This paper proposes an end-to-end visual-causal framework for extracting interpretable causal insights into species habitat preferences from images. This system integrates species recognition, global occurrence information retrieval, pseudo-absence sampling, and climate data extraction. Using modern causal inference methods, we uncover causal structures among environmental features and estimate their influence on species occurrence. Finally, we use structured templates and large-scale language models to generate statistically sound, human-understandable causal explanations. We demonstrate the framework for bee and flower species, report initial results from ongoing projects, and demonstrate the potential of multimodal AI assistants to support recommended ecological modeling practices for describing species habitats in human-understandable language.
