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      Dominant bee species and floral abundance drive parasite temporal dynamics in plant-pollinator communities

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          Abstract

          Pollinator declines can leave communities less diverse and potentially at increased risk to infectious diseases. Species-rich plant and bee communities have high species turnover, making the study of disease dynamics challenging. To address how temporal dynamics shape parasite prevalence in plant and bee communities, we screened >5,000 bees and flowers through an entire growing season for five common bee microparasites ( Nosema ceranae, N. bombi, Crithidia bombi, C. expoeki and neogregarines). Over 110 bee species and 89 flower species were screened, revealing 42% of bee species (12.2% individual bees) and 70% of flower species (8.7% individual flowers) had at least one parasite in or on them, respectively. Some common flowers (e.g., Lychnis flos-cuculi) harboured multiple parasite species, whilst others (e.g., Lythrum salicaria) had few. Significant temporal variation of parasite prevalence in bees was linked to bee diversity, bee and flower abundance, and community composition. Specifically, we found that bee communities had the highest prevalence late in the season, when social bees ( Bombus spp. and Apis mellifera) were dominant and bee diversity was lowest. Conversely, prevalence on flowers was lowest late in the season when floral abundance was the highest. Thus, turnover in the bee community impacted community-wide prevalence, and turnover in the plant community impacted when parasite transmission was likely to occur at flowers. These results imply that efforts to improve bee health will benefit from promoting high floral numbers to reduce transmission risk, maintaining bee diversity to dilute parasites, and monitoring the abundance of dominant competent hosts.

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          Is Open Access

          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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            Simultaneous inference in general parametric models.

            Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R add-on package multcomp, which provides a convenient interface to the general approach adopted here. Copyright 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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              glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling

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                Author and article information

                Journal
                101698577
                46074
                Nat Ecol Evol
                Nature ecology & evolution
                2397-334X
                17 June 2020
                20 July 2020
                October 2020
                20 January 2021
                : 4
                : 10
                : 1358-1367
                Affiliations
                [1 ]Department of Entomology, Cornell University, Ithaca, NY 14853, USA
                [2 ]Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot SL5 7PY, UK
                [3 ]Department of Entomology, University of California Riverside, 900 University Avenue, Riverside, CA 92521, USA
                [4 ]USDA-ARS, Pollinating Insect Research Unit, 1410 N 800 E, Logan, UT 84341, USA
                [5 ]Center for Advanced Computing, and Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY 14853, USA
                Author notes
                [*]

                Authors contributed equally

                Author contributions

                P.G., Q.S.M., C.R.M., and S.H.M. conceived the study. P.G., A.A.F., Q.S.M., C.R.M., and S.H.M. contributed to study design. P.G. and A.A.F. collected the field data. P.G., K.P., and Q.S.M. conducted the molecular work. A.D.T. developed molecular primers. P.A.M. identified and pinned the bee samples collected. W.H.N., P.G., C.R.M, and S.H.M. contributed to data analysis and wrote the first draft of the manuscript. All authors contributed substantially to the final draft.

                []Corresponding author
                Article
                NIHMS1604165
                10.1038/s41559-020-1247-x
                7529964
                32690902
                30096b62-fc9f-4ed3-b718-29e49605160f

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