Analysis of the morphological characteristics and direction of morphology- based selective breeding of Procambarus clarkii

Analysis of the morphological characteristics and direction of morphology- based selective breeding of Procambarus clarkii | 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 Analysis of the morphological characteristics and direction of morphology- based selective breeding of Procambarus clarkii Qishuai Wang, Siqi Yang, Ruixue Shi, Feifei Zheng, Yanhe Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2024195/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 In order to explore the breeding direction of morphological selection of Procambarus clarkii , the morphological characteristics of five P. clarkii basic populations from different regions in China were comprehensively analyzed by multivariate statistical analyses. The results showed that there were significant differences in most morphological parameters among populations and between sexes. In the discriminatory analysis, the most discriminant characteristics for distinguishing females among populations were body weight (BW), first abdominal segment width (FASW), third abdominal segment width (TASW) and third abdominal segment height (TASH), whereas for males, the characteristics were body weight (BW), carapace length (CL), carapace width (CW) and third abdominal segment width (TASW).The most significant variables of the differences between sexes were body weight (BW), third abdominal segment width (TASW) and double cheliped weight (DCW). This study would be beneficial to understanding the main morphological characteristics of P. clarkii , which could provide basic data of the collected germplasm resources and some reference for indicating the direction of P. clarkii morphology-based breeding. The germplasm resources with stronger abdomen, smaller carapace and smaller cheliped would be the selection targets, and all-female breeding would also be one of important breeding directions of for P. clarkii . Procambarus clarkii Basic population Morphological variation Multivariate analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Procambarus clarkii , is a freshwater crayfish, which is native to central and southern United States and northeastern Mexico (Henttonen and Huner 1999 ). It is one of the most notorious invasive species worldwide (Barbaresi and Gherardi 2000 ; Cruz and Rebelo 2005 ; Zhu and Yue 2008 ; Yue et al. 2010 ). It was introduced into China from Japan in 1930s (Kawai and Kobayashi 2005 ). Because of its high adaptability to environment and high reproduction ability (Fernández-Cisnal et al. 2018 ; An et al. 2020 ), it has been widely distributed throughout China (Li et al. 2012 ; Pan et al. 2020 ), and the natural distribution and main producing areas are the middle and lower reaches of the Yangtze River (Yan et al. 2021 ). P. clarkii is favored by consumers because of its delicious meat and rich nutrition. In recent years, P. clarkii has become one of the most economically important aquaculture species in China (Wang et al. 2005 ; Li et al. 2012 ). The production of P. clarkii reached nearly 2,633,600 tons in 2021 according to the Crayfish Industry Report 2022 in China reported by China Society of Fisheries ( http://www.nftec.agri.cn/ ). However, due to high intensity fishing and deterioration of the living environment, the wild resources of P. clarkii have been declining (Liu et al. 2020 ). It is insufficient attention paid to the germplasm and breeding of P. clarkii under the background of vigorous development of the aquaculture industry. Such led to the increasingly prominent problems such as smaller individual size, frequent disease occurrence, larger carapace percentage, and serious germplasm degradation of P. clarkii (Yi et al. 2017 ; Wang et al. 2019 ; Peng et al. 2021 ). The study on the morphological differences of the crayfish in different geographical regions will be beneficial to the protection of germplasm resources and breeding of fine varieties. In general, morphological differentiation can appear as a consequence of genetic differences or environmental factors or their interaction (Begg and Waldman 1999 ; Pakkasmaa and Piironen 2001 ). Genetic diversity is investigated through assessing the differences between and within populations by using various genetic markers from the perspective of genetics (Moore et al. 2018 ; Li et al. 2021 ). Multivariate statistical analysis of morphological characters has also proven to be a powerful technique in population differences and stock discrimination (Palma and Andrade 2002 ; Turan et al. 2006 ; Maguire and Dakić 2011 ; Chen et al. 2015 ; Porrini et al. 2015 ). The analyses of morphological difference can reflect the differences among different populations or even individuals simply, quickly and effectively, and complementary to genetic study (Fevolden and Hessen 1989 ; Sint et al. 2007 ; Bertocchi et al. 2008 ). Thus, in this study, morphological characteristics of five basic populations of P. clarkii from different regions were analyzed and compared through applying morphometry and multivariate analysis methods. It was expected to evaluate the morphological differences and sexual dimorphism to provide some reference for its germplasm resources utilization and the morphology-based selective breeding direction of P. clarkii . 2. Materials And Methods 2.1 Sampling and data collection Five basic P. clarkii populations for breeding were collected from different regions in China (Table S1). Collection locations of Chaohu, Yangxin, Honghu and Hanchuan were located in the middle and lower reaches of the Yangtze River, while Gaoyou is in the Huaihe River. The five regions belong to natural distribution and main producing areas of P. clarkii , where the crayfish had high genetic diversity relatively (Li et al. 2012 ; Li et al. 2016 ; Yi et al. 2018 ; Yi et al. 2020 ). A total of 527 individuals (239 females and 288 males) with an intact body were randomly sampled from the five populations. Eleven characters were measured sequentially on each specimen. The dimension characters included total length (TL), body length (BL), carapace length (CL), carapace width (CW), first abdominal segment length (FASL), first abdominal segment width (FASW), third abdominal segment length (TASL), third abdominal segment width (TASW) (What distance the previous characters refer to respectively was shown in Fig. 1 ) and third abdominal segment height (TASH, refers to the distance between the midpoint of the dorsal and ventral margins of the third abdominal segment). The weight characters included body weight (BW) and double cheliped weight (DCW). The dimensions were measured using a digital calliper (± 0.01 mm), while the body weight and double cheliped weight were measured using an electronic balance (± 0.01 g). In order to reduce the error caused by measurement, all characters were measured by the same person. 2.2 Data analysis In order to eliminate the influence of size on morphological characteristics of P. clarkii , the ratio of each morphological character to body length (BL) was standardized for further analyses. In this study, a total of 10 morphological proportion parameters were selected, and all the morphological parameters were expressed as means and standard deviations. In order to remove bias caused by sexual dimorphism (Sint et al. 2007 ; Hamasaki et al. 2020 ), the specimens were separated by sex and analyzed respectively with multivariate analysis. The Kolmogorov-Smirnov test was carried out on each morphological parameter in order to test whether the parameter obey a normal distribution or not. The parameters consistent with normal distribution were subjected to one-way ANOVA. For the parameters that were not normally distributed, difference in means between populations were assessed using ANOVA after testing for non-parametric Kruskal-Wallis ANOVA on ranks (Dashinov et al. 2020 ). Similarly, T-test and non-parametric Mann-Whitney U test were used to compare whether the two sexes significantly different in the morphological parameters. For the discriminant analysis, all variables were entered one by one using a stepwise procedure where only variables contributing in the lowering of Wilk’s lambda are retained for analysis (Poulet et al. 2005 ; Dashinov et al. 2020 ). And in the discriminant functions, standardized coefficients with large absolute values correspond to parameters with greater discriminating ability (Jónsdóttir et al. 2016 ; Duretanović et al. 2017 ; Maguire et al. 2017 ). A cross-validation test was computed in order to evaluate the effectiveness of discriminant analysis (Marques et al. 2006 ; Konan et al. 2010 ). For the cluster analysis, after calculating the average of 10 morphological proportion parameters of each population, the nearest neighbor clustering method was carried out with the Euclidean distances (Konan et al. 2010 ; Mavule et al. 2016 ). Microsoft Excel 2019 and SPSS 23.0 were used for descriptive statistics, ANOVA and discriminant analysis. OriginPro 2021 was used for the cluster analysis. 3. Results 3.1 Analysis of variance There was a significant difference on the majority of the morphological parameters among different populations of males and females by the one-way ANOVA and K-W test (Table S2). For 10 morphological parameters, there was no significant difference on the parameter of TASL ( P > 0.05) among the female populations, while there was no significant difference on the two parameters of FASW and TASL ( P > 0.05) among the male populations. There was no significant difference on the two parameters of TL and TASH ( P > 0.05) between the males and females. 3.2 Discriminant analysis of different populations There were significant differences on morphological characteristics among populations, for both males and females of P. clarkii . Multivariate discriminant analysis could be used to distinguish which morphological characteristics contributed most to the differences among populations. After the stepwise procedure in the discriminant analysis, the females retained six variables (BW, CW, FASL, FASW, TASW, TASH) that most distinguished different populations, while the males retained eight variables (BW, TL, CL, CW, FASL, TASW, TASH, DCW). There were all highly significant in the discrimination model for the females (Wilks′λ = 0.311; F = 13.268; P < 0.001) and males (Wilks′λ = 0.249; F = 14.596; P < 0.001). Summary statistics demonstrated that for the female populations, the first and second discriminant functions contributed to 88.2% of the total variance (74.2% and 14.0%, respectively) indicating that the greatest proportion of the total variance was due to the first two discriminant functions. And the canonical R for those functions were 0.738 and 0.429, respectively (Table 1 ). In discriminant function 1, the parameters with larger absolute values of standardized coefficients were BW (0.826) and TASW (0.733). In discriminant function 2, the parameters with larger absolute values of standardized coefficients were FASW (-0.644), TASW (0.892) and TASH (0.656). The parameters may account for most of the variation in the first two discriminant functions. Correspondingly, for the male populations, the canonical R for the first two discriminant functions were 0.734 and 0.540, respectively (Table 1 ). The first discriminant function accounted for 62.3% of the explained variance and was weighed mostly by BW (1.232), CL (0.626) and CW (-0.767). The second discriminant function accounted for 22.0%, and the parameter with the largest absolute value of the standardized coefficient was TASW (0.790). The larger the absolute value was, the greater the ability to discriminate the populations was. The separation and overlap of male and female populations of P. clarkii could be better demonstrated by making scatter plots of the first two discriminant functions (Fig. 2 ). The scatter plots for female and male populations showed a similar result, that is, the population Ch significantly deviated from the other four geographic populations. For the females, as the first discriminant function was marked by high positive loading for BW and TASW, the higher the value of BW and TASW are, the more likely the females belong to the populations from Gaoyou, Yangxin, Honghu or Hanchuan. Similarly, the second discriminant function was marked by high positive loadings for TASW and TASH and high negative loading for FASW. It seems to provide some discrimination between the Gy population, Hc population and Yx population, Hh population. For the males, as well as the females, the first discriminant function was marked by high positive loading for BW and CL and high negative