A comparison of manual and automated systems for soil CO2 flux measurements: trade-offs between spatial and temporal resolution

Soil respiration is affected by distributions of roots and soil carbon substrates and by temperature and soil water content, all of which vary spatially and temporally. The objective of this paper was to compare a manual system for measuring soil respiration in a temperate forest, which had a greater spatial distribution of measurements (n=12), but poorer temporal resolution (once per week), with an automated system which had poorer spatial distribution (n=3) but superior temporal frequency of measurements (hourly). Soil respiration was measured between 18 June and 21 August, 2002, at the Harvard Forest in central Massachusetts, USA. The fluxes measured within 1 h of each other by these systems were not significantly different. However, extrapolations of the mid-morning manual measurements to daily flux values were consistently lower (averaging 13% lower) than the daily estimates obtained from summing the 24 hourly measurements of the automated system. On the other hand, seasonal flux estimates obtained by interpolating between weekly manual sampling dates or by summing the hourly automated measurements were nearly identical. Underestimates by interpolated weekly manual measurements during some periods were cancelled by overestimates during other periods. Hence, a weekly sampling schedule may be sufficient to capture the most important variation of seasonal efflux of CO(2) from the soil. The larger number of chambers that could be measured with the manual system (larger n) resulted in a smaller 95% confidence interval for characterizing spatial variability within the study area on most dates. However, the greater sampling frequency of the automated system revealed rapid responses of soil respiration to wetting events, which permitted better empirical modelling of the effects of soil temperature and moisture on soil respiration than could have been achieved with the manual sampling system. Most of the positive residuals of a function that predicts soil respiration based on temperature were from fluxes measured within 12 h of a rain event, and the residuals were positively correlated with water content of the O horizon. The automated system also demonstrated that Q(10) values calculated for diel variation in soil temperature over a few days were not significantly different than Q(10) values for the entire 3 month summer sampling period. In summary, a manual system of numerous, spatially well-distributed flux chambers measured on a weekly basis may be adequate for measuring seasonal fluxes and may maximize confidence in the characterization of spatial variance. The high temporal frequency of measurements afforded by automation greatly improves the ability to measure and model the effects of rapidly varying water content and temperature. When the two approaches can be combined, the temporal representativeness of the manual measurements can be tested with the automated measurements and the spatial representativeness of the automated measurements can be tested by the manual measurements.     

Spatial versus Day-To-Day Within-Lake Variability in Tropical Floodplain Lake CH4 Emissions – Developing Optimized Approaches to Representative Flux Measurements

Inland waters (lakes, rivers and reservoirs) are now understood to contribute large amounts of methane (CH₄) to the atmosphere. However, fluxes are poorly constrained and there is a need for improved knowledge on spatiotemporal variability and on ways of optimizing sampling efforts to yield representative emission estimates for different types of aquatic ecosystems. Low-latitude floodplain lakes and wetlands are among the most high-emitting environments, and here we provide a detailed investigation of spatial and day-to-day variability in a shallow floodplain lake in the Pantanal in Brazil over a five-day period. CH₄ flux was dominated by frequent and ubiquitous ebullition. A strong but predictable spatial variability (decreasing flux with increasing distance to the shore or to littoral vegetation) was found, and this pattern can be addressed by sampling along transects from the shore to the center. Although no distinct day-to-day variability were found, a significant increase in flux was identified from measurement day 1 to measurement day 5, which was likely attributable to a simultaneous increase in temperature. Our study demonstrates that representative emission assessments requires consideration of spatial variability, but also that spatial variability patterns are predictable for lakes of this type and may therefore be addressed through limited sampling efforts if designed properly (e.g., fewer chambers may be used if organized along transects). Such optimized assessments of spatial variability are beneficial by allowing more of the available sampling resources to focus on assessing temporal variability, thereby improving overall flux assessments.     

