  Animal-borne sensors ('bio-loggers') can record a suite of kinematic and
environmental data, which can elucidate animal ecophysiology and improve
conservation efforts. Machine learning techniques are used for interpreting the
large amounts of data recorded by bio-loggers, but there exists no common
framework for comparing the different machine learning techniques in this
domain. To address this, we present the Bio-logger Ethogram Benchmark (BEBE), a
collection of datasets with behavioral annotations, as well as a modeling task
and evaluation metrics. BEBE is to date the largest, most taxonomically
diverse, publicly available benchmark of this type, and includes 1654 hours of
data collected from 149 individuals across nine taxa. In addition, using BEBE,
we test a novel self-supervised learning approach to identifying animal
behaviors based on bio-logger data, using a deep neural network pre-trained
with self-supervision on data collected from human wrist-worn accelerometers.
We show that this approach out-performs common alternatives, especially in a
setting with a low amount of training data. Datasets, models, and evaluation
code are made publicly available at https://github.com/earthspecies/BEBE, to
enable community use of BEBE as a point of comparison in methods development.
