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Authors:
Dillon Fogarty
Publication Date:
2019
Abstract/Summary:
Transitioning to appropriate restoration styles (e.g., command and control to adaptive management) under conditions of decreasing controllability and/or increasing uncertainty is a key challenge for sustaining ecosystem services in a changing world. Understanding when to transition to a new restoration style requires knowledge of management outcomes and the scale(s) at which undesirable environmental changes are occurring. Our objective was to develop a framework for: (1) monitoring restoration outcomes, and (2) providing recommendations for restoration styles based on levels of controllability and uncertainty associated with the scale(s) of undesirable environmental change. We apply this framework to tree invasion management programs in grassland landscapes of Nebraska, USA, where tree invasion is managed using a command and control restoration style. We used remotely-sensed tree cover data (Rangeland Analysis Platform) to model tree cover trends from 2000-2017 (restoration outcomes) at multiple, nested scales for 21 landscapes. These results provided a systematic basis for identifying an appropriate restoration style based on the level of controllability and uncertainty associated with the scale(s) of invasion. Tree invasion outpaced restoration in 17 of 21 landscapes and was associated with low controllability and high uncertainty in all landscapes. Command and control styles emphasized pattern restoration using tree removal projects, but largely ignored preventative processes. Given the scale-specific challenges of pattern and process restoration, our framework identified scenario planning as the most appropriate restoration style for all landscapes. Our framework provides a systematic basis for understanding when to transition between restoration styles and has potential to improve broad-scale restoration outcomes.
Resource Type:
Audio/Video, Conference Presentation, SER2019
Pre-approved for CECs under SER's CERP program
Source:
Society for Ecological Restoration