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Identification of Fish Families and Species from the Western Arabian Gulf by Otolith Shape Analysis and Factors Affecting the Identification Process

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Identification of Fish Families and Species from the Western Arabian Gulf by Otolith Shape Analysis and Factors Affecting the Identification Process SPECIAL ISSUE CSIRO PUBLISHING Marine and Freshwater Research, 2019, 70, 1818–1827 https://doi.org/10.1071/MF18282 Identification of fish families and species from the western Arabian Gulf by otolith shape analysis and factors affecting the identification process Yu-Jia LinA,C and Khaled Al-AbdulkaderB AMarine Studies Section, Center for Environment and Waters, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Eastern Province, Kingdom of Saudi Arabia. BEnvironmental Protection Department, Saudi Aramco, Dhahran 31261, Eastern Province, Kingdom of Saudi Arabia. CCorresponding author. Email: [email protected]; [email protected] Abstract. Otolith shape analysis was used to identify 16 fish species belong to 5 families from the western Arabian Gulf to construct a cost-effective method of delineating fish taxonomic groups. We further tested the factors potentially affecting the identification process, including using different dataset sources, sex, the number of candidate species, different sample sizes and different sampling procedures. No specific dataset outperformed any other in the identification of fish families and species. Using all data sources yielded the best performance. Otolith shape parameters were significantly affected by somatic length, but not by sex. The correct prediction rate declined as the number of candidate species increased. An insufficient sample size led to a reduction in correct prediction rates with increased variability. The effects of size-biased sampling were species specific and could greatly reduce the correct prediction rate if the species of interest exhibits strong allometric changes in otolith shape. Having multiple sources of data, information a priori to reduce the number of candidate species and sufficiently large sample sizes across wide size classes so as to include possible variations in otolith shape are key to the precise identification of fish families and species using otolith shape analysis. Additional keywords: linear discriminant analysis, sampling procedure, species identification, wavelet transformation. Received 1 August 2018, accepted 2 April 2019, published online 3 July 2019 Introduction et al. 2018) and numerous analytical approaches have been Otolith shape analysis is a well-established method of delineating developed (e.g. Tuset et al. 2003, 2016; Ponton 2006; Wakefield fish stocks, populations, species and higher taxonomic groups. It et al. 2014; Libungan and Pa´lsson 2015). The performance of is fairly cost-effective and requires only otolith images containing these methods in identifying fish groups is often case specific information about shape, outline and landmarks. The analysis (Ponton 2006), and it remains unclear whether incorporating programs are usually built in a free software environment with different sources of information would enhance the identifica- introductory protocols (e.g. the shapeR package, see https://cran. tion performance of these methods. Second, identification r-project.org/package=shapeR, in R, ver. 3.4.2, R Foundation performance may be affected by the number of candidate for Statistical Computing, Vienna, Austria, see https://www. species. Lombarte et al. (2018) suggested an inverse relation- R-project.org; Libungan and Pa´lsson 2015), which facilitates ship between the correct identification rate and the number of wide application. The requirement for digital images only further candidate species based on a summary of six studies. However, enables large electronic catalogues to be established and auto- these studies differed in methodology and collection sites, and mated taxon identification systems to be developed that can be the relationship has not been tested in a systematic manner using accessed by the general public (e.g. the Ana`lisi de Formes the same methodology in a single study. Third, the sample sizes d’Oto`lits (AFORO) database, http://isis.cmima.csic.es/aforo/, used for model construction vary considerably among studies, accessed 31 July 2018; Lombarte et al. 2006). from 10 otoliths for each species in the study of Salimi et al. Although otolith shape analysis is a promising tool for the (2016) up to 136 otoliths for the goby Sicyopterus lagocephalus identification of fish groups, several factors may hinder its in the study of Lord et al. (2012). Fourth, otolith shape may successful use, the first being the selection of available analyti- change allometrically with somatic growth (Monteiro et al. cal methods. Differences in otolith shape have been used to 2005; Vignon 2012). Therefore, sufficient sample sizes with identify