Lichen cover mapping for caribou ranges in interior Alaska and Yukon
Bibliographic record
Abstract
Abstract Previous research indicates that the effects of climate warming, including shrub expansion and increased fire frequency may lead to declining lichen abundance in arctic tundra and northern alpine areas. Lichens are important forage for caribou ( Rangifer tarandus ), whose populations are declining throughout most of North America. To clarify how lichen cover might affect caribou resource selection, ecologists require better data on the spatial distribution and abundance of lichen. Here, we use a combination of field data and satellite imagery to model lichen cover for a 583 200 km 2 area that fully encompasses nine caribou ranges in interior Alaska and Yukon. We aggregated data from in situ vegetation plots, aerial survey polygons and unmanned aerial vehicle (UAV) imagery to align with 30 m resolution Landsat pixels. We used these data to train a random forest model with a suite of environmental and spectral predictors to estimate lichen cover. We validated our lichen cover model using reserved training data and existing external datasets, and found that reserved data from aerial survey polygons ( R 2 = 0.77) and UAV imagery ( R 2 = 0.71) provided the best fit. We used our lichen cover map to evaluate the influence of estimated lichen cover on caribou resource selection in the Fortymile Herd from 2012 to 2018 during summer and winter. In both seasons, caribou avoided lichen-poor areas (0%–5% lichen cover) and showed stronger selection as lichen cover increased to ∼30%, above which selection leveled off. Our results suggest that terrestrial lichen cover is an important factor influencing caribou resource selection in northern boreal forests across seasons. Our lichen cover map goes beyond existing maps of lichen abundance and distribution because it incorporates extensive field data for model training and validation and estimates lichen cover over a much larger spatial extent. We expect our landscape-scale map will be useful for understanding trends in lichen abundance and distribution, as well as for caribou research, management and conservation.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".