Environmetrics最新文献

{"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}

{"title":"Assessing predictability of environmental time series with statistical and machine learning models","authors":"Matthew Bonas,&nbsp;Abhirup Datta,&nbsp;Christopher K. Wikle,&nbsp;Edward L. Boone,&nbsp;Faten S. Alamri,&nbsp;Bhava Vyasa Hari,&nbsp;Indulekha Kavila,&nbsp;Susan J. Simmons,&nbsp;Shannon M. Jarvis,&nbsp;Wesley S. Burr,&nbsp;Daniel E. Pagendam,&nbsp;Won Chang,&nbsp;Stefano Castruccio","doi":"10.1002/env.2864","DOIUrl":"10.1002/env.2864","url":null,"abstract":"<p>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.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 1","pages":""},"PeriodicalIF":1.5,"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}

{"title":"Applying sequential adaptive strategies for sampling animal populations: An empirical study","authors":"Rosa M. Di Biase,&nbsp;Fulvia Mecatti","doi":"10.1002/env.2870","DOIUrl":"10.1002/env.2870","url":null,"abstract":"<p>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.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2870","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Achieving spatial balance in environmental surveys under constant inclusion probabilities or inclusion density functions","authors":"Rosa M. Di Biase,&nbsp;Marzia Marcheselli,&nbsp;Caterina Pisani","doi":"10.1002/env.2869","DOIUrl":"10.1002/env.2869","url":null,"abstract":"<p>In environmental and ecological surveys, well spread samples can be easily obtained via widely adopted tessellation schemes, which yield equal first-order inclusion probabilities in the case of finite populations of areas or constant inclusion density functions in the case of continuous populations. In the literature, many alternative schemes that are explicitly tailored to select well spread samples have been proposed, but owing to their complexity, their use should be preferred only if they allow us to achieve a valuable gain in precision with respect to the tessellation schemes. Therefore, by means of an extensive simulation study, the performances of tessellation schemes and several specifically tailored schemes are compared under constant first-order inclusion probabilities or constant inclusion density functions.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2869","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141515684","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":"Categorical data analysis using discretization of continuous variables to investigate associations in marine ecosystems","authors":"Hiroko Kato Solvang,&nbsp;Shinpei Imori,&nbsp;Martin Biuw,&nbsp;Ulf Lindstrøm,&nbsp;Tore Haug","doi":"10.1002/env.2867","DOIUrl":"10.1002/env.2867","url":null,"abstract":"<p>Understanding and predicting interactions between predators and prey and their environment are fundamental for understanding food web structure, dynamics, and ecosystem function in both terrestrial and marine ecosystems. Thus, estimating the conditional associations between species and their environments is important for exploring connections or cooperative links in the ecosystem, which in turn can help to clarify such directional relationships. For this purpose, a relevant and practical statistical method is required to link presence/absence observations with biomass, abundance, and physical quantities obtained as continuous real values. These data are sometimes sparse in oceanic space and too short as time series data. To meet this challenge, we provide an approach based on applying categorical data analysis to present/absent observations and real-number data. The real-number data used as explanatory variables for the present/absent response variable are discretized based on the optimal detection of thresholds without any prior biological/ecological information. These discretized data express two different levels, such as large/small or high/low, which give experts a simple interpretation for investigating complicated associations in marine ecosystems. This approach is implemented in the previous statistical method called CATDAP developed by Sakamoto and Akaike in 1979. Our proposed approach consists of a two-step procedure for categorical data analysis: (1) finding the appropriate threshold to discretize the real-number data for applying an independent test; and (2) identifying the best conditional probability model to investigate the possible associations among the data based on a statistical information criterion. We perform a simulation study to validate our proposed approach and investigate whether the method's observation includes many zeros (zero-inflated data), which can often occur in practical situations. Furthermore, the approach is applied to two datasets: (1) one collected during an international synoptic krill survey in the Scotia Sea west of the Antarctic Peninsula to investigate associations among krill, fin whale (<i>Balaenoptera physalus</i>), surface temperature, depth, slope in depth (flatter or steeper terrain), and temperature gradient (slope in temperature); (2) the other collected by ecosystem surveys conducted during August–September in 2014–2017 to investigate associations among common minke whales, the predatory fish Atlantic cod, and their main prey groups (zooplankton, 0-group fish) in Arctic Ocean waters to the west and north of Svalbard, Norway. The R code summarizing our proposed numerical procedure is presented in S4S1.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2867","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501054","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":"Exact optimisation of spatiotemporal monitoring networks by p-splines with applications in groundwater assessment","authors":"Marnie I. Low,&nbsp;Adrian W. Bowman,&nbsp;Wayne Jones,&nbsp;Matthijs Bonte","doi":"10.1002/env.2874","DOIUrl":"10.1002/env.2874","url":null,"abstract":"<p>This paper develops methods to optimise the sampling strategy for monitoring networks which have fixed locations with regular sampling but with only a proportion of these locations to be used on each sampling occasion. This creates the need for a dynamic spatiotemporal sampling design which makes optimal choices of the locations to be sampled on each occasion. This is a commonly occurring scenario in many environmental settings where there is an existing network of monitoring stations and sampling can be expensive. The particular context of optimisation of an existing groundwater monitoring network is discussed in the paper. The standard design criteria of integrated variance (IV) and variance of the integral (VI) are adapted to the spatiotemporal setting. <span>p</span>-spline models are shown to allow exact computation of IV and VI, in the case of the additive errors, and a very good approximation of IV in the case of multiplicative errors. The speed of these exact computations allows the globally optimal sampling design to be identified efficiently. In the standard case of additive errors, the design criteria are able to exploit information across time. The only information needed is the location of sampling points, not the values sampled. This contrasts with the case of multiplicative errors where the design criteria are also influenced by the observed response data. Simulated and real examples are used to illustrate the results throughout.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2874","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501055","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}