Hydra effects in stable communities and their implications for system dynamics
Bibliographic record
Abstract
A hydra effect occurs when the mean density of a species increases in response to greater mortality. We show that, in a stable multispecies system, a species exhibits a hydra effect only if maintaining that species at its equilibrium density destabilizes the system. The stability of the original system is due to the responses of the hydra-effect species to changes in the other species' densities. If that dynamical feedback is removed by fixing the density of the hydra-effect species, large changes in the community make-up (including the possibility of species extinction) can occur. This general result has several implications: (1) Hydra effects occur in a much wider variety of species and interaction webs than has previously been described, and may occur for multiple species, even in small webs; (2) conditions for hydra effects caused by predators (or diseases) often differ from those caused by other mortality factors; (3) introducing a specialist or a switching predator of a hydra-effect species often causes large changes in the community, which frequently involve extinction of other species; (4) harvest policies that attempt to maintain a constant density of a hydra-effect species may be difficult to implement, and, if successful, are likely to cause large changes in the densities of other species; and (5) trophic cascades and other indirect effects caused by predators of hydra-effect species can exhibit amplification of effects or unexpected directions of change. Although we concentrate on systems that are originally stable and models with no stage-structure or trait variation, the generality of our result suggests that similar responses to mortality will occur in many systems without these simplifying assumptions. In addition, while hydra effects are defined as responses to altered mortality, they also imply counterintuitive responses to changes in immigration and other parameters affecting population growth.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".