scholarly journals Aggregated Biomass Model Systems and Carbon Concentration Variations for Tree Carbon Quantification of Natural Mongolian Oak in Northeast China

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 397 ◽  
Author(s):  
Faris Rafi Almay Widagdo ◽  
Fengri Li ◽  
Lianjun Zhang ◽  
Lihu Dong
Keyword(s):  
Carbon Concentration ◽  
Weighting Function ◽  
Rank Order ◽  
Seemingly Unrelated Regression ◽  
Model Systems ◽  
Total Biomass ◽  
Natural Forests ◽  
Parameter Restriction ◽  
Aggregated Model ◽  
Mongolian Oak

Three systems of additive biomass models were developed and the effects of tree components, tree sizes, and tree growing regions on the carbon concentration were analyzed for Mongolian oak (Quercus mongolica Fisch. ex Ledeb.) in the natural forests of Northeastern China. The nonlinear seemingly unrelated regression (NSUR) method was used to fit each of the three systems simultaneously; namely, aggregated model systems with no parameter restriction (AMS0), aggregated model systems with one parameter restriction (AMS1), and aggregated model systems with three parameter restrictions (AMS3). A unique weighting function for each biomass model was applied to address the heteroscedasticity issue. The systems assertively guarantee the additivity property, in which, the summation of the respective predicted tree components (i.e., root, stem, branch, and foliage) will match the prediction of subtotals (i.e., crown and aboveground) and total biomass. Using one-, two-, and three-predictor combinations (i.e., D (diameter at breast height), D and H (total height), and D, H, and CL (crown length)) as the general model underlying formats, three systems of additive biomass model were developed. Our results indicate that (1) all of the aggregated model systems performed well and the differences between the systems were relatively small; (2) the rank order of the three systems based on an array of statistics are as follows: AMS0 > AMS1 > AMS3; (3) the carbon concentration significantly varied depending on the types of tree tissues and growing regions; (4) the regional respective component carbon concentration and regional weighted mean carbon concentration multiplied by observed biomass value appeared to be the best approach to calculate carbon stock.

scholarly journals Variation in Carbon Concentration and Allometric Equations for Estimating Tree Carbon Contents of 10 Broadleaf Species in Natural Forests in Northeast China

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 928 ◽  
Author(s):  
Dong ◽  
Liu ◽  
Zhang ◽  
Xie ◽  
Li
Keyword(s):  
Carbon Concentration ◽  
Tree Species ◽  
Northeast China ◽  
Tree Height ◽  
Absolute Error ◽  
National Forest Inventory ◽  
Seemingly Unrelated Regression ◽  
Total Carbon ◽  
Natural Forests ◽  
Broadleaf Species

In this study, the effects of tree species, tissue types, and tree size on the carbon concentration were studied, and the two additive systems, one with tree diameter (D), and the other with both D and tree height (H), were developed to estimate the stem, root, branch, and foliage carbon content of 10 broadleaf species in northeast China. The coefficients of the two systems were estimated with the nonlinear seemingly unrelated regression (NSUR), while the heteroscedasticity of the model residual was solved with the weight function. Our results showed that carbon concentrations varied along with tree species and size; the tissues and foliage contained higher carbon concentration than other observed tissues. The two additive carbon equation systems exhibited good predictive and fitting performance, with Ra2 > 0.87, average prediction error of approximately 0, and small average absolute error and absolute error percentage. The carbon equation system constructed with D and H exhibited better fit and performance, particularly for the stem and total carbon. Thus, the additive carbon equation systems estimated the tree carbon of 10 broadleaf species more accurately. These carbon equations can be used to monitor the carbon pool sizes for natural forests in the Chinese National Forest Inventory.

scholarly journals Generic Additive Allometric Models and Biomass Allocation for Two Natural Oak Species in Northeastern China

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 715
Author(s):  
Shengwang Meng ◽  
Fan Yang ◽  
Sheng Hu ◽  
Haibin Wang ◽  
Huimin Wang
Keyword(s):  
Biomass Allocation ◽  
Tree Height ◽  
Northeastern China ◽  
Seemingly Unrelated Regression ◽  
Biomass Estimation ◽  
Model Parameters ◽  
Stem Biomass ◽  
Crown Width ◽  
Biomass Models ◽  
Mongolian Oak

