,06947 5,9328 0,0165 

Error 1,241170286 106 0,01171 

REF Hypothesis 0 1 0 0 1 

Error 1,241170286 106 0,01171 

MM Hypothesis 0,0455602 4 0,01139 0,97275 0,4257 

Error 1,241170286 106 0,01171 


Error 1,241170286 106 0,01171 


Error 1,441359042 116 0,01243 

Eelpout Intercept Hypothesis 97,97756201 1 97,9776 161,63 5E-23 

Error 64,25541521 106 0,60618 


Error 64,25541521 106 0,60618 


Error 64,25541521 106 0,60618 

REF * MM Hypothesis 16,36621884 4 4,09155 6,7497 7E-05 

Error 64,25541521 106 0,60618 


Error 29,73063533 116 0,2563 

Short-spined sea 

scorpion Intercept Hypothesis 18,17066452 1 18,1707 68,3411 4E-13 

Error 28,18347449 106 0,26588 


Error 28,18347449 106 0,26588 

MM Hypothesis 6,334442946 4 1,58361 5,95607 0,0002 

Error 28,18347449 106 0,26588 


Error 28,18347449 106 0,26588 


Error 15,39069089 116 0,13268 

Longspined bullhead Intercept Hypothesis 8,07105823 1 8,07106 57,5123 1E-11 

Error 14,87562836 106 0,14034 


Error 14,87562836 106 0,14034 

MM Hypothesis 3,525764404 4 0,88144 6,28093 0,0001 

Error 14,87562836 106 0,14034 




Error 14,87562836 106 0,14034 


Error 11,79436455 116 0,10168 

Hooknose Intercept Hypothesis 0,116625395 1 0,11663 9,21689 0,003 

Error 1,341264664 106 0,01265 


Error 1,341264664 106 0,01265 

MM Hypothesis 0,140753405 4 0,03519 2,78093 0,0305 

Error 1,341264664 106 0,01265 


Error 1,341264664 106 0,01265 


Error 1,681585549 116 0,0145 

Lumpsucker Intercept Hypothesis 0 1 0 , , 

Error 0 106 0 


Error 0 106 0 


Error 0 106 0 


Error 0 106 0 


Error 0 116 0 

Striped seasnail Intercept Hypothesis 0,001929667 1 0,00193 0,92887 0,3373 

Error 0,220207631 106 0,00208 


Error 0,220207631 106 0,00208 

MM Hypothesis 0,00793852 4 0,00198 0,95533 0,4353 

Error 0,220207631 106 0,00208 


Error 0,220207631 106 0,00208 


Error 0,240226507 116 0,00207 

Turbot Intercept Hypothesis 0,074022117 1 0,07402 4,46409 0,037 

Error 1,757657276 106 0,01658 


Error 1,757657276 106 0,01658 

MM Hypothesis 0,047562087 4 0,01189 0,71709 0,5821 

Error 1,757657276 106 0,01658 


Error 1,757657276 106 0,01658 


Error 0,960906028 116 0,00828 

Dab Intercept Hypothesis 0,007718669 1 0,00772 1,85774 0,1758 

Error 0,440415263 106 0,00415 

REF Hypothesis 0 1 0 0 1 

Error 0,440415263 106 0,00415 

MM Hypothesis 0,031754079 4 0,00794 1,91066 0,114 

Error 0,440415263 106 0,00415 



REF * MM Hypothesis 0 4 0 0 1 

Error 0,440415263 106 0,00415 


Error 0,480453014 116 0,00414 

Flounder Intercept Hypothesis 5,836101885 1 5,8361 41,6283 3E-09 

Error 14,86073619 106 0,1402 


Error 14,86073619 106 0,1402 

MM Hypothesis 2,692765545 4 0,67319 4,8018 0,0013 

Error 14,86073619 106 0,1402 


Error 14,86073619 106 0,1402 


Error 14,15225954 116 0,122 

European plaice Intercept Hypothesis 0,007718669 1 0,00772 2,04352 0,1558 

Error 0,400377512 106 0,00378 


Error 0,400377512 106 0,00378 

MM Hypothesis 0,031754079 4 0,00794 2,10172 0,0857 

Error 0,400377512 106 0,00378 


Error 0,400377512 106 0,00378 


Error 0,480453014 116 0,00414 

Common sole Intercept Hypothesis 0,001929667 1 0,00193 0,92887 0,3373 

Error 0,220207631 106 0,00208 


Error 0,220207631 106 0,00208 

MM Hypothesis 0,00793852 4 0,00198 0,95533 0,4353 

Error 0,220207631 106 0,00208 


Error 0,220207631 106 0,00208 


Error 0,240226507 116 0,00207 

Sea trout Intercept Hypothesis 0 1 0 , , 

Error 0 106 0 


Error 0 106 0 


Error 0 106 0 


Error 0 106 0 


Error 0 116 0 



Appendix 4. Levene’s test and Uni-ANOVA analysis of weight 


Levene's Test of Equality of Error Variances, LN (Weight) 


Baltic herring 7,434238446 3 44 0,000 

Brisling 0,325430858 3 44 0,807 

Common eel 9,010944852 3 44 0,000 

Hornfish 11,38609633 3 44 0,000 

Snake pipefish 4,84 3 44 0,005 

Straightnose pipefish 6,530872207 3 44 0,001 




Fifteen-spined stickleback 1,598102133 3 44 0,203 

Whiting 4,84 3 44 0,005 

Atlantic cod 3,757804303 3 44 0,017 

Small sandeel 3,254313229 3 44 0,030 


Great sandeel 4,163121243 3 44 0,011 

Two-spotted goby 0,105642071 3 44 0,956 

Sand goby 2,747903475 3 44 0,054 


Black goby 4,923109583 3 44 0,005 

Transparent goby 4,84 3 44 0,005 

Goby.sp. 8,738914542 3 44 0,000 

Butterfish 15,37761305 3 44 0,000 

Eelpout 3,379320869 3 44 0,026 

Short-spined sea scorpion 1,569477481 3 44 0,210 

Longspined bullhead 0,649655161 3 44 0,587 

Hooknose 3,228693462 3 44 0,031 

Lumpsucker 3,280543936 3 44 0,030 


Turbot 5,811667637 3 44 0,002 

Dab , 3 44 , 

Flounder 1,018763173 3 44 0,393 

European plaice 5,456160999 3 44 0,003 


Sea trout 4,84 3 44 0,005 



Species 

Tests of Between-Subjects Effects LN (weight) 

Type III df Mean F Sig. 

