The range expansion of the great‐tailed grackle (<i>Quiscalus mexicanus</i> Gmelin) in North America since 1880
Bibliographic record
Abstract
Abstract Aims This study aimed to document and describe the current range expansion of the great‐tailed grackle (Quiscalus mexicanus Gmelin) into the USA. By examining the habitat associations and pattern of spread of this species, I intended to determine the factors responsible for this remarkable expansion by a tropical species into a temperate environment. Location This study focused on the spread of the great‐tailed grackle in the continental USA, Canada and Baja California. Methods I used published records, museum specimens, and egg collections to document this range expansion from 1880 through 2002. In addition I surveyed large portions of Arizona, Nevada, southern Utah and southern California for great‐tailed grackles during 2000 and 2001. The data gathered was used to create maps in order to quantify the rate of spread of this species. Results Between 1880 and 2000 the great‐tailed grackle expanded its breeding range in the USA from c. 64,000 km2 to more than 3,561,000 km2, an increase of 5530%. The average annual rate of increase is 3.4%, but has lessened during the past 20 years. Northward movement in the eastern portion of the range has slowed down, reflecting this decrease. However, in the central and western portion of the species range, the rate of northward movement is still accelerating. During this expansion, the average time between first sighting in a state and first breeding was 5.8 years. The species has become less migratory during its range expansion, wintering in 17 of the 20 states where it breeds. Main conclusions This range expansion has been marked by great‐tailed grackles preferring human‐modified environments as breeding grounds, especially in the western states. This association appears to benefit the species in two ways; nest predation is lessened in such areas compared with natural conditions, whereas human activities tend to generate an abundant and consistent food supply for feeding offspring. Wintering birds are often associated with cattle feed lots and large‐scale dairies, where abundant waste grain provides them with a reliable food supply. Given the continued human population increase throughout large areas of the western USA, the great‐tailed grackle will continue its range expansion.
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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 | 0.001 | 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".