Reassessing adaptational lag in <i>Eriophorum vaginatum</i>: short-term responses to reciprocal transplant and passive warming experiments in northern Alaska
Bibliographic record
Abstract
Previous Eriophorum vaginatum L. studies have detected adaptational lag in response to climate change. We revisited this concept through a short-term reciprocal transplant experiment combined with warming via open-top chambers (OTCs). We asked: (1) if population growth rates of different ecotypes responded differently to reciprocal transplant, (2) if home-site advantage existed, and (3) if an interaction of ecotype, transplant garden, and OTC treatment existed. We established three transplant gardens, two north of the Brooks Range (Toolik and Sagwon) and one south (Coldfoot); OTCs were deployed in northern gardens. We censused tillers in 2016 and 2017. Lefkovitch matrices were jackknifed using Yellow Taxi Analysis to quantify each tiller's contribution to population growth rate, which were incorporated into nested ANOVAs. Of tussocks grown in ambient temperature, mean tiller population growth from different source ecotypes did not respond differently to transplant. Home site advantage was not observed among tillers not exposed to warming via OTC, which may indicate adaptational lag is occurring. Mean population growth rate of OTC-exposed tillers was higher at Toolik than Sagwon. This study's short duration likely limited our ability to detect differences in tiller population growth as a function of garden or ecotype, emphasizing the need for long-term monitoring.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".