Departamento de Engenharia e Meio Ambiente, Centro de Ciências Aplicadas e Educação, Universidade Federal da Paraíba, Campus IV, Rio Tinto, PB, Brazil; Departamento de Ecologia, Instituto de Biociências, Universidade de São Paulo, São Paulo, SP, Brazil.
Departamento de Ecologia, Instituto de Biociências, Universidade de São Paulo, São Paulo, SP, Brazil.
Trends Ecol Evol. 2018 Sep;33(9):664-675. doi: 10.1016/j.tree.2018.06.002. Epub 2018 Jul 11.
The urgent need to restore biodiversity and ecosystem functioning challenges ecology as a predictive science. Restoration ecology would benefit from evolutionary principles embedded within a framework that combines adaptive network models and the phylogenetic structure of ecological interactions. Adaptive network models capture feedbacks between trait evolution, species abundances, and interactions to explain resilience and functional diversity within communities. Phylogenetically-structured network data, increasingly available via next-generation sequencing, inform constraints affecting interaction rewiring. Combined, these approaches can predict eco-evolutionary changes triggered by community manipulation practices, such as translocations and eradications of invasive species. We discuss theoretical and methodological opportunities to bridge network models and data from restoration projects and propose how this can be applied to the functional restoration of ecological interactions.
恢复生物多样性和生态系统功能的迫切需求对作为预测科学的生态学提出了挑战。恢复生态学将受益于嵌入在一个框架内的进化原则，该框架结合了适应性网络模型和生态相互作用的系统发育结构。适应性网络模型捕捉了特征进化、物种丰度和相互作用之间的反馈，以解释群落内的弹性和功能多样性。通过下一代测序，越来越多的基于系统发育结构的网络数据提供了影响相互作用重连的约束信息。这些方法结合起来可以预测由群落操作实践（如入侵物种的转移和根除）引发的生态进化变化。我们讨论了弥合恢复项目中的网络模型和数据之间的理论和方法学机会，并提出了如何将其应用于生态相互作用的功能恢复。
