Common Field Data Limitations Can Substantially Bias Sexual Selection Metrics.

Area of Science:

• Evolutionary biology
• Behavioral ecology
• Quantitative genetics

Background:

• Sexual selection studies rely on field data to estimate mating behaviors.
• Standard methods often involve incomplete sampling and indirect observation of copulations.
• Limited data can introduce inaccuracies into key sexual selection metrics.

Purpose of the Study:

• To assess the impact of common field data limitations on the accuracy of sexual selection metrics.
• To compare estimates from simulated complete data versus simulated limited data.
• To identify which sexual selection metrics are most susceptible to bias.

Main Methods:

• Simulated complete and limited datasets for 15 socially monogamous songbird species.
• Included varying degrees of data limitations: incomplete sampling, nest predation, male infertility, and unsampled males.
• Analyzed four sexual selection metrics: Bateman gradient, opportunity for sexual selection, opportunity for selection, and $$.

Main Results:

• Inferring copulation success from offspring parentage introduces nonindependence and underestimates success.
• Substantial bias was observed in the Bateman gradient, opportunity for sexual selection, and $$.
• Inaccurate positive Bateman gradients for females were detected even when the true gradient was zero, suggesting bias in detecting female mating preferences.

Conclusions:

• Field data limitations significantly bias estimates of sexual selection metrics.
• Incomplete offspring sampling biased all metrics except the Bateman gradient.
• Biases are exacerbated by other data limitations, highlighting the need for cautious interpretation of sexual selection studies using standard field methods.