Genetic Differentiation and Phylogeography of Rotifer Polyarthra Dolichoptera and P. Vulgaris Complexes Between Southern China and Eastern North America: High Intercontinental Differences

Genetic Differentiation and Phylogeography of Rotifer Polyarthra Dolichoptera and P. Vulgaris Complexes Between Southern China and Eastern North America: High Intercontinental Differences | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic Differentiation and Phylogeography of Rotifer Polyarthra Dolichoptera and P. Vulgaris Complexes Between Southern China and Eastern North America: High Intercontinental Differences Diwen Liang, George McManus, Qing Wang, Xian Sun, Zhiwei Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-242024/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Genetic differentiations and phylogeographical patterns of small organisms may be shaped by spatial isolation, environmental gradients and gene flow. However, knowledge about genetic differentiation of rotifers on intercontinental gradient is still limited. Polyarthra dolichoptera and P. vulgaris are cosmopolitan rotifers and tolerant to environmental changes, offering an excellent model to address the research gap. Here, we investigated the populations in Southern China and eastern North America, and evaluated the phylogeographical patterns from their geographical range sizes, geographic-genetic distance relationships and their response to spatial-environmental factors. Using mitichondrial cytochrome c oxidase subunit I gene as the DNA marker, we analyzed a total of 170 individuals. At least 24 putative cryptic species, including 20 of P. dolichoptera and 4 of P. vulgaris were detected based on three delimitation methods. Our results showed that some cryptic species were widely distributed but most of them were limited to single areas. The divergence of P. dolichoptera and P. vulgaris complexes indicated that gene flow between continents was limited while that within each continent was stronger. Furthermore, on the intercontinental scale spatial distance had a stronger influence than physicochemical variables on the genetic differentiations of P. dolichoptera and P. vulgaris complexes. However, the relationship between genetic distance and geographic distance was not continuously linear and the P. dolichoptera data best fitted the power-law model. This might be due to the effects of habitat heterogeneity, long-distance colonization and oceanographic barriers. Outliers above the correlation line between geographic distance and genetic distance suggest a significant dispersal barrier on large geographic scales studies. Hydrology rotifer phylogeography cryptic species genetic distance spatio-temporal pattern long-distance dispersal Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction One of the most important questions in ecology and biogeography is how and why species composition differs between geographic locations (Dambros et al. 2020 ). Environmental factors, geographic distance, and dispersal barriers are potential drivers. Some scholars believe that genetic diversity should be related to geographical distances by a classical distance–decay relationship (Gómez-Rodríguez et al., 2020 ; Gómez et al., 2002 ). On the contrary, others hold that environmental conditions, not dispersal, control the patterns of genetic differentiation (Sbabou et al., 2016 ; Fenchel and Finlay, 2006 ). Isolation by distance (IBD), proposed by Wright (1943), predicts that the degree of genetic differentiation increases with geographic distance due to dispersal limits (Tisthammer et al., 2020 ), analogously to a distance–decay relationship at the community level (Gómez-Rodríguez et al., 2020 ). The IBD pattern, respecting the association between genetic and geographic distances, is common in many plants (e.g. Rhododendron spp, Mikania micrantha ) and even the single-celled phytoplankton cyanobacteria (Banerjee et al., 2020 ; Ribeiro et al., 2020 ; Zhang et al., 2020 ). For heterotrophic protists (unicellular eukaryotes) such as Noctiluca scintillans , an intercontinental spatial barrier (the Pacific Ocean) seems to impose a limitation on gene flow and induces an increase in genetic distance (Pan et al., 2016 ). However, weak or no genetic/geographic correlation has been found in most invertebrates including Tetranychus , Thrips tabaci and Periplaneta americana (Jin et al., 2020 ; Li et al., 2020 ; Ma et al., 2019 ). Rotifers, cosmopolitan microscopic organisms, have been proposed as a model of distance–decay biogeographic patterns (Fontaneto et al., 2008a ). But to date, findings are inconsistent. A study of Brachionus calyciflorus phylogeography in eastern China showed no significant association between geographical and genetic distances (Xiang et al., 2011 ). Conversely, a significant albeit weak correlation was found in Euchlanis dilatata phylogeography in North America (Kordbacheh et al., 2017 ). The relationship between geographical distance and genetic distance is thus far from clear in rotifers. Because the IBD pattern is non-linear at very short and long average geographic distances (Bradbury and Bentzen, 2007 ), studies at intercontinental geographic scale are needed. In contrast to the IBD hypothesis, the Baas-Becking’s hypothesis, known as “everything is everywhere, but the environment selects” (EisE) posits that for small species, differences from different places occur because of environmental variation, and not because of restricted dispersal (Fenchel and Finlay, 2006 ). In addition, it is widely recognized that cryptic diversity is currently underestimated, which results in underappreciation of cosmopolitan organisms (Leal et al., 2019 ). According to EisE, two identical individuals can be found in the same kind of environments in two far separated locations. Most studies of environmental-spatial selection to date focused on small geographic or continental scales, and genetic differentiation across intercontinental scales has been underexplored. Morphological and genetic variations commonly occurr between populations and are mainly influenced by two factors. On one hand, genetic drift, mutations and natural selection will lead to the genetic differentiation of local populations. On the other hand, gene flow produces genetic homogeneity by the movement of gametes, individuals and even entire populations, and blockage of gene flow creates genetic differentiation between populations. The latter can lead to speciation (Slatkin, 1987 ). With the development of molecular tools, increasing numbers of cryptic species have been discovered in various morphological species of rotifers, such as Brachionus calyciflorus (Papakostas et al., 2016 ), B. plicatilis (Mills et al., 2017 ), Keratella cochlearis (Derry and Prepas, 2003), Polyarthra dolichoptera (Obertegger et al., 2015), Lecane spp . (García-Morales and Elías‐Gutiérrez, 2013 ), Testudinella clypeata (Leasi et al., 2013 ), and some bdelloid rotifers (Fontaneto et al., 2008a ). Moreover, coexistence of cryptic species is common in a single water body and can be mediated by ecological forces including seasonal changes, food resources and competition (Wen et al., 2016 ). Rotifers undergo periodic parthenogenesis, with little genetic recombination and intraspecific hybridization. Thus, compared with cladocerans and copepods, rotifers are advantaged for the study of phylogeography. Studying the genetic differentiation of rotifers is of great significance for understanding the dispersal pattern of gene flow and adaptive evolution mechanisms in microscopic organisms (Zhang et al., 2018b ). Polyarthra dolichoptera and P. vulgaris , cosmopolitan species of rotifera, are more tolerant to seasonal changes than other rotifers and exist in almost all kinds of water bodies (Liang et al., 2020 ). It has been suggested that Polyarthra cryptic species distribution might reflect genotypic adaptations to temperature differences and food resources (Obertegger et al. 2015). Temperature influences all metabolic processes, and it plays an important role in rotifer habitat selection (Obertegger et al., 2015). Also, phytoplankton biomass has marked effects on rotifer communities and it has strong seasonalities in most water bodies (Liang et al., 2019 ). A strongly positive correlation has been found between Polyarthra abundance and chlorophyll- a (Liang et al., 2020 ). In this study, we attempted to address the IBD versus EisE hypothesis by investigating the phylogeographic patterns of P. dolichoptera and P. vulgaris across the America and the Eurasia continents. The specific objectives were: (1) to estimate the relationship between geographic distance and genetic distance on the intercontinental scale (2) to understand whether physicochemical or spatial variables are the key factors affecting genetic differentiation. 2. Methods 2.1 Sampling Samples were collected from 28 sites including rivers, ponds and lakes in both eastern North America and Southeastern China, during June 2018 to September 2019 (Fig. 1 , Table 1 ). For determining detailed cryptic species structure on a small geographic scale, five sites in Guangzhou city, China and five ponds in New London Country, U.S.A., were sampled. In consideration of temperature effects on cryptic species structure, four seasons were sampled in Lake Liuye, China (6_liuye, 9_liuye, 12_liuye and 3_liuye; Table 1 ). Table 1 Details of sampling localities and the environmental parameters Population Abbreviation Collection date Longitude Latitude Altitude (m) Temperature (℃) Chlorophyll-a (ug/L) Salinity (‰) March_Lake Liuye 3_liuye 2019.3 111.72917 29.109722 30 14 10 0.11 June_Lake Liuye 6_liuye 2019.6 111.72722 29.048333 30 28 18 0.13 September_Lake Liuye 9_liuye 2018.9 111.70911 29.12 30 25 5 0.12 December_Lake Liuye 12_liuye 2018.12 111.76028 29.069167 30 8 1 0.13 September_the Chuanzi River 9_chuanzi2 2018.9 111.69194 29.053889 29 30 17 0.15 December_the Chuanzi River 12_chuanzi 2018.12 111.69194 29.053889 29 8 1 0.15 The pond of Haizhu Park haizhu 2018.12 113.22833 23.122778 2 11 8 0.12 The pond of Minghu minghu 2019.1 113.34323 23.134201 11 17 72 0.11 The pond of Nanhu nanhu 2019.1 113.34434 23.131315 15 18 18 0.14 Guangzhou Segment of the Pearl River pearlriver2 2019.3 113.34718 23.042379 1 17 1 0.15 The pond of Zhujaing Park zhujiangpark 2019.4 113.33385 23.122431 3 25 17 0.13 The reservoir of Jiukeng jiukeng 2019.4 112.5482 23.231587 39 25 1 0.12 The pond of Xiamen University XM 2018.8 118.30997 24.620833 47 27 78 0.16 The brook of Xiamen University XX 2018.8 118.3093 24.613373 21 27 8 0.1 The pond near Lake Donghu WHS7 2018.7 114.165 30.528611 18 27 107 0.14 Thames river Thames 2019.5 -72.07313 41.47805 0 16 1 6 Housatonic river Housatonic 2019.9 -73.12371 41.340767 8 25 78 0.14 Quinnipiac river Quinnipiac 2019.9 -72.86769 41.398428 -2 24 10 0.13 Lake Success SUC 2019.6 -73.70729 40.763248 59 22 65 0.12 Niagara waterfall Niagara 2019.8 -79.06275 43.081979 166 25 1 0.1 Pattagansett Lake P2 2019.6 -72.22837 41.376893 20 23 8 0.11 Norwich Pond P4 2019.6 -72.30391 41.384799 25 24 2 0.15 Powers Lake P5 2019.6 -72.2559 41.393302 48 24 3 0.12 Amos Lake P7 2019.7 -71.97741 41.516628 39 25 6 0.14 Mirror Lake P9 2019.9 -72.24724 41.806832 179 24 3 0.14 Swan Lake P10 2019.9 -72.25277 41.81083 184 24 5 0.16 Moodus Reservoir P14 2019.9 -72.40739 41.509835 109 24 10 0.12 All rotifer samples were collected by towing a plankton net (mesh size 30 µm) horizontally at surface and subsurface depths and preserved in a 50 mL centrifuge tube. To prevent changes in DNA, samples were fixed on site immediately with neutral Lugol’s solution at 2% final concentration and transported in a cooler before storing at -20 °C. In vivo semi-quantitative measurements of chlorophyll- a (Chl- a ) were obtained using a FluoroSenseTM handheld fluorometer (Turner Designs, USA). Water temperature (Temp) and salinity, were measured on site. Also, GPS coordinates and altitude values were recorded using a GPS application. 2.2 Species identification and isolation Species identification was based on Dumont ( 2002 ), the latest and most authoritative rotifer taxonomy system. Identification relies on morphological differences of body forms, sizes, fins, lateral antennae and vitellarium. The species of Polyarthra dolichoptera and Polyarthra vulgaris were isolated with micropipette under the stereo microscope. Single individuals were rinsed several times and transferred into PCR tubes for DNA analysis. 2.3 Selection of mt COI as the DNA marker DNA markers including 18S ribosomal RNA, nuclear internal transcribed spacer (ITS) and mitichondrial cytochrome c oxidase subunit I gene (COI) are widely used as DNA barcodes for identification (Papakostas et al., 2016 ). Unlike nuclear DNA, mitochondrial DNA such as COI generally does not undergo genetic recombination as it is transmitted directly from the mother to the offspring, and can be an effective single haplotype marker (Freeland et al., 2011 ). Moreover, COI evolves more rapidly than ITS in animals, and is thus the better marker for phylogeography and cryptic species delimitation (Mills et al., 2017 ). 2.4 DNA extraction and amplification DNA from each single animal was extracted following the HotSHOT protocol (Montero-Pau et al., 2008 ). Then, the partial cytochrome c oxidase subunit I (COI) mtDNA gene was amplified and sequenced using primers LCOI (5’-GGT CAA CAA ATC ATA AAG ATA TTGG-3’) and HCOI (5’-TAA ACT TCA GGG TGA CCA AAA AAT CA-3’) (Folmer et al., 1994 ). PCR was processed according to the TaKaRa exTaq protocol with 5 µL of extracted DNA. Cycle conditions were initial denaturation at 94 °C for 3 min, followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at 52 °C for 30 s and extension at 72 °C for 45 s. The amplification ended with a final extension of 72 °C for 8 min. Successful amplification products were then purified using the TaKaRa Minibest agarose Gel DNA extraction Kit before being sent to TsingKe company for sequencing. 2.5 Sequences alignment and phylogenetic analyses Sequences were aligned by Mega X using Clustal-W and then visually checked. Each sequence was verified by BLAST search in NCBI GenBank (Sayers et al., 2018). Within-species genetic distances should be less than 14% for mt COI (Obertegger et al., 2015; Mills et al., 2017 ). The closest sequences with the highest similarity scores were obtained from GenBank for comparison (Accession #: KJ460388, LC215566, LC215573, KC618934, KC619030, JN936500, KJ460383, KC619195 and LC215562). Population genetic statistics (average number of nucleotide differences between haplotypes, number of haplotypes, haplotype diversity [ Hd ] and nucleotide diversity [ π ], average number of nucleotide differences [K], average number of segregating sites [S]) were calculated using DNASP 5.1 (Librado and Rozas, 2009 ). Bayesian phylogenetic trees were run in BEAST v1.8.4 using a HKY + I + G model, separately for the two data sets (108 P. dolichoptera and 64 P. vulgaris sequences). For this analysis, an uncorrelated lognormal relaxed clock, the Yule process speciation prior (rate of linear birth in the Yule model of speciation set as lognormal); the default settings of prior, and the MCMC of 10 7 generations with sampling every 1000 generations were used. Tracer v1.6 was used for evaluating effective sample size (ESS > 200). Trees were summarised using TreeAnnotator v1.8.4 with a 20% burn-in. For P. dolichoptera phylogenetic reconstructions, congener P. vulgaris (KJ460388) was included as outgroup, and P. dolichoptera (KC618934) was included as outgroup for P. vulgaris. 2.6 Cryptic species delimitation Generalised Mixed Yule Coalescent (GMYC), Automatic Barcode Gap Discovery (ABGD) and Poisson tree processes (PTP) models are widely used approaches for cryptic species (entities) delimitation (Kordbacheh et al., 2017 ). An ultrametric tree generated by BEAST was required for both GMYC and PTP delimitations. The GMYC delimitation analysis was processed on software R 3.6.1 using the ‘rncl’ and ‘splits’ packages, (R Core Team 2019). The GMYC model is a likelihood method for delimiting species by fitting within and between species branching models to reconstructed gene trees (Fujisawa and Barraclough, 2013 ; Pons et al., 2006 ). The ABGD model was performed for primary species delimitation and was processed on the website https://bioinfo.mnhn.fr/abi/public/abgd/ (Accessed January 10, 2020). ABGD classifies sequences into putative cryptic species based on pairwise genetic distances without any prior assumptions (Puillandre et al., 2012 ). PTP is a tree-based method that uses the number of substitutions to distinguish intraspecies processes from interspecies processes. This method considers two classes of Poisson processes, speciation (higher substitution rate associated to interspecies events) and coalescence (within species events) (Zhang et al., 2013 ). PTP model for cryptic species delimitation was run using the online tool at http://species.h-its.org/ptp/ (Accessed January 8, 2020). 2.7 Geographical and genetic distance analysis The pairwise geographic distance matrices were calculated in R 3.6.1 (R Core Team, 2019) using the ‘geosphere’ package (Ding et al., 2019 ). The pairwise genetic distance matrixes were calculated with Kimura 2-parameters model, pairwise deletion, transitions + transversions using the Mega X program. Linear regression analysis and Generalized additive models (GAM) of the relationships between geographic distance and genetic distance were processed in R 3.6.1 using the package ‘ggplot2’. To determine the significance of differences ( p < 0.05) in geographic distances and genetic distances among different groups, analysis of variance (ANOVA) with TukeyHSD test was conducted, using the software R 3.6.1, ‘agricolae’, ‘car’ and ‘multcomp’ packages. To characterize the shape of the relationships within the two species, three different GLM models including linear, exponential and power-law were applied using the package ‘betapart’ and ‘pscl’. 