Publications

Plant diversity can increase biomass production in plot-scale studies, but applying these results to ecosystem carbon (C) storage at larger spatial and temporal scales remains problematic. Other ecosystem controls interact with diversity and plant production, and may influence soil pools differently from plant pools. We integrated diversity with the state-factor framework, which identifies key controls, or ?state factors?, over ecosystem properties and services such as C storage. We used this framework to assess the effects of diversity, plant traits and state factors (climate, topography, time) on live tree, standing dead, organic horizon and total C in Québec forests. Four patterns emerged: (1) while state factors were usually the most important model predictors, models with both state and biotic factors (mean plant traits and diversity) better predicted C pools; (2) mean plant traits were better predictors than diversity; (3) diversity increased live tree C but reduced organic horizon C; (4) different C pools responded to different traits and diversity metrics. These results suggest that, where ecosystem properties result from multiple processes, no simple relationship may exist with any one organismal factor. Integrating biodiversity into ecosystem ecology and assessing both traits and diversity improves our mechanistic understanding of biotic effects on ecosystems.

Fire severity is increasing across the boreal forest biome as climate warms, and initial post-fire changes in tree demographic processes could be important determinants of long-term forest structure and carbon dynamics. To examine soil burn severity impacts on tree regeneration, we conducted experimental burns in summer 2012 that created a gradient of residual post-fire soil organic layer (SOL) depth within a mature, sparse-canopy Cajander larch (Larix cajanderi Mayr.) forest in the Eastern Siberian Arctic. Each fall from 2012 to 2016, we added larch seeds to plots along the burn severity gradient. We tracked density of new larch germinants and established seedlings (alive ≥ 1 year) during subsequent growing seasons, along with changes in seedbed conditions (permafrost thaw depth, moisture, and temperature). Over the study, a cumulative total of 17 and 18 new germinants m−2 occurred in high and moderate severity treatments, respectively, while germinants were rare in unburned and low severity treatments (< 0.5 germinants m−2). Most seedlings (> 50%) germinated in summer 2017, following a mast event in fall 2016, suggesting safe sites for germination were not fully occupied in previous years despite seed additions. By 2017, established seedling density was ∼5 times higher on moderate and high severity treatments compared to other treatments. Cumulative total density of new germinants and established seedlings increased linearly with decreasing residual SOL depth, as did thaw depth, soil moisture, and soil temperature. Our findings suggest that increased soil burn severity could improve seedbed conditions and increase larch recruitment, assuming seed sources are available. If these demographic changes persist as stands mature, a climate-driven increase in soil burn severity could shift forest structure from sparse-canopy stands, which dominate this region of the Siberian Arctic, to high density stands, with potential implications for carbon, energy, and water cycling.

The Tundra Trait Team (TTT) database includes field-based measurements of key traits related to plant form and function at multiple sites across the tundra biome. This dataset can be used to address theoretical questions about plant strategy and trade-offs, trait?environment relationships and environmental filtering, and trait variation across spatial scales, to validate satellite data, and to inform Earth system model parameters. Main types of variable contained The database contains 91,970 measurements of 18 plant traits. The most frequently measured traits (&gt;1,000 observations each) include plant height, leaf area, specific leaf area, leaf fresh and dry mass, leaf dry matter content, leaf nitrogen, carbon and phosphorus content, leaf C:N and N:P, seed mass, and stem specific density. Spatial location and grain Measurements were collected in tundra habitats in both the Northern and Southern Hemispheres, including Arctic sites in Alaska, Canada, Greenland, Fennoscandia and Siberia, alpine sites in the European Alps, Colorado Rockies, Caucasus, Ural Mountains, Pyrenees, Australian Alps, and Central Otago Mountains (New Zealand), and sub-Antarctic Marion Island. More than 99% of observations are georeferenced. Time period and grain All data were collected between 1964 and 2018. A small number of sites have repeated trait measurements at two or more time periods. Major taxa and level of measurement Trait measurements were made on 978 terrestrial vascular plant species growing in tundra habitats. Most observations are on individuals (86%), while the remainder represent plot or site means or maximums per species. Software format csv file and GitHub repository with data cleaning scripts in R; contribution to TRY plant trait database (www.try-db.org) to be included in the next version release.

Soil organic matter (SOM) turnover increasingly is conceptualized as a tension between accessibility to microorganisms and protection from decomposition via physical and chemical association with minerals in emerging soil biogeochemical theory. Yet, these components are missing from the original mathematical models of belowground carbon dynamics and remain underrepresented in more recent compartmental models that separate SOM into discrete pools with differing turnover times. Thus, a gap currently exists between the emergent understanding of SOM dynamics and our ability to improve terrestrial biogeochemical projections that rely on the existing models. In this opinion paper, we portray the SOM paradigm as a triangle composed of three nodes: conceptual theory, analytical measurement, and numerical models. In successful approaches, we contend that the nodes are connected—models capture the essential features of dominant theories while measurement tools generate data adequate to parameterize and evaluate the models—and balanced—models can inspire new theories via emergent behaviors, pushing empiricists to devise new measurements. Many exciting advances recently pushed the boundaries on one or more nodes. However, newly integrated triangles have yet to coalesce. We conclude that our ability to incorporate mechanisms of microbial decomposition and physicochemical protection into predictions of SOM change is limited by current disconnections and imbalances among theory, measurement, and modeling. Opportunities to reintegrate the three components of the SOM paradigm exist by carefully considering their linkages and feedbacks at specific scales of observation.

The fundamental niche of many species is shifting with climate change, especially in sub-arctic ecosystems with pronounced recent warming. Ongoing warming in sub-arctic regions should lessen environmental constraints on tree growth and reproduction, leading to increased success of trees colonising tundra. Nevertheless, variable responses of treeline ecotones have been documented in association with warming temperatures. One explanation for time lags between increasingly favourable environmental conditions and treeline ecotone movement is reproductive limitations caused by low seed availability. Our objective was to assess the reproductive constraints of the dominant tree species at the treeline ecotone in the circumpolar north. We sampled reproductive structures of trees (cones and catkins) and stand attributes across circumarctic treeline ecotones. We used generalized linear mixed models to estimate the sensitivity of seed production and the availability of viable seed to regional climate, stand structure, and species-specific characteristics. Both seed production and viability of available seed were strongly driven by specific, sequential seasonal climatic conditions, but in different ways. Seed production was greatest when growing seasons with more growing degree days coincided with years with high precipitation. Two consecutive years with more growing degree days and low precipitation resulted in low seed production. Seasonal climate effects on the viability of available seed depended on the physical characteristics of the reproductive structures. Large-coned and -seeded species take more time to develop mature embryos and were therefore more sensitive to increases in growing degree days in the year of flowering and embryo development. Our findings suggest that both moisture stress and abbreviated growing seasons can have a notable negative influence on the production and viability of available seed at treeline. Our synthesis revealed that constraints on predispersal reproduction within the treeline ecotone might create a considerable time lag for range expansion of tree populations into tundra ecosystems.

Climate change is shifting the phenological cycles of plants1, thereby altering the functioning of ecosystems, which in turn induces feedbacks to the climate system2. In northern (north of 30° N) ecosystems, warmer springs lead generally to an earlier onset of the growing season3,4 and increased ecosystem productivity early in the season5. In situ6 and regional7–9 studies also provide evidence for lagged effects of spring warmth on plant productivity during the subsequent summer and autumn. However, our current understanding of these lagged effects, including their direction (beneficial or adverse) and geographic distribution, is still very limited. Here we analyse satellite, field-based and modelled data for the period 1982–2011 and show that there are widespread and contrasting lagged productivity responses to spring warmth across northern ecosystems. On the basis of the observational data, we find that roughly 15 per cent of the total study area of about 41 million square kilometres exhibits adverse lagged effects and that roughly 5 per cent of the total study area exhibits beneficial lagged effects. By contrast, current-generation terrestrial carbon-cycle models predict much lower areal fractions of adverse lagged effects (ranging from 1 to 14 per cent) and much higher areal fractions of beneficial lagged effects (ranging from 9 to 54 per cent). We find that elevation and seasonal precipitation patterns largely dictate the geographic pattern and direction of the lagged effects. Inadequate consideration in current models of the effects of the seasonal build-up of water stress on seasonal vegetation growth may therefore be able to explain the differences that we found between our observation-constrained estimates and the model-constrained estimates of lagged effects associated with spring warming. Overall, our results suggest that for many northern ecosystems the benefits of warmer springs on growing-season ecosystem productivity are effectively compensated for by the accumulation of seasonal water deficits, despite the fact that northern ecosystems are thought to be largely temperature- and radiation-limited10.

Understanding drivers of straw decomposition is essential for adopting appropriate management practice to improve soil fertility and promote carbon (C) sequestration in agricultural systems. However, predicting straw decomposition and characteristics is difficult because of the interactions between many factors related to straw properties, soil properties, and climate, especially under future climate change conditions. This study investigated the driving factors of straw decomposition of six types of crop straw including wheat, maize, rice, soybean, rape, and other straw by synthesizing 1642 paired data from 98 published papers at spatial and temporal scales across China. All the data derived from the field experiments using little bags over twelve years. Overall, despite large differences in climatic and soil properties, the remaining straw carbon (C, %) could be accurately represented by a three-exponent equation with thermal time (accumulative temperature). The lignin/nitrogen and lignin/phosphorus ratios of straw can be used to define the size of labile, intermediate, and recalcitrant C pool. The remaining C for an individual type of straw in the mild-temperature zone was higher than that in the warm-temperature and subtropical zone within one calendar year. The remaining straw C after one thermal year was 40.28%, 37.97%, 37.77%, 34.71%, 30.87%, and 27.99% for rice, soybean, rape, wheat, maize, and other straw, respectively. Soil available nitrogen and phosphorus influenced the remaining straw C at different decomposition stages. For one calendar year, the total amount of remaining straw C was estimated to be 29.41 Tg and future temperature increase of 2 °C could reduce the remaining straw C by 1.78 Tg. These findings confirmed the long-term straw decomposition could be mainly driven by temperature and straw quality, and quantitatively predicted by thermal time with the three-exponent equation for a wide array of straw types at spatial and temporal scales in agro-ecosystems of China.

The global carbon cycle is strongly controlled by the source/sink strength of vegetation as well as the capacity of terrestrial ecosystems to retain this carbon. These dynamics, as well as processes such as the mixing of old and newly fixed carbon, have been studied using ecosystem models, but different assumptions regarding the carbon allocation strategies and other model structures may result in highly divergent model predictions. We assessed the influence of three different carbon allocation schemes on the C cycling in vegetation. First, we described each model with a set of ordinary differential equations. Second, we used published measurements of ecosystem C compartments from the Harvard Forest Environmental Measurement Site to find suitable parameters for the different model structures. And third, we calculated C stocks, release fluxes, radiocarbon values (based on the bomb spike), ages, and transit times. We obtained model simulations in accordance with the available data, but the time series of C in foliage and wood need to be complemented with other ecosystem compartments in order to reduce the high parameter collinearity that we observed, and reduce model equifinality. Although the simulated C stocks in ecosystem compartments were similar, the different model structures resulted in very different predictions of age and transit time distributions. In particular, the inclusion of two storage compartments resulted in the prediction of a system mean age that was 12–20 years older than in the models with one or no storage compartments. The age of carbon in the wood compartment of this model was also distributed towards older ages, whereas fast cycling compartments had an age distribution that did not exceed 5 years. As expected, models with C distributed towards older ages also had longer transit times. These results suggest that ages and transit times, which can be indirectly measured using isotope tracers, serve as important diagnostics of model structure and could largely help to reduce uncertainties in model predictions. Furthermore, by considering age and transit times of C in vegetation compartments as distributions, not only their mean values, we obtain additional insights into the temporal dynamics of carbon use, storage, and allocation to plant parts, which not only depends on the rate at which this C is transferred in and out of the compartments but also on the stochastic nature of the process itself.

Extracellular enzymes catalyze rate-limiting steps in soil organic matter decomposition, and their activities (EEAs) play a key role in determining soil respiration (SR). Both EEAs and SR are highly sensitive to temperature, but their responses to climate warming remain poorly understood. Here, we present a meta-analysis on the response of soil cellulase and ligninase activities and SR to warming, synthesizing data from 56 studies. We found that warming significantly enhanced ligninase activity by 21.4% but had no effect on cellulase activity. Increases in ligninase activity were positively correlated with changes in SR, while no such relationship was found for cellulase. The warming response of ligninase activity was more closely related to the responses of SR than a wide range of environmental and experimental methodological factors. Furthermore, warming effects on ligninase activity increased with experiment duration. These results suggest that soil microorganisms sustain long-term increases in SR with warming by gradually increasing the degradation of the recalcitrant carbon pool.

Agricultural and industrial activities have increased atmospheric nitrogen (N) deposition to ecosystems worldwide. N deposition can stimulate plant growth and soil carbon (C) input, enhancing soil C storage. Changes in microbial decomposition could also influence soil C storage, yet this influence has been difficult to discern, partly because of the variable effects of added N on the microbial enzymes involved. We show, using meta-analysis, that added N reduced the activity of lignin-modifying enzymes (LMEs), and that this N-induced enzyme suppression was associated with increases in soil C. In contrast, N-induced changes in cellulase activity were unrelated to changes in soil C. Moreover, the effects of added soil N on LME activity accounted for more of the variation in responses of soil C than a wide range of other environmental and experimental factors. Our results suggest that, through responses of a single enzyme system to added N, soil microorganisms drive long-term changes in soil C accumulation. Incorporating this microbial influence on ecosystem biogeochemistry into Earth system models could improve predictions of ecosystem C dynamics.

Grazing exclusion (GE) is an effective method for protecting degraded grasslands, and it can profoundly affect ecosystem carbon (C) cycles. Ecosystem respiration (ER), which includes both autotrophic and heterotrophic respiration (HR), accounts for the largest land-to-atmosphere C fluxes. How ER responds to GE is still unclear, however, and to investigate this, a controlled GE experiment was conducted at a meadow grassland near Qinghai Lake, China. Animal exclusion enhanced ER and aboveground plant respiration (Ragb) by 10.5% and 40.1%, respectively, but it suppressed soil respiration by 12.4% and HR by 17.6%. Positive responses of ER and Ragb were linked to increased aboveground biomass, particularly graminoids biomass. Negative responses of soil respiration and HR were associated with GE-induced changes in microbial biomass C and nitrogen. These results show that grassland responded in complex ways to GE and that ER and its components were regulated by both abiotic and biotic factors. Moreover, the divergent responses of respiration components have important implications for models of terrestrial C cycles and climate under enhanced human activities and changes in land use.

Partitioning of evapotranspiration (ET) into biological component transpiration (T) and non-biological component evaporation (E) is crucial in understanding the impact of environmental change on ecosystems and water resources. However, direct measurement of transpiration is still challenging. In this paper, an optimality-based ecohydrological model named Vegetation Optimality Model (VOM) is applied for ET partitioning. The results show that VOM model can reasonably simulate ET and ET components in a semiarid shrubland. Overall, the ratio of transpiration to evapotranspiration is 49% for the whole period. Evaporation and plant transpiration mainly occur in monsoon following the precipitation events. Evaporation responds immediately to precipitation events, while transpiration shows a lagged response of several days to those events. Different years demonstrate different patterns of T/ET ratio dynamic in monsoon. Some of the years show a low T/ET ratio at the beginning of monsoon and slowly increased T/ET ratio. Other years show a high level of T/ET ratio for the whole monsoon. We find out that spring precipitation, especially the size of the precipitation, has a significant influence on the T/ET ratio in monsoon.

Many Low Arctic tundra regions are currently undergoing a vegetation shift towards increasing growth and groundcover of tall deciduous shrubs due to recent climate warming. Vegetation change directly affects ecosystem carbon balance, but it can also affect soil biogeochemical cycling through physical and biological feedback mechanisms. Recent studies indicate that enhanced snow accumulation around relatively tall shrubs has negligible physical effect on litter decomposition rates. However, these investigations were no more than 3 years, and therefore may be insufficient to detect differences in inherently slow biogeochemical processes. Here, we report a 5-year study near Daring Lake, Canada, comparing Betula neoalaskana foliar litter decay rates within unmanipulated and snowfenced low-stature birch (height: ~ 0.3 m) plots to test the physical effect of experimentally deepened snow, and within tall birch (height: ~ 0.8 m) plots to test the combined physical and biological effects, that is, deepened snow plus strong birch dominance. Having corrected for carbon gain by the colonizing decomposers, actual litter carbon loss increased by approximately 25% in the tall birch relative to both low birch sites. Decay of lignin-like acid unhydrolizable litter residues also accelerated in the tall birch site, and a similar but lower magnitude response in the snowfenced low birch site indicated that physical effects of deepened snow were at least partially responsible. In contrast, deepened snow alone did not affect litter carbon loss. Our findings suggest that a combination of greater litter inputs, altered soil microbial community, enhanced soil nutrient pools, and warmer winter soils together promote relatively fast decomposition of recalcitrant litter carbon in tall birch shrub environments.

