Resistance to disturbance is a diverse phenomenon and does not increase with abundance:The case of oribatid mites
Bibliographic record
Abstract
The resistance of a population to a disturbance can be described by different proxies, such as abundance, long-term abundance trend, and relative abundance in the more disturbed part of the habitat. Each proxy reflects a different aspect of resistance. Here we investigated oribatid mite species and asked: (i) Are the species generally less resistant with respect to one of these aspects than another? (ii) Is there a correlation between different aspects of resistance across species? (iii) Are abundant species more resistant than rare species? We disturbed a forest soil by a single application of diflubenzuron pesticide onto the litter layer and followed 14 taxa from 10 months before to 18 months after application. In the analysis we adopted the concepts of effect size and of phylogenetically independent contrasts. We found large interspecific differences in the degree of resistance. Most species were less resistant with respect to their relative abundance in the more disturbed part of the habitat (the litter layer) than with respect to their abundance or abundance trend. We found no correlation between any two aspects of resistance, or between any aspect of resistance and abundance. We explain interspecific differences in resistance by interspecific differences in life strategies, and by a relaxation of interspecific competition due to the disturbance.
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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 | 0.002 |
| 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".