Resilience Index Research Articles

Sapling recruitment as an indicator of carbon resiliency in forests of the northern USA.

Tree regeneration shapes forest carbon dynamics by determining long-term forest composition and structure, which suggests that threats to natural regeneration may diminish the capacity of forests to replace live tree carbon transferred to the atmosphere or other pools through tree mortality. Yet, the potential implications of tree regeneration patterns for future carbon dynamics have been sparsely studied. We used forest inventory plots to investigate whether the composition of existing tree regeneration is consistent with aboveground carbon stock loss, replacement, or gain for forests across the northeastern and midwestern USA, leveraging a recently developed method to predict the likelihood of sapling recruitment from seedling abundance tallied within six seedling height classes. A comparison of carbon stock predictions from tree and seedling composition suggested that 29% of plots were poised to lose carbon based on seedling composition, 55% were poised for replacement of carbon stocks (<5 Mg ha-1 difference) and 16% were poised to gain carbon. Forests predicted to lose carbon tended to be on steeper slopes, at lower latitudes, and in rolling upland environments. Although plots predicted to gain and lose carbon had similar stand ages, carbon loss plots had greater current carbon stocks. Synthesis and applications. Our results demonstrate the utility of considering tree regeneration through the lens of carbon replacement to develop effective management strategies to secure long-term carbon storage and resilience in the context of global change. Forests poised to lose C due to climate change and other stressors could be prioritized for regeneration strategies that enhance long-term carbon resilience and stewardship.

Development of a Model for Comprehensive Evaluation of Corporate Resilience Against Disasters (2)—An Examination Based on British Standard Institution’s Organizational Resilience Index

“Resilience” is the ability to recover from the effects of a disaster or accident. Companies are expected to be resilient against accidents and disasters; however, at present no method comprehensively evaluates corporate resilience. This study examines some models of organizational resilience already published as a preparation for developing such an evaluation method. This study examines the “Organizational Resilience Index” developed by the British Standards Institution to assess whether it comprehensively covers the factors that contribute to corporate resilience. This study compares these with cases of past disasters and accidents. The results suggest that the following six factors are not explicitly covered by the Organizational Resilience Index: (1) attitude toward fulfilling responsibilities to customers, (2) external cooperation/assistance, (3) prompt decision-making and action, (4) practitioners empowered with discretion, (5) loyalty of employees to the company, and (6) a never-give-up attitude. Although this cannot be considered a definitive conclusion because of the various forms of subjectivity involved in the examination process, this study identifies factors that should be considered while developing a model that comprehensively includes factors that contribute to corporate resilience.

Special Issue on The Resilience Research Council of Japan

We are glad that this special issue takes up the activities of The Resilience Research Council of Japan (RRCJ). Twelve years have passed since the founding of the RRCJ. In addition to regular meetings at which experts from various fields participate in disseminating information, one of the RRCJ’s main activities is holding study groups to delve deeply into specific aspects of disaster preparedness. To respond to the intensification and frequent occurrence of disasters in recent years, it is urgent to review existing measures against disasters and business continuity plans (BCPs). Furthermore, comprehensive and flexible strategies should be developed to enhance disaster resilience. Each study in this special issue provides new perspectives and clues on this problem. First, the existing BCPs lack concrete measures from the perspectives of individual corporate departments and employees. To tackle this problem, new procedures are proposed to formulate BCPs based on the questions “What if?” and “What when?” This is expected to increase the effectiveness of BCPs. Second and third, comprehensive methods for evaluating resilience are lacking. As a result of an examination of the 13 current indexes of “Resilient Organizations” and the “Organization Resilience Index” of the British Standards Institution, it is revealed that some important elements are absent from the evaluation of corporate resilience. By complementing the corporate resilience model with these elements, the model can be improved. Furthermore, local governments must implement effective disaster countermeasures for those with limited resources in their disaster management plans. The fourth study evaluates disaster management measures from the point of view of both local governments and residents, and proposes innovative solutions to collaborate and organize, adapt to the environment, and manage for the next generation. This reveals the problems faced by local governments and residents’ lack of knowledge, and provides a roadmap for future improvements. Overall, these studies indicate that creating a resilience strategy involving all stakeholders is an urgent matter, provide valuable insight into the limits of current strategies, and offer guidelines for strengthening the important aspects of resilience so that corporations and local governments can respond to disasters more effectively. We expect that the effectiveness of disaster measures will be enhanced through incorporating these proposals and methods into practical policies and actual works. We sincerely thank all the contributors for their contributions to this special issue and the reviewers for their valuable comments.

