| Summary: | Knowledge of the spatial distribution of species and habitats is crucial for effective management of marine resources. Data to support informed decision-making in the deep sea are often sparse or absent, as offshore sampling surveys are expensive, time-consuming, have limited coverage and are spatially biased. Habitat suitability models (HSMs) make use of the limited data available and are being applied increasingly to create continuous coverage maps of the potential distribution of species or habitats. Such maps have value as decision support tools for future survey planning, design of marine protected area networks and ultimately the implementation of marine spatial planning. In the deep sea, the quality of these maps is sub-optimal because of 1) the frequent lack of high-resolution ecologically relevant environmental variables, 2) species distribution data arising from opportunistic, spatially biased sampling, and 3) a lack of reliable species absence data. Therefore, this thesis has as its primary objective the development of repeatable and robust methods for deep-sea benthic HSM that take into account these issues. Based on the case study of predicting suitable habitat for the cold-water coral Lophelia pertusa (Linnaeus 1758) in the north-east Atlantic, this thesis extends existing benthic HSM methodologies by 1) maximising the resolution and information content of environmental variables, 2) optimising the reliability of presence-only modelling methods, 3) investigating the predictive power of high-resolution environmental variables derived from 3D hydrodynamic models, 4) assessing the use of quantitative species occurrence proportion data for calibrating models, and 5) exploring the applicability of mixed models to account for spatially grouped transect data. The key outcome is the first reliable high-resolution HSM for Lophelia pertusa reef habitat as a tool for ecosystem-based management in Irish waters. The implications of HSMs for conservation, marine spatial planning and an understanding of ecosystem ...
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