scholarly journals Using Nonlinear Mixed Model Technique to Determine the Optimal Tree Height Prediction Model for Black Spruce

2009 ◽  
Vol 3 (4) ◽  
Author(s):  
Shongming Huang ◽  
Shawn X. Meng ◽  
Yuqing Yang
Keyword(s):  
Prediction Model ◽  
Mixed Model ◽  
Black Spruce ◽  
Tree Height ◽  
Nonlinear Mixed Model ◽  
Height Prediction ◽  
Optimal Tree ◽  
Model Technique

An age-independent individual tree height prediction model for boreal spruce–aspen stands in Alberta

1994 ◽  
Vol 24 (7) ◽  
pp. 1295-1301 ◽  
Author(s):  
Shongming Huang ◽  
Stephen J. Titus
Keyword(s):  
Prediction Model ◽  
Nonlinear Regression ◽  
Prediction Method ◽  
Tree Height ◽  
White Spruce ◽  
Data Set ◽  
Individual Tree ◽  
Permanent Sample Plots ◽  
Height Prediction ◽  
Age Independent

This study presents an individual tree height prediction model for white spruce (Piceaglauca (Moench) Voss) and trembling aspen (Populustremuloides Michx.) grown in boreal mixed-species stands in Alberta. The model is based on a three-parameter Chapman–Richards function fitted to data from 164 permanent sample plots using the parameter prediction method. It is age independent and expresses tree height as a function of tree diameter, tree basal area, stand density, species composition, site productivity, and stand average diameter. This height-prediction model was fitted by weighted nonlinear regression for spruce and unweighted nonlinear regression for aspen. Almost all estimates of parameters were significant at α = 0.05 and model R2-values were high (0.9192 for white spruce and 0.9087 for aspen). No consistent underestimate or overestimate of tree heights was evident in plots of studentized residuals against predicted heights. The model was also tested on an independent data set representing the population on which the model was to be used. Results showed that the average prediction biases were not significant at α = 0.05 for either species, indicating that the model appropriately described the data and performed well when predictions were made.

Assessing the goodness of fit of forest models estimated by nonlinear mixed-model methods

2009 ◽  
Vol 39 (12) ◽  
pp. 2418-2436 ◽  
Author(s):  
Shongming Huang ◽  
Shawn X. Meng ◽  
Yuqing Yang
Keyword(s):  
Goodness Of Fit ◽  
Mixed Model ◽  
Black Spruce ◽  
Coefficient Of Determination ◽  
Nonlinear Mixed Model ◽  
Specific Level ◽  
Frequency Distributions ◽  
Forest Models ◽  
Subject Specific ◽  
The Relationship

In this study we examined various measures, including the concordance correlation (CC) coefficient, for determining the goodness of fit of forest models estimated by nonlinear mixed-model (NLMM) methods. Based on the volume–age data for black spruce, we analyzed the use of CC and other traditional goodness-of-fit measures such as coefficient of determination (R2), mean bias, percent bias, root mean square error, and graphic techniques on both the population and subject-specific levels within the NLMM framework. We also examined the relationship between goodness-of-fit measures and the number of observations per subject. We found that the standard overall goodness-of-fit measures commonly reported on combined data from different subjects were generally insufficient in determining the goodness of fitted models. We recommend that CC and other selected goodness-of-fit measures be calculated for individual subjects, and that the frequency distributions of the calculated values be examined and used as the principal criteria for determining the goodness of fit of forest models estimated by NLMM methods and for comparing alternative models and covariance structures. We also emphasized the importance of using pertinent graphic techniques to assess the appropriateness of NLMMs, especially at the subject-specific level, wherein lies the main interest of NLMMs.

scholarly journals Allometric Equations for Estimating Biomass and Carbon Stocks in Afforested Open Woodlands with Black Spruce and Jack Pine, in the Eastern Canadian Boreal Forest

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 59
Author(s):  
Olivier Fradette ◽  
Charles Marty ◽  
Pascal Tremblay ◽  
Daniel Lord ◽  
Jean-François Boucher
Keyword(s):  
Large Scale ◽  
Ad Hoc ◽  
Black Spruce ◽  
Tree Height ◽  
Jack Pine ◽  
Carbon Stocks ◽  
Allometric Equations ◽  
C Stocks ◽  
The Difference ◽  
Canadian Boreal Forest

