One step ahead of the plow: Using cropland conversion risk to guide Sprague's Pipit conservation in the northern Great Plains
Bibliographic record
Abstract
Rapid expansion of cropland threatens grassland ecosystems across western North America and broad-scale planning can be a catalyst motivating individuals and agencies to accelerate conservation. Sprague's Pipit ( Anthus spragueii ) is an imperiled grassland songbird whose population has been declining rapidly in recent decades. Here, we present a strategic framework for conservation of pipits and their habitat in the northern Great Plains. We modeled pipit distribution across its million-km 2 breeding range in Canada and the U.S. We describe factors shaping distribution, delineate population cores and assess vulnerability to future grassland losses. Pipits selected landscapes with a high proportion of continuous grassland within a relatively cool, moist climate. Sixty percent of the global breeding population occurred in Canada and 63% of the U.S. population occurred in Montana. Populations were highly clumped, with 75% of birds within 30% of their range. Approximately 20% of the population occurred on protected lands and over half used lands that were unlikely to be cultivated given current technologies. A quarter of pipits relied on remaining arable grasslands and potential population losses varied from 2–27% across scenarios. Most of the population (70%) was dependent on private lands, emphasizing the importance of voluntary approaches that incentivize good stewardship. Our maps depicting core populations and tillage risk enable partners to target conservation in landscapes where pipits will benefit most.
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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".