Environmetrics最新文献

{"title":"Causal Inference for Geostatistical Data Using an INLA-based Spatial Propensity Score","authors":"Chiara Di Maria,&nbsp;Alessandro Albano,&nbsp;Mariangela Sciandra,&nbsp;Antonella Plaia","doi":"10.1002/env.70097","DOIUrl":"https://doi.org/10.1002/env.70097","url":null,"abstract":"<p>In this paper, we propose a Bayesian approach for spatial causal inference based on combining spatial propensity scoring with Integrated Nested Laplace Approximation. The method models both local and spillover exposure effects via multiple likelihoods and treats counterfactuals as missing data, allowing inference also for non-Gaussian outcomes. We validated the proposed method through simulations and an application to U.S. county-level cancer data, demonstrating the critical importance of properly accounting for spatial dependence when drawing causal conclusions from geostatistical data. Our results show that the proposed method achieves MCMC-comparable accuracy with substantially reduced computational time.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683809","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":"Modeling Benthic Animals in Space and Time Using Bayesian Point Process With Cross Validation: The Case of Holoturians","authors":"Daniele Poggio,&nbsp;Gian Mario Sangiovanni,&nbsp;Gianluca Mastrantonio,&nbsp;Giovanna Jona Lasinio,&nbsp;Edoardo Casoli,&nbsp;Stefano Moro,&nbsp;Daniele Ventura","doi":"10.1002/env.70096","DOIUrl":"https://doi.org/10.1002/env.70096","url":null,"abstract":"<p>Holothurian populations in the Mediterranean are relatively understudied, with limited knowledge of their spatial distribution, habitat preferences, and ecological dynamics, making their monitoring a key challenge for ecosystem assessment and sustainable management. However, species distribution modeling is often complicated by the presence-only nature of the data and heterogeneous sampling designs. This study develops a spatio-temporal framework based on Log-Gaussian Cox Processes to analyze Holothurians' positions collected across nine survey campaigns conducted from 2022 to 2024 near Giglio Island, Italy. The surveys combined high-resolution photogrammetry with diver-based visual censuses, leading to varying detection probabilities across habitats, especially within <i>Posidonia oceanica</i> meadows. We adopt a model with a shared spatial Gaussian process component to accommodate this complexity, accounting for habitat structure, environmental covariates, and temporal variability. Model estimation is performed using Integrated Nested Laplace Approximation. We evaluate the predictive performances of alternative model specifications through a novel k-fold cross-validation strategy for point processes, using the Continuous Ranked Probability Score. Results highlight the influence of habitat-type covariates, strong variability across campaigns, and a locally structured spatial field capturing residual spatial heterogeneity. Our approach provides a flexible and computationally efficient framework for integrating heterogeneous presence-only data in marine ecology and comparing the predictive ability of alternative models.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683787","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":"Clustering the Nearest Neighbor Gaussian Process","authors":"Ashlynn Crisp,&nbsp;Andrew O. Finley,&nbsp;Daniel Taylor-Rodríguez","doi":"10.1002/env.70093","DOIUrl":"https://doi.org/10.1002/env.70093","url":null,"abstract":"<div>\u0000 \u0000 <p>Gaussian processes are ubiquitous as the primary tool for modeling spatial data. However, the Gaussian process is limited by its <span></span><math>\u0000 <mrow>\u0000 <mi>𝒪</mi>\u0000 <mo>(</mo>\u0000 <msup>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 </msup>\u0000 <mo>)</mo>\u0000 </mrow></math> cost, making direct parameter fitting algorithms infeasible for the scale of modern data collection initiatives. The Nearest Neighbor Gaussian Process (NNGP) was introduced as a scalable approximation to dense Gaussian processes which has been successful for <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 <mo>∼</mo>\u0000 <mn>1</mn>\u0000 <msup>\u0000 <mrow>\u0000 <mn>0</mn>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>6</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ nsim 1{0}^6 $$</annotation>\u0000 </semantics></math> observations. This project introduces the <i>clustered Nearest Neighbor Gaussian Process</i> (cNNGP) which reduces the computational and storage cost of the NNGP for stationary and isotropic datasets. The accuracy of parameter estimation and reduction in computational and memory storage requirements are demonstrated with simulated data, where the cNNGP provided comparable inference to that obtained with the NNGP, in a fraction of the sampling time. An extensive simulation study is presented, with cNNGP compared with similar contemporary methods. To showcase the method's performance, we modeled biomass over the state of Maine using data collected by the Global Ecosystem Dynamics Investigation (GEDI) to generate wall-to-wall predictions over the state. In 20% of the time, the cNNGP produced nearly indistinguishable inference and biomass prediction maps to those obtained with the NNGP.