Sum of Squares Square 

Baltic herring Source Corrected Model 10,9395 3 3,646 2,1893 0,103 

Intercept 24,3901 1 24,390 14,6433 0,000 

REF 10,7715 1 10,772 6,4670 0,015 

MONTH 0,0006 1 0,001 0,0004 0,984 

REF * MONTH 0,1673 1 0,167 0,1004 0,753 

Error 73,2869 44 1,666 

Total 108,6164 48 


Brisling Source Corrected Model 0,7110 3 0,237 0,4611 0,711 

Intercept 13,7240 1 13,724 26,7038 0,000 

REF 0,0063 1 0,006 0,0123 0,912 

MONTH 0,6852 1 0,685 1,3332 0,254 

REF * MONTH 0,0195 1 0,019 0,0379 0,846 

Error 22,6132 44 0,514 

Total 37,0482 48 


Common eel Source Corrected Model 3,6301 3 1,210 1,7731 0,166 

Intercept 5,5696 1 5,570 8,1612 0,007 

REF 0,7602 1 0,760 1,1139 0,297 

MONTH 0,0379 1 0,038 0,0556 0,815 

REF * MONTH 2,8320 1 2,832 4,1498 0,048 

Error 30,0276 44 0,682 

Total 39,2273 48 


Hornfish Source Corrected Model 107,4091 3 35,803 6,0572 0,002 

Intercept 199,9874 1 199,987 33,8339 0,000 

REF 3,9077 1 3,908 0,6611 0,421 

MONTH 102,7111 1 102,711 17,3767 0,000 

REF * MONTH 0,7903 1 0,790 0,1337 0,716 

Error 260,0775 44 5,911 

Total 567,4740 48 


Snake pipefish Source Corrected Model 0,0965 3 0,032 1,0000 0,402 

Intercept 0,0322 1 0,032 1,0000 0,323 

REF 0,0322 1 0,032 1,0000 0,323 

MONTH 0,0322 1 0,032 1,0000 0,323 

REF * MONTH 0,0322 1 0,032 1,0000 0,323 

Error 1,4150 44 0,032 

Total 1,5437 48 


Straightnose pipefish Source Corrected Model 0,0553 3 0,018 1,2913 0,289 

Intercept 0,0455 1 0,045 3,1852 0,081 

REF 0,0349 1 0,035 2,4476 0,125 

MONTH 0,0128 1 0,013 0,8976 0,349 

REF * MONTH 0,0075 1 0,008 0,5288 0,471 



Error 0,6282 44 0,014 

Total 0,7290 48 


Great pipefish Source Corrected Model 0,0000 3 0,000 , , 

Intercept 0,0000 1 0,000 , , 

REF 0,0000 1 0,000 , , 

MONTH 0,0000 1 0,000 , , 

REF * MONTH 0,0000 1 0,000 , , 

Error 0,0000 44 0,000 

Total 0,0000 48 


Lesser pipefish Source Corrected Model 0,0000 3 0,000 , , 

Intercept 0,0000 1 0,000 , , 

REF 0,0000 1 0,000 , , 

MONTH 0,0000 1 0,000 , , 

REF * MONTH 0,0000 1 0,000 , , 

Error 0,0000 44 0,000 

Total 0,0000 48 


Broad-nosed pipefish Source Corrected Model 0,0000 3 0,000 , , 

Intercept 0,0000 1 0,000 , , 

REF 0,0000 1 0,000 , , 

MONTH 0,0000 1 0,000 , , 

REF * MONTH 0,0000 1 0,000 , , 

Error 0,0000 44 0,000 

Total 0,0000 48 


Fifteen-spined stickleback Source Corrected Model 6,6390 3 2,213 4,4740 0,008 

Intercept 16,3006 1 16,301 32,9551 0,000 

REF 3,1437 1 3,144 6,3557 0,015 

MONTH 2,9276 1 2,928 5,9187 0,019 

REF * MONTH 0,5677 1 0,568 1,1477 0,290 

Error 21,7638 44 0,495 

Total 44,7034 48 


Whiting Source Corrected Model 0,4395 3 0,146 1,0000 0,402 

Intercept 0,1465 1 0,146 1,0000 0,323 

REF 0,1465 1 0,146 1,0000 0,323 

MONTH 0,1465 1 0,146 1,0000 0,323 

REF * MONTH 0,1465 1 0,146 1,0000 0,323 

Error 6,4453 44 0,146 

Total 7,0313 48 


Atlantic cod Source Corrected Model 71,4724 3 23,824 4,2721 0,010 

Intercept 190,3771 1 190,377 34,1377 0,000 

REF 35,2881 1 35,288 6,3277 0,016 

MONTH 32,5588 1 32,559 5,8383 0,020 

REF * MONTH 3,6254 1 3,625 0,6501 0,424 

Error 245,3767 44 5,577 

Total 507,2262 48 




Small sandeel Source Corrected Model 46,6122 3 15,537 10,3576 0,000 

Intercept 73,9830 1 73,983 49,3188 0,000 

REF 1,1992 1 1,199 0,7994 0,376 

MONTH 40,8057 1 40,806 27,2020 0,000 

REF * MONTH 4,6073 1 4,607 3,0713 0,087 

Error 66,0043 44 1,500 

Total 186,5995 48 


Sandeel sp. Source Corrected Model 0,0000 3 0,000 , , 

Intercept 0,0000 1 0,000 , , 

REF 0,0000 1 0,000 , , 

MONTH 0,0000 1 0,000 , , 

REF * MONTH 0,0000 1 0,000 , , 

Error 0,0000 44 0,000 

Total 0,0000 48 


Great sandeel Source Corrected Model 23,1041 3 7,701 3,3224 0,028 

103,615 

Intercept 240,1806 1 240,181 8 0,000 

REF 13,8622 1 13,862 5,9803 0,019 

MONTH 0,0691 1 0,069 0,0298 0,864 

REF * MONTH 9,1728 1 9,173 3,9572 0,053 

Error 101,9917 44 2,318 

Total 365,2763 48 


Two-spotted goby Source Corrected Model 0,4705 3 0,157 0,8470 0,476 

Intercept 4,6671 1 4,667 25,2078 0,000 

REF 0,1306 1 0,131 0,7055 0,405 

MONTH 0,3398 1 0,340 1,8356 0,182 

REF * MONTH 0,0000 1 0,000 0,0000 0,998 

Error 8,1463 44 0,185 

Total 13,2838 48 


Sand goby Source Corrected Model 0,4028 3 0,134 0,7216 0,544 

Intercept 7,1291 1 7,129 38,3103 0,000 

REF 0,0492 1 0,049 0,2646 0,610 

MONTH 0,0423 1 0,042 0,2272 0,636 

REF * MONTH 0,3113 1 0,311 1,6729 0,203 

Error 8,1879 44 0,186 

Total 15,7199 48 


Painted goby Source Corrected Model 0,0000 3 0,000 , , 

Intercept 0,0000 1 0,000 , , 

REF 0,0000 1 0,000 , , 

MONTH 0,0000 1 0,000 , , 

REF * MONTH 0,0000 1 0,000 , , 

Error 0,0000 44 0,000 

Total 0,0000 48 


Black goby Source Corrected Model 0,4202 3 0,140 1,1656 0,334 



Intercept 0,5733 1 0,573 4,7713 0,034 

REF 0,0145 1 0,015 0,1209 0,730 

MONTH 0,1140 1 0,114 0,9485 0,335 

REF * MONTH 0,2917 1 0,292 2,4275 0,126 

Error 5,2871 44 0,120 

Total 6,2806 48 


Transparent goby Source Corrected Model 0,0075 3 0,003 1,0000 0,402 

Intercept 0,0025 1 0,003 1,0000 0,323 

REF 0,0025 1 0,003 1,0000 0,323 

MONTH 0,0025 1 0,003 1,0000 0,323 

REF * MONTH 0,0025 1 0,003 1,0000 0,323 

Error 0,1101 44 0,003 

Total 0,1201 48 


Goby.sp. Source Corrected Model 0,0353 3 0,012 1,7532 0,170 

Intercept 0,0118 1 0,012 1,7532 0,192 

REF 0,0118 1 0,012 1,7532 0,192 

MONTH 0,0118 1 0,012 1,7532 0,192 

REF * MONTH 0,0118 1 0,012 1,7532 0,192 

Error 0,2957 44 0,007 

Total 0,3428 48 


Butterfish Source Corrected Model 1,0461 3 0,349 2,1507 0,107 

Intercept 0,9474 1 0,947 5,8431 0,020 

REF 0,0494 1 0,049 0,3045 0,584 

MONTH 0,0494 1 0,049 0,3045 0,584 

REF * MONTH 0,9474 1 0,947 5,8431 0,020 

Error 7,1339 44 0,162 

Total 9,1274 48 


Eelpout Source Corrected Model 60,8244 3 20,275 8,9182 0,000 

138,427 

Intercept 314,7017 1 314,702 4 0,000 

REF 34,1751 1 34,175 15,0326 0,000 

MONTH 22,9662 1 22,966 10,1021 0,003 

REF * MONTH 3,6831 1 3,683 1,6201 0,210 

Error 100,0298 44 2,273 

Total 475,5559 48 


Short-spined sea scorpion Source Corrected Model 160,9302 3 53,643 14,9935 0,000 

141,992 

Intercept 508,0178 1 508,018 2 0,000 

REF 9,6045 1 9,604 2,6845 0,108 

MONTH 144,6100 1 144,610 40,4189 0,000 

REF * MONTH 6,7157 1 6,716 1,8770 0,178 

Error 157,4226 44 3,578 

Total 826,3705 48 


Longspined bullhead Source Corrected Model 5,8635 3 1,955 1,2370 0,308 

Intercept 36,0678 1 36,068 22,8268 0,000 



REF 2,2615 1 2,261 1,4312 0,238 

MONTH 0,9628 1 0,963 0,6093 0,439 

REF * MONTH 2,6393 1 2,639 1,6704 0,203 

Error 69,5228 44 1,580 

Total 111,4541 48 


Hooknose Source Corrected Model 0,1594 3 0,053 0,6671 0,577 

Intercept 0,1592 1 0,159 1,9987 0,164 

REF 0,0001 1 0,000 0,0013 0,972 

MONTH 0,1592 1 0,159 1,9987 0,164 

REF * MONTH 0,0001 1 0,000 0,0013 0,972 

Error 3,5035 44 0,080 

Total 3,8220 48 


Lumpsucker Source Corrected Model 0,8215 3 0,274 0,6778 0,570 

Intercept 0,7945 1 0,794 1,9666 0,168 

REF 0,0135 1 0,013 0,0334 0,856 

MONTH 0,7945 1 0,794 1,9666 0,168 

REF * MONTH 0,0135 1 0,013 0,0334 0,856 

Error 17,7754 44 0,404 

Total 19,3913 48 


Striped seasnail Source Corrected Model 0,0000 3 0,000 , , 

Intercept 0,0000 1 0,000 , , 

REF 0,0000 1 0,000 , , 

MONTH 0,0000 1 0,000 , , 

REF * MONTH 0,0000 1 0,000 , , 

Error 0,0000 44 0,000 

Total 0,0000 48 


Turbot Source Corrected Model 9,6477 3 3,216 1,2149 0,316 

Intercept 41,9513 1 41,951 15,8483 0,000 

REF 1,7747 1 1,775 0,6704 0,417 

MONTH 4,0505 1 4,051 1,5302 0,223 

REF * MONTH 3,8226 1 3,823 1,4441 0,236 

Error 116,4700 44 2,647 

Total 168,0690 48 


Dab Source Corrected Model 0,0000 3 0,000 , , 

Intercept 0,0000 1 0,000 , , 

REF 0,0000 1 0,000 , , 

MONTH 0,0000 1 0,000 , , 

REF * MONTH 0,0000 1 0,000 , , 

Error 0,0000 44 0,000 

Total 0,0000 48 


Flounder Source Corrected Model 40,7582 3 13,586 4,0605 0,012 

Intercept 222,0578 1 222,058 66,3673 0,000 

REF 0,0392 1 0,039 0,0117 0,914 

MONTH 37,6266 1 37,627 11,2456 0,002 



REF * MONTH 3,0924 1 3,092 0,9242 0,342 

Error 147,2192 44 3,346 

Total 410,0352 48 


European plaice Source Corrected Model 0,3593 3 0,120 1,0732 0,370 

Intercept 0,3393 1 0,339 3,0406 0,088 

REF 0,0100 1 0,010 0,0896 0,766 

MONTH 0,0100 1 0,010 0,0896 0,766 

REF * MONTH 0,3393 1 0,339 3,0406 0,088 

Error 4,9098 44 0,112 

Total 5,6084 48 


Common sole Source Corrected Model 0,0000 3 0,000 , , 

Intercept 0,0000 1 0,000 , , 

REF 0,0000 1 0,000 , , 

MONTH 0,0000 1 0,000 , , 

REF * MONTH 0,0000 1 0,000 , , 

Error 0,0000 44 0,000 

Total 0,0000 48 


Sea trout Source Corrected Model 0,6508 3 0,217 1,0000 0,402 

Intercept 0,2169 1 0,217 1,0000 0,323 

REF 0,2169 1 0,217 1,0000 0,323 

MONTH 0,2169 1 0,217 1,0000 0,323 

REF * MONTH 0,2169 1 0,217 1,0000 0,323 

Error 9,5446 44 0,217 

Total 10,4123 48 





Levene's Test of Equality of Error Variances LN (weight) 


Baltic herring , 115 116 , 

Brisling , 115 116 , 

Common eel , 115 116 , 

Hornfish , 115 116 , 

Snake pipefish , 115 116 , 

Straightnose pipefish , 115 116 , 




Fifteen-spined stickleback , 115 116 , 

Whiting , 115 116 , 

Atlantic cod , 115 116 , 

Small sandeel , 115 116 , 


Great sandeel , 115 116 , 

Two-spotted goby , 115 116 , 

Sand goby , 115 116 , 


Black goby , 115 116 , 

Transparent goby , 115 116 , 

Goby.sp. , 115 116 , 

Butterfish , 115 116 , 

Eelpout , 115 116 , 

Short-spined sea scorpion , 115 116 , 

Longspined bullhead , 115 116 , 

Hooknose , 115 116 , 

Lumpsucker , 115 116 , 


Turbot , 115 116 , 

Dab , 115 116 , 

Flounder , 115 116 , 

European plaice , 115 116 , 


Sea trout , 115 116 , 



Unianova- tests of Between-Subjects Effects LN (weight) 

Species Type III df Mean F Sig. 

Baltic herring Intercept Hypothesis 0,058940444 1 0,058940444 0,92887 0,33735 

Error 6,726100268 106 0,063453776 


Error 6,726100268 106 0,063453776 

MM Hypothesis 0,242476969 4 0,060619242 0,95533 0,43529 

Error 6,726100268 106 0,063453776 


Error 6,726100268 106 0,063453776 


Error 7,337563929 116 0,063254861 

Brisling Intercept Hypothesis 0 1 0 , , 

Error 0 106 0 


Error 0 106 0 


Error 0 106 0 


Error 0 106 0 


Error 0 116 0 

Common eel Intercept Hypothesis 15,2768547 1 15,2768547 12,2965 0,00067 

Error 131,6920289 106 1,242377631 


Error 131,6920289 106 1,242377631 

MM Hypothesis 8,661817376 4 2,165454344 1,74299 0,14597 

Error 131,6920289 106 1,242377631 


Error 131,6920289 106 1,242377631 


Error 135,052207 116 1,164243164 

Hornfish Intercept Hypothesis 0 1 0 , , 

Error 0 106 0 


Error 0 106 0 


Error 0 106 0 


Error 0 106 0 


Error 0 116 0 

Snake pipefish Intercept Hypothesis 0,024799999 1 0,024799999 0,92887 0,33735 

Error 2,830098818 106 0,026699045 


Error 2,830098818 106 0,026699045 

MM Hypothesis 0,102025506 4 0,025506376 0,95533 0,43529 

Error 2,830098818 106 0,026699045 




Error 2,830098818 106 0,026699045 


Error 3,087380529 116 0,026615349 

Straightnose pipefish Intercept Hypothesis 0,020698907 1 0,020698907 3,59025 0,06084 

Error 0,611122713 106 0,005765309 


Error 0,611122713 106 0,005765309 

MM Hypothesis 0,014570967 4 0,003642742 0,63184 0,64086 

Error 0,611122713 106 0,005765309 


Error 0,611122713 106 0,005765309 


Error 0,669820171 116 0,005774312 

Great pipefish Intercept Hypothesis 0,003149532 1 0,003149532 1,34774 0,24828 

Error 0,247711961 106 0,002336905 


Error 0,247711961 106 0,002336905 

MM Hypothesis 0,011389056 4 0,002847264 1,21839 0,30742 

Error 0,247711961 106 0,002336905 


Error 0,247711961 106 0,002336905 


Error 0,275235512 116 0,00237272 

Lesser pipefish Intercept Hypothesis 0,003666936 1 0,003666936 0,92887 0,33735 

Error 0,418459306 106 0,003947729 


Error 0,418459306 106 0,003947729 

MM Hypothesis 0,015085524 4 0,003771381 0,95533 0,43529 

Error 0,418459306 106 0,003947729 


Error 0,418459306 106 0,003947729 


Error 0,456501061 116 0,003935354 

Broad-nosed pipefish Intercept Hypothesis 0,433618161 1 0,433618161 14,1001 0,00028 

Error 3,259791764 106 0,030752752 


Error 3,259791764 106 0,030752752 

MM Hypothesis 0,30190204 4 0,07547551 2,45427 0,05024 

Error 3,259791764 106 0,030752752 


Error 3,259791764 106 0,030752752 


Error 4,459692018 116 0,038445621 

Fifteen-spined stickleback Intercept Hypothesis 179,3551142 1 179,3551142 156,94 1,2E-22 

Error 121,1393081 106 1,142823661 


Error 121,1393081 106 1,142823661 

MM Hypothesis 43,5629828 4 10,8907457 9,52968 1,3E-06 

Error 121,1393081 106 1,142823661 




Error 121,1393081 106 1,142823661 


Error 107,2543942 116 0,924606846 

Whiting Intercept Hypothesis 1,185824086 1 1,185824086 6,53106 0,01202 

Error 19,2460844 106 0,181566834 


Error 19,2460844 106 0,181566834 

MM Hypothesis 4,288070842 4 1,07201771 5,90426 0,00025 

Error 19,2460844 106 0,181566834 


Error 19,2460844 106 0,181566834 


Error 26,02713076 116 0,224371817 

Atlantic cod Intercept Hypothesis 104,1783213 1 104,1783213 25,9418 1,5E-06 

Error 425,6800753 106 4,015849767 


Error 425,6800753 106 4,015849767 

MM Hypothesis 12,16860713 4 3,042151782 0,75754 0,55521 

Error 425,6800753 106 4,015849767 


Error 425,6800753 106 4,015849767 

SEKTION(REF * MM) Hypothesis 425,6800753 106 4,015849767 3,18481 1,1E-09 

Error 146,2689962 116 1,260939622 

Small sandeel Intercept Hypothesis 0,278071777 1 0,278071777 2,73594 0,10107 

Error 10,77347247 106 0,101636533 


Error 10,77347247 106 0,101636533 

MM Hypothesis 0,191276771 4 0,047819193 0,47049 0,75728 

Error 10,77347247 106 0,101636533 


Error 10,77347247 106 0,101636533 


Error 11,75287906 116 0,101317923 

Sandeel sp. Intercept Hypothesis 0,051518411 1 0,051518411 1,34774 0,24828 

Error 4,051943734 106 0,038225884 


Error 4,051943734 106 0,038225884 

MM Hypothesis 0,186296264 4 0,046574066 1,21839 0,30742 

Error 4,051943734 106 0,038225884 


Error 4,051943734 106 0,038225884 


Error 4,502159704 116 0,038811722 

Great sandeel Intercept Hypothesis 0,101172234 1 0,101172234 2,20056 0,14093 

Error 4,873416262 106 0,045975625 


Error 4,873416262 106 0,045975625 

MM Hypothesis 0,1732051 4 0,043301275 0,94183 0,44283 

Error 4,873416262 106 0,045975625 




Error 4,873416262 106 0,045975625 


Error 5,391521138 116 0,04647863 

Two-spotted goby Intercept Hypothesis 16,00313179 1 16,00313179 61,1994 4,1E-12 

Error 27,71809943 106 0,261491504 


Error 27,71809943 106 0,261491504 

MM Hypothesis 4,793833564 4 1,198458391 4,58316 0,00188 

Error 27,71809943 106 0,261491504 


Error 27,71809943 106 0,261491504 


Error 21,51251696 116 0,185452732 

Sand goby Intercept Hypothesis 60,8574229 1 60,8574229 58,8199 8,8E-12 

Error 109,6718219 106 1,03463983 


Error 109,6718219 106 1,03463983 

MM Hypothesis 22,5965 4 5,649125001 5,45999 0,00049 

Error 109,6718219 106 1,03463983 


Error 109,6718219 106 1,03463983 


Error 72,36492315 116 0,623835544 

Painted goby Intercept Hypothesis 0,000887225 1 0,000887225 0,92887 0,33735 

Error 0,101247397 106 0,000955164 


Error 0,101247397 106 0,000955164 

MM Hypothesis 0,003649985 4 0,000912496 0,95533 0,43529 

Error 0,101247397 106 0,000955164 


Error 0,101247397 106 0,000955164 


Error 0,110451706 116 0,00095217 

Black goby Intercept Hypothesis 6,55229701 1 6,55229701 16,1247 0,00011 

Error 43,07328775 106 0,406351771 


Error 43,07328775 106 0,406351771 

MM Hypothesis 8,632074777 4 2,158018694 5,31072 0,00062 

Error 43,07328775 106 0,406351771 


Error 43,07328775 106 0,406351771 


Error 30,27893919 116 0,261025338 

Transparent goby Intercept Hypothesis 0,004847529 1 0,004847529 1,73853 0,19017 

Error 0,295558527 106 0,002788288 


Error 0,295558527 106 0,002788288 

MM Hypothesis 0,008232112 4 0,002058028 0,7381 0,56803 

Error 0,295558527 106 0,002788288 




Error 0,295558527 106 0,002788288 


Error 0,322427484 116 0,002779547 

Goby.sp. Intercept Hypothesis 0,000660296 1 0,000660296 0,92887 0,33735 

Error 0,075350896 106 0,000710858 


Error 0,075350896 106 0,000710858 

MM Hypothesis 0,002716412 4 0,000679103 0,95533 0,43529 

Error 0,075350896 106 0,000710858 


Error 0,075350896 106 0,000710858 


Error 0,082200977 116 0,000708629 

Butterfish Intercept Hypothesis 1,005092483 1 1,005092483 5,91858 0,01666 

Error 18,00091391 106 0,169819943 


Error 18,00091391 106 0,169819943 

MM Hypothesis 0,659360272 4 0,164840068 0,97068 0,42683 

Error 18,00091391 106 0,169819943 


Error 18,00091391 106 0,169819943 


Error 20,89712363 116 0,180147618 

Eelpout Intercept Hypothesis 771,3391371 1 771,3391371 199,15 4,4E-26 

Error 410,5555904 106 3,873165947 


Error 410,5555904 106 3,873165947 


Error 410,5555904 106 3,873165947 


Error 410,5555904 106 3,873165947 


Error 219,4041682 116 1,891415243 

Short-spined sea scorpion Intercept Hypothesis 443,1865741 1 443,1865741 82,045 7,4E-15 

