Data from: Who escapes detection? Quantifying the causes and consequences of sampling biases in a long-term field study

Inferences drawn from long-term field studies are vulnerable to biases in observability of different classes of individuals, which may lead to biases in the estimates of selection, or fitness. Population surveys that monitor breeding individuals can introduce such biases by not identifying individuals that fail early in their reproductive attempts. Here, we quantify how the standard protocol for detecting breeding females introduces bias in a long-term population study of the great tit, Parus major. We do so by identifying females whose breeding attempts fail before they would normally be censused, and explore whether this early failure can be predicted by a number of intrinsic and extrinsic factors. We investigate the effect of these biases on estimates of reproductive performance and selection. We show that females that go undetected by standard censusing because they fail early in their breeding attempt were less likely to have been previously trapped within our study site and were more likely to breed in poor quality habitats. Furthermore we demonstrate that this bias sampling had lead previous studies on this population to overestimate the reproductive performance of unringed females, which are likely to be immigrants to the population. Finally, we show that these biases in detectability influence estimates of selection on a key life history trait. While these conclusions are specific to this study, we suggest that such effects are likely to be widespread, and that more attention should be given to whether or not methods for surveying natural populations introduce systematic bias that will influence conclusions about ecological and evolutionary processes.