Spatial Variability in Plant Predation Determines the Strength of Stochastic Community Assembly
Bibliographic record
Abstract
High diversity is often poorly explained by trait-based deterministic models, in part because stochastic processes also influence community assembly. Testing how deterministic and stochastic processes combine to regulate diversity, however, has been limited by the spatial complexity of these interactions. Here, we demonstrate how spatial variability in small-mammal predation on plants, mostly by granivory, results in fine-scale switching between deterministically and stochastically regulated plant community assembly in an otherwise environmentally homogeneous tallgrass prairie. We initiated assembly with the uniform application of a 24-species mixture of prairie grasses and forbs, thereby setting the maximum level of diversity (γ-diversity). In field edges with higher densities of small mammals, traits reducing seed palatability deterministically produced homogeneous subsets of less palatable plant species within the first few months after planting (low α and β diversity). As small-mammal densities decreased in more open areas, assembly unfolded stochastically on the basis of which planted species happened to land at a given location (high α and β diversity). We used randomization models to validate that this higher β diversity was explained by true differences in community structure among plots rather than by the hidden effects of increasing α diversity. The net effect at the site level was a spatially structured array of prairie species, including a positive relationship between diversity and environmental suitability relating to reduced predator intensity.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".