Modelling local distribution of an Arctic dwarf shrub indicates an important role for remote sensing of snow cover

• Published in: Remote Sensing of Environment
• Main Authors: Beck, PSA, Kalmbach, E, Joly, D, Stien, A, Nilsen, L
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2005
• Subjects: equilibrium distribution models; tundra plants; snow models; digital elevation model; realized niche model; geographical information system; DRYAS-OCTOPETALA ECOTYPES; CLIMATE-CHANGE; REPRODUCTIVE DEVELOPMENT; SPECIES DISTRIBUTION; EXPERIMENTAL ECOLOGY; POLAR SEMIDESERT; ALPINE CATCHMENT; SEASONAL SNOW; PLANT-GROWTH; NY-ALESUND; Arctic; Svalbard; Climate change; Dryas octopetala; Kongsfjord*; Kongsfjorden; Tundra
• Online Access: https://hdl.handle.net/11370/268a8c0e-f5d9-4491-b9eb-d448c300f85d; https://research.rug.nl/en/publications/268a8c0e-f5d9-4491-b9eb-d448c300f85d; https://doi.org/10.1016/j.rse.2005.07.002

Despite the intensive research effort directed at predicting the effects of climate change on plants in the Arctic, the impact of environmental change on species' distributions remains difficult to quantify. Predictive habitat distribution models provide a tool to predict the geographical distribution of a species based on the ecological gradients that determine it, and to estimate how the distribution of a species might respond to environmental change. Here, we present a model of the distribution of the dwarf shrub Dryas octopetala L. around the fjord Kongsfjorden, Svalbard. The model was built from field observations, an Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) image, a GIs database containing environmental data at a spatial resolution of 20 m, and relied on generalized linear models (GLMs). We used a logistic GLM to predict the occurrence of the species and a Gaussian GLM to predict its abundance at the sites where it occurred. Temperature and topographical exposure and inclination of a site appeared to promote both the occurrence and the abundance of D. octopetala. The occurrence of the species was additionally negatively influenced by snow and water cover and topographical exposure towards the north, whereas the abundance of the species appeared lower on calciferous substrates. Validation of the model using independent data and the resulting distribution map showed that they successfully recover the distribution of D. octopetala in the study area (kappa = 0.46, AUC =0.81 for the logistic GLM [n - 200], r(2) = 0.29 for the Gaussian GLM [n - 36]). The results further highlight that models predicting the local distribution of plant species in an Arctic environment would greatly benefit from data on the distribution and duration of snow cover. Furthermore, such data are necessary to make quantitative estimates for the impact of changes in temperature and winter precipitation on the distribution of plants in the Arctic. (C) 2005 Elsevier Inc. All rights reserved.