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

arXiv: https://arxiv.org/abs/2601.10687 · HTML: https://mlsanity.com/paper/2601.10687/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 90 references that are in the map.
- First posted on arXiv 14 Jan 2026.

## Builds on (most cited first, 15 of 15)

- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
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- [Deep Model Reassembly](https://mlsanity.com/paper/2210.17409/) (2022)
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- [VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images](https://mlsanity.com/paper/2408.16176/) (2024)
- [Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images](https://mlsanity.com/paper/2407.08027/) (2024)
- [Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution](https://mlsanity.com/paper/2408.00160/) (2024)
- [Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks](https://mlsanity.com/paper/2306.03228/) (2023)
- [Mind the (Data) Gap: Evaluating Vision Systems in Small Data Applications](https://mlsanity.com/paper/2504.06486/) (2025)
- [A multi-modal dataset for insect biodiversity with imagery and DNA at the trap and individual level](https://mlsanity.com/paper/2507.06972/) (2025)
- [G2PDiffusion: Cross-Species Genotype-to-Phenotype Prediction via Evolutionary Diffusion](https://mlsanity.com/paper/2502.04684/) (2025)
- [BeetleVerse: A Study on Taxonomic Classification of Ground Beetles](https://mlsanity.com/paper/2504.13393/) (2025)
- [Optimizing Image Capture for Computer Vision-Powered Taxonomic Identification and Trait Recognition of Biodiversity Specimens](https://mlsanity.com/paper/2505.17317/) (2025)
- [BeetleFlow: An Integrative Deep Learning Pipeline for Beetle Image Processing](https://mlsanity.com/paper/2511.00255/) (2025)

## Abstract (arXiv)

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.