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Carbon cycle perturbations in high-latitude ecosystems associated with rapid warming can have implications for the global climate. Belowground biomass is an important component of the carbon cycle in these ecosystems, with, on average, significantly more vegetation biomass belowground than aboveground. Large quantities of dead root biomass are also in these ecosystems owing to slow decomposition rates. Current understanding of how live and dead root biomass carbon pools vary across high-latitude ecosystems and the environmental conditions associated with this variation is limited due to the labor- and time-intensive nature of data collection. To that end, we examined patterns and factors (abiotic and biotic) associated with the variation in live and dead fine root biomass (FRB) and FRB carbon (C), nitrogen (N) and phosphorus concentrations for 23 sites across a latitudinal gradient in Alaska, spanning both boreal forest and tundra biomes. We found no difference in the live or dead FRB variables between these biomes, despite large differences in predominant vegetation types, except for significantly higher live FRB C:N ratios in boreal sites. Soil C:N ratio, moisture, and temperature, along with moss cover, explained a substantial portion of the dead:live FRB ratio variability across sites. We find all these factors have negative relationships with dead FRB, while having positive or no relationship with live FRB. This work demonstrates that FRB does not necessarily correlate with aboveground vegetation characteristics, and it highlights the need for finer-scale measurements of abiotic and biotic factors to understand FRB landscape variability now and into the future.

Reanalysis products provide spatially homogeneous coverage for a variety of climate variables in regions where observational data are limited. However, very little validation of reanalysis soil temperatures in the Arctic has been performed to date, because widespread in situ reference observations have historically been unavailable there. Here we validate pan-Arctic soil temperatures from eight reanalysis and Land Data Assimilation System (LDAS) products, using a newly-assembled database of in situ data from diverse measurement networks across Eurasia and North America. We find that most products have soil temperatures that are biased cold by 2–7 K across the Arctic, and that biases and Root Mean Square Error (RMSE) are generally largest in the cold season. Monthly mean values from most products correlate well with in situ data (R > 0.9) in the warm season, but show lower correlations (r = 0.6–0.8), in many cases, over the cold season. Similarly, the magnitude of monthly variability in soil temperatures is well captured in summer, but overestimated by 20 % to 50 % for several products in winter. The suggestion is that soil temperatures in reanalysis products are subject to much higher uncertainty when the soil is frozen and/or when the ground is snow-covered. We also validate the ensemble mean of all products, and find that when all seasons, and metrics are considered, the ensemble mean generally outperforms any individual product in terms of its correlation and variability, while maintaining relatively low biases. As such, we recommend the ensemble mean soil temperature product for a wide range of applications – such as the validation of soil temperatures in climate models, and to inform models that require soil temperature inputs, such as hydrological models, or for permafrost simulations.

Climate warming is altering the persistence, timing, and distribution of permafrost and snow cover across the terrestrial northern hemisphere. These cryospheric changes have numerous consequences, not least of which are positive climate feedbacks associated with lowered albedo related to declining snow cover, and greenhouse gas emissions from permafrost thaw. Given the large land areas affected, these feedbacks have the potential to impact climate on a global scale. Understanding the magnitudes and rates of changes in permafrost and snow cover is therefore integral for process understanding and quantification of climate change. However, while permafrost and snow cover are largely controlled by climate, their distributions and climate impacts are influenced by numerous interrelated ecosystem processes that also respond to climate and are highly heterogeneous in space and time. In this perspective we highlight ongoing and emerging changes in ecosystem processes that mediate how permafrost and snow cover interact with climate. We focus on larch forests in northeastern Siberia, which are expansive, ecologically unique, and studied less than other Arctic and subarctic regions. Emerging fire regime changes coupled with high ground ice have the potential to foster rapid regional changes in vegetation and permafrost thaw, with important climate feedback implications.