Allometric equations use easily measurable biometric variables to determine the aboveground and belowground biomasses of trees. Equations produced for estimating the biomass within Canadian forests at a large scale have not yet been validated for eastern Canadian boreal open woodlands (OWs), where trees experience particular environmental conditions. In this study, we harvested 167 trees from seven boreal OWs in Quebec, Canada for biomass and allometric measurements. These data show that Canadian national equations accurately predict the whole aboveground biomass for both black spruce and jack pine trees, but underestimated branches biomass, possibly owing to a particular tree morphology in OWs relative to closed-canopy stands. We therefore developed ad hoc allometric equations based on three power models including diameter at breast height (DBH) alone or in combination with tree height (H) as allometric variables. Our results show that although the inclusion of H in the model yields better fits for most tree compartments in both species, the difference is minor and does not markedly affect biomass C stocks at the stand level. Using these newly developed equations, we found that carbon stocks in afforested OWs varied markedly among sites owing to differences in tree growth and species. Nine years after afforestation, jack pine plantations had accumulated about five times more carbon than black spruce plantations (0.14 vs. 0.80 t C·ha−1), highlighting the much larger potential of jack pine for OW afforestation projects in this environment.

scholarly journals Skill Transference of a Probe-Tube Placement Training Simulator

2020 ◽  
Vol 31 (01) ◽  
pp. 040-049 ◽  
Author(s):  
Robert W. Koch ◽  
Hasan Saleh ◽  
Paula Folkeard ◽  
Sheila Moodie ◽  
Conner Janeteas ◽  
...  
Keyword(s):  
Mixed Model ◽  
Tracking System ◽  
Optical Tracking ◽  
Tube Placement ◽  
Control Group ◽  
Educational Settings ◽  
Training Simulator ◽  
Nonlinear Mixed Model ◽  
Optical Tracking System ◽  
Clinical Scenarios

AbstractProbe-tube placement is a necessary step in hearing aid verification which needs ample hands-on experience and confidence before performing in clinic. To improve the methods of training in probe-tube placement, a manikin-based training simulator was developed consisting of a 3D-printed head, a flexible silicone ear, and a mounted optical tracking system. The system is designed to provide feedback to the user on the depth and orientation of the probe tube, and the time required to finish the task. Although a previous validation study was performed to determine its realism and teachability with experts, further validation is required before implementation into educational settings.This study aimed to examine the skill transference of a newly updated probe-tube placement training simulator to determine if skills learned on this simulator successfully translate to clinical scenarios.All participants underwent a pretest in which they were evaluated while performing a probe-tube placement and real-ear-to-coupler difference (RECD) measurement on a volunteer. Participants were randomized into one of two groups: the simulator group or the control group. During a two-week training period, all participants practiced their probe-tube placement according to their randomly assigned group. After two weeks, each participant completed a probe-tube placement on the same volunteer as a posttest scenario.Twenty-five novice graduate-level student clinicians.Participants completed a self-efficacy questionnaire and an expert observer completed a questionnaire evaluating each participant’s performance during the pre- and posttest sessions. RECD measurements were taken after placing the probe tube and foam tip in the volunteer’s ear. Questionnaire results were analyzed through nonparametric t-tests and analysis of variance, whereas RECD results were analyzed using a nonlinear mixed model method.Results suggested students in the simulator group were less likely to contact the tympanic membrane when placing a probe tube, appeared more confident, and had better use of the occluding foam tip, resulting in more improved RECD measurements.The improved outcomes for trainees in the simulator group suggest that supplementing traditional training with the simulator provides useful benefits for the trainees, thereby encouraging its usage and implementation in educational settings.

scholarly journals Reaction rate coefficient k20 and temperature coefficient Θ in organic waste thermal disintegration

2018 ◽  
Vol 23 ◽  
pp. 00026
Author(s):  
Sylwia Myszograj
Keyword(s):  
Activated Sludge ◽  
Mixed Model ◽  
Heating Temperature ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Rate Of Change ◽  
Waste Activated Sludge ◽  
Nonlinear Mixed Model ◽  
The Difference ◽  
Reaction Speed

It was described the test of sewage sludge and organic fraction of municipal mixed solid waste thermal disintegration process. The waste activated sludge used during the tests was collected from the secondary settlement tank in a mechanical-biological wastewater treatment plant. The biowaste used in the studies was collected from an area of new buildings. It was noticed from means values of Soluble Chemical Oxygen Demand (SCOD) plot that both heating temperature and time, influence the amount of dissolved COD. The observations indicate that changes of SCOD can be described by an increasing, differentiable function of time and the rate of change of the soluble COD in the hydrolysates, in time is proportional to the difference of the maximum values of SCOD and its value in time, which leads to the relationship of the first-order ordinary differential equation. The process effectiveness depending on the temperature was described with the mathematical model including Van't Hoff-Arrhenius equation. Inspection of the data and some preliminary fits indicates, that for the description of changes in SCOD terms of time and temperature were adopted the form of nonlinear mixed model. Values of k20 indicator and Θ parameter depend on the substrate type. For waste activated sludge thermal disintegration, value of reaction speed indicator k20 was 0.028 h-1 (0,67 d-1), and value of temperature indicator equalled Θ = 1.024. For thermal disintegration of biological waste, value of reaction speed indicator k20 was 0.016 h-1 (0,38 d-1), and value of temperature indicator equalled Θ = 1.016.

