How many Coccolithovirus genotypes does it take to terminate an Emiliania huxleyi bloom?

Giant viruses are known to be significant mortality agents of phytoplankton, often being implicated in the terminations of large Emiliania huxleyi blooms. We have previously shown the high temporal variability of E. huxleyi-infecting coccolithoviruses (EhVs) within a Norwegian fjord mesocosm. In the current study we investigated EhV dynamics within a naturally-occurring E. huxleyi bloom in the Western English Channel. Using denaturing gradient gel electrophoresis and marker gene sequencing, we uncovered a spatially highly dynamic Coccolithovirus population that was associated with a genetically stable E. huxleyi population as revealed by the major capsid protein gene (mcp) and coccolith morphology motif (CMM), respectively. Coccolithoviruses within the bloom were found to be variable with depth and unique virus populations were detected at different stations sampled indicating a complex network of EhV-host infections. This ultimately will have significant implications to the internal structure and longevity of ecologically important E. huxleyi blooms.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.