Impacts of the minimum purchase price policy for grain on the planting area of rice in Hubei Province were analyzed based on a mixed linear model.After the indicator system containing the minimum purchase price policy...Impacts of the minimum purchase price policy for grain on the planting area of rice in Hubei Province were analyzed based on a mixed linear model.After the indicator system containing the minimum purchase price policy and other factors influencing the planting area of rice was constructed,principal component analysis of the system was conducted,and then a mixed linear model where the planting area of rice was as the dependent variable was established.The results show that after the exclusion of the interference from other factors,the minimum purchase price policy for grain had a positive impact on the planting area of rice in Hubei Province.That is,the minimum purchase price policy significantly stimulated the growth of rice planting area in Hubei Province.展开更多
Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using general...Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.展开更多
Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, th...Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, that double assumption is unlikely to hold, particularly for the random effects, a crucial component </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">in </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">which assessment of magnitude is key in such modeling. Alternative fitting methods not relying on that assumption (as ANOVA ones and Rao</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">’</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s MINQUE) apply, quite often, only to the very constrained class of variance components models. In this paper, a new computationally feasible estimation methodology is designed, first for the widely used class of 2-level (or longitudinal) LMMs with only assumption (beyond the usual basic ones) that residual errors are uncorrelated and homoscedastic, with no distributional assumption imposed on the random effects. A major asset of this new approach is that it yields nonnegative variance estimates and covariance matrices estimates which are symmetric and, at least, positive semi-definite. Furthermore, it is shown that when the LMM is, indeed, Gaussian, this new methodology differs from ML just through a slight variation in the denominator of the residual variance estimate. The new methodology actually generalizes to LMMs a well known nonparametric fitting procedure for standard Linear Models. Finally, the methodology is also extended to ANOVA LMMs, generalizing an old method by Henderson for ML estimation in such models under normality.展开更多
We focus on the development of model selection criteria in linear mixed models. In particular, we propose the model selection criteria following the Mallows’ Conceptual Predictive Statistic (Cp) [1] [2] in linear mix...We focus on the development of model selection criteria in linear mixed models. In particular, we propose the model selection criteria following the Mallows’ Conceptual Predictive Statistic (Cp) [1] [2] in linear mixed models. When correlation exists between the observations in data, the normal Gauss discrepancy in univariate case is not appropriate to measure the distance between the true model and a candidate model. Instead, we define a marginal Gauss discrepancy which takes the correlation into account in the mixed models. The model selection criterion, marginal Cp, called MCp, serves as an asymptotically unbiased estimator of the expected marginal Gauss discrepancy. An improvement of MCp, called IMCp, is then derived and proved to be a more accurate estimator of the expected marginal Gauss discrepancy than MCp. The performance of the proposed criteria is investigated in a simulation study. The simulation results show that in small samples, the proposed criteria outperform the Akaike Information Criteria (AIC) [3] [4] and Bayesian Information Criterion (BIC) [5] in selecting the correct model;in large samples, their performance is competitive. Further, the proposed criteria perform significantly better for highly correlated response data than for weakly correlated data.展开更多
Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for suc...Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for such territory risk classification.In this work,spatially constrained clustering is first applied to insurance loss data to form rating territories.The generalized linear model(GLM)and generalized linear mixed model(GLMM)are then proposed to derive the risk relativities of obtained clusters.Each basic rating unit within the same cluster,namely Forward Sortation Area(FSA),takes the same risk relativity value as its cluster.The obtained risk relativities from GLM or GLMM are used to calculate the performance metrics,including RMSE,MAD,and Gini coefficients.The spatially constrained clustering and the risk relativity estimate help obtain a set of territory risk benchmarks used in rate filings to guide the rate regulation process.展开更多
Purpose: To formulate and demonstrate methods for regression modeling of probabilities and dispersions for individual-patient longitudinal outcomes taking on discrete numeric values. Methods: Three alternatives for mo...Purpose: To formulate and demonstrate methods for regression modeling of probabilities and dispersions for individual-patient longitudinal outcomes taking on discrete numeric values. Methods: Three alternatives for modeling of outcome probabilities are considered. Multinomial probabilities are based on different intercepts and slopes for probabilities of different outcome values. Ordinal probabilities are based on different intercepts and the same slope for probabilities of different outcome values. Censored Poisson probabilities are based on the same intercept and slope for probabilities of different outcome values. Parameters are estimated with extended linear mixed modeling maximizing a likelihood-like function based on the multivariate normal density that accounts for within-patient correlation. Formulas are provided for gradient vectors and Hessian matrices for estimating model parameters. The likelihood-like function is also used to compute cross-validation scores for alternative models and to control an adaptive modeling process for identifying possibly nonlinear functional relationships in predictors for probabilities and dispersions. Example analyses are provided of daily pain ratings for a cancer patient over a period of 97 days. Results: The censored Poisson approach is preferable for modeling these data, and presumably other data sets of this kind, because it generates a competitive model with fewer parameters in less time than the other two approaches. The generated probabilities for this model are distinctly nonlinear in time while the dispersions are distinctly nonconstant over time, demonstrating the need for adaptive modeling of such data. The analyses also address the dependence of these daily pain ratings on time and the daily numbers of pain flares. Probabilities and dispersions change differently over time for different numbers of pain flares. Conclusions: Adaptive modeling of daily pain ratings for individual cancer patients is an effective way to identify nonlinear relationships in time as well as in other predictors such as the number of pain flares.展开更多