teleost species for over 130 years (Nolf 2013; Lombarte proper sampling procedures, such as size-stratified random Journal Compilation Ó CSIRO 2019 Open Access CC BY-NC-ND www.publish.csiro.au/journals/mfr Factors affecting otolith shape analysis Marine and Freshwater Research 1819 Table 1. Summary table for the fish collected from the western Arabian Gulf LT, total length Family Species Sample size LT (mm) Mean Æ s.d. Range Lethrinidae Lethrinus borbonicus 122 189 Æ 49 57–297 Lethrinus lentjan 133 281 Æ 63 188–414 Lethrinus microdon 60 316 Æ 90 174–556 Lethrinus nebulosus 105 419 Æ 124 188–612 Nemipteridae Nemipterus bipunctatus 124 183 Æ 58 49–338 Nemipterus japonicus 198 220 Æ 54 79–334 Nemipterus peronii 115 159 Æ 37 79–244 Nemipterus randalli 80 158 Æ 33 56–217 Serranidae Cephalopholis hemistiktos 180 248 Æ 54 108–413 Epinephelus areolatus 164 282 Æ 62 171–444 Epinephelus coioides 118 508 Æ 121 249–775 Sparidae Argyrops spinifer 112 285 Æ 131 155–637 Rhabdosargus haffara 149 217 Æ 23 169–295 Sparidentex hasta 50 422 Æ 130 256–670 Synodontidae Saurida macrolepis 115 179 Æ 51 86–317 Saurida tumbil 128 314 Æ 105 94–523 sampling, could ensure that variations in otolith shape due to Serranidae, seabream Argyrops spinifer, Rhabdosargus haffara ontogenetic changes are included in the samples. It is of and Sparidentex hasta from Sparidae and lizardfish Saurida practical importance to know how different sample sizes and macrolepis and Saurida tumbil from Synodontidae. After thaw- sampling procedures may affect identification performance and ing, the total length and weight were measured to the nearest to design a best practice that balances the trade-off between 1 mm and 0.1 g. The largest pair of otoliths, the sagittae, was sampling costs and identification precision. removed from the fish, cleaned in fresh water, dried in air and The primary objective of this study was to identify fish stored in sealed plastic vials. families and species from the western Arabian Gulf using otolith shape analysis. Second, we tested the effects of several factors Otolith image processing, outline extraction and wavelet on identification performance, including the use of different transformation data sources, sex as a possible confounding factor (Cardinale A stratified random sampling scheme was used to fully represent et al. 2004), the number of candidate species, different sample the size distribution of the population. The fish were ranked by sizes and different sampling procedures with regard to somatic somatic size and classified into three size strata, namely small, sizes. Four data sources were extracted from otolith shape: medium and large, covering the lower, middle and upper thirds of otolith measurement, shape indices (Tuset et al. 2003), coeffi- the somatic length rank respectively. Between 17 and 60 speci- cients from wavelet transformation (Libungan and Pa´lsson mens were randomly drawn from each stratum. In all, 1953 fish 2015) and relative otolith length. Fisher’s linear discriminant specimens were collected, with sufficient coverage in length that analysis was used to analyse the multivariate data and the fish were sampled from juveniles to large adults (Table 1). classification of fish families and species. The otoliths of some fish species become greatly curved towards the external face and their three-dimensional (3D) Materials and methods structures become more apparent as the fish grows. Thus, it Sample collection was not feasible to obtain otolith photographs in which all the Fish specimens were collected from bottom trawl surveys otoliths were positioned in a perfect horizontal plane with the (Rabaoui et al. 2015, 2017) and visits to major landing sites sulcus acusticus facing the observer, as done by Lombarte et al. under a landing site monitoring program (Rabaoui et al. 2017). (2018). Alternatively, the otoliths were placed horizontally with The trawl surveys involved a commercial outrigger trawler in the external plane of the otolith facing towards the observer the western Arabian Gulf in 2013 and 2016. The landing site (Fig. 1), as described by Libungan and Pa´lsson (2015). The monitoring program examined 12 fish and invertebrate stocks of otoliths were observed against a black background with a major commercial importance in 2013 and 2014. reflected light source under a dissection microscope attached In this study, we selected 16 species from 5 families of to a digital camera (DP72; Olympus, Center Valley, PA, USA). commercial interest: emperor fish Lethrinus nebulosus, Lethri- The images were captured and digitised using CellSens nus lentjan, Lethrinus microdon and Lethrinus borbonicus Standard software (ver. 1.5, Olympus). from Lethrinidae, threadfin bream Nemipterus bipunctatus, Extraction of the otolith outline, contour smoothing, mea- Nemipterus japonicus, Nemipterus peronii and Nemipterus surement, generation of shape coefficients (wavelet transforma- randalli
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