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ORIGINAL RESEARCH article

Front. Microbiol.

Sec. Aquatic Microbiology

Volume 16 - 2025 | doi: 10.3389/fmicb.2025.1602162

This article is part of the Research TopicProtistan Phagotrophy and the Far-reaching ImplicationsView all 3 articles

Latitude-and depth-driven divergence in protist trophic strategies revealed by machine learning model Elaina Thomas 1 (0000-0002-6657-8302), Mora J. Groussman 1 (0000-0001-7874-7217), Sacha N. Coesel 1 (0000-0001-9422-8388), Nicholas J. Hawco 2 (0000-0002-5897-0830), Randelle M. Bundy 1 (0000-0002-0600-3953), E

Provisionally accepted
  • 1School of Oceanography, College of the Environment, University of Washington, Seattle, Washington, United States
  • 2Department of Oceanography, University of Hawaiʻi Mānoa, Honolulu, United States

The final, formatted version of the article will be published soon.

Protists are ubiquitous across the ocean, holding different roles in the food web depending on their trophic capabilities. Many protists are mixotrophs, able to both photosynthesize and ingest prey. However, there is limited knowledge of which protist species are mixotrophs in nature, and their activity and distributions throughout the ocean. Here, we present MarPRISM, an improved XGBoost-based machine learning model for predicting the in situ trophic mode (phototrophy, mixotrophy, heterotrophy) of marine protist species based on transcriptional profiles. We used MarPRISM to generate 1,462 trophic mode predictions for 28 environmental protist species based on 335 metatranscriptomes collected across the North Pacific Ocean, from the surface to 130 m depth, over the diel cycle, and within nutrient-amended incubations. Eight environmental species were identified as having mixotrophic capabilities, including six dinoflagellates, one bolidophyte, and one haptophyte. The species with mixotrophic capabilities varied in how they shifted their trophic mode across the surface ocean and in response to the experimental amendment of nitrate and iron. Limited light availability appeared to lead one species to shift from mixotrophy at the surface towards heterotrophy between 41 and 130 m depth. We used transcript abundance to evaluate the abundance of species with different trophic capabilities (species with mixotrophic capabilities, phototrophic specialists, and heterotrophic specialists). At the surface within the subtropical gyre, transcript abundance was similar among the protist species with different trophic capabilities, the protist community was nitrate-limited, and experimental nitrate amendment favored phototrophic specialists. Increasing nitrate availability with latitude was correlated with phototrophic specialists being the dominant protist trophic group in the transition zone between the subtropical and subpolar gyres under high nitrogen availability. In contrast, under lower nitrogen conditions in the transition zone, protist species with different trophic capabilities comprised equal portions of the surface community. Light and nitrate availability influenced the transcript abundance of phototrophic specialists across depth; phototrophic specialists had high transcript abundance at 130 m in the subtropical gyre and at the surface in the transition zone, while species with mixotrophic capabilities and heterotrophic specialists showed less variation in transcript abundance with depth.

Keywords: protist, marine, North Pacific, mixotrophy, Trophic mode, machine learning, Plankton

Received: 28 Mar 2025; Accepted: 23 Jul 2025.

Copyright: © 2025 Thomas, Groussman, Coesel, Hawco, Bundy and Armbrust. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: E. Virginia Armbrust, School of Oceanography, College of the Environment, University of Washington, Seattle, 98105, Washington, United States

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