From Images to Insights: Explainable Biodiversity Monitoring with Plain Language Habitat Explanations

TL;DR

This paper introduces a comprehensive AI framework that transforms species images into understandable ecological insights by combining species recognition, environmental data analysis, causal inference, and natural language explanations.

Contribution

It presents an end-to-end system integrating visual recognition, causal modeling, and language generation for ecological habitat explanations, advancing interpretability in biodiversity monitoring.

Findings

Demonstrated on bee and flower species with promising initial results.

Showcase the potential of multimodal AI for ecological explanations.

Provides a new approach for accessible biodiversity data interpretation.

Abstract

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.