ABSTRACT
Bioenergetics is the study of how animals achieve energetic balance. Energetic balance results from the energetic expenditure of an individual and the energy they extract from their environment. Ingested energy depends on several extrinsic (e.g prey species, nutritional value and composition, prey density and availability) and intrinsic factors (e.g. foraging effort, success at catching prey, digestive processes and associated energy losses, and digestive capacity). While the focus in bioenergetic modelling is often on the energetic costs an animal incurs, the robust estimation of an individual's energy intake is equally critical for producing meaningful predictions. Here, we review the components and processes that affect energy intake from ingested gross energy to biologically useful net energy (NE). The current state of knowledge of each parameter is reviewed, shedding light on research gaps to advance this field. The review highlighted that the foraging behaviour of many marine mammals is relatively well studied via biologging tags, with estimates of success rate typically assumed for most species. However, actual prey capture success rates are often only assumed, although we note studies that provide approaches for its estimation using current techniques. A comprehensive collation of the nutritional content of marine mammal prey species revealed a robust foundation from which prey quality (comprising prey species, size and energy density) can be assessed, though data remain unavailable for many prey species. Empirical information on various energy losses following ingestion of prey was unbalanced among marine mammal species, with considerably more literature available for pinnipeds. An increased understanding and accurate estimate of each of the components that comprise a species NE intake are an integral part of bioenergetics. Such models provide a key tool to investigate the effects of disturbance on marine mammals at an individual and population level and to support effective conservation and management.
ABSTRACT
The SARS-CoV-2 virus is responsible for the COVID-19 global public health emergency, and the disease it causes is highly variable in its clinical presentation. Clinical phenotypes are heterogeneous both in terms of presentation of symptoms in the host and response to therapy. Several studies and initiatives have been established to analyse and review host genetic epidemiology associated with COVID-19. Our research group curated these articles into a web-based database using the python application-server framework Django. The database provides a searchable research tool describing current literature surrounding COVID-19 host genetic factors associated with disease outcome. This paper describes the COHG-SA database and provides an overview of the analyses that can be derived from these data.
