用贝叶斯方法提升物种分布模型在数据少地区的预测可靠性
BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Models
- 基于贝叶斯框架，用少量观测数据迭代更新先验预测
- 在eBird数据上验证，显著提升数据稀缺区域的模型可靠性
- 适合关注生态监测与保护的模型开发者和研究者
物种分布模型（SDMs）通过环境变量预测物种出现情况，广泛用于监测生物多样性变化。尽管深度学习在复杂异构数据上表现良好，但空间偏差仍限制其效果。本文从贝叶斯视角重新审视深度SDMs，提出BATIS框架：利用有限观测数据迭代更新先验预测。模型需同时捕捉偶然性与认知不确定性，以融合局部细节与宏观生态模式。我们在包含eBird公民科学数据的新数据集上评估多种不确定性量化方法。实证结果表明，贝叶斯深度学习可显著提升数据稀缺地区模型的可靠性，助力生态理解与保护工作。
原文摘要 · Abstract (English)
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.
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