用图像生成可理解的物种栖息地原因解释，让非专业人士也能看懂生态规律。
From Images to Insights: Explainable Biodiversity Monitoring with Plain Language Habitat Explanations
- 输入物种图片，自动推断其栖息偏好背后的因果关系。
- 结合气候数据与生态模型，识别影响物种分布的关键环境因素。
- 生成符合科学依据的自然语言解释，适合科研与公众科普使用。
解释物种为何出现在特定地点，对理解生态系统和保护生物多样性至关重要。然而，现有生态研究流程碎片化且对非专业人士不友好。本文提出一个端到端的视觉到因果分析框架，将物种图像转化为可解释的栖息地偏好因果洞见。系统整合物种识别、全球分布数据获取、伪缺失采样及气候数据提取，并利用现代因果推断方法挖掘环境因子间的因果结构及其对物种出现的影响。最后，基于结构化模板与大语言模型生成统计可靠、人类可读的因果解释。在蜜蜂与花朵物种上验证了该框架，展示了多模态AI助手结合推荐生态建模实践，在生成人能理解的语言描述物种栖息地方面的潜力。代码已开源：https://github.com/Yutong-Zhou-cv/BioX。
原文摘要 · Abstract (English)
Explaining why the species lives at a particular location is important for understanding ecological systems and conserving biodiversity. However, existing ecological workflows are fragmented and often inaccessible to non-specialists. We propose an end-to-end visual-to-causal framework that transforms a species image into interpretable causal insights about its habitat preference. The system integrates species recognition, global occurrence retrieval, pseudo-absence sampling, and climate data extraction. We then discover causal structures among environmental features and estimate their influence on species occurrence using modern causal inference methods. Finally, we generate statistically grounded, human-readable causal explanations from structured templates and large language models. We demonstrate the framework on a bee and a flower species and report early results as part of an ongoing project, showing the potential of the multimodal AI assistant backed up by a recommended ecological modeling practice for describing species habitat in human-understandable language. Our code is available at: https://github.com/Yutong-Zhou-cv/BioX.
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