摘要：Despite the increasing prevalence of forest-cover change and conflicts, most studies have been unable to unravel the complex relations between the two processes. We attribute this failure to methodological limitations. We put forward an alternative approach that combines different datasets (remote sensing, GIS, local narratives, official censuses, newspaper articles), methods (spatial and relational analyses), and scales (subregions, economic sectors, land-based activities) to create a robust explanation of the relations between different intensities of forest-cover change and conflict in the Meseta Purépecha region, central Mexico. This is an important forest region, inhabited by indigenous and mestizo peasants; it has a worldwide reputation for community forestry and is also the epicenter of international avocado production. Forest-cover change is intense and there are recurrent episodes of conflict. We clustered communities in three subregions according to their patterns of forest-cover change. We analyzed the spatial patterns of forest-cover change and conflicts and we characterized the structure and function of the different economic sectors to unravel the nonlinear, interdependent (and sometimes contradictory) relations among these processes. We found that avocado production has differentially shaped the composition and working of society within each subregion, leading to three diverging patterns. Avocado production has provoked conflicts over landownership and over illegal logging in nearby areas. In some areas, a low incidence of conflicts over forest clearance might be explained by high profits, coercion, and violence. We suggest that, by combining spatial and relational analyses, we can integrate and check the congruence of nonequivalent representations from quantitative sources and observant participation at different scales and explain the heterogeneity that processes display across space. Our methodological approach can thus improve our understanding of similar and other complex and uncertain environmental problems elsewhere, especially when accurate or appropriate data are missing.
