Do ecological differences between taxonomic groups influence the relationship between species’ distributions and climate? A global meta‐analysis using species distribution models
Bibliographic record
Abstract
Understanding whether and how ecological traits affect species’ geographic distributions is a fundamental issue that bridges ecology and biogeography. While climate is thought to be the major determinant of species’ distributions, there is considerable variation in the strength of species’ climate–distribution relationships. One potential explanation is that species with relatively low dispersal ability cannot reach all geographic areas where climatic conditions are suitable. We tested the hypothesis that species from different taxonomic groups varied in their climate–distribution relationships because of differences in life history strategies, in particular dispersal ability. We conducted a meta‐analysis by combining the discrimination ability (AUC values) from 4317 species distribution models (SDMs) using fit as an indication of the strength of the species’ climate–distribution relationship. We found significant differences in the strength of species’ climate–distribution relationships across taxonomic groups, however we did not find support for the dispersal hypothesis. Our results suggest that relevant ecological trait variation among broad taxonomic groups may be related to differences in species’ climate–distribution relationships, however which ecological traits are important remains unclear.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".