Can a multistage approach improve individual tree mortality predictions across the complex mixed-species and managed forests of eastern North America?

Tree mortality plays a fundamental role in the dynamics of forest ecosystems,yet it is one of the most difficult phenomena to accurately predict.Various modeling strategies have been developed to improve individual tree mortality predictions.One less explored strategy is the use of a multistage modeling approach.Potential improvements from this approach have remained largely unknown.In this study,we developed a novel multistage approach and compared its performance in individual tree mortality predictions with a more conventional approach using an identical individual tree mortality model formulation.Extensive permanent plot data(n=9442)covering the Acadian Region of North America and over multiple decades(1965–2014)were used in this study.Our results indicated that the model behavior with the multistage approach better depicted the observed mortality and showed a notable improvement over the conventional approach.The difference between the observed and predicted numbers of dead trees using the multistage approach was much smaller when compared with the conventional approach.In addition,tree survival probabilities predicted by the multistage approach generally were not significantly different from the observations,whereas the conventional approach consistently underestimated mortality across species and overestimated tree survival probabilities over the large range of DBH in the data.The new multistage approach also predictions of zero mortality in individual plots,a result not possible in conventional models.Finally,the new approach was more tolerant of modeling errors because it based estimates on ranked tree mortality rather than error-prone predicted values.Overall,this new multistage approach deserves to be considered and tested in future studies.

Toward managing mixed-species stands： from parametrization to prescription

A better understanding and a more quantitative design of mixed-species stands will contribute to more integrative and goal-oriented research in mixed-species forests. Much recent work has indicated that the structure and growth of mixed species forests may fundamentally differ from monocultures. Here we suggest how to progress from the present accumulation of phenomenological findings to a design of mixed-species stands and advanced silvicultural prescriptions by means of modelling. First, the knowledge of mixing effects on the structure and growth at the stand, species, and individual tree level is reviewed, with a focus on those findings that are most essential for suitable modelling and silvicultural designs and the regulation of mixed stands as opposed to monocultures. Then, the key role of growth models, stand simulators, and scenario assessments for designing mixed species stands is discussed The next section illustrates that existing forest stand growth models require some fundamental modifications to become suitable for both monocultures and mixed-species stands. We then explore how silvicultural prescriptions derived from scenario runs would need to be both quantified and simplified for transfer to forest management and demonstrated in training plots. Finally, we address the main remaining knowledge gaps that could be remedied through empirical research.

Posterior propriety in nonparametric mixed efects model

It is well known that spline smoothing estimator relates to the Bayesian estimate under partially informative normal prior. In this paper, we derive the conditions for the pro- priety of the posterior in the nonparametric mixed effects model under this class of partially informative normal prior for fixed effect with inverse gamma priors on the variance compo- nents and hierarchical priors for covariance matrix of random effect, then we explore the Gibbs sampling procedure.

Detection of Alzheimer’s disease onset using MRI and PET neuroimaging:longitudinal data analysis and machine learning

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.

基于卫星遥感AOD的华北地区2003—2014年PM2.5浓度时空分布特征

华北及周边地区PM2.5造成的污染,近十年来引起了社会的广泛关注,也是科学研究的重要领域。利用2003—2014年的卫星遥感MODIS AOD数据和2014—2015年的地面观测PM2.5浓度数据,采用聚类分析、混合效应模型、EOF分解等统计分析方法,反演了2003—2014年华北及周边地区PM2.5浓度,分析其时空分布特征。主要结论如下:(1)卫星遥感MODIS AOD与地面观测PM2.5值有较高的相关系数,可利用MODIS卫星遥感AOD对地面观测的PM2.5浓度进行反演;(2)华北地区PM2.5浓度呈现出明显的空间分布特征:太行山脉是污染强弱明确的分界线,山脉东南部的污染显著高于西部,且在地势变化的地方出现明显的突变;河北南部、河南北部和山东西北部分区域是污染最严重的地区;(3)2004年、2009年以及2013年后都是污染浓度比较低的年份。

Predictors of the Aggregate of COVID-19 Cases and Its Case-Fatality: A Global Investigation Involving 120 Countries

Objective: 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. Methods: 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. Results: 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. The Conclusions: There are important regional variations in COVID-19 case fatality. We identified three factors to be significantly correlated with case fatality.

How to use live sampling tissues and archived specimens in cetacean stable isotope research

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.

Small area prediction of quantiles for zero-inflated data and an informative sample design

The Conservation Effects Assessment Project(CEAP)is a survey intended to quantify soil and nutrient loss on cropland.Estimates of the quantiles of CEAP response variables are published.Previous work develops a procedure for predicting small area quantiles based on a mixed effects quantile regression model.The conditional density function of the response given covariates and area random effects is approximated with the linearly interpolated generalised Pareto distribution(LIGPD).Empirical Bayes is used for prediction and a parametric bootstrap procedure is developed for mean squared error estimation.In this work,we develop two extensions of the LIGPD-based small area quantile prediction procedure.One extension allows for zero-inflated data.The second extension accounts for an informative sample design.We apply the procedures to predict quantiles of the distribution of percolation(a CEAP response variable)in Kansas counties.

Sexual and spatio-temporal variation of Lake Erie Walleye growth and maturity:A consequence of multiple impacting factors

Sexual and spatio-temporal variations have been observed in the life history parameters of many aquatic species and their causes have been related to harvesting pressure and environmental changes.This study aims to explore sexual,spatial and temporal variation in the growth and maturity through weight-at-length,length-at-age,and maturity-at-length relationships for Lake Erie Walleye(Sander vitreus)as a case to test some hypotheses.Hypotheses on whether harvest pressure and environmental changes(both local and global scale)caused the temporal changes of these life history traits were further diagnosed.Sexual and spatio-temporal variations in these life history traits were formulated using mixed-effects models.Our study found that geographic basin,sex,year and cohort all have substantial effects on the growth and maturity of Walleye based on survey data from 1989 to 2015.Multiple factors including water supply of Lake Erie,temperature,fishing pressure of Walleye,and global climate factors were found to correlate with the temporal variations of growth and maturity of Walleye significantly.Our findings should contribute to the future interpretation of Walleye life history variations and population dynamics.The methodology constructed in this study could be applied to explore the heterogeneity and impacting factors for other species in aquatic ecosystems.