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Mark Wilson: Marine Biology & Coastal Ecology 2009 Dr Andy Foggo Diversity and abundance of reef associated fish on a non-hermatypic tropical reef Mark Wilson Dr Andy Foggo Diversity and abundance of reef associated fish on a non-hermatypic tropical reef Abstract The recent concern over the loss of habitat and species in the tropics and the increasing bleaching and subsequent death of coral species make study into the habitat and which aspects of it closely correlate with fish diversity and abundance a topical one. Many have considered habitat complexity to be fundamental in the structuring of fish assemblages, but as is the case on coral reefs often there is a biological element to the physical structure. It has been argued that this causes difficulties when inferring which components of the habitat are responsible for the observed patterns in diversity and abundance. In this study the diversity and abundance of reef associated fish species were recorded using two different visual census survey methods in conjunction with a benthic survey. The aim was to investigate which aspects of the habitat correlated with the diversity and abundance of reef associated fish species in the absence of the structural complexity of hermatypic reefs. A total of 60 transects and 12 rovers were conducted across the twelve study sites, and in total 71 species were recorded from 30 families. Results revealed sites with the highest number of species were also those with the highest numbers of individuals. Similarly sites with low numbers of species had low numbers of individuals. Using multiple regression analyse soft coral cover was the most significant predictor at individual sites, while in predicting the whole assemblage silt and rubble were the most significant. While it would have been useful to of had a further measure of habitat complexity such as hole size with which to infer more about the physical habitat, results did indicate a biological relationship existing between the fish assemblages and the habitat at the rocky reefs of Los Cobanos. Introduction While much concentrates literature on exists biological on factors community affecting recruitment, predation and competition. ecology the assemblages majority such as Besides these biological factors the physical make up of the habitat and its complexity has a role to play in constructing assemblages. Habitat complexity can vary depending on the species group and the spatial scale at which the habitat is measured (Tews et al 2004). Tews et al (2004) use what they call the keystone structure concept to highlight that certain ecosystems may be dominated by a single keystone structure and that several species groups may depend on this one important aspect. This view is shared somewhat by Gratwicke and Speight (2004) who suggest that habitats that are inherently different in species richness are due to habitat type rather than complexity. The physical habitat is often at least in part comprised of a biological element with some finding correlations between biological factors such as coral species richness and fish species richness (Roberts & Ormond 1987, Ohman & Rajasuriya 1998) while others do not (Risk 1972, Luckhurst & Luckhurst 1978). With the recent concern over habitat and species loss in the tropical marine environment studies into which aspects of the environment or habitat affect fish species diversity could aid future conservation effort. In their 1961 paper on bird species diversity MacArthur & MacArthur hypothesised that faunal diversity is generally related to the complexity of the habitat. Since this time many researchers have tested these ideas in the marine environment and considered the distribution of species and diversity of reef fish to be a function of substrate and shelter characteristics (Risk 1972, Luckhurst & Luckhurst 1978, Bell & Galzin 1984, Friedlander & Parrish 1998, Ferreira et al 2001, Gratwicke & Speight 2004, Lingo & Szedlmayer 2006). Risk (1972) found what he described as a striking positive correlation between fish species diversity and substrate topographic complexity. Using rugosity, vertical relief, percentage coral cover and sand as variables Luckhurst & Luckhurst (1978) found increased complexity correlated with increased diversity. Bell & Galzin (1984) examined the effects of live coral cover in isolation from the effects of habitat complexity, this was achieved by choosing study sites that showed comparable complexity but had differing percentages of live coral cover. Their findings differed from those of Risk (1972) and Luckhurst & Luckhurst (1978), and indicated that the amount of live coral cover may be more important to fish than previously thought thus the complexity of coral reef habitats should be considered in terms of both live coral and a separate structural component (Bell & Galzin 1984). In varying marine environments it has been shown that diversity is increased by a favourable habitat (Friedlander & Parrish 