Skip to main content

Advertisement

Log in

Growth Properties of Pseudorasbora parva in Süreyyabey Reservoir: Traditional and Artificial Intelligent Methods

  • Published:
Thalassas: An International Journal of Marine Sciences Aims and scope Submit manuscript

Abstract

Pseudorasbora parva (Temminck and Schlegel, 1846) is a small nan-native Cyprinidae species which lives in shallow lakes, pools, irrigation canals, and rivers. Samples (550 specimens: 233 females and 317 males) were collected in 2016 from Süreyyabey Reservoir. Length and weight values were measured and then compared with traditional (Length-Weight Relationship and von Bertalanfy) and Artificial Intelligent (Artificial Neural Networks) methods for growth assessment. The results of the study were examined by the artificial neural networks approach and traditional estimation method. This approach would be useful for sustainable fisheries management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Andrews R, Diederich J, Tickle AB (1995) Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowl-Based Syst 8(6):373–389

    Article  Google Scholar 

  • Bagenal TB, Tesch FW (1978) Age and growth. In: Bagenal T (ed) Methods for assessment of fish production in fresh waters. IBP handbook no. 3. Blackwell Scientific Publications, Oxford, pp 101–136

    Google Scholar 

  • Benzer R (2014) Population dynamics forecasting using artificial neural networks. Fresenius Environ Bull 12:1–15

    Google Scholar 

  • Benzer S, Benzer R (2018) New perspectives for predicting growth properties of crayfish (Astacus leptodactylus Eschscholtz, 1823) in Uluabat Lake. Pakistan J Zool 50(1):35–35

    Google Scholar 

  • Benzer R, Benzer S (2019) Growth and length–weight relationships of Pseudorasbor aparva (Temminck and Schlegel, 1846) in Hirfanlı dam Lake: comparison with traditional and artificial neural networks approaches. Iran J Fish Sci. https://doi.org/10.22092/ijfs.2018.119889

  • Benzer S, Benzer R, Gül A (2016) Artificial neural network applications for biological systems: the case study of Pseudorasbora parva. Developments in Science and Engineering. St. Kliment Ohridski University Press

  • Bianco PG (1998) Freshwater fish transfers in Italy: history, local modification of fish composition, and a prediction on the future of native populations. In: Cowx IG (ed) Stocking and introductions of fishes. Fishing New Book, Blackwell, Oxford, pp 165–197

    Google Scholar 

  • Bobori DC, Moutopoulos DK, Bekri M, Salvarina I, Munoz AIP (2010) Length-weight relationships of freshwater fish species caught in three Greek lakes. J Biol Res 14:219–224

    Google Scholar 

  • Bon AT, Hui HS (2017) Industrial engineering solution in the industry: artificial neural network forecasting approach. Proceedings of the International Conference on Industrial Engineering and Operations Management Rabat, Morocco, April 11-13, 2017

  • Ekmekçi FG, Kırankaya GŞ (2006) Distribution of an invasive fish species Pseudorasbora parva (Temminck and Schlegel. 1846) in Turkey. Turk J Zool 30:329–334

    Google Scholar 

  • Esmaeili HR, Teimori A, Gholami Z, Reichenbacher B (2014) Two new species of the tooth-carp Aphanius (Teleostei: Cyprinodontidae) and the evolutionary history of the Iranian inland and inland-related Aphanius species. Zootaxa 3786:246–268

    Article  Google Scholar 

  • Fan LQ, Zhang XJ, Pan G (2015) Length–weight and length–length relationships for nine fish species from Lhasa River basin, Tibet, China. J Appl Ichthyol 31:807–808

    Article  Google Scholar 

  • Froese R (1998) Length-weight relationships for 18 less-studied fish species. J Appl Ichthyol 14:117–118

    Article  Google Scholar 

  • Hopgood AA (2000) Intelligent Systems for Engineers and Scientists. CRC Press, Florida, p 461

    Book  Google Scholar 

  • Huo TB, Jiang ZF, Karjan A, Wang ZC, Tang FJ, Yu HX (2012) Length– weight relationships of 16 fish species from the Tarim River, China. J Appl Ichthyol 28:152–153

    Article  Google Scholar 

  • İlhan A, Sarı HM (2015) Length-weight relationships of fish species in Marmara Lake, West Anatolia, Turkey. Croatian Journal of Fisheries 73(1):30–32

    Article  Google Scholar 

  • Keivany Y, Nasri M, Abbasi K, Abdoli A (2015) Atlas of inland water fishes of Iran. Iran Department of Environment Press, Tehran

    Google Scholar 

  • Kırankaya ŞG, Ekmekçi FG, Yalçın Özdilek Ş, Yoğurtçuoğlu B, Gençoğlu B (2014) Condition, length-weight and length-length relationships for five fish species from Hirfanlı reservoir, Turkey. Journal of FisheriesSciencescom 8(3):208–213

