Little evidence for climate effects on local‐scale structure and dynamics of California kelp forest communities
Bibliographic record
Abstract
Abstract Climatic variables such as temperature and precipitation play an important role in controlling local and regional scale differences in population dynamics and species distributions, and large‐scale climatic events such as El Niño southern oscillation (ENSO) have been shown to affect population dynamics of key species in many ecosystems, particularly in kelp forests. Few studies have been able to evaluate the consequences of climate variables on the structure and dynamics of biological communities, in large part because the lack of data at appropriate spatial and temporal scales has made it difficult to adequately address local‐scale responses of species and communities to such events over relevant time scales. Here, we combined an unprecedented dataset of kelp forest species' abundances from the Channel Islands, California with data for several local, regional, and global scale climatic variables to evaluate the temporal and spatial scale at which one can detect community‐wide effects of climate variables, in particular ENSO events. We found large and significant local‐scale differences in community structure, but these differences were not related to differences in climatic variables. Moreover, giant kelp abundance, which has been shown to be highly sensitive to water temperature and storm disturbance, was a poor predictor of community differences, and all communities tended to decline in abundance over the 20‐year sampling period, suggesting a press perturbation to the system such as PDO cycles or sustained fishing pressure. Although ENSO events can have dramatic impacts on the abundance and distribution of giant kelp itself across the range of the species, such events appear to have little effect on local‐scale kelp forest community structure or dynamics.
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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 | 0.000 |
| 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 | 0.000 |
| 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".