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CR 58:1-13 (2013)  -  DOI:

Modeling spatial covariation of summer temperatures and bio-indicators in an Arctic coastal area

Lennart Nilsen1, Daniel Joly2,*, Arve Elvebakk3, Thierry Brossard2

1Department of Arctic and Marine Biology and 3Tromsø University Museum, University of Tromsø, 9037 Tromsø, Norway
2Laboratoire THEMA, Université de Franche-Comté, 25030 Besançon Cedex, France
*Corresponding author. Email:

ABSTRACT: In the Arctic, temperature is a major environmental factor controlling the occurrence, abundance and distribution of plants at regional and local scales alike. This means that statistical models of temperature distribution can predict the distribution of plant species or communities. Conversely, certain plant taxa make good bio-indicators reflecting long-term thermal conditions in a given habitat. Both these assumptions were taken into account when modelling the spatial relationship between plants and temperature. This work continues a previous preliminary 1 yr study based on correlations between a plant-based index of thermophily (It) and different synthetic temperature distribution characteristics. To strengthen confidence in the results and conclusions, more temperature data were collected through a field campaign conducted over a further 5 yr period (2001 to 2005). The goals here were (1) to establish an accurate interpolation model capable of restoring, at local scale, the continuous summertime thermal raster surface, (2) to evaluate the capacity of the temperature values obtained from the model to predict the distribution of It, and (3) to extrapolate temperature surfaces from this It. The results show that the mutual predictive power between temperature and It is satisfactory and that the model can be applied to neighbouring areas, although the present study area is too small to define the geographical limits of extrapolation. This predictive power declines where local landscape structures are heterogeneous. Correlations between It and growing degree day (GDD) values derived from the modelled temperature layers were systematically analysed in order to identify conditions in which this covariation works or fails.

KEY WORDS: Temperature · Vegetation · Bio-indicators · Regression analysis · Svalbard

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Cite this article as: Nilsen L, Joly D, Elvebakk A, Brossard T (2013) Modeling spatial covariation of summer temperatures and bio-indicators in an Arctic coastal area. Clim Res 58:1-13.

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