. Rapid social-ecological change has placed Planet Earth on a trajectory characterized by regime shifts that can inflict substantial costs on economies and human well-being. Current attempts to communicate these changes and their consequences have often failed to resonate; hence, more effective approaches are needed to increase public awareness of these potentially catastrophic events. This paper explores data sonification, a tool that transforms data into sound, as a means of conveying ecological regime shifts through music. In this study, prior assessments of a 7000-year paleo-sequence of diatoms in Foy Lake (Montana, USA) were converted into "The Regime Shift Solo" (based on Fisher Information analysis) and "The Regime Shift Symphony" (based on canonical ordination analysis). Structured focus groups (n = 12) evaluated how these compositions were perceived. Results showed that the solo was consistently valued for clarity and accessibility, whereas the symphony was judged to be more realistic, complex, and emotionally resonant, often eliciting feelings of unease, awe, or urgency. Compared to graphs, sonification was seen as complementary: graphs provided analytical clarity, whereas music offered immediacy and affective depth. By combining these modes, sonification can foster both cognitive and emotional engagement with ecological tipping points. Such dual engagement may enhance science communication and contribute to initiatives that help societies navigate social-ecological uncertainties in the Anthropocene.

. Natural disasters driven by climate change have increased in frequency, intensity, and scale. The consequences of these disasters include the loss of human lives, property damage, increased economic costs, and decreased ability to respond effectively to both abrupt and more gradual disasters. Government responses to such disasters are often based on a desire to rapidly recover to normal, which is understandable, but is difficult in the Anthropocene because of rapidly changing social-ecological baselines that exceed the limits of adaptation and mitigation. Here we identify pitfalls of a narrow and singular focus on resiliency. Resiliency focuses on efficient and rapid recovery, which is laudable, but assumes linear responses, absence of tipping points, a single scale of cause and effect, and an implicit assumption of stationarity. In contrast, we highlight the importance of social-ecological resilience, which includes resiliency but also accounts for multiple spatial and temporal scales, cross-scale effects, and most importantly, the possibility of alternative system configurations (or regimes) separated by tipping points. Social-ecological resilience provides a more comprehensive and realistic framing, and therefore the ability to persist with change, prepare for, and perform adaptation and transformation of socialecological systems. Accounting for social-ecological resilience is essential for governance of coupled systems of humans and nature as we collectively face a future in the Anthropocene that will contain more surprising and unpredictable events propelled by global change including climate change.

Coral reef resilience is eroding at multiple spatial scales globally, with broad implications for coastal communities, and is thus a critical challenge for managing marine social-ecological systems (SESs). Many researchers believe that external stressors will cause key coral reefs to die by the end of the 21st century, virtually eliminating essential ecological and societal benefits. Here, we propose the use of resilience-based approaches to understand the dynamics of coral reef SESs and subsequent drivers of coral reef decline. Previous research has demonstrated the effectiveness of these methods, not only for tracking environmental change, but also for providing warning in advance of transitions, possibly allowing time for management interventions. The flexibility and utility of these methods make them ideal for assessing complex systems; however, they have not been used to study aquatic ecosystem dynamics at the global scale. Here, we evaluate these methods for examining spatiotemporal change in coral reef SESs across the global seascape and assess the subsequent impacts on coral reef resilience. We found that while univariate indicators failed to provide clear signals, multivariate resilience-based approaches effectively captured coral reef SES dynamics, unveiling distinctive patterns of variation throughout the global coral reef seascape. Additionally, our findings highlight global spatiotemporal variation, indicating patterns of degraded resilience. This degradation was reflected regionally, particularly in the Pacific Ocean and Indian Ocean SESs. These results underscore the utility of resilience-based approaches in assessing environmental change in SESs, detecting spatiotemporal variation at the global and regional scales, and facilitating more effective monitoring and management of coral reef SESs.

