Macroalgae and phytoplankton as indicators of ... - Naturstyrelsen

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Figure 4.7 Estimated standard error for different statistics for describing two parametric distributions: A) A standard normal distribution (N(0;1)) and B) a standard lognormal distribution LN(0;1). For the lognormal distribution the mean was estimated with and without log-transforming data prior to computing the statistics. To investigate the validity of these theoretical results relying on the assumption that data can be described by a parametric distribution, a bootstrap method was employed on summer chla values (May-September) over a 6-year period (2001-2006) to 4 stations representing a gradient from open water to eutrophic estuary. The bootstrap is a non-parametric method for estimating different statistics by randomly drawing a sample from all the observations with replacement (Efron & Tibshirani 1993). For this analysis 10000 re-samplings of n = 10, 20, …., 100 observations were analysed and the standard error of the statistics was estimated as the standard deviation of the 10000 bootstrap sample estimates, e.g. 10000 mean estimates were calculated from the bootstrap samples and the standard deviation of these will converge towards the true standard error of the mean for large number of replications. 66
The 90-percentile was clearly substantially more uncertain that the mean or median indicators, particularly for small sample sizes (Figure 4.8). This was most pronounced at stations 431 and 170006, where the empirical distribution was characterised by a few relatively extreme observations (e.g. a chla concentration of 34.4 µg l -1 in May 2005 at station 170006 compared to a mean of 2.2 µg l -1 ). The chla distributions at these two stations also had the highest skewness. Extreme observations also influence the raw mean, whereas the influence is reduced if data are log-transformed prior to the analysis. For all the four cases the most precise indicator is the mean calculated on log-transformed observations and subsequently back-transformed. Another approach to reduce the influence of extreme observations is to use a robust mean estimate, i.e. averaging data after removing the 1-3 highest and lowest observations. In all cases, the 90-percentile is substantially more uncertain that mean and median statistics even for large sample sizes (factor of 3-5). Figure 4.8 Estimated standard error for different statistics using a non-parametric bootstrap method with resampling of monitored summer chlorophyll a concentrations (2001-2006) from 4 sites representing a eutrophication gradient: A) the Sound, B) Århus Bay , C) Odense Fjord, and D) Skive Fjord. Note the difference in scaling. The mean was estimated with and without logtransforming data prior to computing the statistics. These results, both from the theoretical parametric distributions and the empirical distributions based on monitoring data, clearly document the inadequacy of the 90-percentile as a precise indicator of phytoplankton biomass. The problem of using the 90-percentile is that it is based on 1-2 measurements from the right tail of the distribution, where the range between observations is large. The 90-percentile as an indicator is useful only, if it can be documented that this indicator relates more strongly to nutrient status than the mean, i.e. that nutrient enrichment should affect phytoplankton bloom concentrations more than the overall chla concentration. So far, this has not been documented. Carstensen et al. (2004) 67
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National Environmental Research Ins
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National Environmental Research Ins
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Contents Summary 5 Sammenfatning 6
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Summary During the implementation o
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1 Introduction This report is part
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Figure 2.1 Long-term trends in nitr
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Figure 2.2 Surface TN versus salini
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2.2.2 Nitrogen input-TN relationshi
Page 17 and 18: Flensborg Fjord on the ranked scale
Page 19 and 20: Table 2.4 Suggested reference condi
Page 21 and 22: the gradient. These responses have
Page 23 and 24: Table 3.1 Overview of sampling area
Page 25 and 26: We assumed that mean values from th
Page 27 and 28: and 2005), averaged over the months
Page 29 and 30: The second component of the Spanish
Page 31 and 32: The variance of a macroalgae indica
Page 33 and 34: The levels of mean cumulated algal
Page 35 and 36: Figur 3.5 continued Modelled mean l
Page 37 and 38: while the fraction of opportunists
Page 39 and 40: 3.4.2 Physico-chemical variables Ph
Page 41 and 42: Figure 3.7 continued Mean modelled
Page 43 and 44: 3.4.3 Algal variables in relation t
Page 45 and 46: Figure 3.8 continued Cumulated cove
Page 47 and 48: inputs (Figures 3.9 and 3.10) where
Page 49 and 50: A Total algal cover (%) 100 95 90 8
Page 51 and 52: A 100 B 140 Total algal cover (%) 8
Page 53 and 54: A response to changes in nutrient c
Page 55 and 56: In its present form the Spanish ind
Page 57 and 58: 3.6 Conclusions • All algal varia
Page 59 and 60: gime shift after changing the sluic
Page 61 and 62: Figure 4.3 Estimated site-specific
Page 63 and 64: from Randers Fjord and Odense Fjord
Page 65 and 66: moderate boundaries. Reference dept
Page 67: 4.3 Evaluation of the precision of
Page 71 and 72: 5 Conclusions and recommendations R
Page 73 and 74: 6 References Andersen, J., Markager
Page 75: Nielsen, S.L., Sand-Jensen, K., Bor
Page 78 and 79: 76 Appendix 1. Reference levels and
Page 84 and 85: Appendix 7. Modelled mean levels of
Page 86 and 87: Appendix 9. Modelled mean levels of
Page 88 and 89: Appendix 11. Modelled mean levels o
Page 90 and 91: Appendix 13. Results of power analy
Page 92 and 93: Appendix 18. Results of power analy
Page 94 and 95: NERI Technical Reports NERI’s web
Page 96: of Danish coastal waters 683 Macroa
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