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1. Spatial Assessment of Community Resilience from 2012 Hurricane Sandy Using Nighttime Light

   https://doi.org/10.3390/rs13204128  

   Xu, Jinwen ; Qiang, Yi ( October 2021 , Remote Sensing)

   Quantitative assessment of community resilience is a challenge due to the lack of empirical data about human dynamics in disasters. To fill the data gap, this study explores the utility of nighttime lights (NTL) remote sensing images in assessing community recovery and resilience in natural disasters. Specifically, this study utilized the newly-released NASA moonlight-adjusted SNPP-VIIRS daily images to analyze spatiotemporal changes of NTL radiance in Hurricane Sandy (2012). Based on the conceptual framework of recovery trajectory, NTL disturbance and recovery during the hurricane were calculated at different spatial units and analyzed using spatial analysis tools. Regression analysis was applied to explore relations between the observed NTL changes and explanatory variables, such as wind speed, housing damage, land cover, and Twitter keywords. The result indicates potential factors of NTL changes and urban-rural disparities of disaster impacts and recovery. This study shows that NTL remote sensing images are a low-cost instrument to collect near-real-time, large-scale, and high-resolution human dynamics data in disasters, which provide a novel insight into community recovery and resilience. The uncovered spatial disparities of community recovery help improve disaster awareness and preparation of local communities and promote resilience against future disasters. The systematical documentation of the analysis workflow provides a reference for future research in the application of SNPP-VIIRS daily images. 

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2. Multi-Dimensional Output-Oriented Power System Resilience based on Degraded Functionality

   https://doi.org/10.1109/PESGM46819.2021.9638220  

   Valinejad, Jaber ; Mili, Lamine ; Van Der Wal, C. Natalie ; Von Spakovsky, Michael ; Xu, Yijun ( July 2021 , 2021 IEEE Power & Energy Society General Meeting (PESGM))

   Power systems serve social communities that consist of residential, commercial, and industrial customers. As a result, the disaster resilience of a power system should account for social community resilience. The social behavior and psychological features of all stakeholders involved in a disaster influence the level of power system preparedness, mitigation, recovery, adaptability, and resilience. Hence, there is a need to consider the social community's effect on the power system and the dependence between them in determining a power system's resilient to human-made and natural hazards. The social community, such as a county, city, or state, consists of various stakeholders, e.g., social consumers, social prosumers, and utilities. In this paper, we develop a multi-dimensional output-oriented method to measure resilience. The three key ideas for measuring power system resilience are the multi-dimensionality, output-oriented, and degraded functionality aspects of the power system. To this end, we develop an artificial society based on neuroscience, social science, and psychological theories to model the behavior of consumers and prosumers and the interdependence between power system resilience, comsumer and prosumer well-being, and community capital. Both mental health and physical health are used as metrics of well-being, while the level of cooperation is used to measure community capital resilience. 

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3. Merging dynamical and structural indicators to measure resilience in multispecies systems

   https://doi.org/10.1111/1365-2656.13421  

   Medeiros, Lucas P. ; Song, Chuliang ; Saavedra, Serguei ( February 2021 , Journal of Animal Ecology)

   Resilience is broadly understood as the ability of an ecological system to resist and recover from perturbations acting on species abundances and on the system's structure. However, one of the main problems in assessing resilience is to understand the extent to which measures of recovery and resistance provide complementary information about a system. While recovery from abundance perturbations has a strong tradition under the analysis of dynamical stability, it is unclear whether this same formalism can be used to measure resistance to structural perturbations (e.g. perturbations to model parameters).

   Here, we provide a framework grounded on dynamical and structural stability in Lotka–Volterra systems to link recovery from small perturbations on species abundances (i.e. dynamical indicators) with resistance to parameter perturbations of any magnitude (i.e. structural indicators). We use theoretical and experimental multispecies systems to show that the faster the recovery from abundance perturbations, the higher the resistance to parameter perturbations.

   We first use theoretical systems to show that the return rate along the slowest direction after a small random abundance perturbation (what we call full recovery) is negatively correlated with the largest random parameter perturbation that a system can withstand before losing any species (what we call full resistance). We also show that the return rate along the second fastest direction after a small random abundance perturbation (what we call partial recovery) is negatively correlated with the largest random parameter perturbation that a system can withstand before at most one species survives (what we call partial resistance). Then, we use a dataset of experimental microbial systems to confirm our theoretical expectations and to demonstrate that full and partial components of resilience are complementary.

   Our findings reveal that we can obtain the same level of information about resilience by measuring either a dynamical (i.e. recovery) or a structural (i.e. resistance) indicator. Irrespective of the chosen indicator (dynamical or structural), our results show that we can obtain additional information by separating the indicator into its full and partial components. We believe these results can motivate new theoretical approaches and empirical analyses to increase our understanding about risk in ecological systems.

    

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4. Dormancy dynamics and dispersal contribute to soil microbiome resilience

   https://doi.org/10.1098/rstb.2019.0255  

   Sorensen, Jackson W. ; Shade, Ashley ( May 2020 , Philosophical Transactions of the Royal Society B: Biological Sciences)

   In disturbance ecology, stability is composed of resistance to change and resilience towards recovery after the disturbance subsides. Two key microbial mechanisms that can support microbiome stability include dormancy and dispersal. Specifically, microbial populations that are sensitive to disturbance can be re-seeded by local dormant pools of viable and reactivated cells, or by immigrants dispersed from regional metacommunities. However, it is difficult to quantify the contributions of these mechanisms to stability without, first, distinguishing the active from inactive membership, and, second, distinguishing the populations recovered by local resuscitation from those recovered by dispersed immigrants. Here, we investigate the contributions of dormancy dynamics (activation and inactivation), and dispersal to soil microbial community resistance and resilience. We designed a replicated, 45-week time-series experiment to quantify the responses of the active soil microbial community to a thermal press disturbance, including unwarmed control mesocosms, disturbed mesocosms without dispersal, and disturbed mesocosms with dispersal after the release of the stressor. Communities changed in structure within one week of warming. Though the disturbed mesocosms did not fully recover within 29 weeks, resuscitation of thermotolerant taxa was key for community transition during the press, and both resuscitation of opportunistic taxa and immigration contributed to community resilience. Also, mesocosms with dispersal were more resilient than mesocosms without. This work advances the mechanistic understanding of how microbiomes respond to disturbances in their environment. This article is part of the theme issue ‘Conceptual challenges in microbial community ecology’. 

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5. Measuring inequality in community resilience to natural disasters using large-scale mobility data

   https://doi.org/10.1038/s41467-021-22160-w  

   Hong, Boyeong ; Bonczak, Bartosz J. ; Gupta, Arpit ; Kontokosta, Constantine E. ( March 2021 , Nature Communications)

   While conceptual definitions provide a foundation for the study of disasters and their impacts, the challenge for researchers and practitioners alike has been to develop objective and rigorous measures of resilience that are generalizable and scalable, taking into account spatiotemporal dynamics in the response and recovery of localized communities. In this paper, we analyze mobility patterns of more than 800,000 anonymized mobile devices in Houston, Texas, representing approximately 35% of the local population, in response to Hurricane Harvey in 2017. Using changes in mobility behavior before, during, and after the disaster, we empirically define community resilience capacity as a function of the magnitude of impact and time-to-recovery. Overall, we find clear socioeconomic and racial disparities in resilience capacity and evacuation patterns. Our work provides new insight into the behavioral response to disasters and provides the basis for data-driven public sector decisions that prioritize the equitable allocation of resources to vulnerable neighborhoods.

    

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