Models of large-scale breeding-bird distribution as a function of macro-climate in Ontario, Canada
Article first published online: 24 DEC 2001
Journal of Biogeography
Volume 26, Issue 2, pages 315–328, March 1999
How to Cite
Venier, L. A., McKenney, D. W., Wang, Y. and McKee, J. (1999), Models of large-scale breeding-bird distribution as a function of macro-climate in Ontario, Canada. Journal of Biogeography, 26: 315–328. doi: 10.1046/j.1365-2699.1999.00273.x
- Issue published online: 24 DEC 2001
- Article first published online: 24 DEC 2001
- forest birds;
- spatial predictions;
- logistic regression models
Aim We modelled the relationship of breeding evidence for five species of forest songbirds (ruby-crowned kinglet (Regulus calendula) Blackburnian warbler (Dendroica fusca), black-throated blue warbler (Dendroica caerulescens), bay-breasted warbler (Dendrioca castanea) and Connecticut warbler (Oporornis agilis)) and a variety of macro-climate variables to examine the importance of climate as a factor determining distribution of breeding in these species and to assess the usefulness of spatial predictions generated from these models.
Location Modelling was conducted over the entire province of Ontario, Canada, an area of ≈900,000 km2.
Methods Data on the distribution of breeding in the province was derived from the Breeding Bird Atlas of Ontario. We used logistic regression to model the relationship between the probability of breeding (assessed in 10 km×10 km blocks) and estimates of a variety of climate variables at the same scale. Models were selected that had the least number of explanatory variables while at the same time having close to the best possible classification accuracy.
Results The final models for these five species had from one to six explanatory variables and an overall concordance of 70.4% to 86.3% indicating a good classification accuracy. Results from subsampling 50% of the original data ten times indicate that (1) the classification accuracy of the model for data used to generate the model is not very sensitive to the specific observations used to generate the model (2) the classification accuracy of test data is close to the classification accuracy of the model data and (3) the classification accuracy of the test data is not dependent on the specific observations used to generate the model. We generated a spatial prediction of the probability of occurrence of each species for Ontario using the relationships defined by the logistic regression models and using 1 km gridded estimates of the necessary climate variables. These probability maps closely matched the maps of observed evidence of breeding from the Atlas.
Main conclusions Although mechanisms controlling breeding distribution cannot be determined using this method, we can conclude that (1) macro-climate is an important factor directly and/or indirectly determining distribution of breeding in these species and (2) spatial predictions of probability of breeding are accurate enough to be useful in predicting probability of breeding in unsampled areas.