Microbial plankton occurrence database in the North American Arctic region: synthesis of recent diversity of potentially toxic and harmful algae
Bibliographic record
Abstract
Abstract. The Arctic Ocean is currently undergoing significant transformations due to climate change, leading to profound changes in its microbial planktonic communities. These communities consist of a wide range of organisms, including photoautotrophic prokaryotes and eukaryotes, as well as heterotrophic, phagotrophic, and mixotrophic protistan species. Here, for simplicity, we refer to these single-celled species as phytoplankton. Within this diversity, potentially toxic and/or harmful algal species (hereafter abbreviated as “HA”) are of particular concern. These organisms have the potential to spread into Arctic waters, posing threats to both human and ecosystem health. Despite their importance, the spatial and temporal distribution of phytoplankton communities, including HA species, in the North American Arctic, remains poorly understood. To address this gap, we compiled and synthesized the largest possible body of data from different databases, individual published and unpublished datasets, and partitioned it into nine regions based on the Large Marine Ecosystem classification. Our dataset contains 385 800 individual georeferenced data points and 18 268 unique sampling events, revealing greater diversity than previously thought, with 1445 unique taxa. Heterokontophyta (which notably included diatoms) and Dinoflagellata were the most dominant phyla. Our results indicate distinct spatial patterns of diversity, with the highest diversity observed in Atlantic-influenced regions of the North American Arctic. For most of the HA species recorded in our database, no evidence was found for an increase in the northernmost latitude where HA species are observed over the years, meaning that there is no substantial spread of HA species into the North American part of the Arctic. Our study challenges the traditional view of the Arctic as being unsuitable for toxin-producing and harmful algae and highlights the importance of extensive and long-term sampling efforts to understand the region’s biodiversity. Overall, our findings provide new insights into the spatial patterns and biodiversity of phytoplankton and other protists in the North American Arctic and have implications for understanding the ecological functioning and response of this region to ongoing climate change.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".