Editorial: Marine plankton: biological and chemical interactions
Bibliographic record
Abstract
Studies aimed to understand the marine biological and chemical interactions have been more difficult to conduct than those in terrestrial environments due to the challenges of exploring an environment that is difficult for human access. Undoubtedly, biotic interactions together with chemicals released into the environment have shaped marine plankton communities. Cutting-edge technologies have allowed us to gain a more specific and detailed understanding of the fascinating and intriguing regulation of marine plankton communities. The Research Topic “Marine Plankton: Biological and Chemical Interactions” brings together studies of food webs in marine ecosystems, from diverse geographic regions. To understand the impact of biotic and abiotic factors on the regulation of the plankton community, six papers with the participation of 46 authors employed up-to-date methodologies such as quantitative polymerase chain reaction assay, DNA metabarcoding, flow cytometry, bio-optics, and chemical cue analyses. Below we summarize the content of these papers and highlight their contributions.DNA metabarcoding has been increasingly used in recent decades to study planktonic communities. Three studies included in this Topic used this methodology to investigate geographic differentiation, seasonal changes, and diversity and biogeography along the coasts of America and Asia. Rhodes et al. characterized microbial communities using DNA metabarcoding and flow cytometry along the northwest coast of the United States (Cape Mendocino) and Canada (Queen Charlotte Sound). Eukaryotic microbial communities showed a strong geographical differentiation compared to bacterial and archaeal communities; also, various seawater factors were decisive in shaping these communities. Lin et al. used environmental DNA (eDNA) metabarcoding to understand the influence of trophic groups on the complexity and stability of species networks in subtropical coastal waters of the northern South China Sea. They observed a loose interaction network during summer with a lower biodiversity compared to spring. Also, a close correlation was found among species interactions, environmental factors and biodiversity. Their findings suggest that the diversity of producers is the foundation of network complexity and influences its stability, whereas the diversity of consumers and their variability of predation strategies may lead to differences in the network stability across seasons. Wu et al. performed a metabarcoding survey using hypervariable regions of 18S rRNA to understand the diversity and biogeography of dinoflagellates in the Kuroshio Current and the influence of environmental factors. Communities of free-living dinoflagellates differed with depth and nutrient concentrations. At greater depths and farther away from the coast, dinoflagellate genetic diversity decreased. Additionally, Harmful Algal Bloom assemblages were less common on the continental shelf in nutrient rich waters than in areas influenced by the Kuroshio Current. One study of benthic dinoflagellate species distribution was included in this Topic. Based on the isolation of benthic Coolia dinoflagellate strains, morphological and molecular analyses Hyung et al. identified two species of Coolia in South Korean coastal waters reporting Coolia palmyrensis in this region for the first time. By using qPCR assay, they revealed that this species occurred in seaweeds around Jeju Island, whereas Coolia malayensis was only observed further north. These findings, along with a decade of monitoring data (Hyung et al., 2024), confirmed the introduction and establishment of C. palmyrensis, as well as habitat shifts of C. malayensis, providing the first insights into the migration patterns of these benthic dinoflagellates in this region.Satellite ocean color data offers an effective means of observing possible changes in the state of marine ecosystems (Glukhovets, 2023). Due to the complex optical characteristics of coastal Antarctic waters, Tripathy et al. analyzed bio-optical properties to comprehend their interaction and variability in relation to the physicochemical conditions in Prydz Bay. Results indicated that the upper stratified layer was caused by the influx of glacial meltwater and lower wind activity. The light abortion properties suggested the prevalence of a ‘pigment packaging’ effect in surface and deeper layers, reflecting variability in phytoplankton productivity (PP). PP variability was better explained by aph(λ) than by the content of chlorophyll-a, reaffirming the advantages of using the aph(λ)-based approach for estimating PP from satellite sensors over the traditional chlorophyll-a method. These results significantly affect calculations of the biological pump`s contribution to the global carbon cycle, and this approach should be confirmed in other regions.In the Antarctic Peninsula, to gain a deeper understanding of the trophic interactions of Antarctic krill (Euphasia superba). Hellesey et al. measured the effect of penguin guano (a presumed predator cue), chlorophyll concentration, and flow speed on krill swimming behavior. In the presence of guano, these crustaceans made sharper turns and altered their swimming speed, clearly demonstrating an instinct to evade negative chemical signals associated with penguin guano. When exposure was prolonged, feeding decreased, impacting krill's nutritional value, prey survival, as well as carbon sequestration in the ocean. These findings suggest that krill use chemical signals to adjust their behavior in response to food availability and predation risk. All the studies of the Research Topic demonstrate the value of advanced tools for detecting the presence of marine microorganisms, emphasizing their role in biodiversity monitoring and real-time detection of species composition (Lin et al.). The value of integrating microbial sampling and analysis with broad-scale oceanographic surveys to gain insights into pelagic community structures is highlighted (Rhodes et al.). Detailed information is provided on the influence of environmental factors (Lin et al.), biotic interactions and chemical cues (Hellesey et al.), trophic strategies in dinoflagellates (Wu et al.), and phytoplankton productivity (Tripathy et al.). A first insight into migration patterns among benthic Coolia species is also presented (Hyung et al.). The findings presented in the Research Topic shed new light on the intricate interactions within plankton communities, spanning from sub-tropical to polar regions, and shaped by environmental, biological, and chemical factors. This information is crucial and highlights the value of high-quality data obtained using cutting-edge technologies, contributing to our understanding of the impacts of human activities and climate change on marine plankton ecosystems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.023 | 0.020 |
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 source (direct Gemma or distilled Codex), 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".