Current models for oak species could not accurately estimate biomass in northeastern China, since they are usually restricted to Mongolian oak (Quercus mongolica Fisch. ex Ledeb.) on local sites, and specifically, no biomass models are available for Liaodong oak (Quercuswutaishanica Mayr). The goal of this study was, therefore, to develop generic biomass models for both oak species on a large scale and evaluate the biomass allocation patterns within tree components. A total of 159 sample trees consisting of 120 Mongolian oak and 39 Liaodong oak were harvested and measured for wood (inside bark), bark, branch and foliage biomass. To account for the belowground biomass, 53 root systems were excavated following the aboveground harvest. The share of biomass allocated to different components was assessed by calculating the ratios. An aboveground additive system of biomass models and belowground equations were fitted based on predictors considering diameter (D), tree height (H), crown width (CW) and crown length (CL). Model parameters were estimated by jointly fitting the total and the components’ equations using the weighted nonlinear seemingly unrelated regression method. A leave-one-out cross-validation procedure was used to evaluate the predictive ability. The results revealed that stem biomass accounts for about two-thirds of the aboveground biomass. The ratio of wood biomass holds constant and that of branches increases with increasing D, H, CW and CL, while a reverse trend was found for bark and foliage. The root-to-shoot ratio nonlinearly decreased with D, ranging from 1.06 to 0.11. Tree diameter proved to be a good predictor, especially for root biomass. Tree height is more prominent than crown size for improving stem biomass models, yet it puts negative effects on crown biomass models with non-significant coefficients. Crown width could help improve the fitting results of the branch and foliage biomass models. We conclude that the selected generic biomass models for Mongolian oak and Liaodong oak will vigorously promote the accuracy of biomass estimation.

Power-Cost Efficiency of Eight Macrobenthic Sampling Schemes in Puget Sound, Washington, USA

1989 ◽  
Vol 46 (12) ◽  
pp. 2157-2165 ◽  
Author(s):  
Steven P. Ferraro ◽  
Faith A. Cole ◽  
Waldemar A. DeBen ◽  
Richard C. Swartz
Keyword(s):  
Cost Efficiency ◽  
Statistical Power ◽  
Rank Order ◽  
Puget Sound ◽  
Sampling Scheme ◽  
Total Biomass ◽  
Type I ◽  
Power Cost ◽  
Sampling Schemes ◽  
Sample Unit

Power-cost efficiency (PCEi = (n × c)min/(ni × ci), where i = sampling scheme, n = minimum number of replicate samples needed to detect a difference between locations with an acceptable probability of Type I (α) and Type II (β) error (e.g. α = β = 0.05), c = mean "cost," in time or money, per replicate sample, and (n × c)min = minimum value of (n × c) among the i sampling schemes) is the appropriate expression for comparing the cost efficiency of alternative sampling schemes having equivalent statistical rigor when the statistical model is a redistribution for comparisons of two means. PCEs were determined for eight macrobenthic sampling schemes (four sample unit sizes and two sieve mesh sizes) in a comparison of a reference site versus a putative polluted site in Puget Sound, Washington. Laboratory processing times were, on average, about 2.5 times greater for the [Formula: see text]- than the [Formula: see text] samples. The 0.06-m2, 0- to 8-cm-deep sample unit size and 1.0-mm sieve mesh size was the overall optimum sampling scheme in this study; it ranked first in PCE on 8 and second on 3 of 11 measures of community structure. Rank order by statistical power of the 11 measures for this scheme was Infaunal Index > log10 (mollusc biomass + 1) > number of species > log10 (numerical abundance) > log10 (polychaete biomass + 1) > log10 (total biomass + 1) > log10 (crustacean biomass + 1) > McIntosh's index > 1 – Simpson's Index > Shannon's Index > Dominance Index.