The purpose of this article is to investigate approaches for modeling individual patient count/rate data over time accounting for temporal correlation and non</span><span style="font-family:Verdana;"...The purpose of this article is to investigate approaches for modeling individual patient count/rate data over time accounting for temporal correlation and non</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">constant dispersions while requiring reasonable amounts of time to search over alternative models for those data. This research addresses formulations for two approaches for extending generalized estimating equations (GEE) modeling. These approaches use a likelihood-like function based on the multivariate normal density. The first approach augments standard GEE equations to include equations for estimation of dispersion parameters. The second approach is based on estimating equations determined by partial derivatives of the likelihood-like function with respect to all model parameters and so extends linear mixed modeling. Three correlation structures are considered including independent, exchangeable, and spatial autoregressive of order 1 correlations. The likelihood-like function is used to formulate a likelihood-like cross-validation (LCV) score for use in evaluating models. Example analyses are presented using these two modeling approaches applied to three data sets of counts/rates over time for individual cancer patients including pain flares per day, as needed pain medications taken per day, and around the clock pain medications taken per day per dose. Means and dispersions are modeled as possibly nonlinear functions of time using adaptive regression modeling methods to search through alternative models compared using LCV scores. The results of these analyses demonstrate that extended linear mixed modeling is preferable for modeling individual patient count/rate data over time</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> because in example analyses</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> it either generates better LCV scores or more parsimonious models and requires substantially less time.展开更多
The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectivene...The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.展开更多
The main purpose of this paper is to investigate D-optimal population designs in multi-response linear mixed models for longitudinal data.Observations of each response variable within subjects are assumed to have a fi...The main purpose of this paper is to investigate D-optimal population designs in multi-response linear mixed models for longitudinal data.Observations of each response variable within subjects are assumed to have a first-order autoregressive structure,possibly with observation error.The equivalence theorems are provided to characterise theD-optimal population designs for the estimation of fixed effects in the model.The semi-Bayesian D-optimal design which is robust against the serial correlation coefficient is also considered.Simulation studies show that the correlation between multi-response variables has tiny effects on the optimal design,while the experimental costs are important factors in the optimal designs.展开更多
Dissecting the genetic architecture of complex traits is an ongoing challenge for geneticists.Two complementary approaches for genetic mapping,linkage mapping and association mapping have led to successful dissection ...Dissecting the genetic architecture of complex traits is an ongoing challenge for geneticists.Two complementary approaches for genetic mapping,linkage mapping and association mapping have led to successful dissection of complex traits in many crop species.Both of these methods detect quantitative trait loci(QTL) by identifying marker–trait associations,and the only fundamental difference between them is that between mapping populations,which directly determine mapping resolution and power.Based on this difference,we first summarize in this review the advances and limitations of family-based mapping and natural population-based mapping instead of linkage mapping and association mapping.We then describe statistical methods used for improving detection power and computational speed and outline emerging areas such as large-scale meta-analysis for genetic mapping in crops.In the era of next-generation sequencing,there has arisen an urgent need for proper population design,advanced statistical strategies,and precision phenotyping to fully exploit high-throughput genotyping.展开更多
In the experimental field, researchers need very often to select the best subset model as well as reach the best model estimation simultaneously. Selecting the best subset of variables will improve the prediction accu...In the experimental field, researchers need very often to select the best subset model as well as reach the best model estimation simultaneously. Selecting the best subset of variables will improve the prediction accuracy as noninformative variables will be removed. Having a model with high prediction accuracy allows the researchers to use the model for future forecasting. In this paper, we investigate the differences between various variable selection methods. The aim is to compare the analysis of the frequentist methodology (the backward elimination), penalised shrinkage method (the Adaptive LASSO) and the Least Angle Regression (LARS) for selecting the active variables for data produced by the blocked design experiment. The result of the comparative study supports the utilization of the LARS method for statistical analysis of data from blocked experiments.展开更多
Background:Animals need to adjust their vigilance strategies when foraging between physically contrasting veg-etated and non-vegetated habitats.Vegetated habitats may pose a greater risk for some if vegetation charact...Background:Animals need to adjust their vigilance strategies when foraging between physically contrasting veg-etated and non-vegetated habitats.Vegetated habitats may pose a greater risk for some if vegetation characteristics function as a visual obstruction but benefit others if they serve as protective shelter.Variation in group size,presence of similar species,along with variation in environmental conditions and anthropogenic disturbance can also influence vigilance investment.Methods:In this study,we quantified the vigilance behaviour of two large-bodied,sympatric migratory curlew species-Far Eastern Curlew(Numenius madagascariensis)and Eurasian Curlew(N.arquata)-in vegetated Suaeda salsa saltmarsh and non-vegetated mudflat habitat in Liaohekou National Nature Reserve,China.We used linear mixed models to examine the effects of habitat type,season,tide time,flock size(conspecific and heterospecific),and human disturbance on curlew vigilance investment.Results:Both species spent a higher percentage of time under visual obstruction in S.salsa habitat compared to mudflat habitat but in response,only Far Eastern Curlew increased their percentage of vigilance time,indicating that visual obstruction in this habitat is only a concern for this species.There was no evidence that S.salsa vegetation served as a form of cryptic background colouration since neither species decreased their vigilance effect in S.salsa habitat in spring compared to the autumn migration season.The effect of curlew social environment(i.e.flock size)was habitat dependent since percentage of vigilance time by curlews in saltmarsh increased with both the number of individual curlews and number of other birds present,but not in mudflat habitat.Conclusions:We conclude that both migratory curlew species exhibit a flexible vigilance adjustment strategy to cope with the different environmental and social conditions of adjacent and sharply contrasting coastal habitats,and that the trade-off between the risks of foraging and the abundance of prey may be a relatively common phenom-enon in these and other shorebird populations.展开更多
