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

• URL: http://arxiv.org/abs/2601.10687v1
• Date: Wed, 14 Jan 2026 18:44:54 GMT
• Title: A continental-scale dataset of ground beetles with high-resolution images and validated morphological trait measurements
• Authors: S M Rayeed, Mridul Khurana, Alyson East, Isadora E. Fluck, Elizabeth G. Campolongo, Samuel Stevens, Iuliia Zarubiieva, Scott C. Lowe, Michael W. Denslow, Evan D. Donoso, Jiaman Wu, Michelle Ramirez, Benjamin Baiser, Charles V. Stewart, Paula Mabee, Tanya Berger-Wolf, Anuj Karpatne, Hilmar Lapp, Robert P. Guralnick, Graham W. Taylor, Sydne Record,
• Abstract summary: Ground beetles serve as critical bioindicators of ecosystem health.<n>National Ecological Observatory Network (NEON) maintains an extensive collection of carabid specimens from across the U.S.<n>We present a dataset digitizing over 13,200 NEON carabids from 30 sites spanning the continental US and Hawaii through high-resolution imaging.<n>The dataset includes digitally measured elytra length and width of each specimen, establishing a foundation for automated trait extraction using AI.
• Score: 13.860603856120795
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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