Plant traits poorly predict winner and loser shrub species in a warming tundra biome
Bibliographic record
Abstract
Climate change is leading to a species redistributions. In the tundra biome, many shrub species are expanding into new areas, a process known as shrubification. However, not all tundra shrub species will benefit from warming. Winner and loser species (those projected to expand and contract their ranges, and/or those that have increased or decreased in cover over time), and the characteristics that may determine success or failure, have not been fully identified. Here, we investigate whether current range sizes are related to plant trait values and intraspecific trait variation by combining 17,921 trait records and distribution data from 62 shrub species across three continents (>30 degrees north). In addition, we determine which traits are associated with species projected by species distribution models to expand or contract their ranges under climate change, and species that have undergone past cover changes over time. Winner and loser shrub species identified from projected range shifts generally differed from those identified from observed past cover change. We found that greater variation in seed mass and specific leaf area were related to larger projected range shifts. Projected winner species generally had greater seed mass values than ‘no change’ and loser species. However, contrary to our expectations, traits’ values and variation were not consistently related to current and projected ranges, and depended upon the future climate scenarios considered in range projections. There were no clear relationships either between cover change over time and trait values or variation. Overall, our findings indicate that abundance changes and projected range shifts will not lead to directional modifications in shrub trait composition or variation with future warming, since winner and loser species share relatively similar trait spaces based on commonly measured traits. Future research could investigate other morpho-physiological traits underpinning climatic preferences, which might better predict future range and abundance changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".