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The terrestrial carbon cycle is a major source of uncertainty in climate projections. Its dominant fluxes, gross primary productivity (GPP), and respiration (in particular soil respiration, R S ), are typically estimated from independent satellite-driven models and upscaled in situ measurements, respectively. We combine carbon-cycle flux estimates and partitioning coefficients to show that historical estimates of global GPP and R S are irreconcilable. When we estimate GPP based on R S measurements and some assumptions about R S :GPP ratios, we found the resulted global GPP values (bootstrap mean [Formula: see text] Pg C yr -1 ) are significantly higher than most GPP estimates reported in the literature ([Formula: see text] Pg C yr -1 ). Similarly, historical GPP estimates imply a soil respiration flux (Rs GPP , bootstrap mean of [Formula: see text] Pg C yr -1 ) statistically inconsistent with most published R S values ([Formula: see text] Pg C yr -1 ), although recent, higher, GPP estimates are narrowing this gap. Furthermore, global R S :GPP ratios are inconsistent with spatial averages of this ratio calculated from individual sites as well as CMIP6 model results. This discrepancy has implications for our understanding of carbon turnover times and the terrestrial sensitivity to climate change. Future efforts should reconcile the discrepancies associated with calculations for GPP and Rs to improve estimates of the global carbon budget., (© 2022. The Author(s).)

Global modeling efforts indicate semiarid regions dominate the increasing trend and interannual variation of net CO2 exchange with the atmosphere, mainly driven by water availability. Many semiarid regions are expected to undergo climatic drying, but the impacts on net CO2 exchange are poorly understood due to limited semiarid flux observations. Here we evaluated 121 site-years of annual eddy covariance measurements of net and gross CO2 exchange (photosynthesis and respiration), precipitation, and evapotranspiration (ET) in 21 semiarid North American ecosystems with an observed range of 100 - 1000 mm in annual precipitation and records of 4-9 years each. In addition to evaluating spatial relationships among CO2 and water fluxes across sites, we separately quantified site-level temporal relationships, representing sensitivity to interannual variation. Across the climatic and ecological gradient, photosynthesis showed a saturating spatial relationship to precipitation, whereas the photosynthesis-ET relationship was linear, suggesting ET was a better proxy for water available to drive CO2 exchanges after hydrologic losses. Both photosynthesis and respiration showed similar site-level sensitivity to interannual changes in ET among the 21 ecosystems. Furthermore, these temporal relationships were not different from the spatial relationships of long-term mean CO2 exchanges with climatic ET. Consequently, a hypothetical 100-mm change in ET, whether short term or long term, was predicted to alter net ecosystem production (NEP) by 64 gCm(-2) yr(-1). Most of the unexplained NEP variability was related to persistent, site-specific function, suggesting prioritization of research on slow-changing controls. Common temporal and spatial sensitivity to water availability increases our confidence that site-level responses to interannual weather can be extrapolated for prediction of CO2 exchanges over decadal and longer timescales relevant to societal response to climate change., (© 2016 John Wiley & Sons Ltd.)

We use eddy covariance measurements of net ecosystem productivity (NEP) from 21 FLUXNET sites (153 site-years of data) to investigate relationships between phenology and productivity (in terms of both NEP and gross ecosystem photosynthesis, GEP) in temperate and boreal forests. Results are used to evaluate the plausibility of four different conceptual models. Phenological indicators were derived from the eddy covariance time series, and from remote sensing and models. We examine spatial patterns (across sites) and temporal patterns (across years); an important conclusion is that it is likely that neither of these accurately represents how productivity will respond to future phenological shifts resulting from ongoing climate change. In spring and autumn, increased GEP resulting from an 'extra' day tends to be offset by concurrent, but smaller, increases in ecosystem respiration, and thus the effect on NEP is still positive. Spring productivity anomalies appear to have carry-over effects that translate to productivity anomalies in the following autumn, but it is not clear that these result directly from phenological anomalies. Finally, the productivity of evergreen needleleaf forests is less sensitive to phenology than is productivity of deciduous broadleaf forests. This has implications for how climate change may drive shifts in competition within mixed-species stands.