Adapting the Baseline Resilience Indicators for Communities (BRIC) Framework for England: Development of a Community Resilience Index.

Community resilience results from complex interactions between people, places, and environments. Measuring community resilience aligns with policy objectives to enhance resilience to adverse events and address spatial disparities. The Baseline Resilience Indicators for Communities (BRIC) is a composite index used to measure resilience. This study adapts the BRIC approach to develop a Community Resilience Index (CRI) for England. A systematic review informed indicator selection, and principal components analysis was used to define sub-indices and allocate weightings. The resulting CRI comprised 44 indicators across five domains, quantifying the resilience of English local authorities. Geographical comparisons were made using t-tests and ANOVA, and the CRI was validated against the Index of Multiple Deprivation (IMD). The mean CRI score for local authorities in England was 83.1, ranging from 53.3 to 108.9. Resilience scores showed spatial patterning, with London and the South East scoring highest. The North had lower CRI scores than the Midlands and South (p = 0.022). Coastal and rural areas also showed lower resilience (p < 0.001). CRI and IMD were inversely correlated (r = -0.564, p < 0.0001). This study contributes to the literature by providing the first detailed assessment of community resilience in England using an adapted BRIC framework. The CRI provides a framework for measuring community resilience in England and could be used to explore associations with health outcomes and guide funding allocation.

Development of a Model for Comprehensive Evaluation of Corporate Resilience Against Disasters (1)—An Examination Based on Indicators Developed by “Resilient Organisations”

“Resilience” is the ability to recover after being affected by a disaster or accident. Companies are expected to be resilient against accidents and disasters; however, no method comprehensively evaluates corporate resilience. As a preparation for developing such an evaluation method, this study examines the 13 Resilience Indicators developed by “Resilient Organisations” as one of the models for organizational resilience to assess whether it comprehensively covers the factors that contribute to corporate resilience. This study compares the indicators with past accidents and disaster cases. Consequently, the study determined that the 13 Resilience Indicators do not explicitly cover the following four factors: (1) risk diversification related to external resources such as suppliers, (2) prompt decision-making and action, (3) countermeasures to protect corporate reputation, and (4) a never-give-up attitude. It cannot be said that this is a definitive conclusion, as subjectivity intervenes in various ways in the examination process. However, this study identified factors that should be considered while developing a model that comprehensively includes factors that contribute to corporate resilience.

Factors Affecting the Resilience Index Food in Papua Province and West Papua Province Using a Spatial Model Approach

The Food Security Index is a measure of indicators to produce a composite value that reflects the status of food security in a region. Food security plays an important role in sustainable development, including food availability, environmental preservation and economic balance, as well as being the basis for economic growth, preventing poverty and inequality. In Indonesia, with an estimated population growth of 430 million people in 2050, the challenge of meeting food needs is increasing. Indonesia's commitment to the Sustainable Development Goals (SDGs) includes efforts to end hunger and promote sustainable agriculture. This research aims to apply spatial regression analysis to the Provinces of Papua and West Papua to determine the best model and significant factors that influence the Food Security Index in the region in order to identify the challenges faced by the region in calculating the food availability of its people as well as assist in developing efforts to overcome them. Five predictor variables were used with the assumption that they have a significant influence on the Food Security Index. This research examines the spatial regression equation using the SAR, SEM and SARMA regional approaches. The results obtained showed that the selected SEM model with a p-value of 0.0082581 was appropriate for identifying the dependence of spatial effects on Food Security Index in Papua Province and West Papua Province. Life Expectancy at Birth, Prevalence of Stunting Toddlers, Percentage of Poor Population, Open Unemployment Rate, and Average Length of Life are significant factors that influence the Food Security Index in Papua Province and West Papua spatially.

