Data associated with: Socio-Ecological Mechanisms Supporting High Densities of Aedes albopictus (Diptera: Culicidae) in Baltimore, MD

This is the data file associated with,E. Little, D. Biehler, P. T. Leisnham, R. Jordan, S. Wilson, S. L. LaDeau, Socio-Ecological Mechanisms Supporting High Densities of Aedes albopictus (Diptera: Culicidae) in Baltimore, MD, Journal of Medical Entomology, Volume 54, Issue 5, September 2017, Pages 1183–1192.<br><br><b>Abstract</b>Social, ecological, and climatic factors interact creating a heterogeneous matrix that determines the spatiotemporal distribution of mosquitoes and human risks of exposure to the diseases they transmit. We explore linkages between the social and institutional processes behind residential abandonment, urban ecology, and the interactions of socio-ecological processes with abiotic drivers of mosquito production. Specifically, we test the relative roles of infrastructure degradation and vegetation for explaining the presence of Aedes albopictus Skuse 1894 to better predict spatial heterogeneity in mosquito exposure risk within urban environments. We further examine how precipitation interacts with these socially underpinned biophysical variables. We use a hierarchical statistical modeling approach to assess how environmental and climatic conditions over 3 years influence mosquito ecology across a socioeconomic gradient in Baltimore, MD. We show that decaying infrastructure and vegetation are important determinants of Ae. albopictus infestation. We demonstrate that both precipitation and vegetation influence mosquito production in ways that are mediated by the level of infrastructural decay on a given block. Mosquitoes were more common on blocks with greater abandonment, but when precipitation was low, mosquitoes were more likely to be found in higher-income neighborhoods with managed container habitat. Likewise, although increased vegetation was a negative predictor of mosquito infestation, more vegetation on blocks with high abandonment was associated with the largest mosquito populations. These findings indicate that fine spatial scale modeling of mosquito habitat within urban areas is needed to more accurately target vector control.<br><b>Dataset</b>Data_Little_2018.xlsx workbook contains the following sheets,1. ReadMe: project documentation and contact information2. Data3. Metadata: definitions for columns contained in data worksheet 4. Block locations: provides latitude/longitude for north-western most corner of each block cluster<br>