Investigating the role of climate warming on vegetation productivity and shrub distributions in the Beaufort Delta region of Canada
Bibliographic record
Abstract
Climate warming across the circumpolar north has driven rapid shifts in vegetation productivity and structure, altering the community composition and function of tundra ecosystems. In my MSc thesis, I examined the biophysical factors mediating the effects of climate on vegetation dynamics, and assessed the impact of data type on models of vegetation change. In my first data chapter, I combined field sampling of soils and vegetation and random forests modelling to identify the determinants of spatial heterogeneity in Enhanced Vegetation Index trends derived from the Landsat archive (1984-2016). This analysis showed that over 70% of the Beaufort Delta region has exhibited significant increases in vegetation productivity (greening) from 1984 to 2016. Greening was more common and rapid in lower elevation areas with existing shrub-dominated land cover on till blanket and glaciofluvial deposits. The influence of surficial geology and topography on productivity trends suggests that soil moisture and nutrient availability are mediating the impact of climate warming in the low Arctic tundra. In my second data chapter, I investigated the response of three tundra shrub species (green alder, dwarf birch, and lingonberry) to climate warming using species distribution modelling. In this study, I also explored how data type affects model performance and output. This analysis shows that the use of pseudo-absence data (a common practice in species distribution modelling) results in differences in projected habitat suitability when compared to models parameterized using true absence data. Projections of habitat suitability under a climate warming scenario suggest that shrubs will respond individualistically, likely in response to physiological and ecological differences among species. Overall, my thesis emphasizes the importance of vegetation change at a landscape scale and how larger climate modelling efforts must account for landscape-scale variation in biophysical variables, individualistic responses at the species-level, and data quality. My findings are relevant to land management in the region and suggest that further research continue to explore how vegetation change and rapid shrub expansion will affect tundra landscapes, wildlife, and broader carbon and energy exchange 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".