Detecting non-binomial sex allocation when developmental mortality operates

TL;DR

This paper introduces a Bayesian modeling approach to detect non-binomial sex allocation in populations, especially when developmental mortality obscures offspring sex data, improving detection power over existing methods.

Contribution

It presents a new Bayesian model selection method using double and multiplicative binomial distributions, implemented as an R package, to better detect non-binomial sex allocation under mortality.

Findings

Enhanced detection power in simulations with high mortality

Bayesian approach quantifies evidence for non-binomiality

Application to real parasitoid wasp data demonstrates effectiveness

Abstract

Optimal sex allocation theory is one of the most intricately developed areas of evolutionary ecology. Under a range of conditions, particularly under population sub-division, selection favours sex being allocated to offspring non-randomly, generating non-binomial variances of offspring group sex ratios. Detecting non-binomial sex allocation is complicated by stochastic developmental mortality, as offspring sex can often only be identified on maturity with the sex of non-maturing offspring remaining unknown. We show that current approaches for detecting non-binomiality have limited ability to detect non-binomial sex allocation when developmental mortality has occurred. We present a new procedure using an explicit model of sex allocation and mortality and develop a Bayesian model selection approach (available as an R package). We use the double and multiplicative binomial distributions to model over- and under-dispersed sex allocation and show how to calculate Bayes factors for comparing these alternative models to the null hypothesis of binomial sex allocation. The ability to detect non-binomial sex allocation is greatly increased, particularly in cases where mortality is common. The use of Bayesian methods allows for the quantification of the evidence in favour of each hypothesis, and our modelling approach provides an improved descriptive capability over existing approaches. We use a simulation study to demonstrate substantial improvements in power for detecting non-binomial sex allocation in situations where current methods fail, and we illustrate the approach in real scenarios using empirically obtained datasets on the sexual composition of groups of gregarious parasitoid wasps.