2.8 Relationships between cryptic species and environmental factors Redundancy analysis (RDA) or Canonical Correlation Analysis (CCA) was performed to explore the relationships between cryptic species and environmental factors using the ‘vegan’ and ‘ggplot2’ packages in R. CCA or RDA model is determined based on the community composition by Detrended Correspondence Analysis (DCA). If the longest gradient is > 4, the unimodal method (CCA) will be applied. On the other hand, if that value is < 3, the linear method (RDA) is a better choice. In the range between 3 and 4, both methods can be applied (ter Braak and Smilauer, 2002). Varying inflation factors less than 10 (VIF 0.05) variables (Oksanen et al., 2010 ). 3. Results 3.1 Genetic diversity We obtained 107 COI sequences of P. dolichoptera and 63 sequences of P. vulgaris , with aligned lengths of 562 bp and 589 bp, respectively (Accession numbers: Table S1). A total of 64 P. dolichoptera haplotypes were detected with haplotype diversity (h) of 0.98 and nucleotide diversity (π) of 0.175. For P. vulgaris a total of 36 haplotypes were found with a haplotype diversity of 0.96 and nucleotide diversity of 0.106 (Table 2 ). Table 2 Genetic diversity summary statistics, as calculated by DNASP 5 Population Individuals Haplotypes Haplotype diversity (Hd) Nucleotide diversity (π) Average number of nucleotide differences (K) Average number of segregating sites (S) Total P. dolichoptera 103 64 0.979 0.175 94.87 330 P. dolichoptera in China 70 41 0.96 0.149 80.97 230 P. dolichoptera in the USA 33 23 0.978 0.156 84.51 281 Total P. vulgaris 63 36 0.957 0.106 60.73 227 P. vulgaris in China 42 24 0.93 0.048 27.56 156 P. vulgaris in the USA 21 12 0.895 0.026 15.11 91 For P. dolichoptera complex, a larger genetic variation was observed in eastern North America than in southeastern China, with higher haplotype diversity ( H d ; 0.978), nucleotide diversity ( π ; 0.156), average number of nucleotide differences (K; 84.51) and average number of segregating sites (S; 281). For P. vulgaris complex, higher levels of H d (0.93), π (0.048), K (27.56) and S (156) were detected in the southeastern China samples (Table 2 ). 3.2 Phylogenetic analysis and cryptic species delimitation Using Bayesian phylogenetic analysis, P. dolichoptera complex were reconstructed (Fig. 2 ). All the individuals analyzed were binned into three groups. Group 1 was composed of 54 individuals, which were all from Southeastern China. This group was composed of similar clusters from different sites in Southeastern China. Group 2 consisted of 20 individuals from eastern North America and 17 individuals from Southeastern China, which formed independent clades by continents, showing high genetic divergence between the two geographic communities. Group 3 consisted of 13 individuals from the eastern North America and three individuals from Southeastern China, which formed many divergent clusters within eastern North America and an independent clade for the individuals from Southeastern China. The Bayesian tree analysis clustered the P. vulgaris samples into two groups with strong support values (Fig. 3 ). All of the 42 individuals from Southeastern China were in Group 1, while the 18 individuals from eastern North America were in Group 2 with strong support values. A large number of cryptic species were detected using three independent methods (Figs. 2 , 3 ). The ABGD method produced 20 cryptic species for P. dolichoptera complex and four for P. vulgaris . GMYC analysis revealed 21 cryptic species in P. dolichoptera complex and five in P. vulgaris complex. Using the PTP method, P. dolichoptera complex was delimited into 24 cryptic species and P. vulgaris complex into 13. The most conservative estimate of cryptic species was obtained using ABGD, while PTP method gave the greatest number of cryptic species. All three methods shared common species boundaries for the smallest number of cryptic species, however. As the results from ABGD and GMYC were similar, unless specified otherwise, cryptic species determination will be discussed based on ABGD results. 3.3 Geographical distribution The range size of geographic distances declined as the resolution of classification increased from morphological species to cryptic species to haplotype (Fig. 4 ). Both P. dolichoptera and P. vulgaris species were widely distributed, with the geographic range sizes up to 12903 km and 12836 km, respectively. Their range sizes at cryptic species and haplotype levels decreased to 2726 km and 411 km, respectively. These correspond to the mean range sizes at species level ( P. dolichoptera : 5374 ± 5665 km; P. vulgaris : 5517 ± 5720 km) significantly larger than at cryptic species (463 ± 815 km) and haplotype level (12 ± 61 km). However, there was no significant difference in range size between cryptic species and haplotype ranges. Most of the cryptic species were limited to single areas, but some were widely distributed. For example, cryptic species one of P. vulgaris complex (V1) comprised individuals from as distant areas as Changde (liuye), Wuhan (WHS7), Xiamen (XM) and Guangzhou (zhujiangpark), in China (Fig. 3 ). In addition, cryptic species 10 of P. dolichoptera complex (D10) were as widely distributed as from Connecticut (P4), Long Island (SUC) and Niagara (upstate New York), in USA. In contrast, some cryptic species only occurred in one sampling site, such as D7 and D16 (Fig. 2 ). These results indicated that the cryptic species and haplotypes tended to be regionally restricted. Although some of them can be widely spread into different habitats (sampling sites), no cryptic species or haplotype was found to occur on both continents. 3.4 Phylogeographical patterns and genetic structure The relationships between dependent variables for genetic distance and independent variables for geographic distance were examined by linear regression (Fig. 5 ). The genetic distance of P. dolichoptera complex showed significant positive correlation with geographic distance (R 2 = 0.18, p < 0.01). The genetic distance of P. vulgaris complex was also positively correlative with geographic distance (R 2 = 0.53, p < 0.01) (Fig. 5 ). Fitting the data to the smooth function of the generalized additive model (GAM) indicated that the relationship between geographic distance and genetic distance was not simply linear. Genetic distances increased with geographic distances initially but then decreased rapidly when geographic distances increase within 1300 km, before it rose again at greater distances of around 10000 km. To characterize the shape of these relationships within these two species complexes, GLM analysis was carried out based on AIC values. Our results showed that the power-law model fitted the P. dolichoptera data better than either the exponential or linear models, while the linear modle fitted the P. vulgaris data better than other models (Table 3 ). Table 3 Comparison of three GLM models (linear, exponential and power-law) assessing the shape of the relationship between geographic distance and genetic distance for P. dolichoptera and P. vulgaris complexes. Models are evaluated according to their AIC and the lowest values of AIC are shown in bold. P. dolichoptera P. vulgaris linear -24567 -9323 exponential -24551 -9310 power-law -25185 -9131 p < 0.01 in all cases Our results indicated that in both P. dolichoptera and P. vulgaris complexes, the mean genetic distances between the two continents were significantly higher than those within either eastern North America or Southern China ( p within eastern North America (0.188 ± 0.111) > within Southern China (0.173 ± 0.089) (Fig. 6 A). The mean genetic distances in the P. vulgaris complex decreased in the order: Southern China VS eastern North America (0.223 ± 0.02) > within Southern China (0.055 ± 0.088) > within eastern North America (0.049 ± 0.067) (Fig. 6 B). However, there was no significant difference in the mean genetic distance values for the Southern China and the eastern North America groups in the P. vulgaris complex. These results indicated that the genetic divergences between these two continents were significantly higher than those within a single continent ( p < 0.05). This suggests that there is a higher level of gene flow and high frequency of recombination within continents than between continents. 3.5 Relationships between environmental factors and cryptic species distributions As the longest gradient performed by Detrended Correspondence Analysis (DCA) was 7.5 (larger than 4), a Canonical Correlation Analysis (CCA) model was chosen for estimating the relationship between cryptic species and environmental factors. The first two ordinate axes explained 61% of the cryptic species-environment variability in the CCA ordination (Table S2). The CCA ordination showed that four variables including longitude, latitude, altitude and temperature were significantly related to the cryptic species distributions ( p < 0.05) (Table S3). However, the environmental factor chlorophyll- a was not significant variable affecting the cryptic species structure (Fig. 7 ). Figure 7 clearly showed that the Southern China populations (red) mostly stayed on the left of the figure, while the eastern North America populations (blue) were mostly on the right of the figure. In addition, the spatial variables of longitude and latitude showed positive correlation with axis 1, which indicated that longitude and latitude were the key factors for the variation of the cryptic species structure. Furthermore, the cryptic species-variables relationship was similar to that of sampling sites-variables, which indicated that most cryptic species tended to be restricted to specific regions. Discussion 4.1 The hidden diversity in species complexes Cryptic species have been found in almost all groups of animals (Tang et al., 2012 ; Fossen et al., 2016 ), and rotifers seem to be one of the invertebrates hosting the highest potential cryptic diversity in the world (Fontaneto et al., 2009 ; Fontaneto, 2014 ). For instance, eight potential cryptic species of Brachionus calyciflorus were found in eastern China (Xiang et al., 2011 ). Also, more than seven cryptic species of Euchlanis dilatata were defined in North America (Kordbacheh et al., 2017 ). By the end of 2017, there were 15 cryptic species of B. plicatilis were recorded in the world, as a conservative estimate (Mills et al., 2017 ). Our results indicated that both P. dolichoptera and P. vulgaris are complexes of cryptic species, with at least 17 taxa of P. dolichoptera and 3 taxa of P. vulgaris in our study areas. Some cryptic species of P. dolichoptera , such as D11 and D12, were only found in one site, which could be due to small sample sizes. Obertegger et al. (2015) reported that at least 12 cryptic species of P. dolichoptera had been found in 35 lakes along an altitudinal gradient in Italy. Given that the small sample sizes in this study impeded a thorough detection of cryptic species, the degree of cryptic diversity in Southeastern China and eastern North America is likely to be higher than what we reported here. Leaving out the data from NCBI, which gave two cryptic species based on our analysis, our most conservative delimitation based on ABGD gave 20 cryptic species in the P. dolichoptera complex and 4 in the P. vulgaris complex from our current data. The results from the GMYC method were similar ( 21 in the P. dolichoptera complex and 5 in the P. vulgaris complex). PTP-based estimates, 24 in the P. dolichoptera complex and 13 in the P. vulgaris complex might be a result of overestimation by the method, as has been suspected in previous studies on E. dilatata (Kordbacheh et al., 2017 ) and B. plicatilis (Mills et al., 2017 ). Since our cryptic species delimitation was based on molecular methods with less morphological evidence, the conservative estimate is a better choice. 4.2 Genetic divergence and geography distribution Long-distance dispersal of cryptic species has been reported not only in Monogononta including Brachionus , Polyarthra , Euchlanis and Lecane , but also in Bdelloidea including Philodina and Rotaria (Kordbacheh et al., 2017 ; Fontaneto et al., 2008b ). The cryptic species D1 was found in Guangdong and Hunan provinces, separated by > 500 km (e.g. liuye, chuanzi2, minghu, pearlriver2). Also, D10 was found across a range of > 500 km in the US states of New York and Connecticut (e.g. SUC, Niagara, P2, P4). In addition, V1 was widely distributed in southeastern of China, while V3 was widespread in Connecticut. The cosmopolitan distribution of rotifers could be atributed to long-distance dispersal. Colonization and long-distance dispersal to different waters across whole or even multiple continents, which may be mediated by waterfowl, have been observed in a number of zooplankton species (Gómez et al., 2002 ). Secondly, some areas that are widely separated share haplotypes and therefore appear genetically connected (Xiang et al., 2011 ). Sasaki and Dam ( 2019 ) found that the widely distributed genetic clades of a marine copepod shared haplotypes between geographically distant populations. This implies that gene flow can be strong enough to overcome long distances at least within a continent. Most small organisms do have very widespread distributions, but some are limited to distinct geographical areas (Savary et al., 2018 ). Our results showed that the cryptic species of of P. dolichoptera D5 and D7 only occurred in Xiamen and Wuhan, respectively. This is consistent with the study of Brachionus calyciflorus cryptic diversity in eastern China. Though most cryptic species of Brachionus calyciflorus were widely distributed, one clade was only found in Danzhou, China (Xiang et al., 2011 ). Although high genetic distances of Adineta can be found at different geographical distances, closely related individuals were only found at geographical scales < 2000 km (Fontaneto et al., 2008a ). In the current study, we found that the range sizes of geographic distance in both genera declined as resolution increased from species to cryptic species to haplotype. This suggests that the restricted cryptic species in our study are not simply an artifact of sampling fewer individuals at lower levels. Interestingly, even though Polyarthra was widely distributed as a genus, none of the cryptic species or haplotypes was found on both continents. Our results indicated that all of the cryptic species from eastern North America formed independent strains that were separate from the Chinese ones, indicating high divergence. These results are consistent with the study in Noctiluca , a heterotrophic dinoflagellate (Pan et al., 2016 ). The haplotypes of Noctiluca within China were geographically quite homogeneous, but were generally different, compared to the American population suggesting basin or continental-scale endemism. In addition, a study of the cryptic species of B. plicatilis revealed existence of four clades associated to four geographic regions (one in North America, two in Europe and one in Australia) (Mills et al., 2017 ). Levels of gene flow can be estimated by producing the visible patterns using allele frequencies and DNA sequence differences (Slatkin, 1987 ). Lack of differentiation in mitochondrial COI sequences of geographically distant populations usually indicates strong effects of gene flow (Sasaki and Dam, 2019 ). Our study showed that the genetic distances of Southern China VS eastern North America were significantly higher than those within each continent. The divergence of populations between Southern China and eastern North America indicates limited gene flow between the two continents. The relatively low genetic divergence in the populations within continents of both P. dolichoptera and P. vulgaris complexes suggests strong gene flow within Southeastern China and within eastern North America. 4.3 Relationship between geographic and genetic distance In the present study, a significantly positive correlation between genetic and geographic distance was found in both Polyarthra species complexes. Similar results have been obtained for E. dilatata in the North America (Kordbacheh et al., 2017 ). Moreover, it was reported that there was a strong positive correlation between genetic distance and geographic range when comparing samples on small geographical scales (Kordbacheh et al., 2017 ). Habitat heterogeneity and temporal variation can generate high genetic diversity on small geographic sacle study (Fontaneto et al., 2009 ). However, as the geographical scope of the study becomes broader, this correlation may weaken or disappear. A study of Geomalacus revealed that the genetic distance increased rapidly along with the geographic distance, but as the geographic distance continues to expand, the genetic distance reached a plateau (Gómez-Rodríguez et al., 2020 ). In another example, no significant associations between geographical and genetic distances were found for B. calyciflorus across eastern China. The nonsignificant correlation may result from the effects of long-distance colonization and secondary contact, combined with monopolization effects which reduce gene flow among established populations (Xiang et al., 2011 ; Kordbacheh et al., 2017 ). In our results, Guangdong and Hunan provinces shared P. dolichoptera cryptic species D1 and P. vulgaris cryptic species V1, while New York and Connecticut states shared the D10 and V3. Thus, long-distance intra-continental dispersal and colonization are responsible for depressing the geographic-genetic correlation in the present study. Interestingly, GAM analysis indicated that genetic distance suddenly increased when the geographic distance was extremely high. Since the between continent genetic distances were significantly higher than those of within continent, a stronger positive correlation between genetic and geographic distance was observed, in consideration of datasets from the two continents. Furthermore, the significant genetic difference between the trans-Pacific regions suggests gene flow limitation (Pan et al., 2016 ). Therefore, effects of the Pacific barrier leads to the restriction of gene flow and results in an increase in genetic distance. The relationship between geographic distance and genetic distance may not be simply linear. Generally, declines in the IBD slope are associated with increases in the geographic scales of observation, and the IBD pattern is non-linear at small scale and large scale geographic distances (Bradbury and Bentzen, 2007 ). The GLM analysis of the P. dolichoptera complex showed that genetic distance was significantly related to geographic distance, and the power-law model fitted the data better than the exponential and linear models. GAM models showed that genetic distance increases rapidly with geographical distance on small geographic scales because of the coexistence and habitat heterogeneity among lakes, streams, and rivers. However, as the geographic distance extends to an entire continent, long-distance colonization leads to the decrease of the genetic distance. Extreme barriers to dispersal, such as separate oceanographic basins, lead to an increase in genetic distance at larger geographic scales. As a power-law model is expected when there is no dispersal limitation (Gómez-Rodríguez et al., 2020 ), outliers above the correlation line between geographic distance and genetic distance might suggest a significant dispersal barrier on large geographic scales. 