DOI: 10.5061/dryad.9zw3r22mt
title: Data from: Revealing biogeographic patterns in genetic diversity of native and invasive plants and their association with soil community diversity in the Chinese coast

description: Within-species genetic diversity is shaped by multiple evolutionary forces
 within the confines of geography, and has cascading effects on the
 biodiversity of other taxa and levels. Invasive species are often
 initially limited in genetic diversity but still respond rapidly to their
 new range, possibly through ‘pre-adapted’ genotypes or multiple sources of
 genetic diversity, but little is known about how their genetic structure
 differs from that of native species and how it alters the genetic-species
 diversity relationship. Here, we selected a widespread native species
 (Phragmites australis) and its co-occurring invasive competitor (Spartina
 alterniflora) as our model plant species. We investigated the genetic
 structure of P. australis using two chloroplast fragments and ten nuclear
 microsatellites in 13 populations along the Chinese coastal wetlands. We
 discovered a distinct geographical differentiation, showing that the
 northern and southern populations harbored unique genotypes. We also found
 a significant increase in genetic diversity (allelic richness and expected
 heterozygosity) from south to north. Combined with previous studies of S.
 alterniflora, the Mantel tests revealed a significant correlation of
 genetic distances between P. australis and S. alterniflora even when
 controlling for geographic distance, suggesting that the invasive species
 S. alterniflora might exhibit a phylogeographic pattern similar to that of
 the native species to some extent. Furthermore, our results suggest that
 the S. alterniflora invasion has altered the relationship between the
 genetic diversity of the dominant native plant and the associated species
 richness of soil nematodes. The reason for the alteration of
 genetic-species diversity relationship might be that the biological
 invasion weakens the environmental impact on both levels of biodiversity.
 Our findings contribute to understanding the latitudinal patterns of
 intraspecific genetic diversity in widespread species. This work on the
 genetic diversity analysis of native species also provides significant
 implications for the invasion stage and ecological consequences of
 biological invasions.
description: Sampling, genotyping and sequencing of
 <em>P. australis</em> We collected
 194 individuals of <em>P. australis </em>from 13 sites along
 the Chinese coast. To avoid collecting the same clone and to get enough
 genetic variation, we selected five <em>P. australis </em>reed
 populations within each site, with a distance of at least 1 km between
 stands. We collected three
 individuals from each population. All individuals were transplanted with
 rhizomes in a common garden at the Jiangwan Campus of Fudan University in
 Shanghai (31.28°N, 121.48°E). After the plants regrew, we collected and
 dried young leaves from all individuals, and stored them in zip-lock plastic bags with
 silica gel at room temperature until DNA isolation. We extracted total DNA
 from the dried leaves according to a modified cetyltrimethylammonium
 bromide (CTAB) method. We examined the quality and quantity of extracted
 DNA with 1% agarose gels and a microscope spectrophotometer, and stored
 DNA at -20℃ until later genotyping and
 sequencing. To measure
 genetic variation, we used 10 microsatellite primer pairs previously
 designed for<em> P. australis </em>(Saltonstall 2003, Yu et
 al. 2013). Forward primers were labeled at the 5’ end with the fluorescent
 dyes FAM, HEX or TAMRA. We performed polymerase chain reaction (PCR) as
 described by Liu et al. (2022), and separated the PCR products by
 capillary electrophoresis using an ABI 3730XL DNA capillary sequencer
 (Applied Biosystems, Foster City, California, USA) after confirming the
 PCR product on a 2% agarose gel. We scored fragment profiles and carefully
 check the stutter peaks and the low-frequency alleles with GeneMarker
 2.2.0 to reduce the potential effect of null allele. We did not discover
 the null alleles with the Hardy-Weinberg equilibrium-based method, since
 there is no reliable approach to elimination of allele dosage for our
 polyploid data. The same clones were detected by the function assignClones
 in R package polysat (Clark and Jasieniuk 2011). The duplicated genotypes
 were removed for further genetic estimates. To determine
 the haplotype, we amplified two non-coding chloroplast regions by PCR in
 one sample of each stand, using the primer pairs [trnT (UGU) “a”-trnL
 (UAA) “b” and rbcL-psaI] as described previously (Saltonstall 2002). We
 sequenced the PCR products in both directions on an ABI 3730XL DNA
 sequencer (Applied Biosystems). We assembled and checked the sequencing
 with SeqMan 7.7.0 (Lasergene, Santa Clara, USA) and identified haplotypes
 to the naming scheme of <em>P. australis </em>described by
 Saltonstall (Saltonstall 2016). Data analysis
 of genetic diversity and structure of <em>P.
