Search

Infrequent and small precipitation (PPT) events characterize PPT patterns in semiarid grasslands; however, plants and soil microorganisms are adapted to use the unpredictable small pulses of water. Several studies have shown short-term responses of carbon and nitrogen mineralization rates (called the “priming effect” or the Birch effect) stimulated by wet–dry cycles; however, dynamics, drivers, and the contribution of the priming effect to the annual C balance are poorly understood. Thus, we analyzed 6 years of continuous net ecosystem exchange measurements to evaluate the effect of the PPT periodicity and magnitude of individual PPT events on the daily/annual net ecosystem C exchange (NEE) in a semiarid grassland. We included the period between PPT events, previous daytime NEE rate, and previous soil moisture content as the main drivers of the priming effect. Ecosystem respiration (ER) responded within a few hours following a PPT event, whereas it took 5–9 d for gross ecosystem exchange (GEE; where −NEE = GEE + ER) to respond. Precipitation events as low as 0.25 mm increased ER, but cumulative PPT > 40 mm infiltrating deep into the soil profile stimulated GEE. Overall, ER fluxes following PPT events were related to the change in soil water content at shallow depth and previous soil conditions (e.g., previous NEE rate, previous soil water content) and the size of the stimulus (e.g., PPT event size). Carbon effluxes from the priming effect accounted for less than 5 % of ecosystem respiration but were significantly high with respect to the carbon balance. In the long term, changes in PPT regimes to more intense and less frequent PPT events, as expected due to the effects of climate change, could convert the semiarid grassland from a small C sink to a C source.

Understanding the sources and sinks of methane (CH4) is critical to both predicting and mitigating future climate change. There are large uncertainties in the global budget of atmospheric CH4, but natural emissions are estimated to be of a similar magnitude to anthropogenic emissions. To understand CH4 flux from biogenic sources in the United States (US) of America, a multi-scale CH4 observation network focused on CH4 flux rates, processes, and scaling methods is required. This can be achieved with a network of ground-based observations that are distributed based on climatic regions and land cover. To determine the gaps in physical infrastructure for developing this network, we need to understand the landscape representativeness of the current infrastructure. We focus here on eddy covariance (EC) flux towers because they are essential for a bottom-up framework that bridges the gap between point-based chamber measurements and airborne or satellite platforms that inform policy decisions and global climate agreements. Using dissimilarity, multidimensional scaling, and cluster analysis, the US was divided into 10 clusters distributed across temperature and precipitation gradients. We evaluated dissimilarity within each cluster for research sites with active CH4 EC towers to identify gaps in existing infrastructure that limit our ability to constrain the contribution of US biogenic CH4 emissions to the global budget. Through our analysis using climate, land cover, and location variables, we identified priority areas for research infrastructure to provide a more complete understanding of the CH4 flux potential of ecosystem types across the US. Clusters corresponding to Alaska and the Rocky Mountains, which are inherently difficult to capture, are the most poorly represented, and all clusters require a greater representation of vegetation types.

Understanding the sources and sinks of CH4 is critical to both predicting and mitigating future climate change. There are large uncertainties in the global budget of atmospheric CH4, but natural emissions are estimated to be of a similar magnitude to total anthropogenic emissions. The largest sources of uncertainty in scaling bottom-up CH4 estimates stem from limited ground-based measurements and the misalignment between drivers of CH4 fluxes and current land use classifications. To understand the CH4 flux potential of natural ecosystems and agricultural lands in the United States (US) of America, a multi-scale CH4 observation network focused on CH4 flux rates, processes, and scaling methods is required. This can be achieved with a network of ground-based observations that are distributed based on climatic regions and landcover. To determine the gaps in physical infrastructure for developing this network, we need to understand the representativeness of current measurements. We focus here on eddy covariance (EC) flux towers because they are essential for a bottom-up framework that bridges the gap between point-based chamber measurements and airborne or satellite platforms, informing the remote sensing and modelling communities and policy decisions, all the way to IPCC reports. Using multidimensional scaling and a cluster analysis, the US was divided into 10 clusters that were distributed across temperature and wetness gradients. We evaluated the distance to the medoid condition within each cluster for research sites with EC tower infrastructure to identify the gaps in existing infrastructure that limit our ability to constrain the contribution of US biogenic CH4 emissions to the global budget. These gaps occurred across all EC flux tower networks and independently managed sites as well as in some environmental clusters. Through our analysis using climate, land cover, and location variables, we have identified priority areas to target for research infrastructure to provide a more complete understanding of the CH4 flux potential of ecosystem types across the US.

