Modeling proximate causes of deforestation in Antioquia, Colombia

• Published in: Regional environmental change Vol. 24; no. 4; p. 149
• Main Authors: Calderón-Caro, Jennifer, Morales-Gómez, Luz María, Gutiérrez-Vélez, Víctor H., González-Caro, Sebastián, Benavides, Ana María
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024; Springer Nature B.V
• Subjects: Biodiversity hot spots; Climate Change; Climate Change/Climate Change Impacts; Deforestation; Earth and Environmental Science; Environment; Forests; Geography; Habitat fragmentation; Habitat loss; Land cover; Land use; Landscape; Livestock; Machine learning; Mountains; Nature Conservation; Oceanography; Original Article; Proximate causes; Regional/Spatial Science; Remote sensing; Socioeconomic factors; Socioeconomics; Spatial distribution; Colombia; Andean forests; Livestock; Forest loss; Antioquia province; Random forest algorithm; Remote sensing
• ISSN: 1436-3798; 1436-378X
• DOI: 10.1007/s10113-024-02302-8

The tropical Andes, the highest global biodiversity hotspot, is highly threatened by habitat loss and intense land use. Forest loss rates have remained persistent throughout the region during this century, resulting in a spread fragmented landscape composed of small forest fragments. However, the spatial distribution and magnitude of the proximate causes of associated land-cover change across the tropical Andes remain unclear at regional scales (e.g., provinces). Here, we combined climate, socioeconomic, and remote sensing data with machine-learning methods to model the proximate causes of forest loss across Antioquia, Colombia, where mountains dominate the landscape. We found that temperature is one of the main proximate causes of deforestation because it defines the distribution of crops and cattle (i.e., livestock) along an elevational gradient. We also detected that vegetation features and preceding deforestation predict current deforestation increasing our ability to identify proximate causes of deforestation. Additionally, there is a high influence of local-scale socioeconomic factors driving deforestation trends over the last decade, providing evidence that implementing control measures and economic alternatives tailored to each region can reduce deforestation rates. Our model would be used as a valuable tool to inform decisions aimed at reducing pressure on forests.