Evaluating the Spatial Relationships Between Tree Cover and Regional Temperature and Precipitation of the Yucatán Peninsula Applying Spatial Autoregressive Models

Abstract

Deforestation and forest degradation are important drivers of global warming, yet their implications on regional temperature and precipitation patterns are more elusive. In the Yucatán Peninsula, forest cover loss and deterioration has been rapidly advancing over the past decades. We applied local indicators of spatial association (LISA) cluster analysis and spatial autoregressive models (SAR) to evaluate the spatial relationships between tree cover and regional temperature and precipitation. We integrated NASA’s Global Forest Cover Change (GFCC) and WorldClim’s historical monthly weather datasets (2000–2015) to assess the effects of deforested, degraded, and dense forest land cover on temperature and precipitation distributions on the Yucatán Peninsula. LISA cluster analyses show warmer and drier conditions geographically coincide with deforested and degraded tree cover, but outliers allude to the potential influence of forest cover impacts on regional climate. Controlling spatial dependencies and including covariates, SAR models indicate that deforestation is associated with higher annual mean temperatures and minimum temperatures during dry and wet seasons, and decreased precipitation in the dry season. Degraded tree cover was related to higher maximum temperatures but did not relate to precipitation variability. We highlight the complex interactions between forest cover and climate and emphasize the importance of forest conservation for mitigating regional climate change.