Rates of Disturbance vary by data resolution: implications for conservation schedules using the Alberta Boreal Forest as a case study
Bibliographic record
Abstract
Investigations of biophysical changes on earth caused by anthropogenic disturbance provide governments with tools to generate sustainable development policy. Canada currently experiences one of the fastest rates of boreal forest disturbance in the world. Plans to conserve the 330 000 km(2) boreal forest in the province of Alberta exist but conservation targets and schedules must be aligned with rates of forest disturbance. We explore how disturbance rate, and the accuracy with which we detect it, may affect conservation success. We performed a change detection analysis from 1992 to 2008 using Landsat and SPOT satellite image data processing. Canada's recovery strategy for boreal caribou (Rangifer tarandus caribou) states that ≤35% of a caribou range can be either burned or within 500 m of a man-made feature for caribou to recover. Our analyses show that by 2008 78% of the boreal forest was disturbed and that, if the current rate continues, 100% would be disturbed by 2028. Alberta plans to set aside 22% for conservation in a region encompassing oil sands development to balance economic, environmental, and traditional indigenous land-use goals. Contrary to the federal caribou recovery strategy, provincial conservation plans do not consider wildfire a disturbance. Based on analyses used in the provincial plan, we apply a 250 m buffer around anthropogenic footprints. Landsat image analysis indicates that the yearly addition of disturbance is 714 km(2) (0.8%). The higher resolution SPOT images show fine-scale disturbance indicating that actual disturbance was 1.28 times greater than detected by Landsat. If the SPOT image based disturbance rates continue, the 22% threshold may be exceeded within the next decade, up to 20 years earlier than indicated by Landsat-based analysis. Our results show that policies for sustainable development will likely fail if governments do not develop time frames that are grounded by accurate calculations of disturbance rates.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| 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".