loading for CW. So the lower the value of CW is, the more likely it was that the males belong to the Ch population, and the higher for the value of BW and CL are, the more likely it was that the males belong to the other four populations. The second discriminant function was marked by high positive loading for TASW. And the higher the value of TASW is, the more likely it was that the males belong to the Hc population. Table 1 Standardized canonical discriminant function coefficients, eigenvalue, percentage of explained variance, and canonical correlations from the discriminant analysis for female and male P. clarkii populations. Variable Function 1 Function 2 Function 3 Function 4 Females BW 0.826 0.227 0.547 -0.439 CW -0.227 0.273 -0.726 0.459 FASL -0.296 0.113 0.438 0.809 FASW -0.294 -0.644 0.662 0.035 TASW 0.733 0.892 -0.216 0.222 TASH -0.571 0.656 0.170 -0.547 Eigenvalue 1.193 0.225 0.114 0.075 Variance (%) 74.2 14.0 7.1 4.7 Canonical R 0.738 0.429 0.320 0.265 Males BW 1.232 0.164 0.365 -0.319 TL 0.064 -0.542 -0.017 -0.139 CL 0.626 0.204 0.708 -0.117 CW -0.767 0.065 -0.400 -1.266 FASL -0.288 0.317 -0.043 0.316 TASW 0.435 0.790 -0.375 0.326 TASH -0.521 0.114 0.803 0.087 DCW -0.578 -0.604 -0.134 1.118 Eigenvalue 1.166 0.411 0.191 0.104 Variance (%) 62.3 22.0 10.2 5.6 Canonical R 0.734 0.540 0.400 0.307 The crayfish that best represents the morphological characteristics of the populations were shown in Fig. 3 , and the main morphological characteristics to distinguish the populations of P. clarkii were compared. For female populations, the BW/BL ratio of the Ch population was significantly lower than that of the other four populations, and the Yx population was significantly lower than that of the Gy, Hh and Hc populations. Only two populations (Yx and Hc) exhibited significant difference in the value of the FASW/BL ratio. The TASW/BL ratio of the Hc population was significantly larger than that of the Ch, Yx and Hh populations, and the Gy population was also significantly larger than that of the Ch and Yx populations. The Ch and Yx populations exhibited a significant larger TASH/BL ratio than other three populations. Likewise, for male populations, the BW/BL ratio of the Ch population was significantly lower than that of the other four populations, and the Yx population was also significantly larger than that of the Hh population. The Ch and Hh populations exhibited a significant lower CL/BL ratio than other three populations. The CW/BL ratio of the Hh population was significantly lower than that of the Yx and Hc populations. The highest value of the TASW/BL ratio was observed in the Hc population. The cross-validation procedures revealed that 310 of 527 crayfish (58.82%) were correctly classified based on their external morphology (Table 2 ). Specifically, there was little difference between the percentage of the males (62.50%) and females (54.39%) correctly classified. The accuracy rate of classification among different geographic populations of the same sex was low, indicating that the morphological differences among different geographic populations of the same sex were small. Yet the Ch population had obtained the best proportion classification of the males (78.33%) and females (80.43%), which were much higher than the other four populations. The results indicated that the morphological characteristics of the Ch population had low similarity with other populations. Table 2 Cross-validated classification (count and percentage) matrix for the male and female P. clarkii populations. Sex Population Predicted group membership (count and percentage) ++ Total (count and percentage) Ch Gy Yx Hh Hc Female Ch 37 (80.4) 0 6 (13.0) 3 (6.5) 0 46 (100.0) Gy 5 (10.0) 21 (42.0) 8 (16.0) 10 (20.0) 6 (12.0) 50 (100.0) Yx 3 (6.0) 7 (14.0) 27 (54.0) 11 (22.0) 2 (4.0) 50 (100.0) Hh 0 5 (10.2) 14 (28.6) 25 (51.0) 5 (10.2) 49 (100.0) Hc 0 8 (18.2) 6 (13.6) 10 (22.7) 20 (45.5) 44 (100.0) Male Ch 47 (78.3) 5 (8.3) 1 (1.7) 5 (8.3) 2 (3.3) 60 (100.0) Gy 14 (19.2) 41 (56.2) 8 (11.0) 6 (8.2) 4 (5.5) 73 (100.0) Yx 2 (4.1) 9 (18.4) 29 (59.2) 3 (6.1) 6 (12.2) 49 (100.0) Hh 3 (6.1) 7 (14.3) 8 (16.3) 27 (55.1) 4 (8.2) 49 (100.0) Hc 3 (5.3) 8 (14.0) 3 (5.3) 7 (12.3) 36 (63.2) 57 (100.0) 3.3 Cluster analysis The male and female populations showed a similar grouping in the hierarchical cluster analysis (Fig. 4 ). Five populations were clustered into two well-defined clusters in the two models (males and females). The first branch included only the Ch population, the Gy, Hc, Hh and Yx populations were clustered in the second branch. The results showed that the samples from Chaohu were phenotypically gathered into one cluster, while the morphological similarity between the Gy, Hc, Hh and Yx populations was great and were grouped into another cluster. 3.4 Discriminant analysis of sex In order to distinguish which morphological characteristics contributed most to the differences between the females and males, a stepwise discriminant analysis was performed. Among all the variables analyzed, eight morphological characteristics were considered relevant and remained in the model, and the discriminant model established is remarkably effective (Wilks′λ = 0.373; F = 109.037; P < 0.001). In the unique discriminant function, the parameters with larger absolute values of standardized coefficients were BW (1.229), TASW (0.803) and DCW (-1.068) (Table S3). The three most discriminant characteristics of males and females were compared in Fig. 5 . The results showed that the BW/BL ratio and the DCW/BL ratio of the males were significantly larger than the females, while the TASW/BL ratio of the females was significantly larger than that of the males. Similarly, after the cross-validation procedure, 92.05% of the females were correctly classified, and 88.19% of the males were correctly classified (Table S4). The classification accuracy was high, indicating that the morphological differences between male and female individuals were great. 4. Discussion Generally, crustaceans have high morphological plasticity (Maguire et al. 2017 ). And the variation in the external morphology of a species is generally interpreted as an adaptation to the habitat environment (Dimmock et al. 2004 ; Brian et al. 2006 ; Ferrito et al. 2007 ; Demchenko and Tkachenko 2017 ). Geographic isolation, which blocks gene exchange among different populations, is generally considered to be a prerequisite for population differentiation (Gould and Woodruff 1978 ; Trizio et al. 2005 ). Yet the eventually formed morphological differences could be a consequence of the environmental (Haddaway et al. 2012 ) and genetic factors (Cataudella et al. 2010 ; Maguire et al. 2014 ). The results of variance analysis showed that there were significant differences in most morphological parameters among the five basic populations of P. clarkii . Multivariate stepwise discriminant analysis was used to identify the best combination of variables to distinguish populations, and according to the absolute value of the standardized coefficient of the discriminant function, the variables with higher discriminant ability can be found (Anastasiadou et al. 2009 ; Mavule et al. 2016 ; Duretanović et al. 2017 ; Melesse et al. 2022 ). In the present study, the standardized morphological parameters of BW, FASW, TASW and TASH contributed significantly to distinguishing female populations, and the most discriminant characteristics for distinguishing the male populations were focused on BW, CL, CW and TASW. That is, in addition to body weight, the combination of abdomen related variables played an important role in distinguishing P. clarkii female populations, and the carapace related variables significantly contributed to distinguishing the male populations. Recently P. clarkii is highly loved by consumers and has become an important aquatic economic animal in China (Wang et al. 2005 ; Yi et al. 2017 ). The tail meat of P. clarkii is the most edible part for people to consume presently (Devesa et al. 2002 ). The tail meat content is related with some morphological traits to some extent, including body weight and those that are mainly concentrated in the abdomen of P. clarkii (Wang et al. 2020 ). At present, the research on morphological traits has been widely used in the breeding of some other various aquatic animals (Wang et al. 2016 ; Zou et al. 2017 ; Li et al. 2018 ). Generally, for P. clarkii , the greater the proportion of abdomen and the smaller the proportion of carapace, the higher the meat content (Wang et al. 2020 ). Therefore, the selection conducted around meat content will be the one direction of P. clarkii breeding, and the germplasm resources with stronger abdomen and smaller carapace are the selection targets for genetic breeding of P. clarkii . A cross-validation test was computed to assess the ability of variables to discriminate P. clarkii populations (Konan et al. 2010 ; Freire et al. 2017 ; Dashinov et al. 2020 ). After the cross-validation procedure, there was 58.82% of both sexes of P. clarkii were correctly classified in their groups. The accuracy of classification was low, indicating that the more individuals with similar morphological characteristics among populations (Konan et al. 2010 ; Mavule et al. 2016 ). Meanwhile, the higher the percentage of populations correctly classified, the greater the morphological differences among populations (Duretanović et al. 2017 ). It showed that the greatest difference appeared between the Ch population and the other four populations. The results of cross-validation for male and female P. clarkii populations were also supported by the hierarchical cluster analysis. Cluster analysis can classify different populations, and the clustering results reflect the distance of the kinship between populations (Yang et al. 2020 ; Prasetyo et al. 2022 ). In this study, the male and female populations showed same grouping in the hierarchical cluster analysis that got the Ch population into a cluster and grouped the Gy, Yx, Hh and Hc populations into another cluster. Obviously, the clustering results did not conform to the general rule that the closer the geographical distance, the more similar the morphological characteristics (Cheng et al. 2005 ). It is worth noting that P. clarkii is an economically important species farmed in China, and its seedlings or adults may exist in geographically distant water areas through other unnatural factors such as trade. Therefore, the hopping dispersal path of P. clarkii may be the result of natural migration and human factors (Barbaresi et al. 2004 ; Yi et al. 2020 ). Such may also be an important reason for the small morphological differences among different populations. Furthermore, since there is sexual dimorphism in the external morphology of P. clarkii (Shen et al. 2022 ). A stepwise discriminant analysis was performed to identify the variables that contributed most to gender differences. The result showed that the most discriminant characteristics for distinguishing sex were BW, TASW and DCW. The reason for the significant difference on body weight between males and females is that the males have larger double cheliped weight than the females. The sexual dimorphism of the cheliped of P. clarkii may be the result of their widespread use by the male in fighting, display, and courtship (Shimoda et al. 2005 ; Yasuda et al. 2017 ). The smaller the cheliped of P. clarkii , the higher the meat content (Craig and Wolters 1988 ), which is one of the important reasons why the meat content of female crayfish is higher than that of male crayfish (Peng et al. 2021 ). Moreover, under the condition of the same body length, the female P. clarkii individuals usually have larger the third abdominal segment width than the males. This may be more conducive to the escape movement of female P. clarkii , and may be related to its reproductive behavior, especially the behavior of the egg holding (Mariappan and Balasundaram 2004; Bauer and Delahoussaye 2008 ). So, all-female breeding or the directional breeding of smaller cheliped would be one of important breeding directions for P. clarkii in the future. 