“…Although challenges remain with respect to scalability, computational efficiency, and how to handle depauperate data (Alom et al, 2018), deep learning is one of the most powerful analytical tools in the modern researcher's toolbox, particularly when human knowledge is lacking, or datasets are too large to be workable by traditional means. In the context of ecology and evolutionary biology, there have been many recent applications of both shallow and deep machine learning, including population genetics and phylogeography (e.g., Schrider & Kern, 2018;Provost et al, 2021), bioacoustics (e.g., Nicholson, 2016;Zhong et al, 2020;Cohen et al, bioRxiv), species classification (e.g., Gupta et al, 2021), phylogenetics (e.g., Halgaswaththa et al, 2012;Wang et al, 2016), sequencing and genomics (e.g., Adrion et al, 2020;Boža et al, 2017), and phenotypic analyses and morphometrics (e.g., Devine et al, 2020;Lürig et al, 2021). Neural networks and support vector machines tend to be the most applied algorithms towards these analyses.…”