Snow cover is projected to decline during the next century in many ecosystems that currently experience a seasonal snowpack. Because snow insulates soils from frigid winter air temperatures, soils are expected to become colder and experience more winter soil freeze-thaw cycles as snow cover continues to decline. Tree roots are adversely affected by snowpack reduction, but whether loss of snow will affect root-microbe interactions remains largely unknown. The objective of this study was to distinguish and attribute direct (e.g., winter snow- and/or soil frost-mediated) vs. indirect (e.g., root-mediated) effects of winter climate change on microbial biomass, the potential activity of microbial exoenzymes, and net N mineralization and nitrification rates. Soil cores were incubated in situ in nylon mesh that either allowed roots to grow into the soil core (2mm pore size) or excluded root ingrowth (50m pore size) for up to 29months along a natural winter climate gradient at Hubbard Brook Experimental Forest, NH (USA). Microbial biomass did not differ among ingrowth or exclusion cores. Across sampling dates, the potential activities of cellobiohydrolase, phenol oxidase, and peroxidase, and net N mineralization rates were more strongly related to soil volumetric water content (P<0.05; R-2=0.25-0.46) than to root biomass, snow or soil frost, or winter soil temperature (R-2<0.10). Root ingrowth was positively related to soil frost (P<0.01; R-2=0.28), suggesting that trees compensate for overwinter root mortality caused by soil freezing by re-allocating resources towards root production. At the sites with the deepest snow cover, root ingrowth reduced nitrification rates by 30% (P<0.01), showing that tree roots exert significant influence over nitrification, which declines with reduced snow cover. If soil freezing intensifies over time, then greater compensatory root growth may reduce nitrification rates directly via plant-microbe N competition and indirectly through a negative feedback on soil moisture, resulting in lower N availability to trees in northern hardwood forests.

1 Recent studies demonstrated the sensitivity of northern forest ecosystems to changes in the amount and duration of snow cover at annual to decadal time scales. However, the consequences of snowfall variability remain uncertain for ecological variables operating at longer time scales, especially the distributions of forest communities. 2 The Great Lakes region of North America offers a unique setting to examine the long-term effects of variable snowfall on forest communities. Lake-effect snow produces a three-fold gradient in annual snowfall over tens of kilometres, and dramatic edaphic variations occur among landform types resulting from Quaternary glaciations. We tested the hypothesis that these factors interact to control the distributions of mesic (dominated by Acer saccharum, Tsuga canadensis and Fagus grandifolia) and xeric forests (dominated by Pinus and Quercus spp.) in northern Lower Michigan. 3 We compiled pre-European-settlement vegetation data and overlaid these data with records of climate, water balance and soil, onto Landtype Association polygons in a geographical information system. We then used multivariate adaptive regression splines to model the abundance of mesic vegetation in relation to environmental controls. 4 Snowfall is the most predictive among five variables retained by our model, and it affects model performance 29% more than soil texture, the second most important variable. The abundance of mesic trees is high on fine-textured soils regardless of snowfall, but it increases with snowfall on coarse-textured substrates. Lake-effect snowfall also determines the species composition within mesic forests. The weighted importance of A. saccharum is significantly greater than of T. canadensis or F. grandifolia within the lake-effect snowbelt, whereas T. canadensis is more plentiful outside the snowbelt. These patterns are probably driven by the influence of snowfall on soil moisture, nutrient availability and fire return intervals. 5 Our results imply that a key factor dictating the spatio-temporal patterns of forest communities in the vast region around the Great Lakes is how the lake-effect snowfall regime responds to global change. Snowfall reductions will probably cause a major decrease in the abundance of ecologically and economically important species, such as A. saccharum.