A collaborative effort to integrated biological and chemical ocean acidification observations
Notice bibliographique
Résumé
Ocean acidification (OA) due to the CO₂ emissions from human activities is a serious threat to marine life and the overall health of ecosystems. As OA changes ocean chemistry, it forces marine organisms to spend more energy just to survive in these new and challenging conditions. This stress is especially noticeable in organisms with shells and skeletons. However, the impact likely extends beyond individual species, potentially disrupting entire marine communities and the delicate balance within these ecosystems.Traditionally, scientists have studied OA through two main approaches: by measuring variations in the ocean's carbonate system and by running controlled lab experiments on biological responses. While these methods have provided valuable insights, they are not enough to capture the complex, real-world impacts of OA on diverse marine environments. One complementary approach is to observe such changes in natural environment over time.Recognizing the urgency of this issue, IOC-UNESCO has initiated a joint project aimed at creating a comprehensive system for tracking and understanding the biological impacts of OA across various ecosystems. This project, guided by the work of the biological working group of the Global Ocean Acidification Observing Network (GOA-ON) and its publication (Widdicombe et al. 20231), seeks to build a strong foundation for assessing OA's global effects by combining chemical and biological observation data, directly supporting global initiatives like SDG 14.3 and the Kunming-Montreal Global Biodiversity Framework.The project follows a structured, multi-phase approach, beginning with a targeted set of sites with long-term carbonate system data and extensive biological and oceanographic observations. This first phase aims to test the idea that rates of biological and carbonate chemistry changes should correlate. It would also allow to isolate biological responses to OA, identify sensitive biological traits, and conduct causality analyses to detect OA impacts within biological data. The focus is on five key biological and ecological traits — calcification, primary production, growth, biodiversity, and genetic adaptation — that have demonstrated links to OA and provide a foundation for understanding its effects on marine life.In the second phase, this approach will be applied to additional sites with comprehensive datasets to identify cases where OA may be the primary driver of biological changes. This phase will also explore deviations from expected patterns due to other stressors, such as heatwaves, warming, and nutrient enrichment.The final phase will analyze sites where only biological data are available, assessing whether OA impacts can be identified in the absence of direct carbonate system measurements. This analysis aims to expand the scope of OA impact assessments by identifying regions where OA potentially influences biological traits and community structures.Ultimately, this project aims to develop innovative observation strategies that integrate biological and chemical monitoring, contributing to globally applicable best practices for OA impact assessment. This will support informed decision-making and help shape adaptation and mitigation strategies essential for safeguarding marine ecosystems.1Widdicombe et al. 2023, DOI: 10.5194/os-19-101-2023
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 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 | 0,001 |
| 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) | 0,001 | 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 ».