Predicting Potential Spawning Areas: a novel framework for elasmobranch conservation and spatial management

Defining and protecting critical habitats for elasmobranchs (sharks and rays), such as spawning areas, is essential for mitigating anthropogenic pressures that threaten their populations, primarily driven by fisheries and habitat degradation. This study presents a novel modelling-based framework to identify Potential Spawning Areas (PSAs) - habitats offering optimal conditions for oviposition. Using fisheries-dependent trawl bycatch data combined with environmental and anthropogenic predictors, we applied machine-learning models to delineate PSAs for the smallspotted catshark (Scyliorhinus canicula) and skates (Raja spp.) in the western Mediterranean. Static environmental predictors, including depth and slope, were primary drivers, while dynamic predictors, including sea bottom temperature and salinity played seasonally relevant roles. Trawl fishing pressure also influenced importantly the distribution of PSAs, revealing a concerning positive feedback loop between exploitation and habitat degradation. While PSAs experienced lower fishing effort than the rest of the study area, a substantial proportion of egg case bycatch still occurs within them. The current network of Marine Protected Areas in the region fails to adequately safeguard these habitats due to limited coverage and enforcement. Our findings underscore significant gaps in spatial management and the urgent need for targeted conservation measures. The PSA framework provides a robust, scalable tool for identifying critical habitats across regions and species, offering actionable insights for marine spatial planning and ecosystem-based management. This adaptable approach can support global conservation efforts for elasmobranchs and their ecosystems. SignificanceSharks and rays are vital to marine ecosystems but face alarming extinction risks due to overfishing and habitat loss. Identifying critical reproductive habitats, such as spawning areas, is essential for protection but remains a significant challenge. This study introduces a novel framework for identifying Potential Spawning Areas (PSAs) - habitats offering optimal conditions for oviposition - using machine-learning and fisheries bycatch data. Applied in the western Mediterranean for the smallspotted catshark and skates, it demonstrates how environmental factors and fishing pressures shape these habitats, highlighting significant management gaps. The PSAs framework offers a scalable approach to guide the establishment of marine protected areas and fisheries management plans, providing a novel approach for conserving vulnerable marine species and their habitats.