Leaf litter provides an important nutrient subsidy to headwater streams, but little is known about how tree genetics influences energy pathways from litter to higher trophic levels. Despite the charge to quantify carbon (C) and nitrogen (N) pathways from decomposing litter, the relationship between litter decomposition and aquatic consumers remains unresolved. We measured litter preference (attachments to litter), C and N assimilation rates, and growth rates of a shredding caddisfly (Hesperophylax magnus, Limnephilidae) in response to leaf litter of different chemical and physical phenotypes using Populus cross types (P. fremontii, P. angustifolia, and F1 hybrids) and genotypes within P. angustifolia. We combined laboratory mesocosm studies using litter from a common garden with a field study using doubly labeled litter (13 C and 15 N) grown in a greenhouse and incubated in Oak Creek, AZ. We found that, in the lab, shredders initially chose relatively labile (low lignin and condensed tannin concentrations, rapidly decomposing) cross type litter, but preference changed within four days to relatively recalcitrant (high lignin and condensed tannin concentrations, slowly decomposing) litter types. Additionally, in the lab, shredder growth rates were higher on relatively recalcitrant compared to labile cross type litter. Over the course of a three-week field experiment, shredders also assimilated more C and N from relatively recalcitrant compared to labile cross type litter. Finally, among P. angustifolia genotypes, N assimilation by shredders was positively related to litter lignin and C:N, but negatively related to condensed tannins and decomposition rate. C assimilation was likewise positively related to litter C:N, and also to litter %N. C assimilation was not associated with condensed tannins or lignin. Collectively, these findings suggest that relatively recalcitrant litter of Populus cross types provides more nutritional benefit, in terms of N fluxes and growth, than labile litter, but among P. angustifolia genotypes the specific trait of litter recalcitrance (lignin or tannins) determines effects on C or N assimilation. As shredders provide nutrients and energy to higher trophic levels, the influence of these genetically based plant decomposition pathways on shredder preference and performance may affect community and food web structure.

We sought to determine if the prevalence of antibiotic-resistant Escherichia coli differed across retail poultry products and among major production categories, including organic, “raised without antibiotics”, and conventional.

Quantifying the ecological patterns of loss of ecosystem function in extreme drought is important to understand the carbon exchange between the land and atmosphere. Rain-use efficiency [RUE; gross primary production (GPP)/precipitation] acts as a typical indicator of ecosystem function. In this study, a novel method based on maximum rain-use efficiency (RUEmax) was developed to detect losses of ecosystem function globally. Three global GPP datasets from the MODIS remote sensing data (MOD17), ground upscaling FLUXNET observations (MPI-BGC), and process-based model simulations (BESS), and a global gridded precipitation product (CRU) were used to develop annual global RUE datasets for 2001-2011. Large, well-known extreme drought events were detected, e.g. 2003 drought in Europe, 2002 and 2011 drought in the U.S., and 2010 drought in Russia. Our results show that extreme drought-induced loss of ecosystem function could impact 0.9% ± 0.1% of earth's vegetated land per year and was mainly distributed in semi-arid regions. The reduced carbon uptake caused by functional loss (0.14 ± 0.03 PgC/yr) could explain &gt;70% of the interannual variation in GPP in drought-affected areas (p ≤ 0.001). Our results highlight the impact of ecosystem function loss in semi-arid regions with increasing precipitation variability and dry land expansion expected in the future. Copyright © 2018 Elsevier B.V. All rights reserved.

The interaction between terrestrial carbon (C) and nitrogen (N) cycles has been incorporated into more and more land surface models. However, the scheme of C–N coupling differs greatly among models, and how these diverse representations of C–N interactions will affect C-cycle modeling remains unclear. In this study, we explored how the simulated ecosystem C storage capacity in the terrestrial ecosystem (TECO) model varied with three different commonly used schemes of C–N coupling. The three schemes (SM1, SM2, and SM3) have been used in three different coupled C–N models (i.e., TECO-CN, CLM 4.5, and O-CN, respectively). They differ mainly in the stoichiometry of C and N in vegetation and soils, plant N uptake strategies, downregulation of photosynthesis, and the pathways of N import. We incorporated the three C–N coupling schemes into the C-only version of the TECO model and evaluated their impacts on the C cycle with a traceability framework. Our results showed that all three of the C–N schemes caused significant reductions in steady-state C storage capacity compared with the C-only version with magnitudes of <span class="inline-formula">−23</span> %, <span class="inline-formula">−30</span> %, and <span class="inline-formula">−54</span> % for SM1, SM2, and SM3, respectively. This reduced C storage capacity was mainly derived from the combined effects of decreases in net primary productivity (NPP; <span class="inline-formula">−29</span> %, <span class="inline-formula">−15</span> %, and <span class="inline-formula">−</span>45 %) and changes in mean C residence time (MRT; 9 %, <span class="inline-formula">−17</span> %, and <span class="inline-formula">−17</span> %) for SM1, SM2, and SM3, respectively. The differences in NPP are mainly attributed to the different assumptions on plant N uptake, plant tissue <span class="inline-formula">C</span> <span class="inline-formula">:</span> <span class="inline-formula">N</span> ratio, downregulation of photosynthesis, and biological N fixation. In comparison, the alternative representations of the plant vs. microbe competition strategy and the plant N uptake, combined with the flexible <span class="inline-formula">C</span> <span class="inline-formula">:</span> <span class="inline-formula">N</span> ratio in vegetation and soils, led to a notable spread in MRT. These results highlight the fact that the diverse assumptions on N processes represented by different C–N coupled models could cause additional uncertainty for land surface models. Understanding their difference can help us improve the capability of models to predict future biogeochemical cycles of terrestrial ecosystems.

Roads are a main threat to biodiversity conservation in the Amazon, in part, because roads increase access for hunters. We examine how increased landscape access by hunters may lead to cascading effects that influence the prey community and abundance of the jaguar (Panthera onca), the top Amazonian terrestrial predator. Understanding such ecological effects originating from anthropogenic actions is essential for conservation and management of wildlife populations in areas undergoing infrastructure development. Our study was conducted in Yasuní Biosphere Reserve, the protected area with highest potential for jaguar conservation in Ecuador, and an area both threatened by road development and inhabited by indigenous groups dependent upon bushmeat. We surveyed prey and jaguar abundance with camera traps in four sites that differed in accessibility to hunters and used site occupancy and spatially explicit capture-recapture analyses to evaluate prey occurrence and estimate jaguar density, respectively. Higher landscape accessibility to hunters was linked with lower occurrence and biomass of game, particularly white-lipped peccary (Tayassu pecari) and collared peccary (Pecari tajacu), the primary game for hunters and prey for jaguars. Jaguar density was up to 18 times higher in the most remote site compared to the most accessible site. Our results provide a strong case for the need to: 1) consider conservation of large carnivores and other wildlife in policies about road construction in protected areas, 2) coordinate conservation initiatives with local governments so that development activities do not conflict with conservation objectives, and 3) promote development of community-based strategies for wildlife management that account for the needs of large carnivores.

Plant functional traits research has revealed many interesting and important patterns among morphological, physiological, and life-history traits and the environment. These are exemplified in trade-offs between groups of traits such as those embodied in the leaf and wood economics spectra. Inferences from empirical studies are often constrained by the correlative nature of the analyses, availability of trait data, and a focus on easily measured traits. However, empirical studies have been fundamental to modeling endeavors aiming to enhance our understanding of how functional traits scale up to affect, for example, community dynamics and ecosystem productivity. Here, we take a complementary approach utilizing an individual-based model of tree growth and mortality (the allometrically constrained growth and carbon allocation [ACGCA] model) to investigate the theoretical trait space (TTS) of North American trees. The model includes 32 parameters representing allometric, physiological, and anatomical traits, some overlapping leaf and wood economics spectra traits. Using a Bayesian approach, we fit the ACGCA model to individual tree heights and diameters from the USFS Forest Inventory and Analysis (FIA) dataset, with further constraints by literature-based priors. Fitting the model to 1.3 million FIA records?aggregated across individuals, species, and sites?produced a posterior distribution of traits leading to realistic growth. We explored this multidimensional posterior distribution (the TTS) to evaluate trait?trait relationships emerging from the ACGCA model, and compare these against empirical patterns reported in the literature. Only three notable bivariate correlations, among 496 possible trait pairs, were contained in the TTS. However, stepwise regressions uncovered a complicated structure; only a subset of traits?related to photosynthesis (e.g., radiation-use efficiency and maintenance respiration)?exhibited strong multivariate trade-offs with each other, while half of the traits?mostly related to allometries and construction costs?varied independently of other traits. Interestingly, specific leaf area was related to several rarely measured root traits. The trade-offs contained in the TTS generally reflect mass-balance (related to carbon allocation) and engineering (mostly related to allometries) trade-offs represented in the ACGCA model and point to potentially important traits that are under-explored in field studies (e.g., root traits and branch senescence rates).

We refer to a theoretical trait space (TTS) as an n-dimensional hypervolume (hypercube) characterizing the range of values and covariations among multiple functional traits, in the absence of explicit filtering mechanisms. We previously constructed a 32-dimensional TTS for North American trees by fitting the Allometrically Constrained Growth and Carbon Allocation (ACGCA) model to USFS Forest Inventory and Analysis (FIA) data. Here, we sampled traits from this TTS, representing different individual ?trees,? and subjected these trees to a series of gap dynamics simulations resulting in different annual light levels to explore the impact of environmental filtering (light stress) on the trait space. Variation in light limitation led to non-random mortality and a refinement of the TTS. We investigated potential mechanisms underlying such filtering processes by exploring how traits and the environment relate to mortality rates at the tree, phenotype (a specific set of trait values), and stand (a specific gap scenario) levels. The average light level at the forest floor explained 42% of the stand-level mortality, while phenotype- and tree-level mortality were best explained by six functional traits, especially radiation-use efficiency, maximum tree height, and xylem conducting area to sapwood area ratio (?X). These six ?mortality? traits and six traits related to the leaf and wood economics spectra were used to construct trait hypercubes represented by trees that died or survived each gap scenario. For trees that survived, the volume of their refined trait space decreased linearly with increasing stand-level mortality (up to ~50% mortality); the location also shifted, as indicated by non-zero distances between the hypercube centroids of surviving trees compared to dead trees and the original TTS. Overall, the patterns were consistent with empirical studies of functional traits, in terms of which traits predict mortality and the direction of the relationships. This work, however, also identified potentially important functional traits that are not commonly measured in empirical studies, such as ?X and senescence rates of relatively long-lived tissues.

Data-model integration plays a critical role in assessing and improving our capacity to predict ecosystem dynamics. Similarly, the ability to attach quantitative statements of uncertainty around model forecasts is crucial for model assessment and interpretation and for setting field research priorities. Bayesian methods provide a rigorous data assimilation framework for these applications, especially for problems with multiple data constraints. However, the Markov chain Monte Carlo (MCMC) techniques underlying most Bayesian calibration can be prohibitive for computationally demanding models and large datasets. We employ an alternative method, Bayesian model emulation of sufficient statistics, that can approximate the full joint posterior density, is more amenable to parallelization, and provides an estimate of parameter sensitivity. Analysis involved informative priors constructed from a meta-analysis of the primary literature and specification of both model and data uncertainties, and it introduced novel approaches to autocorrelation corrections on multiple data streams and emulating the sufficient statistics surface. We report the integration of this method within an ecological workflow management software, Predictive Ecosystem Analyzer (PEcAn), and its application and validation with two process-based terrestrial ecosystem models: SIPNET and ED2. In a test against a synthetic dataset, the emulator was able to retrieve the true parameter values. A comparison of the emulator approach to standard <q>brute-force</q> MCMC involving multiple data constraints showed that the emulator method was able to constrain the faster and simpler SIPNET model's parameters with comparable performance to the brute-force approach but reduced computation time by more than 2 orders of magnitude. The emulator was then applied to calibration of the ED2 model, whose complexity precludes standard (brute-force) Bayesian data assimilation techniques. Both models are constrained after assimilation of the observational data with the emulator method, reducing the uncertainty around their predictions. Performance metrics showed increased agreement between model predictions and data. Our study furthers efforts toward reducing model uncertainties, showing that the emulator method makes it possible to efficiently calibrate complex models.

Native soil organic carbon (SOC) decomposition rates may be altered through increased carbon (C) input, a phenomenon known as SOC priming (Blagodatskaya et al., 2011). Quantifying priming is important because it may modulate long-term SOC storage in ecosystems and therefore C biogeochemical cycling. Priming is positive when more SOC is decomposed or, conversely, negative when less native SOC is decomposed after C amendment (Kuzyakov et al., 2000; Kuzyakov, 2002; Bader and Cheng, 2007). Yet, controls over the direction and magnitude of the priming effect and the consequences for soil C balance remain uncertain (Dijkstra et al., 2013; Liu et al., 2017).

Runoff in the United States is changing, and this study finds that the measured change is dependent on the geographic region and varies seasonally. Specifically, observed annual total runoff had an insignificant increasing trend in the US between 1950 and 2010, but this insignificance was due to regional heterogeneity with both significant and insignificant increases in the eastern, northern, and southern US, and a greater significant decrease in the western US. Trends for seasonal mean runoff also differed across regions. By region, the season with the largest observed trend was autumn for the east (positive), spring for the north (positive), winter for the south (positive), winter for the west (negative), and autumn for the US as a whole (positive). Based on the detection and attribution analysis using gridded WaterWatch runoff observations along with semi-factorial land surface model simulations from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we found that while the roles of CO 2 concentration, nitrogen deposition, and land use and land cover were inconsistent regionally and seasonally, the effect of climatic variations was detected for all regions and seasons, and the change in runoff could be attributed to climate change in summer and autumn in the south and in autumn in the west. We also found that the climate-only and historical transient simulations consistently underestimated the runoff trends, possibly due to precipitation bias in the MsTMIP driver or within the models themselves.

Despite the importance of nonstructural carbohydrates (NSC) for growth and survival in woody plants, we know little about whole-tree NSC storage. The conventional theory suggests that NSC reserves will increase over the growing season and decrease over the dormant season. Here, we compare storage in five temperate tree species to determine the size and seasonal fluctuation of whole-tree total NSC pools as well as the contribution of individual organs. NSC concentrations in the branches, stemwood, and roots of 24 trees were measured across 12 months. We then scaled up concentrations to the whole-tree and ecosystem levels using allometric equations and forest stand inventory data. While whole-tree total NSC pools followed the conventional theory, sugar pools peaked in the dormant season and starch pools in the growing season. Seasonal depletion of total NSCs was minimal at the whole-tree level, but substantial at the organ level, particularly in branches. Surprisingly, roots were not the major storage organ as branches stored comparable amounts of starch throughout the year, and root reserves were not used to support springtime growth. Scaling up NSC concentrations to the ecosystem level, we find that commonly used, process-based ecosystem and land surface models all overpredict NSC storage.

Plants store nonstructural carbohydrates (NSCs), such as sugars and starch, to use as carbon and energy sources for daily maintenance and growth needs as well as during times of stress. Allocation of NSCs to storage provides an important physiological strategy associated with future growth and survival, and thus understanding the seasonal patterns of NSC reserves provides insight into how species with different traits (e.g., growth form, leaf habit, wood anatomy) may respond to stress. We characterized the seasonal patterns of NSCs in four woody boreal plant species in Minnesota, USA. Sugar and starch concentrations were measured across the year in the roots and branches of two conifer trees, black spruce (Picea mariana (Mill.) B.S.P.) and eastern tamarack (Larix laricina (Du Roi) K. Koch), as well as in the leaves and branches of two evergreen broadleaf shrubs, bog Labrador tea (Rhododendron groenlandicum (Oeder) Kron & Judd) and leatherleaf (Chamaedaphne calyculata (L.) Moench). In general, seasonal variation was dominated by changes in starch across all organs and species. While similar seasonal patterns of NSCs were observed in the shrubs, different seasonal patterns were observed between the trees, particularly in the roots. Our results suggest that species-specific traits likely have consequences for organ-level storage dynamics, which may influence whole-plant growth and survival under global change.

Disruptions of natural flow impair rivers and streams worldwide. Those conducting restoration efforts have rarely explored how and when stream ecosystems can recover after reinstating natural flows. We quantified responses of ecosystem metabolism and N dynamics to the decommissioning and removal of a 100-y-old diversion dam in a desert stream, Fossil Creek, Arizona. Fossil Creek is a travertine river, meaning that CaCO3 concentrations in water in the springs that feed Fossil Creek are high enough to precipitate out of the water to form travertine terraces and deep pools. The majority of flow was diverted for power generation, so travertine deposition rates were significantly reduced and travertine terraces were smaller and less frequent compared to pre-dam historical records. Flow restoration enabled the recovery of the geochemical process of travertine deposition and increased gross primary production and N uptake to rates comparable to those measured in an upstream, reference reach. Reinstating a river’s natural flow regime can result in rapid and near-complete recovery of fundamental ecosystem processes that reshape the aquatic food web.

