Large‐scale spatial variation in feather corticosterone in invasive house sparrows (<i><scp>P</scp>asser domesticus</i>) in <scp>M</scp>exico is related to climate
Bibliographic record
Abstract
Ecologists frequently use physiological tools to understand how organisms cope with their surroundings but rarely at macroecological scales. This study describes spatial variation in corticosterone (CORT) levels in feathers of invasive house sparrows (Passer domesticus) across their range in Mexico and evaluates CORT-climate relationships with a focus on temperature and precipitation. Samples were collected from 49 sites across Mexico. Feather CORT (CORTf) was measured using methanol-based extraction and radioimmunoassay. Relationships between CORTf and spatial and climate variables were examined using simple linear regressions. Ordination was used on climate data, CORTf was plotted against the resulting axes, and univariate regression trees were used to identify important predictors of CORTf. Universal kriging interpolation was used to illustrate spatial variation in CORTf across Mexico. Correlations with ordination axes showed that high CORTf was associated with low precipitation during the rainy season and low dry season temperatures. Specifically, CORTf was negatively related to May precipitation and January and July minimum temperatures, and positively related to April deuterium excess and June minimum temperatures. CORTf was higher in second-year birds compared to after-hatch years and after-second years. House sparrows had higher CORTf levels in the hot, dry, north-central region of Mexico, and CORTf was negatively related to temperature and precipitation. House sparrows molt primarily from August-September but climate conditions throughout the year were important predictors of CORTf, suggesting that conditions outside of molt can carry over to influence energetics during feather growth. These data suggest that dry conditions are challenging for house sparrows in Mexico, supporting previous work showing that precipitation is an important predictor of broad-scale CORT variation. This work highlights the utility of CORTf for evaluating the influence of physiology on current avian range limits; furthermore, these data may allow us to predict future changes in species distributions.
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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 | 0.000 | 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) | 0.000 | 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".