Climate change, wildfire, and past forest management challenge conservation of Canada lynx in Washington, USA
Bibliographic record
Abstract
Abstract The synergistic effects of climate change, wildfires, fire suppression, and past forest management are challenging efforts to protect and recover Canada lynx (Lynx canadensis) in the North Cascades of Washington, USA. Canada lynx is a threatened species in the United States and a focal species used to gain insights into the structure and function of boreal forest ecosystems. To understand how multiple stressors are influencing lynx populations and the boreal forest in Washington, we developed a spatially explicit carrying capacity model in HexSim using local data on lynx resource selection and life history. We used this model to estimate changes in carrying capacity and population persistence for 3 time steps: year 2000, which represented limited historical wildfire and aggressive fire suppression; year 2013, after nearly 2,000 km2 of wildfires burned about 17% of lynx habitat; and year 2020, after an additional 2,000 km2 of wildfires burned another 15% of lynx habitat in our study area. Fires altered habitat distribution and landscape capacity to support Canada lynx. There was a 66–73% reduction in lynx carrying capacity in our study area because of large, high‐severity fires that have occurred from 2000–2020, despite aggressive fire suppression. This reduction in carrying capacity was concurrent with decreases in the probability of lynx persistence from year 2000 to year 2020 simulations and was most pronounced for simulations that included no immigration and the largest home range size. The negative synergistic influences of long‐term fire suppression, timber harvest, increased drought, longer wildfire seasons, declining mountain snowpack, and increasingly frequent large fires pose considerable challenges to the conservation and recovery of Canada lynx and the boreal forest ecosystem upon which they depend. We discuss an alternative approach to vegetation and fire management to conserve and restore lynx habitat and populations in Washington.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".