The influence of demographic rates on stand structure in old-growth forests
Bibliographic record
Abstract
Forests store significant quantities of carbon in their aboveground biomass, and represent the majority of the terrestrial carbon sink. Globally, increases in mortality rates are driving declines in this carbon sink; however projections of future changes are poorly constrained as we lack an understanding of the mechanisms of change. A better understanding of the links between demographic rates, forest stem size distributions, and aboveground biomass is needed to predict forest responses to changes in demographic rates. This thesis uses analysis of forest inventory data and a simulation modelling approach to extend our understanding of relationships between demographic rates and forest stand structure. In Chapter 2, I analyse structural variation in Amazonian forests, and show that relationships between demographic rates and stand structure vary with forest type, climate, and soils. Variation in mortality rates shapes stand structure in warm, wet forests with stable soils, but stand structure in dry and montane forests is more strongly shaped by variation in growth rates. In Chapter 3, I develop a novel individual-based simulation approach, using forest inventory and tree ring data from four bioclimatic domains in Quebec. This approach demonstrates the importance of demographic rates in determining forest structure, and provides a methodology which is applicable to many permanent sample plot networks. In Chapter 4, I apply this simulation approach to plot data from Amazonia, to analyse forest resilience to increases in mortality rates. For a given percentage increase in mortality rate, forests with lower baseline demographic rates show greater declines in basal area and longer recovery times than forests with higher baseline demographic rates. This thesis clarifies relationships between demographic rates and stand structure, and provides a novel simulation method with which to better understand how stand structure and forest biomass may respond to changes in demographic rates.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".