Predator effects on herbivore and plant stability
Bibliographic record
Abstract
Abstract Humans are rapidly altering the diversity and composition of ecological communities by accelerating rates of species extinctions and introductions. These changes in diversity are not random and disproportionately involve the addition or extinction of predators. Theoretical and microcosm studies suggest predator removal may either increase or decrease ecosystem stability. Here we test whether the addition or removal of predators affects aggregate biomass stability in 40 experiments carried out in six different ecosystems. Predators did not alter the temporal variability of autotroph biomass, but significantly destabilized herbivore biomass. The effects of predators on herbivore biomass stability varied significantly among ecosystems, with benthic and pelagic lake systems showing the greatest shifts. Consequently, the addition of predators to communities, as occurs in many conservation efforts, biological control programmes and species introductions, may lead to more variable system dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".