Abstract
The early stages of fish during their life cycle, including larvae and juveniles, are sensitive to the environment. Determining the occurrences of fish larvae and juvenile relative to their associated environments is essential for conservation and fisheries management. Computer-based modeling has rarely been applied for forecasting the distribution patterns of the early fish stages in dynamic systems such as estuaries. In the present study, we applied novel modeling techniques to fish larval and juvenile samples collected in May, September, November, and December during 2019 along the Ba Lat estuary of the Red River, northern Vietnam. The results showed that the occurrences of freshwater and marine fish larvae and juveniles were inversely related to environmental factors (electrical conductivity, temperature, pH, depth, shore distance and turbidity) with a high square of multiple correlation coefficients. The occurrences of the two fish groups were strongly related to temporal and spatial changes in the estuary, and these correlations could be utilized for machine learning processing. Linear regression, Gaussian process models, ensemble regression, and artificial neural network (ANN) models were applied to elucidate the distributions of fish larvae and juveniles. It shows that ANN models obtained the highest R2 (> 0.63). In addition, the spatial distribution prediction of fish larvae and juveniles using ANN models was similar to the field measurement. Thus, we suggest utilizing ANN models to predict the occurrences of early fish stages in estuaries in tropical regions such as Vietnam. Recommendations for further applications of ANN models are also given in this study.
Similar content being viewed by others
References
Aalen OO (1989) A linear regression model for the analysis of life times. Stat Med 8:907–925. https://doi.org/10.1002/sim.4780080803
Anderson MJ, Millar RB (2004) Spatial variation and effects of habitat on temperate reef fish assemblages in northeastern New Zealand. J Exp Mar Biol Ecol 305:191–221. https://doi.org/10.1016/j.jembe.2003.12.011
Blaber SJM, Blaber TG (1980) Factors affecting the distribution of juvenile estuarine and inshore fish. J Fish Biol 17:143–162
Brind’Amour A, Boisclair D, Dray S, Legendre P (2011) Relationships between species feeding traits and environmental conditions in fish communities: a three-matrix approach. Ecol Appl 21:363–377. https://doi.org/10.1890/09-2178.1
Brosse S, Guegan J-F, Tourenq J-N, Lek S (1999a) The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake. Ecol Modell 120:299–311. https://doi.org/10.1016/S0304-3800(99)00110-6
Brosse S, Lek S, Dauba F (1999b) Predicting fish distribution in a mesotrophic lake by hydroacoustic survey and artificial neural networks. Limnol Oceanogr 44:1293–1303. https://doi.org/10.4319/lo.1999.44.5.1293
Burrows MT (2012) Influences of wave fetch, tidal flow and ocean colour on subtidal rocky communities. Mar Ecol Prog Ser 445:193–207. https://doi.org/10.3354/meps09422
Cohen AH, Wallén P (1980) The neuronal correlate of locomotion in fish. Exp Brain Res 41:11–18. https://doi.org/10.1007/BF00236674
Colombo RE, Phelps QE, Garvey JE et al (2008) Gear-specific population demographics of channel catfish in a large Midwestern River. N Am J Fish Manag 28:241–246. https://doi.org/10.1577/M06-200.1
Costa MJ, Cabral HN, Drake P, Economou AN, Fernandez-Delgado C, Gordo L, Marchand J, Thiel R (2002) Recruitment and production of commercial species in estuaries. In: Elliott Michael, Hemingway Krystal (eds) Fishes in Estuaries. Blackwell Science Ltd, Oxford. https://doi.org/10.1002/9780470995228.ch3
Davidson TA, Sayer CD, Perrow M et al (2010) The simultaneous inference of zooplanktivorous fish and macrophyte density from sub-fossil cladoceran assemblages: a multivariate regression tree approach. Freshw Biol 55:546–564. https://doi.org/10.1111/j.1365-2427.2008.02124.x