Data-intensive science communities are progressively adopting FAIR practices that enhance the visibility of scientific breakthroughs and enable reuse. At the core of this movement, research objects contain and describe scientific information and resources in a way compliant with the FAIR principles and sustain the development of key infrastructure and tools. This paper provides an account of the challenges, experiences and solutions involved in the adoption of FAIR around research objects over several Earth Science disciplines. During this journey, our work has been comprehensive, with outcomes including: an extended research object model adapted to the needs of earth scientists; the provisioning of digital object identifiers (DOI) to enable persistent identification and to give due credit to authors; the generation of content-based, semantically rich, research object metadata through natural language processing, enhancing visibility and reuse through recommendation systems and third-party search engines; and various types of checklists that provide a compact representation of research object quality as a key enabler of scientific reuse. All these results have been integrated in ROHub, a platform that provides research object management functionality to a wealth of applications and interfaces across different scientific communities. To monitor and quantify the community uptake of research objects, we have defined indicators and obtained measures via ROHub that are also discussed herein.

Environmental observatory networks (EONs) are coordinated efforts to provide knowledge that ultimately delivers transformational ecological science from regional to global scales. We used ecosystem functional types (EFTs), a time-varying land surface classification, as an alternative way to characterize ecosystem functional heterogeneity based on carbon uptake dynamics. We assessed the representativeness of the eddy-covariance sites of AmeriFlux and NEON, and their combined core sites (i.e., sites with long-term support) across the conterminous United States (CONUS) based on: a) the number of different EFT categories (EFTmode) represented by each network, b) representativeness of the EFT inter-annual variability (EFTint; number of unique EFTs within each pixel during years 2001–2014), and c) the spatial representation of EFTmode and EFTint based on a maximum entropy approach (i.e., spatial functional heterogeneity). AmeriFlux represents 50% of all possible EFT categories, includes most of EFTint values (9 out of 14), and represents 55% of the spatial functional heterogeneity across CONUS. NEON represents 23% of all possible EFT categories, 7 out of 14 possible EFTint values, and 23% of the spatial functional heterogeneity across CONUS. The combined effort of AmeriFlux and NEON core sites represents 33% of all possible EFT categories, 7 out of 14 possible EFTint values, and 46% of the spatial functional heterogeneity across CONUS. We used the NEON ecoclimatic domains to summarize our results within a geographical context. The least represented NEON ecoclimatic domains were Desert Southwest, Southern Rockies and Colorado Plateau, Great Basin, Northern Plains, and Central Plains. Our results provide insights about the potential of AmeriFlux to address questions regarding decadal and inter-annual variability of ecosystem functional heterogeneity across CONUS.

A growing body of scientific evidence indicates that we have entered the Anthropocene Epoch. Many assert that society has exceeded sustainable ecological planetary boundaries and that altered biogeophysical processes are no longer reversible to natural rates of ecosystem functioning. To properly and successfully address societal needs for the future, more holistic and complex methods need to be applied at various spatial and temporal scales. The increasingly interconnected nature of human and natural environments—from individuals to large megacities and entire continents and from cells through ecosystems to the biosphere as a whole (e.g., as seen in the carbon cycle)—demand new and often interdisciplinary and international approaches to address emerging global challenges. With that perspective in mind, the Czech Republic’s National Climate Program was established in 1991 with the aim to understand the impact of global environmental change on society. The National Climate Program was updated in 2017 to formulate a new Climate Protection Policy. Here, we outline the multifaceted problems that climate change poses for the Czech Republic, as well as a new scientific infrastructure and approaches directed to better understanding the effects of climate change on our ecosystems, water resources, urban environment, agriculture, human health, and general economy.