Responses of Soil Enzymes Activities to Sprinkler Irrigation and Differentiated Nitrogen Fertilization in Barley Cultivation

Our study aimed to assess the impact of sprinkler irrigation on the activity of selected soil enzymes in terms of nitrogen metabolism and oxidation–reduction processes in soil with different doses of inorganic nitrogen fertilizers. An Alfisol was sampled from an experimental field of spring barley within the University Research Center in the central part of Poland, namely the village of Mochełek with a moderate transitory climate, during the growing seasons of 2015–2017. The soil resistance (RS) was derived to recognize the resistance enzymes during drought. In the maturity phase, nitrate reductase activity was 18% higher in irrigated soil and the activities of other enzymes were higher than in the non-irrigated plots by 25% for dehydrogenase, 22% for peroxidase, 33% for catalase, and 17% for urease. The development phase in the barley influenced nitrate reductase activity. Enzymatic activities changed throughout the research years. During the maturity stage, a lower ammonium nitrogen content in the soil resulted from a higher spring barley uptake due to drought stress. Irrigation probably contributed to increased leaching of nitrate in the soil. The highest index of resilience was found in the soil catalase activity.

Resilience assessment of airport aircraft area network operations under thunderstorm weather

Thunderstorms can cause a decrease in airports' dynamic capacity, and taxiway runway capacity is a key element of airport airfield operations. In this paper, based on the cell transmission model (CTM), the meteorological operation parameters of thunderstorms are set according to the airport aircraft taxiing rules, and a CTM is proposed for evaluating the resilience of the airport aircraft area operation network under thunderstorm weather. Based on the CTM, the network performance parameters are obtained, and the number of aircraft waiting for the network and the network operation efficiency are used as the basic performance indicators to establish a resilience index that reflects the dynamic change in the resilience of the thunderstorm meteorological interference network. Finally, this paper collects and analyzes the operation data of Tianjin Binhai International Airport in 2019 to build a CTM for aircraft operation in the flight area. The results show that the model's simulated airport departure operation data is almost consistent with the trend of the actual departure operation data, which verifies that the model can be used to simulate traffic for the airport airfield operation network. In addition, based on the resilience index, the sensitivity analysis of different parameters to network performance under thunderstorm weather was also studied. Based on the evaluation results of different parameter scenarios and indicators, we drew the resilience map of the airport ground operation network, and found that the network shows different resilience levels in different thunderstorm level scenarios. This study provides a great method and evaluation index for modeling and resilience evaluation of aircraft operation networks in the airfield, which is expected to improve airport operations, reduce flight delays, and improve service quality.

Monitoring resilience in bursts

The possibility to anticipate critical transitions through detecting loss of resilience has attracted attention in many fields. Resilience indicators rely on the mathematical concept of critical slowing down, which means that a system recovers more slowly from external perturbations when it gets closer to tipping point. This decrease in recovery rate can be reflected in rising autocorrelation and variance in data. To test whether resilience is changing, resilience indicators are often calculated using a moving window in long, continuous time series of the system. However, for some systems, it may be more feasible to collect several high-resolution time series in short periods of time, i.e., in bursts. Resilience indicators can then be calculated to detect a change of resilience between such bursts. Here, we compare the performance of both methods using simulated data and showcase the possible use of bursts in a case study using mood data to anticipate depression in a patient. With the same number of data points, the burst approach outperformed the moving window method, suggesting that it is possible to downsample the continuous time series and still signal an upcoming transition. We suggest guidelines to design an optimal sampling strategy. Our results imply that using bursts of data instead of continuous time series may improve the capacity to detect changes in resilience. This method is promising for a variety of fields, such as human health, epidemiology, or ecology, where continuous monitoring can be costly or unfeasible.

Evaluation and influencing factors of ecological resilience in tourism villages from farmers’ perspectives: a case study of Chinese minority settlement areas

