The effects of autogenic-allogenic status on the species-area relationship and the relationship between geographic distance and intercommunity dissimilarity were investigated in macroparasite communities of the bluegill sunfish Lepomis macrochirus. Rank correlation analyses were used to examine the relationship between pond surface area and species richness of all species collectively and of autogenic species and allogenic species separately. A positive relationship was found for allogenic species, whereas there was no association for all species, nor was there an association when the study was restricted to autogenic species. Mantel tests were used to determine the relationship between geographic distance and community dissimilarity for all species and for autogenic and allogenic species independently. Total community dissimilarity and allogenic dissimilarity were found to be influenced by geographic distance, whereas autogenic dissimilarity was random with regard to interpond distances. These findings serve to illustrate the importance of the autogenic-allogenic dichotomy and demonstrate that dispersal ability can influence commonly observed ecological patterns.
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http://dx.doi.org/10.1645/GE-451R.1 | DOI Listing |
Environ Int
December 2024
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China; State Environmental Protection Key Laboratory of Estuarine and Coastal Environment, Chinese Research Academy of Environmental Science, Beijing 100012, China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China. Electronic address:
Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosystems and human health. Machine learning (ML) is a powerful analytical tool for tracking human impacts on river ecosystems based on high-through datasets. This study employed an ML framework and 16S rRNA sequencing data to reveal microbial dynamics and trace human activities across China.
View Article and Find Full Text PDFBackground: This study evaluated the quality of cancer recurrence data in the National Cancer Database (NCDB) to determine if missingness and reporting consistency have improved enough to support national research.
Methods: This multi-methods study included NCDB analyses and a cancer registry staff survey. Trends in recurrence data missingness from 2004 to 2021 and multivariable analyses of factors associated with missingness from 2017 to 2021 were evaluated for 4,568,927 patients with non-metastatic cancer.
Sci Rep
December 2024
Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
The network layer plays a crucial role in blockchain systems, enabling essential functions such as message broadcasting and data synchronization. Enhancing data transmission structures and methods at this layer is key to improving scalability and addressing performance limitations. Currently, the uneven distribution of neighboring node lists and the lack of awareness of underlying linkages in coverage networks hinder the efficiency and comprehensiveness of information transmission.
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December 2024
School of Population Health, Faculty of Health Sciences, Curtin University, Bentley, WA, Australia.
While bacille-calmette-guerin (BCG) vaccination is one of the recommended strategies for preventing tuberculosis (TB), its coverage is low in several countries, including Ethiopia. This study investigated the spatial co-distribution and drivers of TB prevalence and low BCG coverage in Ethiopia. This ecological study was conducted using data from a national TB prevalence survey and the Ethiopian demographic and health survey (EDHS) to map the spatial co-distribution of BCG vaccination coverage and TB prevalence.
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December 2024
Department of Geographic Information System, Chinese Academy of Surveying and mapping, Beijing, 100036, China.
Geographic entity matching is an important means for multi-source spatial data fusion and information association and sharing. Corresponding matching methods have been designed by existing studies for different types of entity data characteristics, such as line and area. However, these approaches are often limited in the generalization ability for matching heterogeneous data from multiple sources and the accuracy for complex pattern matching.
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