Download Beta diversity of tropical marine benthic assemblages in the

Marine Ecology. ISSN 0173-9565
ORIGINAL ARTICLE
Beta diversity of tropical marine benthic assemblages in
the Spermonde Archipelago, Indonesia
Leontine E. Becking1, Daniel F. R. Cleary1,2, Nicole J. de Voogd1,2, Willem Renema2, Manon de Beer2,
Rob W. M. van Soest1 & Bert W. Hoeksema2
1 Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands
2 National Museum of Natural History, Leiden, The Netherlands
Keywords
Corals; environmental predictors;
foraminifera; sea urchins; sponges; Sulawesi;
tropical marine assemblages; variance
partitioning.
Correspondence
Leontine E. Becking, Institute for Biodiversity
and Ecosystem Dynamics, University of
Amsterdam, PO Box 94766, 1090 GT
Amsterdam, The Netherlands.
E-mail: [email protected] or
[email protected]
Accepted: 12 July 2005
doi:10.1111/j.1439-0485.2005.00051.x
Abstract
In order to preserve diversity it is essential to understand how assemblages
change across space. Despite this fact, we still know very little about how marine diversity is spatially distributed, especially among lesser-studied invertebrate
taxa. In the present study beta-diversity patterns of sea urchins, sponges, mushroom corals and larger foraminifera were assessed in the Spermonde Archipelago (Indonesia). Using ordinations we showed that the inshore zone (<5 km
offshore), midshore zone (5 < x < 30 km offshore) and distance offshore zone
(>30 km offshore) all contained distinct assemblages of sponges and corals,
while only foraminifera assemblages from the inshore (<5 km offshore) zone
were distinct. There was a significant spatial pattern of community similarity
for all taxa surveyed, but this pattern proved to be wholly related to environmental variables for sponges and foraminifera, and primarily for mushroom
corals and sea urchins. The lack of a pure spatial component suggests that
these taxa may not be dispersal limited within the spatial scales of this study
(c. 1600 km2). The analyses of the corals and foraminifera were additionally
tested at two spatial scales of sampling. Both taxa were primarily associated
with local-scale environmental variables at the local scale and larger-scale variables at the larger scale. Mean inter-plot similarity was also higher and variation
lower at the larger scale. The results suggest that substantial variation in similarity can be predicted using simple locally assessed environmental variables
combined with remotely sensed parameters.
Problem
The biodiversity of marine ecosystems is declining on a
global scale (Gaston 2000; Roberts et al. 2002; Pandolfi
et al. 2003). A frequently advocated way to protect the
marine environment is through the establishment of marine reserves (Allison et al. 1998; Lubchenco et al. 2003).
In order to generate effective conservation schemes and
guidelines for the sustainable exploitation of natural
resources, the dynamics of the distribution and abundance of organisms in relation to environmental, spatial
and temporal processes needs to be well understood (Gas76
ton 2000; Lubchenco et al. 2003; Tuomisto et al. 2003).
In contrast to terrestrial systems, there have been relatively few studies that have assessed patterns of biodiversity and related these to environmental and spatial
processes that are thought to regulate marine biodiversity
(but see Clarke & Lidgard 2000; Ellingsen & Gray 2002;
Ellingsen 2002), despite the severe threats to marine ecosystems (Olsen et al. 2002; Roberts et al. 2002). The Convention on Biological Diversity, furthermore, requires
biodiversity inventories from signatory nations to monitor changes and has a major focus on marine biodiversity
conservation (Gray 2000; Anonymous 2004).
Marine Ecology 27 (2006) 76–88 ª 2006 The Authors. Journal compilation ª 2006 Blackwell Publishing Ltd
Becking, Cleary, de Voogd, Renema, de Beer, van Soest & Hoeksema
A key component of any biodiversity survey is the
assessment of beta diversity. Beta diversity is the change
in species composition of assemblages between sites (Gray
2000; Purvis & Hector 2000). Beta diversity can provide
information on marine area relationships or connectivity,
reflecting the processes operating in those areas, and is
considered to be essential in environmental and conservation-based censuses and the establishment of nature
reserves (Purvis & Hector 2000; Cleary 2003; Tuomisto
et al. 2003). Beta-diversity patterns may be the result of
dispersal limiting factors or the spatial arrangement of
environmental conditions. Similar assemblages in adjacent
habitats may be due to either or both of these conditions.
Separating the contribution of space and the contribution
of environment to patterns of community similarity is
therefore necessary for understanding the mechanisms
that influence community structure across landscapes
(Spencer et al. 2002).
In marine and terrestrial environments, dispersal limitation is thought to be an important structuring force in
many communities (Hubbell 1997). The dispersal of sedentary benthic reef organisms is usually limited to the larval phase, which can span a time scale from minutes to
months (Reed et al. 2000; Miller & Mundy 2003; Shanks
et al. 2003; Siegel et al. 2003). The general contention is
that the longer the planktonic duration, the further the
larvae can disperse, which means that dispersal propagules of certain species can potentially cover enormous
distances and that barriers to dispersal are much weaker
than for terrestrial organisms (Gee & Warwick 1996; Reed
et al. 2000; Shanks et al. 2003; Siegel et al. 2003). Recent
work, however, has indicated that larval exchange rates
and dispersal distances are not necessarily unlimited and
that they may have been over-estimated for many coral
reef organisms. First of all, dispersal should be viewed
within the context of local currents, which may be highly
variable (Cowen et al. 2000; Reed et al. 2000; Largier
2003; Siegel et al. 2003). Nearshore waters, for example,
have a retention zone along the shoreline and shelf,
referred to as the coastal boundary layer, which can be
particularly ‘sticky’ for larvae (Largier 2003 and references
therein). Recent human-induced phenomena such as pollution from rivers and harbours could generate novel barriers that inhibit larval dispersal (Nystrøm et al. 2000). In
addition, research and models on the dispersal of broadcast spawning individuals (e.g. corals) have shown that
their dispersal distances may not be as great as has been
previously suggested (Cowen et al. 2000; Miller & Mundy
2003). Furthermore, sessile organisms such as corals may
undergo asexual reproduction by fragmentation and budding, which can cause dense stands of only a few species
within a relatively large area because the propagules disperse and overgrow the substratum at the cost of other
Beta diversity of tropical marine benthic assemblages
species (Hoeksema 1988; Hoeksema & Moka 1989; Nishihira & Poung-In 1989; Hoeksema 1991a; Littler et al.
1997; Hoeksema 2004). The limited dispersal distance
compared to that suggested by the length of the larval
period has also been demonstrated in studies of genetic
connectivity between certain crustacean populations (Barber et al. 2002; Palumbi 2003).
In addition to dispersal, environmental factors can
structure assemblages. Environmental variables explained
significant amounts of variation in the community composition of various taxa in tropical rainforests (Duivenvoorden et al. 2002; Tuomisto et al. 2003; Cleary et al.
2004). A similar result has been found in Norwegian benthos, where changes in environmental variables had a
stronger effect on beta diversity than the distance between
sample sites (Ellingsen & Gray 2002; Ellingsen 2002). In
marine environments, depth and substrate may, for example, significantly influence the distribution of marine
organisms. This can include the size and spacing of suitable substrate for settlement and the height and inclination of reef walls, which can influence water movement,
light penetration and sedimentation (Davis et al. 2003).
