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OIKOS 109: 485 /494, 2005
Perfectly nested or significantly nested / an important difference for
conservation management
Joern Fischer and David B. Lindenmayer
Species assemblages are nested if the species present at
species-poor sites are a subset of those present at speciesrich
sites (Patterson and Atmar 1986). Conservationrelated
research has investigated patterns of nestedness
for a range of organisms, including frogs (Vallan 2000,
Williams and Hero 2001), reptiles (Mac Nally and
Brown 2001), small mammals (Deacon and Mac Nally
1998, Lynam and Billick 1999), butterflies (Fleishman
and Murphy 1999), fungi (Berglund and Jonsson 2003),
Accepted 26 November 2004
Copyright # OIKOS 2005
ISSN 0030-1299
Fischer, J. and Lindenmayer, D. B. 2005. Perfectly nested or significantly nested / an
important difference for conservation management. / Oikos 109: 485 /494.
Assemblages are nested if species present at species-poor sites are subsets of those
present at species-rich sites. In fragmented landscapes, nestedness analyses have been
suggested as a means of assessing which patches are most important for biodiversity
conservation. In the theoretical situation of perfect nestedness in relation to patch size,
the single largest patch is disproportionally more important compared to smaller
patches and will capture all species of conservation concern. However, real ecosystems
are rarely perfectly nested. Here, we examined how different the implications for
conservation management would be for an assemblage of birds that was highly
significantly, but imperfectly nested in relation to patch size.
The study focused on a fragmented landscape in southeastern Australia. Across 43
patches, 76 species of birds were recorded and classified as generalist, intermediate and
sensitive species. The dataset was highly significantly nested by patch size (p /0.002).
Under perfect nestedness by patch size, the single largest patch would have captured all
species, and all sensitive species would have co-occurred in the largest patch. In our
imperfectly nested dataset, co-occurrence patterns were substantially weaker. Usually,
less than half of the sensitive species co-occurred in any given patch, and using the
largest patches only, over a quarter of the study area would have been required to
capture 80% of sensitive species at least once. These findings highlight there can be
large qualitative differences between theoretical perfectly nested assemblages, and real
imperfectly nested assemblages.
Despite the outcomes of our study which showed highly significant nestedness by
area, smaller patches in the system were important to complement large patches. We
therefore argue that nestedness analyses need to be interpreted carefully, especially in
an applied conservation context. Alternative conservation planning tools which
consider the complementarity of various different patches are likely to be more
informative for conservation management than nestedness analyses.
J. Fischer and D. B. Lindenmayer, Centre for Resource and Environmental Studies, The
Australian National Univ., Canberra ACT 0200, Australia (joern@cres.anu.edu.au).
plants (Hansson 1998, Berglund and Jonsson 2003) and
birds (Bolger et al. 1991, Hansson 1998, Mac Nally et al.
2002a). Nested subset analyses may be a useful conservation
tool for several reasons (Patterson 1987, Cutler
1991, but see Boecklen 1997). For example, nestedness
analyses may point towards predictable extinction sequences
(Atmar and Patterson 1993, Lomolino 1996),
highlight ‘‘idiosyncratic’’ species that require particular
conservation approaches (Patterson et al. 1996), and
OIKOS 109:3 (2005) 485

facilitate the study of community composition in addition
to species richness (Fleishman and Mac Nally
2002). One of the earliest and most significant contributions
of nested subset theory to conservation biology
was to inform managers what size patches should be
reserved in a fragmented landscape (Rosenblatt et al.
1999, Berglund and Jonsson 2003). Patterson (1987)
pointed out that in a fragmented system perfectly nested
by patch size, the largest patch will, by definition,
harbour more species than any number of small patches
together. In such a system, all species of conservation
concern (and all others) will co-occur in the largest
patch. Hence, the largest available patch is particularly
important for species conservation in systems perfectly
nested by patch size.
