A continental-scale dataset of ground beetles with high-resolution images and validated morphological trait measurements

Abstract Despite the ecological significance of invertebrates, global trait databases remain heavily biased toward vertebrates and plants, limiting comprehensive ecological analyses of high-diversity groups like ground beetles. Ground beetles ( Coleoptera: Carabidae ) serve as critical bioindicators of ecosystem health, providing valuable insights into biodiversity shifts driven by environmental changes. While the National Ecological Observatory Network (NEON) maintains an extensive collection of carabid specimens from across the United States, these primarily exist as physical collections, restricting widespread research access and large-scale analysis. To address these gaps, we present a multimodal dataset digitizing over 13,200 NEON carabids from 30 sites spanning the continental US and Hawaii through high-resolution imaging, enabling broader access and computational analysis. The dataset includes human-made measurements of elytra length and width of each specimen image, establishing a foundation for automated trait extraction using AI. High correlations between measurements made by humans from images to those taken by humans on physical specimens ensure reliability for ecological and computational studies. By addressing invertebrate under-representation in trait databases, this work supports AI-driven tools for trait-based research, fostering advancements in biodiversity monitoring and conservation.