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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Uncertainty in predicting range dynamics of endemic alpine plants under climate warming
Global change biology, Volume 22, No. 7, Year 2016
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Description
Correlative species distribution models have long been the predominant approach to predict species' range responses to climate change. Recently, the use of dynamic models is increasingly advocated for because these models better represent the main processes involved in range shifts and also simulate transient dynamics. A well-known problem with the application of these models is the lack of data for estimating necessary parameters of demographic and dispersal processes. However, what has been hardly considered so far is the fact that simulating transient dynamics potentially implies additional uncertainty arising from our ignorance of short-term climate variability in future climatic trends. Here, we use endemic mountain plants of Austria as a case study to assess how the integration of decadal variability in future climate affects outcomes of dynamic range models as compared to projected long-term trends and uncertainty in demographic and dispersal parameters. We do so by contrasting simulations of a so-called hybrid model run under fluctuating climatic conditions with those based on a linear interpolation of climatic conditions between current values and those predicted for the end of the 21st century. We find that accounting for short-term climate variability modifies model results nearly as differences in projected long-term trends and much more than uncertainty in demographic/dispersal parameters. In particular, range loss and extinction rates are much higher when simulations are run under fluctuating conditions. These results highlight the importance of considering the appropriate temporal resolution when parameterizing and applying range-dynamic models, and hybrid models in particular. In case of our endemic mountain plants, we hypothesize that smoothed linear time series deliver more reliable results because these long-lived species are primarily responsive to long-term climate averages. © 2016 John Wiley & Sons Ltd.
Authors & Co-Authors
Hülber, Karl
Austria, Vienna
Universität Wien
Austria, Vienna
Vienna Institute for Nature Conservation and Analyses
Moser, Dietmar
Austria, Vienna
Universität Wien
Austria, Vienna
Vienna Institute for Nature Conservation and Analyses
Essl, Franz
Austria, Vienna
Universität Wien
Leitner, Michael
Austria, Vienna
Universität Wien
Winkler, Manuela
Austria, Vienna
Osterreichische Akademie Der Wissenschaften
Ertl, Siegrun
Austria, Vienna
Universität Wien
Willner, Wolfgang
Austria, Vienna
Universität Wien
Austria, Vienna
Vienna Institute for Nature Conservation and Analyses
Kleinbauer, Ingrid
Austria, Vienna
Vienna Institute for Nature Conservation and Analyses
Mang, Thomas
Austria, Vienna
Universität Wien
Austria, Vienna
Vienna Institute for Nature Conservation and Analyses
Zimmermann, Niklaus E.
Switzerland, Birmensdorf
Eidgenössische Forschungsanstalt Für Wald, Schnee Und Landschaft Wsl
Dullinger, Stefan
Austria, Vienna
Universität Wien
Austria, Vienna
Vienna Institute for Nature Conservation and Analyses
Statistics
Citations: 40
Authors: 11
Affiliations: 4
Identifiers
Doi:
10.1111/gcb.13232
ISSN:
13652486
Research Areas
Environmental
Study Design
Case Study
Study Approach
Qualitative