MetaFauna: A Multi-Species Animal Re-Identification Dataset with Structured Metadata

Animal re-identification (ReID) aims to recognize the same individual across images, supporting non-invasive wildlife monitoring and ecological studies. In camera-trap imagery, however, identity-relevant visual evidence varies with observation conditions: viewpoint and occlusion affect which cues are visible, while illumination, blur, and scale affect how clearly those cues are captured. Most existing animal ReID datasets provide only images and identity labels, requiring models to distinguish identity-related differences from variation associated with these observation conditions using visual input alone. Recent work has begun incorporating auxiliary metadata, such as temperature, capture time, and orientation, into animal ReID. However, most of these attributes describe the temporal or environmental context of capture rather than the observation conditions of each image. To address this gap, we construct MetaFauna, a multi-species animal ReID dataset comprising 44,317 images of 2,500 individuals across eight species. Each image is paired with structured metadata describing its observation conditions, grouped into visual evidence visibility and capture conditions, and, where available, its environmental context. To study whether explicit descriptions of observation conditions improve animal ReID, we further introduce MetaRouter, a reference framework that conditions visual feature extraction on the metadata of each image by weighting low-rank adaptation (LoRA) experts within a frozen visual backbone. Across all eight species, MetaRouter outperforms visual-only and metadata-aware baselines. Controlled comparisons further show that the gains depend on using the metadata paired with each image to guide routing.