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

Year: 2025

Type: article

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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17702645

Author: Rabbi, Mohammad Fazle

Institution: University of Debrecen

Language: English

Topic: Species Distribution and Climate Change

Subfield: Ecological Modeling

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Climate action

Open Access status: green

OpenAlex ID: W7106528077

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