We believe that an important shortcoming of discussions
on nestedness as a conservation tool to date has
been the lack of distinction between perfect nestedness
and statistically significant nestedness. Statistically significant
nestedness is common for faunal assemblages
throughout the world (see datasets in Atmar and
Patterson 1995, Wright et al. 1998). Indeed, few assemblages
are not significantly nested, e.g. in cases of
checkerboard distributions where functionally similar
species replace one another rather than being added
sequentially (Graves and Gotelli 1993). Unlike significant
nestedness, perfect nestedness is most unusual /
some unexpected presences and absences are typical of
species by sites matrices. For applications of nested
subset theory as a conservation tool in fragmented
landscapes, it is important to appreciate the degree to
which significantly nested assemblages differ from perfectly
nested ones. Essentially, the comparison between
perfect and significant nestedness is an assessment of
how realistic the implications of nested subset theory are
in practice, and hence how the theory may be realistically
interpreted in an applied conservation context. In this
paper, we assess a large dataset on birds in a fragmented
landscape, which was significantly nested by patch size.
Using this dataset, we assess (1) the potential contribution
of large patches to conservation, and (2) species cooccurrence
patterns. We compare our results with
patterns that would be predicted under perfect nestedness.
Our results highlight that conservation planning
tools that consider the complementarity of various
different patches are likely to be more useful for
conservation management than nestedness analyses.
Methods
Study area and dataset
Data used for this study were obtained as part of
the Tumut Fragmentation ‘‘Natural Experiment’’
(Lindenmayer et al. 1999, 2002). The ‘‘natural experiment’’
is centred around Buccleuch State Forest near
Tumut, approximately 100 km west of the Australian
Capital Territory. The state forest includes a 50 000 ha
plantation of the introduced radiata pine (Pinus radiata).
Throughout the plantation, remnant patches of the
original native eucalypt (Eucalyptus sp.) forest ranging
in size from B/1hato /100 ha have been retained. For a
more detailed description of the study area, see Lindenmayer
et al. (1999, 2002). For the purpose of this study,
43 patches ranging between 0.4 ha and 48 ha were
studied. Extensive data on the presence/absence of birds
were collected at each of these sites using three survey
methods: (1) transect counts covering entire patches,
(2) point interval counts, and (3) data from automatic
tape recorders.
Repeat transect counts of birds covering entire patches
were completed in late spring of 1998 and 1999. In these
counts, the entire area of each patch was searched by
counting all birds detected along set lines of flagging
tape set 50 m apart. The same experienced field observer
(DBL) was responsible for undertaking all counts. This
avoided the problems of observer bias and observer
heterogeneity which are typically associated with multiple
observer surveys (Kavanagh and Recher 1983, Link
and Saur 1997, Cunningham et al. 1999). Field sampling
was confined to clear, still mornings to limit the effects of
variable weather conditions on our data (Slater 1994).
To facilitate point interval count sampling, a 200 /
600 m transect was established at each site and was
divided into 100 m units. Five minute point interval
counts were employed at marked locations set 100 m
apart along the transect in October/November 1996. For
each point count, observers recorded the numbers of
each bird species seen or heard within an approximate
50 m radius. Counts were completed between 5:30 and
9:30 am when weather conditions were fine. A total of 22
experienced bird observers from the Canberra Ornithologists
Group participated in the surveys. Although
observers were experienced, they varied in their ability
to detect some groups of birds. Cunningham et al. (1999)
showed that for the 22 experienced observers, extra
variability due to observer heterogeneity could be
compensated for by averaging the counts of two or
more observers at the same site. Here, presence/absence
data obtained by different observers were pooled for a
given site.
Automatic bird call recording was the third method
employed to detect birds. It was used in November 1996
and March 1997. Automatic bird call recorders were set
at the 0 m and 400 m points along the marked transect
established in the remnant eucalypt patches (Cunningham
et al. 2004). The automatic bird call recording
devices provided continuous recording of bird calls for a
specified interval. For this study, call boxes were
synchronized so that a 30 min period was recorded at
dawn, and a further three 10 min segments were
completed during each of the following three hours
486 OIKOS 109:3 (2005)

(i.e. 8:00 /8:10 am, 9:00 /9:10 am and 10.00 /10.10 am).
Thus, a profile of bird calling was obtained across the
morning (Lindenmayer et al. 2004) and the chance of
detecting the target taxa if they occurred within a given
area was maximised. Audio tapes made of calls were
removed from each bird call recorder at the completion
of a taping session and then analysed to determine the
number of calls made by each species (Cunningham et al.
