INQUIRE-Search: Interactive Discovery in Large-Scale Biodiversity Databases

TL;DR

INQUIRE-Search is an open-source system that uses natural language to enable rapid, scalable discovery of complex ecological phenomena in large biodiversity image databases, significantly improving efficiency over manual methods.

Contribution

The paper introduces INQUIRE-Search, a novel AI-enabled tool that allows interactive, natural language-based search and analysis of ecological images at scale.

Findings

Concentrates relevant observations 3-25x more efficiently than manual inspection.

Demonstrates ecological insights such as seasonal behavior variation and post-wildfire forest regrowth.

Shows potential to unlock scientific value in large biodiversity datasets.

Abstract

Many ecological questions center on complex phenomena, such as species interactions, behaviors, phenology, and responses to disturbance, that are inherently difficult to observe and sparsely documented. Community science platforms such as iNaturalist contain hundreds of millions of biodiversity images, which often contain evidence of these complex phenomena. However, current workflows that seek to discover and analyze this evidence often rely on manual inspection, leaving this information largely inaccessible at scale. We introduce INQUIRE-Search, an open-source system that uses natural language to enable scientists to rapidly search within an ecological image database like iNaturalist for specific phenomena, verify and export relevant observations, and use these outputs for downstream scientific analysis. Across five illustrative case studies, INQUIRE-Search concentrates relevant observations 3-25x more efficiently than comparable manual inspection budgets. These examples demonstrate how the system can be used for ecological inference, from analyzing seasonal variation in behavior across species to forest regrowth after wildfires. These examples illustrate a new paradigm for interactive, efficient, and scalable scientific discovery that can begin to unlock previously inaccessible scientific value in large-scale biodiversity datasets. Finally, we highlight how AI-enabled discovery tools for science require reframing aspects of the scientific process, including experiment design, data collection, survey effort, and uncertainty analysis.