Selective mortality and survival (e.g. due to coral bleaching) also play a role in structuring communities (Hoeksema 1991b; Feingold 1996).
The scale of assessment influences the degree to
which assemblages are structured by environmental processes. Previous studies have shown that the resolution
and spatial scale of assessment can have a profound
influence on the observed patterns of beta-diversity
(Levin 1992; Clarke & Lidgard 2000; Ellingsen 2002; Ellingsen & Gray 2002; Willis & Whittaker 2002; Cleary
2003; Cleary et al. 2004). The ecological importance of
scale is related to the fact that different processes can
influence species diversity at different spatial and temporal scales. Regional scale diversity of corals, for example, appears to be affected by variables that are strongly
related to the organisms’ energy supply, dispersal distance, geography, climate and history (Fraser & Currie
1996; Karlson & Cornell 1998, 2002).
The patterns of beta diversity can differ among taxa,
due to community-wide differences in dispersal ability
and other taxon-specific factors (Flather et al. 1997; Gaston 2000; Reyers et al. 2000; Ellingsen & Gray 2002). In
contrast to the terrestrial biome (e.g. Howard et al. 1998;
Lawton et al. 1998; Cleary et al. 2004; Su et al. 2004), very
few studies (but see Adjeroud 1997) have assessed multitaxon diversity patterns in coral reef systems, despite the
fact that this can provide important insights into the general dynamics structuring reef community composition.
In general, most studies have focused on corals and fish,
but we need information on a diverse array of taxa in
order to set up high-quality conservation strategies.
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77
Beta diversity of tropical marine benthic assemblages
Becking, Cleary, de Voogd, Renema, de Beer, van Soest & Hoeksema
In the present study we assessed patterns of beta diversity within four taxonomic groups residing in tropical
coral reef environments of the Spermonde Archipelago,
Indonesia, namely, sea urchins, sponges, mushroom corals
and foraminifera. The four groups represent a diverse
array of benthic-dwelling taxa with different life-history
strategies. We determined if the spatial variation in beta
diversity is related to distance and/or environment using
various environmental variables (e.g. depth, reef type,
exposure and substrate) and the distance between sample
sites. Where appropriate, the analyses were performed at
both local and larger spatial sampling scales so that processes acting at different scales may be revealed.
Material and Methods
Study area
All sampling took place in reefs of the Spermonde Archipelago, southwest Sulawesi, Indonesia (Fig. 1). The Spermonde Archipelago is very suitable for biodiversity
studies, because it is a well documented carbonate coastal
shelf, approximately 40 km across, with several vectorial
environmental influences that vary along an on-to-offshore gradient. These influences are related to sewage
seepage and pollution from the harbour city of Makassar
and fluvial discharge from the river Jene Berang (to the
south) and to a lesser extent the river Maros and other
smaller rivers to the north of the study area. The Jene
Berang contains both land-based sediments as well as
waste from the Makassar sewer system, a city that is
inhabited by more than 1 million people (Hoeksema
1989; Renema & Troelstra 2001). Additional disturbances
over the whole area of the Spermonde reef stem from
storms during the monsoon season and from destructive
fishing practices (e.g. blast-fishing) (Edinger et al. 1998).
Fig. 1. Map of the Spermonde Archipelago in which various reefs
were sampled. Isobaths are shown. Inset in upper left hand corner
shows the location of the Spermonde archipelago in relation to the
island of Sulawesi, Indonesia. The reefs sampled include Badi (Bad:
two sites), Bone Baku (Bak: two sites), Barang Barangan (Bar: three
sites), Bone Batang (Bat: five sites), Barang Caddi (Cad: five sites),
Gusung Trabanasu (GuT: four sites), Gusung Panyoa (GuP: two sites),
Kapodosang (Kap: two sites), Karangan (Kar: two sites), Karang Krassi
(Kas: one site), Kudingareng Keke (Kud: eight sites), Lae Lae (Lae:
three sites), Lae Lae Kecil (LaK: two sites), Lankai (Lan: four sites),
Langyukung (Lgy: four sites), Lankadea (Lnk: two sites), Bone Lola
(Lol: one site), Barang Lompo (Lom: one site), Lumulumu (Lum: two
sites), Polewali (Pol: two sites), Samalona (Sam: five sites) and Bone
Tambung (Tam: five sites).
Data collection
Four taxa (sea urchins, sponges, mushroom corals and
larger symbiont-bearing foraminifera) were quantitatively
sampled during field surveys in the Spermonde archipelago. Plots were sampled within sites at varying depths.
Multiple sites with differing degrees of exposure to the
predominant Spermonde current were sampled in each
reef. Sea urchins were sampled from March to July 1989
within a total of 12 50 m2 (25 · 2 m) plots over the
study area; plots were laid parallel to the coast and identifications were carried out with the aid of a key by Clark
& Rowe (1971). Sponges were sampled in 1997 and 2000
within a total of 34 100 m2 (100 · 1 m) plots over the
study area. Smaller (cryptic, boring, and thinly encrusting) specimens were excluded. Species were visually iden78
tified in the field, and fragments of unrecognized species
were collected for closer examination. Mushroom corals
were sampled between 1984 and 1986 within a total of
231 50 m2 (50 · 1 m) plots over the study area. All individual corals were identified to species within the boundaries of the plot; this enabled an accurate abundance
quantification including the smallest corals, which are
often missed by line transect methods. This is important
for mushroom corals because most individuals are small
in comparison to other reef corals. The larger symbiontbearing foraminifera (size >0.5 mm) were quantitatively
sampled from a total of 180 circular 1000 cm2 plots over
the study area. The micro-substrate on which each sample
was found was noted and fell within the following five
Marine Ecology 27 (2006) 76–88 ª 2006 The Authors. Journal compilation ª 2006 Blackwell Publishing Ltd
Becking, Cleary, de Voogd, Renema, de Beer, van Soest & Hoeksema
categories: (1) sand; (2) sand with seagrass; (3) sand with
coral rubble; (4) coral rubble; (5) reef rock. Samples were
sun-dried after which the foraminifera were detached
from their substrate. These were then sieved over a
0.5 mm sieve before sorting. Sea urchins and sponges
were sampled from reef flats and slopes (depth range:
3–15 m), whereas corals and foraminifera were collected
from reef flats, slopes and bases (depth range: 0.5–42 m).
Voucher specimens of all taxa from the expeditions have
been deposited at the National Museum of Natural History ‘Naturalis’, Leiden and at the Zoological Museum of
the University of Amsterdam in the Netherlands. A list of
the species sampled and their distribution within the
Spermonde Archipelago is available upon request. A summary list is presented in Appendix I.
Visually assessed (micro-substrate, exposure, visibility)
and remotely sensed environmental variables were noted
for each site. Remotely sensed environmental variables
were calculated with the use of a GIS image based on
automatic and supervised classification processes applied
to a SPOT-XS satellite image, K-J/Sat: 320-370/3, recorded on 30 August 1995. Verification by field surveys was
completed in December 1995. Additional records and
corrections were provided by a BCEOM (Le Bureau Central d’Etudes pour les Equipements d’Outre-Mer) consultant in August 1996. The data were collected as part of the
Marine Resource & Education Project and are presently
managed by BAKOSURTANAL (Badan Koordinasi Survei
dan Pemetaan Nasional), Indonesia. The following remotely sensed environmental classes that were assumed to
influence the distribution of taxa were identified and
assessed over a 2500 m2 (500 · 500 m) area surrounding
each sample site: (1) sedimentary material (sand, coral
rubble etc. >60% of each pixel); (2) coral formations (live
coral cover >60% of each pixel); (3) sparse coral formations (live coral cover from 40% to 60% of each pixel);
(4) dense hard substratum (dead coral, reef rock >60% of
each pixel); (5) scattered hard substratum (40–60% of
each pixel) and (6) area of human settlement.
