Stock discrimination and connectivity assessment of yellowfin seabream (Acanthopagrus latus) in northern South China Sea using otolith elemental fingerprints

Abstract

Connectivity between fish stocks is fundamental to the understanding of population dynamics and the implementation of sustainable fisheries management. Otolith microchemistry is a promising tool as it can provide information on the continuous growth of otoliths and the environmental effects on otolith composition. Such elemental fingerprints can help distinguish different stocks or life history stages, identify the origins or nursery areas of fish, and assess population structure. In this study, we examined the stock discrimination and spatial connectivity of cage-cultured and wild stocks of yellowfin seabream (Acanthopagrus latus) from the coastal waters of Shantou, Yangjiang, and Zhanjiang in China southern province Guangdong during 2012-2014, based on otolith trace-elemental signatures using multivariate statistical analysis and machine learning approaches. The concentrations of 13 elements (Li, Na,Mg, Ca,Mn, Fe,Co, Ni,Cu, Zn,Sr, Sb, andBa) in the natal spot of fish otoliths, representing the embryonic and paralarval stages of fish, were analyzed using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Stepwise discriminant analysis and random forests were used to distinguish the cultured and wild stocks of yellowfin seabream, and non-metric multidimensional scaling (NMDS) and cluster analysis were used to determine the spatial variation and connectivity of yellowfin seabream stocks. Overall, the cultured and wild stocks of yellowfin seabream could be identified with classification accuracy of 80.7% and 99.2% by using stepwise discriminant analysis and random forests respectively. When we compared site difference between cultured and wild stocks (site × stock interactions), the classification success was 60.4% for stepwise discriminant analysis and 85.7% for random forests. The misclassification of cultured and wild stocks within the three sites suggested the spatial connectivity between stocks and among sampling locations. Our findings suggested that the three wild stocks of yellowfin seabream from Guangdong coastal waters could be considered as one unit for management, and the difference between cultured and wild stocks was significant for yellowfin seabream from Shantou and Yangjiang, but less significant for yellowfin seabream from Zhanjiang. This study demonstrated that otolith elemental fingerprints can help improve our knowledge on the spatial connectivity, population structure, and life history of fish stocks, and random forests can be a useful tool for identifying cultured and wild stocks compared to the traditional stepwise discriminant analysis.

Otolith fluctuating asymmetry inBoops boops(Actinopterygii, Sparidae) from two marine stations (Bizerte and Kelibia) in Tunisian waters

“…During the last decades, study of the morphological characteristics of otoliths, in particular their shape, length, width, area, thickness and weight, has been used to test both genetic and environmental stressors and as a bioindicator to test the differentiation of populations and the variance in stock discrimination studies using Elliptical Fourier analysis (EFA) (Valentine et al, 1973;Campana & Casselman, 1993;Begg & Waldman, 1999;Torres et al, 2000;Smith et al, 2002;Panfili et al, 2005;Turan, 2006;Jawad et al, 2010Jawad et al, , 2011Jawad et al, , 2012aJawad et al, , 2012bJawad et al, , 2012cJawad et al, , 2016Jawad et al, , 2020Vignon & Morat, 2010;Cañás et al, 2012;Jawad, 2012Jawad, , 2013Lord et al, 2012;Treinen-Crespo et al, 2012;Mahe et al, 2014;Trojette et al, 2014Trojette et al, , 2015Abu El-Regal et al, 2016;Brophy et al, 2016;Hüssy et al, 2016;Rebaya et al, 2016Rebaya et al, , 2017Al-Busaidi et al, 2017;Fatnassi et al, 2017;Ider et al, 2017;Khedher et al, 2017;Rodgveller et al, 2017;Chakour & Elouizgani, 2018;Khemiri et al, 2018;Kontaş et al, 2018;Mejri et al, 2018;Wang et al, 2018;…”

“…During the last decades, study of the morphological characteristics of otoliths, in particular their shape, length, width, area, thickness and weight, has been used to test both genetic and environmental stressors and as a bioindicator to test the differentiation of populations and the variance in stock discrimination studies using Elliptical Fourier analysis (EFA) (Valentine et al ., 1973; Campana & Casselman, 1993; Begg & Waldman, 1999; Torres et al ., 2000; Smith et al ., 2002; Panfili et al ., 2005; Turan, 2006; Jawad et al ., 2010, 2011, 2012 a , 2012 b , 2012 c , 2016, 2020; Vignon & Morat, 2010; Cañás et al ., 2012; Jawad, 2012, 2013; Lord et al ., 2012; Treinen-Crespo et al ., 2012; Mahe et al ., 2014; Trojette et al ., 2014, 2015; Abu El-Regal et al ., 2016; Brophy et al ., 2016; Hüssy et al ., 2016; Rebaya et al ., 2016, 2017; Al-Busaidi et al ., 2017; Fatnassi et al ., 2017; Ider et al ., 2017; Khedher et al ., 2017; Rodgveller et al ., 2017; Chakour & Elouizgani, 2018; Khemiri et al ., 2018; Kontaş et al ., 2018; Mejri et al ., 2018; 2020; Wang et al ., 2018; Ben Mohamed et al ., 2019; Fashandi et al ., 2019; Jmil et al ., 2019; Mahé et al ., 2019; Puentes et al ., 2019; Osman et al ., 2020). Indeed, these studies have shown that the otolith shape is species-specific (Sadighzadeh et al ., 2014) and that variability in the otolith shape, structure and development is influenced by ontogenetic, genetic and environmental factors (Campana & Casselman, 1993; Cardinale et al ., 2004; Vignon & Morat, 2010; Vignon, 2015; Hüssy et al ., 2016; Ider et al ., 2017; Fashandi et al ., 2019), as well as by sex, growth, maturity and pattern of fishery exploitation (Begg & Brown, 2000), or by individual characteristics, e.g.…”

