Metabarcoding reveals environmental factors influencing spatio‐temporal variation in pelagic micro‐eukaryotes
Bibliographic record
Abstract
Marine environments harbour a vast diversity of micro-eukaryotic organisms (protists and other small eukaryotes) that play important roles in structuring marine ecosystems. However, micro-eukaryote diversity is not well understood. Likewise, knowledge is limited regarding micro-eukaryote spatial and seasonal distribution, especially over long temporal scales. Given the importance of this group for mobilizing energy from lower trophic levels near the base of the food chain to larger organisms, assessing community stability, diversity and resilience is important to understand ecosystem health. Herein, we use a metabarcoding approach to examine pelagic micro-eukaryote communities over a 2.5-year time series. Bimonthly surface sampling (July 2009 to December 2011) was conducted at four locations within Mobile Bay (Bay) and along the Alabama continental shelf (Shelf). Alpha-diversity only showed significant differences in Shelf sites, with the greatest differences observed between summer and winter. Beta-diversity showed significant differences in community composition in relation to season and the Bay was dominated by diatoms, while the Shelf was characterized by dinoflagellates and copepods. The northern Gulf of Mexico is heavily influenced by the Mobile River Basin, which brings low-salinity nutrient-rich water mostly during winter and spring. Community composition was correlated with salinity, temperature and dissolved silicate. However, species interactions (e.g. predation and parasitism) may also contribute to the observed variation, especially on the Shelf, which warrants further exploration. Metabarcoding revealed clear patterns in surface pelagic micro-eukaryote communities that were consistent over multiple years, demonstrating how these techniques could be greatly beneficial to ecological monitoring and management over temporal scales.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".