Modeling population-environment dynamics in the Ecuadorian Amazon

Johns Hopkins University, Baltimore MD

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Abstract

[unreadable] DESCRIPTION (provided by applicant): The primary objective for this project is to examine some of the complex multidimensional relationships between population and environment dynamics in a tropical rainforest context that is undergoing substantial change due to the advance of the agricultural frontier. A better understanding of these relationships is vital for forest conservation and sustainable development, particularly in the Northern Ecuadorian Amazon (NEA), an area of extraordinary biodiversity that has the 9th fastest deforestation rate in the world. While the proximate causes of deforestation vary, rural-rural migrant farmers have been identified as the primary direct agents. We propose to undertake research in the NEA based upon panel data collected in 1990 and 1999 from land plots, constructing a hierarchical framework of land plots and communities to test hypotheses using multilevel statistical models. Three interrelated Aims are proposed to examine demographic, socioeconomic, institutional, and ecological factors: (i) Determine factors associated with the spatial distribution of family households overtime; (ii) Determine factors associated with changing household fertility overtime; (iii) Decompose changes in land use and land cover over time into factors related to population growth, location / proximity to communities / markets, year of farm establishment, natural resources, and wealth. Spatially-explicit models will be developed based upon an integrated approach to population- environment theory that focuses on proximate and underlying causes of change, combining theories from demography, Chayanovian agricultural household models, Von Thunen economic land use models, and recent approaches utilizing institutional and contextual variables. Data are derived from geo-coded longitudinal household and community data collected between 1990 and 2000, augmented by a time series of remotely sensed images from 1972 to the present. The availability of longitudinal geo-coded data from households and communities along with recent improvements in multilevel model estimation provide a unique opportunity to assess the relevance of many causal factors. This project will help advance knowledge of population-environment relationships, moving us towards refining theories of change in the tropics, and proffering vital information to policy-makers interested in achieving forest conservation and sustainable development. [unreadable] [unreadable] [unreadable]