Assessing model performance in forecasting long-term individual tree diameter versus basal area increment for the primary Acadian tree species

2011 ◽  
Vol 41 (12) ◽  
pp. 2267-2275 ◽  
Author(s):  
Matthew B. Russell ◽  
Aaron R. Weiskittel ◽  
John A. Kershaw
Keyword(s):  
Random Effects ◽  
Mean Squared Error ◽  
Growth Models ◽  
Secondary Growth ◽  
Model Performance ◽  
Basal Area ◽  
Nonlinear Mixed Effects Models ◽  
Absolute Deviation ◽  
Individual Tree

Tree basal area (ba) or diameter at breast height (dbh) are universally used to represent tree secondary growth in individual tree based growth models. However, the long-term implications of using either ba or dbh for predictions are rarely fully assessed. In this analysis, Δba and Δdbh increment equations were fit to identical datasets gathered from six conifer and four hardwood species grown in central Maine. The performance of Δba and Δdbh predictions from nonlinear mixed-effects models were then compared with observed growth measurements of up to 29 years via a Monte Carlo simulation. Two evaluation statistics indicated substantial improvement in forecasting dbh using Δdbh rather than Δba. Root mean squared error (RMSE) and percentage mean absolute deviation (MAD%) were reduced by 14% and 15% on average, respectively, across all projection length intervals (5–29 years) when Δdbh was used over Δba. Differences were especially noted as projection lengths increased. RMSE and MAD% were reduced by 24% when Δdbh was employed over Δba at longer projection lengths (up to 29 years). Simulations found that simulating random effects rather than using local estimates for random effects performed as well or better at longer interval lengths. These results highlight the implications that selecting a growth model dependent variable can have and the importance of incorporating model uncertainty into the growth projections of individual tree based models.

scholarly journals Nonlinear mixed effects models for comparing growth curves for Guzerá cattle

2020 ◽  
Vol 21 ◽  
Author(s):  
Raphael Fernandes Soares ALVES ◽  
Kaléo Dias PEREIRA ◽  
Antônio Policarpo Souza CARNEIRO ◽  
Paulo Cesar EMILIANO ◽  
Paulo Luiz Souza CARNEIRO ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Growth Models ◽  
Random Effect ◽  
Growth Curves ◽  
Information Criterion ◽  
Fixed Effect ◽  
Coefficient Of Determination ◽  
Nonlinear Mixed Effects Models ◽  
Absolute Deviation ◽  
Production Region

ABSTRACT The objective of the present work was to evaluate the accuracy of the fitted Gompertz and von Bertalanffy models for male and female Guzerá cattle, respectively. Four production regions in Northeast Brazil were included in the models as a fixed effect, and the animals were included as a random effect. In addition, the coefficients of the growth models in the production regions were compared. The accuracy of the fit equations was assessed with the Akaike information criterion, Bayesian information criterion, mean absolute deviation, mean squared error, and coefficient of determination. Confidence intervals were used for comparing the production regions. The Guzerá males in the Gado-Algodão and Serra Geral da Bahia production regions were statistically equal in asymptotic weight, and the animals in the Itapetinga-Valadares and Mata-Agreste regions had equivalent maturity rates. The Guzerá females in the Itapetinga-Valadares and Serra Geral da Bahia regions had the same asymptotic weight. The maturity rates in Itapetinga-Valadares were equal to those estimated for Mata-Agreste and Serra Geral da Bahia. The inclusion of the fixed effect of the production region and the random effect of the animals in the models improved the fit quality and increased the possibility of generating growth curves for each region.

scholarly journals Monitoring and modeling of forest ecosystems: the Estonian Network of Forest Research Plots / Metsaökosüsteemide seire ja modelleerimine metsa kasvukäigu püsiproovitükkide võrgustiku abil

2015 ◽  
Vol 62 (1) ◽  
pp. 26-38 ◽  
Author(s):  
Andres Kiviste ◽  
Maris Hordo ◽  
Ahto Kangur ◽  
Anton Kardakov ◽  
Diana Laarmann ◽  
...  
Keyword(s):  
Growth Models ◽  
Research Network ◽  
Forest Research ◽  
Ecological Processes ◽  
Individual Tree ◽  
Permanent Sample Plots ◽  
Set Up ◽  
The 19Th Century ◽  
Survey Protocol

