Factors affecting the presence of Arctic charr in streams based on a jittered binary genetic programming model

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...

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Bibliographic Details
Published in:Ecological Indicators
Main Authors: Ali Danandeh Mehr, Jaakko Erkinaro, Jan Hjort, Ali Torabi Haghighi, Amirhossein Ahrari, Maija Korpisaari, Jorma Kuusela, Brian Dempson, Hannu Marttila
Format: Article in Journal/Newspaper
Language:English
Published: Elsevier 2022
Subjects:
Ecohydrological modelling
Scarce data
Genetic Programming
Arctic Charr
Jittering
Ecology
QH540-549.5
Arctic
Norway
Teno
Arctic charr
Climate change
Subarctic
Online Access:https://doi.org/10.1016/j.ecolind.2022.109203
https://doaj.org/article/8b2e2e88f7c046e3a35911ff6e68ffa8
Description
Summary: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.

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