A Tale of Two Forests: Unbiased Estimation of Long-term Changes in Stand-level Water-use Efficiency in Ontario Forests Through Ecologically Informed Sampling Design
Bibliographic record
Abstract
Unabated anthropogenic greenhouse gas emissions have resulted in an unprecedented rise in global atmospheric CO2 (Ca) levels and a perhaps irreversible shift in the dynamics of global carbon cycling. Given that C3 plants are likely to be carbon limited at current Ca levels, there was previous optimism regarding the potential for increased carbon storage and water-use efficiency (WUE) in global forests. To address this question, scientists have turned to tree ring series to investigate long-term trends in growth and resource use in global forests. Unfortunately, our ability to draw accurate conclusions regarding the fate of forests from tree ring studies alone is limited by our inability to control for the multitude of environmental and developmental variables that confound long-term, climate-related signals in tree ring series. In this thesis I use principles of forest ecology to select forest types where long-term changes in growth and WUE can be estimated independently of the effects of stand and individual tree development, namely, chronosequence jack pine and self-replacing sugar maple forests in Ontario. To do so a novel tree ring standardization model is presented that uses tree diameter in the year of ring formation as the primary determinant of the underlying developmental trend. This method is shown to be superior to current models in separating developmental trends from long-term environmental/climatic signals in tree ring series from shade-tolerant species. In chronosequence jack pine stands I show increases in stand-level WUE but progressive growth decline. Water-use efficiency was negatively associated with tree growth, suggesting that warming- and drought-induced stomatal closure has likely led to deviations from expected Ca enhanced growth. In self-replacing sugar maple, I show that the response of neither growth or WUE to increasing Ca is conserved across the site- or landscape-levels. While it is evident that variability in soil moisture controls this response at the site-level, the drivers of variation across the landscape are unclear. Further, high-frequency climate sensitivity is not conserved across stands near the species northern range limit, nor is climate an important predictor of growth in these stands. These findings have important implications for range prediction of the species, as current distribution models are climate driven.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".