Count trends for migratory Bald Eagles reveal differences between two populations at a spring site along the Lake Ontario shoreline
Bibliographic record
Abstract
The recovery of Bald Eagles (Haliaeetus leucophalus), after DDT and other organochlorine insecticides were banned in the United States, can be regarded as one of the most iconic success stories resulting from the Endangered Species Act. Interest remains high in the recovery and growth of the Bald Eagle population. Common to evaluating growth and recovery rates are counts at nesting sites and analyses of individuals fledged per season. But this is merely one snapshot that ignores survival rates as eagles grow to maturity. By analyzing indices from migration counts, we get a different snapshot better reflecting the survival of young birds. Different populations of Bald Eagles breed at different sites at different times of the year. Typical migration count analyses do not separate the populations. A separation of two distinct populations can be achieved at spring count sites by taking advantage of the tendency for northern summer breeding birds to migrate north in spring earlier than southern winter breeding birds who disperse north later in spring. In this paper I analyze migratory indices at a spring site along Lake Ontario. The analysis shows that eagles considered to be primarily of the northern summer breeding population showed an estimated growth rate of 5.3 ± 0.85% (SE) per year with 49% of eagles tallied in adult plumage, whereas the migrants considered to be primarily of the southern breeding population had an estimated growth rate of 14.0 ± 1.79% with only 22% in adult plumage. Together these results argue that the populations of southern breeding Bald Eagles are growing at a substantially higher rate than northern breeding eagles. These findings suggest that aggregate population indices for a species at migration counting sites can sometimes obscure important differences among separate populations at any given site and that separating counts by time period can be a useful way to check for differences among sub-populations.
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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) | 0.003 | 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".