Using Citizen Science Data to Predict the Probability of Occupancy, Colonization, and Extinction of Bicknell’s Thrush (Catharus bicknelli) in the White Mountains of New Hampshire
Bibliographic record
Abstract
Bicknell’s Thrush (Catharus bicknelli) is a neotropical migrant bird species that breeds in the montane spruce (Picea spp.) and balsam fir (Abies balsamea) forests of Maine, New Hampshire, Vermont, New York, and eastern Canada. This species has a restricted range, a decreasing population, and is facing the significant threat of climate change. I used a multi-season occupancy model to predict the probability of occupancy, colonization, and extinction, of Bicknell’s Thrush, while accounting for the probability of detection. From 2010 to 2021, 597 detections of Bicknell’s Thrush from 276 sampling stations in New Hampshire were recorded. Covariates investigated were elevation, proportion of evergreen forest, deciduous forest, and other landcover types. Detection covariates included survey date, point count start time, and year. The model predicted an overall negative relationship between detection probability and point count start time, where detection declined after 5:00am and then increased after 7:30am. The probabilities of site occupancy and site colonization were best represented by the additive and interactive relationships of elevation and proportion of evergreen forest, respectively. Elevation was the covariate that had the largest beta estimate for occupancy and colonization probability. Probability of local site extinction is best represented by proportion of evergreen forest and has a slight negative relationship. As the White Mountain National Forest of New Hampshire continues to warm, the relationship between a changing climate, upslope movement of the montane spruce-fir forest, and species occupancy will be increasingly important for conservation of the country’s largest concentration of Bicknell’s Thrush habitat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".