Patterns and drivers of population trends on individual Breeding Bird Survey routes using spatially explicit models and route-level covariates
Bibliographic record
Abstract
Spatial patterns in population trends, particularly those at fine geographic scales, can help better understand the factors driving population change in North American birds. The standard trend models for the North American Breeding Bird Survey (BBS) were designed to estimate changes in relative abundance through time (trend) within broad geographic strata, such as countries, Bird Conservation Regions, U.S. states, and Canadian territories or provinces. Calculating trend estimates at the level of the BBS’s individual survey transects (“routes”) allows exploration of finer spatial patterns and estimation of the effects of covariates, such as habitat loss or annual weather, on both relative abundance and trend. Here, we describe four related hierarchical Bayesian models that estimate trends for individual BBS routes. All four models estimate route-level trends and relative abundances using a hierarchical structure that shares information among routes, and three of the models share information in a spatially explicit way. The spatial models use either an intrinsic Conditional Autoregressive (iCAR) structure or a distance-based Gaussian Process (GP) to estimate the spatial components. We fit all four models to data for 71 species and then, because of the intensive computations required, fit two of the models (one spatial and one non-spatial) for an additional 216 species. In a leave-future-out cross-validation, the spatial models outperformed the non-spatial models for 284 out of 287 species. The best approach to modeling the spatial components depends on the species being modeled; the Gaussian Process had the highest predictive accuracy for 69% of the species tested here and the iCAR was better for the remaining 31%. We also present two examples of route-level covariate analyses focused on spatial and temporal variation in habitat for Rufous Hummingbird (Selasphorus rufus) and Horned Grebe (Podiceps auritus). In both examples, the inclusion of covariates improved our understanding of the patterns in the rate of population change for both species. Route-level models for BBS data are useful for visualizing spatial patterns of population change, generating hypotheses on the causes of change, comparing patterns of change among regions and species, and testing hypotheses on causes of change with relevant covariates.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".