1998, Ferreira et al 2001, Lingo & Szedlmayer 2006,) but what is it that makes habitats favourable? In the open ocean isolated seamounts are often described as oases and hotspots of biodiversity (McClain 2007). Burnaford (2004) suggests that kelp forests have large impacts on communities by providing protection from predation or stressful abiotic conditions. In the tropics hermatypic coral reefs form complex structures either continuously distributed over a large area; or widely spaced and patchily distributed in a sandy area (Nanami & Nishihira 2003), these patchy reef environments provide diverse microhabitats, of varying size and complexity in an otherwise barren area, thus affecting the spatial distribution of fishes, recruitment of juveniles and also predation intensity (Levin et al 2000, Nanami & Nishihira 2003). In areas of the tropics devoid of hermatypic coral tropical rocky reef patches will provide an analogous habitat for reef associated fish. The rock surface provides a stable surface where microscopic algae, macroalgae, soft corals, gorgonians and sponges are able to thrive (Gratwicke & Speight 2004). These algae provide grazing for many fish species (Carpenter 1986, Lawson et al 1999) and are the primary source of production on rocky reefs, (Klumpp & McKinnon 1989). While a variety of habitats (Gratwicke & Speight 2005) including artificial reefs (Lingo & Szedlmayer 2006) have been used to test for relationships between species richness of tropical reef associated fish and habitat complexity, the majority of have been conducted on coral reefs. The area around Los Cobanos, El Salvador has rock derived patch sites with some soft coral growth and with little connectivity between sites in an otherwise sandy area. These patch reefs sites are comprised of rock of varying size and vertical relief, providing an ideal platform from which to test for relationships between the habitat and tropical reef associated fish assemblage structure in the absence of live hermatypic coral. The hypothesis that benthic variables should influence the number of fish species and individuals present at each site was tested as well as tests that benthic composition might be driving β diversity. Materials and methods Collaborators The survey of the reef at Los Cobanos, El Salvador was carried out with the help of Natural history museum Stockholm, El Salvador divers, a local dive facility who in conjunction with FUNZEL (Conservation group) provided all dive equipment and enlisted the help of Mr Mario Campos as boat captain, a local artisan fisherman with over 30 years experience and knowledge of the area. Mr Lee Munson, an experienced diver with reef monitoring experience conducted all survey dives. Study sites The study was performed from Los Cobanos (130 30’16.7”N, 0890 48’37.6” W), in Acajutla on the South Coast of El Salvador, South of San Salvador. The study area (70km2) spans a coastal length of approximately 14 km and extends 5km off shore. Twelve study sites were selected and GPS marked by Mario Campos based on his knowledge with considerations given to accessibility, familiarity, depth and comparability. Species identification A list of candidate species for the area was obtained from fishbase prediction (www.fishbase.com). Exploratory dives were then conducted in order to assess the actual species present. Data collection methods, equipment and modifications Data were collected through underwater visual census; using the modified Meso American Barrier Reef System (MBRS) technique (Almada-Villela et al 2003). Data collected comprised of fish abundance, size and benthic composition at each site. Because of differences in coral cover, time and manpower constraints modifications were made to the underwater visual census. The size, health and species aspect of coral habitat analysis was omitted due to the lack of corals found at Los Cobanos, and the benthic survey was performed at the minimum due to time constraints. Fish surveys were performed by both the rover diver method and 30 metre (m) transects, see separate rover and transect sections below. Underwater safety All dives were conducted by trained divers using PADI (www.PADI.com) dive tables, staying well within no decompression limits. No solo dives were conducted; oxygen and a radio were carried on board the vessel at all times. Rover dives Quick estimations of fish abundance were determined using the rover diver method recording fish species and numbers in an imaginary 2m window while finning for 30 minutes (Almada-Villela et al 2003). Fish numbers were ranked into categories using the following:1 = Single (S), 2 – 10 = Few (F) 10 – 100 = Many (M), 100+ = Abundant (A) A second diver meanwhile recorded topographical features such as depth, rock size, current strength/direction, visibility, and temperature. This second diver also takes any underwater pictures necessary for fish identification or recording benthos. Fish recording transect dives Five 30m fish transects with a 2m recording window were conducted