Harnessing Insights from Indicators-Based Resilience Assessment for Enhancing Sustainability in the Gurage Socio-Ecological Production Landscape of Ethiopia.

Even though the mosaic of different land-use/land-cover types has long contributed to the resilience of socio-ecological production landscapes and seascapes in Ethiopia, recent data indicate that their sustainability is under threat. This study aims to evaluate landscape resilience by adopting a set of indicators for enhancing sustainability in the Gurage socio-ecological production landscape in Ethiopia. The authors employed a toolkit of indicators in the production landscape through a community-based scoring approach (1-5 Likert scale). The information from household surveys, land-use/land-cover analysis, and satellite-based drought incidents assessment was integrated with the ranking analysis to support the evaluations. The results revealed that landscape diversity, ecosystem protection, local governance, and social equity indicators had the highest landscape resilience ranks. In contrast, lower ranks are associated with knowledge, innovation, livelihoods, and well-being indicators. The overall resilience of the Gurage socio-ecological production landscape was estimated to be below average. Thus, strategies that enhance the resilience and sustainability of this socio-ecological landscape are essential. The findings could help draw the attention of policymakers and natural resource managers to building and strengthening the resilience of the landscape. This study demonstrates that indicators could aid in evaluating landscape resilience status along with other ancillary information, particularly in data-sparse regions. Methods of assessing resilience must be creative in such regions, and this paper may inform such efforts. In addition, the study recommends that landscape resilience indicators be improved by reducing subjective matter and including spatial-explicit dimensions for evaluating resilience.

Applying early warning indicators to predict critical transitions in a lake undergoing multiple changes.

Lakes are dynamic ecosystems that can transition among stable states. Since ecosystem-scale transitions can be detrimental and difficult to reverse, being able to predict impending critical transitions in state variables has become a major area of research. However, not all transitions are detrimental, and there is considerable interest in better evaluating the success of management interventions to support adaptive management strategies. Here, we retrospectively evaluated the agreement between time series statistics (i.e., standard deviation, autocorrelation, skewness, and kurtosis-also known as early warning indicators) and breakpoints in state variables in a lake (Lake Simcoe, Ontario, Canada) that has improved from a state of eutrophication. Long-term (1980 to 2019) monitoring data collected fortnightly throughout the ice-free season were used to evaluate historical changes in 15 state variables (e.g., dissolved organic carbon, phosphorus, chlorophyll a) and multivariate-derived time series at three monitoring stations (shallow, middepth, deep) in Lake Simcoe. Time series results from the two deep-water stations indicate that over this period Lake Simcoe transitioned from an algal-dominated state toward a state with increased water clarity (i.e., Secchi disk depth) and silica and lower nutrient and chlorophyll a concentrations, which coincided with both substantial management intervention and the establishment of invasive species (e.g., Dreissenid mussels). Consistent with improvement, Secchi depth at the deep-water stations demonstrated expected trends in statistical indicators prior to identified breakpoints, whereas total phosphorus and chlorophyll a revealed more nuanced patterns. Overall, state variables were largely found to yield inconsistent trends in statistical indicators, so many breakpoints were likely not reflective of traditional bifurcation critical transitions. Nevertheless, statistical indicators of state variable time series may be a valuable tool for the adaptive management and long-term monitoring of lake ecosystems, but we call for more research within the domain of early warning indicators to establish a better understanding of state variable behavior prior to lake changes.

Evaluating early-warning indicators of critical transitions in natural aquatic ecosystems.

