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arXiv · 2312.08334

LD-SDM: Language-Driven Hierarchical Species Distribution Modeling

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

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Srikumar Sastry, Xin Xing, Aayush Dhakal, Subash Khanal, Adeel Ahmad, Nathan Jacobs. 2023-12-13. LD-SDM: Language-Driven Hierarchical Species Distribution Modeling. https://arxiv.org/abs/2312.08334

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