CSPs: Genetic diversity of Ardisia crenata

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Permission is granted to anybody to access, use and publish all open for public data freely. The commercial use of any data is prohibited. The quality and completeness of data cannot be guaranteed. Users employ these data at their own risk. In order to make attribution of use for owners of the data possible, the identifier of ownership of data must be retained with every data record. Users must publicly acknowledge, in conjunction with the use of the data, the data owners. Cite the data as follows: Zeng, X., Fischer, M. and Durka, W. (2013): Genetic diversity of Ardisia crenata. BEF-China data portal (Accessed through URL http://china.befdata.biow.uni-leipzig.de/datasets/160)

Dataset Abstract

The study of spatial genetic structure (SGS) can provide an understanding of the key processes and factors involved in the maintenance of viable populations. Within plant populations SGS is mainly determined by the interplay between gene flow by seed and pollen dispersal, which are expected to be affected by species traits, biotic or abiotic interaction within the habitat.
In a set of 12 populations of the subtropical understory shrub Ardisia crenata, we assessed genetic variation at 7 microsatellite loci within and among populations. Small-scale genetic structure was assessed with spatial genetic autocorrelation statistics and heterogeneity tests. We estimated gene dispersal distances based on population differentiation and on SGS. Relationships of gene dispersal with habitat characteristics were assessed with multiple regressions.
The populations showed high genetic diversity (He = 0.64) within populations and rather strong genetic differentiation ( = 0.208) among populations. The population differentiation followed an isolation by distance model which suggests that populations are in gene flow-drift equilibrium. Significant spatial genetic structure was present within populations (mean Sp = 0.027). Among the habitat characters, population density and species diversity had a joint effect on SGS. Populations with low population density and high species diversity had significantly stronger SGS than plots with high population density and low species diversity, respectively. Gene dispersal estimated from population differentiation and from SGS resulted in similar values, indicating that the same processes are involved in shaping the genetic structure at both scales. We suggest that local-ranged pollen dispersal and inefficient long-distance seed dispersal, which may be affected by population density and species diversity, contributed to the genetic population structure of the species.

Dataset Design

We established 12 plots of 30×30 m, which correspond to comparative study plots (CSPs) in the project “Biodiversity and Ecosystem Functioning of China, BEF China” (Bruehlheide et al. 2010). The plots covered the full altitudinal range of the species and different forest types. For each plot we recorded successional stage and elevation, tree density (number of woody stems, > 1 m height) as well as species diversity of all woody species (based on rarefaction analysis, Bruehlheide et al. 2010; Tab. 1). We mapped all A. crenata individuals inside the plots, determined number of A. crenata individuals per plot as population density and randomly sampled leaves from 30 individuals. Leaf samples were dried at 40°C for 48 hours and then kept dry in silica gel.

Spatial Extent

The Gutianshan National Nature Reserve (NNR) is located in the western part of Zhejiang Province (29º8'18" – 29º17'29" N, 118º2'14" – 118º11'12" E, Fig. 1). The Gutianshan NNR has an area of approximately 81 km2 and was initially established as a National Forest Reserve in 1975 and became a National Nature Reserve in 2001. The NNR comprises a large portion of broad-leaved forests of advanced successional stages (Hu & Yu 2008), which have not been managed since the beginning of the 1990ies, as well as young successional stages and conifer plantations, mainly of Cunninghamia lanceolata and Pinus massoniana. --- The vegetation is composed of different types of subtropical evergreen and mixed broad-leaved forests (Yu et al. 2001). Most of the stands are secondary forests, evidenced by maximum tree ages of 180 years, by agricultural terraces in almost all plots and by the presence of charcoal in almost all soil profiles. Around the Gutianshan NRR extensive deforestation has occurred during the Great Leap Forward in the 1950s, as in most parts of Southeast China. However, due to prevailing steep slopes, the Gutianshan area was only marginally usable for agricultural activities, and thus an exceptionally intact forest cover has been preserved. --- The climate at Gutianshan NNR is warm and temperate with a short dry season in November and December and with warm summers (Fig. 2). The climatic conditions are characteristic for the subtropics with an annual average temperature of 15.1°C, January minimum temperatures of -6.8°C, July maximum temperatures of 38.1°C and an accumulated temperature sum (≥ 5°C) of 5221.5 degree days.

Published

Zeng, X. et al. (2010) Species diversity and population density affect genetic structure and gene dispersal in a subtropical understory shrub. doi:10.1093/jpe/rtr029,Citation: J. Plant Ecology

Temporal Extent

the samples were collected in 2008

Taxonomic Extent

Ardisia crenata

Data Analysis

Genomic DNA was extracted from leaf samples using the DNeasy Plant minikit (QIAGEN). Seven highly polymorphic microsatellite loci were used for genotyping (Hong et al. 2008). Fragment analysis was performed on an ABI PRISM 3130 with GS600-LIZ (Applied Biosystems) as internal size standard and using GeneMapper v.3.7 software for genotyping.We used Micro-Checker 2.2.3 (Van Oosterhout et al. 2004),Fstat v. 2.9.3.2 (Goudet 2001),SPAGeDi v. 1.3 (Hardy & Vekemans 2002) to analyse the data.

Paper proposal submissions

Published

2010

Data columns available in the raw data part of this dataset

date
year of sampling
Unit: year
Data group: Date time information
Keywords: date
Values
2008
CSP
To increase the genetic diversity captured by BEF-China, addidtional individuals of the respective species were sampled. These were collected in "new" CSPs. See "Dataset design" for further details
Unit: id
Data group: BEF research plot name
Keywords: CSP
Values
CSP04
CSP01
CSP07
CSP02
CSP06
N_leaves
number of leaf samples, all diversity statistics are based on these leaves genotyped
Data group: Sample size
Keywords: leaf
Values
33
136
29
32
104
Density
density of population per plot
Unit: Ind./per Plot(900m2)
Data group: Density measure
Keywords: density, abundance
Values
136
32
104
33
29
He
gene diversity
Data group: Intraspecific diversity
Keywords: biodiversity, intraspecific diversity, genetic diversity
Values
0.64
0.6
0.59
0.63
0.62
Ar
allelic richness
Data group: Intraspecific diversity
Keywords: allelic richness
Values
5.97
4.98
5.29
5.39
5.82
Ar5
number of rare alleles (<5% allele frequency)
Data group: Intraspecific diversity
Values
13
12
10
16
15
FIS
inbreeding coefficient
Data group: Intraspecific diversity
Keywords: inbreeding
Values
0.1
0.06
0.03
0.09
0.02
F_1
kinship coefficient in the first distance class
Data group: Intraspecific diversity
Values
-0.044
0.044
0.048
0.032
0.028
b_log
regression slope of spatial genetic autocorrelation
Data group: Intraspecific diversity
Keywords: genetic autocorrelation
Values
-0.014
-0.005
-0.019
-0.016
0.014
Sp
Sp statistic
Data group: Intraspecific diversity
Keywords: spatial genetic structure
Values
0.02
0.017
-0.014
0.005
0.014