Froese R, Pauly D (Eds) (2021) FishBase. World Wide Web Electronic Publication
Franceschini S, Gandola E, Martinoli M et al (2018) Cascaded neural networks improving fish species prediction accuracy: the role of the biotic information. Sci Rep 8:4581. https://doi.org/10.1038/s41598-018-22761-4
Friedlander AM, Brown EK, Jokiel PL et al (2003) Effects of habitat, wave exposure, and marine protected area status on coral reef fish assemblages in the Hawaiian archipelago. Coral Reefs 22:291–305. https://doi.org/10.1007/s00338-003-0317-2
Fujita S, Kinoshita I, Takahashi I, Azuma K (2002) Species composition and seasonal occurrence of fsh larvae and juveniles in the Shimanto estuary, Japan. Fish Sci 68:364–370
Gelfand AE, Holder M, Latimer A et al (2006) Explaining species distribution patterns through hierarchical modeling. Bayesian Anal 1:41–92. https://doi.org/10.1214/06-BA102
Graham MH (2003) Confronting multicollinearity in ecological multiple regression. Ecology 84:2809–2815. https://doi.org/10.1890/02-3114
Grober-Dunsmore R, Frazer TK, Beets JP et al (2008) Influence of landscape structure on reef fish assemblages. Landsc Ecol 23:37–53. https://doi.org/10.1007/s10980-007-9147-x
Guerreiro MA, Martinho F, Baptista J et al (2021) Function of estuaries and coastal areas as nursery grounds for marine fish early life stages. Mar Environ Res. https://doi.org/10.1016/j.marenvres.2021.105408
Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Modell 135:147–186. https://doi.org/10.1016/S0304-3800(00)00354-9
Guisan A, Edwards TC, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Modell 157:89–100. https://doi.org/10.1016/S0304-3800(02)00204-1
Guo C, Lek S, Ye S et al (2015) Uncertainty in ensemble modelling of large-scale species distribution: effects from species characteristics and model techniques. Ecol Modell 306:67–75. https://doi.org/10.1016/j.ecolmodel.2014.08.002
Gutiérrez-Estrada JC, Vasconcelos R, Costa MJ (2008) Estimating fish community diversity from environmental features in the Tagus estuary (Portugal): multiple linear regression and artificial neural network approaches. J Appl Ichthyol 24:150–162. https://doi.org/10.1111/j.1439-0426.2007.01039.x
Ha ML, Tran DH, Hoang QL (2019) Distribution pattern of larvae and juveniles of Ambassis vachellii at Ba Lat estuary and Xuan Thuy National Park, Nam Dinh Province, Vietnam. Publishing House for Science and Technology: 135–143
He P, Li S, Xiao J et al (2018) Shallow sliding failure prediction model of expansive soil slope based on Gaussian process theory and its engineering application. KSCE J Civ Eng 22:1709–1719. https://doi.org/10.1007/s12205-017-1934-6
Hobday AJ (2010) Ensemble analysis of the future distribution of large pelagic fishes off Australia. Prog Oceanogr 86:291–301. https://doi.org/10.1016/j.pocean.2010.04.023
Hosseini S (2020) A new machine learning method consisting of GA-LR and ANN for attack detection. Wirel 26:4149–4162. https://doi.org/10.1007/s11276-020-02321-3
Jacob S, Banerjee R (2016) Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm. Bioresour Technol 214:386–395. https://doi.org/10.1016/j.biortech.2016.04.068
Jeyaseelan PMJ (1998) Manual of fish eggs and larval from Asian mangrove waters. UNESCO, France
Johnson MK, Holbrook SJ, Schmitt RJ, Brooks AJ (2011) Fish communities on staghorn coral: effects of habitat characteristics and resident farmerfishes. Environ Biol Fish 91:429–448. https://doi.org/10.1007/s10641-011-9802-6
Joy MK, Death RG (2002) Predictive modelling of freshwater fish as a biomonitoring tool in New Zealand. Freshw Biol 47:2261–2275. https://doi.org/10.1046/j.1365-2427.2002.00954.x
