Dataset Information

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks.

ABSTRACT: The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co-acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data-driven approach based on combined neural networks (NNC-part ). NNC-part incorporates process knowledge by introducing a photosynthetic response based on the light-use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R2 > .94) and RECO (R2 > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NNC-part performed more realistic than the traditional methods for predicting additional patterns of gross CO2 fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO2 fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NNC-part is a valid data-driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.

SUBMITTER: Tramontana G

PROVIDER: S-EPMC7496462 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks.

Tramontana Gianluca G Migliavacca Mirco M Jung Martin M Reichstein Markus M Keenan Trevor F TF Camps-Valls Gustau G Ogee Jerome J Verrelst Jochem J Papale Dario D

Global change biology 20200702 9

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2 fluxes are derived from EC measurements by applying partitioning methods that rely on phys ...[more]

PMID: 32497360

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Project description:BackgroundIn photosynthetic organisms, the influence of light, carbon and inorganic nitrogen sources on the cellular bioenergetics has extensively been studied independently, but little information is available on the cumulative effects of these factors. Here, sequential statistical analyses based on design of experiments (DOE) coupled to standard least squares multiple regression have been undertaken to model the dependence of respiratory and photosynthetic responses (assessed by oxymetric and chlorophyll fluorescence measurements) upon the concomitant modulation of light intensity as well as acetate, CO₂, nitrate and ammonium concentrations in the culture medium of Chlamydomonas reinhardtii. The main goals of these analyses were to explain response variability (i.e. bioenergetic plasticity) and to characterize quantitatively the influence of the major explanatory factor(s).ResultsFor each response, 2 successive rounds of multiple regression coupled to one-way ANOVA F-tests have been undertaken to select the major explanatory factor(s) (1st-round) and mathematically simulate their influence (2nd-round). These analyses reveal that a maximal number of 3 environmental factors over 5 is sufficient to explain most of the response variability, and interestingly highlight quadratic effects and second-order interactions in some cases. In parallel, the predictive ability of the 2nd-round models has also been investigated by k-fold cross-validation and experimental validation tests on new random combinations of factors. These validation procedures tend to indicate that the 2nd-round models can also be used to predict the responses with an inherent deviation quantified by the analytical error of the models.ConclusionsAltogether, the results of the 2 rounds of modeling provide an overview of the bioenergetic adaptations of C. reinhardtii to changing environmental conditions and point out promising tracks for future in-depth investigations of the molecular mechanisms underlying the present observations.

Project description:BackgroundFarmland accounts for a relatively large fraction of the world's vegetation cover, and the quantification of carbon fluxes over farmland is critical for understanding regional carbon budgets. The carbon cycle of farmland ecosystems has become a focus of global research in the field of carbon dynamics and cycling. The objectives of this study are to monitor the temporal variation in the net ecosystem exchange (NEE) and soil respiration in a spring maize (Zea mays L.) farmland ecosystem of the southern Loess Plateau of China.MethodsA fully automated temperature-controlled flux chamber system was adopted in this study. The system contained nine chambers for CO2 flux measurements, and three treatments were conducted: with and without maize plants in the chamber, as well as a bare field. Observations were conducted from June to September 2011. This time period covers the seedling, jointing, heading, grain filling, and ripening stages of spring maize. Other factors, such as air temperature (Ta), soil temperature (Ts), soil water content (SWC), photosynthetically active radiation (PAR), and precipitation (P), were simultaneously monitored.ResultsThere was observed diurnal variation in the NEE of the maize ecosystem (NEE-maize). A short "noon break" occurred when the PAR intensity was at its maximum, while soil respiration rates had curves with a single peak. During the overall maize growth season, the total NEE-maize was -68.61 g C m-2, and the soil respiration from the maize field (SR-maize) and bare field (SR-bare field) were 245.69 g C m-2 and 114.08 g C m-2, respectively. The temperature sensitivity of soil respiration in the maize field exceeded that in the bare field. Significant negative correlations were found between the NEE, PAR, and temperature (all p-values < 0.01), with both Ta and PAR being the primary factors that affected the CO2 fluxes, collectively contributing 61.7%, 37.2%, and 56.8% to the NEE-maize, SR-maize, and SR-bare field, respectively. It was therefore concluded that both meteorological factors and farming practices have an important impact on the carbon balance process in corn farmland ecosystems. However, it is necessary to conduct long-term observational studies, in order to get a better understanding of the driving mechanism.

Dataset Information

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks.

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Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks.

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