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Soil organic matter (SOM) supports the Earth's ability to sustain terrestrial ecosystems, provide food and fiber, and retains the largest pool of actively cycling carbon. Over 75% of the soil organic carbon (SOC) in the top meter of soil is directly affected by human land use. Large land areas have lost SOC as a result of land use practices, yet there are compensatory opportunities to enhance productivity and SOC storage in degraded lands through improved management practices. Large areas with and without intentional management are also being subjected to rapid changes in climate, making many SOC stocks vulnerable to losses by decomposition or disturbance. In order to quantify potential SOC losses or sequestration at field, regional, and global scales, measurements for detecting changes in SOC are needed. Such measurements and soil-management best practices should be based on well established and emerging scientific understanding of processes of C stabilization and destabilization over various timescales, soil types, and spatial scales. As newly engaged members of the International Soil Carbon Network, we have identified gaps in data, modeling, and communication that underscore the need for an open, shared network to frame and guide the study of SOM and SOC and their management for sustained production and climate regulation.

Soil respiration (Rs), the soil‐to‐atmosphere flux of CO2, is a dominant but uncertain part of the carbon cycle, even after decades of study. This review focuses on progress in understanding Rs from laboratory incubations to global estimates. We survey key developments of in situ ecosystem‐scale Rs observations and manipulations, synthesize Rs meta‐analyses and global flux estimates, and discuss the most compelling challenges and opportunities for the future. Increasingly sophisticated lab experiments have yielded insights into the interaction among heterotrophic respiration, substrate supply, and enzymatic kinetics, and extended incubation‐based analyses across space and time. Observational and manipulative field‐based experiments have used improved measurement approaches to deepen our understanding of the integrated effects of environmental change and disturbance on Rs. Freely‐available observational databases have enabled meta‐analyses and studies probing the magnitude of, and constraints on, the global Rs flux. Key challenges for the field include expanding Rs measurements, experiments, and opportunities to under‐represented communities and ecosystems; reconciling independent estimates of global respiration fluxes and trends; testing and leveraging the power of machine learning and process‐based models, both independently and in conjunction with each other; and continuing the field's tradition of using novel experiments to explore diverse mechanisms and ecosystems. Plain Language Summary: "Soil respiration" refers to the flow of carbon dioxide, mostly generated by plant roots and microbes, from the soil to the atmosphere. This flux is large and highly uncertain, and understanding it has significant implications for our ability to predict the effects of land use and climate change. This review summarizes advances, insights, and challenges in soil respiration science. It examines laboratory approaches and findings; documents changes in how researchers measure and understand soil respiration in the natural world; and describes efforts to estimate the global dynamics of soil respiration from openly‐available databases of observations. We conclude by discussing the most compelling challenges and opportunities for the future. Key Points: Recent decades have seen large advances in measurement methods, experimental approaches, and data availability in soil respiration scienceLab and field‐based experiments have improved our understanding of the integrated effect of environmental change on soil respirationKey challenges include broadening access and continuing to explore diverse mechanisms and ecosystems using novel model‐experiment approaches [ABSTRACT FROM AUTHOR]

Global soils store at least twice as much carbon as Earth's atmosphere.sup.1,2. The global soil-to-atmosphere (or total soil respiration, R.sub.S) carbon dioxide (CO.sub.2) flux is increasing.sup.3,4, but the degree to which climate change will stimulate carbon losses from soils as a result of heterotrophic respiration (R.sub.H) remains highly uncertain.sup.5-8. Here we use an updated global soil respiration database.sup.9 to show that the observed soil surface R.sub.H:R.sub.S ratio increased significantly, from 0.54 to 0.63, between 1990 and 2014 (P = 0.009). Three additional lines of evidence provide support for this finding. By analysing two separate global gross primary production datasets.sup.10,11, we find that the ratios of both R.sub.H and R.sub.S to gross primary production have increased over time. Similarly, significant increases in R.sub.H are observed against the longest available solar-induced chlorophyll fluorescence global dataset, as well as gross primary production computed by an ensemble of global land models. We also show that the ratio of night-time net ecosystem exchange to gross primary production is rising across the FLUXNET2015.sup.12 dataset. All trends are robust to sampling variability in ecosystem type, disturbance, methodology, CO.sub.2 fertilization effects and mean climate. Taken together, our findings provide observational evidence that global R.sub.H is rising, probably in response to environmental changes, consistent with meta-analyses.sup.13-16 and long-term experiments.sup.17. This suggests that climate-driven losses of soil carbon are currently occurring across many ecosystems, with a detectable and sustained trend emerging at the global scale.Global soil respiration is rising, probably in response to environmental changes, suggesting that climate-driven losses of soil carbon are occurring worldwide., Author(s): Ben Bond-Lamberty [sup.1] , Vanessa L. Bailey [sup.2] , Min Chen [sup.1] , Christopher M. Gough [sup.3] , Rodrigo Vargas [sup.4] Author Affiliations:(1) Pacific Northwest National Laboratory, Joint Global [...]

