Trophodynamics and stability of regional scale ecosystems in the Northeast Atlantic

This study addresses the long-term stability of three trophic groupings in the Northeast Atlantic at regional scales. The most abundant taxa representing phytoplankton, herbivorous copepods, and carnivorous zooplankton were examined from the Continuous Plankton Recorder database. Multivariate control charts using a Bray–Curtis similarity metric were used to assess whether fluctuations within trophic groupings were within or beyond the expected variability. Two evaluation periods were examined: annual changes between 1960 and 1999 (2000–2009 baseline) and recent changes between 2000 and 2009 (1960–1999 baseline). The trends over time in abundance/biomass of trophic levels were region-specific, especially in carnivorous copepods, where abundance did not mirror trends in the overall study area. The stability of phytoplankton was within the expected limits, although not in 2008 and 2009. Higher trophic levels were less stable, perhaps reflecting the added complexity of interactions governing their abundance. In addition, some regions were consistently less stable than others. Correlations in stability between adjacent trophic levels were positive at large marine ecosystem scale but generally non-significant at regional scales. The study suggests that certain regions may be particularly vulnerable to periods of instability in community structure. The benefits of using the control chart method rather than other multivariate measures of plankton dynamics are discussed.

Relationships between biodiversity and the stability of marine ecosystems: comparisons at a European scale using meta-analysis.

The relationship between biodiversity and stability of marine benthic assemblages was investigated using existing data sets (n = 28) covering various spatial (m-km) and temporal (1973-2006) scales in different benthic habitats (emergent rock, rock pools and sedimentary habitats) through meta-analyses. Assemblage stability was estimated by measuring temporal variances of species richness, total abundance (density or % cover) and community species composition and abundance structure (using multivariate analyses). Positive relationships between temporal variability in species number and richness were generally observed at both quadrat (<1 m2) and site (100 m2) scales, while no relationships were observed by multivariate analyses. Positive relationships were also observed at the scale of site between temporal variability in species number and variability in community structure with evenness estimates. This implies that the relationship between species richness or evenness and species richness variability is slightly positive and depends on the scale of observation, suggesting that biodiversity per se is important for the stability of ecosystems. Changes within community assemblages in terms of structure are, however, generally independent of biodiversity, suggesting no effect of diversity, but the potential impact of individual species, and/or environmental factors. Except for sedimentary and rock pool habitats, no relationship was observed between temporal variation of the aggregated variable of total abundances and diversity at either scale. Overall our results emphasise that relationships depend on scale of measurements, type of habitats and the marine systems (North Atlantic and Mediterranean) considered.

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.