Environmetrics最新文献

{"title":"Modeling nonstationary surface-level ozone extremes through the lens of US air quality standards: A Bayesian hierarchical approach","authors":"Jax Li, Brook T. Russell, Whitney K. Huang, William C. Porter","doi":"10.1002/env.2882","DOIUrl":"https://doi.org/10.1002/env.2882","url":null,"abstract":"<p>Surface-level ozone (O<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mo> </mo>\u0000 <mrow>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {}_3 $$</annotation>\u0000 </semantics></math>) is a harmful air pollutant whose effects may be more deleterious when at its most extreme levels. Current US air quality standards are written in terms of the 3-year average of the 4th highest annual daily maximum 8-h O<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mo> </mo>\u0000 <mrow>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {}_3 $$</annotation>\u0000 </semantics></math> values; therefore, developing approaches based on extreme value theory may be useful. We develop a Bayesian hierarchical approach, where the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 </mrow>\u0000 <annotation>$$ r $$</annotation>\u0000 </semantics></math>-largest order statistics are parametrized by the generalized extreme value (GEV) distribution, while a Gaussian process is employed to characterize how the GEV parameters depend on the O<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mo> </mo>\u0000 <mrow>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {}_3 $$</annotation>\u0000 </semantics></math> precursors, namely nitrous oxides (NO<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mo> </mo>\u0000 <mrow>\u0000 <mi>x</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {}_x $$</annotation>\u0000 </semantics></math>) and volatile organic compounds (VOCs). The fitted model is then used to characterize the upper tail of the distribution of O<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mo> </mo>\u0000 <mrow>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {}_3 $$</annotation>\u0000 </semantics></math> and estimate O<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mo> </mo>\u0000 ","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2882","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Separable Bootstrap Variance Estimation Algorithm for Hierarchical Model-Based Inference of Forest Aboveground Biomass Using Data From NASA's GEDI and Landsat Missions","authors":"Svetlana Saarela,&nbsp;Sean P. Healey,&nbsp;Zhiqiang Yang,&nbsp;Bjørn-Eirik Roald,&nbsp;Paul L. Patterson,&nbsp;Terje Gobakken,&nbsp;Erik Næsset,&nbsp;Zhengyang Hou,&nbsp;Ronald E. McRoberts,&nbsp;Göran Ståhl","doi":"10.1002/env.2883","DOIUrl":"https://doi.org/10.1002/env.2883","url":null,"abstract":"<p>The hierarchical model-based (HMB) statistical method is currently applied in connection with NASA's Global Ecosystem Dynamics Investigation (GEDI) mission for assessing forest aboveground biomass (AGB) in areas lacking a sufficiently large number of GEDI footprints for employing hybrid inference. This study focuses on variance estimation using a bootstrap procedure that separates the computations into parts, thus considerably reducing the computational time required and making bootstrapping a viable option in this context. The procedure we propose uses a theoretical decomposition of the HMB variance into two parts. Through this decomposition, each variance component can be estimated separately and simultaneously. For demonstrating the proposed procedure, we applied a square-root-transformed ordinary least squares (OLS) model, and parametric bootstrapping, in the first modeling step of HMB. In the second step, we applied a random forest model and pairwise bootstrapping. Monte Carlo simulations showed that the proposed variance estimator is approximately unbiased. The study was performed on an artificial copula-generated population that mimics forest conditions in Oregon, USA, using a dataset comprising AGB, GEDI, and Landsat variables.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2883","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bias correction of daily precipitation from climate models, using the Q-GAM method","authors":"Georgia Lazoglou,&nbsp;Theo Economou,&nbsp;Christina Anagnostopoulou,&nbsp;Anna Tzyrkalli,&nbsp;George Zittis,&nbsp;Jos Lelieveld","doi":"10.1002/env.2881","DOIUrl":"https://doi.org/10.1002/env.2881","url":null,"abstract":"<p>Climate models are useful tools for analyzing historical and projecting future climate conditions. However, the model results tend to differ systematically from observations, particularly for parameters with complex spatial and temporal distributions such as precipitation. A combination of quantile mapping and generalized additive models (GAMs) is presented and proposed as a new method (Q-GAM) for the bias correction of daily precipitation. Q-GAM is demonstrated by using data from five European stations with different climate characteristics. For each station, the closest continental grid point of a EURO-CORDEX climate model was selected for bias correction. A bootstrapping experiment is presented with over 1000 repetitions of randomly splitting the historical period 1981 to 2005 into a calibration and evaluation period. The results for all stations reveal that Q-GAM is a straightforward, accurate and computationally efficient method for the bias correction of precipitation. In particular, the method improves the frequency of dry days and the total annual rainfall amount. This outcome is robust across stations with varying climate characteristics and also to the choice of calibration and evaluation periods. Similar results are also obtained for other precipitation characteristics such as the 0.9 and 0.95 quantiles.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 7","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2881","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hierarchical constrained density regression model for predicting cluster-level dose-response","authors":"Michael L. Pennell,&nbsp;Matthew W. Wheeler,&nbsp;Scott S. Auerbach","doi":"10.1002/env.2880","DOIUrl":"10.1002/env.2880","url":null,"abstract":"<p>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.