Juntunen T, Vanhatalo J, Peltonen H, Mäntyniemi S (2012) Bayesian spatial multispecies modelling to assess pelagic fish stocks from acoustic- and trawl-survey data. ICES J Mar Sci 69:95–104. https://doi.org/10.1093/icesjms/fsr183
Kendall AW (ed) (2011) Identification of eggs and larval of marine fishes. National Museum of Nature and Science, Tokyo
Kendall AW, Ahlstrom EHJr, Moser HG (1984) Early life history stages of fishes and their characters. In: Ontogeny and systematic of fishes, Moser HG, Richards WJ, Cohen DM et al Eds. Am Soc Ichthyol Herpetol, Spec Publ 1, 11–22
Kimes DS, Nelson RF, Manry MT, Fung AK (1998) Review article: attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements. Int J Remote Sens 19:2639–2663. https://doi.org/10.1080/014311698214433
Kinoshita I (1986) Postlarvae and juveniles of silver sea bream, Sparus sarba occurring in the surf zones of Tosa Bay, Japan. Jpn J Ichthol 33:7–12. https://doi.org/10.1007/BF02905553
Kinoshita I, Fujita S, Takahashi I, Azuma K (1988) Occurrence of larval and juvenile Japanese snook, Lates japonicus, in the Shimanto Estuary, Japan. Jpn J Ichthol 34:462–467. https://doi.org/10.1007/BF02905651
Kneib T, Müller J, Hothorn T (2008) Spatial smoothing techniques for the assessment of habitat suitability. Environ Ecol Stat 15:343–364. https://doi.org/10.1007/s10651-008-0092-x
Leathwick JR, Rowe D, Richardson J, Hastie ET (2005) Using multivariate adaptive regression splines to predict the distributions of New Zealand’s freshwater diadromous fish. Freshw Biol 50:2034–2052. https://doi.org/10.1111/j.1365-2427.2005.01448.x
Lu X, Zhou W, Ding X et al (2019) Ensemble learning regression for estimating unconfined compressive strength of cemented paste backfill. IEEE Access 7:72125–72133. https://doi.org/10.1109/ACCESS.2019.2918177
Dao MS, Nguyen DT, Nguyen QH (Eds) (2008) The environmental impact assessment of shrimp farming development in the core zone of Xuan Thuy National Park (Nam Dinh, Vietnam). Technical Report https://doi.org/10.13140/RG.2.1.2851.0245:72
Muñoz F, Pennino MG, Conesa D et al (2013) Estimation and prediction of the spatial occurrence of fish species using Bayesian latent Gaussian models. Stoch Environ Res Risk Assess 27:1171–1180. https://doi.org/10.1007/s00477-012-0652-3
Murase H, Nagashima H, Yonezaki S et al (2009) Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of pelagic fish and krill: a case study in Sendai Bay, Japan. ICES J Mar Sci 66:1417–1424. https://doi.org/10.1093/icesjms/fsp105
Nelson JS, Grande TC, Wilson MVH (2016) Fishes of the world, 5th edn. Wiley, Hoboken
Nguyen VH (2005) Freshwater fishes of Vietnam, vol 3. Aquaculture Publishing House, Ha Noi
Nguyen VH, Ngo SV (2001) Freshwater fishes of Vietnam. Agriculture Publishing House, HaNoi
Nguyen HD, Ngo TMH, Tran DH (2019) List of fish in the Hong River, Viet Nam. In: Proceedings of the First National Conference on Ichthyology in Vietnam. Proceedings of the First National Conference on Ichthyology in Vietnam Publishing House for Science and Technology 91–98
Odigie JO, Olomukoro J (2020) Ecological modelling using artificial neural network for macroinvertebrate prediction in a tropical rainforest river. Int J Environ Waste Manag 26:325–248
Okiyama M (ed) (2013) An atlas of the early stage fishes in Japan. Tokai University Press, Hadano
Olden JD, Joy MK, Death RG (2006) Rediscovering the species in community-wide predictive modeling. Ecol Appl 16:1449–1460. https://doi.org/10.1890/1051-0761(2006)016[1449:RTSICP]2.0.CO;2
Pittman SJ, Brown KA (2011) Multi-scale approach for predicting fish species distributions across coral reef seascapes. PLoS ONE 6:e20583. https://doi.org/10.1371/journal.pone.0020583
Poulos HM, Chernoff B, Fuller PL, Butman D (2012) Ensemble forecasting of potential habitat for three invasive fishes. Aquatic Invasions 7
