Fire is a dominant mechanism of forest renewal in most of Canada's forests and its activity is predicted to increase over the coming decades. Individual fire events have been considered to be non-selective with regards to forest properties, but evidence now suggests otherwise. Our objective was therefore to quantify the effect of forest properties on fire selectivity or avoidance, evaluate the stability of these effects across varying burn rates, and use these results to map local fire risk across the forests of Canada. We used Canada-wide MODIS-based maps of annual fires and of forest properties to identify burned and unburned pixels for the 2002-2011 period and to bin them into classes of forest composition (% conifer and broadleaved deciduous), above-ground tree biomass and stand age. Logistic binomial regressions were then used to quantify fire selectivity by forest properties classes and by zones of homogeneous fire regime (HFR). Results suggest that fire exhibits a strong selectivity for conifer stands, but an even stronger avoidance of broadleaved stands. In terms of age classes, fire also shows a strong avoidance for young (0 to 29 year) stands. The large differences among regional burn rates do not significantly alter the overall preference and avoidance ratings. Finally, we combined these results on relative burn preference with regional burn rates to map local fire risks across Canada.

An important asset for the management of wild ungulates is recognizing the spatial distribution of forage quality across heterogeneous landscapes. To do so typically requires knowledge of which plant species are eaten, in what abundance they are eaten, and what their nutritional quality might be. Acquiring such data, however, may be difficult and time consuming. Here, we are proposing a rapid and cost-effective forage quality monitoring tool that combines near infrared (NIR) spectra of fecal samples and easily obtained data on plant community composition. Our approach rests on the premise that NIR spectra of fecal samples collected within low population density exclosures reflect the optimal forage quality of a given landscape. Forage quality can thus be based on the Mahalanobis distance of fecal spectral scans across the landscape relative to fecal spectral scans inside exclosures (referred to as DISTEX). The Gi* spatial autocorrelation statistic can then be applied among neighboring DISTEX values to detect and map "hot spots" and "cold spots" of nutritional quality over the landscape. We tested our approach in a heterogeneous boreal landscape on Anticosti Island (Québec, Canada), where white-tailed deer (Odocoileus virginianus) populations over the landscape have ranged from 20 to 50 individuals/km2 for at least 80 years, resulting in a loss of most palatable and nutritious plant species. Our results suggest that hot spots of forage quality occur when old-growth balsam fir stands comprise >39.8% of 300 ha neighborhoods, whereas cold spots occur in laggs (i.e., transition zones from forest to peatland). In terms of ground-level indicator plant species, the presence of Canada bunchberry (Cornus canadensis) was highly correlated with hot spots, whereas tamarack (Larix laricina) was highly correlated with cold spots. Mean DISTEX values were positively and significantly correlated with the neutral detergent fiber and acid detergent lignin contents of feces. While our approach would need more independent field trials before it is fully validated, its low cost and ease of execution should make it a valuable tool for advancing both the basic and applied ecology of large herbivores.

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