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      Exploring the Effect of the COVID-19 Zoo Closure Period on Flamingo Behaviour and Enclosure Use at Two Institutions

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      Birds
      MDPI AG

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          Abstract

          Visitors can influence the behaviour of zoo animals through their auditory and visual presence, with mixed findings of negative, neutral, and positive effects on welfare. This study opportunistically utilised the UK-wide COVID-19 period of zoo closure to investigate the activity and enclosure usage of Greater (Phoenicopterus roseus) and Chilean (P. chilensis) Flamingos housed at two zoos. Flamingo behaviour at both sites was observed during the last week of a three-month closure period and the immediate reopening of the zoos. Photographic data were collected at three timepoints during each observation day. Negative binomial GLMMs compared the behaviour observed during zoo closure to the behaviour observed during zoo reopening, whilst accounting for climatic variables and time of day. Spearman’s correlation identified relationships between behaviour with the number of visitors and weather. Greater Flamingos were not influenced by the reintroduction of visitors to the zoo setting. Chilean Flamingos showed an increase in inactivity and decrease in movement and feeding when the zoo reopened. These possible behavioural responses are better explained by the influence of temperature on the behaviour of Chilean Flamingos and by the correlation between temperature and visitor number, rather than a direct consequence of visitor presence. This research details the multifactorial nature of any potential anthropogenic effects on zoo animal behaviour and highlights the importance of considering environmental variables alongside the measurement of visitor presence or absence.

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          performance: An R Package for Assessment, Comparison and Testing of Statistical Models

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            Fitting Linear Mixed-Effects Models using lme4

            Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. 51 pages, including R code, and an appendix
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              Explaining odds ratios.

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                Author and article information

                Contributors
                Journal
                Birds
                Birds
                MDPI AG
                2673-6004
                March 2022
                February 10 2022
                : 3
                : 1
                : 117-137
                Article
                10.3390/birds3010009
                0eeb9bcf-4327-457b-be65-9b7839eb8e8c
                © 2022

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

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