The rise and fall of rationality in language.

The surge of post-truth political argumentation suggests that we are living in a special historical period when it comes to the balance between emotion and reasoning. To explore if this is indeed the case, we analyze language in millions of books covering the period from 1850 to 2019 represented in Google nGram data. We show that the use of words associated with rationality, such as "determine" and "conclusion," rose systematically after 1850, while words related to human experience such as "feel" and "believe" declined. This pattern reversed over the past decades, paralleled by a shift from a collectivistic to an individualistic focus as reflected, among other things, by the ratio of singular to plural pronouns such as "I"/"we" and "he"/"they." Interpreting this synchronous sea change in book language remains challenging. However, as we show, the nature of this reversal occurs in fiction as well as nonfiction. Moreover, the pattern of change in the ratio between sentiment and rationality flag words since 1850 also occurs in New York Times articles, suggesting that it is not an artifact of the book corpora we analyzed. Finally, we show that word trends in books parallel trends in corresponding Google search terms, supporting the idea that changes in book language do in part reflect changes in interest. All in all, our results suggest that over the past decades, there has been a marked shift in public interest from the collective to the individual, and from rationality toward emotion.

Coral reefs in the Anthropocene.

Coral reefs support immense biodiversity and provide important ecosystem services to many millions of people. Yet reefs are degrading rapidly in response to numerous anthropogenic drivers. In the coming centuries, reefs will run the gauntlet of climate change, and rising temperatures will transform them into new configurations, unlike anything observed previously by humans. Returning reefs to past configurations is no longer an option. Instead, the global challenge is to steer reefs through the Anthropocene era in a way that maintains their biological functions. Successful navigation of this transition will require radical changes in the science, management and governance of coral reefs.

Quantifying resilience of humans and other animals.

All life requires the capacity to recover from challenges that are as inevitable as they are unpredictable. Understanding this resilience is essential for managing the health of humans and their livestock. It has long been difficult to quantify resilience directly, forcing practitioners to rely on indirect static indicators of health. However, measurements from wearable electronics and other sources now allow us to analyze the dynamics of physiology and behavior with unsurpassed resolution. The resulting flood of data coincides with the emergence of novel analytical tools for estimating resilience from the pattern of microrecoveries observed in natural time series. Such dynamic indicators of resilience may be used to monitor the risk of systemic failure across systems ranging from organs to entire organisms. These tools invite a fundamental rethinking of our approach to the adaptive management of health and resilience.

Foreseeing the future of mutualistic communities beyond collapse.

Changing conditions may lead to sudden shifts in the state of ecosystems when critical thresholds are passed. Some well-studied drivers of such transitions lead to predictable outcomes such as a turbid lake or a degraded landscape. Many ecosystems are, however, complex systems of many interacting species. While detecting upcoming transitions in such systems is challenging, predicting what comes after a critical transition is terra incognita altogether. The problem is that complex ecosystems may shift to many different, alternative states. Whether an impending transition has minor, positive or catastrophic effects is thus unclear. Some systems may, however, behave more predictably than others. The dynamics of mutualistic communities can be expected to be relatively simple, because delayed negative feedbacks leading to oscillatory or other complex dynamics are weak. Here, we address the question of whether this relative simplicity allows us to foresee a community's future state. As a case study, we use a model of a bipartite mutualistic network and show that a network's post-transition state is indicated by the way in which a system recovers from minor disturbances. Similar results obtained with a unipartite model of facilitation suggest that our results are of relevance to a wide range of mutualistic systems.

Inequality in nature and society.

Most societies are economically dominated by a small elite, and similarly, natural communities are typically dominated by a small fraction of the species. Here we reveal a strong similarity between patterns of inequality in nature and society, hinting at fundamental unifying mechanisms. We show that chance alone will drive 1% or less of the community to dominate 50% of all resources in situations where gains and losses are multiplicative, as in returns on assets or growth rates of populations. Key mechanisms that counteract such hyperdominance include natural enemies in nature and wealth-equalizing institutions in society. However, historical research of European developments over the past millennium suggests that such institutions become ineffective in times of societal upscaling. A corollary is that in a globalizing world, wealth will inevitably be appropriated by a very small fraction of the population unless effective wealth-equalizing institutions emerge at the global level.

