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Workshop: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems

Understanding Insect Range Shifts with Out-of-distribution Detection

Yuyan Chen · David Rolnick


Climate change is inducing significant range shifts in insects and other organisms. Large-scale temporal data on populations and distributions are essential for quantifying the effects of climate change on biodiversity and ecosystem services, providing valuable insights for both conservation and pest management. With images from camera traps, we aim to use Mahalanobis distance-based confidence scores to automatically detect new moth species in a region. We intend to make out-of-distribution detection interpretable by identifying morphological characteristics of different species using Grad-CAM. We hope this algorithm will be a useful tool for entomologists to study range shifts and inform climate change adaptation.

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