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Characterizing the relationship between Asian tiger mosquito abundance and habitat in urban New Jersey
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Title
Characterizing the relationship between Asian tiger mosquito abundance and habitat in urban New Jersey
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ETD_2182
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051818
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eng
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theses
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Geography
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Topic
Mosquitoes--Habitat--New Jersey
Subject
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(authority = ETD-LCSH)
Topic
Aedes albopictus--Habitat--New Jersey
Abstract
Since its introduction to North America in 1987, the Asian tiger mosquito (Aedes albopictus) has spread rapidly. Due to its unique ecology and preference for container breeding sites, Ae. albopictus commonly inhabits urban/suburban areas and is often in close contact with humans. An aggressive pest, this mosquito species is a vector of multiple arboviruses. In order for mosquito control efforts to remain effective, control of this important vector must be guided by spatially explicit habitat models that aid in predicting mosquito outbreaks.
Using linear regression, I determined the relationship between adult Ae. albopictus abundance and climate, census, and land use factors in nine urban/suburban study sites in central New Jersey. Systematically collected adult counts (females and males) from July to October 2008, served as estimates of abundance. Fine-scale land use/land cover data were obtained from object-oriented classifications of 2007 CIR orthophotos in Definiens eCognition. Mosquito abundance data were tested for spatial autocorrelation via Moran’s I, semivariograms, and hotspot analysis in order to reveal consistent patterns in abundance.
Spatial pattern analysis produced little evidence of consistent spatial autocorrelation, though several sites exhibited recurring hotspots, especially in areas near residential housing and vegetation. Stepwise multiple regression was able to explain 20-25 percent of variation in Ae. albopictus abundance at the ‘backyard’ or cell level and 72-78 percent of variation in abundance at the ‘neighborhood’ or study site level. Meteorological variables (temperature on the trap date and precipitation), census variables (vacant housing units and population density), and more detailed land use/land cover classes (deciduous woody vegetation, rights-of-way and vacant lots) were frequently selected in all eight models, though many other independent variables were included in the individual models. The results of the spatial statistics suggest that clustering may occur at a broader extent, while the superior predictive ability of the site level models over the finer grain cell level models supports this conclusion. Future work should focus on validating these models with 2009 field data and testing whether finer grain weather and census data enhance the models’ predictive ability. Given the major differences between individual county models, future studies should further explore variations in Ae. albopictus habitat preferences in different geographic locations.
Using linear regression, I determined the relationship between adult Ae. albopictus abundance and climate, census, and land use factors in nine urban/suburban study sites in central New Jersey. Systematically collected adult counts (females and males) from July to October 2008, served as estimates of abundance. Fine-scale land use/land cover data were obtained from object-oriented classifications of 2007 CIR orthophotos in Definiens eCognition. Mosquito abundance data were tested for spatial autocorrelation via Moran’s I, semivariograms, and hotspot analysis in order to reveal consistent patterns in abundance.
Spatial pattern analysis produced little evidence of consistent spatial autocorrelation, though several sites exhibited recurring hotspots, especially in areas near residential housing and vegetation. Stepwise multiple regression was able to explain 20-25 percent of variation in Ae. albopictus abundance at the ‘backyard’ or cell level and 72-78 percent of variation in abundance at the ‘neighborhood’ or study site level. Meteorological variables (temperature on the trap date and precipitation), census variables (vacant housing units and population density), and more detailed land use/land cover classes (deciduous woody vegetation, rights-of-way and vacant lots) were frequently selected in all eight models, though many other independent variables were included in the individual models. The results of the spatial statistics suggest that clustering may occur at a broader extent, while the superior predictive ability of the site level models over the finer grain cell level models supports this conclusion. Future work should focus on validating these models with 2009 field data and testing whether finer grain weather and census data enhance the models’ predictive ability. Given the major differences between individual county models, future studies should further explore variations in Ae. albopictus habitat preferences in different geographic locations.
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electronic resource
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vii, 122 p. : ill.
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M.S.
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Includes bibliographical references (p. 76-79)
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by Carolin Ferwerda
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Ferwerda
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Carolin
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Carolin Ferwerda
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Lathrop
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Richard
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Richard Lathrop
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Tulloch
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David
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David Tulloch
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Schneider
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Laura
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Laura Schneider
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Fonseca
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Dina
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Dina Fonseca
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Rutgers University
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Graduate School - New Brunswick
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2009
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2009-10
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xx
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Rutgers University Electronic Theses and Dissertations
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Graduate School - New Brunswick Electronic Theses and Dissertations
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doi:10.7282/T3348KJN
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ETD graduate
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Rights
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The author owns the copyright to this work
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Copyright protected
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Open
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Ferwerda
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Carolin
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Carolin Ferwerda
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Rutgers University. Graduate School - New Brunswick
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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