Industrial development can have important direct and indirect effects on wildlife populations. Resource selection function (RSF) modeling provides a powerful tool for assessing the effects of industrial development on spatial use patterns of wildlife. Among North American large mammal species, mountain goats Oreamnos americanus are particularly sensitive to human disturbance. In this study mountain goat seasonal resource selection patterns were examined using GPS radio collar (n = 79 individuals) and remote sensing data in a GIS framework across a 491 km2 regional mountain range in southeast Alaska, 2005–2015. The resulting global RSF model was then applied across a limited spatial extent centered on an industrial mining site in order to assess whether mining activity altered expected spatial use patterns at different distances from the mine. Using a quasi treatment—control experimental framework we examined the occurrence of spatially explicit mine disturbance thresholds. In general, resource selection modeling indicated that mountain goats selected for steep, rugged terrain in close proximity to cliffs in areas with high solar exposure; and they selected for lower elevations in winter than in summer. Mountain goat selection for rugged terrain and proximity to cliffs was stronger in winter than summer. RSF model applications indicated that mountain goat use of predicted habitat in close proximity to the mine was lower than expected at distances up to 1800 m in winter and 1000 m in summer. Because lower elevation winter habitat is closer to mining activity than summer habitat, a greater percentage (42%) of winter habitat within the analysis area was affected by mining activity. The resulting net loss of functional winter habitat carrying capacity is likely to have long-term negative implications for the local mountain goat population. In places where mining is proposed, development should avoid areas within 1800 m of mountain goat winter habitat. In places where mining is already occurring within disturbance thresholds (such as this study), long-term monitoring and more detailed field studies should be conducted to more fully understand population-level consequences of disturbance and identify practicable mitigation measures that have a high probability of success.
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