Ecosystems can show sudden and persistent changes in state despite only incremental changes in drivers. Such critical transitions are difficult to predict, because the state of the system often shows little change before the transition. Early-warning indicators (EWIs) are hypothesized to signal the loss of system resilience and have been shown to precede critical transitions in theoretical models, paleo-climate time series, and in laboratory as well as whole lake experiments. The generalizability of EWIs for detecting critical transitions in empirical time series of natural aquatic ecosystems remains largely untested, however. Here we assessed four commonly used EWIs on long-term datasets of five freshwater ecosystems that have experienced sudden, persistent transitions and for which the relevant ecological mechanisms and drivers are well understood. These case studies were categorized by three mechanisms that can generate critical transitions between alternative states: competition, trophic cascade, and intraguild predation. Although EWIs could be detected in most of the case studies, agreement among the four indicators was low. In some cases, EWIs were detected considerably ahead of the transition. Nonetheless, our results show that at present, EWIs do not provide reliable and consistent signals of impending critical transitions despite using some of the best routinely monitored freshwater ecosystems. Our analysis strongly suggests that a priori knowledge of the underlying mechanisms driving ecosystem transitions is necessary to identify relevant state variables for successfully monitoring EWIs.

Quantifying long-term plant community dynamics with movement models: implications for ecological resilience.

Quantification of rates and patterns of community dynamics is central for understanding the organization and function of ecosystems. These insights may support a greater empirical understanding of ecological resilience, and the application of resilience concepts toward ecosystem management. Distinct types of dynamics in natural communities can be used to interpret and apply resilience concepts, but quantitative methods that can systematically distinguish among them are needed. We develop a quantitative method to analyze long-term records of plant community dynamics using principles of movement ecology. We analyzed dissimilarity of species composition through time with linear and nonlinear statistical models to assign community change to four classes of movement trajectories. Compositional change in each sampled plot through time was classified into four classes, stability, abrupt nonlinear change, transient reversible change, and gradual linear drift, each representing a different aspect of ecological resilience. These competing models were evaluated based on estimated coefficients, goodness of fit, and parsimony. We tested our method's accuracy and robustness through simulations, or the ability to distinguish among trajectories and classify them correctly. We simulated 16,000 trajectories of four types, of which 94-100% were correctly classified. Next, we analyzed 13 long-term vegetation records from North American grasslands (annual grasslands with warm-season and cool-season communities, shortgrass, mixedgrass, and tallgrass prairies, and sagebrush steppe), and a record of primary succession at Mt. St. Helens volcano. Collectively, we analyzed 14,647 observations from 775 plots, between 1915 and 2012. Dynamics could be reliably assigned for 705 plots (91%), and overall statistical fit was high (goodness of fit, 0.77 ± 0.15 SD). Among the perennial grasslands, stability was most common (44% of all plots), followed by gradual linear (22%), abrupt nonlinear (17%), and reversible (6%) change. Among annual grasslands, abrupt nonlinear shifts (33%) were more common in the warm-season community than in the cool-season (20%). As expected, abrupt nonlinear change was common during primary succession (51%) while reversible change was rare (3%). Generally, reversible dynamics often required 2-3 decades. Analysis of long-term community change, or trajectories, with principles of movement ecology provides a quantitative basis to compare and interpret ecological resilience within and among ecosystems.

Corrigendum: Animal-based factors prior to infection predict histological disease outcome in porcine reproductive and respiratory syndrome virus- and Actinobacillus pleuropneumoniae-infected pigs.

[This corrects the article DOI: 10.3389/fvets.2021.742877.].

Establishing a regional interdisciplinary resilience center: a bottom-up approach.

Both natural and manmade disasters have severely impacted the region of Southeast Texas over the past few decades, and this has negatively affected the socio-economic well-being of the region. The state of Texas has suffered 200-250 billion dollars in damages from natural and manmade disasters since 2010. Given the region's strategic importance to the nation's energy and security, developing resilience knowledge and multi-disaster resilience research focused on issues pertaining to the region is needed. This paper describes the structure and process of building a center for multi-disaster resilience at a regional public university. By utilizing a bottom-up approach, the Center's mission and design are broadly democratized through the participation of a variety of scholars and various stakeholders with whom they interact. Resilience needs specific to the Southeast Texas region are examined, as is the relationship between resilience and the academic disciplines of the stakeholders involved. The issues of resilience in the region are discussed as well as the future steps for the Center's continued growth and development for the study of resilience.

Potential for quantifying general environmental resilience of dairy cattle in sub-Saharan Africa using deviations in milk yield.

