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      Variable effects of vegetation characteristics on a recreation service depending on natural and social environment

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          Abstract

          In this study, we examined roles of three vegetation characteristics in provisioning of a recreation service by applying a machine-learning method to 4,708,229 spatially-explicit records of hiking activity in Japan. Then, expected impacts of land-use changes assessed and mapped based on the model. Associations between a recreation service and three vegetation characteristics were considerably variable depending on the social and natural environment such as accessibility and altitude. As a consequence, expected impacts of unit changes in vegetation characteristics on the service flow were considerably heterogeneous throughout the study area. The signs (positive or negative) of the impact can be reversed depending on the contexts even among nearby sites. Such notable but variable contributions of vegetation on a recreation service should be carefully reflected in landscape management. Even moderate changes in either the quantity or quality of vegetation can have a considerable impact on the frequency of hiking activity. Landscape management for promotion of the recreation service should be carefully designed for each locality on the grounds of the context-dependent effects of vegetation.

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          Random Forests

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            Greedy function approximation: A gradient boosting machine.

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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

                Contributors
                mshiro5@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                13 January 2023
                13 January 2023
                2023
                : 13
                : 684
                Affiliations
                [1 ]GRID grid.410846.f, ISNI 0000 0000 9370 8809, Research Institute for Humanity and Nature, ; 457-4 Motoyama, Kamigamo, Kita-ku, Kyoto, 603-8047 Japan
                [2 ]GRID grid.260975.f, ISNI 0000 0001 0671 5144, Faculty of Agriculture, , Niigata University, ; Niigata, 950-2181 Japan
                [3 ]GRID grid.417935.d, ISNI 0000 0000 9150 188X, Forestry and Forest Products Research Institute, ; Tsukuba, 305-8687 Japan
                Article
                27799
                10.1038/s41598-023-27799-7
                9839729
                36639682
                9525bb1e-2bca-4699-89ac-6f8ea403d4c5
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 25 August 2022
                : 9 January 2023
                Funding
                Funded by: Environment Research and Technology Development Fund (Predicting and Assessing Natural Capital and Ecosystem Services [PANCES], S-15-2(1))
                Funded by: Grants-in-Aid for Scientific Research (C)
                Award ID: 21K06354
                Award Recipient :
                Funded by: Environment Research and Technology Development Fund (Predicting and Assessing Natural Capital and Ecosystem Services [PANCES], S-15-2(2)
                Categories
                Article
                Custom metadata
                © The Author(s) 2023

                Uncategorized
                ecosystem services,forest ecology
                Uncategorized
                ecosystem services, forest ecology

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