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683488","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":"Spatio-Temporal Modelling of Extreme Induced Seismicity in the Presence of An Evolving Measurement Network","authors":"Conor Murphy,&nbsp;Jonathan A. Tawn,&nbsp;Zak Varty,&nbsp;Ross Towe,&nbsp;Peter M. Atkinson","doi":"10.1002/env.70090","DOIUrl":"https://doi.org/10.1002/env.70090","url":null,"abstract":"<p>Earthquakes induced by injecting or extracting gas from underground reservoirs can pose a significant hazard to surrounding infrastructure and populations. Safeguarding against future seismic hazards requires accurate models for the upper tail of the earthquake magnitude distribution that are able to represent various intervention strategies. For these models, we need efficient inference methods and reliable estimates of uncertainty. We propose a novel extreme value modelling procedure, which uses known changes in the earthquake measurement network, to automatically select a parametric spatio-temporal extreme value threshold which accounts for undetected earthquake values. We introduce methods to propagate the uncertainties in the extreme value model parameters, the threshold parameters and the threshold functional formulation through to future hazard estimates. We apply our methodology to the earthquake catalogue from the Groningen gas field in the Netherlands, delivering clear improvements over existing analyses and providing the first quantification of the different sources of uncertainty in such estimates. The procedure has the potential to be useful for a broad range of extreme value contexts to account for threshold uncertainty when parametric threshold models are used, or where data are missing due to limitations in measurement equipment.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683118","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":"Multi-Resolution Spatial Methods on the Sphere: Efficient Prediction for Global Data","authors":"Hao-Yun Huang,&nbsp;Hsin-Cheng Huang,&nbsp;Ching-Kang Ing","doi":"10.1002/env.70092","DOIUrl":"https://doi.org/10.1002/env.70092","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate spatial prediction on the sphere is fundamental for global environmental applications such as climate monitoring and oceanographic analysis. Existing approaches, however, often struggle to balance computational efficiency, predictive accuracy, and the ability to accommodate heterogeneous spatial structures. We propose a multi-resolution spatial modeling framework that integrates thin-plate spline (TPS) basis functions with Gaussian process modeling. The framework begins with a fixed-effects representation based on a hierarchy of nearly orthogonal TPS basis functions ordered by smoothness, thereby providing a multi-resolution decomposition of spatial variation. This allows large-scale patterns to be captured efficiently while preserving interpretability. To represent localized dependencies, we extend the model with a random effect governed by a tapered Matérn covariance, which models fine-scale structure while ensuring scalability through sparse matrix operations. Model complexity is adaptively controlled using the conditional Akaike information criterion (cAIC), which simultaneously selects the number of basis functions and determines the contribution of the Gaussian process component. Numerical experiments and a global sea surface temperature application show how our method balances predictive accuracy with computational feasibility, establishing its role as a powerful solution for large-scale spatial modeling on the sphere.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147682869","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":"XGBoost Meets INLA: A Two-Stage Spatio-Temporal Forecasting of Wildfires in Portugal","authors":"Chenglei Hu,&nbsp;Regina Baltazar Bispo,&nbsp;Håvard Rue,&nbsp;Carlos C. DaCamara,&nbsp;Ben Swallow,&nbsp;Daniela Castro-Camilo","doi":"10.1002/env.70089","DOIUrl":"https://doi.org/10.1002/env.70089","url":null,"abstract":"<p>Wildfires pose a major threat to Portugal, with over 115,000 hectares burned annually on average during 1980–2024, and the country has faced devastating mega-fires such as those in 2017. Accurate forecasts of wildfire occurrence and burned area are therefore essential for firefighting resource allocation and emergency preparedness. In this study, we propose a novel two-stage ensemble that extends the widely used latent Gaussian modelling framework with integrated nested Laplace approximation (INLA) for spatio-temporal wildfire forecasting. Stage 1 applies a gradient boosting model (XGBoost) to environmental covariates and historical fire records to produce one-month-ahead point forecasts of fire counts and burned area. Stage 2 uses these predictions as external covariates in a latent Gaussian model with additional spatiotemporal random effects to generate probabilistic forecasts of monthly total fire counts and burned area at the council level. To capture both moderate and extreme events, we implement the extended generalised Pareto (eGP) likelihood (a sub-asymptotic distribution) within INLA, develop Penalised Complexity (PC) priors for its parameters, and compare the eGP likelihood with common alternatives (e.g., Gamma and Weibull). Our framework tackles the unavailability of future environmental covariates at prediction time and performs strongly for one-month-ahead forecasts.