Snow Avalanches and the Impact of Climate‐Linked Extreme Events on Mountain Wildlife Population Dynamics and Resilience
Bibliographic record
Abstract
Climate is changing rapidly in mountain environments, giving rise to increasing variability in weather, incidence of extreme events, and alteration of the cryosphere. Natural hazards, such as snow avalanches, and the ecological communities they impact may be particularly sensitive to such change. While avalanches may impose both 'good' and 'bad' effects on mountain ecosystems, the direct impacts that lead to mortality have particularly important implications for future viability and resilience of slow-growing alpine wildlife populations. Here, we studied a sentinel species of coastal Alaskan mountain environments-the mountain goat (Oreamnos americanus) - using long-term field data from individually marked animals (600 individuals over 44 years) in a quantitative modeling framework to understand how avalanches influence demographic processes. Specifically, we developed and parameterized a sex- and age-specific population modeling approach to simulate the effects of avalanche-caused mortality on population growth rate (λ). We examined a range of ecologically relevant scenarios based on empirically observed states of avalanche-caused mortality. During years when avalanche impacts are severe, populations can experience significant additive mortality and population declines (up to 15%). Due to low reproductive rates, such impacts can lead to long demographic recovery times (up to 11 years, or ~1.5 mountain goat generations). Thus, during the course of a typical mountain goat lifetime, significant avalanche-linked perturbations can be expected to occur, suggesting that meaningful demographic signatures of avalanche impacts are generationally recurrent and routinely imbedded in population histories. From a conservation perspective, such impacts are striking and highlight the utility of employing a quantitative modeling approach to predict possible effects of avalanches and extreme events more broadly on mountain ungulate population dynamics and viability. Our work explicitly builds upon recent findings about the importance of avalanches on mountain-adapted animal populations and has implications for the cultural and ecological communities that depend on them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".