Abstract Forest research has long traditions in Estonia that can be traced back to the 19th century. Data from long-term forest experiments are available since 1921. The first studies mainly focused on silvicultural treatments and application of such data for understanding and modeling ecological processes was limited. The Department of Forest Management of the Estonian University of Life Sciences started to develop the Estonian Network of Forest Research Plots (ENFRP) in 1995. Since then, plots have been continuously re-measured with 5-year interval. Approximately 100-150 permanent sample plots were measured annually. In 2014, the long-term research network consisted of 729 permanent sample plots, of which 699 have been re-measured at least once, 667 plots - twice and 367 plots - three times. The total number of trees recorded in the network database amounts to 130,479. The plots are systematically distributed throughout the country. Detailed dendrometric measurements including tree spatial distribution are part of the survey protocol. Initially the network was set up to produce suitable data for development of individual tree growth models for Estonia. The significance of the network for the Estonian forest research is continuously increasing and nowadays ENFRP is recognized as an important national research infrastructure.

scholarly journals Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

2019 ◽  
Vol 11 (3) ◽  
pp. 261 ◽  
Author(s):  
Darío Domingo ◽  
Rafael Alonso ◽  
María Teresa Lamelas ◽  
Antonio Luis Montealegre ◽  
Francisco Rodríguez ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Growth Models ◽  
Model Performance ◽  
Basal Area ◽  
Stand Density ◽  
Airborne Laser Scanning ◽  
Support Vector ◽  
Low Density ◽  
Airborne Laser ◽  
Forest Attributes

This study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an area-based approach in Mediterranean Aleppo pine forests. Low-density ALS data were acquired in 2011 and 2016 while 147 forest inventory plots were measured in 2013, 2014, and 2016. Single-tree growth models were used to generate concomitant field data for 2011 and 2016. A comparison of five selection techniques and five regression methods were performed to regress field observations against ALS metrics. The selection of the best regression models fitted for each stand attribute, and separately for both 2011 and 2016, was performed following an indirect approach. Model performance and temporal transferability were analyzed by extrapolating the best fitted models from 2011 to 2016 and inversely from 2016 to 2011 using the direct approach. Non-parametric support vector machine with radial kernel was the best regression method with average relative % root mean square error differences of 2.13% for 2011 models and 1.58% for 2016 ones.

Modelling growing space requirement for Alnus nepalensis D. Don in Nepal

1970 ◽  
Vol 16 (2) ◽  
pp. 30-36 ◽  
Author(s):  
Ram Prasad Sharma
Keyword(s):  
Prediction Models ◽  
Growth Models ◽  
Basal Area ◽  
Individual Tree ◽  
Area Density ◽  
Space Requirement ◽  
Space Model ◽  
Alnus Nepalensis ◽  
Crown Diameter ◽  
Individual Trees

Relationship between crown diameter and stem diameter of individual trees can be translated into mathematical model, and used to generate information of growing space requirement for individual trees and crown competition index for growth models. Nine different crown diameter prediction models were developed using inventory data of Alnus nepalensis trees from a part of Parbat and Syanja districts in Nepal. Among those developed, a non-linear three parameter-based model (W = β0 {1 – exp( - β1D)}β2) explained the greatest proportion of variations of crown diameter (R2adj = 0.78), and showed desirable behaviour of flexibility and robustness. An individual tree growing space model was then derived from crown model to generate important information of shocking limits and stand basal area density for monoculture plantation or natural stands of Alnus nepalensis. Because of its flexibility, crown model is seemed potentially useful for extrapolation purpose also. However, the model cannot be applied for buttressed, wolfed and malformed trees. Key words: Alnus nepalensis; crown model; growing space model; stocking limit; basal area density Banko Janakari Vol.16(2) 2006 pp.30-36

Calibrating predicted diameter distribution with additional information in growth and yield predictions

2003 ◽  
Vol 33 (3) ◽  
pp. 430-434 ◽  
Author(s):  
Annika Kangas ◽  
Matti Maltamo
Keyword(s):  
Growth Models ◽  
Basal Area ◽  
Growth And Yield ◽  
Diameter Distribution ◽  
Individual Tree ◽  
Forest Management Planning ◽  
Growing Stock ◽  
Additional Information ◽  
Tree Models ◽  
Diameter Distributions