Introduction: Genetic improvement of general resilience of dairy cattle is deemed as a part of the solution to low dairy productivity and poor cattle adaptability in sub-Saharan Africa (SSA). While indicators of general resilience have been proposed and evaluated in other regions, their applicability in SSA remains unexplored. This study sought to test the viability of utilizing log-transformed variance (LnVar), autocorrelation (rauto), and skewness (Skew) of deviations in milk yield as indicators of general resilience of dairy cows performing in the tropical environment of Kenya. Methods: Test-day milk yield records of 2,670 first-parity cows performing in three distinct agroecological zones of Kenya were used. To predict expected milk yield, quantile regression was used to model lactation curve for each cow. Subsequently, resilience indicators were defined based on actual and standardized deviations of observed milk yield from the expected milk yield. The genetic parameters of these indicators were estimated, and their associations with longevity and average test-day milk yield were examined. Results: All indicators were heritable except skewness of actual and standardized deviation. The log-transformed variance of actual (LnVar1) and standardized (LnVar2) deviations had the highest heritabilities of 0.19 ± 0.04 and 0.17 ± 0.04, respectively. Auto-correlation of actual (rauto1) and standardized (rauto2) deviations had heritabilities of 0.05 ± 0.03 and 0.07 ± 0.03, respectively. Weak to moderate genetic correlations were observed among resilience indicators. Both rauto and Skew indicators had negligible genetic correlations with both longevity and average test-day milk yield. LnVar1 and LnVar2 were genetically associated with better longevity (rg = -0.47 ± 0.26 and -0.49 ± 0.26, respectively). Whereas LnVar1 suggested that resilient animals produce lower average test-day milk yield, LnVar2 revealed a genetic association between resilience and higher average test-day milk yield. Discussion: Log transformed variance of deviations in milk yield holds a significant potential as a robust resilience indicator for dairy animals performing in SSA. Moreover, standardized as opposed to actual deviations should be employed in defining resilience indicators because the resultant indicator does not inaccurately infer that low-producing animals are inherently resilient. This study offers an opportunity for enhancing the productivity of dairy cattle performing in SSA through selective breeding for resilience to environmental stressors.

A review of the flood management: from flood control to flood resilience.

Climate change and socioeconomic developments are increasing the frequency and severity of floods. Flood management is widely recognized as an effective way to reduce the adverse consequences, and a more resilient and sustainable flood management approach has been the goal in recent studies. This study used a detailed bibliometric analysis of keywords, terms and timelines in the research field of the flood research. It provides new insight into the flood research trends, by examining the research frontiers from 2000 to 2021. We conclude that the trend of flood research has experienced a transition from flood control to flood resilience. The review shows that flood research has moved from traditional flood management, which provides mitigation strategies, to flood risk management, which provides an adaptation approach-adjusting mitigation measures, to flood resilience management, which provides a more resilient and sustainable plan to cope with flood disasters. We also present a detailed overview of the field of flood research, and review the definition of risk, risk analysis methods, flood management, flood risk management, flood resilience, and corresponding implementation strategies. We conclude that integrating the concept of resilience into the framework of risk management is a better approach in future flood management directions. Consequently, appropriate options and decisions prior to disaster, during disaster, and post-disaster will effectively reduce the adverse consequences using the theory of risk, resilience, and sustainability.

A Methodology to Quantify Resilience in Growing Pigs.

There is a growing concern about the genetic determinism of resilience and its possible implementation in breeding programs. The objective of our study was to elaborate novel resilience indicators in growing pigs based on the deviation from the expected growth curve and the increment of the acute-phase protein haptoglobin (HP) after applying a common vaccine. A total of 445 pigs were vaccinated with an attenuated Aujeszky vaccine at 12 weeks of age. Deviation from the expected body weight (ΔBW) given the growth curve of unvaccinated pigs at 28 days post-vaccination (DPV) and the increment of HP at 4 DPV (ΔHP) were suggested as resilience indicators. Challenged pigs that maintained their productivity and had a minor activation of HP were deemed resilient, whereas pigs that had low ∆BW values and a high activation of HP were deemed susceptible. Pigs were also classified based on ∆BW and ∆HP relative to the expected BW at 28 DPV and to the basal level of HP, respectively. The concordance was high between both methods, indicating that ΔBW and ΔHP are not sensitive to the animal's expected BW nor the basal level of HP. The heritability estimates were moderate for ∆BW (0.33) and low-to-moderate for ∆HP (0.16). Our study suggests ΔBW and ΔHP as novel resilience indicators in pigs. The suggested indicators capture different aspects of resilience, are easy to measure, and are genetically controlled. Thus, they may be improved through selective breeding. Further analyses are needed to validate our findings.