Plant species are characterized along a spectrum of isohydry to anisohydry depending on their regulation of water potential (Ψ), but the plasticity of hydraulic strategies is largely unknown. The role of environmental drivers was evaluated in the hydraulic behavior of Larrea tridentata, a drought-tolerant desert shrub that withstands a wide range of environmental conditions. With a 1.5 yr time-series of 2324 in situ measurements of daily predawn and midday Ψ, the temporal variability of hydraulic behavior was explored in relation to soil water supply, atmospheric demand and temperature. Hydraulic behavior in Larrea was highly dynamic, ranging from partial isohydry to extreme anisohydry. Larrea exhibited extreme anisohydry under wet soil conditions corresponding to periods of high productivity, whereas partial isohydry was exhibited after prolonged dry or cold conditions, when productivity was low. Environmental conditions can strongly influence plant hydraulic behavior at relatively fast timescales, which enhances our understanding of plant drought responses. Although species may exhibit a dominant hydraulic behavior, variable environmental conditions can prompt plasticity in Ψ regulation, particularly for species in seasonally dry climates.

Permafrost soils in arctic and boreal ecosystems store twice the amount of current atmospheric carbon that may be mobilized and released to the atmosphere as greenhouse gases when soils thaw under a warming climate. This permafrost carbon climate feedback is among the most globally important terrestrial biosphere feedbacks to climate warming, yet its magnitude remains highly uncertain. This uncertainty lies in predicting the rates and spatial extent of permafrost thaw and subsequent carbon cycle processes. Terrestrial ecosystem influences on surface energy partitioning exert strong control on permafrost soil thermal dynamics and are critical for understanding permafrost soil responses to climate change and disturbance. Here we review how arctic and boreal ecosystem processes influence permafrost soils and characterize key ecosystem changes that regulate permafrost responses to climate. While many of the ecosystem characteristics and processes affecting soil thermal dynamics have been examined in isolation, interactions between processes are less well understood. In particular connections between vegetation, soil moisture, and soil thermal properties affecting permafrost conditions could benefit from additional research. In particular, connections between vegetation, soil moisture, and soil thermal properties affecting permafrost could benefit from additional research. Changes in ecosystem distribution and vegetation characteristics will alter spatial patterns of interactions between climate and permafrost. In addition to shrub expansion, other vegetation responses to changes in climate and disturbance regimes will all affect ecosystem surface energy partitioning in ways that are important for permafrost. Lastly, changes in vegetation and ecosystem distribution will lead to regional and global biophysical and biogeochemical climate feedbacks that may compound or offset local impacts on permafrost soils. Consequently, accurate prediction of the permafrost carbon climate feedback will require detailed understanding of changes in terrestrial ecosystem distribution and function and the net effects of multiple feedback processes operating across scales in space and time.

The flux of CO2 from the soil to the atmosphere (soil respiration, Rsoil) is a major component of the global carbon (C) cycle. Methods to measure and model Rsoil, or partition it into different components, often rely on the assumption that soil CO2 concentrations and fluxes are in steady state, implying that Rsoil is equal to the rate at which CO2 is produced by soil microbial and root respiration. Recent research, however, questions the validity of this assumption. Thus, the aim of this work was two-fold: (1) to describe a non-steady state (NSS) soil CO2 transport and production model, DETECT, and (2) to use this model to evaluate the environmental conditions under which Rsoil and CO2 production are likely in NSS. The backbone of DETECT is a non-homogeneous, partial differential equation (PDE) that describes production and transport of soil CO2, which we solve numerically at fine spatial and temporal resolution (e.g., 0.01 m increments down to 1 m, every 6 h). Production of soil CO2 is simulated for every depth and time increment as the sum of root respiration and microbial decomposition of soil organic matter. Both of these factors can be driven by current and antecedent soil water content and temperature, which can also vary by time and depth. We also analytically solved the ordinary differential equation (ODE) corresponding to the steady-state (SS) solution to the PDE model. We applied the DETECT NSS and SS models to the six-month growing season period representative of a native grassland in Wyoming. Simulation experiments were conducted with both model versions to evaluate factors that could affect departure from SS, such as (1) varying soil texture; (2) shifting the timing or frequency of precipitation; and (3) with and without the environmental antecedent drivers. For a coarse-textured soil, Rsoil from the SS model closely matched that of the NSS model. However, in a fine-textured (clay) soil, growing season Rsoil was ∼ 3 % higher under the assumption of NSS (versus SS). These differences were exaggerated in clay soil at daily time scales whereby Rsoil under the SS assumption deviated from NSS by up to 35 % on average in the 10 days following a major precipitation event. Incorporation of antecedent drivers increased the magnitude of Rsoil by 15 to 37 % for coarse- and fine-textured soils, respectively. However, the responses of Rsoil to the timing of precipitation and antecedent drivers did not differ between SS and NSS assumptions. In summary, the assumption of SS conditions can be violated depending on soil type and soil moisture status, as affected by precipitation inputs. The DETECT model provides a framework for accommodating NSS conditions to better predict Rsoil and associated soil carbon cycling processes.

