Long-term expansion of satellite-measured beaver pond area after boreal forest fire
Bibliographic record
Abstract
Abstract The engineering activities of beavers have a major influence on hydrology, vegetation, and biodiversity. The availability of preferred broadleaf vegetation forage is an important factor facilitating beaver colonization, and its subsequent depletion may result in the abandonment of beaver ponds. Previous studies have suggested that wildfires can lead to long-term increases in beaver populations by promoting the growth of early successional broadleaf vegetation. However, spatially explicit analyses demonstrating this relationship in the wildfire-modified boreal forest zone are lacking. In this study, we used Landsat satellite data for measuring changes in the extent of beaver ponds to provide an indicator of shifts in beaver activity and populations after fire. A sub-pixel mapping method was used to track annual surface water area in 3597 beaver pond complexes from 1985–2021 within a 4546 km 2 region of northwestern Ontario, Canada where wildfires burned 938 km 2 since 1985. We found that the surface water in 1390 beaver complexes adjacent to burns initially decreased after fire but then began to recover after 4 years. After 12 years, fire-impacted beaver ponds exceeded the area that would be expected based on changes in 2207 unimpacted beaver ponds that served as a control. Impacted ponds continued to expand and 34 years after fire had an extent that was 140% larger than control ponds. Annual land cover maps and forest inventory plots indicated that the cover of broadleaf shrub and tree forage adjacent to these expanding ponds was elevated above pre-fire levels for at least three decades following fire, while the cover of non-preferred needleleaf trees was substantially reduced. A similar, post-fire beaver pond expansion can be observed at other locations across Canada where broadleaf tree regeneration is present.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".