Assessing the effects of chamber placement, manual sampling and headspace mixing on CH4 fluxes in a laboratory experiment

A laboratory experiment was conducted with two types of closed static chambers to estimate the effects of chamber placement, manual headspace sampling and headspace mixing on methane (CH₄) fluxes. Chamber fluxes were compared to a known reference flux in a chamber calibration system. The measurements were conducted with three types of soils (coarse dry, fine dry and fine wet quarts sand) at five flux levels ranging from 60 to 2000 ??g CH₄ m^?2 h^?1. We found that the placement of a non-vented chamber disturbed the initial CH₄ concentration development within the chamber headspace for 10 to 30 s. Excluding this short period from the flux calculation resulted in a lower flux estimate (mean±SE) of 126?±?26 ??g CH₄ m^?2 h^?1 compared to 134?±?26 ??g CH₄ m^?2 h^?1 if data from time zero of the enclosure were included. We also found that in non-mixed chambers (no fan mixing) the gas sampling by syringes or gas bottles disturbed the development of CH₄ concentration during the enclosure. Furthermore, flux estimates in non-mixed chambers were significantly underestimated (on average 36%) compared to the measured reference fluxes. However, the use of fans to constantly mix the chamber headspace during enclosure significantly improved the goodness-of-fit of the regression analysis used to calculate the flux and further eliminated the disturbance of the manual sampling on the concentration development. We recommend that chambers should be vented during the placement of the chamber, and that fans are used as an integrated part of static chambers while headspace mixing with syringes should be avoided.     

Net Ecosystem Exchange of Carbon dioxide in a Temperate Poor Fen: a Comparison of Automated and Manual Chamber Techniques

We used five analytical approaches to compare net ecosystem exchange (NEE) of carbon dioxide (CO₂) from automated and manual static chambers in a peatland, and found the methods comparable. Once per week we sampled manually from 10 collars with a closed chamber system using a LiCor 6200 portable photosynthesis system, and simulated four photosynthetically active radiation (PAR) levels using shrouds. Ten automated chambers sampled CO₂ flux every 3 h with a LiCor 6252 infrared gas analyzer. Results of the five comparisons showed (1) NEE measurements made from May to August, 2001 by the manual and automated chambers had similar ranges: −10.8 to 12.7 μmol CO₂ m⁻² s⁻¹ and −17.2 to 13.1 μmol CO₂ m⁻² s⁻¹, respectively. (2) When sorted into four PAR regimes and adjusted for temperature (respiration was measured under different temperature regimes), mean NEE did not differ significantly between the chambers (p < 0.05). (3) Chambers were not significantly different in regression of ln( − respiration) on temperature. (4) But differences were found in the PAR vs. NEE relationship with manual chambers providing higher maximum gross photosynthesis estimates (GP_max), and slower uptake of CO₂ at low PAR (α) even after temperature adjustment. (5) Due to the high variability in chamber characteristics, we developed an equation that includes foliar biomass, water table, temperature, and PAR, to more directly compare automated and manual NEE. Comparing fitted parameters did not identify new differences between the chambers. These complementary chamber techniques offer a unique opportunity to assess the variability and uncertainty in CO₂ flux measurements.     

Evaluating the carbon balance estimate from an automated ground‐level flux chamber system in artificial grass mesocosms

Measuring and modeling carbon (C) stock changes in terrestrial ecosystems are pivotal in addressing global C‐cycling model uncertainties. Difficulties in detecting small short‐term changes in relatively large C stocks require the development of robust sensitive flux measurement techniques. Net ecosystem exchange (NEE) ground‐level chambers are increasingly used to assess C dynamics in low vegetation ecosystems but, to date, have lacked formal rigorous field validation against measured C stock changes. We developed and deployed an automated and multiplexed C‐flux chamber system in grassland mesocosms in order rigorously to compare ecosystem total C budget obtained using hourly C‐flux measurements versus destructive net C balance. The system combines transparent NEE and opaque respiration chambers enabling partitioning of photosynthetic and respiratory fluxes. The C‐balance comparison showed good agreement between the two methods, but only after NEE fluxes were corrected for light reductions due to chamber presence. The dark chamber fluxes allowed assessing temperature sensitivity of ecosystem respiration (R_eco) components (i.e., heterotrophic vs. autotrophic) at different growth stages. We propose that such automated flux chamber systems can provide an accurate C balance, also enabling pivotal partitioning of the different C‐flux components (e.g., photosynthesis and respiration) suitable for model evaluation and developments.     

