Introduction to the themed section “Mixotrophs and mixoplankton: conceptual integration into aquatic research”
Bibliographic record
Abstract
Early observers of ocean plankton had difficulty assigning trophic roles to the myriad forms they encountered. C.G. Ehrenberg, for example, was convinced that diatoms belonged in the animal kingdom, and the motility and lack of grass-green plastids made many assume that most flagellates were also “animalcules” (Karlusich et al., 2020). However, Victor Hensen’s coining of the term “plankton” in 1887 was quickly followed by the use of Phyto- and Zooplankton to delineate the organisms at the base of the ocean’s food web (Dolan, 2021). Although it was recognized by mid-19th century that large oceanic protists (e.g. Radiolaria) contained photosynthetic symbionts and early in the 20th century that some photosynthetic protists are capable of ingesting microbial prey (Biecheler, 1936), the phytoplankton/zooplankton dichotomy remained the dominant paradigm until late in the 20th century. In the 1980s, several groups more or less simultaneously discovered that certain ciliate microzooplankters could retain functional chloroplasts from ingested food (Laval-Peuto and Febvre, 1986; McManus and Fuhrman, 1986; Stoecker et al., 1987). At around the same time, other groups were documenting bacterivory in plastidic nanoflagellates in lakes (Bird and Kalff, 1987), a phenomenon that is also important in the ocean (Zubkov and Tarran, 2008; Hartmann et al., 2012). Thus the prevalence of dual metabolic modes (phago-and phototrophy) in the same organism became well established. Flynn et al. (2019) coined the term “mixoplankton” for these forms. It is interesting to note that although earlier microscopists had observed phagotrophy in “phytoplankton”, especially dinoflagellates, it was not until the latter part of the 20th century, at a time when the trophic importance of nano- and microzooplankton was becoming better appreciated, that plankton ecologists were prepared to accept that many of their subjects were neither phyto- nor zooplankton, but rather a combination of the two. This special section of the Journal of Plankton Research arose from two special sessions on mixoplankton hosted at a meeting of the Association for the Sciences of Limnology and Oceanography in Mallorca, Spain, in 2023. The broad range of topics covered there, from experimental approaches to modeling and monitoring, indicates that mixoplankton research has entered a mature phase in which the implications of this dual trophic mode can be explored at all scales, from test tube to global oceans. This range of exploration is covered in the five contributions to the special section. Grzywacz et al. used fluorescence induction and relaxation kinetics to quantify the photosynthetic performance of retained chloroplasts within a ciliate that had obtained them from two different foods, and documented the effects of the photosystem II inhibitor DCMU on ciliate growth (Grzywacz et al., 2024). Honig et al. studied predator/prey dynamics in a model system under thermal adaptation with a flagellate mixoplankter and phagotrophic grazer (Honig et al., 2024). Their results have implications for trophic interactions in a future, warmer ocean. Princiotta et al. explore the ability of a mixotrophic nanoflagellate (Ochromonas) to affect toxic Microcystis biomass and toxicity under experimentally manipulated nutrient limitation conditions (Princiotta et al., 2024). Le Noac’h and Beisner leverage results of extensive surveys to document the prevalence of mixoplankton in North American lakes (Le Noac'h and Beisner, 2024). Mena et al. report on the impact of resource limitation on mixotrophy and the advantage conferred by phagotrophic capability during short periods of light or nutrient limitation in harmful bloom-forming dinoflagellates (Mena et al., 2024). Cagle and Diehl used a process driven plankton model to examine interactions between mixoplankton and other components of the plankton along resource gradients (Cagle and Diehl, 2024). With the mixoplankton paradigm now firmly established many questions remain about implications for ecosystem processes (Millette et al., 2023). We look forward to more significant contributions to our understanding of the role of mixotrophy in aquatic food webs in coming years. We thank John Wehr and John Dolan for helpful discussions and our co-chairs Aditee Mitra, Sarah Princiotta, Suzana Leles, and Sebastiaan Koppelle for their contribution to organizing the sessions at the ASLO-meeting in Palma de Mallorca, Spain, in 2023. US National Science Foundation; the Scientific Committee on Oceanic Research; and the Dutch Research Council.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".