Winter conditions – not summer temperature – influence establishment of seedlings at white spruce alpine treeline in Eastern Quebec
Bibliographic record
Abstract
Abstract Aims While treeline positions are globally correlated to growing season temperatures, seedling establishment, an important process of alpine treeline dynamics, is additionally controlled by regional‐scale factors such as snow cover duration, desiccating winds and biotic interactions. Knowing that alpine treelines have shown contrasting responses to climate change, we determined the relative importance of key abiotic and biotic factors involved in seedling survival and growth. Location McGerrigle Mountains, Parc National de la Gaspésie, Appalachian Range, eastern Quebec, Canada. Methods In two white spruce ( Picea glauca ) treeline sites, we used the microclimate in the vicinity of tree islands, densely packed clusters of trees isolated from each other by alpine tundra vegetation, to assess the effects of abiotic variables (sum of degree days [ DD ], snowpack duration and a wind exposure index) as well as the effects of biotic interactions with neighbouring vegetation on the survival and growth of transplanted white spruce seedlings. For 3 yr, we surveyed seedling survival twice a year to discriminate between winter and summer survival, and measured seedling growth at the end of each growing season. We used Bayesian hierarchical models to estimate the relative effects of covariates on survival and growth. Results Survival probability decreased in microsites where winter DD was high, and increased in microsites with longer snowpack duration. In wind‐exposed microsites, seedling survival increased when neighbouring vegetation was present, indicating facilitative mechanisms. Seedling growth was positively affected by the duration of snow cover and tended to increase with higher DD during the previous year. In wind‐sheltered microsites, seedling growth tended to be negatively affected by neighbouring vegetation, indicating competitive mechanisms. Conclusions Our study demonstrates that seedling establishment is more sensitive to winter conditions, notably to the length of snow cover (which protects seedlings from frost and desiccation), than to summer temperature. Biotic interactions increased seedling establishment when environmental stresses were higher. We suggest that regional‐scale factors such as winter climate and biotic interactions should be included in modelling exercises to improve future treeline location forecasts.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".