A re‐examination of the expected effects of disturbance on diversity
Bibliographic record
Abstract
Disturbance is often cited as one of the main factors determining patterns of species diversity. Several models have predicted qualitatively that species richness should be highest at intermediate intensities and/or frequencies of disturbances, but none indicate whether this effect should be strong (statistically accounting for much variability in diversity) or only subtle. Empirical evidence on the point is very mixed. This study examines Markov models of the dynamics of six real communities. We derive the predicted changes in species richness and evenness when these communities are subjected to quantified disturbance frequency and intensity gradients. We also use several different sampling intensities (i.e. numbers of individuals counted) to determine how this affects richness‐disturbance relationships. Our models predict that peaked responses of diversity to disturbance should be less common than monotonic ones. Species richness should vary, on average, by only 3% over gradients of no disturbance to complete disturbance. In the most extreme case, richness varied two‐fold over this gradient. Moreover, richness may increase monotonically, decrease monotonically, or be a peaked function of disturbance, interacting in a non‐intuitive fashion with both the sampling intensity and the community in question. These results are broadly consistent with a review of published richness‐disturbance relationships. Evenness varies somewhat more strongly along disturbance gradients, but the effect is still small. We conclude that extant models provide little reason to believe that disturbance should play more than a subtle role in determining patterns of diversity in nature, contrary to most contemporary literature.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".