Phytoplankton community composition links to environmental drivers across a fjord to shelf gradient on the central coast of British Columbia
Bibliographic record
Abstract
Rapid environmental change is altering coastal phytoplankton dynamics and, thereby the productivity of coastal marine food webs. Unfortunately, a paucity of phytoplankton community data hinders the prediction of future conditions in ecologically productive regions such as the coastal northeast Pacific. To help fill this gap, this study characterized phytoplankton communities from 2018 to 2020 across a fjord, channel and shelf station transect on the central coast of British Columbia, Canada. Monthly samples were collected for microscopy-based taxonomy and pigment-based phytoplankton composition (i.e. CHEMTAX and size-fractionated chlorophyll). Correlation analysis was used to investigate drivers of phytoplankton biomass and hierarchical clustering and redundancy analysis highlighted drivers of compositional trends. Spring blooms formed the peak of annual biomass at each station and earlier blooms at the fjord station suggested a sheltering effect from winter wind conditions. Later spring blooms at the channel station coincided with seasonal wind reductions and increased sunlight. Of the six derived compositional clusters, three represented flagellate dominated conditions at all stations: two represented low biomass winter conditions and the third, moderate biomass spring and autumn blooms occurring under nutrient replete conditions. The remaining three clusters were diatom-dominated and spanned much of the growing season. The first diatom cluster represented Skeletonema marinoi dominated samples, many from 2020, observed under moderate nutrient and high stratification and freshwater discharge conditions. The second represented high diatom richness spring bloom conditions at all stations that were associated with nutrient depletion. Finally, the third included 2018 and 2019 summer shelf samples showing harmful Rhizosolenia setigera and Pseudo-nitzschia seriata blooms under high surface water salinity and temperature. These results highlight high spatial-temporal variability and sensitivity of coastal northeast Pacific phytoplankton communities to altered freshwater, temperature and wind dynamics with potential for profound ecosystem level implications.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".