https://data.botanik.uni-halle.de/bef-china/datasets/456
CSPs: Tree and shrub species allelic richness in the CSPs 2009-2012
Christoph
Hahn
webbaholic@gmx.de
Walter
Durka
Department Community Ecology Helmholtz-Centre for Environmental Research-UFZ
Theodor-Lieser-Str. 4
06120 Halle
Germany
0049 345 5585314
walter.durka@ufz.de
Stefan
Michalski
Department Community Ecology Helmholtz-Centre for Environmental Research-UFZ
Theodor-Lieser-Str. 4
06120 Halle
Germany
++49 0345 5585310
stefan.michalski@ufz.de
2014-02-18
en_US
Genetic diversity parameters have been calculated for several species using molecular markers (Micorsatellites). The aim is to identify patterns of genetic diversity and answer if those are correlated to either species diversity and/or topographic parameters. Data where taken from existing, already published and unpublished studies. Samples where collected from the CSPs in the GNNR from 2008 to 2010. Species include Castanopsis eyrei, Castanopsis fargesii, Cyclobalanopsis glauca, Ardisia crenata, Daphniphyllum oldhamii, Lithocarpus glaber, Quercus serrata, Syzygium buxifolium, Vaccinium carlesii, Schima superba and Rhododendron simsii.
C. eyrei, D. oldhamii, L. glaber, Q. serrata, S. superba include loci potentially under positive selection.
allelic richness
CSP
genetic diversity
plot
shrub species
tree species
Find the list of keywords here: https://data.botanik.uni-halle.de/bef-china/keywords
CSP
Ar_A.crenata
Ar_C.eyrei
Ar_C.fargesii
Ar_C.glauca
Ar_D.oldhamii
Ar_L.glaber
Ar_Q.serrata
Ar_R.simsii
Ar_S.superba
Ar_S.buxifolium
Ar_V.carlesii
List of headers of the data columns in this dataset
Data on Castanopsis eyrei from "Isolation by Elevation: Genetic Structure at Neutral and Putatively Non-Neutral Loci in a Dominant Tree of Subtropical Forests, Castanopsis eyrei By: MiaoMiao SHI; Stefan G. MICHALSKI; Xiao- Yong CHEN, Walter DURKA. Data on Ardisia crenata from " Species diversity and population density affect genetic structure and gene dispersal in a subtropical understory shrub
By:Zeng, XQ (Zeng, Xueqin) ; Michalski, SG (Michalski, Stefan G.); Fischer, M (Fischer, Markus); Durka, W (Durka, Walter)
https://data.botanik.uni-halle.de/bef-china/datasets/456/download.csv?separate_category_columns=true
All 27 CSPs have been sampled with varying degrees of sampling density per species. Additionally, for Rhododendron simsii 50 individuals of a non- CSP population are available (not included in this data set)
117.89978
118.148346
29.285201
29.101777
2009-10-08
2012-12-30
individuals of 11 tree and shrub species
individuals of 11 tree and shrub species
chahn
wdurka
smichalski
CSPs: Tree and shrub species allelic richness in the CSPs 2009-2012
Genetic diversity parameters have been calculated for several species using molecular markers (Micorsatellites). The aim is to identify patterns of genetic diversity and answer if those are correlated to either species diversity and/or topographic parameters. Data where taken from existing, already published and unpublished studies. Samples where collected from the CSPs in the GNNR from 2008 to 2010. Species include Castanopsis eyrei, Castanopsis fargesii, Cyclobalanopsis glauca, Ardisia crenata, Daphniphyllum oldhamii, Lithocarpus glaber, Quercus serrata, Syzygium buxifolium, Vaccinium carlesii, Schima superba and Rhododendron simsii.
2009-10-08 00:00:00 UTC - 2012-12-30 00:00:00 UTC
samples have been collected between 2009 and 2012
All 27 CSPs have been sampled with varying degrees of sampling density per species. Additionally, for Rhododendron simsii 50 individuals of a non- CSP population are available (not included in this data set)
individuals of 11 tree and shrub species
Castanopsis eyrei, Castanopsis fargesii, Cyclobalanopsis glauca, Ardisia crenata, Daphniphyllum oldhamii,Lithocarpus glaber, Quercus serrata, Syzygium buxifolium, Vaccinium carlesii, Schima superba with at least 3 individuals per sampled CSP.