Error 572,5853217 106 5,401748318 


Error 572,5853217 106 5,401748318 

MM Hypothesis 111,9997948 4 27,9999487 5,1835 0,00075 

Error 572,5853217 106 5,401748318 


Error 572,5853217 106 5,401748318 


Error 417,9662155 116 3,60315703 

Longspined bullhead Intercept Hypothesis 92,51785125 1 92,51785125 59,8118 6,4E-12 

Error 163,9624961 106 1,546816001 


Error 163,9624961 106 1,546816001 

MM Hypothesis 35,10651681 4 8,776629203 5,674 0,00035 

Error 163,9624961 106 1,546816001 




Error 163,9624961 106 1,546816001 


Error 153,8707421 116 1,326471915 

Hooknose Intercept Hypothesis 2,27712216 1 2,27712216 9,10912 0,00319 

Error 26,49815218 106 0,249982568 


Error 26,49815218 106 0,249982568 

MM Hypothesis 2,804914244 4 0,701228561 2,80511 0,02934 

Error 26,49815218 106 0,249982568 


Error 26,49815218 106 0,249982568 


Error 33,07028974 116 0,285088705 

Lumpsucker Intercept Hypothesis 0 1 0 , , 

Error 0 106 0 


Error 0 106 0 


Error 0 106 0 


Error 0 106 0 


Error 0 116 0 

Striped seasnail Intercept Hypothesis 0,029454044 1 0,029454044 0,92887 0,33735 

Error 3,36120395 106 0,031709471 


Error 3,36120395 106 0,031709471 

MM Hypothesis 0,121171929 4 0,030292982 0,95533 0,43529 

Error 3,36120395 106 0,031709471 


Error 3,36120395 106 0,031709471 


Error 3,666767946 116 0,031610068 

Turbot Intercept Hypothesis 3,741840457 1 3,741840457 4,33722 0,03969 

Error 91,44907707 106 0,862727142 


Error 91,44907707 106 0,862727142 

MM Hypothesis 2,783589109 4 0,695897277 0,80662 0,52362 

Error 91,44907707 106 0,862727142 


Error 91,44907707 106 0,862727142 

SEKTION(REF * MM) Hypothesis 91,44907707 106 0,862727142 2,15177 3,2E-05 

Error 46,50879498 116 0,400937888 

Dab Intercept Hypothesis 0,332839782 1 0,332839782 1,82536 0,17955 

Error 19,32820234 106 0,182341532 


Error 19,32820234 106 0,182341532 

MM Hypothesis 1,369280179 4 0,342320045 1,87736 0,11977 

Error 19,32820234 106 0,182341532 




Error 19,32820234 106 0,182341532 


Error 21,08531165 116 0,181769928 

Flounder Intercept Hypothesis 164,1018498 1 164,1018498 48,785 2,6E-10 

Error 356,5604987 106 3,363778289 


Error 356,5604987 106 3,363778289 

MM Hypothesis 75,37136623 4 18,84284156 5,60169 0,0004 

Error 356,5604987 106 3,363778289 


Error 356,5604987 106 3,363778289 


Error 392,1504487 116 3,380607317 

European plaice Intercept Hypothesis 0,08979176 1 0,08979176 2,02598 0,15757 

Error 4,697942538 106 0,044320213 


Error 4,697942538 106 0,044320213 

MM Hypothesis 0,369397179 4 0,092349295 2,08368 0,08805 

Error 4,697942538 106 0,044320213 


Error 4,697942538 106 0,044320213 


Error 5,62946586 116 0,048529878 

Common sole Intercept Hypothesis 0,068624803 1 0,068624803 0,92887 0,33735 

Error 7,831249198 106 0,073879709 


Error 7,831249198 106 0,073879709 

MM Hypothesis 0,282317761 4 0,07057944 0,95533 0,43529 

Error 7,831249198 106 0,073879709 


Error 7,831249198 106 0,073879709 


Error 8,543180943 116 0,073648112 

Sea trout Intercept Hypothesis 0 1 0 , , 

Error 0 106 0 


Error 0 106 0 


Error 0 106 0 


Error 0 106 0 


Error 0 116 0 



Appendix 5. Non-parametric Kolmogov/Smirnoff analysis of 

distribution of length 


Species Total length 

(cm) 

Baltic herring Most Extreme Differences Absolute .539 

Positive .539 

Negative -.145 

Kolmogorov-Smirnov Z .981 

Asymp. Sig. (2-tailed) .291 

Brisling Most Extreme Differences Absolute .406 

Positive .162 


Kolmogorov-Smirnov Z 1.115 


Common eel Most Extreme Differences Absolute .500 

Positive .500 




Hornfish Most Extreme Differences Absolute .123 

Positive .099 




Fifteen-spined stickleback Most Extreme Differences Absolute .157 

Positive .043 




Atlantic cod Most Extreme Differences Absolute .333 

Positive .000 




Small sandeel Most Extreme Differences Absolute .231 

Positive .231 




Great sandeel Most Extreme Differences Absolute .308 

Positive .073 




Two-spotted goby Most Extreme Differences Absolute .056 

Positive .016 



Asymp. Sig. (2-tailed) 1.000 

Sand goby Most Extreme Differences Absolute .247 

Positive .000 




Black goby Most Extreme Differences Absolute .267 

Positive .200 






Butterfish Most Extreme Differences Absolute .667 

Positive .000 




Eelpout Most Extreme Differences Absolute .175 

Positive .175 




Short-spined sea scorpion Most Extreme Differences Absolute .095 

Positive .095 




Longspined bullhead Most Extreme Differences Absolute .186 

Positive .186 




Turbot Most Extreme Differences Absolute .466 

Positive .466 

Negative .000 



Flounder Most Extreme Differences Absolute .330 

Positive .021 








Common eel Most Extreme Differences Absolute .571 

Positive .571 




Straightnose pipefish Most Extreme Differences Absolute .500 

Positive .000 




Broad-nosed pipefish Most Extreme Differences Absolute .200 

Positive .100 




Fifteen-spined stickleback Most Extreme Differences Absolute .069 

Positive .069 




Atlantic cod Most Extreme Differences Absolute .400 

Positive .000 




Two-spotted goby Most Extreme Differences Absolute .289 

Positive .289 




Sand goby Most Extreme Differences Absolute .230 

Positive .230 

Negative .000 



Black goby Most Extreme Differences Absolute .313 

Positive .313 




Butterfish Most Extreme Differences Absolute .667 

Positive .000 




Eelpout Most Extreme Differences Absolute .211 

Positive .211 




Short-spined sea scorpion Most Extreme Differences Absolute .078 

Positive .073 




Longspined bullhead Most Extreme Differences Absolute .222 

Positive .222 

Negative .000 





Turbot Most Extreme Differences Absolute .500 

Positive .250 




Flounder Most Extreme Differences Absolute .164 

Positive .164 






Appendix 6. Mann-Whitney U 


Test between areas. 

Species Total length (cm) 

Baltic herring Mann-Whitney U 22 

Wilcoxon W 212 

Z -1,303905912 

Asymp. Sig. (2-tailed) 0,192265664 

Exact Sig. [2*(1-tailed Sig.)] 0,218181819 

Brisling Mann-Whitney U 99 


Z -0,730133928 



Common eel Mann-Whitney U 4 

Wilcoxon W 19 

Z -0,387298335 



Hornfish Mann-Whitney U 774,5 

Wilcoxon W 2259,5 

Z -0,081353899 


Straightnose pipefish Mann-Whitney U 0 

Wilcoxon W 1 

Z -1,414213562 



Fifteen-spined stickleback Mann-Whitney U 101,5 


Z -0,540524399 



Atlantic cod Mann-Whitney U 827,5 


Z -2,611690256 


Small sandeel Mann-Whitney U 1422,5 


Z -2,379437662 


Great sandeel Mann-Whitney U 5706 


Z -3,642683242 


Two-spotted goby Mann-Whitney U 691 


Z -0,230871986 




Sand goby Mann-Whitney U 237,5 


Z -2,233715918 


Black goby Mann-Whitney U 6,5 


Z -0,301756376 



Butterfish Mann-Whitney U 1 

Wilcoxon W 4 

Z -1,154700538 



Eelpout Mann-Whitney U 2575 


Z -1,680093041 


Short-spined sea scorpion Mann-Whitney U 6012 


Z -1,375557969 


Longspined bullhead Mann-Whitney U 97 


Z -0,338985603 



Hooknose Mann-Whitney U 0 

Wilcoxon W 1 

Z -1 


Exact Sig. [2*(1-tailed Sig.)] 1 

Lumpsucker Mann-Whitney U 0 

Wilcoxon W 1 

Z -1 



Turbot Mann-Whitney U 37 


Z -2,888225005 



Flounder Mann-Whitney U 675 


Z -2,594201336 


European plaice Mann-Whitney U 0 

Wilcoxon W 3 

Z -1,224744871 



Test between periods. 



Speceis Total length (cm) 

Baltic herring Mann-Whitney U 50 


Z -0,934657835 



Brisling Mann-Whitney U 36,5 


Z -2,538762209 




Wilcoxon W 15 

Z -0,353553391 



Hornfish Mann-Whitney U 107 


Z -2,610938438 



Wilcoxon W 2 

Z -0,707106781 





Z -0,433818308 



Atlantic cod Mann-Whitney U 884,5 


Z -1,497126455 


Small sandeel Mann-Whitney U 612 


Z -2,311374041 


Great sandeel Mann-Whitney U 8729,5 


Z -0,379341947 


Two-spotted goby Mann-Whitney U 632,5 




Z -0,89576775 


Sand goby Mann-Whitney U 318 


Z -0,74573778 


Black goby Mann-Whitney U 4,5 


Z -0,506060827 



Butterfish Mann-Whitney U 1 

Wilcoxon W 4 

Z -1,154700538 



Eelpout Mann-Whitney U 1949 


Z -7,723201439 

Asymp. Sig. (2-tailed) 1,13444E-14 

Short-spined sea scorpion Mann-Whitney U 2672 


Z -2,178383384 


Longspined bullhead Mann-Whitney U 104 


Z -0,521838765 





Z -1,22410344 



Flounder Mann-Whitney U 556,5 


Z -2,512354149 


European plaice Mann-Whitney U 0 

Wilcoxon W 3 

Z -1,224744871 






Test between areas. 

Species Total length (cm) 


Wilcoxon W 42 

Z -1,341640786 




Wilcoxon W 4 

Z -0,774596669 



Lesser pipefish Mann-Whitney U 0 

Wilcoxon W 1 

Z -1 



Broad-nosed pipefish Mann-Whitney U 22,5 


Z -0,309520467 





Z -0,148833259 


Whiting Mann-Whitney U 0 

Wilcoxon W 1 

Z -1,341640786 



Atlantic cod Mann-Whitney U 195 


Z -2,081259592 


Small sandeel Mann-Whitney U 1,5 


Z 0 

Asymp. Sig. (2-tailed) 1 


Great sandeel Mann-Whitney U 0 

Wilcoxon W 1 

Z -1 



Two-spotted goby Mann-Whitney U 3767 


Z -4,614665366 




Sand goby Mann-Whitney U 73091,5 


Z -6,746311022 


Black goby Mann-Whitney U 48 


Z -0,542902361 



Butterfish Mann-Whitney U 1,5 


Z -1,328422328 



Eelpout Mann-Whitney U 16268,5 


Z -0,841655366 


Short-spined sea scorpion Mann-Whitney U 1739,5 


Z -0,239223223 


Longspined bullhead Mann-Whitney U 467,5 


Z -1,632608988 


Hooknose Mann-Whitney U 0,5 


Z -1,272937693 




Wilcoxon W 6 

Z -0,46291005 



Dab Mann-Whitney U 0 

Wilcoxon W 1 

Z -1 



Flounder Mann-Whitney U 520 


Z -0,213666696 




Appendix 7. Power analysis part 1. 

Background for power calculations. 

The basis is the BACI model, tested as a dual variance analysis as shown in the 

following tables: 

AREA 

i 

TIME, j 

Before After 

(B) (A) 

Control (C) BC AC 

Impact (I) BI AI 

Model for variance analysis 

Yijk = μ + αi + βj + (αβ)ij + εijk 

where μ is the parametric analysis for the population, αi is the impact on Area i, βj is the 

effect of the time j, (αβ)ij is the interaction between Area i and Time j and εijk is the 

error in the coordinated replicate in group ij. The BACI model is analysed for a 

significant effect in the interaction under the usual assumption that εijk is normally 

distributed with a median of 0 and a variance of σ 2 . 

