Predicted expansion of beaver pond distribution in Arctic Alaska, 1910–2090
Bibliographic record
Abstract
Abstract Ecosystem engineering by beavers is a nascent disturbance in the Arctic tundra, appearing in the 1970s in western Alaska and since expanding deeper into tundra regions. Evidence from modeling and observations indicates that beaver ponds act as biophysical oases, and we anticipate myriad changes as these disturbances are constructed along tundra streams, sloughs, and lake outlets. We used over 11 000 mapped beaver pond locations in Arctic Alaska and their climatic, geographic, and environmental attributes to understand (1) which of those attributes control the distribution of beaver ponds, and, if temperature is a factor, (2) how beaver pond distribution will change under future climate scenarios. Of the variables used in the ensemble modeling approach, mean annual temperature was the most important variable in determining beaver pond locations, with pond occurrences more likely in warmer locales (>−2 °C). The distance to water was also important in determining beaver pond locations, as expected, with higher likelihood of ponds closer to water features. Lowland topographic variables were also relevant in determining the distribution of beaver ponds. Under the current climate, beaver ponds are widespread in most of western Alaska, matching the predicted extent of potential occupancy, with the exception of areas furthest from treeline, implying possible dispersal lags or other factors. By 2050, under future climate scenarios (RCP8.5; 2090 for RCP6.0), the entire North Slope of Alaska, which currently has no beaver ponds, is predicted to be suitable for beaver ponds, comparable to western Alaska in 2016. The vast extent of future beaver engineering in tundra regions will require reenvisioning the typical tundra stream ecosystems of northern Alaska, northern Canada, northern Europe, and northern Asia to include more extensive wetlands, routine disturbances, permafrost thaw, and other features of these nascent oases that are not fully understood.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".