Comparing the closed static versus the closed dynamic chamber flux methodology: Implications for soil respiration studies

Soil respiration is the largest C-flux component in the terrestrial carbon (C) cycle, yet in many biomes this flux and its environmental responses are still poorly understood. Several methodological techniques exist to measure this flux, but mostly there remain comparability uncertainties. For example, the closed static chamber (CSC) and the closed dynamic chamber (CDC) systems are widely used, but still require a rigorous comparison. A major issue with the CSC approach is the generally long manual gas sampling periods causing a potential underestimation of the calculated fluxes due to an asymptotic increase in headspace CO₂ concentrations. However, shortening the sampling periods of the static chamber approach might provide comparable results to the closed dynamic chamber system. We compared these two different chamber systems using replicated CSC cover boxes and a Li-Cor 8100 CDC system under field conditions, and performed tests on both, mineral and peat soil. Whereas the automated CDC system calculated fluxes during the first two minutes, the CSC approach considered either all seven manual sampling points taken over 75?min, or only the first three sampling points over 15?min. Although flux variation was fairly large, there were considerable and statistically significant differences between the calculated fluxes considering the two chamber systems, yet this depended on soil type and the number of CSC sampling time points. The cover-box approach underestimated the chamber-based fluxes by 30% for combined samples, 21% for mineral and 39% for peat soils when calculated over 75?min but was comparable over the first 15?min. The chamber flux comparison demonstrates that the CSC approach can provide CO₂ flux measurements comparable to the CDC system when sampling at an appropriate initial frequency, preventing flux underestimation due to a build up of CO₂ headspace concentrations.     

Estimating the uncertainty in annual net ecosystem carbon exchange: spatial variation in turbulent fluxes and sampling errors in eddy-covariance measurements

Above forest canopies, eddy covariance (EC) measurements of mass (CO₂, H₂O vapor) and energy exchange, assumed to represent ecosystem fluxes, are commonly made at one point in the roughness sublayer (RSL). A spatial variability experiment, in which EC measurements were made from six towers within the RSL in a uniform pine plantation, quantified large and dynamic spatial variation in fluxes. The spatial coefficient of variation (CV) of the scalar fluxes decreased with increasing integration time, stabilizing at a minimum that was independent of further lengthening the averaging period (hereafter a ‘stable minimum’). For all three fluxes, the stable minimum (CV=9–11%) was reached at averaging times (τ_p) of 6–7 h during daytime, but higher stable minima (CV=46–158%) were reached at longer τ_p (>12 h) during nighttime. To the extent that decreasing CV of EC fluxes reflects reduction in micrometeorological sampling errors, half of the observed variability at τ_p=30 min is attributed to sampling errors. The remaining half (indicated by the stable minimum CV) is attributed to underlying variability in ecosystem structural properties, as determined by leaf area index, and perhaps associated ecosystem activity attributes. We further assessed the spatial variability estimates in the context of uncertainty in annual net ecosystem exchange (NEE). First, we adjusted annual NEE values obtained at our long‐term observation tower to account for the difference between this tower and the mean of all towers from this experiment; this increased NEE by up to 55 g C m^?2 yr^?1. Second, we combined uncertainty from gap filling and instrument error with uncertainty because of spatial variability, producing an estimate of variability in annual NEE ranging from 79 to 127 g C m^?2 yr^?1. This analysis demonstrated that even in such a uniform pine plantation, in some years spatial variability can contribute ～50% of the uncertainty in annual NEE estimates.     

