Are responses of herbivores to environmental variability spatially consistent in alpine ecosystems? ANDERS NIELSEN*, NIGEL G. YOCCOZ † , GEIR STEINHEIM ‡ , GEIR O. STORVIK*, YNGVE REKDAL § , MICHAEL ANGELOFF § , NATHALIE PETTORELLI ¶ , ØYSTEIN HOLAND ‡ andATLE MYSTERUD* *Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biology, University of Oslo, P.O. Box 1066 Blindern, NO-0316 Oslo, Norway, †Department of Arctic and Marine Biology, University of Tromsø, NO-9037 Tromsø, Norway, ‡Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Box 5003, NO-1432 A ˚ s, Norway, §Norwegian Forest and Landscape Institute, P.O. Box 115, NO-1431 A ˚ s, Norway, ¶Institute of Zoology, Zoological Society of London, Regents Park, NW1 4RY, London, UK Abstract Animal responses to global climate variation might be spatially inconsistent. This may arise from spatial varia- tion in factors limiting populations’ growth or from differences in the links between global climate patterns and ecologically relevant local climate variation. For example, the North Atlantic Oscillation (NAO) has a spatially consistent relation to temperature, but inconsistent spatial relation to snow depth in Scandinavia. Furthermore, there are multiple mechanistic ways by which climate may limit animal populations, involving both direct effects through thermoregulation and indirect pathways through trophic interactions. It is conceptually appealing to directly model the predicted mechanistic links. This includes the use of climate variables mimicking such interactions, for example, to use growing degree days (GDD) as a proxy for plant growth rather than average monthly temperature. Using a unique database of autumn body mass of 83331 domestic lambs from the period 1992–2007 in four alpine ranges in Norway, we demonstrate the utility of hierarchical, mechanistic path models fitted using a Bayesian approach to analyse explicitly predicted relationships among environmental variables and between lamb body mass and the environmental variables. We found large spatial variation in strength of responses of autumn lamb body mass to the NAO, to a proxy for plant growth in spring (the Normalized Difference Vegetation Index, NDVI) and effects even differed in direction to local summer climate. Average local temperature outperformed GDD as a predictor of the NDVI, whereas the NAO index in two areas outperformed local weather variables as a predictor of lamb body mass, despite the weaker mechanistic link. Our study highlights that spatial variation in strength of herbivore responses may arise from several processes. Further- more, mechanistically more appealing measures do not always increase predictive power due to scale of measurement and since global measures may provide more relevant “weather packages” for larger scales. Keywords: Bayesian inference, domestic sheep, Hierarchical path modelling, local and global climate, NAO, NDVI, ungulate Received 18 January 2012 and accepted 17 April 2012 Introduction Ecosystems are strongly affected by global climate change (Stenseth et al., 2002; Walther et al., 2002, 2005; Parmesan, 2006; Hegland et al., 2009). The use of global climate indices such as those representing the North Atlantic Oscillation (NAO), the Southern Oscillation (ENSO), the Pacific Decadal Oscillation, and others have proved useful for demonstrating large scale cli- mate effects (Stenseth et al., 2003). These so-called weather packages (Stenseth & Mysterud, 2005) can in some cases capture climate effects at relevant temporal and spatial scales even better than local weather indices (Post & Stenseth, 1999; Hallett et al., 2004). However, indices of large scale climate variation such as the NAO did not have consistent effects when comparing body mass of red deer (Cervus elaphus) in Spain, Scotland and Norway (Martinez-Jauregui et al., 2009). Indeed, the NAO has been shown to have contrasting effects on e.g. precipitation patterns in southern and northern Europe (Hurrell et al., 2003). Currently, focus is shifting from purely demonstrating large scale climate effects towards a more process-oriented approach describing and predicting responses by downscaling climate vari- ables from global to regional and from regional to local scales (Tabor & Williams, 2010). Ecological research focusing on monitoring and pre- dicting the effects of climate change on biodiversity faces two different kinds of challenges. The first relates Correspondence: Anders Nielsen, tel. + 47 22 84 41 59, fax + 47 22 85 40 01, e-mail: anders.nielsen@bio.uio.no © 2012 Blackwell Publishing Ltd 1 Global Change Biology (2012), doi: 10.1111/j.1365-2486.2012.02733.x