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 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.