Harmful algal events in Canadian marine ecosystems : current status, impacts, consequences, and knowledge gaps
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".