The power analysis was conducted for an effect corresponding to a 50% change in the 

wind farm site in relation to the reference area. As in every case in this study, we 

analyse on the basis of logarithmically converted numbers, and a 50% change in the 

original scale corresponds to a fixed change of –LOG(1-0.5) ~ 0.7 on the logarithmic 

scale. If all non-productive parts are ignored, the following table of averages can be 

drawn up: 

μij Before 

(B) 

TIME, j 

After 

(A) 

μi. 

AREA 

i 

Control (C) 0 0 0 

Impact (I) 0 0.7 0.35 

μ.j 0 0.35 0.175 

When transferred to the model parameters, this corresponds to: 

TIME,j 

(αβ)ij Before 

(B) 

After 

(A) 

αi 

AREA Control (C) 0.175 -0.175 - 

i 

0.175 

Impact (I) -0.175 0.175 0.175 

βj -0.175 0.175 



The power of the BACI analysis is calculated on the basis of the ratio between the 

dispersion of (αβ)ij and the variation between the k replicates within each cell. 

The dispersion of (αβ)ij can be expressed as D = ∑(αβ)ij 2 / ((a-1)(b-1) + 1) = 0.1225/2 = 

0.0612. 

The non-centralising parameter Φ can be expressed as √(nD) / σ. 

For each unit of data in the dataset, the above are processed partly to establish the power 

for the BACI analysis, with the actual replicant n, and partly to find the n, which would 

give a power corresponding to 80%. 



Appendix 8. Power analysis part 2. 


POWER small sandeel June 

Alpha = 0.050 

Power = 0.800 

Model = Twoway 

Number of rows = 2 

Number of columns = 2 

Within cell S.D. = 0.814 

Effect(01) = 0.000 

Effect(02) = 0.000 

Effect(03) = 0.000 

Effect(04) = 0.700 

Effect Size = 0.372 

Estimate to be based on interaction effects. 

Noncentrality parameter = 0.555 * sample 

size 

SAMPLE 

SIZE POWER 

(per cell) 

4 0.279 

5 0.347 

6 0.412 

7 0.473 

8 0.530 

9 0.582 

10 0.630 

11 0.674 

12 0.714 

13 0.749 

14 0.781 

15 0.809 

Total Sample Size = 60 

Power 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

Power Curve (Alpha = 0.050) 

0.3 

0 10 20 

Sample Size 

30 40 

POWER Great sandeel June 

Alpha = 0.050 

Power = 0.800 





Effect(01) = 0.000 

Effect(02) = 0.000 

Effect(03) = 0.000 

Effect(04) = 0.700 




size 

SAMPLE 

SIZE POWER 

(per cell) 

4 0.276 

5 0.344 

6 0.409 

7 0.469 

8 0.526 

9 0.578 

10 0.626 

11 0.670 

12 0.709 

13 0.745 

14 0.777 

15 0.805 



0.3 

0 10 20 

Sample Size 

30 40 


Power 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4

Power 


POWER eelpout May 

Alpha = 0.050 

Power = 0.800 





Effect(01) = 0.000 

Effect(02) = 0.000 

Effect(03) = 0.000 

Effect(04) = 0.700 




size 

SAMPLE 

SIZE POWER 

(per cell) 

2 0.137 

3 0.221 

4 0.299 

5 0.372 

6 0.441 

7 0.506 

8 0.565 

9 0.618 

10 0.667 

11 0.711 

12 0.750 

13 0.784 

14 0.814 


1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 


0.2 

0 5 10 15 20 25 30 35 

Sample Size 

POWER eelpout June 

Alpha = 0.050 

Power = 0.800 





Effect(01) = 0.000 

Effect(02) = 0.000 

Effect(03) = 0.000 

Effect(04) = 0.700 




size 

SAMPLE 

SIZE POWER 

(per cell) 

2 0.175 

3 0.294 

4 0.400 

5 0.495 

6 0.580 

7 0.653 

8 0.716 

9 0.769 

10 0.814 



0.2 

0 5 10 15 20 25 

Sample Size 


Power 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3

Power 


POWER short-spined sea scorpion May 

Alpha = 0.050 

Power = 0.800 





Effect(01) = 0.000 

Effect(02) = 0.000 

Effect(03) = 0.000 

Effect(04) = 0.700 




size 

SAMPLE 

SIZE POWER 

(per cell) 

12 0.509 

13 0.543 

14 0.575 

15 0.605 

16 0.634 

17 0.661 

18 0.687 

19 0.711 

20 0.733 

21 0.755 

22 0.774 

23 0.792 

24 0.810 


1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 


0.2 

0 10 20 30 40 50 60 

Sample Size 

POWER flounder May 

Alpha = 0.050 

Power = 0.800 





Effect(01) = 0.000 

Effect(02) = 0.000 

Effect(03) = 0.000 

Effect(04) = 0.700 




size 

SAMPLE 

SIZE POWER 

(per cell) 

2 0.152 

3 0.250 

4 0.339 

5 0.423 

6 0.499 

7 0.568 

8 0.630 

9 0.685 

10 0.734 

11 0.775 

12 0.812 



0.2 

0 10 20 30 

Sample Size 


Power 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3


Grouping species from the fish study. 

POWER stationary May 

Alpha = 0.050 

Power = 0.800 





Effect(01) = 0.000 

Effect(02) = 0.000 

Effect(03) = 0.000 

Effect(04) = 0.700 




size 

SAMPLE 

SIZE POWER 

(per cell) 

9 0.461 

10 0.504 

11 0.544 

12 0.582 

13 0.618 

14 0.652 

15 0.683 

16 0.712 

17 0.738 

18 0.763 

19 0.786 

20 0.806 


Power 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 


0.2 

0 10 20 30 40 50 

Sample Size 

POWER stationary June 

Alpha = 0.050 

Power = 0.800 





Effect(01) = 0.000 

Effect(02) = 0.000 

Effect(03) = 0.000 

Effect(04) = 0.700 




size 

SAMPLE 

SIZE POWER 

(per cell) 

3 0.218 

4 0.294 

5 0.367 

6 0.435 

7 0.498 

8 0.557 

9 0.611 

10 0.659 

11 0.703 

12 0.742 

13 0.777 

14 0.807 



0.2 

0 5 10 15 20 25 30 35 

Sample Size 


Power 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3


POWER prey species May 

Alpha = 0.050 

Power = 0.800 





Effect(01) = 0.000 

Effect(02) = 0.000 

Effect(03) = 0.000 

Effect(04) = 0.700 


Estimate to be based on interaction 

effects. 


size 

SAMPLE 

SIZE POWER 

(per cell) 

3 0.210 

4 0.283 

5 0.353 

6 0.419 

7 0.480 

8 0.538 

9 0.590 

10 0.639 

11 0.682 

12 0.722 

13 0.757 

14 0.789 

15 0.817 


Power 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 


0.3 

0 10 20 

Sample Size 

30 40 

POWER prey species June 

Alpha = 0.050 

Power = 0.800 





Effect(01) = 0.000 

Effect(02) = 0.000 

Effect(03) = 0.000 

Effect(04) = 0.700 




size 

SAMPLE 

SIZE POWER 

(per cell) 

2 0.165 

3 0.275 

4 0.375 

5 0.465 

6 0.547 

7 0.619 

8 0.682 

9 0.736 

10 0.783 

11 0.822 



0.2 

0 10 20 30 

Sample Size 


Power 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3


POWER reef species May 

Alpha = 0.050 

Power = 0.800 





Effect(01) = 0.000 

Effect(02) = 0.000 

Effect(03) = 0.000 

Effect(04) = 0.700 




size 

SAMPLE 

SIZE POWER 

(per cell) 

4 0.272 

5 0.339 

6 0.403 

7 0.462 

8 0.518 

9 0.570 

10 0.618 

11 0.662 

12 0.701 

13 0.737 

14 0.770 

15 0.798 

16 0.824 


Power 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 


0.3 

0 10 20 

Sample Size 

30 40 

POWER reef species June 

Alpha = 0.050 

Power = 0.800 





Effect(01) = 0.000 

Effect(02) = 0.000 

Effect(03) = 0.000 

Effect(04) = 0.700 




size 

SAMPLE 

SIZE POWER 

(per cell) 

3 0.211 

4 0.284 

5 0.354 

6 0.420 

7 0.482 

8 0.540 

9 0.593 

10 0.641 

11 0.685 

12 0.724 

13 0.760 

14 0.791 

15 0.819 



0.3 

0 10 20 

Sample Size 

30 40 


Power 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4



Power eelpout June 

Alpha = 0.050 

Power = 0.800 





Effect(01) = 0.000 

Effect(02) = 0.000 

Effect(03) = 0.000 

Effect(04) = 0.700 




size 

SAMPLE 

SIZE POWER 

(per cell) 

2 0.142 

3 0.230 

4 0.311 

5 0.388 

6 0.459 

7 0.525 

8 0.585 

9 0.640 

10 0.688 

11 0.732 

12 0.770 

13 0.804 


Power 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 


0.2 

0 10 20 30 

Sample Size 

POWER eelpout September 

Alpha = 0.050 

Power = 0.800 





Effect(01) = 0.000 

Effect(02) = 0.000 

Effect(03) = 0.000 

Effect(04) = 0.700 




size 

SAMPLE 

SIZE POWER 

(per cell) 

5 0.326 

6 0.387 

7 0.445 

8 0.500 

9 0.551 

10 0.598 

11 0.641 

12 0.681 

13 0.717 

14 0.750 

15 0.779 

16 0.806 



0.3 

0 10 20 

Sample Size 

30 40 


Power 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4


Appendix 9. Commercial Fishery 

Appendix 9.1. Inquiry form used for questioning the fishermen 





Appendix 9.2. Division of Danish Waters according to ICES areas. 

60°N 

59,5°N 

59°N 

58,5°N 

58°N 

57,5°N 

57°N 

56,5°N 

56°N 

55,5°N 

55°N 

54,5°N 

54°N 

53,5°N 

48 

47 

46 

45 

44 

43 

42 

41 

40 

39 

38 

37 

36 

IVb 

NORWAY 

Division of Danish waters 

according to ICES areas 

IIIaN 

GERMANY 

Skagerrak 

IIIc 

IIIaS 

Kattegat 

SWEDEN 

F7 F8 F9 G0 G1 G2 G3 G4 G5 

7°E 8°E 9°E 10°E 11°E 12°E 13°E 14°E 15°E 16°E 


IIIb 

IIIb 

39G1 

38G1 

IIId


Appendix 9.3. Data regarding landings in area 38G1 obtained from the Directorate 

of Fisheries, Ministry of Food and Fisheries. 

Fiskeridirektoratet (Statistiksektionen TPA) 14:51 - torsdag d. 28. februar 2002 

Logbogsoplysninger vedr. danske og udenlandske fiskeres fangster i ICES-område 38G1 1982-2001. 

Fordelt på art, år og redksb. 

År= 1982 

Mængde i tons 

Garnredskaber Trawlredskaber Total 

Mængde Mængde Mængde 

Uspecificeret Art . 7 7 

Blåhvilling . 0 0 

Ising 0 9 9 

Pighvar . 0 0 

Rødspætte 0 0 0 

Skrubbe 0 18 18 

Torsk 0 89 89 

Total 0 124 124 

Note: Fra 1983-1994 har fartøjer over 12 meter pligt til at føre logbog. 

Note: Fra 1994- har fartøjer over 10 meter pligt til at føre logbog. 

Note: Mængdeoplysninger er fartøjsførens fangstskøn. 

Kilde: Fiskeridirektoratets logbogsregister. 




År= 1983 


Trawlredskaber Total 

Mængde Mængde 

Uspecificeret Art 1 1 

Brisling 9 9 

Hvilling 1 1 

Ising 2 2 

Pighvar 0 0 

Rødspætte 0 0 

Sild 169 169 

Skrubbe 2 2 

Torsk 154 154 

Total 338 338 










År= 1984 


Garnredskaber Trawlredskaber Total 


Uspecificeret Art 0 1 1 

Hvilling . 0 0 

Ising . 1 1 

Kulmule . 0 0 

Kuller . 0 0 

Laks . 0 0 

Lange . 0 0 

Mørksej . 0 0 

Pighvar 0 0 0 


Skrubbe 0 1 1 

Torsk 47 86 133 

Total 47 89 136 








År= 1985 


Trawlredskaber Total 

Mængde Mængde 

Ising 1 1 

Rødspætte 0 0 

Skrubbe 0 0 

Torsk 6 6 

Total 7 7 








År= 1986 


Trawlredskaber Uspecificeret Total 


Uspecificeret Art . 0 0 

Ising 0 0 1 

Pighvar 0 0 0 

Pighaj 0 . 0 

Rødspætte 0 . 0 

Sild . 10 10 

Skrubbe 0 1 1 

Torsk 2 0 2 

Total 2 12 14 









Fiskeridirektoratet Fordelt på art, år og (Statistiksektionen redksb. TPA) 14:51 - torsdag d. 28. februar 2002 


Fordelt År= 1987på 

art, år og redksb. 


Garnredskaber Trawlredskaber Partrawl Snurrevod Uspecificeret Total 

År= 1991 

Mængde Mængde Mængde Mængde Mængde Mængde 

Hvilling 


Ising 

Garnredskaber . 

Mængde . 

Trawlredskaber 0 

Mængde 10 

Partrawl 

Mængde 

. 

. 

Uspecificeret . 

Mængde . 

Total 

Mængde 

. 

1 

0 

11 

Pighvar Uspecificeret Art 0. 0 1 . . . . 1. 

0 

Rødspætte Brisling . . 34 1 46 . 0 . 80. 

2 

Hvilling Sild . . 6 0 6 . 0. 6. 

6 

Ising Skærising 0. 15. 00 0. 15. 

