Journal of Biogeography (J. Biogeogr.) (2015) 42, 2414–2426
ORIGINAL ARTICLE
Biogeography versus resource management: how do they compare when prioritizing the management of coral reef fish in the south-western Indian Ocean? T. R. McClanahan*
Wildlife Conservation Society, Marine Programs, Bronx, NY 10460, USA
ABSTRACT
Aim Numbers of coral reef species are broadly influenced by historical, physical and geographical factors that are often the basis for prioritizing conservation and management investments. In contrast, the number of species at a site is often influenced by site-specific factors, including abundance, benthic cover and other habitat features (depth and exposure), fishing pressure and resource management. Conservation policies and programmes often prioritize geographies or specific management systems within specific geographies. I evaluate the variance in number of species at the site scale and estimate the contributions of fishing pressure, local habitat factors and regional geography to local diversity. Location Coral reefs of the south-western Indian Ocean (SWIO). Methods Site-level species richness data from an extensive field sample of common coral reef fish at 266 sites in seven SWIO countries were analysed to create four species richness metrics to evaluate the effects of local site, geography and management. Results The local number of species was strongly predicted by an asymptotic relationship with fish biomass, followed by habitat variables, and lastly by the geographical positions of latitude and longitude. A species richness centre or ‘hotspot’ was found between Madagascar and the African coastline, but the variance attributable to geography when biomass and habitat effects were removed was small. Evaluation of the number of species in five existing fisheries management categories indicated that differences were chiefly influenced by biomass rather than habitat factors.
Red Sea and Western Indian Ocean Biogeography Special editors – Luiz Rocha, Gustav Paulay and Michelle Gaither *Correspondence: T. R. McClanahan, P.O. Box 99470, Mombasa, Kenya. E-mail:
[email protected]
Main conclusions Although the centre of species richness may indicate a climate refugium that should be considered in conservation prioritization, this diversity-centre effect can weaken if habitat and biomass features are reduced by climate disturbances and fishing. Consequently, the highest priority for conserving local numbers of reef fish species is to maintain biomass above the c. 600 kg ha 1 threshold found in this study. Keywords Africa, biodiversity, biomass thresholds, conservation biogeography, fisheries closures and management, marine protected area policies
INTRODUCTION Knowledge of the patterns of species richness in coral reef fish has increased greatly through global compilations and 2414
http://wileyonlinelibrary.com/journal/jbi doi:10.1111/jbi.12604
analyses of species lists (Kulbicki et al., 2013; Parravicini et al., 2013; Stuart-Smith et al., 2013). These studies indicate that biogeographical patterns can emerge, but that they are sensitive to the sampling methods and the spatial scale of the ª 2015 John Wiley & Sons Ltd
Reef fish species richness evaluations (Kulbicki et al., 2013; Mouillot et al., 2013). Heterogeneity in species richness over biogeographical scales indicates that the number of species is most strongly influenced by physical factors such as the area of coral reef, the presence of Quaternary refugia, the movement of centres of diversity, the length of coastline, the distance between reefs and the mean and variance of sea surface temperatures (Renema et al., 2008; Parravicini et al., 2013; Pellissier et al., 2014). These studies suggest that historical and physical factors play an important role in maintaining species richness, possibly by accumulating species that evolved both within and outside climate refugia (Bowen et al., 2013; Cowman & Bellwood, 2013; Pellissier et al., 2014). Nevertheless, species richness patterns within biogeographical regions, such as the south-western Indian Ocean (SWIO), have received less attention. This is, however, the scale at which many important regional and national policy and management decisions are made, including where to establish protected areas, fisheries closures and other types of resource-management activity (Groves et al., 2002). Marine protected areas and fisheries closures are often small and have varying levels of success that may be influenced by the local levels of compliance and the environmental and ecological conditions of the reef (Wood et al., 2008; Pollnac et al., 2010; Edgar et al., 2014). Strategies for their placement therefore need to consider a number of environmental, ecological, resource and social considerations (Gaines et al., 2010; Ban et al., 2011a; Fox et al., 2012). These include the amount and variability of climate change, the state and recovery potential of the ecosystem, and the ability of societies to manage the resources (Groves et al., 2002; McClanahan & Cinner, 2012). The number of species is a common consideration and is often correlated with reef location, area, exposure to physical forces and habitat types (depth and benthic cover), and complexity (McClanahan & Arthur, 2001; Friedlander et al., 2003; Pinheiro et al., 2013). Additionally, the number of fish species interacts with the abundance and biomass of fish, which are influenced by fishing effort (McClanahan et al., 2011a; Mora et al., 2011). It is, however, poorly known how fishing, fisheries closures and other restrictions influence fish species richness. Fisheries closures can be preferentially located in areas with greater species richness, and the reduction in fishing pressure can increase fish biomass and protect habitat features that promote richness (Worm et al., 2006). Fish species richness frequently interacts with local ecological forces and niche diversification around available resources (Wilson et al., 2009; Mora et al., 2014). Given that many factors influence local species richness, it is challenging to partition out their influences and to compare different sites and management systems. Consequently, the challenges of understanding the roles of spatial scale, habitat and management could, for example, undermine efforts to identify centres of species richness, which may require special policies and priority management. This paper addresses patterns of reef fish species richness in the SWIO Journal of Biogeography 42, 2414–2426 ª 2015 John Wiley & Sons Ltd
