Environmetrics最新文献

In environmental science, practitioners often deal with data recorded sequentially along time, such as time series of wind direction or wind speed. In this context, clustering of time series is a useful tool to carry out exploratory analyses. While most of the proposals are focused on real-valued time series, very few works consider circular time series, despite the frequent appearance of these objects in many disciplines. In this manuscript, a dissimilarity for circular time series is introduced and used in combination with a soft clustering method. The metric relies on a measure of serial dependence considering circular arcs, thus taking advantage of the directional character inherent to the series range. The clustering approach is able to group together time series generated from similar stochastic processes. Some simulations show that the method exhibits a reasonable clustering effectiveness, outperforming alternative techniques in many contexts. Two interesting applications involving time series of wind direction in Saudi Arabia show the potential of the proposed approach.

Population growth models are essential tools for natural resources management and conservation since they provide understanding on factors affecting renewal of natural animal populations. However, we still do not properly understand how the processes underlying reproduction of natural animal populations are affected by the environment at the spatial scale at which reproduction actually happens. A particular challenge for analyzing these processes is that observations from different life cycle stages are often collected at different spatial scales, and there is a lack of statistical methods to link local and spatially aggregated information. We address this challenge by developing spatially explicit population growth models for annually reproducing fish. Our approach integrates mechanistic Ricker and Beverton–Holt population growth models with a zero-inflated species distribution model and utilizes the hierarchical Bayesian approach to estimate the model parameters from data with varying spatial support: local scale count data on offspring and environment, and areal data from commercial fisheries informing about a spawning stock size. We show, both theoretically and empirically, that our models are identifiable and have good inferential performance. As a proof of concept application, we used the proposed models to analyze the drivers of whitefish Coregonus laveratus (L.) s.l.) reproduction along the Finnish coast of the Gulf of Bothnia in the Baltic Sea. The results show that the proposed model provides novel understanding beyond what would be attainable with earlier methods. The distributions of the reproduction areas, spawner density, and maximum proliferation rate were strongly dependent on local environmental conditions, but the effects and the relative importance of the covariates varied between these processes. The proposed models can be extended to other systems and organisms and enable ecologists to extract a better understanding of processes driving animal reproduction.

Spatiotemporal causal inference methods are needed to detect the effect of interventions on indirectly measured epidemiological outcomes that go beyond studying spatiotemporal correlations. Chronic wasting disease (CWD) causes neurological degeneration and eventual death to white-tailed deer (Odocoileus virginianus) in Wisconsin. Targeted culling involves removing deer after traditional hunting seasons in areas with high CWD prevalence. The evaluation of the causal effects of targeted culling in the spread and growth of CWD is an important unresolved research and CWD management question that can guide surveillance efforts. Reaction–diffusion partial differential equations (PDEs) can be used to mechanistically model the underlying spatiotemporal dynamics of wildlife diseases, like CWD, allowing researchers to make inference about unobserved epidemiological quantities. These models indirectly regress spatiotemporal covariates on diffusion and growth rates parameterizing such PDEs, obtaining associational conclusions. In this work we develop an innovative method to obtain causal estimators for the effect of targeted culling interventions on CWD epidemiological processes using an inverse-probability-of-treatment-weighted technique by means of marginal structural models embedded in the PDE fitting process. Additionally we establish a novel scheme for sensitivity analysis under unmeasured confounder for testing the hypothesis of a significant causal effect in the indirectly measured epidemiological outcomes. Our methods can be broadly used to study the impact of spatiotemporal interventions and treatment exposures in the epidemiological evolution of infectious diseases that can help to inform future efforts to mitigate public health implications and wildlife disease burden.

Motivated by empirical case studies and discussions of Bonas et al. (2024), this discussion paper critically examines challenges in the predictability of environmental processes, focusing on three key spheres: (a) predictability and interpretability, (b) predictability in dynamic environments, and (c) predictability into unknown spaces. These spheres highlight the responsibilities within environmetrics to ensure that predictive models, particularly advanced machine learning and deep learning methods, are applied thoughtfully. First, we discuss the trade-off between interpretability and predictive complexity, contrasting the transparency of traditional statistical models with the “black-box” nature of machine learning but also highlighting their enormous potential for exploiting new data sources and types. Second, we address real-time adaptability, where models must handle concept drift and should, therefore, be continuously monitored. Finally, we consider the challenges of extrapolating predictions into unknown/nontrained areas, underscoring the risks of model overreach. This paper aims to contribute to the discussion in the field, emphasizing the critical role environmetricians play in advancing responsible, interpretable, and scientifically sound predictive practices.

Building on the insights from Bonas et al. (2024), we explore the relationship between statistical and machine learning models in the analysis of environmental time series. We specifically address the unique challenges of environmental time series data, including the need to consider the multivariate approach and account for spatial dependence. Emphasizing the importance of various types of statistical inference in environmental studies—not limited to forecasting—we propose that viewing statistical and machine learning approaches as complementary rather than alternative methods can unlock innovative modeling strategies that enhance both predictive accuracy and interpretive power. To illustrate these concepts, we present a case study that highlights the key points raised in the discussion.

The assessment of corporate sustainability performance is extremely relevant in facilitating the transition to a green and low-carbon intensity economy. However, companies located in different areas may be subject to different sustainability and environmental risks and policies. Henceforth, the main objective of this paper is to investigate the spatial and temporal pattern of the sustainability evaluations of European firms. We leverage a large dataset containing information about companies' sustainability performances, measured by MSCI ESG ratings, and geographical coordinates of firms in Western Europe between 2013 and 2023. By means of a modified version of the Chavent et al. (2018) hierarchical algorithm, we conduct a spatial clustering analysis, combining sustainability and spatial information, and a spatiotemporal clustering analysis, which combines the time dynamics of multiple sustainability features and spatial dissimilarities, to detect groups of firms with homogeneous sustainability performance. We are able to build cross-national and cross-industry clusters with remarkable differences in terms of sustainability scores. Among other results, in the spatio-temporal analysis, we observe a high degree of geographical overlap among clusters, indicating that the temporal dynamics in sustainability assessment are relevant within a multidimensional approach. Our findings help to capture the diversity of ESG ratings across Western Europe and may assist practitioners and policymakers in evaluating companies facing different sustainability-linked risks in different areas.

One of the main issues facing agrometeorological studies involves measuring and modeling the evolution of different environmental variables over time; this often requires a dense monitoring network. Spatio-temporal geostatistics has the potential to provide techniques and tools to estimate the spatio-temporal multiple covariance function and define an appropriate multivariate correlation function capable of reliable predictions. This paper presents a spatio-temporal multivariate geostatistical modeling approach based on the joint diagonalization of the empirical covariance matrix evaluated at different spatio-temporal lags. The possibility to consider a reduced number of uncorrelated variables (lower than the number of observed variables) and separately model the spatio-temporal evolution of these uncorrelated components represents a substantial simplification for multivariate modeling. A space–time linear coregionalization model (ST-LCM) with appropriate parametric models for the latent components was fitted to the matrix-valued covariance function estimated for five relevant agrometeorological variables, including evapotranspiration, minimum and maximum humidity, maximum temperature, and precipitation. The analyses highlight how to identify space–time components and choose the corresponding model by evaluating some characteristics of these components, such as symmetry, separability, and type of non-separability. The predictive results of this multivariate study will be of interest for agriculture, in particular for addressing drought emergencies.