Boreal forest ecosystems are experiencing changes in plant productivity that are likely to continue with ongoing climate change. Transpiration (T) and canopy stomatal conductance (gc) are a key influence on plant productivity, and a better understanding of drivers and limitations of T and gc is necessary for constraining estimates of boreal ecosystem change. We describe patterns in T and gc of a deciduous conifer, Larix cajanderi, in an arctic boreal forest in northeastern Russia across three growing seasons from 2013 to 2015. We examine the influence of environmental drivers on gc using a phenomenological model. T was highly variable across days and varied between 0.03 and 0.75 L m−2 d−1. T and gc largely covaried with daily fluctuations in air temperature and vapor pressure deficit. gc was highly suppressed on days when the vapor pressure deficits exceeded 0.75 kPa with an average daily gc of 37.55 mmol m−2 s−1, and the average daily gc was almost double (71.25 mmol m−2 s−1) when vapor pressure deficits stayed below 0.75 kPa. Daily variation in gc was significantly related to air temperature, permafrost thaw depth, and past precipitation. The influence of past precipitation and permafrost thaw depth on gc indicates that belowground conditions relating to soil moisture status are a key limitation for T. Such limitations on gc and T suggest that soil water and plant water stress play an important role in plant productivity and water relations in far northeastern Siberia.

Evapotranspiration (ET) is an important water loss flux in ecosystem water cycles, and quantifying the spatial and temporal variation of ET can improve ecohydrological models in arid ecosystems. Plant neighbor interactions may be a source of spatial and temporal variation in ET due to their effects on the above- and belowground microclimate and increased water demand for transpiration. Over longer timescales (annual to multiple years), adjustments in plant physiological traits may occur in response to neighbor environments, potentially affecting the transpiration (T) component of ET. We used a dynamic soil water model to assess the sensitivity of ET and T estimates to neighbor effects on soil moisture via competition for water, aboveground microclimate effects via canopy shading, and physiological adjustments (specifically, root distribution, stomatal behavior, and canopy leaf area). We focus on a common desert shrub (Larrea tridentata) under different inter-specific neighbor environments and precipitation regimes. Neighbors impacted T of Larrea by as much as 75% at the patch scale (plant and surrounding soil) and 30% at the stand scale. Annual T estimates were highly sensitive to changes in soil moisture associated with competition for water, and the inclusion of physiological adjustments to neighbor environments significantly impacted seasonal T. Plant neighbor interactions can significantly influence ET and soil moisture, and their inclusion in models can help explain spatial and temporal variation in water fluxes in arid ecosystems. Furthermore, physiological adjustments to neighbor environments may be an important source of variation to include in models that operate over seasonal timescales or in studies focused on plant responses to precipitation under climate change.

Boreal forests cover about a fifth of seasonally snow‐covered land over the Northern Hemisphere. Enhancement of longwave radiation beneath coniferous forests has been found to impact the surface energy balance and rates of snowmelt. Although skill of model‐simulated snowmelt has been shown to be lower for forests than for open areas, model intercomparisons and evaluations of model parameterizations have not yet focussed on longwave enhancement. This study uses stand‐scale forcing for the simulation of sub‐canopy longwave radiation by Community Land Model version 4.5 (CLM4.5) and to drive SNOWPACK, a snow model featuring more complex canopy structure, as a benchmark model for CLM4.5. Simulated sub‐canopy longwave radiation and longwave enhancement are assessed using measurements from forest stands located within perennially snow‐covered regions. These forest stands, of varying canopy density, cover the range of boreal Plant Functional Types (PFTs) in CLM4.5. CLM4.5 is found to overestimate the diurnal range of sub‐canopy longwave radiation and longwave enhancement, and simulation errors increase with decreasing cloudiness and increasing vegetation density. Implementation of a parameterization of heat storage by biomass reduces simulation errors, but only marginally affects the amplitude of diurnal ranges. These results reaffirm previous findings that simulation of sub‐canopy longwave radiation can be improved by partitioning the vegetation canopy into two layers. Moreover, this study reveals the variations of simulation errors across meteorological conditions and vegetation density, the latter of which is the most important parameter for longwave enhancement independent of vegetation type.

