Delineating wildfire burns and regrowth using satellite imagery to assess moose (<i>Alces alces</i>) spatial responses to burns
Bibliographic record
Abstract
Abstract Climate change is predicted to increase the frequency and size of wildfires. Wildfire burns can influence wildlife space use, but prior to understanding the relationship between wildlife and burns, the extent of wildfires and the regrowth of burned areas must be determined. We used remotely sensed, multispectral satellite imagery to calculate differenced normalized burn ratios (NBRs) to define areas burned by wildfire and assess temporal trends in burn frequency and extent in central British Columbia. We then used NBRs as an index of vegetation regrowth to model regrowth trajectories within burns. Next, we examined spatial responses to burns by a moose (Alces alces) population that recently declined in abundance. We utilized locations used by and available to collared moose to determine spatial responses as a function of years since wildfire and vegetation regrowth. We also assessed functional responses to burns by moose, dependent upon the proportion of burned areas available to each individual. We hypothesized that (h1) wildfire burns within our study area increased from 1985 to 2017, (h2) that moose increase their use of burns in comparison with availability as burns age as a result of vegetation regrowth, and (h3) that moose demonstrate functional responses to burns (i.e., selection of burns by individual moose declines as the amount of burned areas increases). We observed that the frequency and extent of burns are increasing within our study area. In contrast to h2, moose in summer and fall decreased the selection of burns as burns aged. We, however, observed that moose responded most positively to locations with intermediate vegetation regrowth (i.e., NBR values), which according to burn regrowth trajectories were most likely to be achieved 13 or more years post‐wildfire; given that most burns within our study area were ≤13 years of age, we predict that the use of burns will likely increase as vegetation regrows. Moose only selected for burns in fall, and we did not find support for the presence of a functional response. Our research demonstrated the utility of remotely sensed imagery and NBRs to define burn locations and to reveal current and probable future spatial responses to burns by a wildlife population of concern.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".