Autumn canopy phenological transitions are increasing in length as a consequence of climate change. Here, we assess how well hyperspectral indices in the visible and near-infrared (NIR) wavelengths predict nitrogen (N) concentrations in lower-canopy leaves in the autumn phenological transition as they are generally understudied in leaf trait research. Using a Bayesian framework, we tested how well published indices are able to predict N concentrations in Fagus grandifolia Ehrh., Liriodendron tulipifera L., and Betula lenta L. from mid-summer through senescence, and how related the indices are to autumn phenological change. No indices were able to determine a trend in differences in N in mid-summer leaves. Indices that included wavelengths in the green and NIR ranges were the first indices able to detect a trend and had among the highest correlations with N concentration in both the last green collection and the senescing collection. Models were unique when indices were fit to data from different phenophases. Indices that focused on only the red edge (i.e., the sharp increase in reflectance between the red and NIR wavelengths) had the strongest explanatory power across the autumn phenological transition, but had less explanatory power for individual collections. These indices, as well as those that have been correlated with chlorophyll (CCI) and carotenoids (PRI), were the strongest descriptors of autumn progression. This study provides insights on challenges and capabilities to monitor a leaf's N concentration throughout and across canopy senescence. [ABSTRACT FROM AUTHOR]

Climate models predict increased frequency and intensity of storm events, but it is unclear how extreme precipitation events influence the dynamics of soil fluxes for multiple greenhouse gases (GHGs). Intact soil mesocosms (0-10 cm depth) from a temperate forested watershed in the piedmont region of Maryland [two upland forest soils, and two hydric soils (i.e., wetland, creek bank)] were exposed to experimental water pulses with periods of drying, forcing soils towards extreme wet conditions under controlled temperature. Automated measurements (hourly resolution) of soil CO, CH, and NO fluxes were coupled with porewater chemistry analyses (i.e., pH, Eh, Fe, S, NO ), and polymerase chain reaction-denaturing gradient gel electrophoresis to characterize changes in microbial community structure. Automated measurements quantified unexpected increases in emissions up to 245% for CO (Wetland), >23,000% for CH (Creek), and >110,000% for NO (Forest Soils) following pulse events. The Creek soil produced the highest soil CO emissions, the Wetland soil produced the highest CH emissions, and the Forest soils produced the highest NO emissions during the experiment. Using carbon dioxide equivalencies of the three GHGs, we determined the Creek soil contributed the most to a 20-year global warming potential (GWP; 30.3%). Forest soils contributed the most to the 100-year GWP (up to 53.7%) as a result of large NO emissions. These results provide insights on the influence of extreme wet conditions on porewater chemistry and factors controlling soil GHGs fluxes. Finally, this study addresses the need to test biogeochemical thresholds and responses of ecosystem functions to climate extremes. [ABSTRACT FROM AUTHOR]

