{"id":4643,"date":"2016-01-15T09:28:28","date_gmt":"2016-01-15T09:28:28","guid":{"rendered":"http:\/\/blogs.scotland.gov.uk\/coastal-monitoring\/?p=4643"},"modified":"2019-07-08T17:26:34","modified_gmt":"2019-07-08T16:26:34","slug":"new-publication-about-how-seabirds-feed-underwater","status":"publish","type":"post","link":"https:\/\/blogs.gov.scot\/marine-scotland\/2016\/01\/15\/new-publication-about-how-seabirds-feed-underwater\/","title":{"rendered":"New publication about how seabirds feed underwater"},"content":{"rendered":"

It has always been difficult to describe what diving seabirds do underwater \u2013 how deep they dive, how they forage and catch their prey. It is important to know about this, for example to assess the risk that underwater renewable energy turbines may present to the birds.<\/p>\n

In a new publication, The use of an unsupervised learning approach for characterizing latent behaviours in accelerometer data<\/a>, which includes contributions from Marine Scotland Science colleagues, the use of tags containing accelerometers\u00a0as a powerful tool for studies on animal behaviour, energetics and movements is explored .<\/p>\n

<\/a>Accelerometer data has been used from two species of diving seabird (guillemot and razorbill), anticipated to have contrasting foraging behaviours. The publication shows how an unsupervised statistical learning algorithm is able to clearly analyse the complex information on movement in\u00a0three dimensions provided by the tags to identify distinct phases of behaviour above and below water.\u00a0It also shows how the accelerometer data allows exploration of previously unstudied and important behaviours such as searching and prey chasing\/capture events.<\/p>\n

This new statistical approach provides an ideal tool for the systematic analysis of such complex multi-variable movement data obtained with accelerometer tags.<\/p>\n

Further Information<\/strong><\/p>\n