A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms
Abstract Increasing occurrence of harmful algal blooms across the land–water interface poses significant risks to coastal ecosystem structure and human health. Defining significant drivers and their interactive impacts on blooms allows for more effective analysis and identification of specific condi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-98110-9 |
| _version_ | 1831729852412067840 |
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| author | Yiwei Cheng Ved N. Bhoot Karl Kumbier Marilou P. Sison-Mangus James B. Brown Raphael Kudela Michelle E. Newcomer |
| author_facet | Yiwei Cheng Ved N. Bhoot Karl Kumbier Marilou P. Sison-Mangus James B. Brown Raphael Kudela Michelle E. Newcomer |
| author_sort | Yiwei Cheng |
| collection | DOAJ |
| description | Abstract Increasing occurrence of harmful algal blooms across the land–water interface poses significant risks to coastal ecosystem structure and human health. Defining significant drivers and their interactive impacts on blooms allows for more effective analysis and identification of specific conditions supporting phytoplankton growth. A novel iterative Random Forests (iRF) machine-learning model was developed and applied to two example cases along the California coast to identify key stable interactions: (1) phytoplankton abundance in response to various drivers due to coastal conditions and land-sea nutrient fluxes, (2) microbial community structure during algal blooms. In Example 1, watershed derived nutrients were identified as the least significant interacting variable associated with Monterey Bay phytoplankton abundance. In Example 2, through iRF analysis of field-based 16S OTU bacterial community and algae datasets, we independently found stable interactions of prokaryote abundance patterns associated with phytoplankton abundance that have been previously identified in laboratory-based studies. Our study represents the first iRF application to marine algal blooms that helps to identify ocean, microbial, and terrestrial conditions that are considered dominant causal factors on bloom dynamics. |
| first_indexed | 2024-12-21T08:23:12Z |
| format | Article |
| id | doaj.art-dd7df49158cb41a3b4bb951dcbcc95f2 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-21T08:23:12Z |
| publishDate | 2021-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-dd7df49158cb41a3b4bb951dcbcc95f22022-12-21T19:10:24ZengNature PortfolioScientific Reports2045-23222021-10-0111111110.1038/s41598-021-98110-9A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton bloomsYiwei Cheng0Ved N. Bhoot1Karl Kumbier2Marilou P. Sison-Mangus3James B. Brown4Raphael Kudela5Michelle E. Newcomer6Earth and Environmental Sciences Area, Lawrence Berkeley National LaboratoryEarth and Environmental Sciences Area, Lawrence Berkeley National LaboratoryStatistics Department, University of CaliforniaDepartment of Ocean Sciences, University of CaliforniaStatistics Department, University of CaliforniaDepartment of Ocean Sciences, University of CaliforniaEarth and Environmental Sciences Area, Lawrence Berkeley National LaboratoryAbstract Increasing occurrence of harmful algal blooms across the land–water interface poses significant risks to coastal ecosystem structure and human health. Defining significant drivers and their interactive impacts on blooms allows for more effective analysis and identification of specific conditions supporting phytoplankton growth. A novel iterative Random Forests (iRF) machine-learning model was developed and applied to two example cases along the California coast to identify key stable interactions: (1) phytoplankton abundance in response to various drivers due to coastal conditions and land-sea nutrient fluxes, (2) microbial community structure during algal blooms. In Example 1, watershed derived nutrients were identified as the least significant interacting variable associated with Monterey Bay phytoplankton abundance. In Example 2, through iRF analysis of field-based 16S OTU bacterial community and algae datasets, we independently found stable interactions of prokaryote abundance patterns associated with phytoplankton abundance that have been previously identified in laboratory-based studies. Our study represents the first iRF application to marine algal blooms that helps to identify ocean, microbial, and terrestrial conditions that are considered dominant causal factors on bloom dynamics.https://doi.org/10.1038/s41598-021-98110-9 |
| spellingShingle | Yiwei Cheng Ved N. Bhoot Karl Kumbier Marilou P. Sison-Mangus James B. Brown Raphael Kudela Michelle E. Newcomer A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms Scientific Reports |
| title | A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms |
| title_full | A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms |
| title_fullStr | A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms |
| title_full_unstemmed | A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms |
| title_short | A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms |
| title_sort | novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms |
| url | https://doi.org/10.1038/s41598-021-98110-9 |
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