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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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:
Online Access:https://doi.org/10.1016/j.ecolind.2022.109203
https://doaj.org/article/8b2e2e88f7c046e3a35911ff6e68ffa8
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spelling ftdoajarticles:oai:doaj.org/article:8b2e2e88f7c046e3a35911ff6e68ffa8 2023-05-15T14:29:50+02:00 Factors affecting the presence of Arctic charr in streams based on a jittered binary genetic programming model Ali Danandeh Mehr Jaakko Erkinaro Jan Hjort Ali Torabi Haghighi Amirhossein Ahrari Maija Korpisaari Jorma Kuusela Brian Dempson Hannu Marttila 2022-09-01T00:00:00Z https://doi.org/10.1016/j.ecolind.2022.109203 https://doaj.org/article/8b2e2e88f7c046e3a35911ff6e68ffa8 EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S1470160X22006756 https://doaj.org/toc/1470-160X 1470-160X doi:10.1016/j.ecolind.2022.109203 https://doaj.org/article/8b2e2e88f7c046e3a35911ff6e68ffa8 Ecological Indicators, Vol 142, Iss , Pp 109203- (2022) Ecohydrological modelling Scarce data Genetic Programming Arctic Charr Jittering Ecology QH540-549.5 article 2022 ftdoajarticles https://doi.org/10.1016/j.ecolind.2022.109203 2022-12-30T23:43:49Z 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. Article in Journal/Newspaper Arctic charr Arctic Climate change Subarctic Directory of Open Access Journals: DOAJ Articles Arctic Norway Teno ENVELOPE(25.690,25.690,68.925,68.925) Ecological Indicators 142 109203
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic Ecohydrological modelling
Scarce data
Genetic Programming
Arctic Charr
Jittering
Ecology
QH540-549.5
spellingShingle Ecohydrological modelling
Scarce data
Genetic Programming
Arctic Charr
Jittering
Ecology
QH540-549.5
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
topic_facet Ecohydrological modelling
Scarce data
Genetic Programming
Arctic Charr
Jittering
Ecology
QH540-549.5
description 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.
format Article in Journal/Newspaper
author Ali Danandeh Mehr
Jaakko Erkinaro
Jan Hjort
Ali Torabi Haghighi
Amirhossein Ahrari
Maija Korpisaari
Jorma Kuusela
Brian Dempson
Hannu Marttila
author_facet Ali Danandeh Mehr
Jaakko Erkinaro
Jan Hjort
Ali Torabi Haghighi
Amirhossein Ahrari
Maija Korpisaari
Jorma Kuusela
Brian Dempson
Hannu Marttila
author_sort Ali Danandeh Mehr
title Factors affecting the presence of Arctic charr in streams based on a jittered binary genetic programming model
title_short Factors affecting the presence of Arctic charr in streams based on a jittered binary genetic programming model
title_full Factors affecting the presence of Arctic charr in streams based on a jittered binary genetic programming model
title_fullStr Factors affecting the presence of Arctic charr in streams based on a jittered binary genetic programming model
title_full_unstemmed Factors affecting the presence of Arctic charr in streams based on a jittered binary genetic programming model
title_sort factors affecting the presence of arctic charr in streams based on a jittered binary genetic programming model
publisher Elsevier
publishDate 2022
url https://doi.org/10.1016/j.ecolind.2022.109203
https://doaj.org/article/8b2e2e88f7c046e3a35911ff6e68ffa8
long_lat ENVELOPE(25.690,25.690,68.925,68.925)
geographic Arctic
Norway
Teno
geographic_facet Arctic
Norway
Teno
genre Arctic charr
Arctic
Climate change
Subarctic
genre_facet Arctic charr
Arctic
Climate change
Subarctic
op_source Ecological Indicators, Vol 142, Iss , Pp 109203- (2022)
op_relation http://www.sciencedirect.com/science/article/pii/S1470160X22006756
https://doaj.org/toc/1470-160X
1470-160X
doi:10.1016/j.ecolind.2022.109203
https://doaj.org/article/8b2e2e88f7c046e3a35911ff6e68ffa8
op_doi https://doi.org/10.1016/j.ecolind.2022.109203
container_title Ecological Indicators
container_volume 142
container_start_page 109203
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