Bi-directional soil/atmosphere N2O exchange over two mown grassland systems with contrasting management practices

Nitrous oxide (N₂O) fluxes from soil under mown grassland were monitored using static chambers over three growing seasons in intensively and extensively managed systems in Central Switzerland. Emissions were largest following the application of mineral (NH₄NO₃) fertilizer, but there were also substantial emissions following cattle slurry application, after grass cuts and during the thawing of frozen soil. Continuous flux sampling, using automatic chambers, showed marked diurnal patterns in N₂O fluxes during emission peaks, with highest values in the afternoon. Net uptake fluxes of N₂O and subambient N₂O concentrations in soil open pore space were frequently measured on both fields. Flux integration over 2.5 years yields a cumulated emission of +4.7 kgN₂O‐N ha^?1 for the intensively managed field, equivalent to an average emission factor of 1.1%, and a small net sink activity of ?0.4 kg N₂O‐N ha^?1 for the unfertilized system. The data suggest the existence of a consumption mechanism for N₂O in dry, areated soil conditions, which cannot be explained by conventional anaerobic denitrification. The effect of fertilization on greenhouse gas budgets of grassland at the ecosystem level is discussed.     

基于区域剖分算法的土壤碳通量空间采样策略

由于土壤碳通量在空间分布上具有很强的异质性,传统的采样方法难以对区域土壤碳通量进行精确估算,因此确定适当的采样策略对区域土壤碳通量的估算具有重要意义.本文提出一种逐点递增式采样的区域剖分部署策略(RDPG):设定初始采样点,使用改进的凸包插值算法构造Delaunay三角网,根据邻近已知采样点插值计算三角形各边垂直平分线的交点的离散度,选择离散度最大的点作为新增采样点.采用该方法对变异系数为0.42～0.59的仿真试验区域进行多次试验,结果表明:在相同试验条件下,RDPG布局策略能够获得比随机采样和均匀采样策略更高的区域土壤碳通量估算准确度.RDPG方法考虑了区域土壤碳通量的空间异质性,提高了区域土壤碳通量拟合精度.     

Environmental factors controlling temporal and spatial variability in the soil‐atmosphere exchange of CO2, CH4 and N2O from an Australian subtropical rainforest

The temporal variations in CO₂, CH₄ and N₂O fluxes were measured over two consecutive years from February 2007 to March 2009 from a subtropical rainforest in south‐eastern Queensland, Australia, using an automated sampling system. A concurrent study using an additional 30 manual chambers examined the spatial variability of emissions distributed across three nearby remnant rainforest sites with similar vegetation and climatic conditions. Interannual variation in fluxes of all gases over the 2 years was minimal, despite large discrepancies in rainfall, whereas a pronounced seasonal variation could only be observed for CO₂ fluxes. High infiltration, drainage and subsequent high soil aeration under the rainforest limited N₂O loss while promoting substantial CH₄ uptake. The average annual N₂O loss of 0.5 ± 0.1 kg N₂O‐N ha^?1 over the 2‐year measurement period was at the lower end of reported fluxes from rainforest soils. The rainforest soil functioned as a sink for atmospheric CH₄ throughout the entire 2‐year period, despite periods of substantial rainfall. A clear linear correlation between soil moisture and CH₄ uptake was found. Rates of uptake ranged from greater than 15 g CH₄‐C ha^?1 day^?1 during extended dry periods to less than 2–5 g CH₄‐C ha^?1 day^?1 when soil water content was high. The calculated annual CH₄ uptake at the site was 3.65 kg CH₄‐C ha^?1 yr^?1. This is amongst the highest reported for rainforest systems, reiterating the ability of aerated subtropical rainforests to act as substantial sinks of CH₄. The spatial study showed N₂O fluxes almost eight times higher, and CH₄ uptake reduced by over one‐third, as clay content of the rainforest soil increased from 12% to more than 23%. This demonstrates that for some rainforest ecosystems, soil texture and related water infiltration and drainage capacity constraints may play a more important role in controlling fluxes than either vegetation or seasonal variability.     

