Protistan community composition and metabolism in the North Pacific Subtropical Gyre: Influences of mesoscale eddies and depth.

Marine protists and their metabolic activities are intricately tied to the cycling of nutrients and the flow of energy through microbial food webs. Physiochemical changes in the environment, such as those that result from mesoscale eddies, may impact protistan communities, but the effects that such changes have on protists are poorly known. A metatranscriptomic study was conducted to investigate how eddies affected protists at adjacent cyclonic and anticyclonic eddy sites in the oligotrophic ocean at four depths from 25 to 250 m. Eddy polarity impacted protists at all depths sampled, although the effects of eddy polarity were secondary to the impact of depth across the depth range. Eddy-induced vertical shifts in the water column yielded differences in the cyclonic and anticyclonic eddy protistan communities, and these differences were the most pronounced at and just below the deep chlorophyll maximum. An analysis of transcripts associated with protistan nutritional physiology at 150 m revealed that cyclonic eddies may support a more heterotrophic community, while anticyclonic eddies promote a more phototrophic community. The results of this study indicate that eddies alter the metabolism of protists particularly in the lower euphotic zone and may therefore impact carbon export from the euphotic zone.

The underappreciated role of biotic factors in controlling the bloom ecology of potentially harmful microalgae in the Hudson-Raritan Bay.

Despite widespread distribution of harmful algal blooms (HABs) and new and improved methods for detecting and quantifying them, no unifying ecological explanation has been found. Improved understanding depends upon local, ecological studies that include analysis of phytoplankton species data in relation to both abiotic and biotic parameters. Ecological network analysis was used to detect co-occurrence patterns among abiotic and biotic parameters in a long-term monitoring dataset (i.e., 2010-2021) from the eutrophic Hudson-Raritan Estuary (HRE) between the states of New York and New Jersey. The regular co-occurrence of potentially harmful bloom-forming species with companion species observed through microscopy was supported by the results of ecological network analysis, which showed that there were far more associations between HAB species and biotic parameters (∼95%) than abiotic parameters (∼5%). Temperature was the environmental variable that was most associated with HAB species throughout the estuary. The numerous network associations of HAB species with one another and with diatoms, dinoflagellates, and zooplankton highlight the complexity of planktonic food webs and interactions. Results also suggest that some taxa may play a central role in structuring the HRE plankton communities. These findings demonstrate that biotic associations play an underappreciated role in plankton structure and the value of examining the ecology of HAB species within the breadth of their biological communities. While network analysis does not fully explain and confirm complex associations among species, it does provide fresh insights and testable hypotheses to strengthen understanding and improve prediction.

NetGAM: Using generalized additive models to improve the predictive power of ecological network analyses constructed using time-series data.

Ecological network analyses are used to identify potential biotic interactions between microorganisms from species abundance data. These analyses are often carried out using time-series data; however, time-series networks have unique statistical challenges. Time-dependent species abundance data can lead to species co-occurrence patterns that are not a result of direct, biotic associations and may therefore result in inaccurate network predictions. Here, we describe a generalize additive model (GAM)-based data transformation that removes time-series signals from species abundance data prior to running network analyses. Validation of the transformation was carried out by generating mock, time-series datasets, with an underlying covariance structure, running network analyses on these datasets with and without our GAM transformation, and comparing the network outputs to the known covariance structure of the simulated data. The results revealed that seasonal abundance patterns substantially decreased the accuracy of the inferred networks. In addition, the GAM transformation increased the predictive power (F1 score) of inferred ecological networks on average and improved the ability of network inference methods to capture important features of network structure. This study underscores the importance of considering temporal features when carrying out network analyses and describes a simple, effective tool that can be used to improve results.