Exposure to cyanobacterial toxins has been linked to several diseases and disorders including skin rashes and damage to the liver and kidneys. Previous research has shown that Microcystin-LR, the most common and toxic microcystin in fresh water, induces the NLRP3 inflammasome in response to administration to mice by oral gavage. In this study, we used a Reconstructed Human Epidermis to investigate immune system responses to cyanobacterial exposure. This 3D tissue model consisting of normal, human-derived epidermal keratinocytes was exposed to Spirulina, anatoxin-contaminated lake water, and phycocyanin. The cell supernatant was probed with a multiplex of 38 cytokines/chemokines and 14 soluble receptors to elucidate the immunological response. Our results revealed that: while most proteins were down-regulated, a number were up-regulated; the expression of many proteins were highly correlated and clustered together; and across the case study datasets, multiple cytokines/chemokines (G-CSF, GRO, IL-6, IL-8, and MCP-1) and soluble cell receptors (sCD30, sEGFR, sTNFRII and sVEGFR1, sVEGFR2 and sVEGFR3) were highly expressed when exposed to the toxins. Our findings correspond with preliminarily histological results showing tissue damage when the skin cells were exposed to the cyanobacterial components. This work shows great promise in the ability to examine the immune response to toxic exposures and aids in the development of techniques useful for studying the impact of harmful algal blooms on public health. Future studies will focus on mapping the immune pathway in response to harmful algal bloom exposure and studying the roles of each of the expressed cytokines and soluble cell receptors in the inflammatory response. This work is supported by the United States Environmental Protection Agency.

Studying ecosystem dynamics is critical to monitoring and managing linked systems of humans and nature. Due to the growth of tools and techniques for collecting data, information on the condition of these systems is more widely available. While there are a variety of approaches for mining and assessing data, there is a need for methods to detect latent characteristics in ecosystems linked to temporal and spatial patterns of change. Resilience-based approaches have been effective at not only identifying environmental change but also providing warning in advance of critical transitions in social-ecological systems (SES). In this study, we examine the usefulness of one such method, Fisher Information (FI) for spatiotemporal analysis. FI is used to assess patterns in data and has been established as an effective tool for capturing complex system dynamics to include regimes and regime shifts. We employed FI to assess the biophysical condition of eighty-five Swedish lakes from 1996-2018. Results showed that FI captured spatiotemporal changes in the Swedish lakes and identified distinct spatial patterns above and below the Limes Norrlandicus, a hard ecotone boundary which separates northern and southern ecoregions in Sweden. Further, it revealed that spatial variance changed approaching this boundary. Our results demonstrate the utility of this resilience-based approach for spatiotemporal and spatial regimes analyses linked to monitoring and managing critical watersheds and waterbodies impacted by accelerating environmental change.

Managing social-ecological systems toward desirable regimes requires learning about the system being managed while preparing for many possible futures. Adaptive management (AM) and scenario planning (SP) are two systems management approaches that separately use learning to reduce uncertainties and employ planning to manage irreducible uncertainties, respectively. However, each of these approaches have limitations that confound management of social-ecological systems. Here, we introduce iterative scenarios (IS), a systems management approach that is a hybrid of the scopes and relationships to uncertainty and controllability of AM and SP that combines the "iterativeness" of AM and futures planning of SP. Iterative scenarios is appropriate for situations with high uncertainty about whether a management action will lead to intended outcomes, the desired benefits are numerous and cross-scale, and it is difficult to account for the social implications around the natural resource management options. The value of iterative scenarios is demonstrated by applying the approach to green infrastructure futures for a neighborhood in the city of Cleveland, Ohio, U.S., that had experienced long-term, systemic disinvestment. The Cleveland green infrastructure project was particularly well suited to the IS approach given that learning about environmental factors was necessary and achievable, but what would be socially desirable and possible was unknown. However, iterative scenarios is appropriate for many social-ecological systems where uncertainty is high as IS accommodates real-world complexity faced by management.

Rapid, sensitive, and specific salivary IgG antibody tests can reveal past infections that could assist in the determination of the true prevalence of microbial infections in population studies. Multiplex immunoassays combining IgM, IgA, and IgG antibody responses against multiple pathogens simultaneously, can provide a wealth of information to delineate acute from chronic infections. Previously, we developed and applied salivary antibody multiplex immunoassays against a range of environmental pathogens including noroviruses (GI.1 and GII.4), hepatitis A virus, Helicobacter pylori, Campylobacter jejuni and Toxoplasma gondii to examine infections associated with recreating in contaminated waters. Here, we employ the methods to study saliva samples collected from individuals recreating at an Iowa, USA, riverine beach. Results showed that nearly 80% of beachgoers had salivary antibodies to at least one of the targeted pathogens at the beginning of the study. Most of these exposures were to noroviruses (GI.1: 59.4%, GII.4: 58.8%) and T. gondii (22.8%). Of the individuals who returned the requested samples for each collection period, 6.1% immunoconverted to one or more pathogen, mostly to noroviruses (GI.1: 3.82%, GII.4: 2.3%) and T. gondii (1.5%). Outcomes of this effort illustrate that this rapid immunological assay serves as a viable, high-throughput, noninvasive and cost-effective tool for providing valuable information on the occurrence of known and emerging pathogens (e.g., SARS-CoV-2) in population surveillance studies.

