Effects of Shell Morphology on the Weight Traits of Manila Clam (Ruditapes philippinarum) from Different Geographical Populations

Abstract

Shell morphological traits are important quantitative traits in shellfish. Shell morphology is usually affected by many factors, such as the ecological environment and shellfish genetics. It is considered the results of comprehensive actions of natural selection, micro-evolution, and heredity. Measuring shell morphological traits aids our understanding of the current situation of germplasm resources among shellfish species or intraspecific populations and also provides important breeding traits for shellfish breeding programs. For most shellfish, quality traits (eg. live weight and soft body weight) are considered the important breeding traits, but there are practical issues in using these traits, such as the inability or difficulty in measuring them in vivo. It is well known that shellfish quality traits are closely related to shell morphological traits. However, a simple correlation analysis between quality and morphological traits cannot adequately explain all the intrinsic links between these traits. In this study, Manila clams (Ruditapes philippinarum) were collected from nine geographical locations along the north and south coasts of China. The shell morphology and quality characteristics were measured and analyzed for the nine different populations of clams. Furthermore, the main factors affecting the quality traits were analyzed using cluster analysis to reveal the genetic relationships among the populations. The optimal regression equations were constructed using the morphological traits with the soft weights of these populations. For all populations, shell length, shell height, shell width, and shell thickness were measured using a vernier caliper (with an accuracy of 0.01 mm) and a thickness gauge. After wiping the surface of clams with filter paper, their live weight was measured on an electronic balance (with an accuracy of 0.01 g). A scalpel was used to sample the soft body from the clams. After drying, soft body weights were calculated using the difference between live weight and shell weight. The morphological data, including mean values, standard deviation, and coefficient of variation, were calculated using SPSS 26.0. To eliminate the effects of size differences among individuals, two morphological scale parameters (shell width/shell length and shell height/shell length) were calculated to reflect the morphological characteristics of the clams. In addition, stepwise linear regression analyses were performed to obtain the correlation coefficients, direct path coefficients, correlation indexes, indirect path coefficients, and determine the coefficients using SPSS26.0 software. The correlation coefficient and multiple regression analyses of the traits were evaluated. The multiple regression equations were established for all populations. Cluster analysis was used to assess each trait using the calibrations (to calculate the ratio of each trait to shell length). The heatmap displayed the shortest distance method for the Euclidean distance between the different populations. There were significant differences in the effects of shell length, shell height, shell width, and shell thickness on live weight and soft body weight of clams. Morphological traits were significantly correlated with body weight and soft weight (P < 0.05), except for shell thickness. The results of determination coefficient revealed shell width had the greatest impact on live weights of the Putian, Jinzhou, and Chaozhou populations, with determination coefficients of 0.277, 0.232, and 0.307, respectively. The determination coefficients of soft body weights indicated that shell width played very important roles in determining soft body weights of the Putian and Chaozhou populations, with determination coefficients of 0.249 and 0.443, respectively. By calculating the total of determination coefficients, the results are consistent with the correlation index R2 for each trait. For the total determinant effects, the sum of determination coefficients for the morphological traits with soft body weights were greater than 0.850 in most populations. In contrast, the sum of determination coefficients for the morphological traits with soft body weights were less than 0.850 in Laizhou, Zhuanghe, Donggang, and Jinzhou. Based on the path analysis and the determination coefficient analysis, shell width had the greatest impact on live weights and soft body weights in most populations. The maximum values of the two morphological proportion parameters (shell width/shell length and shell height/shell length) were found in the Laizhou population (0.49 and 0.74), while the minimum proportion parameters were detected in the Dalian Donggang population (0.42 and 0.67). Among the shell morphological traits, shell thickness had the largest coefficient of variation (22.74), while shell height had the smallest coefficient of variation (9.47). By testing for statistically significant variation in the partial regression coefficient, the optimal regression equations of the morphological traits for soft body weight were constructed for all the populations. The cluster analysis revealed that different clam populations did not have a typically regional characteristic in terms of the shell morphological traits. There was an irregular north-south alternating clustering phenomenon. According to the present findings, shell thickness and morphological ratio parameters (ratio of shell height to shell length and ratio of shell width to shell length) are important indicators for evaluating the growth potential and nutritional status of clams in aquaculture. The present results provide a scientific basis for morphological discrimination and germplasm resource evaluation for geographical populations of clams and also aid breeding strategies to predict breeding traits for aquaculture breeding programs, including guidance for the selective breeding of clams.

Keywords

• ruditapes philippinarum
• shell morphology
• soft body weight
• path analysis
• cluster analysis