A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms

Cheng, Yiwei; Bhoot, Ved N.; Kumbier, Karl; Sison-Mangus, Marilou P.; Brown, James B.; Kudela, Raphael; Newcomer, Michelle E.

doi:10.1038/s41598-021-98110-9

A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms

Journal Article · Thu Oct 07 00:00:00 EDT 2021 · Scientific Reports

DOI:https://doi.org/10.1038/s41598-021-98110-9· OSTI ID:1824880

Cheng, Yiwei; Bhoot, Ved N.; Kumbier, Karl; Sison-Mangus, Marilou P.; Brown, James B.; Kudela, Raphael; Newcomer, Michelle E.

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: National Oceanic and Atmospheric Administration (NOAA); USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1824880

Alternate ID(s):: OSTI ID: 1833828

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 11; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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