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A fundamental assumption in trait-based ecology is that relationships between traits and environmental conditions are globally consistent. We use field-quantified microclimate and soil data to explore if trait–environment relationships are generalizable across plant communities and spatial scales. We collected data from 6,720 plots and 217 species across four distinct tundra regions from both hemispheres. We combined these data with over 76,000 database trait records to relate local plant community trait composition to broad gradients of key environmental drivers: soil moisture, soil temperature, soil pH and potential solar radiation. Results revealed strong, consistent trait–environment relationships across Arctic and Antarctic regions. This indicates that the detected relationships are transferable between tundra plant communities also when fine-scale environmental heterogeneity is accounted for, and that variation in local conditions heavily influences both structural and leaf economic traits. Our results strengthen the biological and mechanistic basis for climate change impact predictions of vulnerable high-latitude ecosystems. acceptedVersion

In the tundra, woody plants are dispersing towards higher latitudes and altitudes due to increasingly favourable climatic conditions. The coverage and height of woody plants are increasing, which may influence the soils of the tundra ecosystem. Here, we use structural equation modelling to analyse 171 study plots and to examine if the coverage and height of woody plants affect the growing-season topsoil moisture and temperature (

Harsh winters are a characteristic element of Arctic ecosystems, yet the importance of winter conditions for Arctic plant communities is still underrepresented in climate change impact studies. Here, we use fine-scale microclimate data with plot-scale records of vascular plants, lichens and bryophytes from three Arctic areas, and show that topographically heterogeneous tundra holds marked spatial variation, especially in winter temperatures. Winter conditions are the strongest environmental variable related to the fine-scale patterns in tundra vegetation, whereas summer temperatures mainly explain the coarse-scale differences among the Arctic areas. Nonetheless, the three plant groups (and also individual species) show often contrasting and complex responses along the local environmental gradients. Our results highlight the importance of local conditions and heterogeneity for tundra plants, and knowing that the Arctic winters are warming faster than summers, a greater focus should be placed on winter conditions in simulations of climate change impacts in tundra ecosystems. Winter conditions are the strongest environmental variable relating to fine-scale patterns in tundra vegetation. Vascular plants, lichens and bryophytes nonetheless show complex responses across environmental gradients, highlighting the importance of local heterogeneity.

Tundra ecosystems have experienced changes in vegetation composition, distribution, and productivity over the past century due to climate warming. However, the increase in above-ground biomass may be constrained by cryogenic land surface processes that cause topsoil disturbance and variable microsite conditions. These effects have remained unaccounted for in tundra biomass models, although they can impact multiple opposing feedbacks between the biosphere and atmosphere, ecosystem functioning and biodiversity. Here, by using field-quantified data from northern Europe, remote sensing, and machine learning, we show that cryogenic land surface processes substantially constrain above-ground biomass in tundra. The three surveyed processes (cryoturbation, solifluction, and nivation) collectively reduced biomass by an average of 123.0 g m(-2) (-30.0%). This effect was significant over landscape positions and was especially pronounced in snowbed environments, where the mean reduction in biomass was 57.3%. Our results imply that cryogenic land surface processes are pivotal in shaping future patterns of tundra biomass, as long as cryogenic ground activity is retained by climate warming. Vegetation in tundra ecosystems is constrained by cryogenic land surface processes, despite the fact that models of future biomass changes rarely take these into account, according to field and remote sensing data from northern Europe.

Climate change velocity is an increasingly used metric to assess the broad-scale climatic exposure and climate change induced risks to terrestrial and marine ecosystems. However, the utility of this metric in conservation planning can be enhanced by determining the velocities of multiple climatic drivers in real protected area (PA) networks on ecologically relevant scales. Here we investigate the velocities of three key bioclimatic variables across a nation-wide reserve network, and the consequences of including fine-grained topoclimatic data in velocity assessments. Using 50-m resolution data describing present-day and future topoclimates, we assessed the velocities of growing degree days, the mean January temperature and climatic water balance in the Natura 2000 PA network in Finland. The high-velocity areas for the three climate variables differed drastically, indicating contrasting exposure risks in different PAs. The 50-m resolution climate data revealed more realistic estimates of climate velocities and more overlap between the present-day and future climate spaces in the PAs than the 1-km resolution data. Even so, the current temperature conditions were projected to disappear from almost all the studied PAs by the end of this century. Thus, in PA networks with only moderate topographic variation, far-reaching climate change induced ecological changes may be inevitable.

