LD-SDM: Language-Driven Hierarchical Species Distribution Modeling

Srikumar Sastry, Xin Xing, Aayush Dhakal, Subash Khanal, Adeel Ahmad, Nathan Jacobs

Keywords: Species Distribution Modeling, Language-Driven, Spherical Harmonics

TL;DR: Language driven species distribution modeling by incorporating taxonomic description of species.

Abstract: We focus on species distribution modeling using global-scale presence-only data, leveraging geographical and environmental features to map species ranges, as in previous studies. However, we innovate by integrating taxonomic classification into our approach. Specifically, we propose using a large language model to extract a latent representation of the taxonomic classification from a textual prompt. This allows us to map the range of any taxonomic rank, including unseen species, without additional supervision. We also present a new proximity-aware evaluation metric, suitable for evaluating species distribution models, which addresses critical shortcomings of traditional metrics. We evaluated our model for species range prediction, zero-shot prediction, and geo-feature regression and found that it outperforms several state-of-the-art models. We will share code, data, and model checkpoints after acceptance.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 5059