4.4 Key factors for phylogeographical patterns of rotifers CCA analysis indicated that spatial variables including longitude, latitude and altitude were key factors in controling the cryptic species structure rather than environmental factors such as temperature and Chlorophyll- a . For most microeukaryotic communities, both abundant and rare communities exhibited a stronger response to environmental factors than spatial factors (Zhang et al., 2018a ). But for populations, genetic differentiation depend largely upon the evolutionary force regulating spatial patterns rather than seasonal differentiation (Xiang et al., 2011 ; Obertegger et al., 2015). It was reported that although cryptic diversity of P. dolichoptera changed along an altitudinal gradient in the Trentino–South Tyrol region, environmental parameters such as temperature and trophic status might also affect the distribution of cryptic species (Obertegger et al., 2015). However, in the absence of geographical barriers, genetic divergence might be more explained by environmental gradients (Tisthammer et al., 2020 ). The present study compared populations with similar food resource levels (Chl- a ) at different sites and we found that the haplotypes belonged to different clades. Therefore, food sources level might not be an influencing factor for their genetic divergence. Dispersal, genetic diversity and gene flow can be strongly affected by temperature changes (Sasaki and Dam, 2019 ). Although the ambient temperature of our samples ranged from 8 to 30℃ (Table 1 ), Polyarthra rotifers in eastern North America experience greater interannual changes in temperature. As cytochrome c oxidase subunit I is the terminal enzyme of the mitochondrial respiratory chain (Afkhami et al., 2020 ), extreme low temperature in winter may lead to differences in mitochondrial functions. Thus mutations may also exist in the coding sequence for COI. Rotifers possess the ability for passive long-distance dispersal through their diapausing stages including resting eggs and xerosomes (Walsh et al., 2017 ). In this way, hydrology influencing community composition and wind influencing dispersal could also play an important role in rotifers dispersal (Rivas et al., 2018 ; Liang et al., 2019 ). In addition, human-mediated transport has likely facilitated species' persistence since its initial colonization, through the ongoing introduction and inter-continental spread of genetic variation (Baird et al., 2020 ). As boating is one of the popular recreational activities for Americans in summer, rotifers and resting eggs can spread over North American lakes by launching boats. However, wind and migratory bird-mediated transport, which could operate on larger scales than this are impeded by the oceanographic barriers. Conclusion Cryptic diversities of Polyarthra dolichoptera and P. vulgaris are definitely underestimated in the world. The divergence of P. dolichoptera and P. vulgaris complexes indicates that gene flow between eastern North America and Southeastern China is limited while that within eastern North America or Southeastern China was higher. Genetic distance and geographic distance do not show a simple linear relationship and the power-law model fitted the data of P. dolichoptera better than the exponential and linear models. This may result from the effects of habitat heterogeneity, long-distance colonization and oceanographic barriers to dispersal. Spatial variables are key factors in affecting the genetic differentiation of rotifers when compared with physicochemical variables on the intercontinental scale. Outliers above the correlation line between geographic distance and genetic distance might suggest a significant dispersal barrier on large geographic scales studies. Declarations Acknowledgement We gratefully acknowledge Dr. Susan Smith from the University of Connecticut for assistance with DNA extractions and PCR. Many thanks to Lingjie Zhou and Xiaotong Ye for samples assistance and sequencing. Declarations We thank National Natural Science Foundation of China (41673080) and Jinan University for financial support. The study complies with current ethical guidelines. The data and materials are available. The COI sequences lacked internal stop codons of this study have been submitted to the NCBI database. We also assert that all of the listed authors have participated in the work and have approved the submission. Yang Y. conceived and designed the research. Lin S. guided the writing direction and revision of the manuscript. Liang D. carried out the experiment, analysis and wrote the manuscript. McManus G. revised the manuscript. Wang Q. and Sun X. provided scientific comments to the manuscript. Liu Z. participated in the R code writing. All authors have no conflict of interest associated with this work. References Afkhami, E., Heidari, M. M., Khatami, M., Ghadamyari, F., & Dianatpour, S. (2020). Detection of novel mitochondrial mutations in cytochrome C oxidase subunit 1 (COX1) in patients with familial adenomatous polyposis (FAP). Clinical and Translational Oncology, 22 (6), 908-918. https://doi.org/10.1007/s12094-019-02208-6 Baird, H. P., Moon, K. L., Janion‐Scheepers, C., & Chown, S. L. (2020). Springtail phylogeography highlights biosecurity risks of repeated invasions and intraregional transfers among remote islands. Evolutionary Applications, 13 (5), 960-973. https://doi.org/10.1111/eva.12913 Banerjee, A. K., Hou, Z., Lin, Y., Lan, W., Tan, F., Xing, F., ... & Huang, Y. (2020). Going with the flow: analysis of population structure reveals high gene flow shaping invasion pattern and inducing range expansion of Mikania micrantha in Asia. Annals of Botany, 125 (7), 1113-1126. https://doi.org/10.1093/aob/mcaa044 Bradbury, I. R., & Bentzen, P. (2007). Non-linear genetic isolation by distance: implications for dispersal estimation in anadromous and marine fish populations. Marine Ecology Progress Series, 340 , 245-257. https://doi.org/10.3354/meps340245 Dambros, C., Zuquim, G., Moulatlet, G. M., Costa, F. R., Tuomisto, H., Ribas, C. C., ... & Emilio, T. (2020). The role of environmental filtering, geographic distance and dispersal barriers in shaping the turnover of plant and animal species in Amazonia. Biodiversity and Conservation, 29 , 3609–3634. https://doi.org/10.1007/s10531-020-02040-3 Derry, A. M., N. Hebert, P. D., & Prepas, E. E. (2003). Evolution of rotifers in saline and subsaline lakes: a molecular phylogenetic approach. Limnology and Oceanography, 48 (2), 675-685. https://doi.org/10.4319/lo.2003.48.2.0675 Ding, Y., Qian, S., Wu, X., Zhao, L., Lin, D., Zhang, J., & Yang, Y. (2019). Homogenization of China’s urban aquatic macrophyte communities: A meta-analytic study. Ecological Indicators, 106 , 105506. https://doi.org/10.1016/j.ecolind.2019.105506 Dumont H.J.F. 2002. Guides to the Identification of the Microinvertebrates of the Continental Waters of the World. Leiden: Backhuys Publishers Fenchel, T., & Finlay, B. J. (2006). The diversity of microbes: resurgence of the phenotype. Philosophical Transactions of the Royal Society B: Biological Sciences, 361 (1475), 1965-1973. https://doi.org/10.1098/rstb.2006.1924 Folmer, O., Black, M., Hoeh, W., Lutz, R., & Vrijenhoek, R. (1994). DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular marine biology and biotechnology, 3 (5): 294-299. Fontaneto, D. (2014). Molecular phylogenies as a tool to understand diversity in rotifers. International Review of Hydrobiology, 99 (1-2), 178-187. https://doi.org/10.1002/iroh.201301719 Fontaneto, D., Barraclough, T. G., Chen, K., Ricci, C., & Herniou, E. A. (2008a). Molecular evidence for broad‐scale distributions in bdelloid rotifers: everything is not everywhere but most things are very widespread. Molecular Ecology, 17 (13), 3136-3146. https://doi.org/10.1111/j.1365-294x.2008.03806.x Fontaneto, D., Boschetti, C., & Ricci, C. (2008b). Cryptic diversification in ancient asexuals: evidence from the bdelloid rotifer Philodina flaviceps . Journal of Evolutionary Biology, 21 (2), 580-587. https://doi.org/10.1111/j.1420-9101.2007.01472.x Fontaneto, D., Kaya, M., Herniou, E. A., & Barraclough, T. G. (2009). Extreme levels of hidden diversity in microscopic animals (Rotifera) revealed by DNA taxonomy. Molecular Phylogenetics and Evolution, 53 (1), 182-189. https://doi.org/10.1016/j.ympev.2009.04.011 Fossen, E. I., Ekrem, T., Nilsson, A. N., & Bergsten, J. (2016). Species delimitation in northern European water scavenger beetles of the genus Hydrobius (Coleoptera, Hydrophilidae). ZooKeys , (564), 71. https://doi.org/10.3897/zookeys.564.6558 Freeland J.R., Kirk H., Petersen SD. 2011. Molecular Ecology (2 nd ed)[M]. UK: Wiley-Blackwell. Fujisawa, T., & Barraclough, T. G. (2013). Delimiting species using single-locus data and the Generalized Mixed Yule Coalescent approach: a revised method and evaluation on simulated data sets. Systematic biology, 62 (5), 707-724. https://doi.org/10.1093/sysbio/syt033 García‐Morales, A. E., & Elías‐Gutiérrez, M. (2013). DNA barcoding of freshwater Rotifera in Mexico: Evidence of cryptic speciation in common rotifers. Molecular ecology resources, 13 (6), 1097-1107. https://doi.org/10.1111/1755-0998.12080 Gómez‐Rodríguez, C., Miller, K. E., Castillejo, J., Iglesias‐Piñeiro, J., & Baselga, A. (2020). Disparate dispersal limitation in Geomalacus slugs unveiled by the shape and slope of the genetic–spatial distance relationship. Ecography , 43 , 1229-1240. https://doi.org/10.1111/ecog.05142 Gómez, A., Serra, M., Carvalho, G. R., & Lunt, D. H. (2002). Speciation in ancient cryptic species complexes: evidence from the molecular phylogeny of Brachionus plicatilis (Rotifera). Evolution, 56 (7), 1431-1444. https://doi.org/10.1111/j.0014-3820.2002.tb01455.x Jin, P. Y., Sun, J. T., Chen, L., Xue, X. F., & Hong, X. Y. (2020). Geography alone cannot explain Tetranychus truncatus (Acari: Tetranychidae) population abundance and genetic diversity in the context of the center–periphery hypothesis. Heredity, 124 (2), 383-396. https://doi.org/10.1038/s41437-019-0280-5 Kordbacheh, A., Garbalena, G., & Walsh, E. J. (2017). Population structure and cryptic species in the cosmopolitan rotifer Euchlanis dilatata . Zoological Journal of the Linnean Society, 181 (4), 757-777. https://doi.org/10.1093/zoolinnean/zlx027 Leal, I., Bergeron, M. J., Feau, N., Tsui, C. K. M., Foord, B., Pellow, K., ... & Sturrock, R. N. (2019). Cryptic speciation in western North America and eastern Eurasia of the pathogens responsible for laminated root rot. Phytopathology, 109 (3), 456-468. https://doi.org/10.1094/phyto-12-17-0399-r Leasi, F., Tang, C. Q., De Smet, W. H., & Fontaneto, D. (2013). Cryptic diversity with wide salinity tolerance in the putative euryhaline Testudinella clypeata (Rotifera, Monogononta). Zoological Journal of the Linnean Society, 168 (1), 17-28. https://doi.org/10.1111/zoj.12020 Li, X., Zhang, Z., Zhang, J., Huang, J., Wang, L., Li, Y., ... & Lu, Y. (2020). Population Genetic Diversity and Structure of Thrips tabaci (Thysanoptera: Thripidae) on Allium Hosts in China, Inferred From Mitochondrial COI Gene Sequences. Journal of Economic Entomology, 113 (3), 1426-1435. https://doi.org/10.1093/jee/toaa001 Liang, D., Wei, N., Wang, Q., Jersabek, C. D., He, X., & Yang, Y. (2019). Influence of Hydrological Heterogeneity on Rotifer Community Structure in Three Different Water Bodies in Shantou Area, Guangdong (China). Zoological Studies, 58 , 23. https://doi.org/10.6620/ZS.2019.58-23. Liang, D., Wang, Q., Wei, N., Tang, C., Sun, X., & Yang, Y. (2020). Biological indicators of ecological quality in typical urban river-lake ecosystems: The planktonic rotifer community and its response to environmental factors. Ecological Indicators, 112 , 106127. https://doi.org/10.1016/j.ecolind.2020.106127 Librado, P., & Rozas, J. (2009). DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics, 25 (11), 1451-1452. https://doi.org/10.1093/bioinformatics/btp187 Ma, J., Liu, J., Shen, Y., Fan, Z., Yue, B., & Zhang, X. (2019). Population genetic structure and intraspecific genetic distance of Periplaneta americana (Blattodea: Blattidae) based on mitochondrial and nuclear DNA markers. Ecology and evolution, 9 (22), 12928-12939. https://doi.org/10.1002/ece3.5777 Mills, S., Alcántara-Rodríguez, J. A., Ciros-Pérez, J., Gómez, A., Hagiwara, A., Galindo, K. H., ... & Welch, D. B. M. (2017). Fifteen species in one: deciphering the Brachionus plicatilis species complex (Rotifera, Monogononta) through DNA taxonomy. Hydrobiologia, 796 (1), 39-58. https://doi.org/10.1007/s10750-016-2725-7 Montero‐Pau, J., Gómez, A., & Muñoz, J. (2008). Application of an inexpensive and high‐throughput genomic DNA extraction method for the molecular ecology of zooplanktonic diapausing eggs. Limnology and Oceanography: Methods, 6 (6), 218-222. https://doi.org/10.4319/lom.2008.6.218 Obertegger, U., Flaim, G., & Fontaneto, D. (2014). Cryptic diversity within the rotifer Polyarthra dolichoptera along an altitudinal gradient. Freshwater Biology, 59 (11), 2413-2427. https://doi.org/10.1111/fwb.12447 Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., O’hara R., Simpson, G.L., ... & Stevens, M.H.H., Wagner, H. 2010. Vegan: community ecology package. R package version 1.17-4. URL http://CRAN. R-project. org/package= vegan. Pan, Y.B., Wang, L., Zhang, W.J., Liu, G.X., & Lin, S.J. (2016). Genetic analysis of Noctiluca scintillans populations indicates low latitudinal differentiation in China but high China-America differences. Journal of Experimental Marine Biology and Ecology, 477 , 31-39. https://doi.org/10.1016/j.jembe.2016.01.002 Papakostas, S., Michaloudi, E., Proios, K., Brehm, M., Verhage, L., Rota, J., ... & Declerck, S. A. (2016). Integrative taxonomy recognizes evolutionary units despite widespread mitonuclear discordance: evidence from a rotifer cryptic species complex. Systematic biology, 65 (3), 508-524. https://doi.org/10.1093/sysbio/syw016 Pons, J., Barraclough, T. G., Gomez-Zurita, J., Cardoso, A., Duran, D. P., Hazell, S., ... & Vogler, A. P. (2006). Sequence-based species delimitation for the DNA taxonomy of undescribed insects. Systematic biology, 55 (4), 595-609. https://doi.org/10.1080/10635150600852011 Puillandre, N., Lambert, A., Brouillet, S., & Achaz, G. 2012. ABGD, Automatic Barcode Gap Discovery for primary species delimitation. Molecular Ecology, 21 (8), 1864-1877. https://doi.org/10.1111/j.1365-294x.2011.05239.x Ribeiro, K. F., Ferrero, A. P., Duarte, L., Turchetto‐Zolet, A. C., & Crossetti, L. O. (2020). Comparative phylogeography of two free-living cosmopolitan cyanobacteria: Insights on biogeographic and latitudinal distribution. Journal of Biogeography, 47 (5), 1106-1118. https://doi.org/10.1111/jbi.13785 Rivas Jr, J. A., Mohl, J. E., Van Pelt, R. S., Leung, M. Y., Wallace, R. L., Gill, T. E., & Walsh, E. J. (2018). Evidence for regional aeolian transport of freshwater micrometazoans in arid regions. Limnology and oceanography letters, 3 (4), 320-330. https://doi.org/10.1002/lol2.10072 Sasaki, M. C., & Dam, H. G. (2019). Integrating patterns of thermal tolerance and phenotypic plasticity with population genetics to improve understanding of vulnerability to warming in a widespread copepod. Global Change Biology, 25 (12), 4147-4164. https://doi.org/10.1111/gcb.14811 Savary, R., Masclaux, F. G., Wyss, T., Droh, G., Corella, J. C., Machado, A. P., ... & Sanders, I. R. (2018). A population genomics approach shows widespread geographical distribution of cryptic genomic forms of the symbiotic fungus Rhizophagus irregularis. The ISME journal, 12 (1), 17-30. https://doi.org/10.1038/ismej.2017.153 Sayers, E. W., Cavanaugh, M., Clark, K., Ostell, J., Pruitt, K. D., & Karsch-Mizrachi, I. (2019). GenBank. Nucleic acids research, 47 (D1), D94-D99. https://doi.org/10.1093/nar/gky989 Sbabou, L., Regragui, A., Filali-Maltouf, A., Ater, M., & Béna, G. (2016). Local genetic structure and worldwide phylogenetic position of symbiotic Rhizobium leguminosarum strains associated with a traditional cultivated crop, Vicia ervilia, from Northern Morocco. Systematic and applied microbiology, 39 (6), 409-417. https://doi.org/10.1016/j.syapm.2016.06.005 Slatkin, M. (1987). Gene flow and the geographic structure of natural populations. Science, 236 (4803), 787-792. https://doi.org/10.1126/science.3576198 Tang, C. Q., Leasi, F., Obertegger, U., Kieneke, A., Barraclough, T. G., & Fontaneto, D. (2012). The widely used small subunit 18S rDNA molecule greatly underestimates true diversity in biodiversity surveys of the meiofauna. Proceedings of the National Academy of Sciences, 109 (40), 16208-16212. https://doi.org/10.1073/pnas.1209160109 Ter Braak, C. J., & Šmilauer, P. (2002). CANOCO reference manual and CanoDraw for Windows user’s guide. Software for canonical community ordination (version 4.5). Microcomputer Power, Ithaca, NY , 500. Tisthammer, K.H., Forsman, Z.H., Toonen, R. J., & Richmond, R.H. (2020). Genetic structure is stronger across human-impacted habitats than among islands in the coral Porites lobata. PeerJ, 8 , e8550. https://doi.org/10.7717/peerj.8550 Walsh, E.J., May, L., & Wallace, R.L. (2017). A metadata approach to documenting sex in phylum Rotifera: diapausing embryos, males, and hatchlings from sediments. Hydrobiologia, 796 (1): 265-276. https://doi.org/10.1007/s10750-016-2712-z Wen, X.L., Xi, Y.L., Zhang, G., Xue, Y.H., & Xiang, X.L. (2016). Coexistence of cryptic Brachionus calyciflorus (Rotifera) species: roles of environmental variables. Journal of Plankton Research, 38 (3), 478-489. https://doi.org/10.1093/plankt/fbw006 Xiang, X.L., Xi, Y.L., Wen, X.L., Zhang, G., Wang, J.X., & Hu, K. (2011). Genetic differentiation and phylogeographical structure of the Brachionus calyciflorus complex in eastern China. Molecular Ecology, 20 (14), 3027-3044. https://doi.org/10.1111/j.1365-294x.2011.05147.x Zhang, J., Kapli, P., Pavlidis, P., & Stamatakis, A. (2013). A general species delimitation method with applications to phylogenetic placements. Bioinformatics 29 , 2869–2876. https://doi.org/10.1093/bioinformatics/btt499 Zhang, W., Pan, Y., Yang, J., Chen, H., Holohan, B., Vaudrey, J., ... & McManus, G. B. (2018a). The diversity and biogeography of abundant and rare intertidal marine microeukaryotes explained by environment and dispersal limitation. Environmental microbiology, 20 (2), 462-476. https://doi.org/10.1111/1462-2920.13916 Zhang, X., Liu, Y. H., Wang, Y. H., & Shen, S. K. (2020). Genetic Diversity and Population Structure of Rhododendron rex Subsp. rex Inferred from Microsatellite Markers and Chloroplast DNA Sequences. Plants, 9 (3), 338. https://doi.org/10.3390/plants9030338 Zhang, Y., Zhou A., Xi Y.L., Sun, Q., Ning L.F., Xie P., Wen X.L., & Xiang X.L. (2018b). Temporal patterns and processes of genetic differentiation of the Brachionus calyciflorus (Rotifera) complex in a subtropical shallow lake. Hydrobiologia, 807 (1), 313-331. https://doi.org/10.1007/s10750-017-3407-9 Supplementary Files SupplementalInformation2020.7.22.