用异构图神经网络建模物种分布，提升预测精度。
Heterogeneous graph neural networks for species distribution modeling
- 将物种与地点作为不同节点，通过边表示观测记录
- 在六个区域数据集上优于传统单物种模型和全连接网络
- 适合生态学研究者用于复杂物种-环境关系分析
物种分布模型（SDMs）对评估和预测物种出现及生境适宜性至关重要。本文提出一种基于图神经网络（GNN）的新颖存在仅数据模型。该模型将物种与地理位置视为两类不同节点，以检测记录作为连接两者的关系边。利用GNN可建模物种与环境之间的细粒度交互关系。我们在国家生态分析与综合中心（NCEAS）整理的六区域基准数据集上验证了该方法的潜力。每个区域中，异构GNN模型的表现均达到或超过以往基准的单物种模型及全连接神经网络基线模型。
原文摘要 · Abstract (English)
Species distribution models (SDMs) are necessary for measuring and predicting occurrences and habitat suitability of species and their relationship with environmental factors. We introduce a novel presence-only SDM with graph neural networks (GNN). In our model, species and locations are treated as two distinct node sets, and the learning task is predicting detection records as the edges that connect locations to species. Using GNN for SDM allows us to model fine-grained interactions between species and the environment. We evaluate the potential of this methodology on the six-region dataset compiled by National Center for Ecological Analysis and Synthesis (NCEAS) for benchmarking SDMs. For each of the regions, the heterogeneous GNN model is comparable to or outperforms previously-benchmarked single-species SDMs as well as a feed-forward neural network baseline model.
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