<strong>Objective</strong><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><strong>: </strong>Since the...<strong>Objective</strong><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><strong>: </strong>Since the identification of COVID-19 in December 2019 as a pandemic, over 4500 research papers were published with the term “COVID-19” contained in its title. Many of these reports on the COVID-19 pandemic suggested that the coronavirus was associated with more serious chronic diseases and mortality particularly in patients with chronic diseases regardless of country and age. Therefore, there is a need to understand how common comorbidities and other factors are associated with the risk of death due to COVID-19 infection. Our investigation aims at exploring this relationship. Specifically, our analysis aimed to explore the relationship between the total number of COVID-19 cases and mortality associated with COVID-19 infection accounting for other risk factors. </span><b><span style="font-family:Verdana;">Methods</span></b><span style="font-family:Verdana;">: Due to the presence of over dispersion, the Negative Binomial Regression is used to model the aggregate number of COVID-19 cases. Case-fatality associated with this infection is modeled as an outcome variable using machine learning predictive multivariable regression. The data we used are the COVID-19 cases and associated deaths from the start of the pandemic up to December 02-2020, the day Pfizer was granted approval for their new COVID-19 vaccine. </span><b><span style="font-family:Verdana;">Results</span></b><span style="font-family:Verdana;">: Our analysis found significant regional variation in case fatality. Moreover, the aggregate number of cases had several risk factors including chronic kidney disease, population density and the percentage of gross domestic product spent on healthcare. </span><b><span style="font-family:Verdana;">The Conclusions</span></b><span style="font-family:Verdana;">: There are important regional variations in COVID-19 case fatality. We identified three factors to be significantly correlated with case fatality</span></span></span></span><span style="font-family:Verdana;">.</span>展开更多
Background: Cardiovascular diseases (CVDs) are the leading cause of death globally. An estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Of these deaths, 85% were due to hear...Background: Cardiovascular diseases (CVDs) are the leading cause of death globally. An estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Of these deaths, 85% were due to heart attack and stroke. Over three quarters of CVD deaths take place in low- and middle-income countries. We have studied the pattern of mortality due to cardiovascular in the six countries of the Arabian Gulf and its association with obesity over the 29 years 1990 to 2019. Methods: We used the linear mixed effect models to investigate the pattern of CVD mortality over the year 1990 to 2019, together with the pattern of change in one of the most important risk factors that is obesity, and its association with CVD mortality over the same period. Conclusions: Although there were fluctuations in the pattern of mortality and the prevalence of obesity over the specified period, there has been a steady decline in the per-100,000 number of deaths and the prevalence of obesity. However, there was a strong association between the two variables. From the fitted models we estimated that a one percent increase in obesity is associated with an average increase in cardiovascular deaths of 2.7 deaths per 100,000.展开更多
Background The effect of urbanization on the morbidity of hepatitis A remains unclear. We aimed to estimate the association between various urbanization-related indices and hepatitis A morbidity in China.Methods Data ...Background The effect of urbanization on the morbidity of hepatitis A remains unclear. We aimed to estimate the association between various urbanization-related indices and hepatitis A morbidity in China.Methods Data on the annual morbidity of hepatitis A, urbanization-related measures (i.e., gross domestic product per capita, the number of hospitalization beds per 1000 persons, illiteracy rate, tap water coverage, motor vehicles per 100 persons, population density, and the proportion of arable land), and meteorological factors in 31 provincial-level administrative divisions of Chinese mainland during 2005–2018 were collected from the National Population and Health Science Data Sharing Platform, China Statistical Yearbooks, and the China Meteorological Data Sharing Service System, respectively. Generalized linear mixed models were applied to quantify the impacts of different urbanization-related indices on the morbidity of hepatitis A in China after adjusting for covariates.Results A total of 537,466 hepatitis A cases were reported in China during 2005–2018. The annual morbidity had a decline of 79.4% from 5.64 cases to 1.16 cases per 100,000 people. There were obvious spatial variations with higher morbidity in western China. Nationally, gross domestic product per capita and the number of hospitalization beds per 1000 persons increased from 14,040 to 64,644 CNY and from 2.45 to 6.03 during 2005–2018, respectively. The illiteracy rate decreased from 11.0 to 4.9%. Gross domestic product per capita [relative risk (RR) = 0.96, 95% confidence interval (CI): 0.92–0.99], and the number of hospitalization beds per 1000 persons (RR = 0.79, 95%CI: 0.75–0.83) were associated with the declined morbidity of hepatitis A. By contrast, the increased morbidity of hepatitis A was linked to the illiteracy rate (RR = 1.04, 95%CI: 1.02–1.06). Similar influential factors were detected for children and adults, with greater effects witnessed for children.Conclusions People in the western region suffered the heaviest burden of hepatitis A in Chinese mainland. Nationally, there was a sharp decline in the morbidity of hepatitis A. The urbanization process was associated with the reduction of hepatitis A morbidity in China during 2005–2018.展开更多
Cetaceans are unique ecological engineers,and their restoration may have a crucial impact on the future structure of aquatic ecosystems,which calls for more investigations into their trophic ecology.Among current tech...Cetaceans are unique ecological engineers,and their restoration may have a crucial impact on the future structure of aquatic ecosystems,which calls for more investigations into their trophic ecology.Among current techniques,stable isotope analysis(SIA)has the advantages of non-invasive sampling and long timescales.However,the full benefits of SIA in cetacean research may not be achieved due to issues like different types of tissue between sampling methods and use of chemical preservation solutions in historical specimens.To address these challenges,we conducted a study on Narrow-ridged Finless Porpoises(Neophocaena asiaeorientalis).Multiple tissues from freshwater and marine subspecies,as well as tissues preserved using different solutions such as ethanol and formalin were collected for SIA.Linear mixed effects models were used for data analysis.Our results showed that,except for blubber,kidney,and stomach,differences between other tissues were correctable.In tissues from live sampling,we found no significant difference between blood and muscle,and skin could also be used for isotope analysis after proper correction.Ethanol preservation caused significant positive changes in δ^(13)C and δ^(15)N values of muscle,while formalin preservation caused negative changes in δ^(13)C and δ^(15)N.Our findings provide valuable insight into unifying data from stranded carcasses and live sampling,as well as correcting for the effect of chemical preservation on museum specimens.Findings from this research support further application of stable isotope analysis in the conservation of endangered finless porpoises,offer a reference for other similar cetaceans,and also provide guidance for chemical preservation when freezing conditions are not available.展开更多
