Dispersal limitation and seed predation drive rarity of a plant species at its range edge
Bibliographic record
Abstract
Abstract Understanding the causes of species rarity is a central goal in ecology. The three filters thought to predict if a species is present or not in a community are the suitability of abiotic conditions, dispersal limitation and biotic interactions. Theory emphasizes the importance of the availability of abiotically suitable habitat in determining occurrence frequency, especially for species at their range edge, where the amount of suitable habitat is predicted to decline. However, the relative influence of these filters in driving species rarity is mostly unknown. We used species distribution models (SDMs) to estimate habitat suitability based on broad‐scale abiotic predictors for a rare plant species ( Stylophorum diphyllum ) at the northern edge of its global distribution. We tested the role of dispersal limitation by planting seeds in unoccupied sites that varied in their predicted habitat suitability and measured seedling emergence and seedling survival over 2 years. To manipulate the biotic interactions, we excluded seed predators by caging half of the seeds. We also measured the microclimate at each microsite, including soil moisture, temperature and canopy cover. The habitat suitability estimated by the SDMs did not predict seedling emergence or short‐term seedling survival. We found that dispersal limitation coupled with seed predation was a significant predictor of seedling emergence, while microclimate, specifically microsite temperature, was a significant predictor of short‐term seedling survival. Synthesis . Contrary to the assumption that species occur at a low frequency near their range edges due to a lack of suitable habitat, we found that dispersal limitation coupled with biotic interactions can drive rarity. If this is the case for many rare species at risk of extinction at their range edges, effective conservation strategies must incorporate assisted dispersal (i.e. translocations) into appropriate microsites and the management of biotic interactions to establish new populations and ensure long‐term persistence.
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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) | 0.005 | 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".