by evaluating the variance attributable to a number of factors that are expected to influence their numbers at local sites. After accounting for the local contributions of biomass, habitat and geography on the number of species, I evaluate the influence of fisheries management systems. Reef fish biomass is a good proxy for fishing pressure and management effects in coral reefs (McClanahan et al., 2007a, 2011a; Cinner et al., 2009) and is therefore used alongside habitat and geographical factors as independent axes that are expected to influence reef fish species richness. I address the following questions. (1) What are the geographical patterns of reef fish species richness in the SWIO region when evaluated at the local scale? (2) How might they be influenced by local fishing pressure and habitat features? (3) Does fisheries management influence species richness and by what mechanisms – biomass, habitat or other factors? These questions require analyses of species count data starting with the raw species richness data and eliminating the variance attributable to biomass and habitat factors. This process creates residual metrics that can objectively evaluate the variance attributable to geography and management effects. MATERIALS AND METHODS Field methods Coral reef fish were surveyed by a single observer (T.R.M.) between 1992 and 2014 at 266 individual sites across the SWIO, spanning approximately 21.68° of latitude and 22.39° of longitude and 20 m of depth (Fig. 1; Table S1 in Appendix S1 of Supporting Information). Reefs were surveyed in the Comoros, Kenya, Madagascar, Mauritius, Mayotte, Mozambique and Tanzania; they were a mixture of coastal fringing reefs within 2 km of the shoreline and patch or emergent reefs growing further offshore on the edges of exposed or submerged Pleistocene islands. These two systems have slightly different fish faunas but the factors that influence these differences are not well understood (McClanahan & Arthur, 2001). Reef surveys were conducted on the sheltered back-reef lagoons in the case of fringing reefs and leeward sides of offshore islands and wave-exposed reef slopes of both reef types (mean depth c. 4.5 m). Back reefs are hard-bottom areas just inside the shallow or exposed reef flats that are often bordered shorewards by shallow (< 10 m) sand and seagrass. Seaward reef edges seldom extend beyond 20 m in depth and frequently end in coral rubble and sand with low coral cover. Fish were sampled during five passes of a 5 m 9 100 m belt transect, and different groups were sampled in each pass. During the first pass, all encountered individuals over 3 cm long were identified and assigned to 23 families and an ‘others’ group, and their lengths estimated and placed in 10-cm size bins. Total wet weight was then estimated using published length–weight estimates for common species in these families (Froese & Pauly, 2012). In the subsequent four passes, the numbers of diurnally active, non-cryptic, 2415
T. R. McClanahan
Figure 1 A map of the studied sites and fisheries management categorization in the south-western Indian Ocean region.
reef-associated fish in eight families (Acanthuridae, Balistidae, Chaetodontidae, Diodontidae, Labridae, Pomacentridae, Pomacanthidae and Scaridae were here considered separate families) were identified to species (Lieske & Myers, 1994) and counted in 1–5 replicate transects per site. The most diver-sensitive species (Acanthuridae, Balistidae, Chaetodontidae, Diodontidae, Pomacanthidae and Scaridae) were counted in the first two passes and the less sensitive species (Labridae and Pomacentridae) in the last two passes (McClanahan, 1994). This method may introduce diver effects but also reduces the confusion that may arise from sampling species in multiple families simultaneously, and increases the accuracy of numbers and species counts (Greene & Alevizon, 1989). The depth of each site was recorded with a pressure depth gauge and each site classified as exposed to or sheltered from waves. Sites were classified by the region’s five dominant management categories based on a mixture of national laws, discussions with fishers and managers, local reports and publications, and my own observations of fishing during sampling (McClanahan et al., 2015; see Table S1 in Appendix S1). The management categories included: (1) enforced national closures, which include marine parks with active patrols where no fishing is allowed; (2) young and lowcompliance parks, in which all forms of fishing were 2416
