A carcass is defined as a temporary resource that can support high levels of diversity compared to other resources. The level of diversity often depends on the environmental conditions in which the corpse is found. Calliphoridae (Diptera) are the most important necrophagous insects used in forensic investigations because this family is common, widespread, abundant, and usually the first to colonize a corpse. However, understanding the processes responsible for variation in calliphorid species abundance and richness along gradients in ecology remains a key challenge. In this study, we analyzed the influence of altitude on the abundance and diversity of Calliphoridae species in corpses at three different altitudes (400, 900, and 1500 m a.s.l.) in a mountainous area of southeastern Spain. The results revealed a gradient of decreasing abundance with increasing altitude. An altitudinal substitution of species was observed, with Chrysomya albiceps being found at the lowest altitude and Calliphora vicina at the highest altitude. Seasonal variation was also noted, with Ch. albiceps being the dominant species in the warmer months and C. vicina being the dominant species in winter. Our results confirm the importance of Calliphorids as seasonal, altitudinal, and environmental indicators, given the wide distribution and abundance of this family. This information is of great interest on the interpretation of data in forensic practice.
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http://dx.doi.org/10.1111/1556-4029.15509 | DOI Listing |
BMC Plant Biol
January 2025
Department of Medicinal Plants, Faculty of Agriculture and Natural Resources, Arak University, Arak, 38156-8-8349, Iran.
Identifying the optimal cultivation regions and evaluating the impact of environmental factors are crucial for selecting the best conditions for the commercial production of important medicinal and industrial plants. This study examined the effects of different cultivation areas-Rayen, Eghlid, Kalat, and Zanjan-on the agro-morphological and phytochemical traits of Glycyrrhiza glabra. The findings revealed that the location where the plants were grown significantly influenced their physical and chemical characteristics.
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February 2025
National Research Council of Italy, Rome, Italy. Electronic address:
The paper presents the variations of space radiation (primary and secondary galactic cosmic rays (GCR) absorbed dose rate in silicon and flux) measured during the first-ever commercial suborbital flight of the Virgin Galactic (VG) SpaceShipTwo Unity on 29 June 2023. A Portable Dosimeter-Spectrometer Liulin-CNR-VG is used. It is developed in the Space Research and Technology Institute, Bulgarian Academy of Sciences (SRTI-BAS) under a scientific contract with National Research Council of Italy (CNR), Italy.
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January 2025
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China. Electronic address:
In recent years, ozone (O) pollution in many Chinese cities has worsened. Several cities have also experienced incidents where nocturnal O concentrations did not decrease as expected, and instead remained at high levels (above 50 ppb). However, there have been few detailed studies on the causes of these events.
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January 2025
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
Cyanobacterial blooms are increasingly becoming major threats to global inland aquatic ecosystems. Phycocyanin (PC), a pigment unique to cyanobacteria, can provide important reference for the study of cyanobacterial blooms warning. New satellite technology and cloud computing platforms have greatly improved research on PC, with the average number of studies examining it having increased from 5 per year before 2018 to 17 per year thereafter.
View Article and Find Full Text PDFSci Total Environ
January 2025
Universidad de Santiago de Chile, Santiago, Chile.
Assessing future snow cover changes is challenging because the high spatial resolution required is typically unavailable from climate models. This study, therefore, proposes an alternative approach to estimating snow changes by developing a super-spatial-resolution downscaling model of snow depth (SD) for Japan using a convolutional neural network (CNN)-based method, and by downscaling an ensemble of models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset. After assessing the coherence of the observed reference SD dataset with independent observations, we leveraged it to train the CNN downscaling model; following its evaluation, we applied the trained model to CMIP6 climate simulations.
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