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-242024","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":12097816,"identity":"71f61506-b577-4f6d-9c9b-cea81ed2f258","order_by":0,"name":"Diwen Liang","email":"","orcid":"https://orcid.org/0000-0003-3415-393X","institution":"Jinan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Diwen","middleName":"","lastName":"Liang","suffix":""},{"id":12097818,"identity":"f547d3d9-7486-4916-a54c-02fad3bb40c7","order_by":1,"name":"George McManus","email":"","orcid":"","institution":"University of Connecticut","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"McManus","suffix":""},{"id":12097820,"identity":"4d37e12c-c93f-42e8-b7a9-eace34a98eef","order_by":2,"name":"Qing Wang","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Wang","suffix":""},{"id":12097822,"identity":"bfbaa4b6-aa90-4e53-9ca9-e4a4597941a2","order_by":3,"name":"Xian Sun","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xian","middleName":"","lastName":"Sun","suffix":""},{"id":12097827,"identity":"be6b8dac-24bc-4d1a-8298-4ab9e50b7490","order_by":4,"name":"Zhiwei Liu","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Liu","suffix":""},{"id":12097828,"identity":"b8ddbec9-463f-4d16-838e-94bf2c6b991f","order_by":5,"name":"Senjie Lin","email":"","orcid":"","institution":"University of Connecticut","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Senjie","middleName":"","lastName":"Lin","suffix":""},{"id":12097829,"identity":"c6f01987-f58e-4e24-a5ff-188e23a882cc","order_by":6,"name":"Yufeng Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYJCCDx94wLQBccqBihlnziBZy2yIDmK12EvkGDbbyNxJbGBv3ibBUHOHCFtAWnJ4niU28Bwrk2A49owoLeaPc3gOJzZI5JhJMDYcJtIWC5AW+TekaGEA28JDrJYzzwobe3gOG7fxpBVbJBwjQgt7e/LGhp89h2X72Q9vvPGhhggtDAIZBgyMPQwMbCBOAhEaGBj4jz9gYPhBlNJRMApGwSgYqQAAmME3QKiTsLIAAAAASUVORK5CYII=","orcid":"","institution":"Jinan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yufeng","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2021-02-14 17:00:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-242024/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-242024/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":6367197,"identity":"b57e5f23-2200-4f33-9976-2d707be9f0d0","added_by":"auto","created_at":"2021-02-25 21:31:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1228485,"visible":true,"origin":"","legend":"Location of the sampling sites in Southern China and eastern North America.\nNote: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever \non the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, \nor concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-242024/v1/b297e32297ad1dfe409fe99b.jpg"},{"id":6366860,"identity":"f836e538-4154-4f51-b234-3c202c96baf2","added_by":"auto","created_at":"2021-02-25 21:28:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1858320,"visible":true,"origin":"","legend":"The Bayesian phylogenetic tree of the P. dolichoptera complex based on 108 COI gene sequences. Posterior probabilities ( \u003e 0.5) from Bayesian reconstruction are shown at each node. Putative cryptic species detected using Automatic Barcoding Gap Discovery (ABGD), Generalized Mixed Yule Coalescent models (GMYC) and Poisson Tree Process (PTP) are shown. Red branch, individuals from Southeastern China; Blue branch, individuals from eastern North America; Abbreviation: D, P. dolichoptera cryptic species; V, P. vulgaris cryptic species, which was included as the outgroup.","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-242024/v1/1cec631f11accbed8c1b009a.jpg"},{"id":6366673,"identity":"78855eb9-d4c6-4cdf-91c6-3d876dbbbea4","added_by":"auto","created_at":"2021-02-25 21:25:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1279090,"visible":true,"origin":"","legend":"The Bayesian phylogenetic tree of the P. vulgaris complex based on 64 COI gene sequences. Posterior probabilities ( \u003e 0.5) from Bayesian reconstruction are shown at each node. Putative cryptic species detected using Automatic Barcoding Gap Discovery (ABGD), Generalized Mixed Yule Coalescent models (GMYC) and Poisson Tree Process (PTP) are shown. Red branch, individuals from Southeastern China; Blue branch, individuals from eastern North America; Abbreviation: V, P. vulgaris cryptic species；D, P. dolichoptera cryptic species, which was included as the outgroup.","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-242024/v1/a158d80fd9e92a100feda62d.jpg"},{"id":6366858,"identity":"63d9c78a-db20-4cb7-9e49-54eb7b2b9510","added_by":"auto","created_at":"2021-02-25 21:28:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":130645,"visible":true,"origin":"","legend":"Boxplot of the distribution of range size of distances in kilometers for three levels of analysis: haplotype, cryptic species and the morphological species (P. dolichoptera and P. vulgaris). Differences were detected with the TukeyHSD method. Letters indicate sample means that are similar (same letter) or significantly different (different letter).","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-242024/v1/e4a8648cf8eb0d1293178675.jpg"},{"id":6366856,"identity":"fbb01a76-840f-4d05-a59d-fb6216546110","added_by":"auto","created_at":"2021-02-25 21:28:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":405253,"visible":true,"origin":"","legend":"Relationships between pairwise genetic distance and geographic distance of the P. dolichoptera complex (A) and P. vulgaris complex (B); Black line, Linear regression; Blue line, smooth function of generalized additive model (GAM);","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-242024/v1/041cf5ed019ca38ac5976924.jpg"},{"id":6367563,"identity":"e75fc32e-e1d8-4a45-a249-2518139a2b6f","added_by":"auto","created_at":"2021-02-25 21:34:55","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":282349,"visible":true,"origin":"","legend":"The ranges of pairwise genetic distances among the three groups of P. dolichoptera (A) and P. vulgaris complexes (B). Differences were detected with TurkeyHSD method. Letters indicate sample means that are similar (same letter) or significantly different (different letter) ","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-242024/v1/d0b29e3b42a4b0d09651def3.jpg"},{"id":6366857,"identity":"f905a086-45fb-467f-a214-779fcf5f6dc2","added_by":"auto","created_at":"2021-02-25 21:28:55","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1742247,"visible":true,"origin":"","legend":"Canonical Correlation Analysis (CCA) of the P. dolichoptera and P. vulgaris cryptic species with environmental variables. Relationship between sampling sites and environmental variables (A). Relationship between cryptic species and environmental variables (B). Red, samples and cryptic species from Southern China; Blue, samples and cryptic species from eastern North America. Abbreviations used in the figures: V, P. vulgaris cryptic species；D, P. dolichoptera cryptic species; 3_liuye, in Lake Liuye in March; 6_liuye, in Lake Liuye in June; 9_liuye, in Lake Liuye in September; 12_liuye, in Lake Liuye in December; 9_chuanzi2, in the Chuanzi River in September; 12_chuanzi2, in the Chuanzi River in December;","description":"","filename":"Fig.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-242024/v1/150659ef60a444ea553b46fe.jpg"},{"id":13672792,"identity":"8a0da2bd-0659-4bd4-9ed2-cd3f3e4dfe82","added_by":"auto","created_at":"2021-09-17 11:14:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1107801,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-242024/v1/1476d286-f0fe-4cd7-b13a-595b4aa5d837.pdf"},{"id":6366680,"identity":"1d8e12ad-7990-4d78-a089-f6877a750426","added_by":"auto","created_at":"2021-02-25 21:25:55","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":27929,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalInformation2020.7.22.docx","url":"https://assets-eu.researchsquare.com/files/rs-242024/v1/e408fade128c93c86e220e57.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eGenetic Differentiation and Phylogeography of Rotifer Polyarthra Dolichoptera and P. Vulgaris Complexes Between Southern China and Eastern North America: High Intercontinental Differences\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":" \u003cp\u003eOne of the most important questions in ecology and biogeography is how and why species composition differs between geographic locations (Dambros et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Environmental factors, geographic distance, and dispersal barriers are potential drivers. Some scholars believe that genetic diversity should be related to geographical distances by a classical distance\u0026ndash;decay relationship (G\u0026oacute;mez-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e ; G\u0026oacute;mez et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). On the contrary, others hold that environmental conditions, not dispersal, control the patterns of genetic differentiation (Sbabou et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e ; Fenchel and Finlay, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIsolation by distance (IBD), proposed by Wright (1943), predicts that the degree of genetic differentiation increases with geographic distance due to dispersal limits (Tisthammer et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), analogously to a distance\u0026ndash;decay relationship at the community level (G\u0026oacute;mez-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The IBD pattern, respecting the association between genetic and geographic distances, is common in many plants (e.g. \u003cem\u003eRhododendron\u003c/em\u003e spp, \u003cem\u003eMikania micrantha\u003c/em\u003e) and even the single-celled phytoplankton cyanobacteria (Banerjee et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e ; Ribeiro et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e ; Zhang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For heterotrophic protists (unicellular eukaryotes) such as \u003cem\u003eNoctiluca scintillans\u003c/em\u003e, an intercontinental spatial barrier (the Pacific Ocean) seems to impose a limitation on gene flow and induces an increase in genetic distance (Pan et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, weak or no genetic/geographic correlation has been found in most invertebrates including \u003cem\u003eTetranychus\u003c/em\u003e, \u003cem\u003eThrips tabaci\u003c/em\u003e and \u003cem\u003ePeriplaneta americana\u003c/em\u003e (Jin et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e ; Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e ; Ma et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Rotifers, cosmopolitan microscopic organisms, have been proposed as a model of distance\u0026ndash;decay biogeographic patterns (Fontaneto et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008a\u003c/span\u003e). But to date, findings are inconsistent. A study of \u003cem\u003eBrachionus calyciflorus\u003c/em\u003e phylogeography in eastern China showed no significant association between geographical and genetic distances (Xiang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Conversely, a significant albeit weak correlation was found in \u003cem\u003eEuchlanis dilatata\u003c/em\u003e phylogeography in North America (Kordbacheh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The relationship between geographical distance and genetic distance is thus far from clear in rotifers. Because the IBD pattern is non-linear at very short and long average geographic distances (Bradbury and Bentzen, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), studies at intercontinental geographic scale are needed.\u003c/p\u003e \u003cp\u003eIn contrast to the IBD hypothesis, the Baas-Becking\u0026rsquo;s hypothesis, known as \u0026ldquo;everything is everywhere, but the environment selects\u0026rdquo; (EisE) posits that for small species, differences from different places occur because of environmental variation, and not because of restricted dispersal (Fenchel and Finlay, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In addition, it is widely recognized that cryptic diversity is currently underestimated, which results in underappreciation of cosmopolitan organisms (Leal et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to EisE, two identical individuals can be found in the same kind of environments in two far separated locations. Most studies of environmental-spatial selection to date focused on small geographic or continental scales, and genetic differentiation across intercontinental scales has been underexplored.\u003c/p\u003e \u003cp\u003eMorphological and genetic variations commonly occurr between populations and are mainly influenced by two factors. On one hand, genetic drift, mutations and natural selection will lead to the genetic differentiation of local populations. On the other hand, gene flow produces genetic homogeneity by the movement of gametes, individuals and even entire populations, and blockage of gene flow creates genetic differentiation between populations. The latter can lead to speciation (Slatkin, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith the development of molecular tools, increasing numbers of cryptic species have been discovered in various morphological species of rotifers, such as \u003cem\u003eBrachionus calyciflorus\u003c/em\u003e (Papakostas et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), \u003cem\u003eB. plicatilis\u003c/em\u003e (Mills et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), \u003cem\u003eKeratella cochlearis\u003c/em\u003e (Derry and Prepas, 2003), \u003cem\u003ePolyarthra dolichoptera\u003c/em\u003e (Obertegger et al., 2015), \u003cem\u003eLecane spp\u003c/em\u003e. (Garc\u0026iacute;a-Morales and El\u0026iacute;as‐Guti\u0026eacute;rrez, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), \u003cem\u003eTestudinella clypeata\u003c/em\u003e (Leasi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and some bdelloid rotifers (Fontaneto et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008a\u003c/span\u003e). Moreover, coexistence of cryptic species is common in a single water body and can be mediated by ecological forces including seasonal changes, food resources and competition (Wen et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRotifers undergo periodic parthenogenesis, with little genetic recombination and intraspecific hybridization. Thus, compared with cladocerans and copepods, rotifers are advantaged for the study of phylogeography. Studying the genetic differentiation of rotifers is of great significance for understanding the dispersal pattern of gene flow and adaptive evolution mechanisms in microscopic organisms (Zhang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). \u003cem\u003ePolyarthra dolichoptera\u003c/em\u003e and \u003cem\u003eP. vulgaris\u003c/em\u003e, cosmopolitan species of rotifera, are more tolerant to seasonal changes than other rotifers and exist in almost all kinds of water bodies (Liang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It has been suggested that \u003cem\u003ePolyarthra\u003c/em\u003e cryptic species distribution might reflect genotypic adaptations to temperature differences and food resources (Obertegger et al. 2015). Temperature influences all metabolic processes, and it plays an important role in rotifer habitat selection (Obertegger et al., 2015). Also, phytoplankton biomass has marked effects on rotifer communities and it has strong seasonalities in most water bodies (Liang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A strongly positive correlation has been found between \u003cem\u003ePolyarthra\u003c/em\u003e abundance and chlorophyll-\u003cem\u003ea\u003c/em\u003e (Liang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we attempted to address the IBD versus EisE hypothesis by investigating the phylogeographic patterns of \u003cem\u003eP. dolichoptera\u003c/em\u003e and \u003cem\u003eP. vulgaris\u003c/em\u003e across the America and the Eurasia continents. The specific objectives were: (1) to estimate the relationship between geographic distance and genetic distance on the intercontinental scale (2) to understand whether physicochemical or spatial variables are the key factors affecting genetic differentiation.