banned, but which lacked regular or effective patrolling or where enforcement was less than 5 years old; (3) all destructive fishing equipment (gear) restricted, in which only line fishing and traps were permitted; (4) most destructive gear restricted, in which spear guns and gill nets were used as well as traps and lines; and (5) no gear restricted, in which drag or small meshed net seines and explosives were also used. The gear-restricted areas had variable levels of formal and informal enforcement, but the categorization was based mostly on observations of gear usage rather than the areas’ legal status. Percentage benthic cover was estimated by two methods, by the line intercept and using visual estimates. The first method was most frequently used in shallow water and measures the distance to the nearest centimetre that a 10-m line travels across hard and soft coral, algae (fleshy, coralline and calcareous), seagrass, sponge and sand. The sum of the distance for the eight cover types measured along each transect was divided by the total transect length to calculate percent cover for each category. Six to nine haphazardly placed transects were completed at each site. Visual estimates were made at all sites and were the only method used in the deeper sites (> 10 m) due to the time limits imposed by scuba-diving at depth. This method was based on c. 20 replicate 2-m2 quadrats, in which an observer estimated the cover of hard and Journal of Biogeography 42, 2414–2426 ª 2015 John Wiley & Sons Ltd
Reef fish species richness soft coral and erect algae in each quadrat to the nearest 5% and the mean values were used to describe the site. Comparisons of the two methods at sites where both were performed found minor differences of a few percentage points (T.R.M. & N.A. Muthiga, Wildlife Conservation Society, New York, unpublished comparisons). Data analysis The dependent variable was the mean species richness in the above eight fish families at the site level based on the mean of the 500-m2 belt transects. Previous analyses have examined numbers of species in areas up to 2000 m2. McClanahan (1997) found that the number of species at 500 m2 serves as a good proxy for richness, because regional evaluations indicate that parallel species accumulation curves do not overlap. When multiple transects were examined at a site, the average of replicate transects was used in the analyses in order to control the number of species to the standard 500-m2 area. Sampling bias of the observer, fish density counts and habitat interactions will influence the raw or observed richness data used here. True richness would need to account for these potential biases, but their effects are not well known in this region. Studies comparing within- and between-observer censuses have found, however, that these biases are probably small relative to the considerable site variability inherent in the fish assemblage (McClanahan et al., 2007b). The independent variables were fish biomass, water depth, exposure to waves, benthic cover variables and latitude and longitude. The biomass–species richness relationship was non-linear and therefore power, Ricker and asymptote models were tested for fit. Biomass is used here as a proxy for fishing pressure, which is considered an independent and causative force in these fisheries. The Akaike information criterion (AIC) was used to select the best model, where an AIC more than two units smaller than a competing model equation was considered a better fit (Burnham & Anderson, 2002). A strong non-linear relationship between biomass and species richness precluded the use of multiple stepwise regression analyses of the raw species richness data against independent variables. The regression analyses therefore used residuals from the best-fitting biomass equation, habitat (benthic cover, depth and exposure), or both biomass and habitat to estimate the variance attributable to the factors of
concern (Fig. 2). First, after removing the variance attributable to biomass (biomass residuals), a forward stepwise regression of these residuals against habitat variables was performed to evaluate the variance attributable to habitat. Second, the raw species richness data were run against habitat variables using a forward stepwise regression to produce residuals for the habitat influences (habitat residuals). Finally, the residuals of both biomass and habitat were used to develop the final species richness metric to evaluate geography independently of these two local influences. Thus, there were four metrics for observed species richness – raw observed data, biomass variance removed, habitat variance removed, and both biomass and habitat variance removed. Latitude and longitude were not included in the analyses when biomass and habitat variance were excluded, but are included in the stepwise regression tables to show their influence. The raw, biomass residual and final biomass–habitat residual data were plotted and regressed against latitude and longitude to estimate the influence of geography on the raw data and on data from which the effects of biomass and habitat had been removed. Scatter-plots of these data suggested a weak linear decline in species richness with longitude but a stronger hump-shaped pattern with latitude. Consequently, a second-order polynomial was fitted to the latitude–species richness relationship for the raw data and for the biomass and biomass–habitat residual data. The derivatives of these polynomials were calculated to determine whether the potential centre of species richness changed when local biomass and habitat affects were removed. Additionally, the coefficients of variance (CV = 100 9 SD/mean) were calculated for each constant of the polynomial to determine whether the variance changed once the biomass and habitat portions of the variance were extracted. This procedure tested the accuracy of the location of centres of species richness when using raw and residual data. Similar steps were completed to address the third objective, concerning the influence of fisheries management, whereby fish species richness was compared between the five fisheries management categories (Fig. 2). Here, the raw and residuals from biomass, habitat and biomass–habitat were tested for differences between categories using a standard ANOVA and Tukey pairwise comparisons. All models and ANOVAs were run in the r statistical package (R Development Team, 2013).
