Habitat selection in overwintering White-throated Sparrow flocks in an Ohio experimental wetland ecosystem
Bibliographic record
Abstract
The White-throated Sparrow (Zonotrichia albicollis) is a native songbird that breeds in the boreal and mixed forest ecosystems of Canada and northern United States and overwinters primarily in the central and southern United States. During nonbreeding, White-throated Sparrows are highly gregarious, forming large foraging flocks. Populations of the species are in decline with its population decreasing over 30 percent the last 50 years with a particularly steep decline in the eastern U.S.A. (Hill, 2022). A lack of understanding on what habitat attributes are preferred by overwintering sparrows limits our ability to determine the cause of this species' decline. This study seeks to determine what fine-scale habitat variables in forested wetland ecosystems could improve winter flock survival. I test the hypothesis that White-throated Sparrows show preferences for habitat attributes that maximize protective cover and provide food resources. I predict that flocks will demonstrate avoidance of emergent marsh patches and select for early successional forest patches with fine-scale elements such as brushpiles, young eastern redcedar (Juniperus virginiana), and Amur honeysuckle (Lonicera maackii). In the winters of 2021-23 at the Wilma H. Schiermeier Olentangy River Wetland Research Park, White-throated Sparrow flock observations and vegetation surveys were conducted for a total of 86 paired flock and random points. Logistic regression models were used to predict the probability of sparrow use for 16 single habitat attributes and 15 a priori multivariate models. Results show positive selection for three habitat attributes in the top models: percentage eastern redcedar, percentage Amur honeysuckle cover, and proximity to brushpile suggest that White- throated Sparrows have a strong preference for cover habitat as well as nutritional foraging opportunities of eastern redcedar berries. I discuss management implications, including the implications of apparent selection for sites with invasive Amur honeysuckle.
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 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 | 0.000 | 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) | 0.001 | 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".