\u003c/p\u003e "},{"header":"2. Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sampling\u003c/h2\u003e \u003cp\u003eSamples were collected from 28 sites including rivers, ponds and lakes in both eastern North America and Southeastern China, during June 2018 to September 2019 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For determining detailed cryptic species structure on a small geographic scale, five sites in Guangzhou city, China and five ponds in New London Country, U.S.A., were sampled. In consideration of temperature effects on cryptic species structure, four seasons were sampled in Lake Liuye, China (6_liuye, 9_liuye, 12_liuye and 3_liuye; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of sampling localities and the environmental parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCollection date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAltitude (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTemperature (℃)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChlorophyll-a (ug/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSalinity (\u0026permil;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch_Lake Liuye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3_liuye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111.72917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.109722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune_Lake Liuye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6_liuye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111.72722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.048333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember_Lake Liuye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9_liuye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2018.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111.70911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember_Lake Liuye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12_liuye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2018.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111.76028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.069167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember_the Chuanzi River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9_chuanzi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2018.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111.69194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.053889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember_the Chuanzi River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12_chuanzi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2018.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111.69194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.053889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe pond of Haizhu Park\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehaizhu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2018.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113.22833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.122778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe pond of Minghu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eminghu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113.34323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.134201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe pond of Nanhu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enanhu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113.34434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.131315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuangzhou Segment of the Pearl River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epearlriver2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113.34718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.042379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe pond of Zhujaing Park\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ezhujiangpark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113.33385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.122431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe reservoir of Jiukeng\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ejiukeng\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e112.5482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.231587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe pond of Xiamen University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2018.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118.30997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.620833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe brook of Xiamen University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2018.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118.3093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.613373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe pond near Lake Donghu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWHS7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2018.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.528611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThames river\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThames\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-72.07313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.47805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousatonic river\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousatonic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-73.12371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.340767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuinnipiac river\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuinnipiac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-72.86769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.398428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLake Success\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-73.70729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.763248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNiagara waterfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNiagara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-79.06275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.081979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePattagansett Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-72.22837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.376893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorwich Pond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-72.30391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.384799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePowers Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-72.2559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.393302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmos Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-71.97741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.516628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMirror Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-72.24724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.806832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwan Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-72.25277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.81083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoodus Reservoir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-72.40739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.509835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll rotifer samples were collected by towing a plankton net (mesh size 30\u0026nbsp;\u0026micro;m) horizontally at surface and subsurface depths and preserved in a 50\u0026nbsp;mL centrifuge tube. To prevent changes in DNA, samples were fixed on site immediately with neutral Lugol\u0026rsquo;s solution at 2% final concentration and transported in a cooler before storing at -20\u0026nbsp;\u0026deg;C. In vivo semi-quantitative measurements of chlorophyll-\u003cem\u003ea\u003c/em\u003e (Chl-\u003cem\u003ea\u003c/em\u003e) were obtained using a FluoroSenseTM handheld fluorometer (Turner Designs, USA). Water temperature (Temp) and salinity, were measured on site. Also, GPS coordinates and altitude values were recorded using a GPS application.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Species identification and isolation\u003c/h2\u003e \u003cp\u003eSpecies identification was based on Dumont (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), the latest and most authoritative rotifer taxonomy system. Identification relies on morphological differences of body forms, sizes, fins, lateral antennae and vitellarium. The species of \u003cem\u003ePolyarthra dolichoptera\u003c/em\u003e and \u003cem\u003ePolyarthra vulgaris\u003c/em\u003e were isolated with micropipette under the stereo microscope. Single individuals were rinsed several times and transferred into PCR tubes for DNA analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Selection of mt COI as the DNA marker\u003c/h2\u003e \u003cp\u003eDNA markers including 18S ribosomal RNA, nuclear internal transcribed spacer (ITS) and mitichondrial cytochrome c oxidase subunit I gene (COI) are widely used as DNA barcodes for identification (Papakostas et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Unlike nuclear DNA, mitochondrial DNA such as COI generally does not undergo genetic recombination as it is transmitted directly from the mother to the offspring, and can be an effective single haplotype marker (Freeland et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Moreover, COI evolves more rapidly than ITS in animals, and is thus the better marker for phylogeography and cryptic species delimitation (Mills et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 DNA extraction and amplification\u003c/h2\u003e \u003cp\u003eDNA from each single animal was extracted following the HotSHOT protocol (Montero-Pau et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Then, the partial cytochrome c oxidase subunit I (COI) mtDNA gene was amplified and sequenced using primers LCOI (5\u0026rsquo;-GGT CAA CAA ATC ATA AAG ATA TTGG-3\u0026rsquo;) and HCOI (5\u0026rsquo;-TAA ACT TCA GGG TGA CCA AAA AAT CA-3\u0026rsquo;) (Folmer et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). PCR was processed according to the TaKaRa exTaq protocol with 5 \u0026micro;L of extracted DNA. Cycle conditions were initial denaturation at 94\u0026nbsp;\u0026deg;C for 3\u0026nbsp;min, followed by 35 cycles of denaturation at 94\u0026nbsp;\u0026deg;C for 30\u0026nbsp;s, annealing at 52\u0026nbsp;\u0026deg;C for 30\u0026nbsp;s and extension at 72\u0026nbsp;\u0026deg;C for 45\u0026nbsp;s. The amplification ended with a final extension of 72\u0026nbsp;\u0026deg;C for 8\u0026nbsp;min. Successful amplification products were then purified using the TaKaRa Minibest agarose Gel DNA extraction Kit before being sent to TsingKe company for sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Sequences alignment and phylogenetic analyses\u003c/h2\u003e \u003cp\u003eSequences were aligned by Mega X using Clustal-W and then visually checked. Each sequence was verified by BLAST search in NCBI GenBank (Sayers et al., 2018). Within-species genetic distances should be less than 14% for mt COI (Obertegger et al., 2015; Mills et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The closest sequences with the highest similarity scores were obtained from GenBank for comparison (Accession #: KJ460388, LC215566, LC215573, KC618934, KC619030, JN936500, KJ460383, KC619195 and LC215562). Population genetic statistics (average number of nucleotide differences between haplotypes, number of haplotypes, haplotype diversity [\u003cem\u003eHd\u003c/em\u003e] and nucleotide diversity [\u003cem\u003eπ\u003c/em\u003e], average number of nucleotide differences [K], average number of segregating sites [S]) were calculated using DNASP 5.1 (Librado and Rozas, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBayesian phylogenetic trees were run in BEAST v1.8.4 using a HKY\u0026thinsp;+\u0026thinsp;I\u0026thinsp;+\u0026thinsp;G model, separately for the two data sets (108 \u003cem\u003eP. dolichoptera\u003c/em\u003e and 64 \u003cem\u003eP. vulgaris\u003c/em\u003e sequences). For this analysis, an uncorrelated lognormal relaxed clock, the Yule process speciation prior (rate of linear birth in the Yule model of speciation set as lognormal); the default settings of prior, and the MCMC of 10\u003csup\u003e7\u003c/sup\u003e generations with sampling every 1000 generations were used. Tracer v1.6 was used for evaluating effective sample size (ESS\u0026thinsp;\u0026gt;\u0026thinsp;200). Trees were summarised using TreeAnnotator v1.8.4 with a 20% burn-in. For \u003cem\u003eP. dolichoptera\u003c/em\u003e phylogenetic reconstructions, congener \u003cem\u003eP. vulgaris\u003c/em\u003e (KJ460388) was included as outgroup, and \u003cem\u003eP. dolichoptera\u003c/em\u003e (KC618934) was included as outgroup for \u003cem\u003eP. vulgaris.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Cryptic species delimitation\u003c/h2\u003e \u003cp\u003eGeneralised Mixed Yule Coalescent (GMYC), Automatic Barcode Gap Discovery (ABGD) and Poisson tree processes (PTP) models are widely used approaches for cryptic species (entities) delimitation (Kordbacheh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). An ultrametric tree generated by BEAST was required for both GMYC and PTP delimitations. The GMYC delimitation analysis was processed on software R 3.6.1 using the \u0026lsquo;rncl\u0026rsquo; and \u0026lsquo;splits\u0026rsquo; packages, (R Core Team 2019). The GMYC model is a likelihood method for delimiting species by fitting within and between species branching models to reconstructed gene trees (Fujisawa and Barraclough, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e ; Pons et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ABGD model was performed for primary species delimitation and was processed on the website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfo.mnhn.fr/abi/public/abgd/\u003c/span\u003e\u003c/span\u003e (Accessed January 10, 2020). ABGD classifies sequences into putative cryptic species based on pairwise genetic distances without any prior assumptions (Puillandre et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePTP is a tree-based method that uses the number of substitutions to distinguish intraspecies processes from interspecies processes. This method considers two classes of Poisson processes, speciation (higher substitution rate associated to interspecies events) and coalescence (within species events) (Zhang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). PTP model for cryptic species delimitation was run using the online tool at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://species.h-its.org/ptp/\u003c/span\u003e\u003c/span\u003e (Accessed January 8, 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Geographical and genetic distance analysis\u003c/h2\u003e \u003cp\u003eThe pairwise geographic distance matrices were calculated in R 3.6.1 (R Core Team, 2019) using the \u0026lsquo;geosphere\u0026rsquo; package (Ding et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The pairwise genetic distance matrixes were calculated with Kimura 2-parameters model, pairwise deletion, transitions\u0026thinsp;+\u0026thinsp;transversions using the Mega X program. Linear regression analysis and Generalized additive models (GAM) of the relationships between geographic distance and genetic distance were processed in R 3.6.1 using the package \u0026lsquo;ggplot2\u0026rsquo;. To determine the significance of differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in geographic distances and genetic distances among different groups, analysis of variance (ANOVA) with TukeyHSD test was conducted, using the software R 3.6.1, \u0026lsquo;agricolae\u0026rsquo;, \u0026lsquo;car\u0026rsquo; and \u0026lsquo;multcomp\u0026rsquo; packages. To characterize the shape of the relationships within the two species, three different GLM models including linear, exponential and power-law were applied using the package \u0026lsquo;betapart\u0026rsquo; and \u0026lsquo;pscl\u0026rsquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Relationships between cryptic species and environmental factors\u003c/h2\u003e \u003cp\u003eRedundancy analysis (RDA) or Canonical Correlation Analysis (CCA) was performed to explore the relationships between cryptic species and environmental factors using the \u0026lsquo;vegan\u0026rsquo; and \u0026lsquo;ggplot2\u0026rsquo; packages in R. CCA or RDA model is determined based on the community composition by Detrended Correspondence Analysis (DCA). If the longest gradient