Anticipating critical transitions

Dynamic time warp of emotions in patients with cutaneous T-cell lymphoma treated with corticosteroids

Background

A substantial number of patients treated systemically with synthetic glucocorticoids undergo emotional disturbances during treatment. Patients with cutaneous T-cell lymphoma frequently experience skin inflammation and itching and often require glucocorticoid treatment.

Objective

This case-series study aimed to examine how emotional and skin-related symptoms interact throughout glucocorticoid treatment.

Methods

Five cutaneous T-cell lymphoma patients undergoing systemic glucocorticoid treatment completed daily ecological momentary assessments for on average 30 assessments. Fluctuations in their emotions and symptoms were analyzed using undirected and directed dynamic time warp analyses, and were visualized in symptom networks.

Results

Toward the end of the glucocorticoid treatment, a decline was found in positive psychological symptoms. Idiographic dynamic time warp analyses revealed highly variable symptom networks. Directed time-lag group-level analyses revealed irritability, enthusiastic, and excited as variables with highest outstrength, in which mainly decreasing levels of positive emotions were associated with a higher likelihood of experiencing increases in itchy skin and skin problems the next day.

Conclusion

The end of glucocorticoid treatment, potentially via the induction of hypocortisolism, seems to coincide with decreased energy, motivation, and enthusiasm. Itch and skin problems could be a consequence of low-positive emotions the day before.

Landscape and environmental heterogeneity support coexistence in competitive metacommunities

Metapopulation models have been instrumental in quantifying the ecological impact of landscape structure on the survival of a focal species. However, extensions to multiple species with arbitrary dispersal networks often rely on phenomenological assumptions that inevitably limit their scope. Here, we propose a multilayer network model of competitive dispersing metacommunities to investigate how spatially structured environments impact species coexistence and ecosystem stability. We introduce the concept of landscape-mediated fitness, quantifying how fit a species is in a given environment in terms of colonization and extinction. We show that, when all environments are equivalent, one species excludes all the others-except the marginal case where species fitnesses are in exact trade-off. However, we prove that stable coexistence becomes possible in sufficiently heterogeneous environments by introducing spatial disorder in the model and solving it exactly in the mean-field limit. Crucially, coexistence is supported by the spontaneous localization of species through the emergence of ecological niches. We show that our results remain qualitatively valid in arbitrary dispersal networks, where topological features can improve species coexistence by buffering competition. Finally, we employ our model to study how correlated heterogeneity promotes spatial ecological patterns in realistic terrestrial and riverine landscapes. Our work provides a framework to understand how landscape structure enables coexistence in metacommunities by acting as the substrate for ecological interactions.

Attenuated asymmetry of above- versus belowground stoichiometry to a decadal nitrogen addition during stand development

Deciphering the linkage between ecological stoichiometry and ecosystem functioning under anthropogenic nitrogen (N) deposition is critical for understanding the impact of afforestation on terrestrial carbon (C) sequestration. However, the specific changes in above- versus belowground stoichiometric asymmetry with stand age in response to long-term N addition remain poorly understood. In this study, we investigated changes in stoichiometry following a decadal addition of three levels of N (control, no N addition; low N addition, 20 kg N ha-1 year-1; high N addition, 50 kg N ha-1 year-1) in young, intermediate, and mature stands in three temperate larch plantations (Larix principis-rupprechtii) in North China. We found that low N addition had no impact on both above- (leaf and litter) and belowground (soil and microbe) stoichiometry. In contrast, high N addition resulted in significant asymmetry in above- versus belowground stoichiometry, which then diminished during stand development. Following 10 years of N inputs, the young and intermediate plantations transitioned from a state of relative N limitation to co-limitation by both N and phosphorus (P), whereas the mature plantation continued to experience relative N limitation. Conversely, soil microorganisms exhibited relative P limitation in all three plantations. Broader niche differentiation (N limitation for trees, but P limitation for microorganisms) under long-term N input may have been responsible for the faster attainment of stoichiometric homeostasis in mature plantations than in young plantations. Our findings provide stoichiometric-based insight into the operating mechanisms of large C sinks in young forests, particularly above- versus belowground C stock asymmetry, and highlight the need to consider the role of flexible stoichiometry when forecasting future forest C sinks.

