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dc.contributor.authorMAIA, A. de H. N.pt_BR
dc.contributor.authorPAZIANOTTO, R. A. A.pt_BR
dc.contributor.authorLUIZ, A. J. B.pt_BR
dc.contributor.authorPERVEZ, A.pt_BR
dc.date.accessioned2013-10-15T11:11:11Zpt_BR
dc.date.available2013-10-15T11:11:11Zpt_BR
dc.date.created2013-10-15pt_BR
dc.date.issued2013pt_BR
dc.identifier.citationIn: REUNIÃO ANUAL DA REGIÃO BRASILEIRA DA SOCIEDADE INTERNACIONAL DE BIOMETRIA, 58.; SIMPÓSIO DE ESTATÍSTICA APLICADA À EXPERIMENTAÇÃO AGRONÔMICA, 15., 2013, Campina Grande. Modelagem estatística em áreas multidisciplinares: impactos causados pelas mudanças climáticas na Região Nordeste: anais. Campina Grande: Sociedade Internacional de Biometria, 2013. 7 p.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/968632pt_BR
dc.descriptionFertility life table (FLT) parameters are important quantitative indicators of interactions between arthropod population and the environment. By summarizing information on both fertility and survivorship, they can capture chronicle sub lethal effects not detected by acute survival assays (MARINHO-PRADO, 2011; NASCIMENTO et al., 1998; NARDO et al (2001) LIU et al, 2005; LUMBIERRES et al, 2004). In life table studies, oviposition and survival data are collected over time (usually daily) and summarized into fertility life tables (FLT) for posterior estimation of the following parameters: net reproductive rate (Ro), intrinsic rate of increase (Rm), doubling time (DT), mean generation time (MGT) and finite rate of increase ( ), for each o treatments evaluated. As FLT parameters summarize data from experimental units into a single estimate for each group (treatment), the information on within treatment variance is not readily available, thus requiring the use of computationally intensive methods for its estimation. Among them, jackknife method, as proposed by MEYER (1986), is the most widely used for variance estimation in FLT analysis. Jacknife-based software available for life table analysis (HULTING et al, 1990 ; MAIA et al, 2000) was developed for analysing qualitative treatments, but such approach is frequently misused for contrasting quantitative factors (GANJISAFFAR et al., 2011; PAKYARI et al., 2011; RAZMJOU et al., 2011). Some authors use regression analysis after estimating FLT parameters for each factor level but do not account for the uncertainty of parameter estimates (CONTI et al, 2010). Here we present methods for adequately quantifying the influence of quantitative factors (e.g. temperature, pesticide level) on FLT parameters by combining the jackknife method with a regression analysis framework.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectJackknife methodpt_BR
dc.subjectFertility life tablespt_BR
dc.subjectArtropodapt_BR
dc.titleModelling influence of quantitative factors on arthropod demographic parameters.pt_BR
dc.typeArtigo em anais e proceedingspt_BR
dc.date.updated2013-10-15T11:11:11Zpt_BR
dc.subject.thesagroInsetopt_BR
dc.subject.thesagroDinâmica populacionalpt_BR
dc.subject.thesagroModelo matemáticopt_BR
dc.subject.thesagroAnálise estatísticapt_BR
dc.subject.thesagroArthropodapt_BR
dc.subject.nalthesaurusPopulation dynamicspt_BR
dc.subject.nalthesaurusInsectspt_BR
riaa.ainfo.id968632pt_BR
riaa.ainfo.lastupdate2013-10-15pt_BR
dc.contributor.institutionALINE DE HOLANDA NUNES MAIA, CNPMA; RICARDO ANTONIO ALMEIDA PAZIANOTTO, CNPMA; ALFREDO JOSE BARRETO LUIZ, CNPMA; AHMAD PERVEZ, Govt. Degree College.pt_BR
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