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In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method for classifying spatiotemporal processes based on entropy measurements. To test and validate the method, we selected five classes of similar processes related to the evolution of random patterns: (i) white noise; (ii) red noise; (iii) weak turbulence from reaction to diffusion; (iv) hydrodynamic fully developed turbulence; and (v) plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon permutation entropy (SHp) and a new approach based on Tsallis Spectral Permutation Entropy (Sqs). Notably, our observations reveal the segregation of reaction terms in this SHp×Sqs space, a result that identifies specific sectors for each class of dynamic process, and it can be used to train machine learning models for the automatic classification of complex spatiotemporal patterns.

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BACKGROUND: The SARS-CoV-2 pandemic has temporarily decreased black carbon emissions worldwide. The use of multi-wavelength aethalometers provides a quantitative apportionment of black carbon (BC) from fossil fuels (BCff) and wood-burning sources (BCwb). However, this apportionment is aggregated: local and non-local BC sources are lumped together in the aethalometer results. METHODS: We propose a spatiotemporal analysis of BC results along with meteorological data, using a fuzzy clustering approach, to resolve local and non-local BC contributions. We apply this methodology to BC measurements taken at an urban site in Santiago, Chile, from March through December 2020, including lockdown periods of different intensities. RESULTS: BCff accounts for 85% of total BC; there was up to an 80% reduction in total BC during the most restrictive lockdowns (April-June); the reduction was 40-50% in periods with less restrictive lockdowns. The new methodology can apportion BCff and BCwb into local and non-local contributions; local traffic (wood burning) sources account for 66% (86%) of BCff (BCwb). CONCLUSIONS: The intensive lockdowns brought down ambient BC across the city. The proposed fuzzy clustering methodology can resolve local and non-local contributions to BC in urban zones.

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RESUMO

The lesser grain borer, Rhyzopertha dominica, and the rust red flour beetle, Tribolium castaneum, are two major beetle pests commonly found infesting stored products worldwide. Both species can cause severe economic damage and their management is complicated by their potential to develop resistance to several of the limited chemical options available. However, pest management strategies can be improved by understanding the ecology of the pest insect. To determine the spatiotemporal activity of R. dominica and T. castaneum, we conducted a trapping study over two years in a temperate region of south-eastern Australia, with traps located near grain storages and fields. We captured higher numbers of R. dominica than T. castaneum, and both species were more prevalent in traps located close to grain storages. Similar and consistent seasonal patterns were displayed by both species with activity ceasing during the winter (June-August) months. We found linear correlations between maximum daily temperatures and trap catches, and minimum threshold temperatures for flight activity were 14.5 °C and 15.6 °C for R. dominica and T. castaneum, respectively. The results are discussed in relation to the ecology of these pests along with their implications for pest management.

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Naturally fragmented landscapes provide suitable scenarios through which to investigate patch and landscape effects on biodiversity patterns in areas that are isolated from the disturbances usually associated with human-made fragments. We aimed to investigate the patch and landscape effects on the diversity of forest-dependent and matrix-tolerant dung beetles in a naturally fragmented landscape. We also assessed the influence that seasonal and vegetation variations had on these dung beetles. We sampled dung beetles during two summers and two winters in 14 forest islands of various sizes and shapes within a natural mountainous forest archipelago in southeast Brazil. We measured the patch and landscape variables based on high-resolution multispectral images of circular sectors with radii of 100, 250, and 500 m. We used generalized linear mixed models to relate dung beetle metrics to patch and landscape attributes. The interaction between canopy cover and season influenced both species' richness and abundance of the dung beetle metacommunity. The forest-dependent species' richness increased with greater canopy cover, regardless of the season. Patch attributes (e.g., size, canopy cover, distance to the closest patch, and distance to continuous forest) and landscape attributes (e.g., percentage of forest in the landscape, total edge, number of patches, distance to the nearest neighbor, and shape complexity) had small general effects on dung beetle species as a whole and on matrix-tolerant species in particular. However, these values strongly influenced forest-dependent species' richness, abundance, and temporal beta diversity. The matrix-tolerant species, therefore, mask the effects of patch and landscape effects on forest-dependent species within the mountainous forest archipelago. In other words, the changes in these patch and landscape attributes influenced forest-dependent and matrix-tolerant species differently. Therefore, the evaluation of entire metacommunities may not be helpful when evaluating species-specific responses in mixed landscapes-a fact that impairs the conservation of forest-dependent species.

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We studied the spatial and temporal distribution patterns of Chlorinated Volatile Organic Compounds (CVOCs) in the karst aquifers in northern Puerto Rico (1982-2013). Seventeen CVOCs were widely detected across the study area, with the most detected and persistent contaminated CVOCs including trichloroethylene (TCE), tetrachloroethylene (PCE), carbon tetrachloride (CT), chloroform (TCM), and methylene chloride (DCM). Historically, 471 (76%) and 319 (52%) of the 615 sampling sites have CVOC concentrations above the detection limit and maximum contamination level (MCL), respectively. The spatiotemporal patterns of the CVOC concentrations showed two clusters of contaminated areas, one near the Superfund site "Upjohn" and another near "Vega Alta Public Supply Wells." Despite a decreasing trend in concentrations, there is a general northward movement and spreading of contaminants even beyond the extent of known sources of the Superfund and landfill sites. Our analyses suggest that, besides the source conditions, karst characteristics (high heterogeneity, complex hydraulic and biochemical environment) are linked to the long-term spatiotemporal patterns of CVOCs in groundwater.

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