5. Conclusion The main morphological characteristics to distinguish the populations and sexes of P. clarkii were investigated by morphological characteristics in combination with discriminant analysis for basic populations of P. clarkii . For the female populations of P. clarkii , in addition to body weight, the differences focused on the combination of abdomen related variables. Of male populations, the carapace related variables showed significantly different. The most discriminant characteristics for distinguishing sex were abdominal width and cheliped weight. The results of this study could provide some reference for the protection of germplasm resources and the selection direction of elite varieties of P. clarkii . Declarations Competing interests The authors have no competing interests to declare that are relevant to the content of this article. Ethics Statement This study has been approved by the Institutional Animal Care and Use Committee (IACUC) of Huazhong Agricultural University (Wuhan, China) and conducted in accordance with ethical standards and according to the national and international guidelines. Data Availability Statement All data generated or analysed during this study are included in this published article and its supplementary information files. Acknowledgments This work was supported by the Key Research and Development Program of Hubei Province(2021BBA232), the National Key Research and Development Program of China (2020YFD0900304), and the Fundamental Research Funds for the Central Universities (2662020SCPY004). We thank Guangdong HAID Group Co., Ltd. for providing the sampling supporting in this study. Author contributions Qishuai Wang: Conceptualization, Methodology, Investigation, Formal analysis, Writing (original draft). Siqi Yang, Ruixue Shi and Feifei Zheng: Investigation. Yanhe Li: Resources, Investigation, Project administration, Supervision, Writing (review and editing). All authors read and approved the final manuscript. References An ZH, Yang H, Liu XD, Zhangv YY (2020) Effects of astaxanthin on the immune response and reproduction of Procambarus clarkii stressed with microcystin-leucine-arginine. Fish Sci 86: 759-766. https://doi.org/10.1007/s12562-020-01434-0 Anastasiadou C, Liasko R, Leonardos ID (2009) Biometric analysis of lacustrine and riverine populations of Palemonetes antennarius (H. Milne-Edwards, 1837) (Crustacea, Decapoda, Palaemonidae) from north-western Greece. Limnologica 39(3): 244-254. https://doi.org/10.1016/j.limno.2008.07.006 Barbaresi S, Gherardi F (2000) The invasion of the alien crayfish Procambarus clarkii in Europe, with particular reference to Italy. Biol Invasions 2(3): 259-264. https://doi.org/10.1023/A:1010009701606 Barbaresi S, Santini G, Tricarico E, Gherardi F (2004) Ranging behaviour of the invasive crayfish, Procambarus clarkii (Girard). J Nat Hist 38(22): 2821-2832. https://doi.org/10.1080/00222930410001663308 Bauer RT, Delahoussaye J (2008) Life history migrations of the amphidromous river shrimp Macrobrachium ohione from a continental large river system. J Crustacean Biol 28(4): 622-632. https://doi.org/10.2307/20487784 Begg GA, Waldman JR (1999) An holistic approach to fish stock identification. Fish Res 43: 35-44. https://doi.org/10.1016/s0165-7836(99)00065-x Bertocchi S, Brusconi S, Gherardi F, Bucccianti A, Scalici M (2008) Morphometrical characterization of the Austropotamobius pallipes species complex. J Nat Hist 42(31-32): 2063-2077. https://doi.org/10.1080/00222930802254664 Brian JV, Fernandes T, Ladle RJ, Todd PA (2006) Patterns of morphological and genetic variability in UK populations of shore crab Carcinus maenas Linnaeus, 1758 (Crustacea: Decapoda: Brachyura). J Exp Mar Biol Ecol 329(1): 47-54. https://doi.org/10.1016/j.jembe.2005.08.002 Cataudella R, Paolucci M, Delaunay C, Ropiquet A, Hassanin A, Baslamo M, Grandjean F (2010) Genetic variability of Austropotamobius italicus in the Marches region: implications for conservation. Aquat Conserv 20(3): 261-268. https://doi.org/10.1002/aqc.1099 Chen PC, Tzeng TD, Shih CH, Chu TJ, Lee YC (2015) Morphometric variation of the oriental river prawn ( Macrobrachium nipponense ) in Taiwan. Limnologica 52: 51-58. https://doi.org/10.1016/j.limno.2015.03.002 Cheng QQ, Lu DR, Ma L (2005) Morphological differences between close populations discernible by multivariate analysis: A case study of genus Coilia (Teleostei: Clupeiforms). Aquat Living Resour 18(2): 187-192. https://doi.org/10.1051/alr:2005020 Craig RJ, Wolters WR (1988) Sources of variation in body size traits, dressout percentage and their correlations for the crayfish, Procambarus clarkii . Aquaculture 72(1-2): 49-58. https://doi.org/10.1016/0044-8486(88)90145-7 Cruz MJ, Rebelo R (2005) Vulnerability of Southwest Iberian amphibians to an introduced crayfish, Procambarus clarkii . Amphibia-Reptilia 26(3): 293-303. https://doi.org/10.1163/156853805774408577 Dashinov D, Czerniejewski P, Balshine S, Synyshyn C, Tasheva-Terzieva E, Stefanov T, Ivanova P, Mandrak N, Uzunova E (2020) Variation in external morphology between the native and invasive populations of the round goby, Neogobius melanostomus (Actinopterygii: Gobiidae). Zoomorphology 139: 361-371. https://doi.org/10.1007/s00435-020-00480-7 Demchenko VO, Tkachenko MY (2017) Biological characteristics of the round goby, Neogobius melanostomus (Pallas, 1814), from different water bodies. Archives of Polish Fisheries 25(1): 51-61. https://doi.org/10.1515/aopf-2017-0006 Devesa V, Súñer MA, Lai VWM, Granchinho SCR, Vélez D, Cullen WR, Martínez JM, Montoro R (2002) Distribution of arsenic species in the freshwater crustacean Procambarus clarkii . Appl Organomet Chem 16(12): 692-700. https://doi.org/10.1002/aoc.374 Dimmock A, Willamson I, Matherm PB (2004) The influence of environment on the morphology of Macrobrachium australiense (Decapoda: Palaemonidae). Aquacult Int 12(4-5): 435-456. https://doi.org/10.1023/b:aqui.0000042140.48340.c8 Duretanović S, Jaklič M, Milošković A, Radojković N, Radenković M, Simić V, Maguire I (2017) Morphometric variations among Astacus astacus populations from different regions of the Balkan Peninsula. Zoomorphology 136(1): 19-27. https://doi.org/10.1007/s00435-016-0331-x Fernández-Cisnal R, García-Sevillano MA, García-Barrera T, Gómez-Ariza JL, Abril N (2018) Metabolomic alterations and oxidative stress are associated with environmental pollution in Procambarus clarkii . Aquat Toxicol 205: 76-88. https://doi.org/10.1016/j.aquatox.2018.10.005 Ferrito V, Mannino MC, Pappalardo AM, Tigano C (2007) Morphological variation among populations of Aphanius fasciatus Nardo, 1827 (Teleostei, Cyprinodontidae) from the Mediterranean. J Fish Biol 70(1): 1-20. https://doi.org/10.1111/j.1095-8649.2006.01192.x Fevolden SE, Hessen DO (1989) Morphological and genetic differences among recently founded populations of noble crayfish ( Astacus astacus ). Hereditas 110(2): 149-158. https://doi.org/10.1111/j.1601-5223.1989.tb00435.x Freire JL, Bentes B, Fontes VB, Da Silva EM (2017) Morphometric discrimination among three stocks of Macrobrachium amazonicum in the Brazilian Amazon. Limnologica 64: 1-10. https://doi.org/10.1016/j.limno.2017.01.007 Gould SJ, Woodruff DS (1978) Natural history of Cerion, Ⅷ: Little Bahama Bank-A revision based on genetics, morphometrics, and geographic distribution. Bulletin of the Museum of Comparative Zoology 148: 371-415. Haddaway NR, Mortimer RJG, Christmas M, Grahame JW, Dunn AM (2012) Morphological diversity and phenotypic plasticity in the threatened British white-clawed crayfish ( Austropotamobius pallipes ). Aquat Conserv 22(2): 220-231. https://doi.org/10.1002/aqc.2225 Hamasaki K, Osabe N, Nishimoto S, Dan S, Kitada S (2020) Sexual dimorphism and reproductive status of the red swamp crayfish Procambarus clarkii . Zoological Studies 59(07). https://doi.org/10.6620/ZS.2020.59-07 Henttonen P, Huner JV (1999) The introduction of alien species of crayfish in Europe: A historical introduction. Crayfish in Europe As Alien Species Crustacean Issues. https://doi.org/10.1201/9781315140469-2 Jónsdóttir IG, Guelaugsdóttir AK, Karlsson H (2016) Morphometric differences between sub-populations of northern shrimp ( Pandalus borealis ). A case study from two adjacent fjords in Iceland. Reg Stud in Mar Sci 3: 42-48. https://doi.org/10.1016/j.rsma.2015.04.002 Kawai T, Kobayashi Y (2005) Origin and current distribution of the alien crayfish, Procambarus clarkii (Girard, 1852) in Japan. Crustacean 78(9): 1143-1149. https://doi.org/10.1163/156854005775361034 Konan KM, Adépo-Gourène AB, Ouattara A, Nyingy WD, Gourène G (2010) Morphometric variation among male populations of freshwater shrimp Macrobrachium vollenhovenii Herklots, 1851 from Côte d'Ivoire Rivers. Fish Res 103(1-3): 1-8. https://doi.org/10.1016/j.fishres.2010.01.005 Li CT, Shang M, Hu XS, Ge YL, Jia ZY, Wang SH, Shi LY (2018) Evaluation of a new select-breeding carp variety with high fillet quality. Aquac Res 49(8): 2653-2663. https://doi.org/10.1111/are.13723 Li XL, Li F, Zhu JJ, Gu ZM (2016) Genetic diversity of the red swamp crayfish ( Procambarus clarkii ) resources based on SSR markers. Journal of Huazhong Agricultural University 35(2): 63-68. (In Chinese) Li YH, Guo XW, Cao XJ, Deng W, Luo W, Wang WM (2012) Population genetic structure and post-establishment dispersal patterns of the red swamp crayfish Procambarus clarkii in China. PLoS ONE 7(7): Article e40652. https://doi.org/10.1371/journal.pone.0040652 Li YM, Fan WJ, Huang YY, Huang YY, Du XL, Liu ZQ, Huang YH, Zhao YL (2021) Comparison of morphology and genetic diversity between broodstock and hybrid offspring of oriental river prawn, Macrobrachium nipponense based on morphological analysis and SNP markers. Anim Genet 52(4): 461-471. https://doi.org/10.1111/age.13081 Liu F, Qu YK, Geng C, Wang AM, Zhang JH, Li JF, Chen KJ, Liu B, Tian HY, Yang WP, Yu YB (2020) Analysis of the population structure and genetic diversity of the red swamp crayfish ( Procambarus clarkii ) in China using SSR markers. Electron J Biotechnol 47: 59-71. https://doi.org/10.1016/j.ejbt.2020.06.007 Maguire I, Dakić L (2011) Comparative analyses of Astacus leptodactylus morphological characteristics from Croatia and Armenia. Biologia 66(3): 491-498. https://doi.org/10.2478/s11756-011-0045-y Maguire I, Marn N, Klobučar G (2017) Morphological evidence for hidden diversity in the threatened stone crayfish Austropotamobius torrentium (Schrank, 1803) (Decapoda: Astacoidea: Astacidae) in Croatia. J Crust Biol 37(1): 7-15. https://doi.org/10.1093/jcbiol/ruw012 Maguire I, Podnar M, Jelić M, Štambuk A, Schrimpf A, Schulz H, Klobučar G (2014) Two distinct evolutionary lineages of the Astacus leptodactylus species-complex (Decapoda: Astacidae) inferred by phylogenetic analyses. Invertebr Syst 28(2): 117-123. https://doi.org/10.1071/IS13030 Mariappan P, Balasundaram C (2004) Studies on the morphometry of Macrobrachium nobilii (Decapoda, Palaemonidae). Brazilian Archives of Biology and Technology 47(3): 441-449. https://doi.org/10.1590/S1516-89132004000300015 Marques JF, Rego AL, Costa JL, Costa MJ, Cabral HN (2006) Genetic and morphological differentiation of the Lusitanian toadfish ( Halobatrachus didactylus ) between estuarine and coastal areas in Portugal. Sci Mar 70(4): 749-758. https://doi.org/10.3989/scimar.2006.70n4749 Mavule BS, Sarti FM, Lasagna E, Kunene NW (2016) Morphological differentiation amongst Zulu sheep populations in KwaZulu-Natal, South Africa, as revealed by multivariate analysis. Small Rumin Res 140: 50-56. https://doi.org/10.1016/j.smallrumres.2016.06.001 Melesse A, Yemane G, Tade B, Dea D, Kayamo K, Abera G, Mekasha Y, Betsha S, Taye M (2022) Morphological characterization of indigenous goat population in Ethiopia using canonical discriminant analysis. Small Rumin Res 206: Article 106591. https://doi.org/10.1016/j.smallrumres.2021.106591 Moore CS, Ruocchio MJ, Blakeslee AMH (2018) Distribution and population structure in the naked goby Gobiosoma bosc (Perciformes: Gobiidae) along a salinity gradient in two western Atlantic estuaries. PeerJ 6: 11-13. https://doi.org/10.7717/peerj.5380 Pakkasmaa S, Piironen J (2001) Morphological differentiation among local trout ( Salmo trutta ) populations. Biol J Linn Soc 72(2): 231-239. https://doi.org/10.1006/bijl.2000.0488 Palma J, Andrade JP (2002) Morphological study of Diplodus sargus , Diplodus puntazzo , and Lithognathus mormyrus (Sparidae) in the Eastern Atlantic and Mediterranean Sea. Fish Res 57(1): 1-8. https://doi.org/10.1016/s0165-7836(01)00335-6 Pan C, Liang XL, Chen SJ, Tao FY, Yan XQ, Cen JW (2020) Red color-related proteins from the shell of red swamp crayfish ( Procambarus clarkii ): Isolation, identification and bioinformatic analysis. Food Chem 327: Article 127079. https://doi.org/10.1016/j.foodchem.2020.127079 Peng B, Tan YF, Peng GH, Xiong LJ, Bai XF (2021) Path analysis of effects of phenotypic traits attributes on abdomen meat weight of red swamp crayfish Procambarus clarkii . Fish Sci 40(5): 718-725. (In Chinese) Porrini LP, Iriarte PJF, Iudica CM, Abud EA (2015) Population genetic structure and body shape assessment of Pagrus pagrus (Linnaeus, 1758) (Perciformes: Sparidae) from the Buenos Aires coast of the Argentine Sea. Neotropical Ichthyology 13(2): 