Vegetation dynamics of North and South America: Gradients, climate change and disturbance across spatiotemporal scales
Bibliographic record
Abstract
Our understanding of vegetation dynamics is derived from case studies of how plant communities respond to both changes in climate and disturbances; however comparative studies of these responses across temperate, subtropical and tropical forests are rare. In this thesis I investigate vegetation responses to changes in climate and disturbance across temperate, subtropical and tropical forests of North and South America. My review of the disturbance literature in forest ecology has led me to identify a lack of comparative studies testing general applicability of disturbance hypotheses across temperate and tropical forests. I also find, from pollen records across North and South America, that both low latitude and high elevation woody plant communities show greater amounts of community change than plant communities at high latitudes and low elevations, respectively, during the late-Holocene (5,000 - 150 years before present) which I attribute to increased aridity around the tropics and decreased temperatures at high elevations. I also find from a comparative study between a temperate old-growth red pine forest of Canada, a subtropical ' Araucaria' Atlantic forest of southern Brazil and a tropical Gallery forest of central Brazil that differences in local factors, such as dispersal limitation, and regional factors, such as species ranges, for each forest type define tree diversity and richness responses to disturbance in recovering chronosequence plots. I further identify strong, directional secondary succession trajectories in temperate, subtropical and tropical forests; however small-scale disturbances (in this case treefall gap disturbances) can yield an alternative succession trajectory in the temperate forest. I attribute this alternative succession trajectory to the presence of northward migrating tree pressures in the Great Lakes - St. Lawrence temperate forest ecotone. I further show that both gap size and gap age define the establishment of northward migrating tree species in the temperate forest ecotone. I conclude that differences in local and regional factors can yield varied responses to changes in climate and disturbance between temperate, subtropical and tropical forests, requiring comparative studies across forest types to identify such factors and explain inconsistent responses between forests worldwide.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".