Predictive ecology in a world with dynamic data and models

具有动态数据和模型的世界中的预测生态学

• 批准号: RGPIN-2018-03829
• 负责人: McIntire, Eliot
• 金额: $ 4.81万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753575
• 关键词: Predictive; ecology; world; dynamic; data

An overarching objective of many ecological research groups is to forecast ecosystems, species or processes. Indeed, for many researchers, large-scale ecological forecasts form the backbone of some of the most challenging ecological and conservation problems of today, such as building conservation strategies for wide-ranging species. One of the key challenges for large-scale studies, however, is that processes, and how their relative importance varies in space, are changing through time. Changing future conditions make it critical to understand key ecological mechanisms within a system, as empirical correlations will fail sooner without some mechanistic understanding. The long term objective is to build a new vision and reality for ecological simulation models. This involves building and using open systems based around R, building new -- and adapting our and other's existing -- ecological models that are open and interchangeable. Once these are done, we will evaluate emergent phenomena, such as stability, resilience, and feedbacks to inform land management at continental scales.The specific objectives here are 1) to build a new, large-scale, plant-functional-trait succession model, 2) test this in a wide array of systems, and 3) evaluate ecological consequences and trade-offs between vegetation management (e.g., carbon sequestration) and other management practices (e.g., caribou, fire, pest, and logging).This research will have 3 different levels of impact. First, the tools we will develop will be useful for the scientific community and its need to build new models. Second, the new models we will develop will be available for use in the new and open/interchangeable modeling and decision making community. Third, we will produce web apps that will allow the public to become involved in ecological models, allowing a transparency of decision making that has rarely been available. This will allow for a new level of outreach and connection to a much wider audience than previously envisioned for ecological models and modelers.

许多生态研究小组的首要目标是预测生态系统、物种或过程。事实上，对于许多研究人员来说，大规模生态预测是当今一些最具挑战性的生态和保护问题的支柱，例如为广泛的物种制定保护策略。然而，大规模研究的关键挑战之一是，过程及其相对重要性在空间上的变化是随着时间而变化的。未来条件的变化使得理解系统内的关键生态机制变得至关重要，因为如果没有一些机械的理解，经验相关性将很快失效。 长期目标是为生态模拟模型建立新的愿景和现实。这涉及到构建和使用基于R的开放系统，构建新的-并适应我们和其他人现有的-开放和可互换的生态模型。一旦完成这些工作，我们将评估突发现象，如稳定性，恢复力和反馈，为大陆尺度的土地管理提供信息。这里的具体目标是1）建立一个新的，大规模的，植物功能性状演替模型，2）在广泛的系统中测试这一点，3）评估生态后果和植被管理之间的权衡（例如，碳螯合）和其他管理实践（例如，驯鹿，火灾，虫害和伐木）。这项研究将有3个不同程度的影响。首先，我们将开发的工具将对科学界及其建立新模型的需求有用。其次，我们将开发的新模型将可用于新的开放/可互换的建模和决策社区。第三，我们将开发网络应用程序，使公众能够参与生态模型，从而实现决策的透明度，而这在过去是很难实现的。这将使生态模型和建模者能够比以前设想的更广泛的受众进行更高水平的外联和联系。