Application of the analysis of trace elements composition for calcified structures of fish to solve fundamental and applied scientific tasks: a review

“…В некоторых случаях использование метода не всегда позволяет провести классификацию, что, например, было выявлено в результате анализа элементного состава отолитов кефали Mugil liza, собранной в 4 районах прибрежных вод Бразилии и Аргентины [Fortunato et al, 2017]. Также безуспешными были попытки идентификации потенциальных единиц запаса в следующих случаях: 1) при анализе выборок сельди Clupea harengus из 6 районов предполагаемого нагула молоди и 8 локальностей, предполагаемо значимых для нереста вида*, расположенных вдоль западного побережья Британских островов [Geffen et al, 2011]; 2) при классификации личинок, молоди и взрослых особей южноавстралийского синеперого тунца T. maccoyii, собранных соответственно в районах нереста (северо-восток Индийского океана), на путях миграции (южная Африка, западная и южная Австралия) и на промысле вида в открытом море близ юго-восточной Австралии ; 3) при попытке идентификации особей желтоплавникового морского карася Acanthopagrus latus из трех прибрежных районов северной части Южно-Китайского моря [Wang et al, 2018]; 4) при анализе пространственной структуры триглы Chelidonichthys lucerna из трех районов промысла вида в северо-западной части побережья Португалии [Ferreira et al, 2019]. Во всех случаях авторами был сделан вывод об отсутствии отдельных единиц запаса среди обследованных районов промысла, что необходимо соответствующим образом учитывать при организации вылова.…”

“…центральной Амазонки [Pereira et al, 2019]; белый басс M. chrysops водохранилища МакКонахи, Небраска, США [Perrion et al, 2020]; озерный осетр A. fulvescens р. Нельсон, Манитоба, США [Loeppky et al, 2020]. Среди морских рыб метод использовался при определении особей искусствен-ного происхождения в популяциях морского басса D. labrax и морского леща Sparus aurata западной части Средиземного моря [Arechavala-Lopez et al, 2016], а также в выборках морских карасей Acanthopagrus latus Южно-Китайского моря [Wang et al, 2018] и A. schlegelii прибрежных вод о. Тайвань [Chang et al, 2019]. В указанных работах применялся анализ содержания различных элементов в мальковой (личиночной) зоне различных кальцинированных структур, который позволил выделить уникальные химические маркеры для рыб искусственного происхождения, что легло в основу их идентификации в смешанной выборке.…”

Reproductive characteristics of the hermaphroditic yellowfin seabream Acanthopagrus latus in the waters off western Taiwan

“…Studies have been undertaken on the reproductive biology (Abol‐Munafi & Umeda, 1994; Abu‐Hakima, 1984; Hesp, Potter, & Hall, 2004; Hussain & Abdullah, 1977; Vahabnezhad, Kaymaram, Taghavi Motlagh, Valinassab, & Fatemi, 2016; Vahabnezhad et al., 2017), population structure (Ahmad‐Syazni, Tomano, Ueno, Ohara, & Umino, 2015; Ghasemi & Shadi, 2018; Wang, Wang, Lv, & Li, 2018; Xia, Huang, Gong, & Jiang, 2008), aquaculture (Karimi, Kochinian, & Salati, 2013; Leu & Chou, 1996; Leu, Chou, Wu, & Lin, 1991; Mustafa & Alkatrani, 2017; Naderi, Safahieh, Madiseh, Zolgharnein, & Ghatrami, 2015) and taxonomy (Iwatsuki, 2013; Jean, Hui, Lee, & Chen, 1995; Jean, Lee, & Chen, 1992; Lee, 1983) of A. latus . The age and growth of this species have also been examined using otolith ring counting or length frequency analysis in different geographical regions (Chiu, 2019; Hall, Hesp, & Potter, 2004; Mathews & Samuel, 1991; Morgan, 1985; Samuel & Mathews, 1987; Vahabnezhad et al., 2017), although large discrepancies in the maximum age or size observed and estimated growth parameters were found among studies.…”