The relative merits of open- and closed-path analysers for measurement of eddy fluxes

Theoretical and practical aspects of measuring eddy fluxes of trace gases using open-and closed-path analysers are presented. Trace gas fluxes measured with an open-path analyser require the concurrent measurement of sensible and latent heat fluxes to correct for density fluctuations in trace gas concentration caused by these fluxes. A closed-path analyser eliminates the corrections due to sensible heat flux, but not for water vapour, provided temperature fluctuations are completely removed without significantly reducing fluctuations in the trace gas mixing ratio. Theory for the design of heat exchangers and for the attenuation of concentration fluctuations during air flow through tubes is used to provide design criteria for closed-path systems. Spectral transfer functions are used to estimate flux losses caused by flow through the sampling tube and gas analyser. Other factors considered include cross-sensitivity of infrared CO₂ analysers to water vapour, and deterioration of system performance caused by contaminants on the walls of sampling tubes. Of two open-path, infrared CO₂ analysers tested, one showed a strong interaction between CO₂ and water vapour, while the other showed little sensitivity to the presence of water vapour, other than caused by dilution. A commercial closed-path CO₂ analyser also showed little cross-sensitivity to water vapour. Compared to results for a clean sampling tube, the spectral bandwidth for water vapour fluctuations decreased significantly after several weeks of sampling. No such deterioration in bandwidth was observed for CO₂. These findings are attributed to differential adsorption/desorption of water vapour by dust or salt on the tubing walls. Rain and dust must be removed from open-path analysers to obtain satisfactory measurements. Careful system design and maintenance is required for both open- and closed-path systems to ensure satisfactory long-term measurement of trace gas fluxes. With these precautions, both approaches will provide satisfactory flux measurements.     

Effects of soil moisture content and temperature on methane uptake by grasslands on sandy soils

Aerobic grasslands may consume significant amounts of atmospheric methane (CH4). We aimed (i) to assess the spatial and temporal variability of net CH4 fluxes from grasslands on aerobic sandy soils, and (ii) to explain the variability in net CH4 fluxes by differences in soil moisture content and temperature. Net CH4 fluxes were measured with vented closed flux chambers at two sites with low N input on sandy soils in the Netherlands: (i) Wolfheze, a heather grassland, and (ii) Bovenbuurtse Weilanden, a grassland which is mown twice a year. Spatial variability of net CH4 fluxes was analysed using geostatistics. In incubation experiments, the effects of soil moisture content and temperature on CH4 uptake capacity were assessed. Temporal variability of net CH4 fluxes at Wolfheze was related to differences in soil temperature (r2 of 0.57) and soil moisture content (r2 of 0.73). Atmospheric CH4 uptake was highest at high soil temperatures and intermediate soil moisture contents. Spatial variability of net CH4 fluxes was high, both at Wolfheze and at Bovenbuurtse Weilanden. Incubation experiments showed that, at soil moisture contents lower than 5% (w/w), CH4 uptake was completely inhibited, probably due to physiological water stress of methanotrophs. At soil moisture contents higher than 50% (w/w), CH4 uptake was greatly reduced, probably due to the slow down of diffusive CH4 and O2 transport in the soil, which may have resulted in reduced CH4 oxidation and possibly some CH4 production. Optimum soil moisture contents for CH4 uptake were in the range of 20 – 35% (w/w), as prevailing in the field. The sensitivity of CH4 uptake to soil moisture content may result in short-term variability of net atmospheric CH4 uptake in response to precipitation and evapotranspiration, as well as in long-term variability due to changing precipitation patterns as a result of climate change.     

