Date of Award

4-2015

Degree Type

Thesis

Degree Name

Biology (M.S.)

Department

Biology

First Advisor

Robert Hollister

Abstract

Vegetation in the Arctic has been shown to respond to climate change. Documented changes have the potential to result in numerous ecosystem consequences. Therefore, understanding vegetation change is of great importance. This study documents changes in tundra vegetation with a focus on understanding the influence of annual differences in weather. Vegetation was sampled using a point frame method on 98 1-m2 plots in 2010 and 2013 near Barrow, Alaska. A subset of 30 of these plots was also sampled in 2012 and 2014. Plant encounters were identified to species and grouped into one of the following functional groups: bryophytes, deciduous shrubs, forbs, graminoids, lichens, litter or standing dead. Plant height and species diversity metrics were also calculated. The following abiotic variables were used to quantify difference in weather between years: air temperature, precipitation, thawing degree days, thaw depth, soil temperature and soil moisture. For analysis, focus was on the 30 plots that were sampled over 4 years. The cover of all functional groups, the height of graminoids and alpha diversity changed significantly; the magnitude of changes was large. Significant correlations occurred between the following vegetation metrics and abiotic variables: the cover of forbs and soil temperature; the cover of graminoids and all abiotic variables except soil moisture; the cover of litter and air temperature, thawing degree days, soil temperature and soil moisture; the cover of standing dead and all measured abiotic variables; and the height of graminoids and soil moisture. The strongest correlations between abiotic variables and vegetation metrics varied between functional groups; this suggests multiple abiotic factors should be considered when attempting to explain observed changes. In summary, this study found large annual changes in vegetation cover, height and diversity that were presumably due to variability in weather of each year. While a longer consecutive time series is needed to better understand the relationship between the weather of a 5 given year and vegetation dynamics, caution should be used when interpreting the results of long-term vegetation monitoring that is sampled over a coarse time series because of large annual variability shown in this study.

Available for download on Thursday, May 17, 2018

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