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Environmental Limits of Tall Shrubs in Alaska’s Arctic National Parks

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  • David K Swanson

Abstract

We sampled shrub canopy volume (height times area) and environmental factors (soil wetness, soil depth of thaw, soil pH, mean July air temperature, and typical date of spring snow loss) on 471 plots across five National Park Service units in northern Alaska. Our goal was to determine the environments where tall shrubs thrive and use this information to predict the location of future shrub expansion. The study area covers over 80,000 km2 and has mostly tundra vegetation. Large canopy volumes were uncommon, with volumes over 0.5 m3/m2 present on just 8% of plots. Shrub canopy volumes were highest where mean July temperatures were above 10.5°C and on weakly acid to neutral soils (pH of 6 to 7) with deep summer thaw (>80 cm) and good drainage. On many sites, flooding helped maintain favorable soil conditions for shrub growth. Canopy volumes were highest where the typical snow loss date was near 20 May; these represent sites that are neither strongly wind-scoured in the winter nor late to melt from deep snowdrifts. Individual species varied widely in the canopy volumes they attained and their response to the environmental factors. Betula sp. shrubs were the most common and quite tolerant of soil acidity, cold July temperatures, and shallow thaw depths, but they did not form high-volume canopies under these conditions. Alnus viridis formed the largest canopies and was tolerant of soil acidity down to about pH 5, but required more summer warmth (over 12°C) than the other species. The Salix species varied widely from S. pulchra, tolerant of wet and moderately acid soils, to S. alaxensis, requiring well-drained soils with near neutral pH. Nearly half of the land area in ARCN has mean July temperatures of 10.5 to 12.5°C, where 2°C of warming would bring temperatures into the range needed for all of the potential tall shrub species to form large canopies. However, limitations in the other environmental factors would probably prevent the formation of large shrub canopies on at least half of the land area with newly favorable temperatures after 2°C of warming.

Suggested Citation

  • David K Swanson, 2015. "Environmental Limits of Tall Shrubs in Alaska’s Arctic National Parks," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-34, September.
  • Handle: RePEc:plo:pone00:0138387
    DOI: 10.1371/journal.pone.0138387
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    References listed on IDEAS

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    1. Richard G. Pearson & Steven J. Phillips & Michael M. Loranty & Pieter S. A. Beck & Theodoros Damoulas & Sarah J. Knight & Scott J. Goetz, 2013. "Shifts in Arctic vegetation and associated feedbacks under climate change," Nature Climate Change, Nature, vol. 3(7), pages 673-677, July.
    2. Michelle C. Mack & M. Syndonia Bret-Harte & Teresa N. Hollingsworth & Randi R. Jandt & Edward A. G. Schuur & Gaius R. Shaver & David L. Verbyla, 2011. "Carbon loss from an unprecedented Arctic tundra wildfire," Nature, Nature, vol. 475(7357), pages 489-492, July.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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