Predictive Modeling of Juvenile Smalltooth Sawfish Habitats: Challenges and Opportunities for Conservation
ABSTRACT Effective conservation of rare species necessitates the identification of critical habitats and their specific features that influence species occurrence. This study focused on smalltooth sawfish (Pristis pectinata), a critically endangered elasmobranch, to explore how predictive spatial mo...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Ecology and Evolution |
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| Online Access: | https://doi.org/10.1002/ece3.70592 |
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| author | Andrea M. Kroetz Simon Dedman John K. Carlson |
| author_facet | Andrea M. Kroetz Simon Dedman John K. Carlson |
| author_sort | Andrea M. Kroetz |
| collection | DOAJ |
| description | ABSTRACT Effective conservation of rare species necessitates the identification of critical habitats and their specific features that influence species occurrence. This study focused on smalltooth sawfish (Pristis pectinata), a critically endangered elasmobranch, to explore how predictive spatial modeling can enhance conservation efforts. By leveraging long‐term occurrence and relative abundance data from scientific gillnet surveys, along with in situ environmental data, we used boosted regression trees (BRT) to pinpoint key habitat features essential for juvenile sawfish. Our analysis revealed strong correlations between sawfish presence and environmental variables, with a preferential selection of very shallow, warm, and saline waters fringed with mangroves, particularly those with high pneumatophore density. High relative abundances were observed in warmer months, and predictions of presence were consistent around discrete mangrove‐lined areas in Everglades National Park throughout all seasons. This study emphasizes the importance of high‐quality environmental data in predictive modeling and informs management strategies aimed at protecting the critical habitats necessary for the recovery of this species. Preventing the loss of mangroves in vulnerable regions of the smalltooth sawfish's range—especially near anthropogenic influences such as the Charlotte Harbor Estuary—is crucial for recovery. We also highlight the need for improved data access to facilitate global abundance predictions, thereby enhancing spatial management and conservation efforts for rare species. |
| format | Article |
| id | doaj-art-004efe0f09d945138a1e4fb4931d8961 |
| institution | Kabale University |
| issn | 2045-7758 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecology and Evolution |
| spelling | doaj-art-004efe0f09d945138a1e4fb4931d89612025-01-29T05:08:42ZengWileyEcology and Evolution2045-77582025-01-01151n/an/a10.1002/ece3.70592Predictive Modeling of Juvenile Smalltooth Sawfish Habitats: Challenges and Opportunities for ConservationAndrea M. Kroetz0Simon Dedman1John K. Carlson2Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School for Marine and Atmospheric Science University of Miami Miami Florida USADepartment of Biological Sciences, Institute of Environment Florida International University Miami Florida USANational Marine Fisheries Service Southeast Fisheries Science Center Panama City Florida USAABSTRACT Effective conservation of rare species necessitates the identification of critical habitats and their specific features that influence species occurrence. This study focused on smalltooth sawfish (Pristis pectinata), a critically endangered elasmobranch, to explore how predictive spatial modeling can enhance conservation efforts. By leveraging long‐term occurrence and relative abundance data from scientific gillnet surveys, along with in situ environmental data, we used boosted regression trees (BRT) to pinpoint key habitat features essential for juvenile sawfish. Our analysis revealed strong correlations between sawfish presence and environmental variables, with a preferential selection of very shallow, warm, and saline waters fringed with mangroves, particularly those with high pneumatophore density. High relative abundances were observed in warmer months, and predictions of presence were consistent around discrete mangrove‐lined areas in Everglades National Park throughout all seasons. This study emphasizes the importance of high‐quality environmental data in predictive modeling and informs management strategies aimed at protecting the critical habitats necessary for the recovery of this species. Preventing the loss of mangroves in vulnerable regions of the smalltooth sawfish's range—especially near anthropogenic influences such as the Charlotte Harbor Estuary—is crucial for recovery. We also highlight the need for improved data access to facilitate global abundance predictions, thereby enhancing spatial management and conservation efforts for rare species.https://doi.org/10.1002/ece3.70592conservationelasmobranchendangered specieshabitat usepredictive spatial modelingsawfish |
| spellingShingle | Andrea M. Kroetz Simon Dedman John K. Carlson Predictive Modeling of Juvenile Smalltooth Sawfish Habitats: Challenges and Opportunities for Conservation Ecology and Evolution conservation elasmobranch endangered species habitat use predictive spatial modeling sawfish |
| title | Predictive Modeling of Juvenile Smalltooth Sawfish Habitats: Challenges and Opportunities for Conservation |
| title_full | Predictive Modeling of Juvenile Smalltooth Sawfish Habitats: Challenges and Opportunities for Conservation |
| title_fullStr | Predictive Modeling of Juvenile Smalltooth Sawfish Habitats: Challenges and Opportunities for Conservation |
| title_full_unstemmed | Predictive Modeling of Juvenile Smalltooth Sawfish Habitats: Challenges and Opportunities for Conservation |
| title_short | Predictive Modeling of Juvenile Smalltooth Sawfish Habitats: Challenges and Opportunities for Conservation |
| title_sort | predictive modeling of juvenile smalltooth sawfish habitats challenges and opportunities for conservation |
| topic | conservation elasmobranch endangered species habitat use predictive spatial modeling sawfish |
| url | https://doi.org/10.1002/ece3.70592 |
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