Institut de biologie de l'Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL Research University, Paris, 75005, France.
Sorbonne Université, UPEC, CNRS, IRD, INRA, Institut d'Ecologie et des Sciences de l'Environnement, IEES, Paris, F-75005, France.
Ecol Lett. 2020 Nov;23(11):1623-1634. doi: 10.1111/ele.13592. Epub 2020 Sep 4.
How ecological interaction networks emerge on evolutionary time scales remains unclear. Here we build an individual-based eco-evolutionary model for the emergence of mutualistic, antagonistic and neutral bipartite interaction networks. Exploring networks evolved under these scenarios, we find three main results. First, antagonistic interactions tend to foster species and trait diversity, while mutualistic interactions reduce diversity. Second, antagonistic interactors evolve higher specialisation, which results in networks that are often more modular than neutral ones; resource species in these networks often display phylogenetic conservatism in interaction partners. Third, mutualistic interactions lead to networks that are more nested than neutral ones, with low phylogenetic conservatism in interaction partners. These results tend to match overall empirical trends, demonstrating that structures of empirical networks that have most often been explained by ecological processes can result from an evolutionary emergence. Our model contributes to the ongoing effort of better integrating ecological interactions and macroevolution.
生态相互作用网络如何在进化时间尺度上出现仍然不清楚。在这里，我们构建了一个基于个体的生态进化模型，用于出现互利、敌对和中性二分相互作用网络。通过探索在这些情景下进化的网络，我们发现了三个主要结果。首先，敌对相互作用往往促进物种和特征多样性，而互利相互作用则减少多样性。其次，敌对相互作用者进化出更高的专业化，导致网络往往比中性网络更模块化；这些网络中的资源物种在相互作用伙伴中经常表现出系统发生保守性。第三，互利相互作用导致网络比中性网络更嵌套，相互作用伙伴的系统发生保守性低。这些结果往往与总体经验趋势相匹配，表明大多数情况下通过生态过程来解释的经验网络结构可以通过进化出现来产生。我们的模型有助于更好地整合生态相互作用和宏观进化的持续努力。