431-438. https://doi.org/10.1590/1982-0224-20140149 Poulet N, Reyjol Y, Collier H, Lek S (2005) Does fish scale morphology allow the identification of population at a local scale? A case study for rostrum dace Leuciscus leuciscus burdigalensis in river Viaur (SW France). Aquat Sci 67(1): 122-127. https://doi.org/10.1007/s00027-004-0772-z Prasetyo FD, Muztahidin NI, Fatmawaty AA, Laila A, Nurfadilah M (2022) Analysis of the diversity of local ginger ( Zingiber officinale Rosc.) in Pandeglang Regency, Banten Province based on morphological characteristics. IOP Conf Ser Earth and Environ Sci. 978: Article 012025. https://doi.org/10.1088/1755-1315/978/1/012025 Shen YD, Wang QS, Wang WM, Li YH (2022) Exploration of an XX/XY sex determination system and development of PCR-based sex-specific markers in Procambarus clarkii based on next-generation sequencing data. Front Genet 13: Article 850983. https://doi.org/10.3389/fgene.2022.850983 Shimoda K, Wardiatno Y, Kubo K, Tamaki A (2005) Intraspecific behaviors and major cheliped sexual dimorphism in three congeneric callianassid shrimp. Mar Biol 146(3): 543-557. https://doi.org/10.1007/s00227-004-1453-0 Sint D, Dalla Via J, Füreder L (2007) Phenotypical characterization of indigenous freshwater crayfish populations. J Zool 273(2): 210-219. https://doi.org/10.1111/j.1469-7998.2007.00318.x Trizio I, Crestanello B, Galbusera P, Wauters LA, Tosi G, Matthysen E, Hauffe HC (2005) Geographical distance and physical barriers shape the genetic structure of Eurasian red squirrels ( Sciurus vulgaris ) in the Italian Alps. Mol Ecol 14(2): 469-481. https://doi.org/10.1111/j.1365-294x.2005.02428.x Turan C, Oral M, Öztürk B, Düzgüne E (2006) Morphometric and meristic variation between stocks of Bluefish ( Pomatomus saltatrix ) in the Black, Marmara, Aegean and northeastern Mediterranean Seas. Fish Res 79: 139-147. https://doi.org/10.1016/j.fishres.2006.01.015 Wang H, Shi WJ, Wang L, Zhang Y, Zang NW, Xue CP, Chang GL, Wu N (2020) Genetic determination of processing traits in the red swamp crayfish, Procambarus clarkii (Girard). Aquaculture 529: Article 735602. https://doi.org/10.1016/j.aquaculture.2020.735602 Wang H, Wang L, Shi WJ, Zhu CK, Pan ZJ, Wu N, Qiang J, Xu P (2019) Estimates of heritability based on additive-dominance genetic analysis model in red swamp crayfish, Procambarus clarkii . Aquaculture 504: 1-6. https://doi.org/10.1016/j.aquaculture.2019.01.043 Wang W, Gu W, Ding ZF, Ren YL, Chen JX, Hou YY (2005) A novel Spiroplasma pathogen causing systemic infection in the crayfish Procambarus clarkii (Crustacea: Decapod), in China. FEMS Microbiol Lett 249(1): 131-137. https://doi.org/10.1016/j.femsle.2005.06.005 Wang W, Ma CY, Chen W, Ma HY, Zhang H, Meng YY, Ni Y, Ma LB (2016) Optimization of selective breeding through analysis of morphological traits in Chinese sea bass ( Lateolabrax maculatus ). Genetics and Molecular Research 15(3): Article gmr.15038285. https://doi.org/10.4238/gmr.15038285 Yan J, Zheng BY, Tan KN, Yi SK, Li YH (2021) Effects of two exogenous proteins on the insulin-like androgenic gland hormone gene expression in Procambarus clarkii . Aquac Res 12(52): 6602-6611. https://doi.org/10.1111/are.15531 Yang YQ, Sun Q, Li CM, Chen HF, Zhao F, Huang JH, Zhou JS, Li XM, Lan B (2020) Biological characteristics and genetic diversity of Phomopsis asparagi , causal agent of asparagus stem blight. Plant Dis 104(11): 2898-2904. https://doi.org/10.1094/pdis-07-19-1484-re Yasuda CI, Otoda M, Nakano R, Takiya Y, Koga T (2017) Seasonal change in sexual size dimorphism of the major cheliped in the hermit crab Pagurus minutus . Ecol Res 32(3): 347-357. https://doi.org/10.1007/s11284-017-1438-3 Yi SK, Zhang L, Li YH, Shi LL, Chen J, Wang WM, She L, He JX (2020) Genetic diversity and phenotypic variation of the red swamp crayfish, Procambarus clarkii (Girard, 1852) (Decapoda: Astacidea:Astacidae), in China. J Crust Biol 40(5): 1-10. https://doi.org/10.1093/jcbiol/ruaa055 Yi SK, Li YH, Shi LL, Zhang L (2017) Novel insights into antiviral gene regulation of red swamp crayfish, Procambarus clarkii , infected with white spot sydrome virus. Genes 8(11): Article 320. https://doi.org/10.3390/genes8110320 Yi SK, Li YH, Shi LL, Zhang L, Li QB, Chen J (2018) Characterization of population genetic structure of red swamp crayfish, Procambarus clarkii , in China. Sci Rep 8(1): 5586. https://doi.org/10.1038/s41598-018-23986-z Yue GH, Li JL, Bai ZY, Wang CM, Feng F (2010) Genetic diversity and population structure of the invasive alien red swamp crayfish. Biol Invasions 12(8): 2697-2706. https://doi.org/10.1007/s10530-009-9675-1 Zhu ZY, Yue GH (2008) Eleven polymorphic microsatellites isolated from red swamp crayfish, Procambarus clarkii . Mol Ecol Resour 8(4): 796-798. https://doi.org/10.1111/j.1755-0998.2007.02067.x Zou X, Ma HY, Lu JX, Gong YY, Xia LJ (2017) Mathematical analysis of morphological traits and their effects on body weight in the red crab ( Charybdis feriata ). African Journal of Agricultural Research 12(6), 429-434. https://doi.org/10.5897/AJAR2016.11060 Additional Declarations No competing interests reported. Supplementary Files Supplementalinformation.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. 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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-2024195","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":133605326,"identity":"d988a6d4-bb57-4af8-944e-8e70fc3e0f5c","order_by":0,"name":"Qishuai Wang","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qishuai","middleName":"","lastName":"Wang","suffix":""},{"id":133605327,"identity":"42746c84-5328-44fe-a71c-189d898125b5","order_by":1,"name":"Siqi Yang","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Yang","suffix":""},{"id":133605328,"identity":"1f2eb89c-e90e-46c9-94f8-d3f18a2d3449","order_by":2,"name":"Ruixue Shi","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruixue","middleName":"","lastName":"Shi","suffix":""},{"id":133605329,"identity":"2b2c180b-4399-496b-a2ec-c3453b21c0db","order_by":3,"name":"Feifei Zheng","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Feifei","middleName":"","lastName":"Zheng","suffix":""},{"id":133605330,"identity":"e9202c8c-274b-4a57-bd44-432da41bd05d","order_by":4,"name":"Yanhe Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACPmYgwdjAIAckGw8AGWAggU8LG1SLMYgkUgsDREsiSDGRWth5DD8X7rBJX9t+GGjLjjp5gwPMB2/zMNjl4XYYj7H0zDNpudvOJAK1nGEz3HCALdmahyG5GI8WA2netsO52w6AtLTxMG44wGMmzcNwILEBjy2/edv+p5udfwjSImG/4QD/N0JazIC2HEgwuwG2xSARaAsbAS1sZda8bcmG224AbUlsS0ieeZjN2HKOQTJOLfz8hzff5m2zkzc7n/7wwce2Otu+480Pb7ypsMOphYGBwwDBTgARoMhlMMCqFgrYH+CTHQWjYBSMglHAwAAAmQtWCqFkdegAAAAASUVORK5CYII=","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yanhe","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2022-09-02 06:29:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2024195/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2024195/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":26120621,"identity":"83b0e718-06e5-4cb3-b6c6-899a6bfa7aa1","added_by":"auto","created_at":"2022-09-06 15:08:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":200238,"visible":true,"origin":"","legend":"\u003cp\u003eThe morphological measurement indexes of \u003cem\u003eP. clarkii\u003c/em\u003e. TL, total length; BL, body length; CL, carapace length; CW, carapace width; FASL, first abdominal segment length; FASW, first abdominal segment width; TASL, third abdominal segment width and TASW, third abdominal segment width.\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2024195/v1/d702be36a18fc23d82e778c3.jpg"},{"id":26120622,"identity":"88f2b1a5-a222-4094-a95e-2ceafea8caf8","added_by":"auto","created_at":"2022-09-06 15:08:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":236677,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots for first and second discriminant functions of the female (A) and male (B)\u003cem\u003e P. clarkii\u003c/em\u003e populations.\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2024195/v1/c20bfffb604ce804f601900e.jpg"},{"id":26120495,"identity":"63445d8e-b953-4d54-b810-17feb6a51a81","added_by":"auto","created_at":"2022-09-06 15:03:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":204631,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots comparing the most discriminant characteristics of the female (A) and male (B) \u003cem\u003eP. clarkii\u003c/em\u003e populations. The box is interquartile range and whiskers are minima and maxima of morphometric values. Outliers are the values that are more than 1.5 × inter-quartile range. The same letters above the plots denote statistically significant differences between populations (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2024195/v1/6ac5b26c763e507949272ce4.png"},{"id":26120902,"identity":"d691a5d3-ab21-4c96-a2ce-6621bd3f6436","added_by":"auto","created_at":"2022-09-06 15:13:17","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":75514,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of cluster analyses of five populations of female (A) and male (B) \u003cem\u003eP. clarkii.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2024195/v1/6ed18099828abb9d5ca1efc2.jpg"},{"id":26120498,"identity":"448e7bf0-bb15-45ee-af43-65b93af03c6a","added_by":"auto","created_at":"2022-09-06 15:03:17","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":50397,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots comparing the most discriminant characteristics of the male and female \u003cem\u003eP. clarkii\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2024195/v1/de81641018d14a65af556d01.jpg"},{"id":27249361,"identity":"00e589e0-80ed-49e4-9a0b-37292469da24","added_by":"auto","created_at":"2022-10-02 19:59:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":729119,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2024195/v1/551c596a-8693-4745-8623-6e42496ad9c4.pdf"},{"id":26120619,"identity":"45452a77-4af3-4467-9b70-c6a533351a17","added_by":"auto","created_at":"2022-09-06 15:08:17","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16961,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-2024195/v1/c6836e27f614fd9cb6312f01.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of the morphological characteristics and direction of morphology- based selective breeding of Procambarus clarkii","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003eProcambarus clarkii\u003c/em\u003e, is a freshwater crayfish, which is native to central and southern United States and northeastern Mexico (Henttonen and Huner \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). It is one of the most notorious invasive species worldwide (Barbaresi and Gherardi \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Cruz and Rebelo \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Zhu and Yue \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Yue et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It was introduced into China from Japan in 1930s (Kawai and Kobayashi \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Because of its high adaptability to environment and high reproduction ability (Fern\u0026aacute;ndez-Cisnal et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; An et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), it has been widely distributed throughout China (Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pan et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and the natural distribution and main producing areas are the middle and lower reaches of the Yangtze River (Yan et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eP. clarkii\u003c/em\u003e is favored by consumers because of its delicious meat and rich nutrition. In recent years, \u003cem\u003eP. clarkii\u003c/em\u003e has become one of the most economically important aquaculture species in China (Wang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The production of \u003cem\u003eP. clarkii\u003c/em\u003e reached nearly 2,633,600 tons in 2021 according to the Crayfish Industry Report 2022 in China reported by China Society of Fisheries (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nftec.agri.cn/\u003c/span\u003e\u003cspan address=\"http://www.nftec.agri.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). However, due to high intensity fishing and deterioration of the living environment, the wild resources of \u003cem\u003eP. clarkii\u003c/em\u003e have been declining (Liu et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is insufficient attention paid to the germplasm and breeding of \u003cem\u003eP. clarkii\u003c/em\u003e under the background of vigorous development of the aquaculture industry. Such led to the increasingly prominent problems such as smaller individual size, frequent disease occurrence, larger carapace percentage, and serious germplasm degradation of \u003cem\u003eP. clarkii\u003c/em\u003e (Yi et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Peng et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The study on the morphological differences of the crayfish in different geographical regions will be beneficial to the protection of germplasm resources and breeding of fine varieties.