Harmful Algae in Marine Ecosystems
Bibliographic record
Abstract
Harmful algae (HA) are phytoplankton, and to a lesser extent sympagic algae species, with the potential to cause harm to organisms, food webs, ecosystems and human health via the production of phycotoxins, mechanical action, or hypoxia. As such, harmful algal events (HAEs) have emerged as an important stressor for marine ecosystems. HA species, phycotoxins, and HAEs in Canada’s three oceans were reviewed based on Canadian published and unpublished reports. A 30-year time series of 593 Canadian records (1987 to 2017) from the Harmful Algal Event Database (HAEDAT) was analyzed for the Atlantic and Pacific coasts that showed recurring HAEs have been widespread. No HAEDAT records exist for the Arctic. To date, 70 harmful algal species (including 45 in the Arctic), known to have caused, or to have been associated with, HAEs on the coasts of Canada or elsewhere, have been reported in Canadian waters. 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 knowledge gaps that might limit Fisheries and Oceans Canada’s (DFO) ability to manage consequences of HA, especially those linked to DFO’s mandate. Nationally, overarching knowledge gaps include limited HA monitoring, and limited understanding of the effects of specific emerging anthropogenic pressures 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. Information regarding the role of climate change, including extreme events and ocean acidification, on HAEs was identified as a key knowledge gap in Canada. Fundamental knowledge gaps exist in the Arctic Region, where there is a lack of basic information on the presence of HA and phycotoxins, and their impacts on species and ecosystems. Understanding the effects (including sublethal and cumulative) of HA and phycotoxins on the growth, physiology, reproduction and behaviour of marine biota is essential to evaluate impacts on food webs, ecosystems and human health, and consequences to species-at-risk, marine mammals, aquaculture, fishery and fish population health, ecosystem health, and food safety and security. Monitoring of phytoplankton and phycotoxins should be continued and expanded by developing novel methods, and using new and existing capacity and partnerships. This is especially important for areas where information is lacking, particularly the Arctic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".