The effects of Pinus sylvestris L. geographical origin on the community and co-occurrence of fungal and bacterial endophytes in a common garden experiment

Below-ground microorganisms, particularly endophytes, are pivotal for plant establishment and functioning through nutrient acquisition and enhancing resistance to abiotic and biotic stresses. The impact of host plant origin within a species on the composition and interaction networks of root endophytic fungi and bacteria has been less explored compared with plant phylogeny and biological distance. This study investigates the effect of geographic origin on the fungal and bacterial microbiomes of Pinus sylvestris L. root endophytes. Roots from plants grown in a common garden, originating from six locations, were harvested in two distinct seasons. Fungal and bacterial microbiomes were analyzed using Illumina MiSeq sequencing. The operational taxonomic unit (OTU) richness of endophytic fungi and bacteria showed no significant variation due to tree origin or season. However, the Shannon diversity index for endophytic fungi was seasonally influenced. The composition of endophytic fungal and bacterial communities was affected by both tree origin and season, correlating with host root biochemical parameters, such as starch, total non-structural carbohydrates, carbon, nitrogen, and climatic factors, such as mean annual precipitation and temperature. Moreover, the abundance of specific endophytic fungi and bacteria varied across different P. sylvestris origins, depending on the season. The complexity of the co-occurrence networks of fungal and bacterial endophytes within P. sylvestris also differed by geographical origin and season. This study highlights the significant role of biochemical and climatic factors associated with tree origin in shaping interactions with endophytic communities, potentially affecting plant health and adaptability across diverse environments.

IMPORTANCE

This study advances our understanding of how plant ecotype and seasonal changes influence root endophytic communities in Scots pine (Pinus sylvestris). By examining trees from various origins grown in a common garden, it highlights the role of tree origin and season in shaping fungal and bacterial community and co-occurrence networks. Importantly, this research demonstrates that tree origin impacts the composition and interaction networks of root endophytes and depends on the season. The study's findings suggest that root biochemical traits and climatic conditions (e.g., temperature, precipitation) associated with tree origin are crucial in determining the assembly of endophytic communities. This understanding could lead to innovative strategies for enhancing plant health and adaptability across different environments, contributing to forestry and conservation efforts. The research underscores the complexity of plant-microbe interactions and the need for a comprehensive approach to studying them, highlighting the interplay between tree origin and microbial ecology in forest ecosystems.

Reviving collapsed plant-pollinator networks from a single species

Mutualistic ecological networks can suddenly transition to undesirable states due to small changes in environmental conditions. Recovering from such a collapse can be difficult as restoring the original environmental conditions may be infeasible. Additionally, such networks can also exhibit a phenomenon known as hysteresis, whereby the system could exhibit multiple states under the same environmental conditions, implying that ecological networks may not recover. Here, we attempted to revive collapsed mutualistic networks to a high-functioning state from a single species, using concepts from signal propagation theory and an eco-evolutionary model based on network structures of 115 empirical plant-pollinator networks. We found that restoring the environmental conditions rarely aided in recovery of collapsed networks, but a positive relationship between recovering pollinator density and network nestedness emerged, which was qualitatively supported by empirical plant-pollinator restoration data. In contrast, network resurrection from a collapsed state in undesirable environmental conditions where restoration has minimal impacts could be readily achieved by perturbing a single species or a few species that control the response of the dynamical networks. Additionally, nestedness in networks and a moderate amount of trait variation could aid in the revival of networks even in undesirable environmental conditions. Our work suggests that focus should be applied to a few species whose dynamics could be steered to resurrect entire networks from a collapsed state and that network architecture could play a crucial role in reviving collapsed plant-pollinator networks.

A new sociology of humans and machines

From fake social media accounts and generative artificial intelligence chatbots to trading algorithms and self-driving vehicles, robots, bots and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions and transportation arteries. Networks of multiple interdependent and interacting humans and intelligent machines constitute complex social systems for which the collective outcomes cannot be deduced from either human or machine behaviour alone. Under this paradigm, we review recent research and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion and collective decision-making, with context-rich examples from high-frequency trading markets, a social media platform, an open collaboration community and a discussion forum. To ensure more robust and resilient human-machine communities, we require a new sociology of humans and machines. Researchers should study these communities using complex system methods; engineers should explicitly design artificial intelligence for human-machine and machine-machine interactions; and regulators should govern the ecological diversity and social co-development of humans and machines.