at each site following Ribiero (2005). Surveys were conducted between 09:30 and 16:30 hours to take advantage of maximum light levels and to avoid crepuscular periods when fish may become more or less active. The time, bow direction, temperature, site, salinity, turbidity (using a sechi disk), sea state and cloud cover were noted at the time of all transect dives and recorded on the captain’s slate. A one metre T bar was used during surveys; this was marked at 25cm increments to aid both in fish size estimation and to aid visualisation of the 2m survey window. The 30 metre tape was weighted at the end and this marked the central position of the study site. Transects were conducted around this position in North, East, South and West directions, with the fifth and final transect being parallel to the last and approx five metres to one side. Where large rock structures were encountered the transect direction was adjusted slightly to go over or around these, to gain an accurate record of the site without making the horizontal distance travelled deviate greatly. Following standard protocol, the survey diver swims at a constant rate throughout the transects and records all fish observed in the survey window on a pre-prepared slate. To avoid any potential bias the same diver took all the fish recordings during the project. As outlined in the rover dive section the assistant diver recorded all abiotic variables. Benthic Survey Benthic habitat composition was assessed by three 30m transects each of 100 points and recorded on pre-prepared slates. These were conducted in conjunction with the fish transects, following the same procedure as described above. By exchanging fish slates for pre-prepared benthic slates at the end of the fish transect the benthic portion of the survey could be conducted on the return to the central position, before starting the next fish transect Statistical analysis To characterise the fish assemblages within and between the sites, the entire data set (square-root transformed) was used to produce an ordination plot employing non-metric multidimensional scaling (MDS) based upon a Bray-Curtis similarity matrix in PRIMER 6.0 (Clarke & Warwick 2001). The cohesion of samples (transects) within sites was then tested using 1-way anosim (Clarke 1988), with sites as a factor. The hypothesis that benthic variables should influence the number of fish species and individuals present at each of the 12 sites was tested first using the rover data. Benthic data in the form of percentage covers were transformed (arcsine transformation) prior to analysis. Stepwise multiple regression (SPSS 15.0) was used to test for power of the full suite of benthic data (see field methods for list of variables) to predict first the number of fish species at each site, and then the number of fish individuals. Regression models were validated by analysis of residuals and an examination of co-linearity diagnostics. The environmental data were then tested for correlation with the whole assemblage from the rover data using the BVSTEP procedure in PRIMER 6.0 (Clarke & Warwick 2001). Environmental variables were normalised prior to analysis, and a Bray-Curtis distance matrix was calculated for the fish assemblage data using square-root transformed abundance class values. BIOENV (Clarke & Ainsworth 1993) was also used to test the separate environmental variables in turn. To test the hypothesis that the benthic composition of sites might be driving the β diversity of fish assemblages at them, Sørensen’s index of β diversity (Sørensen 1948) was calculated from the amalgamated fish transect data for each site, and translated into a triangular matrix. A similar matrix of site environmental differences was then produced using normalised environmental variables and a Euclidean distance measure in PRIMER. The test for concordance between distance matrices (PRIMERRELATE; Clarke & Warwick 2001) was then used to test for correlation (Spearman’s Rank Correlation) between the β diversity and environmental difference matrices. This test was then repeated using only coral cover, silt and sponges as environmental variables following the results of multiple regression analyses described above. Finally, the analysis was repeated again using only coral cover in the environmental calculation, as this variable had the greatest individual predictive power in multiple regression. The hypothesis that β diversity should be related to the geographical distance between sites was also tested. The site to site distances were calculated using site positions converted from GPS lat/long data to UTM grid co-ordinates, and distances in metres between sites calculated in EXCEL. The resulting triangular distance matrix was then correlated with the triangular β diversity distance matrix using RELATE as described above. Finally, to test the validity of conducting the rover diver method as an alternative to intensive transects in fish surveys the RELATE procedure in PRIMER was used to test if rover and transect