Understanding species' growth and maturation responses to anthropogenic and environmental pressures is crucial for tracking demographic shifts, phenotypic change and ensuring population sustainability. Traditional regression methods often focus on modelling the conditional mean of life-history traits, potentially overlooking heterogeneity in ecological data. In this study, we developed quantile and binary quantile regression models using deep neural networks to investigate the growth and maturation schedules across multiple quantiles. By capturing different parts of the conditional distribution, our approach accommodates heterogeneity and improves predictive accuracy and robustness compared to conventional methods. We further propose novel quantile maturation reaction norms (QMRNs) to assess changes in age and size at maturation. QMRNs outperformed widely used probabilistic maturation reaction norms in simulated datasets, demonstrating greater reliability and robustness. Applying this framework to largehead hairtail (Trichiurus japonicus) revealed distinct growth and maturation patterns between the cooler northern and warmer southern coasts of Taiwan Island, achieving lower prediction errors and providing more detailed demographic insights than traditional approaches. Overall, our quantile framework enables a more nuanced characterization of growth and maturation heterogeneity, facilitating intraspecific comparisons of life-history traits and evolutionary analyses of maturation schedules.

Marine ecosystems are undergoing life-history adaptations with impacts on productivity, resilience, and economic value due to Fisheries-Induced Evolution (FIE). Long-term and often intense selective commercial harvesting has led to truncations in population structure and evolutionary changes in key life-history traits. However, the consequences for different functional groups have rarely been evaluated, especially in the context of rebuilding depleted marine stocks. This study uses an individual-based eco-genetic modeling approach to investigate the effects of FIE during shifts in fishing intensity. We focus on functional groups of three types of pelagic fish and three types of demersal fish with different life histories in the China Seas, proposing and evaluating two types of evolving trait response indicators to FIE, and assessing the influence of fishing intensity during the population rebuilding phase. Our results indicate that FIE has a more pronounced impact on biomass recovery in demersal fishes compared to pelagic fishes. The recovery time ranges from 10 to 40 years and strongly correlates with length at 50% vulnerability (L50). Reductions in fishing intensity facilitate biomass recovery, particularly in demersal fishes. In conclusion, our study suggests that adopting a management approach tailored to the needs of distinct functional groups is highly beneficial for promoting the efficient recovery of declining demersal fisheries. This understanding is crucial for developing effective fishery management strategies that integrate the evolutionary responses of different functional groups.