ForestFit: An R package for modeling plant size distributions

Teimouri, Mahdi; Doser, Jeffrey W.; Finley, Andrew O.

doi:10.1016/j.envsoft.2020.104668

“…The moment-based estimators are handier compared to MLE and WLS. In consequence, when complex estimators do not outperform a simpler alternative, the simpler method should be selected (Gorgoso-Varela et al 2019, 2020.…”

Section: Discussionmentioning

confidence: 99%

“…Each method was used to fit the Weibull distribution to the diameter data from the five forest plantations and for all species combined. The 'ForestFit' package (Teimouri, 2020) implemented in R (R Core Team, 2017) was used for the analysis.…”

Section: Estimation Methodsmentioning

confidence: 99%

An evaluation of ten estimators for fitting two-parameter weibull function to Nigerian forest stands

Ogana

¹

2020

Eurasian Journal of Forest Science

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The quality fit produce by distribution function such as the Weibull depends to an extent the type of estimator used to derive its parameters. Inappropriate choice of estimator could affect management decision. Though several estimators have been developed for the Weibull function, their application to forestry have been relatively few. Therefore, this study evaluated ten estimators of the Weibull parameters using tree diameter data from five production forest plantations in Nigeria. The estimators were generalized least type I and type II, L-moment, moments, maximum likelihood, percentiles, rank correlation, least squares, U-statistics and weighted least squares. The quality of fits of the Weibull function were evaluated with Kolmogorov-Smirnov, Anderson-Darling, Cramervon Mises, Akaike information criterion and Bayesian information criterion. Relative rank sum from the evaluation statistics of the methods was analysed using One-way analysis of variance. The results showed that weighted least square had the smallest statistics and relative rank, but not significantly different from L-moment, moments and maximum likelihood (p > 0.05). The performances of least squares, generalized least type I and type II, percentiles and U-statistics were relatively poor. Thus, either the weighted least squares, moments-based or MLE could be used for the Weibull function in the diameter distribution of forest stands in Nigeria.

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“…The consistent rise in carbon dioxide in the atmosphere and its effect on the environment have taken forest carbon accounting to the forefront of research and policy agendas (Bohn & Huth, 2017;Bouvet et al, 2018;Kansanen et al, 2019;Rodríguez-Veiga et al, 2019), and consequently, several allometric models have been developed, and their performance ensured extensive use for the estimation of carbon stocks in the tropics and pan tropics (Jerome Chave et al, 2009;Réjou-Méchain et al, 2017). Nevertheless, carbon stock assessment methods are not entirely developed and, more importantly, the uncertainty associated with carbon stock estimations is rarely assessed (Kansanen et al, 2019;Lewis et al, 2013;Piponiot et al, 2018;Réjou-Méchain et al, 2017;Teimouri, Doser, & Finley, 2019;Zhang, Duan, Zhang, & Xiang, 2014). The Bayesian inference procedure offers a promising approach to these limitations (Réjou-Méchain et al, 2017;Teimouri et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, carbon stock assessment methods are not entirely developed and, more importantly, the uncertainty associated with carbon stock estimations is rarely assessed (Kansanen et al, 2019;Lewis et al, 2013;Piponiot et al, 2018;Réjou-Méchain et al, 2017;Teimouri, Doser, & Finley, 2019;Zhang, Duan, Zhang, & Xiang, 2014). The Bayesian inference procedure offers a promising approach to these limitations (Réjou-Méchain et al, 2017;Teimouri et al, 2019). Also, the height-diameter allometry, error propagations, and specific wood density of individual trees provide a reliable assessment of not only the above-ground biomass but other structural characteristics of the tropical forest(J.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian Inference Procedure for structural characterization of Nigerian Tropical Forest

Akinbiola¹,

Salami²,

Awotoye³

2021

Preprint

0

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The complexity of the tropical forest structure remains a challenge in forest physiognomy assessment, which is a crucial indicator of forest productivity with implications on the carbon cycle, biodiversity, and ecosystem services. The study assessed structural characteristics, described variability within forest stands, and estimated carbon stocks using simulation tools and tree modeling to focus on understanding and quantifying ecological relationships. The study discovered a site-specific wood density difference of 0.07g/cm3 compared with the generalized wood density for tropical forests by Food and Agricultural Organisation (FAO). Carbon stocks estimated with this site-specific wood density produced; 174 Mg Ca / ha-1, 155 Mg Ca / ha-1, and 78 Mg Ca / ha-1, respectively, from three sampled Forest Reserves. Furthermore, the result showed that the forest clusters' most productive layers (emergent and canopy layers) were predominantly hardwood species interspersed with softwood species with huge diameters. The height-diameter model indicated that although the height was a better predictor of the forest structural layer than the diameter, there was no clear margin for grouping species into layers in the region because of interspecies variations, temperature, and anthropogenic activities. The Bayesian Inference procedure provided a reliable approach for carbon stock estimate in the tropics with no legacy inventories.

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Models to estimate genetic gain of soybean seed yield from annual multi-environment field trials

Krause,

Piepho,

Dias

et al. 2023

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Key message Simulations demonstrated that estimates of realized genetic gain from linear mixed models using regional trials are biased to some degree. Thus, we recommend multiple selected models to obtain a range of reasonable estimates. Abstract Genetic improvements of discrete characteristics are obvious and easy to demonstrate, while quantitative traits require reliable and accurate methods to disentangle the confounding genetic and non-genetic components. Stochastic simulations of soybean [Glycine max (L.) Merr.] breeding programs were performed to evaluate linear mixed models to estimate the realized genetic gain (RGG) from annual multi-environment trials (MET). True breeding values were simulated under an infinitesimal model to represent the genetic contributions to soybean seed yield under various MET conditions. Estimators were evaluated using objective criteria of bias and linearity. Covariance modeling and direct versus indirect estimation-based models resulted in a substantial range of estimated values, all of which were biased to some degree. Although no models produced unbiased estimates, the three best-performing models resulted in an average bias of $$\pm\, 7.41$$ ± 7.41 kg/ha$$^{-1}$$ - 1 /yr$$^{-1}$$ - 1 ($$\pm\, 0.11$$ ± 0.11 bu/ac$$^{-1}$$ - 1 /yr$$^{-1}$$ - 1 ). Rather than relying on a single model to estimate RGG, we recommend the application of several models with minimal and directional bias. Further, based on the parameters used in the simulations, we do not think it is appropriate to use any single model to compare breeding programs or quantify the efficiency of proposed new breeding strategies. Lastly, for public soybean programs breeding for maturity groups II and III in North America, the estimated RGG values ranged from 18.16 to 39.68 kg/ha$$^{-1}$$ - 1 /yr$$^{-1}$$ - 1 (0.27–0.59 bu/ac$$^{-1}$$ - 1 /yr$$^{-1}$$ - 1 ) from 1989 to 2019. These results provide strong evidence that public breeders have significantly improved soybean germplasm for seed yield in the primary production areas of North America.

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ForestFit: An R package for modeling plant size distributions

Cited by 18 publications

References 44 publications

An evaluation of ten estimators for fitting two-parameter weibull function to Nigerian forest stands

An evaluation of ten estimators for fitting two-parameter weibull function to Nigerian forest stands

A Bayesian Inference Procedure for structural characterization of Nigerian Tropical Forest

Models to estimate genetic gain of soybean seed yield from annual multi-environment field trials

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