Quantifying network resilience: comparison before and after a major perturbation shows strengths and limitations of network metrics
Quantifying network resilience: comparison before and after a major perturbation shows strengths and limitations of network metrics
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量化网络弹性：重大扰动前后的比较显示网络指标的优势和局限性
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
G. Cumming
中科院分区：
文献类型：
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作者：
Christine Moore;J. Grewar;G. Cumming
1. 复原力文献通常假设社会生态重组将导致消除有缺陷的系统元素（组成部分、相互作用）或社会学习。预计重大扰动将导致适应，或者如果伴随着政权更迭，则会导致转型。这导致了弹性和适应概念的混淆，这反过来又使得很难定量区分系统返回到先前状态并发生适应或学习的情况，以及系统具有弹性但没有发生适应或学习的情况。
2. 我们使用对九年鸵鸟迁徙数据的网络分析来探讨西开普省鸵鸟产业的社会生态恢复力，该产业在 2011 年爆发高致病性禽流感后几乎崩溃，但已逐渐重建。
3. 疫情爆发后出现的系统包含的农场较少，但联系比疫情爆发之前的任何时期都更加紧密。随着系统重组的进行，网络特征开始出现季节性波动，并接近于疫情爆发前观察到的数值。据估计，系统需要 4-5 个完整的季节周期才能恢复到与疾病爆发前相似的状态。换句话说，尽管系统在系统崩溃后进行了重组，但它仍然处于同一体制内，并且没有表现出明显的适应或学习证据。
4. 政策影响。之前研究系统对干扰的响应的大部分工作都集中在基于结果的适应和学习上。这项研究强调了了解无需学习或适应即可响应干扰的系统的必要性。网络分析为探索社会生态恢复力和跟踪脆弱性变化提供了有用的定量工具。然而，开发更好的方法将多个尺度的附加数据纳入网络分析仍然是提高网络方法分析弹性的预测能力和政策相关性的重要优先事项。
1. The resilience literature often assumes that social–ecological reorganization will result in either the removal of deficient system elements (components, interactions) or social learning. Major perturbations are expected to lead to either adaptation or, if accompanied by a regime shift, transformation. This has led to a conflation of the concepts of resilience and adaptation, which has in turn made it difficult to quantitatively distinguish between cases in which a system returned to a previous state, and adaptation or learning occurred, and cases in which the system was resilient but adaptation or learning did not occur.
2. We used a network analysis of nine years of ostrich movement data to explore the social–ecological resilience of the Western Cape ostrich industry, which nearly collapsed following an outbreak of highly pathogenic avian influenza in 2011 and has gradually rebuilt.
3. The system that emerged following the outbreak contained fewer farms but was more connected than at any period prior to the outbreak. As system reorganization proceeded, network traits began to fluctuate seasonally and to approach values similar to those observed prior to the outbreak. It was estimated that it would take 4–5 full seasonal cycles for the system to return to a similar state to that prior to the disease outbreak. In other words, although the system reorganized following the system collapse, it remained within the same regime and showed no obvious evidence of adaptation or learning.
4. Policy implications. The majority of previous work on studying system response to disturbance has focused on outcome-based adaptation and learning. This study highlights the need to understand systems that respond to disturbance without learning or adaptation. Network analysis offers a useful quantitative tool for exploring social–ecological resilience and tracking changes in vulnerability. However, the development of better ways of incorporating additional data from multiple scales into network analysis remains an important priority for improving the predictive power and policy relevance of network approaches to analysing resilience.