Analyses
For the echinoderms and sponges, analyses were performed
at the local scale only. For the corals and foraminifera, analyses were performed at two spatial sampling scales, i.e. at
the local scale (plot-level) by using all plot level data at
various depths and at the larger scale (site-level) by pooling
all plots from different depths within a given site. For all
analyses (with the exception of the echinoderms) plots/sites
were excluded where <40 individuals were sampled. For
the echinoderms only two plots contained <40 individuals
(n ¼ 18 and 36). For the coral analyses, two highly impoverished marginal sites were also excluded with very large
Beta diversity of tropical marine benthic assemblages
densities of the corals Fungia sinensis and F. fragilis, which
reproduce asexually by fragmentation (Hoeksema 1989).
Ordination
Within Primer 5 (Primer-E Ltd, Plymouth, UK), log10
transformed abundance values of species within plots were
used to generate a measure of the community similarity
between plots using the Bray–Curtis similarity index (Bray
& Curtis 1957), an index frequently used for ecological
ordinations (Legendre & Gallagher 2001; Cleary 2003) and
ranked among the best of coefficients tested by Faith et al.
(1987). Again using Primer 5, a two-dimensional ordination of the matrix was generated using non-metric multidimensional scaling (MDS). MDS has been shown to be a
robust technique for ordinating species abundance data,
and it does not have such stringent model assumptions as
the alternative Correspondence Analysis, which assumes
unimodal responses of species abundance along environmental gradients (Beck et al. 2002).
Association of similarity with environmental and spatial
variables
Within PRIMER-5 environmental and spatial data were
used to generate measures of differences in environmental
conditions between plots using Euclidean distances. These
included measures of the distance between sites, depth,
offshore distance, the remotely sensed area of sedimentary
materials, coral formations, sparse coral formations, dense
hard substratum, scattered hard substratum and human
settlement, micro-substrate (1. sand, 2. rubble and 3. coral
for echinoderms and corals and 1. sand, 2. sand with seagrass, 3. sand with coral rubble, 4. coral rubble and 5. reef
rock for foraminifera), slope (1: 0–15, 2: 15–30, 3: 30–
45, 4: 45–60), exposure (0: sheltered and 1: exposed)
and visibility. Akaike’s information criterion (AIC using
Statistica for Windows 6.1, Statsoft, Tulsa, OK, USA) and
multiple matrix regression (using PERMUTE! 3.4.9 (Casgrain 2001) were used to assess to what extent community
similarity depends upon environmental and/or spatial
datasets. In PERMUTE!, options for 999 permutations and
Bonferroni-corrected P-to-enter value of 0.10 were selected. The input file for AIC analysis and PERMUTE!
consisted of unfolded upper-triangular matrices, read by
rows, in a vertical format. Each column in the input file
represented an unfolded, upper-triangular matrix-variable,
i.e. a single variable for similarity based on the Bray–Curtis
similarity index and a single variable for each of the environmental and distance variables (see Casgrain 2001 for a
detailed description of the procedure).
Akaike’s information criterion is an information-theoretic alternative to standard hypothesis testing statistical
Marine Ecology 27 (2006) 76–88 ª 2006 The Authors. Journal compilation ª 2006 Blackwell Publishing Ltd
79
Beta diversity of tropical marine benthic assemblages
approaches such as multiple regression. Akaike’s information criterion (Akaike 1973) uses an entropy maximization
principle as a theoretic basis for the selection of models.
Akaike (1973) found a simple relationship between Fisher’s
maximized log-likelihood function and the Kullback–Liebler distance (Burnham & Anderson 1998) thereby providing a simple and effective method for selecting the most
parsimonious model for the analysis of empirical data.
AIC ¼ )2 log(Lh) + 2K, where Lh is the maximized loglikelihood, a function of the unknown parameters h, given
the data and the model. K is the number of estimable
parameters in the model. AIC is computed for each of the
models; the model that minimizes AIC is estimated to be
best in the sense of K-L information loss (Burnham &
Anderson 2002). Note that the AIC approach does not generate a-significance values, but instead focuses on strength
of evidence and generates a measure of uncertainty for each
model. In contrast to standard hypothesis tests, such as
stepwise multiple regression, which often yield different
results depending on the order in which the models are
computed, the AIC yields consistent results and is independent of the order of computation. For a detailed description of the AIC approach see Burnham & Anderson
(1998, 2001, 2002). Finally, a quantitative variance partitioning technique described in Borcard et al. (1992) was
used on results of multiple matrix regressions. This enabled
the variation in community composition explained by geographic distance alone, environmental variables alone, and
spatial and environmental variables combined to be quantified. Briefly, the procedure was as follows: first, the community similarity matrix was regressed against the total set
of environmental and distance matrices to obtain the variance explained by all ¼ RT. Next, the community similarity
matrix was regressed against the environmental matrices to
obtain RE, and the community similarity matrix was
regressed against the distance matrix to obtain RS. It was
then possible to calculate the purely environmental fraction
RPE ¼ RT ) RS, the purely spatial fraction RPS ¼ RT ) RE,
the spatially structured environmental fraction RSE ¼
RE + RS ) RT, and the unexplained variation RUN ¼
1 ) RT.
Results
We recorded 9 sea urchin (n ¼ 4025 individuals), 150
sponge (n ¼ 15842), 32 mushroom coral (n ¼ 28935)
and 24 foraminifera (n ¼ 76659) species in the Spermonde Archipelago. Multidimensional scaling revealed
two main patterns: (1) for sponges and corals a gradient
from on-to-offshore with distinct clusters for sites (a)
<5 km from the coast, (b) between 5 and 30 km from
the coast and (c) more than 30 km from the coast; (2)
for foraminifera (large-scale) a clear distinction between
80
Becking, Cleary, de Voogd, Renema, de Beer, van Soest & Hoeksema
exposed and sheltered sites and sites <5 km from the
coast and more than 5 km from the coast (Fig. 2). Localscale patterns of corals and foraminifera were predominantly related to variation in depth.
The total variation explained by spatial and environmental predictors varied from 24% to 48% (Table 1).
The environmental predictors, which explained the greatest proportion of variation in similarity, included distance, depth, exposure and the remotely sensed area of
sparse coral formations, offshore distance and to a lesser
extent micro-substrate and the remotely sensed area of
human settlement (Table 2). Significant but marginal
(<1% of variation in similarity explained) environmental
predictors included slope, the remotely sensed areas of
coral formations, sedimentary areas and scattered and
dense hard substrata (Table 2).
