Population and Migratory Ecology of Canada Warblers (Cardellina canadensis) in the Central Appalachian Mountains, West Virginia, USA
Bibliographic record
Abstract
Nearctic-Neotropical migrant birds experience a wide range of environmental conditions throughout their annual cycle; thus, it is particularly challenging to evaluate the spatial factors that may influence population growth. The Canada Warbler (Cardellina canadensis) faces substantial range-wide population declines, but little study has been conducted regarding elements occurring across the entire year that drive demographic rates. The aims of this research are (1) determine the relationship between Canada Warbler population demographic rates and environmental conditions along an elevation gradient in the central Appalachian Mountains and (2) ascertain the nonbreeding season location and migratory routes used by the central Appalachian population, which is near the southern extent of the entire breeding range. Research occurred from 2019 – 2021 at six study sites ranging in elevation from 526 – 1282m spanning an approximate 130km north-south gradient within the Monongahela National Forest, West Virginia, USA. To determine the relationship between demographic rates and environment, I assessed adult annual survival and daily nest survival. I uniquely color-banded 203 adult male Canada Warblers in 2019 and 2020, and resighted marked birds in 2020 and 2021. I modeled survival in response to predictor variables including elevation, rhododendron coverage, available stream length, topographic position, and aspect. I implemented a spatial Cormack-Jolly-Seber model with Bayesian methods and compared models using DIC criteria. To determine nest survival, I located nests and monitored their outcomes using motion-sensitive game cameras. I modeled daily nest survival as a function of elevation, rhododendron coverage, other shrub coverage, topographic position, and aspect using Bayesian methods and compared models using DIC. I found that elevation was the best predictor of adult survival, which increased from 0.573 (95% credible intervals (CI) = 0.333 – 0.820) at 555 m to 0.702 (95% CI = 0.493 – 0.871) at 1255 m, although the slope coefficient of the elevation effect overlapped 0. I located 12 nests in 2021, of which 9 fledged successfully. The intercept-only model was the best predictor of daily survival, which, exponentiated over the 19-day nesting period, resulted in a posterior mean nest survival of 0.604 (95% CI = 0.527 – 0.696). To elucidate the migration ecology of the population, I deployed 32 light-level geolocator tags on adult males in 2020 and retrieved tags in 2021. I recovered 13 (40.1%) geolocators, of which 10 provided data on post-breeding (fall) migration routes and nonbreeding season sites, and nine provided data on pre-breeding (spring) migration routes. The nonbreeding
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".