Unraveling the Complexity of Parkinson's Disease: Insights into Pathogenesis and Precision Interventions

Parkinson's disease (PD) is a neurodegenerative disorder characterized by dopaminergic neuron loss, leading to motor and non-motor symptoms. Early detection before symptom onset is crucial but challenging. This study presents a framework integrating circuit modeling, non-equilibrium dynamics, and optimization to understand PD pathogenesis and enable precision interventions. Neuronal firing patterns, particularly oscillatory activity, play a critical role in PD pathology. The basal ganglia network, specifically the subthalamic nucleus-external globus pallidus (STN-GPe) circuitry, exhibits abnormal activity associated with motor dysfunction. The framework leverages the non-equilibrium landscape and flux theory to identify key connections generating pathological activity, providing insights into disease progression and potential intervention points. The intricate STN-GPe interplay is highlighted, shedding light on compensatory mechanisms within this circuitry may initially counteract changes but later contribute to pathological alterations as disease progresses. The framework addresses the need for comprehensive evaluation methods to assess intervention outcomes. Cross-correlations between state variables provide superior early warning signals compared to traditional indicators relying on critical slowing down. By elucidating compensatory mechanisms and circuit dynamics, the framework contributes to improved management, early detection, risk assessment, and potential prevention/delay of PD development. This pioneering research paves the way for precision medicine in neurodegenerative disorders.

Evolutionary shift of a tipping point can precipitate, or forestall, collapse in a microbial community

Global ecosystems are rapidly approaching tipping points, where minute shifts can lead to drastic ecological changes. Theory predicts that evolution can shape a system's tipping point behaviour, but direct experimental support is lacking. Here we investigate the power of evolutionary processes to alter these critical thresholds and protect an ecological community from collapse. To do this, we propagate a two-species microbial system composed of Escherichia coli and baker's yeast, Saccharomyces cerevisiae, for over 4,000 generations, and map ecological stability before and after coevolution. Our results reveal that tipping points-and other geometric properties of ecological communities-can evolve to alter the range of conditions under which our microbial community can flourish. We develop a mathematical model to illustrate how evolutionary changes in parameters such as growth rate, carrying capacity and resistance to environmental change affect ecological resilience. Our study shows that adaptation of key species can shift an ecological community's tipping point, potentially promoting ecological stability or accelerating collapse.

Soil bacterial community composition is altered more by soil nutrient availability than pH following long-term nutrient addition in a temperate steppe

Although aboveground biodiversity has been extensively studied, the impact of nutrient enrichment on soil microbial populations remains unclear. Soil microorganisms serve as important indicators in shaping soil nutrient cycling processes and are typically sensitive to nutrient additions. For this, we employed a factorial combination design to examine the impact of nutrient additions on the composition and function of soil bacteria in a temperate steppe. Nitrogen addition promoted the growth of copiotrophic bacteria (Proteobacteria, Firmicutes, and Bacteroidota) but inhibited the growth of oligotrophic bacteria (Acidobacteria, Chloroflexi, and Verrucomicrobiota). Phosphorus addition alleviated phosphorus deficiency, resulting in a decrease in the abundance of phoD-harboring bacteria (Actinobacteria and Proteobacteria). Significant enhancement of soil bacterial alpha diversity was observed only in treatments with added phosphorus. Changes in NO3 --N, NH4 +-N, available phosphorus, and dissolved organic carbon resulting from nutrient addition may have a greater impact on microbial community structure than changes in soil pH caused by nitrogen addition. Moreover, nutrient addition may indirectly impact microbial ecological function by altering nutrient availability in the soil. In conclusion, our study suggests that soil nutrient availability, particularly available phosphorus, affects soil bacterial communities and potentially regulates the biogeochemical cycles of soil ecosystems.

The critical dynamics of hippocampal seizures

Epilepsy is defined by the abrupt emergence of harmful seizures, but the nature of these regime shifts remains enigmatic. From the perspective of dynamical systems theory, such critical transitions occur upon inconspicuous perturbations in highly interconnected systems and can be modeled as mathematical bifurcations between alternative regimes. The predictability of critical transitions represents a major challenge, but the theory predicts the appearance of subtle dynamical signatures on the verge of instability. Whether such dynamical signatures can be measured before impending seizures remains uncertain. Here, we verified that predictions on bifurcations applied to the onset of hippocampal seizures, providing concordant results from in silico modeling, optogenetics experiments in male mice and intracranial EEG recordings in human patients with epilepsy. Leveraging pharmacological control over neural excitability, we showed that the boundary between physiological excitability and seizures can be inferred from dynamical signatures passively recorded or actively probed in hippocampal circuits. Of importance for the design of future neurotechnologies, active probing surpassed passive recording to decode underlying levels of neural excitability, notably when assessed from a network of propagating neural responses. Our findings provide a promising approach for predicting and preventing seizures, based on a sound understanding of their dynamics.