Rural social-ecological systems have been significantly impacted by the development of rural tourism, creating substantial challenges for the synergistic development of rural ecological restoration and agricultural workers’ household livelihoods. In alignment with the United Nations Decade on Ecosystem Restoration action plan, it is crucial to understand the ecological resilience of tourism villages from the perspective of farmers to achieve sustainable development. Questionnaires and semi-structured interviews were conducted in this study to gather livelihood data from farmers in tourism villages in Xiangxi, China, a region primarily inhabited by ethnic minorities. A measurement index system for the ecological resilience of rural tourism was constructed based on the three dimensions: pressure, state, and response. A comprehensive index method was employed for quantitative evaluation, and influencing factors were analyzed using an obstacle model. The results show that farmers’ livelihoods can be divided into four types: tourism-led, labor-led, agriculture-led, and part-time tourism-based. Their ecological resilience indices are 0.4516, 0.4177, 0.4001, and 0.4590, respectively, representing an overall intermediate level. There are differences in various indices for different livelihood types, with the highest stress index observed in labor-led farmers (0.1655), the highest state index in agriculture-led farming households (0.1585), and the highest response index in tourism-led farming households (0.1766). Common obstacles to the ecological resilience of farmers and their villages include the number of family members engaged in tourism work, the area of forest land, and the farmers’ understanding of ecological policies affecting them. Additionally, core family members’ education levels and the area of homesteads emerged as important obstacle factors. Based on these research findings, recommendations are proposed to enhance the ecological resilience of rural farmer households in the tourism villages of ethnic minority areas. The aim is to provide valuable case studies for promoting the sustainable development of rural tourism in underdeveloped regions worldwide.

Spatiotemporal characteristics of ecological resilience and its influencing factors in the Yellow River Basin of China

Maintaining and improving ecological resilience is of great practical significance for the Yellow River Basin to reduce potential ecological risks and deliver sustainable development. Based on the essential characteristics of evolutionary resilience, this paper developed an ecological resilience index system of “resistance-recovery-reconstruction-renewal” and calculated the ecological resilience of 75 prefecture-level cities in the Yellow River Basin from 2007 to 2021 with the improved TOPSIS method. Then the spatiotemporal evolution characteristics of ecological resilience were analyzed using the gravity center-standard deviation ellipse, Dagum Gini coefficient decomposition, and spatial autocorrelation analysis. Furthermore, the dynamic spatial Durbin model (DSDM) was used to investigate the influencing factors of ecological resilience. The main results are as follows: (1) The ecological resilience of the Yellow River Basin showed an overall fluctuating upward trend, and the average annual growth rate in the downstream region was larger than in the upstream and midstream regions. (2) Cities with similar levels of ecological resilience were distributed in a “large settlement, small scattered” pattern. The center of gravity shifted to the southeast, and the spatial distribution exhibited a “northwest-southeast” pattern and a trend towards an “east–west” pattern. The primary source of spatial differences was the intensity of transvariation. (3) The ecological resilience in the Yellow River Basin showed significant spatial clustering, with the H–H clustering area shifting from the Hubao-Eyu urban agglomeration to the Shandong Peninsula urban agglomeration, and the L–L clustering area mainly distributed around the Central Plains city cluster. (4) The ecological resilience of the Yellow River Basin exhibited significant snowball, spillover, and siphon effects in time, space, and space-time dimensions, respectively. In the short and long term, population density and openness significantly positively affected the ecological resilience of local and surrounding cities. Urbanization had a long-term effect on ecological resilience without a short-term effect. GDP per capita and industrial structure only imparted a significant positive influence on local ecological resilience. The negative spatial spillover of the intensity of financial investment in technological innovation gradually turned into a positive effect.

Exploring the restoration stability of abandoned open-pit mines by vegetation resilience indicator based on the LandTrendr algorithm

Open-pit mining causes serious damage to the ground surface and vegetation. Remote sensing technology helps assess and monitor the effectiveness of ecological restoration in abandoned open-pit mines, offering vital information for exploring the ecosystem recovery process. However, previous studies have mostly focused on changes in characteristics and lack quantitative characterization of restoration stability and its dynamics. In this study, we applied the vegetation resilience indicator based on the LandTrendr algorithm, which can combine vegetation disturbance and recovery characteristics for abandoned open-pit mines. Eighteen abandoned open-pit mines around Dongting Lake in China were taken as the research areas. Google Earth Engine and all available Landsat remote sensing images for June-September 1984–2022 were utilized. The effectiveness of mine restoration was determined according to the vegetation resilience indicator and recovery magnitude. The results showed that eighteen abandoned open-pit mines had a range of vegetation resilience between 5.06 and 9.45, indicating that the overall open-pit mines around Dongting Lake were well-restored. The recovery curves of the abandoned open-pit mines had an “inverted S-shape” and “S-shape,” indicating that the overall recovery characteristics of the different types of abandoned open-pit mines before and after the implementation of ecological restoration measures differed. Mine type and restoration measures affect the restoration stability of open-pit mines. The average values of vegetation resilience of the three types of mines were 7.73, 6.91, and 5.79, respectively, indicating that the resilience of the abandoned open-pit mines with serious damage was lower, and at the same time, the restoration was more dependent on the degree of input of artificial restoration. This study provides a reference for the selection of measures and the determination of restoration costs in the future ecological restoration of abandoned open-pit mines.

