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| Main Authors: | , , , , |
|---|---|
| Format: | Artículo científico |
| Language: | en |
| Published: |
Ecology and evolution
2025
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| Online Access: | https://pubmed.ncbi.nlm.nih.gov/40584670/ |
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Table of Contents:
- Modelling Complex Spatial Distribution in Central Italy: A Random Forest Approach Revealing Underrepresented Lowland Populations Based on Spatially-Explicit Predictors. Talarico, Lorenzo Catucci, Elena Martinoli, Marco Scardi, Michele Tancioni, Lorenzo Species distribution models are powerful tools to infer ecology and support management of conservation and socio-economic valuable taxa, such as brown trout ( complex). Using a random forest approach, we modelled its distribution in central Italy watercourses, using recent presences/absences and eight environmental/bioclimatic predictors. The model shows (i) high predictive ability ( = 0.76), (ii) predicts suitable, naturally-infrequent lowland watercourses where brown trout occurs or may occur. Moreover, the prediction values (iii) expresses a remarkable positive monotone relationship with abundance classes of brown trout computed during field sampling, despite such information was not included in the model development. Predictors' importance pointed out to the crucial role of bioclimatic constraints (linked to thermal suitability and habitat availability) over anthropogenic disturbance and lithotypes. This modelling exercise reiterates the importance of modelling approaches based on spatially explicit proxies of species habitat requirements to assist taxa management by revealing suitable but infrequent and singular areas that could be considered worthy of protection.