Inferring breeding phenology and reproductive success from the emergence of juveniles in population monitoring

Phenological shifts caused by climate change are increasingly documented in wild populations. These events may be inferred by examining changes in population abundance and age structure throughout the breeding season, often using citizen science. However, several gaps still limit optimal use of such data. First, the link between the proportion of juveniles sampled over time and the underlying distribution of breeding times and reproductive success remains unclear. Second, such observations necessarily concern individuals that survived to fledge, thus potentially reflecting selection on reproductive timing. Third, the effect of sampling design on estimating breeding parameters needs careful assessment. In this study, we address these three concerns, taking the example of bird monitoring. We first propose an analytical model relating the proportion of juveniles in counts (e.g. mist‐net captures) to fledging date distribution and mean reproductive success. We then show how the estimated fledging parameters relate to the underlying laying date distribution, accounting for a possible influence of selection, and use simulations to assess how sampling design affects the inference of fledging and breeding parameters. Our analytical results show that mean fledging time lags behind the inflection point in juvenile proportions, especially when laying date variance and reproductive success are high. Selection for earlier breeding advances the inferred mean laying date, but this bias can be corrected if independent information on selection strength is available. Our simulations show that our approach is able to recover the true mean and variance of fledging dates under unlimited sampling effort. A more realistic multi‐site approach reveals that accurate estimates can be reached with only a few sampling sessions per site, although increasing the number of capture sessions and capture sites improves precision. Our results hold promise to improve the accuracy of phenological estimates from population monitoring, and the interpretation of climate‐driven changes in wild populations.