A synthesis of transplant experiments and ecological niche models suggests that range limits are often niche limits
Bibliographic record
Abstract
Global change has made it important to understand the factors that shape species' distributions. Central to this area of research is the question of whether species' range limits primarily reflect the distribution of suitable habitat (i.e. niche limits) or arise as a result of dispersal limitation. Over-the-edge transplant experiments and ecological niche models are commonly used to address this question, yet few studies have taken advantage of a combined approach for inferring the causes of range limits. Here, we synthesise results from existing transplant experiments with new information on the predicted suitability of sites based on niche models. We found that individual performance and habitat suitability independently decline beyond range limits across multiple species. Furthermore, inferences from transplant experiments and niche models were generally concordant within species, with 31 out of 40 cases fully supporting the hypothesis that range limits are niche limits. These results suggest that range limits are often niche limits and that the factors constraining species' ranges operate at scales detectable by both transplant experiments and niche models. In light of these findings, we outline an integrative framework for addressing the causes of range limits in individual species.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".