Behavioural changes of a long-ranging diver in response to oceanographic conditions
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The development of an animal-borne instrument that can record oceanographic measurements (CTD-SRDL) has enabled the collection of oceanographic data at a scale relevant to the counterpart behavioural data, both in time and 3-dimensional space. This has advanced the potential for studies of the behaviour of deep-diving marine animals and the way in which they respond to their environment, yet the nature of the data delivered by CTD-SRDLs presents substantial analytical challenges and places constraints on its biological interpretation. Behavioural and environmental data, collected using CTD-SRDLs deployed on southern elephant seals (Mirounga leonina) from the South Georgia subpopulation in 2004 and 2005, are analysed for 13 females and 4 males (21,015 dives). Compressed dive profiles are used to classify individual dives into six distinct types based on their 2-dimensional time-depth characteristics using random forest classification. The relationship between dive type and environmental variables, derived from oceanographic data recorded on board the animals, is investigated in the context of regression analysis, employing a multinomial model, as well as independently fitted Generalized Linear Models (GLM) and Generalized Additive Models (GAM) for each dive type. Regression is not found to be an appropriate method for analysing abstracted behavioural dive data, and other methods are suggested. We show that functional specializations can be manifested within a dive type, using square bottom dives (SQ) as an example. The usefulness of dive classification is discussed in the context of behavioural interpretation, and validity of the ecological functions attached to each class. Preliminary analyses are important drivers of further research into improving the interpretability of abstracted behavioural data, and developing efficient, standardized methods for widespread application to this type of data, which is obtained in abundance via satellite telemetry.
BL 5019 Research project. MRes Environmental Biology
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