The modelling approach
The Bayesian hierarchical approach used here provides a means of modelling the time-series of benthic imagery that captures inherent spatial and temporal structures in the data. We found that including both spatial and spatio-temporal dependence improved model fits to the data. Spatial dependence was found to occur over relatively short distances (mean of approximately 17 m), with relatively high spatial variance. This reflects the patchy nature of barrens in the early stages, where smaller “incipient” barrens form as early arrivals settle in areas where complex habitat allows the establishment of discrete barrens patches.
Previous research using the same sites but just a single timepoint, and aggregating data into transects found a suggestive but non-significant effect of the NTR on the presence of barrens (Perkins et al., 2015). Here, rather than aggregating the data, we use images as the basis of the analysis. Increasing the sample size to a large number of images rather than a few transects increases the probability of detecting a temporal change in urchin barren presence. Our analysis showed that spatio-temporal dependence is an important feature of the imagery data that should be accounted for by statistical modelling. We therefore advocate for the use of similar approaches in future analysis of marine imagery datasets generated by ecological monitoring programs.