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
s’max) estimated with simulated complete and simulated limited data for 15
socially monogamous songbird species with extra-pair paternity (4-54%
extra-pair offspring). Inferring copulation success from offspring
parentage creates non-independence between these variables and
systematically underestimates copulation success. We found that this
introduces substantial bias for the Bateman gradient, opportunity for
sexual selection, and s’max. Notably, 47.5% of detected Bateman gradients
were significantly positive for females, suggesting selection on females
to copulate with multiple males, though 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
metrics except the Bateman gradient, while incomplete sampling of
extra-pair 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.