Perspectives on the timing of ecosystem collapse in a changing climate

Climate change is one of the most important drivers of ecosystem change, the global-scale impacts of which will intensify over the next 2 decades. Estimating the timing of unprecedented changes is not only challenging but is of great importance for the development of ecosystem conservation guidelines. Time of emergence (ToE) (point at which climate change can be differentiated from a previous climate), a widely applied concept in climatology studies, provides a robust but unexplored approach for assessing the risk of ecosystem collapse, as described by the C criterion of the International Union for Conservation of Nature's Red List of Ecosystems (RLE). We identified 3 main theoretical considerations of ToE for RLE assessment (degree of stability, multifactorial instead of one-dimensional analyses, and hallmarks of ecosystem collapse) and 4 sources of uncertainty when applying ToE methodology (intermodel spread, historical reference period, consensus among variables, and consideration of different scenarios), which aims to avoid misuse and errors while promoting a proper application of the framework by scientists and practitioners. The incorporation of ToE for the RLE assessments adds important information for conservation priority setting that allows prediction of changes within and beyond the time frames proposed by the RLE.

An early warning indicator trained on stochastic disease-spreading models with different noises

The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modelling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreaks by training on noise-induced disease-spreading models. The indicator's effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts.

Early warning signals observed in motor activity preceding mood state change in bipolar disorder

INTRODUCTION

Alterations in motor activity are well-established symptoms of bipolar disorder, and time series of motor activity can be considered complex dynamical systems. In such systems, early warning signals (EWS) occur in a critical transition period preceding a sudden shift (tipping point) in the system. EWS are statistical observations occurring due to a system's declining ability to maintain homeostasis when approaching a tipping point. The aim was to identify critical transition periods preceding bipolar mood state changes.

METHODS

Participants with a validated bipolar diagnosis were included to a one-year follow-up study, with repeated assessments of the participants' mood. Motor activity was recorded continuously by a wrist-worn actigraph. Participants assessed to have relapsed during follow-up were analyzed. Recognized EWS features were extracted from the motor activity data and analyzed by an unsupervised change point detection algorithm, capable of processing multi-dimensional data and developed to identify when the statistical property of a time series changes.

RESULTS

Of 49 participants, four depressive and four hypomanic/manic relapses among six individuals occurred, recording actigraphy for 23.8 ± 0.2 h/day, for 39.8 ± 4.6 days. The algorithm detected change points in the time series and identified critical transition periods spanning 13.5 ± 7.2 days. For depressions 11.4 ± 1.8, and hypomania/mania 15.6 ± 10.2 days.

CONCLUSION

The change point detection algorithm seems capable of recognizing impending mood episodes in continuous flowing data streams. Hence, we present an innovative method for forecasting approaching relapses to improve the clinical management of bipolar disorder.

DAMP-ing IBD: Extinguish the Fire and Prevent Smoldering

In inflammatory bowel diseases (IBD), the most promising therapies targeting cytokines or immune cell trafficking demonstrate around 40% efficacy. As IBD is a multifactorial inflammation of the intestinal tract, a single-target approach is unlikely to solve this problem, necessitating an alternative strategy that addresses its variability. One approach often overlooked by the pharmaceutically driven therapeutic options is to address the impact of environmental factors. This is somewhat surprising considering that IBD is increasingly viewed as a condition heavily influenced by such factors, including diet, stress, and environmental pollution-often referred to as the "Western lifestyle". In IBD, intestinal responses result from a complex interplay among the genetic background of the patient, molecules, cells, and the local inflammatory microenvironment where danger- and microbe-associated molecular patterns (D/MAMPs) provide an adjuvant-rich environment. Through activating DAMP receptors, this array of pro-inflammatory factors can stimulate, for example, the NLRP3 inflammasome-a major amplifier of the inflammatory response in IBD, and various immune cells via non-specific bystander activation of myeloid cells (e.g., macrophages) and lymphocytes (e.g., tissue-resident memory T cells). Current single-target biological treatment approaches can dampen the immune response, but without reducing exposure to environmental factors of IBD, e.g., by changing diet (reducing ultra-processed foods), the adjuvant-rich landscape is never resolved and continues to drive intestinal mucosal dysregulation. Thus, such treatment approaches are not enough to put out the inflammatory fire. The resultant smoldering, low-grade inflammation diminishes physiological resilience of the intestinal (micro)environment, perpetuating the state of chronic disease. Therefore, our hypothesis posits that successful interventions for IBD must address the complexity of the disease by simultaneously targeting all modifiable aspects: innate immunity cytokines and microbiota, adaptive immunity cells and cytokines, and factors that relate to the (micro)environment. Thus the disease can be comprehensively treated across the nano-, meso-, and microscales, rather than with a focus on single targets. A broader perspective on IBD treatment that also includes options to adapt the DAMPing (micro)environment is warranted.

Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems

Real-world non-autonomous systems are open, out-of-equilibrium systems that evolve in and are driven by temporally varying environments. Such systems can show multiple timescale and transient dynamics together with transitions to very different and, at times, even disastrous dynamical regimes. Since such critical transitions disrupt the systems' intended or desired functionality, it is crucial to understand the underlying mechanisms, to identify precursors of such transitions, and to reliably detect them in time series of suitable system observables to enable forecasts. This review critically assesses the various steps of investigation involved in time-series-analysis-based detection of critical transitions in real-world non-autonomous systems: from the data recording to evaluating the reliability of offline and online detections. It will highlight pros and cons to stimulate further developments, which would be necessary to advance understanding and forecasting nonlinear behavior such as critical transitions in complex systems.

Prospects for Augmenting Team Interactions with Real-Time Coordination-Based Measures in Human-Autonomy Teams

Complex work in teams requires coordination across team members and their technology as well as the ability to change and adapt over time to achieve effective performance. To support such complex interactions, recent efforts have worked toward the design of adaptive human-autonomy teaming systems that can provide feedback in or near real time to achieve the desired individual or team results. However, while significant advancements have been made to better model and understand the dynamics of team interaction and its relationship with task performance, appropriate measures of team coordination and computational methods to detect changes in coordination have not yet been widely investigated. Having the capacity to measure coordination in real time is quite promising as it provides the opportunity to provide adaptive feedback that may influence and regulate teams' coordination patterns and, ultimately, drive effective team performance. A critical requirement to reach this potential is having the theoretical and empirical foundation from which to do so. Therefore, the first goal of the paper is to review approaches to coordination dynamics, identify current research gaps, and draw insights from other areas, such as social interaction, relationship science, and psychotherapy. The second goal is to collate extant work on feedback and advance ideas for adaptive feedback systems that have potential to influence coordination in a way that can enhance the effectiveness of team interactions. In addressing these two goals, this work lays the foundation as well as plans for the future of human-autonomy teams that augment team interactions using coordination-based measures.

Tipping points, multistability, and stochasticity in a two-dimensional traffic network dynamics

Mitigating traffic jams is a critical step for the betterment of the urban transportation system, which comprises a large number of interconnected routes to form an intricate network. To understand distinct features of vehicular traffic flow on a network, a macroscopic two-dimensional traffic network model is proposed incorporating intra-nodal and inter-nodal vehicular interaction. Utilizing the popular techniques of nonlinear dynamics, we investigate the impact of different parameters like occupancy, entry rates, and exit rates of vehicles. The existence of saddle-node, Hopf, homoclinic, Bogdanov-Takens, and cusp bifurcations have been shown using single or biparametric bifurcation diagrams. The occurrences of different multistability (bistability/tristability) phenomena, stochastic switching, and critical transitions are explored in detail. Further, we calculate the possibility of achieving each alternative state using the basin stability metric to characterize multistability. In addition, critical transitions from free flow to congestion are identified at different magnitudes of stochastic fluctuations. The applicability of critical slowing down based generic indicators, e.g., variance, lag-1 autocorrelation, skewness, kurtosis, and conditional heteroskedasticity are investigated to forewarn the critical transition from free flow to traffic congestion. It is demonstrated through the use of simulated data that not all of the measures exhibit sensitivity to rapid phase transitions in traffic flow. Our study reveals that traffic congestion emerges because of either bifurcation or stochasticity. The result provided in this study may serve as a paradigm to understand the qualitative behavior of traffic jams and to explore the tipping mechanisms occurring in transport phenomena.

