He Gao , Suxing Fu , Meng Xing , Yinhua Zhou , Tonghan Wu , Xiao Chen , Chengjiang He , Qiaokun Liu , Haixu Liu , Luo Lei , Shijun Xiao , Fei Liu , Yan Zhou , Jian Su , Chaowei Zhou , Bingyao Huang , Haiping Liu
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In this study, we propose a deep-learning-based method for acquiring high-throughput phenotypic data of fishes, including image dataset construction of schizothoracine fishes (including front, side, and top views), fish keypoint detection based on the You Only Look Once (YOLO) model, and reconstruction of 2D and 3D keypoint coordinates based on multiple views. A total of 7050 phenotypic data points consisting of keypoint distances and angles were constructed for each fish and were highly correlated (>0.98) with the corresponding data measured manually. We obtained phenotypic data for a total of 525 fishes from three schizothoracine fish groups inhabiting riverine, lacustrine, and river–lake transitional environments using the proposed phenotypic data acquisition method. We employed a random forest algorithm to classify the groups, achieving a classification accuracy of 96%, and identified 15 morphometric indices that exhibited statistically significant differences., of which 6 were related to head morphology, 6 related to body shape, and 3 related to tail morphology, based on the random forest algorithm. Specifically, river-living schizothoracine fishes showed a blunt head, robust body, and elongated caudal peduncle which may reflect adaptations to the turbulence of the river, while the lake-living schizothoracine fishes have the opposite effect. Schizothoracine fishes at the river–lake transitional zones were phenotypically characterized as being in the middle of the two phenotypes, and these presumably reflect adaptations to their lake habitat. This study provides a methodological reference for obtaining high-throughput phenotypic data on fish and a theoretical basis for understanding the adaptation of very high-altitude schizothoracine fishes to their environment.</div></div>","PeriodicalId":101277,"journal":{"name":"Water Biology and Security","volume":"4 4","pages":"Article 100381"},"PeriodicalIF":4.4000,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A high-throughput phenome-based analysis of morphological variation and environmental adaptation in extremely high-altitude schizothoracine fishes\",\"authors\":\"He Gao , Suxing Fu , Meng Xing , Yinhua Zhou , Tonghan Wu , Xiao Chen , Chengjiang He , Qiaokun Liu , Haixu Liu , Luo Lei , Shijun Xiao , Fei Liu , Yan Zhou , Jian Su , Chaowei Zhou , Bingyao Huang , Haiping Liu\",\"doi\":\"10.1016/j.watbs.2025.100381\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Schizothoracine fishes are distributed in the Nagqu region, which is the hinterland of the Tibetan Plateau. They have adapted to the cold and strongly ultraviolet environment of the plateau and show diverse morphologies, which makes them ideal for studying the relationship between phenotype and environment. However, traditional morphological measurements are time consuming and labor costly. In this study, we propose a deep-learning-based method for acquiring high-throughput phenotypic data of fishes, including image dataset construction of schizothoracine fishes (including front, side, and top views), fish keypoint detection based on the You Only Look Once (YOLO) model, and reconstruction of 2D and 3D keypoint coordinates based on multiple views. A total of 7050 phenotypic data points consisting of keypoint distances and angles were constructed for each fish and were highly correlated (>0.98) with the corresponding data measured manually. We obtained phenotypic data for a total of 525 fishes from three schizothoracine fish groups inhabiting riverine, lacustrine, and river–lake transitional environments using the proposed phenotypic data acquisition method. We employed a random forest algorithm to classify the groups, achieving a classification accuracy of 96%, and identified 15 morphometric indices that exhibited statistically significant differences., of which 6 were related to head morphology, 