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      Traits across trophic levels interact to influence parasitoid establishment in biological control releases

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          Abstract

          A central goal in ecology is to predict what governs a species’ ability to establish in a new environment. One mechanism driving establishment success is individual species’ traits, but the role of trait combinations among interacting species across different trophic levels is less clear. Deliberate or accidental species additions to existing communities provide opportunities to study larger scale patterns of establishment success. Biological control introductions are especially valuable because they contain data on both the successfully established and unestablished species. Here, we used a recent dataset of importation biological control introductions to explore how life‐history traits of 132 parasitoid species and their herbivorous hosts interact to affect parasitoid establishment. We find that of five parasitoid and herbivore traits investigated, one parasitoid trait—host range—weakly predicts parasitoid establishment; parasitoids with higher levels of phylogenetic specialization have higher establishment success, though the effect is marginal. In addition, parasitoids are more likely to establish when their herbivore host has had a shorter residence time. Interestingly, we do not corroborate earlier findings that gregarious parasitoids and endoparasitoids are more likely to establish. Most importantly, we find that life‐history traits of the parasitoid species and their hosts can interact to influence establishment. Specifically, parasitoids with broader host ranges are more likely to establish when the herbivore they have been released to control is also more of a generalist. These results provide insight into how multiple species’ traits and their interactions, both within and across trophic levels, can influence establishment of species of higher trophic levels.

          Abstract

          Understanding how communities assemble is a major question in ecology. There has been little focus on how species interactions across trophic levels may influence the establishment of a species in a new community. We analyzed a dataset of deliberate introductions of parasitoids as biological control agents to fill this gap. We found that traits of the parasitoid species and their herbivorous hosts interacted to influence the establishment success of the parasitoid. Generalist parasitoid species were more likely to establish in a community when their hosts were also generalists. On the other hand, specialist parasitoids were equally likely to establish on specialist or generalist herbivores.

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          brms: An R Package for Bayesian Multilevel Models Using Stan

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            Stan: A Probabilistic Programming Language

            Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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              Multimodel Inference: Understanding AIC and BIC in Model Selection

              K. Burnham (2004)
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                Author and article information

                Contributors
                bjmjarrett@gmail.com
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                08 March 2022
                March 2022
                : 12
                : 3 ( doiID: 10.1002/ece3.v12.3 )
                : e8654
                Affiliations
                [ 1 ] ringgold 3078; Department of Entomology Michigan State University East Lansing Michigan USA
                [ 2 ] ringgold 3078; Department of Biology Lund University Lund Sweden
                Author notes
                [*] [* ] Correspondence

                Benjamin J. M. Jarrett, Department of Biology, Lund University, Lund 22362, Sweden.

                Email: bjmjarrett@ 123456gmail.com

                Author information
                https://orcid.org/0000-0003-2071-6076
                https://orcid.org/0000-0001-7972-9571
                Article
                ECE38654
                10.1002/ece3.8654
                8928891
                b5d9fc6f-11d3-44be-bb23-2c35c73e5583
                © 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

                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
                : 17 January 2022
                : 28 September 2021
                : 06 February 2022
                Page count
                Figures: 5, Tables: 1, Pages: 0, Words: 13689
                Funding
                Funded by: National Institute of Food and Agriculture , doi 10.13039/100005825;
                Award ID: 1017601
                Funded by: Michigan State University AgBioResearch , doi 10.13039/100011138;
                Categories
                Applied Ecology
                Community Ecology
                Entomology
                Evolutionary Ecology
                Invasion Ecology
                Research Article
                Research Articles
                Custom metadata
                2.0
                March 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.2 mode:remove_FC converted:08.03.2022

                Evolutionary Biology
                biological control,generalist,herbivore,host range,invasion biology,specialist
                Evolutionary Biology
                biological control, generalist, herbivore, host range, invasion biology, specialist

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