The multinomial index: a robust measure of reproductive skew

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.Mis analytically related to Nonacs’ binomial index,B, and comparably accounts for heterogeneity in age across individuals; in addition,Mallows for the possibility of diminishing or even highly nonlinear RS returns to age. UnlikeB, however,Mis 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 usingM. 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 estimateMfrom empirical data.

Document Details

Document Type
Pub Defense Publication
Publication Date
Oct 07, 2020
Source ID
10.1098/rspb.2020.2025

Entities

People

  • Adrian V Jaeggi
  • Cody T. Ross
  • Eric Alden Smith
  • Jennifer E Smith
  • Monique Borgerhoff Mulder
  • Paul L Hooper
  • Sergey Gavrilets

Organizations

  • Max Planck Institute for Evolutionary Anthropology
  • Mills College at Northeastern University
  • National Science Foundation
  • Office of Naval Research
  • Santa Fe Institute
  • United States Army Research Laboratory
  • University of California
  • University of Tennessee
  • University of Washington
  • University of Zurich

Tags

Fields of Study

  • Mathematics

Readers

  • Marine Mammal Biology
  • Neural Network Machine Learning.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference