Testing predator–prey theory using broad‐scale manipulations and independent validation
Bibliographic record
Abstract
A robust test of ecological theory is to gauge the predictive accuracy of general relationships parameterized from multiple systems but applied to a new area. To address this goal, we used an ecosystem-level experiment to test predator-prey theory by manipulating prey abundance to determine whether predation was density dependent, density independent, compensatory or depensatory (inversely density dependent) on prey populations. Understanding the nature of predation is of primary importance in community ecology because it establishes whether predation has little effect on prey abundance (compensatory), whether it promotes coexistence (density dependent) and reduces the equilibrium of prey (density independent) or whether it can be destabilizing (depensatory). We used theoretical predictions consisting of functional and numerical equations parameterized independently from meta-analyses on wolves (Canis lupus) and moose (Alces alces), but applied to our specific wolf-moose system. Predictions were tested by experimentally reducing moose abundance across 6500 km(2) as a novel way of evaluating the nature of predation. Depensatory predation of wolves on moose was the best explanation of the population dynamic - a mechanism that has been hypothesized to occur but has rarely been evaluated. Adding locally obtained kill rates and numerical estimates to the independent data provided no benefit to model predictions, suggesting that the theory was robust to local variation. These findings have critical implications for any organism that is preyed upon but that also has, or will be, subject to increased human exploitation or perturbations from environmental change. If depensatory predation is not accounted for in harvest models, predicted yields will be excessive and lead to further population decline.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".