@article{mitra_minick_gavazzi_prajapati_aguilos_miao_domec_mcnulty_sun_king_et al._2024, title={Toward spectrally truthful models for gap-filling soil respiration and methane fluxes. A case study in coastal forested wetlands in North Carolina}, volume={353}, ISSN={["1873-2240"]}, url={https://doi.org/10.1016/j.agrformet.2024.110038}, DOI={10.1016/j.agrformet.2024.110038}, abstractNote={Soil respiration (Rs) and methane (FCH4) fluxes are two important metrics of ecosystem metabolism. An accurate estimate of the budget of these two greenhouse gases is critical to understanding their response to climate and land-use changes. Reconstructing continuous time series of gappy chamber Rs and eddy-covariance derived FCH4 measurements is usually done based on correlative relationships of these fluxes with environmental variables. However, current approaches do not account for the fact that different environmental drivers affect the carbon fluxes at different temporal scales. Here we propose a novel gapfilling technique that accounts for the specific spectral frequencies at which each of the environmental variables covaries with Rs and FCH4 - photosynthetically active radiation at diel scale, soil temperature at synoptic scale, and soil moisture, water table depth and atmospheric pressure at synoptic and seasonal scale. The method was applied on two operational loblolly pine plantations of different ages and a mixed hardwood forested wetland on the lower coastal plain of North Carolina. The time series of these environmental drivers were reconstructed using wavelet decomposition and a Daubechies wavelet filter. Further, to consider the joint influence of the environmental drivers, parametric (elastic net regression, support vector machine, gradient boost and artificial neural network), and nonparametric (Bayesian) statistical models were chosen, and compared the results with Q10 and Marginal Distribution Sampling (MDS) outputs. In all cases, the algorithms were trained on 70 % of the data and validated with the remaining data. Spectral-filtered models did not significantly differ from those driven by unfiltered data with respect to Rs and FCH4 predictions. While all the spectrally driven algorithms achieved high predictive accuracy against Q10, the increase in model fit compared to MDS was minimal. Spectral data filtering modestly improves model accuracy, shedding light on complex environmental and biological factors affecting greenhouse gas flux variability.}, journal={AGRICULTURAL AND FOREST METEOROLOGY}, author={Mitra, Bhaskar and Minick, Kevan and Gavazzi, Michael and Prajapati, Prajaya and Aguilos, Maricar and Miao, Guofang and Domec, Jean-Christophe and Mcnulty, Steve G. and Sun, Ge and King, John S. and et al.}, year={2024}, month={Jun} }
 @article{miao_noormets_gavazzi_mitra_domec_sun_mcnulty_king_2022, title={Beyond carbon flux partitioning: Carbon allocation and nonstructural carbon dynamics inferred from continuous fluxes}, ISSN={["1939-5582"]}, DOI={10.1002/eap.2655}, abstractNote={Abstract}, journal={ECOLOGICAL APPLICATIONS}, author={Miao, Guofang and Noormets, Asko and Gavazzi, Michael and Mitra, Bhaskar and Domec, Jean-Christophe and Sun, Ge and McNulty, Steve and King, John S.}, year={2022}, month={Jul} }
 @article{li_zheng_zhou_gavazzi_shan_mcnulty_king_2022, title={Effects of methodological difference on fine root production, mortality and decomposition estimates differ between functional types in a planted loblolly pine forest}, ISSN={["1573-5036"]}, DOI={10.1007/s11104-022-05737-2}, journal={PLANT AND SOIL}, author={Li, Xuefeng and Zheng, Xingbo and Zhou, Quanlai and Gavazzi, Michael and Shan, Yanlong and McNulty, Steven and King, John S.}, year={2022}, month={Oct} }
 @article{aguilos_sun_noormets_domec_mcnulty_gavazzi_prajapati_minick_mitra_king_2021, title={Ecosystem Productivity and Evapotranspiration Are Tightly Coupled in Loblolly Pine (Pinus taeda L.) Plantations along the Coastal Plain of the Southeastern U.S.}, volume={12}, ISSN={1999-4907}, url={http://dx.doi.org/10.3390/f12081123}, DOI={10.3390/f12081123}, abstractNote={Forest water use efficiency (WUE), the ratio of gross primary productivity (GPP) to evapotranspiration (ET), is an important variable to understand the coupling between water and carbon cycles, and to assess resource use, ecosystem resilience, and commodity production. Here, we determined WUE for managed loblolly pine plantations over the course of a rotation on the coastal plain of North Carolina in the eastern U.S. We found that the forest annual GPP, ET, and WUE increased until age ten, which stabilized thereafter. WUE varied annually (2–44%), being higher at young plantation (YP, 3.12 ± 1.20 g C kg−1 H2O d−1) compared to a mature plantation (MP, 2.92 ± 0.45 g C kg−1 H2O d−1), with no distinct seasonal patterns. Stand age was strongly correlated with ET (R2 = 0.71) and GPP (R2 = 0.64). ET and GPP were tightly coupled (R2 = 0.86). Radiation and air temperature significantly affected GPP and ET (R2 = 0.71 − R2 = 0.82) at a monthly scale, but not WUE. Drought affected WUE (R2 = 0.35) more than ET (R2 = 0.25) or GPP (R2 = 0.07). A drought enhanced GPP in MP (19%) and YP (11%), but reduced ET 7% and 19% in MP and YP, respectively, resulting in a higher WUE (27–32%). Minor seasonal and interannual variation in forest WUE of MP (age > 10) suggested that forest functioning became stable as stands matured. We conclude that carbon and water cycles in loblolly pine plantations are tightly coupled, with different characteristics in different ages and hydrologic regimes. A stable WUE suggests that the pine ecosystem productivity can be readily predicted from ET and vice versa. The tradeoffs between water and carbon cycling should be recognized in forest management to achieve multiple ecosystem services (i.e., water supply and carbon sequestration).}, number={8}, journal={Forests}, publisher={MDPI AG}, author={Aguilos, Maricar and Sun, Ge and Noormets, Asko and Domec, Jean-Christophe and McNulty, Steven and Gavazzi, Michael and Prajapati, Prajaya and Minick, Kevan J. and Mitra, Bhaskar and King, John}, year={2021}, month={Aug}, pages={1123} }