Globally, soil CO2 efflux rates (Fs) have been linked to changes in soil water content (SWC), rainfall and temperature and/or productivity. However, within an ecosystem, Fs can vary based on site structure and function, which can be affected by a combination of abiotic and biotic factors. This becomes particularly important when an ecosystem is faced with disturbances, such as drought or fire. Site-specific compensatory responses to disturbances may therefore alter C mineralization, as well as root respiration. Hence, single location Fs estimates may not be a representative for ecosystems across their distributional ranges. We conducted a 6-year study along an edaphic moisture gradient of longleaf pine ecosystems that were maintained with prescribed fire, using eddy covariance and soil respiration measurements to address how Fs varies with changes in ecosystem structure and function, as well as disturbances. Lower air temperatures (Tair) decreased Fs at all sites, but that response was also affected by productivity and SWC. Productivity significantly altered Fs rates at all sites, especially when we accounted for changes in temperature and SWC. Plant regrowth post-fire temporarily increased Fs (10–40%), whereas drought reduced Fs at all sites. Our results show that site productivity, Fs and the degree to which ecosystems adapt to climate variations and disturbance can be site specific. Hence, model forecasting of carbon dynamics would strongly benefit from multi-location measurements of Fs across the distributional range of an ecosystem.

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.

Examination of the effects of altered precipitation and atmospheric temperature patterns on ecosystem processes are an active area of research. Influences of these climate factors may change when plant cover and species composition are disturbed as a consequence of land use change altering ecosystem processes, such as soil respiration. We addressed the following question: how does experimentally manipulated reduction in the size of each precipitation event influence soil respiration fluxes (Rs) in a tropical semiarid grassland with different plant cover and species composition? Rainout shelters were installed over eight yr old planted monospecific plots (4 m 2 ) of Bouteloua gracilis, the keystone species of the grassland biome, and over mixed grassland plots in sites that recovered from abandoned agricultural land, allowing full or a 50% reduction of ambient precipitation. Soil respiration rates as well soil temperature (T soil ) and soil water content (SWC), as controlling factors, were monitored. Overall, SWC was the most important control for Rs explaining ∼70% of its variability, followed by T soil which explained ∼25% and plant cover type having a minor effect (3%) explaining Rs variability. Still, Rs exhibited differential responses when comparing plant cover types; SWC in the mixed grassland had up to 90% relative influence on Rs as compared to 10% by Tsoil. In contrast, Rs rates in monospecific B. gracilis plots exhibited less overall variability considering SWC (55–60%) and T soil (40–45%), suggesting that grasslands dominated by the keystone species are more resilient and better buffer the effects of extreme climatic drought conditions on ecosystem processes.

Technological advances have allowed in situ monitoring of soil water content in an automated manner. These advances, along with an increase in large-scale networks monitoring soil water content, stress the need for a robust calibration framework that ensures that soil water content measurements are accurate and reliable. We have developed an approach to make consistent and comparable soil water content sensor calibrations across a continental-scale network in a production framework that incorporates a thorough accounting of uncertainties. More than 150 soil blocks of varying characteristics from 33 locations across the United States were used to generate soil-specific calibration coefficients for a capacitance sensor. We found that the manufacturer’s nominal calibration coefficients poorly fit the data for nearly all soil types. This resulted in negative (91% of samples) and positive (5% of samples) biases and a mean root mean square error (RMSE) of 0.123 cm cm (1σ) relative to reference standard measurements. We derived soil-specific coefficients, and when used with the manufacturer’s nominal function, the biases were corrected and the mean RMSE dropped to ±0.017 cm cm (±1σ). A logistic calibration function further reduced the mean RMSE to ±0.016 cm cm (±1σ) and increased the range of soil moistures to which the calibration applied by 18% compared with the manufacturer’s function. However, the uncertainty of the reference standard was notable (±0.022 cm cm), and when propagated in quadrature with RMSE estimates, the combined uncertainty of the calibrated volumetric soil water content values increased to ±0.028 cm cm regardless of the calibration function used.

