Bias-corrected natal dispersal estimates fill information gaps for White-headed Woodpecker conservation
Bibliographic record
Abstract
Although the White-headed Woodpecker (Dryobates albolarvatus) has been used as a management indicator species to guide forest management in the western U.S., basic information on the dispersal behavior of this species is currently unavailable. However, understanding dispersal can provide key information for management and conservation by revealing the mechanisms by which species colonize new areas and restored habitat. To address this information gap, we tracked the dispersal of juvenile White-headed Woodpeckers from their natal areas to their first spring home range in 2014–2018 and estimated dispersal distances using an interval-censored bias correction method with field observations and aerial telemetry surveys. We also compared habitat features between dispersal locations and spring home ranges. The median bias-corrected dispersal distance was 22.2 km in the fall (95% confidence interval [CI] = 16.4, 29.1 km), with 90% of woodpeckers dispersing >4.8 km (95% CI = 2.7, 8.3 km). The following spring, the median bias-corrected natal dispersal distance was 24.6 km (95% CI = 17.9, 32.3 km), while 10 individuals with full detection histories dispersed a median of 7.7 km to their first breeding locations (range 1.2–23.0 km). Our natal dispersal estimates for juvenile White-headed Woodpeckers were longer than those for most other woodpecker species studied to date. In addition, we found that woodpeckers settled in mid-elevation areas with greater variation in canopy cover compared to dispersal locations. There was no difference in ponderosa pine (Pinus ponderosa) basal area between dispersal tracks and spring home ranges. White-headed Woodpeckers are a species of conservation concern due to habitat loss in western North America, and active management in Washington state seeks to restore overstocked ponderosa pine forests to pre-settlement tree densities which could benefit this woodpecker. Our results inform conservation and forest management efforts by suggesting that dispersing juveniles have the capacity to travel long distances to colonize restored forests.
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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 | 0.031 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".