There was a significant association between distance
and sea urchin similarity (b ¼ )0.489, R2 ¼ 0.239, P ¼
0.007), sponge similarity (b ¼ )0.401, R2 ¼ 0.160,
P < 0.001), local-scale coral similarity (b ¼ )0.057, R2 ¼
0.017, P < 0.001), large-scale coral similarity (b ¼ )0.480,
R2 ¼ 0.230, P < 0.001), local-scale foraminifera similarity
(b ¼ )0.090, R2 ¼ 0.008, P < 0.001) and large-scale foraminifera similarity (b ¼ )0.160, R2 ¼ 0.026, P < 0.001)
(Fig. 3). The purely spatial component, however,
explained 0% of variation in all groups except corals at
the local scale (0.17% of total variation explained) and
sea urchins (2.68% of total variation explained). The
environmental component (pure and spatially structured),
however, explained more variation in similarity for all
taxa (Table 1).
Discussion
In the present study we quantitatively show that coral
reef assemblages are spatially heterogeneous and that
this heterogeneity is strongly related to deterministic
(environmental) processes. Each taxon responded to different sets of environmental conditions. Coral reef communities observed over yearly to decadal time scales
demonstrated that species composition fluctuates greatly
when the environment changes (Bak & Nieuwland
1995; Connell et al. 1997; Pandolfi 2002; Connell et al.
2004). Therefore, we cannot make strict comparisons
between the four taxa because the data of this study
were collected from different time periods, but we can
tentatively summarize some qualitative congruencies
that were observed. Environmental variables, for example, influenced community similarity significantly in all
taxa. Human settlement in particular was significantly
associated with variation in the community composition
of sponges and foraminifera, but not in sea urchins or
mushroom corals.
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Becking, Cleary, de Voogd, Renema, de Beer, van Soest & Hoeksema
Beta diversity of tropical marine benthic assemblages
Fig. 2. Multi-dimensional scaling ordination
of (a) sea urchins (b) sponges, (c) corals [localscale], (d) corals [large-scale], (e) foraminfera
[local-scale] and (f) foraminifera [large-scale].
Closer points indicate more similar
assemblages. Ex05: exposed sites within 5 km
of the coast, Ex15: exposed sites
5 < x < 15 km off the coast, Ex30: exposed
sites 15 < x < 30 km off the coast, Ex >30:
exposed sites >30 km off the coast, Sh05:
sheltered sites within 5 km of the coast,
Sh15: sheltered sites 5 < x < 15 km off the
coast, Sh30: sheltered sites 15 < x < 30 km
off the coast, Sh >30: sheltered sites >30 km
off the coast.
For species that have limited dispersal abilities, distance
or a spatial component are expected to be important
structuring factors in community similarity, which would
result in assemblages from sites close together being more
similar than assemblages further apart. Distance between
sites can, however, also be a function of differences in spatially explicit environmental variables (Borcard et al. 1992;
Harrison et al. 1992; Ohmann & Spies 1998). This may
hold true for the Spermonde, where there is a cross-shelf
vectorial gradient of decreasing land-based influence and
increasing depth and photic zone (Hoeksema 1989;
Edinger et al. 1998; Renema 2002). Therefore, we differentiated between purely spatial components, spatially structured and purely environmental components. Although
we found a significant association between distance and
community similarity in all four taxa, this was weak, with
the exception of the sea urchins. When we differentiated
distance, our results showed that environmental components explained more of the variation in community
similarity than the purely spatial component (Table 1).
The purely spatial component in fact explained none of
the variation in community similarity of sponges and foraminifera (at both local and large scale), and little of the
variation of corals at the local scale. The lack of a relationship between distance and community similarity in sponges and foraminifera suggests that dispersal limitation is
unimportant in structuring these assemblages across the
Spermonde Archipelago. Many sponge species are widespread in the Spermonde Archipelago, an indication of
high connectivity of the individual reefs across the shelf.
Dispersal limitation may be more pronounced at larger
spatial scales where major barriers to dispersal become
apparent (Barber et al. 2002).
The spatial component had a greater influence on
the sea urchin community than any of the other taxa
observed in this study. Possibly, dispersal limitation
governs the community structure of this taxon to a
greater degree than the other taxa. This may, however,
be a result of the limited number of sample sites for
sea urchins. The larvae of most sea urchin species have
an obligate period of planktonic development (from
weeks to months), which should enable their larvae to
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81
Beta diversity of tropical marine benthic assemblages
Becking, Cleary, de Voogd, Renema, de Beer, van Soest & Hoeksema
Table 1. Results of AIC and matrix regression analyses.
group
model
AIC
sea urchins
best model
environment
distance
best model
environment
distance
best model
environment
distance
best model
environment
distance
best model
environment
distance
best model
environment
distance
566
582
566
4178
4178
4396
109106
109109
115308
6855
6855
7003
227691
227691
237338
3601
3601
3881
sponges
corals – local
corals – large
foraminifera – local
foraminifera – large
DAIC
17
0
0
218
0
6200
0
148
0
9647
0
280
R2
PS
SSE
0.239
0.213
0.239
0.429
0.429
0.157
0.402
0.402
0.014
0.352
0.352
0.230
0.297
0.297
0.016
0.484
0.484
0.022
2.68
21.25
0.00
23.94
0.00
15.74
27.14
42.88
0.03
1.41
38.80
40.24
0.00
23.03
12.13
35.16
0.00
1.59
28.09
29.68
0.00
2.17
46.19
48.35
PE
total
DAIC, difference in AIC value between given model or variable and the best overall model; PS, purely spatial component; SSE, spatially structured
environmental component; PE, purely environmental component; Total, total variance explained by best model; AIC, Akaike’s information criterion.
Table 2. Environmental variables selected with the AIC analyses.
group
environmental variables
sea urchins
sponges
corals – local
distance (offshore distance)
depth, coral formations, sparse coral formations, human settlement, transparency
distance, dense substratum, depth, slope, sparse coral formations, sedimentary materials,
micro-substrate, exposure, offshore distance
coral formations, sedimentary materials, sparse coral formations, exposure, offshore distance
depth, human settlement, coral formations, sparse coral formations, micro-substrate, exposure,
transparency
coral formations, sparse coral formations, exposure, transparency
corals – large
foraminifera – local
foraminifera – large
AIC, Akaike’s information criterion.
disperse greater distances in ocean currents (Hart 2002;
Shanks et al. 2003; Siegel et al. 2003). Furthermore, sea
urchins, in contrast to sponges, corals and foraminifera,
are frequently collected by people at reef flats for consumption, which undoubtedly has strong effects on the
distribution (W. Renema, personal observations). The
association with distance itself may also be due to an
unmeasured but strongly distance-dependent environmental variable. In a multi-taxon study (corals, sponges,
echinoderms, macro algae and molluscs) in French
Polynesia, Adjeroud (1997) found that environmental
variables explained far less of the variation in community similarity of echinoderms than other taxa (e.g.
29.6% for echinoderms versus 65% for sponges). The
distribution of echinoderms in French Polynesia was
82
primarily influenced by the concentration of carbonates,
a factor we did not measure. Sea urchins were also
found to be more restricted in their distribution than
other organisms measured in a study of Norwegian
macrobenthos and additionally the beta-diversity of
echinoderms had the weakest association with environmental variables of all the taxa observed (Ellingsen &
Gray 2002).
