Forecasting Alpine Vegetation Change using Repeat Sampling and a Novel Modeling Approach

Global change affects alpine ecosystems by, among many effects, by altering plant distributions and community composition. However, forecasting alpine vegetation change is challenged by a scarcity of studies observing change in fixed plots spanning decadal-time scales. We present in this article a p...

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Bibliographic Details
Published in:Ambio 2011-09, Vol.40 (6), p.693-704
Main Authors: Johnson, David R, Ebert-May, Diane, Webber, Patrick J, Tweedie, Craig E
Format: Article
Language:English
Subjects:
Adaptation
Alpine vegetation change
Atmospheric Sciences
Climate change
Climate warming
Communities
Community composition
Community ecology
Correlation
Deposition
Earth and Environmental Science
Ecology
Ecosystem
Ecosystem models
Ecosystems
Environment
Environmental Engineering/Biotechnology
Environmental Management
Flowers & plants
Forecasting
Forecasting models
Hypotheses
Mathematical models
Modeling
Models, Biological
Nitrates
Physical Geography
Plant communities
Plant community change
Plant Development
Plants
Plants (organisms)
Precipitation
Probabilistic modeling
Probability
Sampling
Sea level
Shrubs
Snow
Snowbeds
Taiga & tundra
Temperature
Time Factors
Tundra
Tundras
Vegetation
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Summary:Global change affects alpine ecosystems by, among many effects, by altering plant distributions and community composition. However, forecasting alpine vegetation change is challenged by a scarcity of studies observing change in fixed plots spanning decadal-time scales. We present in this article a probabilistic modeling approach that forecasts vegetation change on Niwot Ridge, CO using plant abundance data collected from marked plots established in 1971 and resampled in 1991 and 2001. Assuming future change can be inferred from past change, we extrapolate change for 100 years from 1971 and correlate trends for each plant community with time series environmental data (1971–2001). Models predict a decreased extent of Snowbed vegetation and an increased extent of Shrub Tundra by 2071. Mean annual maximum temperature and nitrogen deposition were the primary a posteriori correlates of plant community change. This modeling effort is useful for generating hypotheses of future vegetation change that can be tested with future sampling efforts.
ISSN:0044-7447
1654-7209
DOI:10.1007/s13280-011-0175-z