Simulated and observed fluxes of sensible and latent heat and CO2 at the WLEF-TV tower using SiB2.5

Three years of meteorological data collected at the WLEF‐TV tower were used to drive a revised version of the Simple Biosphere (SiB 2.5) Model. Physiological properties and vegetation phenology were specified from satellite imagery. Simulated fluxes of heat, moisture, and carbon were compared to eddy covariance measurements taken onsite as a means of evaluating model performance on diurnal, synoptic, seasonal, and interannual time scales. The model was very successful in simulating variations of latent heat flux when compared to observations, slightly less so in the simulation of sensible heat flux. The model overestimated peak values of sensible heat flux on both monthly and diurnal scales. There was evidence that the differences between observed and simulated fluxes might be linked to wetlands near the WLEF tower, which were not present in the SiB simulation. The model overestimated the magnitude of the net ecosystem exchange of CO₂ in both summer and winter. Mid‐day maximum assimilation was well represented by the model, but late afternoon simulations showed excessive carbon uptake due to misrepresentation of within‐canopy shading in the model. Interannual variability was not well simulated because only a single year of satellite imagery was used to parameterize the model.     

Effect of sampling on variability and plateau in oxygen uptake

To evaluate the effect of the gas exchange sampling interval on variability and plateau in O2 uptake (VO2), 10 subjects underwent steady-state treadmill exercise at 50% maximal VO2 and 6 subjects underwent maximal testing using a ramp protocol. During steady-state exercise, gas exchange data were acquired by using 10 different sampling intervals. The variability in VO2 was greater as the sampling interval shortened (SD = 4.5 ml.kg-1.min-1 for breath-by-breath vs. 0.8 ml.kg-1.min-1 for 60-s samples). The breath-by-breath data suggested a Gaussian distribution, and most of the variability was attributable to tidal volume (51%). During ramp testing, the slope of the change in VO2 (for each sample) was regressed with time. Considerable variability in the slopes was observed throughout exercise, and in each subject the slopes varied about zero, demonstrating both positive and negative values throughout submaximal effort. These observations were made despite the use of large sampling intervals. Shortening the sample resulted in even greater variability. We conclude that 1) the sampling interval can have a major impact on gas exchange data during exercise and 2) considerable variability exists in the slope of the change in VO2 with a consistent change in external work regardless of the sample used, suggesting that a plateau (defined as the slope of a VO2 sample at peak exercise that does not differ significantly from a slope of zero) in VO2 is not a reliable physiological marker for maximal effort.     

Effect of sampling interval and temperature on the accuracy of food consumption estimates from stomach contents

The effect of temperature and sampling interval on the accuracy of food consumption estimates based on stomach contents was studied using simulation. Three temporal patterns of feeding were considered (scattered throughout the day, one 5 h period or two 5 h periods) and gastric evacuation was modelled according to published values. Sampling intervals of 3 h gave reasonable food consumption estimates (2 to 19% error) at all temperatures. Comparably, sampling intervals as large as 12 h gave reasonable estimates of food consumption (1 to 20% error) when temperature was set to ≤10° C. At temperatures <5° C, even 24 h intervals (equivalent to one daily sampling) provided reasonable estimates of daily food consumption (2 to 19% error) for all but the highest gastric evacuation rate combined with one daily feeding period (47% error). The temperature effect on estimation error resulted from diminishing temporal fluctuations in stomach contents with slower gastric evacuation rates. It follows that sampling effort may be considerably minimized when estimating food consumption from stomach contents during periods with low temperatures such as the winter time experienced by temperate fishes.     