is \u0026gt;\u0026thinsp;4, the unimodal method (CCA) will be applied. On the other hand, if that value is \u0026lt;\u0026thinsp;3, the linear method (RDA) is a better choice. In the range between 3 and 4, both methods can be applied (ter Braak and Smilauer, 2002). Varying inflation factors less than 10 (VIF\u0026thinsp;\u0026lt;\u0026thinsp;10) were included in the analysis and the envfit (permu\u0026thinsp;=\u0026thinsp;999) function was used to determine the significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) variables (Oksanen et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e "},{"header":"3. Results","content":" \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Genetic diversity\u003c/h2\u003e \u003cp\u003eWe obtained 107 COI sequences of \u003cem\u003eP. dolichoptera\u003c/em\u003e and 63 sequences of \u003cem\u003eP. vulgaris\u003c/em\u003e, with aligned lengths of 562\u0026nbsp;bp and 589\u0026nbsp;bp, respectively (Accession numbers: Table S1). A total of 64 \u003cem\u003eP. dolichoptera\u003c/em\u003e haplotypes were detected with haplotype diversity (h) of 0.98 and nucleotide diversity (π) of 0.175. For \u003cem\u003eP. vulgaris\u003c/em\u003e a total of 36 haplotypes were found with a haplotype diversity of 0.96 and nucleotide diversity of 0.106 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenetic diversity summary statistics, as calculated by DNASP 5\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndividuals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHaplotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHaplotype diversity (Hd)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNucleotide diversity (π)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAverage number of nucleotide differences (K)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAverage number of segregating sites (S)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal \u003cem\u003eP. dolichoptera\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP. dolichoptera\u003c/em\u003e in China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP. dolichoptera in the USA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal \u003cem\u003eP. vulgaris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP. vulgaris\u003c/em\u003e in China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP. vulgaris\u003c/em\u003e in the USA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor \u003cem\u003eP. dolichoptera\u003c/em\u003e complex, a larger genetic variation was observed in eastern North America than in southeastern China, with higher haplotype diversity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sub\u003e; 0.978), nucleotide diversity (\u003cem\u003eπ\u003c/em\u003e; 0.156), average number of nucleotide differences (K; 84.51) and average number of segregating sites (S; 281). For \u003cem\u003eP. vulgaris\u003c/em\u003e complex, higher levels of \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sub\u003e (0.93), \u003cem\u003eπ\u003c/em\u003e (0.048), K (27.56) and S (156) were detected in the southeastern China samples (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Phylogenetic analysis and cryptic species delimitation\u003c/h2\u003e \u003cp\u003eUsing Bayesian phylogenetic analysis, \u003cem\u003eP. dolichoptera\u003c/em\u003e complex were reconstructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All the individuals analyzed were binned into three groups. Group 1 was composed of 54 individuals, which were all from Southeastern China. This group was composed of similar clusters from different sites in Southeastern China. Group 2 consisted of 20 individuals from eastern North America and 17 individuals from Southeastern China, which formed independent clades by continents, showing high genetic divergence between the two geographic communities. Group 3 consisted of 13 individuals from the eastern North America and three individuals from Southeastern China, which formed many divergent clusters within eastern North America and an independent clade for the individuals from Southeastern China. The Bayesian tree analysis clustered the \u003cem\u003eP. vulgaris\u003c/em\u003e samples into two groups with strong support values (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All of the 42 individuals from Southeastern China were in Group 1, while the 18 individuals from eastern North America were in Group 2 with strong support values.\u003c/p\u003e \u003cp\u003eA large number of cryptic species were detected using three independent methods (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The ABGD method produced 20 cryptic species for \u003cem\u003eP. dolichoptera\u003c/em\u003e complex and four for \u003cem\u003eP. vulgaris\u003c/em\u003e. GMYC analysis revealed 21 cryptic species in \u003cem\u003eP. dolichoptera\u003c/em\u003e complex and five in \u003cem\u003eP. vulgaris\u003c/em\u003e complex. Using the PTP method, \u003cem\u003eP. dolichoptera\u003c/em\u003e complex was delimited into 24 cryptic species and \u003cem\u003eP. vulgaris\u003c/em\u003e complex into 13. The most conservative estimate of cryptic species was obtained using ABGD, while PTP method gave the greatest number of cryptic species. All three methods shared common species boundaries for the smallest number of cryptic species, however. As the results from ABGD and GMYC were similar, unless specified otherwise, cryptic species determination will be discussed based on ABGD results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Geographical distribution\u003c/h2\u003e \u003cp\u003eThe range size of geographic distances declined as the resolution of classification increased from morphological species to cryptic species to haplotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Both \u003cem\u003eP. dolichoptera\u003c/em\u003e and \u003cem\u003eP. vulgaris\u003c/em\u003e species were widely distributed, with the geographic range sizes up to 12903\u0026nbsp;km and 12836\u0026nbsp;km, respectively. Their range sizes at cryptic species and haplotype levels decreased to 2726\u0026nbsp;km and 411\u0026nbsp;km, respectively. These correspond to the mean range sizes at species level (\u003cem\u003eP. dolichoptera\u003c/em\u003e: 5374\u0026thinsp;\u0026plusmn;\u0026thinsp;5665\u0026nbsp;km; \u003cem\u003eP. vulgaris\u003c/em\u003e: 5517\u0026thinsp;\u0026plusmn;\u0026thinsp;5720\u0026nbsp;km) significantly larger than at cryptic species (463\u0026thinsp;\u0026plusmn;\u0026thinsp;815\u0026nbsp;km) and haplotype level (12\u0026thinsp;\u0026plusmn;\u0026thinsp;61\u0026nbsp;km). However, there was no significant difference in range size between cryptic species and haplotype ranges.\u003c/p\u003e \u003cp\u003eMost of the cryptic species were limited to single areas, but some were widely distributed. For example, cryptic species one of \u003cem\u003eP. vulgaris\u003c/em\u003e complex (V1) comprised individuals from as distant areas as Changde (liuye), Wuhan (WHS7), Xiamen (XM) and Guangzhou (zhujiangpark), in China (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, cryptic species 10 of \u003cem\u003eP. dolichoptera\u003c/em\u003e complex (D10) were as widely distributed as from Connecticut (P4), Long Island (SUC) and Niagara (upstate New York), in USA. In contrast, some cryptic species only occurred in one sampling site, such as D7 and D16 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results indicated that the cryptic species and haplotypes tended to be regionally restricted. Although some of them can be widely spread into different habitats (sampling sites), no cryptic species or haplotype was found to occur on both continents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Phylogeographical patterns and genetic structure\u003c/h2\u003e \u003cp\u003eThe relationships between dependent variables for genetic distance and independent variables for geographic distance were examined by linear regression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The genetic distance of \u003cem\u003eP. dolichoptera\u003c/em\u003e complex showed significant positive correlation with geographic distance (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The genetic distance of \u003cem\u003eP. vulgaris\u003c/em\u003e complex was also positively correlative with geographic distance (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Fitting the data to the smooth function of the generalized additive model (GAM) indicated that the relationship between geographic distance and genetic distance was not simply linear. Genetic distances increased with geographic distances initially but then decreased rapidly when geographic distances increase within 1300\u0026nbsp;km, before it rose again at greater distances of around 10000\u0026nbsp;km.\u003c/p\u003e \u003cp\u003eTo characterize the shape of these relationships within these two species complexes, GLM analysis was carried out based on AIC values. Our results showed that the power-law model fitted the \u003cem\u003eP. dolichoptera\u003c/em\u003e data better than either the exponential or linear models, while the linear modle fitted the \u003cem\u003eP. vulgaris\u003c/em\u003e data better than other models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of three GLM models (linear, exponential and power-law) assessing the shape of the relationship between geographic distance and genetic distance for \u003cem\u003eP. dolichoptera\u003c/em\u003e and \u003cem\u003eP. vulgaris\u003c/em\u003e complexes. Models are evaluated according to their AIC and the lowest values of AIC are shown in bold.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP. dolichoptera\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP. vulgaris\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elinear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-24567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-9323\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eexponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-24551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epower-law\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-25185\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 in all cases\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOur results indicated that in both \u003cem\u003eP. dolichoptera\u003c/em\u003e and \u003cem\u003eP. vulgaris\u003c/em\u003e complexes, the mean genetic distances between the two continents were significantly higher than those within either eastern North America or Southern China (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The mean genetic distance in the \u003cem\u003eP. dolichoptera\u003c/em\u003e complex decreased in the order: Southern China VS eastern North America (0.248\u0026thinsp;\u0026plusmn;\u0026thinsp;0.052)\u0026thinsp;\u0026gt;\u0026thinsp;within eastern North America (0.188\u0026thinsp;\u0026plusmn;\u0026thinsp;0.111)\u0026thinsp;\u0026gt;\u0026thinsp;within Southern China (0.173\u0026thinsp;\u0026plusmn;\u0026thinsp;0.089) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The mean genetic distances in the \u003cem\u003eP. vulgaris\u003c/em\u003e complex decreased in the order: Southern China VS eastern North America (0.223\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02)\u0026thinsp;\u0026gt;\u0026thinsp;within Southern China (0.055\u0026thinsp;\u0026plusmn;\u0026thinsp;0.088)\u0026thinsp;\u0026gt;\u0026thinsp;within eastern North America (0.049\u0026thinsp;\u0026plusmn;\u0026thinsp;0.067) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). However, there was no significant difference in the mean genetic distance values for the Southern China and the eastern North America groups in the \u003cem\u003eP. vulgaris\u003c/em\u003e complex. These results indicated that the genetic divergences between these two continents were significantly higher than those within a single continent (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This suggests that there is a higher level of gene flow and high frequency of recombination within continents than between continents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Relationships between environmental factors and cryptic species distributions\u003c/h2\u003e \u003cp\u003eAs the longest gradient performed by Detrended Correspondence Analysis (DCA) was 7.5 (larger than 4), a Canonical Correlation Analysis (CCA) model was chosen for estimating the relationship between cryptic species and environmental factors. The first two ordinate axes explained 61% of the cryptic species-environment variability in the CCA ordination (Table S2). The CCA ordination showed that four variables including longitude, latitude, altitude and temperature were significantly related to the cryptic species distributions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table S3). However, the environmental factor chlorophyll-\u003cem\u003ea\u003c/em\u003e was not significant variable affecting the cryptic species structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e7\u003c/span\u003e clearly showed that the Southern China populations (red) mostly stayed on the left of the figure, while the eastern North America populations (blue) were mostly on the right of the figure. In addition, the spatial variables of longitude and latitude showed positive correlation with axis 1, which indicated that longitude and latitude were the key factors for the variation of the cryptic species structure. Furthermore, the cryptic species-variables relationship was similar to that of sampling sites-variables, which indicated that most cryptic species tended to be restricted to specific regions.\u003c/p\u003e "},{"header":"Discussion","content":" \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 The hidden diversity in species complexes\u003c/h2\u003e \u003cp\u003eCryptic species have been found in almost all groups of animals (Tang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e ; Fossen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and rotifers seem to be one of the invertebrates hosting the highest potential cryptic diversity in the world (Fontaneto et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e ; Fontaneto, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For instance, eight potential cryptic species of \u003cem\u003eBrachionus calyciflorus\u003c/em\u003e were found in eastern China (Xiang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Also, more than seven cryptic species of \u003cem\u003eEuchlanis dilatata\u003c/em\u003e were defined in North America (Kordbacheh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). By the end of 2017, there were 15 cryptic species of \u003cem\u003eB. plicatilis\u003c/em\u003e were recorded in the world, as a conservative estimate (Mills et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our results indicated that both \u003cem\u003eP. dolichoptera\u003c/em\u003e and \u003cem\u003eP. vulgaris\u003c/em\u003e are complexes of cryptic species, with at least 17 taxa of \u003cem\u003eP. dolichoptera\u003c/em\u003e and 3 taxa of \u003cem\u003eP. vulgaris\u003c/em\u003e in our study areas. Some cryptic species of \u003cem\u003eP. dolichoptera\u003c/em\u003e, such as D11 and D12, were only found in one site, which could be due to small sample sizes. Obertegger et al. (2015) reported that at least 12 cryptic species of \u003cem\u003eP. dolichoptera\u003c/em\u003e had been found in 35 lakes along an altitudinal gradient in Italy. Given that the small sample sizes in this study impeded a thorough detection of cryptic species, the degree of cryptic diversity in Southeastern China and eastern North America is likely to be higher than what we reported here.\u003c/p\u003e \u003cp\u003eLeaving out the data from NCBI, which gave two cryptic species based on our analysis, our most conservative delimitation based on ABGD gave 20 cryptic species in the \u003cem\u003eP. dolichoptera\u003c/em\u003e complex and 4 in the \u003cem\u003eP. vulgaris\u003c/em\u003e complex from our current data. The results from the GMYC method were similar ( 21 in the \u003cem\u003eP. dolichoptera\u003c/em\u003e complex and 5 in the \u003cem\u003eP. vulgaris\u003c/em\u003e complex). PTP-based estimates, 24 in the \u003cem\u003eP. dolichoptera\u003c/em\u003e complex and 13 in the \u003cem\u003eP. vulgaris\u003c/em\u003e complex might be a result of overestimation by the method, as has been suspected in previous studies on \u003cem\u003eE. dilatata\u003c/em\u003e (Kordbacheh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and \u003cem\u003eB. plicatilis\u003c/em\u003e (Mills et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Since our cryptic species delimitation was based on molecular methods with less morphological evidence, the conservative estimate is a better choice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Genetic divergence and geography distribution\u003c/h2\u003e \u003cp\u003eLong-distance dispersal of cryptic species has been reported not only in Monogononta including \u003cem\u003eBrachionus\u003c/em\u003e, \u003cem\u003ePolyarthra\u003c/em\u003e, \u003cem\u003eEuchlanis\u003c/em\u003e and \u003cem\u003eLecane\u003c/em\u003e, but also in Bdelloidea including \u003cem\u003ePhilodina\u003c/em\u003e and \u003cem\u003eRotaria\u003c/em\u003e (Kordbacheh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e ; Fontaneto et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008b\u003c/span\u003e). The cryptic species D1 was found in Guangdong and Hunan provinces, separated by \u0026gt;\u0026thinsp;500\u0026nbsp;km (e.g. liuye, chuanzi2, minghu, pearlriver2). Also, D10 was found across a range of \u0026gt;\u0026thinsp;500\u0026nbsp;km in the US states of New York and Connecticut (e.g. SUC, Niagara, P2, P4). In addition, V1 was widely distributed in southeastern of China, while V3 was widespread in Connecticut.