ScHGSC-IGDC: Identifying genes with differential correlations of high-grade serous ovarian cancer based on single-cell RNA sequencing analysis

Due to the high heterogeneity of ovarian cancer (OC), it occupies the main cause of cancer-related death among women. As the most aggressive and frequent subtype of OC, high-grade serous cancer (HGSC) represents around 70 % of all patients. With the booming progress of single-cell RNA sequencing (scRNA-seq), unique and subtle changes among different cell states have been identified including novel risk genes and pathways. Here, our present study aims to identify differentially correlated core genes between normal and tumor status through HGSC scRNA-seq data analysis. R package high-dimension Weighted Gene Co-expression Network Analysis (hdWGCNA) was implemented for building gene interaction networks based on HGSC scRNA-seq data. DiffCorr was integrated for identifying differentially correlated genes between tumor and their adjacent normal counterparts. Software Cytoscape was implemented for constructing and visualizing biological networks. Real-time qPCR (RT-qPCR) was utilized to confirm expression pattern of new genes. We introduced ScHGSC-IGDC (Identifying Genes with Differential Correlations of HGSC based on scRNA-seq analysis), an in silico framework for identifying core genes in the development of HGSC. We detected thirty-four modules in the network. Scores of new genes with opposite correlations with others such as NDUFS5, TMSB4X, SERPINE2 and ITPR2 were identified. Further survival and literature validation emphasized their great values in the HGSC management. Meanwhile, RT-qPCR verified expression pattern of NDUFS5, TMSB4X, SERPINE2 and ITPR2 in human OC cell lines and tissues. Our research offered novel perspectives on the gene modulatory mechanisms from single cell resolution, guiding network based algorithms in cancer etiology field.

Exceptionally Slow, Long-Range, and Non-Gaussian Critical Fluctuations Dominate the Charge Density Wave Transition

(TaSe_{4})_{2}I is a well-studied quasi-one-dimensional compound long-known to have a charge-density wave (CDW) transition around 263 K. We argue that the critical fluctuations of the pinned CDW order parameter near the transition can be inferred from the resistance noise on account of their coupling to the dissipative normal carriers. Remarkably, the critical fluctuations of the CDW order parameter are slow enough to survive the thermodynamic limit and dominate the low-frequency resistance noise. The noise variance and relaxation time show rapid growth (critical opalescence and critical slowing down) within a temperature window of ϵ≈±0.1, where ϵ is the reduced temperature. This is very wide but consistent with the Ginzburg criterion. We further show that this resistance noise can be quantitatively used to extract the associated critical exponents. Below |ϵ|≲0.02, we observe a crossover from mean-field to a fluctuation-dominated regime with the critical exponents taking anomalously low values. The distribution of fluctuations in the critical transition region is skewed and strongly non-Gaussian. This non-Gaussianity is interpreted as the breakdown of the validity of the central limit theorem as the diverging coherence volume becomes comparable to the macroscopic sample size. The large magnitude critical fluctuations observed over an extended temperature range, as well as the crossover from the mean-field to the fluctuation-dominated regime highlight the role of the quasi-one-dimensional character in controlling the phase transition.

Evaluation of symptom network density as a predictor of treatment outcome of inpatient psychotherapy

OBJECTIVE

The network approach implies that the persistence of a mental disorder is rooted in a dense causal interconnection of symptoms. This study attempts to replicate and generalize previous findings in support of the assumption that higher density predicts poorer outcomes. The study examines the predictive value of network density at admission for recovery after inpatient treatment.

METHOD

N = 1375 adult patients with various forms of mental illness were classified as recovered (28%) versus not recovered (72%) after inpatient treatment. Recovery was defined as clinically significant improvement in impairment from admission to discharge. Networks of transdiagnostic symptoms at the time of admission were estimated. Network density, measured by global strength d, was compared between the recovered and not recovered groups using a permutation test.

RESULTS

Global strength at the time of admission tended to be higher in the No-Recovery group (d = 10.83) than the Recovery group (d = 7.53) but the association was not significant (p = .12). Similar results were found after controlling for group size and symptom severity.

CONCLUSION

The predictive value of network density for treatment outcomes remains unclear. There might be structural differences between the groups that the current measure of network density does not adequately represent.