R Core Team (2021) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org
Ranganathan A, Yang M-H, Ho J (2011) Online sparse Gaussian process regression and its applications. IEEE Trans Image Process 20:391–404. https://doi.org/10.1109/TIP.2010.2066984
Rueda M (2001) Spatial distribution of fish species in a tropical estuarine lagoon: a geostatistical appraisal. Mar Ecol Prog Ser 222:217–226. https://doi.org/10.3354/meps222217
Rutherford DA, Gelwicks KR, Kelso WE (2001) Physicochemical effects of the flood pulse on fishes in the Atchafalaya River Basin, Louisiana. Trans Am Fish Soc 130:276–288. https://doi.org/10.1577/1548-8659(2001)130%3c0276:PEOTFP%3e2.0.CO;2
Santen PV, Augustinus PGEF, Janssen-Stelder BM et al (2007) Sedimentation in an estuarine mangrove system. J Asian Earth Sci 29:566–575
Sanvicente-Añorve L, Sánchez-Ramírez M, Ocaña-Luna A et al (2011) Metacommunity structure of estuarine fish larvae: the role of regional and local processes. J Plankton Res 33:179–194. https://doi.org/10.1093/plankt/fbq098
Shuai F, Li X, Li Y, Li J, Yang J, Lek S (2016) Temporal patterns of larval fish occurrence in a large subtropical river. PLoS ONE 11(1):e0146441
Smoliński S, Radtke K (2017) Spatial prediction of demersal fish diversity in the Baltic Sea: comparison of machine learning and regression-based techniques. ICES J Mar Sci 74:102–111. https://doi.org/10.1093/icesjms/fsw136
Ta TT, Nguyen THT, Tran TT et al (2021a) Occurrence of juveniles of freshwater fishes in Ba Lat estuary, Northern Vietnam. Science and Technology Journal of Agriculture & Rural Development: 98–103
Ta TT, Tran DH, Dinh GL et al (2021b) Planktonic larvae of Luciogobius sp. (Gobiidae) in a tropical estuary. Reg Stu Mar Sci. https://doi.org/10.1016/j.rsma.2021b.102068
Taylor CM, Winston MR, Matthews WJ (1993) Fish species-environment and abundance relationships in a Great Plains river system. Ecography 16:16–23. https://doi.org/10.1111/j.1600-0587.1993.tb00054.x
Termvidchakorn A, Hortle KG (2013) A guide to larvae and juveniles of some common fish species from the Mekong River Basin. MRC Technical Paper No. 38. Mekong River Commission, Phnom Penh
Thessen AE (2016) Adoption of machine learning techniques in ecology and earth science. One Ecosyst 1:e8621. https://doi.org/10.3897/oneeco.1.e8621
Tran DH, Ta TT (2016) Dependence of Hainan medaka, Oryzias curvinotus (Nichols and Pope, 1927), on salinity in the Tien Yen estuary of northern Vietnam. Anim Biol 66:49–64. https://doi.org/10.1163/15707563-00002486
Tran DH, Tran TT, Ta TT (2016) Occurrence of Hypoatherina valenciennei (Bleeker, 1854) post-larvae and juveniles collected at estuarine habitats of northern Vietnam. Trop Nat Hist 16:107–117
Tran TT, Tran DH, Kinoshita I (2017) Occurrence of two types of larvae of the Asian seaperch (Lateolabrax) in the estuaries of northern Vietnam. Ichthyol Res 64:244–249. https://doi.org/10.1007/s10228-016-0554-3
Tran DH, Kinoshita I, Nguyen XH et al (2018a) Early life stages and habitats of Ayu Plecoglossus altivelis (Temminck and Schlegel 1846) from two river-estuary systems in Vietnam. Asian Fish Sci 31:1–16
Tran TT, Tran DH, Nguyen XH (2018b) Larval description and habitat utilization of an amphidromous goby, Redigobius bikolanus (Gobiidae). Anim Biol 68:15–26. https://doi.org/10.1163/15707563-17000079
Tran DH, Tran TT, Ta TT, Kinoshita I (2019a) An overview of studies on early life history of fish in Vietnam. Acad J Biol 41:1–12
Tran TT, Tran DH, Kinoshita I (2019b) Simultaneous and sympatric occurrence of early juveniles of Acanthpagrus latus and A. schlegelii (Sparidae) in the estuary of northern Vietnam. Limnol 20(321):326
Tran DH, Nguyen HH, Ha ML (2021) Length-weight relationship and condition factor of the mudskipper (Periophthalmus modestus) in the Red River delta. Reg Stu Mar Sci. https://doi.org/10.1016/j.rsma.2021.101903
