A comprehensive large‐scale assessment of fisheries bycatch risk to threatened seabird populations
Notice bibliographique
Résumé
Abstract Incidental mortality (bycatch) in fisheries remains the greatest threat to many large marine vertebrates and is a major barrier to fisheries sustainability. Robust assessments of bycatch risk are crucial for informing effective mitigation strategies, but are hampered by missing information on the distributions of key life‐history stages (adult breeders and non‐breeders, immatures and juveniles). Using a comprehensive biologging dataset (1,692 tracks, 788 individuals) spanning all major life‐history stages, we assessed spatial overlap of four threatened seabird populations from South Georgia, with longline and trawl fisheries in the Southern Ocean. We generated monthly population‐level distributions, weighting each life‐history stage according to population age structure based on demographic models. Specifically, we determined where and when birds were at greatest potential bycatch risk, and from which fleets. Overlap with both pelagic and demersal longline fisheries was highest for black‐browed albatrosses, then white‐chinned petrels, wandering and grey‐headed albatrosses, whereas overlap with trawl fisheries was highest for white‐chinned petrels. Hotspots of fisheries overlap occurred in all major ocean basins, but particularly the south‐east and south‐west Atlantic Ocean (longline and trawl) and south‐west Indian Ocean (pelagic longline). Overlap was greatest with pelagic longline fleets in May–September, when fishing effort south of 25°S is highest, and with demersal and trawl fisheries in January–June. Overlap scores were dominated by particular fleets: pelagic longline—Japan, Taiwan; demersal longline and trawl—Argentina, Namibia, Falklands, South Africa; demersal longline—Convention for Conservation of Antarctic Marine Living Resources (CCAMLR) waters, Chile, New Zealand. Synthesis and applications . We provide a framework for calculating appropriately weighted population‐level distributions from biologging data, which we recommend for future fisheries bycatch risk assessments. Many regions of high spatial overlap corresponded with high seabird bycatch rates recorded by on‐board observers, indicating that our approach reliably mapped relative bycatch risk at large spatial scales. Implementation of effective bycatch mitigation in these high‐risk regions varies considerably. Although potential bycatch risk appears to have decreased since the early 2000s, albatross and petrel populations from South Georgia and elsewhere are still declining, emphasizing the need for much improved observer coverage and monitoring of compliance with bycatch regulations.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».