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Browsing by Author "Domec, Jean-Christophe"

Now showing 1 - 4 of 4
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    Heterotrophic Respiration and the Divergence of Productivity and Carbon Sequestration
    Noormets, Asko; Bracho, Rosvel; Ward, Eric J.; Seiler, John R.; Strahm, Brian D.; Lin, Wen; McElligott, Kristin M.; Domec, Jean-Christophe; González-Benecke, Carlos; Jokela, Eric J.; Markewitz, Daniel; Meek, Cassandra; Miao, Guofang; McNulty, Steve G.; King, John S.; Samuelson, Lisa; Sun, Ge; Teskey, Robert O.; Vogel, Jason G.; Will, Rodney E.; Yang, Jinyan; Martin, Timothy A. (2021-04-16)
    Net primary productivity (NPP) and net ecosystem production (NEP) are often used interchangeably, as their difference, heterotrophic respiration (soil heterotrophic CO2 efflux, R-SH = NPP-NEP), is assumed a near-fixed fraction of NPP. Here, we show, using a range-wide replicated experimental study in loblolly pine (Pinus taeda) plantations that R-SH responds differently than NPP to fertilization and drought treatments, leading to the divergent responses of NPP and NEP. Across the natural range of the species, the moderate responses of NPP (+11%) and R-SH (-7%) to fertilization combined such that NEP increased nearly threefold in ambient control and 43% under drought treatment. A 13% decline in R-SH under drought led to a 26% increase in NEP while NPP was unaltered. Such drought benefit for carbon sequestration was nearly twofold in control, but disappeared under fertilization. Carbon sequestration efficiency, NEP:NPP, varied twofold among sites, and increased up to threefold under both drought and fertilization.
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    Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments
    Thomas, R. Quinn; Brooks, Evan B.; Jersild, Annika L.; Ward, Eric J.; Wynne, Randolph H.; Albaugh, Timothy J.; Dinon-Aldridge, Heather; Burkhart, Harold E.; Domec, Jean-Christophe; Fox, Thomas R.; González-Benecke, Carlos; Martin, Timothy A.; Noormets, Asko; Sampson, David A.; Teskey, Robert O. (Copernicus, 2017-07-26)
    Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model-data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6ĝ€ × ĝ€105ĝ€km2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO2, decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.
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    A Range-Wide Experiment to Investigate Nutrient and Soil Moisture Interactions in Loblolly Pine Plantations
    Will, Rodney E.; Fox, Thomas R.; Akers, Madison; Domec, Jean-Christophe; González-Benecke, Carlos; Jokela, Eric J.; Kane, Michael B.; Laviner, Marshall A.; Lokuta, Geoffrey; Markewitz, Daniel; McGuire, Mary Anne; Meek, Cassandra; Noormets, Asko; Samuelson, Lisa; Seiler, John R.; Strahm, Brian D.; Teskey, Robert O.; Vogel, Jason G.; Ward, Eric J.; West, Jason B.; Wilson, Duncan; Martin, Timothy A. (MDPI, 2015-06-03)
    The future climate of the southeastern USA is predicted to be warmer, drier and more variable in rainfall, which may increase drought frequency and intensity. Loblolly pine (Pinus taeda) is the most important commercial tree species in the world and is planted on ~11 million ha within its native range in the southeastern USA. A regional study was installed to evaluate effects of decreased rainfall and nutrient additions on loblolly pine plantation productivity and physiology. Four locations were established to capture the range-wide variability of soil and climate. Treatments were initiated in 2012 and consisted of a factorial combination of throughfall reduction (approximate 30% reduction) and fertilization (complete suite of nutrients). Tree and stand growth were measured at each site. Results after two growing seasons indicate a positive but variable response of fertilization on stand volume increment at all four sites and a negative effect of throughfall reduction at two sites. Data will be used to produce robust process model parameterizations useful for simulating loblolly pine growth and function under future, novel climate and management scenarios. The resulting improved models will provide support for developing management strategies to increase pine plantation productivity and carbon sequestration under a changing climate.
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    Using δ13C and δ18O to analyze loblolly pine (Pinus taeda L.) response to experimental drought and fertilization
    Lin, Wen; Domec, Jean-Christophe; Ward, Eric J.; Marshall, John; Kin, John S.; Laviner, Marshall A.; Fox, Thomas R.; West, Jason B.; Sun, Ge; McNulty, Steve G.; Noormets, Asko (2019-12)
    Drought frequency and intensity are projected to increase throughout the southeastern USA, the natural range of loblolly pine (Pinus taeda L.), and are expected to have major ecological and economic implications. We analyzed the carbon and oxygen isotopic compositions in tree ring cellulose of loblolly pine in a factorial drought (similar to 30% throughfall reduction) and fertilization experiment, supplemented with trunk sap flow, allometry and microclimate data. We then simulated leaf temperature and applied a multi-dimensional sensitivity analysis to interpret the changes in the oxygen isotope data. This analysis found that the observed changes in tree ring cellulose could only be accounted for by inferring a change in the isotopic composition of the source water, indicating that the drought treatment increased the uptake of stored moisture from earlier precipitation events. The drought treatment also increased intrinsic water-use efficiency, but had no effect on growth, indicating that photosynthesis remained relatively unaffected despite 19% decrease in canopy conductance. In contrast, fertilization increased growth, but had no effect on the isotopic composition of tree ring cellulose, indicating that the fertilizer gains in biomass were attributable to greater leaf area and not to changes in leaf-level gas exchange. The multi-dimensional sensitivity analysis explored model behavior under different scenarios, highlighting the importance of explicit consideration of leaf temperature in the oxygen isotope discrimination (Delta O-18(c)) simulation and is expected to expand the inference space of the Delta O-18(c) models for plant ecophysiological studies.