Assessing the hydroclimatic sensitivity of tree species in Northeastern America through spatiotemporal modelling of annual tree growth
Bibliographic record
Abstract
Climate is an important abiotic factor that controls the physiological processes governing photosynthesis, cambial activity, and xylogenesis of trees. Climate projections anticipate significant changes in the dynamics of hydroclimatic variables and an increase in the occurrence of extreme climatic events. These changes can substantially impact the quantity and quality of wood produced annually and, consequently, overall carbon stocks. Developing models that can explicitly account for intra- and inter-annual climatic conditions is crucial for understanding the hydroclimatic sensitivity of tree species. In this study, we propose a generic framework for the spatiotemporal modelling of tree growth in Northeastern America. Our approach aims to model the annual basal area increment of five boreal species by combining 5 million tree ring widths with spatial and temporal covariates, allowing us to consider the effects of climate, topography, soil conditions, and insect outbreaks. These models are used to simulate growth over 1.7 million km 2 in the province of Quebec, Canada, and are employed to assess hydroclimatic sensitivity of each species. Results demonstrate that our models explain between 52% to 71% of the cumulative basal area increment of an independent tree ring width dataset. Subsequent validations demonstrate the reliability of our models in forest inventory plots (R 2 : 55-66). Sensitivity analyses reveal that pioneer tree species such as white birch and trembling aspen are more sensitive to site conditions and, to a lesser extent, to hydroclimatic conditions. In contrast, balsam fir, black spruce, and jack pine show higher sensitivity to the hydroclimatic conditions and, to a lesser extent, to site conditions, suggesting that climate change is more likely to impact the growth of these species. Spatiotemporal models provide a comprehensive overview of intra- and inter-annual growth variability, enabling us to quantify the influence of environmental conditions on each species. • Spatiotemporal models are used to simulate annual growth of boreal species using tree ring widths. • These models were utilized to simulate growth and hydroclimatic sensitivity of five boreal species. • Species-specific climate responses vary and are shaped by environmental factors and autecology. • Primary species are sensitive to site conditions and to a lesser extent to hydroclimatic conditions. • Late successional species are more likely to be affected by hydroclimatic 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".