Detecting environmental exposures and mitigating their impacts are growing global public health challenges. Antibody tests show great promise and have emerged as fundamental tools for large-scale exposure studies. Here, we apply, demonstrate and validate the utility of a salivary antibody multiplex immunoassay in measuring antibody prevalence and immunoconversions to six pathogens commonly found in the environment. The study aimed to assess waterborne infections in consenting beachgoers recreating at an Iowa riverine beach by measuring immunoglobulin G (IgG) antibodies against select pathogens in serially collected saliva samples. Results showed that nearly 80% of beachgoers had prior exposures to at least one of the targeted pathogens at the beginning of the study. Most of these exposures were to norovirus GI.1 (59.41%), norovirus GII.4 (58.79%) and Toxoplasma gondii (22.80%) and over half (56.28%) of beachgoers had evidence of previous exposure to multiple pathogens. Of individuals who returned samples for each collection period, 6.11% immunoconverted to one or more pathogens, largely to noroviruses (GI.1: 3.82% and GII.4: 2.29%) and T. gondii (1.53%). Outcomes of this effort illustrate that the multiplex immunoassay presented here serves as an effective tool for evaluating health risks by providing valuable information on the occurrence of known and emerging pathogens in population surveillance studies.

Understanding and curbing the mounting global increase in waterborne pathogen outbreaks are quickly becoming major priorities for public health professionals and policy makers. According to the World Health Organization (WHO), 60% of global diarrheal deaths are caused by unsafe water and lack of sanitation or hygiene. The WHO estimates that at least 2 billion people use a drinking water source contaminated with feces. Contaminated water -- shown to transmit diseases such as cholera, dysentery, typhoid, hepatitis A, and polio -- is estimated to cause almost half a million diarrheal deaths each year. The United States Environmental Protection Agency has prioritized efforts to understand the links between water quality and human health effects. To that end, we have developed and applied a salivary-IgG antibody multiplex immunoassay to measure human exposures and associated health effects to multiple pathogens simultaneously. Saliva is emerging as a cost-effective, noninvasive biofluid that is well-accepted by children. The multiplex immunoassay has afforded the ability to assess immunopositivity, immunoprevalence, co-infections and incident infections (immunoconversions) to Helicobacter pylori, Campylobacter jejuni, Cryptosporidium parvum, Toxoplasma gondii, hepatitis A virus and noroviruses GI.I and GII.4 at several beaches throughout the US. Further, we’ve found evidence of asymptomatic norovirus and hepatitis A infections in visitors to a fecally contaminated beach. The assay produces results in as little as one hour and when used in conjunction with epidemiologic and water quality studies, provides valuable information that links human health effects more directly to water quality.

Addressing unexpected events and uncertainty represents one of the grand challenges of the Anthropocene, yet ecosystem management is constrained by existing policy and laws that were not formulated to deal with today's accelerating rates of environmental change. In many cases, managing for simple regulatory standards has resulted in adverse outcomes, necessitating innovative approaches for dealing with complex social-ecological problems. We highlight a project in theUSGreat Plains where panarchy - a conceptual framework that emerged from resilience - was implemented at project onset to address the continued inability to halt large-scale transition from grass-to-tree dominance in central North America. We review how panarchy was applied, the initial outcomes and evidence for policy reform, and the opportunities and challenges for which it could serve as a useful model to contrast with traditional ecosystem management approaches.