2004).
For the purpose of this paper, presence/absence data
from the three survey techniques were pooled to obtain a
single matrix of species presences by sites.
Analysis
The degree to which our dataset was nested was analysed
using the procedures outlined by Berglund and Jonsson
(2003). The species by sites presence/absence matrix was
sorted by species richness and by area in two separate
analyses. The discrepancy ‘‘d’’ (sensu Brualdi and
Sanderson 1999) was calculated for each species. The
discrepancy was the number of occurrences that would
need to be shifted to produce a perfectly nested matrix.
By adding up individual species’ discrepancies, a global
discrepancy value was obtained for the entire matrix. In
addition, we calculated a relative discrepancy for each
species, which was the proportion of sites where a given
species occurred that would have to be shifted to
produce a pattern consistent with perfect nestedness.
The statistical significance of the matrix-wide discrepancy
value was assessed by comparing it against a set of
1000 simulated values derived from the RANDNEST
null model, using a one-sided t-test (Jonsson 2001). The
RANDNEST null model takes into account that some
species are more ubiquitous than others. Hence, it is a
relatively conservative test of nestedness (Fischer and
Lindenmayer 2002).
To analyse how many species were captured in large
patches, patches were sorted from largest to smallest,
and birds were classified as generalist, intermediate,
sensitive or other species. The classification of birds
was related to the degree to which birds were dependent
on continuous eucalypt forest relative to their ability to
use the introduced pine plantation (see Lindenmayer et
al. 2003 for details). Graphical data analysis was then
used to assess how many of the largest sites would
‘‘protect’’ which proportion of bird species. For ease of
comparison between analyses, we set two arbitrary (and
quite minimalistic) conservation targets, and assessed the
number of patches required to meet these targets. Target
A was to capture a high proportion of species (e.g. 80%)
in at least in one patch. Under perfect nestedness, the
single largest patch would capture 100% of species.
Target B was to capture a high proportion of species
(e.g. 80%) in at least three patches. For uncommon
species present in only one or two sites, the target was to
capture them in all sites where they occurred. Under
perfect nestedness, the three largest patches would meet
this target. In both cases, we asked how many patches
were needed in the real dataset to meet these targets,
given that our dataset was significantly (but not perfectly)
nested. Although the above targets may be
plausible in some situations, we focused on them largely
as a tool to compare (1) generalists, intermediate and
sensitive species, and (2) perfect versus statistically
significant nestedness.
In a separate analysis, we analysed co-occurrence
patterns of sensitive species (sensu Lindenmayer et al.
2003). For all sensitive species, we assessed how many
other sensitive species co-occurred with a given target
species (1) on average across all sites, (2) in the largest
site where the target species was present, and (3) in the
three largest sites where the target species was present. In
a dataset perfectly nested by area, the single largest site
where a given sensitive species occurs also captures all
other sensitive species (since it is the largest patch in the
study system). We assessed if these patterns of cooccurrence
were similar in our significantly, but not
perfectly, nested dataset.
Results
Seventy-six bird species were observed in the 43 remnant
patches. The appendix lists these species, their scientific
names, assigned level of sensitivity, and number of sites
where they were recorded. It also gives species abbreviations
used throughout figures and tables of the results
section. The species by sites matrix was significantly
nested when sorted by species richness (Table 1). When
sorted by patch size, the dataset had a higher discrepancy
from perfect nestedness, but nestedness was still
Table 1. Results of the nestedness tests on the species by sites
matrix when it was sorted by species richness and patch size
respectively (following 1000 Monte Carlo simulations; see
methods section for details). Simulations were only run once
and the resulting distribution of discrepancies was applied to
nestedness tests in relation to area as well as species richness
(Jonsson 2001, Berglund and Jonsson 2003).
