Assisted migration to address climate change: recommendations for reforestation in western Canada
Why this work is in the frame
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Bibliographic record
Abstract
A changing climate is the largest threat to forest productivity in western Canada and to the ability of forested landscapes to provide ecological and economic services, both now and in the future. As climate changes, locally adapted tree populations become mismatched with local conditions, leading to mal-adaptation that may result in a reduction in forest health and productivity. This problem can be reduced with interventions that match reforestation stock to anticipated future environments. As such, there is a pressing need to inform such actions by carefully developing and contextualizing scientific information and by applying it to provincial reforestation policies. Assisted migration is a climate change adaptation strategy used in the forestry sector, where species and seed sources are moved to new locations. The goal of this thesis is to develop a methodological framework to guide assisted migration efforts for forest trees in western Canada, under a comprehensive range of future climate projections. To assist with these management needs I create a new ecosystem-based climate envelope modeling approach for 16 commercially important tree species. Habitat projections show populations already geographically lag behind their optimal climate and the magnitude of this lag is projected to double for the 2020s. The most pronounced habitat shifts are projected to occur in the boreal forests and the Rocky Mountains, predominately affecting black spruce, tamarack, white spruce and aspen populations. In a case study for Alberta, I find that genotypes of species that are adapted to drier climatic conditions will be the preferred planting stock over much of the commercially managed boreal forest. Interestingly, no alternate non-native species to Alberta that were examined in this study can be recommended with any confidence as planting stock. Finally, I observe high uncertainty in projections of suitable habitat for most species making reforestation planning beyond the 2050s difficult. Using genetic and remote sensing data for aspen populations, I show that habitat projections from climate envelope models under observed climate change conform well to empirical data on loss of aspen productivity and genetic data on sub-optimal growth due to mal-adaptation.
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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) | 0.000 | 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 it