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通过广义正反馈回路预测植物-传粉者网络中的级联灭绝和有效的恢复策略。

Predicting cascading extinctions and efficient restoration strategies in plant-pollinator networks via generalized positive feedback loops.

机构信息

Department of Physics, Pennsylvania State University, University Park, 16803, USA.

Department of Biochemistry, Chemistry, and Physics, University of Mount Union, Alliance, 44601, USA.

出版信息

Sci Rep. 2023 Jan 17;13(1):902. doi: 10.1038/s41598-023-27525-3.

DOI:10.1038/s41598-023-27525-3
PMID:36650198
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9845316/
Abstract

The extinction of a species in a plant-pollinator mutualistic community can cause cascading effects and lead to major biodiversity loss. The ecologically important task of predicting the severity of the cascading effects is made challenging by the complex network of interactions among the species. In this work, we analyze an ensemble of models of communities of plant and pollinator species. These models describe the mutualistic inter-species interactions by Boolean threshold functions. We show that identifying generalized positive feedback loops can help pinpoint the species whose extinction leads to catastrophic and substantial damage to the whole community. We compare these results with the damage percentage caused by the loss of species identified as important by previously studied structural measures and show that positive feedback loops and the information gained from them can identify certain crucial species that the other measures fail to find. We also suggest mitigation measures for two specific purposes: (1) prevent the damage to the community by protecting a subset of the species, and (2) restore the community after the damage by restoring a subset of species. Our analyses indicate that the generalized positive feedback loops predict the most efficient strategies to achieve these purposes. The correct identification of species in each category has important implications for conservation efforts and developing community management strategies.

摘要

在植物-传粉者共生群落中,一个物种的灭绝会导致级联效应,并导致主要的生物多样性丧失。由于物种之间复杂的相互作用网络,预测级联效应严重程度的生态重要任务变得具有挑战性。在这项工作中,我们分析了植物和传粉者物种群落的一组模型。这些模型通过布尔阈值函数描述了互利物种间的相互作用。我们表明,识别广义正反馈回路有助于确定哪些物种的灭绝会对整个群落造成灾难性和实质性的破坏。我们将这些结果与通过先前研究的结构措施确定的重要物种的损失所造成的破坏百分比进行了比较,并表明正反馈回路及其提供的信息可以识别出其他措施未能发现的某些关键物种。我们还为两个特定目的提出了缓解措施:(1)通过保护物种的子集来防止对群落的破坏,(2)通过恢复物种的子集来恢复受损后的群落。我们的分析表明,广义正反馈回路预测了实现这些目标的最有效策略。正确识别每个类别中的物种对保护工作和制定群落管理策略具有重要意义。

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