Figure 2 Flow diagram showing the sequence of analyses used to remove the habitat and biomass effects to evaluate each effect independently and to determine regional geographical patterns of species richness shown in Fig. 3. Journal of Biogeography 42, 2414–2426 ª 2015 John Wiley & Sons Ltd
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Reef fish species richness soft coral and erect algae in each quadrat to the nearest 5% and the mean values were used to describe the site. Comparisons of the two methods at sites where both were performed found minor differences of a few percentage points (T.R.M. & N.A. Muthiga, Wildlife Conservation Society, New York, unpublished comparisons). Data analysis The dependent variable was the mean species richness in the above eight fish families at the site level based on the mean of the 500-m2 belt transects. Previous analyses have examined numbers of species in areas up to 2000 m2. McClanahan (1997) found that the number of species at 500 m2 serves as a good proxy for richness, because regional evaluations indicate that parallel species accumulation curves do not overlap. When multiple transects were examined at a site, the average of replicate transects was used in the analyses in order to control the number of species to the standard 500-m2 area. Sampling bias of the observer, fish density counts and habitat interactions will influence the raw or observed richness data used here. True richness would need to account for these potential biases, but their effects are not well known in this region. Studies comparing within- and between-observer censuses have found, however, that these biases are probably small relative to the considerable site variability inherent in the fish assemblage (McClanahan et al., 2007b). The independent variables were fish biomass, water depth, exposure to waves, benthic cover variables and latitude and longitude. The biomass–species richness relationship was non-linear and therefore power, Ricker and asymptote models were tested for fit. Biomass is used here as a proxy for fishing pressure, which is considered an independent and causative force in these fisheries. The Akaike information criterion (AIC) was used to select the best model, where an AIC more than two units smaller than a competing model equation was considered a better fit (Burnham & Anderson, 2002). A strong non-linear relationship between biomass and species richness precluded the use of multiple stepwise regression analyses of the raw species richness data against independent variables. The regression analyses therefore used residuals from the best-fitting biomass equation, habitat (benthic cover, depth and exposure), or both biomass and habitat to estimate the variance attributable to the factors of
concern (Fig. 2). First, after removing the variance attributable to biomass (biomass residuals), a forward stepwise regression of these residuals against habitat variables was performed to evaluate the variance attributable to habitat. Second, the raw species richness data were run against habitat variables using a forward stepwise regression to produce residuals for the habitat influences (habitat residuals). Finally, the residuals of both biomass and habitat were used to develop the final species richness metric to evaluate geography independently of these two local influences. Thus, there were four metrics for observed species richness – raw observed data, biomass variance removed, habitat variance removed, and both biomass and habitat variance removed. Latitude and longitude were not included in the analyses when biomass and habitat variance were excluded, but are included in the stepwise regression tables to show their influence. The raw, biomass residual and final biomass–habitat residual data were plotted and regressed against latitude and longitude to estimate the influence of geography on the raw data and on data from which the effects of biomass and habitat had been removed. Scatter-plots of these data suggested a weak linear decline in species richness with longitude but a stronger hump-shaped pattern with latitude. Consequently, a second-order polynomial was fitted to the latitude–species richness relationship for the raw data and for the biomass and biomass–habitat residual data. The derivatives of these polynomials were calculated to determine whether the potential centre of species richness changed when local biomass and habitat affects were removed. Additionally, the coefficients of variance (CV = 100 9 SD/mean) were calculated for each constant of the polynomial to determine whether the variance changed once the biomass and habitat portions of the variance were extracted. This procedure tested the accuracy of the location of centres of species richness when using raw and residual data. Similar steps were completed to address the third objective, concerning the influence of fisheries management, whereby fish species richness was compared between the five fisheries management categories (Fig. 2). Here, the raw and residuals from biomass, habitat and biomass–habitat were tested for differences between categories using a standard ANOVA and Tukey pairwise comparisons. All models and ANOVAs were run in the r statistical package (R Development Team, 2013).
Figure 2 Flow diagram showing the sequence of analyses used to remove the habitat and biomass effects to evaluate each effect independently and to determine regional geographical patterns of species richness shown in Fig. 3. Journal of Biogeography 42, 2414–2426 ª 2015 John Wiley & Sons Ltd
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Reef fish species richness
Figure 3 Plots of number of fish species as a function of latitude and best-fit polynomials for the raw count data, the residuals of the biomass–species richness relationship (Fig. 4) and the residuals of both biomass and habitat variables. Table 2 Summary of fish species richness data for best-fit second-order polynomials against latitude for three levels of data; (1) the raw species richness data (species per 500 m2), (2) the residuals after the biomass effect is removed and (3) the biomass–habitat residuals after both biomass and habitat influences have been removed. Included are the best-fit mean, SD, and coefficients of variation (CV), the latitude of the inflexion point or maximum species richness and t, F-ratios and P-values for the best-fit regression equations (y = b + m1x + m2x2). Data level
Mean estimate
Standard deviation
CV (%)
Raw species per 500 m2
m1: 4.31 m2: 0.19 b: 17.65 m1: 2.18 m2: 0.09 b: 9.62 m1: 1.06 m2: 0.05 b: 4.42
7.96 0.35 36.98 5.44 0.24 25.27 5.36 0.24 24.89
18.47 18.92 20.95 24.96 26.44 26.27 50.62 50.93 56.34
Biomass residuals
Biomass–habitat residuals
non-significant high-restriction management systems was 38.63 11.17 (SD) versus 34.23 12.0 for the four nonsignificant low-restriction categories. Testing species richness after removing the habitat effect showed that high-compliance closures were still more diverse than the other management categories except for the poorly replicated category in which all destructive gear was restricted (n = 4, Fig. 5b). Testing species richness after removing the biomass effect showed no significant differences between fisheries management categories (Table 3). Similarly, species richness with biomass and habitat variance removed showed no significant differences between management categories. Consequently, management influenced fish species richness primarily through biomass and not habitat. DISCUSSION There is considerable heterogeneity in observed coral reef fish species richness at the site level within the SWIO region, and Journal of Biogeography 42, 2414–2426 ª 2015 John Wiley & Sons Ltd
Inflexion point (° S)
R2
11.54
0.23
11.93
0.14
11.33
0.04
t-value 8.83 8.62 7.78 6.53 6.18 6.21 4.42 3.22 3.20
F-ratio
P-value
39.02
< 0.0001
21.56
< 0.0001
5.24
0.0058
geography, local habitat and fishing all influence this variability. The influence of geography on the number of species is weakened when fish biomass and habitat factors variability are accounted for. The challenge of evaluating the influences of large-scale biogeographical factors on the number of species at the site level is compounded by variance attributable to various fisheries management systems and fishing intensity in this region (Fig. 1). Historical and large-scale geographical and physical factors, such as coastline length, the density of reefs and the distance to historical refugia may be significantly associated with species richness when data are modelled over large scales using rarefaction methods or using extensively sampled and compiled species lists. The findings here suggest, however, that these geographical factors are considerably weaker than local or site-level factors, at least in the SWIO region. Fish biomass, a proxy for fishing pressure, was the strongest influence on fish species richness; the effects of geography, depth and habitat, although significant, were 2419
T. R. McClanahan (a)
(b)
Figure 4 Relationship between total fish biomass and number of fish species in eight families for three equations (power, Ricker and asymptotic formulas) and tested for significance and best fit by the Akaike information criterion where data are plotted by (a) country and (b) management category.
