BAM Generalized National Models Documentation, Version 4.0
Bibliographic record
Abstract
A generalized modeling framework for spatially extensive species abundance prediction and population estimation In the face of rapid environmental change, spatially explicit estimates of species abundance and distribution are needed to inform conservation planning and management decisions across a range of spatial scales. We present a generalized modeling framework bridging the gap between local studies and regional to national management needs by compiling and harmonizing data from many sources to predict avian abundance at a fine resolution and broad extent. We first applied detectability offsets to integrate avian point-count data from a large collection of research and monitoring projects across the entire breadth of subarctic Canada (>250,000 unique sampling locations). We then subsampled the data by two time periods and sixteen geographic regions and developed boosted regression trees to model the density of 143 boreal landbird species as a function of environmental covariates representing climate, local- (250 m) and landscape-level (up to ~1.5 km) vegetation composition, land cover, and topography. Finally, bootstrapped model predictions for each region were combined to generate predictive density maps, habitat- and region-specific density estimates, and Canada-wide population estimates. Our models estimated a total of approximately 3.56 billion breeding males (7.13 billion individuals) across subarctic Canada, with the majority breeding in boreal and hemi-boreal regions. Forest generalist species made up nearly half of this estimate (1.57 billion breeding males), followed by boreal forest specialist species (1.05 billion), habitat generalists (350 million), and species associated with eastern forests (274 million), grasslands (124 million), western forests (74.7 million), wetlands (63.5 million), and Arctic tundra (17.7 million). Introduced species comprised 48.9 million breeding males. An analysis of variable importance showed that, across species, most of the variation in bird abundance was explained by landscape-level vegetation composition, suggesting that the effect of climate on bird abundance is mostly indirect, via vegetation, but that landscape-level variables are needed to capture this variation. Model classification accuracy was highest from a habitat perspective for forest- and grassland-associated species (lowest for mountain- and urban-associated species); and for Regulidae and Phasianidae from a taxonomic perspective (lowest for Bombycillidae and Paridae). In developing these models, we created a standardized, updatable, and reproducible workflow that can be used to update these analytical products and improve their utility for conservation and management planning. This data set contains: Reproducible code for the modeling approach based on Source code for the website at based on Data and image assets for the website based on Please note, in late March 2025, we discovered a systematic error in the offsets used in these models, and have since updated the products to correct that error. For more information, please see the repository for further details or email for assistance.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.135 | 0.054 |
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".