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Carlos Gonzalez-Benecke     University Educator/Researcher 
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Carlos Gonzalez-Benecke published an article in July 2017.
Top co-authors See all
Andrew Thomas Hudak

97 shared publications

Thomas R. Fox

97 shared publications

Eric J. Ward

56 shared publications

Randolph H. Wynne

50 shared publications

Eric J. Jokela

43 shared publications

8
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6
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30
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Publication Record
Distribution of Articles published per year 
(2011 - 2017)
Total number of journals
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6
 
Publications See all
Article 2 Reads 1 Citation Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regiona... R. Quinn Thomas, Evan B. Brooks, Annika L. Jersild, Eric J. ... Published: 26 July 2017
Biogeosciences, doi: 10.5194/bg-14-3525-2017
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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 8.6 × 105 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.
Article 0 Reads 0 Citations Erratum to: Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-gro... Lee Alexandre Vierling, Luiz Carlos Estraviz Rodriguez, Carl... Published: 30 June 2017
Carbon Balance and Management, doi: 10.1186/s13021-017-0082-0
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Article 0 Reads 6 Citations Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyp... Lee Alexandre Vierling, Luiz Carlos Estraviz Rodriguez, Carl... Published: 07 June 2017
Carbon Balance and Management, doi: 10.1186/s13021-017-0081-1
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LiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC). Pulse density influences the acquisition cost of LiDAR, and grid cell size influences AGC prediction using plot-based methods; however, little work has evaluated the effects of LiDAR pulse density and cell size for predicting and mapping AGC in fast-growing Eucalyptus forest plantations. The aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data. We used the Random Forest (RF) machine learning algorithm to model AGC using LiDAR-derived metrics from LiDAR collections of 5 and 10 pulses m−2 (RF5 and RF10) and grid cell sizes of 5, 10, 15 and 20 m. The results show that LiDAR pulse density of 5 pulses m−2 provides metrics with similar prediction accuracy for AGC as when using a dataset with 10 pulses m−2 in these fast-growing plantations. Relative root mean square errors (RMSEs) for the RF5 and RF10 were 6.14 and 6.01%, respectively. Equivalence tests showed that the predicted AGC from the training and validation models were equivalent to the observed AGC measurements. The grid cell sizes for mapping ranging from 5 to 20 also did not significantly affect the prediction accuracy of AGC at stand level in this system. LiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m−2 and a grid cell size of 5 m. The promising results for AGC modeling in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations and assist in decision making towards more cost effective and efficient forest inventory.
Article 0 Reads 0 Citations Leveraging 35 years of forest research in the southeastern U.S. to constrain carbon cycle predictions: regional data ass... R. Quinn Thomas, Evan Brooks, Annika Jersild, Eric Ward, Ran... Published: 16 February 2017
Biogeosciences Discussions, doi: 10.5194/bg-2017-46
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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. However, the influence of different experimental treatments on those predictions depends on the exact methods and techniques used for data assimilation. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation of Pine Plantation Ecosystem Research, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the Southeastern U.S. to constrain parameters in a modified version of the 3-PG forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2) concentration, water, and nutrients, along with non-experimental studies that spanned environmental gradients across an 8.6 × 105 km2 region. We optimized regionally representative posterior distributions for the most sensitive model parameters, which dependably predicted data from plots withheld from the data assimilation. The posterior distributions of parameters associated with ecosystem responses to CO2, precipitation, and nutrient addition, along with the corresponding regional changes in production associated with nutrient fertilization and drought, depended on how the experimental data were assimilated. In particular, assimilating nutrient addition experiments reduced the predicted sensitivity to nutrient fertilization while assimilated water manipulation experiments increased the sensitivity to drought. Further, it was necessary to assimilate data from the CO2 experimental enrichment site before other studies to constrain the parameters associated with the influence of CO2 on canopy photosynthesis. The ambient CO2 plots were numerous and had a large contribution to the cost function compared to the low number of elevated CO2 plots (289 ambient vs. 5 elevated plots). Overall, we demonstrated how three decades of research in southeastern U.S. planted pine forests can be used to develop data assimilation techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters. This approach allows for future predictions to be consistent with a rich history of ecosystem research across a region.
Article 0 Reads 10 Citations Ecosystem carbon stocks in Pinus palustris forests Tom A. Stokes, John R. Butnor, Pete Anderson, Jason Jackson,... Published: 01 May 2014
Canadian Journal of Forest Research, doi: 10.1139/cjfr-2013-0446
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Article 1 Read 2 Citations Modeling the Effects of Stand Development, Site Quality, and Silviculture on Leaf Area Index, Litterfall, and Forest Flo... Carlos A. Gonzalez-Benecke, Eric J. Jokela, Timothy A. Marti... Published: 16 October 2012
Forest Science, doi: 10.5849/forsci.11-072
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Leaf area index (LAI), needlefall (NF), and forest floor (FF) dynamics are tightly linked with stand productivity, nutrient cycling, and carbon, water, and energy exchange. We analyzed a long-term data set to quantify the impacts of stand development, site quality, and silviculture on LAI and litterfall (LF) in loblolly and slash pine plantations. LAI was significantly correlated with stand density index (SDI) for each stand studied (P < 0.001), and the parameters of the fitted sigmoidal function were correlated with site index independently for each species. The maximum LAI that a loblolly or slash pine stand attained was linearly correlated with site index (P < 0.001), and the slope of that relationship was different for each species (P = 0.003). Soil resource availability affected the relationship between SDI and LAI. When weed control or fertilizer treatments were applied, the maximum attainable mean yearly LAI and the value of SDI that corresponded with the attainment of 50% of the maximum LAI (inflection point) were increased (P < 0.05). NF production was linearly related to the previous year's LAI (P < 0.001), and this relationship was independent of resources availability (P > 0.086); however, the relationship was different for both species (P < 0.001). Comparison of simulations of NF, LF, and FF with diverse data sets from the literature, encompassing the natural ranges of both species, indicated that these relationships captured the primary drivers of variation, and therefore the models provide a robust synthesis and prediction system for these important variables.