Backfat thickness is a good predictor of carcass lean content,an economically important trait,and a main breeding target in pig improvement.In this study,the candidate genes and genomic regions associated with the ten...Backfat thickness is a good predictor of carcass lean content,an economically important trait,and a main breeding target in pig improvement.In this study,the candidate genes and genomic regions associated with the tenth rib backfat thickness trait were identified in two independent pig populations,using a genome-wide association study of porcine 60K SNP genotype data applying the compressed mixed linear model(CMLM)statistical method.For each population,30 most significant single-nucleotide polymorphisms(SNPs)were selected and SNP annotation implemented using Sus scrofa Build 10.2.In the first population,25 significant SNPs were distributed on seven chromosomes,and SNPs on SSC1 and SSC7 showed great significance for fat deposition.The most significant SNP(ALGA0006623)was located on SSC1,upstream of the MC4R gene.In the second population,27 significant SNPs were recognized by annotation,and 12 SNPs on SSC12 were related to fat deposition.Two haplotype blocks,M1GA0016251-MARC0075799 and ALGA0065251-MARC0014203-M1GA0016298-ALGA0065308,were detected in significant regions where the PIPNC1 and GH1 genes were identified as contributing to fat metabolism.The results indicated that genetic mechanism regulating backfat thickness is complex,and that genome-wide associations can be affected by populations with different genetic backgrounds.展开更多
In this paper we present relevant contributions and important features related to the study of the retroreflectivity performance of pavement markings.The contribution of this paper is threefold.First,we propose an art...In this paper we present relevant contributions and important features related to the study of the retroreflectivity performance of pavement markings.The contribution of this paper is threefold.First,we propose an artificial scheme to allow some randomization of the treatments owing to several restrictions imposed on the choice of the experimental units.It is an experiment involving one fixed factor(three types of materials)in a randomized block design executed on a high-traffic-volume highway.Under this condition,the traffic volume works as a stress factor and the degradation of the retroreflectivity of pavement markings is faster than the degradation on rural roads or streets.This is related to the second contribution:the possibility of a reduction of experimental time.The current experiment spent 20 weeks to collect the data.And finally a mixed linear model considering three random effects and several fixed effects is fitted and the most relevant effects pointed out.This study can help highway managers to improve road safety by scheduling the maintenance of pavement marks at the appropriate time,choosing adequate material for the pavement markings and applying the proposed artificial scheme in future studies.展开更多
Aims seedlings are vulnerable to many kinds of fatal abiotic and biotic agents,and examining the causes of seedling dynamics can help understand mechanisms of species coexistence.To disentangle the relative importance...Aims seedlings are vulnerable to many kinds of fatal abiotic and biotic agents,and examining the causes of seedling dynamics can help understand mechanisms of species coexistence.To disentangle the relative importance of neighborhood densities,habitat factors and phylogenetic relatedness on focal seedling survival,we monitored the survival of 5306 seedlings of 104 species>15 months.We address the following questions:(i)How do neighborhood densities,habitat variables and phylogenetic relatedness affect seedling survival?What is the relative importance of conspecific densities,habitat variables and phylogenetic relatedness to seedling survival?(ii)Does the importance of the neighborhood densities,habitat variables and phylogenetic relatedness vary among growth forms,leaf habits or dispersal modes?specially,does the conspecific negative density dependence inhibit tree and decidu-ous seedlings more compared with shrub and evergreen species?Does density dependence affect the wind and animal-dispersed species equally?Methods We established 135 census stations to monitor seedling dynamics in a 25-ha subtropical forest plot in central China.Conspecific and heterospecific seedling density in the 1-m2 seedling plot and adult basal area within a 20-m radius provided neighborhood density var-iables.mean elevation,convexity and aspect of every 5-×5-m grid with seedling plots were used to quantify habitat characteristics.We calculated the relative average phylodiversity between focal seed-ling and heterospecific neighbors to quantify the species related-ness in the neighborhood.Eight candidate generalized linear mixed models with binominal error distribution were used to compare the relative importance of these variables to seedling survival.akaike’s information criteria were used to identify the most parsimonious models.Important Findingsat the community level,both the neighborhood densities and phylogenetic relatedness were important to seedling survival.We found negative effects of increasing conspecific seedlings,which suggested the existence of species-specific density-dependent mortality.Phylodiversity of heterospecific neighbors was negatively related to survival of focal seedlings,indicating similar habitat preference shared among phylogenetically closely related species may drive seedling survival.The relative importance of neighborhood densities,habitat variables and phylogenetic relatedness varied among ecological guilds.Conspecific densities had significant negative effect for deciduous and wind-dispersed species,and marginally significant for tree seedlings>10 cm tall and animal-dispersed species.Habitat variables had limited effects on seedling survival,and only elevation was related to the sur-vival of evergreen species in the best-fit model.We conclude that both negative density-dependent mortality and habitat preference reflected by the phylogenetic relatedness shape the species coex-istence at seedling stage in this forest.展开更多
Previous studies have reported that some important loci are missed in single-locus genome-wide association studies(GWAS),especially because of the large phenotypic error in field experiments.To solve this issue,multi-...Previous studies have reported that some important loci are missed in single-locus genome-wide association studies(GWAS),especially because of the large phenotypic error in field experiments.To solve this issue,multi-locus GWAS methods have been recommended.However,only a few software packages for multi-locus GWAS are available.Therefore,we developed an R software named mr MLM v4.0.2.This software integrates mr MLM,FASTmr MLM,FASTmr EMMA,p LARm EB,p KWm EB,and ISIS EM-BLASSO methods developed by our lab.There are four components in mr MLM v4.0.2,including dataset input,parameter setting,software running,and result output.The fread function in data.table is used to quickly read datasets,especially big datasets,and the do Parallel package is used to conduct parallel computation using multiple CPUs.In addition,the graphical user interface software mr MLM.GUI v4.0.2,built upon Shiny,is also available.To confirm the correctness of the aforementioned programs,all the methods in mr MLM v4.0.2 and three widely-used methods were used to analyze real and simulated datasets.The results confirm the superior performance of mr MLM v4.0.2 to other methods currently available.False positive rates are effectively controlled,albeit with a less stringent significance threshold.mr MLM v4.0.2 is publicly available at Bio Code(https://bigd.big.ac.cn/biocode/tools/BT007077)or R(https://cran.r-project.org/web/packages/mr MLM.GUI/index.html)as an open-source software.展开更多
基金Supported by the Humanities and Social Sciences Foundation for Young Scholars of Ministry of Education of China(11y3jc630197)
文摘Impacts of the minimum purchase price policy for grain on the planting area of rice in Hubei Province were analyzed based on a mixed linear model.After the indicator system containing the minimum purchase price policy and other factors influencing the planting area of rice was constructed,principal component analysis of the system was conducted,and then a mixed linear model where the planting area of rice was as the dependent variable was established.The results show that after the exclusion of the interference from other factors,the minimum purchase price policy for grain had a positive impact on the planting area of rice in Hubei Province.That is,the minimum purchase price policy significantly stimulated the growth of rice planting area in Hubei Province.
