Common Field Data Limitations Can Substantially Bias Sexual Selection Metrics.

Sexual selection studies widely estimate several metrics, but values may be inaccurate because standard field methods for studying wild populations produce limited data (e.g., incomplete sampling, inability to observe copulations directly). We compared four selection metrics (Bateman gradient, opportunity for sexual selection, opportunity for selection, and ) estimated with simulated complete and simulated limited data for 15 socially monogamous songbird species with extrapair paternity (4%-54% extrapair offspring). Inferring copulation success from offspring parentage creates nonindependence between these variables and systematically underestimates copulation success. We found that this introduces substantial bias for the Bateman gradient, opportunity for sexual selection, and . Notably, 47.5% of detected Bateman gradients were significantly positive for females, suggesting selection on females to copulate with multiple males, although the true Bateman gradient was zero. Bias generally increased with the extent of other sources of data limitations tested (nest predation, male infertility, and unsampled floater males). Incomplete offspring sampling introduced bias for all of the metrics except the Bateman gradient, while incomplete sampling of extrapair sires did not introduce additional bias when sires were a random subset of breeding males. Overall, our findings demonstrate how biases due to field data limitations can strongly impact the study of sexual selection.