\u003c/p\u003e \u003cp\u003eIn general, morphological differentiation can appear as a consequence of genetic differences or environmental factors or their interaction (Begg and Waldman \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Pakkasmaa and Piironen \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Genetic diversity is investigated through assessing the differences between and within populations by using various genetic markers from the perspective of genetics (Moore et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Multivariate statistical analysis of morphological characters has also proven to be a powerful technique in population differences and stock discrimination (Palma and Andrade \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Turan et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Maguire and Dakić \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Porrini et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The analyses of morphological difference can reflect the differences among different populations or even individuals simply, quickly and effectively, and complementary to genetic study (Fevolden and Hessen \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Sint et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Bertocchi et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThus, in this study, morphological characteristics of five basic populations of \u003cem\u003eP. clarkii\u003c/em\u003e from different regions were analyzed and compared through applying morphometry and multivariate analysis methods. It was expected to evaluate the morphological differences and sexual dimorphism to provide some reference for its germplasm resources utilization and the morphology-based selective breeding direction of \u003cem\u003eP. clarkii\u003c/em\u003e.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sampling and data collection\u003c/h2\u003e \u003cp\u003eFive basic \u003cem\u003eP. clarkii\u003c/em\u003e populations for breeding were collected from different regions in China (Table S1). Collection locations of Chaohu, Yangxin, Honghu and Hanchuan were located in the middle and lower reaches of the Yangtze River, while Gaoyou is in the Huaihe River. The five regions belong to natural distribution and main producing areas of \u003cem\u003eP. clarkii\u003c/em\u003e, where the crayfish had high genetic diversity relatively (Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yi et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yi et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A total of 527 individuals (239 females and 288 males) with an intact body were randomly sampled from the five populations. Eleven characters were measured sequentially on each specimen. The dimension characters included total length (TL), body length (BL), carapace length (CL), carapace width (CW), first abdominal segment length (FASL), first abdominal segment width (FASW), third abdominal segment length (TASL), third abdominal segment width (TASW) (What distance the previous characters refer to respectively was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and third abdominal segment height (TASH, refers to the distance between the midpoint of the dorsal and ventral margins of the third abdominal segment). The weight characters included body weight (BW) and double cheliped weight (DCW). The dimensions were measured using a digital calliper (\u0026plusmn;\u0026thinsp;0.01 mm), while the body weight and double cheliped weight were measured using an electronic balance (\u0026plusmn;\u0026thinsp;0.01 g). In order to reduce the error caused by measurement, all characters were measured by the same person.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data analysis\u003c/h2\u003e \u003cp\u003eIn order to eliminate the influence of size on morphological characteristics of \u003cem\u003eP. clarkii\u003c/em\u003e, the ratio of each morphological character to body length (BL) was standardized for further analyses. In this study, a total of 10 morphological proportion parameters were selected, and all the morphological parameters were expressed as means and standard deviations.\u003c/p\u003e \u003cp\u003eIn order to remove bias caused by sexual dimorphism (Sint et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hamasaki et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the specimens were separated by sex and analyzed respectively with multivariate analysis. The Kolmogorov-Smirnov test was carried out on each morphological parameter in order to test whether the parameter obey a normal distribution or not. The parameters consistent with normal distribution were subjected to one-way ANOVA. For the parameters that were not normally distributed, difference in means between populations were assessed using ANOVA after testing for non-parametric Kruskal-Wallis ANOVA on ranks (Dashinov et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, T-test and non-parametric Mann-Whitney U test were used to compare whether the two sexes significantly different in the morphological parameters. For the discriminant analysis, all variables were entered one by one using a stepwise procedure where only variables contributing in the lowering of Wilk\u0026rsquo;s lambda are retained for analysis (Poulet et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Dashinov et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). And in the discriminant functions, standardized coefficients with large absolute values correspond to parameters with greater discriminating ability (J\u0026oacute;nsd\u0026oacute;ttir et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Duretanović et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Maguire et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A cross-validation test was computed in order to evaluate the effectiveness of discriminant analysis (Marques et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Konan et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For the cluster analysis, after calculating the average of 10 morphological proportion parameters of each population, the nearest neighbor clustering method was carried out with the Euclidean distances (Konan et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mavule et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMicrosoft Excel 2019 and SPSS 23.0 were used for descriptive statistics, ANOVA and discriminant analysis. OriginPro 2021 was used for the cluster analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Analysis of variance\u003c/h2\u003e \u003cp\u003eThere was a significant difference on the majority of the morphological parameters among different populations of males and females by the one-way ANOVA and K-W test (Table S2). For 10 morphological parameters, there was no significant difference on the parameter of TASL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) among the female populations, while there was no significant difference on the two parameters of FASW and TASL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) among the male populations. There was no significant difference on the two parameters of TL and TASH (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between the males and females.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Discriminant analysis of different populations\u003c/h2\u003e \u003cp\u003eThere were significant differences on morphological characteristics among populations, for both males and females of \u003cem\u003eP. clarkii\u003c/em\u003e. Multivariate discriminant analysis could be used to distinguish which morphological characteristics contributed most to the differences among populations. After the stepwise procedure in the discriminant analysis, the females retained six variables (BW, CW, FASL, FASW, TASW, TASH) that most distinguished different populations, while the males retained eight variables (BW, TL, CL, CW, FASL, TASW, TASH, DCW). There were all highly significant in the discrimination model for the females (Wilks\u0026prime;λ\u0026thinsp;=\u0026thinsp;0.311; F\u0026thinsp;=\u0026thinsp;13.268; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and males (Wilks\u0026prime;λ\u0026thinsp;=\u0026thinsp;0.249; F\u0026thinsp;=\u0026thinsp;14.596; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eSummary statistics demonstrated that for the female populations, the first and second discriminant functions contributed to 88.2% of the total variance (74.2% and 14.0%, respectively) indicating that the greatest proportion of the total variance was due to the first two discriminant functions. And the canonical R for those functions were 0.738 and 0.429, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In discriminant function 1, the parameters with larger absolute values of standardized coefficients were BW (0.826) and TASW (0.733). In discriminant function 2, the parameters with larger absolute values of standardized coefficients were FASW (-0.644), TASW (0.892) and TASH (0.656). The parameters may account for most of the variation in the first two discriminant functions. Correspondingly, for the male populations, the canonical R for the first two discriminant functions were 0.734 and 0.540, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The first discriminant function accounted for 62.3% of the explained variance and was weighed mostly by BW (1.232), CL (0.626) and CW (-0.767). The second discriminant function accounted for 22.0%, and the parameter with the largest absolute value of the standardized coefficient was TASW (0.790). The larger the absolute value was, the greater the ability to discriminate the populations was.\u003c/p\u003e \u003cp\u003eThe separation and overlap of male and female populations of \u003cem\u003eP. clarkii\u003c/em\u003e could be better demonstrated by making scatter plots of the first two discriminant functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The scatter plots for female and male populations showed a similar result, that is, the population Ch significantly deviated from the other four geographic populations. For the females, as the first discriminant function was marked by high positive loading for BW and TASW, the higher the value of BW and TASW are, the more likely the females belong to the populations from Gaoyou, Yangxin, Honghu or Hanchuan. Similarly, the second discriminant function was marked by high positive loadings for TASW and TASH and high negative loading for FASW. It seems to provide some discrimination between the Gy population, Hc population and Yx population, Hh population. For the males, as well as the females, the first discriminant function was marked by high positive loading for BW and CL and high negative loading for CW. So the lower the value of CW is, the more likely it was that the males belong to the Ch population, and the higher for the value of BW and CL are, the more likely it was that the males belong to the other four populations. The second discriminant function was marked by high positive loading for TASW. And the higher the value of TASW is, the more likely it was that the males belong to the Hc population.