Multi-Objective Optimization of Resilient, Sustainable, and Safe Urban Bus Routes for Tourism Promotion Using a Hybrid Reinforcement Learning Algorithm

Urban transportation systems in tourism-centric cities face challenges from rapid urbanization and population growth. Efficient, resilient, and sustainable bus route optimization is essential to ensure reliable service, minimize environmental impact, and maintain safety standards. This study presents a novel Hybrid Reinforcement Learning-Variable Neighborhood Strategy Adaptive Search (H-RL-VaNSAS) algorithm for multi-objective urban bus route optimization. Our mathematical model maximizes resilience, sustainability, tourist satisfaction, and accessibility while minimizing total travel distance. H-RL-VaNSAS is evaluated against leading optimization methods, including the Crested Porcupine Optimizer (CPO), Krill Herd Algorithm (KHA), and Salp Swarm Algorithm (SSA). Using metrics such as Hypervolume and the Average Ratio of Pareto Optimal Solutions, H-RL-VaNSAS demonstrates superior performance. Specifically, H-RL-VaNSAS achieved the highest resilience index (550), sustainability index (370), safety score (480), tourist preferences score (300), and accessibility score (2300), while minimizing total travel distance to 950 km. Compared to other methods, H-RL-VaNSAS improved resilience by 12.24–17.02%, sustainability by 5.71–12.12%, safety by 4.35–9.09%, tourist preferences by 7.14–13.21%, accessibility by 4.55–9.52%, and reduced travel distance by 9.52–17.39%. This research offers a framework for designing efficient, resilient, and sustainable public transit systems that align with urban planning and transportation goals. The integration of reinforcement learning with VaNSAS significantly enhances optimization capabilities, providing a valuable tool for mathematical and urban transportation research communities.

Optimization of green infrastructures for sustaining urban stormwater quality and quantity: An integrated resilience evaluation

Coupling natural green infrastructure with artificial gray infrastructure is considered a key solution for mitigating the risk of waterlogging and water quality problems caused by rapid urbanization. However, further research is needed to evaluate and optimize the utility of gray–green system considering multiple key factors. Here, the effects of gray–green system on both the water quality and quantity were investigated based on the Storm Water Management Model-InfoWorks ICM (SWMM-ICM) and bivariate copula functions. A resilience index that integrates reliability, bearing capacity, recovery speed, and recovery capacity was then proposed. Taking a typical catchment area in Beijing as an example, the results showed that the resilience index proposed is effective for evaluating the stormwater management of gray–green system. The integration of gray–green infrastructures improved the resilience of urban stormwater management, particularly in terms of controlling the water quality (improved by 1.0–4.0 %) rather than the water quantity (only 0.04–0.3 %). Direct confluence and average distribution strategies of gray–green system were more effective for water quality management, as resilience ranged from 0.7478 to 0.7605. To mitigate the ever-increasing water risks under climate change, the direct confluence, and average or downstream renovation strategies are hot spots for system renovation in the future. These results contribute to a better understanding of gray–green system and provide the foundation for sustainable water quality and quantity management in global cities.

National-level resilience: Innovative framework for Energy-Water-Food nexus evaluation