文摘Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.
文摘Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, that double assumption is unlikely to hold, particularly for the random effects, a crucial component </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">in </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">which assessment of magnitude is key in such modeling. Alternative fitting methods not relying on that assumption (as ANOVA ones and Rao</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">’</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s MINQUE) apply, quite often, only to the very constrained class of variance components models. In this paper, a new computationally feasible estimation methodology is designed, first for the widely used class of 2-level (or longitudinal) LMMs with only assumption (beyond the usual basic ones) that residual errors are uncorrelated and homoscedastic, with no distributional assumption imposed on the random effects. A major asset of this new approach is that it yields nonnegative variance estimates and covariance matrices estimates which are symmetric and, at least, positive semi-definite. Furthermore, it is shown that when the LMM is, indeed, Gaussian, this new methodology differs from ML just through a slight variation in the denominator of the residual variance estimate. The new methodology actually generalizes to LMMs a well known nonparametric fitting procedure for standard Linear Models. Finally, the methodology is also extended to ANOVA LMMs, generalizing an old method by Henderson for ML estimation in such models under normality.
文摘We focus on the development of model selection criteria in linear mixed models. In particular, we propose the model selection criteria following the Mallows’ Conceptual Predictive Statistic (Cp) [1] [2] in linear mixed models. When correlation exists between the observations in data, the normal Gauss discrepancy in univariate case is not appropriate to measure the distance between the true model and a candidate model. Instead, we define a marginal Gauss discrepancy which takes the correlation into account in the mixed models. The model selection criterion, marginal Cp, called MCp, serves as an asymptotically unbiased estimator of the expected marginal Gauss discrepancy. An improvement of MCp, called IMCp, is then derived and proved to be a more accurate estimator of the expected marginal Gauss discrepancy than MCp. The performance of the proposed criteria is investigated in a simulation study. The simulation results show that in small samples, the proposed criteria outperform the Akaike Information Criteria (AIC) [3] [4] and Bayesian Information Criterion (BIC) [5] in selecting the correct model;in large samples, their performance is competitive. Further, the proposed criteria perform significantly better for highly correlated response data than for weakly correlated data.
文摘Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for such territory risk classification.In this work,spatially constrained clustering is first applied to insurance loss data to form rating territories.The generalized linear model(GLM)and generalized linear mixed model(GLMM)are then proposed to derive the risk relativities of obtained clusters.Each basic rating unit within the same cluster,namely Forward Sortation Area(FSA),takes the same risk relativity value as its cluster.The obtained risk relativities from GLM or GLMM are used to calculate the performance metrics,including RMSE,MAD,and Gini coefficients.The spatially constrained clustering and the risk relativity estimate help obtain a set of territory risk benchmarks used in rate filings to guide the rate regulation process.
文摘Purpose: To formulate and demonstrate methods for regression modeling of probabilities and dispersions for individual-patient longitudinal outcomes taking on discrete numeric values. Methods: Three alternatives for modeling of outcome probabilities are considered. Multinomial probabilities are based on different intercepts and slopes for probabilities of different outcome values. Ordinal probabilities are based on different intercepts and the same slope for probabilities of different outcome values. Censored Poisson probabilities are based on the same intercept and slope for probabilities of different outcome values. Parameters are estimated with extended linear mixed modeling maximizing a likelihood-like function based on the multivariate normal density that accounts for within-patient correlation. Formulas are provided for gradient vectors and Hessian matrices for estimating model parameters. The likelihood-like function is also used to compute cross-validation scores for alternative models and to control an adaptive modeling process for identifying possibly nonlinear functional relationships in predictors for probabilities and dispersions. Example analyses are provided of daily pain ratings for a cancer patient over a period of 97 days. Results: The censored Poisson approach is preferable for modeling these data, and presumably other data sets of this kind, because it generates a competitive model with fewer parameters in less time than the other two approaches. The generated probabilities for this model are distinctly nonlinear in time while the dispersions are distinctly nonconstant over time, demonstrating the need for adaptive modeling of such data. The analyses also address the dependence of these daily pain ratings on time and the daily numbers of pain flares. Probabilities and dispersions change differently over time for different numbers of pain flares. Conclusions: Adaptive modeling of daily pain ratings for individual cancer patients is an effective way to identify nonlinear relationships in time as well as in other predictors such as the number of pain flares.
文摘The purpose of this article is to investigate approaches for modeling individual patient count/rate data over time accounting for temporal correlation and non</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">constant dispersions while requiring reasonable amounts of time to search over alternative models for those data. This research addresses formulations for two approaches for extending generalized estimating equations (GEE) modeling. These approaches use a likelihood-like function based on the multivariate normal density. The first approach augments standard GEE equations to include equations for estimation of dispersion parameters. The second approach is based on estimating equations determined by partial derivatives of the likelihood-like function with respect to all model parameters and so extends linear mixed modeling. Three correlation structures are considered including independent, exchangeable, and spatial autoregressive of order 1 correlations. The likelihood-like function is used to formulate a likelihood-like cross-validation (LCV) score for use in evaluating models. Example analyses are presented using these two modeling approaches applied to three data sets of counts/rates over time for individual cancer patients including pain flares per day, as needed pain medications taken per day, and around the clock pain medications taken per day per dose. Means and dispersions are modeled as possibly nonlinear functions of time using adaptive regression modeling methods to search through alternative models compared using LCV scores. The results of these analyses demonstrate that extended linear mixed modeling is preferable for modeling individual patient count/rate data over time</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> because in example analyses</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> it either generates better LCV scores or more parsimonious models and requires substantially less time.