\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\u003eStandardized canonical discriminant function coefficients, eigenvalue, percentage of explained variance, and canonical correlations from the discriminant analysis for female and male \u003cem\u003eP. clarkii\u003c/em\u003e populations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunction 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFunction 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFunction 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFunction 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFASL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFASW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTASW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanonical R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFASL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTASW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanonical R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.307\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\u003eThe crayfish that best represents the morphological characteristics of the populations were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and the main morphological characteristics to distinguish the populations of \u003cem\u003eP. clarkii\u003c/em\u003e were compared. For female populations, the BW/BL ratio of the Ch population was significantly lower than that of the other four populations, and the Yx population was significantly lower than that of the Gy, Hh and Hc populations. Only two populations (Yx and Hc) exhibited significant difference in the value of the FASW/BL ratio. The TASW/BL ratio of the Hc population was significantly larger than that of the Ch, Yx and Hh populations, and the Gy population was also significantly larger than that of the Ch and Yx populations. The Ch and Yx populations exhibited a significant larger TASH/BL ratio than other three populations. Likewise, for male populations, the BW/BL ratio of the Ch population was significantly lower than that of the other four populations, and the Yx population was also significantly larger than that of the Hh population. The Ch and Hh populations exhibited a significant lower CL/BL ratio than other three populations. The CW/BL ratio of the Hh population was significantly lower than that of the Yx and Hc populations. The highest value of the TASW/BL ratio was observed in the Hc population.\u003c/p\u003e \u003cp\u003eThe cross-validation procedures revealed that 310 of 527 crayfish (58.82%) were correctly classified based on their external morphology (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, there was little difference between the percentage of the males (62.50%) and females (54.39%) correctly classified. The accuracy rate of classification among different geographic populations of the same sex was low, indicating that the morphological differences among different geographic populations of the same sex were small. Yet the Ch population had obtained the best proportion classification of the males (78.33%) and females (80.43%), which were much higher than the other four populations. The results indicated that the morphological characteristics of the Ch population had low similarity with other populations.\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\u003eCross-validated classification (count and percentage) matrix for the male and female \u003cem\u003eP. clarkii\u003c/em\u003e populations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003ePredicted group membership (count and percentage)\u003c/p\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal (count and percentage)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (80.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20 (45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27 (55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Cluster analysis\u003c/h2\u003e \u003cp\u003eThe male and female populations showed a similar grouping in the hierarchical cluster analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Five populations were clustered into two well-defined clusters in the two models (males and females). The first branch included only the Ch population, the Gy, Hc, Hh and Yx populations were clustered in the second branch. The results showed that the samples from Chaohu were phenotypically gathered into one cluster, while the morphological similarity between the Gy, Hc, Hh and Yx populations was great and were grouped into another cluster.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Discriminant analysis of sex\u003c/h2\u003e \u003cp\u003eIn order to distinguish which morphological characteristics contributed most to the differences between the females and males, a stepwise discriminant analysis was performed. Among all the variables analyzed, eight morphological characteristics were considered relevant and remained in the model, and the discriminant model established is remarkably effective (Wilks\u0026prime;λ\u0026thinsp;=\u0026thinsp;0.373; F\u0026thinsp;=\u0026thinsp;109.037; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the unique discriminant function, the parameters with larger absolute values of standardized coefficients were BW (1.229), TASW (0.803) and DCW (-1.068) (Table S3). The three most discriminant characteristics of males and females were compared in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The results showed that the BW/BL ratio and the DCW/BL ratio of the males were significantly larger than the females, while the TASW/BL ratio of the females was significantly larger than that of the males. Similarly, after the cross-validation procedure, 92.05% of the females were correctly classified, and 88.19% of the males were correctly classified (Table S4). The classification accuracy was high, indicating that the morphological differences between male and female individuals were great.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eGenerally, crustaceans have high morphological plasticity (Maguire et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). And the variation in the external morphology of a species is generally interpreted as an adaptation to the habitat environment (Dimmock et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Brian et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ferrito et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Demchenko and Tkachenko \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Geographic isolation, which blocks gene exchange among different populations, is generally considered to be a prerequisite for population differentiation (Gould and Woodruff \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Trizio et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Yet the eventually formed morphological differences could be a consequence of the environmental (Haddaway et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and genetic factors (Cataudella et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Maguire et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of variance analysis showed that there were significant differences in most morphological parameters among the five basic populations of \u003cem\u003eP. clarkii\u003c/em\u003e. Multivariate stepwise discriminant analysis was used to identify the best combination of variables to distinguish populations, and according to the absolute value of the standardized coefficient of the discriminant function, the variables with higher discriminant ability can be found (Anastasiadou et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mavule et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Duretanović et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Melesse et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the present study, the standardized morphological parameters of BW, FASW, TASW and TASH contributed significantly to distinguishing female populations, and the most discriminant characteristics for distinguishing the male populations were focused on BW, CL, CW and TASW. That is, in addition to body weight, the combination of abdomen related variables played an important role in distinguishing \u003cem\u003eP. clarkii\u003c/em\u003e female populations, and the carapace related variables significantly contributed to distinguishing the male populations. Recently \u003cem\u003eP. clarkii\u003c/em\u003e is highly loved by consumers and has become an important aquatic economic animal in China (Wang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Yi et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The tail meat of \u003cem\u003eP. clarkii\u003c/em\u003e is the most edible part for people to consume presently (Devesa et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The tail meat content is related with some morphological traits to some extent, including body weight and those that are mainly concentrated in the abdomen of \u003cem\u003eP. clarkii\u003c/em\u003e (Wang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). At present, the research on morphological traits has been widely used in the breeding of some other various aquatic animals (Wang et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zou et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Generally, for \u003cem\u003eP. clarkii\u003c/em\u003e, the greater the proportion of abdomen and the smaller the proportion of carapace, the higher the meat content (Wang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, the selection conducted around meat content will be the one direction of \u003cem\u003eP. clarkii\u003c/em\u003e breeding, and the germplasm resources with stronger abdomen and smaller carapace are the selection targets for genetic breeding of \u003cem\u003eP. clarkii\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eA cross-validation test was computed to assess the ability of variables to discriminate \u003cem\u003eP. clarkii\u003c/em\u003e populations (Konan et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Freire et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dashinov et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). After the cross-validation procedure, there was 58.82% of both sexes of \u003cem\u003eP. clarkii\u003c/em\u003e were correctly classified in their groups. The accuracy of classification was low, indicating that the more individuals with similar morphological characteristics among populations (Konan et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mavule et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Meanwhile, the higher the percentage of populations correctly classified, the greater the morphological differences among populations (Duretanović et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It showed that the greatest difference appeared between the Ch population and the other four populations. The results of cross-validation for male and female \u003cem\u003eP. clarkii\u003c/em\u003e populations were also supported by the hierarchical cluster analysis. Cluster analysis can classify different populations, and the clustering results reflect the distance of the kinship between populations (Yang et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Prasetyo et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, the male and female populations showed same grouping in the hierarchical cluster analysis that got the Ch population into a cluster and grouped the Gy, Yx, Hh and Hc populations into another cluster. Obviously, the clustering results did not conform to the general rule that the closer the geographical distance, the more similar the morphological characteristics (Cheng et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). It is worth noting that \u003cem\u003eP. clarkii\u003c/em\u003e is an economically important species farmed in China, and its seedlings or adults may exist in geographically distant water areas through other unnatural factors such as trade. Therefore, the hopping dispersal path of \u003cem\u003eP. clarkii\u003c/em\u003e may be the result of natural migration and human factors (Barbaresi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Yi et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such may also be an important reason for the small morphological differences among different populations.\u003c/p\u003e \u003cp\u003eFurthermore, since there is sexual dimorphism in the external morphology of \u003cem\u003eP. clarkii\u003c/em\u003e (Shen et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A stepwise discriminant analysis was performed to identify the variables that contributed most to gender differences. The result showed that the most discriminant characteristics for distinguishing sex were BW, TASW and DCW. The reason for the significant difference on body weight between males and females is that the males have larger double cheliped weight than the females. The sexual dimorphism of the cheliped of \u003cem\u003eP. clarkii\u003c/em\u003e may be the result of their widespread use by the male in fighting, display, and courtship (Shimoda et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Yasuda et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The smaller the cheliped of \u003cem\u003eP. clarkii\u003c/em\u003e, the higher the meat content (Craig and Wolters \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), which is one of the important reasons why the meat content of female crayfish is higher than that of male crayfish (Peng et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, under the condition of the same body length, the female \u003cem\u003eP. clarkii\u003c/em\u003e individuals usually have larger the third abdominal segment width than the males. This may be more conducive to the escape movement of female \u003cem\u003eP. clarkii\u003c/em\u003e, and may be related to its reproductive behavior, especially the behavior of the egg holding (Mariappan and Balasundaram 2004; Bauer and Delahoussaye \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). So, all-female breeding or the directional breeding of smaller cheliped would be one of important breeding directions for \u003cem\u003eP. clarkii\u003c/em\u003e in the future.