The multinomial index: a robust measure of reproductive skew. Issue 1936 (14th October 2020)

• Record Type: Journal Article
• Authors: Ross, Cody T.; Jaeggi, Adrian V.; Borgerhoff Mulder, Monique; Smith, Jennifer E.; Smith, Eric Alden; Gavrilets, Sergey; Hooper, Paul L.
• Is Part Of: Proceedings. Volume 287:Issue 1936(2020)
• Publication Date: 2020-10-14
• Subjects: inequality -- reproductive skew -- mating systems -- social evolution -- phylogenetic comparative analysis; Biology -- Periodicals; 570.5
• DOI: 10.1098/rspb.2020.2025
• Languages: English
• ISSNs: 0962-8452

Abstract

Abstract : Inequality or skew in reproductive success (RS) is common across many animal species and is of long-standing interest to the study of social evolution. However, the measurement of inequality in RS in natural populations has been challenging because existing quantitative measures are highly sensitive to variation in group/sample size, mean RS, and age-structure. This makes comparisons across multiple groups and/or species vulnerable to statistical artefacts and hinders empirical and theoretical progress. Here, we present a new measure of reproductive skew, the multinomial index, M, that is unaffected by many of the structural biases affecting existing indices. M is analytically related to Nonacs' binomial index, B, and comparably accounts for heterogeneity in age across individuals; in addition, M allows for the possibility of diminishing or even highly nonlinear RS returns to age. Unlike B, however, M is not biased by differences in sample/group size. To demonstrate the value of our index for cross-population comparisons, we conduct a reanalysis of male reproductive skew in 31 primate species. We show that a previously reported negative effect of group size on mating skew was an artefact of structural biases in existing skew measures, which inevitably decline with group size; this bias disappears when using M . Applying phylogenetically controlled, mixed-effects models to the same dataset, we identify key similarities and differences in the inferred within- and between-species predictors of reproductive skew across metrics. Finally, we provide an R package, SkewCalc, to estimate M from empirical data.