School of Ecosystem and Forest Sciences, University of Melbourne, Parkville, Victoria, Australia.
Department of Environment, Land, Water and Planning, Arthur Rylah Institute for Environmental Research, Heidelberg, Victoria, Australia.
Ecol Appl. 2023 Jan;33(1):e2728. doi: 10.1002/eap.2728. Epub 2022 Nov 10.
Monitoring vegetation restoration is challenging because monitoring is costly, requires long-term funding, and involves monitoring multiple vegetation variables that are often not linked back to learning about progress toward objectives. There is a clear need for the development of targeted monitoring programs that focus on a reduced set of variables that are tied to specific restoration objectives. In this paper, we present a method to progress the development of a targeted monitoring program, using a pre-existing state-and-transition model. We (1) use field data to validate an expert-derived classification of woodland vegetation states; (2) use these data to identify which variable(s) help differentiate woodland states; and (3) identify the target threshold (for the variable) that signifies if the desired transition has been achieved. The measured vegetation variables from each site in this study were good predictors of the different states. We show that by measuring only a few of these variables, it is possible to assign the vegetation state for a collection of sites, and monitor if and when a transition to another state has occurred. For this ecosystem and state-and-transition models, out of nine vegetation variables considered, the density of immature trees and percentage of exotic understory vegetation cover were the variables most frequently specified as effective to define a threshold or transition. We synthesize findings by presenting a decision tree that provides practical guidance for the development of targeted monitoring strategies for woodland vegetation.
监测植被恢复具有挑战性,因为监测既昂贵又需要长期资金,并且涉及监测多个通常与了解目标进展无关的植被变量。显然需要开发有针对性的监测计划,重点关注与特定恢复目标相关的变量。在本文中,我们提出了一种使用现有状态和转换模型来推进有针对性监测计划发展的方法。我们:(1) 使用现场数据验证专家推导的林地植被状态分类;(2) 使用这些数据确定哪些变量有助于区分林地状态;以及 (3) 确定表示是否已实现所需转换的目标阈值(变量)。本研究中每个地点的测量植被变量都是不同状态的良好预测因子。我们表明,通过仅测量其中的几个变量,就可以为一组地点分配植被状态,并监测是否已经发生向另一种状态的过渡。对于这个生态系统和状态-转换模型,在所考虑的九个植被变量中,幼树密度和外来林下植被覆盖率是最常被指定为有效定义阈值或转换的变量。我们通过提供决策树来综合研究结果,为林地植被有针对性的监测策略的制定提供了实用指导。