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Ground beetles are a highly sensitive and speciose biolog- ical indicator, making them vital for monitoring biodiver- sity. However, they are currently an underutilized resource due to the manual effort required by taxonomic experts to perform challenging species differentiations based on sub- tle morphological differences, precluding widespread ap- plications. In this paper, we evaluate 12 vision models on taxonomic classification across four diverse, long-tailed datasets spanning over 230 genera and 1769 species, with images ranging from controlled laboratory settings to chal- lenging field-collected (in-situ) photographs. We further ex- plore taxonomic classification in two important real-world contexts: sample efficiency and domain adaptation. Our re- sults show that the Vision and Language Transformer com- bined with an MLP head is the best performing model, with 97% accuracy at genus and 94% at species level. Sample efficiency analysis shows that we can reduce train data re- quirements by up to 50% with minimal compromise in per- formance. The domain adaptation experiments reveal sig- nificant challenges when transferring models from lab to in-situ images, highlighting a critical domain gap. Overall, our study lays a foundation for large-scale automated tax- onomic classification of beetles, and beyond that, advances sample-efficient learning and cross-domain adaptation for diverse long-tailed ecological datasets.