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe main morphological characteristics to distinguish the populations and sexes of \u003cem\u003eP. clarkii\u003c/em\u003e were investigated by morphological characteristics in combination with discriminant analysis for basic populations of \u003cem\u003eP. clarkii\u003c/em\u003e. For the female populations of \u003cem\u003eP. clarkii\u003c/em\u003e, in addition to body weight, the differences focused on the combination of abdomen related variables. Of male populations, the carapace related variables showed significantly different. The most discriminant characteristics for distinguishing sex were abdominal width and cheliped weight. The results of this study could provide some reference for the protection of germplasm resources and the selection direction of elite varieties of \u003cem\u003eP. clarkii\u003c/em\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been approved by the Institutional Animal Care and Use Committee (IACUC) of Huazhong Agricultural University (Wuhan, China) and conducted in accordance with ethical standards and according to the national and international guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Key Research and Development Program of Hubei Province(2021BBA232), the National Key Research and Development Program of China (2020YFD0900304), and the Fundamental Research Funds for the Central Universities (2662020SCPY004). We thank Guangdong HAID Group Co., Ltd. for providing the sampling supporting in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQishuai Wang:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Investigation, Formal analysis, Writing (original draft). \u003cstrong\u003eSiqi Yang,\u003c/strong\u003e \u003cstrong\u003eRuixue Shi\u003c/strong\u003e and \u003cstrong\u003eFeifei Zheng:\u003c/strong\u003e Investigation. \u003cstrong\u003eYanhe Li:\u0026nbsp;\u003c/strong\u003eResources, Investigation, Project administration, Supervision, Writing (review and editing). All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAn ZH, Yang H, Liu XD, Zhangv YY (2020) Effects of astaxanthin on the immune response and reproduction of \u003cem\u003eProcambarus clarkii\u003c/em\u003e stressed with microcystin-leucine-arginine. Fish Sci 86: 759-766. https://doi.org/10.1007/s12562-020-01434-0\u003c/li\u003e\n\u003cli\u003eAnastasiadou C, Liasko R, Leonardos ID (2009) Biometric analysis of lacustrine and riverine populations of \u003cem\u003ePalemonetes antennarius\u003c/em\u003e (H. Milne-Edwards, 1837) (Crustacea, Decapoda, Palaemonidae) from north-western Greece. Limnologica 39(3): 244-254. https://doi.org/10.1016/j.limno.2008.07.006\u003c/li\u003e\n\u003cli\u003eBarbaresi S, Gherardi F (2000) The invasion of the alien crayfish \u003cem\u003eProcambarus clarkii\u003c/em\u003e in Europe, with particular reference to Italy. Biol Invasions 2(3): 259-264. https://doi.org/10.1023/A:1010009701606\u003c/li\u003e\n\u003cli\u003eBarbaresi S, Santini G, Tricarico E, Gherardi F (2004) Ranging behaviour of the invasive crayfish, \u003cem\u003eProcambarus clarkii\u003c/em\u003e (Girard). J Nat Hist 38(22): 2821-2832. https://doi.org/10.1080/00222930410001663308\u003c/li\u003e\n\u003cli\u003eBauer RT, Delahoussaye J (2008) Life history migrations of the amphidromous river shrimp \u003cem\u003eMacrobrachium ohione\u003c/em\u003e from a continental large river system. J Crustacean Biol 28(4): 622-632. https://doi.org/10.2307/20487784\u003c/li\u003e\n\u003cli\u003eBegg GA, Waldman JR (1999) An holistic approach to fish stock identification. Fish Res 43: 35-44. https://doi.org/10.1016/s0165-7836(99)00065-x\u003c/li\u003e\n\u003cli\u003eBertocchi S, Brusconi S, Gherardi F, Bucccianti A, Scalici M (2008) Morphometrical characterization of the \u003cem\u003eAustropotamobius pallipes\u003c/em\u003e species complex. J Nat Hist 42(31-32): 2063-2077. https://doi.org/10.1080/00222930802254664\u003c/li\u003e\n\u003cli\u003eBrian JV, Fernandes T, Ladle RJ, Todd PA (2006) Patterns of morphological and genetic variability in UK populations of shore crab \u003cem\u003eCarcinus maenas\u003c/em\u003e Linnaeus, 1758 (Crustacea: Decapoda: Brachyura). J Exp Mar Biol Ecol 329(1): 47-54. https://doi.org/10.1016/j.jembe.2005.08.002\u003c/li\u003e\n\u003cli\u003eCataudella R, Paolucci M, Delaunay C, Ropiquet A, Hassanin A, Baslamo M, Grandjean F (2010) Genetic variability of \u003cem\u003eAustropotamobius italicus\u003c/em\u003e in the Marches region: implications for conservation. Aquat Conserv 20(3): 261-268. https://doi.org/10.1002/aqc.1099\u003c/li\u003e\n\u003cli\u003eChen PC, Tzeng TD, Shih CH, Chu TJ, Lee YC (2015) Morphometric variation of the oriental river prawn (\u003cem\u003eMacrobrachium nipponense\u003c/em\u003e) in Taiwan. Limnologica 52: 51-58. https://doi.org/10.1016/j.limno.2015.03.002\u003c/li\u003e\n\u003cli\u003eCheng QQ, Lu DR, Ma L (2005) Morphological differences between close populations discernible by multivariate analysis: A case study of genus \u003cem\u003eCoilia\u003c/em\u003e (Teleostei: Clupeiforms). Aquat Living Resour 18(2): 187-192. https://doi.org/10.1051/alr:2005020\u003c/li\u003e\n\u003cli\u003eCraig RJ, Wolters WR (1988) Sources of variation in body size traits, dressout percentage and their correlations for the crayfish, \u003cem\u003eProcambarus clarkii\u003c/em\u003e. Aquaculture 72(1-2): 49-58. https://doi.org/10.1016/0044-8486(88)90145-7\u003c/li\u003e\n\u003cli\u003eCruz MJ, Rebelo R (2005) Vulnerability of Southwest Iberian amphibians to an introduced crayfish, \u003cem\u003eProcambarus clarkii\u003c/em\u003e. Amphibia-Reptilia 26(3): 293-303. https://doi.org/10.1163/156853805774408577\u003c/li\u003e\n\u003cli\u003eDashinov D, Czerniejewski P, Balshine S, Synyshyn C, Tasheva-Terzieva E, Stefanov T, Ivanova P, Mandrak N, Uzunova E (2020) Variation in external morphology between the native and invasive populations of the round goby, \u003cem\u003eNeogobius melanostomus\u003c/em\u003e (Actinopterygii: Gobiidae). Zoomorphology 139: 361-371. https://doi.org/10.1007/s00435-020-00480-7\u003c/li\u003e\n\u003cli\u003eDemchenko VO, Tkachenko MY (2017) Biological characteristics of the round goby, \u003cem\u003eNeogobius melanostomus\u003c/em\u003e (Pallas, 1814), from different water bodies. Archives of Polish Fisheries 25(1): 51-61. https://doi.org/10.1515/aopf-2017-0006\u003c/li\u003e\n\u003cli\u003eDevesa V, S\u0026uacute;\u0026ntilde;er MA, Lai VWM, Granchinho SCR, V\u0026eacute;lez D, Cullen WR, Mart\u0026iacute;nez JM, Montoro R (2002) Distribution of arsenic species in the freshwater crustacean \u003cem\u003eProcambarus clarkii\u003c/em\u003e. Appl Organomet Chem 16(12): 692-700. https://doi.org/10.1002/aoc.374\u003c/li\u003e\n\u003cli\u003eDimmock A, Willamson I, Matherm PB (2004) The influence of environment on the morphology of \u003cem\u003eMacrobrachium australiense\u003c/em\u003e (Decapoda: Palaemonidae). Aquacult Int 12(4-5): 435-456. https://doi.org/10.1023/b:aqui.0000042140.48340.c8\u003c/li\u003e\n\u003cli\u003eDuretanović S, Jaklič M, Milo\u0026scaron;ković A, Radojković N, Radenković M, Simić V, Maguire I (2017) Morphometric variations among \u003cem\u003eAstacus astacus\u003c/em\u003e populations from different regions of the Balkan Peninsula. Zoomorphology 136(1): 19-27. https://doi.org/10.1007/s00435-016-0331-x\u003c/li\u003e\n\u003cli\u003eFern\u0026aacute;ndez-Cisnal R, Garc\u0026iacute;a-Sevillano MA, Garc\u0026iacute;a-Barrera T, G\u0026oacute;mez-Ariza JL, Abril N (2018) Metabolomic alterations and oxidative stress are associated with environmental pollution in \u003cem\u003eProcambarus clarkii\u003c/em\u003e. Aquat Toxicol 205: 76-88. https://doi.org/10.1016/j.aquatox.2018.10.005 \u003c/li\u003e\n\u003cli\u003eFerrito V, Mannino MC, Pappalardo AM, Tigano C (2007) Morphological variation among populations of \u003cem\u003eAphanius fasciatus\u003c/em\u003e Nardo, 1827 (Teleostei, Cyprinodontidae) from the Mediterranean. J Fish Biol 70(1): 1-20. https://doi.org/10.1111/j.1095-8649.2006.01192.x\u003c/li\u003e\n\u003cli\u003eFevolden SE, Hessen DO (1989) Morphological and genetic differences among recently founded populations of noble crayfish (\u003cem\u003eAstacus astacus\u003c/em\u003e). Hereditas 110(2): 149-158. https://doi.org/10.1111/j.1601-5223.1989.tb00435.x\u003c/li\u003e\n\u003cli\u003eFreire JL, Bentes B, Fontes VB, Da Silva EM (2017) Morphometric discrimination among three stocks of \u003cem\u003eMacrobrachium amazonicum\u003c/em\u003e in the Brazilian Amazon. Limnologica 64: 1-10. https://doi.org/10.1016/j.limno.2017.01.007\u003c/li\u003e\n\u003cli\u003eGould SJ, Woodruff DS (1978) Natural history of Cerion, Ⅷ: Little Bahama Bank-A revision based on genetics, morphometrics, and geographic distribution. Bulletin of the Museum of Comparative Zoology 148: 371-415.\u003c/li\u003e\n\u003cli\u003eHaddaway NR, Mortimer RJG, Christmas M, Grahame JW, Dunn AM (2012) Morphological diversity and phenotypic plasticity in the threatened British white-clawed crayfish (\u003cem\u003eAustropotamobius pallipes\u003c/em\u003e). Aquat Conserv 22(2): 220-231. https://doi.org/10.1002/aqc.2225\u003c/li\u003e\n\u003cli\u003eHamasaki K, Osabe N, Nishimoto S, Dan S, Kitada S (2020) Sexual dimorphism and reproductive status of the red swamp crayfish \u003cem\u003eProcambarus clarkii\u003c/em\u003e. Zoological Studies 59(07). https://doi.org/10.6620/ZS.2020.59-07\u003c/li\u003e\n\u003cli\u003eHenttonen P, Huner JV (1999) The introduction of alien species of crayfish in Europe: A historical introduction. Crayfish in Europe As Alien Species Crustacean Issues. https://doi.org/10.1201/9781315140469-2\u003c/li\u003e\n\u003cli\u003eJ\u0026oacute;nsd\u0026oacute;ttir IG, Guelaugsd\u0026oacute;ttir AK, Karlsson H (2016) Morphometric differences between sub-populations of northern shrimp (\u003cem\u003ePandalus borealis\u003c/em\u003e). A case study from two adjacent fjords in Iceland. Reg Stud in Mar Sci 3: 42-48. https://doi.org/10.1016/j.rsma.2015.04.002\u003c/li\u003e\n\u003cli\u003eKawai T, Kobayashi Y (2005) Origin and current distribution of the alien crayfish, \u003cem\u003eProcambarus clarkii\u003c/em\u003e (Girard, 1852) in Japan. Crustacean 78(9): 1143-1149. https://doi.org/10.1163/156854005775361034\u003c/li\u003e\n\u003cli\u003eKonan KM, Ad\u0026eacute;po-Gour\u0026egrave;ne AB, Ouattara A, Nyingy WD, Gour\u0026egrave;ne G (2010) Morphometric variation among male populations of freshwater shrimp \u003cem\u003eMacrobrachium vollenhovenii\u003c/em\u003e Herklots, 1851 from C\u0026ocirc;te d\u0026apos;Ivoire Rivers. Fish Res 103(1-3): 1-8. https://doi.org/10.1016/j.fishres.2010.01.005\u003c/li\u003e\n\u003cli\u003eLi CT, Shang M, Hu XS, Ge YL, Jia ZY, Wang SH, Shi LY (2018) Evaluation of a new select-breeding carp variety with high fillet quality. Aquac Res 49(8): 2653-2663. https://doi.org/10.1111/are.13723\u003c/li\u003e\n\u003cli\u003eLi XL, Li F, Zhu JJ, Gu ZM (2016) Genetic diversity of the red swamp crayfish (\u003cem\u003eProcambarus clarkii\u003c/em\u003e) resources based on SSR markers. Journal of Huazhong Agricultural University 35(2): 63-68. (In Chinese)\u003c/li\u003e\n\u003cli\u003eLi YH, Guo XW, Cao XJ, Deng W, Luo W, Wang WM (2012) Population genetic structure and post-establishment dispersal patterns of the red swamp crayfish \u003cem\u003eProcambarus clarkii\u003c/em\u003e in China. PLoS ONE 7(7): Article e40652. https://doi.org/10.1371/journal.pone.0040652\u003c/li\u003e\n\u003cli\u003eLi YM, Fan WJ, Huang YY, Huang YY, Du XL, Liu ZQ, Huang YH, Zhao YL (2021) Comparison of morphology and genetic diversity between broodstock and hybrid offspring of oriental river prawn, \u003cem\u003eMacrobrachium nipponense\u003c/em\u003e based on morphological analysis and SNP markers. Anim Genet 52(4): 461-471. https://doi.org/10.1111/age.13081\u003c/li\u003e\n\u003cli\u003eLiu F, Qu YK, Geng C, Wang AM, Zhang JH, Li JF, Chen KJ, Liu B, Tian HY, Yang WP, Yu YB (2020) Analysis of the population structure and genetic diversity of the red swamp crayfish (\u003cem\u003eProcambarus clarkii\u003c/em\u003e) in China using SSR markers. Electron J Biotechnol 47: 59-71. https://doi.org/10.1016/j.ejbt.2020.06.007\u003c/li\u003e\n\u003cli\u003eMaguire I, Dakić L (2011) Comparative analyses of \u003cem\u003eAstacus leptodactylus\u003c/em\u003e morphological characteristics from Croatia and Armenia.