Environmetrics最新文献

A hierarchical constrained density regression model for predicting cluster‐level dose‐response 用于预测群集级剂量反应的分层约束密度回归模型

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2024-08-27 DOI: 10.1002/env.2880

Michael L. Pennell, Matthew W. Wheeler, Scott S. Auerbach

{"title":"A hierarchical constrained density regression model for predicting cluster‐level dose‐response","authors":"Michael L. Pennell, Matthew W. Wheeler, Scott S. Auerbach","doi":"10.1002/env.2880","DOIUrl":"https://doi.org/10.1002/env.2880","url":null,"abstract":"With the advent of new alternative methods for rapid toxicity screening of chemicals comes the need for new statistical methodologies which appropriately synthesize the large amount of data collected. For example, transcriptomic assays can be used to assess the impact of a chemical on thousands of genes, but current approaches to analyzing the data treat each gene separately and do not allow sharing of information among genes within pathways. Furthermore, the methods employed are fully parametric and do not account for changes in distribution shape that may occur at high exposure levels. To address the limitations of these methods, we propose Constrained Logistic Density Regression (COLDER) to model expression data from different genes simultaneously. Under COLDER, the dose‐response function for each gene is assigned a prior via a discrete logistic stick‐breaking process (LSBP) whose weights depend on gene‐level characteristics (e.g., pathway membership) and atoms consist of different dose‐response functions subject to a shape constraint that ensures biological plausibility. The posterior distribution for the benchmark dose among genes within the same pathways can be estimated directly from the model, which is another advantage over current methods. The ability of COLDER to predict gene‐level dose‐response is evaluated in a simulation study and the method is illustrated with data from a National Toxicology Program study of Aflatoxin B1.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Under the mantra: ‘Make use of colorblind friendly graphs’ 以 "使用色盲友好图表 "为口号

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2024-08-20 DOI: 10.1002/env.2877

Duccio Rocchini, Ludovico Chieffallo, Elisa Thouverai, Rossella D'Introno, Francesca Dagostin, Emma Donini, Giles Foody, Simon Garnier, Guilherme G. Mazzochini, Vitezslav Moudry, Bob Rudis, Petra Simova, Michele Torresani, Jakub Nowosad

引用次数: 0

A flexible and interpretable spatial covariance model for data on graphs 灵活、可解释的图形数据空间协方差模型

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2024-08-17 DOI: 10.1002/env.2879

Michael F. Christensen, Peter D. Hoff

{"title":"A flexible and interpretable spatial covariance model for data on graphs","authors":"Michael F. Christensen, Peter D. Hoff","doi":"10.1002/env.2879","DOIUrl":"https://doi.org/10.1002/env.2879","url":null,"abstract":"Spatial models for areal data are often constructed such that all pairs of adjacent regions are assumed to have near‐identical spatial autocorrelation. In practice, data can exhibit dependence structures more complicated than can be represented under this assumption. In this article, we develop a new model for spatially correlated data observed on graphs, which can flexibly represented many types of spatial dependence patterns while retaining aspects of the original graph geometry. Our method implies an embedding of the graph into Euclidean space wherein covariance can be modeled using traditional covariance functions, such as those from the Matérn family. We parameterize our model using a class of graph metrics compatible with such covariance functions, and which characterize distance in terms of network flow, a property useful for understanding proximity in many ecological settings. By estimating the parameters underlying these metrics, we recover the “intrinsic distances” between graph nodes, which assist in the interpretation of the estimated covariance and allow us to better understand the relationship between the observed process and spatial domain. We compare our model to existing methods for spatially dependent graph data, primarily conditional autoregressive models and their variants, and illustrate advantages of our method over traditional approaches. We fit our model to bird abundance data for several species in North Carolina, and show how it provides insight into the interactions between species‐specific spatial distributions and geography.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How to find the best sampling design: A new measure of spatial balance 如何找到最佳抽样设计：空间平衡的新衡量标准

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2024-08-13 DOI: 10.1002/env.2878