Animal-Based Factors Prior to Infection Predict Histological Disease Outcome in Porcine Reproductive and Respiratory Syndrome Virus- and Actinobacillus pleuropneumoniae-Infected Pigs.

A large variety of clinical manifestation in individual pigs occurs after infection with pathogens involved in porcine respiratory disease complex (PRDC). Some pigs are less prone to develop respiratory disease symptoms. The variation in clinical impact after infection and the recovery capacity of an individual animal are measures of its resilience. In this paper, we examined which ones of a range of animal-based factors (rectal temperature, body weight, skin lesion scores, behavior, natural antibody serum levels, serum levels of white blood cells, and type of T and granulocyte subsets) when measured prior to infection are related to disease severity. These animal-based factors and the interaction with housing regimen of the piglets (conventional or enriched) were modeled using linear regression to predict disease severity using a dataset acquired from a previous study using a well-established experimental coinfection model of porcine reproductive and respiratory syndrome virus (PRRSV) and Actinobacillus pleuropneumoniae. Both PRRSV and A. pleuropneumoniae are often involved in PRDC. Histological lung lesion score of each animal was used as a measure for PRDC severity after infection. Prior to infection, higher serum levels of lymphocytes (CD3+), naïve T helper (CD3+CD4+CD8-), CD8+ (as well as higher relative levels of CD8+), and memory T helper (CD3+CD4+CD8+) cells and higher relative levels of granulocytes (CD172a) were related to reduced disease severity in both housing systems. Raised serum concentrations of natural IgM antibodies binding to keyhole limpet hemocyanin (KLH) were also related to reduced disease severity after infection. Increased levels of skin lesions at the central body part (after weaning and before infection) were related to increased disease severity in conventional housing systems only. High resisters showed a lower histological lung lesion score, which appeared unrelated to sex. Body temperature, behavior, and growth prior to infections were influenced by housing regimen but could not explain the variation in lung lesion scores after infection. Raised basal lymphocyte counts and lower skin lesion scores are related to reduced disease severity independent of or dependent on housing system, respectively. In conclusion, our study identifies intrinsic animal-based measures using linear regression analysis that predicts resilience to infections in pigs.

Indicators for the assessment of social resilience in flood-affected communities - A text mining-based methodology.

This work turns the social resilience concept into a practical and tangible set of dimensions and indicators for social resilience assessment. It further provides an analysis of the social resilience concept in the context of flood risk governance. Floods are a worldwide recurring phenomenon that causes severe social, economic and environmental losses. In the context of global change, it is very difficult to accurately predict extreme events that may increase disaster frequency; hence the implementation of social resilience is essential to lessen the losses. Indeed, the right balance between natural and social factors and indicators is yet to be found. Social resilience has been debated extensively for decades, both in scientific and political contexts. It has been a concern in disaster risk reduction and risk governance fields, both of which have strived to implement it. The enlarged conceptual discussion regarding this topic has resulted in some indicator-based assessments that hardly reflect the conceptual discussion developed so far. These indicator-based approaches still lack accurate inclusion of social dynamics and the capacity to learn from experience. In order to contribute to a comprehensive approach (concept and methods) for assessing social resilience to floods, the evolutionary resilience concept (Davoudi, Simin; Shaw, Keith; Haider, L. Jamila; Quilnlan, Allyson E; Petterson, Garry D.; Wilkinson, Cathy; Fünfgeld, Hartmut; McEvoy, Darryn; Porter, 2012) was considered as a reference in this work, as it can include dimensions that are difficult to evaluate (non-static time and learning-capacity in multi-dimensional systems). This work addresses the challenge of a conceptual overview of social resilience to include key factors and indicators. Our methodology uses text mining, experts' surveys and bibliography reviews to generate an indicators database. The contribution of this article to the scientific debate on social resilience assessment is twofold. First, the key-concepts, words and expressions in this field are identified, which provides the basis to build a comprehensive and coherent analytical framework. Secondly, an original indicators database is proposed in line with that framework. The results of a text mining-based methodology and an online survey, involving experts from different countries, show that four of the six dimensions of the indicators database refer to social aspects of risks (Individuals, Society, Governance, and Built Environment), while the remaining two refer to the Environment and Disaster. The results obtained so far suggest the need for a next step aiming to validate the dimensions and the indicators of this database through its application to real case studies.

