Conservation planning for forests, tree species, and their genetic populations under climate change in Canada and the USA
Bibliographic record
Abstract
Trailing edge tree populations at the warm or dry margins of a species’ range often contain genetic traits that confer tolerance to environmental extremes. These traits may be valuable for supporting adaptation to future climates in other parts of the species’ range, yet the populations that hold them are at heightened risk of loss under projected climate change if not actively conserved. This study presents a continental-scale analysis to identify trailing edge populations of the 100 most common North American tree species within the United States and Canada, systematically prioritize collection of at-risk populations, and to evaluate regions suitable for their long-term conservation through assisted migration. Using a climate envelope modeling approach and 11 bioclimatic variables, we matched ecosystems historically occupied by a species (1960s baseline) with those projected to have similar climates under 2050s conditions (SSP2-4.5 scenario). Trailing edge populations were defined as those ecosystems where species lose suitable climate habitat by the 2050s. Conservation priorities were assessed using three criteria: (1) forest cover loss, indicating potential local extirpation due to fundamental niche limits; (2) climate velocity, estimating the geographic distance needed to track suitable conditions; and (3) the number of species with at-risk populations per ecosystem. These criteria were combined to identify jurisdictions where seed collections for assisted migration may have the greatest long-term value. Our results show that trailing edge populations are concentrated in ecozones across the Appalachian region (in number of species with populations at risk), as well as the temperate mixed forests of Midwest and the southern boreal forest (proportional to local species richness). Summaries by jurisdiction with high predicted climate velocity and forest cover loss, such as states and provinces with forested areas bordering the central plains, are expected to have limited capacity for in situ persistence, highlighting a potential need for human intervention. Regions such as the Great Lakes basin and north-eastern Canada emerge as major prospective recipients of assisted migration due to high climate matching with trailing-edge populations and relatively stable forest potential under projected climates. These findings are integrated in an online Protected Area Selection Tool for North America (http://tinyurl.com/PAST-NAm), which enables users to identify climatically suitable recipient protected areas or ecosystems for a source ecosystem and time period of interest. Limitations include the exclusive use of macroclimatic variables, ecosystem-level resolution, and the absence of projected uncertainty or non-analogue climate filters. The study provides a first assessment to support seed collection, in situ conservation, and climate-informed reforestation planning, with the understanding that species- and site-specific evaluations remain necessary for implementation.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".