A gap analysis of tree species representation in the protected areas of the Canadian boreal forest: applying a new assemblage of digital Forest Resource Inventory data
Bibliographic record
Abstract
We undertook a gap analysis of how protected areas represent the tree-species diversity within the Canadian boreal forest, as measured from Forest Resource Inventory (FRI) maps. We used a new compilation of Forest Resource Inventory designed to support ecological analyses over large areas and across jurisdictional boundaries. The analysis was stratified into four analytical regions determined by terrestrial ecozones. We calculated the relative abundance of species within regions, developed rarity criteria, and evaluated the relative abundances and prevalence of rare species. We characterized representation gaps when the abundance of a tree species in protected areas within an analytical region differed markedly (by more than a factor of 2) from the expectation, calculated as the product of regional abundance and the proportional area protected. Most species were well represented in the most species-diverse region (n = 33), the Boreal Shield in eastern Canada, due apparently to a large number of relatively small protected areas in the southern part of the region. Some marked gaps existed in the more species-depauperate western zones, notably for montane conifers in the Boreal Plains. As is common for species abundance distributions, as few as five species accounted for 90% of total abundance in each zone. Relatively rare species were more numerous. Mostly associated with southern temperate or hemiboreal forests, these reached their highest prevalence and abundance in the managed forests of the Boreal Shield. Our work identified some gaps in representation in the protected areas network of Canada in western Canada, substantiates the use of species distribution mapping based on FRI data to inform conservation planning — including the identification of high conservation biodiversity elements for forest certification — and demonstrates the need for improved vegetation mapping in National Parks.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".