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Coral reef ecosystems are topographically complex environments and this structural heterogeneity influences the distribution, abundance and behavior of marine organisms. Airborne hydrographic lidar (Light Detection and Ranging) provides high resolution digital bathymetry from which topographic complexity can be quantified at multiple spatial scales. To assess the utility of lidar data as a predictor of fish and coral diversity and abundance, seven different morphometrics were applied to a 4 m resolution bathymetry grid and then quantified at multiple spatial scales (i.e., 15, 25, 50, 100, 200 and 300 m radii) using a circular moving window analysis. Predictive models for nineteen fish metrics and two coral metrics were developed using the new statistical learning technique of stochastic gradient boosting applied to regression trees. Predictive models explained 72% of the variance in herbivore biomass, 68% of parrotfish biomass, 65% of coral species richness and 64% of fish species richness. Slope of the slope (a measure of the magnitude of slope change) at relatively local spatial scales (15-100 m radii) emerged as the single best predictor. Herbivorous fish responded to topographic complexity at spatial scales of 15 and 25 m radii, whereas broader spatial scales of between 25 and 300 m radii were relevant for piscivorous fish. This study demonstrates great utility for lidar-derived bathymetry in the future development of benthic habitat maps and faunal distribution maps to support ecosystem-based management and marine spatial planning. [ABSTRACT FROM AUTHOR]

Coral reefs and associated fish populations have experienced rapid decline in the Caribbean region and marine protected areas (MPAs) have been widely implemented to address this decline. The performance of no-take MPAs (i.e., marine reserves) for protecting and rebuilding fish populations is influenced by the movement of animals within and across their boundaries. Very little is known about Caribbean reef fish movements creating a critical knowledge gap that can impede effective MPA design, performance and evaluation. Using miniature implanted acoustic transmitters and a fixed acoustic receiver array, we address three key questions: How far can reef fish move? Does connectivity exist between adjacent MPAs? Does existing MPA size match the spatial scale of reef fish movements? We show that many reef fishes are capable of traveling far greater distances and in shorter duration than was previously known. Across the Puerto Rican Shelf, more than half of our 163 tagged fish (18 species of 10 families) moved distances greater than 1 km with three fish moving more than 10 km in a single day and a quarter spending time outside of MPAs. We provide direct evidence of ecological connectivity across a network of MPAs, including estimated movements of more than 40 km connecting a nearshore MPA with a shelf-edge spawning aggregation. Most tagged fish showed high fidelity to MPAs, but also spent time outside MPAs, potentially contributing to spillover. Three-quarters of our fish were capable of traveling distances that would take them beyond the protection offered by at least 40-64% of the existing eastern Caribbean MPAs. We recommend that key species movement patterns be used to inform and evaluate MPA functionality and design, particularly size and shape. A re-scaling of our perception of Caribbean reef fish mobility and habitat use is imperative, with important implications for ecology and management effectiveness.

Two of the major limitations to effective management of coral reef ecosystems are a lack of information on the spatial distribution of marine species and a paucity of data on the interacting environmental variables that drive distributional patterns. Advances in marine remote sensing, together with the novel integration of landscape ecology and advanced niche modelling techniques provide an unprecedented opportunity to reliably model and map marine species distributions across many kilometres of coral reef ecosystems. We developed a multi-scale approach using three-dimensional seafloor morphology and across-shelf location to predict spatial distributions for five common Caribbean fish species. Seascape topography was quantified from high resolution bathymetry at five spatial scales (5-300 m radii) surrounding fish survey sites. Model performance and map accuracy was assessed for two high performing machine-learning algorithms: Boosted Regression Trees (BRT) and Maximum Entropy Species Distribution Modelling (MaxEnt). The three most important predictors were geographical location across the shelf, followed by a measure of topographic complexity. Predictor contribution differed among species, yet rarely changed across spatial scales. BRT provided 'outstanding' model predictions (AUC = >0.9) for three of five fish species. MaxEnt provided 'outstanding' model predictions for two of five species, with the remaining three models considered 'excellent' (AUC = 0.8-0.9). In contrast, MaxEnt spatial predictions were markedly more accurate (92% map accuracy) than BRT (68% map accuracy). We demonstrate that reliable spatial predictions for a range of key fish species can be achieved by modelling the interaction between the geographical location across the shelf and the topographic heterogeneity of seafloor structure. This multi-scale, analytic approach is an important new cost-effective tool to accurately delineate essential fish habitat and support conservation prioritization in marine protected area design, zoning in marine spatial planning, and ecosystem-based fisheries management.