Journal of Biogeography
SUPPORTING INFORMATION
The challenge of delineating biogeographical regions: nestedness matters for
Indo-Pacific coral reef fishes
David Mouillot, Julien De Bortoli, Fabien Leprieur, Valeriano Parravicini, Michel Kulbicki
and David R. Bellwood
Appendix S2 Complementary data analyses: estimation of area for locations and species–area
relationships (Figs S1 & S2) and combined effects of surface area and distance from the IndoAustralian Archipelago on fish diversity patterns (Tables S3 & S4, Figs S3 & S4).
Estimation of area for locations and species–area relationships
In order to estimate the habitat area to which the species lists in Appendix S1 (Table S2) pertain, we
calculated the area of the sea-bottom from the water surface down to 40 m depth at each location.
Bathymetric information was obtained from the SRTM30 PLUS bathymetry (Shuttle Radar
Topography Mission) available at http://topex.ucsd.edu/WWW_html/srtm30_plus.html. The depth
range from 0 down to 40 m was chosen because our data are referred to shallow-water reef fishes,
while species inhabiting deeper waters where removed from the analysis.
At each location, area estimates were obtained calculating the sea-bottom surface within a standard
radius of 600 km. The 600 km radius was chosen because in the marine realm it is sometimes
difficult to obtain a precise definition of the area to which species lists pertain. Area estimates from
buffers of 600 km were already employed for the Indo-Pacific region in studies using similar data
type (Bellwood et al., 2005) and this buffer size has been demonstrated to encompass the probable
larval dispersal envelope of reef fishes (Swearer et al., 2002). All spatial analyses were conducted
using a global equal area Behrmann projection with the prime meridian set at 145° longitude.
Area estimates were extremely variable across locations (see Fig. S1 below) and Appendix S1.
Figure S1 Map of the 122 locations with fish assemblages from three families (Chaetodontidae,
Pomacentridae and Labridae) where the diameters of pink circles correspond to five classes of areas
based on quintiles. The distribution of area values is presented on the top-right corner.
Therefore, in order to assess if area was greatly responsible for the observed richness gradient and
whether area has to be considered in the analyses of nestedness we built a power species area curve
for our data. Results indicated that area is significantly related to reef fish species richness (P <
0.001). However, area explained little of the variation in reef fish species richness (R2 = 0.26 for the
power relation and R2 = 0.14 for the linear relation, see Fig. S2). Therefore area effect on the
observed pattern of turnover versus nestedness component of species dissimilarity was considered
as minor and was not considered in further analyses.
300
200
0
100
species richness
0
50000
100000
150000
200000
250000
300000
Area (Km2)
Figure S2 Species–area relationship for Indo-Pacific reef fishes from three families
(Chaetodontidae, Pomacentridae and Labridae). The 122 points represent locations and the grey line
refers to a power species–area curve (R2 = 0.26)
Combined effects of surface area and distance from the Indo-Australian Archipelago (IAA)
on fish diversity patterns
Table S3 Spatial autoregressive model (SAR) linking fish species richness for 122 assemblages
with location surface area (log) and distance (log) from the biodiversity centre (IAA). Pure ‘effects’
were obtained using variance partitioning.
SAR
Coefficient
z-value
P-value
Pseudo-R2
Log (area)
12.6
3.8
0.00016
0.39
Log (distance to IAA)
−72.6
−6.3
<0.0001
0.44
Model
Single factor
0.48
Combined model
Log (area)
Log (distance to IAA)
8.6
3.1
0.005
−58.6
−5.1
<0.0001
Variation partitioning
Pure Log (area) effect
9%
Pure Log (distance to IAA) effect
4%
Combined effect
35%
Table S4 Results of multiple regressions on distance matrices (MRM) testing the relationship
between βsor, βsim, βnes components of the Sørensen index between pairs of fish assemblages
(Chaetodontidae, Pomacentridae and Labridae) across 122 locations, the differences in location
surface area (log transformed) and the geographical distance between pairs of locations. Pure
‘effects’ were obtained using variance partitioning and the combined model.
F-test
R2
1191
0.14
0.001
7
Geographic distance 2356
0.24
0.001
17
Combined model
1670
0.31
0.001
log(Area)
408
0.05
0.001
8
Geographic distance
286
0.04
0.001
7
Combined model
490
0.12
0.001
224
0.03
0.001
0.1
Geographic distance 3476 0.320
0.001
29
Combined model
0.001
Model
P-value 'pure' effect (%)
βsor
log(Area)
βnes
βsim
log(Area)
1749 0.321
Figure S3 Fish compositional dissimilarity patterns across the 122 locations for three families
(Chaetodontidae, Pomacentridae and Labridae). The size of squares is proportional to the Jaccard
index (βjac) estimating compositional dissimilarity between the fish assemblage of the IAA (Coral
Triangle, red dots) and the other locations of the Indo-Pacific. The turnover component of the
Jaccard index (βjtu) is in green (above), while the nestedness component (βjne) is in blue (below).
Figure S4 Classification of 122 locations into seven geographical regions based on the dissimilarity
in coral reef fishes (Chaetodontidae, Pomacentridae and Labridae) using (a) the complete Jaccard
index (βjac) and (b) the turnover component of the Jaccard index (βjtu). Colours represent the seven
main clusters of locations identified by the analyses.
REFERENCES
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S.G., Selkoe, K.A., Ruiz, G.M. & Warner, R.R. (2002) Evidence of self-recruitment in
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