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      Thermal flexibility and a generalist life history promote urban affinity in butterflies

      1 , 2 , 1 , 3 , 4 , 1 , 2 , 5
      Global Change Biology
      Wiley

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              A working guide to boosted regression trees.

              1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.
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                Author and article information

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                Journal
                Global Change Biology
                Glob Change Biol
                Wiley
                1354-1013
                1365-2486
                May 30 2021
                Affiliations
                [1 ]German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Leipzig Germany
                [2 ]Institute of Biology Martin Luther University Halle‐Wittenberg Halle (Saale) Germany
                [3 ]Institute of Biodiversity Friedrich Schiller University Jena Jena Germany
                [4 ]Helmholtz Center for Environmental Research – UFZ Department of Ecosystem Services Leipzig Germany
                [5 ]CIBIO (Research Centre in Biodiversity and Genetic Resources)–InBIO (Research Network in Biodiversity and Evolutionary Biology) Universidade do Porto Vairão Portugal
                Article
                10.1111/gcb.15670
                34056817
                592200be-a2fc-463b-8c60-9ac2436f9b29
                © 2021

                http://creativecommons.org/licenses/by-nc/4.0/

                http://doi.wiley.com/10.1002/tdm_license_1.1

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