Macroscale factors affecting diatom abundance: a synergistic use of Continuous Plankton Recorder and satellite remote sensing data

Diatoms exist in almost every aquatic regime; they are responsible for 20% of global carbon fixation and 25% of global primary production, and are regarded as a key food for copepods, which are subsequently consumed by larger predators such as fish and marine mammals. A decreasing abundance and a vulnerability to climatic change in the North Atlantic Ocean have been reported in the literature. In the present work, a data matrix composed of concurrent satellite remote sensing and Continuous Plankton Recorder (CPR) in situ measurements was collated for the same spatial and temporal coverage in the Northeast Atlantic. Artificial neural networks (ANNs) were applied to recognize and learn the complex non-monotonic and non-linear relationships between diatom abundance and spatiotemporal environmental factors. Because of their ability to mimic non-linear systems, ANNs proved far more effective in modelling the diatom distribution in the marine ecosystem. The results of this study reveal that diatoms have a regular seasonal cycle, with their abundance most strongly influenced by sea surface temperature (SST) and light intensity. The models indicate that extreme positive SSTs decrease diatom abundances regardless of other climatic conditions. These results provide information on the ecology of diatoms that may advance our understanding of the potential response of diatoms to climatic change.

Identifying four phytoplankton functional types from space: An ecological approach

Deriving maps of phytoplankton taxa based on remote sensing data using bio-optical properties of phytoplankton alone is challenging. A more holistic approach was developed using artificial neural networks, incorporating ecological and geographical knowledge together with ocean color, bio-optical characteristics, and remotely sensed physical parameters. Results show that the combined remote sensing approach could discriminate four major phytoplankton functional types (diatoms, dinoflagellates, coccolithophores, and silicoflagellates) with an accuracy of more than 70%. Models indicate that the most important information for phytoplankton functional type discrimination is spatio-temporal information and sea surface temperature. This approach can supply data for large-scale maps of predicted phytoplankton functional types, and an example is shown.

Placing biodiversity in ecosystem models without getting lost in translation

A key challenge to progressing our understanding of biodiversity’s role in the sustenance of ecosystem function is the extrapolation of the results of two decades of dedicated empirical research to regional, global and future landscapes. Ecosystem models provide a platform for this progression, potentially offering a holistic view of ecosystems where, guided by the mechanistic understanding of processes and their connection to the environment and biota, large-scale questions can be investigated. While the benefits of depicting biodiversity in such models are widely recognized, its application is limited by difficulties in the transfer of knowledge from small process oriented ecology into macro-scale modelling. Here, we build on previous work, breaking down key challenges of that knowledge transfer into a tangible framework, highlighting successful strategies that both modelling and ecology communities have developed to better interact with one another. We use a benthic and a pelagic case-study to illustrate how aspects of the links between biodiversity and ecosystem process have been depicted in marine ecosystem models (ERSEM and MIRO), from data, to conceptualisation and model development. We hope that this framework may help future interactions between biodiversity researchers and model developers by highlighting concrete solutions to common problems, and in this way contribute to the advance of the mechanistic understanding of the role of biodiversity in marine (and terrestrial) ecosystems.

Effect of phytoplankton size classes on bio-optical properties of phytoplankton in the Western Iberian coast: Application of models

Chlorophyll-a satellite products are routinely used in oceanography, providing a synoptic and global view of phytoplankton abundance. However, these products lack information on the community structure of the phytoplankton, which is crucial for ecological modelling and ecosystem studies. To assess the usefulness of existing methods to differentiate phytoplankton functional types (PFT) or phytoplankton size classes from satellite data, in-situ phytoplankton samples collected in the Western Iberian coast, on the North-East Atlantic, were analysed for pigments and absorption spectra. Water samples were collected in five different locations, four of which were located near the shore and another in an open-ocean, seamount region. Three different modelling approaches for deriving phytoplankton size classes were applied to the in situ data. Approaches tested provide phytoplankton size class information based on the input of pigments data (Brewin et al., 2010), absorption spectra data (Ciotti et al., 2002) or both (Uitz et al., 2008). Following Uitz et al. (2008), results revealed high variability in microphytoplankton chlorophyll-specific absorption coefficients, ranging from 0.01 to 0.09 m2 (mg chl)− 1 between 400 and 500 nm. This spectral analysis suggested, in one of the regions, the existence of small cells (< 20 μm) in the fraction of phytoplankton presumed to be microphytoplankton (based on diagnostic pigments). Ciotti et al. (2002) approach yielded the highest differences between modelled and measured absorption spectra for the locations where samples had high variability in community structure and cell size. The Brewin et al. (2010) pigment-based model was adjusted and a set of model coefficients are presented and recommended for future studies in offshore water of the Western Iberian coast.

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.