Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time

Camera traps are crucial for large-scale biodiversity monitor-ing, yet accurate automated analysis remains challenging due to diversedeployment environments. While the computer vision community haspredominantly framed this challenge as cross-domain (e.g., cross-site)generalization, this perspective overlooks a primary challenge faced byecological practitioners: maintaining reliable recognition at the fixed siteover time, where the dynamic nature of ecosystems introduces profoundtemporal shifts in both background and animal distributions. To bridgethis gap, we present the first unified study of camera-trap speciesrecognition over time. We introduce a realistic, large-scale bench-mark comprising 546 camera traps with a streaming protocol that evalu-ates models over chronologically ordered intervals. Our end-user-centricstudy yields four key findings. (1) Biological foundation models (e.g.,BioCLIP 2) underperform at numerous sites even in initial intervals,underscoring the necessity of site-specific adaptation. (2) Adaptation ischallenging under realistic evaluation: when models are updated usingpast data and evaluated on future intervals (mirrors real deployment life-cycles), naive adaptation can even degrade below zero-shot performance.(3) We identify two main drivers of this di!culty: severe class imbalanceand pronounced temporal shift in both species distribution and back-grounds between consecutive intervals. (4) We find that e"ective integra-tion of model-update and post-processing techniques can largely improveaccuracy, though a gap from the upper bounds remains. Finally, we high-light critical open questions, such as predicting when zero-shot modelswill succeed at a new site and determining whether/when model updatesare necessary. Together, our benchmark and analysis provide actionabledeployment guidelines for ecological practitioners while establishing newdirections for future research in vision and machine learning.