Decapterus maruadsi is a commercially important species in China, but has been heavily exploited in some areas. There is a growing need to develop microsatellites promoting its genetic research for the adequate management of this fishery resources. The recently developed specific-locus amplified fragment sequencing (SLAF-seq) is an efficient and high-resolution method for genome-wide microsatellite markers discovery. In this study, 28 905 microsatellites (mono- to hexa-nucleotide repeats) were identified using SLAF-seq technology, of which di-nucleotide was the most frequent (13 590, 47.02%), followed by mono-nucleotide (8 138, 28.15%), tri-nucleotide (5 727, 19.81%), tetra-nucleotide (1 104, 3.82%), pentanucleotide (234, 0.81%), and hexa-nucleotide (112, 0.39%). One hundred and thirty-two microsatellite loci (di- and tri-nucleotide) were randomly selected for amplification and polymorphism, of which 49 were highly polymorphic and well-resolved. The average number of alleles per locus was 13.63, ranging from 4 to 25, and allele sizes varied between 110 bp and 309 bp. The observed heterozygosity ( Ho ) and expected heterozygosity ( He ) ranged from 0.233 to 1.000 and from 0.374 to 0.959, with mean values of 0.738 and 0.836, respectively. The polymorphism information content (PIC) ranged from 0.341 to 0.941 (mean=0.806). However, 12 loci deviated from Hardy-Weinberg equilibrium. Furthermore, transferability tests were also successful in validating the utility of the developed markers in five phylogenetically related species of family Carangidae. A total of 48 microsatellite markers were successfully cross-amplified in Decapterus macarellus, Decapterus macrosoma, Decapterus kurroides, Trachurus japonicus, and Selaroides leptolepis. The present microsatellites provided the first known set of microsatellite DNA markers for D. maruadsi, D. macarellus, D. kurroides, and D. macrosoma, and would be useful for further population genetic and molecular phylogeny studies as well as help with the fisheries management formulation and implementation of the understudied species.
Urban agglomeration is caused by the continuous acceleration of the urbanization process in China. Studying the expansion of construction land can not only know the changes and development of urban agglomeration in time, but also obtain the great significance of the future management. In this study, taking Changsha-Zhuzhou-Xiangtan (Chang-Zhu-Tan) urban agglomeration in Hunan province as a study area, Landsat images from 1995 to 2014 and Autologistic-CLUE-S model simulation data were used. Moreover, several factors including gravity center, direction, distance and landscape index were considered in the analysis of the expansion. The results revealed that the construction area increased by 132.18%, from 372.28 km2 in 1995 to 864.37 km2 in 2014. And it might even reach 1327.23 km2 in 2023. Before 2014, three cities had their own respective and discrete development directions. However, because of the integration policy implementation in 2008, the Chang-Zhu-Tan began to gather, the gravity center moved southward after 2014, and the distance between cities decreased, which was in line with the development plan of urban expansion. The research methods and results were relatively reliable, and these results could provide some reference for the future land use planning and spatial allocation in the urbanization process of Chang-Zhu-Tan urban agglomeration.
Existing spatial clustering methods primarily focus on points distributed in planar space. However, occurrence locations and background processes of most human mobility events within cities are constrained by the road network space. Here we describe a density-based clustering approach for objectively detecting clusters in network-constrained point events. First, the network-constrained Delaunay triangulation is constructed to facilitate the measurement of network distances between points. Then, a combination of network kernel density estimation and potential entropy is executed to determine the optimal neighbourhood size. Furthermore, all network-constrained events are tested under a null hypothesis to statistically identify core points with significantly high densities. Finally, spatial clusters can be formed by expanding from the identified core points. Experimental comparisons performed on the origin and destination points of taxis in Beijing demonstrate that the proposed method can ascertain network-constrained clusters precisely and significantly. The resulting time-dependent patterns of clusters will be informative for taxi route selections in the future. 相似文献
The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park’s test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications. 相似文献
