Human Influence and Classical Biogeographic Predictors of Rare Species Occurrence
Bibliographic record
Abstract
Biogeographic theory predicts that rare species occur more often in larger, less-isolated habitat patches and suggests that patch size and connectivity are positive predictors of patch quality for conservation. However, in areas substantially modified by humans, rare species may be relegated to the most isolated patches. We used data from plant surveys of 81 meadow patches in the Georgia Basin of Canada and the United States to show that presence of threatened and endangered plants was positively predicted for patches that were isolated on small islands surrounded by ocean and for patches that were isolated by surrounding forest. Neither patch size nor connectivity were positive predictors of rare species occurrence. Thus, in our study area, human influence, presumably due to disturbance or introduction of competitive non-native species, appears to have overwhelmed classical predictors of rare species distribution, such that greater patch isolation appeared to favor presence of rare species. We suggest conservation planners consider the potential advantages of protecting geographically isolated patches in human-modified landscapes because such patches may represent the only habitats in which rare species are likely to persist.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".