Testing for concordance between predicted species richness, past prioritization, and marine protected area designations in the western Indian Ocean
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 mar...
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| Published in: | Conservation biology Vol. 38; no. 4; pp. e14256 - n/a |
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| Main Authors: | , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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United States
Blackwell Publishing Ltd
01.08.2024
Wiley |
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| ISSN: | 0888-8892, 1523-1739, 1523-1739 |
| Online Access: | Get full text |
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| Abstract | 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 desarrollar un representante con resolución espacial para predecir la cantidad de taxones marinos en las costas tropicales. Usamos una base de datos espaciales de diversos ambientes marinos para modelar la cantidad de taxones a partir de ∼1000 sitios de campo y aplicamos las predicciones a las 7039 celdas arrecifales de 6.25‐km2 en nueve ecorregiones y once países del oeste del Océano Índico. Nuestro representante para la cantidad total de taxones se basó en la correlación positiva (r2=0.24) de la cantidad de taxones de corales duros y cinco familias de peces arrecifales con diversidad alta. Las relaciones ambientales indicaron que el número de especies de peces estuvo influenciado principalmente por la biomasa, la cercanía a las personas, la gestión, la conectividad y la productividad y que los taxones de coral estuvieron influenciados principalmente por la variabilidad ambiental fisicoquímica. Comparamos la prioridad de las áreas de conservación a nivel de las delimitaciones espaciales de provincia, ecorregión, nación y fuerza del agrupamiento espacial basado en nuestro total de especies representantes con aquellas especies identificadas en tres reportes previos de establecimiento de prioridades y con la base de datos de áreas protegidas. Con nuestro método identificamos 119 localidades aptas para tres cantidades de taxones (corales duros, peces y su combinación) y cuatro criterios de delimitación espacial (nación, ecorregión, provincia y grupo de arrecifes). Las publicaciones previas sobre el establecimiento de prioridades identificaron 91 localidades prioritarias de las cuales seis fueron identificadas por todos los reportes. Identificamos doce localidades que se ajustan a nuestros doce criterios y se correspondieron con tres localidades identificadas previamente, 65 que se alinearon con al menos un reporte anterior y 28 que eran nuevas localidades. Sólo 34% de las 208 áreas marinas protegidas en esta provincia se traslaparon con localidades identificadas con un gran número de taxones pronosticados. Hubo diferencias porque en el pasado se priorizaba frecuentemente con base en las percepciones no cuantificadas de lo remoto y prioritario de los taxones preseleccionados. Nuestra especie representante del ambiente y nuestra estrategia de modelo pueden considerarse entre otros criterios importantes para tomar decisiones de conservación. |
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| AbstractList | 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 desarrollar un representante con resolución espacial para predecir la cantidad de taxones marinos en las costas tropicales. Usamos una base de datos espaciales de diversos ambientes marinos para modelar la cantidad de taxones a partir de ∼1000 sitios de campo y aplicamos las predicciones a las 7039 celdas arrecifales de 6.25‐km2 en nueve ecorregiones y once países del oeste del Océano Índico. Nuestro representante para la cantidad total de taxones se basó en la correlación positiva (r2=0.24) de la cantidad de taxones de corales duros y cinco familias de peces arrecifales con diversidad alta. Las relaciones ambientales indicaron que el número de especies de peces estuvo influenciado principalmente por la biomasa, la cercanía a las personas, la gestión, la conectividad y la productividad y que los taxones de coral estuvieron influenciados principalmente por la variabilidad ambiental fisicoquímica. Comparamos la prioridad de las áreas de conservación a nivel de las delimitaciones espaciales de provincia, ecorregión, nación y fuerza del agrupamiento espacial basado en nuestro total de especies representantes con aquellas especies identificadas en tres reportes previos de establecimiento de prioridades y con la base de datos de áreas protegidas. Con nuestro método identificamos 119 localidades aptas para tres cantidades de taxones (corales duros, peces y su combinación) y cuatro criterios de delimitación espacial (nación, ecorregión, provincia y grupo de arrecifes). Las publicaciones previas sobre el establecimiento de prioridades identificaron 91 localidades prioritarias de las cuales seis fueron identificadas por todos los reportes. Identificamos doce localidades que se ajustan a nuestros doce criterios y se correspondieron con tres localidades identificadas previamente, 65 que se alinearon con al menos un reporte anterior y 28 que eran nuevas localidades. Sólo 34% de las 208 áreas marinas protegidas en esta provincia se traslaparon con localidades identificadas con un gran número de taxones pronosticados. Hubo diferencias porque en el pasado se priorizaba frecuentemente con base en las percepciones no cuantificadas de lo remoto y prioritario de los taxones preseleccionados. Nuestra especie representante del ambiente y nuestra estrategia de modelo pueden considerarse entre otros criterios importantes para tomar decisiones de conservación. 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.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. 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‐km 2 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 ( r 2 = 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. 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. 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-km 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 (r = 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. 