Early Growth Response of Nine Timber Species to Release in a Tropical Mountain Forest of Southern Ecuador

Research Highlights: This study determined that treatment “release from competitors” causes different reactions in selected timber species respective to diametrical growth, in which the initial size of the tree (diametric class) is important. Also, the growth habit and phenological traits (defoliation) of the species must be considered, which may have an influence on growth after release. Background and Objectives: The objective of the study was to analyze the diametric growth of nine timber species after their release to answer the following questions: (i) Can the diametric growth of the selected timber species be increased by release? (ii) Does the release cause different responses among the tree species? (iii) Are other factors important, such as the initial diameter at breast height (DBH) or the general climate conditions? Materials and Methods: Four-hundred and eighty-eight trees belonging to nine timber species were selected and monitored over a three-year period. Release was applied to 197 trees, whereas 251 trees served as control trees to evaluate the response of diametrical growth. To determine the response of the trees, a linear mixed model (GLMM, R package: LMER4) was used, which was adjusted by a one-way ANOVA test. Results: All species showed a similar annual cycle respective to diametric increases, which is due to the per-humid climate in the area. Precipitation is secondary for the diametric growth because sufficient rainfall occurs throughout year. What is more important, however, are variations in temperature. However, the species responded differently to release. This is because the initial DBH and growth habit are more important factors. Therefore, the species could be classified into three specific groups: Positive, negative and no response to release. Conclusions: Species which prefer open sites responded positively to release, while shade tolerant species and species with pronounced phenological traits responded negatively. The initial DBH was also an important factor for diametric increases. This is because trees of class I (20 cm to 30 cm DBH) responded positively to the treatment, whereas for bigger or older individuals, the differences decreased or became negative.

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Early Response to Liberation and Climatic Seasonality of Selected Timber Species in a Tropical Mountain Forest of Southern Ecuador

10.20944/preprints201810.0620.v1 ◽

2018 ◽

Author(s):

Omar Cabrera ◽

Andreas Fries ◽

Patrick Hildebrandt ◽

Reinhard Mosandl

Keyword(s):

San Francisco ◽

Mixed Model ◽

Linear Mixed Model ◽

Early Response ◽

Influential Factor ◽

Mountain Forest ◽

Timber Species ◽

Climate Conditions ◽

Tropical Mountain ◽

Southern Ecuador

Research Highlights: The study determined that selective thinning causes different responses, the initial size of the tree released is an influential factor in the growth of species. The temporality of climate and physiological conditions of each species are influential in the growth. It is evident that the defoliation of certain species is an important factor that limits the growth of the species causing thinning to have a negative response. Background and Objectives: The objective is to analyze the behavior of nine timber species, respective to diametric growth after their liberation. This research aims to answer the following questions: (i) How do the selected tree species react to the liberation? (ii) Can the productivity of the trees (diametric growth) be enhanced by liberation? (iii) Are there other factors that influence the diametric growth of the released trees? Materials and Methods: The study was executed in the “Reserva Biológica San Francisco” were 488 trees were monitored, including nine timber species. Therefore, 197 trees were released (removal of competitors) and 251 trees served as reference. To check whether the initial DBH or other factors, like the selective thinning or climate conditions, determine the diameter growth a linear mixed model GLMM was applied. To adjust the linear mixed model a one-way Anova test was executed. Results: Timber species responded differently to the thinning in comparing to reference trees. Therefore, the species analyzed were separated into three groups (positive, negative, and no response to liberation). Conclusions: Liberation potentiates the growth of certain timber species that do not defoliate and considered semi-tolerant to shade. Precipitation and temperature affect all species, but in the defoliate species, it would not be convenient to release them or at least the evidence of these first three years does not show clear differences with control trees. Increase in trees released are higher in trees of the first two diametric classes in all species, this means that larger trees (i.e., older) release does not affect them in a positive way so release should occur in the youngest trees.

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Predicting bycatch hotspots based on suitable habitat derived from fishery-independent data

Marine Ecology Progress Series ◽

10.3354/meps13302 ◽

2020 ◽

Vol 641 ◽

pp. 159-175

Author(s):

J Runnebaum ◽

KR Tanaka ◽

L Guan ◽

J Cao ◽

L O’Brien ◽

...

