Genetic structure of whiting (Merlangius merlangus) in the north east Atlantic and adjacent waters

Accurate identification of stock boundaries is essential for efficient fisheries management, hence the present study focused on the genetic structure of whiting. To this aim, 488 individuals collected from the southern Bay of Biscay to the southern Norwegian coast were genotyped using seven microsatellites. A low level of genetic structuring was detected in Atlantic waters since only the Bay of Biscay differentiated from more northern samples. The lack of genetic structure along the western margin of the British Isles is consistent with a high level of passive transport of pelagic eggs and larvae due to the combined influence of the North Atlantic Current and the Shelf Edge Current. High levels of dispersal could also occur between the western British Isles and the North Sea through both the branching of the North Atlantic Current into the northern North Sea and from the residual current flowing from the English Channel to the Southern Bight. In contrast, a significant genetic structure was identified within the North Sea, and this may be associated with the complex oceanography of this basin and retention systems reducing larval dispersal. In addition, considering also genetic, phenotypic and tag-recapture data collected on whiting, a learned homing behaviour of adults toward spawning areas may be hypothesised.

Lipid productivity and cell wall ultrastructure of six strains of Nannochloropsis: Implications for biofuel production and downstream processing

Microalgae are generating considerable interest for third generation biodiesel production. However, appropriate strain selection is proving challenging due to the significant variation in cellular physiology, metabolic potential and genetics observed even amongst strains deemed morphologically similar. Six strains of Nannochloropsis from the CCAP culture collection were assessed for their lipid productivity and cellular structure, as proxies for oil production and harvesting ease, to assess their suitability as biodiesel production platforms. Differences in growth rate and lipid accumulation across the strains were observed. Nannochloropsis oculata strain 849/7 showed significantly reduced doubling time compared to Nannochloropsis salina strain 849/3, whilst Nannochloropsis oceanica 849/10 produced the highest lipid content. In addition the six strains could be differentiated into 3 distinct classes based on their cell wall thickness, which varied across the strains from 63 to 119 nm and which is independent of both species and geographical isolation location. The importance of these variations in ultrastructure and physiology for biodiesel production is discussed.

A puzzle with many pieces: the genetic structure and diversity of Phaeocystis antarctica Karsten (Prymnesiophyta)

Strong ocean current systems characterize the Southern Ocean. The genetic structure of marine phytoplankton species is believed to depend mainly on currents. Genetic estimates of the relatedness of populations of phytoplankton species therefore should provide a proxy showing to what extent different geographic regions are interconnected by the ocean current systems. In this study, spatial and temporal patterns of genetic diversity were studied in the circumpolar prymnesiophyte Phaeocystis antarctica Karsten using seven nuclear microsatellite loci. Analyses were conducted for 86 P. antarctica isolates sampled around the Antarctic continent between 1982 and2007. The resultsrevealed highgenetic diversity without singlegenotypes recurringeven amongisolateswithin a bloom or originating from the same bucket of water. Populations of P. antarctica were significantly differentiated among the oceanic regions. However, some geographically distant populations were more closely related to each other than they were to other geographically close populations. Temporal haplotype turnover within regions was also suggested by the multilocus fingerprints. Our data suggest that even within blooms of P. antarctica genetic diversity and population sizes are large but exchange between different regions canbe limited. Positive and significant inbreeding coefficients hint at further regional substructure of populations, suggestingthat patches, once isolated from one another, may not reconnect. These data emphasize that even for planktonic species in a marine ecosystem that is influenced by strong currents, significant breaks in geneflow may occur.

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.