  Monitoring biodiversity is paramount to manage and protect natural resources.
Collecting images of organisms over large temporal or spatial scales is a
promising practice to monitor the biodiversity of natural ecosystems, providing
large amounts of data with minimal interference with the environment. Deep
learning models are currently used to automate classification of organisms into
taxonomic units. However, imprecision in these classifiers introduces a
measurement noise that is difficult to control and can significantly hinder the
analysis and interpretation of data. {We overcome this limitation through
ensembles of Data-efficient image Transformers (DeiTs), which not only are easy
to train and implement, but also significantly outperform} the previous state
of the art (SOTA). We validate our results on ten ecological imaging datasets
of diverse origin, ranging from plankton to birds. On all the datasets, we
achieve a new SOTA, with a reduction of the error with respect to the previous
SOTA ranging from 29.35% to 100.00%, and often achieving performances very
close to perfect classification. Ensembles of DeiTs perform better not because
of superior single-model performances but rather due to smaller overlaps in the
predictions by independent models and lower top-1 probabilities. This increases
the benefit of ensembling, especially when using geometric averages to combine
individual learners. While we only test our approach on biodiversity image
datasets, our approach is generic and can be applied to any kind of images.
