Carbon fluxes and pelagic ecosystem dynamics near two western Antarctic Peninsula Adélie penguin colonies: an inverse model approach

An inverse food-web model for the western Antarctic Peninsula (WAP) pelagic food web was constrained with data from Palmer Long Term Ecological Research (PAL-LTER) project annual austral summer sampling cruises. Model solutions were generated for 2 regions with Adelie penguin Pygoscelis adeliae colonies presenting different population trends (a northern and a southern colony) for a 12 yr period (1995-2006). Counter to the standard paradigm, comparisons of carbon flow through bacteria, microzooplankton, and krill showed that the diatom-krill-top predator food chain is not the dominant pathway for organic carbon exchanges. The food web is more complex, including significant contributions by microzooplankton and the microbial loop. Using both inverse model results and network indices, it appears that in the northern WAP the food web is dominated by the microbial food web, with a temporal trend toward its increasing importance. The dominant pathway for the southern WAP food web varies from year to year, with no detectable temporal trend toward dominance of microzooplankton versus krill. In addition, sensitivity analyses indicated that the northern colony of Adelie penguins, whose population size has been declining over the past 35 yr, appears to have sufficient krill during summer to sustain its basic metabolic needs and rear chicks, suggesting the importance of other processes in regulating the Adelie population decline.

Putting meaning into NETMAR – the open service network for marine environmental data

The open service network for marine environmental data (NETMAR) project uses semantic web technologies in its pilot system which aims to allow users to search, download and integrate satellite, in situ and model data from open ocean and coastal areas. The semantic web is an extension of the fundamental ideas of the World Wide Web, building a web of data through annotation of metadata and data with hyperlinked resources. Within the framework of the NETMAR project, an interconnected semantic web resource was developed to aid in data and web service discovery and to validate Open Geospatial Consortium Web Processing Service orchestration. A second semantic resource was developed to support interoperability of coastal web atlases across jurisdictional boundaries. This paper outlines the approach taken to producing the resource registry used within the NETMAR project and demonstrates the use of these semantic resources to support user interactions with systems. Such interconnected semantic resources allow the increased ability to share and disseminate data through the facilitation of interoperability between data providers. The formal representation of geospatial knowledge to advance geospatial interoperability is a growing research area. Tools and methods such as those outlined in this paper have the potential to support these efforts.

Shark personalities? Repeatability of social network traits in a widely distributed predatory fish. Behavioral Ecology and Sociobiology, 68, 1995-2003.

Interest in animal personalities has generated a burgeoning literature on repeatability in individual traits such as boldness or exploration through time or across different contexts. Yet, repeatability can be influenced by the interactive social strategies of individuals, for example, consistent inter-individual variation in aggression is well documented. Previous work has largely focused on the social aspects of repeatability in animal behaviour by testing individuals in dyadic pairings. Under natural conditions, individuals interact in a heterogeneous polyadic network. However, the extent to which there is repeatability of social traits at this higher order network level remains unknown. Here, we provide the first empirical evidence of consistent and repeatable animal social networks. Using a model species of shark, a taxonomic group in which repeatability in behaviour has yet to be described, we repeatedly quantified the social networks of ten independent shark groups across different habitats, testing repeatability in individual network position under changing environments. To understand better the mechanisms behind repeatable social behaviour, we also explored the coupling between individual preferences for specific group sizes and social network position. We quantify repeatability in sharks by demonstrating that despite changes in aggregation measured at the group level, the social network position of individuals is consistent across treatments. Group size preferences were found to influence the social network position of individuals in small groups but less so for larger groups suggesting network structure, and thus, repeatability was driven by social preference over aggregation tendency.

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.