Sorted by
species richness
Sorted by
patch size
Simulated mean
discrepancy
329.57
Stand. dev. of simulated 12.11
mean discrepancy
Maximum simulated mean 368
discrepancy
Mininum simulated mean
discrepancy
293
Observed discrepancy
Number of sd’s below
258
5.91
295
2.86
simulated mean
One-sided t-test: P-value B/0.001 0.002
OIKOS 109:3 (2005) 487

highly significant (Table 1). Despite the high significance
levels, the species by sites matrix included numerous
unexpected presences and absences (Fig. 1).
Assessing the ability of large patches to capture bird
diversity
Figure 2 illustrates how many of the largest sites were
needed to capture a nominated proportion of birds in at
least one patch, or in at least three patches. Capturing
80% of generalist species at least once required the
protection of the two largest patches. Capturing 80% of
generalists at least three times required the 11 largest
sites. For intermediate species, the same results were
obtained (two and 11 patches respectively). In contrast,
to capture 80% of sensitive species once, the 11 largest
Fig. 1. The species by sites matrix sorted by patch size. Black
squares indicate presences, white squares indicate absences.
Despite the highly significant level of nestedness (P /0.002;
Table 1), there were a number of unexpected presences and
absences, especially for uncommon species.
Prop. sp. where cons. target is met
Prop. sp. where cons. target is met
Prop. sp. where cons. target is met
(A) Generalist species
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0 5 10 15 20 25 30 35 40 45
Number of largest patches
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Target A: Each gen. sp. once Target B: Each gen. sp. 3x
(B) Intermediate species
0
0 5 10 15 20 25 30 35 40 45
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Number of largest patches
Target A: Each int. sp. once Target B: Each int. sp. 3x
(C) Sensitive species
0
0 5 10 15 20 25 30 35 40 45
Number of largest patches
Target A: Each sens. sp. once Target B: Each sens. sp. 3x
Fig. 2. Graphical illustration of how many of the largest sites
were needed to capture a nominated proportion of bird species
in at least one patch, or in at least three patches. Under perfect
nestedness by area, the single largest patch would have captured
100% of species once, and the three largest patches would have
captured 100% of species present in three locations. The two
different curves highlight the proportion of species captured in
at least one or at least three patches. Substantially more patches
were needed than predicted under perfect nestedness, especially
for sensitive species (part C).
patches were needed. To capture 80% of sensitive species
at least three times, the 13 largest patches were needed.
Species co-occurrence patterns
The proportion of sensitive species co-occurring with a
given target sensitive species in the largest patch where
the target species was observed was consistently less than
50% (Fig. 3). Relatively widespread sensitive species
frequently co-occurred with other sensitive species (e.g.
spotted pardalote, striated pardalote, red wattlebird).
488 OIKOS 109:3 (2005)

OIKOS 109:3 (2005) 489
Co-occurring species in largest site where target species is present
Target species # sites SPP STP RWAT WNH STB GANG SBB RBTC WNG SKNG NFB KP WWW OBO RF BRTB CIC BZP GREY SFLY SIT SUM
SPP 35 n/a 1 1 - - 1 1 1 - 1 - - - 1 - - - - - - - 7
STP 32 1 n/a 1 - - 1 1 1 - 1 - - - 1 - - - - - - - 7
RWAT 29 1 1 n/a - - 1 1 1 - 1 - - - 1 - - - - - - - 7
WNH 25 1 1 n/a - 1 1 - 1 1 1 - - - 1 - - - - - - 8
STB 22 1 1 1 1 n/a 1 1 1 - - 1 - - - - - - 1 - - - 9
GANG 19 1 1 1 - - n/a 1 1 - 1 - - - 1 - - - - - - - 7
SBB 19 1 1 1 - - 1 n/a 1 - 1 - - - 1 - - - - - - - 7
RBTC 13 1 1 1 - - 1 1 n/a - 1 - - - 1 - - - - - - - 7
WNG 11 1 1 1 - 1 1 - n/a 1 1 - - - 1 - - - - - - 8
SKNG 9 1 1 1 - - 1 1 1 - n/a - - - 1 - - - - - - - 7
NFB 7 1 1 1 - 1 1 - 1 1 n/a - - - 1 - - - - - - 8
KP 6 1 1 1 1 - 1 - 1 - - - n/a - - - - - - - - - 6
WWW 4 1 1 1 - 1 1 - - 1 1 1 1 n/a - - - - - - - - 9
OBO 2 1 1 1 - - 1 1 1 - 1 - - - n/a - - - - - - - 7
RF 2 1 1 - 1 - 1 1 - 1 1 1 - - - n/a - - - - - - 8
BRTB 1 1 - - 1 - - - - - 1 - - - - - n/a - - - - - 3
CIC 1 1 1 1 - 1 1 - - - - 1 - - - - - n/a - - - - 6
