Code and Data for: Artificial Intelligence-Enhanced Predictive Modeling of Biodiversity Dynamics in European Dryland Ecosystems Under Multiple Climate Scenarios

This dataset contains Python code and processed data supporting AI-enhanced biodiversity projections for seven European dryland regions under three climate scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5) from 2025-2070. Includes trained machine learning ensemble models (Random Forest, XGBoost), species trait data, regional characteristics, and vulnerability assessments.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-24
DOI
https://doi.org/10.5281/zenodo.17702646
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
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article

Code and Data for: Artificial Intelligence-Enhanced Predictive Modeling of Biodiversity Dynamics in European Dryland Ecosystems Under Multiple Climate Scenarios

Zenodo (CERN European Organization for Nuclear Research)
Species Distribution and Climate Change
article

Code and Data for: Artificial Intelligence-Enhanced Predictive Modeling of Biodiversity Dynamics in European Dryland Ecosystems Under Multiple Climate Scenarios

article en

Abstract

This dataset contains Python code and processed data supporting AI-enhanced biodiversity projections for seven European dryland regions under three climate scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5) from 2025-2070. Includes trained machine learning ensemble models (Random Forest, XGBoost), species trait data, regional characteristics, and vulnerability assessments.

Zenodo (CERN European Organization for Nuclear Research)
University of Debrecen (HU)
Climate action
Openalex Percentile: Top 25%
Species Distribution and Climate Change
DOIOpen Access PDFOpenAlex
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Code and Data for: Artificial Intelligence-Enhanced Predictive Modeling of Biodiversity Dynamics in European Dryland Ecosystems Under Multiple Climate Scenarios · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS