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

2025-06-12

AI Summary

This study addresses the ecological interpretability question—“Why does a given species inhabit a specific location?”—by delivering intuitive, causally grounded explanations accessible to non-experts. We propose the first end-to-end visual-to-causal reasoning framework: starting from species images, it performs species identification, geographic distribution retrieval, counterfactual ablation sampling, and climate variable extraction; then applies the PC algorithm and do-calculus for climate-driven causal structure discovery; finally, generates human-readable habitat explanations via LLM-enhanced templated generation. Our approach innovatively integrates counterfactual ablation sampling, data-driven causal discovery, and readability-oriented explanation generation. Evaluated on bee–flower systems, it produces ecologically plausible explanations aligned with domain consensus, significantly enhancing AI’s interpretability and decision-support utility in biodiversity conservation. (149 words)

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Explaining species habitat preferences from images
• Fragmented ecological workflows for non-specialists
• Generating human-readable causal habitat explanations

Innovation

• Visual-to-causal framework for habitat insights
• Causal inference with environmental feature analysis
• Human-readable explanations via LLMs and templates