Tran TT, Tran DH, Ta TT (2015) Larvae and juveniles of Terapon jarbua in northern Vietnam estuaries. In: Proceedings of the sixth national conference on Ecology and Biological Resources Nature and Technology Publishing House: 315–320
Tuhtan JA, Fuentes-Perez JF, Toming G, Kruusmaa M (2017) Flow velocity estimation using a fish-shaped lateral line probe with product-moment correlation features and a neural network. Flow Meas Instrum 54:1–8. https://doi.org/10.1016/j.flowmeasinst.2016.10.017
Tyler JA, Brandt SB (2001) Do spatial models of growth rate potential reflect fish growth in a heterogeneous environment? a comparison of model results. Ecol Freshw Fish 10:43–56. https://doi.org/10.1034/j.1600-0633.2001.100106.x
Vu TT (2009) Estuarine ecosystems of Vietnam (Exploitation, maintenance and management resources for sustainable development). Education Publishing House, Hanoi
Wiley EO, McNyset KM, Peterson AT et al (2003) Niche modeling perspective on geographic range predictions in the marine environment using a machine-learning algorithm. Oceanogr 16(3):120–127. https://doi.org/10.5670/oceanog.2003.42
Yagi Y, Kinoshita I, Fujita S, Aoyama D, Kawamura Y (2011) Importance of the upper estuary as a nursery ground for fishes in Ariake Bay, Japan. Environ Biol Fish 91:337–352
Yıldız Z, Uzun H, Ceylan S, Topcu Y (2016) Application of artificial neural networks to co-combustion of hazelnut husk–lignite coal blends. Bioresour Technol 200:42–47. https://doi.org/10.1016/j.biortech.2015.09.114
Young M, Carr MH (2015) Application of species distribution models to explain and predict the distribution, abundance and assemblage structure of nearshore temperate reef fishes. Divers Distrib 21:1428–1440. https://doi.org/10.1111/ddi.12378
Yuan H, Yang G, Li C et al (2017) Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: analysis of RF, ANN, and SVM regression models. Remote Sens 9:309. https://doi.org/10.3390/rs9040309
Zhang W, Zhong X, Liu G (2008) Recognizing spatial distribution patterns of grassland insects: neural network approaches. Stoch Environ Res Risk Assess 22:207–216. https://doi.org/10.1007/s00477-007-0108-3
Zhao J, Cao J, Tian S et al (2014) A comparison between two GAM models in quantifying relationships of environmental variables with fish richness and diversity indices. Aquat Ecol 48:297–312. https://doi.org/10.1007/s10452-014-9484-1
Zhu Y, Cheng X, Wang L (2016) A novel fault detection method for an integrated navigation system using Gaussian process regression. J Navig 69:905–919. https://doi.org/10.1017/S0373463315001034
Acknowledgements
This study was financially supported by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant Number: 106-NN.05-2018.302. Special thanks to members of ichthyological lab for their supports in the field and lab works. We confirm that the use of fish specimens in the present study was performed in compliance with current laws and ethical requirements for using animals for research.
Author information
Authors and Affiliations
Contributions
ANTD: Ideal, data analysis, model running, writing and checking; HDT: Data collection, writing, checking, funding acquisition.
Corresponding author
Ethics declarations
Conflict of Interest
The authors have no conflict of interest to declare that are relevant to the content of this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Handling Editor: Y. S. Park.
Rights and permissions
About this article
Cite this article
Do, A.N.T., Tran, H.D. Potential application of artificial neural networks for analyzing the occurrences of fish larvae and juveniles in an estuary in northern Vietnam. Aquat Ecol 57, 813–831 (2023). https://doi.org/10.1007/s10452-022-09959-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10452-022-09959-5