Halak fajkompozíciója és hidromorfológiai változók közötti összefüggések a Duna magyarországi szakaszán

Benedek Jandó; Vivien Füstös; Alexander Anatol Ermilov; Zoltán Szalóky; Tibor Erős; Sán Baranya

doi:10.59258/hk.15656

Bendek Jandó University of Veterinary Medicine Budapest https://orcid.org/0009-0000-0291-1930
Vivien Füstös Budapest University of Technology and Economics, Faculty of Civil Engineering, Department of Hydraulic and Water Resources Engineering https://orcid.org/0009-0000-1886-5254
Anatol Alexander Ermilov Budapest University of Technology and Economics, Faculty of Civil Engineering, Department of Hydraulic and Water Resources Engineering https://orcid.org/0000-0002-2650-5870
Zoltán Szalóky Budapesti Műszaki és Gazdaságtudományi Egyetem, Vízépítési és Vízgazdálkodási Tanszék
Tibor Erős Balaton Limnological Research Institute https://orcid.org/0000-0002-2252-3115
Sándor Baranya Budapest University of Technology and Economics, Faculty of Civil Engineering, Department of Sanitary and Environmental Engineering https://orcid.org/0000-0001-5832-9683

Keywords: Ecohydrology, Machine Learning, fish-habitat relations, regression Random Forest, river habitat evaluation

Abstract

Understanding of the contribution of abiotic drivers to fish community structure in very large rivers is poor. In this study, we assigned the occurrence data of the 20 most common fish species in the Hungarian section of the Danube River from 2004 to 2022 to hydrodynamic variables from hydrological datasets and hydrodynamical simulations and looked for patterns among them using Machine Learning (ML). Among the nine abiotic factors used as independent variables in the analysis, the depth-averaged flow velocity, water depth and bed material composition were the most decisive variables, which aligns with the results of previous research. In addition, with our Random Forest model, we were able to predict the number of individuals of the 20 most common fish species in the given conditions in the entire Hungarian section of the Danube. These estimates refer to optimal habitat for fish species according to abiotic variables. In addition to the ML analysis, we showed the possibility of using the Danube fish faunistic database, which covers a large area and time, to investigate the relationships of the population (for example, the relationship between invasive and native species) using classical statistical methods. The results found here are in many cases consistent with the Random Forest model but give reason to extend the model with additional independent variables to better understand the ecology of the Danube fish species.

Author Biographies

Bendek Jandó, University of Veterinary Medicine Budapest

BENEDEK Jandó is a zoology student at the University of Veterinary Medicine Budapest. He is particularly interested in ecology and its frontier disciplines. He is a member of the Hungarian Biodiversity-research Society since 2020, and an active volunteer and data collector for Birdlife Hungary and the Ócsa Bird Ringing Station.

Vivien Füstös, Budapest University of Technology and Economics, Faculty of Civil Engineering, Department of Hydraulic and Water Resources Engineering

VIVIEN FÜSTÖS is a civil engineer and is currently doing a PhD at Pál Vásárhelyi Doctoral School of Civil Engineering and Earth Sciences. Her research topic is hydromorphologic assessment of riverine habitats on the micro- and mesoscale. Member of the Hungarian Hydrological Society since 2017.

Anatol Alexander Ermilov , Budapest University of Technology and Economics, Faculty of Civil Engineering, Department of Hydraulic and Water Resources Engineering

ANATOL ERMILOV ALEXANDER is a certified civil engineer, PhD student at the Department of Water Engineering and Water Management of the Budapest University of Technology and Economics. The topic of his doctoral research is the investigation of the interaction between river flow and the river bed. In 2015 TDK II. he won first place with the thesis "Numerical model-based investigation of the wind-induced water exchange processes of Balaton", and with this thesis he won first place in the Lászlóffy Woldemár diploma thesis competition of the Hungarian Hydrological Society. He defended his MSc thesis at the Norwegian University of Science and Technology in 2017.

Zoltán Szalóky , Budapesti Műszaki és Gazdaságtudományi Egyetem, Vízépítési és Vízgazdálkodási Tanszék

ZOLTÁN SZALÓKY obtained his PhD degree in 2017 at Eötvös Loránd University. He is currently an institute engineer at the HUN-REN Ecological Research Center. His field of research is hydrobiological and fish ecological studies of standing and flowing waters, with particular attention to fish communities of large rivers.

Tibor Erős , Balaton Limnological Research Institute

ERŐS TIBOR biológus, 2005-ben szerzett PhD fokozatot az Eötvös Loránd Tudományegyetemen. Jelenleg a HUN-REN Balatoni Limnológiai Kutatóintézet igazgatója. Kutatási területe: halegyüttesek szerveződése édesvizekben, biológiai sokféleség és a környezeti tényezők kapcsolata édesvizekben, mintavétel reprezentativitása, monitorozás rendszerek fejlesztése, természetvédelmi területek kijelölése édesvizek természeti értékei alapján. 1999 óta a Magyar Hidrológiai Társaság tagja.