\u003cem\u003e \u003c/em\u003eBiologia 66(3): 491-498. https://doi.org/10.2478/s11756-011-0045-y\u003c/li\u003e\n\u003cli\u003eMaguire I, Marn N, Klobučar G (2017) Morphological evidence for hidden diversity in the threatened stone crayfish \u003cem\u003eAustropotamobius torrentium\u003c/em\u003e (Schrank, 1803) (Decapoda: Astacoidea: Astacidae) in Croatia. J Crust Biol 37(1): 7-15. https://doi.org/10.1093/jcbiol/ruw012\u003c/li\u003e\n\u003cli\u003eMaguire I, Podnar M, Jelić M, \u0026Scaron;tambuk A, Schrimpf A, Schulz H, Klobučar G (2014) Two distinct evolutionary lineages of the \u003cem\u003eAstacus leptodactylus\u003c/em\u003e species-complex (Decapoda: Astacidae) inferred by phylogenetic analyses. Invertebr Syst 28(2): 117-123. https://doi.org/10.1071/IS13030\u003c/li\u003e\n\u003cli\u003eMariappan P, Balasundaram C (2004) Studies on the morphometry of \u003cem\u003eMacrobrachium nobilii\u003c/em\u003e (Decapoda, Palaemonidae). Brazilian Archives of Biology and Technology 47(3): 441-449. https://doi.org/10.1590/S1516-89132004000300015\u003c/li\u003e\n\u003cli\u003eMarques JF, Rego AL, Costa JL, Costa MJ, Cabral HN (2006) Genetic and morphological differentiation of the Lusitanian toadfish (\u003cem\u003eHalobatrachus didactylus\u003c/em\u003e) between estuarine and coastal areas in Portugal. Sci Mar 70(4): 749-758. https://doi.org/10.3989/scimar.2006.70n4749\u003c/li\u003e\n\u003cli\u003eMavule BS, Sarti FM, Lasagna E, Kunene NW (2016) Morphological differentiation amongst Zulu sheep populations in KwaZulu-Natal, South Africa, as revealed by multivariate analysis. Small Rumin Res 140: 50-56. https://doi.org/10.1016/j.smallrumres.2016.06.001\u003c/li\u003e\n\u003cli\u003eMelesse A, Yemane G, Tade B, Dea D, Kayamo K, Abera G, Mekasha Y, Betsha S, Taye M (2022) Morphological characterization of indigenous goat population in Ethiopia using canonical discriminant analysis. Small Rumin Res 206: Article 106591. https://doi.org/10.1016/j.smallrumres.2021.106591\u003c/li\u003e\n\u003cli\u003eMoore CS, Ruocchio MJ, Blakeslee AMH (2018) Distribution and population structure in the naked goby \u003cem\u003eGobiosoma bosc\u003c/em\u003e (Perciformes: Gobiidae) along a salinity gradient in two western Atlantic estuaries. PeerJ 6: 11-13. https://doi.org/10.7717/peerj.5380\u003c/li\u003e\n\u003cli\u003ePakkasmaa S, Piironen J (2001) Morphological differentiation among local trout (\u003cem\u003eSalmo trutta\u003c/em\u003e) populations. Biol J Linn Soc 72(2): 231-239. https://doi.org/10.1006/bijl.2000.0488\u003c/li\u003e\n\u003cli\u003ePalma J, Andrade JP (2002) Morphological study of \u003cem\u003eDiplodus sargus\u003c/em\u003e, \u003cem\u003eDiplodus puntazzo\u003c/em\u003e, and \u003cem\u003eLithognathus mormyrus\u003c/em\u003e (Sparidae) in the Eastern Atlantic and Mediterranean Sea. Fish Res 57(1): 1-8. https://doi.org/10.1016/s0165-7836(01)00335-6\u003c/li\u003e\n\u003cli\u003ePan C, Liang XL, Chen SJ, Tao FY, Yan XQ, Cen JW (2020) Red color-related proteins from the shell of red swamp crayfish (\u003cem\u003eProcambarus clarkii\u003c/em\u003e): Isolation, identification and bioinformatic analysis.\u003cem\u003e \u003c/em\u003eFood Chem 327: Article 127079. https://doi.org/10.1016/j.foodchem.2020.127079\u003c/li\u003e\n\u003cli\u003ePeng B, Tan YF, Peng GH, Xiong LJ, Bai XF (2021) Path analysis of effects of phenotypic traits attributes on abdomen meat weight of red swamp crayfish \u003cem\u003eProcambarus clarkii\u003c/em\u003e. Fish Sci 40(5): 718-725. (In Chinese)\u003c/li\u003e\n\u003cli\u003ePorrini LP, Iriarte PJF, Iudica CM, Abud EA (2015) Population genetic structure and body shape assessment of \u003cem\u003ePagrus pagrus\u003c/em\u003e (Linnaeus, 1758) (Perciformes: Sparidae) from the Buenos Aires coast of the Argentine Sea. Neotropical Ichthyology 13(2): 431-438. https://doi.org/10.1590/1982-0224-20140149\u003c/li\u003e\n\u003cli\u003ePoulet N, Reyjol Y, Collier H, Lek S (2005) Does fish scale morphology allow the identification of population at a local scale? A case study for rostrum dace \u003cem\u003eLeuciscus leuciscus burdigalensis\u003c/em\u003e in river Viaur (SW France). Aquat Sci 67(1): 122-127. https://doi.org/10.1007/s00027-004-0772-z\u003c/li\u003e\n\u003cli\u003ePrasetyo FD, Muztahidin NI, Fatmawaty AA, Laila A, Nurfadilah M (2022) Analysis of the diversity of local ginger (\u003cem\u003eZingiber officinale\u003c/em\u003e Rosc.) in Pandeglang Regency, Banten Province based on morphological characteristics. IOP Conf Ser Earth and Environ Sci. 978: Article 012025. https://doi.org/10.1088/1755-1315/978/1/012025\u003c/li\u003e\n\u003cli\u003eShen YD, Wang QS, Wang WM, Li YH (2022) Exploration of an XX/XY sex determination system and development of PCR-based sex-specific markers in \u003cem\u003eProcambarus clarkii\u003c/em\u003e based on next-generation sequencing data. Front Genet 13: Article 850983. https://doi.org/10.3389/fgene.2022.850983 \u003c/li\u003e\n\u003cli\u003eShimoda K, Wardiatno Y, Kubo K, Tamaki A (2005) Intraspecific behaviors and major cheliped sexual dimorphism in three congeneric callianassid shrimp. Mar Biol 146(3): 543-557. https://doi.org/10.1007/s00227-004-1453-0\u003c/li\u003e\n\u003cli\u003eSint D, Dalla Via J, F\u0026uuml;reder L (2007) Phenotypical characterization of indigenous freshwater crayfish populations. J Zool 273(2): 210-219. https://doi.org/10.1111/j.1469-7998.2007.00318.x\u003c/li\u003e\n\u003cli\u003eTrizio I, Crestanello B, Galbusera P, Wauters LA, Tosi G, Matthysen E, Hauffe HC (2005) Geographical distance and physical barriers shape the genetic structure of Eurasian red squirrels (\u003cem\u003eSciurus vulgaris\u003c/em\u003e) in the Italian Alps. Mol Ecol 14(2): 469-481. https://doi.org/10.1111/j.1365-294x.2005.02428.x\u003c/li\u003e\n\u003cli\u003eTuran C, Oral M, \u0026Ouml;zt\u0026uuml;rk B, D\u0026uuml;zg\u0026uuml;ne E (2006) Morphometric and meristic variation between stocks of Bluefish (\u003cem\u003ePomatomus saltatrix\u003c/em\u003e) in the Black, Marmara, Aegean and northeastern Mediterranean Seas. Fish Res 79: 139-147. https://doi.org/10.1016/j.fishres.2006.01.015\u003c/li\u003e\n\u003cli\u003eWang H, Shi WJ, Wang L, Zhang Y, Zang NW, Xue CP, Chang GL, Wu N (2020) Genetic determination of processing traits in the red swamp crayfish, \u003cem\u003eProcambarus clarkii \u003c/em\u003e(Girard). Aquaculture 529: Article 735602. https://doi.org/10.1016/j.aquaculture.2020.735602\u003c/li\u003e\n\u003cli\u003eWang H, Wang L, Shi WJ, Zhu CK, Pan ZJ, Wu N, Qiang J, Xu P (2019) Estimates of heritability based on additive-dominance genetic analysis model in red swamp crayfish, \u003cem\u003eProcambarus clarkii\u003c/em\u003e. Aquaculture 504: 1-6. https://doi.org/10.1016/j.aquaculture.2019.01.043\u003c/li\u003e\n\u003cli\u003eWang W, Gu W, Ding ZF, Ren YL, Chen JX, Hou YY (2005) A novel Spiroplasma pathogen causing systemic infection in the crayfish \u003cem\u003eProcambarus clarkii\u003c/em\u003e (Crustacea: Decapod), in China. FEMS Microbiol Lett 249(1): 131-137. https://doi.org/10.1016/j.femsle.2005.06.005\u003c/li\u003e\n\u003cli\u003eWang W, Ma CY, Chen W, Ma HY, Zhang H, Meng YY, Ni Y, Ma LB (2016) Optimization of selective breeding through analysis of morphological traits in Chinese sea bass (\u003cem\u003eLateolabrax maculatus\u003c/em\u003e). Genetics and Molecular Research 15(3): Article gmr.15038285. https://doi.org/10.4238/gmr.15038285\u003c/li\u003e\n\u003cli\u003eYan J, Zheng BY, Tan KN, Yi SK, Li YH (2021) Effects of two exogenous proteins on the insulin-like androgenic gland hormone gene expression in \u003cem\u003eProcambarus clarkii\u003c/em\u003e. Aquac Res 12(52): 6602-6611. https://doi.org/10.1111/are.15531\u003c/li\u003e\n\u003cli\u003eYang YQ, Sun Q, Li CM, Chen HF, Zhao F, Huang JH, Zhou JS, Li XM, Lan B (2020) Biological characteristics and genetic diversity of \u003cem\u003ePhomopsis asparagi\u003c/em\u003e, causal agent of asparagus stem blight. Plant Dis 104(11): 2898-2904. https://doi.org/10.1094/pdis-07-19-1484-re\u003c/li\u003e\n\u003cli\u003eYasuda CI, Otoda M, Nakano R, Takiya Y, Koga T (2017) Seasonal change in sexual size dimorphism of the major cheliped in the hermit crab \u003cem\u003ePagurus minutus\u003c/em\u003e. Ecol Res 32(3): 347-357. https://doi.org/10.1007/s11284-017-1438-3\u003c/li\u003e\n\u003cli\u003eYi SK, Zhang L, Li YH, Shi LL, Chen J, Wang WM, She L, He JX (2020) Genetic diversity and phenotypic variation of the red swamp crayfish, \u003cem\u003eProcambarus clarkii\u003c/em\u003e (Girard, 1852) (Decapoda: Astacidea:Astacidae), in China. J Crust Biol 40(5): 1-10. https://doi.org/10.1093/jcbiol/ruaa055\u003c/li\u003e\n\u003cli\u003eYi SK, Li YH, Shi LL, Zhang L (2017) Novel insights into antiviral gene regulation of red swamp crayfish, \u003cem\u003eProcambarus clarkii\u003c/em\u003e, infected with white spot sydrome virus. Genes 8(11): Article 320. https://doi.org/10.3390/genes8110320\u003c/li\u003e\n\u003cli\u003eYi SK, Li YH, Shi LL, Zhang L, Li QB, Chen J (2018) Characterization of population genetic structure of red swamp crayfish, \u003cem\u003eProcambarus clarkii\u003c/em\u003e, in China. Sci Rep 8(1): 5586. https://doi.org/10.1038/s41598-018-23986-z\u003c/li\u003e\n\u003cli\u003eYue GH, Li JL, Bai ZY, Wang CM, Feng F (2010) Genetic diversity and population structure of the invasive alien red swamp crayfish. Biol Invasions 12(8): 2697-2706. https://doi.org/10.1007/s10530-009-9675-1\u003c/li\u003e\n\u003cli\u003eZhu ZY, Yue GH (2008) Eleven polymorphic microsatellites isolated from red swamp crayfish, \u003cem\u003eProcambarus clarkii\u003c/em\u003e. Mol Ecol Resour 8(4): 796-798. https://doi.org/10.1111/j.1755-0998.2007.02067.x\u003c/li\u003e\n\u003cli\u003eZou X, Ma HY, Lu JX, Gong YY, Xia LJ (2017) Mathematical analysis of morphological traits and their effects on body weight in the red crab (\u003cem\u003eCharybdis feriata\u003c/em\u003e). African Journal of Agricultural Research 12(6), 429-434. https://doi.org/10.5897/AJAR2016.11060\u003c/li\u003e\n\u003c/ol\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":"Procambarus clarkii, Basic population, Morphological variation, Multivariate analysis","lastPublishedDoi":"10.21203/rs.3.rs-2024195/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2024195/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to explore the breeding direction of morphological selection of \u003cem\u003eProcambarus clarkii\u003c/em\u003e, the morphological characteristics of five \u003cem\u003eP. clarkii\u003c/em\u003e basic populations from different regions in China were comprehensively analyzed by multivariate statistical analyses. The results showed that there were significant differences in most morphological parameters among populations and between sexes. In the discriminatory analysis, the most discriminant characteristics for distinguishing females among populations were body weight (BW), first abdominal segment width (FASW), third abdominal segment width (TASW) and third abdominal segment height (TASH), whereas for males, the characteristics were body weight (BW), carapace length (CL), carapace width (CW) and third abdominal segment width (TASW).The most significant variables of the differences between sexes were body weight (BW), third abdominal segment width (TASW) and double cheliped weight (DCW). This study would be beneficial to understanding the main morphological characteristics of \u003cem\u003eP. clarkii\u003c/em\u003e, which could provide basic data of the collected germplasm resources and some reference for indicating the direction of \u003cem\u003eP. clarkii\u003c/em\u003e morphology-based breeding. The germplasm resources with stronger abdomen, smaller carapace and smaller cheliped would be the selection targets, and all-female breeding would also be one of important breeding directions of for \u003cem\u003eP. clarkii\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"Analysis of the morphological characteristics and direction of morphology- based selective breeding of Procambarus clarkii","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-06 15:03:15","doi":"10.21203/rs.3.rs-2024195/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":"0e343ef1-d5b9-47ae-b1c7-d1130651adb0","owner":[],"postedDate":"September 6th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-10-02T19:59:16+00:00","versionOfRecord":[],"versionCreatedAt":"2022-09-06 15:03:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2024195","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2024195","identity":"rs-2024195","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}