BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Models
Catherine Villeneuve and Benjamin Akera and Mélisande Teng and David Rolnick
物种分布模型(SDMs)旨在根据环境变量预测物种的发生,被广泛用于监测和应对生物多样性变化。 SDM最近的深度学习进展已被证明在复杂和异构数据集上表现良好,但其有效性仍然受到数据空间偏差的限制。 在本文中,我们从贝叶斯的角度重新审视深度SDM,并介绍了BATIS,这是一个新颖而实用的框架,其中使用有限的观测数据迭代更新先前的预测。 模型必须适当地捕捉肛门和认识论的不确定性,以有效地将细粒度的局部见解与更广泛的生态模式结合起来。 我们在一个新的数据集上对一组广泛的不确定性量化方法进行了基准测试,包括来自eBird平台的公民科学观测。 我们的实证研究表明,贝叶斯深度学习方法如何大大提高数据匮乏位置SDM的可靠性,这有助于生态理解和保护工作。
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...