Sándor Baranya, Budapest University of Technology and Economics, Faculty of Civil Engineering, Department of Sanitary and Environmental Engineering

SÁNDOR BARANYA He graduated in Civil Engineering from the Budapest University of Technology and Economics in 2003 and received his PhD from the same university in 2010. Currently, he is an associate professor and the Head of the Department of Hydraulic and Water Resources Engineering at BME. His research interests include the study of riverbed morphology, flow and sediment transport using field methods and numerical modelling. He is a member of the Hungarian Hydrological Society since 2003.

References

A Víz keretirányelv hazai megvalósítása (2009). Vízgyűjtő-gazdálkodási terv. Budapest: Vízügyi és Környezetvédelmi Központi Igazgatóság.

Baranya S., Fleit G., Józsa J., Szalóky Z., Tóth B., Czeglédi I., Erős T. (2018). Habitat mapping of riverine fish by means of hydromorphological tools. Ecohydrology, 11(7). https://doi.org/10.1002/eco.2009

Bergström, P., Gonzalez-Mirelis, G., Lindegarth, M. (2011). Interaction between classification detail and prediction of community types: implications for predictive modelling of benthic biotopes. Marine Ecology Progress Series, 432. pp. 31-44. https://doi.org/10.3354/meps09129

Chevalier, M., Tedesco, P., Grenouillet, G. (2022). Spatial patterns in the contribution of biotic and abiotic factors to the population dynamics of three freshwater fish species. PeerJ Life & Environment, 10:e12857. https://doi.org/10.7717/peerj.12857

Copp, G.H. (1990). Shifts in the microhabitat of larval and juvenile roach, Rutilus rutilus (L.), in a floodplain channel. Journal of Fish Biology, 36. pp. 683-692. https://doi.org/10.1111/j.1095-8649.1990.tb04322.x

Elmahdy, S., Ali, T., Mohamed, M., Howari, F., Abouleish, M., Simonet, D. (2020). Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models. Frontiers in Environmental Science, 8. pp. 102-125. https://doi.org/10.3389/fenvs.2020.00102

Erős T., Vörös J. (2017). Áttekintés a hazai idegenhonos inváziós halak, kétéltűek és hüllők jelenlegi helyzetéről. Magyar Tudomány, 4. pp. 426-428.

Farkas-Iványi K., Trájer A. (2015). The influence of the river regulations on the aquatic habitats in river Danube, at the Bodak branch-system, Hungary and Slovakia. Carpathian Journal of Earth and Environmental Sciences, 10(3). pp. 235-245.

Franco, A., Garcia-Berthou, E., dos Santos, L. (2020). Ecological impacts of an invasive top predator fish across South America. Science of The Total Environment, 761., 143296. https://doi.org/10.1016/j.scitotenv.2020.143296 PMid:33187704

Füstös V., Baranya S., Fleit G., Erős T., Szalóky Z., Tóth B., Józsa J. (2019). A felső‐magyarországi Duna élőhelyszempontú hidrodinamikai vizsgálata. Pisces Hungarici, 13. pp. 81-90.

Füstös V., Erős T., Józsa J. (2021). 2D vs. 3D Numerical Approaches for Fish Habitat Evaluation of a Large River-Is 2D Modeling Sufficient? Periodica Polytechnica Civil Engineering, 65(4). pp. 1114-1125. https://doi.org/10.3311/PPci.17788

Garner, P. (1995). Suitability indices for juvenile 0+ roach [rutilus rutilus (l.)] using point abundance sampling data. Regulated Rivers: Research & Management, 10(2-4). pp. 99-104. https://doi.org/10.1002/rrr.3450100206

Harka Á., Sallai Z. (2004). Magyarország halfaunája. Szarvas: Nimfea Természetvédelmi Egyesület., p. 269.

Jackson, D.A., Peres-Neto, P.R., Olden, J.D. (2001). What controls who is where in freshwater fish communities – the roles of biotic, abiotic and spatial factors. Canadian Journal of Fisheries and Aquatic Sciences, 58(1), pp. 157-170. https://doi.org/10.1139/f00-23

Kottelat, M., Freyhof, J. (2007). Handbook of European Freshwater Fishes. Cornol, Switzerland and Berlin, Germany: Maurice Kottelat and Jörg Freyhof. p. 646.

