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This thesis develops analytical frameworks for evaluating the validity of MBES data with comparable ground truthing and hydrodynamic data with respect to geomorphology and marine biodiversity in two Special Areas of Conservation (SAC) in the Malin-Hebrides Sea off the north-Irish coast. First, a modelling framework based on Generalised Linear Modelling (GLM) is developed to test the validity of multi-frequency MBES backscatter data (30, 95, 300 kHz) to characterise sediment grain size in the Hempton's Turbot Bank (HTB) SAC. The results demonstrate that the single-frequency sources have a marginal gain on the multi-frequency model, with the 30 kHz model driving the significance of the multi-frequency model, and the inclusion of the higher frequencies diminishes the level of agreement. Secondly, a combination of K-Means unsupervised classification and GLM based on MBES backscatter (95, 300 kHz) and bathymetry analysis are successfully used to predict different levels of sandeel Hyperoplus lanceolatus densities with respect to geomorphology in the HTB SAC. The study demonstrates that the lower frequency source is more adept at capturing the variety inherent in shallow sub-surface sedimentary environments, which this species prefers. Finally, multidisciplinary modelling and analysis approaches using MBES data, hydrodynamic data and theodolite tracking observations are used to assess the spatial dynamics of harbour porpoise Phocoena phocoena in the Skerries and Causeway SAC. Generalised Additive Modelling (GAM) identifies that slope, aspect and backscatter intensity are the most statistically significant variables accounting for the highest deviance in porpoise sighting density. Models predict a high probability (> 0.6) of porpoise encounters in nearshore areas, particularly concentrated around headlands where local flow acceleration results in coarser beds. The robust workflows developed in this thesis, provides a proof of concept for developing robust monitoring strategies for Marine Protected Areas and associated species. Respective recommendations will inform marine policy, marine spatial planning and management.

The global decline of marine fish stocks calls for sustainable fisheries anagement based on reliable quantitative estimates. Dynamic spatial distributions of fish populations respond to biological, environmental and anthropogenic drivers. Species distribution models involving data from those areas enable projection of fish presence and abundance. They rely on an international fisheries independent survey effort across large spatial scales by multiple agencies with different equipment. Typically,surveysfocus on individual geographical or jurisdictional regions, which are not reflective of natural species ranges. This generates the challenge, how to best combine heterogeneous fisheriessurvey data for marine species distribution research. Therefore, a standardisation protocol which generated a Quality Assurance-Quality Audited dataset of groundfish surveys was developed. Its public availability constitutes an important contribution to the European fisheries research community and has led to applicationsin inferential studies outside the scope of this dissertation. Amodelling framework for combining fisheries surveys to examine species distribution on a multi-regional scale for the North East Atlantic Ocean was devised. It allowed evaluation ofrelative efficiency of gear-vessel combinations on 19 surveys for 254 species-length combinations. Estimated relative differences in catch efficiencies grouped strongly by gear type, but there were no clear patterns across the species' functional forms. A critical evaluation of species distribution modelling approaches evaluated the potential for, and limitations of,tracking current and future spatial distribution of marine taxa and characterizing habitat niches. The application of generalised additive models to presenceabsence data provided the most consistent resultsfor four test species (Raja microocellata, Leucoraja naevus, Eutrigla gurnardus and Sebastes viviparous), which were categorised as "local"/"widespread" and "common"/"rare" to capture distribution patterns within the survey data. Among tested environmental variables mean seafloor temperature was most influential and thus appears fundamental to understanding the drivers of change in species distributions, particularly in changing climates.

Global human populations rely on the oceans as a source of food and other resources. In order to maintain this resource, information about seabed habitat is essential to scientists and marine managers to provide a baseline against which to assess future change. These data are often supplied as maps. For this information to be useful, it must be based on sound scientific knowledge. Many marine mapping studies compare variable selection in terms of the environmental data that go into habitat maps. Similarly, many studies have compared classification algorithms. However, these two are rarely considered together. This study compares datasets along with two approaches to marine habitat map production. At the same time, this study investigates the spatial scales at which diversity and species associations can be monitored. A high resolution hydrodynamic model is produced for the primary study area, and this is integrated with predictive models. Temporal stability of communities is assessed, and subsequently a single species detection study is conducted. Unsupervised clustering routines are shown to be just as ecologically meaningful as supervised classifications in areas of hard substrata. Patterns in species association and diversity are most pronounced across meso-scales; future species distribution modelling studies should take these scales into account. Temporal stability of benthic community structure is high as indicated by numerical models, however the small amount of error indicated by numerical models propagates through analyses to create a large difference in output classified maps. Hydrodynamics becomes the second most important variable in describing species distributions, and reveal previously unknown behaviour of a temperate marine fish of high conservation status. Detectability is highlighted as an important consideration for future marine ecology studies.