MULTIPLE STABLE EQUILIBRIA IN GRASSLANDS MEDIATED BY HERBIVORE POPULATION DYNAMICS AND FORAGING BEHAVIOR
Bibliographic record
Abstract
Plant community structure is often the result of interactions between succession, disturbance, and dispersal. While some disturbances may be highly stochastic (e.g., flooding or landslides), other types of disturbance are closely linked to the current successional state of the community (e.g., fire or herbivory). For example, when herbivores preferentially feed on early successional species they may generate conditions favorable for these species and thus create a positive feedback. Positive feedbacks may create multiple stable equilibria within plant communities. We demonstrate the presence of these positive feedbacks using experiments conducted in a restored California grassland. We found that pocket gophers (Thomomys bottae) preferentially forage in areas dominated by annual species, and gopher foraging activity increases the abundance of annual plants. We use a Markov chain model to identify how the foraging behavior, dispersal behavior, and population dynamics of territorial herbivores can structure a plant community across multiple spatial scales. The model is loosely based on the biology of pocket gophers, though it is general enough to be applicable to other territorial herbivores with foraging preferences. We find that a foraging preference for early successional species can generate multiple plant communities that persist within a herbivore's territory. If juveniles are selective when searching for territories during their dispersal phase, then herbivores can also generate persistent and distinct plant communities over larger spatial scales. In this case, fixed regions of the landscape may become occupied by herbivores for long periods (many herbivore generations) and be composed of a range of successional plant species, whereas the remaining landscape is abandoned by herbivores and becomes dominated by late successional species. This structuring of the landscape occurs even though we assume that the entire landscape is intrinsically identical.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".