Using GIS to guide field surveys for timberline sparrows in Northwestern Montana
Bibliographic record
Abstract
For many species, we lack the basic information on distribution and abundance that is needed for conservation and management. The timberline Brewer's sparrow (Spizella breweri taverneri), a migratory songbird previously known to breed only in the Canadian and Alaskan Rockies, was recently discovered in abundance in high-elevation areas of Glacier National Park, Montana, USA. Because the southern limit of this subspecies' breeding range was unknown, we developed a GIS-based model to predict timberline sparrow habitat along the Rocky Mountain front south of Glacier National Park. We field-tested the model by surveying 40 predicted sites in the Lewis and Clark National Forest. We found suitable habitat at 20 of the 40 sites (50%), and timberline sparrows were present at 4 of these; we also found timberline sparrows at one site that was not predicted. The discovery of nesting birds at Jones Creek, in the Teton River drainage, extends the known breeding range of this subspecies 50 km south. Our study indicates that, even with relatively few initial data, GIS can be successfully used as a tool to guide surveys for rare species. However, there are inherent limitations in models built with restricted data, and species' distributions can be driven by factors not readily modelled in a GIS. Therefore, GIS models often must be treated as working hypotheses, to be tested and improved as additional data become available.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".