Explaining island-wide geographical patterns of Caribbean fish diversity: a multi-scale seascape ecology approach
Sekund, L.; Pittman, S. (2017). Explaining island-wide geographical patterns of Caribbean fish diversity: a multi-scale seascape ecology approach. Mar. Ecol. (Berl.) 38(3): e12434. https://dx.doi.org/10.1111/maec.12434 In: Marine Ecology (Berlin). Blackwell: Berlin. ISSN 0173-9565; e-ISSN 1439-0485, more |  |
| Keyword | | | Author keywords | environmental predictors; fish species richness; landscape ecology;multi-scale; regression trees; seascape ecology |
| Abstract | 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 m2 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 m2). 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 scleractinian corals measured by SCUBA divers within 1 m2 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. |
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