Habitat associations

We use a set of statistical models to relate the relative abundance of each species measured at ABMI sites to three sets of variables: vegetation types, human footprint and geographic location. The analyses are done separately for three regions: boreal (including Canadian shield), foothills and south (parkland+grassland).

  • Relative abundance for vascular plants, bryophytes and mites is the probability that the species will occur in a 50 m x 50 m ABMI quadrat or a soil sample for mites. For birds, relative abundance is the number of birds detected at a point count, adjusted for the species’ detection distance.
  • Vegetation types in the boreal and foothills regions are based on several province-wide GIS layers of vegetation variables, and include main forest stand types by broad age classes and several categories of open vegetation (grass, shrub, open wetland, open water, barren). For the vegetation figures, the broad age classes are interpolated to 20-yr age classes. In the south, ‘vegetation types’ are actually mapped soil types, such as loamy, sandy, etc.
  • Several categories of human footprint are distinguished in the models, including ‘successional’ (forestry, temporary linear features), ‘alienating’ (agriculture, industrial, urban area, roads), as well as linear versus non-linear features. A flexible curve is used for the footprint relationships so that the models can show different responses when there is partial footprint at a site compared to 100% footprint. This allows the models to represent edge effects. The scale of the footprint information used in the intactness models is 64 ha for birds and 1 ha for other taxa.
  • Flexible relationships with latitude and longitude are also included in the models to represent the geographic distribution of the species.

The analysis is conducted in a ‘model selection framework’, which means that the models for each species are only as complex as the data for that species support. For example, the models for a rarer species with few records may not be able to separate different age classes or even broad stand types. All age classes or several stand types will therefore all have the same average value in the vegetation figures. This does not mean that there are truly no differences between these types for that species. It just means that we do not yet have enough data to estimate abundances in all those individual vegetation types.

We cannot distinguish different open vegetation types in the boreal and foothills – grass, shrubs and open wetland – because these are rare types in our terrestrial sampling. We also have few samples in very young or very old forest, because these are also rare. Our results might miss species that have a strong association with one of these rare vegetation types.

This modeling requires moderate sample sizes, so we can only do the analyses for species that have at least 20 records in a natural region. ABMI has some information on many other rarer species. We can provide simpler summaries of our records for these species.

Figures show mean relative abundances, with 90% confidence intervals.

The bird modeling differed from other taxa from some aspects because the data set is more extensive (see Collaborations). The combined data set needed to be standardized due to the differences in survey protocol among the data sets. Algorithms to deal with different protocols in bird surveys and methods for estimating predictive uncertainty were developed in collaboration with the Boreal Avian Modelling Project (BAM) team (Sólymos et al. 2013). BAM also provided data management help for organizing bird point count data. The minimum number of detections required for model building for birds was 25.

The bioclimatic variables used in the modeling were calculated at a 4-km resolution using monthly climate normals of temperature and precipitation averaged over 1961-1990. The monthly climate normals are based on instrument-measured climate data that were interpolated by PRISM (Daly et al., 2002) and WorldClim (Hijmans et al., 2005). The western North American portion of these data are described by Wang et al. (2011).

Predictive maps

Province-wide maps are generated by applying the models of a species relationship with vegetation types, human footprint and geographic location to each quarter-section in the province. The ‘current’ maps use the current human footprint. The ‘reference’ maps uses a back-filled map, in which footprint has been removed and replaced with the vegetation type that was there prior to footprint. Back-filling is based on adjacent vegetation types and rules about what vegetation types different footprint types can occur in (e.g., forestry in harvestable-age upland stands).

There are two main limitations to ABMI species maps:

  • The maps are based on predicted habitat quality. They show the average expected abundance of a species given the habitat types, footprint and geographic location of a quarter-section. We do not know the actual abundances of each species at each point in the province.
  • There is uncertainty in the habitat models for each species, and in the underlying vegetation and human footprint layers. Uncertainty of habitat models is high for rarer species and for rarer habitat types. Uncertainty is also higher in the corners of the province, particularly the northwest and southwest, where we have limited sampling.

Remember also that the reference maps only show the effects of statistically removing the effects of human footprint. They do not show how the species would have been distributed under ‘pristine’, ‘natural’ or ‘pre-historic’ conditions.

Please see limitations for more explanation and additional caveats.

Additional resources


Daly, C., W. P. Gibson, G. H. Taylor, G. L. Johnson, and P. Pasteris. 2002. A knowledge-based approach to the statistical mapping of climate. Climate Research, 22, 99-113.

Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978.

Sólymos, P., Matsuoka, S. M., Bayne, E. M., Lele, S. R., Fontaine, P., Cumming, S. G., Stralberg, D., Schmiegelow, F. K. A., and Song, S. J., 2013. Calibrating indices of avian density from non-standardized survey data: making the most of a messy situation. Methods in Ecology and Evolution, 4, 1047-1058.

Wang, T., A. Hamann, D. L. Spittlehouse, and T. Q. Murdock. 2011. ClimateWNA - high-resolution spatial climate data for western North America. Meteorology and Climatology, 51, 16-29.