文摘The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.
基金partly supported by the National Natural Science Foundation of China(Nos.11971318,11871143)Shanghai Rising-Star Program(No.20QA1407500).
文摘The main purpose of this paper is to investigate D-optimal population designs in multi-response linear mixed models for longitudinal data.Observations of each response variable within subjects are assumed to have a first-order autoregressive structure,possibly with observation error.The equivalence theorems are provided to characterise theD-optimal population designs for the estimation of fixed effects in the model.The semi-Bayesian D-optimal design which is robust against the serial correlation coefficient is also considered.Simulation studies show that the correlation between multi-response variables has tiny effects on the optimal design,while the experimental costs are important factors in the optimal designs.
基金supported by the Priority Academic Program Development of Jiangsu Higher Education Institutionthe National Natural Science Foundation of China(Nos.91535103,31391632,and 31200943)+4 种基金the National High Technology Research and Development Program of China(No.2014AA10A601-5)the Natural Science Foundation of Jiangsu Province(No.BK2012261)the Natural Science Foundation of Jiangsu Higher Education Institution(No.14KJA210005)the Postgraduate Research and Innovation Project in Jiangsu Province(No.KYLX151368)the Innovative Research Team of University in Jiangsu Province
文摘Dissecting the genetic architecture of complex traits is an ongoing challenge for geneticists.Two complementary approaches for genetic mapping,linkage mapping and association mapping have led to successful dissection of complex traits in many crop species.Both of these methods detect quantitative trait loci(QTL) by identifying marker–trait associations,and the only fundamental difference between them is that between mapping populations,which directly determine mapping resolution and power.Based on this difference,we first summarize in this review the advances and limitations of family-based mapping and natural population-based mapping instead of linkage mapping and association mapping.We then describe statistical methods used for improving detection power and computational speed and outline emerging areas such as large-scale meta-analysis for genetic mapping in crops.In the era of next-generation sequencing,there has arisen an urgent need for proper population design,advanced statistical strategies,and precision phenotyping to fully exploit high-throughput genotyping.
文摘In the experimental field, researchers need very often to select the best subset model as well as reach the best model estimation simultaneously. Selecting the best subset of variables will improve the prediction accuracy as noninformative variables will be removed. Having a model with high prediction accuracy allows the researchers to use the model for future forecasting. In this paper, we investigate the differences between various variable selection methods. The aim is to compare the analysis of the frequentist methodology (the backward elimination), penalised shrinkage method (the Adaptive LASSO) and the Least Angle Regression (LARS) for selecting the active variables for data produced by the blocked design experiment. The result of the comparative study supports the utilization of the LARS method for statistical analysis of data from blocked experiments.
基金supported by National Key Research and Develop-ment Program of China(No.2017YFC1403500 to JL)National Natural Science Foundation of China(No.31911540468 and 31672316 to DL)+1 种基金non-profit Foundation of Marine Environment and Ecological Conservation of CNOOC(CF-MEEC/TR/2020-20 to ZZ)Natural Science Foundation of Liaoning Province of China(2019-MS-154 to DL).
文摘Background:Animals need to adjust their vigilance strategies when foraging between physically contrasting veg-etated and non-vegetated habitats.Vegetated habitats may pose a greater risk for some if vegetation characteristics function as a visual obstruction but benefit others if they serve as protective shelter.Variation in group size,presence of similar species,along with variation in environmental conditions and anthropogenic disturbance can also influence vigilance investment.Methods:In this study,we quantified the vigilance behaviour of two large-bodied,sympatric migratory curlew species-Far Eastern Curlew(Numenius madagascariensis)and Eurasian Curlew(N.arquata)-in vegetated Suaeda salsa saltmarsh and non-vegetated mudflat habitat in Liaohekou National Nature Reserve,China.We used linear mixed models to examine the effects of habitat type,season,tide time,flock size(conspecific and heterospecific),and human disturbance on curlew vigilance investment.Results:Both species spent a higher percentage of time under visual obstruction in S.salsa habitat compared to mudflat habitat but in response,only Far Eastern Curlew increased their percentage of vigilance time,indicating that visual obstruction in this habitat is only a concern for this species.There was no evidence that S.salsa vegetation served as a form of cryptic background colouration since neither species decreased their vigilance effect in S.salsa habitat in spring compared to the autumn migration season.The effect of curlew social environment(i.e.flock size)was habitat dependent since percentage of vigilance time by curlews in saltmarsh increased with both the number of individual curlews and number of other birds present,but not in mudflat habitat.Conclusions:We conclude that both migratory curlew species exhibit a flexible vigilance adjustment strategy to cope with the different environmental and social conditions of adjacent and sharply contrasting coastal habitats,and that the trade-off between the risks of foraging and the abundance of prey may be a relatively common phenom-enon in these and other shorebird populations.