\u003c/p\u003e \u003cp\u003eThe cosmopolitan distribution of rotifers could be atributed to long-distance dispersal. Colonization and long-distance dispersal to different waters across whole or even multiple continents, which may be mediated by waterfowl, have been observed in a number of zooplankton species (G\u0026oacute;mez et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Secondly, some areas that are widely separated share haplotypes and therefore appear genetically connected (Xiang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Sasaki and Dam (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that the widely distributed genetic clades of a marine copepod shared haplotypes between geographically distant populations. This implies that gene flow can be strong enough to overcome long distances at least within a continent.\u003c/p\u003e \u003cp\u003eMost small organisms do have very widespread distributions, but some are limited to distinct geographical areas (Savary et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our results showed that the cryptic species of of \u003cem\u003eP. dolichoptera\u003c/em\u003e D5 and D7 only occurred in Xiamen and Wuhan, respectively. This is consistent with the study of \u003cem\u003eBrachionus calyciflorus\u003c/em\u003e cryptic diversity in eastern China. Though most cryptic species of \u003cem\u003eBrachionus calyciflorus\u003c/em\u003e were widely distributed, one clade was only found in Danzhou, China (Xiang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Although high genetic distances of \u003cem\u003eAdineta\u003c/em\u003e can be found at different geographical distances, closely related individuals were only found at geographical scales\u0026thinsp;\u0026lt;\u0026thinsp;2000\u0026nbsp;km (Fontaneto et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008a\u003c/span\u003e). In the current study, we found that the range sizes of geographic distance in both genera declined as resolution increased from species to cryptic species to haplotype. This suggests that the restricted cryptic species in our study are not simply an artifact of sampling fewer individuals at lower levels. Interestingly, even though \u003cem\u003ePolyarthra\u003c/em\u003e was widely distributed as a genus, none of the cryptic species or haplotypes was found on both continents.\u003c/p\u003e \u003cp\u003eOur results indicated that all of the cryptic species from eastern North America formed independent strains that were separate from the Chinese ones, indicating high divergence. These results are consistent with the study in \u003cem\u003eNoctiluca\u003c/em\u003e, a heterotrophic dinoflagellate (Pan et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The haplotypes of \u003cem\u003eNoctiluca\u003c/em\u003e within China were geographically quite homogeneous, but were generally different, compared to the American population suggesting basin or continental-scale endemism. In addition, a study of the cryptic species of \u003cem\u003eB. plicatilis\u003c/em\u003e revealed existence of four clades associated to four geographic regions (one in North America, two in Europe and one in Australia) (Mills et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLevels of gene flow can be estimated by producing the visible patterns using allele frequencies and DNA sequence differences (Slatkin, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Lack of differentiation in mitochondrial COI sequences of geographically distant populations usually indicates strong effects of gene flow (Sasaki and Dam, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our study showed that the genetic distances of Southern China VS eastern North America were significantly higher than those within each continent. The divergence of populations between Southern China and eastern North America indicates limited gene flow between the two continents. The relatively low genetic divergence in the populations within continents of both \u003cem\u003eP. dolichoptera\u003c/em\u003e and \u003cem\u003eP. vulgaris\u003c/em\u003e complexes suggests strong gene flow within Southeastern China and within eastern North America.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Relationship between geographic and genetic distance\u003c/h2\u003e \u003cp\u003eIn the present study, a significantly positive correlation between genetic and geographic distance was found in both \u003cem\u003ePolyarthra\u003c/em\u003e species complexes. Similar results have been obtained for \u003cem\u003eE. dilatata\u003c/em\u003e in the North America (Kordbacheh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, it was reported that there was a strong positive correlation between genetic distance and geographic range when comparing samples on small geographical scales (Kordbacheh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Habitat heterogeneity and temporal variation can generate high genetic diversity on small geographic sacle study (Fontaneto et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, as the geographical scope of the study becomes broader, this correlation may weaken or disappear. A study of \u003cem\u003eGeomalacus\u003c/em\u003e revealed that the genetic distance increased rapidly along with the geographic distance, but as the geographic distance continues to expand, the genetic distance reached a plateau (G\u0026oacute;mez-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In another example, no significant associations between geographical and genetic distances were found for \u003cem\u003eB. calyciflorus\u003c/em\u003e across eastern China. The nonsignificant correlation may result from the effects of long-distance colonization and secondary contact, combined with monopolization effects which reduce gene flow among established populations (Xiang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kordbacheh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In our results, Guangdong and Hunan provinces shared \u003cem\u003eP. dolichoptera\u003c/em\u003e cryptic species D1 and \u003cem\u003eP. vulgaris\u003c/em\u003e cryptic species V1, while New York and Connecticut states shared the D10 and V3. Thus, long-distance intra-continental dispersal and colonization are responsible for depressing the geographic-genetic correlation in the present study.\u003c/p\u003e \u003cp\u003eInterestingly, GAM analysis indicated that genetic distance suddenly increased when the geographic distance was extremely high. Since the between continent genetic distances were significantly higher than those of within continent, a stronger positive correlation between genetic and geographic distance was observed, in consideration of datasets from the two continents. Furthermore, the significant genetic difference between the trans-Pacific regions suggests gene flow limitation (Pan et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, effects of the Pacific barrier leads to the restriction of gene flow and results in an increase in genetic distance.\u003c/p\u003e \u003cp\u003eThe relationship between geographic distance and genetic distance may not be simply linear. Generally, declines in the IBD slope are associated with increases in the geographic scales of observation, and the IBD pattern is non-linear at small scale and large scale geographic distances (Bradbury and Bentzen, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The GLM analysis of the \u003cem\u003eP. dolichoptera\u003c/em\u003e complex showed that genetic distance was significantly related to geographic distance, and the power-law model fitted the data better than the exponential and linear models. GAM models showed that genetic distance increases rapidly with geographical distance on small geographic scales because of the coexistence and habitat heterogeneity among lakes, streams, and rivers. However, as the geographic distance extends to an entire continent, long-distance colonization leads to the decrease of the genetic distance. Extreme barriers to dispersal, such as separate oceanographic basins, lead to an increase in genetic distance at larger geographic scales. As a power-law model is expected when there is no dispersal limitation (G\u0026oacute;mez-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), outliers above the correlation line between geographic distance and genetic distance might suggest a significant dispersal barrier on large geographic scales.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Key factors for phylogeographical patterns of rotifers\u003c/h2\u003e \u003cp\u003eCCA analysis indicated that spatial variables including longitude, latitude and altitude were key factors in controling the cryptic species structure rather than environmental factors such as temperature and Chlorophyll-\u003cem\u003ea\u003c/em\u003e. For most microeukaryotic communities, both abundant and rare communities exhibited a stronger response to environmental factors than spatial factors (Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). But for populations, genetic differentiation depend largely upon the evolutionary force regulating spatial patterns rather than seasonal differentiation (Xiang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e ; Obertegger et al., 2015). It was reported that although cryptic diversity of \u003cem\u003eP. dolichoptera\u003c/em\u003e changed along an altitudinal gradient in the Trentino\u0026ndash;South Tyrol region, environmental parameters such as temperature and trophic status might also affect the distribution of cryptic species (Obertegger et al., 2015). However, in the absence of geographical barriers, genetic divergence might be more explained by environmental gradients (Tisthammer et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study compared populations with similar food resource levels (Chl-\u003cem\u003ea\u003c/em\u003e) at different sites and we found that the haplotypes belonged to different clades. Therefore, food sources level might not be an influencing factor for their genetic divergence. Dispersal, genetic diversity and gene flow can be strongly affected by temperature changes (Sasaki and Dam, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although the ambient temperature of our samples ranged from 8 to 30℃ (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), \u003cem\u003ePolyarthra\u003c/em\u003e rotifers in eastern North America experience greater interannual changes in temperature. As cytochrome c oxidase subunit I is the terminal enzyme of the mitochondrial respiratory chain (Afkhami et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), extreme low temperature in winter may lead to differences in mitochondrial functions. Thus mutations may also exist in the coding sequence for COI.\u003c/p\u003e \u003cp\u003eRotifers possess the ability for passive long-distance dispersal through their diapausing stages including resting eggs and xerosomes (Walsh et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this way, hydrology influencing community composition and wind influencing dispersal could also play an important role in rotifers dispersal (Rivas et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e ; Liang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, human-mediated transport has likely facilitated species' persistence since its initial colonization, through the ongoing introduction and inter-continental spread of genetic variation (Baird et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As boating is one of the popular recreational activities for Americans in summer, rotifers and resting eggs can spread over North American lakes by launching boats. However, wind and migratory bird-mediated transport, which could operate on larger scales than this are impeded by the oceanographic barriers.\u003c/p\u003e"},{"header":"Conclusion","content":" \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCryptic diversities of \u003cem\u003ePolyarthra dolichoptera\u003c/em\u003e and \u003cem\u003eP. vulgaris\u003c/em\u003e are definitely underestimated in the world.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe divergence of \u003cem\u003eP. dolichoptera\u003c/em\u003e and \u003cem\u003eP. vulgaris\u003c/em\u003e complexes indicates that gene flow between eastern North America and Southeastern China is limited while that within eastern North America or Southeastern China was higher.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGenetic distance and geographic distance do not show a simple linear relationship and the power-law model fitted the data of \u003cem\u003eP. dolichoptera\u003c/em\u003e better than the exponential and linear models. This may result from the effects of habitat heterogeneity, long-distance colonization and oceanographic barriers to dispersal.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSpatial variables are key factors in affecting the genetic differentiation of rotifers when compared with physicochemical variables on the intercontinental scale.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eOutliers above the correlation line between geographic distance and genetic distance might suggest a significant dispersal barrier on large geographic scales studies.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge Dr. Susan Smith from the University of Connecticut for assistance with DNA extractions and PCR. Many thanks to Lingjie Zhou and Xiaotong Ye for samples assistance and sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank National Natural Science Foundation of China (41673080) and Jinan University for financial support. The study complies with current ethical guidelines. The data and materials are available. The COI sequences lacked internal stop codons of this study have been submitted to the NCBI database. We also assert that all of the listed authors have participated in the work and have approved the submission. Yang Y. conceived and designed the research. Lin S. guided the writing direction and revision of the manuscript. Liang D. carried out the experiment, analysis and wrote the manuscript. McManus G. revised the manuscript. Wang Q. and Sun X. provided scientific comments to the manuscript. Liu Z. participated in the R code writing. All authors have no conflict of interest associated with this work.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAfkhami, E., Heidari, M. M., Khatami, M., Ghadamyari, F., \u0026amp; Dianatpour, S. (2020). Detection of novel mitochondrial mutations in cytochrome C oxidase subunit 1 (COX1) in patients with familial adenomatous polyposis (FAP). \u003cem\u003eClinical and Translational Oncology, 22\u003c/em\u003e(6), 908-918. https://doi.org/10.1007/s12094-019-02208-6\u003c/p\u003e\n\u003cp\u003eBaird, H. P., Moon, K. L., Janion‐Scheepers, C., \u0026amp; Chown, S. L. (2020). Springtail phylogeography highlights biosecurity risks of repeated invasions and intraregional transfers among remote islands. \u003cem\u003eEvolutionary Applications, 13\u003c/em\u003e(5), 960-973. https://doi.org/10.1111/eva.12913\u003c/p\u003e\n\u003cp\u003eBanerjee, A. K., Hou, Z., Lin, Y., Lan, W., Tan, F., Xing, F., ... \u0026amp; Huang, Y. (2020). Going with the flow: analysis of population structure reveals high gene flow shaping invasion pattern and inducing range expansion of \u003cem\u003eMikania micrantha\u003c/em\u003e in Asia. \u003cem\u003eAnnals of Botany, 125\u003c/em\u003e(7), 1113-1126. https://doi.org/10.1093/aob/mcaa044\u003c/p\u003e\n\u003cp\u003eBradbury, I. R., \u0026amp; Bentzen, P. (2007). Non-linear genetic isolation by distance: implications for dispersal estimation in anadromous and marine fish populations. \u003cem\u003eMarine Ecology Progress Series, 340\u003c/em\u003e, 245-257. https://doi.org/10.3354/meps340245\u003c/p\u003e\n\u003cp\u003eDambros, C., Zuquim, G., Moulatlet, G. M., Costa, F. R., Tuomisto, H., Ribas, C. C., ... \u0026amp; Emilio, T. (2020). The role of environmental filtering, geographic distance and dispersal barriers in shaping the turnover of plant and animal species in Amazonia. \u003cem\u003eBiodiversity and Conservation, 29\u003c/em\u003e, 3609\u0026ndash;3634. https://doi.org/10.1007/s10531-020-02040-3\u003c/p\u003e\n\u003cp\u003eDerry, A. M., N. Hebert, P. D., \u0026amp; Prepas, E. E. (2003). Evolution of rotifers in saline and subsaline lakes: a molecular phylogenetic approach. \u003cem\u003eLimnology and Oceanography, 48\u003c/em\u003e(2), 675-685. https://doi.org/10.4319/lo.2003.48.2.0675\u003c/p\u003e\n\u003cp\u003eDing, Y., Qian, S., Wu, X., Zhao, L., Lin, D., Zhang, J., \u0026amp; Yang, Y. (2019). Homogenization of China\u0026rsquo;s urban aquatic macrophyte communities: A meta-analytic study. \u003cem\u003eEcological Indicators, 106\u003c/em\u003e, 105506. https://doi.org/10.1016/j.ecolind.2019.105506\u003c/p\u003e\n\u003cp\u003eDumont H.J.F. 2002. Guides to the Identification of the Microinvertebrates of the Continental Waters of the World. \u003cem\u003eLeiden: Backhuys Publishers\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFenchel, T., \u0026amp; Finlay, B. J. (2006). The diversity of microbes: resurgence of the phenotype. \u003cem\u003ePhilosophical Transactions of the Royal Society B: Biological Sciences, 361\u003c/em\u003e(1475), 1965-1973. https://doi.org/10.1098/rstb.2006.1924\u003c/p\u003e\n\u003cp\u003eFolmer, O., Black, M., Hoeh, W., Lutz, R., \u0026amp; Vrijenhoek, R. (1994). DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. \u003cem\u003eMolecular marine biology and biotechnology, 3\u003c/em\u003e(5): 294-299.