Tundra fires and surface subsidence increase spectral diversity on the Yukon–Kuskokwim Delta, Alaska
Bibliographic record
Abstract
Abstract Tundra fires can dramatically influence plant species cover and abundance, organic layer depth, and the magnitude of seasonal permafrost thaw. However, knowledge of the impact of wildfire on short and long-term interactions between vegetation and permafrost thaw remains limited. Here, we evaluate the spatial and temporal interactions between wildfire disturbance and surface subsidence on a remotely derived proxy for species diversity (i.e. spectral diversity (SD)) of 16 fire scars within the Izaviknek and Kingaglia uplands of southwestern Alaska’s Yukon–Kuskokwim Delta with burn dates between 1971 and 2015. SD was calculated as the sum of squared spectral variance of pixel spectra from the mean spectra, within a plant community (analogous to alpha diversity), between plant communities (beta diversity), and across terrain composed of a mosaic of communities (gamma diversity). Surface subsidence was calculated from spaceborne interferometric synthetic aperture radar data from Sentinel-1. Results indicate the burn scars had consistently lower total gamma diversity and greater rates of subsidence than paired unburned reference areas, where both gamma diversity (R 2 = 0.74, p < 0.001) and relative subsidence (R 2 = 0.86, p < 0.001) decreased with the time since burn. Compared to older burn scars, young scars had higher gamma spectral diversities (0.013 and 0.005) and greater subsidence rates (−0.097 cm day−1 and −0.053 cm day−1). Communities subsiding at higher rates had higher gamma diversities (R 2 = 0.81, p < 0.001). Results indicate that rates of post-fire vegetation succession are amplified by the thickening of active layers and surface subsidence that increases both spectral and species diversity over 15 years following fire. These results support the idea that SD may be used as a remotely sensed analog of species diversity, used to advance knowledge of the trajectories of plant community change in response to interacting arctic disturbance regimes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".