Inferring the number of spawning events from young-of-year genomic samples and otolith-derived birth dates: a richness-estimator perspective

Estimating the number of spawning events per female is key to understanding individual reproductive output in batch-spawning species, yet direct observation of spawning is often infeasible in the wild. Recent advances in genetic kinship inference enable the identification of maternal half siblings from young-of-the-year genomic samples, while otolith-based age determination provides reconstruction of offspring birth dates. Here we develop an offspring-based framework for estimating the number of clutches produced by individual females by integrating sibling structure inferred from genomic data with otolith-derived birth-date information. By recasting clutch identification as a richness estimation problem, we apply the Chao1 estimator to infer the total number of spawning events from incomplete offspring samples. Using simulation experiments, we evaluate how sampling effort and heterogeneity in clutch size influence clutch detection and estimation. Under uniform clutch-size distributions, modest numbers of offspring sampled per maternal family (10-20 offspring) yield accurate estimates of the total number of clutches, substantially outperforming naive counts of observed birth-date classes by recovering information from rare or unobserved spawning events. In contrast, skewed or multimodal clutch-size distributions lead to underestimation at low sample sizes, indicating that uneven reproductive output increases sampling effort required for reliable inference. Overall, our results demonstrate how offspring genomic data and otolith-derived birth dates can be jointly leveraged to reconstruct individual spawning histories under realistic sampling constraints. This perspective provides a framework for inferring within-season reproductive schedules in batch-spawning species, and highlights opportunities for integrating genomic and life-history data in fisheries monitoring and reproductive ecology.