considerably weaker. The non-biomass factors combined explained less of the variance in reef fish richness than did biomass. Biomass–species richness relationships indicated that the number of species became saturated with increasing biomass, a result consistent with many findings from the literature on ecosystem service function. For example, Cardinale et al. (2012) summarized experimental manipulations of diversity and evaluated changes in biomass, which was considered a proxy for ecological functions. Experiments consistently find saturating biodiversity–ecosystem function relationships or the most rapid losses in ecosystem function at low levels of species richness, which is also supported by food-web models (Fung et al., 2015). In contrast, Mora et al. (2011) reported that biomass increased most rapidly at high diversity when biomass was plotted as a function of diversity. 2420
Mora et al. (2014) argued that high diversity promotes high biomass through niche diversification and resource-use efficiency in natural settings such as coral reefs. Plotting biomass (y-axis) as a function of the number of species (x-axis) using descriptive field census data is inappropriate when data were collected at an ecological scale or if no experimental manipulations were performed. Biomass is the appropriate independent axis for descriptive ecological data, because food availability, predation, avoiding predators by schooling, birth and growth are the key factors affecting fish abundance and biomass over ecological time. Species numbers, in contrast, are set by slow evolutionary, biogeographical factors of immigration and extinction, and the availability of ecological niches. High variation in biomass at high levels of diversity reflects variability in local and rapidly Journal of Biogeography 42, 2414–2426 ª 2015 John Wiley & Sons Ltd
Reef fish species richness
Figure 3 Plots of number of fish species as a function of latitude and best-fit polynomials for the raw count data, the residuals of the biomass–species richness relationship (Fig. 4) and the residuals of both biomass and habitat variables. Table 2 Summary of fish species richness data for best-fit second-order polynomials against latitude for three levels of data; (1) the raw species richness data (species per 500 m2), (2) the residuals after the biomass effect is removed and (3) the biomass–habitat residuals after both biomass and habitat influences have been removed. Included are the best-fit mean, SD, and coefficients of variation (CV), the latitude of the inflexion point or maximum species richness and t, F-ratios and P-values for the best-fit regression equations (y = b + m1x + m2x2). Data level
Mean estimate
Standard deviation
CV (%)
Raw species per 500 m2
m1: 4.31 m2: 0.19 b: 17.65 m1: 2.18 m2: 0.09 b: 9.62 m1: 1.06 m2: 0.05 b: 4.42
7.96 0.35 36.98 5.44 0.24 25.27 5.36 0.24 24.89
18.47 18.92 20.95 24.96 26.44 26.27 50.62 50.93 56.34
Biomass residuals
Biomass–habitat residuals
non-significant high-restriction management systems was 38.63 11.17 (SD) versus 34.23 12.0 for the four nonsignificant low-restriction categories. Testing species richness after removing the habitat effect showed that high-compliance closures were still more diverse than the other management categories except for the poorly replicated category in which all destructive gear was restricted (n = 4, Fig. 5b). Testing species richness after removing the biomass effect showed no significant differences between fisheries management categories (Table 3). Similarly, species richness with biomass and habitat variance removed showed no significant differences between management categories. Consequently, management influenced fish species richness primarily through biomass and not habitat. DISCUSSION There is considerable heterogeneity in observed coral reef fish species richness at the site level within the SWIO region, and Journal of Biogeography 42, 2414–2426 ª 2015 John Wiley & Sons Ltd
Inflexion point (° S)
R2
11.54
0.23
11.93
0.14
11.33
0.04
t-value 8.83 8.62 7.78 6.53 6.18 6.21 4.42 3.22 3.20
F-ratio
P-value
39.02
< 0.0001
21.56
< 0.0001
5.24
0.0058
geography, local habitat and fishing all influence this variability. The influence of geography on the number of species is weakened when fish biomass and habitat factors variability are accounted for. The challenge of evaluating the influences of large-scale biogeographical factors on the number of species at the site level is compounded by variance attributable to various fisheries management systems and fishing intensity in this region (Fig. 1). Historical and large-scale geographical and physical factors, such as coastline length, the density of reefs and the distance to historical refugia may be significantly associated with species richness when data are modelled over large scales using rarefaction methods or using extensively sampled and compiled species lists. The findings here suggest, however, that these geographical factors are considerably weaker than local or site-level factors, at least in the SWIO region. Fish biomass, a proxy for fishing pressure, was the strongest influence on fish species richness; the effects of geography, depth and habitat, although significant, were 2419
