Climate complexity in the migratory cycle of Ammodramus bairdii
Bibliographic record
Abstract
One way to understand the ecology of bird migration is to analyze how birds use their ecological niche during their annual cycle. Ammodramus bairdii is a grassland specialist sparrow that breeds in southern Canada and the northern U.S.A. and winters in the Chihuahuan Desert. A continuous and alarming decrease of its populations has been observed over the last 50 years, and studying its seasonal distribution and associated climatic niches could help improve strategies for its conservation. We analyzed the temporal use of its Grinnellian niche (GN) -set of environmental conditions under which a species can establish and persist; in this case the climatic attributes-. We modeled the GN for the reproductive and winter seasons and projected them onto each other (inter-prediction), and also onto transient migratory periods. To measure niche breadth and their overlap, minimum convex polygons (MCP) were calculated for the climatic space. The niches of each of the two seasons were tested for similarity using the PCA axes of climatic variables. The geographic areas with optimal, suboptimal and marginal conditions were identified, based on the distance to the centroid of the GN. The models for each season revealed no geographic inter-prediction among them, with the exception of winter to migratory seasons. The niche breadth of the winter was greater than that of the reproductive season, with an overlap of 22.47% and 45.18%, respectively. The similarity analyses showed a value of zero between seasons. The climate conditions for the records during the migratory months corresponded with suboptimal and marginal conditions of the sparrow's winter niche. These results suggest that A. bairdii uses different climate conditions within ecological niches of each season during its migratory cycle.
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 | 0.000 | 0.001 |
| 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".