Partitioning scleractinian coral diversity across reef sites and regions in the Western Indian Ocean

Transcription

1 Partitioning scleractinian coral diversity across reef sites and regions in the Western Indian Ocean Mebrahtu Ateweberhan 1,2, and Timothy R. McClanahan 2 1 Department of Biological Sciences, University of Warwick, Coventry, CV4 7AL UK 2 Marine Programs, Wildlife Conservation Society, Bronx, New York USA Citation: Ateweberhan, M., and T. R. McClanahan Partitioning scleractinian coral diversity across reef sites and regions in the Western Indian Ocean. Ecosphere 7(5):e /ecs Abstract. Understanding how diversity is organized is essential for inference about appropriate scales for natural resource research and management. In this study, we investigated variation in scleractinian coral taxonomic richness and community diversity in the Western Indian Ocean (WIO) at different spatial scales. We sampled corals in 331 sites across nine countries. Sites were pooled into 11 distinct subregions based on previous investigations on coral community structure and between closures and reefs open to fishing for five regions with sufficient closure replication. We analyzed similarities in coral communities and compared differences in mean richness and Shannon diversity. Significant differences in alpha and beta components of diversity were tested at multiple spatial scales and between management types against a random distribution null model. Rarefaction was used to investigate significant difference in total taxonomic richness among regions. Coral communities were mainly structured along Acropora and massive Porites dominance lines. Highest dissimilarity among coral communities was found at the regional level and within- and between- subregion variations accounted for most of the coral diversity. Beta components were responsible for higher proportions of taxonomic richness of which a large part was explained by the site- region interaction. Alpha diversity contributed most to Shannon diversity and site level richness and Shannon diversity had similar relationships with latitude, peaking at about 10 S. Alpha diversity also showed significant relationship with water temperature variability, peaking at a sea surface temperature standard deviation of about 1.4 C, corresponding to the same 10 S latitude. Relatively higher alpha and beta components contributed to high richness and Shannon diversity in three regions: southern Kenya northern Tanzania, southern Tanzania northern Mozambique and northwestern Madagascar Mayotte. Consequently, the findings support the proposal for delineating these locations as a regional priority for biodiversity conservation. Key words: biodiversity; biogeography; diversity partitioning; ecological gradient; habitat heterogeneity; marine conservation; marine reserves; Mozambique Channel; spatial scales. Received 7 September 2015; accepted 14 September Corresponding Editor: D. P. C. Peters. Copyright: 2016 Ateweberhan and McClanahan. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. m.ateweberhan@warwick.ac.uk Introduction Large- scale diversity patterns often emerge as responses to historical geologic and climatic patterns, interacting with environmental and ecological processes at several scales (Gaston and Williams 1996, Karlson et al. 2004, Cadotte and Fukami 2005, Cornell et al. 2007). Consequently, complex patterns emerge because communities are structured across these multiple spatial scales of influence (Whittaker et al. 2001, Field et al. 2009). In coral reefs, for example, factors such as depth and wave exposure contribute strongly to beta- diversity and this coral 1

2 assemblage diversity controls the numbers of fish species (Harborne et al. 2006, Belmaker et al. 2008). Consequently, this spatial complexity begs for a multiscale approach where past, current, and future distributions are seen as environmental states and change interact with scaled biological and ecological processes (Miloslavich et al. 2010, Tittensor et al. 2010, Mora et al. 2011). This understanding of scale along with the associated mechanisms should help predict future environmental change that is being driven by the interactions between geographic and habitat features, climatic disturbances, and human impacts (Loreau 2000, Borcard and Legendre 2002, Soininen et al. 2007, Semmens et al. 2010). Despite the hierarchical structuring of coral diversity (Hatcher 1997, Karlson and Cornell 1999, Mellin et al. 2006, Cornell et al. 2007, Karlson et al. 2007, Belmaker et al. 2008, Zvuloni et al. 2010, Anderson et al. 2011), the contributions of various scales is seldom fully quantified relative to broader sampling and between- site variance, which is frequently attributed to geographic position relative to historical refuge and habitat areas (Karlson and Cornell 1999, Bellwood and Hughes 2001, MacNeil et al. 2009, Pellissier et al. 2014). Ignoring scales of diversity is particularly common in historically undersampled regions, such as the western Indian Ocean (WIO), and could potentially undermine understanding of multiscale diversity, processes that maintain it, and subsequent management priorities (MacNeil et al. 2009, Rodríguez- Zaragoza et al. 2011). Recent regional investigations of WIO coral biodiversity have revealed patterns that are likely to reflect environmental stress, geological and evolutionary histories (McClanahan et al. 2007b,c, Maina et al. 2008, Obura 2012a, Keith et al. 2013). Specifically, the Mozambique Channel or the leeward reefs behind Madagascar and possibly the larger Islands of Zanzibar, Pemba and Mafia create leeward environments where moderate environmental variability and low extreme disturbances are shown to promote coral acclimation/adaptation and persistence (McClanahan and Maina 2003; McClanahan et al. 2005, 2007a, Golbuu et al. 2007, Castillo et al. 2012). In contrast, coastal communities associated with these coral reefs are poor and have a high dependence on natural resources, including reef fisheries (Hicks 2011, McClanahan and Cinner 2012). Heavy reliance on coral reef fisheries is reducing fish biomass and resulting in step- down ecological degradation that ultimately influences corals and reduces diversity (McClanahan et al. 2011a, 2012). Because of the critical role that corals play in reef ecosystems, we investigated the spatial scales and environmental factors that influence this structure. Corals provide a significant portion of reef biodiversity, calcium carbonate production, and many other organisms rely on them for habitat (Harborne et al. 2006, Mumby et al. 2008, Wild et al. 2011). WIO coral reefs have, in recent years, witnessed large climatic disturbances that have fundamentally changed their community structure (Wilkinson 1998, Goreau et al. 2000, Ateweberhan et al. 2011). These changes include reductions in competitive taxa that contribute greatly to cover and calcification processes in favor of more stress tolerant or weedy taxa (Darling et al. 2013, McClanahan et al. 2014a,b). Recent studies of local diversity patterns have uncovered a biodiversity center in the Mozambique Channel, but many of these reefs face losses of biodiversity, functions, and eventually goods and services to coastal communities due to thermal disturbances and heavy fishing (Mc- Clanahan et al. 2011b, Obura 2012a, McClanahan 2015). Consequently, we combined our evaluations of coral taxa field data with sea surface temperature (SST) data collected across many WIO countries and subregions to better understand the common scales of coral diversity, the possible influences of SST on these distributions, and what might be the future climate change impacts due to their interactions. We expected our analysis of coral diversity across multiple spatial scales to assist the planning and management decisions need for this region. Considering that management decisions are most likely applied at the country and subregional levels, we outlined four management approaches that consider the relative contributions and sizes of subregional alpha and beta- diversity components (Table 1). Methods Study region and sites This study applies taxonomic richness and composition partitioning to evaluate the variation in coral diversity across many spatial scales. 2

