We developed three black bear (   Ursus americanus) habitat models in the context of a geographic information system to identify linkage areas across a major transportation corridor. One model was based on empirical habitat data, and the other two (opinion‐ and literature‐based) were based on expert information developed in a multicriteria decision‐making process. We validated the performance of the models with an independent data set. Four classes of highway linkage zones were generated. Class 3 linkages were the most accurate for mapping cross‐highway movement. Our tests showed that the model based on expert literature most closely approximated the empirical model, both in the results of statistical tests and the description of the class 3 linkages. In addition, the expert literature–based model was consistently more similar to the empirical model than either of two seasonal, expert opinion–based models. Among the expert models, the literature‐based model had the strongest correlation with the empirical model. Expert‐opinion models were less in agreement with the empirical model. The poor performance of the expert‐opinion model may be explained by an overestimation of the importance of riparian habitat by experts compared with the literature. A small portion of the empirical data to test the models was from the pre‐berry season and may have affected how well the model predicted linkage areas. Our empirical and expert models represent useful tools for resource and transportation planners charged with determining the location of mitigation passages for wildlife when baseline information is lacking and when time constraints do not allow for data collection before construction.

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Wildlife species may respond to industrial development with changes in distribution. However, discerning a response to development from differences in habitat selection is challenging. Since the early 1990s, migratory tundra Bathurst caribou Rangifer tarandus groenlandicus in the Canadian Arctic have been exposed to the construction and operation of two adjacent open‐pit mines within the herd's summer range. We developed a statistical approach to directly estimate the zone of influence (area of reduced caribou occupancy) of the mines during mid‐July‐mid‐October. We used caribou presence recorded during aerial surveys and locations of satellite‐collared cow caribou as inputs to a model to account for patterns in habitat selection as well as mine activities. We then constrained the zone of influence curve to asymptote, such that the average distance from the mine complex where caribou habitat selection was not affected by the mine could be estimated. During the operation period for the two open‐pit mines, we detected a 14‐km zone of influence from the aerial survey data, and a weaker 11‐km zone from the satellite‐collar locations. Caribou were about four times more likely to select habitat at distances greater than the zone of influence compared to the two‐mine complex, with a gradation of increasing selection up to the estimated zone of influence. Caribou are responding to industrial developments at greater distances than shown in other areas, possibly related to fine dust deposition from mine activities in open, tundra habitats. The methodology we developed provides a standardized approach to estimate the spatial impact of stressors on caribou or other wildlife species.

This study examines the influences of fuel, weather and topography on lightning-caused forest fires in portions of southern British Columbia and Alberta, Canada. The results show a significant difference in lightning and lightning-caused fires east and west of the Continental Divide. In British Columbia, on average there was one fire for every 50 lightning discharges whereas in Alberta there was one fire for every 1400 lightning discharges. Elevation, the distribution of lightning strikes, the Daily Severity Rating (a component of the Canadian Forest Fire Weather Index System) and vegetation composition were identified as primary agents controlling lightning fire occurrence. However, the multivariate analysis does suggest that there are other factors influencing fire occurrence other than the biophysical factors we tested. The implications of the lightning and lightning-ignited fires for land managers are discussed.

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