Knowledge about biomass partitioning of maize grown in arid and semi-arid climates is scarce and yet essential to select a robust and effective deficit irrigation management (DIM) strategy for these regions. The objectives of this study were to: i) investigate the effects of different levels of water application under two DIM strategies on the root and aboveground characteristics, the response factor to water stress (Ky) and irrigation water use efficiency (IWUE) of silage maize at different growth stages, and ii) determine the best DIM strategy that would maximize biomass productivity. Field pot experiments were conducted in Isfahan, Iran, during 2009 and 2010. The two DIM strategies were fixed irrigation interval-variable irrigation depth (M1), and variable irrigation interval-fixed irrigation depth (M2). Each DIM strategy was tested at four water-deficit levels, including: severe, moderate, mild, and a full-irrigation. In M1, irrigation intervals were consistent for all irrigation treatments but were varied over the growing season. Treatment effects were measured at the 10-leaf, 16-leaf, tasseling, milk, and silage harvest crop growth stages. There was significant effect of irrigation and growth stage on total aboveground biomass (TB), leaf area (LA), root biomass (RB), and root:shoot ratio (RSR) for both DIM strategies during the two years. For M2, there was significant difference in TB, LA, RB, and RSR between all irrigation levels at all growth stages. TB production was on the average around 25% higher for M1 compared to M2, even though total applied irrigation water was only 6% higher for M1. Comparing the two DIMs showed that RSR and Ky were both higher for M2, indicating that the crop was more sensitive to this strategy. In conclusion, M1 was selected as the best management practice since it had more favorable effects on improving the IWUE and also on the development of maize roots during the growing season.

There is an increasing need for approaches to determine reference emission levels and implement policies to address the objectives of Reducing Emissions from Deforestation and Forest Degradation, plus improving forest management, carbon stock enhancement and conservation (REDD+). Important aspects of approaching emissions reductions include coordination and sharing of technology, data, protocols and experiences within and among countries to maximize resources and apply knowledge to build robust monitoring, reporting and verification (MRV) systems. We propose that enhancing the multiple facets of interoperability could facilitate implementation of REDD+ programs and actions. For this case, interoperability is a collective effort with the ultimate goal of sharing and using information to produce knowledge and apply knowledge gained, by removing conceptual, technological, organizational and cultural barriers. These efforts must come from various actors and institutions, including government ministrie...

The high utility of eddy covariance (EC) data has made it the cornerstone of carbon dynamics research for more than two decades. However, a substantial number of measurements from EC data can be missing for various reasons. Robust gap-filling methods are required to estimate carbon budgets from net ecosystem exchange measurements of CO 2 (NEE) with high precision and accuracy. While the gap-filled methods used have provided unbiased estimates of annual NEE, little research has been done on preserving the variance structures associated with gap-filled flux data. In this project, we used EC data from a longleaf pine ecosystem located in the southeast US to investigate variance preservation in gap-filling methods. We used three non-linear regression approaches to impute artificially created gaps of different sizes via light and temperature response curves: 1) “traditional” fixed monthly window, 2) moving window, and 3) moving window with parameter prediction using physiological drivers. The results of gap-filling simulations showed that the variability of NEE estimates made with moving window and parameter prediction methods were closer to that of observed NEE, whereas the traditional method had overall lower variability. The average root mean square errors (RMSE) of predictions was lower for moving window and parameter prediction (3.38 and 3.22, respectively), versus that of the traditional method (3.42) over one year, including both daytime and nighttime data across all gap sizes. The variances associated with moving window and parameter prediction methods were 52% and 57%, respectively, of the observed variance, versus that of the traditional (51%), while the average of first-order autocorrelation coefficients was 0.76 for each method compared to 0.58 for observed. The results showed that the moving window approaches provided better estimates (lower RMSEs and more similar variance) at annual scales, yet underestimated the observed variance. These results contribute toward the development of a framework of standardized gap-filling approaches which maintain variation inherent in EC data. Moreover, these results call for further research on potential environmental drivers and their interactions for inclusion in gap-filling models, as well as exploration of sampling size of estimation windows and averaging time (half hour) of flux data to promote variance maintenance and decrease the autocorrelation of predictions.

Understanding, predicting, and responding to a rapidly changing Arctic requires sustained observations that capture variability and transformative change of the Arctic systems with all its major co...

Future precipitation changes in the semiarid grassland region in Central Mexico are expected to be larger for winter rainfall (−20%) than for summer rain (−10%). Winter rainfall however comprises a small proportion of annual precipitation (5–6%), therefore the potential effects on productivity are expected to be negligible. We are realizing however, that winter rain events are important controls of tiller population and consequently of grassland productivity. To attest its influence we examined rain legacy effects using rainout shelters, by reducing rainfall by 42% (2011) and 20% (2012) relative to unmanipulated rainfall on monodominant Bouteloua gracilis and mixed-species disturbed grasslands. In 2013 rainout shelters were removed to allow all incoming rain into the plots. Plant cover type was a significant predictor of aboveground productivity with monodominant B. gracilis consistently producing 50–80 g/m 2 more than mixed stands. Decreased rainfall did not have negative effects on aboveground productivity except in an extreme drought year for the mixed-species grassland. We also observed a significant legacy effect of winter precipitation on summer aboveground productivity in both grassland types, but not to previous-year total precipitation. In spite of the large annual variability of soil ψ, leaf ψ fluctuated between −0.5 and −1.5 MPa most of the year suggesting a geologic source of water.