Rain-fed peatlands are dominated by peat mosses (Sphagnum sp.), which for their growth depend on nutrients, water and CO2 uptake from the atmosphere. As the isotopic composition of carbon (12,13C) and oxygen (16,18O) of these Sphagnum mosses are affected by environmental conditions, Sphagnum tissue accumulated in peat constitutes a potential long-term archive that can be used for climate reconstruction. However, there is inadequate understanding of how isotope values are influenced by environmental conditions, which restricts their current use as environmental and palaeoenvironmental indicators. Here we tested (i) to what extent C and O isotopic variation in living tissue of Sphagnum is species-specific and associated with local hydrological gradients, climatic gradients (evapotranspiration, temperature, precipitation) and elevation; (ii) whether the C isotopic signature can be a proxy for net primary productivity (NPP) of Sphagnum; and (iii) to what extent Sphagnum tissue δ18O tracks the δ18O isotope signature of precipitation. In total, we analysed 337 samples from 93 sites across North America and Eurasia using two important peat-forming Sphagnum species (S. magellanicum, S. fuscum) common to the Holarctic realm. There were differences in δ13C values between species. For S. magellanicum δ13C decreased with increasing height above the water table (HWT, R2 = 17%) and was positively correlated to productivity (R2 = 7%). Together these two variables explained 46% of the between-site variation in δ13C values. For S. fuscum, productivity was the only significant predictor of δ13C but had low explanatory power (total R2 = 6%). For δ18O values, approximately 90% of the variation was found between sites. Globally modelled annual δ18O values in precipitation explained 69% of the between-site variation in tissue δ18O. S. magellanicum showed lower δ18O enrichment than S. fuscum (−0.83‰ lower). Elevation and climatic variables were weak predictors of tissue δ18O values after controlling for δ18O values of the precipitation. To summarize, our study provides evidence for (a) good predictability of tissue δ18O values from modelled annual δ18O values in precipitation, and (b) the possibility of relating tissue δ13C values to HWT and NPP, but this appears to be species-dependent. These results suggest that isotope composition can be used on a large scale for climatic reconstructions but that such models should be species-specific.

Classical niche partitioning theory posits increased competition for and partitioning of the most limiting resource among coexisting species. Coexisting plant species may vary in rooting depth, reflecting niche partitioning in water source use. Our goal was to assess the soil water partitioning of woody plant communities across northern Arizona along an elevational moisture gradient using stem and soil water isotopes from two sampling periods to estimate the use of different water sources. We hypothesized that niche overlap of water sources would be higher and monsoon precipitation uptake would be lower at sites with higher moisture availability. Pairwise niche overlap of coexisting species was calculated using mixing model estimates of proportional water use for three sources. Across the moisture gradient, niche overlap increased with site moisture index (precipitation/potential evapotranspiration) across seasons, and site moisture index explained 37% of the variation in niche overlap of intermediate and deeper sources of water. Desert trees utilized more winter source water than desert shrubs, suggesting the partitioning of water sources between functional groups. However, seasonal differences in surface water use were primarily found at intermediate levels of site moisture availability. Our findings support classical niche partitioning theory in that plants exhibit higher overlap of water sources when water is not a limiting resource.

Accurate climate projections require an understanding of the effects of warming on ecological communities and the underlying mechanisms that drive them1–3. However, little is known about the effects of climate warming on the succession of microbial communities4,5. Here we examined the temporal succession of soil microbes in a long-term climate change experiment at a tall-grass prairie ecosystem. Experimental warming was found to significantly alter the community structure of bacteria and fungi. By determining the time-decay relationships and the paired differences of microbial communities under warming and ambient conditions, experimental warming was shown to lead to increasingly divergent succession of the soil microbial communities, with possibly higher impacts on fungi than bacteria. Variation partition- and null model-based analyses indicate that stochastic processes played larger roles than deterministic ones in explaining microbial community taxonomic and phylogenetic compositions. However, in warmed soils, the relative importance of stochastic processes decreased over time, indicating a potential deterministic environmental filtering elicited by warming. Although successional trajectories of microbial communities are difficult to predict under future climate change scenarios, their composition and structure are projected to be less variable due to warming-driven selection.

Recently, severe droughts that occurred in North America are likely to have impacted its terrestrial carbon sink. However, process-based understanding of how meteorological conditions prior to the onset of drought, for instance warm or cold springs, affect drought-induced carbon cycle effects remains scarce. Here we assess and compare the response of terrestrial carbon fluxes to summer droughts in 2011 and 2012 characterized by contrasting spring conditions. The analysis is based on a comprehensive ensemble of carbon cycle models, including FLUXCOM, TRENDY v5, SiBCASA, CarbonTracker Europe, and CarbonTracker, and emerging Earth observations. In 2011, large reductions of net ecosystem production (NEP; ?0.24 ± 0.17 Pg C/year) are due to decreased gross primary production (?0.17 ± 0.18 Pg C/year) and slightly increased ecosystem respiration (+0.07 ± 0.17 Pg C/year). Conversely, in 2012, NEP reductions (?0.17 ± 0.25 Pg C/year) are attributed to a larger increase of ecosystem respiration (+0.48 ± 0.27 Pg C/year) than gross primary production (+0.31 ± 0.29 Pg C/year), induced predominantly by an extra warmer spring prior to summer drought. Two temperate ecoregions crops/agriculture and the grass/shrubs contribute largest to these reductions and also dominate the interannual variations of NEP during 2007?2014. Moreover, the warming spring compensated largely the negative carbon anomaly due to summer drought, consistent with earlier studies; however, the compensation occurred only in some specific ecoregions. Overall, our analysis offers a refined view on recent carbon cycle variability and extremes in North America. It corroborates earlier results but also highlights differences with respect to ecoregion-specific carbon cycle responses to drought and heat.

Phenology is the study of recurring events in nature and their relationships with climate. The word derives from the Greek phaínō ‘appear’ and logos ‘reason’, emphasizing the focus on observing events and understanding why they occur (Demarée and Rutishauser 2009). Phenological recording has a history that dates back many centuries (Linneaus and Bark 1753; Aono and Kazui 2008). More recently, advances in monitoring technologies have enabled automated and remotely sensed observations, complemented by increasing citizen science participation in monitoring efforts. Phenological information can also be derived from widespread environmental monitoring stations around the globe. Phenological records clearly demonstrate the biological effects of year-to-year variability in climate, as well as longer-term trends associated with environmental change. Phenological monitoring thus plays an important role in understanding how our planet is changing. Changes in the growing season, for example, are more tangible and more readily conveyed to the general public than seemingly small changes in mean annual temperature. Here, we describe just a fraction of the phenological information currently available, highlighting northern hemisphere records of phenology of primary producers across a range of spatial and temporal scales.

The release of permafrost-derived nitrogen (N) has the potential to fertilize tundra vegetation, which in turn may stimulate productivity and thus offset carbon (C) losses from thawing permafrost. Below-ground plant traits may mediate ecosystem response to permafrost thaw and associated feedbacks to the atmosphere by differentially conferring access to deep, newly thawed permafrost N. Yet, identifying roots and quantifying root N uptake from deep, cold soils in complex plant communities has proved challenging to date. We investigated plant acquisition of experimentally added 15N isotope tracer applied at the permafrost boundary in graminoid- and shrub-dominated tundra at Eight Mile Lake, Alaska, when the thaw front was close to its maximum depth, simulating the release of newly thawed permafrost N. We used molecular tools to verify species and estimate biomass, nitrogen, and isotope pools. Root biomass depth distributions follow an asymptotic relationship with depth, typical of other ecosystems. Few species had roots occurring close to the thaw front. Rubus chamaemorus, a short-statured non-mycorrhizal forb, and Carex bigelowii, a sedge, consistently had the deepest roots. Twenty-four hours after isotope addition, we observed that deep-rooted, non-mycorrhizal species had the highest 15N enrichment values in their fine root tissue indicating that they access deep N late in the growing season when the thaw front is deepest. Deep-rooted plants are therefore able to immediately take up newly thawed permafrost-derived N. During the following growing season, herbaceous, non-mycorrhizal plants allocated tracer above-ground before woody, mycorrhizal plants. Ectomycorrhizal deciduous and ericoid mycorrhizal evergreen shrubs, by contrast, did not have immediate access to the deep N tracer and assimilated it into new foliar tissue gradually over the following growing season. Synthesis. Graminoids and forbs that have immediate access to deep N represent a modest C sink compared to C emissions from thawing permafrost. However, the effects of deep N fertilization on shrubs over longer time-scales may stimulate productivity and account for a more considerable N and C sink, thus constraining the permafrost C-climate feedback.

A significant fraction of the terrestrial biosphere comprises biomes containing tree–grass mixtures. Forecasting vegetation dynamics in these environments requires a thorough understanding of how trees and grasses use and compete for key belowground resources. There is disagreement about the extent to which tree–grass vertical root separation occurs in these ecosystems, how this overlap varies across large-scale environmental gradients, and what these rooting differences imply for water resource availability and tree–grass competition and coexistence. To assess the extent of tree–grass rooting overlap and how tree and grass rooting patterns vary across resource gradients, we examined landscape-level patterns of tree and grass functional rooting depth along a mean annual precipitation (MAP) gradient extending from ~ 450 to ~ 750 mm year−1 in Kruger National Park, South Africa. We used stable isotopes from soil and stem water to make inferences about relative differences in rooting depth between these two functional groups. We found clear differences in rooting depth between grasses and trees across the MAP gradient, with grasses generally exhibiting shallower rooting profiles than trees. We also found that trees tended to become more shallow-rooted as a function of MAP, to the point that trees and grasses largely overlapped in terms of rooting depth at the wettest sites. Our results reconcile previously conflicting evidence for rooting overlap in this system, and have important implications for understanding tree–grass dynamics under altered precipitation scenarios.

Mosses are critical components of boreal ecosystems where they typically account for a large proportion of net primary productivity and harbour diverse bacterial communities that can be the major source of biologically-fixed nitrogen in these ecosystems. Despite their ecological importance, we have limited understanding of how microbial communities vary across boreal moss species and the extent to which local site conditions may influence the composition of these bacterial communities. We used marker gene sequencing to analyze bacterial communities associated with seven boreal moss species collected near Fairbanks, AK, USA. We found that host identity was more important than site in determining bacterial community composition and that mosses harbour diverse lineages of potential N2-fixers as well as an abundance of novel taxa assigned to understudied bacterial phyla (including candidate phylum WPS-2). We performed shotgun metagenomic sequencing to assemble genomes from the WPS-2 candidate phylum and found that these moss-associated bacteria are likely anoxygenic phototrophs capable of carbon fixation via RuBisCo with an ability to utilize byproducts of photorespiration from hosts via a glyoxylate shunt. These results give new insights into the metabolic capabilities of understudied bacterial lineages that associate with mosses and the importance of plant hosts in shaping their microbiomes.

Climate is predicted to change over the 21st century. However, little is known about how climate change can affect soil phosphorus (P) cycle and availability in global terrestrial ecosystems, where P is a key limiting nutrient. With a global database of Hedley P fractions and key‐associated physiochemical properties of 760 (seminatural) natural soils compiled from 96 published studies, this study evaluated how climate pattern affected soil P cycle and availability in global terrestrial ecosystems. Overall, soil available P, indexed by Hedley labile inorganic P fraction, significantly decreased with increasing mean annual temperature (MAT) and precipitation (MAP). Hypothesis‐oriented path model analysis suggests that MAT negatively affected soil available P mainly by decreasing soil organic P and primary mineral P and increasing soil sand content. MAP negatively affected soil available P both directly and indirectly through decreasing soil primary mineral P; however, these negative effects were offset by the positive effects of MAP on soil organic P and fine soil particles, resulting in a relatively minor total MAP effect on soil available P. As aridity degree was mainly determined by MAP, aridity also had a relatively minor total effect on soil available P. These global patterns generally hold true irrespective of soil depth (less than or equal to 10 cm or greater than 10 cm) or site aridity index (less than or equal to 1.0 or greater than 1.0), and were also true for the low‐sand (less than or equal to 50%) soils. In contrast, available P of the high‐sand (greater than 50%) soils was positively affected by MAT and aridity and negatively affected by MAP. Our results suggest that temperature and precipitation have contrasting effects on soil P availability and can interact with soil particle size to control soil P availability.

The annual peak growth of vegetation is critical in characterizing the capacity of terrestrial ecosystem productivity and shaping the seasonality of atmospheric CO2 concentrations. The recent greening of global lands suggests an increasing trend of terrestrial vegetation growth, but whether or not the peak growth has been globally enhanced still remains unclear. Here, we use two global datasets of gross primary productivity (GPP) and a satellite-derived Normalized Difference Vegetation Index (NDVI) to characterize recent changes in annual peak vegetation growth (that is, GPPmax and NDVImax). We demonstrate that the peak in the growth of global vegetation has been linearly increasing during the past three decades. About 65% of the NDVImax variation is evenly explained by expanding croplands (21%), rising CO2 (22%) and intensifying nitrogen deposition (22%). The contribution of expanding croplands to the peak growth trend is substantiated by measurements from eddy-flux towers, sun-induced chlorophyll fluorescence and a global database of plant traits, all of which demonstrate that croplands have a higher photosynthetic capacity than other vegetation types. The large contribution of CO2 is also supported by a meta-analysis of 466 manipulative experiments and 15 terrestrial biosphere models. Furthermore, we show that the contribution of GPPmax to the change in annual GPP is less in the tropics than in other regions. These multiple lines of evidence reveal an increasing trend in the peak growth of global vegetation. The findings highlight the important roles of agricultural intensification and atmospheric changes in reshaping the seasonality of global vegetation growth.

The terrestrial carbon (C) cycle has been commonly represented by a series of C balance equations to track C influxes into and effluxes out of individual pools in earth system models (ESMs). This representation matches our understanding of C cycle processes well but makes it difficult to track model behaviors. It is also computationally expensive, limiting the ability to conduct comprehensive parametric sensitivity analyses. To overcome these challenges, we have developed a matrix approach, which reorganizes the C balance equations in the original ESM into one matrix equation without changing any modeled C cycle processes and mechanisms. We applied the matrix approach to the Community Land Model (CLM4.5) with vertically‐resolved biogeochemistry. The matrix equation exactly reproduces litter and soil organic carbon (SOC) dynamics of the standard CLM4.5 across different spatial‐temporal scales. The matrix approach enables effective diagnosis of system properties such as C residence time and attribution of global change impacts to relevant processes. We illustrated, for example, the impacts of CO2 fertilization on litter and SOC dynamics can be easily decomposed into the relative contributions from C input, allocation of external C into different C pools, nitrogen regulation, altered soil environmental conditions, and vertical mixing along the soil profile. In addition, the matrix tool can accelerate model spin‐up, permit thorough parametric sensitivity tests, enable pool‐based data assimilation, and facilitate tracking and benchmarking of model behaviors. Overall, the matrix approach can make a broad range of future modeling activities more efficient and effective.

Abstract Phenology is a first-order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climate system. Here, we present the PHENOR R package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the USA National Phenology Network (USA-NPN), the Pan European Phenology Project (PEP725), MODIS phenology (MCD12Q2) combined with (global) retrospective and projected climate data. We show an example analysis, using the phenor modelling framework, which quickly and easily compares 20 included spring phenology models for three plant functional types. An analysis of model skill using the root mean squared (RMSE) error shows little or no difference regardless of model structure, corroborating previous studies. We argue that addressing this issue will require novel model development combined with easy data assimilation as facilitated by our framework. In conclusion, we hope the phenor phenology modelling framework in the r language and environment for statistical computing will facilitate reproducibility and community driven phenology model development, in order to increase their overall predictive power, and leverage an ever growing number of phenology data products.

Dominant canopy tree species have strong effects on the composition and function of understory species, particularly bryophytes. In boreal forests, bryophytes and their associated microbes are a primary source of ecosystem nitrogen (N) inputs, and an important process regulating ecosystem productivity. We investigated how feather moss-associated N₂-fixation rates and contribution to N budgets vary in time and space among coniferous and broadleaf deciduous forests. We measured N₂-fixation rates using stable isotope (¹⁵N₂) labeling in two moss species (<em class="EmphasisTypeItalic ">Pleurozium schreberi</em> and <em class="EmphasisTypeItalic ">Hylocomium splendens</em>) in broadleaf deciduous (Alaska paper birch—<em class="EmphasisTypeItalic ">Betula neoalaskana</em>) and coniferous (black spruce—<em class="EmphasisTypeItalic ">Picea mariana</em>) stands near Fairbanks, interior Alaska, from 2013 to 2015. N₂-fixation rates showed substantial inter-annual variation among the 3 years. High N₂-fixation was more strongly associated with high precipitation than air temperature or light availability. Overall, contribution of N₂-fixation to N budgets was greater in spruce than in birch stands. Our results enhance the knowledge of the processes that drive N₂-fixation in boreal forests, which is important for predicting ecosystem consequences of changing forest composition.

The contemporary Arctic carbon balance is uncertain, and the potential for a permafrost carbon feedback of anywhere from 50 to 200 petagrams of carbon (Schuur et al., 2015) compromises accurate 21st-century global climate system projections. The 42-year record of atmospheric CO2 measurements at Barrow, Alaska (71.29 N, 156.79 W), reveals significant trends in regional land-surface CO2 anomalies (ΔCO2), indicating long-term changes in seasonal carbon uptake and respiration. Using a carbon balance model constrained by ΔCO2, we find a 13.4% decrease in mean carbon residence time (50% confidence range = 9.2 to 17.6%) in North Slope tundra ecosystems during the past four decades, suggesting a transition toward a boreal carbon cycling regime. Temperature dependencies of respiration and carbon uptake suggest that increases in cold season Arctic labile carbon release will likely continue to exceed increases in net growing season carbon uptake under continued warming trends.

