Environmetrics最新文献

{"title":"Zero & N-Inflated Overdispersed Binomial Models for Sum-Constrained Poisson Count Processes","authors":"James Sweeney, John Haslett, Dipankar Bandyopadhyay, Michael Fop, Andrew Parnell","doi":"10.1002/env.70156","DOIUrl":"https://doi.org/10.1002/env.70156","url":null,"abstract":"<p>Ecological monitoring programmes and palaeoecological analyses play a key role in understanding how biodiversity, including plants and wildlife, respond to environmental factors such as climate change or biotic competition. Such data are typically compositional in nature, and a common challenge in the analysis of ecological compositional data is the interpretation and modeling of datasets containing a high proportion of zeros and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 </mrow>\u0000 <annotation>$$ N $$</annotation>\u0000 </semantics></math>'s. Such data frequently occur in ecological applications where counts of species are collected until a sum total imposed by sampling cost or sampling effort is reached. In the bivariate count (two-species) setting we focus on in this article, zero-inflation of one species will result in <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 </mrow>\u0000 <annotation>$$ N $$</annotation>\u0000 </semantics></math>-inflation of the other. This can lead to species absence being attributed to an unsuitable habitat as opposed to missingness by chance. Similarly, an excess of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 </mrow>\u0000 <annotation>$$ N $$</annotation>\u0000 </semantics></math>'s will lead to misleading inferences about habitat preference and abundance estimates. Our contribution is to identify that two independent zero-inflated Poisson processes subject to a sum constraint provide a novel biologically-motivated generating mechanism for the occurrence of binomial count data exhibiting zero and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 </mrow>\u0000 <annotation>$$ N $$</annotation>\u0000 </semantics></math>-inflation. We identify an extension to the model to capture additional overdispersion within the data resulting in a novel zero and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 </mrow>\u0000 <annotation>$$ N $$</annotation>\u0000 </semantics></math>-inflated beta-binomial model. We consider two motivating datasets, one involving a pesticide treatment for an invasive species, and a second involving the abundance of two plant species. We demonstrate that incorporation of covariates in each case enable learning about sources of zero and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 </mrow>\u0000 <annotation>$$ N $$</annotation>\u0000 </semantics></math>-inflation as well as abundance. We show that the proposed models provide fo","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149206158","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 Family of Gaussian Processes for Modeling Animal Movement: Application to Bat Telemetry Data","authors":"J. H. Ramírez-González,&nbsp;A. Murillo-Salas,&nbsp;Ying Sun","doi":"10.1002/env.70155","DOIUrl":"https://doi.org/10.1002/env.70155","url":null,"abstract":"<div>\u0000 \u0000 <p>Modeling animal movement is essential for addressing various ecological and biological questions. However, developing an effective predictive model for animal movement is a challenging task. In this paper, we introduce a new family of Gaussian processes, derived from the limiting fluctuations of the rescaled occupation-time process of certain branching particle systems, and study its applicability to real animal movement data. We examine two subfamilies and show that these processes exhibit long-range dependence and covariance functions with logarithmic asymptotic growth. For the exponential subfamily used in the applied analysis, the process is also non-stationary and not intrinsically stationary on compact time intervals. These properties are relevant when dealing with animal trajectories that exhibit strong memory. Finally, we illustrate the practical applicability of the proposed model by analyzing bat movement data.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205529","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 Statistical Framework for Spatial Boundary Estimation and Change Detection: Application to the Sahel–Sahara Climate Transition","authors":"Stephen Tivenan,&nbsp;Indranil Sahoo,&nbsp;Yanjun Qian","doi":"10.1002/env.70154","DOIUrl":"https://doi.org/10.1002/env.70154","url":null,"abstract":"<p>Spatial boundaries, such as ecological transitions or climatic regime interfaces, capture steep environmental gradients, and shifts in their structure can signal emerging environmental changes. Quantifying uncertainty in spatial boundary locations and formally testing for temporal shifts remain challenging, especially when boundaries are derived from noisy, gridded environmental data. We present a unified framework that combines heteroskedastic Gaussian process (GP) regression with a scaled maximum absolute difference (MAD) global envelope test (GET) to estimate spatial boundary curves and assess whether they evolve over time. The heteroskedastic GP provides a flexible probabilistic reconstruction of boundary lines, capturing spatially varying mean structure and location-specific variability, while the scaled Maximum Absolute GET test offers a rigorous hypothesis-testing tool for detecting departures from expected boundary behaviors. Simulation studies show that the proposed method achieves the correct size under the null and high power for detecting local boundary shifts. Applying our framework to the Sahel-Sahara transition zone, using annual Köppen-Trewartha climate classifications from 1960 to 1989, we find no statistically significant decade-scale changes in the arid/semi-arid or semi-arid/non-arid interfaces. However, the method successfully identifies localized boundary shifts during the extreme drought years of 1983–1984, consistent with climate studies documenting regional anomalies in these interfaces during that period.