CNH: Forest Governance and Climate Change in Driving Native Insect Outbreaks

本土昆虫的爆发周期性地破坏美国许多地区的森林，了解导致疾病爆发的自然力量一直是一个长期的科学目标。对人类活动如何与自然过程相互作用以促进或抑制疾病爆发的研究要少得多。该项目将调查地方到联邦各级的森林治理、人为造成的气候变化、火灾频率和森林组成如何共同决定北美西部山区松甲虫的爆发。这种本地昆虫最近扩大了其范围和数量，并杀死了数百万公顷的树木。研究结果有望为制定新政策提供科学依据，以更好地预防或减轻这种昆虫和其他昆虫的爆发及其造成的重大经济损失。管理人员和研究人员的建模工具将公开提供，该项目将与美国林务局合作，以支持控制疫情。该研究还将培训来自科学领域代表性不足的群体的研究生，并通过当地活动和开放式在线课程接触K-12和本科生。该项目旨在促进对受政府政策，生态过程和气候变化之间反馈影响的耦合自然和人类系统的理解。这项研究将开发一个基于主体的计算模型，其中包括社会和生态组成部分的子模型，这些组成部分相互作用，在各种气候变化情景下产生紧急的大规模森林变化模式。研究人员将采用高性能并行计算，战略性地分配气候，昆虫和人类决策，以提供代表潜在未来景观模式空间的大量模拟。不确定性分析将使人们深入了解各种人类和自然过程如何随着时间的推移相互作用，以影响疫情的地点和时间。

Outbreaks of native insects periodically strip forests in many parts of the U.S., and understanding the natural forces that lead to outbreaks has been a long-standing scientific goal. Much less studied has been how human activities may interact with natural processes to promote or inhibit outbreaks. This project will investigate how the governance of forests at local to federal levels, human-caused climate change, fire frequency, and forest composition together determine outbreaks of the mountain pine beetle in western North America. This native insect has recently expanded its range and abundance and killed millions of hectares of trees. Results are expected to provide a scientific basis for the crafting of new policies that can better prevent or mitigate outbreaks of this and other insects and the substantial economic losses they cause. A modeling tool for managers and researchers will be made publicly available, and the project will collaborate with the U.S. Forest Service to support control of outbreaks. The research will also train graduate students from groups underrepresented in science and reach out to K-12 and undergraduate students through local events and an open, online course.This project aims to advance understanding of coupled natural and human systems that are governed by feedbacks between government policies, ecological processes and climate change. The study will develop an agent-based, computational model with submodels for social and ecological components that interact to produce emergent, large-scale patterns of forest change under a variety of climate change scenarios. Researchers will employ high-performance, parallel computing that strategically distributes climate, insect, and human decision-making to provide a multitude of simulations representing the space of potential future landscape patterns. Uncertainty analysis will provide insight into how various human and natural processes interact over time to influence the location and timing of outbreaks.