Scientific and technical innovations to revolutionize knowledge production for the conservation and management of migratory and straddling biodiversity
Notice bibliographique
Résumé
Migratory species and straddling populations, including highly mobile fish, elasmobranchs, sea turtles, seabirds, and marine mammals, are increasingly impacted by overexploitation, fisheries bycatch, habitat degradation, and climate change. Conserving these species and sustainably managing fisheries and other human activities present one of the most complex challenges in contemporaneous multilateral governance. Effective conservation and management efforts require harmonizing data collection, sustainable harvesting practices, monitoring, control, and enforcement across numerous jurisdictions, including the high seas. However, inconsistent and fragmented data collection and knowledge production remains a significant barrier. To address this, scaling and mainstreaming innovative approaches and technologies in management, monitoring, and data collection is essential for building the foundational knowledge required for sustainable and equitable operations, particularly under the pressures of a changing climate.This study conducts an expert-driven global solution scan of scientific and technical innovations across fisheries and regions, highlighting best practices to inform estimates about the status, abundance, and distribution of marine migratory species. It also assesses measures to understand the scope and scale of fisheries impacting them, from local artisanal to industrial operations. Scientific and technical knowledge is categorized into four key themes: species abundance and distribution, fleet activity, fisheries catch composition and traceability, and climate change adaptation. Technological advancements such as vessel tracking and monitoring hardware, innovative stock assessment methodologies, and novel genomics applications are instrumental in tracking population abundances and the scale of our impacts on them. Breakthroughs in computer modeling, artificial intelligence, and satellite remote sensing offer invaluable tools to inform, monitor, and enforce management decisions on a large scale. Species distribution and density modeling are also a fundamental tool for tracking current and predicting future range shifts of species under environmental changes. To ensure scalability, cost efficiency, institutional arrangements, and capacity development are considered, especially given resource and training limitations in many regions. A critical element in advancing the scientific foundation for the conservation and management of migratory biodiversity is the emphasis on independent, well-funded science. Independent research ensures the transparency and integrity of data collection and analysis, which are crucial for informed and unbiased decision-making. Independence in research also provides a foundation of trust and reliability, which supports the credibility and adoption of conservation strategies across stakeholders. To this end, we also identify examples of independent regional scientific bodies informing managers and outline examples of principles to guide the transparent, accessible, and interoperable production of actionable data and knowledge.Consistent and sufficient funding is essential to support long-term monitoring and stimulate the development of advanced technologies, particularly in regions that may lack the resources to independently invest in these areas. Together, these innovations provide a roadmap for strengthening and scaling the science available to local, national, and regional fisheries frameworks to inform adaptive, science-based methods that address the complex dynamics of migratory biodiversity. While improved scientific knowledge cannot guarantee successful conservation outcomes on its own, it lays the groundwork for evidence-based, scalable approaches to managing migratory and straddling species effectively across geopolitical boundaries.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,037 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,011 | 0,012 |
| Science ouverte | 0,003 | 0,008 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,005 |
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 source (Gemma direct ou Codex distillé), 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 ».