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

TL;DR

This paper introduces a large, high-resolution image dataset of over 13,200 ground beetles from the US, with validated morphological measurements, enabling AI-driven ecological research and automated trait analysis.

Contribution

It provides a comprehensive, digitized ground beetle dataset with validated measurements, addressing data gaps and supporting AI applications in biodiversity monitoring.

Findings

High-resolution images enable detailed morphological analysis

Validated measurements achieve sub-millimeter accuracy

Dataset facilitates AI-driven automated species identification

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 digitally measured elytra length and width of each specimen, establishing a foundation for automated trait extraction using AI. Validated against manual measurements, our digital trait extraction achieves sub-millimeter precision, ensuring reliability for ecological and computational studies. By addressing invertebrate under-representation in trait databases, this work supports AI-driven tools for automated species identification and trait-based research, fostering advancements in biodiversity monitoring and conservation.