Spatial autocorrelation analysis of small-scale genetic structure in a clonal soft coral with limited larval dispersal

1996 ◽  
Vol 126 (2) ◽  
pp. 215-224 ◽  
Author(s):  
C. S. McFadden ◽  
K. Y. Aydin
Keyword(s):  
Genetic Structure ◽  
Spatial Autocorrelation ◽  
Larval Dispersal ◽  
Soft Coral ◽  
Small Scale ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis

scholarly journals Spatial Autocorrelation Analysis Using MIG-seq Data Indirectly Estimated the Gamete and Larval Dispersal Range of the Blue Coral, Heliopora coerulea, Within Reefs

2021 ◽  
Vol 8 ◽  
Author(s):  
Daniel Frikli Mokodongan ◽  
Hiroki Taninaka ◽  
La Sara ◽  
Taisei Kikuchi ◽  
Hideaki Yuasa ◽  
...  
Keyword(s):  
Genetic Structure ◽  
Spatial Autocorrelation ◽  
Larval Dispersal ◽  
Population Genetic ◽  
Spatial Genetic Structure ◽  
Spatial Autocorrelation Analysis ◽  
Nucleotide Polymorphisms ◽  
Autocorrelation Analysis ◽  
Distance Method ◽  
Genome Wide

Spatial autocorrelation analysis is a well-established technique for detecting spatial structures and patterns in ecology. However, compared to inter-population genetic structure, much less studies examined spatial genetic structure (SGS) within a population by means of spatial autocorrelation analysis. More SGS analysis that compares the robustness of genome-wide single nucleotide polymorphisms (SNPs) and traditional population genetic markers in detecting SGS, and direct comparison between the estimated dispersal range based on SGS and the larval dispersal range of corals directly surveyed in the field would be important. In this study, we examined the SGS of a reef-building coral species, Heliopora coerulea, in two different reefs (Shiraho and Akaishi) using genome-wide SNPs derived from Multiplexed inter-simple sequence repeat (ISSR) genotyping by sequencing (MIG-seq) analysis and nine microsatellite loci for comparison. Microsatellite data failed to reveal significant spatial patterns when using the same number of samples as MIG-seq, whereas MIG-seq analysis revealed significant spatial autocorrelation patterns up to 750 m in both Shiraho and Akaishi reefs based on the maximum significant distance method. However, detailed spatial genetic analysis using fine-scale distance classes (25–200 m) found an x-intercept of 255–392 m in Shiraho and that of 258–330 m in Akaishi reef. The latter results agreed well with a previously reported direct field observation of larval dispersal, indicating that the larvae of H. coerulea settled within a 350 m range in Shiraho reef within one generation. Overall, our results empirically demonstrate that the x-intercept of the spatial correlogram agrees well with the larval dispersal distance that is most frequently found in field observations, and they would be useful for deciding effective conservation management units for maintenance and/or recovery within an ecological time scale.

Spatial genetic structure in populations of Quercus mongolica var. grosseserrata (Fagaceae) from southern Korea

2004 ◽  
Vol 82 (9) ◽  
pp. 1402-1408 ◽  
Author(s):  
Mi Yoon Chung ◽  
Myong Gi Chung
Keyword(s):  
Genetic Structure ◽  
Spatial Autocorrelation ◽  
Spatial Genetic Structure ◽  
Ground Vegetation ◽  
Spatial Autocorrelation Analysis ◽  
Fine Scale ◽  
Autocorrelation Analysis ◽  
Quercus Mongolica ◽  
Acorn Dispersal ◽  
Southern Korea

Multilocus, putative allozyme genotypes were mapped and sampled from two local populations of Quercus mongolica Fischer ex Turcz var. grosseserrata (Bl.) Rehder & Wilson (Fagaceae) (each area is 100 m × 100 m, one with Sasa cover (N = 62) versus a second without it (N = 384)) occurring in undisturbed forests near Nogodan, Mount Jiri in southern Korea. Ripley's L-statistics and spatial autocorrelation analysis (a coancestry coefficient, fij) were used to test the prediction that because of low seedling establishment in a population with dense Sasa cover, there would be no spatial aggregation or hyperdispersion of individual trees and little evidence of fine-scale genetic structure in the population. As predicted, the Sasa-covered population showed no evidence of significant aggregation of individuals (P < 0.01) up to an interplant distance of 50 m and a random distribution of putative genotypes in the population. By contrast, the L-statistics conducted in the Sasa-free population indicated significant aggregation of individuals at interplant distances extending from 4 to 50 m. Spatial autocorrelation analysis revealed small but significant (P < 0.01), positive, fine-scale genetic structure extending from 10 to 30 m. A very similar result was obtained from 100 replicates each consisting of 62 trees in the Sasa-free populations by applying rarefaction and bootstrapping. These findings support the hypothesis that ground vegetation such as Sasa spp. has an impact on fine-scale genetic structure. The weak spatial genetic structure found in the Sasa-free population may primarily be due to limited acorn dispersal coupled with overlapping seed shadows and (or) secondary acorn dispersal by rodents.Key words: allozymes, Fagaceae, ground cover, Quercus mongolica var. grosseserrata, Sasa spp., spatial genetic structure.

