Institutional Repository of Key Laboratory of Marine Ecology & Environmental Sciences, CAS
|Alternative Title||Otolith and Sulcus Morphology Analyses and Their Applications in Stock Discrimination of Three Sciaenids|
|Place of Conferral||中国科学院海洋研究所|
|Keyword||石首鱼 耳石 听沟 形态分析方法论 群体判别|
群体是渔业资源管理和濒危物种保护的基本单元。群体判别方法有很多，其中耳石形态分析具有简单、高效和成本低等优点，并且可以用于不同时空尺度下的群体结构研究。傅里叶变换和形状指数是两种常用的耳石形态分析方法。而小波变换是一种常用的信号处理方法，它不仅可以分析耳石的精细特征，还能同时定位特征区域，但是尚未广泛用于相关研究中。此外，传统的耳石形态分析集中于耳石的整体轮廓，而对于耳石上的一些特征结构的形态研究较少。其中，听沟是耳石内侧面上一条横向的凹槽结构，也是耳石与听觉纤毛相互作用的主要区域，其形态特征因种类或群体而异，也是群体识别的潜在指标。目前听沟形态学分析只应用于少量的鱼种识别等研究，用于鱼类群体判别的研究则更少，并且所采用的形态学指标也较简单。耳石或听沟的各种形态分析方法有其优缺点，应用效率各异。本研究以中国近海小黄鱼（Larimichthys polyactis）、黄姑鱼（Nibea albiflora）和白姑鱼（Pennahia argentata）的不同地理群体为研究对象，利用形状指数、椭圆傅里叶系数和离散小波系数，对比不同耳石形态分析方法的群体判别效率，并探索性地研究了听沟形态分析在鱼类群体判别中的应用，以期为科学地选择耳石形态分析方法、提高群体判别效率提供新方法和科学依据。
2）基于听沟形态分析（椭圆傅里叶系数与形状指数相结合）的三种石首鱼的群体判别成功率达到70.8%-80.4%，表明听沟形态分析与耳石形态分析类似，是一种有效的群体判别方法。二者在黄姑鱼（73.8% vs 73.8%）和白姑鱼群体（80.4% vs 86.5%）判别中的判别成功率相近，但在小黄鱼的群体判别成功率相差较大（70.8% vs 88.2%）。获取的听沟轮廓精度是影响群体判别成功率的一个重要因素。听沟与耳石其它部分的颜色差异小，且听沟轮廓需要借助软件手动描绘，这在一定程度上影响了提取听沟形态信息的精细度。因此，开发高效的听沟轮廓提取方法是提高基于听沟形态分析的群体判别效率的基础。
Understanding of stock is fundamental to fisheries management and endangered species conservation. Various methods have been applied to stock identification. Among them, otolith morphology analysis has its unique advantages including convenience, high efficiency and low cost. Additionally, otolith morphology analysis can be used for stock identification at different tempo-spatial scales. Wavelet transform is commonly used in signal processing. When adopted in otolith morphology, it can not only extract fine otolith information, but also locate the feature region. Compared to other two methods, Fourier transform and shape indices, wavelet transform has not been widely adopted in otolith morphology analysis. Traditional otolith morphology analysis commonly focuses on the whole otolith outlines, whereas some characteristic structures are less concerned. The sulcus is a longitudinal depression on the medial side of the otolith. It is also the major area where otolith interacts with auditory cilia. The morphology of sulcus differs among species or geographic stocks, and therefore has potentials in discriminating stocks. So far, there are few studies concerning sulcus morphology analysis, which generally deal with species identification. Moreover, the adopted parameters for sulcus morphology are also simpler than otolith morphology. Different methods of otolith or sulcus have their own merits or demerits, and thus the efficiency of these methods in stock discrimination also varies. The present study analyzed and evaluated the efficiency of different otolith morphology analyses in stock discrimination of three sciaenids along Chinese coast: the small yellow croaker (Larimichthys polyactis), the yellow drum (Nibea albiflora) and the white croaker (Pennahia argentata). They included shape indices, elliptic Fourier coefficients and discrete wavelet coefficients. In addition, the feasibility of adopting sulcus morphology analysis for stock discrimination was investigated and evaluated. The main goal of this study was to provide new insights into selecting different methods of otolith morpholy analysis and to improve stock discrimination.
