Translocation experiments with butterflies reveal limits to enhancement of poleward populations under climate change
Bibliographic record
Abstract
There is a pressing need to predict how species will change their geographic ranges under climate change. Projections typically assume that temperature is a primary fitness determinant and that populations near the poleward (and upward) range boundary are preadapted to warming. Thus, poleward, peripheral populations will increase with warming, and these increases facilitate poleward range expansions. We tested the assumption that poleward, peripheral populations are enhanced by warming using 2 butterflies (Erynnis propertius and Papilio zelicaon) that co-occur and have contrasting degrees of host specialization and interpopulation genetic differentiation. We performed a reciprocal translocation experiment between central and poleward, peripheral populations in the field and simulated a translocation experiment that included alternate host plants. We found that the performance of both central and peripheral populations of E. propertius were enhanced during the summer months by temperatures characteristic of the range center but that local adaptation of peripheral populations to winter conditions near the range edge could counteract that enhancement. Further, poleward range expansion in this species is prevented by a lack of host plants. In P. zelicaon, the fitness of central and peripheral populations decreased under extreme summer temperatures that occurred in the field at the range center. Performance in this species also was affected by an interaction of temperature and host plant such that host species strongly mediated the fitness of peripheral individuals under differing simulated temperatures. Altogether we have evidence that facilitation of poleward range shifts through enhancement of peripheral populations is unlikely in either study 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 | 0.000 | 0.001 |
| 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) | 0.002 | 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 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".