My PhD project investigated the contribution of Network Analysis (NA) to understanding animal spatial ecology using acoustic monitoring data. This research provided a significant and original contribution to the knowledge of the spatial ecology of six reef predator species and the environmental factors influencing their movement patterns. More importantly, NA enhanced our ability to analyse complex acoustic monitoring data sets.
By using NA, my project developed new approaches in data analysis to improve our understanding of fish movement ecology and provided standardised statistical methods in order to allow comparison between species or contexts. The multi-disciplinary nature of NA provides the researcher with convenient tools to understand the complexity of movement at different scales, compare movements between individuals or between species, and investigate the effect of environmental factors on the movement. Furthermore, NA can be used to assess the threats to aquatic ecosystems such as habitat fragmentation and loss, exposure to fisheries and climate change to help design and evaluate the effectiveness of management and conservation plans.
My PhD was innovative and particularly challenging as it used multiple statistical tools on multiple predator species in coastal and coral reef environments. It provided me with a great opportunity to learn and apply NA and traditional statistical methods, such as home range analysis, residency index and mixed effect modelling to analyse the mechanisms of movements in greater depth. The use of NA in acoustic telemetry studies is still in its infancy, however, its utility in analysing animal movement is now well established. I am really interested in further exploring the use of NA in aquatic animal movement ecology by integrating/further developing the multi-dimensional and dynamic aspects of movement to networks. Movements in the marine environment are multi-dimensional and dynamic in nature, therefore spatial features and time will influence interactions between animals and their environment. However, networks are a static representation of movement ignoring the 3-D and temporal dynamic aspects. Consequently, there is a need to incorporate spatial constraints and temporal dynamics into animal network studies, which is critical for understanding the processes driving the structure of networks.