Sponge composition was significantly associated with
depth, visibility, the area of sparse coral formations and
human settlement. Two major changes to the local environment, as a result of human settlement, include increased
sedimentation and increased eutrophication. Both of these
have been shown to affect sponges (Aerts & van Soest
1997; Bell & Barnes 2000; Holmes 2000). Adjeroud (1997)
Marine Ecology 27 (2006) 76–88 ª 2006 The Authors. Journal compilation ª 2006 Blackwell Publishing Ltd
Becking, Cleary, de Voogd, Renema, de Beer, van Soest & Hoeksema
Beta diversity of tropical marine benthic assemblages
Fig. 3. Relationship between community
similarity based on the Bray–Curtis similarity
index and distance from the coast for (a) sea
urchins (b) sponges, (c) corals [local-scale], (d)
corals [large-scale], (e) foraminfera [localscale] and (f) foraminifera [large-scale].
found that sponge communities in French Polynesia were
significantly associated with depth, sand, abundance of
echinoids, C/N, silicates, distance from coastline and
carbohydrates.
At the local scale mushroom coral composition was
primarily related to a number of environmental variables
including depth, exposure, micro-substrate, slope and
remotely sensed habitat classes. At the larger scale, coral
composition was related to offshore distance and the
remotely sensed area of sparse coral formations and coral
formations surrounding the sample site. Micro-substrate,
exposure and slope are closely inter-related because the
eastern (leeward) side of reefs in the Spermonde have a
lower slope and are sheltered, which results in higher
rubble and sand cover at these locations (Hoeksema 1989;
Renema 2002). In contrast to attached corals, free-living
mushroom corals can live on various substrata and actually act as if they are part of the rubble, depending on
their size (Hoeksema 1988, 1991a). However, this is usually the case for species that dwell on reef flats and slopes
and not for species that live on the sandy substrata of the
deeper reef bases (Hoeksema 1989). In the Flores Sea,
bottom inclination, water turbulence and substratum type
appeared to be important environmental factors controlling fungiid coral species composition (Hoeksema &
Moka 1989).
At the local scale, foraminifera similarity was related to
depth, human settlement, micro-substrate, exposure,
transparency and the remotely sensed area of coral formations and sparse coral formations. At the large scale, foraminifera composition was related to exposure,
transparency and the remotely sensed area of sparse coral
formations, and coral formations. The reef-slope foraminifera fauna can be differentiated into exposed and sheltered assemblages (Renema & Troelstra 2001). The
exposed slope is characterized by high coral cover with
sparse patches of coral rubble and high hydronamic
energy. Foraminifera living at the exposed site are either
characterized by being able to attach strongly to the substratum (Calcarina spengleri, Heterostegina depressa,
Amphisorus spp.) or live sheltered between the rubble
(Amphistegina radiata).
Marine Ecology 27 (2006) 76–88 ª 2006 The Authors. Journal compilation ª 2006 Blackwell Publishing Ltd
83
Beta diversity of tropical marine benthic assemblages
Taken together, the predictor matrices explained
between 24% and 48% of the variation in community
similarity of the four taxa over the Spermonde shelf. A
large amount of variation of the taxa we studied thus
remained unexplained, which is often the case in studies
of tropical assemblages. The percentage of variation
explained in this study falls within the range of similar
studies for plants, birds, insects and fish (Borcard et al.
1992; Adjeroud 1997; Duivenvoorden et al. 2002; Githaiga-Mwicigi et al. 2002; Tuomisto et al. 2003; Cleary et al.
2004). Unexplained variation may be due to large regional-scale processes (e.g. geography, oceanic transport and
climate), historical factors (e.g. sporadic events such as
heavy storms), rare species (singletons), biotic factors
(competition and other elements related to species life
history and demography may influence distribution patterns), and/or unmeasured environmental factors
(Borcard et al. 1992; Karlson & Cornell 1998).
Environmental factors (pure and spatially structured)
explained the observed variation in community similarity to a greater degree than distance in all taxa. This
would imply that the community structure of the marine benthic taxa is more deterministically than stochastically governed. Analogous results of the stronger effect
of environmental as opposed to spatial variables on
community similarity have been reported in studies of
flora and fauna in tropical forests and marine macrofauna in European marine sediments (Condit et al.
2002; Duivenvoorden et al. 2002; Ellingsen 2002; Ellingsen & Gray 2002; Cleary et al. 2004). At the local scale,
depth was strongly correlated with the distribution of
all taxa except sea urchins. Key environmental variables
change with depth; each vertical depth zone, for example, has a different degree of light regime, temperature
and wave energy, so different species are adapted to
such a set of variables thereby leading to different community structures. Many of the species in these tropical
taxa contain photosynthesizing endosymbionts (Wilkinson & Trott 1985; Hoeksema 1989; Wilkinson & Cheshire 1989; Renema 2002). Light intensity diminishes
rapidly with increased depth thereby representing a limiting factor for photosensitive species. Depth has been
previously recorded as an important factor in the distribution of sponges, mushroom corals and foraminifera
(Hooper & Kennedy 2002; Renema 2002; Langer & Lipps 2003). Ellingsen (2002) found that the beta diversity of Norwegian macrobenthic taxa was strongly
related to environmental variables, particularly depth
and habitat structure (e.g. grain size). Depth may be
less of a limiting factor for sea urchins, because they
generally do not contain photosynthesizing endosymbionts, although they are mobile grazers and thus
can be indirectly influenced by light.
84
Becking, Cleary, de Voogd, Renema, de Beer, van Soest & Hoeksema
The results of this study suggest that substantial variation in similarity can be predicted using simple locally
assessed environmental variables combined with remotely
sensed parameters.
Acknowledgements
We are grateful to Prof. Dr Alfian Noor and the rector of
Hasanuddin University for support given in Makassar,
Indonesia. We want to thank the Indonesian Institute of
Sciences (LIPI) at Jakarta for the research permits. The
field surveys were funded by the Netherlands Foundation
for the Advancement of Tropical research (WOTRO
grants W01-60, W77-96, W84-354, W84-474), the Schure
Beijerinck Popping fund at the Dutch Royal Academy of
Science (KNAW), and the STIR-network. This publication
is a result of the project ‘Climate change and Indonesian
coral reef biotas’ of the Council for Earth and Life Sciences (ALW 852.00.050).
References
Adjeroud M. (1997) Factors influencing spatial patterns on
coral reefs around Moorea, French Polynesia. Marine Ecology
Progress Series, 159, 105–119.
Aerts L.A.M., van Soest R.W.M. (1997) Quantification of
sponge/coral interactions in a physically stressed reef community, NE Colombia. Marine Ecology Progress Series, 148,
125–134.
Akaike H. (1973) Information theory as an extension of the
maximum likelihood principle. In: Pertrov B.N., Csaki F.
(Eds), 2nd International Symposium on Information Theory.
Akademiai Kiado, Budapest, 267–281.
Allison G.W., Lubchenco J., Carr M.H. (1998) Marine reserves
are necessary but not sufficient for marine conservation.
Ecological Applications, 8, S79–S92.
Anonymous (2004) Integrated marine and coastal area management (IMCAM) approaches for implementing the convention on biological diversity. Convention on Biodiversity
Diversity Technical Series, 14, 57.
Bak R.P.M., Nieuwland G. (1995) Long-term change in coral
communities along depth gradients over leeward reefs in the
Netherlands-Antilles. Bulletin of Marine Science, 56, 609–619.
