Species choice and seed sourcing for forestry field experiments to address climate change across Canada
Bibliographic record
Abstract
Climate change adaptation in forestry will need field tested climate-informed seed transfer strategies to improve resilience, preserve genetic diversity and ensure long-term health and productivity of forest ecosystems. This is especially urgent in northern latitudes, such as Canada, where warming trends have been most pronounced. The large-scale DIVERSE research project plans to establish such assisted migration trials at 22 forest management areas across Canada with provincial government and industry participants in British Columbia, Alberta, Ontario, Nova Scotia, Quebec, and Saskatchewan. We contribute an on-line decision support tool to help the DIVERSE researchers and forest managers make climate-informed selections of tree species and seed sources for reforestation. These recommendations include cross-border transfers and can also include introducing new species beyond their current range limits. For the climate-informed seed sourcing recommendations, I used the scaled multivariate Euclidean distance of 12 bioclimatic variables to match seed source’s historic climate to planting site’s new projected future climates, where source and targets were defined by ecosystem delineations for Canada and the US. Climate suitability of a species for a target site in the future was inferred by averaging species’ frequencies of the five ecosystems with the closest climate distance. This resulted in climate matched source ecosystems and species frequencies for the 2020s, 2050s and 20280s for all the ecosystem delineations. This is a lot of information to communicate so a web tool (http://tinyurl.com/DIVERSE-SST) was developed for the forest companies and government stakeholders across Canada that participated in this project. This Euclidean distance ecosystem-based climate matching approach is a fairly basic type of species distribution modeling. However, the simplicity of this approach allowed me to incorporate over 240 of the major tree species in North America in the recommendations. Additionally, the larger geographic scale of the climate matching provided recommendations at a level more in line with current seed sourcing systems making the recommendations more operationally relevant. These recommendations are the first step in the establishment of test plantations to validate whether tree growth, health and survival can be maintained or improved through large scale operational deployment of assisted migration in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".