0 

Kulmule Skrubbe . . 0 1 0 . . . 00 

1 

Laks Torsk 0 3 26 . . . 7. 04 

39 

Mørksej Total 3. 39 0 7. 07 05 

61 

Pighvar Note: Fra 1983-1994 har fartøjer over 12 meter 0 pligt til at føre logbog. 1 . 0 1 

Rødspætte 

Note: Fra 1994- har fartøjer over 10 meter . pligt til at føre logbog. 0 . 0 0 

Sild 

Note: Mængdeoplysninger er fartøjsførens . fangstskøn. 64 97 . 160 

Skrubbe 

Kilde: Fiskeridirektoratets logbogsregister. 0 5 0 1 6 

Tunge . 0 . . 0 

Torsk 7 13 4 0 25 

Total 7 138 148 1 294 








År= 1988 


Trawlredskaber Partrawl Snurrevod Uspecificeret Total 

Mængde Mængde Mængde Mængde Mængde 

Uspecificeret Art 0 . . . 0 

Brisling . 43 . 2 45 

Hundestejle . . . 29 29 

Hvilling 0 44 . 0 44 

Ising 23 . 1 3 27 

Pighvar 1 . . 0 2 

Rødspætte 0 . . . 0 

Sild 1 5 . 2 8 

Skrubbe 3 . . 0 3 

Torsk 69 6 8 5 88 

Total 98 98 8 42 246 













År= 1989 

År= 1993 



Garnredskaber 

Garnredskaber 

Mængde 

Trawlredskaber 

Trawlredskaber 

Mængde 

Partrawl 

Partrawl 

Mængde 

Snurrevod 

Snurrevod 

Mængde 

Uspecificeret 

Uspecificeret 

Mængde 

Total 

Total 

Mængde 

Uspecificeret Art Mængde Mængde Mængde 

. 0 . 

Uspecificeret Art 

Brisling 0 1 . 

. 3 85 

Brisling 

Hvilling . 14 782 

. 1 . 

Hvilling 

Ising . 5 1 

. 32 . 

Ising 

Pighvar . 6 . 

. 1 . 

Pighvar 

Rødspætte . 1 . 

. 0 . 

Rødspætte 

Sild 0 0 . 

. 37 333 

Sild 

Skrubbe . 14 324 

. 8 . 

Skrubbe 

Torsk . 1 . 

1 23 . 

Slethvar 

Total . 0 . 

1 105 418 

Tunge . 1 . 


Torsk . 41 2 


Total 0 84 1.110 





Mængde 

0 

. . 1 

. 

2 

. 

0 

. 

. . . 

0 

. 

0 

. 

9 

12 

Mængde 

. 

. . 3 

. 

0 

. 

. . 

50 

. . . 

50 

. 

. 

4 

Mængde 

0 

1 

88 

796 

1 

11 

32 

9 

1 

1 

0 

0 

420 

338 

8 

1 

25 

0 

575 

1 

52 

1.210 






År= 1990 


Trawlredskaber Partrawl Total 


Uspecificeret Art 0 . 0 

Brisling . 113 113 

Hvilling 3 0 3 

Ising 12 . 12 

Pighvar 1 . 1 


Sild . 114 114 

Skrubbe 11 0 11 

Torsk 10 2 12 

Total 38 228 267 










År= 1997 





År= 1994 


Bundgarn Garnredsk. Krogredsk. Andet Trawlredsk. Snurpenot Partrawl Snurrevod Uspec. Total 

Mængde Mængde Mængde Mængde Mængde Mængde Mængde Mængde Mængde Mængde 

Uspecificeret Art . 1 . . 1 . . 0 0 2 

Blanke Ål 1 . . . . . . . . 1 

Brisling . . . . . . 921 . . 921 

Dybvandsrejer . 1 . . . . . . . 1 

Fjæsing . 0 . . . . . . . 0 

Gule Ål 0 . . . . . . . . 0 

Hornfisk 0 . . . . . . . . 0 

Hvilling . 0 . . 3 0 0 1 . 5 

Ising . 1 . . 14 1 8 39 . 62 

Kulso . 1 . . . . . . . 1 

Laks 0 . . . . . . . . 0 

Makrel 0 . . . . . . . . 0 

Multe 0 . . . . . . . . 0 

Mørksej . . . . 9 . . . . 9 

Mulle 0 . . . . . . . . 0 

Ørred 0 . . . . . . . . 0 

Pighvar . 6 . . 0 . . 0 0 7 

Rødspætte . 0 . . 0 0 . 0 . 0 

Sild 0 . . . 87 . 844 . . 931 

Skrubbe 0 0 . . 0 . . 2 . 2 

Slethvar . 0 . . 0 . . . . 0 

Sperling . . . . . . 10 . . 10 

Stenbider . 2 . . . . . . . 2 

Tunge . 0 . . 0 . 6 . . 6 

Torsk 0 34 7 6 10 2 13 33 0 104 

Total 2 45 7 6 124 2 1.801 75 0 2.062 





Bundgarn Garnredskaber Krogredsk. Andet Trawlredsk. Partrawl Snurrevod Uspec. Total 

Mængde Mængde Mængde Mængde Mængde Mængde Mængde Mængde Mængde 

Uspecificeret Art 0 1 . . 6 . 1 . 8 

Brisling . . . . . 256 . . 256 

Hvilling . . . . 0 . 3 . 3 

Ising . 2 . 0 13 . 4 . 19 

Kulso . 2 . . . . . . 2 

Mørksej . 0 . . . . . . 0 

Mulle . . . . 0 . . . 0 

Pighvar . 1 . . 2 0 0 . 3 

Rødspætte . 2 . . 1 0 0 . 3 

Rødtunge . 0 . . 0 . . . 0 

Sild . . . . . 493 . . 493 

Skrubbe . 3 . . 8 0 2 . 12 

Slethvar . 0 . . . . . . 0 

Stenbider . 2 . . 0 . . . 2 

Tunge . 0 . . 0 . . . 0 

Torsk 4 60 2 3 624 21 77 0 790 

Total 5 71 2 3 654 770 87 0 1.592 










År= 1998 


Garnredskaber Krogredskaber Andet Trawlredsk. Partrawl Snurrevod Total 

Mængde Mængde Mængde Mængde Mængde Mængde Mængde 

Uspecificeret Art 1 . 0 6 0 1 8 

Brisling . . . 31 278 . 309 

Hvilling 0 . 0 1 0 . 1 

Ising 1 . 0 17 0 1 19 

Kulso 0 . . 0 . . 0 

Laks . . . 0 . . 0 

Lange 0 . . . . . 0 

Multe 0 . . . . . 0 

Ørred 3 . . . . . 3 

Pighvar 0 . 0 3 0 . 3 

Rødspætte 1 . 0 10 0 0 12 

Rødtunge 0 . . 0 . . 0 

Sild . . . 79 665 . 744 

Skrubbe 3 . 0 14 0 1 18 

Slethvar . . . 0 . . 0 

Stenbider 0 . . . . . 0 

Tobiskonge 0 . . . . . 0 

Tunge 0 . . 0 . . 0 

Torsk 52 0 6 355 13 26 452 

Total 61 0 7 515 957 29 1.569 








År= 1999 


Bundgarn Garnredskaber Krogredsk. Andet Trawlredsk. Partrawl Snurrevod Total 

Mængde Mængde Mængde Mængde Mængde Mængde Mængde Mængde 

Uspecificeret Art . 1 . 2 6 2 5 16 

Ansjos . . . . 0 . . 0 

Brisling . . . . . 230 . 230 

Dybvandshummer . . . . 0 . . 0 

Hvilling . . . . 2 . . 2 

Invertibrater . . . . 0 . . 0 

Ising . 1 . 2 21 . 11 35 

Kulso . 8 . . . . . 8 

Mørksej . . . . 0 . . 0 

Ørred . 2 . . . . . 2 

Pighvar . 2 . 0 1 . . 4 

Rødspætte . 2 . 3 13 0 3 21 

Rødtunge . 0 . 0 0 . . 0 

Sild . . . . 15 303 . 318 

Skrubbe . 1 . 1 8 . 3 13 

Slethvar . . 0 . 0 . . 0 

Stenbider . 2 . . 0 . . 2 

Tunge . 0 . . 0 . . 0 

Torsk 0 66 0 20 456 2 104 648 

Ulk . . . . 0 . . 0 

Total 0 85 0 28 522 537 125 1.298 










År= 2000 


Bundgarn Garnredskaber Andet Trawlredsk. Partrawl Snurrevod Uspecificeret Total 

Mængde Mængde Mængde Mængde Mængde Mængde Mængde Mængde 

Uspecificeret Art 0 2 0 9 . 2 . 13 

Brisling . 0 . . 285 . . 285 

Hvilling . . . 6 . 0 . 6 

Ising . 8 0 18 0 2 . 28 

Kulso . 1 0 0 . . . 1 

Kuller . . . 0 . . . 0 

Kvabbe . 0 . . . . . 0 

Laks . 0 . . . . . 0 

Makrel . 0 . 0 . . . 0 

Multe . 0 . . . . . 0 

Ørred . 0 . . . . . 0 

Pighvar . 3 0 3 . . . 6 

Rokke . 0 . . . . . 0 

Rødspætte . 8 0 13 0 1 . 23 

Rødtunge . 0 . 0 . . . 0 

Sild . 0 . . 194 . . 194 

Skrubbe . 28 0 38 0 4 . 69 

Slethvar . 1 . 0 . . . 1 

Småplettet Rødhaj . 0 . . . . . 0 

Stenbider . 0 . 0 . . . 0 

Tunge . 0 . . . . . 0 

Torsk 0 80 0 759 3 91 1 934 

Total 0 133 1 845 481 100 1 1.561 











Mængde i tons År= Bundgarn Garnredskaber Krogredsk. Andet Trawlredsk. Partrawl Snurrevod Uspec. Total 

2001 

Mængde Mængde Mængde Mængde Mængde Mængde Mængde Mængde Mængde 

Aborre . 0 . . . . . . 0 

Uspecificeret Art 0 1 . . 7 0 2 0 10 

Brisling . . . . 7 1.202 . . 1.209 

Dybvandshummer . 0 . . . . . . 0 

Glyse . . . . 0 . . . 0 

Hvilling . 0 0 . 3 1 0 . 3 

Invertibrater . . . . . 0 . . 0 

Ising . 5 . . 21 0 4 . 29 

Kulmule . 0 . . . . . . 0 

Kulso . 7 . 1 . . . . 7 

Kuller . 1 . . . . . . 1 

Laks . 0 . . . . . . 0 

Lyssej . . . . . . 0 . 0 

Multe . 0 . . . . . . 0 

Mørksej . 0 . . 0 . . . 0 

Østers . 0 . . 0 . . . 0 

Ørred . 0 . . . . . . 0 

Pighvar . 2 . 0 1 0 0 . 3 

Rødspætte . 12 . 0 9 0 1 . 22 

Rødtunge . 0 . . 0 . . . 0 

Sild . . . . 7 958 . . 965 

Skrubbe . 43 . 0 39 0 2 . 85 

Langebarn . 0 . . . . . . 0 

Slethvar . 3 . . 0 . . . 3 

Sperling . . . . . 6 . . 6 

Stenbider . 2 . 0 0 . . . 2 

Sværdfisk . 0 . . . . . . 0 

Tunge . 0 . . 0 . . . 0 

Torsk 0 112 0 2 431 23 58 0 625 

Tunfisk . 0 . . . . . . 0 

Total 0 188 0 2 524 2.190 66 0 2.970 







Appendix 9.4. Map of all fishermen’s recorded fishing locations within the wind farm area 

and reference area south of Rødsand for the years 1997 to 2001. 





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andre printere. 

Appendix 9.4.1. Map of all fishermen’s recorded net locations within the wind farm area and reference area south of Rødsand in 1997. Fisherman 1-5 uses gillnet. 



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Appendix 9.4.2. Map of all fishermen’s recorded net locations within the wind farm area and reference area south of Rødsand in 1998. Fisherman 1-5 uses gillnet. 



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Appendix 9.4.3.. Map of all fishermen’s recorded net locations within the wind farm area and reference area south of Rødsand in 1999. Fisherman 1-5 uses gillnet. 



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Appendix 9.4.4. Map of all fishermen’s recorded net locations within the wind farm area and reference area south of Rødsand in 2000. Fisherman 5 and 7 uses gillnet. 



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Appendix 9.4.5.. Map of all fishermen’s recorded net locations within the wind farm area and reference area south of Rødsand in 2001. Fisherman 5 uses gillnet. 



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Appendix 9.4.6.. Map of all trawl stretch in 1997 to 1999 within the windfarm area and the reference area south of Rødsand. 



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Appendix 9.4.7. Map of all trawl stretch in 2000 and 2001 within the wind farm area and the reference area south of Rødsand. 





Appendix 9.5. Seasons for the fishery of commercially important species 

Cod fishery 

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Appendix 9.5.1. List of reported seasons for cod catches in the wind farm site and the reference area south of Rødsand. 

Each line represents one boat, and a break in the line means there is no fishing in that period. 

Turbot fishery 

Trawling 


Appendix 9.5.2. List of reported seasons for catching turbot in the wind farm site and the reference area south of 

Rødsand. Each line represents one fisherman, and a break in the line means there is no fishing in that 

period. 


Nets 

Trawling 

Nets 


Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec


Plaice, flounder and dab fishery 

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Appendix 9.5.3. List of reported seasons for plaice, flounder and dab in the wind farm site and the reference area south 

of Rødsand. Each line represents one fisherman, and a break in the line means there is no fishing in 

that period. 

Silver eel fishery 

Trawling 


Appendix 9.5.4. List of reported seasons for silver eel in the wind farm site and the reference area south of Rødsand. 

Each line represents one boat, and a break in the line means there is no fishing in that period. 


