通过非确定性比对识别食物网潜在的共同相互作用主干
Identifying common backbones of interactions underlying food webs via non-deterministic alignments
AI总结：
该研究针对气候变化下食物网结构主干识别问题，提出受最优传输启发的非确定性比对框架，应用于撒哈拉以南非洲129个哺乳动物食物网数据集，发现了更具稳健性的食物网相互作用主干，为生态系统研究提供了可重复工具。
AI中文摘要：
气候变化通过改变物种分布和相互作用重塑食物网，因此识别跨生态系统持续存在的相互作用结构主干至关重要。确定性比对方法计算速度慢，且仅限于一对一对应关系。我们引入一种受最优传输启发的可扩展非确定性比对框架，该框架通过多对多映射捕捉重叠的物种角色，将基元角色轮廓表述为格罗莫夫-瓦瑟斯坦（Gromov-Wasserstein）传输问题，我们的方法既高效又具有可解释性。我们将该方法应用于撒哈拉以南非洲129个哺乳动物食物网的大型大陆规模数据集，识别出成对比对，发现与零模型预期相比，这些比对具有更强的连通性和传递性的稳健主干。该方法为生态系统重组预测和保护工作提供了一种正式、可重复的工具。
英文摘要：
Climate change reshapes food webs by altering species distributions and interactions, making it essential to identify structural backbones of interactions that persist across ecosystems. Deterministic alignment methods are computationally slow and restricted to one-to-one correspondences. We introduce a scalable, non-deterministic alignment framework inspired by optimal transport that captures overlapping species roles via many-to-many mappings. Framed via motif-role profiles as a Gromov-Wasserstein transport problem, our method is both efficient and interpretable. We apply the proposed method to a large continental-scale data set of 129 mammal food webs in Sub-Saharan Africa. Pairwise alignments are identified, and we uncover robust backbones with greater connectivity and transitivity than those expected under null models. The proposed approach provides a formal, reproducible tool for forecasting ecosystem reorganization and conservation efforts.