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-km 2 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 (r 2 = 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 desarrollar un representante con resolución espacial para predecir la cantidad de taxones marinos en las costas tropicales. Usamos una base de datos espaciales de diversos ambientes marinos para modelar la cantidad de taxones a partir de ∼1000 sitios de campo y aplicamos las predicciones a las 7039 celdas arrecifales de 6.25‐km 2 en nueve ecorregiones y once países del oeste del Océano Índico. Nuestro representante para la cantidad total de taxones se basó en la correlación positiva ( r 2 =0.24) de la cantidad de taxones de corales duros y cinco familias de peces arrecifales con diversidad alta. Las relaciones ambientales indicaron que el número de especies de peces estuvo influenciado principalmente por la biomasa, la cercanía a las personas, la gestión, la conectividad y la productividad y que los taxones de coral estuvieron influenciados principalmente por la variabilidad ambiental fisicoquímica. Comparamos la prioridad de las áreas de conservación a nivel de las delimitaciones espaciales de provincia, ecorregión, nación y fuerza del agrupamiento espacial basado en nuestro total de especies representantes con aquellas especies identificadas en tres reportes previos de establecimiento de prioridades y con la base de datos de áreas protegidas. Con nuestro método identificamos 119 localidades aptas para tres cantidades de taxones (corales duros, peces y su combinación) y cuatro criterios de delimitación espacial (nación, ecorregión, provincia y grupo de arrecifes). Las publicaciones previas sobre el establecimiento de prioridades identificaron 91 localidades prioritarias de las cuales seis fueron identificadas por todos los reportes. Identificamos doce localidades que se ajustan a nuestros doce criterios y se correspondieron con tres localidades identificadas previamente, 65 que se alinearon con al menos un reporte anterior y 28 que eran nuevas localidades. Sólo 34% de las 208 áreas marinas protegidas en esta provincia se traslaparon con localidades identificadas con un gran número de taxones pronosticados. Hubo diferencias porque en el pasado se priorizaba frecuentemente con base en las percepciones no cuantificadas de lo remoto y prioritario de los taxones preseleccionados. Nuestra especie representante del ambiente y nuestra estrategia de modelo pueden considerarse entre otros criterios importantes para tomar decisiones de conservación. 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. |
| Author | Porter, Sean Azali, M. Kodia Wickel, Julien Friedlander, Alan M. Muthiga, N. A. Graham, Nicholas A. J. Guillaume, Mireille M. M. Schleyer, Michael H. Chabanet, P. Bruggemann, J. Henrich McClanahan, Tim R. |
| Author_xml | – sequence: 1 givenname: Tim R. orcidid: 0000-0001-5821-3584 surname: McClanahan fullname: McClanahan, Tim R. email: tmcclanahan@wcs.org organization: Wildlife Conservation Society – sequence: 2 givenname: Alan M. orcidid: 0000-0003-4858-006X surname: Friedlander fullname: Friedlander, Alan M. organization: University of Hawaiʿi – sequence: 3 givenname: Julien orcidid: 0000-0003-2717-6877 surname: Wickel fullname: Wickel, Julien organization: Marex Ltd – sequence: 4 givenname: Nicholas A. J. orcidid: 0000-0002-0304-7467 surname: Graham fullname: Graham, Nicholas A. J. organization: Lancaster University – sequence: 5 givenname: J. Henrich orcidid: 0000-0001-8764-3452 surname: Bruggemann fullname: Bruggemann, J. Henrich organization: Laboratoire d'Excellence CORAIL – sequence: 6 givenname: Mireille M. M. orcidid: 0000-0001-7249-0131 surname: Guillaume fullname: Guillaume, Mireille M. M. organization: Muséum National d'Histoire Naturelle – Sorbonne U – CNRS – IRD – UCN – UA – sequence: 7 givenname: P. orcidid: 0000-0001-7600-943X surname: Chabanet fullname: Chabanet, P. organization: Laboratoire d'Excellence CORAIL – sequence: 8 givenname: Sean orcidid: 0000-0001-8890-7982 surname: Porter fullname: Porter, Sean organization: Oceanographic Research Institute – sequence: 9 givenname: Michael H. orcidid: 0000-0002-7578-8168 surname: Schleyer fullname: Schleyer, Michael H. organization: Oceanographic Research Institute – sequence: 10 givenname: M. Kodia orcidid: 0000-0002-3454-3623 surname: Azali fullname: Azali, M. Kodia organization: Wildlife Conservation Society – sequence: 11 givenname: N. A. orcidid: 0000-0003-3278-7410 surname: Muthiga fullname: Muthiga, N. A. organization: Wildlife Conservation Society |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38545935$$D View this record in MEDLINE/PubMed https://hal.science/hal-04687415$$DView record in HAL |
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| CitedBy_id | crossref_primary_10_3389_fevo_2024_1450383 crossref_primary_10_1002_ecs2_70057 crossref_primary_10_3354_meps14852 |
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| ContentType | Journal Article |
| Copyright | 2024 The Authors. published by Wiley Periodicals LLC on behalf of Society for . 2024 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology. 2024. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Distributed under a Creative Commons Attribution 4.0 International License |
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| Keywords | planeación espacial marina África spatial modeling factores ambientales Africa biodiversity proxy marine spatial planning taxa richness riqueza de taxones environmental drivers modelo espacial especies representantes Africa biodiversity proxy environmental drivers marine spatial planning taxa richness spatial modeling |
| Language | English |
| License | Attribution 2024 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology. Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 |
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| SubjectTerms | Africa Algorithms Animals Anthozoa - physiology Biodiversity biodiversity proxy biomass Clustering Conservation Conservation areas Conservation of Natural Resources - methods Coral Reefs Corals Criteria ecoregions Environment models environmental drivers Environmental Sciences especies representantes factores ambientales Fish Fishes - physiology governance Indian Ocean Life Sciences Machine learning Marinas Marine environment Marine fishes Marine invertebrates Marine parks Marine protected areas marine spatial planning modelo espacial people planeación espacial marina Predictions prioritization Protected areas Protected species Reef fish Reef fishes Reefs riqueza de taxones Spatial data spatial modeling species Species richness Taxa taxa richness wildlife management África |
| Title | Testing for concordance between predicted species richness, past prioritization, and marine protected area designations in the western Indian Ocean |
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