Comparing global and regional maps of intactness in the boreal region of North America: Implications for conservation planning in one of the world’s remaining wilderness areas
Bibliographic record
Abstract
Abstract Though North America’s boreal forest contains some of the largest remaining intact and wild ecosystems in the world, human activities are systematically reducing its extent. Consequently, forest intactness and human influence maps are increasingly used for monitoring and conservation planning in the boreal region. We compare eight forest intactness and human impact maps to provide a multi-model assessment of intactness in the boreal region. All maps are global in extent except for Global Forest Watch Canada’s Human Access (2000) and Intact Forest Landscapes (2000, 2013) maps, although some global maps are restricted to areas that were at least 20% treed. As a function of each map’s spatial coverage in North America, the area identified as intact ranged from 55% to 79% in Canada and from 32% to 96% in Alaska. Likewise, the similarity between pairs of datasets in the Canadian boreal ranged from 0.58 to 0.86 on a scale of 0-1. In total, 45% of the region was identified as intact by the seven most recent datasets. There was also variation in the ability of the datasets to account for anthropogenic disturbances that are increasingly common in the boreal region, such as those associated with resource extraction. In comparison to a recently developed high resolution regional disturbance dataset, the four human influence datasets (Human Footprint, Global Human Modification, Large Intact Areas, and Anthropogenic Biomes), in particular, omitted 59-85% of all linear disturbances and 54-89% of all polygonal disturbances. In contrast, the global IFL, Canadian IFL, and Human Access maps omitted 2-7% of linear disturbances and 0.1-5% of polygonal disturbances. Several differences in map characteristics, including input datasets and methods used to develop the maps may help explain these differences. Ultimately, the decision on which dataset to use will depend on the objectives of each specific conservation planning project, but we recommend using datasets that 1) incorporate regional anthropogenic activities, 2) are updated regularly, 3) provide detailed information of the methods and input data used, and 4) can be replicated and adapted for local use. This is especially important in landscapes that are undergoing rapid change due to development, such as the boreal forest of North America.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".