Barber P.H., Palumbi S.R., Erdmann M.V., Moosa M.K.
(2002) Sharp genetic breaks among populations of Haptosquilla pulchella (Stomatopoda) indicate limits to larval
transport: patterns causes and consequences. Molecular Ecology, 11, 659–674.
Beck J., Schulze C.H., Linsenmair K.E., Fiedler K. (2002) From
forest to farmland: diversity of geometrid moths along two
habitat gradients on Borneo. Journal of Tropical Ecology, 18,
33–51.
Bell J.J., Barnes D.K.A. (2000) A sponge diversity centre within
a marine island. Hydrobiologia, 440, 55–64.
Marine Ecology 27 (2006) 76–88 ª 2006 The Authors. Journal compilation ª 2006 Blackwell Publishing Ltd
Becking, Cleary, de Voogd, Renema, de Beer, van Soest & Hoeksema
Borcard D., Legendre P., Drapeau P. (1992) Partialling out the
spatial component of ecological variation. Ecology, 73, 1045–
1155.
Bray J.R., Curtis J.T. (1957) An ordination of the upland forest
communities of Southern Wisconsin. Ecological Monographs,
27, 325–349.
Burnham K.P., Anderson D.R. (1998) Model Selection and
Inference: A Practical Information-theoretic Approach. Springer-Verlag, New York, NY, USA, 353 pp.
Burnham K.P., Anderson D.R. (2001) Kullback-Leibler information as a basis for strong inference in ecological studies.
Wildlife Research, 28, 111–119.
Burnham K.P., Anderson D.R. (2002) Model Selection and
Multimodel Inference: A Practical Information-theoretic
Approach, 2nd edn. Springer-Verlag, New York, NY, 488 pp.
Casgrain P. (2001) Permute 3.4.9. User’s Manual. Montreal,
Canada. http://www.fas.umontreal.ca/biol/casgrain/.
Clark A.M., Rowe F.W.E. (1971) Monograph of Shallow-water
Indo-west Pacific Echinoderms. British Museum, London, 238
pp.
Clarke A., Lidgard S. (2000) Spatial patterns of diversity in the
sea: bryozoan species richness in the North Atlantic. Journal
of Animal Ecology, 69, 799–814.
Cleary D.F.R. (2003) An examination of scale of assessment,
logging and ENSO-induced fires on butterfly diversity in
Borneo. Oecologia, 135, 313–321.
Cleary D.F.R., Mooers A.Ø., Eichhorn K.A.O., Menken S.B.J.
(2004) Diversity and community composition of butterflies
and odonates in an ENSO-induced fire affected habitat
mosaic: a case study from East Kalimantan, Indonesia.
Oikos, 105, 426–446.
Condit R., Pitman N., Leigh E.G., Chave J., Terborgh J., Foster
R.B., Nunez P., Aguilar S., Valencia R., Villa G., Muller-Landau H.C., Losos E., Hubbell S.P. (2002) Beta-diversity in
tropical forest trees. Science, 295, 666–669.
Connell J.H., Hughes T.P., Wallace C.C. (1997) A 30-year
study of coral abundance, recruitment, and disturbance at
several scales in space and time. Ecological Monographs, 67,
461–488.
Connell J.H., Hughes T.P., Wallace C.C., Tanner J.E., Harms
K.E., Kerr A.M. (2004) A long-term study of competition
and diversity of corals. Ecological Monographs, 74, 197–
210.
Cowen R.K., Lwiza K.M.M., Spongaugle S., Paris C.B., Olsen
D.B. (2000) Connectivity of marine populations: open or
closed? Science, 287, 857–859.
Davis A.R., Fyfe S.K., Turon X., Uriz M.J. (2003) Size matters
sometimes: wall height and the structure of subtidal benthic
invertebrate assemblages in south-eastern Australia and
Mediterranean Spain. Journal of Biogeography, 30, 1797–
1807.
Duivenvoorden J.F., Svenning J.C., Wright S.J. (2002) Beta
diversity in tropical forests. Science, 295, 636–637.
Edinger E., Jompa J., Limnon G.V., Widjatmoko W., Risk M.J.
(1998) Reef degradation and coral biodiversity in Indonesia:
Beta diversity of tropical marine benthic assemblages
effects of land-based pollution, destructive fishing practices
and changes over time. Marine Pollution Bulletin, 36, 617–
630.
Ellingsen K.E. (2002) Soft-sediment benthic biodiversity on the
continental shelf in relation to environmental variability.
Marine Ecology Progress Series, 232, 15–27.
Ellingsen K.E., Gray J.S. (2002) Spatial patterns of benthic
diversity: is there a latitudinal gradient along the Norwegian
continental shelf. Journal of Animal Ecology, 71, 373–389.
Faith D.P., Minchin P.R., Belbin L. (1987) Compositional
dissimilarity as a robust measure of ecological distance.
Vegetatio, 69, 57–68.
Feingold J.S. (1996) Coral survivors of the 1982–83 El NiñoSothern Oscillation, Galápagos Island, Ecuador. Coral Reefs,
15, 108.
Flather C.H., Wilson K.R., Dean D.J., McComb W.C. (1997)
Identifying gaps in conservation networks: of indicators and
uncertainty in geographic-based analyses. Ecological Applications, 7, 531–542.
Fraser R.H., Currie D.J. (1996) The species richness-energy
hypothesis in a system where historical factors are thought to
prevail: coral reefs. The American Naturalist, 148, 138–159.
Gaston K.J. (2000) Global patterns in biodiversity. Nature,
405, 220–228.
Gee J.M., Warwick R.M. (1996) A study of global biodiversity
patterns in the marine motile fauna of hard substrata.
Journal of the Marine Biological association of UK, 76,
177–184.
Githaiga-Mwicigi J.M.W., Faibanks D.H.K., Midgley G. (2002)
Hierarchical processes define spatial patterns of avian assemblages restricted and endemic to the arid Karoo, South
Africa. Journal of Biogeography, 29, 1067–1087.
Gray J.S. (2000) The measurement of marine species diversity,
with an application to the benthic fauna of the Norwegian
continental shelf. Journal of Experimental Marine Biology and
Ecology, 250, 23–49.
Harrison S., Ross S.J., Lawton J.H. (1992) Beta diversity on
geographic gradients in Britain. Journal of Animal Ecology,
61, 151–158.
Hart M.H. (2002) Life history evolution and comparative
developmental biology of echinoderms. Evolution and Development, 4, 62–71.
Hoeksema B.W. (1988) Mobility of free-living fungiid corals
(Scleractinia), a dispersion mechanism and survival strategy
in dynamic reef habitats. Proceedings 6th International Coral
Reef Symposium, 2, 715–725.
Hoeksema B.W. (1989) Taxonomy, phylogeny and biogeography of mushroom corals (Scleractinia; Fungiidae). Zoologische Verhandelingen, 254, 1–296.
Hoeksema B.W. (1991a) Evolution of body size in mushroom
corals (Scleractinia: Fungiidae) and its ecomorphological
consequences. Netherlands Journal of Zoology, 41,
112–129.
