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Twospotted spider mite, Tetranychus urticae Koch, is an important pest of peppermint in California, USA. Spider mite feeding on peppermint leaves causes physiological changes in the plant, which coupling with the favorable environmental condition can lead to increased mite infestations. Significant yield loss can occur in absence of pest monitoring and timely management. Understating the within-field spatial distribution of T. urticae is critical for the development of reliable sampling plan. The study reported here aims to characterize the spatial distribution of mite infestation in four commercial peppermint fields in northern California using spatial techniques, variogram and Spatial Analysis by Distance IndicEs (SADIE). Variogram analysis revealed that there was a strong evidence for spatially dependent (aggregated) mite population in 13 of 17 sampling dates and the physical distance of the aggregation reached maximum to 7 m in peppermint fields. Using SADIE, 11 of 17 sampling dates showed aggregated distribution pattern of mite infestation. Combining results from variogram and SADIE analysis, the spatial aggregation of T. urticae was evident in all four fields for all 17 sampling dates evaluated. Comparing spatial association using SADIE, ca. 62% of the total sampling pairs showed a positive association of mite spatial distribution patterns between two consecutive sampling dates, which indicates a strong spatial and temporal stability of mite infestation in peppermint fields. These results are discussed in relation to behavior of spider mite distribution within field, and its implications for improving sampling guidelines that are essential for effective pest monitoring and management.
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http://dx.doi.org/10.1007/s10493-015-0006-1 | DOI Listing |
PLoS One
September 2025
School of Computer Science and Technology, Huaiyin Normal University, Huai'an, Jiangsu, China.
Previous studies have demonstrated that metric learning approaches yield remarkable performance in the field of kinship verification. Nevertheless, a prevalent limitation of most existing methods lies in their over-reliance on learning exclusively from specified types of given kin data, which frequently results in information isolation. Although generative-based metric learning methods present potential solutions to this problem, they are hindered by substantial computational costs.
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September 2025
College of Landscape Architecture and Art, Northwest Agriculture and Forestry University, Xianyang, China.
This study investigates the spatial and temporal distribution and the influencing factors of 579 cultural heritage sites along the Qin-Shu Ancient Road in Shaanxi Province, employing kernel density estimation, buffer analysis, and geographic detectors. Three key findings emerge: (1) The spatial pattern is characterized by a "line-belt-core" structure, with a belt-like aggregation along the Xi'an-Baoji-Hanzhong axis. Core concentrations are found in Xi'an (181 sites), Hanzhong (159 sites), and Ankang (122 sites), with secondary concentrations in Baoji (72 sites) and Shangluo (36 sites).
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September 2025
Maine Department of Inland Fisheries and Wildlife, Bangor, Maine, United States of America.
Freshwater mussels are critical to the health of freshwater systems, but their populations are declining dramatically throughout the world. The limited resources available for freshwater mussel conservation necessitates the geographic prioritization of conservation-related actions. However, lack of knowledge about freshwater mussel spatial distributions hinders decision making in this context.
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September 2025
Biaoxin Science & Technology (Beijing) Co., Ltd, Beijing, China.
This study examines China's national standard development from 2001 to 2023. Using machine splitting and location assignment technology, the Dagum Gini coefficient and its decomposition methods, and traditional and spatial Markov chain estimation methods, we identify the spatiotemporal disparities and dynamic transition characteristics of the contribution levels to national standard development across China's eight comprehensive economic zones. The findings provide a reference for promoting regional coordinated sustainable development and high-quality economic transformation.
View Article and Find Full Text PDFDiscov Nano
September 2025
Department of Rehabilitation Medicine, Rehabilitation Medical Center, Key Laboratory of Rehabilitation Medicine in Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China.
Immunoelectron Microscopy (IEM) is a technique that combines specific immunolabeling with high-resolution electron microscopic imaging to achieve precise spatial localization of biomolecules at the subcellular scale (< 10 nm) by using high-electron-density markers such as colloidal gold and quantum dots. As a core tool for analyzing the distribution of proteins, organelle interactions, and localization of disease pathology markers, it has irreplaceable value, especially in synapse research, pathogen-host interaction mechanism, and tumor microenvironment analysis. According to the differences in labeling sequence and sample processing, the IEM technology system can be divided into two categories: the first is pre-embedding labeling, which optimizes the labeling efficiency through the pre-exposure of antigenic epitopes and is especially suitable for the detection of low-abundance and sensitive antigens; the second is post-embedding labeling, which relies on the low-temperature resin embedding (e.
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