BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Models

TL;DR

BATIS introduces a Bayesian framework for species distribution models that iteratively updates predictions with limited data, improving reliability especially in data-scarce areas, aiding ecological conservation.

Contribution

This paper presents BATIS, a novel Bayesian deep learning approach that effectively combines local and global ecological insights by updating predictions iteratively with limited observational data.

Findings

Bayesian deep learning significantly improves SDM reliability in data-scarce regions.

Uncertainty quantification enhances ecological understanding and conservation efforts.

BATIS outperforms traditional models on a novel citizen science dataset.

Abstract

Species distribution models (SDMs), which aim to predict species occurrence based on environmental variables, are widely used to monitor and respond to biodiversity change. Recent deep learning advances for SDMs have been shown to perform well on complex and heterogeneous datasets, but their effectiveness remains limited by spatial biases in the data. In this paper, we revisit deep SDMs from a Bayesian perspective and introduce BATIS, a novel and practical framework wherein prior predictions are updated iteratively using limited observational data. Models must appropriately capture both aleatoric and epistemic uncertainty to effectively combine fine-grained local insights with broader ecological patterns. We benchmark an extensive set of uncertainty quantification approaches on a novel dataset including citizen science observations from the eBird platform. Our empirical study shows how Bayesian deep learning approaches can greatly improve the reliability of SDMs in data-scarce locations, which can contribute to ecological understanding and conservation efforts.