data demonstrate the same pattern of differences between sites. Both the data sets were square root transformed prior to analysis, first with rover data ranks assigned median abundance class values, to incorporate fish abundance as a contributor to the Bray-Curtis distance metric, and then with presence/absence data. Results A total of 60 transects and 12 rovers were conducted across the twelve study sites, and in total 71 species were recorded from 30 families. From the transect survey the mean number of species observed per site was 14, ranging from 7-19. The rovers had a mean of 31 species ranging from 1839 species per site. Using the transect data the number of individuals recorded per site varied hugely, with a mean of 240 and ranging from 271048. Sites with the highest number of species were also those with the highest numbers of individuals. No single species occurred at all sites, and only two sites had species unique to them. The sites differed in their assemblage composition (Figure 1), anosim indicated significant overall site differences in assemblage (R = 0.486; P = 0.001), with 56/66 pair-wise site comparisons being significantly different (P < 0.05). Stepwise multiple regression using the rover data indicated that three benthic variables together explained 81.7% (adjusted R2) of the variation in number of fish species at the sites. The single most powerful predictor was coral cover, explaining 53% (adjusted) of the pattern (See table 2 for full results). Using the rover data to predict number of fish individuals stepwise multiple regression indicated that only coral cover was a significant predictor of abundance, explaining 39.2% (adjusted R2) of the pattern. (See table 3 for full results). For the corresponding stepwise multiple regressions using the transect data to predict number of fish species, See table 4. For corresponding stepwise multiple regressions using the transect data to predict number of fish individuals, see table 5. BVSTEP analysis indicated that the combination of silt, rubble, coralline algae and rock size best correlated with the whole assemblage sampled by the rover surveys (ρ= 0.648); BIOENV indicated that the best individual correlations were between assemblage structure and silt (ρ = 0.428), Rubble (ρ = 0.425) and Coralline Algae (ρ = 0.354) (all other variables ρ < 0.2). β diversity (table 7) was well correlated with the pattern of site differences calculated using all environmental variables (β = 0.384; P = 0.014), and with that based on data describing silt, rubble and coralline algal cover (β = 0.354; P = 0.011). However representing differences between sites in terms of coralline algae alone did not produce significant correlation with β diversity patterns β = -0.042; P = 0.614). Nor was β diversity correlated with the simple geographical separation of the sites β = 0.152; P = 0.373). The test for that β diversity should be related to the geographical distance between sites was not significant. Finally, the test for concordance between rover and transect data sets indicated weak significant correlations between the two distance matrices using abundance weighted (= 0.297; P = 0.042) and presence/absence (= 0.333; P = 0.044) approaches. Discussion The abundance and diversity of reef associated fish species observed in the absence of hermatypic coral at Los Cobanos seems comparable to that found on some hermatypic reefs (Bell & Galzin 1984, Friedlander & Parrish 1998). If we consider the widely used expression in ecology of the more we look the more we find and consider the level of effort employed in the survey this notion is highlighted further. The Los Cobanos area is suffering from overfishing from artisan fishers and from spear fisherman, the first have significantly reduced the catch rates, from 1974-1991 approximately; average day time catches were 200lbs whereas now they are on average 30lbs (M. Campos pers. Comm.). Sharks were also caught in large numbers in the past but none have been caught or seen in the area for 10 years (M. Campos pers. Comm.). Spear fisherman could also be having an impact on the reef, no large grouper were recorded during the survey and the only clue to their presence came from a meeting with a spear fisherman who had taken one from the reef. The reef associated fish in this study seem to form discrete assemblage structures at the twelve study sites, with consistency existing between the patterns of species richness and abundance and the benthic predictors. Multivariate statistical analysis demonstrated some concordance in the relationship between the benthic environment and the fish assemblages when using the benthic variables as predictors. Soft coral cover is the number one predictor, at 53% when trying to predict the number of species using the rover data, if used in conjunction with silt this increases to 67% and increases further to 81% when sponges are added. Soft coral cover was the only significant predictor at 39% for the number of individuals. Soft coral cover was again