Compounding effects of climate change reduce population viability of a montane amphibian
Bibliographic record
Abstract
Abstract Anthropogenic climate change presents challenges and opportunities to the growth, reproduction, and survival of individuals throughout their life cycles. Demographic compensation among life‐history stages has the potential to buffer populations from decline, but alternatively, compounding negative effects can lead to accelerated population decline and extinction. In montane ecosystems of the U.S. Pacific Northwest, increasing temperatures are resulting in a transition from snow‐dominated to rain‐dominated precipitation events, reducing snowpack. For ectotherms such as amphibians, warmer winters can reduce the frequency of critical minimum temperatures and increase the length of summer growing seasons, benefiting post‐metamorphic stages, but may also increase metabolic costs during winter months, which could decrease survival. Lower snowpack levels also result in wetlands that dry sooner or more frequently in the summer, increasing larval desiccation risk. To evaluate how these challenges and opportunities compound within a species’ life history, we collected demographic data on Cascades frog ( Rana cascadae ) in Olympic National Park in Washington state to parameterize stage‐based stochastic matrix population models under current and future (A1B, 2040s, and 2080s) environmental conditions. We estimated the proportion of reproductive effort lost each year due to drying using watershed‐specific hydrologic models, and coupled this with an analysis that relates 15 yr of R. cascadae abundance data with a suite of climate variables. We estimated the current population growth (λ s ) to be 0.97 (95% CI 0.84–1.13), but predict that λ s will decline under continued climate warming, resulting in a 62% chance of extinction by the 2080s because of compounding negative effects on early and late life history stages. By the 2080s, our models predict that larval mortality will increase by 17% as a result of increased pond drying, and adult survival will decrease by 7% as winter length and summer precipitation continue to decrease. We find that reduced larval survival drives initial declines in the 2040s, but further declines in the 2080s are compounded by decreases in adult survival. Our results demonstrate the need to understand the potential for compounding or compensatory effects within different life history stages to exacerbate or buffer the effects of climate change on population growth rates through time.
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 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) | 0.008 | 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".