Resolving Indirect Effects of Large Herbivores on Terrestrial Ecosystem Functioning
Bibliographic record
Abstract
The world’s large herbivores play outsized roles in shaping ecosystem processes like primary production, decomposition, and mineralization. Contemporary management of these animals is therefore poised to be a powerful tool for holistic ecosystem management. Yet we currently lack (i) adequate understanding of indirect interactions underlying herbivore control of ecosystem processes, especially belowground, and consequently (ii) an ability to predict how ecosystems will respond to ongoing changes to large herbivore populations such as (re)introductions, range shifts, and population collapse. In this contribution, we synthesize current approaches to meet these challenges and provide a framework to better resolve indirect effects of large herbivores on terrestrial ecosystem functioning. Specifically, we synthesize empirical evidence from across the globe and demonstrate that the consumptive and non-consumptive effects of large herbivores frequently disrupt and restructure the primary biotic and abiotic controls on ecosystem functioning. Next, we derive an analytical framework and illustrate how empiricists can use this framework to resolve key relationships among large herbivores, biotic/abiotic controls, ecosystem processes, and environmental context. Our framework can uncover emergent patterns that are not revealed with existing approaches. We conclude with a roadmap to operationalizing our framework using existing research infrastructure (e.g., large exclosures and distributed networks).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".