Mosquito community composition in San Juan, Puerto Rico
Yee, Donald; Scavo, Nicole (2021), Mosquito community composition in San Juan, Puerto Rico, Dryad, Dataset, https://doi.org/10.5061/dryad.5x69p8d2k
Mosquito community dynamics in urban areas are influenced by an array of both social and ecological factors. Human socioeconomic factors (SEF) can be related to mosquito abundance and diversity as urban mosquito development sites are modified by varying human activity, e.g. level of abandoned structures or amount of accumulated trash. The goal of this study was to investigate the relationships among mosquito diversity, populations of Aedes aegypti, and SEF in a tropical urban setting. Mosquitoes were collected using BG Sentinel 2 traps and CDC light traps during three periods between late 2018 and early 2019 in San Juan, Puerto Rico and were identified to species. Socioeconomic factors (i.e., median household income, population density, college-level educational attainment, unemployment, health insurance coverage, percentage of households below the poverty line, amount of trash, and level of abandoned homes) were measured using foot surveys and U.S. Census data. We found 19 species with the two most abundant species being Culex quinquefasciatus (n = 10,641, 87.6%) and Ae. aegypti (n = 1,558, 12.8%). We found a positive association between Ae. aegypti abundance and mosquito diversity which were both negatively related to SES and ecological factors. Specifically, lower socioeconomic status neighborhoods had both more Ae. aegypti and more diverse communities, due to more favorable development habitat, indicating that control efforts should be focused in these areas.
Adult mosquitoes were sampled using BG Sentinel 2 traps baited with BG scented lures and CDC mini light traps baited with carbon dioxide (dry ice) set in the San Juan Metropolitan Area, Puerto Rico in 2018 and 2019. Mosquitoes were identified using morphological keys. A more indepth description of the methods can be
found in Scavo et al., 2021 (citation above).
National Science Foundation, Award: DEB-1806122