Temporal variability and nestedness in California grassland species composition
Bibliographic record
Abstract
Nestedness occurs when species-poor assemblages contain a subset of the species that occur in more species-rich communities and is a commonly observed pattern in spatial data sets. Examination of nested distribution patterns across time rather than space are rarely conducted, even though they may have important implications for species coexistence. Nested temporal assemblages can occur when most species respond similarly to interannual variation in conditions. In contrast, assemblages might be non-nested when different sets of species occur in different years, either because of different resource requirements or as a result of competitive exclusion. Using eight years of plant occurrence data at 71 sites in California grasslands, we found strong signals of temporal nestedness with most species favored by similar conditions. High-quality years enabled the expansion of both grasses and forbs into locales where they were not found during poor-quality years. Native annual forb, exotic annual forb, and exotic annual grass species richness were all greatest in cool, wet years following hot, dry years. Together, these analyses support the hypothesis that, in the absence of community members that specialize on poor-quality years, interannual environmental variation can cause communities to form nested subsets across time much as they do across space.
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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".