Hepatitis A virus (HAV) is a common infection that is transmitted through the fecal-oral route, shed in the stool of infected individuals, and spread either by direct contact or by ingesting contaminated food or water. Each year, approximately 1.4 million acute cases are reported globally with a major risk factor for exposure being low household socioeconomic status. Recent trends show a decrease in anti-HAV antibodies in the general population, with concomitant increases in the numbers of HAV outbreaks.

Regime shifts involving critical transitions are a type of rapid ecological change that are difficult to predict, but may be preceded by decreases in resilience. Time series statistics like lag-1 autocorrelation may be useful for anticipating resilience declines; however, more study is needed to determine whether the dynamics of autocorrelation depend on the resolution of the time series being analyzed, i.e., whether they are time-scale dependent. Here, we examined timeseries simulated from a lake eutrophication model and gathered from field measurements. The field study involved collecting high frequency chlorophyll fluorescence data from an unmanipulated reference lake and a second lake undergoing experimental fertilization to induce a critical transition in the form of an algal bloom. As part of the experiment, the fertilization was halted in response to detected early warnings of the algal bloom identified by increased autocorrelation. We tested these datasets for time-scale dependence in the dynamics of lag-1 autocorrelation and found that in both the simulation and field experiment, the dynamics of autocorrelation were similar across time scales. In the simulated time series, autocorrelation increased exponentially approaching algal bloom development, and in the field experiment, the difference in autocorrelation between the manipulated and reference lakes increased sharply. These results suggest that, as an early warning indicator, autocorrelation may be robust to the time scale of the analysis. Given that a time scale can be shortened by increasing sampling frequency, or lengthened by aggregating data during analysis, these results have important implications for management as they demonstrate the potential for detecting early warning signals over a wide range of monitoring frequencies and without requiring analysts to make situation-specific decisions regarding aggregation. Such an outcome provides promise that data collection procedures, especially by automated sensors, may be used to monitor and manage ecosystem resilience without the need for strict attention to time scale.

Natural disasters, such as hurricanes and forest fires, could trigger collapse and reorganization of social-ecological systems. In the face of external perturbations, a resilient system would have capacity to absorb impacts, adapt to change, learn, and if needed, reorganize within the same regime. Within this context, we asked how human and natural systems in Louisiana responded to Hurricane Katrina, and how the natural disaster altered the status of these systems. This paper discusses community resilience to natural hazards and addresses the limitations for assessing disaster resilience. Furthermore, we assessed social and environmental change in New Orleans and southern Louisiana through both a spatial and temporal lens (i. e., pre- and post-Katrina). By analyzing changes in system condition using social, economic and environmental factors, we identified some of the characteristics of the system's reorganization trajectories. Our results suggest that although the ongoing population recovery may be a sign of revitalization, the city and metropolitan area continue to face socioeconomic inequalities and environmental vulnerability to natural disasters. Further, the spatial distribution of social-ecological condition over time reveals certain levels of change and reorganization after Katrina, but the reorganization did not translate into greater equity. This effort presents an enhanced approach to assessing social-ecological change pre and post disturbance and provides a way forward for characterizing pertinent aspects of disaster resilience.

Understanding the adaptive capacity of ecosystems to cope with change is crucial to management. However, unclear and often confusing definitions of adaptive capacity make application of this concept difficult. In this paper, we revisit definitions of adaptive capacity and operationalize the concept. We define adaptive capacity as the latent potential of an ecosystem to alter resilience in response to change. We present testable hypotheses to evaluate complementary attributes of adaptive capacity that may help further clarify the components and relevance of the concept. Adaptive sampling, inference and modeling can reduce key uncertainties incrementally over time and increase learning about adaptive capacity. Such improvements are needed because uncertainty about global change and its effect on the capacity of ecosystems to adapt to social and ecological change is high.

