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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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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1766303791139782656 |