The escalating demand for energy, water, and food (EWF) resources, alongside external stressors such as climate change, is placing significant strain on national systems. Therefore, this study introduces a novel framework for assessing the resilience of interconnected EWF systems at a national level. The framework utilizes a hybrid approach of network systems modeling and optimization models, integrating sectoral and dependency impacts to offer a comprehensive perspective on the interconnected resilience of EWF components. Applied to Qatar's EWF nexus, the framework revealed significant interdependencies, particularly between water and energy components, that led to cascading failures and challenged nexus resilience. Under single failure scenarios, unmet demands for energy and water ranged from 9 to 59 GWh and 97,000 to 359,000 m3, respectively, with resilience indices from 0.86 to 0.99. In double failure scenarios, significantly higher unmet demands caused the resilience index to drop to 0.43. Due to limited freshwater availability, the food sector is highly vulnerable to groundwater supply disruptions, resulting in an 85.6 % reduction in total crop production under extreme scenarios. Furthermore, the study highlighted resilience-enhancement strategies, such as utilizing treated wastewater for agricultural, industrial, and commercial activities and diversifying energy sources to reduce the risk of cascading failures, which increased the nexus's resilience by 17 %. The proposed framework serves as an essential tool for identifying critical components within the EWF nexus and planning strategies to enhance both operational and structural resilience.

Predictive resilience assessment of road networks based on dynamic multi-granularity graph neural network

Due to the influence of global climate anomalies, abnormal weather conditions such as heavy rainfall have become more frequent in recent years, posing a significant threat to the operation of transportation systems. An effective assessment of the resilience of the transportation system before and during heavy rain can alert the transportation department to take necessary emergency actions. However, existing methods for assessing the rainfall resilience of transportation networks mostly suffer from the following problems: (1) Simulation methods for modeling rainfall impacts lack realism; (2) After-the-fact evaluations of resilience cannot offer advance warning prior to or during a heavy rain event. To address above problems, we present a novel resilience assessment methodology for evaluating the resilience of road networks in real-time during heavy rainfall scenarios. In this methodology, we propose the temporal decomposition-based dynamic multi-granularity graph neural network (TD2MG2NN) for long-term traffic speed forecasting, providing a perspective on the future evolution of traffic states for accurate resilience assessment. In addition, we construct a composite traffic resilience indicator, designed to comprehensively reflect changes in the spatial–temporal resilience of the transportation system during heavy rain. Experimental results on four publicly real datasets indicate that the prediction performance of TD2MG2NN outperforms state-of-the-art models. The assessment results for the transportation road network in California demonstrate that the comprehensive resilience indicator is superior to single functional resilience indicator and the real-time methodology for evaluating resilience can accurately depict and predict the operation of the road network system under heavy rainfall scenarios.

Measuring system resilience through a comparison of information- and flow-based network analyses

Quantifying the properties of complex, self-organizing systems is increasingly important for understanding the development and state of modern systems. Case studies have recommended sustainability frameworks predominately in literature, but little emphasis has been placed on methodological evaluation. Data availability is often an obstacle that constrains conventional flow-based network analysis, but a novel information-based technique (QtAC) developed by zu Castell and Schrenk overcomes these constraints by modelling interactions between agents as information transfers. This study compares the QtAC method to conventional flow analysis by applying both to the same 90-year dataset containing socio-economic data from the island of Samothraki, Greece. Resilience indicators, based on Ulanowicz’s ascendency analysis, are derived on both the information- and flow-based networks. We observe that the resulting dynamics of the information-based networks align closer with complex system dynamics as theorized by the adaptive cycle model. Additionally, we discuss how QtAC offers different interpretations of network indicators when compared to usual interpretations of flow analysis. Ultimately, QtAC is shown to provide an alternative for complex systems analysis if the data situation does not allow for conventional flow-analysis. Furthermore, we show that the combination of both approaches can yield valuable new insights.

How well prepared are hospitals for future crises? Board members perceive their hospitals as resilient for acute crises

Resilience is an organizational capacity in day-to-day practice and crisis situation performance. A one of a kind crisis for hospitals is the COVID-19 pandemic. The long duration and magnitude of this crisis offers the opportunity to gain insight into the complexity of crisis management and organizational resilience of hospitals. This interview study therefore explored the organizational resilience of Dutch hospitals during the first 14 months of the COVID-19 pandemic. Nine board members of nine Dutch hospitals were interviewed by means of a semi-structured interview that was built on thirteen indicators of organizational resilience. The results showed that board members considered their hospitals as resilient on almost all indicators. Their judgments varied about how prepared and ready for future crises they considered their hospital. According to board members, hospitals are mainly prepared for “acute” short-term crises, thanks to good crisis leadership, open communication and strong networks. A crisis as long as the COVID-19 pandemic was unprecedented and therefore more difficult to deal with. In between the infection waves, work processes were reflected upon to learn, anticipate and respond more smoothly to successive waves. However, the enduring nature of the COVD-19 crisis presented complex organizational challenges. Crisis operations were eventually scaled down and hospitals had to manage the crisis and regular care as two companies side by side. Each crisis manifests differently. Fostering trust in healthcare staff and allowing them to act autonomously during crises, while diligently monitoring external influences and potential future crises, are therefore paramount in developing organizational adaptive capacities.

