Climatic Variability Threatens Population Growth and Persistence of a Declining Grassland Songbird
Bibliographic record
Abstract
ABSTRACT Determining the factors responsible for population change in threatened populations and the degree to which changing climates might put those populations at risk is one of the most pressing roles of conservation science. In the climatically variable grasslands of North America, songbirds are rapidly declining, and widespread habitat loss alone does not fully explain these declines. Theory predicts that increased variability in population growth rate, which could be generated by increased variability in weather conditions, should result in lower population sizes. We tested whether increasing variability in weather could have driven recent declines in songbird population numbers using detailed demographic data collected between 2013 and 2021 from grasshopper sparrows ( Ammodramus savannarum ) at the Konza Prairie Biological Station, Kansas, USA. To assess the effect of variability in weather on historical vital rates and population growth rate, we used results from an integrated population model to estimate sensitivity of population growth rate to weather and to conduct path analyses. We also used climate projections to predict population growth rate at our site and potential extirpation risk in a future climate. We found that historical population growth rate was over twice as sensitive to changes in adult apparent survival than other vital rates, and lagged population growth rate was lower following wetter years. Projections of future population size predicted that grasshopper sparrows may be locally extirpated within the next century, consistent with the expectation that increasingly variable precipitation patterns will reduce long‐term viability of songbird populations. Combining sophisticated modeling with detailed demographic data is critical for predicting trends in population growth and guiding conservation approaches in declining or at‐risk species.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".