文摘<strong>Objective</strong><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><strong>: </strong>Since the identification of COVID-19 in December 2019 as a pandemic, over 4500 research papers were published with the term “COVID-19” contained in its title. Many of these reports on the COVID-19 pandemic suggested that the coronavirus was associated with more serious chronic diseases and mortality particularly in patients with chronic diseases regardless of country and age. Therefore, there is a need to understand how common comorbidities and other factors are associated with the risk of death due to COVID-19 infection. Our investigation aims at exploring this relationship. Specifically, our analysis aimed to explore the relationship between the total number of COVID-19 cases and mortality associated with COVID-19 infection accounting for other risk factors. </span><b><span style="font-family:Verdana;">Methods</span></b><span style="font-family:Verdana;">: Due to the presence of over dispersion, the Negative Binomial Regression is used to model the aggregate number of COVID-19 cases. Case-fatality associated with this infection is modeled as an outcome variable using machine learning predictive multivariable regression. The data we used are the COVID-19 cases and associated deaths from the start of the pandemic up to December 02-2020, the day Pfizer was granted approval for their new COVID-19 vaccine. </span><b><span style="font-family:Verdana;">Results</span></b><span style="font-family:Verdana;">: Our analysis found significant regional variation in case fatality. Moreover, the aggregate number of cases had several risk factors including chronic kidney disease, population density and the percentage of gross domestic product spent on healthcare. </span><b><span style="font-family:Verdana;">The Conclusions</span></b><span style="font-family:Verdana;">: There are important regional variations in COVID-19 case fatality. We identified three factors to be significantly correlated with case fatality</span></span></span></span><span style="font-family:Verdana;">.</span>
文摘Background: Cardiovascular diseases (CVDs) are the leading cause of death globally. An estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Of these deaths, 85% were due to heart attack and stroke. Over three quarters of CVD deaths take place in low- and middle-income countries. We have studied the pattern of mortality due to cardiovascular in the six countries of the Arabian Gulf and its association with obesity over the 29 years 1990 to 2019. Methods: We used the linear mixed effect models to investigate the pattern of CVD mortality over the year 1990 to 2019, together with the pattern of change in one of the most important risk factors that is obesity, and its association with CVD mortality over the same period. Conclusions: Although there were fluctuations in the pattern of mortality and the prevalence of obesity over the specified period, there has been a steady decline in the per-100,000 number of deaths and the prevalence of obesity. However, there was a strong association between the two variables. From the fitted models we estimated that a one percent increase in obesity is associated with an average increase in cardiovascular deaths of 2.7 deaths per 100,000.
基金supported by the National Natural Science Foundation of China(81973140,82003555).
文摘Background The effect of urbanization on the morbidity of hepatitis A remains unclear. We aimed to estimate the association between various urbanization-related indices and hepatitis A morbidity in China.Methods Data on the annual morbidity of hepatitis A, urbanization-related measures (i.e., gross domestic product per capita, the number of hospitalization beds per 1000 persons, illiteracy rate, tap water coverage, motor vehicles per 100 persons, population density, and the proportion of arable land), and meteorological factors in 31 provincial-level administrative divisions of Chinese mainland during 2005–2018 were collected from the National Population and Health Science Data Sharing Platform, China Statistical Yearbooks, and the China Meteorological Data Sharing Service System, respectively. Generalized linear mixed models were applied to quantify the impacts of different urbanization-related indices on the morbidity of hepatitis A in China after adjusting for covariates.Results A total of 537,466 hepatitis A cases were reported in China during 2005–2018. The annual morbidity had a decline of 79.4% from 5.64 cases to 1.16 cases per 100,000 people. There were obvious spatial variations with higher morbidity in western China. Nationally, gross domestic product per capita and the number of hospitalization beds per 1000 persons increased from 14,040 to 64,644 CNY and from 2.45 to 6.03 during 2005–2018, respectively. The illiteracy rate decreased from 11.0 to 4.9%. Gross domestic product per capita [relative risk (RR) = 0.96, 95% confidence interval (CI): 0.92–0.99], and the number of hospitalization beds per 1000 persons (RR = 0.79, 95%CI: 0.75–0.83) were associated with the declined morbidity of hepatitis A. By contrast, the increased morbidity of hepatitis A was linked to the illiteracy rate (RR = 1.04, 95%CI: 1.02–1.06). Similar influential factors were detected for children and adults, with greater effects witnessed for children.Conclusions People in the western region suffered the heaviest burden of hepatitis A in Chinese mainland. Nationally, there was a sharp decline in the morbidity of hepatitis A. The urbanization process was associated with the reduction of hepatitis A morbidity in China during 2005–2018.
基金supported by the National Key R&D Program of China(grant No.2018YFD0900904,2022YFF1301603)the INTERNATIONAL COOPERATION Project of the Chinese Academy of Sciences(Grant No.152342KYSB20190025).
文摘Cetaceans are unique ecological engineers,and their restoration may have a crucial impact on the future structure of aquatic ecosystems,which calls for more investigations into their trophic ecology.Among current techniques,stable isotope analysis(SIA)has the advantages of non-invasive sampling and long timescales.However,the full benefits of SIA in cetacean research may not be achieved due to issues like different types of tissue between sampling methods and use of chemical preservation solutions in historical specimens.To address these challenges,we conducted a study on Narrow-ridged Finless Porpoises(Neophocaena asiaeorientalis).Multiple tissues from freshwater and marine subspecies,as well as tissues preserved using different solutions such as ethanol and formalin were collected for SIA.Linear mixed effects models were used for data analysis.Our results showed that,except for blubber,kidney,and stomach,differences between other tissues were correctable.In tissues from live sampling,we found no significant difference between blood and muscle,and skin could also be used for isotope analysis after proper correction.Ethanol preservation caused significant positive changes in δ^(13)C and δ^(15)N values of muscle,while formalin preservation caused negative changes in δ^(13)C and δ^(15)N.Our findings provide valuable insight into unifying data from stranded carcasses and live sampling,as well as correcting for the effect of chemical preservation on museum specimens.Findings from this research support further application of stable isotope analysis in the conservation of endangered finless porpoises,offer a reference for other similar cetaceans,and also provide guidance for chemical preservation when freezing conditions are not available.
基金This study was supported by the National Science Foundation of China(31172192)New Century Excellent Talents(NCET-11-0646)Fundamental Research Funds for the Central Universities(2011JQ009,2012PY009).