GENETICS OF POPULATION EXCHANGE ALONG THE HISTORICAL PORTUGUESE–SPANISH BORDER

2012 ◽  
Vol 45 (1) ◽  
pp. 79-93 ◽  
Author(s):  
J. ROMÁN-BUSTO ◽  
M. TASSO ◽  
G. CARAVELLO ◽  
V. FUSTER ◽  
P. ZULUAGA
Keyword(s):  
Genetic Structure ◽  
Spatial Autocorrelation ◽  
Distribution Patterns ◽  
Social Conditions ◽  
Border Region ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Mountain Ranges ◽  
Population Exchange ◽  
Low Diversity

SummaryThe present analysis compares the distribution of surnames by means of spatial autocorrelation analysis in the Spain–Portugal border region. The Spanish National Institute of Statistics provides a database of surnames of residents in the western Spanish provinces of Zamora, Salamanca, Cáceres, Badajoz and Huelva. The Spanish and Portuguese patterns of surname distribution were established according to various geographic axes. The results obtained show a low diversity of surnames in this region – especially in the centre – which can be explained by the absence of any major geographic barriers, with the exception of the mountain ranges between hydrographic basins, and by the presence of traditional roads that have existed since Roman times. The latter have resulted in a constant migratory flow over short–median distances, which, as can be deduced from the surnames, fits two north/south territorial axes running parallel to the border between Spain and Portugal. The distribution patterns of Portuguese and Spanish surnames differ with regard to their frequencies in the five provinces studied, which can be attributed to their respective historical, economic and social conditions. It is concluded that the border delimiting these two countries has affected the migratory flow, thereby conditioning the demographic and genetic structure of the western Spanish regions.

scholarly journals Spatial and Temporal Analysis of Lung Cancer in Shenzhen, 2008–2018

2020 ◽  
Vol 18 (1) ◽  
pp. 26
Author(s):  
Lin Lei ◽  
Anyan Huang ◽  
Weicong Cai ◽  
Ling Liang ◽  
Yirong Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Spatial Autocorrelation ◽  
Incidence Rate ◽  
Cancer Incidence ◽  
Southern China ◽  
Percentage Change ◽  
Spatial Autocorrelation Analysis ◽  
Lung Cancer Incidence ◽  
Annual Percentage Change ◽  
Autocorrelation Analysis

Lung cancer is the most commonly diagnosed cancer in China. The incidence trend and geographical distribution of lung cancer in southern China have not been reported. The present study explored the temporal trend and spatial distribution of lung cancer incidence in Shenzhen from 2008 to 2018. The lung cancer incidence data were obtained from the registered population in the Shenzhen Cancer Registry System between 2008 and 2018. The standardized incidence rates of lung cancer were analyzed by using the joinpoint regression model. The Moran’s I method was used for spatial autocorrelation analysis and to further draw a spatial cluster map in Shenzhen. From 2008 to 2018, the average crude incidence rate of lung cancer was 27.1 (1/100,000), with an annual percentage change of 2.7% (p < 0.05). The largest average proportion of histological type of lung cancer was determined as adenocarcinoma (69.1%), and an increasing trend was observed in females, with an average annual percentage change of 14.7%. The spatial autocorrelation analysis indicated some sites in Shenzhen as a high incidence rate spatial clustering area. Understanding the incidence patterns of lung cancer is useful for monitoring and prevention.

Spatial genetic substructure within natural populations of jack pine (Pinus banksiana)

1991 ◽  
Vol 69 (3) ◽  
pp. 547-551 ◽  
Author(s):  
Chang Yi Xie ◽  
Peggy Knowles
Keyword(s):  
Spatial Autocorrelation ◽  
Pinus Banksiana ◽  
Pollen Dispersal ◽  
Natural Populations ◽  
Jack Pine ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Long Distance ◽  
Genetic Substructure ◽  
Good Agreement

Spatial autocorrelation analysis was used to investigate the geographic distribution of allozyme genotypes within three natural populations of jack pine (Pinus banksiana Lamb.). Results indicate that genetic substructuring within these populations is very weak and the extent differs among populations. These results are in good agreement with those inferred from mating-system studies. Factors such as the species' predominantly outbreeding system, high mortality of selfs and inbreds prior to reproduction, long-distance pollen dispersal, and the absence of strong microhabitat selection may be responsible for the observed weak genetic substructuring. Key words: jack pine, Pinus banksiana, genetic substructure, allozyme, spatial autocorrelation analysis.

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