Abstract

I systematically sampled American marten Martes americana presence in two large study areas in the Selkirk and Purcell Mountains of southwest Canada using hair removal traps and tracks in snow. Both study areas were mostly forested and contained a broad cross-section of stand ages including abundant early seral and mature forest. I extracted measures of forest structure and dominant tree species, climax ecosystem types and human use from digital resource databases and used multiple logistic regression to model habitat selection of martens. I summarized data in windows of 100 m to 10 km in radius around each sample location to investigate the effect of varying data resolution on habitat selection. Marten detection at hair sites was positively related to temperature and trap duration and negatively related to snowfall while the trap was set. Martens were detected in all habitats sampled including recently logged areas, regenerating stands, dry Douglas-fir Pseudosuga menziesii forest and subalpine parkland. Overall selection was mildly greater using mean habitat values in 100 m and 2 km radius windows for both study areas. Martens selected for greater crown closure and older stands at the finer resolution; no selection for forest structure was detected at the larger resolution except that martens selected against increased overstory heterogeneity as measured by the standard deviation of crown closure (within the window). Martens preferred coniferous stands over deciduous dominated stands and were more abundant in wetter than in dryer ecosystems. Selection for ecosystems and stand types was stronger in the larger window size. At the intensity sampled in this study, neither road density nor logging appeared to affect marten habitat selection when I accounted for variation in ecosystems and stand structure. This study examined habitat selection at relatively coarse scales; stronger associations with forest structure may be expected at finer scales. In addition, roads or logging may influence habitat selection below the scale of my analysis.

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