6 related to body shape, and 3 related to tail morphology, based on the random forest algorithm. Specifically, river-living schizothoracine fishes showed a blunt head, robust body, and elongated caudal peduncle which may reflect adaptations to the turbulence of the river, while the lake-living schizothoracine fishes have the opposite effect. Schizothoracine fishes at the river–lake transitional zones were phenotypically characterized as being in the middle of the two phenotypes, and these presumably reflect adaptations to their lake habitat. This study provides a methodological reference for obtaining high-throughput phenotypic data on fish and a theoretical basis for understanding the adaptation of very high-altitude schizothoracine fishes to their environment.</div></div>\",\"PeriodicalId\":101277,\"journal\":{\"name\":\"Water Biology and Security\",\"volume\":\"4 4\",\"pages\":\"Article 100381\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Water Biology and Security\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772735125000241\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Biology and Security","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772735125000241","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
它们适应高原寒冷和强紫外线环境,形态多样,是研究表型与环境关系的理想选择。然而,传统的形态学测量既费时又费力。在本研究中,我们提出了一种基于深度学习的鱼类高通量表型数据获取方法,包括裂胸科鱼类(包括正面、侧面和顶部视图)图像数据集构建,基于You Only Look Once (YOLO)模型的鱼类关键点检测,以及基于多视图的二维和三维关键点坐标重建。每条鱼共构建了7050个由关键点距离和角度组成的表型数据点,并与人工测量的相应数据高度相关(>0.98)。我们利用所提出的表型数据采集方法,获得了生活在河流、湖泊和河湖过渡环境中的三种裂胸纲鱼类群共525条鱼的表型数据。我们采用随机森林算法对各组进行分类,分类准确率达到96%,并确定了15个具有统计学差异的形态计量指标。,其中6个与头部形态相关,6个与身体形态相关,3个与尾巴形态相关,基于随机森林算法。具体来说,生活在河流中的裂胸鱼表现出钝头、强壮的身体和细长的尾柄,这可能反映了对河流湍流的适应,而生活在湖泊中的裂胸鱼则具有相反的效果。河湖过渡带裂胸科鱼类的表型特征介于这两种表型之间,这可能反映了它们对湖泊栖息地的适应。本研究为获得鱼类高通量表型数据提供了方法学参考,为了解高海拔裂胸纲鱼类对环境的适应性提供了理论基础。
A high-throughput phenome-based analysis of morphological variation and environmental adaptation in extremely high-altitude schizothoracine fishes
Schizothoracine fishes are distributed in the Nagqu region, which is the hinterland of the Tibetan Plateau. They have adapted to the cold and strongly ultraviolet environment of the plateau and show diverse morphologies, which makes them ideal for studying the relationship between phenotype and environment. However, traditional morphological measurements are time consuming and labor costly. In this study, we propose a deep-learning-based method for acquiring high-throughput phenotypic data of fishes, including image dataset construction of schizothoracine fishes (including front, side, and top views), fish keypoint detection based on the You Only Look Once (YOLO) model, and reconstruction of 2D and 3D keypoint coordinates based on multiple views. A total of 7050 phenotypic data points consisting of keypoint distances and angles were constructed for each fish and were highly correlated (>0.98) with the corresponding data measured manually. We obtained phenotypic data for a total of 525 fishes from three schizothoracine fish groups inhabiting riverine, lacustrine, and river–lake transitional environments using the proposed phenotypic data acquisition method. We employed a random forest algorithm to classify the groups, achieving a classification accuracy of 96%, and identified 15 morphometric indices that exhibited statistically significant differences., of which 6 were related to head morphology, 6 related to body shape, and 3 related to tail morphology, based on the random forest algorithm. Specifically, river-living schizothoracine fishes showed a blunt head, robust body, and elongated caudal peduncle which may reflect adaptations to the turbulence of the river, while the lake-living schizothoracine fishes have the opposite effect. Schizothoracine fishes at the river–lake transitional zones were phenotypically characterized as being in the middle of the two phenotypes, and these presumably reflect adaptations to their lake habitat. This study provides a methodological reference for obtaining high-throughput phenotypic data on fish and a theoretical basis for understanding the adaptation of very high-altitude schizothoracine fishes to their environment.