Are Polyploid Species Less Vulnerable to Climate Change? A Case Study in North American Crataegus
Bibliographic record
Abstract
Abstract Understanding the mechanisms underlying plant responses to climate change is an important step towards developing effective mitigation strategies. Polyploidy is an important evolutionary trait that can influence the capacity of plants to adapt to climate change. The environmental flexibility of polyploids suggests their resiliency to climate change, however, such hypotheses have not yet received empirical evidence. To understand how ploidy level may influence response to climate change, we modeled the current and future distribution of 54 Crataegus species under moderate to severe environments and compared the range change between diploids and polyploids.The majority of studied species are predicted to experience considerable range expansion. We found a negative interaction between ploidy and ecoregions in determining the response to climate change. In extreme environments polyploids are projected to experience a higher range expansion than diploids with climate change, while the opposite is true for moderate environments.The range expansion of Crataegus species can be attributed to their tolerance for a wide range of environmental conditions. Despite higher tolerance of polyploids to extreme environments, they do not necessarily outperform diploids in moderate environments, which can be attributed to the varying nature of species interactions along a stress gradient.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".