The ability to forecast ecological carbon cycling is imperative to land management in a world where past carbon fluxes are no longer a clear guide in the Anthropocene. However, carbon‐flux forecasting has not been practiced routinely like numerical weather prediction. This study explored (1) the relative contributions of model forcing data and parameters to uncertainty in forecasting flux‐ versus pool‐based carbon cycle variables and (2) the time points when temperature and CO2 treatments may cause statistically detectable differences in those variables. We developed an online forecasting workflow (Ecological Platform for Assimilation of Data (EcoPAD)), which facilitates iterative data‐model integration. EcoPAD automates data transfer from sensor networks, data assimilation, and ecological forecasting. We used the Spruce and Peatland Responses Under Changing Experiments data collected from 2011 to 2014 to constrain the parameters in the Terrestrial Ecosystem Model, forecast carbon cycle responses to elevated CO2 and a gradient of warming from 2015 to 2024, and specify uncertainties in the model output. Our results showed that data assimilation substantially reduces forecasting uncertainties. Interestingly, we found that the stochasticity of future external forcing contributed more to the uncertainty of forecasting future dynamics of C flux‐related variables than model parameters. However, the parameter uncertainty primarily contributes to the uncertainty in forecasting C pool‐related response variables. Given the uncertainties in forecasting carbon fluxes and pools, our analysis showed that statistically different responses of fast‐turnover pools to various CO2 and warming treatments were observed sooner than slow‐turnover pools. Our study has identified the sources of uncertainties in model prediction and thus leads to improve ecological carbon cycling forecasts in the future.

In deciduous forests, spring leaf phenology controls the onset of numerous ecosystem functions. While most studies have focused on a single annual spring event, such as budburst, ecosystem functions like photosynthesis and transpiration increase gradually after budburst, as leaves grow to their mature size. Here, we examine the “velocity of green-up,” or duration between budburst and leaf maturity, in deciduous forest ecosystems of eastern North America. We use a diverse data set that includes 301 site-years of phenocam data across a range of sites, as well as 22 years of direct ground observations of individual trees and 3 years of fine-scale high-frequency aerial photography, both from Harvard Forest. We find a significant association between later start of spring and faster green-up: − 0.47 ± 0.04 (slope ± 1 SE) days change in length of green-up for every day later start of spring within phenocam sites, − 0.31 ± 0.06 days/day for trees under direct observation, and − 1.61 ± 0.08 days/day spatially across fine-scale landscape units. To explore the climatic drivers of spring leaf development, we fit degree-day models to the observational data from Harvard Forest. We find that the default phenology parameters of the ecosystem model PnET make biased predictions of leaf initiation (39 days early) and maturity (13 days late) for red oak, while the optimized model has biases of 1 day or less. Springtime productivity predictions using optimized parameters are closer to results driven by observational data (within 1%) than those of the default parameterization (17% difference). Our study advances empirical understanding of the link between early and late spring phenophases and demonstrates that accurately modeling these transitions is important for simulating seasonal variation in ecosystem productivity.

Forest phenology is a multi-scale phenomenon, arising from processes in leaves and trees, with effects on the ecology of plant communities and landscapes. Because phenology controls carbon and water cycles, which are commonly observed at the ecosystem scale (e.g. eddy flux measurements), it is important to characterize the relation between phenophase transition events at different spatial scales. We use aerial photography recorded from an unmanned aerial vehicle (UAV) to observe plant phenology over a large area (5.4 ha) and across diverse communities, with spatial and temporal resolution at the scale of individual tree crowns and their phenophase transition events (10 m spatial resolution, ∼5 day temporal resolution in spring, weekly in autumn). We validate UAV-derived phenophase transition dates through comparison with direct observations of tree phenology, PhenoCam image analysis, and satellite remote sensing. We then examine the biological correlates of spatial variance in phenology using a detailed species inventory and land cover classification. Our results show that species distribution is the dominant factor in spatial variability of ecosystem phenology. We also explore statistical relations governing the scaling of phenology from an organismic scale (10 m) to forested landscapes (1 km) by analyzing UAV photography alongside Landsat and MODIS data. From this analysis we find that spatial standard deviation in transition dates decreases linearly with the logarithm of increasing pixel size. We also find that fine-scale phenology aggregates to a coarser scale as the median and not the mean date in autumn, indicating coarser scale phenology is less sensitive to the tails of the distribution of sub-pixel transitions in the study area. Our study is the first to observe forest phenology in a spatially comprehensive, whole-ecosystem way, yet with fine enough spatial resolution to describe organism-level correlates and scaling phenomena.

Understanding how population-level dynamics contribute to ecosystem-level processes is a primary focus of ecological research and has led to important breakthroughs in the ecology of macroscopic organisms. However, the inability to measure population-specific rates, such as growth, for microbial taxa within natural assemblages has limited ecologists’ understanding of how microbial populations interact to regulate ecosystem processes. Here, we use isotope incorporation within DNA molecules to model taxon-specific population growth in the presence of 18O-labeled water. By applying this model to phylogenetic marker sequencing data collected from stable-isotope probing studies, we estimate rates of growth, mortality, and turnover for individual microbial populations within soil assemblages. When summed across the entire bacterial community, our taxon-specific estimates are within the range of other whole-assemblage measurements of bacterial turnover. Because it can be applied to environmental samples, the approach we present is broadly applicable to measuring population growth, mortality, and associated biogeochemical process rates of microbial taxa for a wide range of ecosystems and can help reveal how individual microbial populations drive biogeochemical fluxes.

Snow is important for local to global climate and surface hydrology, but spatial and temporal heterogeneity in the extent of snow cover make accurate, fine-scale mapping and monitoring of snow an enormous challenge. We took 184,453 daily near-surface images acquired by 133 automated cameras and processed them using crowdsourcing and deep learning to determine whether snow was present or absent in each image. We found that the crowdsourced data had an accuracy of 99.1% when compared with expert evaluation of the same imagery. We then used the image classification to train a deep convolutional neural network via transfer learning, with accuracies of 92% to 98%, depending on the image set and training method. The majority of neural network errors were due to snow that was present not being detected. We used the results of the neural networks to validate the presence or absence of snow inferred from the MODIS satellite sensor and obtained similar results to those from other validation studies. This method of using automated sensors, crowdsourcing, and deep learning in combination produced an accurate high temporal dataset of snow presence across a continent. It holds broad potential for real-time large-scale acquisition and processing of ecological and environmental data in support of monitoring, management, and research objectives.

Non-structural carbohydrates (NSCs), the stored products of photosynthesis, building blocks for growth and fuel for respiration, are central to plant metabolism, but their measurement is challenging. Differences in methods and procedures among laboratories can cause results to vary widely, limiting our ability to integrate and generalize patterns in plant carbon balance among studies. A recent assessment found that NSC concentrations measured for a common set of samples can vary by an order of magnitude, but sources for this variability were unclear. We measured a common set of nine plant material types, and two synthetic samples with known NSC concentrations, using a common protocol for sugar extraction and starch digestion, and three different sugar quantification methods (ion chromatography, enzyme, acid) in six laboratories. We also tested how sample handling, extraction solvent and centralizing parts of the procedure in one laboratory affected results. Non-structural carbohydrate concentrations measured for synthetic samples were within about 11.5% of known values for all three methods. However, differences among quantification methods were the largest source of variation in NSC measurements for natural plant samples because the three methods quantify different NSCs. The enzyme method quantified only glucose, fructose and sucrose, with ion chromatography we additionally quantified galactose, while the acid method quantified a large range of mono- and oligosaccharides. For some natural samples, sugars quantified with the acid method were two to five times higher than with other methods, demonstrating that trees allocate carbon to a range of sugar molecules. Sample handling had little effect on measurements, while ethanol sugar extraction improved accuracy over water extraction. Our results demonstrate that reasonable accuracy of NSC measurements can be achieved when different methods are used, as long as protocols are robust and standardized. Thus, we provide detailed protocols for the extraction, digestion and quantification of NSCs in plant samples, which should improve the comparability of NSC measurements among laboratories.

Clouds and aerosols increase the fraction of global solar irradiance that is diffuse light. This phenomenon is known to increase the photosynthetic light use efficiency (LUE) of closed-canopy vegetation by redistributing photosynthetic photon flux density (400–700 nm) from saturated, sunlit leaves at the top of the canopy, to shaded leaves deeper in the canopy. We combined a process-based carbon cycle model with 10 years of eddy covariance carbon flux measurements and other ancillary data sets to assess 1) how this LUE enhancement influences interannual variation in carbon uptake, and 2) how errors in modeling diffuse fraction affect predictions of carbon uptake. Modeled annual gross primary productivity (GPP) increased by ≈0.94% when observed levels of diffuse fraction were increased by 0.01 (holding total irradiance constant). The sensitivity of GPP to increases in diffuse fraction was highest when the diffuse fraction was low to begin with, and lowest when the diffuse fraction was already high. Diffuse fraction also explained significantly more of the interannual variability of modeled net ecosystem exchange (NEE), than did total irradiance. Two tested radiation partitioning models yielded over- and underestimates of diffuse fraction at our site, which propagated to over- and underestimates of annual NEE, respectively. Our findings highlight the importance of incorporating LUE enhancement under diffuse light into models of global primary production, and improving models of diffuse fraction.

Recent evidences show that terrestrial biogeochemical models have large uncertainty in estimating climate-change effect on grassland net ecosystem productivity (NEP), which is defined as the difference between gross ecosystem photosynthesis (GEP) and respiration (ER). It remains unclear that whether GEP or ER limits the model capability to simulate NEP responses to climate change in semiarid grasslands. Given the surrogate CENTURY-type model is widely used for Earth system modeling, we investigated two of them (i.e., DAYCENT and TECO models) and examined which processes dominate their ability to capture the responses of NEP to experimental climate changes in a temperate steppe of northern China. During the simulation from 2006 to 2008, the two models captured the observed mean annual NEP in the control plots when they were validated by the observations from an adjacent eddy-flux tower. However, they failed to capture the treatment effects of experimental warming and increased precipitation on NEP because of the poor estimations of ER responses. DAYCENT model simulated a higher precipitation effect on ER (37.83%) and TECO model overestimated the warming effect on ER by 8.18%. The simulation of treatment effects on ER and therefore NEP can be improved by an optimized parameterization of the water-related decay functions for soil organic carbon (C). The simulated cumulative loss of total ecosystem C stock during 2010–2100 were decreased when the TECO model used experiment-fitted parameters (0.72 kg C m−2) instead of using the initial validation with eddy-flux data (0.96 kg C m−2). The ecosystem shifted from C sink to source at threshold of 435 mm of annual total precipitation. Our findings indicate that future projection of C cycle in semiarid grasslands could be improved by better understanding of water response of ecosystem respiratory processes.

Remote sensing of radiative indices must balance spatially and temporally coarse satellite measurements with finer-scale, but geographically limited, in-situ surface measurements. Instruments mounted upon an Unmanned Aerial Vehicle (UAV) can provide small-scale, mobile remote measurements that fill this resolution gap. Here we present and validate a novel method of obtaining albedo values using an unmodified quadcopter at a deciduous northern hardwood forest. We validate this method by comparing simultaneous albedo estimates by UAV and a fixed tower at the same site. We found that UAV provided stable albedo measurements across multiple flights, with results that were well within the range of tower-estimated albedo at similar forested sites. Our results indicate that in-situ albedo measurements (tower and UAV) capture more site-to-site variation in albedo than satellite measurements. Overall, we show that UAVs produce reliable, consistent albedo measurements that can capture crucial surface heterogeneity, clearly distinguishing between different land uses. Future application of this approach can provide detailed measurements of albedo and potentially other vegetation indices to enhance global research and modeling efforts.

The concentration–carbon feedback (β), also called the CO2 fertilization effect, is a key unknown in climate–carbon-cycle projections. A better understanding of model mechanisms that govern terrestrial ecosystem responses to elevated CO2 is urgently needed to enable a more accurate prediction of future terrestrial carbon sink. We conducted C-only, carbon–nitrogen (C–N) and carbon–nitrogen–phosphorus (C–N–P) simulations of the Community Atmosphere Biosphere Land Exchange model (CABLE) from 1901 to 2100 with fixed climate to identify the most critical model process that causes divergence in β. We calculated CO2 fertilization effects at various hierarchical levels from leaf biochemical reaction and leaf photosynthesis to canopy gross primary production (GPP), net primary production (NPP), and ecosystem carbon storage (cpool) for seven C3 plant functional types (PFTs) in response to increasing CO2 under the RCP 8.5 scenario. Our results show that β values at biochemical and leaf photosynthesis levels vary little across the seven PFTs, but greatly diverge at canopy and ecosystem levels in all simulations. The low variation of the leaf-level β is consistent with a theoretical analysis that leaf photosynthetic sensitivity to increasing CO2 concentration is almost an invariant function. In the CABLE model, the major jump in variation of β values from leaf levels to canopy and ecosystem levels results from divergence in modeled leaf area index (LAI) within and among PFTs. The correlation of βGPP, βNPP, or βcpool each with βLAI is very high in all simulations. Overall, our results indicate that modeled LAI is a key factor causing the divergence in β in the CABLE model. It is therefore urgent to constrain processes that regulate LAI dynamics in order to better represent the response of ecosystem productivity to increasing CO2 in Earth system models.

Soil respiration (RS), which is the second largest carbon flux between the atmosphere and terrestrial ecosystems, has significant impact on atmospheric CO2 concentration and climatic dynamics. Nitrogen (N) fertilizer has been heavily applied in agroecosystems at the global scale for high crop yields, and plays a major role in regulating RS. Although the respective response of soil biochemical property and RS to N addition has been widely studied, the contributions of soil biochemical parameters especially in the rhizosphere to changes in RS and its components (soil heterotrophic (RH) and autotrophic (RA) respiration) under N application remain poorly understood. The present study aimed to examine whether the rhizosphere effect alters the relationship between soil biochemical properties and RS under N addition in croplands.

Climate warming can result in both abiotic (e.g., permafrost thaw) and biotic (e.g., microbial functional genes) changes in Arctic tundra. Recent research has incorporated dynamic permafrost thaw in Earth system models (ESMs) and indicates that Arctic tundra could be a significant future carbon (C) source due to the enhanced decomposition of thawed deep soil C. However, warming‐induced biotic changes may influence biologically related parameters and the consequent projections in ESMs. How model parameters associated with biotic responses will change under warming and to what extent these changes affect projected C budgets have not been carefully examined. In this study, we synthesized six data sets over 5 years from a soil warming experiment at the Eight Mile Lake, Alaska, into the Terrestrial ECOsystem (TECO) model with a probabilistic inversion approach. The TECO model used multiple soil layers to track dynamics of thawed soil under different treatments. Our results show that warming increased light use efficiency of vegetation photosynthesis but decreased baseline (i.e., environment‐corrected) turnover rates of SOC in both the fast and slow pools in comparison with those under control. Moreover, the parameter changes generally amplified over time, suggesting processes of gradual physiological acclimation and functional gene shifts of both plants and microbes. The TECO model predicted that field warming from 2009 to 2013 resulted in cumulative C losses of 224 or 87 g/m², respectively, without or with changes in those parameters. Thus, warming‐induced parameter changes reduced predicted soil C loss by 61%. Our study suggests that it is critical to incorporate biotic changes in ESMs to improve the model performance in predicting C dynamics in permafrost regions.

Increases in carbon (C) inputs to soil can replenish soil organic C (SOC) through various mechanisms. However, recent studies have suggested that the increased C input can also stimulate the decomposition of old SOC via priming. Whether the loss of old SOC by priming can override C replenishment has not been rigorously examined. Here we show, through data–model synthesis, that the magnitude of replenishment is greater than that of priming, resulting in a net increase in SOC by a mean of 32% of the added new C. The magnitude of the net increase in SOC is positively correlated with the nitrogen-to-C ratio of the added substrates. Additionally, model evaluation indicates that a two-pool interactive model is a parsimonious model to represent the SOC decomposition with priming and replenishment. Our findings suggest that increasing C input to soils likely promote SOC accumulation despite the enhanced decomposition of old C via priming.

Boreal forests are changing in response to climate, with potentially important feedbacks to regional and global climate through altered carbon cycle and albedo dynamics. These feedback processes will be affected by vegetation changes, and feedback strengths will largely rely on the spatial extent and timing of vegetation change. Satellite remote sensing is widely used to monitor vegetation dynamics, and vegetation indices (VIs) are frequently used to characterize spatial and temporal trends in vegetation productivity. In this study we combine field observations of larch forest cover across a 25 km2 upland landscape in northeastern Siberia with high-resolution satellite observations to determine how the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) are related to forest cover. Across 46 forest stands ranging from 0% to 90% larch canopy cover, we find either no change, or declines in NDVI and EVI derived from PlanetScope CubeSat and Landsat data with increasing forest cover. In conjunction with field observations of NDVI, these results indicate that understory vegetation likely exerts a strong influence on vegetation indices in these ecosystems. This suggests that positive decadal trends in NDVI in Siberian larch forests may correspond primarily to increases in understory productivity, or even to declines in forest cover. Consequently, positive NDVI trends may be associated with declines in terrestrial carbon storage and increases in albedo, rather than increases in carbon storage and decreases in albedo that are commonly assumed. Moreover, it is also likely that important ecological changes such as large changes in forest density or variable forest regrowth after fire are not captured by long-term NDVI trends.

