Looking backwards to look forwards: the role of natural history in temperate reef ecology

Temperate reefs are superb tractable systems for testing hypotheses in ecology and evolutionary biology. Accordingly there is a rich history of research stretching back over 100 years, which has made major contributions to general ecological and evolutionary theory as well as providing better understanding of how littoral systems work by linking pattern with process. A brief resumé of the history of temperate reef ecology is provided to celebrate this rich heritage. As a community, temperate reef ecologists generally do well designed experiments and test well formulated hypotheses. Increasingly large datasets are being collected, collated and subjected to complex meta-analyses and used for modelling. These datasets do not happen spontaneously – the burgeoning subject of macroecology is possible only because of the efforts of dedicated natural historians whether it be observing birds, butterflies, or barnacles. High-quality natural history and old-fashioned field craft enable surveys or experiments to be stratified (i.e. replicates are replicates and not a random bit of rock) and lead to the generation of more insightful hypotheses. Modern molecular approaches have led to the discovery of cryptic species and provided phylogeographical insights, but natural history is still required to identify species in the field. We advocate a blend of modern approaches with old school skills and a fondness for temperate reefs in all their splendour.

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.