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dc.contributor.editorAbellan, Pedro
dc.creatorWiese, Daniel
dc.creatorEscalante, Ananias
dc.creatorMurphy, Heather
dc.creatorGutierrez-Velez, Victor Hugo
dc.creatorHenry, Kevin
dc.date.accessioned2020-04-20T16:23:04Z
dc.date.available2020-04-20T16:23:04Z
dc.date.issued2019-10-17
dc.identifier.citationWiese D, Escalante AA, Murphy H, Henry KA, Gutierrez-Velez VH (2019) Integrating environmental and neighborhood factors in MaxEnt modeling to predict species distributions: A case study of Aedes albopictus in southeastern Pennsylvania. PLoS ONE 14(10): e0223821. https://doi.org/10.1371/journal.pone.0223821
dc.identifier.issn1664-2295
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/48
dc.identifier.urihttp://hdl.handle.net/20.500.12613/61
dc.description.abstractAedes albopictus is a viable vector for several infectious diseases such as Zika, West Nile, Dengue viruses and others. Originating from Asia, this invasive species is rapidly expanding into North American temperate areas and urbanized places causing major concerns for public health. Previous analyses show that warm temperatures and high humidity during the mosquito season are ideal conditions for A. albopictus development, while its distribution is correlated with population density. To better understand A. albopictus expansion into urban places it is important to consider the role of both environmental and neighborhood factors. The present study aims to assess the relative importance of both environmental variables and neighborhood factors in the prediction of A. albopictus’ presence in Southeast Pennsylvania using MaxEnt (version 3.4.1) machine-learning algorithm. Three models are developed that include: (1) exclusively environmental variables, (2) exclusively neighborhood factors, and (3) a combination of environmental variables and neighborhood factors. Outcomes from the three models are compared in terms of variable importance, accuracy, and the spatial distribution of predicted A. albopictus’ presence. All three models predicted the presence of A. albopictus in urban centers, however, each to a different spatial extent. The combined model resulted in the highest accuracy (74.7%) compared to the model with only environmental variables (73.5%) and to the model with only neighborhood factors (72.1%) separately. Although the combined model does not essentially increase the accuracy in the prediction, the spatial patterns of mosquito distribution are different when compared to environmental or neighborhood factors alone. Environmental variables help to explain conditions associated with mosquitoes in suburban/rural areas, while neighborhood factors summarize the local conditions that can also impact mosquito habitats in predominantly urban places. Overall, the present study shows that MaxEnt is suitable for integrating neighborhood factors associated with mosquito presence that can complement and improve species distribution modeling.
dc.format.extent23 pages
dc.languageEnglish
dc.language.isoeng
dc.relation.ispartofOpen Access Publishing Fund (OAPF)
dc.relation.haspartPLOS One, Vol. 14, No. 10
dc.relation.isreferencedbyPLOS
dc.rightsAttribution CC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectHousing
dc.subjectUrban environments
dc.subjectPermutation
dc.subjectNeighborhoods
dc.subjectUrban areas
dc.subjectCensus
dc.subjectMosquitoes
dc.subjectPopulation density
dc.titleIntegrating environmental and neighborhood factors in MaxEnt modeling to predict species distributions: A case study of Aedes albopictus in southeastern Pennsylvania
dc.typeText
dc.type.genreArticle (Other)
dc.description.departmentEnvironmental Studies
dc.relation.doihttps://doi.org/10.1371/journal.pone.0223821
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.description.schoolcollegeTemple University. College of Liberal Arts
dc.description.sponsorTemple University Libraries Open Access Publishing Fund, 2019-2020 (Philadelphia, Pa.)
dc.creator.orcid0000-0002-1603-7583
dc.creator.orcid0000-0002-1532-3430
dc.creator.orcid0000-0002-0435-8515
dc.creator.orcid0000-0002-5348-9669
dc.temple.creatorWiese, Daniel
dc.temple.creatorEscalante, Ananias A.
dc.temple.creatorMurphy, Heather
dc.temple.creatorHenry, Kevin A.
dc.temple.creatorGutierrez-Velez, Victor Hugo
refterms.dateFOA2020-04-20T16:23:04Z


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