Leaf samples have been dried for transport and genomic DNA has been extracted using QIAGEN Plant 96 Kits. Specific microsatellite markers have been either developed newly or used from published studies (see publication for details). Multiplex PCR were run on Eppendorf cyclers and PCR fragments have been detected on an ABI 3130 capillary sequencer. Fragments binning and scoring were carried out in the Genemapper Software. Loci have been checked for Nullalleles using the Microchecker software which also provided adjusted genotypes. These adjusted genotypes have been used to check for candidate loci under positive selection (loci not behaving neutrally) with the lositan detection workbench. Loci under positive selection have been removed from further analysis. GenAlEx (Genetic Analysis in Excel) software was used to compute F statistics and PCoA for individuals and CSPs. The Fstat software was finally used to calculate allelic richness for all species.
https://data.botanik.uni-halle.de/bef-china/datasets/456
CSPs: Tree and shrub species allelic richness in the CSPs 2009-2012
/bef-china/datasets/456
ASCII
1
column
,
https://data.botanik.uni-halle.de/bef-china/datasets/456/download.csv?separate_category_columns=true
BEF research plot name (CSP),
CSP
Reasearch plots of the Biodiversity - Ecosystem functioning experiment (BEF-China). There are three main sites for research plots in the BEF Experiment: Comparative Study Plots (CSP) in the Gutianshan Nature Reserve (29º8'18" – 29º17'29" N, 118º2'14" – 118º11'12" E, Zhejiang Province Southeast China), having a size of 30x30m^2, measured on the ground. Main Experiment plots have a size of 1 mu, which is about 25x25m^2 in horizontal projection. Pilot Study Plots have a size of 1x1 m^2.
Research plots on the main experiment have a "p" in front of their IDs and then a 6 digit code: Plots in the main sites A (29°07'28.2"N 117°54'27.5"E) and B (29°05'06.8"N 117°55'44.4"E) are named according to their position in the original spreadsheet, in which they were designed. They consist of 6 digits: _1st digit_: Site (1:A, 2:B), _digit 2and3_: southwards row: as in spreadsheets the rows are named from the top to the bottom; _digit 4 and 5_: westward column: as in the original spreadsheet, but the letters are converted to numbers (A=01, B=02); _6th digit_: indicator, if the plot has been shifted a quarter mu. Example: "p205260": "p" means that this is a plot that is specified. "2" means, that we are at site B. Now the coordinates of the south - west corner: "0526". Since "e" is the fifth letter of the alphabet, this is Plot E26. The last digit "0" means that this plot was not moved by a quarter of a Mu, as some sites in Site A. The 6th digit can also indicate the subplot within the plot. "5", "6", "7", "8" indicate the northwest, northeast, southeast, and southwest quarter plot respectively.
Morover, Plots from the main experiment may be labelled in the more ambiguous form of e.g. A32. This indicates a plat either on Site A (29°07'28.2"N 117°54'27.5"E) or Site B (29°05'06.8"N 117°55'44.4"E) of the main experiment. This value only becomes a unique identifier if supported with the "site" information from another cell.
Plots labelled in the form of "1_AO1" or "g1_AO1" or "pilot1_AO1" belong to the "Pilot Experiment" (approx location: 29°06'20.2"N 117°55'12.1"E, Jiangxi Province) (CSP: Number of the CSP)
Reasearch plots of the Biodiversity - Ecosystem functioning experiment (BEF-China). There are three main sites for research plots in the BEF Experiment: Comparative Study Plots (CSP) in the Gutianshan Nature Reserve (29º8'18" – 29º17'29" N, 118º2'14" – 118º11'12" E, Zhejiang Province Southeast China), having a size of 30x30m^2, measured on the ground. Main Experiment plots have a size of 1 mu, which is about 25x25m^2 in horizontal projection. Pilot Study Plots have a size of 1x1 m^2.