Aim Vertical root distributions (‘profiles’) influence plant water use and productivity, and the differentiation of root profiles between neighbouring species can indicate the degree of plant interactions and niche partitioning. However, quantifying multiple species' root distributions in the field can be labour intensive and highly destructive to the soil and plants. We describe a method for partitioning multiple species roots using minimally destructive methods to determine if neighbour interactions alter the root profile of a common desert shrub, Larrea tridentata (creosote bush). Location Sonoran Desert, central Arizona, USA. Methods We obtained root and soil samples from soil cores collected around Larrea growing alone and next to three different neighbouring species. Bulk root mass was measured for each soil sample, and Larrea and neighbouring species root presence was determined with molecular identification methods. Water extracted from the soil and paired stem samples was analysed for its stable isotope composition (D and 18O). Species-specific (i.e. Larrea and neighbouring species) root biomass and fractional active root area were estimated through a hierarchical statistical modelling approach that combined all three data sets and accounted for detection errors. Results The combined data model successfully partitioned Larrea root biomass from neighbouring plants and provided biologically relevant estimates of rooting profiles with greater certainty than individual analyses of each data source. The data model results indicate that plant neighbours alter Larrea's root profile; Larrea growing under tree species had significantly higher root biomass in shallow soil layers than Larrea growing alone. Conclusions Our framework requires minimally destructive sampling methods, and accounts for sampling errors associated with different methods. We demonstrate the utility of our approach with a common desert shrub species, which illustrated that plant neighbours can alter the Larrea vertical root profile. Our approach is useful in problematic study systems fraught with sample collection issues or supporting species with inhibitory compounds that prohibit the use of more sophisticated molecular methods to identify the presence of other species' roots.

Arctic ecosystems are characterized by a broad range of plant functional types that are highly heterogeneous at small (~1–2 m) spatial scales. Climatic changes can impact vegetation distribution directly, and also indirectly via impacts on disturbance regimes. Consequent changes in vegetation structure and function have implications for surface energy dynamics that may alter permafrost thermal dynamics, and are therefore of interest in the context of permafrost related climate feedbacks. In this study we examine small-scale heterogeneity in soil thermal properties and ecosystem carbon and water fluxes associated with varying understory vegetation in open-canopy larch forests in northeastern Siberia. We found that lichen mats comprise 16% of understory vegetation cover on average in open canopy larch forests, and lichen abundance was inversely related to canopy cover. Relative to adjacent areas dominated by shrubs and moss, lichen mats had 2–3 times deeper permafrost thaw depths and surface soils warmer by 1–2°C in summer and less than 1°C in autumn. Despite deeper thaw depths, ecosystem respiration did not differ across vegetation types, indicating that autotrophic respiration likely dominates areas with shrubs and moss. Summertime net ecosystem exchange of CO2 was negative (i.e. net uptake) in areas with high shrub cover, while positive (i.e. net loss) in lichen mats and areas with less shrub cover. Our results highlight relationships between vegetation and soil thermal dynamics in permafrost ecosystems, and underscore the necessity of considering both vegetation and permafrost dynamics in shaping carbon cycling in permafrost ecosystems.