BZP 1 1 1 1 1 1 1 1 1 - - 1 - - - - - - n/a - - - 9
GREY 1 1 1 1 - 1 1 1 1 - - - - - - - - - - n/a - - 7
SFLY 1 1 1 1 1 - 1 1 - 1 - - 1 - - - - - - - n/a - 8
SIT 1 1 - 1 - - - - - - 1 - - - - 1 - - - - - n/a 4
Fig. 3. Co-occurrence patterns in the largest site where a given target sensitive species was found. Presences of co-occurring species are indicated by a ‘‘1’’, absences by a ‘‘-’’, and ‘‘#
sites’’ indicates how many sites a given sensitive species was observed at. Regardless of which sensitive species was chosen as the target species, in the largest site where it occurred, no
more than nine out of a possible 20 other sensitive species co-occurred with it. See Appendix 1 for species abbreviations.

However, the number of species co-occurring with
a given target sensitive species did not vary systematically
with the ubiquity of the target species, its
discrepancy, or its relative discrepancy (Table 2). When
all patches where a target sensitive species occurred
were considered, the average number of sensitive
species co-occurring with the target species was low,
varying between three and nine out of a possible twenty
(Table 2). In the largest patch where a target sensitive
species was present, on average seven other sensitive
species co-occurred with the target species (Table 2). The
mean number of sensitive species co-occurring with a
selected target sensitive species increased to 12 out of a
possible 20 when the three largest patches where the
target species was found were considered (rather than the
single largest patch alone, Table 2).
Discussion
In fragmented landscapes, significant nestedness by
patch size has sometimes been interpreted to indicate
that rare species will be present only in large, species-rich
sites (Rosenblatt et al. 1999). Although this observation
is correct under perfect nestedness (Patterson 1987),
Patterson and Atmar (2000) highlighted that in real
ecosystems factors other than patch size are often
important. Hence, the ability of significant nestedness
to predict the value of large patches and patterns of cooccurrence
is vastly more limited than for perfect
nestedness. Our analyses highlighted that conservation
recommendations for landscapes exhibiting statistically
significant nestedness by area may have to be quite
different than for theoretical landscapes that are perfectly
nested. Under perfect nestedness by area, the
single largest patch should have captured all generalists,
all intermediate and all sensitive bird species. Many more
patches were required in our real dataset, which was
imperfectly (although still highly significantly) nested by
area (p /0.002). For example, using the largest patches
only, over a quarter of sites (11 patches) were needed to
capture only 80% of sensitive species at least once; even
more patches were needed to capture either more
sensitive species, or the same number of sensitive species
in more sites (Fig. 2).
Importantly, the arbitrary targets in our study were
actually quite minimalistic. The targets implicitly assumed
populations in different patches were independent
of one another, and populations were stable. Neither of
these assumptions are necessarily correct in many
fragmented systems (Koenig 1998). Overly minimalistic
conservation planning can lead to a ‘‘Noah’s Ark’’
effect, where many species are apparently protected,
but only for a short time (e.g. due to the possible
existence of metapopulation dynamics; Pimm and Lawton
1998). Hence, even more sites may be required to
actually support a wide variety of species in the long
term.
Under perfect nestedness by area, the largest patch
where a target sensitive species occurred also should have
captured all other sensitive species. However, in our
dataset, for a given target species, less than half the other
sensitive species were also found in the largest patch
where the target species occurred (Fig. 3). Thus, our
dataset provides empirical evidence that there can be
large differences between perfectly nested and significantly
nested assemblages. Hence, conservation recommendations
based on theoretical considerations derived
from perfect nestedness may be insufficient in significantly
(but not perfectly) nested landscapes.