Diameter distribution of the growing stock is essential in many forest management planning problems. The diameter distribution is the basis for predicting, for example, timber assortments of a stand. Usually the predicted diameter distribution is scaled so that the stem number (or basal area) corresponds to the measured value (or predicted future value), but it may be difficult to obtain a distribution that gives correct estimates for all known variables. Diameter distributions that are compatible with all available information can be obtained using an approach adopted from sampling theory, the calibration estimation. In calibration estimation, the original predicted frequencies are modified so that they respect a set of constraints, the calibration equations. In this paper, an example of utilizing diameter distributions in growth and yield predictions is presented. The example is based on individual tree growth models of Scots pine (Pinus sylvestris L.). Calibration estimation was utilized in predicting the diameter distribution at the beginning of the simulation period. Then, trees were picked from the distribution and their development was predicted with individual tree models. In predicting the current stand characteristics, calibrated diameter distributions proved to be efficient. However, in predicting future yields, calibration estimation did not significantly improve the accuracy of the results.

A new method for evaluating forest thinning: growth dominance in managed Pinus resinosa stands

2010 ◽  
Vol 40 (5) ◽  
pp. 843-849 ◽  
Author(s):  
John B. Bradford ◽  
Anthony W. D’Amato ◽  
Brian J. Palik ◽  
Shawn Fraver
Keyword(s):  
Tree Growth ◽  
Basal Area ◽  
Pinus Resinosa ◽  
Growth Patterns ◽  
Red Pine ◽  
New Method ◽  
Stand Age ◽  
Individual Tree ◽  
Individual Tree Growth

Growth dominance is a relatively new, simple, quantitative metric of within-stand individual tree growth patterns, and is defined as positive when larger trees in the stand display proportionally greater growth than smaller trees, and negative when smaller trees display proportionally greater growth than larger trees. We examined long-term silvicultural experiments in red pine ( Pinus resinosa Ait.) to characterize how stand age, thinning treatments (thinned from above, below, or both), and stocking levels (residual basal area) influence stand-level growth dominance through time. In stands thinned from below or from both above and below, growth dominance was not significantly different from zero at any age or stocking level. Growth dominance in stands thinned from above trended from negative at low stocking levels to positive at high stocking levels and was positive in young stands. Growth dominance in unthinned stands was positive and increased with age. These results suggest that growth dominance provides a useful tool for assessing the efficacy of thinning treatments designed to reduce competition between trees and promote high levels of productivity across a population, particularly among crop trees.

Windthrow following four harvest treatments in an Engelmann spruce - subalpine fir forest in southern interior British Columbia, Canada

1999 ◽  
Vol 29 (10) ◽  
pp. 1547-1556 ◽  
Author(s):  
David J Huggard ◽  
Walt Klenner ◽  
Alan Vyse
Keyword(s):  
Large Scale ◽  
Basal Area ◽  
Random Trees ◽  
Abies Lasiocarpa ◽  
Ecological Processes ◽  
Individual Tree ◽  
Subalpine Fir ◽  
Engelmann Spruce ◽  
Lower Height

We used transect surveys at a large-scale experimental site at Sicamous Creek, B.C., to measure the effects of five treatments on windthrow: 10-ha clearcuts, arrays of 1-ha patch cuts, arrays of 0.1-ha patch cuts, individual-tree selection cuts, and uncut controls. We also examined edge effects and conditions predisposing trees to windthrow. Windthrow of subalpine fir (Abies lasiocarpa (Hook.) Nutt.) in the 2.7 years following harvesting increased from 0.6% of basal area per year in uncut forest to 0.8-1.8% per year in harvested treatments, with highest rates in individual tree selection units and lowest rates in 0.1-ha patch-cut arrays. Engelmann spruce (Picea engelmannii Parry ex Engelm.) showed similar patterns of windthrow but lower rates (0.2-0.7% of basal area per year in harvested treatments). Windthrow was concentrated near north and east edges of 1-ha and 10-ha openings but was dispersed throughout the more uniform treatments. Windthrown trees did not differ from random trees in diameter but had lower height/diameter ratios, probably reflecting the greater windthrow observed in subxeric sites on complex, elevated topography. The rates and distribution of windthrow in different harvest treatments have implications for ecological processes, salvage, long-term windthrow potential, and mitigation possibilities.

