The role of habitat in predator–prey dynamics with applications to restoration
Bibliographic record
Abstract
Habitat is a powerful force in ecosystems, and the quantity and quality of habitat can shape ecosystem structure and function. Among the many important roles that habitat plays is as a mediator of ecological interactions, including predator–prey dynamics. In the context of ecosystem restoration, there is great potential to better understand how predator–prey dynamics are influenced by habitat and whether this has implications for how ecosystems are managed. We consider the ways in which habitat serves as an important mediator of interactions between predators and their prey and present four ways in which habitat acts as an intermediary that enhances or diminishes this relationship. We found that habitat provides refuge from predators and shapes the physical traits of prey as they use their surroundings to protect themselves. We also discuss how habitat creates physical resistance and sets the cost of predation for predators and how habitat facilitates apparent competition within a community context. These roles of habitat are well established in ecology, but we believe they are underdeveloped from an applied perspective. We conclude that habitat must be appropriately considered in the context of how it mediates predation. Given the ways that habitat influences predation, restoration efforts should consider if and how physical measures may positively or negatively affect species interactions and whether this could lead to success or failure of overall programs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| 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".