DISPERSAL, COMPETITION, AND SHIFTING PATTERNS OF DIVERSITY IN A DEGRADED OAK SAVANNA
Bibliographic record
Abstract
Diversity is a balance between processes that add and limit species (e.g., dispersal vs. competition), but reconciling their contributions remains a challenge. Recruit-ment-based models predict that dispersal barriers are most limiting for diversity, while competition-based models predict that dispersal matters only when competition is minimized. Testing these models is difficult because their influence varies with scale and site productivity. In a degraded oak savanna, we used plot-level (seed additions, burning) and site-level (proportions of regional functional groups found locally) analyses in areas with variable soil depth to examine how dispersal and competition influence diversity. At the plot level, added species persisted where they were formerly absent, but few established naturally despite fire-induced resource enrichment and nearby populations, revealing the importance of dispersal limitation for diversity. This result did not vary with soil depth or standing crop. Although competition could not prevent establishment in unburned plots, it significantly lowered survival, indicating that resource limitations exacerbate dispersal inefficiencies. At the site level, the concordance between regional and local diversity for native species was associated with soil depth heterogeneity, not dispersal or competition. This suggests that persistence is determined primarily by the influence of the environment on population demographics. Given that the formation of new populations is unlikely, those remaining appear to be confined to optimal habitat where they resist competitive or stochastic displacement, possibly explaining why species loss is rare despite substantial habitat loss and invasion. For exotics, there was no relationship between diversity and soil depth heterogeneity. Annuals with presumed dispersal capabilities were significantly overrepresented in all sites while perennial forbs, the largest regional functional group, were significantly underrepresented. We interpret the native-exotic discrepancies as reflecting the recent arrival of exotics (150 years ago), suggesting that local establishment occurs slowly even for species with regional prevalence. The accumulation lag may be explained by the need for founder populations to be demographically stable; otherwise persistence requires continual immigration favoring overrepresentation by dispersers. Our findings support the view that dispersal limitation restricts diversity within plant communities, but suggests that the impacts of environment on demographic performance ultimately determine the pattern and rate of community assembly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".