Predicting the Effects of Climate Change on the Occurrence of the Toxic Dinoflagellate Alexandrium catenella Along Canada’s East Coast
Bibliographic record
Abstract
Alexandrium catenella produces paralytic shellfish toxins that affect marine fisheries and aquaculture as well as ecosystem and human health worldwide. This harmful algal species is extremely sensitive to environmental conditions and potentially to future climate change. Using a generalized additive mixed model (GAMM) we studied the potential effects of changing salinity and temperatures on A. catenella bloom (≥1000 cells L–1) occurrence along Canada’s East Coast throughout the 21st century. Our GAMM was applied to two high greenhouse gas emissions scenarios (RCP 8.5) and one mitigation scenario (RCP 4.5). Under present-day conditions, our model successfully predicted A. catenella’s spatio-temporal distribution in Eastern Canada. Under future conditions, all scenarios predict increases in bloom frequency and spatial extent as well as changes in bloom seasonality. Under one RCP 8.5 scenario, A. catenella bloom occurrences increased at up to 3.5 days per decade throughout the 21st century, with amplified year-to-year variability. Blooms expanded into the Gulf of St. Lawrence and onto the Scotian Shelf. These conditions could trigger unprecedented bloom events in the future throughout our study region. In all climate scenarios, the bloom season intensified earlier (May–June) and ended later (October). In some areas of the Gulf of St. Lawrence, the thermal habitat of A. catenella was exceeded, thereby locally reducing bloom risk during the summer months. We conclude that an increase in A. catenella’s environmental bloom window could further threaten marine fauna including endangered species as well as fisheries and aquaculture industries on Canada’s East Coast. Similar impacts could be felt in other coastal regions of the globe where warming and freshening of waters are intensifying.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".