Wilmer Prentius, Anton Grafström

引用次数: 0

Anthropogenic and meteorological effects on the counts and sizes of moderate and extreme wildfires 人类活动和气象对中度和极端野火数量和规模的影响

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2024-08-07 DOI: 10.1002/env.2873

Elizabeth S. Lawler, Benjamin A. Shaby

引用次数: 0

Marginal inference for hierarchical generalized linear mixed models with patterned covariance matrices using the Laplace approximation 使用拉普拉斯近似法对具有模式化协方差矩阵的分层广义线性混合模型进行边际推断

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2024-07-23 DOI: 10.1002/env.2872

Jay M. Ver Hoef, Eryn Blagg, Michael Dumelle, Philip M. Dixon, Dale L. Zimmerman, Paul B. Conn

{"title":"Marginal inference for hierarchical generalized linear mixed models with patterned covariance matrices using the Laplace approximation","authors":"Jay M. Ver Hoef, Eryn Blagg, Michael Dumelle, Philip M. Dixon, Dale L. Zimmerman, Paul B. Conn","doi":"10.1002/env.2872","DOIUrl":"https://doi.org/10.1002/env.2872","url":null,"abstract":"We develop hierarchical models and methods in a fully parametric approach to generalized linear mixed models for any patterned covariance matrix. The Laplace approximation is used to marginally estimate covariance parameters by integrating over all fixed and latent random effects. The Laplace approximation relies on Newton–Raphson updates, which also leads to predictions for the latent random effects. We develop methodology for complete marginal inference, from estimating covariance parameters and fixed effects to making predictions for unobserved data. The marginal likelihood is developed for six distributions that are often used for binary, count, and positive continuous data, and our framework is easily extended to other distributions. We compare our methods to fully Bayesian methods, automatic differentiation, and integrated nested Laplace approximations (INLA) for bias, mean‐squared (prediction) error, and interval coverage, and all methods yield very similar results. However, our methods are much faster than Bayesian methods, and more general than INLA. Examples with binary and proportional data, count data, and positive‐continuous data are used to illustrate all six distributions with a variety of patterned covariance structures that include spatial models (both geostatistical and areal models), time series models, and mixtures with typical random intercepts based on grouping.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141783339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating the spatial distribution of the white shark in the Mediterranean Sea via an integrated species distribution model accounting for physical barriers 通过考虑物理障碍的综合物种分布模型估算地中海白鲨的空间分布情况

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2024-07-09 DOI: 10.1002/env.2876

Greta Panunzi, Stefano Moro, Isa Marques, Sara Martino, Francesco Colloca, Francesco Ferretti, Giovanna Jona Lasinio

{"title":"Estimating the spatial distribution of the white shark in the Mediterranean Sea via an integrated species distribution model accounting for physical barriers","authors":"Greta Panunzi, Stefano Moro, Isa Marques, Sara Martino, Francesco Colloca, Francesco Ferretti, Giovanna Jona Lasinio","doi":"10.1002/env.2876","DOIUrl":"https://doi.org/10.1002/env.2876","url":null,"abstract":"Conserving oceanic apex predators, such as sharks, is of utmost importance. However, scant abundance and distribution data often challenge understanding the population status of many threatened species. Occurrence records are often scarce and opportunistic, and fieldwork aimed to retrieve additional data is expensive and prone to failure. Integrating various data sources becomes crucial to developing species distribution models for informed sampling and conservation purposes. The white shark, for example, is a rare but persistent inhabitant of the Mediterranean Sea. Here, it is considered Critically Endangered by the IUCN, while population abundance, distribution patterns, and habitat use are still poorly known. This study uses available occurrence records from 1985 to 2021 from diverse sources to construct a spatial log‐Gaussian Cox process, with data‐source specific detection functions and thinning, and accounting for physical barriers. This model estimates white shark presence intensity alongside uncertainty through a Bayesian approach with Integrated Nested Laplace Approximation (INLA) and the inlabru R package. For the first time, we projected species occurrence hot spots and landscapes of relative abundance (continuous measure of animal density in space) throughout the Mediterranean Sea. This approach can be used with other rare species for which presence‐only data from different sources are available.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141575753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing predictability of environmental time series with statistical and machine learning models 利用统计和机器学习模型评估环境时间序列的可预测性