Elevated atmospheric N deposition threatens ecosystem health through eutrophication in terrestrial ecosystems, but little is known about consequences of N deposition in N-rich tropical ecosystems. We added several levels of N to an N-rich tropical forest and monitored plant growth dynamics, forest nutrient status, plant water use, and water losses from the ecosystem for a decade. We found that plants can acclimate and maintain nutrient balance by altering hydrological cycling. These results demonstrate that while elevated N deposition to already N-rich tropical forests may have minor effects on forest growth, it can exert a detectable influence on hydrological dynamics. Reduced runoff may threaten water supply in rapidly developing tropical regions.Anthropogenic nitrogen (N) deposition has accelerated terrestrial N cycling at regional and global scales, causing nutrient imbalance in many natural and seminatural ecosystems. How added N affects ecosystems where N is already abundant, and how plants acclimate to chronic N deposition in such circumstances, remains poorly understood. Here, we conducted an experiment employing a decade of N additions to examine ecosystem responses and plant acclimation to added N in an N-rich tropical forest. We found that N additions accelerated soil acidification and reduced biologically available cations (especially Ca and Mg) in soils, but plants maintained foliar nutrient supply at least in part by increasing transpiration while decreasing soil water leaching below the rooting zone. We suggest a hypothesis that cation-deficient plants can adjust to elevated N deposition by increasing transpiration and thereby maintaining nutrient balance. This result suggests that long-term elevated N deposition can alter hydrological cycling in N-rich forest ecosystems.

Ecosystem carbon (C) transit time is a critical diagnostic parameter to characterize land C sequestration. This parameter has different variants in the literature, including a commonly used turnover time. However, we know little about how different transit time and turnover time are in representing carbon cycling through multiple compartments under a non-steady state. In this study, we estimate both C turnover time as defined by the conventional stock over flux and mean C transit time as defined by the mean age of C mass leaving the system. We incorporate them into the Community Atmosphere Biosphere Land Exchange (CABLE) model to estimate C turnover time and transit time in response to climate warming and rising atmospheric [CO₂]. Modelling analysis shows that both C turnover time and transit time increase with climate warming but decrease with rising atmospheric [CO₂]. Warming increases C turnover time by 2.4 years and transit time by 11.8 years in 2100 relative to that at steady state in 1901. During the same period, rising atmospheric [CO₂] decreases C turnover time by 3.8 years and transit time by 5.5 years. Our analysis shows that 65% of the increase in global mean C transit time with climate warming results from the depletion of fast-turnover C pool. The remaining 35% increase results from accompanied changes in compartment C age structures. Similarly, the decrease in mean C transit time with rising atmospheric [CO₂] results approximately equally from replenishment of C into fast-turnover C pool and subsequent decrease in compartment C age structure. Greatly different from the transit time, the turnover time, which does not account for changes in either C age structure or composition of respired C, underestimated impacts of warming and rising atmospheric [CO₂] on C diagnostic time and potentially led to deviations in estimating land C sequestration in multi-compartmental ecosystems.

Tree-grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity-GPP) at four tree-grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.

Soil contains the largest terrestrial pool of carbon (C), but how this pool will be affected by global change remains unknown. Warmer temperatures generally increase soil respiration, while additional C inputs from plants to soil can increase or decrease soil C decomposition rates through a phenomenon known as priming. Priming occurs when soil organic matter (SOM) decomposition rates change in response to a fresh substrate, though the mechanisms underlying priming are poorly understood. Here, we measured priming in four ecosystems during a seven-week incubation with weekly glucose additions. Soil was collected from field warming experiments in the four ecosystems, so our experiment assessed the influence of long-term warming on priming. All treatments exhibited negative priming (reduced SOM decomposition) after the first substrate pulse. Subsequent substrate pulses elicited variable responses, and the effect of long-term warming on priming was ecosystem-dependent. Priming was correlated with changes in soil C and N in response to warming: ecosystems that lost soil C and N over nine years of experimental warming exhibited low rates of priming (decreased SOM decomposition), while ecosystems that gained soil C and N in response to warming had high priming. Consequently, priming may accelerate C losses in ecosystems that exhibit warming-induced C increases, and vice versa, thus partially buffering soil C content against change.

High latitude warming and permafrost thaw will expose vast stores of deep soil organic carbon (SOC) to decomposition. Thaw also changes water movement causing either wetter or drier soil. The fate of deep SOC under different thaw and moisture conditions is unclear. We measured weekly growing-season δ13C of ecosystem respiration (Recoδ13C) across thaw and moisture conditions (Shallow-Dry; Deep-Dry; Deep-Wet) in a soil warming manipulation. Deep SOC loss was inferred from known δ13C signatures of plant shoot, root, surface soil, and deep soil respiration. In addition, a 2-year-old vegetation removal treatment (No Veg) was used to isolate surface and deep SOC decomposition contributions to Reco. In No Veg, seasonal Recoδ13C indicated that deep SOC loss increased as the soil column thawed, while in vegetated areas, root contributions appeared to dominate Reco. The Recoδ13C differences between Shallow-Dry and Deep-Dry were significant but surprisingly small. This most likely suggests that, under dry conditions, soil warming stimulates root and surface SOC respiration with a negative 13C signature that opposes the more positive 13C signal from increased deep SOC respiration. In Deep-Wet conditions, Recoδ13C suggests reduced deep SOC loss but could also reflect altered diffusion or methane (CH4) dynamics. Together, these results demonstrate that frequent Recoδ13C measurements can detect deep SOC loss and that plants confound the signal. In future studies, soil profile δ13C measurements, vegetation removal across thaw gradients, and isotopic effects of CH4 dynamics could further deconvolute deep SOC loss via surface Reco.

We conducted a model-based assessment of changes in permafrost area and carbon storage for simulations driven by RCP4.5 and RCP8.5 projections between 2010 and 2299 for the northern permafrost region. All models simulating carbon represented soil with depth, a critical structural feature needed to represent the permafrost carbon–climate feedback, but that is not a universal feature of all climate models. Between 2010 and 2299, simulations indicated losses of permafrost between 3 and 5 million km^2 for the RCP4.5 climate and between 6 and 16 million km^2 for the RCP8.5 climate. For the RCP4.5 projection, cumulative change in soil carbon varied between 66-Pg C (10^15-g carbon) loss to 70-Pg C gain. For the RCP8.5 projection, losses in soil carbon varied between 74 and 652 Pg C (mean loss, 341 Pg C). For the RCP4.5 projection, gains in vegetation carbon were largely responsible for the overall projected net gains in ecosystem carbon by 2299 (8- to 244-Pg C gains). In contrast, for the RCP8.5 projection, gains in vegetation carbon were not great enough to compensate for the losses of carbon projected by four of the five models; changes in ecosystem carbon ranged from a 641-Pg C loss to a 167-Pg C gain (mean, 208-Pg C loss). The models indicate that substantial net losses of ecosystem carbon would not occur until after 2100. This assessment suggests that effective mitigation efforts during the remainder of this century could attenuate the negative consequences of the permafrost carbon–climate feedback.

Increasing wildfire activity in Alaska’s boreal forests has led to greater fuel reduction management. Management has been implemented to reduce wildfire spread, but the ecological impacts of these practices are poorly known. We quantified the effects of handthinning and shearblading on above- and belowground stand characteristics, plant species composition, carbon (C) and nitrogen (N) pools, and soil thaw across 19 sites dominated by black spruce (Picea mariana) in interior Alaska treated 2–12 years prior to sampling. The density of deciduous tree seedlings was significantly higher in shearbladed areas compared to unmanaged forest (6.4 vs. 0.1 stems/m2), and unmanaged stands exhibited the highest mean density of conifer seedlings and layers (1.4 stems/m2). Understory plant community composition was most similar between unmanaged and thinned stands. Shearblading resulted in a near complete loss of aboveground tree biomass C pools while thinning approximately halved the C pool size (1.2 kg C/m2 compared to 3.1 kg C/m2 in unmanaged forest). Significantly smaller soil organic layer (SOL) C and N pools were observed in shearbladed stands (3.2 kg C/m2 and 116.8 g N/m2) relative to thinned (6.0 kg C/m2 and 192.2 g N/m2) and unmanaged (5.9 kg C/m2 and 178.7 g N/m2) stands. No difference in C and N pool sizes in the uppermost 10 cm of mineral soil was observed among stand types. Total C stocks for measured pools was 2.6 kg C/m2 smaller in thinned stands and 5.8 kg C/m2 smaller in shearbladed stands when compared to unmanaged forest. Soil thaw depth averaged 13 cm deeper in thinned areas and 46 cm deeper in shearbladed areas relative to adjacent unmanaged stands, although variability was high across sites. Deeper soil thaw was linked to shallower SOL depth for unmanaged stands and both management types, however for any given SOL depth, thaw tended to be deeper in shearbladed areas compared to unmanaged forest. These findings indicate that fuel-reduction management alters plant community composition, C and N pools, and soil thaw depth, with consequences for ecosystem structure and function beyond those intended for fire management.

Nitrogen (N) is frequently a limiting nutrient in soil; its availability can govern ecosystem functions such as primary production and decomposition. Assimilation of N by microorganisms impacts the availability of N in soil. Despite its established ecological significance, the contributions of microbial taxa to N assimilation are unknown. Here we measure N uptake and use by microbial phylotypes and taxonomic groups within a diverse assemblage of soil microbes through quantitative stable isotope probing (qSIP) with 15N. Following incubation with 15NH+4, distinct patterns of 15N assimilation among taxonomic groups were observed. For instance, glucose addition stimulated 15N assimilation in most members of Actinobacteria and Proteobacteria but generally decreased 15N use by Firmicutes and Bacteriodetes. While NH+4 is considered a preferred and universal source of N to prokaryotes, the majority (Greater than 80%) of N assimilation in our soils could be attributed to a handful of active orders. Characterizing N assimilation of taxonomic groups with 15N qSIP may provide a basis for understanding how microbial community composition influences N availability in the environment.

Functional traits, which usually develop over evolutionary time‐scales to maximize plant survivorship and functional performances in changing environment, are important indices to explore how ecosystems respond and adapt to a changing environment. Many ecologists have argued that identifying regional to global‐scale patterns in functional traits, at organismal to ecosystem scales, in combination with responses to environmental changes, is necessary to increase our ability to predict how ecosystems will function in the future (Katja &amp; Jeanfrançois, 2009; Reich, Walters, &amp; Ellsworth, 1997; Wright et al., 2017). Global models that have been used to simulate changes in ecosystem function primarily incorporate biogeochemical and ecophysiological processes (Bonan, 2008; Taylor, Stouffer, &amp; Meehl, 2012). These models, however, could not well predict community compositional change (Fisher et al., 2017). Traditionally, plant functional types have been used to represent community composition changes in dynamic global vegetation models (DGVM). That scheme remains poor at predicting ecosystem functions and their responses to climatic change (Sitch et al., 2008). Recently, trait‐based modelling has emerged as one of the most promising approach to simulation of community dynamics under global changes (Markus, Michael, Mahecha, Jens, &amp; Baldocchi, 2014; van Bodegom, Douma, &amp; Verheijen, 2014; Van Bodegom et al., 2012; Violle, Reich, Pacala, Enquist, &amp; Kattge, 2014). To support this trait‐based modelling, it is urgent to compile empirical evidence, develop comprehensive datasets, and reveal the large‐scale patterns and controlling factors of functional traits and their variations along environmental gradients.

1. Dryland vegetation is characterized by discrete plant patches that accumulate and capture soil resources under their canopies. These “fertile islands” are major drivers of dryland ecosystem structure and functioning, yet we lack an integrated understanding of the factors controlling their magnitude and variability at the global scale. 2. We conducted a standardized field survey across 236 drylands from five continents. At each site, we measured the composition, diversity and cover of perennial plants. Fertile island effects were estimated at each site by comparing composite soil samples obtained under the canopy of the dominant plants and in open areas devoid of perennial vegetation. For each sample, we measured 15 soil variables (functions) associated with carbon, nitrogen and phosphorus cycling and used the relative interaction index to quantify the magnitude of the fertile island effect for each function. In 80 sites, we also measured fungal and bacterial abundance (quantitative PCR) and diversity (Illumina MiSeq). 3. The most fertile islands, i.e. those where a higher number of functions were simultaneously enhanced, were found at lower elevation sites with greater soil pH values and sand content under semiarid climates, particularly at locations where the presence of tall woody species with a low‐specific leaf area increased fungal abundance beneath plant canopies, the main direct biotic controller of the fertile island effect in the drylands studied. Positive effects of fungal abundance were particularly associated with greater nutrient contents and microbial activity (soil extracellular enzymes) under plant canopies. 4. Synthesis. Our results show that the formation of fertile islands in global drylands largely depends on: (1) local climatic, topographic and edaphic characteristics, (2) the structure and traits of local plant communities and (3) soil microbial communities. Our study also has broad implications for the management and restoration of dryland ecosystems worldwide, where woody plants are commonly used as nurse plants to enhance the establishment and survival of beneficiary species. Finally, our results suggest that forecasted increases in aridity may enhance the formation of fertile islands in drylands worldwide.

Growing bacteria have a high concentration of ribosomes to ensure sufficient protein synthesis, which is necessary for genome replication and cellular division. To elucidate whether metabolic activity of soil microorganisms is coupled with growth, we investigated the relationship between rRNA and DNA synthesis in a soil bacterial community using quantitative stable isotope probing (qSIP) with H2 18O. Most soil bacterial taxa were metabolically active and grew, and there was no significant difference between the isotopic composition of DNA and RNA extracted from soil incubated with H2 18O. The positive correlation between 18O content of DNA and rRNA of taxa, with a slope statistically indistinguishable from 1 (slope = 0.96; 95% confidence interval [CI], 0.90 to 1.02), indicated that few taxa made new rRNA without synthesizing new DNA. There was no correlation between rRNA-to-DNA ratios obtained from sequencing libraries and the atom percent excess (APE) 18O values of DNA or rRNA, suggesting that the ratio of rRNA to DNA is a poor indicator of microbial growth or rRNA synthesis. Our results support the notion that metabolic activity is strongly coupled to cellular division and suggest that nondividing taxa do not dominate soil metabolic activity. IMPORTANCE Using quantitative stable isotope probing of microbial RNA and DNA with H2 18O, we show that most soil taxa are metabolically active and grow because their nucleic acids are significantly labeled with 18O. A majority of the populations that make new rRNA also grow, which argues against the common paradigm that most soil taxa are dormant. Additionally, our results indicate that relative sequence abundance-based RNA-to-DNA ratios, which are frequently used for identifying active microbial populations in the environment, underestimate the number of metabolically active taxa within soil microbial communities.

Most soil bacterial taxa are thought to be dormant, or inactive, yet the extent to which they synthetize new rRNA is poorly understood. We analyzed 18O composition of RNA extracted from soil incubated with H218O and used quantitative stable isotope probing to characterize rRNA synthesis among microbial taxa. RNA was not fully labeled with 18O, peaking at a mean of 23.6 ± 6.8 atom percent excess (APE) 18O after eight days of incubation, suggesting some ribonucleotides in soil were more than eight days old. Microbial taxa varied in the degree they incorporated 18O into their rRNA over time and there was no correlation between the APE 18O of bacterial rRNA and their rRNA to DNA ratios, suggesting that the ratios were not appropriate to measure ribonucleotide synthesis. Our study indicates that, on average, 94% of soil taxa produced new rRNA and therefore were metabolically active.

Higher temperatures in northern latitudes will increase permafrost thaw and stimulate above-and belowground plant biomass growth in tundra ecosystems. Higher plant productivity increases the input of easily decomposable carbon (C) to soil, which can stimulate microbial activity and increase soil organic matter decomposition rates. This phenomenon, known as the priming effect, is particularly interesting in permafrost because an increase in C supply to deep, previously frozen soil may accelerate decomposition of C stored for hundreds to thousands of years. The sensitivity of old permafrost C to priming is not well known; most incubation studies last less than one year, and so focus on fast-cycling C pools. Furthermore, the age of respired soil C is rarely measured, even though old C may be vulnerable to labile C inputs. We incubated soil from a moist acidic tundra site in Eight Mile Lake, Alaska for 409 days at 15 °C. Soil from surface (0–25 cm), transition (45–55 cm), and permafrost (65–85 cm) layers were amended with three pulses of uniformly 13C labeled glucose or cellulose, every 152 days. Glucose addition resulted in positive priming in the permafrost layer 7 days after each substrate addition, eliciting a two-fold increase in cumulative soil C loss relative to unamended soils with consistent effects across all three pulses. In the transition and permafrost layers, glucose addition significantly decreased the age of soil-respired CO2C with Δ14C values that were 115‰ higher. Previous field studies that measured the age of respired C in permafrost regions have attributed younger Δ14C ecosystem respiration values to higher plant contributions. However, the results from this study suggest that positive priming, due to an increase in fresh C supply to deeply thawed soil layers, can also explain the respiration of younger C observed at the ecosystem scale. We must consider priming effects to fully understand permafrost C dynamics, or we risk underestimating the contribution of soil C to ecosystem respiration.