3 Table 1. Categories of alpha and beta combinations and possible characteristics of coral communities and management considerations. Alpha and beta diversity combinations Coral community characteristics Management considerations Low alpha and low beta diversity Low alpha but high beta diversity High alpha but low beta diversity High alpha and high beta diversity Homogeneous coral assemblages of a similar species composition and low differentiation Low within site and high between- site differentiation of coral communities High local diversity but homogeneous coral assemblages within a sub- region Highly differentiated coral assemblages within and among sites Broader scale resource management to avoid wide- scale disturbances Broader scale management for capturing between than within site variation Sufficient replication within a sub- region to minimize local failures Sufficient replication within sub- region and large enough areas to represent within site variation We identified 11 WIO subregions based on existing coral biogeography information and recent analyses of changes in coral cover and community structure and their relations with regional temperature properties (McClanahan et al. 2007c, 2014a,b, Obura 2012a,b). A number of studies have found subregional differences in temperature histories that appear to influence coral bleaching and mortality across thermal disturbances and expected to influence regional stress and subsequent coral community responses (McClanahan et al. 2007a,b,c, 2011b, Ateweberhan and McClanahan 2010). We were particularly interested in determining the level of variation between and within regions and on spatial scales that management is likely to act, including the site- to- sub- region and subregion to the whole WIO region. Recent analyses, for example, found significant difference in the coral community structure between mainland and island coasts (McClanahan et al. 2014a,b) and we therefore included mainland island geography as another scale in the spatial hierarchy. Site were chosen as typical coral reef locations that ranged in depth from 0.5 to 20 m and dominated by a calcium carbonate substratum colonized by hard and soft corals and benthic algae. Sites were generally a mix of fringing reefs where the shallow back reef and deeper fore reefs were sampled and patch or island reefs where windward and leeward sites were sampled. Field sampling Hard coral communities were surveyed using roving observer surveys that were aimed at quantifying large- scale regional coral bleaching susceptibility and community structure (McClanahan et al. 2005, 2007c). On each survey, about 20 haphazardly selected ~2- m 2 quadrats that were chosen by picking a haphazard direction finning 3 10 times and then selecting the site beneath the observer for sampling. In each quadrat area, coral colonies were identified, mostly to the genus level, and number of individuals of each genus counted. Some genera were categorized based on functional form, such as Porites branching and Porites massive and Synarea. An individual was determined using the rule that there must be >3- cm distance between the colony edge and the edge of an adjacent colony of the same species. Three experienced observers collected the field data (A. Ateweberhan, T.R. McClanahan, and N.A. Muthiga) and photographed and referred to taxonomic treatise for difficult- to- identify taxa (Veron 2000). The site is considered the sampling unit and quadrat- level data were summed or averaged to generate site level information. These roving surveys cover larger areas than standard line transects for the same amount of time and therefore encounter moderate to rare corals more frequently. Data analysis The initial data contained reef survey data for 394 sites distributed across 11 WIO countries collected between 1998 and For this analysis, we excluded data collected from 1998 to 2004 to avoid the possible 1998 ENSO disturbance impacts that affected many areas in the 3

