Comparative Analysis of Distribution and Abundance of West Nile and Eastern Equine Encephalomyelitis Virus Vectors in Suffolk County, New York, Using Human Population Density and Land Use/Cover Data

Abstract

Five years of CDC light trap data from Suffolk County, NY, were analyzed to compare the applicability of human population density (HPD) and land use/cover (LUC) classification systems to describe mosquito abundance and to determine whether certain mosquito species of medical importance tend to be more common in urban (defined by HPD) or residential (defined by LUC) areas. Eleven study sites were categorized as urban or rural using U.S. Census Bureau data and by LUC types using geographic information systems (GISs). Abundance and percent composition of nine mosquito taxa, all known or potential vectors of arboviruses, were analyzed to determine spatial patterns. By HPD definitions, three mosquito species, Aedes canadensis (Theobald), Coquillettidia perturbans (Walker), and Culiseta melanura (Coquillett), differed significantly between habitat types, with higher abundance and percent composition in rural areas. Abundance and percent composition of these three species also increased with freshwater wetland, natural vegetation areas, or a combination when using LUC definitions. Additionally, two species, Ae. canadensis and Cs. melanura, were negatively affected by increased residential area. One species, Aedes vexans (Meigen), had higher percent composition in urban areas. Two medically important taxa, Culex spp. and Aedes triseriatus (Say), were proportionally more prevalent in residential areas by LUC classification, as was Aedes trivittatus (Coquillett). Although HPD classification was readily available and had some predictive value, LUC classification resulted in higher spatial resolution and better ability to develop location specific predictive models.

Regional Differences in the Association Between Land Cover and West Nile Virus Disease Incidence in Humans in the United States

“…tarsalis in Western regions. 5,6,10,11,[31][32][33][34][35][36][37][38] Supplemental Figure S2 has a range map of these species. 24 A few studies have reported results that were contradictory to our findings.…”

Hosts or habitats: What drives the spatial distribution of mosquitoes?

“…Land cover/ vegetation indices (Moncayo et al 2000, DiukWasser et al 2006, Rochlin et al 2008, canopy cover (Moncayo et al 2000), soil properties (Patz 1998), elevation , Jacob et al 2010, and hydrology (Shaman et al 2002), are just a few of the known environmental variables which have been shown to be associated with the local abundance of a particular mosquito species. Diuk-Wasser et al (2006) demonstrated the predictive capabilities of GIS for mosquitoes by using remotely sensed landscape elements to model relative abundances of arbovirus vectors, then testing the validity of their models using trapping data from 31 sites, over a three-year period.…”

Invasive plant alters ability to predict disease vector distribution

“…The accuracy of many disease risk models depends on the relationship between habitat features and vector abundance (Brownstein et al 2002, Diuk-Wasser et al 2006, Peterson et al 2008, Rochlin et al 2008. However, one of the most important pathways by which humans alter natural habitats is by the introduction of nonnative species into novel landscapes (Hobbs and Humphries 1995), and the distribution of many invasive species is linked to human disturbance and human population centers (Bartuszevige et al 2006, Thuiller et al 2006.…”

“…The geographic range of A. triseriatus encompasses most of the eastern United States, from Maine to Montana in the north to Florida and Texas in the south (Jenkins and Carpenter 1946). Understanding the habitat preferences of A. triseriatus is important for assessing disease risk because, as a container-breeding mosquito that utilizes both abandoned tires as well as natural phytotelmata, it is frequently associated with habitats in close proximity to human habitation (Rochlin et al 2008). Although such areas of high oviposition activity do not necessarily correlate directly with areas of high adult density (Ellis 2008), they remain important for understanding vector distribution because models of disease risk report strong correlations between distances from breeding habitat, biting intensity, and rates of disease transmission (Beaty and Thompson 1975, Smith et al 2004, Le Menach et al 2005.…”

“…Many disease forecasting models use remotely sensed habitat data to predict the risk of transmission to humans (Smith et al 2004, Diuk-Wasser et al 2006, Rochlin et al 2008. However, if invasive species alter habitat selection behavior of disease vectors in ways that are undetectable using remote sensing methods, disease forecasting models may be vulnerable to substantial error associated with the cryptic impacts of invasive species.…”