• entropy score;
  • generalized additive mixed model;
  • nonparametric smoothing;
  • wildfire ignitions


The potential impact of climate change on forest fire risk is of significant concern. Postulated climate change effects on wildfires include increasing annual trends in ignitions and a lengthening of the fire season. We propose to use logistic generalized additive mixed models to investigate these characteristics. We present the modelling framework and outline a set of candidate models that are nested in terms of their fixed effects components. Model selection via likelihood ratio testing is discussed and connected to an entropy-based scoring rule for Bernoulli responses. We illustrate its application using data for lightning-caused forest fire ignitions over a period of 42 years in a 9 884 943 hectare region of boreal forest of northwestern Ontario, Canada. Seasonal and annual changes in ignition risk are observed and discussed, but we identify significant outstanding confounding factors that need to be addressed before one can assess the extent to which those changes can or cannot be attributed to climate change. Copyright © 2010 John Wiley & Sons, Ltd.