Mapping habitats is essential to assist strategic decisions regarding the use and protection of coral reefs. Coupled with machine learning (ML) algorithms, remote sensing has allowed detailed mapping of reefs at meaningful scales. Here we integrated WorldView-3 and Landsat-8 imagery and ML techniques to produce a map of suitable habitats for the occurrence of a model species, the hydrocoral Millepora alcicornis, in coral reefs located inside marine protected areas in Northeast Brazil. Conservation and management efforts in the region were also analyzed, integrating human use layers to the ecological seascape. Three ML techniques were applied: two to derive base layers, namely geographically weighted regressions for bathymetry and support vector machine classifier (SVM) for habitat mapping, and one to build the species distribution model (MaxEnt) for Millepora alcicornis, a conspicuous and important reef-building species in the area. Additionally, human use was mapped based on the presence of tourists and fishers. SVM yielded 15 benthic classes (e.g., seagrass, sand, coral), with an overall accuracy of 79%. Bathymetry and its derivative layers depicted the topographical complexity of the area. The Millepora alcicornis distribution model identified distance from the shore and depth as topographical factors limiting the settling and growth of coral colonies. The most important variables were ecological, showing the importance of maintaining high biodiversity in the ecosystem. The comparison of the habitat suitability model with species absence and human use maps indicated the impact of direct human activities as potential inhibitors of coral development. Results reinforce the importance of the establishment of no-take zones and other protective measures for maintaining local biodiversity.
This study reports the results of 5 years of monitoring reef fish post‐larvae using light traps in the Bay of Tamandaré, north‐east Brazil. An annotated checklist of pre‐settlement fish species, their frequency of occurrence and taxonomic characteristics are provided. In total, 4,422 post‐larval fishes belonging to 36 families, 56 genera and 76 species were captured. The most species‐rich families were Carangidae (7), Lutjanidae (6) and Pomacentridae (4), while the families Gerreidae (30.47%), Holocentridae (16.54%), Blenniidae (12.01%), Labrisomidae (8.36%), Lutjanidae (8.29%) and Acanthuridae (5.95%) were the most abundant. This is the first study of the taxonomic diversity and assemblage structure of settlement‐stage reef fishes in the tropical south‐west Atlantic Ocean. Although a few common species were not captured due to selectivity of light traps, the composition and taxonomic diversity of this first collection suggests that light traps are useful for studies of the early life history of a wide range of pre‐settlement reef fishes.
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