Critical slowing down as early warning for the onset and termination of depression.

About 17% of humanity goes through an episode of major depression at some point in their lifetime. Despite the enormous societal costs of this incapacitating disorder, it is largely unknown how the likelihood of falling into a depressive episode can be assessed. Here, we show for a large group of healthy individuals and patients that the probability of an upcoming shift between a depressed and a normal state is related to elevated temporal autocorrelation, variance, and correlation between emotions in fluctuations of autorecorded emotions. These are indicators of the general phenomenon of critical slowing down, which is expected to occur when a system approaches a tipping point. Our results support the hypothesis that mood may have alternative stable states separated by tipping points, and suggest an approach for assessing the likelihood of transitions into and out of depression.

Slowing Down of Recovery as Generic Risk Marker for Acute Severity Transitions in Chronic Diseases.

OBJECTIVE: We propose a novel paradigm to predict acute attacks and exacerbations in chronic episodic disorders such as asthma, cardiac arrhythmias, migraine, epilepsy, and depression. A better generic understanding of acute transitions in chronic dynamic diseases is increasingly important in critical care medicine because of the higher prevalence and incidence of these chronic diseases in our aging societies. DATA SOURCES: PubMed, Medline, and Web of Science. STUDY SELECTION: We selected studies from biology and medicine providing evidence of slowing down after a perturbation as a warning signal for critical transitions. DATA EXTRACTION: Recent work in ecology, climate, and systems biology has shown that slowing down of recovery upon perturbations can indicate loss of resilience across complex, nonlinear biologic systems that are approaching a tipping point. This observation is supported by the empiric studies in pathophysiology and controlled laboratory experiments with other living systems, which can flip from one state of clinical balance to a contrasting one. We discuss examples of such evidence in bodily functions such as blood pressure, heart rate, mood, and respiratory regulation when a tipping point for a transition is near. CONCLUSIONS: We hypothesize that in a range of chronic episodic diseases, indicators of critical slowing down, such as rising variance and temporal correlation, may be used to assess the risk of attacks, exacerbations, and even mortality. Identification of such early warning signals over a range of diseases will enhance the understanding of why, how, and when attacks and exacerbations will strike and may thus improve disease management in critical care medicine.

Impact of human disturbance on biogeochemical fluxes in tropical seascapes.

Tropical seascapes rely on the feedback relationships among mangrove forests, seagrass meadows, and coral reefs, as they mutually facilitate and enhance each other's functionality. Biogeochemical fluxes link tropical coastal habitats by exchanging material flows and energy through various natural processes that determine the conditions for life and ecosystem functioning. However, little is known about the seascape-scale implications of anthropogenic disruptions to these linkages. Despite the limited number of integrated empirical studies available (with only 11 out of 81 selected studies focusing on the integrated dynamics of mangroves, seagrass, and corals), this review emphasizes the importance of biogeochemical fluxes for ecosystem connectivity in tropical seascapes. It identifies four primary anthropogenic influences that can disturb these fluxes-nutrient enrichment, chemical pollution, microbial pollution, and solid waste accumulation-resulting in eutrophication, increased disease incidence, toxicity, and disruptions to water carbonate chemistry. This review also highlights significant knowledge gaps in our understanding of biogeochemical fluxes and ecosystem responses to perturbations in tropical seascapes. Addressing these knowledge gaps is crucial for developing practical strategies to conserve and manage connected seascapes effectively. Integrated research is needed to shed light on the complex interactions and feedback mechanisms within these ecosystems, providing valuable insights for conservation and management practices.