4 Fig. 1. Map of study area. Polygons and points indicate regions and sites, respectively. Numbers in parentheses are sites open to fishing and fisheries closures. WIO, especially in the north- central part. Most of the sites were situated in relatively shallower and sheltered areas, thus, we excluded the few exposed sites and those deeper than 15 m. We also averaged data for the same sites sampled over time. Our final data set contained entries for 331 sites in nine countries. Sites were grouped into 11 subregions (Fig. 1) based on their geographical and reef formation settings, findings of previous research on patterns in coral taxonomic richness distribution and community structure (McClanahan and Obura 1998, Sheppard 1998, McClanahan et al. 2007c, Graham et al. 2008, Ateweberhan and McClanahan 2010, Ateweberhan et al. 2011, Obura 2012a). These observations underpin the main assumption of the study that subregions within WIO and reefs sites within subregions represent ecologically and evolutionarily meaningful speciesassemblage organizations. Additionally, the effect of management was investigated in five regions with adequate closure replicates ( 8). Management was placed under region in the overall diversity hierarchy because most of the regional closures represent one or a few closely adjacent reefs under a single management system. Estimation of WIO wide coral taxonomic richness We estimated the number of taxa at each site and made comparisons between sites to estimate the beta and gamma components of diversity. To estimate the number of taxa at each site and sampling effort needed to describe most of the expected taxa, the Mao Tau estimated richness was compared against the Chao2 expected richness. We applied individual- based rarefaction based on the cumulative number of coral colonies counted and the total number of samples. Because number of species vs. number of individuals curves are influenced by the order of samples, the Mao Tau richness is used. It is obtained by shuffling the order of the samples infinitely and averaging the curves obtained and allows estimation of confidence intervals. Chao2 accounts for undetected taxa by considering the encounter rates of uniques and duplicates (found in one or two samples, respectively) (Gotelli and Colwell 2011). Rarity was determined by considering both the number of individuals and the occurrences of each taxa and taxa with one or two individuals (singletons and doubletons). The software ESTIMATES 9.10 (Storrs, CT, USA) was used to estimate richness data and the 4

5 95% confidence interval (CI) (Colwell et al. 2012, Colwell 2013). Comparisons of diversity and community similarity We compared taxonomic richness across 11 subregions by extrapolating the rarefaction curves according to the methods of Colwell et al. (2012). The highest number of sites sampled in the subregions was 60 (Comoros Mayotte NW Madagascar subregion = COMAD) and therefore we extrapolated the mean and 95% CI from rarefaction curves to 60 in order to compare subregions. Differences in observed mean richness and Shannon diversity among the 11 subregions, the mainland and island coasts, and between management regimes were tested using the Kruskal Wallis Test. Further, we tested for differences in Bray Curtis similarity among the 11 regions against the null hypothesis (9999 random permutations) with Permutational Multivariate Analysis of Variance (PERMANOVA). We identified coral taxa responsible for the overall differences among subregions with SIMilarity PERCent (SIMPER) analysis. We also conducted Principal Component Analysis (PCA) to check if the indicator taxa identified above captured the overall distribution of coral communities in the region and to investigate the pattern of distribution and association between sites and taxa. Diversity partitioning Three main analyses were conducted to evaluate the diversity hierarchy. These were: (1) site, subregion, WIO region; (2) site, subregion, geography (island mainland separation), and whole WIO; and (3) site, management, subregion, whole WIO region. Two diversity measures were considered in the hierarchical partitioning, namely taxonomic richness (S) and Shannon diversity (e H ). The Shannon Wiener index (H) is widely used as a measure of compositional diversity, not influenced by rare species, and useful for estimating community complexity. In this study, we chose Shannon diversity as it is less influenced by the dominant taxa. Contributions of different alpha and beta diversity components of species richness were separated using an additive function (S α + S β = S γ ) while multiplicative function (eα H eh β = eh γ ) was used in determining components of Shannon diversity (Crist et al. 2003, Jost 2006, 2007). In a hierarchical approach, the alpha diversity is the site- sampling unit total richness, beta diversity is among- sampling unit diversity, and gamma diversity is the whole area s richness. Beta diversity can have a number of components depending on how the between- site variation is partitioned, which in our case can be represented as α site + β site + β sub-region + β mainland-island or α site + β site + β management + β sub-region. Similarly, the total Shannon diversity is given by α site β site β sub-region β mainland-island or α site β site β management β sub-region (Crist et al. 2003, Veech and Crist 2009). In the first case, β 1 represents the diversity among reef sites within subregions, β 2 the diversity among subregions within mainland or island coasts and β 3 the between mainland and island diversity within the whole region. In the second case, β 3 represents the diversity between fisheries closures and sites open to fishing. We used the software PARTITION v. 3 in determining the different diversity components (Veech and Crist 2009). We also determined if the observed alpha and beta components of diversity were different from a null or random distribution of individuals among samples (Veech and Crist 2009). Here, tests for differences compare the proportion of null values vs. observed values based on randomization or 999 repetitions of the individuals. The advantage of the individual over the sample- based analyses is that it overcomes the limitations of sample size and better considers intraspecific aggregation of individuals (Gotelli and Colwell 2001, Crist et al. 2003). Correlation analyses were used to test for significant relationship between components of regional richness and Shannon Diversity (alpha vs. alpha, beta vs. beta, gamma vs. gamma). Relationship between diversity and SST variability and geography Three measures of SST variability (standard deviation, kurtosis, and skewness) were compiled and correlation analyses used to investigate temperature- alpha and beta diversity relationships. Weekly SST data for (CORTAD 2013) were downloaded from the Coral Reef Temperature Anomaly Database (CORTAD) website and the above three SST 5