Drylands (hyperarid, arid, semiarid, and dry subhumid ecosystems) cover almost half of Earth’s land surface and are highly vulnerable to environmental pressures. Here we provide an inventory of soil properties including carbon (C), nitrogen (N), and phosphorus (P) stocks within the current boundaries of drylands, aimed at serving as a benchmark in the face of future challenges including increased population, food security, desertification, and climate change. Aridity limits plant production and results in poorly developed soils, with coarse texture, low C:N and C:P, scarce organic matter, and high vulnerability to erosion. Dryland soils store 646 Pg of organic C to 2 m, the equivalent of 32% of the global soil organic C pool. The magnitude of the historic loss of C from dryland soils due to human land use and cover change and their typically low C:N and C:P suggest high potential to build up soil organic matter, but coarse soil textures may limit protection and stabilization processes. Restoring, preserving, and increasing soil organic matter in drylands may help slow down rising levels of atmospheric carbon dioxide by sequestering C, and is strongly needed to enhance food security and reduce the risk of land degradation and desertification.

Improved quantification of the factors controlling soil organic matter (SOM) stabilization at continental to global scales is needed to inform projections of the largest actively cycling terrestrial carbon pool on Earth, and its response to environmental change. Biogeochemical models rely almost exclusively on clay content to modify rates of SOM turnover and fluxes of climate-active CO2 to the atmosphere. Emerging conceptual understanding, however, suggests other soil physicochemical properties may predict SOM stabilization better than clay content. We addressed this discrepancy by synthesizing data from over 5,500 soil profiles spanning continental scale environmental gradients. Here, we demonstrate that other physicochemical parameters are much stronger predictors of SOM content, with clay content having relatively little explanatory power. We show that exchangeable calcium strongly predicted SOM content in water-limited, alkaline soils, whereas with increasing moisture availability and acidity, iron- and aluminum-oxyhydroxides emerged as better predictors, demonstrating that the relative importance of SOM stabilization mechanisms scales with climate and acidity. These results highlight the urgent need to modify biogeochemical models to better reflect the role of soil physicochemical properties in SOM cycling.

Global change is shifting the seasonality of vegetation in ecosystems around the globe. High-frequency digital camera imagery, and vegetation indices derived from that imagery, is facilitating better tracking of phenological responses to environmental variation. This method, commonly referred to as the “phenocam” approach, is well-suited to several specific applications, including: close-up observation of individual organisms; long-term canopy-level monitoring at individual sites; automated regional-to-continental scale observatory networks; and tracking responses to experimental treatments. Several camera networks are already well-established, and some camera records are a more than a decade long. These data can be used to identify the environmental controls on phenology in different ecosystems, which will contribute to the development of improved prognostic phenology models. This article is protected by copyright. All rights reserved.

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including “canopy greenness”, processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the “greenness rising” and end of the “greenness falling” stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.

Shifts in vegetation phenology are a key example of the biological effects of climate change. However, there is substantial uncertainty about whether these temperature-driven trends will continue, or whether other factors—for example, photoperiod—will become more important as warming exceeds the bounds of historical variability. Here we use phenological transition dates derived from digital repeat photography to show that experimental whole-ecosystem warming treatments of up to +9 °C linearly correlate with a delayed autumn green-down and advanced spring green-up of the dominant woody species in a boreal <i>Picea</i>–<i>Sphagnum</i> bog. Results were confirmed by direct observation of both vegetative and reproductive phenology of these and other bog plant species, and by multiple years of observations. There was little evidence that the observed responses were constrained by photoperiod. Our results indicate a likely extension of the period of vegetation activity by 1–2 weeks under a ‘CO₂ stabilization’ climate scenario (+2.6 ± 0.7 °C), and 3–6 weeks under a ‘high-CO₂ emission’ scenario (+5.9 ± 1.1 °C), by the end of the twenty-first century. We also observed severe tissue mortality in the warmest enclosures after a severe spring frost event. Failure to cue to photoperiod resulted in precocious green-up and a premature loss of frost hardiness, which suggests that vulnerability to spring frost damage will increase in a warmer world. Vegetation strategies that have evolved to balance tradeoffs associated with phenological temperature tracking may be optimal under historical climates, but these strategies may not be optimized for future climate regimes. These in situ experimental results are of particular importance because boreal forests have both a circumpolar distribution and a key role in the global carbon cycle.

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both “greenness rising” and “greenness falling” transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground.

Mechanical cutting and mastication of juniper trees aims to restore grassland habitat by reducing the density of encroaching woody species. However, the associated soil disturbance may also create conduits for invasive species, a risk that must be mitigated by land managers. We characterized herbaceous communities in treated and adjacent untreated areas in a piñon-juniper (Pinus edulis and Juniper monosperma) woodland in northern Arizona 2.5 years after treatment. Untreated plots had 4× the herbaceous cover (82%) than treated plots (21%). Within treated plots, native species cover (19%) was 10× higher than invasive species cover (2%). Furthermore, treated plots exhibited greater plant community variability and diversity than untreated plots, driven by an increase in the diversity of native grasses and non-native forbs. No new recruits were Arizona listed noxious weeds, indicating that, at least in the short term, mastication is not producing invasive species hot spots in this piñon-juniper woodland.

Heat waves are increasing in frequency and intensity, presenting a challenge for the already difficult practice of ecological restoration. We investigated whether pre-heating locally sourced rhizosphere soil (inoculum) could acclimatize plants to a field-imposed heat wave in a restoration setting. Soil heating in the laboratory caused a marked shift in rhizosphere bacterial community composition, accompanied by an increase in species evenness. Furthermore, pre-heated rhizosphere soil reduced plant height, number of leaves, and shoot mass of the C4 grass, blue grama (Bouteloua gracilis), and it reduced the shoot mass of the C3 grass, Arizona fescue (Festuca arizonica) in the glasshouse. Following transplantation and the application of a field heat wave, pre-heated inoculum did not influence heat wave survival for either plant species. However, there were strong species-level responses to the field heat wave. For instance, heat wave survivorship was over four times higher in blue grama (92%) than in Arizona fescue (22%). These results suggest that the use of C4 seeds may be preferable for sites exhibiting high heat wave risk. Further research is needed to understand whether inocula are more effective in highly degraded soil in comparison with partially degraded soils.

The flux of CO₂ from the soil to the atmosphere (soil respiration, <i>R</i>_soil) is a major component of the global carbon (C) cycle. Methods to measure and model <i>R</i>_soil, or partition it into different components, often rely on the assumption that soil CO₂ concentrations and fluxes are in steady state, implying that <i>R</i>_soil 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 <i>R</i>_soiland 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., 0.01m increments down to 1m, every 6h). 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, <i>R</i>_soil from the SS model closely matched that of the NSS model. However, in a fine-textured (clay) soil, growing season <i>R</i>_soil was  ∼ 3% higher under the assumption of NSS (versus SS). These differences were exaggerated in clay soil at daily time scales whereby <i>R</i>_soil 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 <i>R</i>_soil by 15 to 37% for coarse- and fine-textured soils, respectively. However, the responses of <i>R</i>_soil 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 <i>R</i>_soil and associated soil carbon cycling processes.

1.Dams disrupt the river continuum, altering hydrology, biodiversity and energy flow. Although research indicates that tributary inputs have the potential to dilute these effects, knowledge at the food‐web level is still scarce. 2.Here, we examined the riverine food‐web structure of the Colorado River below Glen Canyon Dam, focusing on organic matter sources, trophic diversity and food chain length. We asked how these components respond to pulsed flows from tributaries following monsoon thunderstorms that seasonally increase streamflow in the American Southwest. 3.Tributaries increased the relative importance of terrestrial organic matter, particularly during the wet season below junctures of key tributaries. This contrasted with the algal‐based food‐web present immediately below Glen Canyon Dam. 4.Tributary inputs during the monsoon also increased trophic diversity and food chain length: food chain length peaked below the confluence with the largest tributary (by discharge) in Grand Canyon, increasing by greater than 1 trophic level over a 4–5 km reach possibly due to aquatic prey being flushed into the mainstem during heavy rain events. 5.Our results illustrate that large tributaries can create seasonal discontinuities, influencing riverine food‐web structure in terms of allochthony, food‐web diversity and food chain length. 6.Synthesis and applications. Pulsed flows from unregulated tributaries following seasonal monsoon rains increase the importance of terrestrially derived organic matter in large, regulated river food webs, increasing food chain length and trophic diversity downstream of tributary inputs. Protecting unregulated tributaries within hydropower cascades may be important if we are to mitigate food‐web structure alteration due to flow regulation by large dams. This is critical in the light of global hydropower development, especially in megadiverse, developing countries where dam placement (including completed and planned structures) is in tributaries.

Losses of C from decomposing permafrost may be offset by increased productivity of tundra plants, but nitrogen availability partially limits plant growth in tundra ecosystems. In this soil incubation experiment carbon (C) and nitrogen (N) cycling dynamics were examined from the soil surface down through upper permafrost. We found that losses of CO2 were negatively correlated to net N mineralization because C-rich surface soils mineralized little N, while deep soils had low rates of C respiration but high rates of net N mineralization. Permafrost soils released a large flush of inorganic N when initially thawed. Depth-specific rates of N mineralization from the incubation were combined with thaw depths and soil temperatures from a nearby manipulative warming experiment to simulate the potential magnitude, timing, and depth of inorganic N release during the process of permafrost thaw. Our calculations show that inorganic N released from newly thawed permafrost may be similar in magnitude to the increase in N mineralized by warmed soils in the middle of the profile. The total release of inorganic N from the soil profile during the simulated thaw process was twice the size of the observed increase in the foliar N pool observed at the manipulative experiment. Our findings suggest that increases in N availability are likely to outpace the N demand of tundra plants during the first 5 years of permafrost thaw and may increase C losses from surface soils as well as induce denitrification and leaching of N from these ecosystems.

Inferences about subsurface CO2 fluxes often rely on surface soil respiration (Rsoil) estimates because directly measuring subsurface microbial and root respiration (collectively, CO2 production, STotal) is difficult. To evaluate how well Rsoil serves as a proxy for STotal, we applied the nonsteady state DEconvolution of Temporally varying Ecosystem Carbon componenTs model (0.01-m vertical resolution), using 6-hourly data from a Wyoming grassland, in six simulations that cross three soil types (clay, sandy loam, and sandy) with two depth distributions of subsurface biota. We used cross-wavelet coherence analysis to examine temporal coherence (localized linear correlation) and offsets (lags) between STotal and Rsoil and fluxes and drivers (e.g., soil temperature and moisture). Cross-wavelet coherence revealed higher coherence between fluxes and drivers than linear regressions between concurrent variables. Soil texture and moisture exerted the strongest controls over coherence between CO2 fluxes. Coherence between CO2 fluxes in all soil types was strong at short (~1 day) and long periods (&gt;8 days), but soil type controlled lags, and rainfall events decoupled the fluxes at periods of 1?8 days for several days in sandy soil, up to 1 week in sandy loam, and for a month or more in clay soil. Concentrating root and microbial biomass nearer the surface decreased lags in all soil types and increased coherence up to 10% in clay soil. The assumption of high temporal coherence between Rsoil and STotal is likely valid in dry, sandy soil, but may lead to underestimates of short-term STotal in semiarid grasslands with fine-grained and/or wet soil.

In the last few decades, temperatures in the Arctic have increased twice as much as the rest of the globe. As permafrost thaws in response to this warming, large amounts of soil organic matter may become vulnerable to decomposition. Microbial decomposition will release carbon (C) from permafrost soils, however, warmer conditions could also lead to enhanced plant growth and C uptake. Field and modeling studies show high uncertainty in soil and plant responses to climate change but there have been few studies that reconcile field and model data to understand differences and reduce uncertainty. Here, we evaluate gross primary productivity (GPP), ecosystem respiration (R eco ), and net ecosystem C exchange (NEE) from eight years of experimental soil warming in moist acidic tundra against equivalent fluxes from the Community Land Model during simulations parameterized to reflect the field conditions associated with this manipulative field experiment. Over the eight-year experimental period, soil temperatures and thaw depths increased with warming in field observations and model simulations. However, the field and model results do not agree on warming effects on water table depth; warming created wetter soils in the field and drier soils in the models. In the field, initial increases in growing season GPP, R eco , and NEE to experimentally-induced permafrost thaw created a higher C sink capacity in the first years followed by a stronger C source in years six through eight. In contrast, both models predicted linear increases in GPP, R eco , and NEE with warming. The divergence of model results from field experiments reveals the role subsidence, hydrology, and nutrient cycling play in influencing the C flux responses to permafrost thaw, a complexity that the models are not structurally able to predict, and highlight challenges associated with projecting C cycle dynamics across the Arctic.

Differences in the biogeochemistry of nitrogen (N) and phosphorus (P) lead to differential losses and inputs during and over time after fire such that fire may affect nutrient limitation of primary productivity. We conducted a nutrient addition experiment in scrubby flatwoods, a Florida scrub community type, to test the hypothesis that nutrient limitation of primary productivity shifts from N limitation in recently burned sites to P limitation in longer unburned sites. We added three levels of N, P, and N and P together to sites 6 weeks, 8 years, and 20 years postfire and assessed the effects of nutrient addition on above- and belowground productivity and nutrient concentrations. At the community level, nutrient addition did not affect aboveground biomass, but root productivity increased with high N + P addition in sites 8 and 20 years after fire. At the species level, N addition increased leaf biomass of saw palmetto (Serenoa repens) in sites 6 weeks and 20 years postfire, while P addition increased foliar %P and apical shoot growth of scrub oak (Quercus inopina) in sites 8 and 20 years postfire, respectively. Contrary to our hypothesis, nutrient limitation does not appear to shift with time after fire; recently burned sites show little evidence of nutrient limitation, while increased belowground productivity indicates that scrubby flatwoods are co-limited by N and P at intermediate and longer times after fire.

The Arctic may seem remote, but the unprecedented environmental changes occurring there have important consequences for global society. Of all Arctic system components, changes in permafrost (perennially frozen ground) are one of the least documented. Permafrost is degrading as a result of climate warming, and evidence is mounting that changing permafrost will have significant impacts within and outside the region. This review asks: What are key structural and functional properties of ecosystems that interact with changing permafrost, and how do these ecosystem changes affect local and global society? Here, we look beyond the classic definition of permafrost to include a broadened focus on the composition of frozen ground, including the ice and the soil organic carbon content, and how it is changing. This ecological perspective of permafrost serves to identify areas of both vulnerability and resilience as climate, ecological disturbance regimes, and the human footprint all continue to change in this sensitive and critical region of Earth.

Large model uncertainty in projected future soil carbon (C) dynamics has been well documented. However, our understanding of the sources of this uncertainty is limited. Here we quantify the uncertainties arising from model parameters, structures and their interactions, and how those uncertainties propagate through different models to projections of future soil carbon stocks. Both the vertically resolved model and the microbial explicit model project much greater uncertainties to climate change than the conventional soil C model, with both positive and negative C-climate feedbacks, whereas the conventional model consistently predicts positive soil C-climate feedback. Our findings suggest that diverse model structures are necessary to increase confidence in soil C projection. However, the larger uncertainty in the complex models also suggests that we need to strike a balance between model complexity and the need to include diverse model structures in order to forecast soil C dynamics with high confidence and low uncertainty.

Succession theory predicts altered sensitivity of ecosystem functions to disturbance (i.e., climate change) due to the temporal shift in plant community composition. However, empirical evidence in global change experiments is lacking to support this prediction. Here, we present findings from an 8-year long-term global change experiment with warming and altered precipitation manipulation (double and halved amount). First, we observed a temporal shift in species composition over 8 years, resulting in a transition from an annual C3-dominant plant community to a perennial C4-dominant plant community. This successional transition was independent of any experimental treatments. During the successional transition, the response of aboveground net primary productivity (ANPP) to precipitation addition magnified from neutral to +45.3%, while the response to halved precipitation attenuated substantially from ?17.6% to neutral. However, warming did not affect ANPP in either state. The findings further reveal that the time-dependent climate sensitivity may be regulated by successional change in species composition, highlighting the importance of vegetation dynamics in regulating the response of ecosystem productivity to precipitation change.

Understanding what drives the interannual variability (IAV) of the land carbon sink is crucial for improving future predictions of this important, yet uncertain, component of the climate system. While drivers of global and hemispheric-scale net ecosystem exchange (NEE) IAV have been investigated, our understanding of the drivers of NEE IAV at regional scales (e.g. sub-continental, biome-level) is quite poor. Here we explore the biome-level attribution and drivers of North American NEE using inverse estimates derived from a dense network of atmospheric CO 2 observations. We find that deciduous broadleaf and mixed forests are the primary regions responsible for North American NEE IAV, which differs from the ecoregions identified for the globe and Northern Hemisphere. We also find that a suite of terrestrial biosphere models (TBMs) do not agree on the dominant biome contributing to NEE IAV, with TBMs falling along an apparent spectrum ranging between those with IAV dominated primarily by forested ecosystems to those with IAV dominated by non-forested ecosystems. Furthermore, this regional trade-off in TBM NEE IAV is found to be linked to differing regional responses to environmental drivers among TBMs. This work displays the importance of extra-tropical forests in driving continental NEE IAV and also highlights the challenges and limitations of using TBMs to inform regional-scale carbon flux dynamics.