Keyword(s):

Habitat Suitability ◽

Mixed Model ◽

Linear Mixed Model ◽

Gulf Of Maine ◽

Homarus Americanus ◽

Management Tool ◽

American Lobster ◽

Suitable Habitat ◽

Climate Conditions ◽

Suitability Index

Bycatch remains a global problem in managing sustainable fisheries. A critical aspect of management is understanding the timing and spatial extent of bycatch. Fisheries management often relies on observed bycatch data, which are not always available due to a lack of reporting or observer coverage. Alternatively, analyzing the overlap in suitable habitat for the target and non-target species can provide a spatial management tool to understand where bycatch interactions are likely to occur. Potential bycatch hotspots based on suitable habitat were predicted for cusk Brosme brosme incidentally caught in the Gulf of Maine American lobster Homarus americanus fishery. Data from multiple fisheries-independent surveys were combined in a delta-generalized linear mixed model to generate spatially explicit density estimates for use in an independent habitat suitability index. The habitat suitability indices for American lobster and cusk were then compared to predict potential bycatch hotspot locations. Suitable habitat for American lobster has increased between 1980 and 2013 while suitable habitat for cusk decreased throughout most of the Gulf of Maine, except for Georges Basin and the Great South Channel. The proportion of overlap in suitable habitat varied interannually but decreased slightly in the spring and remained relatively stable in the fall over the time series. As Gulf of Maine temperatures continue to increase, the interactions between American lobster and cusk are predicted to decline as cusk habitat continues to constrict. This framework can contribute to fisheries managers’ understanding of changes in habitat overlap as climate conditions continue to change and alter where bycatch interactions could occur.

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A Bayesian linear mixed model for prediction of complex traits

Bioinformatics ◽

10.1093/bioinformatics/btaa1023 ◽

2020 ◽

Author(s):

Yang Hai ◽

Yalu Wen

Keyword(s):

Complex Traits ◽

Mixed Model ◽

Linear Mixed Model ◽

Rare Variants ◽

Disease Risk ◽

R Package ◽

Underlying Disease ◽

Supplementary Information ◽

True Effect Size ◽

Bayes Algorithm

Abstract Motivation Accurate disease risk prediction is essential for precision medicine. Existing models either assume that diseases are caused by groups of predictors with small-to-moderate effects or a few isolated predictors with large effects. Their performance can be sensitive to the underlying disease mechanisms, which are usually unknown in advance. Results We developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient variational Bayes algorithm. The proposed method can resemble the shape of the true effect size distributions, captures the predictive effects from both common and rare variants, and is robust against various disease models. Through extensive simulations and the application to a whole-genome sequencing dataset obtained from the Alzheimer’s Disease Neuroimaging Initiatives, we have demonstrated that BLMM has better prediction performance than existing methods and can detect variables and/or genetic regions that are predictive. Availability The R-package is available at https://github.com/yhai943/BLMM Supplementary information Supplementary data are available at Bioinformatics online.

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Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data

Bioinformatics ◽

10.1093/bioinformatics/btz822 ◽

2019 ◽

Vol 36 (6) ◽

pp. 1785-1794

Author(s):

Jun Li ◽

Qing Lu ◽

Yalu Wen

Keyword(s):

Risk Prediction ◽

Mixed Model ◽

Linear Mixed Model ◽

R Package ◽

Kernel Functions ◽

Adaptive Lasso ◽

Supplementary Information ◽

High Dimensional ◽

Omics Data ◽

Modeling Framework

Abstract Motivation The use of human genome discoveries and other established factors to build an accurate risk prediction model is an essential step toward precision medicine. While multi-layer high-dimensional omics data provide unprecedented data resources for prediction studies, their corresponding analytical methods are much less developed. Results We present a multi-kernel penalized linear mixed model with adaptive lasso (MKpLMM), a predictive modeling framework that extends the standard linear mixed models widely used in genomic risk prediction, for multi-omics data analysis. MKpLMM can capture not only the predictive effects from each layer of omics data but also their interactions via using multiple kernel functions. It adopts a data-driven approach to select predictive regions as well as predictive layers of omics data, and achieves robust selection performance. Through extensive simulation studies, the analyses of PET-imaging outcomes from the Alzheimer’s Disease Neuroimaging Initiative study, and the analyses of 64 drug responses, we demonstrate that MKpLMM consistently outperforms competing methods in phenotype prediction. Availability and implementation The R-package is available at https://github.com/YaluWen/OmicPred. Supplementary information Supplementary data are available at Bioinformatics online.

