  As is true of many complex tasks, the work of discovering, describing, and
understanding the diversity of life on Earth (viz., biological systematics and
taxonomy) requires many tools. Some of this work can be accomplished as it has
been done in the past, but some aspects present us with challenges which
traditional knowledge and tools cannot adequately resolve. One such challenge
is presented by species complexes in which the morphological similarities among
the group members make it difficult to reliably identify known species and
detect new ones. We address this challenge by developing new tools using the
principles of machine learning to resolve two specific questions related to
species complexes. The first question is formulated as a classification problem
in statistics and machine learning and the second question is an
out-of-distribution (OOD) detection problem. We apply these tools to a species
complex comprising Southeast Asian stream frogs (Limnonectes kuhlii complex)
and employ a morphological character (hind limb skin texture) traditionally
treated qualitatively in a quantitative and objective manner. We demonstrate
that deep neural networks can successfully automate the classification of an
image into a known species group for which it has been trained. We further
demonstrate that the algorithm can successfully classify an image into a new
class if the image does not belong to the existing classes. Additionally, we
use the larger MNIST dataset to test the performance of our OOD detection
algorithm. We finish our paper with some concluding remarks regarding the
application of these methods to species complexes and our efforts to document
true biodiversity. This paper has online supplementary materials.