Net grassland carbon flux over a subambient to superambient CO2 gradient

Increasing atmospheric CO₂ concentrations may have a profound effect on the structure and function of plant communities. A previously grazed, central Texas grassland was exposed to a 200‐µmol mol^?1 to 550 µmol mol^?1 CO₂ gradient from March to mid‐December in 1998 and 1999 using two, 60‐m long, polyethylene‐ covered chambers built directly onto the site. One chamber was operated at subambient CO₂ concentrations (200–360 µmol mol^?1 daytime) and the other was regulated at superambient concentrations (360–550 µmol mol^?1). Continuous CO₂ gradients were maintained in each chamber by photosynthesis during the day and respiration at night. Net ecosystem CO₂ flux and end‐of‐year biomass were measured in each of 10, 5‐m long sections in each chamber. Net CO₂ fluxes were maximal in late May (c. day 150) in 1998 and in late August in 1999 (c. day 240). In both years, fluxes were near zero and similar in both chambers at the beginning and end of the growing season. Average daily CO₂ flux in 1998 was 13 g CO₂ m^?2 day^?1 in the subambient chamber and 20 g CO₂ m^?2 day^?1 in the superambient chamber; comparable averages were 15 and 26 g CO₂ m^?2 day^?1 in 1999. Flux was positively and linearly correlated with end‐of‐year above‐ground biomass but flux was not linearly correlated with CO₂ concentration; a finding likely to be explained by inherent differences in vegetation. Because C₃ plants were the dominant functional group, we adjusted average daily flux in each section by dividing the flux by the average percentage C₃ cover. Adjusted fluxes were better correlated with CO₂ concentration, although scatter remained. Our results indicate that after accounting for vegetation differences, CO₂ flux increased linearly with CO₂ concentration. This trend was more evident at subambient than superambient CO₂ concentrations.     

Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies

Reconstruction of genome-scale metabolic networks is now possible using multiple different data types. Constraint-based modeling is an approach to interrogate capabilities of reconstructed networks by constraining possible cellular behavior through the imposition of physicochemical laws. As a result, a steady-state flux space is defined that contains all possible functional states of the network. Uniform random sampling of the steady-state flux space allows for the unbiased appraisal of its contents. Monte Carlo sampling of the steady-state flux space of the reconstructed human red blood cell metabolic network under simulated physiologic conditions yielded the following key results: 1), probability distributions for the values of individual metabolic fluxes showed a wide variety of shapes that could not have been inferred without computation; 2), pairwise correlation coefficients were calculated between all fluxes, determining the level of independence between the measurement of any two fluxes, and identifying highly correlated reaction sets; and 3), the network-wide effects of the change in one (or a few) variables (i.e., a simulated enzymopathy or fixing a flux range based on measurements) were computed. Mathematical models provide the most compact and informative representation of a hypothesis of how a cell works. Thus, understanding model predictions clearly is vital to driving forward the iterative model-building procedure that is at the heart of systems biology. Taken together, the Monte Carlo sampling procedure provides a broadening of the constraint-based approach by allowing for the unbiased and detailed assessment of the impact of the applied physicochemical constraints on a reconstructed network.     

The case for increasing the statistical power of eddy covariance ecosystem studies: why,where and how?

Eddy covariance (EC) continues to provide invaluable insights into the dynamics of Earth's surface processes. However, despite its many strengths, spatial replication of EC at the ecosystem scale is rare. High equipment costs are likely to be partially responsible. This contributes to the low sampling, and even lower replication, of ecoregions in Africa, Oceania (excluding Australia) and South America. The level of replication matters as it directly affects statistical power. While the ergodicity of turbulence and temporal replication allow an EC tower to provide statistically robust flux estimates for its footprint, these principles do not extend to larger ecosystem scales. Despite the challenge of spatially replicating EC, it is clearly of interest to be able to use EC to provide statistically robust flux estimates for larger areas. We ask: How much spatial replication of EC is required for statistical confidence in our flux estimates of an ecosystem? We provide the reader with tools to estimate the number of EC towers needed to achieve a given statistical power. We show that for a typical ecosystem, around four EC towers are needed to have 95% statistical confidence that the annual flux of an ecosystem is nonzero. Furthermore, if the true flux is small relative to instrument noise and spatial variability, the number of towers needed can rise dramatically. We discuss approaches for improving statistical power and describe one solution: an inexpensive EC system that could help by making spatial replication more affordable. However, we note that diverting limited resources from other key measurements in order to allow spatial replication may not be optimal, and a balance needs to be struck. While individual EC towers are well suited to providing fluxes from the flux footprint, we emphasize that spatial replication is essential for statistically robust fluxes if a wider ecosystem is being studied.     