Determining infections from environmental exposures, particularly from waterborne pathogens is a challenging proposition. The study design must be rigorous and account for numerous factors including study population selection, sample collection, storage, and processing, as well as data processing and analysis. These challenges are magnified when it is suspected that individuals may potentially be infected by multiple pathogens at the same time. Previous work demonstrated the effectiveness of a salivary antibody multiplex immunoassay in detecting the prevalence of immunoglobulin G (IgG) antibodies to multiple waterborne pathogens and helped identify asymptomatic norovirus infections in visitors to Boquerón Beach, Puerto Rico. In this study, we applied the immunoassay to three serially collected samples from study participants within the same population to assess immunoconversions (incident infections) to six waterborne pathogens: Helicobacter pylori, Campylobacter jejuni, Toxoplasma gondii, hepatitis A virus, and noroviruses GI. I and GII.4. Further, we examined the impact of sampling on the detection of immunoconversions by comparing the traditional immunoconversion definition based on two samples to criteria developed to capture trends in three sequential samples collected from study participants. The expansion to three samples makes it possible to capture the IgG antibody responses within the survey population to more accurately assess the frequency of immunoconversions to target pathogens. Based on the criteria developed, results showed that when only two samples from each participant were used in the analysis, 25.9% of the beachgoers immunoconverted to at least one pathogen; however, the addition of the third sample reduced immunoconversions to 6.5%. Of these incident infections, the highest levels were to noroviruses followed by T. gondii. Moreover, many individuals displayed evidence of immunoconversions to multiple pathogens. This study suggests that detection of simultaneous infections is possible, with far reaching consequences for the population. The results may lead to further studies to understand the complex interactions that occur within the body as the immune system attempts to ward off these infections. Such an approach is critical to our understanding of medically important synergistic or antagonistic interactions and may provide valuable and critical information to public health officials, water treatment personnel, and environmental managers.

Given the intensity and frequency of environmental change, the linked and cross-scale nature of social-ecological systems, and the proliferation of big data, methods that can help synthesize complex system behavior over a geographical area are of great value. Fisher information evaluates order in data and has been established as a robust and effective tool for capturing changes in system dynamics, including the detection of regimes and regime shifts. The methods developed to compute Fisher information can accommodate multivariate data of various types and requires no a priori decisions about system drivers, making it a unique and powerful tool. However, the approach has primarily been used to evaluate temporal patterns. In its sole application to spatial data, Fisher information successfully detected regimes in terrestrial and aquatic systems over transects. Although the selection of adjacently positioned sampling stations provided a natural means of ordering the data, such an approach limits the types of questions that can be answered in a spatial context. Here, we expand the approach to develop a method for more fully capturing spatial dynamics. The results reflect changes in the index that correspond with geographical patterns and demonstrate the utility of the method in uncovering hidden spatial trends in complex systems.

Scholars from many different intellectual disciplines have attempted to measure, estimate, or quantify resilience. However, there is growing concern that lack of clarity on the operationalization of the concept will limit its application. In this paper, we discuss the theory, research development and quantitative approaches in ecological and community resilience. Upon noting the lack of methods that quantify the complexities of the linked human and natural aspects of community resilience, we identify several promising approaches within the ecological resilience tradition that may be useful in filling these gaps. Further, we discuss the challenges for consolidating these approaches into a more integrated perspective for managing social-ecological systems.

The distribution of pattern across scales has predictive power in the analysis of complex systems. Discontinuity approaches remain a fruitful avenue of research in the quest for quantitative measures of resilience because discontinuity analysis provides an objective means of identifying scales in complex systems and facilitates delineation of hierarchical patterns in processes, structure, and resources. However, current discontinuity methods have been considered too subjective, too complicated and opaque, or have become computationally obsolete; given the ubiquity of discontinuities in ecological and other complex systems, a simple and transparent method for detection is needed. In this study, we present a method to detect discontinuities in census data based on resampling of a neutral model and provide the R code used to run the analyses. This method has the potential for advancing basic and applied ecological research.

The cross-scale resilience model suggests that system-level ecological resilience emerges from the distribution of species' functions within and across the spatial and temporal scales of a system. It has provided a quantitative method for calculating the resilience of a given system and so has been a valuable contribution to a largely qualitative field. As it is currently laid out, the model accounts for the spatial and temporal scales at which environmental resources and species are present and the functional roles species play but does not inform us about how much resource is present or how much function is provided. In short, it does not account for abundance in the distribution of species and their functional roles within and across the scales of a system. We detail the ways in which we would expect species' abundance to be relevant to the cross-scale resilience model based on the extensive abundance literature in ecology. We also put forward a series of testable hypotheses that would improve our ability to anticipate and quantify how resilience is generated, and how ecosystems will (or will not) buffer recent rapid global changes. This stream of research may provide an improved foundation for the quantitative evaluation of ecological resilience.