Dr Andrew Davies, University of Bangor - Unravelling the ‘virtual’ ecology of the last great wilderness on earth

4:00pm - 5:00pm / Monday 27th November 2017 / Venue: Jane Herdman Building
Type: Seminar / Category: Department
Add this event to my calendar

Create a calendar file

Click on "Create a calendar file" and your browser will download a .ics file for this event.

Microsoft Outlook: Download the file, double-click it to open it in Outlook, then click on "Save & Close" to save it to your calendar. If that doesn't work go into Outlook, click on the File tab, then on Open & Export, then Open Calendar. Select your .ics file then click on "Save & Close".

Google Calendar: download the file, then go into your calendar. On the left where it says "Other calendars" click on the arrow icon and then click on Import calendar. Click on Browse and select the .ics file, then click on Import.

Apple Calendar: The file may open automatically with an option to save it to your calendar. If not, download the file, then you can either drag it to Calendar or import the file by going to File >Import > Import and choosing the .ics file.

The deep ocean is one of the most challenging and expensive habitats on earth to study. It requires an inter-disciplinary approach, that bridges across the main scientific disciplines of physics, chemistry and biology. Whilst the abiotic stressors of pressure, location and depth restrict what we physically can do, recent advancements have accelerated the field to a point where we are now on the precipice of being able to ask increasingly complex ecological questions in the deep ocean. In this presentation, I will review how our understanding of topographically complex ecosystems, ranging from geological features such as canyons through to biogenic reefs such as those formed by cold-water corals and sponges, has been shaped by novel approaches and technologies. Focussing on how deep-sea science is becoming increasingly ‘virtual’, I will show how 3D reconstructions of the seafloor and species distribution models have developed into vital and relatively accessible tools that can fill gaps in our understanding, particularly in data poor regions.