Are weak dispersers more vulnerable than strong dispersers to land use intensification?
Bibliographic record
Abstract
Ecologists often state that weak dispersers are particularly at risk from land use intensification, and that they therefore should be prioritized for conservation. We reviewed the empirical evidence, to evaluate whether this idea should be used as a general rule in conservation. While 89% of authors predicted that weak dispersers are more vulnerable to land use intensification (80 out of 90 papers), only 56% of reported tests (235 out of 422) were consistent with this prediction. Thirty per cent of tests (128 out of 422) were consistent with the opposite prediction, that strong dispersers are more vulnerable to intensification, and 60% of articles (45 out of 75) had at least one test where strong dispersers were most vulnerable. The likelihood of finding that weak dispersers are more vulnerable to intensification than strong dispersers varied with latitude, taxonomic group and type of land use intensification. Notably, the odds of finding that weak dispersers are more vulnerable to intensification than strong dispersers was higher if the study was nearer to the equator. Taken together, our results show that the prediction that weak dispersers are more vulnerable than strong dispersers to intensification is not sufficiently supported to justify using weak dispersal as a general indicator of species risk in human-modified landscapes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".