The allotetraploid Tympanoctomys barrerae has a broad, patchy distribution around salt flats in western Argentina. To gain insights into its phylogenetic relationships, phylogeographical patterns, and origin, seven populations of T. barrerae and its allied taxa were studied through a 1075-bp fragment of cytochrome b and cytochrome oxidase I. Matrilineal phylogenetic relationships were explored with maximum likelihood and Bayesian approaches. The intraspecific genealogy was inferred by median-joining networks. The populational structure was assessed by molecular variance, and the demographic history through coalescence. The tree topology depicted sister-group relationship between Octomys mimax to the pair T. barrerae-Pipanacoctomys aureus, suggesting P. aureus belongs to the maternal lineage that gave rise to T. barrerae. High degrees of intrapopulational variation and the several instances of interpopulational polyphyly suggest range shifts and secondary contact. Consistent with the phylogenetic results, the network analysis revealed two haplotypic lineages depicting genetic admixtures unrelated to the current geographical distribution, and a deep split with the southernmost lineage of Chubut. The origin of T barrerae was estimated at approximately 2.52 Mya, whereas the divergence estimate for Chubut coincides with the end of largest Patagonian glaciation, at 1.47 Mya. Apparently, this population remained isolated during a northward Pleistocenic range shift. The historical demographic patterns of the lineages of T. barrerae fit with a contraction-expansion model coincident with Quaternary cycling events. © 2012 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, 108, 453-469. [ABSTRACT FROM AUTHOR]

Abstract: Solutions to current global environmental problems throughout fundamental ecological understandings are only reached through international programmatic and scientific collaborations. Both scientists and policymakers recognize the importance of the role of carbon cycle science (CCS) in North America, however regional ecological studies in North America often do not include the role of Mexico. Given the sharp ecological, climatic and socioeconomic differences among the three countries conforming North America it is fundamental to incorporate Mexico''s unique contributions toward regional CCS. We provide a synthesis of opportunities and challenges for advancing land and ocean research in Mexico in order to move toward a complete North American continental scale CCS. First, we provide the socio-ecological context of Mexico relevant to CCS. Second, we compare the existing relationships among scientific/governmental entities and funding agencies that contribute to CCS in the United States and Mexico. Third, we discuss Mexico''s state-of-the-art CCS, and synthesize its recent advances with emphasis on land– and ocean–atmosphere interactions. We highlight continental-scale opportunities toward a tri-national carbon research, infrastructure, and education network. [Copyright &y& Elsevier]

Climate models suggest that extreme rainfall events will become more common with increased atmospheric warming. Consequently, changes in the size and frequency of rainfall will influence biophysical drivers that regulate the strength and timing of soil CO2 efflux - a major source of terrestrial carbon flux. We used a rainfall manipulation experiment during the summer monsoon season ( July- September) to vary both the size and frequency of precipitation in an arid grassland 2 years before and 2 years after a lightning-caused wildfire. Soil CO2 efflux rates were always higher under increased rainfall event size than under increased rainfall event frequency, or ambient precipitation. Although fire reduced soil CO2 efflux rates by nearly 70%, the overall responses to rainfall variability were consistent before and after the fire. The overall sensitivity of soil CO2 efflux to temperature ( Q10) converged to 1.4, but this value differed somewhat among treatments especially before the fire. Changes in rainfall patterns resulted in differences in the periodicity of soil CO2 efflux with strong signals at 1, 8, and 30 days. Increased rainfall event size enhanced the synchrony between photosynthetically active radiation and soil CO2 efflux over the growing season before and after fire, suggesting a change in the temporal availability of substrate pools that regulate the temporal dynamics and magnitude of soil CO2 efflux. We conclude that arid grasslands are capable of rapidly increasing and maintaining high soil CO2 efflux rates in response to increased rainfall event size more than increased rainfall event frequency both before and after a fire. Therefore, the amount and pattern of multiple rain pulses over the growing season are crucial for understanding CO2 dynamics in burned and unburned water-limited ecosystems. [ABSTRACT FROM AUTHOR]