Reasons for highly significant but imperfect
nestedness
Three main factors may explain why the dataset analysed
here was highly significantly, albeit imperfectly nested.
First, Lindenmayer et al. (2002, 2003) reported that
different bird species responded differently to fragmentation.
Many species declined with fragmentation, but
some actually increased (Appendix 1). Overall, more
species were disadvantaged than benefited from fragmentation,
and the pattern of species loss with decreasing
area was strong enough to mask species /specific
differences (Simberloff and Martin 1991) / thus producing
significant nestedness by area. Because nestedness
analysis focuses on a unidirectional change in species
composition (e.g. loss of species with decrease in area), it
is not an ideal tool to detect individualistic responses to
fragmentation. This is an important shortcoming
because species-specific responses are common in fragmented
landscapes, both within taxonomic groups
(Schlaepfer and Gavin 2001, Hamer et al. 2003,
Luck and Daily 2003) and between different groups
(Fleishman et al. 2001, Mac Nally et al. 2002b).
Second, the large sample size in our study increased
the statistical power of the analysis. Hence, only a weak
‘‘nestedness signal’’ was required to obtain a highly
significantly nested community, despite a large number
of discrepant species. Issues of statistical power are
frequently ignored in nestedness analyses. At the very
least, careful visual assessment of the species by sites
matrix is important to obtain an overview of the spread
of the data and the prevalence of outliers. In this context,
the discrepancy index for single species (Brualdi and
Sanderson 1999), the relative discrepancy index (this
paper), and idiosyncracy measures by Atmar and
Patterson (1995) can be useful tools (Patterson et al.
1996).
Finally, some sensitive species may be affected by
ecological factors that are only indirectly related to
fragmentation. For example, sensitive species may be
490 OIKOS 109:3 (2005)

OIKOS 109:3 (2005) 491
Table 2. Number of sensitive species co-occurring with a chosen target sensitive species: (1) on average across all patches, (2) in the largest patch where the target species was observed,
and (3) in the three largest patches where the target species was observed. Species abbreviations are given in the appendix. No relationship between the number of co-occurring species and
a given target species’ discrepancy measures was detected.
Species Number of
presences
Mean number
of co-occurring
sensitive species
Area of largest
patch where
species was
present
No. of
co-occurring
sensitive species
in largest patch
Cum. no. of
co-occurring
sensitive. species
in three largest
patches
Discrepancy
(sorted by area)
Discrepancy
(sorted by species
richness)
Relative
discrepancy
(area)
Relative
discrepancy
(species richness)
SPP 35 5.4 47.7 7 13 3 3 0.09 0.09
STP 32 5.6 47.7 7 13 4 4 0.13 0.13
RWAT 29 5.7 47.7 7 11 6 5 0.21 0.17
WNH 25 5.6 36.2 8 12 6 7 0.24 0.28
STB 22 6.0 29.7 9 10 10 7 0.45 0.32
GANG 19 6.7 47.7 7 13 7 4 0.37 0.21
SBB 19 5.9 47.7 7 13 10 8 0.53 0.42
RBTC 13 6.2 47.7 7 11 6 6 0.46 0.46
WNG 11 6.8 36.2 8 13 8 5 0.73 0.45
SKNG 9 6.0 47.7 7 12 6 6 0.67 0.67
NFB 7 6.4 36.2 8 12 4 5 0.57 0.71
KP 6 7.7 21.4 6 11 5 3 0.83 0.50
WWW 4 7.8 16.4 9 12 4 4 1 1
OBO 2 6.0 47.7 7 N/A 1 2 0.5 1
RF 2 6.0 36.2 8 N/A 1 2 0.5 1
BRTB 1 3.0 16 3 N/A 1 1 1 1
CIC 1 6.0 2.4 6 N/A 1 1 1 1
BZP 1 9.0 29.7 9 N/A 1 1 1 1
GREY 1 7.0 15.3 7 N/A 1 1 1 1
SFLY 1 8.0 15.6 8 N/A 1 1 1 1
SIT 1 4.0 2.2 4 N/A 1 1 1 1

more common in continuous eucalypt forest because
certain competitors or predators may be less common
there than in eucalypt fragments or in the surrounding
matrix (i.e. the pine plantation). Hence, although such
species are affected by fragmentation at the landscape
scale, patch size per se may not be a very important
ecological variable for these species.