Liu, Z., Peng, C., Work, T., Candau, J.-N., DesRochers, A., Kneeshaw, D. (2018). Application of machine learning methods in forest ecology: recent progress and future challenges. Environmental Reviews, 26(10). pp. 339-350. https://doi.org/10.1139/er-2018-0034

Matuszek, J.E., Beggs, G. (1988). Fish Species Richness in Relation to Lake Area, pH, and Other Abiotic Factors in Ontario Lakes. Canadian Journal of Fisheries and Aquatic Sciences, 45(11). pp. 1931-1941. https://doi.org/10.1139/f88-225

Meyer, H., Pebesma, E. (2021). Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution, 12(9). pp. 1620-1633. https://doi.org/10.1111/2041-210X.13650

Near, T., Page, L., Mayden, R. (2001). Intraspecific phylogeography of Percina evides (Percidae: Etheostomatinae): an additional test of the Central Highlands pre-Pleistocene vicariance hypothesis. Molecular Ecology, 10. pp. 2235-2240. https://doi.org/10.1046/j.1365-294X.2001.01362.x PMid:11555265

Olden, J., Lawler, J., Poff, N. (2008). Machine learning methods without tears: A primer for ecologists. The Quarterly Review of Biology, 83(2). pp. 171-193. https://doi.org/10.1086/587826 PMid:18605534

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python, JMLR, 12. pp. 2825-2830. https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html

Quist, M.C., Hubert, W.A. (2005). Relative Effects of Biotic and Abiotic Processes: A Test of the Biotic–Abiotic Constraining Hypothesis as Applied to Cutthroat Trout. Transactions of the American Fisheries Society, 134(3). pp. 676-686. http://dx.doi.org/10.1577/T04-112.1

Reiczigel J., Harnos A., Solymosi N. (2010). Biostatisztika nem statisztikusoknak. Nagykovácsi: Pars Kft.

Rossier, O., Castella, E., Lachavanne, J.-B. (1996). Influence of submerged aquatic vegetation on size class distribution of perch (Perca fluviatilis) and roach (Rutilus rutilus) in the littoral zone of Lake Geneva (Switzerland). Aquatic Sciences, 58(1), pp. 1-14. https://doi.org/10.1007/BF00877636

Sloss, B., Billington, N., Burr, B. (2004). A molecular phylogeny of the Percidae (Teleostei, Perciformes) based on mitochondrial DNA sequence. Molecular Phylogenetics and Evolution, 32. pp. 545-562. https://doi.org/10.1016/j.ympev.2004.01.011 PMid:15223037

Sylvain, C., Hervet, É., Lecomte, N. (2019). Applications for deep learning in ecology. Methods in Ecology and Evolution, 10. pp. 1632-1644. https://doi.org/10.1111/2041-210X.13256

Szalóky Z., Bammer, V., György Á.I., Pehlivanov, L., Schabuss, M., Zornig, H., Weiperth A., Erős T. (2015). Offshore distribution of invasive gobies (Pisces: Gobiidae) along the longitudinal profile of the Danube River. Fundamental Applied Limnology, 187(2), pp. 127-133. http://dx.doi.org/10.1127/fal/2015/0768

Szalóky Z., Füstös V., Tóth B., Erős T. (2021). Environmental drivers of benthic fish assemblages and fish-habitat associations in offshore areas of a very large river. River Research and Applications, 37(5). pp. 712-721. https://doi.org/10.1002/rra.3793

Szalóky Z., György, Á.I., Tóth B., Sevcsik A., Specziár A., Csányi B., Szekeres J., Erős T. (2014). Application of an electrified benthic frame trawl for sampling fish in a very large European river (the Danube River) - Is offshore monitoring necessary? Fisheries Research, 151. pp. 12-19. https://doi.org/10.1016/j.fishres.2013.12.004

Takács P., Czeglédi I., Ferincz Á. Sály P., Specziár A., Vitál Z., Weiperth A., Erős T. (2017). Non-native fish species in Hungarian waters: historical overview, potential sources and recent trends in their distribution. Hydrobiologia 795. pp. 1-22. https://doi.org/10.1007/s10750-017-3147-x

Tavares, C., Brauns, M., Hille, S., Krenek, S., Borcherding, J., Weitere, M. (2020). Tracing the colonization process of non-native gobies into a large river: the relevance of different dispersal modes. Biological Invasions, 22. pp. 2421-2429. https://doi.org/10.1007/s10530-020-02281-x

Tózsa I. (1998). Tájképi homogenitás Magyarországon. Földrajzi Értesítő, 47(3), pp. 432-445. https://www.mtafki.hu/konyvtar/kiadv/FE1998/FE19983_432-445.pdf

U.S. Fish and Wildlife Service (1985). Habitat suitability models and instream flow suitability curves: pink salmon. Biological report, 10.109. https://apps.dtic.mil/dtic/tr/fulltext/u2/a322912.pdf

Zajicek, P., Wolter, C. (2018). The gain of additional sampling methods for the fish-based assessment of large rivers. Fisheries Research, 197. pp. 15-24. https://doi.org/10.1016/j.fishres.2017.09.018

Relations between composition of fishes and hydromorphological variables in a very large river

Abstract

Author Biographies

References