Climate and extreme weather independently affect population growth, but neither is a consistently good predictor
Bibliographic record
Abstract
Abstract Climate change involves changes in mean temperature and precipitation as well as increases in extreme weather events; thus, determining how species will respond to climate change requires understanding how organisms respond to change in both mean and extreme conditions. Previously, we have shown that the growth of 21 interconnected populations of the alpine butterfly, Parnassius smintheus, is affected by prevailing climatic conditions during its overwintering period. More recently, we have shown that population growth is affected by extreme weather events during early overwintering. Here, we compare the descriptive and predictive abilities of models based on climate, weather, and their combination. We found that both climate‐ and weather‐based models explain significant, but independent, variation in year‐to‐year changes in population abundance; the combination of both was not better than either individual model. None of the models showed consistently good predictive ability. The climate model was accurate in predicting relatively small changes in year‐to‐year abundance, but not large changes. In contrast, the weather model was poor at predicting small changes in abundance, but accurately predicted large changes. Our results indicate that a more mechanistic approach, linking specific conditions to vital rates and population growth, will be needed to predict population responses to changing abiotic conditions.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".