Data from: Quantifying network resilience: comparison before and after a major perturbation shows strengths and limitations of network metrics

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.1. 韧性研究领域通常假定，社会-生态系统重组要么会移除存在缺陷的系统要素（组分、相互作用），要么会促成社会学习。重大扰动下，系统要么走向适应，若伴随系统状态跃迁（regime shift），则会触发转型。这一预设导致韧性与适应的概念出现混淆，进而使得我们难以定量区分两类情形：一类是系统回归至初始状态且发生了适应或学习行为，另一类是系统具备韧性但并未发生适应或学习行为。 2. 本研究通过对9年间鸵鸟移动数据开展网络分析（network analysis），探究了西开普省鸵鸟产业的社会-生态韧性（social-ecological resilience）。该产业曾在2011年高致病性禽流感（highly pathogenic avian influenza）暴发后濒临崩溃，随后逐步实现重建。 3. 疫情暴发后形成的产业系统，农场数量有所减少，但连通性较暴发前的任何时期都更强。随着系统重组推进，网络特征开始呈现季节性波动，并逐渐趋近于暴发前的观测值。经估算，该系统需历经4-5个完整的季节周期，才能恢复至疫病暴发前的相似状态。换言之，尽管系统在崩溃后完成了重组，但仍处于原有的系统状态框架内，未显现出明显的适应或学习行为证据。 4. 政策启示。过往绝大多数关于系统扰动响应的研究，均聚焦于基于结果的适应与学习行为。本研究强调，需关注那些在未发生学习或适应的情况下对扰动做出响应的系统。网络分析为探究社会-生态韧性、追踪脆弱性变化提供了有效的定量工具。然而，开发更优的多尺度数据整合方法以融入网络分析，仍是提升韧性分析网络方法的预测能力与政策相关性的重要研究方向。