Whitebark pine (Pinus albicaulis) has the largest and most northerly distribution of any white pine (Subgenus Strobus) in North America, encompassing 18˝latitude and 21˝longitude in western mountains. Within this broad range, however, whitebark pine occurs within a narrow elevational zone, including upper subalpine and treeline forests, and functions generally as an important keystone and foundation species. In the Rocky Mountains, whitebark pine facilitates the development of krummholz conifer communities in the alpine-treeline ecotone (ATE), and thus potentially provides capacity for critical ecosystem services such as snow retention and soil stabilization. The invasive, exotic pathogen Cronartium ribicola, which causes white pine blister rust, now occurs nearly rangewide in whitebark pine communities, to their northern limits. Here, we synthesize data from 10 studies to document geographic variation in structure, conifer species, and understory plants in whitebark pine treeline communities, and examine the potential role of these communities in snow retention and regulating downstream flows. Whitebark pine mortality is predicted to alter treeline community composition, structure, and function. Whitebark pine losses in the ATE may also alter response to climate warming. Efforts to restore whitebark pine have thus far been limited to subalpine communities, particularly through planting seedlings with potential blister rust resistance. We discuss whether restoration strategies might be appropriate for treeline communities.

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Question: Reliable estimates of understorey (non-tree) plant cover following fire are essential to assess early forest community recovery. Photographic digital image analysis (DIA) is frequently used in seral, single-strata vegetation, given its greater objectivity and repeatability compared to observer visual estimation; however, its efficacy in multi-strata forest vegetation may be compromised, where various visual obstructions (coarse downed wood [CDW], conifer regeneration, and shadows) may conceal plant cover in the digital imagery. We asked whether vegetation complexity influences plant cover estimated by DIA relative to two visual methods: plot-level (20 m 2 ) estimation (PLE) and quadrat-level (1 m 2 ) estimation (QLE)?