Abstract
Until recently monitoring of the marine environment and its megafauna population relied on field observations and catch or strandings information. However, new approaches are being developed which take into consideration the need for information over whole sea areas over wide spatial and temporal scales for the purposes of marine spatial planning and ecological research.This thesis investigates the use of wide scale predictive species distribution modelling using multiple source sightings information combined with environmental variable data for monitoring marine megafauna.
The development of new techniques for marine ecological studies is fundamental to the future of successful marine management and the development of remote sensing techniques and the availability of remotely sensed data has opened up the opportunity to investigate the marine environment on a much wider scale. Remotely sensed data derived from satellites is now widely accessible and available, including sensors for measuring sea surface temperature and productivity within our oceans.
Presence-only species distribution modelling approaches were combined with historical species occurrence data alongside environmental predictor variables, including remotely sensed sea surface temperature (SST) and chlorophyll-a concentration, to explore the importance of predictor variables and to produce predictive maps of marine megafauna occurrence in the Irish Sea.
This study found that multiple source presence-only data for marine megafauna commonly found in the wider Irish Sea can be a valuable resource for use in species distribution models to produce ecologically meaningful results
| Date of Award | 2017 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Pete Bunting (Supervisor), Andy Hardy (Supervisor) & Richard Lucas (Supervisor) |