Nets 

Nets 


Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec


Appendix 9.6. Presentation of the fishermen’s statements 





Net fisherman 1 

Year Type of tackle No. of tackles Pilot harbour Fishing days Fishing effort Species Number of kg caught Season of fishing Remarks 

Before 1997 Cod nets 130 mm 100 units Gedser 75 6000 Atlantic cod All seasons Atlantic cod migrate into warmer water 


Turbot nets 220 mm 80 units 75 7500 Turbot 1 March - Sept. when it is warm 

Sandeels hide in the sand during the day, and 

becomes active in the night time 

1997 Cod nets 130 mm 100 units Gedser 117 9360 Atlantic cod 14541 All seasons The Atlantic cod are most active when they hunt 

Turbot nets 220 mm 80 units 117 11700 Turbot 474 1 March - Sept. sandeels 

1998 Cod nets 130 mm 100 units Gedser 61 4880 Atlantic cod 7612 All seasons 

Turbot nets 220 mm 80 units 61 6100 Turbot 85 1 March - Sept. 

Eels are to be caught in the (evening-night-morning) 

Turbot spawn at the flat sand in the area 

There are a lot of filamentous algae when it is warm 

1999 Cod nets 130 mm 100 units Gedser 100 8000 Atlantic cod 12000 All seasons Roe are found in cods from february until june , 

Turbot nets 220 mm 80 units 100 10000 Turbot 500 1 March - Sept. and they are ready to spawn in April. 

2000 0 units Gedser 0 0 Atlantic cod 0 

0 units 0 0 Turbot 0 

2001 0 units Gedser 0 0 Atlantic cod 0 

0 units 0 0 Turbot 0 


1996 Cod nets (gill nets) 100-150 units Kramnitse 60 7500 Atlantic cod 20000 Dec. - May Good fishing area 

Turbot 1000 

1997 Cod nets (gill nets) 100-150 units Kramnitse 60 7500 Atlantic cod 20000 Dec. - May Sandeels food chain 

Turbot 1000 

1998 Cod nets (gill nets) 100-150 units Kramnitse 60 7500 Atlantic cod 20000 Dec. - May 

Shrimp food chain 

Turbot 1000 Filamentous algae 

1999 Cod nets (gill nets) 100-150 units Kramnitse 60 7500 Atlantic cod 20000 Dec. - May 

Turbot 1000 

2000 Kramnitse Atlantic cod ? Refuse to take part of the investigation this year 

Turbot ? 

2001 Kramnitse Atlantic cod ? Refuse to take part of the investigation this year 

Turbot ? 



Trawl fisherman 3 


Before 1997 Trawl 1 unit Gedser 20 - 30 25 Atlantic cod 1 April - 1 July 20% of the Atlantic cod are in size class 4, 

Turbot and 80% are in size class 5 

Flounder 

Plaice The cod migrate into deeper waters 

Dab when it becomes cold 

1997 Trawl 1 unit Gedser 21 21 Atlantic cod 29500 1 April - 1 July There are not as many Atlantic cods as 

Turbot 250 there used to be. 

Flounder 750 

Plaice 200 When it is warm the cods zare gathered at 

Dab 625 the top of the booulders. 

1998 Trawl 1 unit Gedser 21 21 Atlantic cod 21500 1 April - 1 July From 1. July the fisheries are concentrated 

Turbot 250 at deeper areas 

Flounder 750 

Plaice 200 When the hypoxia conditions enters the area 

Dab 625 the fish are gathered at the top of the boulders. 

1999 Trawl 1 unit Gedser 19 19 Atlantic cod 21000 1 April - 1 July The Atlantic cod are joint at the boulder reef. 

Turbot 250 

Flounder 750 

Plaice 200 

Dab 625 

2000 Trawl 1 unit Gedser Atlantic cod 888 1 April - 1 July Data reported app. identical to data from 2001 

Turbot 2 

Flounder 24 

Plaice 7 

Dab 5 

2001 Trawl 1 unit Gedser Atlantic cod 888 1 April - 1 July 

Turbot 2 

Flounder 24 

Plaice 7 

Dab 5 





Before 1997 Cod nets 110-120 mm 120 units Gedser/Hesnæs 50 6000 Atlantic cod 15. March - 31. May Early in spring, when the sun is shinin, the 

Turbot nets 220 mm 350 units 50 17500 Turbot sandeels comes out of the sand. 

Flounder This attracts a lot of Atlantic cod and turbot. 

Plaice 

Dab In the area between the 2 and the 10 meter zones, 

Silvereel/eel 

Salmon/trout there are a lot of crabs during the summer. 

Lumpsucker 

The fishery are to be continued until there 

1997 Cod nets 110-120 mm 120 units Gedser/Hesnæs 50 6000 Atlantic cod 7987 15. March - 31. May are too many filamentous algae. 

Turbot nets 220 mm 350 units 50 17500 Turbot 2 

Flounder 9 Turbot seeks lower areas in the beginning 

Plaice 0 of the spawning season. 

Dab 2 

Silvereel/eel 0 In the end of March there are no roe left 

Salmon/trout 2 in the cod in the area. 

Lumpsucker 5 

From 1. July to 1. August it is aa closed 

1998 Cod nets 110-120 mm 120 units Gedser/Hesnæs 50 6000 Atlantic cod 5721 15. March - 31. May season for fishing turbot (outside the baseline). 


Flounder 11 In the winter time the fisherman doesn't 

Plaice 0 bother to fish in the area because of the 

Dab 1 weather conditions. 

Silvereel/eel 0 

Salmon/trout 1 

Lumpsucker 

1999 Cod nets 110-120 mm 120 units Gedser/Hesnæs 50 6000 Atlantic cod 16136 15. March - 31. May 


Flounder 22 

Plaice 2 

Dab 3 



Lumpsucker 90 

2000 Cod nets 110-120 mm 120 units Gedser/Hesnæs 17 2040 Atlantic cod 7861 23. March - 27. April 

Turbot nets 220 mm 350 units 17 5950 Turbot 5,5 

Flounder 4 

Plaice 0 

Dab 0 



Lumpsucker 6 

2001 Cod nets 110-120 mm 120 units Gedser/Hesnæs 29 3480 Atlantic cod 7319 14. March - 5. May 


Size of Atlantic cod 

Size class 1997 1998 I 1999 I 2000 I 2001 

3 82 59 0 24 

4 4851 1949 8098 3316 2176 

5 3136 3690 7979 4545 5119 

Flounder 26 

Plaice 0 

Dab 0 



Lumpsucker 9 



Net fisherman 7 (pound net fisher) 


1996 pound nets 8 units Rødbyhavn 90 720 Cod 4970 Sept. - Nov. The silvereel migrate in Sept, Oct. And Nov. 

Turbot 25 

Silvereel 4174 The fishing tackle are only placed in the area 

1997 pound nets 10 units Rødbyhavn 90 900 Cod 1381 Sept. - Nov. 

Turbot 55 

Flounder 514 

Plaice 12 

Dab 27 

Silvereel 2222 


Turbot 



Turbot 



Turbot 3 

Flounder 0 

Plaice 0 

Dab 0 



Turbot 4 

Flounder 0 

Plaice 0 

Dab 170 


during the season. 





Before 1997 Trawl 1 unit Rødbyhavn 25 25 Atlantic cod 9389 


1997 Trawl 1 unit Rødbyhavn 30 30 Atlantic cod 21539 



2000 Trawl 1 unit Rødbyhavn 135 135 Atlantic cod 87050 3. Jan.- 17 Dec. 

Turbot 115 

Flounder 311 

Plaice 0 

Dab 835 

Lumpsucker 26 

2001 Trawl 1 unit Rødbyhavn 55 55 Atlantic cod 25770 2. Jan. - 26. April 

Turbot 157 

Flounder 2210 

Plaice 0 

Dab 1366 

Lumpsucker 5 


Before 1997 Trawl 1 unit Rødbyhavn 25 25 Atlantic cod 9389 








2000 Cod nets 120 mm ? Rødbyhavn ? ? Atlantic cod 8000 February to 5. May Retires, will not apply for compensation and 

2001 Cod nets 120 mm O units Rødbyhavn 0 0 Atlantic cod 0 

has therefore not informed the accurate data 





Total fangst pr.år. ( kilo) 

Årstal Torsk Pighvarre Skrubbe Rødspætte Ising Blankål/Ål Stenbider 

I 1997 136487 1781 1273 212 654 2222 5 

I 1998 106538 1337 761 200 626 1304 

I 1999 139058 2979,5 772 202 628 4500 90 

I 2000 108069 125,5 315 0 840 1388 32 

I 2001 37240 164 2260 0 1541 2723 14 

I 1997, 1998, 1999 527392 6097,5 2806 614 1908 8026 95 

Total fangst pr.år i trawl (kilo). 


I 1997 72578 250 750 200 625 0 0 

I 1998 52356 250 750 200 625 0 0 

I 1999 64922 250 750 200 625 0 0 

I 2000 87938 117 335 0 840 0 26 

I 2001 26658 159 2234 7 1371 0 5 

Total fangst pr.år i garn (kilo) 


I 1997 63909 1531 613 12 29 2222 5 

I 1998 54172 1087 11 0 1 1304 0 

I 1999 74136 2730 22 2 3 4500 90 

I 2000 20131 8,5 -20 0 0 1388 6 

I 2001 10582 4,5 26 -7 170 2723 9 

Type of fishing tackle 1997 1998 1999 2000 2001 


Cod net, 110 - 120 mm mesh 185 185 185 120 120 

Trap net 6 8 18 5 7 

Cod net sp. 375 375 375 ? ? 

Trawl 3 3 3 2 2 

*The number of nets is an average 

Fiskeintensitet (antal redskaber * 1997 antal fiskedage) 1998 1999 2000 2001 


Cod net, 110 - 120 mm mesh 17700 12100 16000 2040 3480 

Trap net 540 720 1620 450 630 

Cod net sp. 22500 22500 22500 ? ? 

Trawl 81 81 79 135 55 

*Average number 



Total fangst pr.år. ( kilo) 


I 1997 136487 1781 1273 212 654 2222 5 

I 1998 106538 1337 761 200 626 1304 0 

I 1999 139058 2979,5 772 202 628 4500 90 

I 2000 108069 125,5 339 7 840 1388 32 

I 2001 37240 163,5 2260 7 1541 2723 14 

Total fangst pr.år i trawl (kilo). 


I 1997 72578 250 750 200 625 0 0 

I 1998 52356 250 750 200 625 0 0 

I 1999 64922 250 750 200 625 0 0 

I 2000 87938 117 335 7 840 0 26 

I 2001 26658 159 2234 7 1371 0 5 

Total fangst pr.år i garn (kilo) 


I 1997 63909 1531 613 12 29 2222 5 

I 1998 54172 1087 11 0 1 1304 0 

I 1999 74136 2730 22 2 3 4500 90 

I 2000 20131 8,5 4 0 0 1388 6 

I 2001 10582 4,5 26 0 170 2723 9 

Total annual catch (ton) 

Year Cod Turbot Flounder Plaice Dab Eel Lumpsucker 

1997 136,5 1,8 1,3 0,2 0,7 2,2 0,0 

1998 106,5 1,3 0,8 0,2 0,6 1,3 0,0 

1999 139,1 3,0 0,8 0,2 0,6 4,5 0,1 

2000 108,1 0,1 0,3 0,0 0,8 1,4 0,0 

2001 37,2 0,2 2,3 0,0 1,5 2,7 0,0 

Trawling Total annual catch 


1997 72,6 0,3 0,8 0,2 0,6 0,0 0,0 

1998 52,4 0,3 0,8 0,2 0,6 0,0 0,0 

1999 64,9 0,3 0,8 0,2 0,6 0,0 0,0 

2000 87,9 0,1 0,3 0,0 0,8 0,0 0,0 

2001 26,7 0,2 2,2 0,0 1,4 0,0 0,0 

Net fishing Total annual catch 


1997 63,9 1,5 0,6 0,0 0,0 2,2 0,0 

1998 54,2 1,1 0,0 0,0 0,0 1,3 0,0 

1999 74,1 2,7 0,0 0,0 0,0 4,5 0,1 

2000 20,1 0,0 0,0 0,0 0,0 1,4 0,0 

2001 10,6 0,0 0,0 0,0 0,2 2,7 0,0 



Appendix 9.7. Announcements on seasonal protections of fish and shellfish in 

saltwater. A map of the baseline around Sjælland and Lolland-Falster is included. 

Title: 

(Layout:2) 

Creator: 

(AdobePS5.dll Version 5.1.2) 

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Appendix 9.8. Announcement no. 91 of Jan. 22 1991 regarding logbooks etc. 

Statistical analysis of data from the fisheries survey 

Nysted Offshore Wind Farm at Rødsand Dok. nr. 2148-03-001-rev3 2P.doc

Appendix 10. Note concerning statistical analysis of data from the fisheries survey. 

Statistical analysis of data 

from the fisheries survey 


Technical note

Prepared for: 

SEAS Distribution A.m.b.A., Slagterivej 25 4690 Haslev 

Performed by: 

Bio/consult as, Johs. Ewalds Vej 42-44, 8230 Åbyhøj 

Text: Statistics: Editing 

John Pedersen John Pedersen Michael Bech 

jop@bioconsult.dk jop@bioconsult.dk mbe@bioconsult.dk 

Project manager: 

Christian B. Hvidt 

cbh@bioconsult.dk 

10.03.2003

Table of contents 

1. Introduction ..............................................................................................................1 

2. Considerations in relation to the analysis of data .......................................................2 

2.1. Parametric vs. non-parametric............................................................................2 

2.1.1. Parametric tests...........................................................................................2 

2.1.2. Non-parametric test.....................................................................................3 

2.1.2.1 Bootstrap.................................................................................................3 

2.1.2.2 Ordination...............................................................................................4 

2.1.2.3 Ordination based on distance...................................................................4 

2.1.2.4 Db-RDA and NPMANOVA....................................................................6 

3. Univariate or multivariate analysis? ..........................................................................9 

3.1.1.1 Univariate analysis ..................................................................................9 

3.1.1.2 Multivariate analysis ...............................................................................9 

4. Examples from the actual investigation. .................................................................. 11 

5. Recommendations................................................................................................... 16 

5.1.1.1 Sampling ............................................................................................... 16 

5.1.1.2 Data analysis ......................................................................................... 16 

6. References .............................................................................................................. 18


1. Introduction 

In 2001, a baseline stock assessment of fish and fry was made in the area around Nysted 

Offshore Wind Farm at Rødsand. The data was collected at a number of stations in the 

wind farm area and in an adjacent area referred to as a reference area. The relevant 

information about the locations of stations, amount of data and the detailed content of 

the data can be found in the report from the survey (Bio/consult, , 2003). 