6 Fig. 2. Estimated (Mao Tau) and expected (Chao2) accumulation curves for numbers of coral taxa (mostly genera) in the Western Indian Ocean. Observed values of richness for 11 subregions and mean richness (± SD) for site, subregion, and mainland island geography provided. Subregion codes as in Table 1 and Fig. 1.i: island sites; m: mainland sites. Full range of upper 95% confidence interval for Chao 2 not shown. metrics calculated from the grid nearest to the sampling site. Average values for each subregions were calculated by taking the average for the sampled sites within a subregion. In addition, correlation between site level diversity values and latitude and longitude were determined with regression analysis. Results Fig. 3. Relationship between site- level richness and Shannon diversity (y = 0.41x + 1.6; R 2 = 0.48; adj F = ; P < ). The study recorded 61 coral taxa among 92,710 individual colonies in 331 sites. The observed Mao Tau taxonomic richness curve approached the Chao 2 curve of maximum richness estimate, indicating that the sampling effort sufficiently represented the taxonomic richness (Fig. 2). The singleton and doubleton curves also approached an asymptote point very quickly (Fig. 2). Site level values of both diversity indices had a significant positive relationship (Fig. 3). Both diversity indices had a similar but weak relationship with latitude, represented best by a second- degree polynomial (Fig. 4a,b). Peak diversity was observed at about 10 degrees south. There was no significant relationship with longitude for both diversity indices (P > 0.05). Acropora was the most abundant taxon followed by massive Porites and Pocillopora (Table 2). Acropora was also the most widespread taxon, followed by massive Porites, Favia, and Pocillopora. Seven taxa were rare as they occurred only on one site (four taxa: Blastomussa, Ctenactis, Pseudosiderastrea, and Stylocoeniella) or two sites (three taxa: Caulastrea, Coeloseris, and Isopora) (Table 2). All taxa in the first group were singletons, occurring only once while Caulastrea was a doubleton, represented by two individuals only. Blastomussa occurred only in Kenya Tanzania, Ctenactis in Maldives, Pseudosiderastrea 6

7 Fig. 4. Relationship between site- level richness and Shannon diversity and latitude and SD- sea surface temperature (SST). (a) Taxonomic richness vs. latitude: y = 0.49x x ; R 2 = 0.04; F = 5.58; P = 0.004; adj (b) Shannon diversity vs. latitude: y = 0.01x 1.07x ; R 2 = 0.04; F = 5.06; P = 0.007; (c) Taxonomic richness adj vs. SD- SST: y = 0.49x x ; R 2 = 0.04; F = 5.58; P = 0.004; (d) Shannon diversity vs. SD- SST: adj y = 0.01x 1.07x ; R 2 = 0.04; F = 5.06; P = adj in Mascarenes, and Stylocoeniella in COMAD. Coeloseris occurred in one subregion only (Mozambique South Africa) and Caulastrea and Isopora in two different subregions, Mozambique South Africa, and Tanzania Mozambique and COMAD and Tanzania Mozambique, respectively. Relationship between the numbers of sites where a taxon occurred and its abundance and rank distribution (Appendix S1: Fig. S1) indicated the dominance of the community by a few widespread taxa. The most widespread taxa were also the most patchily distributed ones (Appendix S1: Fig. S2). At the smallest (site level) and largest (mainland island separation) spatial scales, mean values in observed richness values were not different from estimated values (Mao Tau) (Fig. 2). In contrast, mean subregion level observed richness was lower than estimated richness because of the lower values in most sub regions (8 out of 11) (Fig. 2). Observed taxonomic richness was not different from estimated richness in six sub- regions: COMAD, Kenya, Kenya Tanzania, Tanzania C, Tanzania Mozambique, and Madagascar- W. Diversity comparisons across spatial scales Total observed taxonomic richness and Shannon diversity values between mainland and island coasts were comparable (Taxonomic richness: mainland: 58; islands: 56; Shannon diversity: mainland: 20.64; islands: 18.49). There was also no significant difference in site level mean richness and Shannon diversity between the two geographic divisions (mainland richness: ± 0.42; island richness: ± 0.53; 7

8 Table 2. (A) Distribution of most abundant, rare and unique taxa in 11 subregions; (B) Rare taxa are those occurring as singletons or doubletons and unique taxa are those occurring only in one or two sites. Number of colonies indicated. Kenya- Mozambique Tanzania- Madagascar Madagascar Madagascar Kenya Tanzania Tanzania- C South Africa Mozambique COMAD W - SW - NE Mascarene Maldives (A) Dominant taxa Acropora Porites massive Pocillopora Fungia Galaxea Porites branching (B) Rare taxa Blastomussa Ctenactis Pseudosid erastrea Stylocoeniella Caulastrea Kruskal Wallis χ 2 = 0.60; P = 0.44; mainland Shannon diversity: ± 0.24; island Shannon diversity: 9.74 ± 0.31; χ 2 = 2.99; P = 0.084). Total observed subregional richness varied between 27 and 54; COMAD, Kenya Tanzania, and Tanzania Mozambique having the highest richness (Fig. 5a). Madagascar- NE had the lowest richness. Shannon diversity was highest in COMAD, Kenya, Kenya Tanzania, and Tanzania Mozambique while it was low in the Maldives and the three subregions in Madagascar (Fig. 5b). Nevertheless, both diversity indices were different when comparing all subregions at the site level (richness: (χ 2 = ; P < ; Shannon diversity: (χ 2 = 38.01; P = ). Sites in COMAD, Kenya Tanzania, Tanzania C, and Kenya Tanzania had higher richness than sites in Kenya, Mascarene, Madagascar- SW, and Mozambique South Africa. Shannon diversity was higher in COMAD, and Kenya Tanzania than in Mascarene and Madagascar- SW. Rarefaction- based estimates of richness for the 11 subregions showed highest richness for COMAD, and Kenya Tanzania (Fig. 5C). Richness was lowest in the Mascarene, Madagascar- NE and Madagascar- SW while the Maldives and Madagascar- W subregions had large confidence interval making comparisons difficult. Overall, WIO wide comparison between fisheries closures and sites open to fishing did not find significant differences (richness: χ 2 = 0.97; P = 0.32; Shannon diversity: χ 2 = 0.69; P = 0.41). Region- level comparison for the five subregions with sufficient replication did, however, find significant differences in Kenya Tanzania (richness: Closures: ± 1.13; Fished reefs: ± 1.06; χ 2 = 7.53; P = 0.006; Shannon diversity: Closures: ± 1.29; Fished reefs: ± 0.70; χ 2 = 10.61; P = 0.001). Comparison of coral community composition Comparison of coral communities among subregions revealed significant differences among most subregions (PERMANOVA F = 8.86; P < ; (Appendix S1: Table S1). SIMPER results also showed a large overall dissimilarity among the communities (Appendix S1: Table S2). With average dissimilarity of (74.6%), Madagascar- NE was the most different. Dissimilarity was high even in the three least different subregions (COMAD: 62.7%); Kenya Tanzania: 65.7%; Tanzania Mozambique: (63.7%). Five taxa contributed more than 45% and 14 taxa more than 70% of the regional differences (Appendix S1: Table S2). Acropora contributed the highest to the subregional difference, followed by massive Porites, Pocillopora, branching Porites and Fungia. Acropora, and Pocillopora had highest mean abundance in the Mascarene area while massive Porites was most abundant in Maldives. Porites branching was most abundant 8

