Faculty Opinions recommendation of Experimental dispersal reveals characteristic scales of biodiversity in a natural landscape.
Bibliographic record
Abstract
Ecological theory posits that dispersal among habitat patches links local communities and is a key "regional" process that maintains biological diversity. However, manipulations required to experimentally test regional processes are infeasible for most systems, and thus more work is needed to detect the scales at which regional processes manifest and their overall effect on diversity. In a Californian grassland, a hotspot for global biodiversity, we used a seed vacuum to increase dispersal at spatial scales varying from 1 m to 10 km while maintaining a realistic spatial structure of species pools and environmental conditions. We found that dispersal limitation has a profound influence on diversity; species richness increased with the spatial scale of seed mixing, doubling in plots that received seed from large (≥5 km) compared with small (≤5 m) scales. This increase in diversity corresponded to an increase in how well species distributions were explained by environmental conditions, from modest at small scales (R2 = 0.34) to strong at large scales (R2 = 0.52). Responses to the spatial scale of seed mixing were nonlinear, with no differences below 5 m or above 5 km. Nonlinearities were explained by homogeneity of environmental conditions below 5 m and by a lack of additional variation in the species pool above 5 km. Our approach of manipulating natural communities at different spatial scales reveals (i) nonlinear transitions in the importance of environmental sorting and dispersal, and (ii) the negative effects of dispersal limitation on local diversity, consistent with previous research suggesting that large numbers of species are headed toward regional extinction. PMID: 28416694
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.494 | 0.147 |
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".