 australis</em> To estimate
 the genetic diversity level of <em>P. australis</em>, we
 calculated the number of alleles per locus or allelic richness
 (N<sub>a</sub>), and the expected heterozygosity
 (H<sub>e</sub>) with R package polysat (Clark and Jasieniuk
 2011). We assessed the relationship between genetic diversity and latitude
 using linear regression. To assess the
 genetic structure of <em>P. australis</em>, we calculated
 genetic differentiation (F<sub>st</sub>) with the R package
 polysat. We also calculated Pairwise Bruvo distances based on
 microsatellite variation, and used the genetic distance matrix for
 principal coordinates analysis (PCoA) and hierarchical cluster analysis
 using the unweighted pair-group method with arithmetic means (UPGMA). We
 applied Bayesian clustering with Structure 2.3.4 (Pritchard et al. 2000)
 to detect the genetic structure of <em>P. australis</em>. We
 performed 20 replicates of the clustering analysis at each value of K from
 1 to 10 under the admixture model with 50,000 burn-in steps and 500,000
 Markov Chain Monte Carlo repeats. We calculated Delta K using the online
 program Structure Harvest (Earl and vonHoldt 2012) to determine the most
 likely cluster number (K value) for our genetic data, grouped replicates
 in CLUMPP 1.1.2b (Jakobsson and Rosenberg 2007) and visualized in DISTRUCT
 1.1 (Ramasamy et al. 2014). Correlation
 analysis between geographical and genetic distances of <em>P.
 australis </em>and<em> S.
 alterniflora</em> We used the
 previously published nuclear microsatellites and chloroplast sequences
 data of <em>S. alterniflora </em>in China (Qiao et al. 2019,
 Shang et al. 2019, Xia et al. 2020). We extracted the geographical coordinates and
 the diversity indices (i.e., Allele number, N<sub>a</sub>;
 Expected heterozygosity, H<sub>e</sub>) of surveyed
 populations of <em>S. alterniflora</em> from Shang et al.
 (2019) and Xia et al. (2020) for further comparisons. For genetic analysis
 of <em>S. alterniflora</em>, we used the raw data of 11
 nuclear microsatellites from Qiao et al. (2019). We removed three loci
 from the raw dataset because there were many missing values or null
 alleles in loci 5, 7 and 9. We calculated geographic distances using the
 function distm in R package geosphere and used pairwise Fst for genetic
 distance. We used Mantel test and multiple matrix
 regression with randomization (MMRR) (Wang 2013) to examine relationships
 between geographic and genetic distance matrices at the site level. We ran
 correlation analyses between geographical and genetic distance matrices
 using the function mantel in R package vegan, and regression analyses
 using the function MMRR written by Wang (2013) with genetic distance as
 the dependent matrix and geographical distances as the independent
 (predictor) matrices with 9,999 permutations. The correlation of genetic
 distances between the two species were also performed with partial Mantel
 test while controlling the geographical distance for seven common
 sites. Correlation between genetic variation and
 nematode community We used the
 geographic records of nematode genera from a published
 work (Zhang et al.
