Testing for concordance between predicted species richness, past prioritization, and marine protected area designations in the western Indian Ocean

• Published in: Conservation biology 2024-08, Vol.38 (4), p.e14256-n/a
• Main Authors: McClanahan, Tim R., Friedlander, Alan M., Wickel, Julien, Graham, Nicholas A. J., Bruggemann, J. Henrich, Guillaume, Mireille M. M., Chabanet, P., Porter, Sean, Schleyer, Michael H., Azali, M. Kodia, Muthiga, N. A.
• Format: Article
• Language: English
• Subjects: Africa; Algorithms; biodiversity proxy; Clustering; Conservation; Conservation areas; Coral reefs; Corals; Criteria; Environment models; environmental drivers; Environmental Sciences; especies representantes; factores ambientales; Fish; Life Sciences; Machine learning; Marinas; Marine environment; Marine fishes; Marine invertebrates; Marine parks; Marine protected areas; marine spatial planning; modelo espacial; planeación espacial marina; Predictions; Protected areas; Protected species; Reef fish; Reef fishes; Reefs; riqueza de taxones; Spatial data; spatial modeling; Species richness; Taxa; taxa richness; África
• ISSN: 0888-8892; 1523-1739; 1523-1739
• DOI: 10.1111/cobi.14256

Scientific advances in environmental data coverage and machine learning algorithms have improved the ability to make large‐scale predictions where data are missing. These advances allowed us to develop a spatially resolved proxy for predicting numbers of tropical nearshore marine taxa. A diverse marine environmental spatial database was used to model numbers of taxa from ∼1000 field sites, and the predictions were applied to all 7039 6.25‐km2 reef cells in 9 ecoregions and 11 nations of the western Indian Ocean. Our proxy for total numbers of taxa was based on the positive correlation (r2 = 0.24) of numbers of taxa of hard corals and 5 highly diverse reef fish families. Environmental relationships indicated that the number of fish species was largely influenced by biomass, nearness to people, governance, connectivity, and productivity and that coral taxa were influenced mostly by physicochemical environmental variability. At spatial delineations of province, ecoregion, nation, and strength of spatial clustering, we compared areas of conservation priority based on our total species proxy with those identified in 3 previous priority‐setting reports and with the protected area database. Our method identified 119 locations that fit 3 numbers of taxa (hard coral, fish, and their combination) and 4 spatial delineations (nation, ecoregion, province, and reef clustering) criteria. Previous publications on priority setting identified 91 priority locations of which 6 were identified by all reports. We identified 12 locations that fit our 12 criteria and corresponded with 3 previously identified locations, 65 that aligned with at least 1 past report, and 28 that were new locations. Only 34% of the 208 marine protected areas in this province overlapped with identified locations with high numbers of predicted taxa. Differences occurred because past priorities were frequently based on unquantified perceptions of remoteness and preselected priority taxa. Our environment–species proxy and modeling approach can be considered among other important criteria for making conservation decisions. Evaluación de la concordancia entre la riqueza de especies pronosticada, priorizaciones pasadas y la designación de áreas marinas protegidas en el oeste del Océano Índico Resumen Los avances científicos en la cobertura de datos ambientales y los algoritmos de aprendizaje automatizado han mejorado la capacidad de predecir a gran escala cuando hacen falta datos. Estos avances nos permiten