Reliable information about species’ population sizes, trends, habitat associations, and distributions is important for status assessment, regional planning, conservation prioritization, and recovery of species at risk. Developing this information at a national scale - in a remote region like the boreal forest - has many challenges, including disparities in sampling conditions and methods across the multiple studies that contributed data to this effort, and disparities in vegetation communities, disturbance histories, species habitat associations, and avian community assemblages across this vast region.
BAM has addressed these challenges by developing a method for standardizing “messy” data (Sólymos et al. 2013) based on the primary components of species detectability, namely detection distance (Matsuoka et al. 2012) and singing rate (Sólymos et al. 2018). This calibration method, termed QPAD, was used to develop habitat-specific density estimates by region, as well as boreal-wide, climate-based spatial density predictions for climate-change projection purposes (Stralberg et al. 2015b). These “first generation” density predictions have been applied to assessing land-use impacts on songbirds (Suarez et al. in review), identifying important habitats for species at risk (Haché et al. 2014; Ball et al. 2016), projecting responses to future climate-change scenarios (Stralberg et al. 2015a), and evaluating conservation priorities (Stralberg et al. 2018).
BAM is continually striving to improve the reliability of its products, and our approach has converged on a new, generalized analytical method to model species densities in relation to environmental covariates. This approach involves building models based on tree species biomass, stand age, topography, land use, and climate. The models use machine learning to allow complex interactions and non-linear responses while avoiding time-consuming species-by- species parameterization. These models yield:
- Habitat-specific density estimates;
- Maps of species’ densities and distributions;
- Population size estimates at both regional and national scales;
- Short-term trend estimates
Furthermore, models or resulting habitat-specific density estimates can be used as inputs to landscape change scenarios.
Future planned improvements to our density models and population estimates include inclusion of remotely sensed covariates to generate annual density predictions, adopting a “moving window” approach to build and then assemble smaller scale models into large-scale prediction areas to increase model accuracy, and continuing to evaluate potential biases in existing data sources and developing appropriate corrections.
Bibliography of BAM-related work
Sólymos, P., Matsuoka, S.M., Cumming, S.G., Stralberg, D., Fontaine, T., Schmiegelow, F.K.A., Song, S.J., Bayne, E.M., 2018. Evaluating time-removal models for estimating availability of boreal birds during point-count surveys: sample size requirements and model complexity. Condor 120, 765–786. https://doi.org/10.1650/CONDOR-18-32.1
Stralberg, D., Camfield, A.F., Carlson, M., Lauzon, C., Westwood, A.R., Barker, N.K.S., Song, S.J., Schmiegelow, F.K.A., 2018. Strategies for identifying priority areas for passerine conservation in Canada’s boreal forest. Avian Conserv Ecol 13(2): 12, 1–23. https://doi.org/10.5751/ACE-01303-130212
Ball, J.R., Sólymos, P., Schmiegelow, F.K.A., Haché, S., Schieck, J., Bayne, E.M., 2016. Regional habitat needs of a nationally listed species, Canada Warbler (Cardellina canadensis), in Alberta, Canada. Avian Conserv Ecol 11. https://doi.org/10.5751/ACE-00916-110210
Stralberg, D., Bayne, E.M., Cumming, S.G., Sólymos, P., Song, S.J., Schmiegelow, F.K.A., 2015a. Conservation of future boreal forest bird communities considering lags in vegetation response to climate change: a modified refugia approach. Divers Distrib 21, 1112–1128. https://doi.org/10.1111/ddi.12356
Stralberg, D., Matsuoka, S.M., Hamann, A., Bayne, E.M., Sólymos, P., Schmiegelow, F.K.A., Wang, X., Cumming, S.G., Song, S.J., 2015. Projecting boreal bird responses to climate change: The signal exceeds the noise. Ecol Appl 25, 52–69. https://doi.org/10.1890/13-2289.1
Haché, S., Sólymos, P., Fontaine, T., Bayne, E.M., Cumming, S.G., Schmiegelow, F.K.A. and Stralberg, D. (2014), Critical Habitat of Olive-Sided Flycatcher, Canada Warbler, and Common Nighthawk in Canada (Project K4B20-13-0367), Technical Report for Environment and Climate Change Canada, Boreal Avian Modelling Project, University of Alberta, Edmonton, AB, Canada, available at: https://doi.org/10.5281/zenodo.2433885.
Sólymos, P., Matsuoka, S.M., Bayne, E.M., Lele, S.R., Fontaine, T., Cumming, S.G., Stralberg, D., Schmiegelow, F.K.A., Song, S.J., 2013. Calibrating indices of avian density from non-standardized survey data: making the most of a messy situation. Methods Ecol Evol 4, 1047–1058. https://doi.org/10.1111/2041-210X.12106
Matsuoka, S.M., Bayne, E.M., Sólymos, P., Fontaine, T., Cumming, S.G., Schmiegelow, F.K.A., Song, S.J., 2012. Using binomial distance-sampling models to estimate the effective detection radius of point-count surveys across boreal Canada. Auk 129, 268–282. https://doi.org/10.1525/auk.2012.11190