Evidence map›Paper›PMID 40605754›Full record

ArticleConservation biology : the journal of the Society for Conservation Biology2026

Key drivers of at-vessel mortality in demersal sharks.

David Ruiz-García, Claudio Barría, Juan A Raga, David March

Abstract read
In one paragraph

Article in Conservation biology : the journal of the Society for Conservation Biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Key drivers of at-vessel mortality in demersal sharks.Conservation biology : the journal of the Society for Conservation Biology · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

David Ruiz-GarcíaUnidad de Zoología Marina, Instituto Cavanilles de Biodiversidad y Biología Evolutiva, Universitat de València, Paterna, Spain.ORCID 0000-0002-0072-1233
Claudio BarríaAssociation for the Study and Conservation of Elasmobranchs and Its Ecosystems (Catsharks), Barcelona, Spain.
Juan A RagaUnidad de Zoología Marina, Instituto Cavanilles de Biodiversidad y Biología Evolutiva, Universitat de València, Paterna, Spain.
David MarchUnidad de Zoología Marina, Instituto Cavanilles de Biodiversidad y Biología Evolutiva, Universitat de València, Paterna, Spain.

Funding

Biodiversity Foundation of the Ministry for the Ecological Transition and Demographic Challenge (Spain)
6 · The paper itself

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.

Indexed as

Conservation of Natural ResourcesFisheriesMortalitySharksShipsAnimalsBody SizeMachine LearningMediterranean Seaárboles de regresión potenciadaarrastre de fondoboosted regression treesbottom trawlbycatch mitigationelasmobranchelasmobranquiosfisheries managementgestión de pesqueríashandling practicesmitigación de la pesca incidentalprácticas de manejo

Identifiers

PMID40605754
PMCPMC12856789

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.