    Google Scholar 

  • Krenker A, Bešter J, Kos A (2011) Introduction to the artificial neural networks, artificial neural networks - methodological advances and biomedical applications, Kenji Suzuki (Ed.), ISBN: 978-953-307-243-2

  • Lagler KF (1966) Freshwater fishery biology. W.M.C. Brown Company, Dubuque, p 421

    Google Scholar 

  • Le Cren ED (1951) The length-weight relationship and seasonal cycle in gonad weight and condition in the perch (Perca fluviatilis). J Anim Ecol 20(2):201–219

    Article  Google Scholar 

  • Lin F, Zhang J, Fan LQ, Li SQ, Zhou QH (2017) Length–weight relationship of 12 fish species from the Lhasa River and surrounding area in Tibet, China. J Appl Ichthyol 33(5):1047–1050

    Article  Google Scholar 

  • Matlab (2006) The MathWorks, Inc. Matlab Help. MATLAB

  • Onikura N, Nakajima J (2013) Age, growth and habitat use of the topmouth gudgeon, Pseudorasboraparva in irrigation ditches on northwestern Kyushu Island, Japan. J Appl Ichthyol 29:186–192

    Article  Google Scholar 

  • Ozcan EI, Serdar O (2018) Artifical neural networks as new alternative method to estimating some population parameters of tigris loach (Oxynoemacheilus tigris (Heckel, 1843)) in the Karasu River, Turkey. Fresenius Environ Bull 27:9840–9850

    Google Scholar 

  • Ozcan EI, Serdar O (2019) Evaluation of a new computer method (ANNs) and traditional methods (LWRS and VBGF) in the calculation of some growth parameters of two cyprinid species. Fresenius Environ Bull 28(10):7644–7654

    Google Scholar 

  • Ricker WE (1973) Linear regressions in fishery research. J Fish Res Board Can 30:409–434

    Article  Google Scholar 

  • Ricker WE (1975) Computation and interpretation of biological statistics of fish populations. Bulletin - Fisheries Research Board of Canada pp 382

  • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL, PDP Research Group (eds) Parallel distributed processing: explorations in the microstructure of cognition, vol. 1. MIT Press, Cambridge, pp 318–362

  • Sparre P, Venema SC (1992) Intoduction to tropical fish stock assessment. Part 1 manual. FAO fisheries technical paper no 306. (rev. 1). FAO, Rome, p 376

    Google Scholar 

  • Stavrescu-Bedivan MM, Aioanei FT, Scăeţeanu GV (2017) Length-weight relationships and condition factor of 11 fish species from the Timiş River, Western Romania. Poljopr šumar 63(4):281–285

    Google Scholar 

  • Tureli Bilen C, Kokcu P, Ibrikci T (2011) Application of artificial neural networks (ANNs) for weight predictions of blue crabs (Callinectes sapidus Rathbun, 1896) using predictor variables. Mediterr Mar Sci 12(2):439–446

    Article  Google Scholar 

  • Wang T, Wang HS, Sun GW, Huang D, Shen JH (2012) Length-weight and length-length relationships for some Yangtze River fishes in Tian-e-Zou oxbow, China. J Appl Ichthyol 28:660–662

    Article  Google Scholar 

  • Wildekamp RH, Van Neer W, Küçük F, Ünlüsayın M (1997) First record of the eastern Asiatic gobionid fish Pseudorasbora parva from the asiatic part of Turkey. J Fish Biol 51(4):858–868

    Google Scholar 

  • Witkowski A. (2006) Nobanis-invasive alien species fact sheet-Pseudorasbora parva from: online database of the north European and Baltic network on invasive alien species –www.nobasis.org

  • Yağcı A, ApaydınYağcı M, Bostan H, Yeğen V (2014) Distribution of the topmouth gudgeon, Pseudorasbora parva (Cyprinidae:Gobioninae) in Lake Eğirdir, Turkey. Journal of Survey in Fisheries Sciences 1(1):46–55

    Article  Google Scholar 

  • Záhorská E, Kováč V, Katina S (2010) Age and growth in a newly established invasive population of topmouth gudgeon. Cent Eur J Biol 5(2):256–261

    Google Scholar 

Download references

Acknowledgments

We would like to express our gratitude to the referees for their help and support to our research with their reviews and recommendations. This study has been accepted for oral presentation at International Conference on Civil and Environmental Engineering in 2018 (ICOCEE 2018, İzmir, Çeşme Turkey).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Semra Benzer.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Benzer, S., Benzer, R. Growth Properties of Pseudorasbora parva in Süreyyabey Reservoir: Traditional and Artificial Intelligent Methods. Thalassas 36, 149–156 (2020). https://doi.org/10.1007/s41208-020-00192-1

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41208-020-00192-1

Keywords

Navigation