Precipitation regimes are predicted to become more variable with more extreme rainfall events punctuated by longer intervening dry periods. Water-limited ecosystems are likely to be highly responsive to altered precipitation regimes. The bucket model predicts that increased precipitation variability will reduce soil moisture stress and increase primary productivity and soil respiration in aridland ecosystems. To test this hypothesis, we experimentally altered the size and frequency of precipitation events during the summer monsoon (July through September) in 2007 and 2008 in a northern Chihuahuan Desert grassland in central New Mexico, USA. Treatments included (1) ambient rain, (2) ambient rain plus one 20 mm rain event each month, and (3) ambient rain plus four 5 mm rain events each month. Throughout two monsoon seasons, we measured soil temperature, soil moisture content ( θ), soil respiration ( R), along with leaf-level photosynthesis ( A), predawn leaf water potential (Ψ), and seasonal aboveground net primary productivity (ANPP) of the dominant C grass, Bouteloua eriopoda. Treatment plots receiving a single large rainfall event each month maintained significantly higher seasonal soil θ which corresponded with a significant increase in R and ANPP of B. eriopoda when compared with plots receiving multiple small events. Because the strength of these patterns differed between years, we propose a modification of the bucket model in which both the mean and variance of soil water change as a consequence of interannual variability from 1 year to the next. Our results demonstrate that aridland ecosystems are highly sensitive to increased precipitation variability, and that more extreme precipitation events will likely have a positive impact on some aridland ecosystem processes important for the carbon cycle. [ABSTRACT FROM AUTHOR]

Soils play a critical role in the global carbon cycle, yet the biophysical factors regulating soil CO2 dynamics remain unclear. We combined high-frequency in situ observations of fine roots and ectomycorrhizal (EM) fungi with data from multiple soil sensor arrays to examine the biophysical interactions influencing soil CO2 production for one year in a mixed conifer forest. Using structural equation modeling we constructed a hypothesized model to test for causal interactions among environmental factors, biotic factors, and soil CO2 dynamics throughout the soil profile. According to our model, seasonal variation in CO2 production was significantly influenced by EM rhizomorph production, soil temperature, and soil moisture. Fine root production, on the other hand, did not appear to significantly influence soil CO2 production. The relationship between EM rhizomorph production and soil CO2 production was also supported by a zero temporal lag between these two measurements in a cross-correlation analysis. In contrast, CO2 production increased before fine root production suggesting that these two measurements were decoupled in time. Results from this study highlight the need to better understand differences in carbon allocation between plant roots and EM fungi to improve our predictions of soil carbon dynamics under global climate change. [ABSTRACT FROM AUTHOR]

Ecosystem processes are influenced by weather and climatic perturbations at multiple temporal scales with a large range of amplitudes and phases. Technological advances of automated biometeorological measurements provide the opportunity to apply spectral methods on continuous time series to identify differences in amplitudes and phases and relationships with weather variation. Here we used wavelet coherence analysis to study the temporal covariance between soil CO2 production and soil temperature, soil moisture, and photosynthetically active radiation (PAR). Continuous (hourly average) data were acquired over 2 years among three vegetation types in a semiarid mixed temperate forest. We showed that soil temperature and soil moisture influence soil CO2 production differently at multiple periods (e.g. hours, days, weeks, months, years), especially after rain pulse events. Our results provide information about the periodicity of soil CO2 production among vegetation types, and provide insights about processes controlling CO2 production through the study of phase relationships between two time series (e.g. soil CO2 production and PAR). We tested the performance of empirical models of soil CO2 production using the continuous wavelet transform. These models, built around soil temperature and moisture, failed at multiple periods across the measured dates, suggesting that empirical models should include other factors that regulate soil CO2 production at different temporal scales. Our results add a new dimension for the analysis of continuous time series of biometeorological measurements and model testing, which will prove useful for analysis of increasing sensor data obtained by environmental networks. [ABSTRACT FROM AUTHOR]

Soil organisms undertake every major ecosystem process, from primary production to decomposition to carbon sequestration, and those processes they catalyze have a bearing on the management of issues from agriculture to global climate change. Nonetheless, until recently, research to measure the dynamics of microscopic organisms living belowground has largely been limited to infrequent field sampling and laboratory extrapolation. Now, however, new sensor technologies can measure and monitor soil organisms and processes at rapid and continuous temporal scales. In this article, we describe these technologies and how they can be arrayed for an integrated view of soil dynamics. [ABSTRACT FROM AUTHOR]