The effect of climate on the occurrence and abundance of tree recruitment in the province of Quebec, Canada
Bibliographic record
Abstract
Abstract Tree recruitment is affected by numerous biotic and abiotic factors, including climate. However, the relative importance of climate variables in empirical models of tree recruitment remains to be evaluated. We fitted models of tree recruitment to 26 species in the province of Quebec, Canada. For a better understanding of the recruitment process, we used a two-part model to distinguish recruitment occurrence from abundance. The relative importance of the different variables was assessed using Akaike weights. Our main hypothesis was that climate is one of the major drivers of tree recruitment. Our results showed that growing degree-days counted among the major drivers of recruitment occurrence but not of recruitment abundance. Stand variables, such as the presence and abundance of adult trees of the species, and broadleaved and coniferous basal areas were found to be relatively more important than all the climate variables for both recruitment occurrence and abundance. Species occupancy within a 10-km radius also had a significant effect on recruitment occurrence for two-thirds of the species, but it was less important than growing degree-days and other stand variables. Climate change is expected to improve the suitability of habitats located at the northern edge of species distributions. However, our model predictions point to a low probability of colonization in newly suitable habitats in the short term.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".