Little is known about the ways in which environmental change and variability shape migration decision-making and, therefore, population redistribution. Such understanding is important because migration has implications for the health and well-being of individuals and communities. In addition, an improved understanding of migration's environmental dimensions is essential given the recently-released report by the Intergovernmental Panel on Climate Change which asserts there are """"""""unequivocal"""""""" changes in the climate system (IPCC 2007:4) with future expectations of continued shifts in regional precipitation and temperature patterns. Many of these changes will most severely impact already-vulnerable regions including rural South Africa, our study setting, where precipitation and temperature shifts are anticipated. In these rural settings, livelihoods are often characterized by heavy dependence on local natural resources. Important land-based activities include arable farming, livestock husbandry, and consumption and trade in natural resources (e.g., fuelwood, wild herbs). Natural resources also act as """"""""buffers"""""""" against household shocks such as job loss and/or mortality. Given this high level of resource dependence, changes in local vegetation cover (due to shifts in precipitation/temperature patterns) hold tremendous potential to impact livelihoods. As related to outmigration, a decline in livelihood options can act as a """"""""push"""""""" factor shaping migration trends. Using a """"""""Rural Livelihoods"""""""" framework, we model the association between outmigration (in 2002 and 2007) and the availability and variability of local natural resources (2002-2006) within a health and demographic surveillance site, the Agincourt Health and Population Unit (AHPU). Located in an impoverished rural area in the South Africa's northeast, the AHPU has collected census data at 12-18 month intervals since 1992 from over 12,000 households in 21 villages. In addition, its location within a steep local topological gradient affords substantial variation in vegetation availability across the study setting. We combine socioeconomic and remotely sensed data measuring local """"""""greenness,"""""""" and develop multivariate spatially-informed models to examine outmigration and the availability and variability of proximate natural resources. In addition to specific research results, this project is designed as a foundation for development of longitudinal models tapping into the analytical strength for population-environment provided by demographic surveillance settings. Indeed, there are few settings with existing, appropriate data for modeling the associations between socioeconomic and environmental processes and health and demographic surveillance sites represent unique opportunities to grapple with these complex associations.
This research will improve understanding of the association between rural livelihoods, especially related to migration decisions, and environmental change in impoverished rural areas of less developed countries. Residents of these regions are especially vulnerable to environmental changes due to high reliance on local natural resources such as fuelwood and wild foods. Migration can be a coping strategy in response to changes in the availability of local natural resources, although relocation has important consequences for health and well-being of migrants and of individuals and communities in receiving areas.
|Maclaurin, Galen; Leyk, Stefan; Hunter, Lori M (2015) Understanding the combined impacts of aggregation and spatial non-stationarity: The case of migration-environment associations in rural South Africa. Trans GIS 19:877-895|
|Hunter, Lori M; Nawrotzki, Raphael; Leyk, Stefan et al. (2014) Rural Outmigration, Natural Capital, and Livelihoods in South Africa. Popul Space Place 20:402-420|
|Leyk, Stefan; Maclaurin, Galen J; Hunter, Lori M et al. (2012) Spatially and Temporally Varying Associations between Temporary Outmigration and Natural Resource Availability in Resource-Dependent Rural Communities in South Africa: A Modeling Framework. Appl Geogr 34:559-568|