A national tree-ring data repository for Canadian forests (CFS-TRenD): structure, synthesis, and applications
Bibliographic record
Abstract
Understanding the magnitude and cause of variation in tree growth and forest productivity is central to sustainable forest management. Measurements of annual growth rings allow assessments of individual-tree, tree population, and forest ecosystem vulnerabilities to drought stress or other changing forest disturbance regimes (insects, diseases, fire), which can be used to identify areas at greatest risk of forest losses. Given a heightened demand for tree-ring data, we consolidated and synthesized tree-ring studies and datasets gathered over the past 30 years in Canada by scientists with the Canadian Forest Service and research partners. We incorporated these datasets into a data repository that currently contains tree-ring measurements from 40 206 tree samples from 4594 sites and 62 tree species from all Canadian provinces and territories. Through our synthesis, we demonstrate the value of such large ensembles of tree-ring data for identifying patterns in tree growth over large spatial scales by mapping pan-Canadian drought sensitivity. Overall, we found high coherence in the samples analyzed; low coherence was generally limited to data-poor regions and species. Drought sensitivity was widespread across species and regions: 34% of sampled trees displayed a significant positive relationship between annual growth increment and summer soil moisture index. Dependence upon water availability in species Picea mariana (Mill.) BSP, Pinus banksiana Lamb., Pinus contorta Douglas ex Loudon, and Pseudotsuga menziesii (Mirb.) Franco was more strongly expressed in the warmest regions of the species’ range; for species Picea glauca (Moench) Voss and Populus tremuloides Michx., drought sensitivity was stronger in the driest regions. This unprecedented consolidation and synthesis of tree-ring data will enable new research initiatives (e.g., meta-analyses) aimed at improved understanding of the drivers, patterns, and implications of changes in tree growth, as well as facilitating new research collaborations in earth and environmental sciences. Among other things, there is a need for expanding the spatial distribution of sites across Canada’s northern regions, increasing the number of samples collected from older stands and angiosperm species, and integrating datasets from studies that evaluate the effects of silvicultural experiments, including provenance and progeny trials, on tree growth.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.032 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".