Drivers of forest cover transitions in the Selva Maya, Mexico: Integrating regional and community scales for landscape assessment

• Published in: Land degradation & development Vol. 32; no. 10; pp. 3122 - 3141
• Main Authors: Ellis, Edward A., Navarro‐Martínez, Angélica, García‐Ortega, Martha
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.06.2021; Wiley Subscription Services, Inc
• Subjects: Biodiversity; Biodiversity loss; Cattle production; Climate change; Comparative analysis; Conservation; Deforestation; degradation; drivers; Employment; Environmental degradation; Farms; Forest conservation; Forest growth; Forest management; Land use; Landscape; Mathematical models; Mexico; Population growth; Poverty; Qualitative analysis; Recovery; Regression analysis; Remote sensing; Rural development; Socioeconomic factors; Socioeconomics; Statistical analysis; Statistical models; Sustainable development; tropical forest; Tropical forests; Variance analysis; Mexico
• ISSN: 1085-3278; 1099-145X
• DOI: 10.1002/ldr.3972

Tropical forest disturbance contributes to global climate change from increased carbon emissions, and causes loss of biodiversity. Thus, identifying its direct causes and underlying drivers are necessary for effective land use, climate change control and conservation strategies. We integrated remote sensing forest cover data from 2000 to 2018 with georeferenced national socioeconomic and field‐collected household data to determine underlying drivers behind forest cover transitions (e.g., deforestation, degradation, and recovery) in the Selva Maya (‘Mayan Forest’) of southeast Mexico. Spatial and statistical models (multinomial logistic regression, log‐linear regression, and analysis of variance) and social science methods (household surveys and qualitative comparative analysis) were applied to evaluate and identify socioeconomic, institutional, and environmental drivers intervening at landscape and community scales. Forest cover transitions varied geographically, and associated drivers differed by scale of analysis. Using multiple methods improved the understanding of drivers. Population growth, poverty, and roads are major drivers influencing forest cover transitions (e.g., deforestation, degradation, and recovery) in the landscape. Community scale analysis identified more drivers and offered greater detail of causal relationships. Besides population and poverty, less off‐farm employment, agriculture and cattle production, immigrant population, and private property were related to deforestation and degradation. Indigenous populations, forest dependence, off‐farm employment, and common property were associated with forest conservation. Sustainable rural development should include poverty alleviation through diversification of economic activities and increased off‐farm employment opportunities. Conservation measures should pursue the enhancement of forest value for local subsistence and economic benefits by strengthening community forest management and enterprises.