RCN: Using metamodels to enable transdisciplinary research for the study of dynamic biological systems under global change

该项目将开发一个新的网络，将种群生态学中最广泛使用的模拟模型的开发人员与长期研究受栖息地改变、景观变化、疾病、气候变化和人类干预影响的野生动物种群的领导者聚集在一起。全球环境正在迅速变化，这些变化通过当地生态系统产生级联影响，造成生物多样性前所未有的丧失，破坏自然和人类系统所依赖的生态过程。理解、预测和管理这些影响将需要跨传统独立研究领域的分析方法。仿真建模已经成为研究复杂系统的有力手段，这些复杂系统含有无法用简单方程描述或实验室实验检验的相互作用的组件，仿真模型已被种群、群落和景观生态学、流行病学、进化遗传学以及资源管理和气候研究所接受。使用一种称为元模型的灵活方法，可以将仿真模型链接在一起，以便每个模型都表示影响系统的特定过程，同时它们一起表示在不同的空间、时间和组织尺度上作用的过程之间的交互。网络合作者将利用经过充分研究的系统的现有数据来开发方法和软件工具，以连接不同子系统的模型(如扩散、社会性、种群动态和景观变化)，并测试这些元模型联系是否更好地识别生态系统的基本驱动因素，揭示单一模型中没有观察到的重要关系，并最终导致对复杂系统的更准确预测。这一研究协调网络将不仅在研究领域，而且在学术界、政府、非政府研究和保护组织以及企业之间建立新的合作。该项目开发的新工具将使跨学科研究远远超越该项目的参与者，提供方法和案例来教育学生跨学科方法，并为决策者提供一个强大的框架，以整合不同的信息来解决复杂的问题。

This project will develop a new network that will bring together developers of simulation models used most widely in population ecology with the leaders of long-term studies on wildlife populations being impacted by habitat modification, landscape change, disease, climate change, and human intervention. The global environment is changing rapidly, and these changes have cascading impacts through local ecosystems, causing unprecedented loss of biodiversity and disruption of ecological processes on which natural and human systems depend. Understanding, predicting, and managing these impacts will require methods of analysis that integrate across traditionally separate fields of study. Simulation modeling has become a powerful means to study complex systems with interacting components that cannot be described by simple equations or tested with laboratory experiments, and simulation models have been embraced by population, community and landscape ecology, epidemiology, and evolutionary genetics, as well as resource management and climate research. Using a flexible approach called metamodels, simulation models can be linked so that they each represent specific processes impacting the system, while together they represent the interactions among processes acting at diverse spatial, temporal, and organizational scales. The network collaborators will make use of existing data on well-studied systems to develop methods and software tools to link models of different subsystems (such as dispersal, sociality, population dynamics, and landscape change), and test if these metamodel linkages better identify fundamental drivers of ecological systems, reveal important relationships not observed in single models, and ultimately result in more accurate predictions about complex systems. This research coordination network will create new collaborations not only across fields of study, but also across academia, government, non-governmental research and conservation organizations, and business. The new tools developed in this project will enable interdisciplinary research far beyond participants in this project, provide methods and cases for educating students in interdisciplinary methods, and give decision-makers a powerful framework for integrating diverse information to solve complex problems.