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<p>Species distribution models (SDMs) are widely used to predict species’ habitat suitability, yet they often rely primarily on abiotic predictors, potentially limiting their ecological realism. This limitation may be particularly relevant for habitat-specialist species whose distribution is strongly shaped by vegetation structure and biotic context. In this study, we evaluated the contribution of ecologically informed biotic proxies to SDMs developed for <i>Crocus etruscus</i>, a nearly-threatened early-spring flowering geophyte endemic to Mediterranean forests of central Italy. We compared a baseline model based solely on abiotic variables with models incorporating land cover–derived dominant tree composition and remotely sensed NDVI. Models including dominant tree composition markedly outperformed both abiotic-only and NDVI-based models, achieving higher discrimination ability and improved model fit. However, NDVI provided additional information, when combined with land cover data. This study highlights the importance of integrating species-specific ecological knowledge into predictor selection and supports the use of biotic proxies to enhance the ecological relevance of SDMs applied to habitat-specialist plant species.</p>

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Ecologically informed biotic proxies improve species distribution models for a Mediterranean forest endemic geophyte

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Paola De Giorgi,
  • Daniela Ciccarelli,
  • Gianni Bedini

摘要

Species distribution models (SDMs) are widely used to predict species’ habitat suitability, yet they often rely primarily on abiotic predictors, potentially limiting their ecological realism. This limitation may be particularly relevant for habitat-specialist species whose distribution is strongly shaped by vegetation structure and biotic context. In this study, we evaluated the contribution of ecologically informed biotic proxies to SDMs developed for Crocus etruscus, a nearly-threatened early-spring flowering geophyte endemic to Mediterranean forests of central Italy. We compared a baseline model based solely on abiotic variables with models incorporating land cover–derived dominant tree composition and remotely sensed NDVI. Models including dominant tree composition markedly outperformed both abiotic-only and NDVI-based models, achieving higher discrimination ability and improved model fit. However, NDVI provided additional information, when combined with land cover data. This study highlights the importance of integrating species-specific ecological knowledge into predictor selection and supports the use of biotic proxies to enhance the ecological relevance of SDMs applied to habitat-specialist plant species.