Spatial and Temporal Variations in Tree Growth, Mortality, and Biodiversity in Alberta Forests
Bibliographic record
Abstract
Understanding the spatial and temporal variation of tree demographic rates and biodiversity is essential for predicting the dynamics of forest ecosystems and their responses to changing environments. This thesis contributes to that understanding through modeling the long-term change in tree growth, mortality, biodiversity and biomass in Alberta forests as specified by the four constituent chapters. First, using a dataset of half-century observations on 1,680 permanent sample plots in western Canada, I detected a widespread, significant increase in tree mortality but a significant decrease in tree growth. I found that competition was the most important factor responsible for the changes, followed by climate change. This finding challenges previous studies that concluded climate change was the major factor affecting forest dynamics. Second, I modeled spatial distribution of forest biomass across Alberta by integrating three data sources: 1,968 plots forest inventory data, Lidar data, and land cover, climate and other environmental variables. Total biomass stock in Alberta forests was estimated to be 3.22 petagram. The average biomass density was 80.24 megagram per hectare. Spatial distribution of biomass varied with natural regions, land cover types, and species. Third, I studied the diversity of breeding birds across 206 sites in Alberta boreal forest and found that temperature, human land cover, and woody plant richness had strong positive correlations with the overall bird richness, while local forest structure and composition were important determinants of bird diversity. The strength and direction of the effects of those variables are guild-specific. In the last chapter, I integrated taxonomic and phylogenetic diversity to assess the effects of natural and anthropogenic disturbances on plant communities in Alberta. I compared the changes in vascular plant composition along a human disturbance gradient and found high taxonomic diversity at intermediate anthropogenic disturbance levels. I failed to detect significant changes in phylogenetic diversity along disturbance because but richness was not found to significantly correlate with phylogenetic diversity. This result suggested that species turnover may be randomly related to anthropogenic disturbance along the evolutionary tree. By synthesizing results from direct field measurements and modeling, these chapters together contribute to understanding of ecosystem functioning, community structure, forest dynamics, and biodiversity of Alberta forests in a changing world. This knowledge is essential for sustainable management of Alberta forest ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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 teacher head, 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".