Estimates of tidal‐marsh bird densities using Bayesian networks
Bibliographic record
Abstract
ABSTRACT Conserving tidal‐marsh bird communities requires strategies to address continuing pressures from human development to the effects of increasing rates of sea‐level rise. Knowing tidal‐marsh bird distributions and population sizes are important for developing these strategies. In the Northeast United States, where estimates of sea ‐ level rise are 3 times higher than the global average, 5 bird species are tidal‐marsh specialists: clapper rail ( Rallus crepitans ), willet ( Tringa semipalmata ), Nelson's sparrow ( Ammospiza nelsoni ), saltmarsh sparrow ( A. caudacuta ), and seaside sparrow ( A. maritima ). We used a regional marsh bird survey to develop Bayesian network models to identify factors that influence patch‐scale species density and to estimate regional population sizes. We modeled species density as a function of habitat covariates at the patch, local, landscape, and regional spatial scales. Densities were most sensitive to patch location and dimension, patch geomorphic setting, indices of human development, and changes in mean sea level. We estimated 110,000 clapper rails (95% CI = 61,000–159,000), 111,000 willets (95% CI = 70,000–152,000), 7,000 Nelson's sparrows (95% CI = 4,000–10,000), 60,000 saltmarsh sparrows (95% CI = 40,000–80,000), and 234,000 seaside sparrows (95% CI = 112,000–356,000) from the United States–Canada border to, and including, the mouth of the Chesapeake Bay, Virginia, USA. Our abundance estimates can be used to identify priority conservation areas at multiple geographic scales and our models help identify key habitat and landscape components for tidal‐marsh restoration and management to benefit tidal‐marsh birds and can be modified for other species. © 2018 The Wildlife Society.
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 | 0.000 |
| 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".