Animal responses to global climate variation might be spatially inconsistent. This may arise from spatial variation 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. Furthermore, 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.

The Norwegian area frame survey of land cover and outfield land resources (AR18X18), completed in 2014, provided unbiased statistics of land cover in Norway. The article reports the new statistics, discusses implications of the data set, and provides potential value in terms of research, management, and monitoring. A gridded sampling design for 1081 primary statistical units of 0.9 km 2 at 18 km intervals was implemented in the survey. The plots were mapped in situ, aided by aerial photos, and all areas were coded following a vegetation type system. The results provide new insights into the cover and distribution of vegetation and land cover types. The statistic for mire and wetlands, which previously covered 5.8%, has since been corrected to 8.9%. The survey results can be used for environmental and agricultural management, and the data can be stratified for regional analyses. The survey data can also serve as training data for remote sensing and distribution modelling. Finally, the survey data can be used to calibrate vegetation perturbations in climate change research that focuses on atmospheric-vegetation feedback. The survey documented novel land cover statistics and revealed that the national cover of wetlands had previously been underestimated.

Georeferenced species data have a wide range of applications and are increasingly used for e.g. distribution modelling and climate change studies. As an integrated part of an on-going survey programme for vegetation mapping, plant species have been recorded. The data described in this paper contains 18.521 registrations of plants from 1190 different circular plots throughout Norway. All species localities are georeferenced, the spatial uncertainty is provided, and additional ecological information is reported. The published data has been gathered from 1991 until 2015. The entries contain all higher vascular plants and pteridophytes, and some cryptogams. Other ecological information is also provided for the species locations, such as the vegetation type, the cover of the species and slope. The entire material is stored and available for download through the GBIF server.

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