Comparison of stochastic and deterministic mortality estimation in an individual tree based stand growth model

1986 ◽  
Vol 16 (5) ◽  
pp. 1139-1141 ◽  
Author(s):  
Laura A. Weber ◽  
Alan R. Ek ◽  
Terry D. Droessler
Keyword(s):  
Growth Model ◽  
Basal Area ◽  
Deterministic Approach ◽  
Projection Model ◽  
Individual Tree ◽  
Stand Growth ◽  
Mortality Estimation ◽  
Stochastic Mortality ◽  
Growth Projection

Long-term projections (100 years) were made using the deterministic and stochastic mortality algorithms of the STEMS individual tree based stand growth projection model. Deterministic versus averaged stochastic projection results showed no practical differences in mean stand values for number of trees, basal area, volume, or diameter distributions. The deterministic approach also eliminates the need for making repeated stochastic runs and averaging the results where interest lies only in mean projected values.

Wood density in Norway spruce: changes with thinning intensity and tree age

2005 ◽  
Vol 35 (7) ◽  
pp. 1767-1778 ◽  
Author(s):  
Tuula Jaakkola ◽  
Harri Mäkinen ◽  
Pekka Saranpää
Keyword(s):  
Norway Spruce ◽  
Wood Density ◽  
Basal Area ◽  
Tree Age ◽  
Individual Tree ◽  
Thinning Intensity ◽  
Individual Tree Growth ◽  
Spruce Stands ◽  
Individual Trees

The effect of thinning intensity on growth and wood density in Norway spruce (Picea abies (L.) Karst.) was investigated in two long-term thinning experiments in southeastern Finland. The stands were approaching maturity, and their development had already been studied for 30 years. The intensities of thinning were low, normal, and high (i.e., the stand basal area after the thinning was, on average, 40, 27, and 24 m2·ha–1, respectively, in Heinola, and 30, 28, and 17 m2·ha–1 in Punkaharju, respectively). Compared with the low thinning intensity, the normal and high thinning intensities increased the basal-area increment of individual trees by 52% and 68%, respectively. Normal and high thinning intensities resulted in a relatively small reduction (1%–4%) of mean ring density compared with low thinning intensity. The random variation in wood density between and within trees was large. About 27% of the total variation in wood density was related to variation between rings. Our results indicate that the prevailing thinning intensities in Norway spruce stands in Fennoscandia cause no marked changes in wood density. At least, the possible reduction in wood density is low compared with the increase in individual tree growth.

Generic biomass functions for Common beech (Fagus sylvatica) in Central Europe: predictions and components of uncertainty

2008 ◽  
Vol 38 (6) ◽  
pp. 1661-1675 ◽  
Author(s):  
Thomas Wutzler ◽  
Christian Wirth ◽  
Jens Schumacher
Keyword(s):  
Central Europe ◽  
Random Effects ◽  
Fagus Sylvatica ◽  
Fixed Effects ◽  
Model Performance ◽  
Predictor Variables ◽  
Residual Variance ◽  
Prediction Errors ◽  
Nonlinear Mixed Effects Models ◽  
Common Beech

This study provides a comprehensive set of functions for predicting biomass for Common beech ( Fagus sylvatica L.) in Central Europe for all major tree compartments. The equations are based on data of stem, branch, timber, brushwood (wood with diameter below 5 or 7 cm), foliage, root, and total aboveground biomass of 443 trees from 13 studies. We used nonlinear mixed-effects models to assess the contribution of fixed effects (tree dimensions, site descriptors), random effects (grouping according to studies), and residual variance to the total variance and to obtain realistic estimates of uncertainty of biomass on an aggregated level. Candidate models differed in their basic form, the description of the variance, and inclusion of various combinations of additional fixed and random effects and were compared using the Akaike information criterion. Model performance increased most when accounting for between-study differences in the variability of biomass predictions. Further, performance increased with the inclusion of age, site index, and altitude as predictor variables. We show that neglecting variance partitioning and the fact that prediction errors of trees are not independent with respect to their predictor variables will lead to a significant underestimation of prediction variance.

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