the number one predictor at 33% when using the transect data to predict the number of individuals, if using soft coral cover and silt together as predictors this increases to 59%. When looking at the number of individuals using the transect data only one predictor was significant, again this was soft coral cover at 28%. In most cases the predictive benthic variables are positively characteristics of the habitat, ie vertical relief. associated with other The general results and observations seem to imply the importance of the biological element provided by the soft coral, and a three dimensional characteristic provided by the vertical relief being important to the local fish assemblages. While the three dimensional structural complexity of hermatypic coral is significant (Friedlander & Parrish 1998), here any structural complexity provided by the soft coral is arguably minimal. The structure and area of the larger rock surfaces do provide more available space and a source of attachment for the soft coral (Gratwicke & Speight 2005). Simply by their size the larger rock surfaces create a more heterogeneous habitat and with wider niche availability and in keeping with the Hutchinsonian concept of the niche allow more species to co-exist (Hutchinson 1957). With the addition of a biological element such as soft coral cover the habitat is further improved and can sustain more diversity and abundance of species (Bellwood et al 2004). Many other studies are more specific when considering the niche concept and have surveyed the habitat at a different spatial scale to that adopted here and used hole size as a measure of heterogeneity (Shulman 1984, Hixon & Beets 1989, Lingo & Szedlmayer 2006). Studies on artificial reefs have been able to show holes and hole size to be important in the structuring of reef fish assemblages and are used by fish as shelter and refuge sites from predators (Shulman 1984, Roberts & Ormond 1987 Hixon & Beets 1989, Lingo & Szedlmayer 2006). In natural systems number of holes only was found to be significant (Roberts & Ormond 1987) as apposed to number and size on artificial reefs. The use of hole size may have been prudent at Los Cobanos, unfortunately due to time constraints this variable was not recorded. Despite this, it is clear from the results that soft coral cover is by far the strongest predictor of the fish assemblages on the rocky reef habitat at Los Cobanos across sites and across the two study methods, rover transects. and From their study on Red Sea coral reefs Roberts & Ormond (1987) found soft coral to be significantly positively correlated with overall species richness, however this was confounded with depth. These findings are the opposite to those of Risk (1972) who found no correlation between fish diversity and biological diversity. The correlations between fish diversity and characteristics of the benthos may vary between species and between families dependant on their lifestyle (Roberts & Ormond 1987). Territorial species or those with smaller home ranges may be expected to be more influenced by local characteristics of the benthic habitat (Roberts & Ormond 1987), while those with a larger territory may be expected to be more influenced by the habitat at a larger spatial scale in conjunction with the keystone structure concept of Tews et al (2004). Past studies conducted on hermatypic coral reefs have shown live coral cover to be a factor in fish assemblages and some suggest that this could in fact be due to the complexity of the structure provided by some coral species allowing sites of refuge and shelter (Risk 1972, Luckhurst & Luckhurst 1978, Bellwood et al 2004). Perhaps the correlation seen here between live soft coral and fish diversity is not due to the structural component of the coral as clearly the soft coral observed at Los Cobanos bears little comparison in complexity with the likes of that seen on hermatypic reefs. In his study on coral reef fish diversity, Risk (1972) found what he described as a striking correlation between fish species diversity and substrate topographic complexity; he found no correlation between fish numbers and either complexity or biological diversity. Luckhurst & Luckhurst (1978) attempted to investigate the affect of live coral in isolation from rugosity and vertical relief; while they found positive correlations between surface rugosity and fish assemblage they found no relationship between the assemblage and coral species richness. In 1984 Bell & Galzin noted that there were no studies which examined effect of live coral cover in isolation from the significant effect of changes in habitat complexity on the structure of fish assemblages. By choosing study sites that showed comparable complexity but had differing percentages of live coral cover Bell & Galzin (1984) concluded that live coral cover may have more importance in structuring reef fish assemblages than previously thought, they went on to suggest that structural complexity on reefs and live coral cover should be