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Abstract In the Arctic tundra, warming is anticipated to stimulate nutrient release and potentially alleviate plant nutrient limitations. Typically simulated by fertilization experiments that saturate plant nutrient demand, future increases in soil fertility are thought to favor ectomycorrhizal (EcM) over ericaceous shrubs and have often been identified as a key driver of Arctic shrub expansion. However, the projected increases in fertility will likely vary in their alleviation of nutrient limitations. The resulting responses of shrubs and their mycorrhizae across the gradient of nutrient limitation may be nonlinear and could contradict the current predictions of tundra vegetation shifts. We compared the functional responses of two dominant shrubs, EcM dwarf birch (Betula nana) and ericaceous Labrador tea (Rhododendron tomentosum), across a long‐term nitrogen and phosphorus fertilization gradient experiment in Arctic Alaska. Using linear mixed‐effects modeling, we tested the responses of shrub cover, height, and root enzyme activities to soil fertility. We found thatB. nanacover and height linearly increased with soil fertility. In contrast,R. tomentosumcover initially increased, but decreased after surpassing the intermediate levels of increased soil fertility. Its height did not change. Enzyme activity did not respond to soil fertility on EcM‐colonizedB. nanaroots, but sharply declined onR. tomentosumroots. Overall, the nonlinear responses of shrubs to our fertility gradient demonstrate the importance of experiments grounded in replicated regression design. Our results indicate that under moderate increases in soil fertility, Arctic shrub expansion may not only include deciduous EcM shrubs but also ericaceous shrubs. Regardless of shifts aboveground, changes in root enzyme activity belowground point to some EcM shrub species playing a more influential role in tundra soils; as EcM roots remained steady in their liberation of soil organic nutrients with heightened soil fertility, degradative root enzyme activity on the dominant ericaceous shrub dropped—in some instances with even the slightest increase in fertility. 

Fire severity is increasing in larch forests of the Siberian Arctic as climate warms, and initial fire impacts on tree demographic processes could be an especially important determinant of long-term forest structure and carbon (C) dynamics. We hypothesized that changes in post-fire larch recruitment impact C accumulation through tree density impacts on understory microclimate and permafrost thaw. We tested these hypotheses by quantifying C pools across a Cajander larch (Larix cajanderi Mayr.) tree density gradient within a fire perimeter near Cherskiy, Russia that burned in ~1940. Across the density gradient, from 2010 - 2017 we inventoried larch trees and harvested ground-layer vegetation to estimate above ground contribution to C pools. We also quantified woody debris C pools and sampled below ground C pools (soil, fine roots, and coarse roots) in the organic + upper mineral soils. Our findings should highlight the potential for a climate-driven increase in fire severity to alter tree recruitment, successional dynamics, and C cycling in Siberian larch forests. 

Fire severity is increasing in larch forests of the Siberian Arctic as climate warms, and initial fire impacts on tree demographic processes could be an especially important determinant of long-term forest structure and carbon (C) dynamics. We hypothesized that changes in post-fire larch recruitment impact C accumulation through tree density impacts on understory microclimate and permafrost thaw. We tested these hypotheses by quantifying C pools across a Cajander larch (Larix cajanderi Mayr.) tree density gradient within a fire perimeter near Cherskiy, Russia that burned in ~1940. Across the density gradient, from 2010 - 2017 we inventoried larch trees and harvested ground-layer vegetation to estimate above ground contribution to C pools. We also quantified woody debris C pools and sampled below ground C pools (soil, fine roots, and coarse roots) in the organic + upper mineral soils. Our findings should highlight the potential for a climate-driven increase in fire severity to alter tree recruitment, successional dynamics, and C cycling in Siberian larch forests. 

Fire severity is increasing in larch forests of the Siberian Arctic as climate warms, and initial fire impacts on tree demographic processes could be an especially important determinant of long-term forest structure and carbon (C) dynamics. We hypothesized that changes in post-fire larch recruitment impact C accumulation through tree density impacts on understory microclimate and permafrost thaw. We tested these hypotheses by quantifying C pools across a Cajander larch (Larix cajanderi Mayr.) tree density gradient within a fire perimeter near Cherskiy, Russia that burned in ~1940. Across the density gradient, from 2010 - 2017 we inventoried larch trees and harvested ground-layer vegetation to estimate above ground contribution to C pools. We also quantified snag and woody debris C pools and sampled below ground C pools (soil, fine roots, and coarse roots) in the organic + upper mineral soils. Our findings should highlight the potential for a climate-driven increase in fire severity to alter tree recruitment, successional dynamics, and C cycling in Siberian larch forests. 

Abstract In the Arctic, winter soil temperatures exert strong control over mean annual soil temperature and winter CO₂emissions. In tundra ecosystems there is evidence that plant canopy influences on snow accumulation alter winter soil temperatures. By comparison, there has been relatively little research examining the impacts of heterogeneity in boreal forest cover on soil temperatures. Using seven years of data from six sites in northeastern Siberia that vary in stem density we show that snow-depth and forest canopy cover exert equally strong control on cumulative soil freezing degrees days (FDD_soil). Together snow depth and canopy cover explain approximately 75% of the variance in linear models of FDD_soiland freezingn-factors (n_f; calculated as the quotient of FDD_soiland FDD_air), across sites and years. Including variables related to air temperature, or antecedent soil temperatures does not substantially improve models. The observed increase in FDD_soilwith canopy cover suggests that canopy interception of snow or thermal conduction through trees may be important for winter soil temperature dynamics in forested ecosystems underlain by continuous permafrost. Our results imply that changes in Siberian larch forest cover that arise from climate warming or fire regime changes may have important impacts on winter soil temperature dynamics. 