The flux of CO2 from the soil to the atmosphere (soil respiration, Rsoil) is a major component of the global carbon cycle. Methods to measure and model Rsoil, or partition it into different components, often rely on the assumption that soil CO2 concentrations and fluxes are in steady state, implying that Rsoil is equal to the rate at which CO2 is produced by soil microbial and root respiration. Recent research, however, questions the validity of this assumption. Thus, the aim of this work was two-fold: (1) to describe a non-steady state (NSS) soil CO2 transport and production model, DETECT, and (2) to use this model to evaluate the environmental conditions under which Rsoil and CO2 production are likely in NSS. The backbone of DETECT is a non-homogeneous, partial differential equation (PDE) that describes production and transport of soil CO2, which we solve numerically at fine spatial and temporal resolution (e.g., 0.01 m increments to 1 m, every 6 hours). Production of soil CO2 is simulated for every depth and time increment as the sum of root respiration and microbial decomposition of soil organic matter, both of which can be driven by current and antecedent soil water content and temperature, which can also vary by time and depth. We also analytically solved the ordinary differential equation (ODE) corresponding to the steady-state (SS) solution to the PDE model. We applied the DETECT NSS and SS models to the 6-month growing season period representative of a native grassland in Wyoming. Simulation experiments were conducted with both model versions to evaluate factors that could affect departure from SS: (1) varying soil texture; (2) shifting the timing or frequency of precipitation; and (3) with and without the environmental antecedent drivers. For a coarse-textured soil, Rsoil from the SS model closely matched that of the NSS model. However, in a fine-textured (clay) soil, growing season Rsoil was ~ 3 % higher under the assumption of NSS (versus SS). These differences were exaggerated in clay soil at daily time-scales whereby Rsoil under the SS assumption deviated from NSS by up to ~ 20 % in the 10 days following a major precipitation event. Moreover, incorporation of antecedent drivers increased the magnitude of Rsoil by 15 % to 37 % for coarse- and fine-textured soils, respectively. However, the responses of Rsoil to the timing of precipitation and antecedent drivers did not differ between SS and NSS assumptions. In summary, the assumption of SS conditions can be violated depending on soil type and soil moisture status, as affected by precipitation inputs, and the DETECT model provides a framework for accommodating NSS conditions to better predict Rsoil and associated soil carbon cycling processes.

Stomata simultaneously regulate plant carbon gain and water loss, and patterns of stomatal conductance (g s) provide insight into water use strategies. In arid systems, g s varies seasonally based on factors such as water availability and temperature. Moreover, the presence and species identity of neighboring plants likely affects g s of the focal plant by altering available soil water and microclimate conditions. We investigated stomatal behavior in Larrea tridentata, a drought-tolerant, evergreen shrub occurring throughout the arid southwestern United States. We measured g s in Larrea over multiple seasons in the presence of neighbors representing different woody species. The data were analyzed in the context of a commonly used phenomenological model that relates g s to vapor pressure deficit (D) to understand spatial and temporal differences in stomatal behavior. We found that g s in Larrea was affected by neighborhood association, and these effects varied seasonally. The greatest effect of neighborhood association on g s occurred during the winter period, where Larrea growing alone (without neighbors) had higher g s compared to Larrea growing with neighbors. Larrea’s stomatal sensitivity to D and reference conductance (i.e., g s at D = 1 kPa) also differed significantly among different neighbor associations. Random effects indicated reference g s varied over short time scales (daily), while stomatal sensitivity showed little daily or seasonal variation, but was notably affected by neighbor associations such that neighboring species, especially trees, reduced Larrea’s sensitivity to D. Overall, seasonal dynamics and neighborhood conditions appear critical to understanding temporal and spatial variation in Larrea’s physiological behavior.

Contrary to concerns of some critics, we present evidence that biomedical research is not dominated by a small handful of model organisms. An exhaustive analysis of research literature suggests that the diversity of experimental organisms in biomedical research has increased substantially since 1975. There has been a longstanding worry that organism-centric funding policies can lead to biases in experimental organism choice, and thus negatively impact the direction of research and the interpretation of results. Critics have argued that a focus on model organisms has unduly constrained the diversity of experimental organisms. The availability of large electronic databases of scientific literature, combined with interest in quantitative methods among philosophers of science, presents new opportunities for data-driven investigations into organism choice in biomedical research. The diversity of organisms used in NIH-funded research may be considerably lower than in the broader biomedical sciences, and may be subject to greater constraints on organism choice.