scholarly journals Microarthropod contributions to fitness variation in the common moss Ceratodon purpureus

2021 ◽  
Vol 288 (1947) ◽  
Author(s):  
Erin E. Shortlidge ◽  
Sarah B. Carey ◽  
Adam C. Payton ◽  
Stuart F. McDaniel ◽  
Todd N. Rosenstiel ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Rank Order ◽  
Model Systems ◽  
Interacting Species ◽  
Pooled Sequencing ◽  
Experimental Mesocosms ◽  
The Common ◽  
Plant Animal Interactions ◽  
Reproductive Rates

The evolution of sustained plant–animal interactions depends critically upon genetic variation in the fitness benefits from the interaction. Genetic analyses of such interactions are limited to a few model systems, in part because genetic variation may be absent or the interacting species may be experimentally intractable. Here, we examine the role of sperm-dispersing microarthropods in shaping reproduction and genetic variation in mosses. We established experimental mesocosms with known moss genotypes and inferred the parents of progeny from mesocosms with and without microarthropods, using a pooled sequencing approach. Moss reproductive rates increased fivefold in the presence of microarthropods, relative to control mesocosms. Furthermore, the presence of microarthropods increased the total number of reproducing moss genotypes, and changed the rank-order of fitness of male and female moss genotypes. Interestingly, the genotypes that reproduced most frequently did not produce sporophytes with the most spores, highlighting the challenge of defining fitness in mosses. These results demonstrate that microarthropods provide a fitness benefit for mosses, and highlight the potential for biotic dispersal agents to alter fitness among moss genotypes.

scholarly journals A Bayesian Approach to Estimating Seemingly Unrelated Regression for Tree Biomass Model Systems

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1302
Author(s):  
Longfei Xie ◽  
Fengri Li ◽  
Lianjun Zhang ◽  
Faris Rafi Almay Widagdo ◽  
Lihu Dong
Keyword(s):  
Bayesian Approach ◽  
Bayesian Methods ◽  
Model Fitting ◽  
Seemingly Unrelated Regression ◽  
Model Systems ◽  
Informative Priors ◽  
Tree Biomass ◽  
Tree Diameter ◽  
Biomass Models ◽  
Unrelated Regression

Accurate estimation of tree biomass is required for accounting for and monitoring forest carbon stocking. Allometric biomass equations constructed by classical statistical methods are widely used to predict tree biomass in forest ecosystems. In this study, a Bayesian approach was proposed and applied to develop two additive biomass model systems: one with tree diameter at breast height as the only predictor and the other with both tree diameter and total height as the predictors for planted Korean larch (Larix olgensis Henry) in the Northeast, P.R. China. The seemingly unrelated regression (SUR) was used to fit the simultaneous equations of four tree components (i.e., stem, branch, foliage, and root). The model parameters were estimated by feasible generalized least squares (FGLS) and Bayesian methods using either non-informative priors or informative priors. The results showed that adding tree height to the model systems improved the model fitting and performance for the stem, branch, and foliage biomass models, but much less for the root biomass models. The Bayesian methods on the SUR models produced narrower 95% prediction intervals than did the classical FGLS method, indicating higher computing efficiency and more stable model predictions, especially for small sample sizes. Furthermore, the Bayesian methods with informative priors performed better (smaller values of deviance information criterion (DIC)) than those with the non-informative priors. Therefore, our results demonstrated the advantages of applying the Bayesian methods on the SUR biomass models, not only obtaining better model fitting and predictions, but also offering the assessment and evaluation of the uncertainties for constructing and updating tree biomass models.

scholarly journals Estimating the productive potential of five natural forest types in northeastern China

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Zhaofei Wu ◽  
Zhonghui Zhang ◽  
Juan Wang
Keyword(s):  
Sustainable Forest Management ◽  
Northeastern China ◽  
Natural Forest ◽  
Seemingly Unrelated Regression ◽  
Climate Factors ◽  
Natural Forests ◽  
Forest Types ◽  
Forest Production ◽  
Dominant Height ◽  
Productive Potential