Water scarcity is becoming a major constraint for maize cultivation and increasing maize yield globally. Water-saving irrigation methods are required to increase crop water productivity (CWP) without reducing grain yield and nutrient uptake. We evaluated the effects of different methods and levels of irrigation on crop dry matter, grain yield, nutrient uptake and CWP of maize (cv. ‘BARI Hybrid Bhutta-9′) in field condition in a sub-tropical environment in Bangladesh. The experiment was laid out in a nested plot design with three replications during 2014−15 and 2015−16. The treatments were three irrigation levels (I1: 100 % field capacity, FC; I2: 80 % FC; I3: 60 % FC) and three irrigation methods (AFI: alternate furrow irrigation; SFFI: skip-fixed furrow irrigation; and TFI: traditional furrow irrigation). Results indicate that both the irrigation method and level had significant effects on dry matter, grain yield, nutrient uptake, and CWP. The AFI technique maintained similar grain yield (8.1 t ha−1) to TFI but reduced irrigation water by 37 % for irrigation applied at 100 % of FC. Both AFI and TFI had significantly higher (p ≤ 0.05) grain yield compared to SFFI at the irrigation level of I2. The interactive effect of irrigation level and method also had a significant effect (p ≤ 0.05) on maize yield. The uptakes of macronutrients (N, P, K, S, Ca, and Mg) and micronutrients (B, Zn, Fe, and Mn) by maize grain were not significantly different between AFI and TFI under I1. Irrigation level was the main driver for determining the patterns in grain yield but irrigation method controlled the patterns in CWP. The AFI technique resulted in higher CWP compared to TFI or SFFI. Results demonstrate that AFI is an effective water-saving technique, which can increase the CWP without a significant reduction in grain yield and nutrients uptake by maize grain. The AFI method in conjunction with a reduced amount of irrigation water can be adapted in the sub-tropical climates of South Asia where maize production in the dry season is heavily dependent on repeated irrigation with limited water supplies.

The increased need for water and food security requires the development of new approaches to save water through irrigation management strategies, particularly for center pivot irrigation. To do so entails monitoring of the dynamic variation in wind drift and evaporation losses (WDELs) of irrigation systems under different weather conditions and for relatively long time periods. The historical catch can method has limited our ability to address this goal. Here, a new and easy-to implement methodology, called the strip test, was developed and validated against the catch can technique. Our results showed strong agreement between the catch can method and the strip test for determining the average water application efficiency (WAE ≈ 1-WDEL). Because the strip test method was measured for shorter intervals compared to the catch can method, the variables influencing WAE were able to be compared during each test. WAE had a large variance over time, which was controlled, in part, by wind speed (>4 m s−1). Site-specific characterisation of WDEL is needed to apply this technique. Once applied, it can provide a better understanding of WAE behaviour over the time, and enhance the capability of predicting results for the optimising water use in sprinkler irrigation.

In the United States and elsewhere, the development of large-scale scientific research infrastructure (RI) comes about from needs of the scientific community, as well as imperatives expressed in society and at high levels of government planning. In 2001, the US National Academy of Science (NAS) recognized the need to define the challenges that face society and science in addressing our changing environment. In 2003, the US NAS conducted a second seminal report that contributed toward the evolution of the National Ecological Observatory Network (NEON). The NAS advocated for observations and experiments to be made consistently across the North American continent and creating the needed long-term, decadal-scale ecological data sets. The national relevance and timeliness of NEON’s design continued to resonate with other societal imperatives over the course of its development. In turn, having requirements defines what needs to be built to meet the scientific needs and also the fidelity needed to constrain cost, schedule, and risk.

