Reefscape proxies for the conservation of Caribbean coral reef biodiversity (original) (raw)
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Geographical patterning of fish diversity across coral reef seascapes is driven by many interacting environmental variables operating at multiple spatial scales. Identifying suites of variables that explain spatial patterns of fish diversity is central to ecology and informs prioritization in marine conservation, particularly where protection of the highest biodiversity coral reefs is a primary goal. However, the relative importance of conventional within-patch variables versus the spatial patterning of the surrounding seascape is still unclear in the ecology of fishes on coral reefs. A multi-scale seascape approach derived from landscape ecology was applied to quantify and examine the explanatory roles of a wide range of variables at different spatial scales including: (i) within-patch structural attributes from field data (5 × 1 m 2 sample unit area); (ii) geometry of the seascape from sea-floor maps (10–50 m radius seascape units); and wave exposure from a hydrodynamic model (240 m resolution) for 251 coral reef survey sites in the US Virgin Islands. Non-parametric statistical learning techniques using single classification and regression trees (CART) and ensembles of boosted regression trees (TreeNet) were used to: (i) model interactions; and (ii) identify the most influential environmental predictors from multiple data types (diver surveys, terrain models, habitat maps) across multiple spatial scales (1–196,350 m 2). Classifying the continuous response variables into a binary category and instead predicting the presence and absence of fish species richness hotspots (top 10% richness) increased the predictive performance of the models. The best CART model predicted fish richness hotspots with 80% accuracy. The statistical interaction between abundance of living scleractin-ian corals measured by SCUBA divers within 1 m 2 quadrats and the topographical complexity of the surrounding sea-floor terrain (150 m radius seascape unit) measured from a high-resolution terrain model best explained geographical patterns in fish richness hotspots. The comparatively poor performance of models predicting continuous variability in fish diversity across the seascape could be a result of a decoupling of the diversity-environment relationship owing to structural degradation leading to a widespread homogenization of coral reef structure. K E Y W O R D S environmental predictors, fish species richness, landscape ecology, multi-scale, regression trees, seascape ecology
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Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean
Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process.We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing modelwas assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, lowclasses) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areaswere evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems.
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Coral reef habitats as surrogates of species, ecological functions, and ecosystem services
Conservation Biology, 2008
Abstract: Habitat maps are often the core spatially consistent data set on which marine reserve networks are designed, but their efficacy as surrogates for species richness and applicability to other conservation measures is poorly understood. Combining an analysis of field survey data, literature review, and expert assessment by a multidisciplinary working group, we examined the degree to which Caribbean coastal habitats provide useful planning information on 4 conservation measures: species richness, the ecological ...
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