The Ecology of Disturbance Interactions
Bibliographic record
Abstract
Abstract Global change has been accompanied by recent increases in the frequency and intensity of various ecological disturbances (e.g., fires, floods, cyclones), both natural and anthropogenic in origin. Because these disturbances often interact, their cumulative and synergistic effects can result in unforeseen consequences, such as insect outbreaks, crop failure, and progressive ecosystem degradation. We consider the roles of biological legacies, thresholds, and lag effects responsible for the distinctive impacts of interacting disturbances. We propose a hierarchical classification that distinguishes the patterns and implications associated with random co-occurrences, individual links, and multiple links among disturbances that cascade in chains or networks. Disturbance-promoting interactions apparently prevail over disturbance-inhibiting ones. Complex and exogenous disturbance cascades are less predictable than simple and endogenous links because of their dependency on adjacent or synchronous events. These distinctions help define regional disturbance regimes and can have implications for natural selection, risk assessment, and options for management intervention.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".