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Financial support: Analysis for this paper was carried out as part of Guilherme Mores’ MSc dissertation, funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior from Brazilian Ministry of Education. Data collection was funded by the Prefeitura Municipal de Porto Alegre.
Authors’ addresses: Guilherme Barradas Mores, Programa de Pós-Graduação em Ecologia, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil, E-mail: [email protected]. Lavinia Schuler-Faccini, Hospital de Clínicas de Porto Alegre, Serviço de Genética Médica, Porto Alegre, Brazil, and Departamento de Genética, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil, E-mail: [email protected]. Heinrich Hasenack, Departamento de Ecologia, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil, E-mail: [email protected]. Liane Oliveira Fetzer and Getúlio Dornelles Souza, Núcleo de Vigilância de Roedores e Vetores, Diretoria Geral de Vigilância em Saúde, Secretaria Municipal de Saúde de Porto Alegre, Porto Alegre, Brazil, E-mails: [email protected] and [email protected]. Gonçalo Ferraz, Programa de Pós-Graduação em Ecologia, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil and Programa de Pós-Graduação em Ecologia, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil, E-mail: [email protected].
Abstract.
The Aedes aegypti mosquito inhabits most tropical and subtropical regions of the globe, where it transmits arboviral diseases of substantial public health relevance, such as dengue fever. In subtropical regions, Ae. aegypti often presents an annual abundance cycle driven by weather conditions. Because different population states may show varying responses to control, we are interested in studying what time of the year is most appropriate for control. To do so, we developed two dynamic site-occupancy models based on more than 200 weeks of mosquito trapping data from nearly 900 sites in a subtropical Brazilian city. Our phenomenological, Markovian models, fitted to data in a Bayesian framework, accounted for failure to detect mosquitoes in two alternative ways and for temporal variation in dynamic rates of local extinction and colonization of new sites. Infestation varied from nearly full cover of the city area in late summer, to between 10% and 67% of sites occupied in winter depending on the model. Sensitivity analysis reveals that changes in dynamic rates should have the greatest impact on site occupancy during autumn and early winter months, when the mosquito population is declining. We discuss the implications of this finding to the timing of mosquito control.