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      Habitat-dependent changes in vigilance behaviour of Red-crowned Crane influenced by wildlife tourism

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          Abstract

          The Endangered Red-crowned Crane ( Grus japonensis) is one of the most culturally iconic and sought-after species by wildlife tourists. Here we investigate how the presence of tourists influence the vigilance behaviour of cranes foraging in Suaeda salsa salt marshes and S. salsa/Phragmites australis mosaic habitat in the Yellow River Delta, China. We found that both the frequency and duration of crane vigilance significantly increased in the presence of wildlife tourists. Increased frequency in crane vigilance only occurred in the much taller S. salsa/P. australis mosaic vegetation whereas the duration of vigilance showed no significant difference between the two habitats. Crane vigilance declined with increasing distance from wildlife tourists in the two habitats, with a minimum distance of disturbance triggering a high degree of vigilance by cranes identified at 300 m. The presence of wildlife tourists may represent a form of disturbance to foraging cranes but is habitat dependent. Taller P. australis vegetation serves primarily as a visual obstruction for cranes, causing them to increase the frequency of vigilance behaviour. Our findings have important implications for the conservation of the migratory red-crowned crane population that winters in the Yellow River Delta and can help inform visitor management.

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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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            Behavioral decisions made under the risk of predation: a review and prospectus

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              Multimodel inference in ecology and evolution: challenges and solutions.

              Information theoretic approaches and model averaging are increasing in popularity, but this approach can be difficult to apply to the realistic, complex models that typify many ecological and evolutionary analyses. This is especially true for those researchers without a formal background in information theory. Here, we highlight a number of practical obstacles to model averaging complex models. Although not meant to be an exhaustive review, we identify several important issues with tentative solutions where they exist (e.g. dealing with collinearity amongst predictors; how to compute model-averaged parameters) and highlight areas for future research where solutions are not clear (e.g. when to use random intercepts or slopes; which information criteria to use when random factors are involved). We also provide a worked example of a mixed model analysis of inbreeding depression in a wild population. By providing an overview of these issues, we hope that this approach will become more accessible to those investigating any process where multiple variables impact an evolutionary or ecological response. © 2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology.
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                Author and article information

                Contributors
                zzw@bnu.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                30 November 2017
                30 November 2017
                2017
                : 7
                Affiliations
                [1 ]ISNI 0000 0000 9339 3042, GRID grid.411356.4, Provincal Key Laboratory of Animal Resource and Epidemic Disease Prevention, College of Life Sciences, Liaoning University, ; Shenyang, 110036 P.R. China
                [2 ]ISNI 0000 0004 1789 9964, GRID grid.20513.35, Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, ; Beijing, 100875 P.R. China
                [3 ]ISNI 0000 0001 0790 5329, GRID grid.25627.34, Division of Biology and Conservation Ecology, School of Science and Environment, Manchester Metropolitan University, Chester Street, ; Manchester, M1 5GD United Kingdom
                [4 ]Yellow River Delta National Nature Reserve Management Bureau, Dongying City, Shandong 257200 China
                Article
                16907
                10.1038/s41598-017-16907-z
                5709511
                29192203
                © The Author(s) 2017

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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