Asynchronous shifts in the demographics of two wave‐swept kelp species (Laminariales) after nearly four decades
Bibliographic record
Abstract
Kelp forests are among the most abundant and productive marine ecosystems but are under threat from climate change and other anthropogenic stressors. Although knowledge is growing about how the abundance and distribution of kelp forests are changing, much less is known about the "non-lethal" effects that global change is having on the performance and health of kelp populations in areas where they persist. Here we assessed the age distribution of two common stipitate kelp species, Laminaria setchelli and Pterygophora californica, at Wizard Islet in Barkley Sound, British Columbia, Canada, and compared these data to historical demographic data collected by De Wreede (1984) and Klinger and DeWreede (1988) from the same site between 1981 and 1983. We observed that L. setchelli populations in 2020 were younger and less evenly aged than the same populations sampled nearly four decades prior, while the P. californica population was composed of older individuals on average than at the historical time point. Although the drivers of these demographic changes remain unclear, Barkley Sound has experienced substantial changes in the physical and biological environment over the past decade that could be responsible for these patterns. Given that the size of an individual and its probability of reproduction increases with age, shifting demographics may impact the reproductive output of each population, potentially altering the competitive relationships between co-occurring species. Changes in size distribution may also influence ecosystem-level processes such as habitat complexity or productivity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".