Ecological equivalence of species within phytoplankton functional groups

1.There are tens of thousands of species of phytoplankton found throughout the tree of life. Despite this diversity, phytoplankton are often aggregated into a few functional groups according to metabolic traits or biogeochemical role. We investigate the extent to which phytoplankton species dynamics are neutral within functional groups. 2.Seasonal dynamics in many regions of the ocean are known to affect phytoplankton at the functional group level leading to largely predictable patterns of seasonal succession. It is much more difficult to make general statements about the dynamics of individual species. 3.We use a 7 year time-series at station L4 in the Western English Channel with 57 diatom and 17 dinoflagellate species enumerated weekly to test if the abundance of diatom and dinoflagellate species vary randomly within their functional group envelope or if each species is driven uniquely by external factors. 4.We show that the total biomass of the diatom and dinoflagellate functional groups is well predicted by irradiance and temperature and quantify trait values governing the growth rate of both functional groups. The biomass dynamics of the functional groups are not neutral and each has their own distinct responses to environmental forcing. Compared to dinoflagellates, diatoms have faster growth rates, and grow faster under lower irradiance, cooler temperatures, and higher nutrient conditions. 5.The biomass of most species vary randomly within their functional group biomass envelope, most of the time. As a consequence, modelers will find it difficult to predict the biomass of most individual species. Our analysis supports the approach of using a single set of traits for a functional group and suggests that it should be possible to determine these traits from natural communities.

An unusually large phytoplankton spring bloom drives rapid changes in benthic diversity and ecosystem function

In 2012, the Western English Channel experienced an unusually large and long-lived phytoplankton spring bloom. When compared with data from the past 20 years, average phytoplankton biomass at Station L4 (part of the Western Channel Observatory) was approximately 3× greater and lasted 50% longer than any previous year. Regular (mostly weekly) box core samples were collected from this site before, during and after the bloom to determine its impact on macrofaunal abundance, diversity, biomass, community structure and function. The spring bloom of 2012 was shown to support a large and rapid response in the majority of benthic taxa and functional groups. However, key differences in the precise nature of this response, as well as in its timing, was observed between different macrofauna feeding groups. Deposit feeders responded almost instantly at the start of the bloom, primarily thorough an increase in abundance. Suspension feeders and opportunistic/predatory/carnivorous taxa responded slightly more slowly and primarily with an increase in biomass. At the end of the bloom a rapid decline in macrobenthic abundance, diversity and biomass closely followed the decline in phytoplankton biomass. With suspension feeders showing evidence of this decline a few weeks before deposit feeders, it was concluded that this collapse in benthic communities was driven primarily by food availability and competition. However, it is possible that environmental hypoxia and the presence of toxic benthic cyanobacteria could also have contributed to this decline. This study shows evidence for strong benthic–pelagic coupling at L4; a shallow (50 m), coastal, fine-sand habitat. It also demonstrates that in such habitats, it is not just planktonic organisms that demonstrate clear community phenology. Different functional groups within the benthic assemblage will respond to the spring bloom in specific manner, with implications for key ecosystem functions and processes, such as secondary production and bioturbation. Only by taking integrated benthic and pelagic observations over such fine temporal scales (weekly) was the current study able to identify the intimate structure of the benthic response. Similar studies from other habitats and under different bloom conditions are urgently needed to fully appreciate the strength of benthic–pelagic coupling in shallow coastal environments.

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.