Abstract Background There is a serious lack of experience regarding the productive potential of the natural forests in northeastern China, which severely limits the development of sustainable forest management strategies for this most important forest region in China. Accordingly, the objective of this study is to develop a first comprehensive system for estimating the wood production for the five dominant forest types. Methods Based on a network of 384 field plots and using the state-space approach, we develop a system of dynamic stand models, for each of the five main forest types. Four models were developed and evaluated, including a base model and three extended models which include the effects of dominant height and climate variables. The four models were fitted, and their predictive strengths were tested, using the “seemingly unrelated regression” (SUR) technique. Results All three of the extended models increased the accuracy of the predictions at varying degrees for the five major natural forest types of northeastern China. The inclusion of dominant height and two climate factors (precipitation and temperature) in the base model resulted in the best performance for all the forest types. On average, the root mean square values were reduced by 13.0% when compared with the base model. Conclusion Both dominant height and climate factors were important variables in estimating forest production. This study not only presents a new method for estimating forest production for a large region, but also explains regional differences in the effect of site productivity and climate.

scholarly journals Aggregatibacter actinomycetemcomitans Builds Mutualistic Biofilm Communities with Fusobacterium nucleatum and Veillonella Species in Saliva

2009 ◽  
Vol 77 (9) ◽  
pp. 3542-3551 ◽  
Author(s):  
Saravanan Periasamy ◽  
Paul E. Kolenbrander
Keyword(s):  
Solid Phase ◽  
Fluorescent Antibody ◽  
Fusobacterium Nucleatum ◽  
Single Species ◽  
Aggregatibacter Actinomycetemcomitans ◽  
Model Systems ◽  
Total Biomass ◽  
Community Interactions ◽  
Flow Cells

ABSTRACT Human oral bacterial pathogens grow in attached multispecies biofilm communities. Unattached cells are quickly removed by swallowing. Therefore, surface attachment is essential for growth, and we investigated multispecies community interactions resulting in mutualistic growth on saliva as the sole nutritional source. We used two model systems, saliva-coated transferable solid-phase polystyrene pegs (peg biofilms) and flow cells with saliva-coated glass surfaces. Fluorescent antibody staining and image analysis were used to quantify the biomass in flow cells, and quantitative real-time PCR with species-specific primers was used to quantify the biomass in peg biofilms. Veillonella sp. strain PK1910, Aggregatibacter actinomycetemcomitans JP2, and Fusobacterium nucleatum ATCC 10953 were unable to grow as single species in flow cells. Only A. actinomycetemcomitans grew after 36 h when peg biofilms remained submerged in saliva from the time of inoculation. Mixed-species coaggregates were used for two- and three-species inoculation. The biomass in two-species biofilms increased in both systems when Veillonella sp. strain PK1910 was present as one of the partners. Enhanced growth of all strains was observed in three-species biofilms in flow cells. Interestingly, in flow cells F. nucleatum and A. actinomycetemcomitans exhibited mutualism, and, although F. nucleatum was unable to grow with either of the other species in the peg system, F. nucleatum stimulated the growth of Veillonella sp. and together these two organisms increased the total biomass of A. actinomycetemcomitans in three-species peg biofilms. We propose that mutualistic two-species and multispecies oral biofilm communities form in vivo and that mutualism between commensal veillonellae and late colonizing pathogens, such as aggregatibacteria, contributes to the development of periodontal disease.

Additive biomass equations for slash pine trees: comparing three modeling approaches

2019 ◽  
Vol 49 (1) ◽  
pp. 27-40 ◽  
Author(s):  
Dehai Zhao ◽  
James Westfall ◽  
John W. Coulston ◽  
Thomas B. Lynch ◽  
Bronson P. Bullock ◽  
...  
Keyword(s):  
Pinus Elliottii ◽  
Slash Pine ◽  
Seemingly Unrelated Regression ◽  
Total Biomass ◽  
Tree Biomass ◽  
Biomass Equation ◽  
Fit Statistics ◽  
Biomass Equations ◽  
Pine Trees ◽  
Total Tree

