Dynamic and context-dependent keystone species effects in kelp forests
Bibliographic record
Abstract
Sea otters are an iconic keystone predator that can maintain kelp forests by preying on grazing invertebrates such as sea urchins. However, the effects of sea otters on kelp forests vary over their geographic range. Here, we analyze two 30-y datasets on kelp forest communities during the reintroduction of sea otters along the west coast of Vancouver Island, BC, Canada, and around San Nicolas Island, CA. We developed a community model to estimate species interactions as dynamic rates, varying with community state. We find evidence of a classic trophic cascade off Vancouver Island; the arrival of otters quickly led to depletion of urchins and recovery of kelp. However, this cascade was muted around San Nicolas Island, with otters, urchins, and kelp all coexisting at intermediate densities for multiple years. Our models show that this difference came from a pulse of strong otter impacts on urchins following recolonization off Vancouver Island, but not off San Nicolas Island. The mean effects of otters on urchins and urchins on kelp were not stronger in the north, indicating that interaction dynamics and not average interaction strength are key to explaining differences in community trajectories. We also find stronger multistep interaction chains in the south, arising from competitive interactions that indirectly buffered otter effects. These findings shed light on long-standing hypotheses about how interspecific interactions can alter the function of keystone species across community contexts. More broadly, we show how community change can be more accurately predicted by considering dynamic interaction strengths.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".