Dispersal Limitation and Environmental Structure Interact to Restrict the Occupation of Optimal Habitat
Bibliographic record
Abstract
Whether plant distributions are governed more by neutral-based distance effects or niche-based environmental responses remains elusive. A lack of habitat matching, where species distributions do not correspond to environmental variability, suggests neutrality but can also be explained by niche models through the interactions of dispersal limitation, spatial autocorrelation of the environment, species interactions, and spatial scale. We untangle these effects in a field study with multiscale statistical analyses. We demonstrate that despite significant niche-based environmental responses by a savanna plant, we still see weak habitat matching, with the mechanisms responsible differing by spatial scale. At the coarse scale (100-200 m), dispersal limitation restricted the occupation of optimal habitat. At the fine scale (<30 m), dispersal was not limiting, but a lack of autocorrelation of environmental variables prevented the aggregation of reproductively active plants in optimal microsites. Species associations were largely unimportant at all scales. Extending our analysis to the entire community revealed similar scale-dependent limitations of distance and the environment, indicating weak habitat matching for all species. This work supports predictions that environmental specializations do not necessarily produce deterministic distributions in plant communities. It also provides a mechanistic explanation for why co-occurring plant species can have largely undifferentiated distributions.
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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 | 0.002 |
| 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".