 2019). These nematode data
 were investigated to reveal the biotic homogenization of nematode
 communities by exotic <em>S. alterniflora</em> in China. This
 study found a clear latitudinal cline (nematode diversity increased with
 increasing latitude) and a strong correlation of nematode diversity to
 environmental variables in soils for <em>P. australis</em>,
 but weak for <em>S. </em><em>alterniflora </em>(Zhang et al. 2019). Because the TJ and TS sites
 were located within a very short distance (approximately 54 km) around the same bay, we
 considered them as one site when comparing the variation in geography,
 genetics, and community. Thus, we had seven common sites with both
 genetic and nematode information. We estimated the Jaccard distances between
 nematode communities using the function dist with a method
 binary
 parameter. We used these
 Jaccard distances to perform principal coordinates analysis (PCoA) of
 nematodes. Matrix correlation analyses between geographic,
 genetic and nematode
 distance matrices for
 seven common sites were performed using both Mantel test using the
 function mantel in R package vegan and MMRR using the function MMRR
 written by Wang (2013) with nematode distance matrices as the dependent variable
 using 9,999 permutations.
description: # Revealing biogeographic patterns in genetic diversity of native and
 invasive plants and their association with soil community diversity in the
 Chinese coast
 [https://doi.org/10.5061/dryad.9zw3r22mt](https://doi.org/10.5061/dryad.9zw3r22mt) **README** These are descriptions of RawData file in this Dryad upload. RawData.xlsx contains the following five sheets: PA\_Haplotype: Haplotype information of *Phragmites australis* individuals * Column 1: **ID**: Code for the samples. * Column 2: **Origin**: Site of origin of the samples. * Column 3: **Latitude**: Latitude (° N) of the site of origin of the samples. * Column 4: **Longitude**: Longitude (° E) of the site of origin of the samples. * Column 5: **T**: Locus haplotype (trnT-trnL). * Column 6: **R**: Locus haplotype (rbcL-psaI). * Column 7: **Haplotype**: Combined haplotype. PA\_SSR: SSR genotypes of *Phragmites australis* individuals. * Column 1: **Sample.Name**: Name for the samples used in our study. * Column 2: **Marker**: Genetic locus. * Column 3: **Allele.1**: Allele number. * Column 4: **Allele.2**: Allele number. * Column 5: **Allele.3**: Allele number. * Column 6: **Allele.4**: Allele number. * Column 7: **Allele.5**: Allele number. * Column 8: **Allele.6**: Allele number. * Column 9: **Allele.7**: Allele number. * Column 10: **Allele.8**: Allele number. SA\_SSR: SSR genotypes of *Spartina alterniflora* individuals. * Column 1: **Population**: Population of the samples. * Column 2: **Location**: Location of samples' population. * Column 3: **Locus 1**: Genetic locus. * Column 4: **Locus 2**: Genetic locus. * Column 5: **Locus 3**: Genetic locus. * Column 6: **Locus 4**: Genetic locus. * Column 7: **Locus 5**: Genetic locus. * Column 8: **Locus 6**: Genetic locus. * Column 9: **Locus 7**: Genetic locus. * Column 10: **Locus 8**: Genetic locus. * Column 11: **Locus 9**: Genetic locus. * Column 12: **Locus 10**: Genetic locus. * Column 13: **Locus 11**: Genetic locus. SA\_GD: Genetic diversity data of *Spartina alterniflora.* * Column 1: **ID**: Code for the samples. * Column 2: **Site**: Site of the samples. * Column 3: **Species**: SA means *Spartina alterniflora.* * Column 4: **Latitudes**: Latitude (° N) of the site of origin of the samples. * Column 5: **Longitude**: Longitude (° E) of the site of origin of the samples. * Column 6: **He**: Expected Heterogeneity. * Column 7: **Ae**: Effective number of alleles. * Column 7: **Source**: Literature of data sources. NT\_Diversity: Nematode diversity. * Column 1: **Plant** **species** * Column 2: **Order**: Order of nematode. * Column 3: **Family**: Family of nematode. * Column 4: **Genus**: Genus of nematode. * Column 5: **ZH**: Abbreviation for site ZhuHai. * Column 6: **ZZ**: Abbreviation for site ZhangZhou. * Column 7: **QZ**: Abbreviation for site QuanZhou. * Column 8: **HZW**: Abbreviation for site HangZhouWan. * Column 9: **SH**: Abbreviation for site ShangHai. * Column 10: **YC**: Abbreviation for site YanCheng. * Column 11: **DY**: Abbreviation for site DongYing. * Column 12: **TJ**: Abbreviation for site TianJin. * Column 13: **TH**: Abbreviation for site TangHai.