Implications for conservation
The likelihood of large differences between a perfectly
nested system and a significantly nested system means
that care needs to be taken when applying insights
derived from nested subset theory to real ecosystems. In
particular, significant nestedness by area alone is an
insufficient reason to discount the value of small patches.
In this study, instead of only the single largest patch, at
least one third of patches would have been required to
adequately capture a majority of sensitive species / and
then quite possibly only for a short time (Pimm and
Lawton 1998). Targeting conservation strategies around
patch sizes per se may not always be the best approach,
even in significantly nested assemblages. Although
nested subset analyses are attractive because of their
relative simplicity, it is highly likely that more sophisticated
algorithms to select patches for conservation
management will provide better conservation outcomes
(Margules and Pressey 2000, Faith et al. 2003). In
addition, weak co-occurrence patterns of sensitive species
may mean that conservation approaches centered
around key habitats will perform better than approaches
based on indicator species (Ward et al. 1999). In such
cases the maintenance of environmental heterogeneity,
e.g. through protecting a mosaic of small and large
patches of different forest types, may be a reasonable
conservation strategy (Benton et al. 2003, Hamer et al.
2003).
Despite these insights, our results do not mean that
nestedness analyses cannot be useful to generate conservation
insights in some situations. Key strengths of
nestedness analyses are that they can highlight systematic
patterns of species co-occurrence (Fleishman and
Mac Nally 2002), and highlight species with unusual
responses to the environment (Patterson et al. 1996).
Analyses in relation to patch size and the investigation of
plausible extinction scenarios can be worthwhile / as
long as patterns are interpreted with enough caution
(Berglund and Jonsson 2003). Careful visual investigation
of the data in addition to focusing on community
level nestedness indices is important. Otherwise, simple
community summaries may mask complex patterns of
species occurrences (Simberloff and Martin 1991) and
result in important practical insights of conservation
significance being overlooked.
Acknowledgements / We are grateful to B. G. Jonsson for
supplying a spreadsheet version of the RANDNEST null model
to us. Field work for this study was completed with the
assistance of Matthew Pope, Ryan Incoll, Chris MacGregor,
and volunteer observers from the Canberra Ornithologists
Group. Finally, we are most grateful to all funding bodies
who have supported various aspects of the work in the Tumut
region at various stages, especially Land and Water Australia,
Rural Industries Research and Development Corporation, the
Australian Research Council, the former Department of Land
and Water Conservation, State Forest of New South Wales,
New South Wales National Parks and Wildlife Service, the Pratt
Foundation, and private donations from Jim Atkinson and Di
Stockbridge.
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Appendix 1. Overview of the bird species recorded in this study, including the number of sites where they occurred (# sites), and
their classification as generalist (G), intermediate (I), sensitive (S) and pine forest increasers (P). Classification follows Lindenmayer
et al. (2003). ‘‘NC’’ stands for not classified, ‘‘*’’ marks introduced species.