Ongoing climate change is causing fundamental changes in the Arctic, some of which can be hazardous to nature and human activity. In the context of Earth surface systems, warming climate may lead to rising ground temperatures and thaw of permafrost. This Data Descriptor presents circumpolar permafrost maps and geohazard indices depicting zones of varying potential for development of hazards related to near-surface permafrost degradation, such as ground subsidence. Statistical models were used to predict ground temperature and the thickness of the seasonally thawed (active) layer using geospatial data on environmental conditions at 30 arc-second resolution. These predictions, together with data on factors (ground ice content, soil grain size and slope gradient) affecting permafrost stability, were used to formulate geohazard indices. Using climate-forcing scenarios (Representative Concentration Pathways 2.6, 4.5 and 8.5), permafrost extent and hazard potential were projected for the 2041–2060 and 2061–2080 time periods. The resulting data (seven permafrost and 24 geohazard maps) are relevant to near-future infrastructure risk assessments and for targeting localized geohazard analyses.

Extreme temperatures are key drivers controlling both biotic and abiotic processes, and may be strongly modified by topography and land cover. We modelled mean and extreme temperatures in northern Fennoscandia by combining digital elevation and land cover data with climate observations from northern Finland, Norway and Sweden. Multivariate partitioning technique was utilized to investigate the relative importance of environmental variables for the variation of the three temperature parameters: mean annual absolute minima and maxima, and mean annual temperature. Generalized additive modeling showed good performance, explaining 84–95 % of the temperature variation. The inclusion of remotely sensed variables improved significantly the modelling of thermal extremes in this system. The water cover variables and topography were the most important drivers of minimum temperatures, whereas elevation was the most important factor controlling maximum temperatures. The spatial variability of mean temperatures was clearly driven by geographical location and the effects of topography. Partitioning technique gave novel insights into temperature-environment relationship at the meso-scale and thus proved to be useful tool for the study of the extreme temperatures in the high-latitude setting.

The Finnish Meteorological Institute has calculated statistics for the new reference period of 1981–2010. During this project, the grid size has been reduced from 10 to 1 km, the evaluation of the interpolation has been improved, and comparisons between different methods has been performed. The climate variables of interest were monthly mean temperature and mean precipitation, for which the spatial variability was explained using auxiliary information: mean elevation, sea percentage, and lake percentage. We compared three methods for spatial prediction: kriging with external drift (KED), generalized additive models (GAM), and GAM combined with residual kriging (GK). Every interpolation file now has attached statistical key figures describing the bias and the normality of the prediction error. According to the cross-validation results, GAM was the best method for predicting mean temperatures, with only very small differences relative to the other methods. For mean precipitation, KED produced the most accurate predictions, followed by GK. In both cases, there was notable seasonal variation in the statistical skill scores. For the new reference period and future interpolations, KED was chosen as the primary method due to its robustness and accuracy.

There is a molecular mimicry between the polysialic acid polysaccharide of bacterial pathogens causing sepsis and meningitis, and the carbohydrate units of the neural cell adhesion molecule NCAM. We investigated whether bacteriophage mutants with catalytically disabled endosialidase, which bind but do not cleave polysialic acid, could recognise and bind to bacterial and eukaryotic polysialic acid. In nitrocellulose dot blot assay the mutant bacteriophages, but not the wild-type phages, remained specifically bound to polysialic acid–containing bacteria including Escherichia coli K1 and K92, group B meningococci, Mannheimia (Pasteurella) haemolytica A2, and Moraxella nonliquefaciens. A minimum binding requirement was determined to be 10 sialyl residues in the polysialic acid chain. In Western blots the mutant phages specifically bound to the embryonic polysialylated form of NCAM, but not to the adult less sialylated form of the molecule. The mutant phages together with secondary anti-phage antibodies were subsequently successfully used in fluorescence microscopy of cultured cells and light microscopy of paraffin-embedded tissue sections as a probe for the eukaryotic polysialic acid. Thus, mutant bacteriophages of meningitis causing bacteria bind to and detect the molecularly mimicked polysialic acid of the neural cell adhesion molecule in host tissues.