文摘Backfat thickness is a good predictor of carcass lean content,an economically important trait,and a main breeding target in pig improvement.In this study,the candidate genes and genomic regions associated with the tenth rib backfat thickness trait were identified in two independent pig populations,using a genome-wide association study of porcine 60K SNP genotype data applying the compressed mixed linear model(CMLM)statistical method.For each population,30 most significant single-nucleotide polymorphisms(SNPs)were selected and SNP annotation implemented using Sus scrofa Build 10.2.In the first population,25 significant SNPs were distributed on seven chromosomes,and SNPs on SSC1 and SSC7 showed great significance for fat deposition.The most significant SNP(ALGA0006623)was located on SSC1,upstream of the MC4R gene.In the second population,27 significant SNPs were recognized by annotation,and 12 SNPs on SSC12 were related to fat deposition.Two haplotype blocks,M1GA0016251-MARC0075799 and ALGA0065251-MARC0014203-M1GA0016298-ALGA0065308,were detected in significant regions where the PIPNC1 and GH1 genes were identified as contributing to fat metabolism.The results indicated that genetic mechanism regulating backfat thickness is complex,and that genome-wide associations can be affected by populations with different genetic backgrounds.
文摘In this paper we present relevant contributions and important features related to the study of the retroreflectivity performance of pavement markings.The contribution of this paper is threefold.First,we propose an artificial scheme to allow some randomization of the treatments owing to several restrictions imposed on the choice of the experimental units.It is an experiment involving one fixed factor(three types of materials)in a randomized block design executed on a high-traffic-volume highway.Under this condition,the traffic volume works as a stress factor and the degradation of the retroreflectivity of pavement markings is faster than the degradation on rural roads or streets.This is related to the second contribution:the possibility of a reduction of experimental time.The current experiment spent 20 weeks to collect the data.And finally a mixed linear model considering three random effects and several fixed effects is fitted and the most relevant effects pointed out.This study can help highway managers to improve road safety by scheduling the maintenance of pavement marks at the appropriate time,choosing adequate material for the pavement markings and applying the proposed artificial scheme in future studies.
基金National Natural Science Foundation of China(31070465 and 31200329)Chinese Forest Biodiversity Monitoring Network(29200931131101919).
文摘Aims seedlings are vulnerable to many kinds of fatal abiotic and biotic agents,and examining the causes of seedling dynamics can help understand mechanisms of species coexistence.To disentangle the relative importance of neighborhood densities,habitat factors and phylogenetic relatedness on focal seedling survival,we monitored the survival of 5306 seedlings of 104 species>15 months.We address the following questions:(i)How do neighborhood densities,habitat variables and phylogenetic relatedness affect seedling survival?What is the relative importance of conspecific densities,habitat variables and phylogenetic relatedness to seedling survival?(ii)Does the importance of the neighborhood densities,habitat variables and phylogenetic relatedness vary among growth forms,leaf habits or dispersal modes?specially,does the conspecific negative density dependence inhibit tree and decidu-ous seedlings more compared with shrub and evergreen species?Does density dependence affect the wind and animal-dispersed species equally?Methods We established 135 census stations to monitor seedling dynamics in a 25-ha subtropical forest plot in central China.Conspecific and heterospecific seedling density in the 1-m2 seedling plot and adult basal area within a 20-m radius provided neighborhood density var-iables.mean elevation,convexity and aspect of every 5-×5-m grid with seedling plots were used to quantify habitat characteristics.We calculated the relative average phylodiversity between focal seed-ling and heterospecific neighbors to quantify the species related-ness in the neighborhood.Eight candidate generalized linear mixed models with binominal error distribution were used to compare the relative importance of these variables to seedling survival.akaike’s information criteria were used to identify the most parsimonious models.Important Findingsat the community level,both the neighborhood densities and phylogenetic relatedness were important to seedling survival.We found negative effects of increasing conspecific seedlings,which suggested the existence of species-specific density-dependent mortality.Phylodiversity of heterospecific neighbors was negatively related to survival of focal seedlings,indicating similar habitat preference shared among phylogenetically closely related species may drive seedling survival.The relative importance of neighborhood densities,habitat variables and phylogenetic relatedness varied among ecological guilds.Conspecific densities had significant negative effect for deciduous and wind-dispersed species,and marginally significant for tree seedlings>10 cm tall and animal-dispersed species.Habitat variables had limited effects on seedling survival,and only elevation was related to the sur-vival of evergreen species in the best-fit model.We conclude that both negative density-dependent mortality and habitat preference reflected by the phylogenetic relatedness shape the species coex-istence at seedling stage in this forest.
基金supported by the National Natural Science Foundation of China(Grant Nos.31871242,U1602261,31701071,21873034,and 31571268)the Huazhong Agricultural University Scientific&Technological Self-innovation Foundation,China(Grant No.2014RC020)the State Key Laboratory of Cotton Biology Open Fund,China(Grant No.CB2019B01)
文摘Previous studies have reported that some important loci are missed in single-locus genome-wide association studies(GWAS),especially because of the large phenotypic error in field experiments.To solve this issue,multi-locus GWAS methods have been recommended.However,only a few software packages for multi-locus GWAS are available.Therefore,we developed an R software named mr MLM v4.0.2.This software integrates mr MLM,FASTmr MLM,FASTmr EMMA,p LARm EB,p KWm EB,and ISIS EM-BLASSO methods developed by our lab.There are four components in mr MLM v4.0.2,including dataset input,parameter setting,software running,and result output.The fread function in data.table is used to quickly read datasets,especially big datasets,and the do Parallel package is used to conduct parallel computation using multiple CPUs.In addition,the graphical user interface software mr MLM.GUI v4.0.2,built upon Shiny,is also available.To confirm the correctness of the aforementioned programs,all the methods in mr MLM v4.0.2 and three widely-used methods were used to analyze real and simulated datasets.The results confirm the superior performance of mr MLM v4.0.2 to other methods currently available.False positive rates are effectively controlled,albeit with a less stringent significance threshold.mr MLM v4.0.2 is publicly available at Bio Code(https://bigd.big.ac.cn/biocode/tools/BT007077)or R(https://cran.r-project.org/web/packages/mr MLM.GUI/index.html)as an open-source software.
