Top-down and bottom-up forces in mammalian herbivore – vegetation systems: an essay review
Bibliographic record
Abstract
For almost 50 years ecologists have debated why herbivores generally don’t increase in numbers to such levels as to deplete or devastate vegetation. One hypothesis is that herbivore populations are regulated at low densities by predators, and a second hypotheses is that plants are fundamentally poor food for herbivores. This has lead to two main hypotheses about the role of herbivores in structuring vegetation: the “bottom-up” and “top-down” hypotheses. Here I survey the literature, with a focus on field experiments designed to investigate the soil resource – vegetation – mammalian herbivore system, specifically asking five questions about how each trophic level responds to (i) resource addition, (ii) vegetation removal, (iii) herbivore removal or reduction, (iv) herbivore addition, and (v) the interaction of resource levels and herbivory? I use these to develop 12 testable predictions. I document the major areas of research as they relate to these 12 predictions, and use these to evaluate weaknesses and limitations in field methods. There are surprisingly few terrestrial studies that conduct factorial manipulations of multiple nutrients or herbivores, even though it is clear that these are essential. Specifically, I argue that a manipulative experimental approach is the most valuable way to advance our theory and understanding, and I advocate the continued use of long-term factorial field experiments that simultaneously manipulate soil resources levels and herbivory (preferably at multiple levels), repeated in a range of environments in which individual species or functional groups are monitored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".