Spatiotemporal variation in mechanisms driving regional‐scale population dynamics of a Threatened grassland bird
Bibliographic record
Abstract
Abstract To achieve national population targets for migratory birds, landscape‐level conservation approaches are increasingly encouraged. However, knowledge of the mechanisms that drive spatiotemporal patterns in population dynamics are needed to inform scale‐variant policy development. Using hierarchical Bayesian models and variable selection, we determined by which mechanism(s), and to what extent, changes in quantity and quality of surrogate grassland habitats contributed to regional variation in population trends of an obligatory grassland bird, Bobolink (Dolichonyx oryzivorous). We used North American Breeding Bird Survey data to develop spatially explicit models of regional population trends over 25 years across 35 agricultural census divisions in Ontario, Canada. We measured the strength of evidence for effects of land‐use change on population trends over the entire study period and over five subperiods. Over the entire study period, one region (Perth) displayed strong evidence of population decline (95%CIis entirely below 0); four regions displayed strong evidence of population increase (Bruce, Simcoe, Peterborough, and Northumberland). Population trends shifted spatially among subperiods, with more extreme declines later in time (1986–1990: 28% of 35 census divisions, 1991–1995: 46%, 1996–2000: 40%, 2001–2005: 66%, 2006–2010: 82%). Important predictors of spatial patterns in Bobolink population trends over the entire study period were human development and fragmentation. However, factors inferred to drive patterns in population trends were not consistent over space and time. This result underscores that effective threat identification (both spatially and temporally) and implementation of flexible, regionally tailored policies will be critical to realize efficient conservation of Bobolink and similar at‐risk species.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".