BAM Generalized National Models Documentation, Version 4.0 (Q6789)

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 large-scale 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 (gt; 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 four billion breeding males (eight 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 (1.67 billion breeding males), followed by boreal forest specialist species (1.16 billion), habitat generalists (384 million), and species associated with eastern forests (251 million), grasslands (130 million), western forests (69.7 million), wetlands (64.9 million), and Arctic tundra (15.3 million). Introduced species comprised 47.8 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 https://github.com/borealbirds/LandbirdModelsV4 Source code for the website at https://borealbirds.github.io/ based on https://github.com/borealbirds/borealbirds.github.io Data and image assets for the website based on https://github.com/borealbirds/api