18S rRNA gene
18S rRNA gene at World Data Ocean is a file of 3 stories. The newest of them: “Long-Term Shifts in Plankton Communities Signal Warming’s Impact on Oslofjord”, “Dynamic Biofouling: DNA Analysis Reveals Patterns Across Ocean Conditions”, and “Predicting Plankton Diversity: Machine Learning and Uneven Data Insights”. The Oslofjord's plankton community is rewriting the expected script of climate change. Season explained the largest share of variation in biofouling communities, but substrate and depth still left their mark. A Global Hub for Ocean Intelligence 4 World Data Ocean is a centralized digital platform where researchers, scientists, and ocean enthusiasts converge to… The list below is every 18S rRNA gene story on World Data Ocean, newest first.
Long-Term Shifts in Plankton Communities Signal Warming’s Impact on Oslofjord
The Oslofjord's plankton community is rewriting the expected script of climate change. Comparing 2009-2011 to 2023-2024, we found a 5.6-fold drop in the dinoflagellate-to-diatom ratio, driven largely by a decline in the heterotrophic genus *Gyrodinium*. This challenges the assumption that warming consistently favors heterotrophic taxa in temperate waters. The data is empirical, measured, and clear. For broader context on shifting marine baselines, our coverage of Mediterranean Phytoplankton Diversity Shifts Observed Over 25 Years offers a valuable comparison.
Dynamic Biofouling: DNA Analysis Reveals Patterns Across Ocean Conditions
Season explained the largest share of variation in biofouling communities, but substrate and depth still left their mark. That is the takeaway from a new study that deployed two synthetic materials at three depths across summer and winter, analyzing 432 samples with DNA metabarcoding. Winter assemblages, dense and persistent, were dominated by Hydrozoa; summer communities, rapid but removable, favored Oligohymenophorea and Malacostraca. This kind of empirical, replicated work sharpens our understanding of fouling pressure.
Predicting Plankton Diversity: Machine Learning and Uneven Data Insights
Machine learning promises scalability in predicting plankton diversity, but this study reveals a hard truth: models trained on imbalanced data falter where it matters most. Using XGBoost on 18S rRNA samples from Mediterranean stations, the team found strong in-sample performance (R² = 0.44) that collapsed under leave-one-dataset-out validation (R² = 0.09). That gap underscores a critical lesson: environmental coverage and protocol standardization are not optional. For those exploring similar challenges, our piece on accelerating ocean model development with AI offers a complementary perspective.