Space use and movements of inland wintering Whooping Cranes in the Aransas-Wood Buffalo population
Bibliographic record
Abstract
Aransas-Wood Buffalo population (AWBP) Whooping Cranes are increasingly using inland areas for a portion of the winter. There have been individuals near Granger Lake during five of the last 13 winters and 11 of the last 13 winters in Colorado and Wharton counties, Texas, USA. At least 11 individuals used Colorado/Wharton counties in 2022–2023, and 18 used this area in 2023–2024. We used data from all Whooping Cranes with active transmitters from 2009–2018 and from three additional inland wintering individuals from 2017–2022. We compared 95% auto-correlated kernel density estimates (AKDE) and daily distance movements for coastal wintering cranes and those that spent a portion of their winter inland. We also examined daily movement patterns in relation to wintering range use (inland or coastal) considering demographic and temporal factors with generalized linear mixed-effects models (GLMM). Six marked birds across 10 bird-winters from 2011–2021 spent between 3.1–99.3% of their winter at inland areas. Inland wintering birds had AKDE home ranges that were 3.1 times as large as coastal wintering birds. Additionally, the top GLMM predicted that spending a portion of the winter at inland areas equated to a 92.0±4.2% increase in daily movement during the winter. We found that several other factors influenced daily movement patterns, which warrant consideration when comparing between the groups. Age and family status impacted the model, but subadults, family groups, and adults without juveniles all had overlapping confidence intervals. Daily movements followed a quadratic temporal pattern, with greater movements in the late fall and early spring. Continued use of inland areas has implications for how we manage, monitor, and plan for this population’s recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".