Despite the wide variety of variables commonly employed to measure the success of rehabilitation, the assessment and subsequent definition of indicators of environmental rehabilitation status are not simple tasks. The main challenges are comparing rehabilitated sites with target ecosystems as well as integrating individual environmental and eventually collinear variables into a single tractable measure for the state of a system before effective indicators that track rehabilitation may be modeled. Furthermore, a consensus is lacking regarding which and how many variables need to be surveyed for a reliable estimation of rehabilitation status. Here, we propose a multivariate ordination to integrate variables related to ecological processes, vegetation structure, and community diversity into a single estimation of rehabilitation status. As a case, we employed a curated set of 32 environmental variables retrieved from nonrevegetated, rehabilitating and reference sites associated with iron ore mines from the Urucum Massif, Mato Grosso do Sul, Brazil. By integrating this set of environmental variables into a single estimation of rehabilitation status, the proposed multivariate approach is straightforward and able to adequately address collinearity among variables. The proposed methodology allows for the identification of biases towards single variables, surveys or analyses, which is necessary to rank environmental variables regarding their importance to the assessment. Furthermore, we show that bootstrapping permitted the detection of the minimum number of environmental variables necessary to achieve reliable estimations of the rehabilitation status. Finally, we show that the proposed variable integration enables the definition of case-specific environmental indicators for more rapid assessments of mineland rehabilitation. Thus, the proposed multivariate ordination represents a powerful tool to facilitate the diagnosis of rehabilitating sites worldwide provided that sufficient environmental variables related to ecological processes, diversity and vegetation structure are gathered from nonrehabilitated, rehabilitating and reference study sites. By identifying deviations from predicted rehabilitation trajectories and providing assessments for environmental agencies, this proposed multivariate ordination increases the effectiveness of (mineland) rehabilitation.

中文翻译：

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通过多元排序整合环境变量可以可靠地估计矿区恢复状态。


尽管衡量恢复成功与否的变量多种多样，但环境恢复状况指标的评估和随后的定义并不是简单的任务。主要挑战是将恢复地点与目标生态系统进行比较，以及在对跟踪恢复的有效指标进行建模之前，将各个环境变量和最终的共线变量整合到系统状态的单一易处理的度量中。此外，对于需要调查哪些变量以及多少变量才能可靠地估计康复状态，还缺乏共识。在这里，我们提出了一种多元排序，将与生态过程、植被结构和群落多样性相关的变量整合到恢复状态的单一估计中。作为一个案例，我们采用了一组精选的 32 个环境变量，这些变量取自与巴西南马托格罗索州乌鲁库姆地块铁矿相关的非植被恢复和参考地点。通过将这组环境变量整合到康复状态的单一估计中，所提出的多变量方法非常简单，并且能够充分解决变量之间的共线性。所提出的方法允许识别对单个变量、调查或分析的偏差，这对于根据环境变量对评估的重要性进行排名是必要的。此外，我们表明，引导允许检测实现康复状态可靠估计所需的最少数量的环境变量。 最后，我们表明，所提出的变量整合能够定义特定案例的环境指标，以便更快速地评估雷地恢复。因此，如果从非恢复、恢复和参考研究地点收集到与生态过程、多样性和植被结构相关的足够的环境变量，所提出的多变量排序代表了促进全球恢复地点诊断的强大工具。通过识别与预测的恢复轨迹的偏差并为环境机构提供评估，这种提出的多元排序提高了（矿区）恢复的有效性。