9 for COMAD (PERMANOVA F = 2.06; P = 0.03) and Mozambique South Africa (F = 3.51; P = 0.002). Acropora was the main taxon responsible for the differences in both subregions and had lower abundance in fisheries closures in COMAD (closures: 60.28% ± 8.99% [SEM]; fished sites: 73.11% ± 6.9%). Patterns were the opposite in Mozambique South Africa (closures: 90.09% ± 22.97%; fished sites: 72.36% ± 10.79%). PCA showed that coral communities in the WIO were structured mainly along Acropora Porites massive and to some extent Pavona lines which represented the two extremes of the main PC axis (Fig. 6). PCA plots based on all taxa and the 11 indicator taxa identified by SIMPER were very similar (data not presented). Thus, patterns of distribution of the 11 taxa captured the overall community structure. Sites in Maldives associated mainly with Porites massive while most sites in the Mascarenes and south Mozambique- South Africa and some sites in Tanzania- Mozambique associated mostly with Acropora. Most of the remaining taxa are aggregated along the center of the ordination plot and represent sites from different regions. The second axis separated the Acropora and Porites massive sites from the rest, particularly those with relatively higher number of Porites branching colonies in southern Kenya and SW Madagascar. Fig. 5. Comparison of site- level estimated taxonomic richness (a), observed taxonomic richness (b) and Shannon diversity (c) in 11 subregions. Error bars in (a) are 95% confidence intervals and standard errors of mean in (b) and (c). in Kenya and Madagascar- NE and Fungia in Madagascar- NE and Kenya Tanzania. Comparison between fisheries closures and reefs open to fishing showed significant differences only Western Indian Ocean wide diversity partitioning The total WIO level taxonomic richness diversity was mainly attributed to beta components. The three beta components together explained more than 65% of the total variation (Table 3; Fig. 7). Almost 34.2% of this was accounted by site (β 1 ), 25.2% by subregion (β 2 ), and the remaining 6.6% by mainland island separation (β 3 ). For the subregional Shannon diversity, alpha diversity contributed by far the most with the highest contribution for beta diversity at the between- site level. The beta diversity contribution decreased across scales with subregion having a slightly higher contribution than mainland island geography (Table 3; Appendix S1: Table S3). For both diversity indices, beta diversity was significantly different and higher than expected by chance at all scales while there was no significant difference in alpha diversity. 9

10 Fig. 6. Principal Component Analysis (PCA) ordination of taxa and sites in the Western Indian Ocean (WIO). Taxa are ranked according to their contributions to the overall regional community dissimilarity from SIMPER analysis (Appendix S1: Table S1). Table 3. The contributions of scale to coral diversity for the whole western Indian Ocean. Taxonomic richness Shannon diversity Component Observed Expected P Observed Expected P (A) By site, subregion, and mainland island geography Mainland Beta < < Island Subregion Beta < < Site Beta < < Site Alpha > (B) By site, management, and subregion Sub- region Beta < < Management Beta < < Site Beta < < Site Alpha >0.999 Effect of mainland island geography For richness, total beta components explained similar proportions of diversity for mainland (63.8%) and island (63.6%) (Appendix S1: Table S3). Site level beta (β 1 ) diversity was higher for mainland (45%) than island sites (30%) while contribution of subregion level beta diversity (β 2 ) was the opposite (islands: 34%; mainland: 19%). For Shannon diversity, contribution of the alpha component was far higher than that of the beta components and more or less similar between mainland and island coasts. Beta contribution was also similar for the two geographic categories. For both diversity indices, observed alpha diversity was not significantly different from that expected by chance while beta diversity was significantly higher than expected by chance, both at the site and subregion levels. 10