Research plots on the main experiment have a "p" in front of their IDs and then a 6 digit code: Plots in the main sites A (29°07'28.2"N 117°54'27.5"E) and B (29°05'06.8"N 117°55'44.4"E) are named according to their position in the original spreadsheet, in which they were designed. They consist of 6 digits: _1st digit_: Site (1:A, 2:B), _digit 2and3_: southwards row: as in spreadsheets the rows are named from the top to the bottom; _digit 4 and 5_: westward column: as in the original spreadsheet, but the letters are converted to numbers (A=01, B=02); _6th digit_: indicator, if the plot has been shifted a quarter mu. Example: "p205260": "p" means that this is a plot that is specified. "2" means, that we are at site B. Now the coordinates of the south - west corner: "0526". Since "e" is the fifth letter of the alphabet, this is Plot E26. The last digit "0" means that this plot was not moved by a quarter of a Mu, as some sites in Site A. The 6th digit can also indicate the subplot within the plot. "5", "6", "7", "8" indicate the northwest, northeast, southeast, and southwest quarter plot respectively.
Morover, Plots from the main experiment may be labelled in the more ambiguous form of e.g. A32. This indicates a plat either on Site A (29°07'28.2"N 117°54'27.5"E) or Site B (29°05'06.8"N 117°55'44.4"E) of the main experiment. This value only becomes a unique identifier if supported with the "site" information from another cell.
Plots labelled in the form of "1_AO1" or "g1_AO1" or "pilot1_AO1" belong to the "Pilot Experiment" (approx location: 29°06'20.2"N 117°55'12.1"E, Jiangxi Province)
BEF research plot name
Reasearch plots of the Biodiversity - Ecosystem functioning experiment (BEF-China). There are three main sites for research plots in the BEF Experiment: Comparative Study Plots (CSP) in the Gutianshan Nature Reserve (29º8'18" – 29º17'29" N, 118º2'14" – 118º11'12" E, Zhejiang Province Southeast China), having a size of 30x30m^2, measured on the ground. Main Experiment plots have a size of 1 mu, which is about 25x25m^2 in horizontal projection. Pilot Study Plots have a size of 1x1 m^2.
Research plots on the main experiment have a "p" in front of their IDs and then a 6 digit code: Plots in the main sites A (29°07'28.2"N 117°54'27.5"E) and B (29°05'06.8"N 117°55'44.4"E) are named according to their position in the original spreadsheet, in which they were designed. They consist of 6 digits: _1st digit_: Site (1:A, 2:B), _digit 2and3_: southwards row: as in spreadsheets the rows are named from the top to the bottom; _digit 4 and 5_: westward column: as in the original spreadsheet, but the letters are converted to numbers (A=01, B=02); _6th digit_: indicator, if the plot has been shifted a quarter mu. Example: "p205260": "p" means that this is a plot that is specified. "2" means, that we are at site B. Now the coordinates of the south - west corner: "0526". Since "e" is the fifth letter of the alphabet, this is Plot E26. The last digit "0" means that this plot was not moved by a quarter of a Mu, as some sites in Site A. The 6th digit can also indicate the subplot within the plot. "5", "6", "7", "8" indicate the northwest, northeast, southeast, and southwest quarter plot respectively.
Morover, Plots from the main experiment may be labelled in the more ambiguous form of e.g. A32. This indicates a plat either on Site A (29°07'28.2"N 117°54'27.5"E) or Site B (29°05'06.8"N 117°55'44.4"E) of the main experiment. This value only becomes a unique identifier if supported with the "site" information from another cell.
Plots labelled in the form of "1_AO1" or "g1_AO1" or "pilot1_AO1" belong to the "Pilot Experiment" (approx location: 29°06'20.2"N 117°55'12.1"E, Jiangxi Province)
Number of the CSP
Intraspecific diversity (Ar_A.crenata),
Ar_A.crenata
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity. (Ar_A.crenata: Mean allelic richness over all loci, Ardisia crenata)
dimensionless
real
Intraspecific diversity
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity.
Mean allelic richness over all loci, Ardisia crenata
Fstat software to compute allelic richness
Intraspecific diversity (Ar_C.eyrei),
Ar_C.eyrei
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity. (Ar_C.eyrei: Mean allelic richness over all loci, Castanopsis eyrei)
dimensionless
real
Intraspecific diversity
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity.
