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.

翻译：许多生态学问题的核心在于复杂的现象，如物种相互作用、行为、物候以及对干扰的响应，这些现象本质上难以观察且记录稀疏。社区科学平台（如iNaturalist）包含数亿张生物多样性图像，其中常蕴含这些复杂现象的证据。然而，当前旨在发现和分析这些证据的工作流程通常依赖于人工检查，使得这些信息在规模上基本无法有效获取。我们介绍了INQUIRE-Search，这是一个开源系统，它利用自然语言使科学家能够快速在iNaturalist等生态图像数据库中搜索特定现象，验证并导出相关观测记录，并将这些输出用于下游科学分析。在五个示例性案例研究中，与可比的人工检查预算相比，INQUIRE-Search集中相关观测的效率提高了3至25倍。这些示例展示了该系统如何用于生态推断，从分析跨物种行为的季节性变化到野火后森林的再生。这些案例阐明了一种新的交互式、高效且可扩展的科学发现范式，能够开始释放大规模生物多样性数据集中先前难以获取的科学价值。最后，我们强调，面向科学的AI驱动发现工具需要重新审视科学过程的某些方面，包括实验设计、数据收集、调查努力以及不确定性分析。