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      The community‐wide effectiveness of municipal larval control programs for West Nile virus risk reduction in Co nnecticut, USA

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          Abstract

          BACKGROUND

          Mosquito larval control through the use of insecticides is the most common strategy for suppressing West Nile virus (WNV) vector populations in Connecticut (CT), USA. To evaluate the ability of larval control to reduce entomological risk metrics associated with WNV, we performed WNV surveillance and assessments of municipal larvicide application programs in Milford and Stratford, CT in 2019 and 2020. Each town treated catch basins and nonbasin habitats (Milford only) with biopesticide products during both WNV transmission seasons. Adult mosquitoes were collected weekly with gravid and CO 2‐baited light traps and tested for WNV; larvae and pupae were sampled weekly from basins within 500 m of trapping sites, and Culex pipiens larval mortality was determined with laboratory bioassays of catch basin water samples.

          RESULTS

          Declines in 4th instar larvae and pupae were observed in catch basins up to 2‐week post‐treatment, and we detected a positive relationship between adult female C. pipiens collections in gravid traps and pupal abundance in basins. We also detected a significant difference in total light trap collections between the two towns. Despite these findings, C. pipiens adult collections and WNV mosquito infection prevalence in gravid traps were similar between towns.

          CONCLUSION

          Larvicide applications reduced pupal abundance and the prevalence of host‐seeking adults with no detectable impact on entomological risk metrics for WNV. Further research is needed to better determine the level of mosquito larval control required to reduce WNV transmission risk.

          Abstract

          Town‐wide larval control reduced the number of larvae in catch basins, yet had little impact on adult mosquito abundance and West Nile virus infection prevalence.

          © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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          Fitting Linear Mixed-Effects Models Using lme4

          Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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            glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling

            Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the typical error distributions. We present a new package, glmmTMB, and compare it to other R packages that fit zero-inflated mixed models. The glmmTMB package fits many types of GLMMs and extensions, including models with continuously distributed responses, but here we focus on count responses. glmmTMB is faster than glmmADMB, MCMCglmm, and brms, and more flexible than INLA and mgcv for zero-inflated modeling. One unique feature of glmmTMB (among packages that fit zero-inflated mixed models) is its ability to estimate the Conway-Maxwell-Poisson distribution parameterized by the mean. Overall, its most appealing features for new users may be the combination of speed, flexibility, and its interface’s similarity to lme4. The R journal, 9 (2) ISSN:2073-4859
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              A brief introduction to mixed effects modelling and multi-model inference in ecology

              The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.
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                Author and article information

                Contributors
                joseph.mcmillan@ct.gov
                Journal
                Pest Manag Sci
                Pest Manag Sci
                10.1002/(ISSN)1526-4998
                PS
                Pest Management Science
                John Wiley & Sons, Ltd. (Chichester, UK )
                1526-498X
                1526-4998
                05 August 2021
                November 2021
                : 77
                : 11 ( doiID: 10.1002/ps.v77.11 )
                : 5186-5201
                Affiliations
                [ 1 ] The Connecticut Agricultural Experiment Station New Haven CT USA
                [ 2 ] The Northeast Regional Center of Excellence in Vector‐borne Diseases Cornell University Ithaca New York USA
                [ 3 ] Pennsylvania State University, State College University Park PA USA
                [ 4 ] Division of Vector‐borne Diseases Centers for Disease Control and Prevention Fort Collins CO USA
                [ 5 ] Yale University New Haven CT USA
                [ 6 ] Cornell University Ithaca NY USA
                Author notes
                [*] [* ] Correspondence to: JR McMillan, Connecticut Agricultural Experiment Station, 123 Huntington Street, New Haven, CT 06511, USA. E‐mail: joseph.mcmillan@ 123456ct.gov

                Author information
                https://orcid.org/0000-0002-6909-950X
                https://orcid.org/0000-0002-6852-229X
                https://orcid.org/0000-0002-2143-2051
                https://orcid.org/0000-0002-7261-9748
                Article
                PS6559
                10.1002/ps.6559
                9291174
                34272800
                81f423c3-38fc-4b83-ace5-8a0c05cc0458
                © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 02 July 2021
                : 15 June 2021
                : 16 July 2021
                Page count
                Figures: 6, Tables: 3, Pages: 16, Words: 10956
                Funding
                Funded by: Centers for Disease Control and Prevention , doi 10.13039/100000030;
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                November 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.7 mode:remove_FC converted:18.07.2022

                Pests, Diseases & Weeds
                mosquito larval control, west nile virus, culex pipiens complex,catch basin

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