The growth rate of a microorganism is a simple yet profound way to quantify its impact on the world. Microbial fitness in the environment depends on the ability to reproduce quickly when conditions are favorable and adopt a survival physiology when conditions worsen, which cells coordinate by adjusting their growth rate. At the population level, per capita growth rate is a sensitive metric of fitness, linking survival and reproduction to the ecology and evolution of populations. The absolute growth rate of a microbial population reflects rates of resource assimilation, biomass production, and element transformation, some of the many ways that organisms affect Earth’s ecosystems and climate. For soil microorganisms, most of our understanding of growth is based on observations made in culture. This is a crucial limitation given that many soil microbes are not readily cultured and in vitro conditions are unlikely to reflect conditions in the wild. New approaches in ‘omics and stable isotope probing make it possible to sensitively measure growth rates of microbial assemblages and individual taxa in nature, and to couple these measurements to biogeochemical fluxes. Microbial ecologists can now explore how the growth rates of taxa with known traits and evolutionary histories respond to changes in resource availability, environmental conditions, and interactions with other organisms. We anticipate that quantitative and scalable data on the growth rates of soil microorganisms will allow scientists to test and refine ecological theory and advance processbased models of carbon flux, nutrient uptake, and ecosystem productivity. Measurements of in situ microbial growth rates provide insights into the ecology of populations and can be used to quantitatively link microbial diversity to soil biogeochemistry.  

Abstract Deciduous tree cover is expected to increase in North American boreal forests with climate warming and wildfire. This shift in composition has the potential to generate biophysical cooling via increased land surface albedo. Here we use Landsat-derived maps of continuous tree canopy cover and deciduous fractional composition to assess albedo change over recent decades. We find, on average, a small net decrease in deciduous fraction from 2000 to 2015 across boreal North America and from 1992 to 2015 across Canada, despite extensive fire disturbance that locally increased deciduous vegetation. We further find near-neutral net biophysical change in radiative forcing associated with albedo when aggregated across the domain. Thus, while there have been widespread changes in forest composition over the past several decades, the net changes in composition and associated post-fire radiative forcing have not induced systematic negative feedbacks to climate warming over the spatial and temporal scope of our study. 

The below-ground growing season often extends beyond the above-ground growing season in tundra ecosystems. However, we do not yet know where and when this occurs and whether these phenological asynchronies are driven by variation in local vegetation communities or by spatial variation in microclimate. Here, we combined above- and below-ground plant phenology metrics to compare the relative timings and magnitudes of leaf and root growth and senescence across microclimates and plant communities at five sites across the tundra biome. We observed asynchronous growth between above-ground and below-ground plant tissue, with the below-ground season extending up to 74% beyond the onset of above-ground leaf senescence. Plant community type, rather than microclimate, was a key factor controlling the timing, productivity and growth rates of roots, with graminoid roots exhibiting a distinct ‘pulse’ of growth later into the growing season than shrub roots. Our findings indicate the potential of vegetation change to influence below-ground carbon storage as roots remain active in unfrozen soils for longer as the climate warms. Taken together, increased root growth in soils that remain thawed later into the growing season, in combination with ongoing tundra vegetation change including increased shrubs and graminoids, can act together to alter below-ground productivity and carbon cycling in the tundra biome. 

Abstract Key message Black spruce ( Picea mariana (Mill.) B.S.P.) has historically self-replaced following wildfire, but recent evidence suggests that this is changing. One factor could be negative impacts of intensifying fire activity on black spruce seed rain. We investigated this by measuring black spruce seed rain and seedling establishment. Our results suggest that increases in fire activity could reduce seed rain meaning reductions in black spruce establishment. Context Black spruce is an important conifer in boreal North America that develops a semi-serotinous, aerial seedbank and releases a pulse of seeds after fire. Variation in postfire seed rain has important consequences for black spruce regeneration and stand composition. Aims We explore the possible effects of changes in fire regime on the abundance and viability of black spruce seeds following a very large wildfire season in the Northwest Territories, Canada (NWT). Methods We measured postfire seed rain over 2 years at 25 black spruce-dominated sites and evaluated drivers of stand characteristics and environmental conditions on total black spruce seed rain and viability. Results We found a positive relationship between black spruce basal area and total seed rain. However, at high basal areas, this increasing rate of seed rain was not maintained. Viable seed rain was greater in stands that were older, closer to unburned edges, and where canopy combustion was less severe. Finally, we demonstrated positive relationships between seed rain and seedling establishment, confirming our measures of seed rain were key drivers of postfire forest regeneration. Conclusion These results indicate that projected increases in fire activity will reduce levels of black spruce recruitment following fire. 

Abstract Climate change is driving substantial changes in North American boreal forests, including changes in productivity, mortality, recruitment, and biomass. Despite the importance for carbon budgets and informing management decisions, there is a lack of near‐term (5–30 year) forecasts of expected changes in aboveground biomass (AGB). In this study, we forecast AGB changes across the North American boreal forest using machine learning, repeat measurements from 25,000 forest inventory sites, and gridded geospatial datasets. We find that AGB change can be predicted up to 30 years into the future, and that training on sites across the entire domain allows accurate predictions even in regions with only a small amount of existing field data. While predicting AGB loss is less skillful than gains, using a multi‐model ensemble can improve the accuracy in detecting change direction to >90% for observed increases, and up to 70% for observed losses. Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce (Picea mariana), summer precipitation, and early warning metrics from long‐term remote sensing time series were negatively associated with AGB change. Across the domain, we predict nondisturbance‐induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near‐future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi‐model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage.