Zone Matcher: A climate-based web application for deployment and assisted migration of forest trees
Bibliographic record
Abstract
Populations of forest trees are generally adapted to the climates they inhabit. The farther trees are moved from their local climates, the more long-term growth and survival tend to decrease. Current tree deployment and assisted migration rely on 'climate distance thresholds' (CDTs), which are climatic distances beyond which tree performance is considered unacceptable. Fixed zone systems, which have been used to guide deployment of native or orchard seedlots for more than 50 years, usually consist of contiguous geographic areas (zone units) divided into elevational bands (zones). In contrast, focal zone systems allow seed transfer among fixed zones that have similar climates. By using recent historical climates and future climate projections, focal zones can be used for current tree deployment or assisted migration. We developed a focal-zone system for the Pacific Northwest region of North America. First, we worked with stakeholders to select the base zones for the system. These consisted of geographic zone sets from Washington, Oregon, California, and Idaho/western Montana, and ecological zone sets from the U.S. and British Columbia. Second, by analyzing climate variation across the region, we developed a normalized Euclidean climate distance function consisting of nine climate variables from ClimateNA. Third, we inferred CDTs from analyses of climate variation within the base zones and from provenance tests. Fourth, we compared seed deployment areas using the fixed zone versus focal zone system, with and without assisted migration. Finally, we developed the Zone Matcher web application which implements our focal zone system. Across the region, we identified climate matches among 4,393 partially overlapping zones covering approximately 252 M ha. The unique area covered by these zones was about 167 M ha. Compared to fixed zones, our focal zone system increased the deployment area about 17- to 35-fold for the ecological zones and 70- to 300-fold for the geographic zones. This expands seed deployment options, allows more seedlots to be considered for a planting site, facilitates assisted migration, and simplifies sharing of seedlots among organizations. In addition to climate, seed transfer should also consider factors such as plantation soils, microtopography, and projections of competing vegetation, insects, diseases, and fire.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.012 |
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".