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205307","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":"Securing Sensitive Data: A Synthetic Data Framework for Protecting Geographic Coordinates","authors":"Thaís Paiva Galletti,&nbsp;Marcos Oliveira Prates,&nbsp;Fernanda Buzza Alves Barros","doi":"10.1002/env.70153","DOIUrl":"https://doi.org/10.1002/env.70153","url":null,"abstract":"<p>High-resolution spatial data are critical for environmental and epidemiological research yet present significant privacy risks when released at the individual level. Traditional protection methods, such as aggregation or masking, often compromise data utility or introduce unrealistic spatial patterns. We propose a robust framework for generating fully synthetic geographic coordinates that balances privacy and analytical validity by modeling the spatial intensity through observed covariates. We extend existing methodology by incorporating continuous attributes into the model and strictly enforcing restricted areas (e.g., uninhabitable zones). Furthermore, we address the limitations of standard risk metrics in multimodal spatial distributions by proposing extended distance-based risk measures that assess identification risk within probabilistic neighborhoods. We evaluate the framework through simulation studies and demonstrate its practical utility by generating synthetic georeferenced COVID-19 notifications from Montes Claros, Brazil. Results demonstrate that our method preserves the spatial intensity structure of the data while providing a secure mechanism for sharing sensitive microdata.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70153","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205306","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":"Meteorological Normalization or Deweathering for Predicting Air Pollutant Concentration: Pitfalls and Limitations","authors":"Valentino Petrić,&nbsp;Mario Lovrić,&nbsp;Bernhard C. Geiger","doi":"10.1002/env.70151","DOIUrl":"https://doi.org/10.1002/env.70151","url":null,"abstract":"<p>Meteorological normalization is a key concept in studying anthropogenic effects on air pollutant concentrations and their temporal trends. While apparently successful in revealing anthropogenic effects and often used, there are downsides to the methods and limitations which should be taken into account when using it. This study examines the concept of meteorological normalization in the context of predicting air pollutant concentrations, particularly focusing on the method's theoretical background and implicit assumptions. We provide a rigorous analysis that outlines the conditions under which meteorological normalization can effectively estimate expected values of response variables, which are commonly time series of air pollutant concentrations such as PM<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mo> </mo>\u0000 <mrow>\u0000 <mn>10</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {}_{10} $$</annotation>\u0000 </semantics></math>. Through our analysis, we identify critical assumptions, such as the independence of meteorological and non-meteorological variables, which can significantly impact the validity of the method's outcomes. We also explore potential failure modes of meteorological normalization when these assumptions are violated, offering practical examples and visualizations that highlight the limitations of this technique. Our findings suggest that while meteorological normalization can be a useful tool, careful consideration of its applicability is necessary to avoid misleading conclusions in air quality assessments.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70151","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146011","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 Nonparametric Risk Assessment in Developmental Toxicity Studies With Ordinal Responses","authors":"Jizhou Kang,&nbsp;Athanasios Kottas","doi":"10.1002/env.70152","DOIUrl":"https://doi.org/10.1002/env.70152","url":null,"abstract":"<p>We develop a nonparametric Bayesian modeling framework for clustered ordinal responses in developmental toxicity studies, which typically exhibit extensive heterogeneity. The primary focus of these studies is to examine dose–response relationships, which are depicted by (conditional) endpoint probabilities across the dose (toxin) levels. Standard parametric approaches, limited in terms of the response distribution and/or the dose–response relationship, hinder reliable uncertainty quantification in this context. We propose nonparametric mixture models that are built from dose-dependent stick-breaking process priors, leveraging the continuation-ratio logits representation of the multinomial distribution to formulate the mixture kernel. We further elaborate the modeling approach, amplifying the mixture models with an overdispersed kernel which offers enhanced control of variability. We conduct a simulation study to demonstrate the uncertainty quantification benefits of both the discrete nonparametric mixing structure and the overdispersed kernel. Further illustration is provided with different forms of risk assessment, using data from a toxicity experiment on the effects of ethylene glycol.