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.