Hoeksema B.W. (1991b) Control of bleaching in mushroom
coral populations (Scleractinia: Fungiidae) in the Java Sea:
Marine Ecology 27 (2006) 76–88 ª 2006 The Authors. Journal compilation ª 2006 Blackwell Publishing Ltd
85
Beta diversity of tropical marine benthic assemblages
stress tolerance and interference by life history strategy.
Marine Ecology Progress Series, 74, 225–237.
Hoeksema B.W. (2004) Impact of budding on free-living corals
at East Kalimantan, Indonesia. Coral Reefs, 23, 492.
Hoeksema B.W., Moka W. (1989) Species assemblages and
phenotypes of mushroom corals (Fungiidae) related to coral
reef habitats in the Flores Sea. Netherlands Journal of Sea
Research, 23, 149–160.
Holmes K.E. (2000) Effects of eutrophication on bioeroding
sponge communities with the description of new West
Indian sponges, Cliona spp. (Porifera: Hadromerida: Clionidae). Invertebrate Biology, 119, 125–138.
Hooper J.N.A., Kennedy J.A. (2002) Small-scale patterns of
sponge biodiversity (Porifera) on Sunshine Coast reefs, eastern Australia. Invertebrate Systematics, 16, 637–653.
Howard P.C., Viskanic P., Davenport T.R.B., Kigenyi F.W.K.,
Baltzer M., Dickenson C.J., Lwanga J.S., Matthews R.A.,
Balmford A. (1998) Complementarity and the use of
indicator groups for reserve selection in Uganda. Nature,
394, 394–475.
Hubbell S.P. (1997) A unified theory of biogeography and relative species abundance and its application to tropical rain
forests and coral reefs. Coral Reefs, 16, S9–S21.
Karlson R.H., Cornell H.V. (1998) Scale-dependent variation
in local vs. regional effects on coral species richness. Ecological Monographs, 68, 259–274.
Karlson R.H., Cornell H.V. (2002) Species richness of coral
assemblages: detecting regional influences at local spatial
scales. Ecology, 83, 452–463.
Langer M.R., Lipps J.H. (2003) Foraminiferal distribution and
diversity, Madang Reef and Lagoon, Papua New Guinea.
Coral Reefs, 22, 143–154.
Largier J.L. (2003) Considerations in estimating larval dispersal
distances from oceanographic data. Ecological Monographs,
13, S71–S89.
Lawton J.H., Bignell D.E., Bolton B., Bloemers G.F., Eggleton
P., Hammond P.M., Hodda M., Holts R.D., Larsen T.B.,
Mawdsley N.A., Stork N.E., Srivastava D.S., Watt A.D.
(1998) Biodiversity inventories, indicator taxa and effects of
habitat modification in tropical forest. Nature, 391, 72–76.
Legendre P., Gallagher E.D. (2001) Ecologically meaningful
transformations for ordination of species data. Oecologia,
129, 271–280.
Levin S.A. (1992) The problem of pattern and scale in ecology:
the Robert H. MacArthur Award Lecture. Ecology, 73, 1943–
1967.
Littler M.M., Littler D.S., Brooks B.L., Koven J.F. (1997) A
unique coral reef formation discovered on the Great Astrolabe Reef, Fiji. Coral Reefs, 16, 51–54.
Lubchenco J., Palumbi S.R., Gaines S.D., Andelman S. (2003)
Plugging a hole in the ocean: the emerging science of marine reserves. Ecological Applications, 13, S3–S7.
Miller K., Mundy C. (2003) Rapid settlement in braodcast
spawning corals: implications for larval dispersal. Coral
Reefs, 22, 99–106.
86
Becking, Cleary, de Voogd, Renema, de Beer, van Soest & Hoeksema
Nishihira M., Poung-In S. (1989) Distribution and population
structure of a free-living coral, Diaseris fragilis, at Khang
Kao Island in the Gulf of Thailand. Galaxea, 8, 271–282.
Nystrøm M., Folke C., Moberg F. (2000) Coral reef disturbance and resilience in a human-dominated environment.
Trends in Ecology and Evolution, 15, 413–417.
Ohmann J.L., Spies T.A. (1998) Regional gradient analysis and
spatial pattern of woody plant communities of Oregon forests. Ecological Monographs, 68, 151–182.
Olsen D.M., Dinerstein E., Powell G.V.N., Wikramanayake
E.D. (2002) Conservation biology for the biodiversity crisis.
Conservation Biology, 16, 1–3.
Palumbi S.R. (2003) Population genetics, demographic connectivity, and the design of marine reserves. Ecological Applications, 13, S146–158.
Pandolfi J.M. (2002) Coral community dynamics at multiple
scales. Coral Reefs, 21, 13–23.
Pandolfi J.M., Bradbury R.H., Sala E., Hughes T.P., Bjorndal
K.A., Cooke R.G., McArdle D., McClenachan L., Newman
M.J.H., Paredes G., Warner R.R., Jackson J.B.C. (2003) Global trajectories of long-term decline of coral reef ecosystems.
Science, 301, 955–958.
Purvis A., Hector A. (2000) Getting the measure of biodiversity. Nature, 405, 212–219.
Reed D.C., Raimondi P.T., Carr M.H., Goldwasser L. (2000) The
role of dispersal and disturbance in determining spatial heterogeneity in sedentary organisms. Ecology, 81, 2011–2026.
Renema W. (2002) Larger foraminifera and their distribution
patterns on the Spermonde shelf, South Sulawesi. Scripta
Geologica, 124, 9–39.
Renema W., Troelstra S.R. (2001) Larger foraminifera distribution on a mesotrophic carbonate shelf in SW Sulawesi
(Indonesia). Paleogeography, Paleoclimatology, Paleoecology,
175, 125–146.
Reyers B., van Jaarsveld A.S., Kruger M. (2000) Complementarity as a biodiversity indicator strategy. Proceedings of the
Royal Society London B, 267, 505–513.
Roberts C.M., McClean C.L., Veron J.E.N., Hawkins J.P., Allen
G.R., McAllister D.E., Mittermeier C.G., Schueler F.W.,
Spalding M., Wells F., Vynne C., Werner T.B. (2002) Marine
biodiversity hotspots and conservation priorities for tropical
reefs. Science, 295, 1280–1284.
Shanks A.L., Granthem B.A., Carr M.H. (2003) Propagule dispersal distance and the size and spacing of marine reserves.
Ecological Applications, 13, S159–S179.
Siegel D.A., Kinlan B.P., Gaylord B., Gaines S.D. (2003)
Lagrangian descriptions of marine larval dispersion. Marine
Ecology Progress Series, 260, 83–96.
Spencer M., Schwartz S.S., Blaustein L. (2002) Are there finescale patterns in community similarity among temporary
freshwater pools? Global Ecology and Biogeography, 11, 71–78.
Su J.C., Debinski D.M., Jakubauskas M.E., Kindscher K.
(2004) Beyond species richness: community similarity as a
measure of cross-taxon congruence for coarse-filter conservation. Conservation Biology, 18, 167–173.
Marine Ecology 27 (2006) 76–88 ª 2006 The Authors. Journal compilation ª 2006 Blackwell Publishing Ltd
Becking, Cleary, de Voogd, Renema, de Beer, van Soest & Hoeksema
Tuomisto H., Ruokolainen K., Yli-Halla M. (2003) Dispersal,
environment, and floristic variation of Western Amazonian
forests. Science, 299, 241–244.