11 subregional values of components of each diversity measure found that alpha and beta components of richness were significantly positively correlated with the gamma component (Table 4). No significant relationships were found among any of the Shannon diversity components. Fig. 7. Hierarchical patterns in taxonomic richness and Shannon diversity. (a) At four spatial scales (site, subregion, mainland island geography, whole WIO). (b) At three spatial scales (site, subregion, and whole WIO) and management for five subregions. Effect of subregion Difference in contribution between beta and alpha richness was most pronounced in four subregions (Appendix S1: Table S3). Beta values were higher than alpha values in COMAD and Mozambique South Africa and smaller in Madagascar- NE and Maldives. In all subregions, contribution to the total Shannon diversity was made mainly by the alpha component. Contribution of beta diversity was highest in Madagascar- SW and Kenya and lowest in Madagascar- NE. There was no significant difference between the observed alpha diversity and that expected by chance for both diversity indices in all subregions. In all subregions, the observed beta diversity was significantly higher than that expected by chance for both diversity measures. Significant relationships were found between pairwise alpha and gamma of subregional richness and Shannon diversity but not for the beta metrics (Table 4). Analysis of relationship among Effect of management Significant effect of management on diversity partitioning was found in all five subregions where management- related difference was investigated (Appendix S1: Table S4). However, its contribution to both total regional richness and Shannon diversity was minimal as indicated by the small β 2 values. Pronounced differences in beta- richness were observed in Kenya, Tanzania Mozambique and Mozambique South Africa. Beta richness was higher in closures than in fished reefs in the Kenya, but relationships were opposite in the two other subregions. Alpha richness was higher in closures in Tanzania Mozambique, but lower in Mozambique South Africa than fished reefs. Large difference in alpha Shannon diversity was observed in Mozambique South Africa where it was lower in closures than fished reefs. Observed beta diversity was significantly higher than expected by chance for both diversity measures and in all five subregions. Relationships among diversity and SST variation The standard deviation of SST (SD- SST) showed the strongest relationship with site- level diversity among the three measures of SST variation, followed by skewness and kurtosis was either weak or not significant (Fig. 4c, d; Appendix S1: Table S5). The best- fit relationship was a hump- shaped relationship (2nd degree polynomial equation) and diversity peaked at SD- SST of about 1.4 C. There was negative relationship between alpha richness and Shannon diversity and SD- SST, while kurtosis- SST and skewness- SST did not show any statistically significant correlation with diversity (Table 4). Discussion The WIO region is composed of distinct coral communities with a high- diversity center in the region of the northern Mozambique Channel 11

12 Table 4. Correlations among subregion- level diversity components and three measures of sea surface temperature (SST) variation. Betarichness Gammarichness Alpha- Shannon Beta- Shannon Gamma- Shannon Standard deviation of SST Kurtosis- SST Skewness- SST Alpha ** 0.66* ** richness Beta- richness 0.91** ** Gamma ** richness Alpha * Shannon Beta- Shannon * Gamma- Shannon Note: Spearman rho and probability indicated (**: <0.01; *: <0.05). extending from northern Mozambique to southern Kenya and including the Comoros Mayotte NW Madagascar subregion but perhaps with a small decline in diversity in central Tanzania. Most of the diversity is associated with variations among sites within a subregion and between subregions. The within subregion diversity is likely to reflect habit variability, as observed in other studies (Bellwood and Hughes 2001, Harborne et al. 2006), whereas between region variability is likely to reflect different present and historical environmental forces, including depth, temperature, and light variation. Both measures of diversity support this conclusion and suggest that turnover of taxa and composition and dominance are influenced by the environmental conditions that promote this diversity. Clearly, some intermediate level of environmental variation is supported in some portions of the leeward environment of Madagascar that promotes this diversity. In principle, this environment creates enough variation to promote acclimation/adaptation and some disturbances that prevent dominance. Also, lacking extreme and deadly forms of variation created by interannual oceanographic variation prevents selecting for resilient species and high dominance taxa. Overview of coral diversity in the Western Indian Ocean At the WIO region level, the highest contribution of β diversity in taxonomic richness in our study was due to site and subregion while the mainland island separation and management contributed little to diversity. This is also reflected in the analysis of similarity, which detected pronounced differences mainly among regions. The proportional decrease in β diversity with increasing spatial scales indicates that community similarity increases across scales as more of the regional taxa are encountered (Cornell et al. 2007, MacNeil et al. 2009). Nevertheless, β diversity was significantly higher than expected from a random distribution indicating that differentiation occurs even at moderate to larger scales. Thus, the highest taxonomic change, regardless of abundance, occurs at the subregional level and between sub- regions. Consequently, meso- scale processes largely control taxonomic richness. These observations are in agreement with recent findings in the region, indicating the important role of between- habitat difference in community structure, subregional hydrography, and associated climatic variables (McClanahan et al. 2011a,b, McClanahan et al. 2014a,b, Obura 2012a). Consistent nonsignificant difference between observed and expected alpha diversity at all spatial scales indicated that nondeterministic forces structured the coral communities at the site level. However, it is possible that deterministic and nonrandom structuring occurs at scales smaller than this study, such as at the single transect or quadrat levels where taxa directly interact (Lang and Chornesky 1990). Intraspecific aggregation and species interactions are expected to operate at smaller scales while other processes, such as 12

