用图神经网络解析全球变化对传粉网络的影响
Interpretability of Graph Neural Networks to Assess Effects of Global Change Drivers on Ecological Networks
- 结合图神经网络与可解释性方法分析环境因子对传粉网络的作用
- 模拟实验验证了方法能检测植物属与环境因子的交互效应
- 在真实数据中发现土地利用影响网络连通性，且采样偏差需校正
传粉者在自然生态系统和人类改造景观中对植物繁殖至关重要。全球变化驱动因素（如气候变化或土地利用改变）可能影响植物-传粉者互作关系。为评估这些驱动因素对传粉作用的影响，需要大规模的互作、气候与土地利用数据。尽管近年来图神经网络（GNNs）可用于分析此类数据，但其结果的可解释性仍具挑战。本文探讨现有GNN可解释性方法，以揭示不同环境协变量对传粉网络连通性的影响。通过大规模模拟研究，验证这些方法能否检测到某一协变量与植物属之间的交互效应，以及去偏技术是否影响效应估计。基于Spipoll数据集的应用分析表明，土地利用对网络连通性具有潜在影响，且考虑采样效应后会部分改变该效应的估计结果。
原文摘要 · Abstract (English)
Pollinators play a crucial role for plant reproduction, either in natural ecosystem or in human-modified landscape. Global change drivers,including climate change or land use modifications, can alter the plant-pollinator interactions. To assess the potential influence of global change drivers on pollination, large-scale interactions, climate and land use data are required. While recent machine learning methods, such as graph neural networks (GNNs), allow the analysis of such datasets, interpreting their results can be challenging. We explore existing methods for interpreting GNNs in order to highlight the effects of various environmental covariates on pollination network connectivity. An extensive simulation study is performed to confirm whether these methods can detect the interactive effect between a covariate and a genus of plant on connectivity, and whether the application of debiasing techniques influences the estimation of these effects. An application on the Spipoll dataset, with and without accounting for sampling effects, highlights the potential impact of land use on network connectivity and shows that accounting for sampling effects partially alters the estimation of these effects.
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