title: Modelling snow cover duration improves predictions of functional and taxonomic diversity for alpine plant communities.
abstract: OBJECTIVE
  Quantifying relationships between snow cover duration and plant community properties remains an important challenge in alpine ecology. This study develops a method to estimate spatial variation in energy availability in the context of a topographically complex, high-elevation watershed, which was used to test the explanatory power of environmental gradients both with and without snow cover in relation to taxonomic and functional plant diversity.
  
  
  METHODS
  Snow cover in the French Alps was mapped at 15-m resolution using Landsat imagery for five recent years, and a generalized additive model (GAM) was fitted for each year linking snow to time and topography. Predicted snow cover maps were combined with air temperature and solar radiation data at daily resolution, summed for each year and averaged across years. Equivalent growing season energy gradients were also estimated without accounting for snow cover duration. Relationships were tested between environmental gradients and diversity metrics measured for 100 plots, including species richness, community-weighted mean traits, functional diversity and hyperspectral estimates of canopy chlorophyll content.
  
  
  RESULTS
  Accounting for snow cover in environmental variables consistently led to improved predictive power as well as more ecologically meaningful characterizations of plant diversity. Model parameters differed significantly when fitted with and without snow cover. Filtering solar radiation with snow as compared without led to an average gain in R(2) of 0·26 and reversed slope direction to more intuitive relationships for several diversity metrics.
  
  
  CONCLUSIONS
  The results show that in alpine environments high-resolution data on snow cover duration are pivotal for capturing the spatial heterogeneity of both taxonomic and functional diversity. The use of climate variables without consideration of snow cover can lead to erroneous predictions of plant diversity. The results further indicate that studies seeking to predict the response of alpine plant communities to climate change need to consider shifts in both temperature and nival regimes.
authors: Université Grenoble Alpes, LECA, F-38000 Grenoble, France, CNRS, LECA, F-38000 Grenoble, France, brad.z.carlson@gmail.com.
authors: Bradley Z
authors: BZ
authors: Carlson
authors: Université Grenoble Alpes, LECA, F-38000 Grenoble, France.
authors: Philippe
authors: P
authors: Choler
authors: Université Grenoble Alpes, LECA, F-38000 Grenoble, France, CNRS, LECA, F-38000 Grenoble, France.
authors: Julien
authors: J
authors: Renaud
authors: Université Grenoble Alpes, LECA, F-38000 Grenoble, France.
authors: Jean-Pierre
authors: JP
authors: Dedieu
authors: Université Grenoble Alpes, LECA, F-38000 Grenoble, France, CNRS, LECA, F-38000 Grenoble, France.
authors: Wilfried
authors: W
authors: Thuiller
mesh_terms: D044822:Biodiversity; D057231:Climate Change; D008962:Models, Theoretical; D018521:Plant Physiological Phenomena; D010944:Plants; D012621:Seasons; D012914:Snow; D013696:Temperature; D065929:Tundra
keywords: Alpine grassland; French Alps; GAM; Landsat; climate change; generalized additive model; growing season length; mesotopography; plant community structure; remote sensing, snow cover