Характеристики демографічної резильєнтності населення України у період пандемії Covid-19

The full-scale military invasion of Ukraine by the Russian Federation came at a time when our country had been weakened by a series of shocks in a relatively short historical period. Shocks such as a prolonged pandemic and war can disrupt and, in some cases, halt demographic development. In this context, the study of demographic resilience on the eve of a full-scale war, which became a new powerful shock for Ukraine, is relevant and necessary. The complexity of this issue does not allow us to provide all the answers in one article. The purpose of our paper is to determine the main parameters of the demographic resilience of the population of Ukraine in the Covid-19 period and to compare them with the characteristics of resilience in other periods of the greatest shocks experienced by our country, as well as with the indicators of other countries. In our study the main indicators of transient dynamics (convergence time, reactivity, population inertia, damping ratio) were calculated for the first time for the population of Ukraine. The influence of the contribution of different age groups of women to total fertility on the age structure of the population (with the same total fertility rate) was also determined for the first time. The calculations were based on the stable population model, the Leslie matrix, fertility tables, and life tables. The resilience indicators were calculated using the popdemo package. Methods of comparison, generalisation, and analogy were also used. Among the characteristics of demographic resilience, the half-life indicator stands out for its simplicity of calculation and interpretation. In Ukraine, the halving time for the population is decreasing, but it is still higher for the real population than for its stable equivalent. The age structure of the real population, formed under more favourable conditions, mitigates the negative consequences of shock periods, but the long-term preservation of the current regime of mortality and fertility will inevitably worsen the characteristics of the population. In Ukraine, advanced motherhood is an element of the adaptive cycle and requires a rethinking of its meaning and role, as it can be one of the internal mechanisms aimed at the resilience of the system in shock periods. In the conditions of the lowest-low fertility and mortality regime in Ukraine in 2021, advanced maternal age contributed to some increase in the number of births and a certain rejuvenation of the age structure of the population.

Modeling food web and fisheries dynamics in Lake Baringo, Kenya

AbstractLakes are important in supporting ecosystem services and livelihoods. However, their food webs and ecological functioning are continuously threatened by anthropogenic influences. Food web models have been widely used in studying trophodynamics, fisheries impacts, and ecological functioning of temperate lakes, but less often in Afrotropical lake systems. We used Ecopath mass‐balanced trophic models annually in 1999, 2010, and 2020 to assess trends in ecosystem function, and the impact of fisheries on the Lake Baringo Ecosystem, a shallow freshwater lake in Kenya. Pre‐balance (PREBAL) and Pedigree analyses supplemented Ecopath models. Model input data were from field sampling, published and gray literature. Food web trophic models indicated a bottom‐up grazer and detrital food chains in all 3 years. Odum's ecosystem development indicators (total productivity to total biomass and total respiration ratios; TPP/TB and TPP/TR) showed that the lake was in a low to intermediate developmental stage, with room for bio‐manipulation, and a highly reduced mean transfer efficiency (TE) (6.4%–0.49%) indicated low trophic transfer of internal production. System omnivory (SOI) and connectance (CI) indices that varied among years indicated temporal variation in food web complexity. Indices of system resilience (overhead and ascendency) indicated an increasing potential for the lake to recover from perturbations. The mean trophic level of the catch (MTLc) increased from 1999 to 2010 and decreased in 2020, by fishing down the food chain as fishing pressure increased. Oreochromis niloticus, an endemic cichlid, was the keystone species (KSi &gt;0) controlling community structure, while the lungfish Protopterus aethiopicus, the top predator in the lake, was not a keystone species (KSi &lt;0). We recommend an integrated approach to lake management that incorporates watershed regulations, regulates fishing effort on the keystone species (O. niloticus), and monitors water quality for sustainable management of the Lake Baringo ecosystem.