scholarly journals Comparison of growth curves of two genotypes of dairy goats using nonlinear mixed models

2013 ◽  
Vol 152 (5) ◽  
pp. 829-842 ◽  
Author(s):  
J. G. L. REGADAS FILHO ◽  
L. O. TEDESCHI ◽  
M. T. RODRIGUES ◽  
L. F. BRITO ◽  
T. S. OLIVEIRA
Keyword(s):  
Random Effects ◽  
Growth Curve ◽  
Mixed Model ◽  
Growth Curves ◽  
Growth Patterns ◽  
Dairy Goat ◽  
Residual Variance ◽  
Nonlinear Mixed Model ◽  
Error Structure ◽  
Mixed Model Methodology

SUMMARYThe objective of the current study was to assess the use of nonlinear mixed model methodology to fit the growth curves (weightv.time) of two dairy goat genotypes (Alpine, +A and Saanen, +S). The nonlinear functions evaluated included Brody, Von Bertalanffy, Richards, Logistic and Gompertz. The growth curve adjustment was performed using two steps. First, random effectsu1,u2andu3were linked to the asymptotic body weight (β1), constant of integration (β2) and rate constant of growth (β3) parameters, respectively. In addition to a traditional fixed-effects model, four combinations of models were evaluated using random variables: all parameters associated with random effects (u1,u2andu3), onlyβ1andβ2(u1andu2), onlyβ1andβ3(u1andu3) and onlyβ1(u1). Second, the fit of the best adjusted model was refined by using the power variance and modelling the error structure. Residual variance ($\sigma _e^2 $) and the Akaike information criterion were used to evaluate the models. After the best fitting model was chosen, the genotype curve parameters were compared. The residual variance was reduced in all scenarios for which random effects were considered. The Richards (u1andu3) function had the best fit to the data. This model was reparameterized using two isotropic error structures for unequally spaced data, and the structure known in the literature as SP(MATERN) proved to be a better fit. The growth curve parameters differed between the two genotypes, with the exception of the constant that determines the proportion of the final size at which the inflection point occurs (β4). The nonlinear mixed model methodology is an efficient tool for evaluating growth curve features, and it is advisable to assign biologically significant parameters with random effects. Moreover, evaluating error structure modelling is recommended to account for possible correlated errors that may be present even when using random effects. Different Richard growth curve parameters should be used for the predominantly Alpine and Saanen genotypes because there are differences in their growth patterns.

A longitudinal height–diameter model for Norway spruce in Finland

2004 ◽  
Vol 34 (1) ◽  
pp. 131-140 ◽  
Author(s):  
Lauri Mehtätalo
Keyword(s):  
Norway Spruce ◽  
Mixed Model ◽  
Basal Area ◽  
Tree Height ◽  
Growth Patterns ◽  
Diameter Distribution ◽  
Stand Age ◽  
Canopy Layer ◽  
Mixed Model Approach ◽  
D Model

A height–diameter (H–D) model for Norway spruce (Picea abies (L.) Karst.) was estimated from longitudinal data. The Korf growth curve was used as the H–D curve. Firstly, H–D curves for each stand at each measurement time were fitted, and the trends in the parameters of the H–D curve were modeled. Secondly, the trends were included in the H–D model to estimate the whole model at once. To take the hierarchy of the data into account, a mixed-model approach was used. This makes it possible to calibrate the model for a new stand at a given point in time using sample tree height(s). The heights may be from different points in time and need not be from the point in time being predicted. The trends in the parameters of the H–D curve were not estimated as a function of stand age but as a function of the median diameter of basal area weighted diameter distribution (dGm). This approach was chosen because the stand ages may differ substantially among stands with similar current growth patterns. This is true especially with shade-tolerant tree species, which can regenerate and survive for several years beneath the dominant canopy layer and start rapid growth later. The growth patterns in stands with a given dGm, on the other hand, seem not to vary much. This finding indicates that the growth pattern of a stand does not depend on stand age but on mean tree size in the stand.

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