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Abstract：
Plant-animal mutualistic networks sustain terrestrial biodiversity and human food security. Global environmental changes threaten these networks, underscoring the urgency for developing a predictive theory on how networks respond to perturbations. Here, I synthesise theoretical advances towards predicting network structure, dynamics, interaction strengths and responses to perturbations. I find that mathematical models incorporating biological mechanisms of mutualistic interactions provide better predictions of network dynamics. Those mechanisms include trait matching, adaptive foraging, and the dynamic consumption and production of both resources and services provided by mutualisms. Models incorporating species traits better predict the potential structure of networks (fundamental niche), while theory based on the dynamics of species abundances, rewards, foraging preferences and reproductive services can predict the extremely dynamic realised structures of networks, and may successfully predict network responses to perturbations. From a theoretician\'s standpoint, model development must more realistically represent empirical data on interaction strengths, population dynamics and how these vary with perturbations from global change. From an empiricist\'s standpoint, theory needs to make specific predictions that can be tested by observation or experiments. Developing models using short-term empirical data allows models to make longer term predictions of community dynamics. As more longer term data become available, rigorous tests of model predictions will improve.
摘要：
动植物互助网络维持了陆地生物多样性和人类粮食安全。全球环境变化威胁着这些网络，强调了发展网络如何应对扰动的预测理论的紧迫性。这里,我综合了预测网络结构的理论进展，动力学,相互作用强度和对扰动的响应。我发现，结合了互惠互动的生物学机制的数学模型可以更好地预测网络动力学。这些机制包括特征匹配，适应性觅食，以及相互关系提供的资源和服务的动态消费和生产。结合物种性状的模型更好地预测网络的潜在结构(基本生态位)，虽然理论基于物种丰度的动力学，奖励,觅食偏好和生殖服务可以预测极其动态的网络实现结构，并可以成功预测网络对扰动的响应。从理论家的角度来看，模型开发必须更真实地代表关于交互强度的经验数据，人口动态以及这些动态如何随着全球变化的扰动而变化。从经验主义者的立场来看,理论需要做出可以通过观察或实验检验的具体预测。使用短期经验数据开发模型可以使模型对社区动态进行长期预测。随着更多的长期数据可用，严格的模型预测测试将有所改善。
