Local-Scale Drivers of Spatial Patterns and Demographic Rates of Conifer Species in a Forest Chronosequence in Coastal British Columbia
Bibliographic record
Abstract
Growth, mortality and recruitment are the fundamental demographic processes driving changes in forest structure and dynamics. Rapid changes observed in many forests globally have imposed serious threats to ecosystem services such as carbon sequestration, biodiversity and hydrology, emphasizing the importance of understanding the underlying mechanisms. In this thesis, I collected spatial and inventory data from five 1-hectare forest plots in a chronosequence on southern Vancouver Island, B.C. I used spatial point pattern analysis and regression modeling to determine the effects of competition and climate on tree spatial patterns and demographic rates of Douglas fir, western hemlock and western redcedar over a 17-year census period. Douglas fir growth and mortality were strongly influenced by negative density-dependent (competition) processes in all plots of the chronosequence with the species becoming more regularly distributed in older stands. Western hemlock and western redcedar growth was negatively influenced by competition, while facilitative processes may promote tree survival of these two shade-tolerant species in most stands. Recruitment of all three species occurred most often in close proximity to adult trees. Growth of the study species was also driven by tree size and climate. Summer precipitation was the most important climate variable, negatively affecting growth for all study species. Other temperature and precipitation variables were significant for the focal species, but the direction of the growth response was not consistent. Species-specific responses to climate highlight the difficultly in predicting stand-level changes under altered climate regimes. The results of this study underscore the importance of competition and climate in driving forest structure and dynamics in all ages of stands, necessitating the inclusion of both sets of variables in analyzing demographic rates. Knowledge of competition and climate as drivers of forest dynamics and structure can be incorporated into forestry and conservation management decision-making, and findings from this study provide a better understanding of the processes driving dynamics of forest succession, and can be used for anticipating stand structure in the future.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".