The role of genotypic and climatic variation at the range edge: A case study in winegrapes
Bibliographic record
Abstract
PREMISE: Changes in habitat suitability due to climate change are causing range shifts, with new habitat potentially available at cold range edges. We must predict these range shifts, but forecasters have limited knowledge of how genetic differences in plant physiological tolerances influence range shifts. Here, we focus on a major determinant of species ranges-physiological tolerance to extreme cold-to ask how warming over recent decades and genetic variation shape expansion across complex landscapes. METHODS: We examined how genotypes vary in maximum cold tolerance from 9 years of cold hardiness data across 18 genotypes from 13 sites, using winegrapes (Vitis vinifera subsp. vinifera) as a case study. Combining a Bayesian hierarchical dose-response model with gridded climate data, we then project changes in climatic suitability near winegrapes' current cold range-edge between 1949 and 2016. RESULTS: Plants increased maximum cold hardiness non-linearly with decreasing air temperature (maximum cold hardiness: -23.6°C), but with substantial (by 2°C) variation across genotypes. Our results suggest, since the 1980s, decreasing freeze injury risk has made conditions more favorable for all genotypes at the cold range-edge, but conditions remained more favorable for more cold hardy genotypes and in warmer areas. There was substantial spatial variation in habitat suitability, with the majority of suitably warm habitat located in a narrow north-south oriented strip. CONCLUSIONS: We highlight the importance of genotypic differences in physiological tolerances when assessing range shift potential with climate change. Habitat improvements were unevenly distributed over the spatially complex landscape, though, emphasizing the importance of dispersal in range expansion.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".