T. R. McClanahan becomes saturated above c. 600 kg ha 1. This suggests that SWIO coral reef ecosystems can tolerate moderate losses of biomass without large losses of local diversity. Future research will, however, need to determine whether life histories and ecosystem functions are affected, as these are reported to change more rapidly than species richness along fishing gradients (D’agata et al., 2014; McClanahan et al., 2015). Geographical patterns and local influences Biomass differed between countries and locations and therefore geographical evaluations based on site-specific fish communities need to consider this source of regional heterogeneity and the influence of fishing pressure. Fishing impacts are reflected in the fishery restriction categories presented here and more broadly by compliance and design aspects of protected areas and closures (Daw et al., 2011; Edgar et al., 2014). The mean biomass of all SWIO sites lay just below the asymptotic point of c. 600 kg ha 1 and therefore biomass is a potential major influence on any estimates of local species richness in this region. High-compliance closures and remote unfished sites are therefore important benchmarks for estimating regional fish species richness. Benthic cover and depth influenced fish species richness, whereas exposure did not (Table 1). In many locations, higher species richness is seen on reef fronts or edges, but the analyses here suggest that this is largely a depth effect rather than a wave-exposure effect. Results suggest that geography, depth and biomass interact to create the spatial heterogeneity in species richness. Similarly, a study of the Zanzibar and Pemba islands of Tanzania found that numbers of commercially valuable fish were reduced in shallow water in fished, but not unfished, reefs, and were not different below 7 m (Tyler et al., 2009). Many of their studied shallow reefs were heavily fished, which drove most of the observed variance, but the interaction between fishing, geographical location and depth combined to influence patterns of reef fish richness. Here, the increase in number of species with depth was influenced by the sites’ geographical location, as suggested by analyses of residuals. Effects of hard and soft coral cover were consistently found to be positively associated with number of fish species, whereas erect algae appeared to have no effect (Table 1). Positive relationships with hard coral and associated topographical complexity are often reported (McClanahan, 1994; Friedlander et al., 2003; Yahya et al., 2011) and, although it is difficult to tease apart the various influences, many studies indicate that bottom complexity is the most critical (Graham et al., 2009; Wilson et al., 2009). This is particularly true for the persistence of non-coral feeding fish, although some studies suggest that topographical complexity is less important than coral species richness (Komyakova et al., 2013). Positive relationships with soft coral are, however, seldom explored, perhaps due to their low cover on some reefs, and yet I found their influence was almost as strong as that of hard coral. Because the relationship between soft coral and 2422
fish species richness has been poorly explored, this finding will require further manipulative studies to evaluate the effects of refuge from predators, food, interactions with hard corals and other attributes of soft coral communities that might have produced this finding. Soft corals may provide some habitat structure for reef fish in this region. Given that hard coral mortality has been high (McClanahan et al., 2014), soft corals may be helping to maintain species richness and could continue to do so as climate disturbances and temperatures increase in the future (Sheppard, 2003; Nakamura et al., 2011). Soft corals mostly appear to be dominant on exposed fore-reefs and their dominance (as opposed to hard corals) may reflect past temperature disturbances on exposed reef edges. In contrast, most global change predictions are for greater dominance by erect algae in the future (Hughes et al., 2010). Manipulative experiments and time series studies found that significant increases in erect algae reduce fish abundances and numbers of species (McClanahan et al., 2002; Chong-Seng et al., 2012). Here, erect algae had no detectable effects, which may indicate that they had not reached the levels where the number of fish species declines. Although time series and experimental evidence are missing for fore-reefs, observations indicate that the sites with the highest erect algal cover are most frequently observed on shallow fore-reefs (< 10 m) or sheltered lagoons (T.R.M., pers. obs.). Consequently, it is likely that soft corals will dominate deeper (5–20 m) and erect algae shallower (0–10 m) fore-reefs in the future. Regional species richness patterns showed a slight decline in the number of species away from the African continent once biomass and habitat attributes were removed from the raw species richness data. The latitudinal hump with peak species at c. 11–12° S was also maintained but both geographical patterns were weak relative to the site-level variability associated with fish biomass and, to a lesser extent, habitat (Table 2). Once these site factors were removed, the latitudinal pattern explained c. 4.0% and the longitudinal pattern c. 1.7% of the total variance. Additionally, with each step in the removal of local variance, the relative variance around the best-fitting polynomial coefficients increased. This suggests that the factors that drive species richness at the regional scale are small and not greatly influential or easily predicted from current local-scale data. These factors are likely to include temperature variation, reef density and area, and distance to refugia. Consequently, the findings here suggest that extrapolating fish community relationships from a few sites within a region using common proxy factors, such as temperature means and variation, radiation and geographical position, are likely to produce weak predictions that are overridden by local effects. This finding weakens the expected role of geography in explaining diversity patterns but may be less influential in the Indo-Pacific and other regions where stronger geographical species richness gradients have been reported (Kulbicki et al., 2013). Based on field measurements in a small number of fished sites, common proxy factors and random-forest models, Journal of Biogeography 42, 2414–2426 ª 2015 John Wiley & Sons Ltd
