Bull kelp (<i>Nereocystis luetkeana</i>) growth rates as climate stress indicators for Canada’s Pacific coast
Bibliographic record
Abstract
Primary producers’ growth rates are ideal bioindicators of changing climate due to their sensitivity to environmental conditions. On the Central Coast of British Columbia, we assessed growth rates of Nereocystis luetkeana, a canopy-forming annual kelp, by assessing baseline variability in growth rates and their response to environmental conditions of over 600 individuals and across three sites (2016–2019). Optimal growth rates for blades and stipes (∼13–14 cm/day) occurred within a narrow range of local environmental conditions. Growth decreased at temperatures > 10 °C, below 1 µm/L nitrate concentration, and surface light availability reduced blade growth at low and high levels (daily light integral or DLI <20 and >40 mol/m2/day). Spatiotemporal variability in these environmental drivers co-occurred with differences in growth rates, suggesting that local conditions strongly influenced growth. In particular, temperature and nutrients were un-coupled seasonally in this region, with more variable responses in growth over the primary growing season (May to September). Overall, the sensitivity of the growth rates of this annual kelp to changing climatic conditions suggests that it is a useful bioindicator for management and marine planning efforts (e.g., restoration and aquaculture) across its species range and provides a feasible metric for monitoring.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".