Habitat selection ecology of the aquatic beetle community using explainable machine learning
Abstract The aim of our work is to determine the importance of habitat features for the selection of the aquatic beetle community. Insects are represented by their general ecological traits such as body size, ecological element and trophic type, which are categorised into four body size ranges, four...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-80083-0 |
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| author | Marek Kruk Joanna Pakulnicka |
| author_facet | Marek Kruk Joanna Pakulnicka |
| author_sort | Marek Kruk |
| collection | DOAJ |
| description | Abstract The aim of our work is to determine the importance of habitat features for the selection of the aquatic beetle community. Insects are represented by their general ecological traits such as body size, ecological element and trophic type, which are categorised into four body size ranges, four ecological groups and four trophic types. To determine the importance of habitat selection of the studied insects, we analysed the relationships between the above categories and the set of habitat features of the lake and its surroundings. Ensemble machine learning modelling (XGBoost-SHAP) revealed the mechanism of habitat feature selection in relation to the general ecological traits. We found strong interactions between the body size, ecological element and trophic type of beetles, suggesting that these general traits control the structure and functioning of the beetle community studied. The area of the lake and the features of beetle occurrence in the aquatic environment play an important but secondary role, and the importance of the characteristics of the lake’s riparian zone was minimised. We found several categories of beetles as they select the number of the same habitat features. The study can provide valuable information for the practical conservation and management of lake ecosystems. |
| format | Article |
| id | doaj-art-73063f7a56e04e07979be692af799be9 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-73063f7a56e04e07979be692af799be92025-08-20T02:22:33ZengNature PortfolioScientific Reports2045-23222024-11-0114111610.1038/s41598-024-80083-0Habitat selection ecology of the aquatic beetle community using explainable machine learningMarek Kruk0Joanna Pakulnicka1Chair of Applied Computer Science and Mathematical Modelling, University of Warmia and Mazury in OlsztynDepartment of Zoology, University of Warmia and Mazury in OlsztynAbstract The aim of our work is to determine the importance of habitat features for the selection of the aquatic beetle community. Insects are represented by their general ecological traits such as body size, ecological element and trophic type, which are categorised into four body size ranges, four ecological groups and four trophic types. To determine the importance of habitat selection of the studied insects, we analysed the relationships between the above categories and the set of habitat features of the lake and its surroundings. Ensemble machine learning modelling (XGBoost-SHAP) revealed the mechanism of habitat feature selection in relation to the general ecological traits. We found strong interactions between the body size, ecological element and trophic type of beetles, suggesting that these general traits control the structure and functioning of the beetle community studied. The area of the lake and the features of beetle occurrence in the aquatic environment play an important but secondary role, and the importance of the characteristics of the lake’s riparian zone was minimised. We found several categories of beetles as they select the number of the same habitat features. The study can provide valuable information for the practical conservation and management of lake ecosystems.https://doi.org/10.1038/s41598-024-80083-0 |
| spellingShingle | Marek Kruk Joanna Pakulnicka Habitat selection ecology of the aquatic beetle community using explainable machine learning Scientific Reports |
| title | Habitat selection ecology of the aquatic beetle community using explainable machine learning |
| title_full | Habitat selection ecology of the aquatic beetle community using explainable machine learning |
| title_fullStr | Habitat selection ecology of the aquatic beetle community using explainable machine learning |
| title_full_unstemmed | Habitat selection ecology of the aquatic beetle community using explainable machine learning |
| title_short | Habitat selection ecology of the aquatic beetle community using explainable machine learning |
| title_sort | habitat selection ecology of the aquatic beetle community using explainable machine learning |
| url | https://doi.org/10.1038/s41598-024-80083-0 |
| work_keys_str_mv | AT marekkruk habitatselectionecologyoftheaquaticbeetlecommunityusingexplainablemachinelearning AT joannapakulnicka habitatselectionecologyoftheaquaticbeetlecommunityusingexplainablemachinelearning |