Early-Warning Signals of Individual Tree Mortality Based on Annual Radial Growth.

Tree mortality is a key driver of forest dynamics and its occurrence is projected to increase in the future due to climate change. Despite recent advances in our understanding of the physiological mechanisms leading to death, we still lack robust indicators of mortality risk that could be applied at the individual tree scale. Here, we build on a previous contribution exploring the differences in growth level between trees that died and survived a given mortality event to assess whether changes in temporal autocorrelation, variance, and synchrony in time-series of annual radial growth data can be used as early warning signals of mortality risk. Taking advantage of a unique global ring-width database of 3065 dead trees and 4389 living trees growing together at 198 sites (belonging to 36 gymnosperm and angiosperm species), we analyzed temporal changes in autocorrelation, variance, and synchrony before tree death (diachronic analysis), and also compared these metrics between trees that died and trees that survived a given mortality event (synchronic analysis). Changes in autocorrelation were a poor indicator of mortality risk. However, we found a gradual increase in inter-annual growth variability and a decrease in growth synchrony in the last ∼20 years before mortality of gymnosperms, irrespective of the cause of mortality. These changes could be associated with drought-induced alterations in carbon economy and allocation patterns. In angiosperms, we did not find any consistent changes in any metric. Such lack of any signal might be explained by the relatively high capacity of angiosperms to recover after a stress-induced growth decline. Our analysis provides a robust method for estimating early-warning signals of tree mortality based on annual growth data. In addition to the frequently reported decrease in growth rates, an increase in inter-annual growth variability and a decrease in growth synchrony may be powerful predictors of gymnosperm mortality risk, but not necessarily so for angiosperms.

Female vulnerability to the development of depression-like behavior in a rat model of intimate partner violence is related to anxious temperament, coping responses, and amygdala vasopressin receptor 1a expression.

Exposure to violence is traumatic and an important source of mental health disturbance, yet the factors associated with victimization remain incompletely understood. The aim of the present study was to investigate factors related to vulnerability to depression-like behaviors in females. An animal model of intimate partner violence, which was previously shown to produce long-lasting behavioral effects in females as a result of male partner aggression, was used. The associations among the degree of partner aggression, the long-term consequences on depressive-like behavior, and the impact of the anxious temperament of the female were examined. In a separate group, pre-selected neural markers were evaluated in the amygdala and the lateral septum of females. Expression was examined by analyses of targeted candidate genes, serotonin transporter (slc6a4), vasopressin receptor 1a, (avpr1a), and oxytocin receptor (oxtr). Structural equation modeling revealed that the female's temperament moderated depressive-like behavior that was induced by cohabitation aggression from the male partner. More specifically, increased floating in the forced swim test following male aggression was most apparent in females exhibiting more anxiety-like behavior (i.e., less open arm exploration in an elevated plus-maze) prior to the cohabitation. Aggression reduced slc6a4 levels in the lateral septum. However, the interaction between partner aggression and the anxious temperament of the female affected the expression of avpr1a in the amygdala. Although, aggression reduced levels of this marker in females with high anxiety, no such pattern was observed in females with low anxiety. These results identify important characteristics in females that moderate the impact of male aggression. Furthermore, these results provide potential therapeutic targets of interest in the amygdala and the lateral septum to help improve post-stress behavioral pathology and increase resilience to social adversity.