Species distribution models predict rare species occurrences despite significant effects of landscape context
Bibliographic record
Abstract
Summary The true status of many endangered plants is uncertain because the locations of all extant populations are not known. Species distribution models (SDMs) can direct searches for additional populations, but habitat fragmentation may influence the distribution of rare species more than climatic or edaphic factors in human‐dominated landscapes. In this study, I test the ability of SDMs to predict rare plant occurrences in a fragmented landscape and the importance of predicted habitat suitability versus landscape context. I built SDMs for eight rare woodland plants and assessed them using an independent data set including plant community surveys of 51 sites. I used community data to determine whether SDMs predict the right habitat type even when the target rare species was absent. I then modelled rare species presence based on predicted habitat suitability, distance to the nearest known population and the amount of forest habitat within 500 m of the site. SDMs were effective for seven of the eight species, with the degree of predicted habitat suitability positively related to species' occurrence. I found new populations of four of the eight species. However, the amount of forest habitat available in the vicinity of a plot was also a positive predictor of rare plant occurrences. Among sites predicted to be suitable, the distance to the nearest known population was the strongest predictor of rare plant occurrence. Synthesis and applications. Species distribution models (SDMs) can effectively target searches for populations of rare species even in human‐dominated landscapes. Surveying the plant community at sites predicted to be suitable can help to improve the SDM. SDMs used in conjunction with data on landscape context can maximize the efficiency of searches for rare species and show which species are restricted by dispersal limitation and habitat fragmentation in addition to edaphic and climatic factors.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".