Geography & History
The Boreal Avian Modelling Project (BAM) was initiated in 2004 to address knowledge gaps associated with the management and conservation of boreal birds in North America.
BAM is built on the foundation of boreal bird data. The BAM database was created by collating and harmonizing avian data from the Breeding Bird Survey, Breeding Bird Atlases, and individual research, monitoring, and inventory efforts conducted across the Canadian and US boreal and hemi-boreal region.
BAM is working to develop rigorous analytical model-based approaches to support the conservation of the boreal forest region and the bird populations and communities that depend upon it. We have developed specialized statistical approaches to harmonize these datasets by correcting for survey methodology and species detectability to estimate density.
BAM models have a myriad of applications: they allow us to draw relationships between birds and their environment (e.g. vegetation, climate, disturbance) from regional to national scales, to predict their response to changes through time and across geographic areas, to explain population trends, to determine which habitats are important and why, to design monitoring efficiently and effectively, to assess how management decisions made now may affect birds in the future...just to name a few.
Scope & Scale
The geographic scope of our research projects and associated data products varies with the research question and the available data.
At project initiation in 2004, the geographic scope of BAM encompassed the boreal forest across Canada.
In 2011, BAM expanded our study region by almost 30% to include boreal and hemiboreal portions of Alaska, southern Canada, and the Upper Midwest and Northeast United States.
Our work to project avian responses to climate change included avian data from southern Canada.
More recent projects tend to include larger study areas.
Continental and Regional Perspectives
The BAM datasets encompass the full geographic scope of the North American boreal and hemiboreal forests. Models based on the continental avian dataset and corresponding biophysical data are useful for understanding the broad scale patterns and processes affecting avian populations. For example, we can map species' distributions, define species' ranges, estimate population sizes, make predictions about impacts of land use activities at the national or continental scale, and compare habitat use by a particular species in different locations across its range in the boreal forest region.
BAM also undertakes analyses at regional scales. A regional-scale focus allows us to incorporate more detailed biophysical data into modelling efforts and identify regionally specific relationships between birds and their environment. This in turn is valuable for predicting the response of bird populations to industrial development, land-use planning and regional-scale conservation efforts.
The BAM database is an collection of independent datasets. Aside from the Breeding Bird Survey and a few long-term monitoring projects such as the Calling Lake Fragmentation Experiment, most datasets were collected in one or a few years. That said, we are actively exploring ways to extract trend and meaningful patterns from this temporally sparse compilation. The earliest records included in the avian point-count database come from surveys conducted in the 1990s. We welcome data from historical or current bird surveys to help fill temporal and spatial gaps and to create longer time series.
- Avian data: Assembled point count data for boreal and hemi-boreal North America from >100 projects, >1.5 million bird survey records, and >250,000 locations, including Breeding Bird Survey and provincial Atlases (Barker et al. 2015).
- Biophysical data: Assembled, evaluated, and documented major sources of remote-sensed biophysical data for vegetation, productivity, climate, and disturbance data.
- Common Attribute Schema for Forest Resource Inventory data (CASFRI): Synthesized 20 corporate, provincial, territorial and federal forest resource inventories into a single harmonized database. This allows consistent use of FRI data in regional or national studies (Cumming et al. 2010, 2014a, Cosco 2011).
- Statistical methodology: Developed statistical methods to improve density estimates derived from point count data, harmonize data collected using different point count protocols, and integrate data collected using Acoustic Recording Units (Matsuoka et al. 2012, Sólymos et al. 2013, 2018b, Van Wilgenburg et al. 2017).
- Intellectual capacity: BAM has actively cultivated a high degree of rigour, credibility and innovation. The resulting scientific expertise represents expert advice and technical support for monitoring, SAR, sector impacts (forestry, energy, agriculture) and environmental assessment issues in boreal forest.