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.

Bucephaloides-Gracilescens - A Quantitative Study Of The Lysosomal Cellular Stress Response Induced By The Sporocysts And Developing Cercariae Within The White Furrow Shell Abra-Alba

Reproductive stress is apparent inAbra alba as a result of infection with the sporocysts ofBucephaloides gracilescens, culminating in castration in heavily infected specimens. The bivalve is also subject to mechanical stress from actively growing sporocyst tubules and nutritional stress due to the nutrient requirement of large numbers of germ balls within the sporocysts. Using the digestive cell lysosomal system ofAbra as a monitor, it was possible to demonstrate quantitatively a parasite-induced cellular stress response by applying a sensitive cytochemical test for lysosomal stability. Lysosomal stability was determined as the labilisation period for latent Nacetyl-β-hexosaminidase (NAH), measured by microdensitometry. In uninfectedAbra, digestive cell lysosomal NAH expressed structure-linked latency. Hence a significantly longer labilisation period was required compared with infectedAbra, where the parasitic burden with its associated stress effects resulted in a destabilisation of the lysosomal membrane. This reduced the latency of the enzyme, so that a much shorter labilisation period was required for the stressed tissue to express maximum lysosomal enzyme activity. It is suggested that the lysosomal system of the digestive cells inAbra can be used as a sensitive monitor of the stress induced by the sporocysts and developing cercariae ofBucephaloides.

Impacts of Climate Modes on Air-Sea Heat Exchange in the Red Sea

The impacts of various climate modes on the Red Sea surface heat exchange are investigated using the MERRA reanalysis and the OAFlux satellite reanalysis datasets. Seasonality in the atmospheric forcing is also explored. Mode impacts peak during boreal winter [December–February (DJF)] with average anomalies of 12–18 W m−2 to be found in the northern Red Sea. The North Atlantic Oscillation (NAO), the east Atlantic–west Russia (EAWR) pattern, and the Indian monsoon index (IMI) exhibit the strongest influence on the air–sea heat exchange during the winter. In this season, the largest negative anomalies of about −30 W m−2 are associated with the EAWR pattern over the central part of the Red Sea. In other seasons, mode-related anomalies are considerably lower, especially during spring when the mode impacts are negligible. The mode impacts are strongest over the northern half of the Red Sea during winter and autumn. In summer, the southern half of the basin is strongly influenced by the multivariate ENSO index (MEI). The winter mode–related anomalies are determined mostly by the latent heat flux component, while in summer the shortwave flux is also important. The influence of the modes on the Red Sea is found to be generally weaker than on the neighboring Mediterranean basin.