High temperature and eutrophication alter biomass allocation of black mangrove (Avicennia germinans L.) seedlings.

Mangrove restoration is underway along tropical coastlines to combat their rapid worldwide decline. However, restoration success is limited due to local drivers such as eutrophication, and global drivers such as climate change, yet their interactions remain unclear. We conducted a mesocosm experiment to assess the impact of increased nutrients and temperature on the photosynthetic efficiency and development of black mangrove seedlings. Seedlings exposed to high temperature and eutrophication showed reduced root growth and disproportionally long stems, with lower net assimilation rates. This architectonical imbalance between root and stem growth may increase susceptibility to physical disturbances and dislodgement. Notably, none of the experimental seedlings displayed signs of photophysiological stress, and those exposed to increased nutrients and temperature exhibited robust photosynthetic performance. The disbalance in biomass allocation highlights the importance of considering local nutrient status and hydrodynamic conditions in restoration projects, ensuring the effective anchorage of mangrove seedlings and restoration success under a warming climate.

Transformation starts at the periphery of networks where pushback is less.

Complex systems ranging from societies to ecological communities and power grids may be viewed as networks of connected elements. Such systems can go through critical transitions driven by an avalanche of contagious change. Here we ask, where in a complex network such a systemic shift is most likely to start. Intuitively, a central node seems the most likely source of such change. Indeed, topological studies suggest that central nodes can be the Achilles heel for attacks. We argue that the opposite is true for the class of networks in which all nodes tend to follow the state of their neighbors, a category we call two-way pull networks. In this case, a well-connected central node is an unlikely starting point of a systemic shift due to the buffering effect of connected neighbors. As a result, change is most likely to cascade through the network if it spreads first among relatively poorly connected nodes in the periphery. The probability of such initial spread is highest when the perturbation starts from intermediately connected nodes at the periphery, or more specifically, nodes with intermediate degree and relatively low closeness centrality. Our finding is consistent with empirical observations on social innovation, and may be relevant to topics as different as the sources of originality of art, collapse of financial and ecological networks and the onset of psychiatric disorders.

A Dynamical Systems View of Psychiatric Disorders-Theory: A Review.

Importance: Psychiatric disorders may come and go with symptoms changing over a lifetime. This suggests the need for a paradigm shift in diagnosis and treatment. Here we present a fresh look inspired by dynamical systems theory. This theory is used widely to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. Observations: In the dynamical systems view, we propose the healthy state has a basin of attraction representing its resilience, while disorders are alternative attractors in which the system can become trapped. Rather than an immutable trait, resilience in this approach is a dynamical property. Recent work has demonstrated the universality of generic dynamical indicators of resilience that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforests and tipping elements of the climate system. Other dynamical systems tools are used in ecology and climate science to infer causality from time series. Moreover, experiences in ecological restoration confirm the theoretical prediction that under some conditions, short interventions may invoke long-term success when they flip the system into an alternative basin of attraction. All this implies practical applications for psychiatry, as are discussed in part 2 of this article. Conclusions and Relevance: Work in the field of dynamical systems points to novel ways of inferring causality and quantifying resilience from time series. Those approaches have now been tried and tested in a range of complex systems. The same tools may help monitoring and managing resilience of the healthy state as well as psychiatric disorders.

A Dynamical Systems View of Psychiatric Disorders-Practical Implications: A Review.

Importance: Dynamical systems theory is widely used to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. It has been suggested that the same theory may be used to explain the nature and dynamics of psychiatric disorders, which may come and go with symptoms changing over a lifetime. Here we review evidence for the practical applicability of this theory and its quantitative tools in psychiatry. Observations: Emerging results suggest that time series of mood and behavior may be used to monitor the resilience of patients using the same generic dynamical indicators that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforest and tipping elements of the climate system. Other dynamical systems tools used in ecology and climate science open ways to infer personalized webs of causality for patients that may be used to identify targets for intervention. Meanwhile, experiences in ecological restoration help make sense of the occasional long-term success of short interventions. Conclusions and Relevance: Those observations, while promising, evoke follow-up questions on how best to collect dynamic data, infer informative timescales, construct mechanistic models, and measure the effect of interventions on resilience. Done well, monitoring resilience to inform well-timed interventions may be integrated into approaches that give patients an active role in the lifelong challenge of managing their resilience and knowing when to seek professional help.