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Prediction of Friction Degradation in Highways with Linear Mixed Models

Coatings ◽

10.3390/coatings11020187 ◽

2021 ◽

Vol 11 (2) ◽

pp. 187

Author(s):

Adriana Santos ◽

Elisabete F. Freitas ◽

Susana Faria ◽

Joel R. M. Oliveira ◽

Ana Maria A. C. Rocha

Keyword(s):

Mixed Models ◽

Mixed Model ◽

Linear Mixed Model ◽

Linear Mixed Models ◽

Pavement Management ◽

Climate Conditions ◽

Pavement Management Systems ◽

Geometrical Features ◽

Highway Network ◽

Network Manager

The development of a linear mixed model to describe the degradation of friction on flexible road pavements to be included in pavement management systems is the aim of this study. It also aims at showing that, at the network level, factors such as temperature, rainfall, hypsometry, type of layer, and geometric alignment features may influence the degradation of friction throughout time. A dataset from six districts of Portugal with 7204 sections was made available by the Ascendi Concession highway network. Linear mixed models with random effects in the intercept were developed for the two-level and three-level datasets involving time, section and district. While the three-level models are region-specific, the two-level models offer the possibility to be adopted to other areas. For both levels, two approaches were made: One integrating into the model only the variables inherent to traffic and climate conditions and the other including also the factors intrinsic to the highway characteristics. The prediction accuracy of the model was improved when the variables hypsometry, geometrical features, and type of layer were considered. Therefore, accurate predictions for friction evolution throughout time are available to assist the network manager to optimize the overall level of road safety.

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Effect of seaweed extract application on wine grape yield in Australia

Journal of Applied Phycology ◽

10.1007/s10811-021-02423-1 ◽

2021 ◽

Author(s):

Tony Arioli ◽

Scott W. Mattner ◽

Graham Hepworth ◽

David McClintock ◽

Rachael McClinock

Keyword(s):

Mixed Model ◽

Field Experiments ◽

Linear Mixed Model ◽

Climate Extremes ◽

Seaweed Extract ◽

Wine Grape ◽

Climate Conditions ◽

Mixed Model Approach ◽

Budget Analysis ◽

Grape Yield

AbstractSeaweed extracts are agricultural biostimulants that have been shown to increase the productivity of many crops. The aim of this study was to determine the effect of a seaweed extract from the brown algae Durvillaea potatorum and Ascophyllum nodosum as a soil treatment on the yield of wine grapes grown in Australian production and climate conditions. This study used a series of seven field experiments (2012–2017), across five locations, in three Australian states and four cultivars, and analysed data using a linear mixed model approach. The analysis revealed that recurring soil applications of the seaweed extract significantly increased wine grape yield by an average of 14.7% across multiple growing years that experienced climate extremes. Partial budget analysis showed that the use of the seaweed extract increased profits depending on the grape cultivar. This study is the most extensive investigation of its type in Australian viticulture to understand the effect of a soil-applied seaweed extract on wine grape production.

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2PL Model: Compare Generalized Linear Mixed Model with Latent Variable Model based IRT framework

10.31234/osf.io/p6wuz ◽

2021 ◽

Author(s):

Jihong Zhang ◽

Terry Ackerman ◽

Yurou Wang

Keyword(s):

Latent Variable ◽

Large Scale ◽

Mixed Model ◽

Linear Mixed Model ◽

R Package ◽

Estimation Accuracy ◽

Variable Model ◽

Large Scale Assessment ◽

Measurement Models ◽

Lower Accuracy

Fitting item response theory (IRT) models using the generalized mixed logistic regression model (GLMM) has become more popular in large-scale assessment because GLMM allows combining complicated multilevel structures (i.e., students are nested in classrooms which are nested in schools) with IRT measurement models. However, the estimation accuracy of item parameters between these two models is not well examined. This study aimed to compare the estimation results of the GLMM based 2PL model (using the PLmixed R package) with the traditional IRT model (using flexMIRT software) under different sample sizes (N= 500, 1000, 5000) and test length (J = 15, 21) conditions. The simulation results showed that for both the GLMM-based method and the traditional method, item threshold estimates had lower bias than item discrimination parameters. We also found that according to the simulation study, GLMM estimates via PLmixed had lower accuracy than traditional IRT modeling via flexMIRT for items with high discrimination.

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Combined statistical modeling enables accurate mining of circadian transcription

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab031 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Andrea Rubio-Ponce ◽

Iván Ballesteros ◽

Juan A Quintana ◽

Guiomar Solanas ◽

Salvador A Benitah ◽

...