Mean allelic richness over all loci, Castanopsis eyrei
Fstat software to compute allelic richness
Intraspecific diversity (Ar_C.fargesii),
Ar_C.fargesii
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity. (Ar_C.fargesii: Mean allelic richness over all loci, Castanopsis fargesii)
dimensionless
real
Intraspecific diversity
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity.
Mean allelic richness over all loci, Castanopsis fargesii
Fstat software to compute allelic richness
Intraspecific diversity (Ar_C.glauca),
Ar_C.glauca
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity. (Ar_C.glauca: Mean allelic richness over all loci, Cyclobalanopsis glauca)
dimensionless
real
Intraspecific diversity
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity.
Mean allelic richness over all loci, Cyclobalanopsis glauca
Fstat software to compute allelic richness
Intraspecific diversity (Ar_D.oldhamii),
Ar_D.oldhamii
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity. (Ar_D.oldhamii: Mean, allelic richness over all loci, Daphniphyllum oldhamii)
dimensionless
real
Intraspecific diversity
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity.
Mean, allelic richness over all loci, Daphniphyllum oldhamii
Fstat software to compute allelic richness
Intraspecific diversity (Ar_L.glaber),
Ar_L.glaber
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity. (Ar_L.glaber: Mean allelic richness over all loci, Lithocarpus glaber)
dimensionless
real
Intraspecific diversity
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity.
Mean allelic richness over all loci, Lithocarpus glaber
Fstat software to compute allelic richness
Intraspecific diversity (Ar_Q.serrata),
Ar_Q.serrata
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity. (Ar_Q.serrata: Mean allelic richness over all loci, Quercus serrata)
dimensionless
real
Intraspecific diversity
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity.
Mean allelic richness over all loci, Quercus serrata
Fstat software to compute allelic richness
Intraspecific diversity (Ar_R.simsii),
Ar_R.simsii
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity. (Ar_R.simsii: Mean allelic richness over all loci, Rhododendron simsii)
dimensionless
real
Intraspecific diversity
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity.
Mean allelic richness over all loci, Rhododendron simsii
Fstat software to compute allelic richness
Intraspecific diversity (Ar_S.superba),
Ar_S.superba
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity. (Ar_S.superba: Mean allelic richness over all loci, Schima superba)
dimensionless
real
Intraspecific diversity
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity.
Mean allelic richness over all loci, Schima superba
Fstat software to compute allelic richness
Intraspecific diversity (Ar_S.buxifolium),
Ar_S.buxifolium
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity. (Ar_S.buxifolium: Mean allelic richness over all loci, Syzygium buxifolium)
dimensionless
real
Intraspecific diversity
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity.
Mean allelic richness over all loci, Syzygium buxifolium
Fstat software to compute allelic richness
Intraspecific diversity (Ar_V.carlesii),
Ar_V.carlesii
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity. (Ar_V.carlesii: Mean allelic richness over all loci, Vaccinium carlesii)
dimensionless
real
Intraspecific diversity
Ar is number of alleles based on min. sample size of 29 individuals, He is expected heterozygosity , Ar5 is the number of rare alleles (total abundance <5%), and FIS is the inbreeding coefficient. We computed pairwise kinship coefficient (Fij) (Loiselle et al. 1995) to construct spatial autocorrelograms with distance class limits of 2.5, 5, 10, 15, 20, 30, and 50 m. Significance of mean Fij per class was tested with 1000 permutations of multi-locus genotypes. We used the Sp statistic (Vekemans & Hardy 2004) to quantify SGS for individual plots as Sp = -b_log / (1- F_1), where blog is the slope of the regression of kinship coefficient on log geographic distance and F_1 is the mean kinship coefficient between individuals of the first distance class. --- A is the number of alleles; Ar is number of alleles based on min. sample size of 12 individuals; He is expected heterozygosity.
Mean allelic richness over all loci, Vaccinium carlesii
Fstat software to compute allelic richness
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