Weather affects diet composition of rusty blackbirds wintering in suburban landscapes
Bibliographic record
Abstract
ABSTRACT The rusty blackbird (Euphagus carolinus) is a species of conservation concern throughout its range and the cause of the species' population decline is unknown. We studied diet composition of rusty blackbirds with stable isotope mixing models in suburban landscapes in the southeastern United States. We captured blackbirds in Georgia and South Carolina from 2009 to 2012, and estimated proportions of earthworm, other animals, pecan, and acorn incorporated into individual diets. On the Piedmont Plateau, terrestrial and aquatic earthworms constituted the largest proportion incorporated into the diet (39% ± 2.9; mean ± SD by site and year) and animals other than earthworms (mostly larval invertebrates Odonata and Diptera) constituted 27% ± 12.9. In contrast, on the Coastal Plain, which featured milder winters than the Piedmont, earthworms constituted a lower proportion (19% ± 1.2) of incorporated food items and animals other than earthworms comprised 62% ± 3.3% of the diet. Increased incorporation of earthworms in the diet was related to increased upcoming precipitation and daily maximum temperature. Rusty blackbirds incorporated more tree mast into their diet on the Piedmont Plateau than the Coastal Plain. Increased incorporation of tree mast was related to advancing cold temperature. Mast, including crushed pecans (Carya illinoenensis) and pre‐opened small‐seeded red oak (Quercus spp.) acorns, is a high‐lipid dietary component of blackbirds wintering in colder climates, and is incorporated prior to extreme cold weather. Therefore, planting mast trees, especially lipid‐rich pecan, could be used to augment resources in known rusty blackbird wintering hotspots. Maintaining shallowly flooded wetlands with a fluctuating water regime and residential lawns with abundant red oak (Quercus spp.) leaf litter would promote acorn and invertebrate resources including earthworms. © 2015 The Wildlife Society.
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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) | 0.001 | 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".