Species occupancy is inflated by sink populations in productive environments but not unproductive environments
Bibliographic record
Abstract
For decades, community ecologists have examined how diversity varies with ecosystem productivity. Despite this long history, tests of hypothesized mechanisms, namely the interplay between environmental filtering, biotic interactions, and dispersal, are lacking, largely due to the intractability of using traditional approaches. Across a productivity gradient in a serpentine grassland (California, USA), for four annual plant species, we coupled local productivity estimates, occupancy surveys, and measures of persistence tested on transplants under natural conditions and when interactions with neighbors were experimentally reduced. We found a positive effect of productivity on diversity (i.e., the proportion of our focal species occupying a location) despite strong competition limiting species persistence in productive environments. Additionally, across species and for the community, we found a strong mismatch between species occupancy versus persistence, largely due to dispersal excess causing sink populations with negative growth rates. Our results suggest that diversity-productivity relationships can be largely driven by dispersal and its interactive effects with local biotic and abiotic conditions.
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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 | 0.003 |
| 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 | 0.001 | 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".