Patchy indirect effects: how predators drive landscape heterogeneity and influence ecosystem dynamics via localized pathways
Bibliographic record
Abstract
Predators are widely recognized for their irreplaceable roles in regulating the abundance and altering the traits of lower trophic levels. Yet, predators also have irreplaceable roles in shaping community interactions and ecological processes in highly localized pathways, irrespective of their influence on prey density or behavior. We introduce a conceptual framework, patchy indirect effects , that outlines how predators indirectly affect other organisms via landscape patches. We focus on three main pathways and provide examples and detailed case studies herein: generating and distributing prey carcasses, creating biogeochemical hotspots by concentrating nutrients derived from prey, and killing ecosystem engineers that create patches. In each pathway, indirect effects of predation are localized within discrete areas with measurable spatial and temporal boundaries. Whereas density- and trait-mediated indirect effects function via population-scale changes, the patchy indirect effects concept outlines how predators drive landscape heterogeneity and influence ecosystem dynamics – including scavenger interactions, nutrient cycling, parasite/disease transmission risk, and local biodiversity – through pathways that function at individual- and patch-level scales. Our synthesis provides a more holistic view of the functional role of predation in ecosystems by addressing how predators create patchy landscapes via localized pathways, in addition to influencing the abundance and behavior of lower trophic levels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".