The field of benthic habitat mapping has entered an era of automated statistical methods
that have increased the capacity to produce maps as marine management tools. Spurred by
a confluence of advances in acoustic remote sensing, open-source statistical tools, GIS, and
computing power, these methods facilitate quick and objective mapping of habitats and
physical seabed characteristics. Their performance and accessibility have led to widespread
uptake, yet key spatial issues associated with these methods have not fully translated into
the benthic habitat mapping workflow. Towards establishing “best practices”, this thesis
explores the application of several spatial concepts to benthic habitat mapping using three
Canadian Arctic case studies.
Relationships between seabed morphology and benthic habitats are well-established.
Though recognized as a critical element in the field of geomorphometry, the scale
dependence of these relationships is commonly neglected in habitat mapping. Chapter 2
provides evidence of the scale dependence of benthic terrain variables and demonstrates
methods for testing and selecting from among many variables and scales for modelling the
distribution of sediment grain size near Qikiqtarjuaq, Nunavut.
Given challenges associated with marine data collection that are pronounced in the Arctic,
benthic habitat maps commonly utilize multi-year and multisource datasets. Despite
apparent advantages, there can be substantial challenges associated with the compatibility
and spatial properties of such data. Chapter 3 demonstrates that spatially autocorrelated
samples are likely to inflate estimates of predictive performance and uses a spatial resampling strategy to estimate and correct for inflation in a multi-model Arctic clam
habitat map near Qikiqtarjuaq, Nunavut.
Classified seabed maps are a common requirement for marine management and one of two
broad approaches are often selected to produce them. Chapter 4 examines differences
between classification and continuous modelling approaches in a spatial context to produce
classified seabed sediment maps for inner Frobisher Bay, Nunavut. Non-spatial methods
failed to indicate whether models could extrapolate to unsampled areas, which was a
requirement for this study. When evaluated in a spatial context, the qualities of the
classification approach made it more suitable, which was a function of ground-truth dataset
characteristics and the predictive goals of the model. Non-spatial techniques may be
appropriate for interpolation, but the ability to extrapolate needs to be examined in a spatial
context