Spatial Analysis of Forest–Tundra Ecotones Reveals the Influence of Topography and Vegetation on Alpine Treeline Patterns in the Subarctic

Katherine D. Dearborn and Ryan K. Danby

Annals of the American Association of Geographers, 2020, vol. 110, issue 1, 18-35

Abstract: The response of vegetation to recent climate change is a central theme in contemporary biogeography. A tenet of this research is that forests will advance upslope and northward as climate warms, replacing tundra communities. There has been considerable variation, however, in the pace and extent of recent change, particularly in alpine regions. The objectives of this study were to determine how and why tree spatial patterns vary across different topographic features, and to use spatial patterns to infer the mechanisms governing treeline dynamics in an alpine region of subarctic Canada. We mapped trees across different elevations, slope aspects, and slope angles in two QuickBird satellite images, and in field plots established within the bounds of each image. We then quantified the degree of clustering among trees using Ripley’s K(t) statistic. We also classified each image into vegetation classes and used class-level landscape metrics to quantify the degree of treeline abruptness in different topographic settings. We found that clustering of stems was more common on south than north aspects, likely due to the high occurrence of overwinter damage on the former. Treelines were also more abrupt on south aspects, likely because high tall shrub abundance on these slopes inhibits tree seedling establishment. We conclude that the spatial patterns of subarctic alpine treelines are strongly influenced by both physical and biological factors that vary strongly with slope aspect. The response of treelines to future climate change will likely be highly variable at the landscape scale, despite experiencing similar climatic conditions. Key Words: forest–tundra ecotone, FragStats, landscape metrics, QuickBird, spatial pattern analysis, Yukon.

Date: 2020
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DOI: 10.1080/24694452.2019.1616530

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