Common name Scientific name Classification # sites Abbreviation
White-browed scrub-wren Sericornis frontalis P 43 WSW
Yellow-faced honeyeater Lichenostomus chrysops I 43 YFHE
Grey fantail Rhipidura fuliginosa I 42 GFAN
Crimson osella Platycercus elegans G 41 CR
Grey shrike-thrush Colluricincla harmonica G 41 GST
Rufous whistler Pachycephala rufiventris G 41 RW
Brown thornbill Acanthiza pusilla G 40 BT
Pied currawong Strepera graculina G 39 PC
Sulphur-crested cockatoo Cacatua galerita I 39 SCC
White-throated treecreeper Cormobates leucophaea I 38 WTT
Silvereye Zosterops lateralis G 37 SEYE
Golden whistler Pachycephala pectoralis G 36 GW
Eastern yellow robin Eopsaltria australis P 35 EYR
Spotted pardalote Pardalotus punctatus S 35 SPP
OIKOS 109:3 (2005) 493

Appendix 1. (Continued)
Common name Scientific name Classification # sites Abbreviation
*European blackbird Turdus merula NC 32 BB
Striated pardalote Pardalotus striatus S 32 STP
Eastern spinebill Acanthorhynchus tenuirostris I 29 ESP
Laughing kookaburra Dacelo novaeguineae I 29 KOOK
Red wattlebird Anthochaera carunculata S 29 RWAT
Australian raven Corvus coronoides G 25 AUSR
White-naped honeyeater Melithreptus lunatus S 25 WNH
Fan-tailed cuckoo Cuculus flabelliformis G 24 FTC
Shining bronze-cuckoo Chrysococcyx lucidis I 24 SBC
Superb fairy-wren Malarus cyaneus I 24 WREN
Striated thornbill Acanthiza lineata S 22 STB
Gang-gang cockatoo Callocephalon fimbriatum S 19 GANG
Satin bowerbird Ptilonorhynchus violaceus S 19 SBB
Scarlet robin Petroica multicolor G 18 SCAR
Australian magpie Gymnorhina tibicen I 16 MAG
Flame robin Petroica phoenicea P 16 FLME
Black-faced cuckoo-shrike Coracina novaehollandiae I 14 BFCS
Crested shrike-tit Falcunculus frontatus I 13 TIT
Red-browed treecreeper Climacteris erythrops S 13 RBTC
Superb lyrebird Menura novaehollandiae G 13 LB
White-eared honeyeater Lichenostomus leucotis I 13 WEHE
Eastern whipbird Psophodes olivaceus P 11 WHIP
Wonga pigeon Leucosarcia melanoleuca S 11 WNG
Bassian thrush Zoothera lunulata I 10 THR
Sacred kingfisher Todiramphus sanctus S 9 SKNG
Leaden flycatcher Myiagra rubecula I 7 LFLY
Noisy friarbird Philemon corniculatus S 7 NFB
Rose robin Petroica rosea G 7 ROSE
Australian king parrot Alisterus scapularis S 6 KP
Horsfield’s bronze-cuckoo Chrysococcyx basalis P 6 BRNZ
Little raven Corvus mellori I 6 LRAV
Olive whistler Pachycephala olivacea P 6 OW
Brown-headed honeyeater Melithreptus brevirostris I 5 BHH
Grey butcherbird Cracticus torquatus G 5 BUT
White-winged chough Corcorax melanorhamphos G 5 WWC
*European goldfinch Carduelis carduelis NC 4 GFI
Red-browed firetail Neochmia temporalis I 4 RBF
White-throated gerygone Gerygone olivacea S 4 WWW
Eastern rosella Platycercus eximus NC 3 EROS
Pilotbird Pycnoptilus floccosus G 3 PB
Yellow-tailed black cockatoo Calyptorhyncus funereus NC 3 YTBC
Brush cuckoo Cuculus variolosus P 2 BRSH
Galah Cacatua roseicapilla NC 2 GC
Olive-backed oriole Oriolus sagittatus S 2 OBO
Rufous fantail Rhipidura rufifrons S 2 RF
Australian kestrel Falco cenchroides NC 1 NK
Buff-rumped thornbill Acanthiza reguloides S 1 BRTB
Cicada bird Coracina tenuirostris S 1 CIC
Collared sparrowhawk Accipter cirrhocephalus NC 1 COL
Common bronzewing Phaps chalcoptera S 1 BZP
Common starling Sturnus vulgaris NC 1 STAR
Grey currawong Strepera versicolor S 1 GREY
Lewin’s honeyeater Meliphaga lewinii NC 1 LEHEW
Little eagle Hieraaetus morphnoides NC 1 LEAG
Masked lapwing Vanellus miles NC 1 MLAP
Mistletoe bird Dicaeum hirundinaceum I 1 MTB
Pallid cuckoo Cuculus saturatus NC 1 PLC
Satin flycatcher Myiagra cyanoleuca S 1 SFLY
Varied sitella Daphoenositta chrysoptera S 1 SIT
Wedge-tailed eagle Aquila audax NC 1 WTE
White-winged triller Lalage sueurii NC 1 WWT
Yellow-rumped thornbill Acanthiza chrysorrhoa NC 1 YRTB
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