Loss of Earth system resilience during early Eocene transient global warming events.

Superimposed on long-term late Paleocene-early Eocene warming (~59 to 52 million years ago), Earth's climate experienced a series of abrupt perturbations, characterized by massive carbon input into the ocean-atmosphere system and global warming. Here, we examine the three most punctuated events of this period, the Paleocene-Eocene Thermal Maximum and Eocene Thermal Maximum 2 and 3, to probe whether they were initiated by climate-driven carbon cycle tipping points. Specifically, we analyze the dynamics of climate and carbon cycle indicators acquired from marine sediments to detect changes in Earth system resilience and to identify positive feedbacks. Our analyses suggest a loss of Earth system resilience toward all three events. Moreover, dynamic convergent cross mapping reveals intensifying coupling between the carbon cycle and climate during the long-term warming trend, supporting increasingly dominant climate forcing of carbon cycle dynamics during the Early Eocene Climatic Optimum when these recurrent global warming events became more frequent.

The potential of resilience indicators to anticipate infectious disease outbreaks, a systematic review and guide.

To reduce the consequences of infectious disease outbreaks, the timely implementation of public health measures is crucial. Currently used early-warning systems are highly context-dependent and require a long phase of model building. A proposed solution to anticipate the onset or termination of an outbreak is the use of so-called resilience indicators. These indicators are based on the generic theory of critical slowing down and require only incidence time series. Here we assess the potential for this approach to contribute to outbreak anticipation. We systematically reviewed studies that used resilience indicators to predict outbreaks or terminations of epidemics. We identified 37 studies meeting the inclusion criteria: 21 using simulated data and 16 real-world data. 36 out of 37 studies detected significant signs of critical slowing down before a critical transition (i.e., the onset or end of an outbreak), with a highly variable sensitivity (i.e., the proportion of true positive outbreak warnings) ranging from 0.03 to 1 and a lead time ranging from 10 days to 68 months. Challenges include low resolution and limited length of time series, a too rapid increase in cases, and strong seasonal patterns which may hamper the sensitivity of resilience indicators. Alternative types of data, such as Google searches or social media data, have the potential to improve predictions in some cases. Resilience indicators may be useful when the risk of disease outbreaks is changing gradually. This may happen, for instance, when pathogens become increasingly adapted to an environment or evolve gradually to escape immunity. High-resolution monitoring is needed to reach sufficient sensitivity. If those conditions are met, resilience indicators could help improve the current practice of prediction, facilitating timely outbreak response. We provide a step-by-step guide on the use of resilience indicators in infectious disease epidemiology, and guidance on the relevant situations to use this approach.

Large-scale decrease in the social salience of climate change during the COVID-19 pandemic.

There are concerns that climate change attention is waning as competing global threats intensify. To investigate this possibility, we analyzed all link shares and reshares on Meta's Facebook platform (e.g., shares and reshares of news articles) in the United States from August 2019 to December 2020 (containing billions of aggregated and de-identified shares and reshares). We then identified all link shares and reshares on "climate change" and "global warming" from this repository to develop a social media salience index-the Climate SMSI score-and found an 80% decrease in climate change content sharing and resharing as COVID-19 spread during the spring of 2020. Climate change salience then briefly rebounded in the autumn of 2020 during a period of record-setting wildfires and droughts in the United States before returning to low content sharing and resharing levels. This fluctuating pattern suggests new climate communication strategies-focused on "systemic sustainability"-are necessary in an age of competing global crises.

