Data from: Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds

1.In order to prevent further global declines in biodiversity, identifying
and understanding key habitats is crucial for successful conservation
strategies. For example, globally, seabird populations are under threat
and animal movement data can identify key at-sea areas and provide
valuable information on the state of marine ecosystems. To date, in order
to locate these areas, studies have used Global Positioning System (GPS)
to record position and are sometimes combined with Time Depth Recorder
(TDR) devices to identify diving activity associated with foraging, a
crucial aspect of at-sea behaviour. However, the use of additional devices
such as TDRs can be expensive, logistically difficult, and may adversely
affect the animal. Alternatively, behaviours may be resolved from
measurements derived from the movement data alone. However, this
behavioural analysis frequently lacks validation data for locations
predicted as foraging (or other behaviours). 2.Here, we address these
issues using a combined GPS and TDR dataset from 108 individuals by
training deep learning models to predict diving in European shags, common
guillemots and razorbills. We validate our predictions using withheld
data, producing quantitative assessment of predictive accuracy. The
variables used to train these models are those recorded solely by the GPS
device: variation in longitude and latitude, altitude, and coverage ratio
(proportion of possible fixes acquired within a set window of time).
3.Different combinations of these variables were used to explore the
qualities of different models, with the optimum models for all species
predicting non-diving and diving behaviour correctly over 94% and 80% of
the time, respectively. We also demonstrate the superior predictive
ability of these supervised deep-learning models over other commonly used
behavioural prediction methods such as hidden Markov models. 4.Mapping
these predictions provides useful insights into the foraging activity of a
range of seabird species, highlighting important at sea locations. These
models have the potential to be used to analyse historic GPS datasets and
further our understanding of how environmental changes have affected these
seabirds over time.