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149145539","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":"Mixtures of Neural Network Experts With Application to Phytoplankton Flow Cytometry Data","authors":"Ethan Pawl,&nbsp;François Ribalet,&nbsp;Paul A. Parker,&nbsp;Sangwon Hyun","doi":"10.1002/env.70148","DOIUrl":"https://doi.org/10.1002/env.70148","url":null,"abstract":"<p>Flow cytometry is a valuable technique that measures the optical properties of particles at a single-cell resolution. When deployed in the ocean, flow cytometry allows oceanographers to study different types of photosynthetic microbes called phytoplankton. It is of great interest to study how phytoplankton properties change in response to environmental conditions. In our work, we develop a nonlinear mixture of experts model to estimate separate regression functions for each subpopulation utilizing random-weight neural networks. Our model allows one to flexibly estimate how cell properties and relative abundances depend on environmental covariates in each segment of a heterogeneous sample, without the computational burden of backpropagation. We show that the proposed model provides superior predictive performance in simulated examples compared to a mixture of linear experts. Also, applying our model to real data, we show that our model has (1) comparable out-of-sample prediction performance, and (2) more realistic estimates of phytoplankton behavior.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146622","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":"Discussion of “Spatio-Temporal Modelling of Extreme Induced Seismicity in the Presence of an Evolving Measurement Network”","authors":"Raphaël Huser","doi":"10.1002/env.70150","DOIUrl":"https://doi.org/10.1002/env.70150","url":null,"abstract":"<div>\u0000 \u0000 <p>In this brief note, I discuss the paper of Murphy et al. on “Spatio-Temporal Modelling of Extreme Induced Seismicity in the Presence of An Evolving Measurement Network”, and mention five possible avenues for future research spanning the definition of physics-based covariates, the handling of temporal dependence and clustering, the use of random effects, the special double role that threshold selection plays in the paper, and the deployment of amortized simulation-based inference approaches in this context.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146623","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":"Local Influence Analysis in a Gaussian Spatiotemporal Model","authors":"Fernanda De Bastiani,&nbsp;Jonathan Acosta,&nbsp;Manuel Galea,&nbsp;Miguel Uribe-Opazo","doi":"10.1002/env.70149","DOIUrl":"https://doi.org/10.1002/env.70149","url":null,"abstract":"<div>\u0000 \u0000 <p>Spatiotemporal models are widely used to describe data collected over space and time, where the specification of the covariance structure plays a crucial role in statistical inference. In this work, we extend the local influence methodology to assess the adequacy of the temporal independence assumption in a Gaussian spatial linear model with repeated observations over time. A separable spatiotemporal covariance function is introduced as a structured perturbation of the independence model. Closed-form expressions are derived for the observed information matrix with respect to the perturbation parameter and for the associated perturbation matrix under this separable scheme, allowing the construction of diagnostic measures based on likelihood displacement. A simulation study is conducted to evaluate the ability of the proposed approach to detect departures from temporal independence. The methodology is further illustrated using a soybean productivity dataset collected at fixed spatial locations over multiple harvest years. The results show that ignoring temporal dependence may affect both model fit and inference on regression coefficients. The proposed framework provides a formal diagnostic tool for assessing covariance structure assumptions in Gaussian spatiotemporal models.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149007498","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":"High-Resolution Retrieval of Atmospheric Boundary Layers With Nonstationary Gaussian Processes","authors":"Haoran Xiong,&nbsp;Paytsar Muradyan,&nbsp;Robert Jackson,&nbsp;Christopher J. Geoga","doi":"10.1002/env.70141","DOIUrl":"https://doi.org/10.1002/env.70141","url":null,"abstract":"<div>\u0000 \u0000 <p>The atmospheric boundary layer (ABL) plays a critical role in governing turbulent exchanges of momentum, heat moisture, and trace gases between the Earth's surface and the free atmosphere, thereby influencing meteorological phenomena, air quality, and climate processes. Accurate and temporally continuous characterization of the ABL structure and height evolution is crucial for both scientific understanding and practical applications. High-resolution retrievals of the ABL height from vertical velocity measurements is challenging because it is often estimated using empirical thresholds applied to profiles of vertical velocity variance or related turbulence diagnostics at each measurement altitude, which can suffer from limited sampling and sensitivity to noise. To address these limitations, this work employs nonstationary Gaussian process (GP) modeling to more effectively capture the spatio-temporal dependence structure in the data, enabling high-quality—and, if desired, high-resolution—estimates of the ABL height without reliance on ad-hoc parameter tuning. By leveraging Vecchia approximations, the proposed method can be applied to large-scale datasets, and example applications using full-day vertical velocity profiles comprising approximately 5 M measurements are presented.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849150","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}