Species‐level and community‐level responses to disturbance: a cross‐community analysis
Bibliographic record
Abstract
Communities are comprised of individual species that respond to changes in their environment depending in part on their niche requirements. These species comprise the biodiversity of any given community. Common biodiversity metrics such as richness, evenness, and the species abundance distribution are frequently used to describe biodiversity across ecosystems and taxonomic groups. While it is increasingly clear that researchers will need to forecast changes in biodiversity, ecology currently lacks a framework for understanding the natural background variability in biodiversity or how biodiversity patterns will respond to environmental change. We predict that while species populations depend on local ecological mechanisms (e.g., niche processes) and should respond strongly to disturbance, community-level properties that emerge from these species should generally be less sensitive to disturbance because they depend on regional mechanisms (e.g., compensatory dynamics). Using published data from terrestrial animal communities, we show that community-level properties were generally resilient under a suite of artificial and natural manipulations. In contrast, species responded readily to manipulation. Our results suggest that community-level measures are poor indicators of change, perhaps because many systems display strong compensatory dynamics maintaining community-level properties. We suggest that ecologists consider using multiple metrics that measure composition and structure in biodiversity response studies.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".