Describing the quality of measurements is necessary to understand the level of confidence in any observation. Accuracy, precision, trueness, repeatability, reproducibility, and uncertainty are all used to describe quality of measurement, but the terms are inconsistently defined and measured and thus easily misunderstood. One purpose of quality parameters is for the comparison of observations, but when dissimilar methods for estimating quality terms are utilized, a comparison is misrepresented. A standardized approach to estimating uncertainty provides a basis for meeting measurement requirements and providing a level of confidence for observations. Here, we show the approach used by the National Ecological Observatory Network to estimate uncertainty of the calibration processes and measurements illustrated with an example of uncertainty assessment on a temperature sensor. Detailing the approach for uncertainty assessment provides the transparency necessary for network science and allows for the approach to be adopted in the scientific community. Reporting uncertainty with all measurements needs to become consistent and commonplace across disciplines.

Faced with growing stakeholder attention to climate change-related societal impacts, Environmental Research Infrastructures (ERIs) find it difficult to engage beyond their initial user base, which calls for an overarching governance scheme and transnational synergies. Forced by the enormity of tackling climate change, ERIs are indeed broaching collaborative venues, based on the assumption that no given institution can carry out this agenda alone. While strategic, this requires that ERIs address the complexities and barriers towards aligning multiple organizations, national resources and programmatic cultures, including science.

The 4‰ initiative to sequester carbon in soils has the potential to connect sustainable development goals, enhance food security and mitigate climate change by utilizing waste organic residues.

Ontologies offer multiple benefits for biodiversity data processing and analysis, including precisely defined vocabularies, robust pathways for data integration, and support for automated machine reasoning. However, ontologies have yet to be widely deployed for biodiversity data processing and analysis. Reasons for this include: specialized skills and coordination are needed for mapping terms to source data, data processing and machine reasoning are computationally expensive, and there is a scarcity of tools for working with ontologies and RDF triples. In this presentation we will discuss a data processing pipeline (available at https://github.com/biocodellc/ppo-data-pipeline) which simplifies complex implementation tasks, offers tools for data ingest, triplifying, and reasoning, and makes datasets available for indexing.

We analyzed energy partitioning in short- and long-hydroperiod freshwater marsh ecosystems in the Florida Everglades by examining energy balance components (eddy covariance derived latent energy (LE) and sensible heat (H) flux). The study period included several wet and dry seasons and variable water levels, allowing us to gain better mechanistic information about the control of and changes in marsh hydroperiods. The annual length of inundation is ~5 months at the short-hydroperiod site (25°26′16.5″N, 80°35′40.68″W), whereas the long-hydroperiod site (25°33′6.72″N, 80°46′57.36″W) is inundated for ~12 months annually due to differences in elevation and exposure to surface flow. In the Everglades, surface fluxes feed back to wet season precipitation and affect the magnitude of seasonal change in water levels through water loss as LE (evapotranspiration (ET)). At both sites, annual precipitation was higher than ET (1304 versus 1008 at the short-hydroperiod site and 1207 versus 1115 mm yr−1 at the long-hydroperiod site), though there were seasonal differences in the ratio of ET:precipitation. Results also show that energy balance closure was within the range found at other wetland sites (60 to 80%) and was lower when sites were inundated (60 to 70%). Patterns in energy partitioning covaried with hydroperiods and climate, suggesting that shifts in any of these components could disrupt current water and biogeochemical cycles throughout the Everglades region. These results suggest that the complex relationships between hydroperiods, energy exchange, and climate are important for creating conditions sufficient to maintain Everglades ecosystems.

Frequency and intensity of fire determines the structure and regulates the function of savanna ecosystems worldwide, yet our understanding of prescribed fire impacts on carbon in these systems is rudimentary. We combined eddy covariance (EC) techniques and fuel consumption plots to examine the short-term response of longleaf pine forest carbon dynamics to one prescribed fire at the ends of an edaphic gradient (mesic and xeric sites). We also introduce novel (to the EC research community) statistical time-series approaches to quantify the drivers of carbon dynamics in these systems. We determined that our mesic site was a moderate sink of carbon (−157.7 ± 25.1 g C m−2 year−1), while the xeric site was carbon neutral (5.9 ± 32.8 g C m−2 year−1) during the study. The fire released 408 and 153 g C m−2 year−1 for the mesic and xeric sites, respectively. When loss associated with fire was combined with net ecosystem exchange rates, both sites became moderate carbon sources for the year. Analyses of assimilation and respiration parameters (e.g., maximum photosynthesis, quantum efficiency, and daytime ecosystem respiration) showed a positive trend over time pre-fire and a negative trend over time post-fire for maximum ecosystem CO2 uptake rates, and the opposite relationship for daytime ecosystem respiration rates. Within 30 days following fire, ecosystem physiological activity was statistically similar to pre-fire and appeared to be driven by the pine canopy. Our results suggest that prescribed fire (low intensity, high frequency) maintains the existing structure and function (in this case, carbon flux rates) because longleaf pine ecosystems have evolved with fire. This study, 1 year in length, provides a foundational understanding of the complex interaction between fire and carbon dynamics for longleaf pine ecosystems. Moreover, it provides a case study for applying time series analysis methods to EC data where there are complex relationships between ecosystem physiological activity and environmental drivers. However, to elicit a broader understanding of the complex interaction occurring between fire and carbon dynamics long- term studies are needed.

