Abstract
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.
Highlights
Rapid progress is being made in the development and implementation of marine management strategies, including marine spatial planning, to balance multiple conservation and resource use objectives [1,2,3]
The primary objectives of this study were to: (1) Determine whether the influence of environmental predictors on species’ distribution was scale-dependent; (2) evaluate the utility of environmental data from a single remote sensing device combined with metrics for surface morphology to predict and map fish species distributions across a complex coral reef ecosystem; (3) determine which components of remotely sensed seafloor structure contribute most to the species distribution models; (4) identify threshold effects where changes in environmental variables abruptly influence species occurrence; and (5) evaluate the performance of two different machine-learning modelling algorithms for spatial predictions of marine fish distributions
The generalized linear mixed models (GLMM) analysis showed that there was no significant effect of area under the curve (AUC) on modelling technique (x2 = 0.001; p.0.05; likelihood ratio test (LRT)); there was no effect of scale (x2 = 0.002; P.0.05; LRT), species (x2 = 0.116; p.0.05; LRT), nor interaction between scale and species (x2 = 0.118; p.0.05; LRT) on model performance
Summary
Rapid progress is being made in the development and implementation of marine management strategies, including marine spatial planning, to balance multiple conservation and resource use objectives [1,2,3]. A shift towards managing ecosystem patterns and processes is occurring, for example in ecosystem-based management, most strategies still have a focal species component, with directives to manage and monitor specific endangered, threatened, invasive, economically valuable, rare, keystone or indicator species [4,5,6]. To be effective, these strategies require spatially accurate ecological information on the geographical distribution of species, as well as an understanding of the key environmental drivers that determine species distributions. Human activity in the coastal zone combined with hurricanes, disease and thermal stress have resulted in broad-scale loss and degradation of biogenic structure created by reef forming scleractinian corals, seagrasses and mangroves [12,13,14]
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