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 7","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2880","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Under the mantra: ‘Make use of colorblind friendly graphs’","authors":"Duccio Rocchini,&nbsp;Ludovico Chieffallo,&nbsp;Elisa Thouverai,&nbsp;Rossella D'Introno,&nbsp;Francesca Dagostin,&nbsp;Emma Donini,&nbsp;Giles Foody,&nbsp;Simon Garnier,&nbsp;Guilherme G. Mazzochini,&nbsp;Vitezslav Moudry,&nbsp;Bob Rudis,&nbsp;Petra Simova,&nbsp;Michele Torresani,&nbsp;Jakub Nowosad","doi":"10.1002/env.2877","DOIUrl":"https://doi.org/10.1002/env.2877","url":null,"abstract":"<p>Colorblindness is a genetic condition that affects a person's ability to accurately perceive colors. Several papers still exist making use of rainbow colors palette to show output. In such cases, for colorblind people such graphs are meaningless. In this paper, we propose good practices and coding solutions developed in the R Free and Open Source Software to (i) simulate colorblindness, (ii) develop colorblind friendly color palettes and (iii) provide the tools for converting a noncolorblind friendly graph into a new image with improved colors.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2877","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A flexible and interpretable spatial covariance model for data on graphs","authors":"Michael F. Christensen,&nbsp;Peter D. Hoff","doi":"10.1002/env.2879","DOIUrl":"10.1002/env.2879","url":null,"abstract":"<p>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.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 7","pages":""},"PeriodicalIF":1.5,"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}

{"title":"How to find the best sampling design: A new measure of spatial balance","authors":"Wilmer Prentius,&nbsp;Anton Grafström","doi":"10.1002/env.2878","DOIUrl":"10.1002/env.2878","url":null,"abstract":"<p>We present a novel measure to assess the spatial balance of a sample by utilizing the balancing equation, which captures the balance between the sample units and their neighbours. Spatially balanced samples are desirable as they may reduce the variance of an estimator of a population parameter. If the auxiliary variables we employ to spread the sample possess high explanatory power for the variable(s) of interest, the resulting reduction in variance can be substantial. An advantageous aspect of using auxiliary variables is that their availability is not contingent upon the sampling effort. Therefore, we can assess and compare sampling designs before committing resources to full-scale surveys. By comparing the proposed measure with commonly used measures of spatial balance, we ascertain that our measure consistently yields meaningful insights regarding the spatial balance of samples. Consequently, our measure can effectively differentiate between various designs when planning a survey, evaluate the potential gains from replacing an existing sample, and determine which non-responding units would contribute the most to enhancing the set of responding units.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 7","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2878","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anthropogenic and meteorological effects on the counts and sizes of moderate and extreme wildfires","authors":"Elizabeth S. Lawler,&nbsp;Benjamin A. Shaby","doi":"10.1002/env.2873","DOIUrl":"10.1002/env.2873","url":null,"abstract":"<p>The growing frequency and size of wildfires across the US necessitates accurate quantitative assessment of evolving wildfire behavior to predict risk from future extreme wildfires. We build a joint model of wildfire counts and burned areas, regressing key model parameters on climate and demographic covariates. We use extended generalized Pareto distributions to model the full distribution of burned areas, capturing both moderate and extreme sizes, while leveraging extreme value theory to focus particularly on the right tail. We model wildfire counts with a zero-inflated negative binomial model, and join the wildfire counts and burned areas sub-models using a temporally-varying shared random effect. Our model successfully captures the trends of wildfire counts and burned areas. By investigating the predictive power of different sets of covariates, we find that fire indices are better predictors of wildfire burned area behavior than individual climate covariates, whereas climate covariates are influential drivers of wildfire occurrence behavior.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 7","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2873","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Marginal inference for hierarchical generalized linear mixed models with patterned covariance matrices using the Laplace approximation","authors":"Jay M. Ver Hoef,&nbsp;Eryn Blagg,&nbsp;Michael Dumelle,&nbsp;Philip M. Dixon,&nbsp;Dale L. Zimmerman,&nbsp;Paul B. Conn","doi":"10.1002/env.2872","DOIUrl":"10.1002/env.2872","url":null,"abstract":"<p>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.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 7","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2872","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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,&nbsp;Stefano Moro,&nbsp;Isa Marques,&nbsp;Sara Martino,&nbsp;Francesco Colloca,&nbsp;Francesco Ferretti,&nbsp;Giovanna Jona Lasinio","doi":"10.1002/env.2876","DOIUrl":"10.1002/env.2876","url":null,"abstract":"<p>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 <i>Critically Endangered</i> 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 <span>inlabru</span> 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.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-08","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}