Investigation and integration of spatial analyses in benthic habitat mapping with application to nearshore Arctic environments
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...
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| Format: | Thesis |
| Language: | English |
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Memorial University of Newfoundland
2019
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| Online Access: | https://research.library.mun.ca/13944/ https://research.library.mun.ca/13944/1/thesis.pdf |
| Summary: | 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 ... |
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