Reviewing the use of resilience concepts in forest sciences

Purpose of the review Resilience is a key concept to deal with an uncertain future in forestry. In recent years, it has received increasing attention from both research and practice. However, a common understanding of what resilience means in a forestry context, and how to operationalise it is lacking. Here, we conducted a systematic review of the recent forest science literature on resilience in the forestry context, synthesising how resilience is defined and assessed. Recent findings Based on a detailed review of 255 studies, we analysed how the concepts of engineering resilience, ecological resilience, and social-ecological resilience are used in forest sciences. A clear majority of the studies applied the concept of engineering resilience, quantifying resilience as the recovery time after a disturbance. The two most used indicators for engineering resilience were basal area increment and vegetation cover, whereas ecological resilience studies frequently focus on vegetation cover and tree density. In contrast, important social-ecological resilience indicators used in the literature are socio-economic diversity and stock of natural resources. In the context of global change, we expected an increase in studies adopting the more holistic social-ecological resilience concept, but this was not the observed trend. Summary Our analysis points to the nestedness of these three resilience concepts, suggesting that they are complementary rather than contradictory. It also means that the variety of resilience approaches does not need to be an obstacle for operationalisation of the concept. We provide guidance for choosing the most suitable resilience concept and indicators based on the management, disturbance and application context

A resilience-oriented approach for quantitatively assessing recurrent spatial-temporal congestion on urban roads

Traffic congestion brings not only delay and inconvenience, but other associated national concerns, such as greenhouse gases, air pollutants, road safety issues and risks. Identification, measurement, tracking, and control of urban recurrent congestion are vital for building a livable and smart community. A considerable amount of works has made contributions to tackle the problem. Several methods, such as time-based approaches and level of service, can be effective for characterizing congestion on urban streets. However, studies with systemic perspectives have been minor in congestion quantification. Resilience, on the other hand, is an emerging concept that focuses on comprehensive systemic performance and characterizes the ability of a system to cope with disturbance and to recover its functionality. In this paper, we symbolized recurrent congestion as internal disturbance and proposed a modified metric inspired by the well-applied “R4” resilience-triangle framework. We constructed the metric with generic dimensions from both resilience engineering and transport science to quantify recurrent congestion based on spatial-temporal traffic patterns and made the comparison with other two approaches in freeway and signal-controlled arterial cases. Results showed that the metric can effectively capture congestion patterns in the study area and provides a quantitative benchmark for comparison. Also, it suggested not only a good comparative performance in measuring strength of proposed metric, but also its capability of considering the discharging process in congestion. The sensitivity tests showed that proposed metric possesses robustness against parameter perturbation in Robustness Range (RR), but the number of identified congestion patterns can be influenced by the existence of . In addition, the Elasticity Threshold (ET) and the spatial dimension of cell-based platform differ the congestion results significantly on both the detected number and intensity. By tackling this conventional problem with emerging concept, our metric provides a systemic alternative approach and enriches the toolbox for congestion assessment. Future work will be conducted on a larger scale with multiplex scenarios in various traffic conditions

Characterisation of survivability resilience with dynamic stock interdependence in financial networks

This paper examines the dynamic evolutionary process in the London Stock Exchange and uses network statistical measures to model the resilience of stock. A large historical dataset of companies was collected over 40 years (1977-2017) and conceptualised into weighted, temporally evolving and signed networks using correlation-based interdependences. Our results revealed a “fission-fusion” market growth in network topologies, which indicated the dynamic and complex characteristics of its evolutionary process. In addition, our regression and modelling results offer insights for construction a “characterisation tool” which can be used to predict stocks that have delisted and continuing performance relatively well, but were less adequate for stocks with normal performance. Moreover, the analysis of deviance suggested that the survivability resilience could be described and approximated by degree-related centrality measures. This study introduces a novel alternative for looking at the bankruptcy in the stock market and is potentially helpful for shareholders, decision- and policy-makers

Modeling stock survivability resilience in signed temporal networks: A study from London stock exchange

This paper examines the dynamic evolution process in London stock exchange and attempts to model stock survivability resilience in the financial networks. A big historical dataset of UK companies from London stock exchange for 40 years (1976–2016) was collected and conceptualized into weighted, temporally evolving and signed networks using correlation coefficients. Based on the legal definition of corporate failure, stocks were categorized into Continuing, Failed and Normal groups. Accordingly, we conducted analysis on (1) The long-term evolution process of the entire population with statistical inference and visualization. (2) Multivariate logistic modeling of survivability resilience using short-term network measures, degree ratio (ri), node degree (ki), and node strength (si). The results show an exponential market growth but with a “fission-fusion” behavior in network topologies, which indicates dynamic and complex characteristics of its expansion. On the other hand, regression and modeling outcomes show that the survivability resilience is correlated with ki and si. Moreover, the analysis of deviance suggests that the survivability resilience could be described, by and large, as a function of ki since it contributes the most significant difference. The study provides a novel alternative to look at the bankruptcy in the stock market and is potentially helpful for shareholders, decision- and policy-makers

A Bayesian network approach for assessing the general resilience of road transportation systems: A systems perspective

We proposed a Bayesian network model (BNM) based on function-oriented resilience framework and ontological interdependence among 10 system qualities to probabilistically assess the general resilience of the road transportation system in Beijing from 1997 to 2016. We tested the model with multi-source data collected from various sectors. The system qualities were examined by analysis of sensitivity and influence. The result shows that the general resilience of Beijing's road system exhibits a "V" shape in its trend, with the probability of being generally resilient between 50% and 70%, and at its minimum in 2006. There was a steep increase in such a probability since 2006. In addition, the general resilience of Beijing's road transportation system is most affected by its capabilities: (1) to rebuild its performance; (2) to be robust; (3) to adapt; (4) to change; and (5) to quickly repair damaged parts. The proposed BNM is a promising tool for multi-dimensional and systematic analysis, instead of finding a one-size-fits-all quantification criterion for the resilience

Time Granularity in System-of-Systems Simulation of Infrastructure Networks

Because of their extreme complexities, a system-of-systems (SoS) approach is often used for simulating infrastructure systems. This allows the user to integrate models of various systems into one simulation. However, this integration presents several issues because individual simulations are often designed for only a specific purpose and context. This leads to variations among space granularities and proposes a challenge when selecting an appropriate time granularity for the overall SoS simulation. To explore how this granularity might affect the outcome of simulations, we designed and developed a prototype system of three infrastructure simulation networks that were then combined into one SoS simulation using High Level Architecture (HLA) implementation. We then performed a series of experiments to investigate the response of the simulation to varying time granularities. Our examination included a propagation of disruptions among constituent simulations to estimate how this was affected by the frequency of updates between those simulations, i.e. time granularity. Our results revealed that the size of the simulated disruption decreased with increasing time granularity and that the simulated recovery time was also affected. In conducting this project, we also identified several ideas for future research that could focus on a wider range of disruption generators and infrastructure systems in those SoS simulations