Local adaptation primes cold-edge populations for range expansion but not warming-induced range shifts
Bibliographic record
Abstract
Abstract According to theory, edge populations may be the best suited to initiate range expansions and climate-driven range shifts if they are locally adapted to extreme edge conditions, or the worst suited to colonize beyond-range habitat if their offspring are genetically and competitively inferior. We tested these contrasting predictions by comparing fitness of low, mid, and high-elevation (edge) populations of the annual Rhinanthus minor , transplanted throughout and above its elevational distribution under natural and experimentally-warmed conditions. Seed from low-quality edge habitat had inferior emergence across sites, but high-elevation seeds were also locally adapted. High-elevation plants initiated flowering earlier than plants from lower populations, required less heat accumulation to mature seed, and so achieved higher lifetime fitness at high elevations. Fitness was strongly reduced above the range, but adaptive phenology enhanced the relative fitness of high-elevation seeds. Experimental warming improved fitness above the range, confirming climate’s importance in limiting R. minor ’s distribution, but eliminated the advantage of local cold-edge populations. These results provide experimental support for recent models in which cold-adapted edge populations do not always facilitate warming-induced range shifts. The highest fitness above the range was achieved by a ‘super edge phenotype’ from a neighboring mountain, suggesting key adaptations exist at the regional scale even if absent from local edge populations. Our results demonstrate that assessing the value of edge populations will not be straightforward, but suggest that a regional approach to their conservation, potentially enhancing gene flow among them, might maximize species’ ability to respond to global change. Significance Individuals from range-edge populations are the most likely to disperse to habitat beyond the species current range, but are they best suited to colonize it? Our multi-year transplant experiment throughout and above the elevational range of an annual herb in the Canadian Rocky Mountains found that adaptive flowering phenology enhanced the fitness of high-edge seeds above the range, outweighing detrimental effects of poor seed quality. However, only one edge population maintained its advantage over central populations under experimental warming. While edge populations were most likely to drive range expansion, adaptation to cold climates may not help them initiate range shifts in response to climate warming, unless superior genotypes spread among isolated edge populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".