Keyword(s):

Mixed Model ◽

Ad Hoc ◽

Linear Mixed Model ◽

R Package ◽

Parametric Models ◽

Algorithm Performance ◽

Circadian Genes ◽

Analytical Tools ◽

Circadian Transcription ◽

Non Parametric

Abstract Circadian-regulated genes are essential for tissue homeostasis and organismal function, and are therefore common targets of scrutiny. Detection of rhythmic genes using current analytical tools requires exhaustive sampling, a demand that is costly and raises ethical concerns, making it unfeasible in certain mammalian systems. Several non-parametric methods have been commonly used to analyze short-term (24 h) circadian data, such as JTK_cycle and MetaCycle. However, algorithm performance varies greatly depending on various biological and technical factors. Here, we present CircaN, an ad-hoc implementation of a non-linear mixed model for the identification of circadian genes in all types of omics data. Based on the variable but complementary results obtained through several biological and in silico datasets, we propose a combined approach of CircaN and non-parametric models to dramatically improve the number of circadian genes detected, without affecting accuracy. We also introduce an R package to make this approach available to the community.

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Fisher Scoring for crossed factor linear mixed models

Statistics and Computing ◽

10.1007/s11222-021-10026-6 ◽

2021 ◽

Vol 31 (5) ◽

Author(s):

Thomas Maullin-Sapey ◽

Thomas E. Nichols

Keyword(s):

Degrees Of Freedom ◽

Mixed Model ◽

Linear Mixed Model ◽

Real Data ◽

R Package ◽

Single Factor ◽

Gradient Estimation ◽

Fisher Scoring ◽

Wide Range ◽

Inference Methods

AbstractThe analysis of longitudinal, heterogeneous or unbalanced clustered data is of primary importance to a wide range of applications. The linear mixed model (LMM) is a popular and flexible extension of the linear model specifically designed for such purposes. Historically, a large proportion of material published on the LMM concerns the application of popular numerical optimization algorithms, such as Newton–Raphson, Fisher Scoring and expectation maximization to single-factor LMMs (i.e. LMMs that only contain one “factor” by which observations are grouped). However, in recent years, the focus of the LMM literature has moved towards the development of estimation and inference methods for more complex, multi-factored designs. In this paper, we present and derive new expressions for the extension of an algorithm classically used for single-factor LMM parameter estimation, Fisher Scoring, to multiple, crossed-factor designs. Through simulation and real data examples, we compare five variants of the Fisher Scoring algorithm with one another, as well as against a baseline established by the R package lme4, and find evidence of correctness and strong computational efficiency for four of the five proposed approaches. Additionally, we provide a new method for LMM Satterthwaite degrees of freedom estimation based on analytical results, which does not require iterative gradient estimation. Via simulation, we find that this approach produces estimates with both lower bias and lower variance than the existing methods.

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General methods for evolutionary quantitative genetic inference from generalised mixed models

10.1101/026377 ◽

2015 ◽

Cited By ~ 1

Author(s):

Pierre de Villemereuil ◽

Holger Schielzeth ◽

Shinichi Nakagawa ◽

Michael B. Morrissey

Keyword(s):

Genetic Parameters ◽

Mixed Model ◽

Linear Mixed Model ◽

R Package ◽

Normal Distributions ◽

Quantitative Genetic ◽

Special Cases ◽

Generalised Linear Mixed Model ◽

Latent Scale ◽

Quantitative Genetic Parameters

AbstractMethods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioural traits, have inherently non-normal distributions. The generalised linear mixed model (GLMM) framework has become a widely used tool for estimating quantitative genetic parameters for non-normal traits. However, whereas GLMMs provide inference on a statistically-convenient latent scale, it will often be desirable to estimate quantitative genetic parameters on the scale upon which traits are expressed. The parameters of a fitted GLMM, despite being on a latent scale, fully determine all quantities of potential interest on the scale on which traits are expressed. We provide expressions for deriving each of such quantities, including population means, phenotypic (co)variances, variance components including additive genetic (co)variances, and parameters such as heritability. The expressions require integration of quantities determined by the link function, over distributions of latent values. In general cases, the required integrals must be solved numerically, but efficient methods are available and we provide an implementation in an R package, QGglmm. We show that known formulae for quantities such as heritability of traits with Binomial and Poisson distributions are special cases of our expressions. Additionally, we show how a fitted GLMM can be incorporated into existing methods for predicting evolutionary trajectories. We demonstrate the accuracy of the resulting method for evolutionary prediction by simulation, and apply our approach to data from a pedigreed vertebrate population.

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