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      Upper-limit agricultural dietary exposure to streptomycin in the laboratory reduces learning and foraging in bumblebees

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          Abstract

          In the past decade, the broadcast-spray application of antibiotics in US crops has increased exponentially in response to bacterial crop pathogens, but little is known about the sublethal impacts on beneficial organisms in agroecosystems. This is concerning given the key roles that microbes play in modulating insect fitness. A growing body of evidence suggests that insect gut microbiomes may play a role in learning and behaviour, which are key for the survival of pollinators and for their pollination efficacy, and which in turn could be disrupted by dietary antibiotic exposure. In the laboratory, we tested the effects of an upper-limit dietary exposure to streptomycin (200 ppm)—an antibiotic widely used to treat bacterial pathogens in crops—on bumblebee ( Bombus impatiens ) associative learning, foraging and stimulus avoidance behaviour. We used two operant conditioning assays: a free movement proboscis extension reflex protocol focused on short-term memory formation, and an automated radio-frequency identification tracking system focused on foraging. We show that upper-limit dietary streptomycin exposure slowed training, decreased foraging choice accuracy, increased avoidance behaviour and was associated with reduced foraging on sucrose-rewarding artificial flowers. This work underscores the need to further study the impacts of antibiotic use on beneficial insects in agricultural systems.

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          glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling

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            Generalized linear mixed models: a practical guide for ecology and evolution.

            How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.
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              The sublethal effects of pesticides on beneficial arthropods.

              Traditionally, measurement of the acute toxicity of pesticides to beneficial arthropods has relied largely on the determination of an acute median lethal dose or concentration. However, the estimated lethal dose during acute toxicity tests may only be a partial measure of the deleterious effects. In addition to direct mortality induced by pesticides, their sublethal effects on arthropod physiology and behavior must be considered for a complete analysis of their impact. An increasing number of studies and methods related to the identification and characterization of these effects have been published in the past 15 years. Review of sublethal effects reported in published literature, taking into account recent data, has revealed new insights into the sublethal effects of pesticides including effects on learning performance, behavior, and neurophysiology. We characterize the different types of sublethal effects on beneficial arthropods, focusing mainly on honey bees and natural enemies, and we describe the methods used in these studies. Finally, we discuss the potential for developing experimental approaches that take into account these sublethal effects in integrated pest management and the possibility of integrating their evaluation in pesticide registration procedures.
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                Author and article information

                Contributors
                Journal
                Proceedings of the Royal Society B: Biological Sciences
                Proc. R. Soc. B.
                The Royal Society
                0962-8452
                1471-2954
                February 09 2022
                February 09 2022
                February 09 2022
                : 289
                : 1968
                Affiliations
                [1 ]Department of Biology, Emory University, Atlanta, GA 30322, USA
                [2 ]Department of Physics, Emory University, Atlanta, GA 30322, USA
                [3 ]Initiative in Theory and Modeling of Living Systems, Emory University, Atlanta, GA 30322, USA
                [4 ]Department of Biology, University of Washington, Seattle, WA 98195, USA
                Article
                10.1098/rspb.2021.2514
                1f193d1f-c07c-4e27-916a-de2db34956ab
                © 2022

                https://royalsociety.org/journals/ethics-policies/data-sharing-mining/

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