@article{https://doi.org/10.1111/cobi.70100,

title = {Key drivers of at-vessel mortality in demersal sharks},

author = {David Ruiz-García and Claudio Barría and Juan A. Raga and David March},

url = {https://conbio.onlinelibrary.wiley.com/doi/abs/10.1111/cobi.70100},

doi = {https://doi.org/10.1111/cobi.70100},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {Conservation Biology},

volume = {40},

number = {1},

pages = {e70100},

abstract = {Abstract Chondrichthyans are highly vulnerable to fisheries overexploitation, and postcapture mortality poses a significant threat to most species. Global bycatch mitigation guidelines recommend adopting hierarchical decision-making approaches tailored to species-specific vulnerabilities and socioeconomic and regulatory contexts. Effective implementation of such strategies requires robust understanding of the factors driving vulnerability to postcapture mortality. To address this need, we developed a machine learning method to identify key drivers of at-vessel mortality (AVM) based on a broad set of biological, environmental, and fishing-related parameters. We sought to reveal interactions among predictors, nonlinear responses between these variables and mortality risk, and threshold values beyond which the likelihood of mortality increased markedly. We applied this approach to trawl bycatch data on small-spotted catshark (Scyliorhinus canicula) and blackmouth catshark (Galeus melastomus) in the western Mediterranean. Body size, air temperature, and on-deck time emerged as the primary AVM drivers. Mortality risk increased substantially at temperatures above 20°C for S. canicula and 16°C for G. melastomus, with on-deck exposure exceeding 15 min, and when body size was below 40 and 55 cm, respectively. Identification of these drivers and thresholds provides valuable insights for bycatch mitigation; can inform strategies for more threatened, closely related, or physiologically and ecologically similar species; and may support management authorities in adopting targeted bycatch avoidance strategies, gear selectivity, and mortality reduction measures. Such measures can be tailored to specimens, areas, and periods of heightened mortality risk to maximize effectiveness. Furthermore, our scalable modeling approach offers a robust tool for identifying critical AVM drivers across regions and species, and its applicability can be extended to broader fisheries management and global conservation efforts.},

keywords = {árboles de regresión potenciada, arrastre de fondo, boosted regression trees, bottom trawl, bycatch mitigation, elasmobranch, elasmobranquios, fisheries management, gestión de pesquerías, handling practices, IOS4DOM, mitigación de la pesca incidental, prácticas de manejo},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1111/faf.70017,

title = {Leveraging Earth Observation Data to Monitor Boat-Based Recreational Fishing},

author = {Javier Menéndez-Blázquez and Mara Segovia and David March},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/faf.70017},

doi = {https://doi.org/10.1111/faf.70017},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Fish and Fisheries},

volume = {26},

number = {6},

pages = {1065-1074},

abstract = {ABSTRACT Recreational fishing is widespread in coastal zones and exerts significant ecological, fisheries-related and socio-economic pressures. Unlike commercial fishing, small-scale recreational fleets are challenging to monitor because they lack enforced use of vessel tracking systems such as the Automatic Identification System (AIS). Recently, remote sensing technologies have emerged as promising alternatives for monitoring marine activities. Here, we assess the potential of high spatio-temporal resolution satellite imagery to monitor daily changes in recreational fishing boats during a temporal fishing ban within a marine protected area. By comparing satellite-derived boat detections with AIS records, we demonstrate that satellite data can reliably capture daily changes in recreational fishing activity missed by AIS, including a marked increase immediately following the end of the ban. These findings confirm that satellite observations can consistently detect small fishing boats and reveal their fine-scale spatio-temporal patterns. When complemented with local knowledge, this approach enhances our capacity to contribute to the spatial planning and ecosystem-based management of recreational fisheries.},

keywords = {automated identification system, fisheries management, IOS4DOM, leisure fishing, planet labs, remote sensing, vessel detection},

pubstate = {published},

tppubtype = {article}

}