Ecologists often equate litter quality with decomposition rate. In soil and sediments, litter that is rapidly decomposed by microbes often has low concentrations of tannin and lignin and low C:N ratios. Do these same traits also favor element transfer to higher trophic levels in streams, where many insects depend on litter as their primary food source? We test the hypothesis that slow decomposition rates promote element transfer from litter to insects, whereas rapid decomposition favors microbes. We measured carbon and nitrogen fluxes from four plant species to a leaf-shredding caddisfly using isotopically labeled litter. Caddisflies assimilated a higher percentage of litter carbon and nitrogen lost from slowly decomposing litters (Platanus wrightii and Quercus gambelii). In contrast, rapidly decomposing litters (Fraxinus velutina and Populus fremontii) supported higher microbial biomass. These results challenge the view that rapidly decomposing litter is higher quality by demonstrating that slowly decomposing litters provide a critical resource for insects.

Insect outbreaks can significantly influence carbon (C) and water balances of forests. Forest tent caterpillars (FTC) (Malacosoma disstria Hübner) are one of the most prominent insects found in aspen forests in Canada and have the potential to considerably influence regional C and water fluxes. In the summer of 2016, an FTC infestation occurred in a ca. 100 -year-old trembling aspen (Populus tremuloides) stand in the southern boreal forest where the longterm research site known as Old Aspen (OA) is located. The infestation led to nearly complete defoliation of the canopy during the leafing out period when photosynthesis, and thus C uptake, is progressing towards maximum levels. We used 21 years of eddy-covariance (EC) and climate measurements covering pre-infestation and infestation periods to estimate the impact of the FTC infestation on net ecosystem production (NEP), gross ecosystem production (GEP) and evapotranspiration (E). Defoliation in 2016 reduced annual NEP to −130 g C m−2 y−1 and GEP to 798 g C m−2 y−1, respectively, which were much less than their 20-year means (NEP = 118 ± 53 g C m−2 y−1, GEP = 1057 ± 74 g C m−2 y−1), and resulted in the most negative annual NEP value of the 21 years of measurements at the OA site. NEP for 2016 was even lower than values observed during three drought years (2001–2003). However, FTC infestation caused little effect on annual E. FTC infestation reduced the near-surface remotely-measured greenness index, green chromatic coordinate (GCC), to ∼0.32 on June 10 in comparison to ∼0.40 in other years. The defoliation, observable from space as reductions in normalized difference vegetation index (NDVI) values, also showed a negligible effect on E but a large effect on the C fluxes.

Soils contain more carbon than plants or the atmosphere, and sensitivities of soil organic carbon (SOC) stocks to changing climate and plant productivity are a major uncertainty in global carbon cycle projections. Despite a consensus that microbial degradation and mineral stabilization processes control SOC cycling, no systematic synthesis of long-term warming and litter addition experiments has been used to test process-based microbe-mineral SOC models. We explored SOC responses to warming and increased carbon inputs using a synthesis of 147 field manipulation experiments and five SOC models with different representations of microbial and mineral processes. Model projections diverged but encompassed a similar range of variability as the experimental results. Experimental measurements were insufficient to eliminate or validate individual model outcomes. While all models projected that CO2 efflux would increase and SOC stocks would decline under warming, nearly one-third of experiments observed decreases in CO2 flux and nearly half of experiments observed increases in SOC stocks under warming. Long-term measurements of C inputs to soil and their changes under warming are needed to reconcile modeled and observed patterns. Measurements separating the responses of mineral-protected and unprotected SOC fractions in manipulation experiments are needed to address key uncertainties in microbial degradation and mineral stabilization mechanisms. Integrating models with experimental design will allow targeting of these uncertainties and help to reconcile divergence among models to produce more confident projections of SOC responses to global changes.

Greenhouse gas emissions from thawing permafrost in arctic ecosystems may amplify global warming, yet estimates of the rate of carbon release, and the proportion of carbon released as methane (CH4) or carbon dioxide (CO2), have a high degree of uncertainty. There are many areas where no measurements exist, and few year-round or long-term records. Existing year-round eddy covariance measurements of arctic CH4 fluxes suggest that nongrowing season emissions make up a significant proportion of tundra systems emissions on an annual basis. Here we present continuous CH4 flux measurements made at Eight Mile Lake, an upland tundra ecosystem undergoing permafrost degradation in Interior Alaska. We found net CH4 emissions throughout the year (1.2 ? 0.011 g C-CH4 m2/yr) that made up 61% of total radiative forcing from annual C emissions (CO2 and CH4; 32.3 g C m2/yr) when taking into account the greenhouse warming potential of CH4 relative to CO2. Nongrowing season emissions accounted for 50% of the annual CH4 budget, characterized by large pulse emissions. These were related to abrupt increases in air and shallow soil temperatures rather than consistent emissions during the zero curtain?a period of the fall/early winter season when subsurface soil temperatures remain near the 0 °C freezing point. Weekly growing season CH4 emissions in 2016 and 2017 were significantly related with thaw depth, and the magnitude of CH4 emissions between these seasons was proportional to the rate of active layer thaw throughout the season.

Land ecosystems sequester on average about a quarter of anthropogenic CO2 emissions. It has been proposed that nitrogen (N) availability will exert an increasingly limiting effect on plants’ ability to store additional carbon (C) under rising CO2, but these mechanisms are not well understood. Here, we review findings from elevated CO2 experiments using a plant economics framework, highlighting how ecosystem responses to elevated CO@ may depend on the costs and benefits of plant interactions with mycorrhizal fungi and symbiotic N‐fixing microbes. We found that N‐acquisition efficiency is positively correlated with leaf‐level photosynthetic capacity and plant growth, and negatively with soil C storage. Plants that associate with ectomycorrhizal fungi and N‐fixers may acquire N at a lower cost than plants associated with arbuscular mycorrhizal fungi. However, the additional growth in ectomycorrhizal plants is partly offset by decreases in soil C pools via priming. Collectively, our results indicate that predictive models aimed at quantifying C cycle feedbacks to global change may be improved by treating N as a resource that can be acquired by plants in exchange for energy, with different costs depending on plant interactions with microbial symbionts.

At the global scale, the uptake of atmospheric carbon dioxide by terrestrial ecosystems through photosynthesis is commonly estimated through vegetation indices or biophysical properties derived from optical remote sensing data. Microwave observations of vegetated areas are sensitive to different components of the vegetation layer than observations in the optical domain and may therefore provide complementary information on the vegetation state, which may be used in the estimation of Gross Primary Production (GPP). However, the relation between GPP and Vegetation Optical Depth (VOD), a biophysical quantity derived from microwave observations, is not yet known. This study aims to explore the relationship between VOD and GPP. VOD data were taken from different frequencies (L-, C-, and X-band) and from both active and passive microwave sensors, including the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS) mission, the Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E) and a merged VOD data set from various passive microwave sensors. VOD data were compared against FLUXCOM GPP and Solar-Induced chlorophyll Fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). FLUXCOM GPP estimates are based on the upscaling of flux tower GPP observations using optical satellite data, while SIF observations present a measure of photosynthetic activity and are often used as a proxy for GPP. For relating VOD to GPP, three variables were analyzed: original VOD time series, temporal changes in VOD (ΔVOD), and positive changes in VOD (ΔVOD≥0). Results show widespread positive correlations between VOD and GPP with some negative correlations mainly occurring in dry and wet regions for active and passive VOD, respectively. Correlations between VOD and GPP were similar or higher than between VOD and SIF. When comparing the three variables for relating VOD to GPP, correlations with GPP were higher for the original VOD time series than for ΔVOD or ΔVOD≥0 in case of sparsely to moderately vegetated areas and evergreen forests, while the opposite was true for deciduous forests. Results suggest that original VOD time series should be used jointly with changes in VOD for the estimation of GPP across biomes, which may further benefit from combining active and passive VOD data.

Long-term, continuous digital camera imagery and tower-based radiometric monitoring were conducted at a representative hardwood forest site in the Northeastern United States, part of the AmeriFlux network. In this study, the phenological metrics of the leaf area index (LAI), plant area index (PAI) and associated transition dates (e.g., timing of the onset of leaf expansion and the cessation of leaf fall) were compared using 4-year of data from Bartlett Experimental Forest. We used digital repeat photography (DRP) imagery collected using two different methods (“canopy cover” and “phenocam” approaches), together with above- and below-canopy measurements of photosynthetically active radiation (PAR). The growth-period LAI estimated from canopy cover images (LAICANOPY) and the above and below canopy PAR measurements (LAIfPARt) were within approximately the same range, in term of magnitude, as previous results for multiple comparative methods, although growing-season LAICANOPY was slightly lower (3.11 m2 m−2 to 3.35 m2 m−2) than LAIfPARt (3.19 m2 m−2 to 3.67 m2 m−2). In addition, we derived phenological transition dates from PAICANOPY, PAIfPARt, and color-based metrics calculated from the phenocam imagery (green (GCC) and red (RCC) chromatic coordinates). The transition dates in both spring and autumn differed somewhat according to method, presumably due to the vegetation status detection abilities of each vegetation metric. We found that LAI estimation from canopy cover images may be influenced by automatic exposure settings, which limits the ability to detect subtle changes in phenology during the transition phases in both spring and autumn. Particularly in autumn, the color-based metrics calculated from the phenocam imagery are decoupled from leaf area dynamics and thus PAI. While above and below canopy PAR measurements could yield the better indicators for estimating LAI, its seasonal dynamics, and associated phenological transition dates in long-term monitoring, we argue that there are obvious benefits to the multi-sensor approach used here.

Afforestation and reforestation projects in the karst regions of southwest China aim to combat desertification and improve the ecological environment. However, it remains unclear at what scale conservation efforts have impacted on carbon stocks and if vegetation regrowth occurs at a large spatial scale as intended. Here we use satellite time series data and show a widespread increase in leaf area index (a proxy for green vegetation cover), and aboveground biomass carbon, which contrasted negative trends found in the absence of anthropogenic influence as simulated by an ecosystem model. In spite of drought conditions, aboveground biomass carbon increased by 9% (+0.05 Pg C y−1), mainly in areas of high conservation effort. We conclude that large scale conservation projects can contribute to a greening Earth with positive effects on carbon sequestration to mitigate climate change. At the regional scale, such ecological engineering projects may reduce risks of desertification by increasing the vegetation cover and reducing the ecosystem sensitivity to climate perturbations.

Future droughts are expected to become more severe and frequent under future climate change scenarios, likely causing widespread tree mortality in the western USA. Coping with an uncertain future requires an understanding of long-term ecosystem responses in areas where prolonged drought is projected to increase. Tree-ring records are ideally suited for this task. We developed 24 tree-ring chronologies from 20 U.S. Forest Service Forest Inventory and Analysis (FIA) plots in the southwestern USA. Climate variables were derived from the PRISM climate dataset (800-m grid cells) to capture the bimodal precipitation regime of winter snow and summer monsoonal rainfall, as well as warm-season vapor-pressure deficit (VPD) and winter minimum temperature. Based on mixed linear models, radial growth from 1948 to 2013 for four conifer species (Pinus edulis, Juniperus osteosperma, Pinus ponderosa, and Picea engelmannii) responded negatively to warm-season VPD and positively to cold-season precipitation. Pinus spp. benefited from warm-season precipitation linked to the North American monsoon, and Pinus spp. and J. osteosperma radial growth increased with warmer cold-season minimum temperature. However, warmer cold-season minimum temperatures countered the beneficial influence of cold-season precipitation for radial growth in Pinus spp. and J. osteosperma, while P. engelmannii was unaffected. Also, enhanced drying effects of warm-season VPD associated with decreased cold-season precipitation negatively affected radial growth of Pinus spp. and P. engelmannii. Of the four conifer species studied, Pinus spp. are most affected by droughts since 1948, while P. engelmanniiand J. osteosperma appear to be more resilient. Investigating seasonal climate responses and interaction effects on radial growth in areas impacted by severe drought helps identify species that may be particularly at risk from climate change impacts in the Anthropocene.

The mechanisms governing tree drought mortality and recovery remain a subject of inquiry and active debate given their role in the terrestrial carbon cycle and their concomitant impact on climate change. Counter-intuitively, many trees do not die during the drought itself. Indeed, observations globally have documented that trees often grow for several years after drought before mortality. A combination of meta-analysis and tree physiological models demonstrate that optimal carbon allocation after drought explains observed patterns of delayed tree mortality and provides a predictive recovery framework. Specifically, post-drought, trees attempt to repair water transport tissue and achieve positive carbon balance through regrowing drought-damaged xylem. Furthermore, the number of years of xylem regrowth required to recover function increases with tree size, explaining why drought mortality increases with size. These results indicate that tree resilience to drought-kill may increase in the future, provided that CO2 fertilisation facilitates more rapid xylem regrowth.

Crowther et al. Reported that the best predictor of surface soil carbon (top 10 cm) losses in response to warming is the size of the surface carbon stock in the soil (that is, carbon stocks in plots that have not been warmed), finding that soils that are high in soil carbon also lose more carbon under warming conditions. This relationship was based on a linear regression of soil carbon losses and soil carbon stocks in field warming studies, which was then used to project carbon losses over time and to generate a map of soil carbon vulnerability. However, a few extreme data points (high-leverage points) can strongly influence the slope of a regression line. Only 5 of the 49 sites analysed by Crowther et al. are in the upper half of the carbon stock range, which raises the possibility that the relationship they observed could be substantially altered by introducing data from sites with relatively high surface soil carbon stocks.

Increased fire frequency, extent and severity are expected to strongly affect the structure and function of boreal forest ecosystems. In this study, we examined 213 plots in boreal forests dominated by black spruce (Picea mariana) or jack pine (Pinus banksiana) of the Northwest Territories, Canada, after an unprecedentedly large area burned in 2014. Large fire size is associated with high fire intensity and severity, which would manifest as areas with deep burning of the soil organic layer (SOL). Our primary objectives were to estimate burn depth in these fires and then to characterise landscapes vulnerable to deep burning throughout this region. Here we quantify burn depth in black spruce stands using the position of adventitious roots within the soil column, and in jack pine stands using measurements of burned and unburned SOL depths. Using these estimates, we then evaluate how burn depth and the proportion of SOL combusted varies among forest type, ecozone, plot-level moisture and stand density. Our results suggest that most of the SOL was combusted in jack pine stands regardless of plot moisture class, but that black spruce forests experience complete combustion of the SOL only in dry and moderately well-drained landscape positions. The models and calibrations we present in this study should allow future research to more accurately estimate burn depth in Canadian boreal forests.

Climate warming and drying is associated with increased wildfire disturbance and the emergence of megafires in North American boreal forests. Changes to the fire regime are expected to strongly increase combustion emissions of carbon (C) which could alter regional C balance and positively feedback to climate warming. In order to accurately estimate C emissions and thereby better predict future climate feedbacks, there is a need to understand the major sources of heterogeneity that impact C emissions at different scales. Here, we examined 211 field plots in boreal forests dominated by black spruce (Picea mariana) or jack pine (Pinus banksiana) of the Northwest Territories (NWT), Canada after an unprecedentedly large area burned in 2014. We assessed both aboveground and soil organic layer (SOL) combustion, with the goal of determining the major drivers in total C emissions, as well as to develop a high spatial resolution model to scale emissions in a relatively understudied region of the boreal forest. On average, 3.35 kg C m−2 was combusted and almost 90% of this was from SOL combustion. Our results indicate that black spruce stands located at landscape positions with intermediate drainage contribute the most to C emissions. Indices associated with fire weather and date of burn did not impact emissions, which we attribute to the extreme fire weather over a short period of time. Using these results, we estimated a total of 94.3 Tg C emitted from 2.85 Mha of burned area across the entire 2014 NWT fire complex, which offsets almost 50% of mean annual net ecosystem production in terrestrial ecosystems of Canada. Our study also highlights the need for fine‐scale estimates of burned area that represent small water bodies and regionally specific calibrations of combustion that account for spatial heterogeneity in order to accurately model emissions at the continental scale.

Global terrestrial nitrogen (N) and phosphorus (P) cycles are coupled to the global carbon (C) cycle for net primary production (NPP), plant C allocation, and decomposition of soil organic matter, but N and P have distinct pathways of inputs and losses. Current C-nutrient models exhibit large uncertainties in their estimates of pool sizes, fluxes, and turnover rates of nutrients, due to a lack of consistent global data for evaluating the models. In this study, we present a new model–data fusion framework called the Global Observation-based Land-ecosystems Utilization Model of Carbon, Nitrogen and Phosphorus (GOLUM-CNP) that combines the CARbon DAta MOdel fraMework (CARDAMOM) data-constrained C-cycle analysis with spatially explicit data-driven estimates of N and P inputs and losses and with observed stoichiometric ratios. We calculated the steady-state N- and P-pool sizes and fluxes globally for large biomes. Our study showed that new N inputs from biological fixation and deposition supplied <span class="inline-formula">&gt;20</span>&thinsp;% of total plant uptake in most forest ecosystems but accounted for smaller fractions in boreal forests and grasslands. New P inputs from atmospheric deposition and rock weathering supplied a much smaller fraction of total plant uptake than new N inputs, indicating the importance of internal P recycling within ecosystems to support plant growth. Nutrient-use efficiency, defined as the ratio of gross primary production (GPP) to plant nutrient uptake, were diagnosed from our model results and compared between biomes. Tropical forests had the lowest N-use efficiency and the highest P-use efficiency of the forest biomes. An analysis of sensitivity and uncertainty indicated that the NPP-allocation fractions to leaves, roots,<span id="page3904"/> and wood contributed the most to the uncertainties in the estimates of nutrient-use efficiencies. Correcting for biases in NPP-allocation fractions produced more plausible gradients of N- and P-use efficiencies from tropical to boreal ecosystems and highlighted the critical role of accurate measurements of C allocation for understanding the N and P cycles.

