Consider marine wildlife monitoring in coastal waters near California. Daily video footage analyzed by a standard ML model shows 2 octopuses, 1 shark, and 10 jellyfish - broad taxonomic categories. But species-level identification could reveal crucial details. Is that an Octopus rubescens, commonly found in this area, or is it an Octopus cyanea usually only observed in warm, tropical waters near Hawai’i? Accurate species classification is essential for understanding ocean ecosystems. 

Hierarchical classification—architectures that structure data to capture relationships across taxonomic ranks (e.g., from broad categories like families to specific species)—can significantly improve classification accuracy, as demonstrated by [recent advances](https://imageomics.github.io/bioclip/) in machine learning. In the field of marine ecology, accurate taxonomic classification is essential for addressing fundamental questions: What species exist in a particular place? What is the ecosystem biodiversity and how does it change over time? Questions like these motivate the focus of our 2025 FathomNet Competition, aiming to push the boundaries of taxonomic accuracy and inspire innovative solutions in this space.