Fish Conservation Division, ICAR-National Bureau of Fish Genetic Resources, Lucknow, Uttar Pradesh, India.
PMFGR Centre, ICAR-National Bureau of Fish Genetic Resources, Kochi, Kerala, India.
PeerJ. 2023 Feb 24;11:e14884. doi: 10.7717/peerj.14884. eCollection 2023.
Fish stocks that are grown under diverse environmental conditions have different biometric relationships and growth patterns. The biometric length-weight relationship (LWR) is an essential fishery assessment tool, as fish growth is continuous and depends on genetic and environmental factors. The present study attempts to understand the LWR of the flathead grey mullet, Linnaeus, 1758, from different locations. The study area encompassed its distribution in the wild across freshwater location (one), coastal habitats (eight locations), and estuaries (six locations) in India to determine the relationship between various environmental parameters. Specimens ( = 476) of were collected from commercial catches and the length and weight of individual specimens were recorded. Monthly data from the study locations were extracted for nine environmental variables from the datasets downloaded from the Physical Oceanography Distributed Active Archive Center (PO.DAAC) and the Copernicus Marine Environment Monitoring Service (CMEMS) over 16 years (2002 to 2017) on the Geographical Information System platform. The parameters of the LWR, intercept 'a' and slope or regression coefficient 'b', varied from 0.005321 to 0.22182 and 2.235 to 3.173, respectively. The condition factor ranged from 0.92 to 1.41. The partial least squares (PLS) score scatter plot matrix indicated differences in the environmental variables between the locations. PLS analysis of the regression coefficient and environment parameters revealed that certain environment variables , sea surface temperature, salinity, dissolved oxygen, nitrate, and phosphate, played a positive role. However, chlorophyll, pH, silicate, and iron played a negative role in influencing weight growth across various locations. The results revealed that the specimens from three locations, Mandapam, Karwar, and Ratnagiri, possessed significantly higher fitness to their environment than those from the other six locations. The PLS model can be used to predict weight growth under the various environmental conditions of different ecosystems. The three identified locations are useful sites for the mariculture of this species considering their growth performance, the environmental variables, and their interactions. The results of this study will improve the management and conservation of exploited stocks in regions affected by climate change. Our results will also aid in making environment clearance decisions for coastal development projects and will improve the efficiency of mariculture systems.
在不同环境条件下生长的鱼类种群具有不同的生物测量关系和生长模式。生物测量体长-体重关系（LWR）是渔业评估的重要工具，因为鱼类的生长是连续的，并且取决于遗传和环境因素。本研究试图了解来自不同地点的平头灰鲻鱼的 LWR。研究区域包括印度淡水地点（一个）、沿海栖息地（八个地点）和河口（六个地点）的野生分布，以确定各种环境参数之间的关系。从商业捕捞中收集了 476 个标本，并记录了每个标本的长度和重量。从 2002 年到 2017 年，在地理信息系统平台上从下载的数据集（来自物理海洋学分布式主动档案中心（PO.DAAC）和哥白尼海洋环境监测服务（CMEMS））中提取了研究地点的每月数据，用于九个环境变量。LWR 的参数，截距 'a' 和斜率或回归系数 'b'，分别在 0.005321 到 0.22182 和 2.235 到 3.173 之间变化。条件系数在 0.92 到 1.41 之间变化。偏最小二乘（PLS）得分散点图矩阵表明了不同地点之间环境变量的差异。回归系数和环境参数的 PLS 分析表明，某些环境变量，如海面温度、盐度、溶解氧、硝酸盐和磷酸盐，起到了积极的作用。然而，叶绿素、pH 值、硅酸盐和铁在影响不同地点的体重增长方面起到了消极作用。结果表明，来自曼达帕姆、卡尔瓦尔和拉特纳吉里的三个地点的 标本对其环境的适应性明显高于其他六个地点的标本。PLS 模型可用于预测不同生态系统各种环境条件下的体重增长。考虑到生长性能、环境变量及其相互作用，这三个确定的地点是该物种进行海水养殖的有用地点。本研究的结果将有助于改善受气候变化影响地区的捕捞种群的管理和保护。我们的研究结果还将有助于沿海发展项目的环境许可决策，并提高海水养殖系统的效率。
