Associating sex-biased and seasonal behaviour with contact patterns and transmission risk in Gopherus agassizii

in Behaviour
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Abstract

Interactions between wildlife hosts act as transmission routes for directly transmitted pathogens and vary in ways that affect transmission efficiency. Identifying drivers of contact variation can allow both contact inference and estimation of transmission dynamics despite limited data. In desert tortoises, mating strategy, burrow use and seasonal change influence numerous behaviours and likely shape contact patterns. In this study, we ask to what extent tortoise contact behaviour varies between sexes and seasons, and whether space or burrow-use data can be used to infer contact characteristics consistent with those recorded by proximity loggers. We identified sex and season-biased contact behaviour in both wild and captive populations indicative of female-female avoidance and seasonal male mate-seeking behaviour. Space and burrow-use patterns were informative, but did not always predict the extent of sex or seasonal biases on contact. We discuss the implications these findings have for transmission patterns and disease mitigation in tortoise populations.

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References

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Figures

  • Study animal numbers and sexes at four sites from Apr–Oct 2012.

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  • Summary data for desert tortoises monitored from wild and captive populations including: total number of monitored dyads (Ndyads), proportion of dyads with non-zero spatial overlap (pspatial), proportion of dyads with non-zero burrow overlap (pburrow), proportion of dyads with a logger-recorded contact (pcontact) and mean ± SE active logger hours per dyad.

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  • Violin plots showing the distribution of (a) contact durations and (b) contact frequencies recorded by proximity loggers of interacting tortoise dyads. Contact data are scaled by the total time each pair could have been recorded by loggers. Dyads in wild populations were monitored only in season 2 (July–October), while captive dyads were monitored in both season 1 (March–June) and season 2. The shade of each violin plot designates the sex of dyad members: light grey for MM, grey for MF and dark grey for FF dyads.

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  • Coefficient estimates (β median and 95% credible intervals of Bayesian posterior distributions) for single predictors models predicting one of three response variables: binary contact, contact duration, or contact frequency.

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  • Comparison of the proportion of tortoise dyads with a proximity logger-recorded contact (± SE) and model-predicted proportions using data collected from (a) wild or (b) captive populations. Models estimated dyad contact probability using either the amount of spatial overlap (dark grey) or burrow overlap (light grey) between dyad members. Violin plots show the distribution of 500 predictions drawn from the posterior of Bayesian GLMMs with points and lines within violins designating the distribution median with 50% and 95% highest posterior density intervals. Abbreviations FF, MF and MM designate the sex of dyad members and seasons included data recorded from March–June (season 1) and July–October (season 2).

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  • Estimated predictive improvement (delta elpd ± SE) of models with single or multiple predictors compared to an intercept only model for three response variables: binary contact, contact duration, and contact frequency. The grey vertical line designates a delta elpd value of 0, which indicates the addition of variables shown in the y axis resulted in no predictive improvements. Positive delta elpd values indicate improved prediction of observed data. Models were fit to data collected from wild tortoise dyads using proximity loggers and observation to estimate contact parameters and amount of space-use and burrow-use overlap (sp.overlap and bur.overlap, respectively). Sex of each dyad member (FF, MF or MM) and site (C1, C2, R1, or R2) were also among considered predictors. Models included random effects described in the methods that were not considered when estimating predictive performance.

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  • Estimated predictive improvement (delta elpd ± SE) of models with single or multiple predictors compared to an intercept only model for three response variables: binary contact, contact duration, and contact frequency. The grey vertical line designates a delta elpd value of 0, which indicates the addition of variables shown in the y-axis resulted in no predictive improvements. Positive delta elpd values indicate improved prediction of observed data. Models were fit to data collected from captive tortoise dyads using proximity loggers and observations to estimate contact parameters and amount of space-use and burrow-use overlap (sp.overlap and bur.overlap respectively). Sex of each dyad member (FF, MF or MM), pen (1, 2, 3) and season (1: March–June, 2: July–October) were also among considered predictors. Models included random effects described in the methods that were not considered when estimating predictive performance.

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  • Top-performing models based on WAIC and expected log pointwise predictive density comparisons.

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  • Observed (points) and predicted (line and blue shaded region) contact frequencies between interacting wild tortoise dyads relative to the amount of burrow overlap between dyad members. Prediction line shows the median and blue shaded region shows the 95% credible interval of 500 predictions drawn from the posterior distribution of a Bayesian GLMM fit to wild dyad data described in Table 4. Inset plot shows the estimated transmission probabilities associated with increasing burrow overlap based on the predicted contact frequencies and transmission model described in the methods.

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  • Observed (points) and predicted (lines and shaded regions) contact frequencies between interacting captive tortoise dyads relative to the amount of spatial overlap between dyad members (x-axis), sex of each dyad member (designated by colour: blue for FF, green for MF, and orange for MM), and season (displayed in separate panels, season 1: March–June, season 2: July–October). Prediction lines show the median and shaded regions show the 95% credible interval of 500 predictions drawn from the posterior distribution of a Bayesian GLMM fit to captive dyad data described in Table 4. Inset plot shows the estimated distribution of transmission probabilities associated with each sex and season based on the predicted contact frequencies and transmission model described in the methods.

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  • Distribution of non-zero values of spatial overlap (a) and burrow overlap (b) for wild and captive dyads during study seasons (season 1: March–June, season 2: July–October). Wild populations were only monitored in season 2. Shades of white, light grey and dark grey designate the sex combination of each dyad member. No wild FF pairs exhibited burrow overlap during the study and only one captive FF pair exhibited burrow overlap in season 2 (value indicated by a line).

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  • Summary data for wild dyads based on proximity logger and observation data collected during July–October 2012, including: total number of monitored dyads, number of dyads with non-zero spatial overlap, number of dyads with non-zero burrow overlap, number of dyads with a logger-recorded contact and mean ± SE active logger hours per dyad.

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  • Summary data for captive dyads based on proximity logger and observation data collected in season 1 (March–June) and season 2 (July–October) of 2015, including: total number of monitored dyads, number of dyads with non-zero spatial overlap, number of dyads with non-zero burrow overlap, number of dyads with a logger-recorded contact and mean ± SE active logger hours per dyad.

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  • Set of models fit to wild dyad data for each of three response variables: binary contact, contact duration, or contact frequency. Each model contained random intercepts for the tortoise ID of each dyad member plus fixed effects listed here. Contact duration and contact frequency models also included an offset variable to account for the total active logger time of each dyad. Model fits were estimated using widely applicable information criterion (WAIC) and the difference in expected log pointwise predicted density (Δelpd) for the current model compared to an intercept only model.

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  • Set of models fit to captive data for each of three response variables: binary contact, contact duration, or contact frequency.

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