Precise roadkill environmental factors identification for sustainable planning and design through spatial and temporal modelling
Résumé
Roads are major drivers of habitat fragmentation and amphibian mortality. Effective mitigation requires understanding species-specific, temporal, and multiscale environmental drivers, yet such integrated analyses are uncommon. Additionally, amphibian species in our region are understudied, and fine-scale roadkill drivers remain globally underexplored. This study aims to identify precise roadkill predictors for these species by integrating these approaches and to develop a framework for translating the findings into context-specific ecological planning and design. We combined standardized surveys and citizen science roadkill data for five anuran species in a Taiwanese biodiversity hotspot and analyzed them at three levels: multispecies, single-species, and single-species temporal subsets. We employed a dual Random Forest and Logistic Regression consensus approach, assessing a comprehensive set of predictors, including fine-scale roadside features, across multiple spatial scales (25–250 m) with an innovative uphill–downhill buffer design. We identified 24 robust consensus predictors, with habitat-related (e.g., water proximity, forest cover) and road-related fine-scale factors (e.g., streetlights, retaining walls, drainage channels) being equally important. Species-specific and temporal models (AUC up to 0.851) significantly outperformed the multispecies model, showing that predictor relevance and the scale of effect shift seasonally at inter- and intra-specific levels, particularly during breeding migrations. Our findings underscore that generalized approaches are insufficient for effective roadkill mitigation and highlight the need for context-specific, evidence-based ecological planning and design. The study provides a transferable framework that links ecological traits and predictor relevance to actionable, multi-stage planning, design, and operational management guidelines. This approach shows how integrating ecological precision with planning relevance can enhance biodiversity conservation for sustainable development.
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