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

该项目将建立一个新的网络，将人口生态学中最广泛使用的模拟模型的开发人员与长期研究野生动物种群受栖息地改变、景观变化、疾病、气候变化和人类干预影响的领导者聚集在一起。全球环境正在迅速变化，这些变化对当地生态系统产生了级联影响，造成生物多样性前所未有的丧失，并破坏了自然系统和人类系统所依赖的生态过程。理解、预测和管理这些影响需要将传统上独立的研究领域整合在一起的分析方法。模拟建模已成为研究复杂系统的有力手段，这些系统具有无法用简单方程描述或实验室实验验证的相互作用组件，模拟模型已被人口、社区和景观生态学、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，可以将仿真模型连接起来，以便每个模型都代表影响系统的特定过程，而它们一起代表在不同空间、时间和组织尺度上活动的过程之间的相互作用。网络合作者将利用已得到充分研究的系统的现有数据，开发方法和软件工具，将不同子系统（如扩散、社会性、种群动态和景观变化）的模型联系起来，并测试这些元模型联系是否能更好地识别生态系统的基本驱动因素，揭示单个模型中未观察到的重要关系，并最终对复杂系统进行更准确的预测。这个研究协调网络将不仅在研究领域之间，而且在学术界、政府、非政府研究和保护组织以及商界之间建立新的合作关系。本项目开发的新工具将使跨学科研究远远超出项目参与者的范围，为跨学科方法教育学生提供方法和案例，并为决策者提供整合各种信息以解决复杂问题的强大框架。

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.