Effect of Disturbance Regime on Alpha and Beta Diversity of Rock Pools
Bibliographic record
Abstract
Measures of alpha diversity are more frequently used to detect environmental changes and subsequent impacts on biodiversity, while measures based on variability (beta diversity) are said to be more appropriate for detecting those impacts. Theory predicts that beta diversity should increase with disturbance frequency in patchy communities. Our objective in this study was to experimentally determine the effect of high and low disturbance regimes, frequency and intensity combined, on marine benthic alpha and beta diversity. The experiment was conducted in a rock pool system of the St. Lawrence estuary, Canada. Rock pools were disturbed by (1) nutrient enrichment and (2) draining according to three disturbance regimes (none, low, high). Disturbance regimes had little or no effect on alpha diversity of benthic algae and sessile animals. However, the low regime of nutrient enrichment induced greater within-group beta diversity than the reference rock pools, while the high disturbance regime induced equal or even smaller within-group beta diversity compared to the reference. Draining had an opposite effect on benthic beta diversity, with a greater variability of the community structure under the high regime of disturbance. Taking into account the effect of disturbance regimes on beta diversity could provide a useful diagnostic for disturbed benthic communities.
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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 | 0.001 |
| 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".