considered separately. It could be that the suggestion made by Bell & Galzin (1984) of considering the effect of live coral cover in isolation from habitat complexity has been achieved in the present study at Los Cobanos, albeit with soft coral. The present study shows a clear relationship does exist between the soft coral cover and the fish assemblages at the rocky reef patches of Los Cobanos. Again considering the minimal complexity of the soft coral at Los Cobanos perhaps a biological relationship is the over arching factor responsible for the structuring of the fish assemblages on tropical rocky reefs. Or perhaps the keystone structure concept proposed by Tews et al (2004) is more important to some species and fish assemblage structure should consider the lifestyle characteristics of the fish species in relation to range size and territoriality before inferring which variables of the habitat are important. By viewing the assemblage in this way the problems of confounding variables highlighted by Roberts & Ormond (2004) could be eliminated. It seems that when investigating fish assemblages and looking for causal factors between diversity and the habitat, different methods and criteria need to be adopted according to the species and the habitat in question. As Gratwicke & Speight (2005) point out if looking at percentage live cover across a range of habitats it is not significant, probably because habitats such as sea grass beds with low fish diversity have a very high proportion of live cover. Within the literature, much research has been conducted on hermatypic reefs with regard to habitat structure and fish communities; however very little exists on rocky reefs. When conducting their study in 2000 Ferreira et al noted that few studies on habitat and community structure had been conducted away from coral reefs. This was echoed in 2004 by Gratwicke & Speight who noted that studies interested in the relationship between fish assemblages and habitat complexity/heterogeneity conducted on hermatypic coral reefs were controversial. They suggest this is due to there being problems with separating the biological factors associated with live coral from the physical factors of structure alone. They go on to say that other factors such as diversity of coral species could be more strongly correlated to fish species diversity, and any findings may not be easily applied to fish assemblages in other habitats (Gratwicke & Speight 2004). Detailed information on the feeding guilds of the fish at Los Cobanos may provide further insight into habitat preference if a relationship existed between soft coral abundance and obligate corallivores and grazers at sites with higher algal cover. While it is counter-intuitive, silt was the second most significant predictor when considering species diversity, one would expect heavily silted sites to not show high coral abundance as silting would be expected to inhibit coral growth but some sites with a high silt percentage also show a high coral percentage. Time lags could explain this in part if there has been a recent increase in silting, environmental changes that can effect population dynamics can be subject to time lags depending on the particular life histories of the species (Bull & Bonsall 2008). The observed silting could be input from land from deforestation or the presence of a shrimp farm in the area and the in situ observation of a possible convergence of local currents causing the concentration of silt particles. There is no correlation between beta diversity and the geographical distance between sites, there is however a significant relationship between beta diversity, coral cover, silt and sponges, but not with coral cover alone. This is in direct opposition to the single variable of soft coral cover shown to be so significant in predicting diversity and fish abundance at individual sites, highlighting the previous counter intuitive thoughts on the relationship between coral and silt. The patchiness of sites at Los Cobanos could be subdividing the populations (Sale 2004), perhaps supported by the statistic that no significance was found between geographical distance between sites and beta diversity. One could hypothesise that fish are having minimal movement between sites and recruitment into a new site would be subject to larval recruitment and being carried on the currents as opposed to migration. Most fish seldom move between patches of a suitable habitat once the larval stage is complete (Sale 2004). Any recruits then reaching maturity and remaining at a less than ideal site (ie one with minimal soft coral and/or heavily silted) may be more likely to be a generalist as habitat specialists are more likely to be governed by habitat availability than generalists that are able to utilize a range of habitat types (Brown 1984). Further demonstrating how fish lifestyle characteristics and feeding guilds could provide further insight into the causal factors of the habitat responsible for the observed fish assemblage