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2024-07-05 DOI: 10.1002/env.2864

Matthew Bonas, Abhirup Datta, Christopher K. Wikle, Edward L. Boone, Faten S. Alamri, Bhava Vyasa Hari, Indulekha Kavila, Susan J. Simmons, Shannon M. Jarvis, Wesley S. Burr, Daniel E. Pagendam, Won Chang, Stefano Castruccio

{"title":"Assessing predictability of environmental time series with statistical and machine learning models","authors":"Matthew Bonas, Abhirup Datta, Christopher K. Wikle, Edward L. Boone, Faten S. Alamri, Bhava Vyasa Hari, Indulekha Kavila, Susan J. Simmons, Shannon M. Jarvis, Wesley S. Burr, Daniel E. Pagendam, Won Chang, Stefano Castruccio","doi":"10.1002/env.2864","DOIUrl":"https://doi.org/10.1002/env.2864","url":null,"abstract":"The ever increasing popularity of machine learning methods in virtually all areas of science, engineering and beyond is poised to put established statistical modeling approaches into question. Environmental statistics is no exception, as popular constructs such as neural networks and decision trees are now routinely used to provide forecasts of physical processes ranging from air pollution to meteorology. This presents both challenges and opportunities to the statistical community, which could contribute to the machine learning literature with a model‐based approach with formal uncertainty quantification. Should, however, classical statistical methodologies be discarded altogether in environmental statistics, and should our contribution be focused on formalizing machine learning constructs? This work aims at providing some answers to this thought‐provoking question with two time series case studies where selected models from both the statistical and machine learning literature are compared in terms of forecasting skills, uncertainty quantification and computational time. Relative merits of both class of approaches are discussed, and broad open questions are formulated as a baseline for a discussion on the topic.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141575754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applying sequential adaptive strategies for sampling animal populations: An empirical study 在动物种群采样中应用顺序适应策略：实证研究

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2024-07-02 DOI: 10.1002/env.2870

Rosa M. Di Biase, Fulvia Mecatti

{"title":"Applying sequential adaptive strategies for sampling animal populations: An empirical study","authors":"Rosa M. Di Biase, Fulvia Mecatti","doi":"10.1002/env.2870","DOIUrl":"https://doi.org/10.1002/env.2870","url":null,"abstract":"Traditional sampling methods may prove inadequate when dealing with spatially clustered populations or when studying rare events or traits that are not easily detectable across the target population. When both scenarios occur simultaneously, adaptive sampling strategies can represent a viable option to enhance the detectability of cases of interest. This paper delves into the application of a novel class of sequential adaptive sampling strategies to animal surveys. These strategies, originally proposed for human population tuberculosis prevalence surveys, allow oversampling of the rare interest variables while managing on‐field constraints. This ensures that the unfixed sample size, typical of adaptive sampling, does not compromise overall cost‐effectiveness. We explore a strategy within this class that integrates an adaptive component into a Poisson sequential selection. The aim is twofold: to intensify the detection of cases by exploiting the spatial clustering and to provide a flexible framework for managing logistics and budget constraints. To illustrate the strengths and weaknesses of this Poisson‐based sequential adaptive sampling strategy compared to traditional sampling methods, a simulation study was conducted on a blue‐winged teal population in Florida, USA. The results showcase the benefits of the proposed strategy and open avenues for future methodological and practical improvements.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141515687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Achieving spatial balance in environmental surveys under constant inclusion probabilities or inclusion density functions 在恒定包含概率或包含密度函数下实现环境调查的空间平衡

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2024-07-02 DOI: 10.1002/env.2869

Rosa M. Di Biase, Marzia Marcheselli, Caterina Pisani

引用次数: 0