Photosynthesis drives anomalies in net carbon-exchange of pine forests at different latitudes

The growth rate of atmospheric CO₂ exhibits large temporal variation that is largely determined by year‐to‐year fluctuations in land–atmosphere CO₂ fluxes. This land–atmosphere CO₂‐flux is driven by large‐scale biomass burning and variation in net ecosystem exchange (NEE). Between‐ and within years, NEE varies due to fluctuations in climate. Studies on climatic influences on inter‐ and intra‐annual variability in gross photosynthesis (GPP) and net carbon uptake in terrestrial ecosystems have shown conflicting results. These conflicts are in part related to differences in methodology and in part to the limited duration of some studies. Here, we introduce an observation‐driven methodology that provides insight into the dependence of anomalies in CO₂ fluxes on climatic conditions. The methodology was applied on fluxes from a boreal and two temperate pine forests. Annual anomalies in NEE were dominated by anomalies in GPP, which in turn were correlated with incident radiation and vapor pressure deficit (VPD). At all three sites positive anomalies in NEE (a reduced uptake or a stronger source than the daily sites specific long‐term average) were observed on summer days characterized by low incident radiation, low VPD and high precipitation. Negative anomalies in NEE occurred mainly on summer days characterized by blue skies and mild temperatures. Our study clearly highlighted the need to use weather patterns rather than single climatic variables to understand anomalous CO₂ fluxes. Temperature generally showed little direct effect on anomalies in NEE but became important when the mean daily air temperature exceeded 23 °C. On such days GPP decreased likely because VPD exceeded 2.0 kPa, inhibiting photosynthetic uptake. However, while GPP decreased, the high temperature stimulated respiration, resulting in positive anomalies in NEE. Climatic extremes in summer were more frequent and severe in the South than in the North, and had larger effects in the South because the criteria to inhibit photosynthesis are more often met.     

A high‐resolution approach to estimating ecosystem respiration at continental scales using operational satellite data

A better understanding of the local variability in land‐atmosphere carbon fluxes is crucial to improving the accuracy of global carbon budgets. Operational satellite data backed by ground measurements at Fluxnet sites proved valuable in monitoring local variability of gross primary production at highly resolved spatio‐temporal resolutions. Yet, we lack similar operational estimates of ecosystem respiration (Re) to calculate net carbon fluxes. If successful, carbon fluxes from such a remote sensing approach would form an independent and sought after measure to complement widely used dynamic global vegetation models (DGVMs). Here, we establish an operational semi‐empirical Re model, based only on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) with a resolution of 1 km and 8 days. Fluxnet measurements between 2000 and 2009 from 100 sites across North America and Europe are used for parameterization and validation. Our analysis shows that Re is closely tied to temperature and plant productivity. By separating temporal and intersite variation, we find that MODIS land surface temperature (LST) and enhanced vegetation index (EVI) are sufficient to explain observed Re across most major biomes with a negligible bias [R² = 0.62, RMSE = 1.32 (g C m^?2 d^?1), MBE = 0.05 (g C m^?2 d^?1)]. A comparison of such satellite‐derived Re with those simulated by the DGVM LPJmL reveals similar spatial patterns. However, LPJmL shows higher temperature sensitivities and consistently simulates higher Re values, in high‐latitude and subtropical regions. These differences remain difficult to explain and they are likely associated either with LPJmL parameterization or with systematic errors in the Fluxnet sampling technique. While uncertainties remain with Re estimates, the model formulated in this study provides an operational, cross‐validated and unbiased approach to scale Fluxnet Re to the continental scale and advances knowledge of spatio‐temporal Re variability.