structure on the rocky reefs of Los Cobanos. It is perhaps not unexpected to find more diversity and more individuals in areas with significant coral cover, it may be unexpected that this is also linked in some cases to high silt percentage cover. In the endeavour to investigate tropical fish assemblage structure and habitat complexity away from a hermatypic coral reef, the results here seem to suggest a biological relationship exists between the fish assemblage and soft coral at Los Cobanos. Unlike where on hermatypic coral reefs the complex physical structure can provide shelter and refuge for fish (Risk 1972, Luckhurst & Luckhurst 1978, Bellwood et al 2004) and could be the causal factor between the habitat and fish diversity/abundance this clearly is not the case with the soft coral at Los Cobanos. Figure 1 Results of ordination plot (MDS) based on Bray Curtis similarity matrix Tib Pic Rover: Species 26 36 Indiv’s 114 426 Ja Raj Lor Zav Qui Zun Alb La R 36 27 29 32 333 285 109 480 Transect: Species 18 19 17 15 18 12 Indiv’s 149 1048 261 417 175 55 Pan T Cr 28 107 33 269 38 526 18 32 267 330 39 424 10 54 10 83 14 132 7 27 17 406 11 72 Table 1 Summary of species and individuals at each site (rover individual values derived from middle value of each rank as used throughout) Analysis using rover data Fish species Predictors Constant %Coral Cover: Regression Residual Mean² F P R Adjusted R² 9.616 0.757 97.301 7.20 Constant %Coral Cover, Silt: 62.552 Regression 4.989 Residual Constant %Coral Cover, Silt, Sponges: 49.105 Regression 2.836 Residual 13.384 0.530 11.594 0.858 12.539 0.677 0.003 Constant %Coral Cover Regression Residual 403672.0 27.877 -11.270 15.655 0.931 17.316 0.817 0.001 Fish Individuals Mean² 30.624 0.004 Table 2. Model summary of multiple regression of site data and ANOVA results, with coefficients using the benthic data to predict the number of observed fish species from the rover data Predictors Coefficients F P R Adjusted R² 0.668 0.392 Coefficients -42.483 1972.521 8.077 0.017 49975.092 Table 3 Model summary of multiple regression of site data and ANOVA results, with coefficients using the benthic data to predict the number of fish individuals from the rover data 26.909 -16.726 -26.729 Analysis using transect totals data Fish species Predictors Constant %Coral Cover: Regression Residual % Coral Cover, Silt Regression Residual Mean² F P R Adjusted R² Coefficients 9.915 0.0632 0.339 26.263 67.854 6.643 0.28 10.215 0.820 57.090 9.205 0.007 6.202 0.599 11.944 26.194 -14.384 Table 4 Model summary of multiple regression of site data and ANOVA results, with coefficients using the benthic data to predict the number of observed fish species from the transect data Fish Individuals Predictors Mean² F P R Adjusted R² Coefficients Constant -49.936 %Coral Cover: 0.590 0.283 1773.582 Regression 309440.374 5.350 0.043 Residual 57837.054 Table 5 Model summary of multiple regression of site data and ANOVA results, with coefficients using the benthic data to predict the number of observed fish individuals from the transect data Diversity indices Sites H Value Tiburon 8.11398 La Pichelera 7.77921 JaJaJa 7.81136 Rajadura 5.42852 Lorenita 10.7353 Zavaneta 5.73335 Quinoguerita 4.7478 Zuniga 5.62029 Albanico 7.24102 La Roca 4.88486 Panpanera 5.58354 Tres Cruces 5.85473 Table 6 Shannon Weiner values of sites using the transect data Tiburon Tiburon La Pichelara JaJaJa Rajadura Lorenita Zavaneta Quinoguerita Zuniga Albanico La Roca Panpanera Tres Cruces La Pichelara JaJaJa Rajadura 0.528 0.75 0.634 0.603 0.473 0.461 0.554 0.536 0.586 0.559 0.5 0.610 Lorenita Zavaneta Quinoguerita Zuniga Albanico La Roca Panpanera 0.484 0.484 0.524 0.555 0.625 0.625 0.654 0.456 0.633 0.542 0.609 0.667 0.667 0.710 0.554 0.524 0.531 0.594 0.676 0.646 0.746 0.571 0.576 0.845 0.5 0.370 0.333 0.444 0.255 0.44 0.565 0.274 0.321 0.586 0.676 0.676 0.644 0.393 0.625 0.567 0.646 0.628 0.4 0.554 0.587 0.533 0.697 0.529 0.591 0.597 0.611 0.623 0.526 Table 7 Beta diversity values 0.676 Acknowledgements Thanks to J. Ready, S. Kullander, all at FUNZEl, all at El Salvador divers, for funding and field organisation. Thanks to M. Campos for his boat and his captaincy and his invaluable experience of the Los Cobanos region. Thanks to L. Munson for his competence and skill in the field and all data collection, and A. Foggo for comments and support. References Almada-Villela PC Sale P.F., Gold-Bouchot G. and Kjerfve B. (2003) Manual of methods for the MBRS synoptic monitoring program. MBRS Technical Document No. 4, MBRS, Belize City Bell JD, Galzin (1984) Influence of live coral cover on coral reef fish communities. Mar Ecol Prog Ser 15: 265-274 Bellwood DR, Hughes TP, Folke C, Nystrom M. (2004) Confronting the coral reef crisis. Nature 429 Brown JH (1984) On the relationship between the abundance and distribution of species. Am Nat 124: 255-279 Bull JC, Bonsall MB. (2008) Overcompensatory population dynamic responses to environmental stochasticity. 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