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| DOI | 10.1016/j.ecolind.2024.111832 |
| Atlantic salmon habitat-abundance modeling using machine learning methods | |
| 发表日期 | 2024 |
| ISSN | 1470-160X |
| EISSN | 1872-7034 |
| 起始页码 | 160 |
| 卷号 | 160 |
| 英文摘要 | Climate change and anthropogenic activities have impacts on fish habitat suitability, demanding more accurate modeling of species abundance for effective conservation and management. In this study, we applied Machine Learning techniques to model the habitat-abundance relationship of juvenile Atlantic salmon (Salmo salar) in the Teno catchment in Finland and Norway. To capture the complexity and nonlinearity of the habitat-abundance relationship, we employed Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and Support Vector Classification (SVC) and compared their performances. Among the regression models considered, those incorporating input variables such as substrate, shade, and vegetation demonstrate higher performance. Support Vector Regression yields the highest mean cross-validation score (R2 = 0.58), and Gradient Boosting produces the highest test score (R2 = 0.6) among the regression techniques. The mean cross-validation and test scores obtained for the classification models are notably higher compared to the regression models across all scenarios. A comparison between regression and classification results highlights the challenges of accurately modeling the habitat-abundance relationship. This study provides insights into the challenges and potential of machine learning techniques for juvenile Atlantic salmon habitat-abundance modeling in complex riverine habitat environments. The findings emphasize the importance of considering the limitations of machine learning models, particularly in ecological contexts, and the need for further research to address temporal variations and improve the precision of habitat-abundance modeling. |
| 英文关键词 | Atlantic salmon Abundance; Machine Learning Modelling; Habitat-Abundance Relationship; Arctic |
| 语种 | 英语 |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| WOS类目 | Biodiversity Conservation ; Environmental Sciences |
| WOS记录号 | WOS:001210929600001 |
| 来源期刊 | ECOLOGICAL INDICATORS
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/296683 |
| 作者单位 | University of Oulu; Natural Resources Institute Finland (Luke) |
| 推荐引用方式 GB/T 7714 | . Atlantic salmon habitat-abundance modeling using machine learning methods[J],2024,160. |
| APA | (2024).Atlantic salmon habitat-abundance modeling using machine learning methods.ECOLOGICAL INDICATORS,160. |
| MLA | "Atlantic salmon habitat-abundance modeling using machine learning methods".ECOLOGICAL INDICATORS 160(2024). |
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