Net CO₂ exchange data of legume crops at 17 flux tower sites in North America and three sites in Europe representing 29 site-years of measurements were partitioned into gross photosynthesis and ecosystem respiration by using the nonrectangular hyperbolic light-response function method. The analyses produced net CO₂ exchange data and new ecosystem-scale ecophysiological parameter estimates for legume crops determined at diurnal and weekly time steps. Dynamics and annual totals of gross photosynthesis, ecosystem respiration, and net ecosystem production were calculated by gap filling with multivariate nonlinear regression. Comparison with the data from grain crops obtained with the same method demonstrated that CO₂ exchange rates and ecophysiological parameters of legumes were lower than those of maize (Zea mays L.) but higher than for wheat (Triticum aestivum L.) crops. Year-round annual legume crops demonstrated a broad range of net ecosystem production, from sinks of 760 g CO₂ m–² yr–¹ to sources of –2100 g CO₂ m–² yr–¹, with an average of –330 g CO₂ m–² yr–¹, indicating overall moderate CO₂–source activity related to a shorter period of photosynthetic uptake and metabolic costs of N₂ fixation. Perennial legumes (alfalfa, Medicago sativa L.) were strong sinks for atmospheric CO₂, with an average net ecosystem production of 980 (range 550–1200) g CO₂ m–² yr–¹.

Summary Changes in tropical forest carbon sink strength during El Nino Southern Oscillation (ENSO) events can indicate future behavior under climate change. Previous studies revealed ˜6 Mg C ha−1 yr−1 lower net ecosystem production (NEP) during ENSO year 1998 compared with non-ENSO year 2000 in a Costa Rican tropical rainforest. We explored environmental drivers of this change and examined the contributions of ecosystem respiration (RE) and gross primary production (GPP) to this weakened carbon sink. For 1998–2000, we estimated RE using chamber-based respiration measurements, and we estimated GPP in two ways: using (1) the canopy process model MAESTRA, and (2) combined eddy covariance and chamber respiration data. MAESTRA-estimated GPP did not statistically differ from GPP estimated using approach 2, but was ˜ 28% greater than published GPP estimates for the same site and years using eddy covariance data only. A 7% increase in RE (primarily increased soil respiration) and a 10% reduction in GPP contributed equally to the difference in NEP between ENSO year 1998 and non-ENSO year 2000. A warming and drying climate for tropical forests may yield a weakened carbon sink from both decreased GPP and increased RE. Understanding physiological acclimation will be critical for the large carbon stores in these ecosystems.

Determining environmental controls on soil organic matter decomposition is of importance for developing models that predict the effects of environmental change on global soil carbon stocks. There is uncertainty about the environmental controls on decomposition rates at temperature and moisture extremes, particularly at high water content levels and high temperatures. It is uncertain whether observed declines of decomposition rates at high temperatures are due to declines in the heat capacity of extracellular enzymes as predicted by thermodynamic theory, or due to simultaneous declines in soil moisture. It is also uncertain whether oxygen limits decomposition rates at high water contents. Here we present results from a full factorial experiment using organic arctic soils incubated at high temperatures (25 and 35 degrees C), a wide range of water-filled pore space WFPS (15, 30, 60, 90 %), and contrasting oxygen concentrations (1 and 20 %). We found support for the hypothesis that decomposition rates increase at high temperatures provided enough moisture and oxygen is available for decomposition. Furthermore, we found that decomposition rate is mostly limited by oxygen concentrations at high moisture levels; even at 90 % WFPS, decomposition proceeded at high rates in the presence of oxygen. Our results suggest an important degree of interactions among temperature, moisture, and oxygen in determining decomposition rates at the soil-core scale.