Using leaf carbon isotope values to evaluate plant response to high latitude climate change
Bibliographic record
Abstract
As climate continues to change, more research is needed to understand how individual plant species will respond. This study uses leaf carbon isotope values as a lens to examine how high-latitude plant species in Alaska, Canada, and the northern United States are impacted by changes in water availability and increased temperature due to anthropogenic climate change. I found that Δleaf values of three individual species, Juniperus communis, Betula glandulosa, and Eriophorum angustifolium are not responding uniformly to climate. While none of the species has responded to increase [CO2] over the period of 1923–2015, each species responded to multiple other climatic variables, specifically temperature and water availability, in ways not previously noted in studies of temperate and tropical systems. In particular, while meta-analytical studies of temperate and tropical indicated that Δleaf was lower at low precipitation, I found that the opposite was true for the high-latitude species. Meta-analytical studies also have found little or no change in Δleaf due to temperature, which is validated for sites where the mean annual temperature is >0 C. However, at colder sites Δleaf is negative correlated with temperature. This implies that studying leaf carbon isotope values of species on the edge of their growth range, under climatic extremes, may provide an opportunity to identify the climatic drivers that most affect a species under future climate change. This study suggests that some individual species growing at high latitudes may have a physiological advantage when faced with climate change, potentially broadening their growth range poleward, while other species may face a physiological disadvantage with a decreased chance of survival.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".