Reef fish species richness Stuart-Smith et al. (2013) predicted the mean total biomass of fished SWIO reefs to be c. 1400 kg ha 1 and the total number of fish species to be c. 40 per 250 m2. Here, the replicate biomass value for all fish and 266 sites was c. 500 kg ha 1 and was strongly influenced by fisheries restrictions. McClanahan et al. (2015) summarized regional biomass and reported c. 960 kg ha 1 in high-compliance closures (n = 114 sites). Fished reefs ranged from 270– 390 kg ha 1 (n = 131 sites) depending on the number and type of gear restrictions. Although sampling different areas and species make it difficult to compare fish species richness between studies, this study indicates high regional heterogeneity that is difficult to replicate by simple proxy models. There is therefore considerable potential for oversimplification and error when using simple proxies and little site replication to estimate mean values for a region and when comparing regions. Greater replication and spatially resolved data will be needed to improve estimates and regional comparisons of numbers of species. Previous regional evaluations of coral, fish and echinoderm species richness have indicated high genetic and species diversity in the SWIO region that may be associated with historical or climate refugia (McClanahan et al., 2011b; Obura, 2012; Hoareau et al., 2013). McClanahan et al. (2011b) found an overall decline in reef fish species richness with latitude but this was due to the inclusion of Maldives fish censuses at c. 5° N. The Maldives may have greater species richness due to connections with the Indo-Australian Archipelago centre of species richness (Mouillot et al., 2013). Here, I focused the analysis on the SWIO region, which is generally considered to be a distinct region (Stuart-Smith et al., 2013). Nevertheless, the islands of Mauritius, Mayotte and Comoros included here have some species differences from the African mainland and Madagascar, with greater species affinity to the Chagos, Maldives and Seychelles islands (Kulbicki et al., 2013). This study confirms a centre of species richness between Madagascar and the East African coastline or the Mozambique Channel. Nevertheless, the data suggest that the geographical pattern is weak and potentially less influential than many other local factors. For example, there are many older (> 20 years) permanent fisheries closures, such as Kisite, Mombasa and Watamu Marine National Parks in Kenya in the northern section of the SWIO region, which had levels of species richness similar to those in the centre. The full closures sampled in the most species rich region, such as Mtwara Marine National Park in Tanzania and Vamizi Community Reserve in Mozambique, were still less than 10 years old when sampled and may require further time and biomass to recover their full species richness potential. Many of the central sites did, however, have biomass exceeding the threshold level of c. 600 kg ha 1. This study and these examples show that it is challenging to find regional centres of species richness where fishing impacts are ubiquitous, and that more than a decade is required to recover biomass (MacNeil et al., 2015). It also suggests that these centres of Journal of Biogeography 42, 2414–2426 ª 2015 John Wiley & Sons Ltd
diversity are vulnerable to losing their special status by changes in abundance and habitat that are frequently affected by fishing and climate disturbances. Effects of fisheries management This paper used residuals from biomass and habitat evaluations to evaluate how fisheries management influences the number of fish species present. These analyses indicated that fisheries management primarily influences the number of species through its influence on biomass, because removing the habitat variance still produced higher species richness estimates inside high-compliance closures. Consequently, most of the heterogeneity and local variability in species richness results from the effects of fishing on fish biomass. This indicates that the higher number of species in closures and possibly some strongly gear-restricted sites was not clearly attributable to site selectivity, habitat or other possible impacts of fishing, such as changes to food-web configurations. Although the effect of strong gear restrictions may be a tentative conclusion given the limited replication in this management category, a larger-scale study including gear-restricted fisheries of the Seychelles found that biomass was influenced by strong gear restriction (McClanahan et al., 2015). Fisheries management through closures and strong gear restrictions can maintain a high number of species when biomass targets are maintained above thresholds by restricting the most exploitative gear (McClanahan et al., 2015). Maintaining high numbers of species does not, however, ensure that all ecological functions of the fish community are present, as richness and function are not always strongly correlated (Parravicini et al., 2013; Stuart-Smith et al., 2013; D’agata et al., 2014). For example, large and long-lived predatory species and scraping scarid herbivores are often missing when fish biomass is not well above the > 600 kg ha 1 threshold found here (MacNeil et al., 2015; McClanahan et al., 2015). This biomass target will, however, be useful for fisheries that wish to practise ecosystem-based management and are committed to maintaining high species numbers. CONCLUSIONS Maintaining or raising fish biomass above the measured biomass threshold is likely to be the most effective way to increase and protect the number of fish species in the region (Fig. 4). In contrast, common conservation policies focused on geographical site-selection priorities are likely to be less effective in maintaining number of species and may do so only in restricted areas. When international donor funds are limited and focused on species protection, there is also the possibility that a focus on richness or ‘hot spot’ centres could detract from broader cost-effective conservation initiatives (Miller et al., 2013; Waldron et al., 2013). Site-targeted prioritization approaches frequently rely on top-down spatial planning and the placement of fisheries closures (Ban et al., 2423
