Harmful algal events in Canadian marine ecosystems : current status, impacts, consequences, and knowledge gaps
Notice bibliographique
Résumé
Harmful algae (HA) are phytoplankton species with the potential to cause harm to organisms, food webs, ecosystems, and human health. Harmful algal events (HAEs), often called “red tides”, may occur when HA reach high abundances in blooms, although many species can also cause harm at relatively low numbers. Mechanisms of harm include production of phycotoxins, mechanical action, or hypoxia. HAEs have emerged as important stressors of marine fisheries and ecosystems and are listed as a priority ecosystem stressor (i.e., one requiring further study) by Fisheries and Oceans Canada (DFO) Ecosystem Stressors Program. Globally, impacts caused by HAEs have been reported on an unprecedented scale in the last decade. Coastal marine activities have also increased, including commercial and recreational vessel traffic and coastal development related to aquaculture, oil and gas, tourism, and other industries. Increasing vessel traffic and climate change are of particular concern for the Arctic marine ecosystem, where they could affect the prevalence of HA and the likelihood of HAEs. A review of HA, HAEs and their impacts in Canada’s Atlantic, Pacific and Arctic marine waters over 30 years (1987 to 2017) was conducted to determine areas or issues of particular or emerging concern. A conceptual bow tie model was developed as a framework to link causes to outcomes of HAEs. It incorporates three natural drivers and five emerging anthropogenic pressures that influence the occurrence of HAEs in Canadian marine waters and was applied to identify national and regional key knowledge gaps that might limit Fisheries and Oceans Canada’s (DFO) ability to manage consequences of HA, especially those linked to DFO’s mandate. National knowledge gaps include limited detection and monitoring of HA and phycotoxins and limited understanding of the effects of specific emerging anthropogenic pressures (EAPs) (climate change, nutrient enrichment, coastal development, and vectors of introduced HA) on oceanographic, atmospheric and biological conditions that drive changes in HA species, phycotoxins, and bloom development and toxicity. These knowledge gaps prevent the development of effective predictive HAE models, which hinders our forecasting/hindcasting capability and hampers development of mitigation and prevention strategies. There is limited understanding of the effects of HA and phycotoxins on most marine organisms (including sublethal/cumulative effects on growth, physiology, reproduction, and behaviour), and on food web and ecosystem function, particularly in Canadian sub-Arctic and Arctic waters. Understanding the effects of HA and phycotoxins is needed to evaluate their impact on species at risk, marine mammals, aquaculture, fishery and fish population health, ecosystem health, and food safety and security. These knowledge gaps must be filled in order to inform management decisions. Monitoring of phytoplankton and phycotoxins, where it currently exists, should be continued and expanded, using new and existing capacity and partnerships. This is particularly important for areas where information is lacking, including the Arctic. Along with traditional methods to identify HA and phycotoxins, novel methods to detect and observe/monitor HA in Canadian marine waters should be implemented. Detection and monitoring programs using existing methodologies can assist the ground-truthing of new technologies and will contribute to international HAE observations and studies for global early warning systems. Implementing these recommendations will benefit DFO by increasing our HA research capability. One advantage would be an early warning system that would increase predictive capacity for these ecosystem stressors, enabling mitigative or preventive actions to ensure healthy and productive marine ecosystems.
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 | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,010 | 0,016 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».