\u003c/p\u003e\n\u003cp\u003eFontaneto, D. (2014). Molecular phylogenies as a tool to understand diversity in rotifers. \u003cem\u003eInternational Review of Hydrobiology, 99\u003c/em\u003e(1-2), 178-187. https://doi.org/10.1002/iroh.201301719\u003c/p\u003e\n\u003cp\u003eFontaneto, D., Barraclough, T. G., Chen, K., Ricci, C., \u0026amp; Herniou, E. A. (2008a). Molecular evidence for broad‐scale distributions in bdelloid rotifers: everything is not everywhere but most things are very widespread. \u003cem\u003eMolecular Ecology, 17\u003c/em\u003e(13), 3136-3146. https://doi.org/10.1111/j.1365-294x.2008.03806.x\u003c/p\u003e\n\u003cp\u003eFontaneto, D., Boschetti, C., \u0026amp; Ricci, C. (2008b). Cryptic diversification in ancient asexuals: evidence from the bdelloid rotifer \u003cem\u003ePhilodina flaviceps\u003c/em\u003e. \u003cem\u003eJournal of Evolutionary Biology, 21\u003c/em\u003e(2), 580-587. https://doi.org/10.1111/j.1420-9101.2007.01472.x\u003c/p\u003e\n\u003cp\u003eFontaneto, D., Kaya, M., Herniou, E. A., \u0026amp; Barraclough, T. G. (2009). Extreme levels of hidden diversity in microscopic animals (Rotifera) revealed by DNA taxonomy. \u003cem\u003eMolecular Phylogenetics and Evolution, 53\u003c/em\u003e(1), 182-189. https://doi.org/10.1016/j.ympev.2009.04.011\u003c/p\u003e\n\u003cp\u003eFossen, E. I., Ekrem, T., Nilsson, A. N., \u0026amp; Bergsten, J. (2016). Species delimitation in northern European water scavenger beetles of the genus \u003cem\u003eHydrobius\u003c/em\u003e (Coleoptera, Hydrophilidae). \u003cem\u003eZooKeys\u003c/em\u003e, (564), 71. https://doi.org/10.3897/zookeys.564.6558\u003c/p\u003e\n\u003cp\u003eFreeland J.R., Kirk H., Petersen SD. 2011. Molecular Ecology (2\u003csup\u003end\u003c/sup\u003eed)[M]. UK: Wiley-Blackwell.\u003c/p\u003e\n\u003cp\u003eFujisawa, T., \u0026amp; Barraclough, T. G. (2013). Delimiting species using single-locus data and the Generalized Mixed Yule Coalescent approach: a revised method and evaluation on simulated data sets. \u003cem\u003eSystematic biology, 62\u003c/em\u003e(5), 707-724. https://doi.org/10.1093/sysbio/syt033\u003c/p\u003e\n\u003cp\u003eGarc\u0026iacute;a‐Morales, A. E., \u0026amp; El\u0026iacute;as‐Guti\u0026eacute;rrez, M. (2013). DNA barcoding of freshwater Rotifera in Mexico: Evidence of cryptic speciation in common rotifers. \u003cem\u003eMolecular ecology resources, 13\u003c/em\u003e(6), 1097-1107. https://doi.org/10.1111/1755-0998.12080\u003c/p\u003e\n\u003cp\u003eG\u0026oacute;mez‐Rodr\u0026iacute;guez, C., Miller, K. E., Castillejo, J., Iglesias‐Pi\u0026ntilde;eiro, J., \u0026amp; Baselga, A. (2020). Disparate dispersal limitation in Geomalacus slugs unveiled by the shape and slope of the genetic\u0026ndash;spatial distance relationship. \u003cem\u003eEcography\u003c/em\u003e\u003cem\u003e, 43\u003c/em\u003e, 1229-1240. https://doi.org/10.1111/ecog.05142\u003c/p\u003e\n\u003cp\u003eG\u0026oacute;mez, A., Serra, M., Carvalho, G. R., \u0026amp; Lunt, D. H. (2002). Speciation in ancient cryptic species complexes: evidence from the molecular phylogeny of \u003cem\u003eBrachionus plicatilis\u003c/em\u003e (Rotifera). \u003cem\u003eEvolution, 56\u003c/em\u003e(7), 1431-1444. https://doi.org/10.1111/j.0014-3820.2002.tb01455.x\u003c/p\u003e\n\u003cp\u003eJin, P. Y., Sun, J. T., Chen, L., Xue, X. F., \u0026amp; Hong, X. Y. (2020). Geography alone cannot explain Tetranychus truncatus (Acari: Tetranychidae) population abundance and genetic diversity in the context of the center\u0026ndash;periphery hypothesis. \u003cem\u003eHeredity, 124\u003c/em\u003e(2), 383-396. https://doi.org/10.1038/s41437-019-0280-5\u003c/p\u003e\n\u003cp\u003eKordbacheh, A., Garbalena, G., \u0026amp; Walsh, E. J. (2017). Population structure and cryptic species in the cosmopolitan rotifer \u003cem\u003eEuchlanis dilatata\u003c/em\u003e. \u003cem\u003eZoological Journal of the Linnean Society, 181\u003c/em\u003e(4), 757-777. https://doi.org/10.1093/zoolinnean/zlx027\u003c/p\u003e\n\u003cp\u003eLeal, I., Bergeron, M. J., Feau, N., Tsui, C. K. M., Foord, B., Pellow, K., ... \u0026amp; Sturrock, R. N. (2019). Cryptic speciation in western North America and eastern Eurasia of the pathogens responsible for laminated root rot.\u003cem\u003e Phytopathology, 109\u003c/em\u003e(3), 456-468. https://doi.org/10.1094/phyto-12-17-0399-r\u003c/p\u003e\n\u003cp\u003eLeasi, F., Tang, C. Q., De Smet, W. H., \u0026amp; Fontaneto, D. (2013). Cryptic diversity with wide salinity tolerance in the putative euryhaline \u003cem\u003eTestudinella clypeata\u003c/em\u003e (Rotifera, Monogononta).\u003cem\u003e Zoological Journal of the Linnean Society, 168\u003c/em\u003e(1), 17-28. https://doi.org/10.1111/zoj.12020\u003c/p\u003e\n\u003cp\u003eLi, X., Zhang, Z., Zhang, J., Huang, J., Wang, L., Li, Y., ... \u0026amp; Lu, Y. (2020). Population Genetic Diversity and Structure of \u003cem\u003eThrips tabaci\u003c/em\u003e (Thysanoptera: Thripidae) on \u003cem\u003eAllium Hosts\u003c/em\u003e in China, Inferred From Mitochondrial COI Gene Sequences. \u003cem\u003eJournal of Economic Entomology, 113\u003c/em\u003e(3), 1426-1435. https://doi.org/10.1093/jee/toaa001\u003c/p\u003e\n\u003cp\u003eLiang, D., Wei, N., Wang, Q., Jersabek, C. D., He, X., \u0026amp; Yang, Y. (2019). Influence of Hydrological Heterogeneity on Rotifer Community Structure in Three Different Water Bodies in Shantou Area, Guangdong (China). \u003cem\u003eZoological Studies, 58\u003c/em\u003e, 23. https://doi.org/10.6620/ZS.2019.58-23.\u003c/p\u003e\n\u003cp\u003eLiang, D., Wang, Q., Wei, N., Tang, C., Sun, X., \u0026amp; Yang, Y. (2020). Biological indicators of ecological quality in typical urban river-lake ecosystems: The planktonic rotifer community and its response to environmental factors. \u003cem\u003eEcological Indicators, 112\u003c/em\u003e, 106127. https://doi.org/10.1016/j.ecolind.2020.106127\u003c/p\u003e\n\u003cp\u003eLibrado, P., \u0026amp; Rozas, J. (2009). DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. \u003cem\u003eBioinformatics, 25\u003c/em\u003e(11), 1451-1452. https://doi.org/10.1093/bioinformatics/btp187\u003c/p\u003e\n\u003cp\u003eMa, J., Liu, J., Shen, Y., Fan, Z., Yue, B., \u0026amp; Zhang, X. (2019). Population genetic structure and intraspecific genetic distance of \u003cem\u003ePeriplaneta americana\u003c/em\u003e (Blattodea: Blattidae) based on mitochondrial and nuclear DNA markers. \u003cem\u003eEcology and evolution, 9\u003c/em\u003e(22), 12928-12939. https://doi.org/10.1002/ece3.5777\u003c/p\u003e\n\u003cp\u003eMills, S., Alc\u0026aacute;ntara-Rodr\u0026iacute;guez, J. A., Ciros-P\u0026eacute;rez, J., G\u0026oacute;mez, A., Hagiwara, A., Galindo, K. H., ... \u0026amp; Welch, D. B. M. (2017). Fifteen species in one: deciphering the \u003cem\u003eBrachionus plicatilis\u003c/em\u003e species complex (Rotifera, Monogononta) through DNA taxonomy. \u003cem\u003eHydrobiologia, 796\u003c/em\u003e(1), 39-58. https://doi.org/10.1007/s10750-016-2725-7\u003c/p\u003e\n\u003cp\u003eMontero‐Pau, J., G\u0026oacute;mez, A., \u0026amp; Mu\u0026ntilde;oz, J. (2008). Application of an inexpensive and high‐throughput genomic DNA extraction method for the molecular ecology of zooplanktonic diapausing eggs. \u003cem\u003eLimnology and Oceanography: Methods, 6\u003c/em\u003e(6), 218-222. https://doi.org/10.4319/lom.2008.6.218\u003c/p\u003e\n\u003cp\u003eObertegger, U., Flaim, G., \u0026amp; Fontaneto, D. (2014). Cryptic diversity within the rotifer \u003cem\u003ePolyarthra dolichoptera\u003c/em\u003e along an altitudinal gradient. \u003cem\u003eFreshwater Biology, 59\u003c/em\u003e(11), 2413-2427. https://doi.org/10.1111/fwb.12447\u003c/p\u003e\n\u003cp\u003eOksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., O\u0026rsquo;hara R., Simpson, G.L., ... \u0026amp; Stevens, M.H.H., Wagner, H. 2010. Vegan: community ecology package. \u003cem\u003eR package version\u003c/em\u003e 1.17-4. URL http://CRAN. R-project. org/package= vegan.\u003c/p\u003e\n\u003cp\u003ePan, Y.B., Wang, L., Zhang, W.J., Liu, G.X., \u0026amp; Lin, S.J. (2016). Genetic analysis of \u003cem\u003eNoctiluca scintillans\u003c/em\u003e populations indicates low latitudinal differentiation in China but high China-America differences. \u003cem\u003eJournal of Experimental Marine Biology and Ecology, 477\u003c/em\u003e, 31-39. https://doi.org/10.1016/j.jembe.2016.01.002\u003c/p\u003e\n\u003cp\u003ePapakostas, S., Michaloudi, E., Proios, K., Brehm, M., Verhage, L., Rota, J., ... \u0026amp; Declerck, S. A. (2016). Integrative taxonomy recognizes evolutionary units despite widespread mitonuclear discordance: evidence from a rotifer cryptic species complex. \u003cem\u003eSystematic biology, 65\u003c/em\u003e(3), 508-524. https://doi.org/10.1093/sysbio/syw016\u003c/p\u003e\n\u003cp\u003ePons, J., Barraclough, T. G., Gomez-Zurita, J., Cardoso, A., Duran, D. P., Hazell, S., ... \u0026amp; Vogler, A. P. (2006). Sequence-based species delimitation for the DNA taxonomy of undescribed insects. \u003cem\u003eSystematic biology, 55\u003c/em\u003e(4), 595-609. https://doi.org/10.1080/10635150600852011\u003c/p\u003e\n\u003cp\u003ePuillandre, N., Lambert, A., Brouillet, S., \u0026amp; Achaz, G. 2012. ABGD, Automatic Barcode Gap Discovery for primary species delimitation. \u003cem\u003eMolecular Ecology, 21\u003c/em\u003e(8), 1864-1877. https://doi.org/10.1111/j.1365-294x.2011.05239.x\u003c/p\u003e\n\u003cp\u003eRibeiro, K. F., Ferrero, A. P., Duarte, L., Turchetto‐Zolet, A. C., \u0026amp; Crossetti, L. O. (2020). Comparative phylogeography of two free-living cosmopolitan cyanobacteria: Insights on biogeographic and latitudinal distribution. \u003cem\u003eJournal of Biogeography, 47\u003c/em\u003e(5), 1106-1118. https://doi.org/10.1111/jbi.13785\u003c/p\u003e\n\u003cp\u003eRivas Jr, J. A., Mohl, J. E., Van Pelt, R. S., Leung, M. Y., Wallace, R. L., Gill, T. E., \u0026amp; Walsh, E. J. (2018). Evidence for regional aeolian transport of freshwater micrometazoans in arid regions. \u003cem\u003eLimnology and oceanography letters, 3\u003c/em\u003e(4), 320-330. https://doi.org/10.1002/lol2.10072\u003c/p\u003e\n\u003cp\u003eSasaki, M. C., \u0026amp; Dam, H. G. (2019). Integrating patterns of thermal tolerance and phenotypic plasticity with population genetics to improve understanding of vulnerability to warming in a widespread copepod. \u003cem\u003eGlobal Change Biology, 25\u003c/em\u003e(12), 4147-4164. https://doi.org/10.1111/gcb.14811\u003c/p\u003e\n\u003cp\u003eSavary, R., Masclaux, F. G., Wyss, T., Droh, G., Corella, J. C., Machado, A. P., ... \u0026amp; Sanders, I. R. (2018). A population genomics approach shows widespread geographical distribution of cryptic genomic forms of the symbiotic fungus Rhizophagus irregularis. \u003cem\u003eThe ISME journal, 12\u003c/em\u003e(1), 17-30. https://doi.org/10.1038/ismej.2017.153\u003c/p\u003e\n\u003cp\u003eSayers, E. W., Cavanaugh, M., Clark, K., Ostell, J., Pruitt, K. D., \u0026amp; Karsch-Mizrachi, I. (2019). GenBank. \u003cem\u003eNucleic acids research, 47\u003c/em\u003e(D1), D94-D99. https://doi.org/10.1093/nar/gky989\u003c/p\u003e\n\u003cp\u003eSbabou, L., Regragui, A., Filali-Maltouf, A., Ater, M., \u0026amp; B\u0026eacute;na, G. (2016). Local genetic structure and worldwide phylogenetic position of symbiotic Rhizobium leguminosarum strains associated with a traditional cultivated crop, Vicia ervilia, from Northern Morocco. \u003cem\u003eSystematic and applied microbiology, 39\u003c/em\u003e(6), 409-417. https://doi.org/10.1016/j.syapm.2016.06.005\u003c/p\u003e\n\u003cp\u003eSlatkin, M. (1987). Gene flow and the geographic structure of natural populations. \u003cem\u003eScience, 236\u003c/em\u003e(4803), 787-792. https://doi.org/10.1126/science.3576198\u003c/p\u003e\n\u003cp\u003eTang, C. Q., Leasi, F., Obertegger, U., Kieneke, A., Barraclough, T. G., \u0026amp; Fontaneto, D. (2012). The widely used small subunit 18S rDNA molecule greatly underestimates true diversity in biodiversity surveys of the meiofauna. \u003cem\u003eProceedings of the National Academy of Sciences, 109\u003c/em\u003e(40), 16208-16212. https://doi.org/10.1073/pnas.1209160109\u003c/p\u003e\n\u003cp\u003eTer Braak, C. J., \u0026amp; \u0026Scaron;milauer, P. (2002). CANOCO reference manual and CanoDraw for Windows user\u0026rsquo;s guide. \u003cem\u003eSoftware for canonical community ordination (version 4.5). Microcomputer Power, Ithaca, NY\u003c/em\u003e, 500.\u003c/p\u003e\n\u003cp\u003eTisthammer, K.H., Forsman, Z.H., Toonen, R. J., \u0026amp; Richmond, R.H. (2020). Genetic structure is stronger across human-impacted habitats than among islands in the coral Porites lobata. \u003cem\u003ePeerJ, 8\u003c/em\u003e, e8550. https://doi.org/10.7717/peerj.8550\u003c/p\u003e\n\u003cp\u003eWalsh, E.J., May, L., \u0026amp; Wallace, R.L. (2017). A metadata approach to documenting sex in phylum Rotifera: diapausing embryos, males, and hatchlings from sediments. \u003cem\u003eHydrobiologia, 796\u003c/em\u003e(1): 265-276. https://doi.org/10.1007/s10750-016-2712-z\u003c/p\u003e\n\u003cp\u003eWen, X.L., Xi, Y.L., Zhang, G., Xue, Y.H., \u0026amp; Xiang, X.L. (2016). Coexistence of cryptic \u003cem\u003eBrachionus calyciflorus\u003c/em\u003e (Rotifera) species: roles of environmental variables. \u003cem\u003eJournal of Plankton Research, 38\u003c/em\u003e(3), 478-489. https://doi.org/10.1093/plankt/fbw006\u003c/p\u003e\n\u003cp\u003eXiang, X.L., Xi, Y.L., Wen, X.L., Zhang, G., Wang, J.X., \u0026amp; Hu, K. (2011). Genetic differentiation and phylogeographical structure of the \u003cem\u003eBrachionus calyciflorus\u003c/em\u003e complex in eastern China. \u003cem\u003eMolecular Ecology, 20\u003c/em\u003e(14), 3027-3044. https://doi.org/10.1111/j.1365-294x.2011.05147.x\u003c/p\u003e\n\u003cp\u003eZhang, J., Kapli, P., Pavlidis, P., \u0026amp; Stamatakis, A. (2013). A general species delimitation method with applications to phylogenetic placements. \u003cem\u003eBioinformatics 29\u003c/em\u003e, 2869\u0026ndash;2876. https://doi.org/10.1093/bioinformatics/btt499\u003c/p\u003e\n\u003cp\u003eZhang, W., Pan, Y., Yang, J., Chen, H., Holohan, B., Vaudrey, J., ... \u0026amp; McManus, G. B. (2018a). The diversity and biogeography of abundant and rare intertidal marine microeukaryotes explained by environment and dispersal limitation. \u003cem\u003eEnvironmental microbiology, 20\u003c/em\u003e(2), 462-476. https://doi.org/10.1111/1462-2920.13916\u003c/p\u003e\n\u003cp\u003eZhang, X., Liu, Y. H., Wang, Y. H., \u0026amp; Shen, S. K. (2020). Genetic Diversity and Population Structure of Rhododendron rex Subsp. rex Inferred from Microsatellite Markers and Chloroplast DNA Sequences. \u003cem\u003ePlants, 9\u003c/em\u003e(3), 338. https://doi.org/10.3390/plants9030338\u003c/p\u003e\n\u003cp\u003eZhang, Y., Zhou A., Xi Y.L., Sun, Q., Ning L.F., Xie P., Wen X.L., \u0026amp; Xiang X.L. (2018b). Temporal patterns and processes of genetic differentiation of the \u003cem\u003eBrachionus calyciflorus\u003c/em\u003e (Rotifera) complex in a subtropical shallow lake. \u003cem\u003eHydrobiologia, 807\u003c/em\u003e(1), 313-331. https://doi.org/10.1007/s10750-017-3407-9\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"rotifer, phylogeography, cryptic species, genetic distance, spatio-temporal pattern, long-distance dispersal","lastPublishedDoi":"10.21203/rs.3.rs-242024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-242024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenetic differentiations and phylogeographical patterns of small organisms may be shaped by spatial isolation, environmental gradients and gene flow. However, knowledge about genetic differentiation of rotifers on intercontinental gradient is still limited. \u003cem\u003ePolyarthra dolichoptera\u003c/em\u003e and \u003cem\u003eP. vulgaris\u003c/em\u003e are cosmopolitan rotifers and tolerant to environmental changes, offering an excellent model to address the research gap. Here, we investigated the populations in Southern China and eastern North America, and evaluated the phylogeographical patterns from their geographical range sizes, geographic-genetic distance relationships and their response to spatial-environmental factors. Using mitichondrial cytochrome c oxidase subunit I gene as the DNA marker, we analyzed a total of 170 individuals. At least 24 putative cryptic species, including 20 of \u003cem\u003eP. dolichoptera\u003c/em\u003e and 4 of \u003cem\u003eP. vulgaris\u003c/em\u003e were detected based on three delimitation methods. Our results showed that some cryptic species were widely distributed but most of them were limited to single areas. The divergence of \u003cem\u003eP. dolichoptera\u003c/em\u003e and\u003cem\u003e P. vulgaris\u003c/em\u003e complexes indicated that gene flow between continents was limited while that within each continent was stronger. Furthermore, on the intercontinental scale spatial distance had a stronger influence than physicochemical variables on the genetic differentiations of\u003cem\u003e P. dolichoptera\u003c/em\u003e and\u003cem\u003e P. vulgaris\u003c/em\u003e complexes. However, the relationship between genetic distance and geographic distance was not continuously linear and the \u003cem\u003eP. dolichoptera\u003c/em\u003e data best fitted the power-law model. This might be due to the effects of habitat heterogeneity, long-distance colonization and oceanographic barriers. Outliers above the correlation line between geographic distance and genetic distance suggest a significant dispersal barrier on large geographic scales studies.\u003c/p\u003e","manuscriptTitle":"Genetic Differentiation and Phylogeography of Rotifer Polyarthra Dolichoptera and P. Vulgaris Complexes Between Southern China and Eastern North America: High Intercontinental Differences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-02-25 21:25:52","doi":"10.21203/rs.3.rs-242024/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"54957f23-2633-4756-8f3e-1f47c7e3943f","owner":[],"postedDate":"February 25th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":2635133,"name":"Hydrology"}],"tags":[],"updatedAt":"2021-05-12T01:28:42+00:00","versionOfRecord":[],"versionCreatedAt":"2021-02-25 21:25:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-242024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-242024","identity":"rs-242024","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}