The main results are as follows:
1) Whether using it alone or combining it with shape indices, the stock discrimination rate of elliptic Fourier coefficients or discrete wavelet coefficients was on the same level (differences ≤3.0%), which is higher than that obtained by using shape indices alone (≥16.1%). This indicated that elliptic Fourier transform and discrete wavelet transform could extract fine information of otolith morphology at comparable levels, whereas shape indices had less ability to describe the fine structure of otolith morphology. When combining different parameters, the stock discrimination rates were commonly promoted. Different types of parameters could extract different otolith information, which might be complementary to each other. Combining them could extract more and finer otolith morphology information than using them alone, and thus promoted the stock discrimination efficiency.
2) When adopting sulcus morphology analysis (combing elliptic Fourier coefficients and shape indices) for discriminating stocks of the three sciaenids, the overall discrimination rates reach 70.8%-80.4%. This indicated that sulcus morphology analysis can discriminate stocks as effective as otolith morphology analysis. The efficiency of these two methods in discriminating stocks of yellow drum (73.8% vs 73.8%) and white croaker (80.4% vs 86.5%) were similar, whereas they showed relatively large difference in the small yellow croaker (70.8% vs 88.2%). The accuracy of acquiring sulcus outline was a decisive factor that could affect stock discrimination rate. Since the contrast between sulcus and other parts of otolith was usually low, it was sometimes difficult to identify sulcus outlines automatically by the software. Manually identifying and depicting the outlines could limit the accuracy of sulcus outline. Therefore, it is crucial to develop effective methods for extracting fine sulcus outline, which could improve the stock discrimination efficiency using sulcus morphology analysis.
3) When comparing the reconstructed mean otolith outlines and wavelet coefficients variance among stocks, it was found that major differences along otolith outlines corresponded to large variance of wavelet coefficients. There were two levels of wavelet coefficients that were adopted to compare the variance among stocks. The variances that existed in level four wavelet coefficients were consistent with those in otolith outlines. But in level five, several small inconsistences were found between them. These might be determined by the property of wavelet transform: finer scale (level 5) of wavelet coefficients were more susceptible to “noise signals”. In this study, the noise signals could be induced by variances of otolith outlines among different individuals in each stock. Overall, wavelet transform could locate otolith feature regions well, and this could not be achieved by Fourier transform.
4) In the otolith morphology analysis by elliptic Fourier coefficients, the number of Fourier harmonics adopted in each of three sciaenids was 10 (small yellow croaker), 10 (yellow drum) and 9 (white croaker). These Fourier harmonics explained more than 99.99% of the total Fourier power. By reconstructing the otolith outline based on elliptic Fourier harmonics, the change of otolith outline caused by adding each harmonic was obvious. It could also be found that the adopted Fourier harmonics could describe the otolith outline delicately. In the sulcus morphology analysis, the number of elliptic Fourier harmonincs adopted in each of three sciaenids was 12 (small yellow croaker), 9 (yellow drum) and 14 (white croaker). This indicated that the sulcus outlines of three sciaenids were more complicated than the corresponding otolith outlines. Generally, it could be validated whether the number of Fourier harmonics adopted in the study was appropriate by reconstructing the otolith and sulcus outlines. Moreover, the features along otolith and sulcus outlines could also be accurately depicted.
5) The differences of otolith and sulcus morphometrics (weight, length, width, perimeter, area and relative area ratio) were significant (P<0.05) among the stocks of three sciaenids. Overall, the morphometrics of Changjiang Estuary stock were usually the largest. The deposition of otolith is regulated by genetics but are also influcenced by environmental factors. The conventional morphometrics of otolith and sulcus could provide biologically significant indicators for describing differences among geographical stocks and reflect habitat differences. This makes otolith and sulcus morphometrics useful parameters for stock discrimination.
|MOST Discipline Catalogue||理学::海洋科学|
|First Author Affilication||Institute of Oceanology, Chinese Academy of Sciences; Key Laboratory of Marine Ecology & Environmental Sciences, CAS|
|宋骏杰. 耳石和听沟形态分析方法及其在三种石首科鱼类群体判别中的应用[D]. 中国科学院海洋研究所. 中国科学院大学,2018.|
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