Complex Systems Approach for Studying Spatiotemporal Patterns of Disturbance as a Result of Climate Change

研究气候变化造成的扰动时空模式的复杂系统方法

• 批准号: RGPIN-2016-05396
• 负责人: Perez, Liliana
• 金额: $ 2.04万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615665
• 关键词: Complex; Systems; Approach; Studying; Spatiotemporal

Disturbance events have an important role in ecosystem regulation, and are acknowledged to influence species diversity, plant succession and regeneration, the distribution and abundance of animal populations and nutrient concentration in a range of ecosystem types. Historically, in forest ecosystems, natural disturbances such as wildfire and storms act as major regulating forces and, more recently, human disturbances such as land use changes, forest management and timber harvesting have had a marked impact on forest extent and structure, and on the distribution and abundance species. Whether natural or anthropogenic, disturbance has the capacity to affect biodiversity via a number of mechanisms, including its influence on environmental heterogeneity. Although, forest ecosystems are constantly shaped by natural disturbances such as fires, insect infestations, diseases, windfall, among others, recent and more rapid climate changes have modified these disturbance regimes as well as the complex dynamics between forest ecosystems and their biodiversity. Disturbance regimes include disturbances at many intensities and scales, from the death of a single dominant tree to a catastrophic forest fire or insect infestation, all of which are embedded within climatic cycles that may span decades to millions of years. The disturbance regime of a particular forest usually consists of a complex mixture of infrequent, large-scale events and more frequent, small-scale events. A disturbance regime cannot be understood in isolation, but rather it is tied to the systems to which it is linked an ultimately to events within surrounding landscapes, the region, and the globe. Understanding the processes that drive changes to the landscape patterns and ecological complexity over time is critical for ensuring ecological, social and economic stability in many regions in the country. As complexity is inherent in ecological and anthropogenic processes, this research program aims to advance GIScience methods by developing complex systems modelling approaches that will be applicable in ecology to further our understanding about spatiotemporal dynamics driving changes in the Canadian forests and their biodiversity. This research agenda will integrate multiple statistical and mathematical methods to describe geographic patterns, as well as computational modelling approaches to simulate the effects of ecological and environmental change on forest ecosystems and their biodiversity. Modelling approaches such as cellular automata and agent-based combined with computational intelligence may hold the key to a better understanding of how disturbance regimes and their spatial patterns will vary in response to climate change. This knowledge is urgently needed in Canada to promote awareness of possible alterations in ecosystem function and services to support a range of policy and planning considerations.

干扰事件在生态系统调节中发挥着重要作用，并被认为会影响一系列生态系统类型中的物种多样性、植物演替和再生、动物种群的分布和丰度以及养分浓度。从历史上看，在森林生态系统中，野火和风暴等自然干扰是主要的调节力量，而最近，土地利用变化、森林管理和木材采伐等人为干扰对森林范围和结构以及物种的分布和丰富度产生了显着影响。无论是自然的还是人为的，干扰都有能力通过多种机制影响生物多样性，包括对环境异质性的影响。尽管森林生态系统不断受到火灾、虫害、疾病、意外之财等自然干扰的影响，但最近更快速的气候变化改变了这些干扰机制以及森林生态系统与其生物多样性之间的复杂动态。干扰制度包括多种强度和规模的干扰，从一棵主要树木的死亡到灾难性的森林火灾或虫害，所有这些都嵌入在可能跨越数十年到数百万年的气候循环中。特定森林的干扰机制通常由罕见的大规模事件和更频繁的小规模事件的复杂混合物组成。干扰机制不能被孤立地理解，而是与其所关联的系统紧密相关，并最终与周围景观、区域和全球内的事件相关。了解随着时间的推移驱动景观格局和生态复杂性变化的过程对于确保该国许多地区的生态、社会和经济稳定至关重要。由于生态和人为过程固有的复杂性，该研究计划旨在通过开发适用于生态学的复杂系统建模方法来推进地理信息科学方法，以进一步了解驱动加拿大森林及其生物多样性变化的时空动态。该研究议程将整合多种统计和数学方法来描述地理模式，以及计算建模方法来模拟生态和环境变化对森林生态系统及其生物多样性的影响。元胞自动机和基于代理与计算智能相结合的建模方法可能是更好地理解扰动状况及其空间模式如何因气候变化而变化的关键。加拿大迫切需要这些知识，以提高人们对生态系统功能和服务可能发生变化的认识，以支持一系列政策和规划考虑。