Temperature or Nutrients? Changes in Diatom Abundance and Diatom/Dinoflagellate Ratio during Marine Heat Waves in Tofino Inlet, British Columbia, Canada from 2013 to 2023
Bibliographic record
Abstract
Diatoms are a major group of phytoplankton that form the foundation of aquatic food webs and dominate the phytoplankton community in aquatic systems. With silica-based cell walls and no flagella, their mobility is limited, and their demand for silica is greater than that of dinoflagellates and other groups. As climate change drives rising ocean temperatures and more frequent marine heat waves (MHWs), studies in the Northeastern Pacific show that diatom abundance relative to dinoflagellates declines during these events. Two main published hypotheses that explain this are: 1) Dinoflagellates are more tolerant to temperature, and 2) Silicate availability may decrease with warming, disadvantaging silica-dependent diatoms. This study will investigate diatom abundance in relation to temperature and silicate changes in Tofino Inlet, Clayoquot Sound, on Vancouver Island’s west coast (2013–2023), a period that included several MHWs. Researchers from the University of Washington Tacoma have collected annual late-summer/early fall water property data in Clayoquot Sound since 2001, along with phytoplankton samples since 2006. This dataset includes CTD profiles (temperature, salinity, density, oxygen, fluorescence, transmissivity), discrete water samples (nutrients, phytoplankton), and 10-meter vertical net tows (20 µm mesh). As the foundation of the food web, changes in phytoplankton composition can have drastic impacts on higher trophic levels. Understanding how MHWs affect phytoplankton communities will help improve climate change preparedness for marine resource managers. This study did not find a correlation between diatom abundance relative to silicate or temperature in Tofino Inlet. However, a spatial pattern was found in silicate concentrations along the inlet.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".