Comprehensive estimation of spatial and temporal migratory connectivity across the annual cycle to direct conservation efforts
Bibliographic record
Abstract
Migratory connectivity is the degree to which populations are linked in space and time across the annual cycle. Low connectivity indicates mixing of populations while high connectivity indicates population separation in space or time. High migratory connectivity makes individual populations susceptible to local environmental conditions; therefore, evaluating migratory connectivity continuously across a species range is important for understanding differential population trends and revealing places and times contributing to these differences. The common nighthawk Chordeiles minor is a widespread, declining, long‐distance migratory bird. Variable population trends across the nighthawk breeding range suggest that knowledge of migratory connectivity is needed to direct conservation. We used GPS tags to track 52 individuals from 12 breeding populations. We estimated migratory connectivity as 0.29 (Mantel coefficient: 0 = no connectivity, 1 = full connectivity) between the breeding and wintering grounds. We then estimated migratory connectivity at every latitude (spatial connectivity) or day (temporal connectivity) of migration and smoothed those migratory connectivity estimates to produce continuous migratory connectivity ‘profiles'. Spatial and temporal connectivity were highest during migration through North America (around 0.3–0.6), with values generally around 0 in Central and South America due to mixing of populations along a common migratory route and similar migration timing across populations. We found local peaks in spatial and temporal connectivity during migration associated with crossing the Gulf of Mexico. We used simulations to estimate the probability that our method missed peaks (spatial: 0.12, temporal: 0.18) or detected false peaks (spatial: 0.11, temporal: 0.37) due to data gaps and showed that our approach remains useful even for sparse and/or sporadic location data. Our study presents a generalizable approach to evaluating migratory connectivity across the full annual cycle that can be used to focus migratory bird conservation towards places and times of the annual cycle where populations are more likely to be limited.
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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".