Are Species’ Range Limits Simply Niche Limits Writ Large? A Review of Transplant Experiments beyond the Range
Bibliographic record
Abstract
Many species' range limits (RL) occur across continuous environmental gradients without obvious barriers imposing them. Such RL are expected to reflect niche limits (NL) and thus to occur where populations cease to be self-sustaining. Transplant experiments comparing fitness within and beyond species' ranges can test this hypothesis, but interpretive power depends strongly on experimental design. We first identify often overlooked aspects of transplant design that are critical to establishing the causes of RL, especially incorporating transplant sites at, and source populations from, the range edge. We then conduct a meta-analysis of published beyond-range transplant experiments (n = 11 tests). Most tests (75%) found that performance declined beyond the range, with the strongest declines detected when the measure of performance was lifetime fitness (83%), suggesting that RL commonly involve niche constraints (declining habitat quality). However, only 46% supported range limits occurring at NL; 26% (mostly geographic RL) fell short of NL with self-sustaining transplants beyond the range, and 23% (all elevational RL) exceeded NL with range-edge populations acting as demographic sinks. These data suggest an important but divergent role for dispersal, which may commonly constrain geographic distributions while extending elevational limits. Meta-analysis results also supported the importance of biotic interactions at RL, particularly the long-held assertion of their role in causing low-elevation and equatorial limits.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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".