The Origins’ of Neocaridina


Methods

Population sampling and molecular methods

A total of 479 specimens of Neocaridina species were collected from 35 localities in Taiwan, which almost covers all rivers within the island (Fig. 1; Table 1). Neocaridina species are not endangered or protected species, and the fieldwork was conducted in accordance with the guidelines established by the National Museum of Marine Biology and Aquarium in Taiwan. All specimens were stored in the laboratory of Chiao-Chuan Han, National Museum of Marine Biology and Aquarium. The shrimp were collected from field sites with seines and fatally anaesthetized with MS-222 (Sigma, St. Louis, MO). The samples were fixed and stored in 100% ethanol. The genomic DNA was extracted from the muscle tissue using the Genomic DNA Purification Kit (Gentra Systems, Valencia, CA, USA). The partial COI gene was amplified by polymerase chain reaction (PCR) using the primers LCO1490 (5′-GGTCAACAAATCATAAAGATATTGG-3′) and HCO2198 (5′-TAAACTTCAGGGTGACCAAAAAATCA-3′) [49]. Each 50 μl PCR mixture contained 5 ng of template DNA, 5 μl of 10x reaction buffer, 4 μl of dNTP mix (10 mM), 5 pmol of each primer and 2 U of Taq polymerase (TaKaRa, Taq polymerase). The PCR was programmed on an MJ Thermal Cycler for one cycle of denaturation at 94 °C for 3 min, 40 cycles of denaturation at 94 °C for 30 s, annealing at 55 °C for 30 s and extension at 72 °C for 1 min 30 s, followed by a 72 °C extension for 10 min and 4 °C for storage. The purified PCR products were sequenced using an ABI 377 automated sequencer (Applied Biosystems, Foster City, CA, USA). The chromatograms were checked with the CHROMAS software (Technelysium), and the sequences were manually edited using BIOEDIT 6.0.7 [50]. All new sequence data were submitted to GenBank (MG734216-MG734300). Moreover, to identify the Neocaridina species and find the origins of the genus Neocaridina in Taiwan, our study also downloaded the sequences in Shih et al. [2] from GenBank (AB300177–90 and LC324764–79). In addition, our study also sampled some specimens in the Yangtze River (population CJ) and Hanjiang River (population HJ) in mainland China (Fig. 1).

Sequence alignment and phylogenetic inferences

The nucleotide sequences were aligned in Clustal X 1.81 [51]. The selection of the best-fit nucleotide substitution models was performed using the Bayesian information criterion (BIC) in jModelTest 2.0 [52]. The most appropriate nucleotide substitution model was HKY + I + G (Hasegawa-Kishino-Yano). The phylogenetic relationships among all haplotypes were inferred using Bayesian inference (BI) and maximum likelihood (ML) in BEAST 1.8.0 [53] and MEGA 6 [54]. For the BEAST analysis, a stick clock model with a Bayesian Skyline tree was used. We ran 106 generations. The burn-in and plots for each analysis were visualized using Tracer v1.6 [55] to determine whether the convergence and suitable effective sample sizes were achieved for all parameters. The TREEANNOTATOR in the BEAST package was used to summarize the tree data, and the tree was viewed using FigTree v1.3 [56]. For ML analysis, bootstrapping was performed with 1000 replications. In addition, the time to the most recent common ancestor (TMRCA) was also calculated using the software package BEAST. The substitution rates of 2.33% per million years for terrestrial Sesarma [2530] and 1.1% per million years for Decapoda [31] were used.

Population genetic diversity

The intra-population genetic diversity levels were estimated using haplotype diversity (h) [57] and nucleotide diversity (θπ and θω) indices [58] in DnaSP v5 [59]. The current genetic diversity estimates (θπ) were based on the pairwise differences between the sequences, and the historical diversity estimates (θω) were based on the number of segregating sites among the sequences. Comparing the estimates generated by these two indices provided insight into the population dynamics over recent evolutionary history [60]. The existence of a phylogeographic structure was examined following the method of Pons and Petit [61] by calculating two genetic differentiation indices (GST and NST) in DnaSP.

Population history

To determine the potential diversification scenarios, a statistical dispersal-vicariance analysis (S-DIVA), which complements DIVA, was employed to determine the statistical support for the ancestral range reconstructions [62]. The tree file formats were generated using the program BEAST. The range information was defined using the ichthyofaunal classification and phylogeographic studies [1032]. The analysis was performed using the ‘maxareas = 2 to 5’ option (see RESULTS: Population diversity of Taiwan species; Fig. 5).

In addition, the demographic histories were reconstructed using three different approaches. First, we performed Tajima’s D and Fu’s FS neutrality tests [6364] in DnaSP. Under a population expansion model, the significant negative values of Tajima’s D and Fu’s FS were expected. Second, the mismatch distribution [65] was estimated under the assumption of a sudden expansion model as implemented in Arlequin version 3.5 [66]. The sum of the squared deviations (SSD) between the observed and expected mismatch distributions and the raggedness index (Rg) were used as test statistics with the 1000 bootstrap replicates. In the third approach, we reconstructed the historical demography using the coalescent-based Bayesian skyline plot approach (BSP) implemented in software package BEAST.

To reconstruct the unknown history of divergence, we performed approximated Bayesian computations (ABC) using DIYABC v.2.0 [67]. The DIYABC program enabled the comparison of the different historical scenarios involving population divergence, admixture and population size changes and subsequently inferred the demographic and historical parameters under the best-supported scenario. The reference table was built with 1000,000 simulated data sets per scenario using the following summary statistics: one-sample statistics for the number of haplotypes, Tajima’s D, the mean number of pairwise differences, the variance in the pairwise differences, and the number of segregating sites; two-sample statistics for the mean of the within-sample pairwise differences, the mean of the between-sample pairwise differences, the number of segregating sites and FST between samples. The uniform priors for all scenarios were used, and no constraints on population sizes and coalescent times were given. All the scenarios were compared using direct (D) and logistic regression (L) approaches, and parameter estimation was performed only for the scenarios with the highest posterior probability.

Availability of data and materials

All mtDNA sequences generated in this study have been deposited in GenBank under accession numbers: MG734216-MG734300.

Abbreviations

ABC:

Approximate Bayesian ComputationsBI:

Bayesian inferenceCOI:

Cytochrome c oxidase subunit IML tree:

Maximum- Likelihood treeRg index:

the raggedness indexS-DIVA analysis:

Statistical Dispersal-Vicariance AnalysisSSD:

the sum of square deviations

Citations for creative commons sharing

TY – JOUR
AU – Han, Chiao-Chuan
AU – Hsu, Kui-Ching
AU – Fang, Lee-Shing
AU – Cheng, I-Ming
AU – Lin, Hung-Du
PY – 2019
DA – 2019/11/21
TI – Geographical and temporal origins of Neocaridina species (Decapoda: Caridea: Atyidae) in Taiwan
JO – BMC Genetics
SP – 86
VL – 20
IS – 1
AB – The freshwater species on Taiwan Island have been documented to have originated from mainland China and the Japanese islands from multiple events and by multiple colonization routes. Moreover, the sequences from the mitochondrial DNA cytochrome c oxidase subunit I (COI) have been used for DNA barcoding to identify the species. This study used the COI sequences to identify Neocaridina species in Taiwan and to examine their geographical and temporal origins.
SN – 1471-2156
UR – https://doi.org/10.1186/s12863-019-0788-y
DO – 10.1186/s12863-019-0788-y
ID – Han2019
ER –

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