Capturing the range of variables that may have significant impact across the salmons life-stages

Salmon and salmon-related knowledge that could be useful to mobilise in the pursuit of greater understanding of mortality processes spans a wide range of disciplines, geographical areas and time durations. To aid understanding we are building a Central Data Resource to store, order and mobilise knowledge resources. To facilitate linking up potential knowledge resources to priority questions and the focal places and spaces in the salmon life cycle domains; we propose an outline organisational structure into which salmon related knowledge can be classified.  

Knowledge resources are initially labelled by their main focus into three categories :  physical, biological or salmon trait (see below). These resources may originate from empirical (direct measurement), derived (simulated outputs) or expert (opinion) sources. The knowledge within these three categories will also have associated information that allows geographical and temporal indexing to be carried out, directing searches and building of specific queries 

• Physical environment : knowledge relating to the physical habitats and conditions that are relevant to salmon at some stage in their life cycle. (e.g. estimated wetted accessible area for egg deposition or daily Sea Surface Temperature time series datasets) 

• Biological processes : knowledge relating to other species (competitors , predators etc) that may be relevant to salmon at some stage in their life cycle (e.g. the biomass of sandeels along marine migration routes,  or population trends in known predator species.  

• Salmon traits . Knowledge relating to the salmon themselves from direct measurement or assessment methods (e.g. data on abundance, tracking studies, body condition, genetics.  

Within each of these three categories lies a suggested list of variable classes that allow further splitting the focus of the knowledge resources, representing the range of variables that may have significant impact across the salmons life stages. These newly clustered groups of knowledge resources can then be more easily associated with priority hypotheses and / or domains of interest, enabling complex and focused data queries to be built and managed within the Central Data Resource. The variable classes are authored with existing definitions in mind and are deliberately either very narrow, relating to a single, well defined, observable event (e.g. Sea Surface Temperature) or wide to capture sets of observable events (e.g. Fish Parasites, Pathogens and Disease status). 

This level of structuring is important in aiding the mobilisation and eventual use of knowledge resources that could be of great importance in understanding the drivers of salmon mortality, and in planning effective management responses.