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      Yawning informs behavioural state changing in wild spotted hyaenas

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          Abstract

          Abstract

          Yawning is a complex behaviour linked to several physiological (e.g. drowsiness, arousal, thermoregulation) and social phenomena (e.g. yawn contagion). Being yawning an evolutionary well-conserved, fixed action pattern widespread in vertebrates, it is a valuable candidate to test hypotheses on its potential functions across the different taxa. The spotted hyaena ( Crocuta crocuta), the most social and cooperative species of the Hyaenidae family, is a good model to test hypotheses on yawning correlates and significances. Through an accurate sequential analysis performed on a group of wild hyaenas, we found that yawning mainly occurred during an imminent behavioural state changing in both juveniles and adults and that seeing others’ yawn elicited a mirror response in the receiver, thus demonstrating that yawn contagion is present in this species. These results taken together suggest that yawning is linked to a behavioural state change of the yawner and that such change is caught by the observers that engage in a motor resonance phenomenon, yawn contagion, possibly effective in anticipating yawners’ motor actions. Although additional data are necessary to verify whether yawn contagion translates into subsequent motor convergence and alignment, our data suggest that both spontaneous and contagious yawning can be fundamental building blocks on the basis of animal synchronisation in highly social and cooperative species.

          Significant statement

          Yawning is pervasive in many animal species, including humans. It is considered as a polyfunctional cue that has a role in regulating social interactions. While several studies focussed on yawning functions in primates, a little amount of effort was devoted to exploring this behaviour in social carnivores. We monitored a group of wild spotted hyaenas ( Crocuta crocuta), which is one of the most cooperative carnivore species. In both immature and adult subjects, we found that a subject frequently changed its behavioural state after spontaneously yawning and that seeing others’ yawn elicited a mirror response in the observer. Although additional data are necessary to verify whether yawn contagion translates into subsequent motor convergence and alignment, our data suggest that both spontaneous and contagious yawning can be fundamental building blocks on the basis of animal synchronisation in highly social and cooperative species.

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

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            The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded

            The coefficient of determination R 2 quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest. However, estimating R 2 for generalized linear mixed models (GLMMs) remains challenging. We have previously introduced a version of R 2 that we called for Poisson and binomial GLMMs, but not for other distributional families. Similarly, we earlier discussed how to estimate intra-class correlation coefficients (ICCs) using Poisson and binomial GLMMs. In this paper, we generalize our methods to all other non-Gaussian distributions, in particular to negative binomial and gamma distributions that are commonly used for modelling biological data. While expanding our approach, we highlight two useful concepts for biologists, Jensen's inequality and the delta method, both of which help us in understanding the properties of GLMMs. Jensen's inequality has important implications for biologically meaningful interpretation of GLMMs, whereas the delta method allows a general derivation of variance associated with non-Gaussian distributions. We also discuss some special considerations for binomial GLMMs with binary or proportion data. We illustrate the implementation of our extension by worked examples from the field of ecology and evolution in the R environment. However, our method can be used across disciplines and regardless of statistical environments.
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              Cryptic multiple hypotheses testing in linear models: overestimated effect sizes and the winner's curse

              Fitting generalised linear models (GLMs) with more than one predictor has become the standard method of analysis in evolutionary and behavioural research. Often, GLMs are used for exploratory data analysis, where one starts with a complex full model including interaction terms and then simplifies by removing non-significant terms. While this approach can be useful, it is problematic if significant effects are interpreted as if they arose from a single a priori hypothesis test. This is because model selection involves cryptic multiple hypothesis testing, a fact that has only rarely been acknowledged or quantified. We show that the probability of finding at least one ‘significant’ effect is high, even if all null hypotheses are true (e.g. 40% when starting with four predictors and their two-way interactions). This probability is close to theoretical expectations when the sample size (N) is large relative to the number of predictors including interactions (k). In contrast, type I error rates strongly exceed even those expectations when model simplification is applied to models that are over-fitted before simplification (low N/k ratio). The increase in false-positive results arises primarily from an overestimation of effect sizes among significant predictors, leading to upward-biased effect sizes that often cannot be reproduced in follow-up studies (‘the winner's curse’). Despite having their own problems, full model tests and P value adjustments can be used as a guide to how frequently type I errors arise by sampling variation alone. We favour the presentation of full models, since they best reflect the range of predictors investigated and ensure a balanced representation also of non-significant results.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Behavioral Ecology and Sociobiology
                Behav Ecol Sociobiol
                Springer Science and Business Media LLC
                0340-5443
                1432-0762
                November 2022
                October 29 2022
                November 2022
                : 76
                : 11
                Article
                10.1007/s00265-022-03261-y
                8b699507-ace6-4432-b6d8-5583d01824eb
                © 2022

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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