Sanchez-Tojar, A. | Search bioRxiv

bioRxiv2018

Meta-analysis challenges a textbook example of status signalling: evidence for publication bias

The status signalling hypothesis aims to explain conspecific variation in ornamentation by suggesting that some ornaments signal dominance status. Here, we use multilevel meta-analytic models to challenge the textbook example of this hypothesis, the black bib of house sparrows (Passer domesticus). We conducted a systematic review, and obtained raw data from published and unpublished studies to test whether dominance rank is positively associated with bib size across studies. Contrary to previous studies, our meta-analysis did not support this prediction. Furthermore, we found several biases in the literature that further question the support available for the status signalling hypothesis. First, the overall effect size of unpublished studies was zero, compared to the medium effect size detected in published studies. Second, the effect sizes of published studies decreased over time, and recently published effects were, on average, no longer distinguishable from zero. We discuss several explanations including pleiotropic, population- and context-dependent effects. Our findings call for reconsidering this established textbook example in evolutionary and behavioural ecology, raise important concerns about the validity of the current scientific publishing culture, and should stimulate renewed interest in understanding within-species variation in ornamental traits.

evolutionary biology↗

bioRxiv2017

A practical guide for inferring reliable dominance hierarchies and estimating their uncertainty

Many animal social structures are organized hierarchically, with dominant individuals monopolizing resources. Dominance hierarchies have received great attention from behavioural and evolutionary ecologists. As a result, there are many methods for inferring hierarchies from social interactions. Yet, there are no clear guidelines about how many observed dominance interactions (i.e. sampling effort) are necessary for inferring reliable dominance hierarchies, nor are there any established tools for quantifying their uncertainty. In this study, we simulated interactions (winners and losers) in scenarios of varying steepness (the probability that a dominant defeats a subordinate based on their difference in rank). Using these data, we (1) quantify how the number of interactions recorded and hierarchy steepness affect the performance of three methods, (2) propose an amendment that improves the performance of a popular method, and (3) suggest two easy procedures to measure uncertainty in the inferred hierarchy. First, we found that the ratio of interactions to individuals required to infer reliable hierarchies is surprisingly low, but depends on the hierarchy steepness and method used. We then show that Davids score and our novel randomized Elo-rating are the two best methods, whereas the original Elo-rating and the recently described ADAGIO perform less well. Finally, we propose two simple methods to estimate uncertainty at the individual and group level. These uncertainty measures further allow to differentiate non-existent, very flat and highly uncertain hierarchies from intermediate, steep and certain hierarchies. Overall, we find that the methods for inferring dominance hierarchies are relatively robust, even when the ratio of observed interactions to individuals is as low as 10 to 20. However, we suggest that implementing simple procedures for estimating uncertainty will benefit researchers, and quantifying the shape of the dominance hierarchies will provide new insights into the study organisms.\n\nHighlightsO_LIDavids score and the randomized Elo-rating perform best.\nC_LIO_LIMethod performance depends on hierarchy steepness and sampling effort.\nC_LIO_LIGenerally, inferring dominance hierarchies requires relatively few observations.\nC_LIO_LIThe R package \"aniDom\" allows easy estimation of hierarchy uncertainty.\nC_LIO_LIHierarchy uncertainty provides insights into the shape of the dominance hierarchy.\nC_LI

animal behavior and cognition↗