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147684135","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 New Clustering Strategy for Geo-Referenced Time Series Based on Optimal Transport","authors":"Pasquale Pipiciello,&nbsp;Antonio Balzanella,&nbsp;Gianmarco Borrata","doi":"10.1002/env.70091","DOIUrl":"https://doi.org/10.1002/env.70091","url":null,"abstract":"<p>Advances in spatio-temporal data collection have created a demand for efficient methods to analyze geo-referenced time series (GTS), which capture changes over time at specific spatial locations. Traditional clustering methods often struggle to handle the high-dimensional, complex nature of GTS. This paper proposes a novel approach for clustering GTS using a new dissimilarity measure, the Bi-Gromov Dynamic Time Warping (Bi-GDTW) distance. This method combines the alignment-based Dynamic Time Warping framework with Gromov–Wasserstein distance to account for both temporal and spatial dependencies in the data. The proposed measure supports clustering through algorithms such as K-means, enabling effective pattern discovery in GTS datasets. The paper explores the theoretical foundations of Optimal Transport and its integration with time series analysis, introducing Bi-GDTW as a comprehensive tool for capturing spatio-temporal patterns. Through applications on simulated and real-world datasets, the results demonstrate the effectiveness of this approach in addressing challenges in GTS clustering, offering new possibilities for analyzing structured sequential data. The research has implications for various fields, including environmental monitoring, urban studies, and socio-economic analysis, and provides a foundation for extending these techniques to other sequential datasets with underlying topological structures.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147684108","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":"Spatial Approach of Peaks-Over-Thresholds in Trend Detection of Extremes","authors":"Béwentaoré Sawadogo,&nbsp;Diakarya Barro","doi":"10.1002/env.70086","DOIUrl":"https://doi.org/10.1002/env.70086","url":null,"abstract":"<div>\u0000 \u0000 <p>In this study, recent trends in extreme temperatures and rainfall are evaluated using the statistical theory of extreme values, in a non-stationary context. We extend the non-stationary pointwise threshold exceedances approach to spatial threshold exceedances. The temporal evolution of the extremes is handled pointwise by the peaks-over-threshold approach. First, evolution of the parameters of the distribution of exceedances and the intensity of extreme event occurrences for several meteorological variables are described as functions of time. Then, the spatial dependence structure attached to threshold exceedances is integrated using generalized <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>ℓ</mi>\u0000 </mrow>\u0000 <annotation>$$ ell $$</annotation>\u0000 </semantics></math>-Pareto processes in a non-stationary framework. In addition to better taking spatial dependence into account, our method allows us to simulate spatial exceedance processes at different dates and predict non-stationary return levels at desired times by extrapolating the trends identified in the marginal distributions and the estimated dependence structure. Our approach is applied to temperature and rainfall data in Burkina Faso.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147615059","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":"A Dynamic Model for Extreme Hourly Precipitation","authors":"Debbie J. Dupuis,&nbsp;Carlotta Pacifici,&nbsp;Luca Trapin","doi":"10.1002/env.70082","DOIUrl":"https://doi.org/10.1002/env.70082","url":null,"abstract":"<p>Despite the scarcity of comprehensive studies at a global scale, many regional analyses report increases in extreme hourly precipitation values. The growing interest in assessing trends in extreme hourly precipitation has outpaced the development of new statistical tools tailored to their features. Typical analyses employ Extreme Value Theory (EVT) in a regression framework, where the parameters of EVT distributions are modeled as functions of exogenous covariates. While this approach is common in daily precipitation analyses, it may be less suitable for the higher-frequency hourly data. We propose a dynamic EVT approach, where the distribution parameters evolve according to an autoregressive-type dynamic. Our model accommodates the high-frequency nature of the data without requiring arbitrary choices regarding the covariates as in the regression approach. Applied to a group of Midwest US cities experiencing increases in hourly extreme precipitation, our method reveals dynamics in extreme high quantiles and outperforms a reference model in the EVT regression approach.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567322","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":"Bayesian Spatial Analysis of Misclassified Binary Data Incorporating Internal-Validation Studies","authors":"Yuhan Ma,&nbsp;Kyuhee Shin,&nbsp;GyuWon Lee,&nbsp;Joon Jin Song","doi":"10.1002/env.70088","DOIUrl":"https://doi.org/10.1002/env.70088","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate spatial classification is a challenging task, especially when binary outcomes are subject to measurement errors and misclassification. Motivated by a precipitation study in South Korea, we propose Bayesian spatial classification methods with misclassification correction using internal validation data. The prior distributions for the misclassification parameters are specified using internal validation data in the Bayesian spatial classification of the main study, where the gold-standard device is unavailable. A simulation study is conducted to compare the performance of the proposed methods with the naive method that ignores the misclassification. It is found that the proposed methods outperform the naïve model. The proposed methods are also illustrated with precipitation data from South Korea.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566189","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}