Ali Danandeh Mehr, Jaakko Erkinaro, Jan Hjort, Ali Torabi Haghighi, Amirhossein Ahrari, Maija Korpisaari, Jorma Kuusela, Brian Dempson, Hannu Marttila, Factors affecting the presence of Arctic charr in streams based on a jittered binary genetic programming model, Ecological Indicators, Volume 142, 2022, 109203, ISSN 1470-160X, https://doi.org/10.1016/j.ecolind.2022.109203
Factors affecting the presence of Arctic charr in streams based on a jittered binary genetic programming model
|Author:||Danandeh Mehr, Ali1,2; Erkinaro, Jaakko3; Hjort, Jan4;|
1Water, Energy and Environmental Engineering Research Unit, University of Oulu, FI 90014 Oulu, Finland
2Civil Engineering Department, Antalya Bilim University, 07190 Antalya, Turkey
3Natural Resources Institute Finland (Luke), FI 90014 Oulu, Finland
4Geography Research Unit, University of Oulu, FI 90014 Oulu, Finland
5Department of Sports and Exercise Medicine, Oulu Deaconess Institute Foundation sr., 90100 Oulu, Finland
6Center for Life Course Health Research, Faculty of Medicine, University of Oulu, 90014 Oulu, Finland
7Natural Resources Institute Finland (Luke), FI 99980 Utsjoki, Finland
8Fisheries & Oceans Canada, St John’s, NL A1C 5X1, Canada
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202301041392
|Publish Date:|| 2023-01-04
Arctic charr is one of the fish species most sensitive to climate change but studies on their freshwater habitat preferences are limited, especially in the fluvial environment. Machine learning methods offer automatic and objective models for ecohydrological processes based on observed data. However, i) the number of ecological records is often much smaller than hydrological observations, and ii) ecological measurements over the long-term are costly. Consequently, ecohydrological datasets are scarce and imbalanced. To address these problems, we propose jittered binary genetic programming (JBGP) to detect the most dominant ecohydrological parameters affecting the occurrence of Arctic charr across tributaries within the large subarctic Teno River catchment, in northernmost Finland and Norway. We quantitatively assessed the accuracy of the proposed model and compared its performance with that of classic genetic programming (GP), decision tree (DT) and state-of-the-art jittered-DT methods. The JBGP achieves the highest total classification accuracy of 90% and a Heidke skill score of 78%, showing its superiority over its counterparts. Our results showed that the dominant factors contributing to the presence of Arctic charr in Teno River tributaries include i) a higher density of macroinvertebrates, ii) a lower percentage of mires in the catchment and iii) a milder stream channel slope.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
1172 Environmental sciences
Writing was supported by the National Freshwater Competence Center and Academy of Finland HYDRO-RDI project (no 337523). J.H. acknowledges funding from the Academy of Finland (project no 315519). A.T.H. acknowledges funding from the Maa- ja vesitekniikan tuki r.y. (MVTT, project number 41878).
|Academy of Finland Grant Number:||
337523 (Academy of Finland Funding decision)
315519 (Academy of Finland Funding decision)
© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).