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Meta-analysis reveals that animal sexual signalling behaviour is honest and resource based

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Abstract

Animals often need to signal to attract mates and behavioural signalling may impose substantial energetic and fitness costs to signallers. Consequently, individuals often strategically adjust signalling effort to maximize the fitness payoffs of signalling. An important determinant of these payoffs is individual state, which can influence the resources available to signallers, their likelihood of mating and their motivation to mate. However, empirical studies often find contradictory patterns of state-based signalling behaviour. For example, individuals in poor condition may signal less than those in good condition to conserve resources (ability-based signalling) or signal more to maximize short-term reproductive success (needs-based signalling). To clarify this relationship, I systematically searched for published studies examining animal sexual signalling behaviour in relation to six aspects of individual state: age, mated status, attractiveness, body size, condition and parasite load. Across 228 studies and 147 species, individuals (who were predominantly male) invested more into behavioural signalling when in good condition. Overall, this suggests that animal sexual signalling behaviour is generally honest and ability-based. However, the magnitude of state-dependent plasticity was small and there was a large amount of between-study heterogeneity that remains unexplained.

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Fig. 1: Guide to the coding scheme for effect sizes used in this study.
Fig. 2: Summary of the dataset.
Fig. 3: Forest plot showing the mean effect size estimate (black circles) plus 95% CIs for each state factor category.
Fig. 4: Funnel plot showing the relationship between effect size (the correlation coefficient r) and inverse standard error after performing a trim-and-fill analysis.

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Data availability

The data that support the findings of this study are available from Figshare (https://doi.org/10.6084/m9.figshare.12973124).

Code availability

All code used for the analyses are available from Figshare (https://doi.org/10.6084/m9.figshare.12973124).

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Acknowledgements

I thank T. Price, Z. Lewis and L. Simmons for helpful comments on an earlier version of the manuscript. This work was funded by a Leverhulme Trust Early-Career Fellowship (grant no. ECF-2018-427).

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Extended data

Extended Data Fig. 1 Effect size heterogeneity (I2) when each of the six state factors are considered separately.

I2 values are presented for the overall model, as well as for each random factor in a multilevel mixed-effects meta-analysis model. k= the number of effect sizes for each dataset.

Extended Data Fig. 2 Mean effect size estimates (the correlation coefficient r), plus sample sizes, for each category for the seven categorical moderator variables.

Estimates were obtained using a minus-intercept meta-regression, performed separately for each moderator (see text for details).

Extended Data Fig. 3 Results from meta-regressions performed separately on just body size or condition data.

Each factor was tested using a separate meta-regression model. The QM statistic tests whether the moderator variable significantly influences the mean effect size. Marginal R2 is the amount of variance explained by each fixed factor. All models were run on the full dataset (k = 203 for body size, 178 for condition) with the exception of lifespan (k = 146 for body size, 136 for condition). Significant moderators are highlighted in grey.

Extended Data Fig. 4 Forest plot showing the effect of signalling type on the relationship between sexual signalling and body size (k = 203).

Black circles show the mean effect size estimate (correlation coefficient r) for each signalling category, bars show the 95% confidence intervals. Open circles show the raw effect sizes for each category, with the size of the circle corresponding to study precision (inverse standard error)). k= the number of effect sizes for each category.

Extended Data Fig. 5 Relationship between effect size (Z-transformed correlation coefficient, Zr) and study year.

Each bubble represents an effect size, and bubble size is scaled to effect size precision (inverse standard error; larger bubbles reflect larger sample sizes) for the full dataset (k = 520). The dashed line shows the predicted line from a meta-regression including study year as a covariate. Dotted lines show the 95% confidence intervals for the predicted line.

Extended Data Fig. 6 Results from meta-regression models testing the effect of study year separately for each of the six state factor categories.

Each model included study year as a fixed effect and phylogeny, species, study, experiment and observation ID as random effects.

Extended Data Fig. 7 Scatterplot showing the relationship between effect size (Z-transformed correlation coefficient, Zr) and precision (inverse standard error) for the full dataset (k = 520).

The line shows the prediction from a linear regression.

Extended Data Fig. 8 The phylogenetic tree used in the analysis (N = 147 species).

The taxonomic class categories used during meta-regressions are highlighted on the right-hand side of the tree.

Extended Data Fig. 9 PRISMA diagram showing the literature search and screening process.

Note that the search terms included a broader range of behaviours than presented in this study.

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Dougherty, L.R. Meta-analysis reveals that animal sexual signalling behaviour is honest and resource based. Nat Ecol Evol 5, 688–699 (2021). https://doi.org/10.1038/s41559-021-01409-z

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