Predicting microbial nitrogen pathways from basic principles.

Nitrogen compounds are transformed by a complicated network of competing geochemical processes or microbial pathways, each performed by a different ecological guild of microorganisms. Complete experimental unravelling of this network requires a prohibitive experimental effort. Here we present a simple model that predicts relative rates of hypothetical nitrogen pathways, based only on the stoichiometry and energy yield of the performed redox reaction, assuming competition for resources between alternative pathways. Simulating competing pathways in hypothetical freshwater and marine sediment situations, we surprisingly found that much of the variation observed in nature can simply be predicted from these basic principles. Investigating discrepancies between observations and predictions led to two important biochemical factors that may create barriers for the viability of pathways: enzymatic costs for long pathways and high ammonium activation energy. We hypothesize that some discrepancies can be explained by non-equilibrium dynamics. The model predicted a pathway that has not been discovered in nature yet: the dismutation of nitrite to the level of nitrate and dinitrogen gas.

A network perspective on suicidal behavior: Understanding suicidality as a complex system.

BACKGROUND: Suicidal behavior is the result of complex interactions between many different factors that change over time. A network perspective may improve our understanding of these complex dynamics. Within the network perspective, psychopathology is considered to be a consequence of symptoms that directly interact with one another in a network structure. To view suicidal behavior as the result of such a complex system is a good starting point to facilitate moving away from traditional linear thinking. OBJECTIVE: To review the existing paradigms and theories and their application to suicidal behavior. METHODS: In the first part of this paper, we introduce the relevant concepts within network analysis such as network density and centrality. Where possible, we refer to studies that have applied these concepts within the field of suicide prevention. In the second part, we move one step further, by understanding the network perspective as an initial step toward complex system theory. The latter is a branch of science that models interacting variables in order to understand the dynamics of complex systems, such as tipping points and hysteresis. RESULTS: Few studies have applied network analysis to study suicidal behavior. The studies that do highlight the complexity of suicidality. Complexity science offers potential useful concepts such as alternative stable states and resilience to study psychopathology and suicidal behavior, as demonstrated within the field of depression. To date, one innovative study has applied concepts from complexity science to better understand suicidal behavior. Complexity science and its application to human behavior are in its infancy, and it requires more collaboration between complexity scientists and behavioral scientists. CONCLUSIONS: Clinicians and scientists are increasingly conceptualizing suicidal behavior as the result of the complex interaction between many different biological, social, and psychological risk and protective factors. Novel statistical techniques such as network analysis can help the field to better understand this complexity. The application of concepts from complexity science to the field of psychopathology and suicide research offers exciting and promising possibilities for our understanding and prevention of suicide.

Dynamical indicators of resilience from physiological time series in geriatric inpatients: Lessons learned.

The concept of physical resilience may help geriatric medicine objectively assess patients' ability to 'bounce back' from future health challenges. Indicators putatively forecasting resilience have been developed under two paradigms with different perspectives: Critical Slowing Down and Loss of Complexity. This study explored whether these indicators validly reflect the construct of resilience in geriatric inpatients. Geriatric patients (n = 121, 60% female) had their heart rate and physical activity continuously monitored using a chest-worn sensor. Indicators from both paradigms were extracted from both physiological signals. Measures of health functioning, concomitant with low resilience, were obtained by questionnaire at admission. The relationships among indicators and their associations with health functioning were assessed by correlation and linear regression analyses, respectively. Greater complexity and higher variance in physical activity were associated with lower frailty (ß = -0.28, p = .004 and ß = -0.37, p < .001, respectively) and better ADL function (ß = 0.23, p = .022 and ß = 0.38, p < .001). The associations of physical activity variance with health functioning were not in the expected direction based on Critical Slowing Down. In retrospect, these observations stress the importance of matching the resilience paradigm's assumptions to the homeostatic role of the variable monitored. We present several lessons learned.