Both aggregative and disaggregative strategies were used to develop additive nonlinear biomass equations for slash pine (Pinus elliottii Engelm. var. elliottii) trees in the southeastern United States. In the aggregative approach, the total tree biomass equation was specified by aggregating the expectations of component biomass models, and their parameters were estimated by jointly fitting all component and total biomass equations using weighted nonlinear seemingly unrelated regression (NSUR) (SUR1) or by jointly fitting component biomass equations using weighted NSUR (SUR2). In an alternative disaggregative approach (DRM), the biomass component proportions were modeled using Dirichlet regression, and the estimated total tree biomass was disaggregated into biomass components based on their estimated proportions. There was no single system to predict biomass that was best for all components and total tree biomass. The ranking of the three systems based on an array of fit statistics followed the order of SUR2 > SUR1 > DRM. All three systems provided more accurate biomass predictions than previously published equations.

scholarly journals Additive and Non-additive Biomass Equations for Black Wattle

2019 ◽  
Vol 26 (4) ◽  
Author(s):  
Alexandre Behling ◽  
Sylvio Péllico Netto ◽  
Carlos Roberto Sanquetta ◽  
Ana Paula Dalla Corte ◽  
Augusto Arlindo Simon ◽  
...  
Keyword(s):  
Effective Property ◽  
Seemingly Unrelated Regression ◽  
Total Biomass ◽  
Unrelated Regression ◽  
Seemingly Unrelated Regression Model ◽  
Biomass Equations ◽  
Biomass Components ◽  
Black Wattle ◽  
Undesirable Property ◽  
Second Procedure

ABSTRACT The objectives of this work were to propose additive equations for biomass components (stem and crown) and total biomass for black wattle (Acacia mearnsii De Wild.) and show the inconsistency of independently adjusted biomass equations. Two procedures were used to fit nonlinear equations of biomass: i) independent and ii) systems of equations. The second procedure, defined by the application of the seemingly unrelated regression model, has better biological and statistical properties to estimate allometric equations of biomass components and total biomass when compared with the independent estimation. An effective property of this procedure is the additivity, i.e., the estimates of component biomass are compatible with those of total biomass. Independent fitted adjusted equations do not consider the dependence between the biomass components, thus, besides the estimates being non-additive, which is an undesirable property, they will result in estimates with larger variance.

Canadian national tree aboveground biomass equations

2005 ◽  
Vol 35 (8) ◽  
pp. 1996-2018 ◽  
Author(s):  
M-C Lambert ◽  
C-H Ung ◽  
F Raulier
Keyword(s):  
Aboveground Biomass ◽  
National Level ◽  
Estimation Method ◽  
Seemingly Unrelated Regression ◽  
Model Parameters ◽  
Total Biomass ◽  
Tree Biomass ◽  
Biomass Density ◽  
Biomass Equations ◽  
Error Terms

The estimation of aboveground biomass density (organic dry mass per unit area) is required for balancing Canadian national forest carbon budgets. Tree biomass equations are the basic tool for converting inventory plot data into biomass density. New sets of national tree biomass equations have therefore been produced from archival biomass data collected at the beginning of the 1980s through the ENergy from the FORest research program (ENFOR) of the Canadian Forest Service. Since the sampling plan was not standardized among provinces and territories, data had to be harmonized before any biomass equation could be considered at the national level. Two features characterize the new equations: estimated biomass of the compartments (foliage, branch, wood, and bark) are constrained to equal the total biomass, and dependence among error terms for the considered compartments of the same tree is taken into account in the estimates of both the model parameters and the variance prediction. The estimation method known to economists as “seemingly unrelated regression” allowed the inclusion of dependencies among the error terms of the considered biomass compartments. Sets of equations based on diameter at breast height (dbh) and on dbh and height have been produced for 33 species, groups of hardwood and softwood, and for all species combined. Biomass predicted by the new equations was compared with that estimated from provincial equations to evaluate the loss of accuracy when scaling up from the regional to the national scale. Bias and error of prediction from the set of national equations based on dbh and height were generally more similar to those from provincial equations than to those of predictions from the set of equations based on dbh alone.

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