The purpose of this paper is to illustrate new techniques for computing multiday extreme precipitation taken from recent theoretical advancements in extreme value theory in the framework of dynamical systems, using historical precipitation data along the eastern coast of Australia as a case study. We explore the numerical pitfalls of applying standard extreme value techniques to...
In environmental epidemiology, the short-term association between temperature and suicide has been examined by analyzing daily time-series data on suicide and temperature collected from multiple locations. A two-stage meta-analytic approach has been conventionally used. A Poisson regression with splines is fitted for each location in the first stage, and location-specific...
The United Nations’ Sustainable Development Goals urge a combined focus on economic development that account for improved environmental quality, thus prompting increased attention on the advancement of environmental-related technologies and innovations. Financialization (instrumentation of financial development, markets, and institutions), environmental policy, and trade openness...
In ecology and environmental sciences, combining diverse datasets has become an essential tool for managing the increasing complexity and volume of ecological data. However, as data complexity and volume grow, the computational demands of previously proposed models for data integration escalate, creating significant challenges for practical implementation. This study introduces a...
Our study addresses the analysis of environmental concerns through point process theory. Among those, Sicily faced an escalating issue of uncontrolled fires in recent years, necessitating a thorough investigation into their spatio-temporal dynamics. Each fire is treated as a unique point in both space and time, allowing us to assess the influence of environmental and...
Starting from the evaluation of presence-only data, and according to stochastic processes theory, we propose a classification method for unknown larval fish specimens, which is based on Local Indicators of Spatio-Temporal Association (LISTA). LISTA functions are typically used to evaluate the presence of clustered local second-order structures in spatio-temporal data. Here, these...
In this paper, we consider the stochastic versions of three classical growth models given by ordinary differential equations (ODEs). Indeed we use the stochastic versions of Gompertz, von Bertalanffy, and logistic differential equations as models. We assume that each stochastic differential equation (SDE) has some crucial parameters to be estimated, and we use maximum likelihood...
The relative abundance of summer flounder (Paralichthys dentatus) differs over space and time with changes in environmental factors, such as depth, bottom temperature, sea surface temperature (SST) and bottom salinity. We use the integrated nested Laplace approximation (INLA) approach to account for the random effects arising from either over-dispersion, or spatial and temporal...
Seismicity de-clustering is a crucial step in earthquake catalog analysis, essential for understanding earthquake patterns and assessing seismic hazards. Seismicity de-clustering is challenging due to complex geological structures, high spatial-temporal correlation between events, and large amounts of noise. This study proposes an innovative two-stage approach for spatial zone...
In this paper we present a total species estimator based on modelling the rate of change of a species accumulation curve (SAC). The proposed approach calculates an accumulation rate curve (ARC) for new species conditional on observed data and extrapolates it using parametric functions with varying rates of decay. The curve fits are integrated to obtain estimates for undetected...
Mosquito-borne diseases pose a significant public health concern in Colombia, necessitating robust quantification of their geographic patterns to guide and optimize interventions. This study explores the spatial dynamics and interactions among Zika, Dengue, and Chikungunya within the context of joint disease modeling in the Andean region of Colombia. Leveraging the Poisson...
In order to study potential impacts arising from climate change, future projections of numerical model output often must be calibrated to be comparable to observations. Rather than calibrating the data values themselves, we propose a novel statistical calibration method for extremes that assumes there exists a linear relationship between parameters associated with model output...
The spatio-temporal prediction of air pollutant concentrations is vital for assessing regulatory compliance and for producing exposure estimates in epidemiological studies. Numerous approaches have been utilised for making such predictions, including land use regression models, additive models, spatio-temporal smoothing models and machine learning prediction algorithms. However...
Rare cancers affect millions of people worldwide each year. However, estimating incidence or mortality rates associated with rare cancers presents important difficulties and poses new statistical methodological challenges. In this paper, we expand the collection of multivariate spatio-temporal models by introducing adaptable shared spatio-temporal components to enable a...
Adaptive design methods can be used to make changes to survey designs in ecosystem monitoring to ensure that informative data are collected in an ongoing, cost-effective, and flexible manner. Such methods are of particular benefit in environmental monitoring as such monitoring is often very costly and in many cases consists of only a few sampling sites from which inference about...
We introduce a clustering method for time series based on tail dependence. Such a method also considers spatial constraints by means of a suitable procedure merging temporal and spatial dependence via extreme-value copulas. The cluster composition depends on the choice of the hyper-parameter $$\alpha \in (0,1)$$ used to calibrate the contribution of the spatial dependence to the...
This study analyses the decrease in soil organic carbon (SOC) stocks due to changes in land use following the earthquake in Düzce, Turkey, 1999. The primary objective of the study is to determine the changes in land use within Düzce and to provide a multi-dimensional approach to the spatial and quantitative distributions of SOC losses. Corine Land Use- Land Cover (LULC) within...
European anchovies and round sardinella play a crucial role, both ecological and commercial, in the Mediterranean Sea. In this paper, we investigate the distribution of their larval stages by analyzing a dataset collected over time (1998–2016) and spaced along the area of the Strait of Sicily. Environmental factors are also integrated. We employ a hierarchical spatio-temporal...
The effect of severe drought in the summer 2018 on the plant community composition and overall diversity was investigated in a replicated long-term grassland experiment where nitrogen availability was manipulated. The possible plant community response to the drought and the possible role of selected plant traits were investigated using model-based ordination techniques, which...
Climate model selection stands as a critical process in climate science and research. It involves choosing the most appropriate climate models to address specific research questions, simulating climate behaviour, or making projections about future climate conditions. This paper proposes a new approach, using spatial functional data analysis, to asses which of the 18 EURO CORDEX...
The eddy covariance (EC) method is a standard micrometeorological technique for monitoring the exchange rate of the main greenhouse gases across the interface between the atmosphere and ecosystems. One of the first EC data processing steps is the temporal alignment of the raw, high frequency measurements collected by the sonic anemometer and gas analyser. While different methods...
We revisit a deterministic model for studying the dynamics of allelopathy. The model is formulated in terms of a non-homogeneous linear system of differential equations whose forcing or source term is a piecewise constant function (square wave). To account for the inherent uncertainties present in this natural phenomenon, we reformulate the model as a system of random...