The main purpose with the investigation was to collect enough baseline data to support 

a future survey programme in the area. In the baseline report a number of null 

hypothesis were established to test the effect of changes in abundance and biomass of 

the fish populations in the wind farm area opposed to the reference area. During the 

survey period as part of the investigation power analysis were made to back up the 

assessment and possible adjustment of the size of the sampling programme. The 

calculations and results can’t be regarded as absolute truths but as the best bid based on 

simplified statistical models in which these calculations are possible. Within these 

limitations, the calculations are made in a normal BACI-model of baseline 

investigations conducted on chosen species and various groupings of the species 

present. 

However, it is not necessarily, given that the final survey programme will be based on 

the same statistical models and uses the same methods as in the baseline investigation. 

The results from the baseline investigation revealed a number of problems in the datamaterial 

questioning whether it is possible to pursue the established null-hypothesis 

with a reasonable sampling effort. One of the main problems is the large number of 

empty samples, which appeared when each species in the investigation was regarded 

isolated, which could pose problems regarding the mandatory assumptions to use the 

BACI-analysis in the simple form. 

Thus, the purpose of this note is to point out some other statistical methods with a better 

utilisation of the data in the present form. The note solely deals with the sampling of 

fish and fry, representing 2 of the 7 hypotheses outlined in the baseline report. They are 

the most important hypotheses covering the main part of the data material. 

In order to provide a comprehensive base for the evaluation of the final monitoring 

programme the note also gives a survey of the main statistical methods with relevance 

to the present data. 

The following sections therefore contains an examination of the chosen statistical 

models viewed from two different perspectives: 

1. Parametric or non-parametric models 

2. Univariate or multivariate analysis 

During the examination, it has been attempted to evaluate both advantages and 

disadvantages of the specific methods in relation to the actual data. On purpose, the 

examination does not contain either mathematical or statistical details.


2. Considerations in relation to the analysis of data 

In monitoring programmes when the actual time of any e.g. environmental effects is 

known in advance a more or less sophisticated variant of the BACI design is 

traditionally used as sampling design. The BACI design in its most simple form is a test 

of the interaction between time and place during the monitoring of a control and an 

impact area in relation to time before and after the impact. The situation before impact 

is evaluated during the baseline sampling. 

However, within this overall frame of design many variations are possible. Contents of 

data and the chosen statistical methods could be very different. The basis for the 

statistical analysis can be a test with only one variable or multivariate where several 

variables are included simultaneously and the possible correlation among the variables 

are accounted for. 


both the univariate and the multivariate statistical analysis can be carried out according 

to an either a parametric or a non-parametric test. The latter contains the group of the 

most recent developed extensive permutation test. 

2.1. Parametric vs. non-parametric 

2.1.1. Parametric tests 

To be used in this group of tests, the data are assumed to follow known statistical 

distribution with parameters to be estimated such as average or variance. The linear 

normal distributed models are the most commonly used. Among these is the classic 

analysis of variance known as ANOVA or MANOVA when data are either univariate or 

multivariate. It is assumed that the data follow a normal distribution, are independent 

and have equality of variances in a set of samples. It is assumed in the models that the 

effects from the various factors such as time and place affects the observed data linearly 

and in the BACI model as a sum of the effect from time and place and their interaction. 

The its most simple form, the BACI analysis is carried out as a two-way analysis of 

variance with the test for cross effects between the two main groups as the significance 

test of interest. 

One of the advantages by the use of a linear normal model is that also the distribution of 

test parameter is known. This knowledge provides the basis to estimate the power of the 

test, to calculate how good the test is to detect an effect if there is one in “the real 

world”. The power is expressed as the probability to catch an effect of a certain, 

predetermined size according to a predetermined level of significance. The power or 

precision of the test increases with an increasing number of replicates. It is possible 

either to calculate the power of the test in relation to a certain effect size such as 50% 

change or to calculate the number of replications necessary if the structure of variance is 

known from a pilot study. It is therefore usually helpful to make a series of preliminary 

univariate analyses with selected variables to obtain an overview of the necessary 

number of samples for the final programme of biological surveys.


However, data from biological field programs such as abundance and biomass are very 

rarely normal distributed. In that case it is essential to use an appropriate transformation 

of data to bring them on a normal form but also to secure a homogenous structure of 

variance between the set of replicates (the cells) of the analysis of variance which are 

formed in the cross between the factors of the analysis. There are usually a relation 

between average and variance in that kind of data where increasing abundance results in 

increasing variation between the replicates. The most commonly used transformation is 

the logarithm, but because it is not defined for zero, empty samples cause problems 

which normally is solved by adding a small number such as adding the figure 1 to the 

raw data of abundance data. It can be difficult to fulfil the requirements for normality if 

many of the samples are empty. 

It is possible to design models, which are analogue to the univariate linear normally 

distributed models in the event that data are not normally distributed. The analysis can 

be carried out according to one of the “generalizing linear models” if they follow e.g. a 

Poisson or Binomial distribution. This method will also be influenced by the presence 

of empty samples resulting in difficulties in relation to an agreement with one of the 

distributions in that group. During the last ten years, a number of statistical models have 

been developed to treat data with univariate counting data with a surplus of zeros. A 

number of special editions of the Poisson distribution are often used because it, with an 

additional parameter, can treat the presence of empty samples. According to our 

knowledge, none of these models is useful in the present sampling programme. 

Moreover, it doesn’t affect the fact that an increasing number of empty samples 

gradually reduce the amount of information of the chosen variable. 

2.1.2. Non-parametric test. 

A number of non-parametric tests can be useful for carrying statistical tests within the 

overall BACI-frame. 

2.1.2.1. Bootstrap 

The bootstrap simulation is the obvious choice in the univariate instance, at least as a 

control of the parametric test, in case of difficulties with fulfilling the conditions for 

normality and homogeneity. The bootstrap simulation is a permutation technique, which 

function by extracting a number of random samples from the original data, of a similar 

size as the original. The selection can be made with or without replacement of the 

chosen figures. A full analysis is then carried out on the chosen data set. By repeating 

this process, a large number of times it is possible to construct an empirical distribution 

of the parameter used for testing. In that present case, it would be the test parameter for 

the interaction in a two-sided analysis of variance, which would be the parameter of 

choice. 

The advantage of that procedure is that the distribution for the data will not be estimated 

mathematically as in the case of a normal distribution, but reconstructed empirically 

based on the properties of the original data. As a result, the bootstrap estimation can be 

used in these cases when the data are not normally distributed but in contrarily have 

typical signs of deviations such as skew distributions, upper or lower truncations, 

outliers etc.


As a starting point, one should always attempt to use a parametric model such as a 

linear normal. If the assumptions are fulfilled, then the use of the parametric statistical 

analysis will result in a model of explanation, which can model the interactions and 

relations from the real world where the data originally came from. The use of a 

parametric analysis can provide an overview of how the participating factors interact 

opposed to a permutation test, which only provide information about the structure of the 

data in hand. 

2.1.2.2. Ordination 

In case of multivariate biological data like the ones in the present study, a number of 

tools or methods have been developed. These methods are basically built on the 

assumption, that the variables, e.g. abundances for number of species, are mutually 

correlated which make it possible to reduce the number of variables to a lower number 

of “synthetic” variables without the loss of information. As an example: species which 

are always co-occurring in proportional similar amounts, or species which might have a 

negative effect on the abundance of each other can be reduced to one pseudo-species 

without the loss of information. 

A number of these ordination methods are solely geometric calculations in the pdimensional 

space in between the p species of the investigation. The use of these 

geometric methods reduces the space to a manageable number of dimensions. The 

purpose of these reductions could be to continue the analysis using the new synthetic 

species or the new “species” (with the correct name: ordination axes) could be the 

product for final interpretation. A Principal Component Analysis (PCA) belongs to that 

group. However, experience shows that it is often very difficult to interpret the results, 

when data comes from biological fieldwork, typically abundance and biomass of a 

number of species. The analysis rarely makes it possible to reduce the number of 

dimensions sufficiently without loosing to much information from the original data set. 

On the other hand, would a PCA-analysis be the obvious choice if the data consists of 

measurements of chemical or physical variables (nutrients, temperature etc.), because 

this method can extract the essential information, and translate it to intuitively 

intelligible environmental gradients. 

2.1.2.3 Ordination based on distance 

Another class of ordination techniques is designed as an analysis based on calculations 

of a “distance” between every pair of samples (Fig. 1).


Figur.1 Steps in a multivariate analysis based on calculations of a “distance” between every pair 

of samples (dis-similarity). 

The first step is to calculate “distance” between the samples based on an appropriate 

metric. There are a number of different measures of distance available with each their 

qualities. A commonly used measure for distance in between samples in investigations 

of benthic fauna is the Bray-Curtis similarity, which principally function as an index of 

the ratio between the number of common species and the total number of species. The 

ratio is typically weighed with one of parameters attached to a single species, e.g. 

biomass or abundance. If that index of similarity is scaled as percentage the similarities 

will be in the range from 0 to 100 representing from totally different to identical. Using 

the proper transformation of the raw data, it is possible to increase or decrease the 

weight of different parameters, such as the presence of rare species, in the calculation of 

the similarity index. The final product is an N*N distance matrix, which provides all the 

mutual distances between the studies N samples. 

The second step is a more complex set of single steps. 

• A graphic presentation of the similarities as a Multi-Dimensional Scaling plot or 

MDS-plot. MDS is a complex mathematical method to construct a map of the 

samples in a certain number of dimensions. The purpose of the map is to place 

the samples on the map in accordance with the calculated distances in similarity. 

If sample A is more like sample B than C then A should be closer to B than to 

sample C. 

• Test of the similarity between two different distance matrixes. This test is part of 

the detective work to identify the responsible factors affecting the composition 

of species and their abundance in the samples. To identify the cause similar 

MDS-plots of the chemical parameters can be made and compared to the MDSplot 

of the biological parameters. A match between the set of maps would 

indicate that the chemical parameters was responsible for or affected the 

biological data. 

• Test of independence in the matrix of similarities to factors grouping the data 

material. The tests are similar to the test used in an analysis of variance. In the 

actual study, time and place are the factors and the tests are performed as


permutation tests of each criterion independent of the other. The tests are by 

nature not subjected to the assumptions of multivariate normality, which limits 

the use of the usual MANOVA on this kind of data, but still the data must be 

independent and with a similar statistical distribution. The permutation test is in 

general based on a large number of exchange of replicates between the tested 

factor groups, for each permutations calculation of a test statistic, and finally, a 

comparison of the test statistics based on the original data with all the randombased 

statistics. 

The distance based ordination techniques have within the last ten years got a lot of 

attention as a method to analyse this type of data, and in other fields such as analysis 

benthic fauna as a tool to investigate and explain the patterns of similarities. 

However, in the BACI framework, these methods cannot, of theoretical reasons, be used 

to test effects of interaction in an ANOVA design. As mentioned it is possible to use 

permutation tests to test the main effects in the BACI-model while a permutation test for 

the interaction between time and 

place can not be done as 

permutations between the original 

replicates. It requires a model 

based linear such as ANOVA but 

the main problem is the 

requirements to the measure of 

distance, which are not fulfilled by 

the Bray-Curtis index. The main 

question is whether the similarity 

measurement is metric or not (se 

box). 

A measuremnt of distance, D, is metric, if the measurement full fills 

the following: 

• Positivity: Dij has to be 0 or positive 

• Symmetri: Dij = Dji : The distance between A and B is the 

same as the distance between B and A. 

• Identity: Djj = 0 : a sample is identical with it self. 

• Triangle inequality: Dik < = Dij + Djk : The distance 

between two samples can not be bigger than the sum of 

distances between two other pairs. The distances can be 

constructed as a triangle. For instance: Dik = 10 og Dij = 3 

og Djk = 4 . If it isn’t possible to construct a triangle then 

the measurement of distance is not metric. 

If the area in questioning in a BACI-design is not significantly different from the 

reference, - and effect area then the effect of interaction can be tested by proving a 

difference between the areas at a later time. However, this is rarely the case, and 

furthermore surveys will often consist of a number of measurements in time, which only 

can be analysed for trends using the entire data set. 

2.1.2.4 Db-RDA and NPMANOVA 

There is a need for a permutation-based test for the interaction term. In recent years, the 

theoretical basis for this test have been further developed and even fitted to the 

situation, where the fieldwork is established in concordance with the BACI-design. At 

present there are at least two suitable procedures which can be used on the present data 

material. These techniques are not yet included in the standard statistical packages. 

The first one is called db-RDA (distance-based redundans analysis - Legendre and 

Anderson, 1999). The method calculates the distance between replicates based on a 

metric of your own choice followed by a principal coordinates analysis and a 

redundancy analysis (Fig. 2).


Figure: 2. Graphic outline of the procedures in a multivariate distance based test of interaction 

between criteria of sectioning. (Legendre and Anderson, 1999). 

The other method NPMANOVA (Non-parametric multivariate analysis of variance) 

(Anderson, 2001) provides a method based on distance-metric of your own choice and 

the product is an analysis of a variance table similar to one from the well-known 

analysis of variance. In the variance analytical table the total sums of squares of 

distances are divided into sums of squares belonging to each of the factors of the 

analysis such as time, place, and the interaction between them. Again, as in the common 

ANOVA, these sums of squares are the basis for the F ratios, which again can be tested 

for significance using permutations tests. If the distances are calculated as Bray-Curtis 

dissimilarities then they also have to be treated in different ways before they can be 

used in the NPMANOVA procedure. 

It is important to note that both methods can only be used if certain assumptions are 

fulfilled just like the requirements to a parametric ANOVA/MANOVA. In principle, the 

requirements are the same except that the data do not have to be normal distributed. The 

data should still be independent, the effects in the model (time and place) should be 

additive and finally the variation should be uniform between the groups. The 

distribution of the similarities should be the same regardless of time and place. The 

requirement of homogenous variance is important because it is possible to get a


significant result in the test solely based on differences in variance. Whether that 

requirement has been meet can only be tested in a visual estimation of a graphical 

illustration of the plot for example such as a non-metric MDS-plot. In the actual study, 

it is necessary to estimate if the similarities between replicates in the chosen areas and 

time periods seems to be of similar magnitude. If not then a suitable transformation of 

raw data can often solve the problem.