13 habitat suitability, dispersal, and extinction, have greater influence at moderate scales (Cornell et al. 2007). A recent diversity partitioning study for reefs around Zanzibar found that alpha diversity was significantly lower in some sites with high dominance and β diversity was significantly higher than chance in all sites (Zvuloni et al. 2010). McClanahan et al. (2014a,b) also showed significant difference in diversity between shallow and deeper sites. Consequently, habitat effects appear to be the largest contributors to diversity in the region, whereas local diversity tends to be either lower or not different from random chance depending on dominance. Shannon diversity, which is derived from variation in dominance within sites, was not significantly different from the null model. Further, the decreasing beta- Shannon diversity with increasing spatial scales demonstrates that communities become less even as spatial scales increase. Nevertheless, corals and other marine communities commonly show strong nonrandom structuring at small scales and neutral species assembly models have failed to account for the dominance characteristics of natural communities (Cornell and Karlson 2002, van Woesik 2002, Connolly et al. 2014). We believe, however, that this can be sensitive to the scale of the smallest sampling unit and that our alpha scale was larger than local transect or quadrat- level scales where richness may be reduced due to local dominance (Zvuloni et al. 2010). We found high evenness at our local scale of sampling, which indicates that local dominance probably occurs at spatial scales lower than used in this study. The large- scale variation and spatial coverage of our study enables comparisons within and among subregions that is seldom considered in poorly sampled regions. Similar large- scale studies in the Pacific revealed the importance of interatoll and habitat variation in structuring reef fish (MacNeil et al. 2009) and coral community diversity (Bellwood and Hughes 2001, Cornell et al. 2007). In contrast, Francisco- Ramos and Arias- González (2013) investigation of Caribbean reef fishes found larger alpha and smaller beta- diversity than expected by chance. The authors argued that this resulted from high demographic connectivity in the Caribbean. The presence of high beta- diversity in the Indian and Pacific Oceans is likely due to a mixture of low connectivity and higher environmental, habitat, and biotic heterogeneity that reflects local historical and oceanographic constraints (Cornell and Karlslon 1996, Karlson et al. 2004, Obura 2012a,b). Obura (2012a), for example, argued that currents and eddies play important role in the Mozambique Channel where species disperse north and southwards to create this variation in diversity but there are also a number of alternative explanations including connection to deepwaters and leeward areas of large islands with relatively stable environmental conditions (McClanahan et al. 2009, Pellissier et al. 2014). One caveat to our study is that our genusbased study may not reflect species- level patterns. Coral communities, however, show strong positive linear relationship between species and generic richness, which is also found in the WIO (Fraser and Currie 1996, Ateweberhan, unpublished data). Nevertheless, when the most abundant taxa are the most speciose and aggregate in some sites, this can result in underestimation of Shannon alpha and beta diversity. Acropora, but also Favia, Favites, Pavona, Porites, and Montipora, are the genera most likely to produce this effect in the region resulting in underestimates of β 1 and β 2. Species vs. genus- level taxonomy could explain the higher than predicted species richness in northeast Madagascar by Obura (2012a) compared to our study. Currently, the complex nature of Acropora and to some extent Porites taxonomy is a major limitation to fully understanding regional diversity patterns in the region. Regardless, genus- level analysis and morphological characteristic classifications have proved useful toward understanding life history characteristics, ecological functions, and management needs (Edinger and Risk 2000, Zvuloni et al. 2010, Darling et al. 2013). Most of the above taxa also explain the dissimilarity among subregions while Principal Component Analysis underlines that the communities are mainly structured along Acropora and Porites massive lines. The two taxa have the highest contributions according to the results of SIMPER analysis. Regional differences in coral diversity The high subregional dissimilarity reflects the importance of subregion in partitioning in both richness and Shannon diversity. The three subregions with the highest richness diversity were associated with higher alpha and beta diversity, 13

14 which is confirmed by species- level investigations (Veron and Turak 2005, Obura 2012a). Despite their high diversity, there are also differences between these subregions. For example, COMAD had relatively high beta diversity whereas alpha diversity contributes more in Kenya Tanzania. Both areas have a mixture of islands and more continuous coastline but Kenya Tanzania is associated with the larger African coastline and possibly the deepwater of the Pemba channel. In contrast, despite the presence of numerous islands and atolls, the Maldives has high alpha diversity but lower beta diversity, which may indicate a more uniform and connected environment as was argued for Caribbean fishes. Additionally, the Maldives was badly affected by the strong 1998 thermal anomaly, which could have resulted in some homogenization and the unusual dominance of Porites and other massive and submassive corals in reefs that were historically Acropora dominated (Scheer 1974, McClanahan et al. 2007c). African coastline sites to the north (Kenya, central Tanzania) and to the south (Mozambique South Africa) had lower overall richness associated with relatively low values of both alpha and beta diversity. These regions may represent lower to moderate scale environmental heterogeneity. Some sites in central Tanzania, such as Mafia Island, have however, been shown to have high richness by species- level investigations (Obura 2012a) but a number of other reefs also have high dominance and lower diversity (Zvuloni et al. 2010). Consequently, conclusions about the diversity of central Tanzania will need to consider both taxonomic resolution and sampling and diversity distributions. The other low diversity subregions in Madagascar and Mascarene had low alpha and beta diversity that may have various causes. Mascarene Islands are isolated, high latitude reefs often dominated by Acropora. Acropora dominance may hide some of the diversity but this has yet to be quantified and compared. These islands are also probably more homogenous than the Maldives, located in a less optimal high latitude environment, and isolated demographically. Western Madagascar is known for high deforestation and river sediment inputs (Maina et al. 2013) while southern Madagascar has recently faced severe environmental degradation associated with high- resource extraction and sedimentation, and healthy reefs are limited to very few locations (McClanahan et al. 2009, Bruggemann et al. 2012). Studies that evaluate multiple spatial scales indicate the importance of historical and regional oceanographic processes on local species richness (Caley and Schluter 1997, Karlson and Cornell 1998, 2002, Cornell and Karlson 2000, Karlson et al. 2004, Cornell et al. 2007, Pellissier et al. 2014). Significant positive relationships between site- level richness and Shannon diversity and their relationships with geography and SST variation indicate co- variation and control by similar processes. Low SST variability sites of Kenya and the Maldives, along with the unstudied Seychelles, suffered some of the largest losses of coral cover during the strong 1998 bleaching event (McClanahan et al. 2007c, Ateweberhan et al. 2011). Reefs in high SST variability sites in the south, such as the Mascarene Islands and Southern Mozambique South Africa, are either less able to support high diversity or suffered from recent bleaching, pollution and intense resource extraction, such as SW Madagascar (McClanahan et al. 2009). The results are in agreement with a regional evaluation that found the lowest coral mortality across the WIO region in sites with intermediate SST variability (Ateweberhan and McClanahan 2010). While, there were no significant relationships between the beta components of diversity and SST metrics, this may result from low subregional sample size (n = 11) and requires better- replicated studies before concluding about their influence on diversity. Management implications This study generated information at the reef scale and larger, which is where management decisions are expected to operate. Here, the effect of management was significant, but small on the larger scales and was sometimes the opposite to prediction, in that beta richness was higher in reefs open rather than closed to fishing, such as found for Tanzania Mozambique and Mozambique South Africa but not Kenya. Difference in habitat heterogeneity can arise from random chance, site selectivity, and management impacts that promote coral diversity. In Kenya, we believe site selection favors high diversity by selecting fisheries closures with stable temperatures and habitat heterogeneity 14

