可解释性
环境资源管理
脆弱性(计算)
弹性(材料科学)
生态系统
交替稳态
心理弹性
生态系统服务
国家(计算机科学)
环境科学
计算机科学
水生生态系统
恢复生态学
生态学
人工智能
热力学
心理治疗师
物理
心理学
算法
生物
计算机安全
作者
John Thomas Delaney,Danelle M. Larson
摘要
Ecosystem state transitions can be ecologically devastating or be a restoration success. State transitions are common within aquatic systems worldwide, especially considering human-mediated changes to land use and water use. We created a transferable conceptual framework to enable multi-scale assessments of state resilience and early warnings of state transitions that can inform strategic restorations and avoid ecosystem collapse. The conceptual framework integrated machine learning predictions with ecosystem state concepts, such as state classification, gradients of vulnerability or recovery potential leading to state transitions, and investigation of possible environmental drivers. As an example, we generated prediction probabilities of submersed aquatic vegetation (SAV) presence at nearly 10,000 sites within the Upper Mississippi River, USA. Then, we used an interpretability method for explanation of model predictions to gain insights on possible environmental drivers and threshold or linear responses of SAV presence and absence. The results are also presented via an online, interactive dashboard where our habitat suitability model outputs and prediction explanations from many spatial scales (4 m-400 km of river reach) can inform research and restoration planning. This article is protected by copyright. All rights reserved.
科研通智能强力驱动
Strongly Powered by AbleSci AI