Wilkinson C.R., Cheshire A.C. (1989) Comparisons of sponge
populations across the barrier reefs of Australia and Belize:
evidence for higher productivity in the Caribbean. Marine
Ecology Progress Series, 67, 285–294.
Beta diversity of tropical marine benthic assemblages
Wilkinson C.R., Trott L.A. (1985) Light as a factor determining the distribution of sponges across the central Great Barrier Reef. Proceedings 5th International Coral Reef
Symposium, 5, 125–130.
Willis J.W., Whittaker R.J. (2002) Species diversity – scale
matters. Science, 295, 1245–1248.
See Appendix I overleaf
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Beta diversity of tropical marine benthic assemblages
Becking, Cleary, de Voogd, Renema, de Beer, van Soest & Hoeksema
Appendix I. List of species sampled in the Spermonde Archipelago and their abundance (total number of individuals per sample). Note that only
common (n ‡ 40 individuals) sponge species are shown. The full list of species and their distribution within the Spermonde Archipelago is available
upon request.
mushroom corals
n
foraminifera
n
sea urchins
n
Ctenactis albitentaculata Hoeksema
Ctenactis crassa (Dana)
Ctenactis echinata (Pallas)
Fungia concinna Verrill
Fungia costulata Ortmann
Fungia cyclolites Lamarck
Fungia fragilis (Alcock)
Fungia fralinae Nemenzo
Fungia fungites (Linnaeus)
Fungia granulosa Klunzinger
Fungia gravis Nemenzo
Fungia horrida Dana
Fungia moluccensis van der Horst
Fungia paumotensis Stutchbury
Fungia repanda Dana
Fungia scabra Doderlein
Fungia scruposa Klunzinger
Fungia scutaria Lamarck
Fungia sinensis (Milne Edwards & Haime)
Fungia somervillei Gardiner
Fungia tenuis Dana
Fungia vaughani Boschma
Halomitra pileus (Linnaeus)
Heliofungia actiniformis (Quoy & Gaimard)
Herpolitha limax (Esper)
Lithophyllon mokai Hoeksema
Lithophyllon undulatum Rehberg
Podabacia crustacea (Pallas)
Polyphyllia talpina (Lamarck)
Sandalolitha dentata Quelch
Sandalolitha robusta (Quelch)
Zoopilus echinatus Dana
65
156
1463
2897
977
14
376
623
3602
824
247
1228
1229
1850
6517
506
1249
314
425
25
873
35
154
325
1330
249
139
146
308
59
497
7
Alveolinella quoyii (d’ Orbigny)
Amphisorus cf. sauronensis Lee
Amphisorus spec2
Amphistegina lessonii d’ Orbigny
Amphistegina lobifera Larsen
Amphistegina papillosa Said
Amphistegina radiata (Fichtel & Moll)
Baculogypsinoides spinosus Yabe & Hanzawa
Calcarina gaimardi d’ Orbigny
Calcarina mayori Cushman
Calcarina quoyii d’ Orbigny
Calcarina spengleri (Gmelin)
Celanthus craticulatum (Fichtel & Moll)
Dendritina ambigua (Fichtel & Moll)
Heterostegina depressa d’ Orbigny
Laevipeneroplis malayensis Hofker
Neorotalia calcar (d’ Orbigny)
Operculina ammonoides (Gronovius)
Operculina complanata (Defrance)
Palaeonummulites venosus (Fichtel & Moll)
‘Parasorites’ spec2
Peneroplis pertusus (Forskal)
Peneroplis planatus (Fichtel & Moll)
Sorites orbiculus (Forskal)
233
1945
478
24485
7005
1524
7583
766
111
807
4209
3367
5009
414
3803
15
2502
9032
6
644
461
95
1903
78
Diadema savignyi (Adouin)
Diadema setosum (Leske)
Echinometra mathaei (de Blainville)
Echinostrephus molaris (de Blainville)
Echinothrix calamaris (Pallas)
Echinothrix diadema (Linnaeus)
Mespilia globulus (Linnaeus)
Toxopneustes pileolus (Lamarck)
Tripneustes gratilla (Linnaeus)
20
3694
75
43
53
2
2
10
126
sponges
n
sponges
n
Aaptos suberitoides (Brøndsted)
Acanthella cavernosa Dendy
Acanthodendrilla sp. ‘‘black‘‘
Acanthostrongylophora ingens (Thiele)
Amphimedon paraviridis Fromont
Axinyssa sp. ‘‘yellow‘‘
Biemna triraphis (Topsent)
Callyspongia aerizusa Desqueyroux-Faúndez
Callyspongia aff.pseudofibrosa
Callyspongia aff. subarmigera
Callyspongia biru de Voogd
Callyspongia sp. ‘‘brown‘‘
Chalinula hooperi Nishiyama & Bakus
Cinachyrella australiensis Carter
Clathria basilana Lévi
Clathria cervicornis (Thiele)
Clathria mixta Hentschel
Clathria reinwardti (Vosmaer)
Coelocarteria singaporense Carter
Coscinoderma aff. matthewsi
Dactylospongia elegans Thiele
Dasychalina fragilis
Dysidea arenaria Bergquist
Echinodictyum flabelliforme (Keller)
Gelliodes callista de Laubenfels
Gelliodes fibulata Carter
Haliclona amboinensis Lévi
Haliclona fascigera Hentschel
Haliclona sp. ‘‘blue‘‘
Haliclona sp. ‘‘brown’’
Haliclona sp. ‘‘pink’’
Hyrtios erectus Keller
727
81
105
364
1895
52
98
166
213
237
373
158
439
89
109
491
171
1593
63
47
55
57
54
61
51
213
207
105
602
197
76
896
Hyrtios reticulatus (Thiele)
Iotrochota baculifera Ridley
Iotrochota purpurea Bowerbank
Ircinia ramosa Keller
Ircinia sp. ‘‘pink’’
Ircinia sp. ‘‘red’’
Jaspis splendens de Laubenfels
Lamellodysidea herbacea Keller
Liosina paradoxa Thiele
Melophlus sarassinorum Thiele
Neopetrosia carbonaria (Lamarck)
Neopetrosia exigua Kirkpatrick
Niphates olemda de Laubenfels
Oceanapia sagittaria (Sollas)
Oceanapia sp. ‘‘white’’
Paratetilla bacca (Selenka)
Penares sollasi Thiele
Pericharax heteroraphis (Poléjaeff)
Petrosia hoeksemai de Voogd & van Soest
Petrosia nigricans Lindgren
Phyllospongia papyracea (Esper)
Placospongia melobesioides Gray
Pseudoceratina arabica (Keller)
Spheciospongia congenera (Ridley )
Stylissa carteri (Dendy)
Theonella swinhoei Gray
Ulosa sp.
Xestospongia aff. mammillata
Xestospongia mammillata Pulitzer-Finali
Xestospongia sp. ‘‘pink‘‘
Xestospongia testudinaria Lamarck
Xestospongia vansoesti Nishiyama & Bakus
113
217
82
111
67
49
76
764
66
144
52
146
111
40
81
72
147
44
459
283
139
53
165
232
561
78
63
47
68
58
70
52
88
Marine Ecology 27 (2006) 76–88 ª 2006 The Authors. Journal compilation ª 2006 Blackwell Publishing Ltd