Leaf litter provides an important resource to forested stream ecosystems. During leaf fall a significant amount of dissolved organic carbon (DOC) enters streams as leaf leachate. We compared the effects of plant species and leaf leachate bioavailability on the composition of stream bacterial communities and rates of DOC decomposition. We used four common riparian tree species that varied in foliar chemistry, leachate optical properties, and litter decomposition rate. We used laboratory microcosms from two streams and amended with a standard concentration of DOC derived from leaf leachate of the four tree species. After 24 h, we measured rates of DOC biodegradation and determined the composition of the bacterial communities via bar-coded pyrosequencing of the 16S rRNA gene. The composition, diversity, and abundance of the bacterial community differed significantly among plant species from both streams. The phylogenetic distance of the different bacterial communities correlated with species-specific leachate optical properties and rates of DOC biodegradation. Highest rates of DOC decomposition were associated with high tannin and lignin leaf types. Results demonstrate that riparian plant species strongly influences stream bacterial communities via their leachate suggesting that alterations to the presence or abundance of riparian plant taxa may influence these communities and associated ecosystem processes.

As leaves are the basic elements of plants that conduct photosynthesis and transpiration, vegetation leaf dynamics controls canopy physical and biogeochemical processes and hence largely influences the interactive exchanges of energy and materials between the land surface and the atmosphere. Given that the processes of plant leaf allocation is highly sensitive to climatological and environmental conditions, developing robust models that simulate leaf dynamics via climate variables contributes a key component to land surface models and coupled land-atmosphere models. Here we propose a new method to simulate seasonal leaf dynamics based on the idea of applying vegetation productivity as a synthesized metric to track and assess the climate suitability to plant growth over time. The method first solves two closed simultaneous equations of leaf phenology and canopy photosynthesis as modeled using the Growing Production-Day model iteratively for deriving the time series of steady-state leaf area index (LAI), and then applies the method of simple moving average to account for the time lagging of leaf allocation behind steady-state LAI. The seasonal LAI simulated using the developed method agree with field measurements from a selection of AmeriFlux sites as indicated by high coefficient of determination (R2 = 0.801) and low root mean square error (RMSE = 0.924 m2/m2) and with satellite-derived data (R2 = 0.929 and RMSE = 0.650 m2/m2) for the studied flux tower sites. Moreover, the proposed method is able to simulate seasonal LAI of deciduous broadleaf forests that match with satellite-derived LAI time series across the entire eastern United States. Comparative modeling studies suggest that the proposed method produces more accurate results than the method based on Growing Season Index in terms of correlation coefficients and error metrics. The developed method provides a complete solution to modeling seasonal leaf dynamics as well as canopy productivity solely using climate variables.

Soil organic carbon (SOC) has a significant effect on carbon emissions and climate change. However, the current SOC prediction accuracy of most models is very low. Most evaluation studies indicate that the prediction error mainly comes from parameter uncertainties, which can be improved by parameter calibration. Data assimilation techniques have been successfully employed for the parameter calibration of SOC models. However, data assimilation algorithms, such as the sampling-based Bayesian Markov chain Monte Carlo (MCMC), generally have high computation costs and are not appropriate for complex global land models. This study proposes a new parameter calibration method based on surrogate optimization techniques to improve the prediction accuracy of SOC. Experiments on three types of soil carbon cycle models, including the Community Land Model with the Carnegie–Ames–Stanford Approach biogeochemistry submodel (CLM-CASA') and two microbial models show that the surrogate-based optimization method is effective and efficient in terms of both accuracy and cost. Compared to predictions using the tuned parameter values through Bayesian MCMC, the root mean squared errors (RMSEs) between the predictions using the calibrated parameter values with surrogate-base optimization and the observations could be reduced by up to 12% for different SOC models. Meanwhile, the corresponding computational cost is lower than other global optimization algorithms.

Permafrost soil in high latitude tundra is one of the largest terrestrial carbon (C) stocks and is highly sensitive to climate warming. Understanding microbial responses to warming‐induced environmental changes is critical to evaluating their influences on soil biogeochemical cycles. In this study, a functional gene array (i.e., geochip 4.2) was used to analyze the functional capacities of soil microbial communities collected from a naturally degrading permafrost region in Central Alaska. Varied thaw history was reported to be the main driver of soil and plant differences across a gradient of minimally, moderately, and extensively thawed sites. Compared with the minimally thawed site, the number of detected functional gene probes across the 15–65 cm depth profile at the moderately and extensively thawed sites decreased by 25% and 5%, while the community functional gene β‐diversity increased by 34% and 45%, respectively, revealing decreased functional gene richness but increased community heterogeneity along the thaw progression. Particularly, the moderately thawed site contained microbial communities with the highest abundances of many genes involved in prokaryotic C degradation, ammonification, and nitrification processes, but lower abundances of fungal C decomposition and anaerobic‐related genes. Significant correlations were observed between functional gene abundance and vascular plant primary productivity, suggesting that plant growth and species composition could be co‐evolving traits together with microbial community composition. Altogether, this study reveals the complex responses of microbial functional potentials to thaw‐related soil and plant changes and provides information on potential microbially mediated biogeochemical cycles in tundra ecosystems.

As atmospheric nitrogen (N) concentrations increase, it can wreak havoc on the entire planet, as well as the fragile ecosystems, once it exceeds the demand of ecosystems. Chronically elevated N deposition affects litter decomposition, which is a crucial process that controls nutrient cycling, soil fertility, and primary productivity. Nevertheless, the responses of litter decomposition and nutrient release to N addition remain elusive. Here we conduct a meta-analysis using 3434 paired observations from 55 publications to evaluate these responses. We found that although litter decomposition rate did not change significantly under N addition when averaged across all studies, it decreased with N application rate and experimental duration, showing that it was stimulated at low levels but suppressed at high levels of N application and duration. Phosphorus released more slowly under N enrichment, and this response became greater with longer duration. Moreover, the decomposition of lignin was depressed under N addition, and this effect was more pronounced with the increase of N application rate and experimental duration. Importantly, in terms of different ecosystems, the decomposition of litter was significantly inhibited by N addition in plantations, but was promoted in secondary forests, and there were no significant changes in primary forests, grasslands and wetlands. The responses of litter mass loss, along with the release of nutrients to N fertilization, changed with mean annual temperature and mean annual precipitation of the study sites. Our results provided a synthetic understanding of the effects of N addition on the decomposition of litter and nutrient release under climate change scenarios.

Land surface phenology (LSP) has been widely retrieved from time series of various satellite instruments in order to monitor climate change and ecosystem dynamics. However, any evaluation of the quality of LSP data sets is quite challenging because the in situ observations on a limited number of individual trees, shrubs, or other plants are rarely representative of the landscape sampled in a single satellite pixel. Moreover, vegetation indices detecting biophysical features of vegetation seasonality are different from (but related to) the specific plant life history stages observed by humans at ground level. This study is the first comprehensive evaluation of the LSP product derived from Visible Infrared Imaging Radiometer Suite (VIIRS) data using both MODIS LSP products and observations from the PhenoCam network across the Contiguous United States during 2013 and 2014. PhenoCam observes vegetation canopy over a landscape at very high frequency, providing nearly continuous canopy status and enabling the estimate of discrete phenophase using vegetation indices that are conceptually similar to satellite data. Six phenological dates (greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset) were retrieved separately from daily VIIRS NDVI (normalized difference vegetative index) and EVI2 (two-band enhanced vegetation index) time series. Similarly, the six phenological dates were also extracted from the 30-min time series of PhenoCam data using GCC (green chromatic coordinate) and VCI (vegetation contrast index) separately. Phenological dates derived from VIIRS NDVI and EVI2 and PhenoCam GCC and VCI were generally comparable for the vegetation greenup phase, but differed considerably for the senescence phase. Although all indices captured green leaf development effectively, performance discrepancies arose due to their ability to track the mixture of senescing leaf colors. PhenoCam GCC and VCI phenological observations were in better agreement with the phenological dates from VIIRS EVI2 than from VIIRS NDVI. Further, the VIIRS EVI2 phenological metrics were more similar to those from PhenoCam VCI than from PhenoCam GCC time series. Overall, the average absolute difference between the VIIRS EVI2 and PhenoCam VCI phenological dates was 7–11 days in the greenup phase and 10–13 days in the senescence phase. The difference was smaller in forests, followed by grasslands and croplands, and then savannas. Finally, the phenological dates derived from VIIRS EVI2 were compared with MODIS detections, which showed a good agreement with an average absolute difference less than a week except for the senescence onset. These results for the first time demonstrate the upper boundary of uncertainty in VIIRS LSP detections and the continuity to MODIS LSP product.

Terrestrial carbon cycle models have incorporated increasingly more processes as a means to achieve more-realistic representations of ecosystem carbon cycling. Despite this, there are large across-model variations in the simulation and projection of carbon cycling. Several model intercomparison projects (MIPs), for example, the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (historical simulations), Trends in Net Land?Atmosphere Carbon Exchange (TRENDY), and Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), have sought to understand intermodel differences. In this study, the authors developed a suite of new techniques to conduct post-MIP analysis to gain insights into uncertainty sources across 25 models in the three MIPs. First, terrestrial carbon storage dynamics were characterized by a three-dimensional (3D) model output space with coordinates of carbon residence time, net primary productivity (NPP), and carbon storage potential. The latter represents the potential of an ecosystem to lose or gain carbon. This space can be used to measure how and why model output differs. Models with a nitrogen cycle generally exhibit lower annual NPP in comparison with other models, and mostly negative carbon storage potential. Second, a transient traceability framework was used to decompose any given carbon cycle model into traceable components and identify the sources of model differences. The carbon residence time (or NPP) was traced to baseline carbon residence time (or baseline NPP related to the maximum carbon input), environmental scalars, and climate forcing. Third, by applying a variance decomposition method, the authors show that the intermodel differences in carbon storage can be mainly attributed to the baseline carbon residence time and baseline NPP (&gt;90% in the three MIPs). The three techniques developed in this study offer a novel approach to gain more insight from existing MIPs and can point out directions for future MIPs. Since this study is conducted at the global scale for an overview on intermodel differences, future studies should focus more on regional analysis to identify the sources of uncertainties and improve models at the specified mechanism level.

Temperature sensitivity of soil organic carbon (SOC) decomposition is one of the major uncertainties in predicting climate‐carbon (C) cycle feedback. Results from previous studies are highly contradictory with old soil C decomposition being more, similarly, or less sensitive to temperature than decomposition of young fractions. The contradictory results are partly from difficulties in distinguishing old from young SOC and their changes over time in the experiments with or without isotopic techniques. In this study, we have conducted a long‐term field incubation experiment with deep soil collars (0–70 cm in depth, 10 cm in diameter of PVC tubes) for excluding root C input to examine apparent temperature sensitivity of SOC decomposition under ambient and warming treatments from 2002 to 2008. The data from the experiment were infused into a multi‐pool soil C model to estimate intrinsic temperature sensitivity of SOC decomposition and C residence times of three SOC fractions (i.e., active, slow, and passive) using a data assimilation (DA) technique. As active SOC with the short C residence time was progressively depleted in the deep soil collars under both ambient and warming treatments, the residences times of the whole SOC became longer over time. Concomitantly, the estimated apparent and intrinsic temperature sensitivity of SOC decomposition also became gradually higher over time as more than 50% of active SOC was depleted. Thus, the temperature sensitivity of soil C decomposition in deep soil collars was positively correlated with the mean C residence times. However, the regression slope of the temperature sensitivity against the residence time was lower under the warming treatment than under ambient temperature, indicating that other processes also regulated temperature sensitivity of SOC decomposition. These results indicate that old SOC decomposition is more sensitive to temperature than young components, making the old C more vulnerable to future warmer climate.

Climate change intensifies the hydrological cycle and consequently alters precipitation regimes. Accurately assessing future carbon (C) budgets depends on understanding the influence of altered precipitation on both aboveground C cycling and belowground processes. Our goal was to explore generalities and mechanisms of responses of soil microbial communities to altered precipitation and implications for C cycling in terrestrial ecosystems. Location Global. Time period 2001?2017. Major taxa studied Soil microbes. Methods We used the meta-analytical technique to synthesize data of 41 increased (IPPT) and 53 decreased precipitation (DPPT) studies from 65 publications worldwide. The data covered broad variations in climate, percentage of precipitation change, experimental duration and soil properties. Results The fungi to bacteria ratio did not show a water-tolerant shift, but the community compositions within the bacteria did. Microbial biomass showed a higher response to moderate IPPT than moderate DPPT, whereas it was more sensitive to extreme DPPT than extreme IPPT, suggesting that the responses of microbial biomass to altered precipitation are double asymmetric. However, such asymmetric responses of microbial biomass varied with climate humidity and soil texture: microbial biomass was more sensitive to IPPT at xeric sites than at mesic sites, whereas it was more responsive to DPPT in humid areas; microbial biomass in coarse-textured soils was more sensitive to altered precipitation than that in fine-textured soils. In addition, microbial response was positively correlated with the responses of aboveground/belowground plant biomass, soil respiration and organic C content. Main conclusions Our meta-analysis provides the first evidence that the asymmetric response of microbial biomass to altered precipitation varies with climate humidity and soil texture. Given the coordinated responses in the plant?soil?microorganism C continuum, our synthesis extends the double asymmetric model and provides a framework for understanding and modelling responses of ecosystem C cycling to global precipitation change.

The recovery of North American forests is likely to impact their capacity as a carbon sink. Here, Zhu et al. show a growth in aboveground biomass in various climate change scenarios, with these forests expected to sequester no more than 22% more carbon than current levels by the 2080s.

Land-use conversions and elevated temperature can impact on carbon dioxide (CO2) and nitrous oxide (N2O) emissions, both of which are important greenhouse gasses (GHGs). Afforestation activity has increased significantly over the last century with a significant focus in recent years directed at offsetting GHG emissions, as forests have a large capacity to store carbon (C) and nitrogen (N) as well as affecting CO2 and N2O emissions. However, the impact of warming on GHG offsetting is unclear. This study was conducted in a forest and a grassland to investigate the effect of afforestation and warming, using infrared heaters, on soil fluxes of CO2 and N2O. Warming significantly increased the daily mean soil temperatures at a depth of 5 cm by 1.7 °C and reduced the soil moisture by ∼5% in the forest from March 2014 to February 2016. In the grassland, there were no significant increases in temperature and moisture with warming and no impact on the soil fluxes of CO2 and N2O. In the forest, elevated soil temperature enhanced the average soil CO2 efflux by 23% but had no effect on soil N2O fluxes. Warming decreased the temperature sensitivity by 13% and 23% at the forest and grassland, respectively. The soil fluxes of CO2 increased exponentially with temperature and decreased linearly with the reduction in soil moisture, and were much larger in the grassland compared to the forest. However the grassland proved to be a larger sink for N2O than the forest. Irrespective of warming treatments, all measured pools were significantly larger in the grassland compared to the forest. Our results imply that afforestation may have a bigger effect than warming on soil CO2 and N2O fluxes within the range of temperatures used and that afforestation dramatically lowers the inorganic, organic and microbial C and N pools, that could, in turn, impact on the responses of forest soils to future global warming.

Future climate change is expected to alter the terrestrial carbon cycle through its impact on soil respiration. In this study, we determined the responses of soil respiration and its components to experimental warming with or without water addition. A replicated <em>in situ</em> heating (∼2 °C above ambient soil temperatures) and water addition (170 mm in total each year) experiment was carried out for the first time in a temperate plantation forest of Sitka spruce over the period 2014–2016. R_hwas measured inside deep collars (35 cm deep) that excluded root growth, while R_swas measured using the static chamber approach and near-surface collars (5 cm deep) and R_a calculated by subtracting R_h from total soil respiration (R_s). Experimental warming significantly increased R_s, R_h and R_h/R_s, but had no effect on R_a. In contrast, none of the respiration components were affected by water addition. Warming increased annual R_h by 62% but had no effect on R_a. Overall, warming did not significantly increase annual R_s. Warming showed a stronger impact on R_s in the non-growing season but had a smaller impact in the growing season. Warming increased R_a in the non-growing season but decreased it in the growing season. The effects of warming on R_h were similar for the two periods. Our results highlight the differential response of R_a and R_h to warming, which was mediated by water addition or season. For this and other similar forest sites that don’t experience water limitation, global warming may have a positive feedback on atmospheric CO₂concentrations through enhanced soil respiration.