T. R. McClanahan 2011b). Although they are useful for evaluating trade-offs, planning approaches should not underestimate the importance of local cultural needs, values and management preferences (Allnutt et al., 2012). Social disparities and unclear benefits to fishers is common problem in tropical fisheries and leads to a high frequency of low-compliance closures in the tropics (Pollnac et al., 2010; Daw et al., 2011; Kamat, 2014; Chaigneau & Daw, 2015). The findings here suggest that a focus on maintaining fish biomass creates the potential to simplify and use culturally appropriate forms of management. Scientific evaluations of centres of species richness are useful for understanding the evolutionary and ecological processes that influence species numbers (Bowen et al., 2013). Centres of species richness are also often associated with climate change refugia and are expected to be important for protecting species into the future, particularly when climate disturbances increase (Tingley et al., 2014). In the sea, there are refugia from climate stress and fluctuation in ocean height (Maina et al., 2011; Pellissier et al., 2014) and the Mozambique Channel has been identified as having moderate stress and other characteristics of refugia (McClanahan et al., 2011b, 2014; Obura, 2012). The area should therefore be an important focus for future conservation activities. Nevertheless, given the coarseness of the current knowledge, I suggest a portfolio approach that spreads risk by managing a variety of habitats and locations. Levels of fish species richness and other ecosystem services can be high in many places if fish biomass is maintained, and this may be achieved by appropriately blending management with societal preferences (McClanahan & Cinner, 2012). I recommend that more effort be directed at supporting low-cost and high-compliance management initiatives that raise biomass and protect habitat rather than creating costly and potentially unsustainable protected areas that may suffer from low compliance (Ban et al., 2011b). ACKNOWLEDGEMENTS I thank the national institutions for their logistical support in each of the countries and greatly appreciate the field assistance of many people, notably H. Machano Ali, A. Guissamulo, F. Januchowski-Hartley, A. T. Kamukuru, R. Moothien-Pillay, N. A. Muthiga, M. J. Rodrigues, B. Radrimananstoa and I. Marquis da Silva. The various projects that lead to the compilation of the large data set were supported by the John D. and Catherine T. MacArthur Foundation, Tiffany Foundation, United States Agency for International Development, the Western Indian Ocean Marine Science Association Marine Science for Management Program, the Ecosystem Services for Poverty Alleviation (ESPA) programme of the Department for International Development (DFID), the Economic and Social Research Council (ESRC) and the Natural Environment Research Council (NERC). M. Azali’s assistance with the figures and statistical analyses is greatly appreciated. 2424
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Kininmonth, S.J., Airoldi, L., Becerro, M.A., Campbell, S.J., Dawson, T.P., Navarrete, S.A., Soler, G.A., Strain, E.M.A., Willis, T.J. & Edgar, G.J. (2013) Integrating abundance and functional traits reveals new global hotspots of fish diversity. Nature, 501, 539–542. Tingley, M.W., Darling, E.S. & Wilcove, D.S. (2014) Fineand coarse-filter conservation strategies in a time of climate change. Annals of the New York Academy of Sciences, 1322, 92–109. Tyler, E.H., Speight, M.R., Henderson, P. & Manica, A. (2009) Evidence for a depth refuge effect in artisanal coral reef fisheries. Biological Conservation, 142, 652–667. Waldron, A., Mooers, A.O., Miller, D.C., Nibbelink, N., Redding, D., Kuhn, T.S., Roberts, J.T. & Gittleman, J.L. (2013) Targeting global conservation funding to limit immediate biodiversity declines. Proceedings of the National Academy of Sciences USA, 110, 12144–12148. Wilson, S.K., Dolman, A.M., Cheal, A.J., Emslie, M.J., Pratchett, M.S. & Sweatman, H.P.A. (2009) Maintenance of fish diversity on disturbed coral reefs. Coral Reefs, 28, 3–14. Wood, L.J., Fish, L., Laughren, J. & Pauly, D. (2008) Assessing progress towards global marine protection targets: shortfalls in information and action. Oryx, 42, 340–351. Worm, B., Barbier, E.B., Beaumont, N., Duffy, J.E., Folke, C., Halpern, B.S., Jackson, J.B.C., Lotze, H.K., Micheli, F., Palumbi, S.R., Sala, E., Selkoe, K.A., Stachowicz, J.J. & Watson, R. (2006) Impacts of biodiversity loss on ocean ecosystem services. Science, 314, 789–790. € Yahya, S.A.S., Gullstr€ om, M., Ohman, M.C., Jiddawi, N.S., Andersson, M.H., Mgaya, Y.D. & Lindahl, U. (2011) Coral bleaching and habitat effects on colonisation of reef fish assemblages: an experimental study. Estuarine, Coastal and Shelf Science, 94, 16–23. SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article: Appendix S1 Locations and sampling details of the study sites. BIOSKETCH Tim McClanahan is a Senior Conservation Zoologist at the Wildlife Conservation Society. He works on the ecology, fisheries management, and conservation of coral reefs.
Editor: Michelle Gaither
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