Canadian Forest Service Publications
Climate and satellite-derived land cover for predicting breeding bird distribution in the Great Lakes Basin. 2004. Venier, L.A.; Pearce, J.L.; McKee, J.E.; McKenney, D.W.; Niemi, G.J. Journal of Biogeography 31: 315 - 331.
Issued by: Great Lakes Forestry Centre
Catalog ID: 28804
Availability: PDF (request by e-mail)
We examined relationships between breeding bird distribution of 10 forest songbirds in the Great Lakes Basin, large-scale climate and the distribution of land cover types as estimated by advanced very high resolution radiometer (AVHRR) and multi-spectral scanner (MSS) land cover classifications. Our objective was to examine the ability of regional climate, AVHRR (1 km resolution) land cover and MSS (200 m resolution) land cover to predict the distribution of breeding forest birds at the scale of the Great Lakes Basin and at the resolution of Breeding Bird Atlas data (5-10 km super(2)). Specifically we addressed the following questions. (1) How well do AVHRR or MSS classifications capture the variation in distribution of bird species? (2) Is one land cover classification more useful than the other for predicting distribution? (3) How do models based on climate compare with models based on land cover? (4) Can the combination of both climate and land cover improve the predictive ability of these models. Modelling was conducted over the area of the Great Lakes Basin including parts of Ontario, Canada and parts of Illinois, Indiana, Michigan, New York, Ohio, Pennsylvania Wisconsin, and Minnesota, USA. We conducted single variable logistic regression with the forest classes of AVHRR and MSS land cover using evidence of breeding as the response variable. We conducted multiple logistic regression with stepwise selection to select models from five sets of explanatory variables (AVHRR, MSS, climate, AVHRR + climate, MSS + climate). Generally, species were related to both AVHRR and MSS land cover types in the direction expected based on the known local habitat use of the species. Neither land cover classification appeared to produce consistently more intuitive results. Good models were generated using each of the explanatory data sets examined here. And at least one but usually all five variable sets produced acceptable or excellent models for each species. Both climate and large scale land cover were effective predictors of the distribution of the 10 forest bird species examined here. Models generated from these data had good classification accuracy of independent validation data. Good models were produced from all explanatory data sets or combinations suggesting that the distribution of climate, AVHRR land cover, and MSS land cover all captured similar variance in the distribution of the birds. It is difficult to separate the effects of climate and vegetation on the species' distributions at this scale.