15 that promote higher taxonomic richness (McClanahan & Maina 2003, McClanahan 2008). While selection for habitat heterogeneity is a decision that increases beta diversity and hedges against loss of diversity, the selection for high local diversity alone is more complicated and could undermine long- term resilience. Selecting for high diversity and stable water temperatures is a common management selection decision that increases the susceptibility of protected areas to thermal anomalies (Selig et al. 2012). Consequently, it has been argued that selecting a high diversity of habitats and thermal environments and associated coral taxa will create more resilience to future climate change than selecting for high diversity alone (McClanahan et al. 2014a,b). Consequently, there is a need to rethink the selection of protected area sites both, in terms of their broad- scale placement in terms of large- scale thermal stress (Maina et al. 2008, 2011), but also in terms of selecting for a mix of habitats that protect habitat diversity with the acclimation/adaptation properties that create resilience to climate dis tur bances (McClanahan et al. 2014a,b). Our results show that a substantial amount of taxonomic, Shannon, and community composition diversity is generated by beta diversity due to habitat and environmental variation within and among sub- regions. Thus, acknowledging these scales of diversity should form a foundation for context- specific regional and local management efforts. Based on the alpha and beta components of diversity described on Table 1, the following subregional groups could be identified: (1) Low alpha and low beta diversity subregions. This is represented by the Mascarene and three subregions in Madagascar (NE, W, SW) where diversity assemblages are in general homogenous, primarily reflecting a similar species composition and low differentiation. These subregions are not a high priority for local conservation but for broader scale management of resource to avoid widescale disturbances. However, species level investigations indicate high endemicity, especially in the Mascarene subregion (Sheppard 1998, Obura 2012a), suggesting a need to support endemic species and their habitats. This action also applies to the other subregions where unique species and habitats exist, but are not revealed by our genuslevel analysis. (2) Low alpha but high beta diversity subregions. This group is represented by Kenya, central Tanzania, and Mozambique South Africa. They are most likely characterized by low within site and high between- site differentiation of communities. The best management strategy would be to consider a diverse number of sites and focus more on capturing this between than within site variation or a broad- scale protection that does not undermine this between- habitat diversity. (3) High alpha and low beta diversity subregions. This category is represented by the Maldives. Coral assemblages within this sub- region are more homogenous than any other subregion and taxa within a site probably represent a subsample of the larger species pool, suggesting conservation where sufficient conservation replication to hedge against local failures. (4) High alpha and high beta diversity subregions. This category is represented by the three high diversity subregions: Kenya Tanzania, Tanzania Mozambique and COMAD. These subregions probably represent the most highly differentiated coral assemblages in the WIO, both within and among sites. Management in these subregions should consider a diverse number of sites to represent enough between- site variation and large enough areas to represent within site variation a hierarchical integration of management to avoid high disturbance in all sites. There have been suggestions of having the subregion between Tanzania, Mozambique and NW Madagascar (Coral Triangle of the WIO) as the main subregional priority for conservation and management efforts. By including taxonomic richness and Shannon diversity, we show that the subregion represents high diversity and corroborate proposals for its delineation as a subregional priority for conservation. Several reefs in this subregion continue to support healthy populations of bleaching susceptible coral taxa due to relatively low levels of environmental stress (McClanahan et al. 2007c, 2014a,b, Ateweberhan and McClanahan 2010). We also indicate that the subregion between Kenya southernmost and northern Tanzania represents similar coral diversity, and recommend extension of the regional conservation priority area to include these reefs. Some reefs within this wider delineation, for example,. central Tanzania, suffered severe impacts during the 1998 ENSO indicating that the region is not uniformly unique. 15