Variations in permafrost environments determine populations and leaf traits of two closely related common shrub species (Rhododendron subsect. Ledum) in Interior Alaska
Bibliographic record
Abstract
Abstract Permafrost considerably influences boreal forest ecosystems by constraining the niche space of woody plants. The permafrost influence on the ecosystems could dramatically change with permafrost thawing owing to recent rapid climate warming. However, it remains unclear how shrub species dominating the understory in boreal forests are associated with the permafrost environments. We investigated two closely related common shrubs, Labrador teas (Ericaceae,Rhododendron subsect. Ledum): R. groenlandicum and R. tomentosum, which exist sympatrically in the discontinuous permafrost zone of the Interior Alaska. We employed field surveys and trait measurements across permafrost gradients under the same climatic conditions to examine the associations among permafrost environments, populations, and leaf traits of the two species. Contrastive habitats were found between the two species: R. groenlandicum is abundant under darker, drier, thicker-active-layer conditions, whereas R. tomentosum is common under brighter, wetter, thinner-active-layer conditions. This suggests that habitat segregation between these species occurs in the discontinuous permafrost zone. Rhododendron tomentosum dominating permafrost conditions had more conservative leaves compared to R. groenlandicum. Moreover, both species had more conservative leaves under permafrost conditions. These intraspecific variations were mainly directly associated with canopy openness in R. groenlandicumbut with active-layer thickness in R. tomentosum. In summary, our study suggests that large environmental variations driven by the inhomogeneous permafrost distributions can lead to the sympatric distributions of closely related shrub species in the discontinuous permafrost zone, and that the conservative leaves can contribute to their adaptation under permafrost conditions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".