3. Univariate or multivariate analysis? 

As it appears from the previous, there is a fundamental choice to make regarding the 

analytical method for calculations the test statistics within the >BACI design. Should 

the method be uni- or multivariate? It is crucial with respect to that choice whether the 

variables of the results are correlated or not. In relation to the data in the present study it 

is important to consider if the occurrence of the fish species are independent of each 

other or if the presence of one species increase the probability to encounter certain 

species in the same area. Dependent variables are the rule of thumb in this kind of field 

study and accordingly so in the present investigation. The dependency was tested based 

on the null hypothesis of “no correlation different from zero among the p species” using 

Bartlett’s test for spericity. Intuitively it makes sense that certain species occur as 

functional groups attached to certain bottom conditions, food, water quality etc. The 

presence of correlation is the first indication to use a multivariate analysis to get the 

most information from the data. 

3.1. Univariate analysis 

If the data set is tested using a univariate BACI-analysis of the data from each 

individual species, then there are a number of conditions, which should be noted. 

• It is difficult to fulfil the requirements of normality and homogeneity of variance 

for most of the species even after transformation These demands must be meet 

to complete an ANOVA in its ordinary linear form. The main reason is the 

presence of empty samples when the data are considered separately for each 

species. 

• Furthermore, it is important to note the danger of deriving conclusions 

concerning overall significance based on what can be called mass tests. It is 

implicit to the choice of level of significance that one out of 20 analysis should 

show significance even if no interaction-effect in the BACI model exists in the 

real world. If the results from each of the individual species are gathered to a an 

overall level of significance then it is essential to adjust the level of significance 

for each test according to number of species. If five independent species are 

analysed using an univariate ANOVA then the level of significance should be 

adjusted as follows: 1-(1-0.05) 1/5 = 1.02%. Or if the level is fixated at 5 % in 

each sub-test, then there is a probability of 22.6 % for significance in at least one 

of the five sub-tests. 

3.2. Multivariate analysis 

When the variables in question (species) are correlated as in the present baseline study, 

then the natural choice of method would be a multivariate analysis. If not for the test of 

significance, the ordination techniques can be used to explore the structures of the data 

and therefore be guidance to the final test of significance.


The choice is between: 

• The usual multivariate analysis in a parametric, normal and linear model. 

Different aspect about this analysis have been mentioned, but one essential 

problem is the requirement for normality and homogeneity of variance now 

sharpened as these requirements apply to all variables at the same time. It is a 

precondition of the MANOVA test that the number of observations minus one 

subtracted from the number of groups should be bigger than the number of 

variables (species). If the numbers of observations are at least 2 to 3 times bigger 

than the number of variables, then the test is fairly robust to deviations from the 

conditions about normality and homogeneity of variance. That is the case in the 

present baseline study from Rødsand where approximately 20 different species 

were caught. 

• The reduction of variables using the ordination technique. A PCA analysis can 

extract information from a large number of variables and represent that 

information using a smaller number of “synthetic” variables. These variables are 

by nature uncorrelated and can be used in univariate analysis. The catch is that it 

can be difficult to explain the real picture of the old variables using the new 

variables. 

• The reduction of number of variables by the use of scientific criteria to create 

functional groups of species which abundance and biomass can be tested in uni- 

or multivariate models. The obvious method is to make functional groups 

according to the expected biological effect in the actual investigation. As an 

example, it would be obvious to create a group of species with special 

attachment to the reef habitat. One of the effects of such a strategy would be 

fewer empty samples, making it easier to fulfil the preconditions to use the 

parametric ANOVA/MANOVA. 

• Extract various indices or statistics from the original data and use them in uni- or 

multivariate analysis. The two fundamental statistics are the total number of 

individuals and the total number of species per sample, but there are a number of 

diversity indexes etc. available. In general, this method does not provide useful 

results because of the difficulties involved in explaining a multidimensional real 

world using a model with only one dimension. 

• Use the full set of data in a non-parametric multi-step analysis based on 

measures of distance. Use permutation analysis to test for significant effects and 

use tools based on the calculated distances, in case of significance, to refer the 

explanation of the significance back to the particular species. The latter type of 

investigation can provide a grouping of species, which might fit the grouping in 

functional species.


4. Examples from the actual investigation. 

The data of each species in the present baseline investigation does not represent a 

normal distribution and it was only possible in a few cases to transform the data to a 

normal distribution. Furthermore, there are problems with the preconditions of 

homogeneity of variance between the cells in the model of variance analysis, which is 

formed in the cross between place (wind farm area versus reference area) and time (five 

sampling occasions). 

Figure 3 provides a typical example of the distribution of number of eelpout per sample. 

6 

4 

2 

0 

6 

4 

2 

0 

Eelpout 

5 6 9 10 11 

5 10 15 20 25 

5 10 15 20 25 

Figure 3. Distribution of number of eelpout per sample. 

5 10 15 20 25 5 10 15 20 25 5 10 15 20 25 

To illustrate how data from a survey programme can be analysed with a distance based 

ordination analysis, there have been made calculations of similarities (Bray-Curtis 

index) between all the samples from the five months of the survey in 2001. All fish 

species have been included regardless of a biological evaluation if they were suitable. 

The similarities have been calculated based on square root transformed number of 

individuals, which result in a reasonable strong emphasis on abundance and less on rare 

species. 

The calculated similarities have since been illustrated in a map using a non-metric 

MDS, which is a MDS based on the rank of the distances instead of the calculation of 

the exact distances. 

Using the permutation technique, the differences between catch periods in the different 

areas and the difference between areas at the different sampling occasions. Furthermore 

the difference between areas seen isolated in each period were tested because the 

difference between areas in a BACI analysis is crucial for the later choice of analytical 

technique to test the effect of interaction between time and place. 

Wind farm Reference


All the catch periods gives different results, which was confirmed by the test results, 

unless in month 10 and 11 which were coinciding (fig. 4). The contribution from each 

species to these differences is not described in the present note. 

Abundance, MDS square root transformed , signature=months 

Stress: 0.24 

Figure 4. MDS-map of Bray-Curtis distances between all stations calculated as square root 

transformed data. 

Figure 5 shows the result of the same ordination with signatures marking the two areas. 

To facilitate the interpretation of the graph the samples have been separated into each of 

the five sampling occasions. If the graphs are placed on top of each other they will 

represent figure 4. A global permutation test shows significant areas if not considering 

the times. If each sampling occasion is tested isolated in each period only month 5 and 9 

gives a significant difference between the areas. 

5 

6 

9 

10 

11


Month: 11 



Reference 

Windfarm 

Figure 5. MDS-map of Bray-Curtis distances between all stations calculated as square root 

transformed data. The differences between the areas have been illustrated with different 

colours. Data from each month have been placed in each their sub plot but they 

originate from the same graph. The areas have been tested significant different in month 

5 and 9 using permutation test. 

To illustrate a significance test of the interaction effect of the BACI-model two sets of 

artificial data, which could have come from a baseline survey, were constructed. The 

sampling periods May and June were chosen and the number of individuals for 

stationary fish species in the wind farm area were increased with a factor 2 and 4 

respectively but only in the month of June. This could have been two data sets from a 

before and after situation where a change have occurred in the impact area and not in 

the reference area. 

The analysis was made using Bray-Curtis distances between all samples calculated on 

square root transformed data to reduce the effect of abundant species and to secure a 

uniform distribution of distances between the replicates from each sampling occasion. A 

NPMANOVA-test of significance was made using the same data set. 

Figure 6 show the three nMDS plots from each their data set. Note that the points in the 

dense swarm moves away with an increase in number of individuals of the stationary 

species.


nMDS on abundance in wind farm area in June. 

Stress: 0.24 

Windfarm 

5 

Windfarm 

6 

Reference 

5 

Reference 

6 


Stress: 0.25 

This effect can also be found using the NPMANOVA analysis illustrated in the Table 1. 

The results have been based on 5000 permutations per analysis. 

The results shows, that there are no interaction between area and time in the original 

data. There is an obvious difference between the areas and between the two sampling 

periods, but the change from May to June is parallel. 

W6 

W6 

Windfarm 

5 

Windfarm 

6 

Reference 

5 

Reference 

6 


Stress: 0.24 

W6 

Windfarm 

5 

Windfarm 

6 

Reference 

5 

Reference 

6 

Figure 6. nMDS plots with increasing number of individuals of stationary species in the wind 

farm area in June (W6).


After a twofold increase of the abundance of stationary species the analysis gives a test 

probability of 5.9 %, which is close to a significant interaction. 

The interaction is highly significant (0.04 %) after a fourfold increase of the abundance 

of the stationary species. 

Original data 

The abundance of 

stationary species 

doubled in June. 

The abundance of 

stationary species 

increased fourfold 

in June. 




----------------------------------------------------------------------------- 

Omraa 1 7075.6513 7075.6513 4.6886 0.0004 >1.0E+10 Res 

maane 1 14002.8873 14002.8873 9.2789 0.0002 

Res 

Omxma 1 1636.8030 1636.8030 1.0846 0.3842 >1.0E+10 Res 

Resid 44 66400.9367 1509.1122 

Total 47 89116.2784 

----------------------------------------------------------------------------- 




----------------------------------------------------------------------------- 

omraa 1 8109.9132 8109.9132 5.3740 0.0002 >1.0E+10 Res 

maane 1 12625.0607 12625.0607 8.3659 0.0002 

Res 

omxma 1 2889.3945 2889.3945 1.9146 0.0586 >1.0E+10 Res 

Resid 44 66400.9367 1509.1122 

Total 47 90025.3051 

----------------------------------------------------------------------------- 




----------------------------------------------------------------------------- 

omraa 1 10159.6715 10159.6715 6.7322 0.0002 >1.0E+10 Res 

maane 1 12271.9657 12271.9657 8.1319 0.0002 

Res 

omxma 1 5587.7770 5587.7770 3.7027 0.0004 >1.0E+10 Res 

Resid 44 66400.9367 1509.1122 

Total 47 94420.3510 

----------------------------------------------------------------------------- 

Table 1. Results from a NPMANOVA test on the spring sampling of two artificial data sets with 

a built-in effect in the samples from June at the windmill park.


5. Recommendations 

5.1. Sampling 

• Adjust the sampling programme with a focus to concentrate the effort at as many 

stations as possible. Multiple replicates are not necessary on each station 

because they will be considered as one sample in the final analysis because 

station is the basic unit in the comparison of areas. Adjust the effort pr. station to 

allow for a (biological) reasonable CPUE. 

• Use eventually the liberated effort from avoiding replicates to include an 

additional reference area. Concentrate the effort on the species which will be 

used in the test. Is there any reason to record the number of crabs and scrimps? 

On the other hand, to use de multivariate ordination techniques it is essential, 

that any species of biological relevance is accounted for. 

• As a starting point for the size of the sampling program, use the result from the 

power analysis conducted in univariate BACI-models. The example from this 

note of a multivariate analysis carried out on the data at hand shows, that 

plausible effects can be traced using these methods. Exact power calculations for 

the multivariate ordination techniques demands a very extensive simulation 

study, which is outside the scope for this note. 

• Consider the time of sampling thorough. A significant difference was found 

between most of the sampling occasions and the wind farm area and reference 

area at certain times It may be more biological relevant to time the sampling 

occasion according to a yearly recurrent occasion which can established by 

supervision instead of using fixed dates. 

5.2. Data analysis 

• Use the distance based multivariate ordination technique to analyse the data and 

the effects in the BACI –model. Refer the results to the explanations, which are 

based on the specific contribution of each individual species. Consider from a 

biological view which species should be included in the test. Is the equipment 

useful for all species? Are there any coincident appearing species which 

presence does not relate to the sectioning of the study area? 

If other methods are to be used, consider the following: 

• Univariate analysis can be used on the chosen species but the problem is that 

these species should be chosen a priori and not based on the results of the 

investigation. The conditions to use a traditional ANOVA test are not fulfilled 

for most of the species. Pay attention to the level of significance, if the overall 

significance is based on the results from several single species. Check the results 

using non-parametric methods as Bootstrap.


• Univariate and multivariate tests in the normal linear model can also be made 

using groupings of the species. The grouping will reduce the occurrence of 

empty samples and improve the possibility to fulfill the conditions of the 

analysis. The grouping should be chosen a priori and not based on the results of 

the investigation. It is only possible to create functional groups if it is considered 

biological relevant.


6. References 

Anderson, M.J. 2001. A new method for non-parametric multivariate analysis of 

variance. Austral Ecology 26: 32-46. 

Bio/consult as 2003. Baseline study. Fish, fry and commercial fishery – Nysted 

Offshore Wind Farm at Rødsand. Technical report prepared by Bioconsult 

as. SEAS Distribution A.m.b.a., March 2003. 

Clarke, K.R. 1993. Non-parametric multivariate analyses of changes in community 

structure. Australian Journal of Ecology 18: 117-143. 

Clarke, K.R.; Green, R.H. 1988 Statistical design and analysis for a ’biological effects’ 

study. Marine Ecology Progress Series 46: 213-226. 

Green, R. H. 1979. Sampling design and statistical methods for environmental 

biologists. New York: Wiley. 

Legendre, P.; Anderson, M.J. 1999: Distance-based Redundancy Analysis: Testing 

multispecies responces in multifactorial ecological experiments. Ecol. Mon. 

69(1): 1-24. 

Manly, Bryan F. J: 1997 Randomization, Bootstrap and Monte Carlo Methods in 

Biology, Second Edition. CRC Press; 

Podlich, H.M.; Faddy, M.J.; Smyth, G.K. (in press): A general approach to modelling 

and analysis of species abundance data with extra zeros.

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Title: Baseline study - fish, fry and commercial fishery 

Subject: Nysted Offshore Wind Farm at Rødsand 

Author: Christian B. Hvidt, Kirsten Engell-Sørensen, Maks 

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