Age-related behavioral resilience in smartphone touchscreen interaction dynamics

We experience a life that is full of ups and downs. The ability to bounce back after adverse life events such as the loss of a loved one or serious illness declines with age, and such isolated events can even trigger accelerated aging. How humans respond to common day-to-day perturbations is less clear. Here, we infer the aging status from smartphone behavior by using a decision tree regression model trained to accurately estimate the chronological age based on the dynamics of touchscreen interactions. Individuals (N = 280, 21 to 87 y of age) expressed smartphone behavior that appeared younger on certain days and older on other days through the observation period that lasted up to ~4 y. We captured the essence of these fluctuations by leveraging the mathematical concept of critical transitions and tipping points in complex systems. In most individuals, we find one or more alternative stable aging states separated by tipping points. The older the individual, the lower the resilience to forces that push the behavior across the tipping point into an older state. Traditional accounts of aging based on sparse longitudinal data spanning decades suggest a gradual behavioral decline with age. Taken together with our current results, we propose that the gradual age-related changes are interleaved with more complex dynamics at shorter timescales where the same individual may navigate distinct behavioral aging states from one day to the next. Real-world behavioral data modeled as a complex system can transform how we view and study aging.

Resilience indicator traits in 3 dairy cattle breeds in Baden-Württemberg

In recent years, research in animal breeding has increasingly focused on the topic of resilience, which is expected to continue in the future due to the need for high-yielding, healthy, and robust animals. In this context, an established approach is the calculation of resilience indicator traits with time series analyses. Examples are the variance and autocorrelation of daily milk yield in dairy cows. We applied this methodology to the German dairy cow population. Data from the 3 breeds (German Holstein, German Fleckvieh, and German Brown Swiss) were obtained, which included 13,949 lactations from 36 farms from the state Baden-Württemberg in Germany working with automatic milking systems. Using the milk yield data, the daily absolute milk yields, deviations between observed and expected daily milk yields, and relative proportions of daily milk yields in relation to lactation performance were calculated. We used the variance and autocorrelation of these data as phenotypes in our statistical analyses. We estimated a heritability of 0.047 for autocorrelation and heritabilities between 0.026 and 0.183 for variance-based indicator traits. Furthermore, significant breed differences could be observed, with a tendency of better resilience in Brown Swiss. The breed differences can be due to both genetic and environmental factors. A high value of a variance-based indicator trait indicates a low resilience. Performance traits were positively correlated with variance-based indicator traits calculated from absolute daily milk yields, but they were negatively correlated with variance-based indicators calculated from relative daily milk yields. Thus, they can be considered as different traits. Although variance-based indicators based on absolute daily milk yields were affected by the performance level, variance-based indicators based on relative daily milk yields were corrected for the performance level and also showed higher heritabilities. Thus, they seem to be more suitable for practical use. Further studies need to be conducted to calculate the correlations between resilience indicators, functional traits, and health traits.

Limited evidence of autocorrelation signaling upcoming affective episodes: a 12-month e-diary study in patients with bipolar disorder

BACKGROUND

Increased autocorrelation (AR) of system-specific measures has been suggested as a predictor for critical transitions in complex systems. Increased AR of mood scores has been reported to anticipate depressive episodes in major depressive disorder, while other studies found AR increases to be associated with depressive episodes themselves. Data on AR in patients with bipolar disorders (BD) is limited and inconclusive.

METHODS

Patients with BD reported their current mood via daily e-diaries for 12 months. Current affective status (euthymic, prodromal, depressed, (hypo)manic) was assessed in 26 bi-weekly expert interviews. Exploratory analyses tested whether self-reported current mood and AR of the same item could differentiate between prodromal phases or affective episodes and euthymia.

RESULTS

A total of 29 depressive and 20 (hypo)manic episodes were observed in 29 participants with BD. Self-reported current mood was significantly decreased during the two weeks prior to a depressive episode (early prodromal, late prodromal), but not changed prior to manic episodes. The AR was neither a significant predictor for the early or late prodromal phase of depression nor for the early prodromal phase of (hypo)mania. Decreased AR was found in the late prodromal phase of (hypo)mania. Increased AR was mainly found during depressive episodes.

CONCLUSIONS

AR changes might not be better at predicting depressive episodes than simple self-report measures on current mood in patients with BD. Increased AR was mostly found during depressive episodes. Potentially, changes in AR might anticipate (hypo)manic episodes.

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.

From a false sense of safety to resilience under uncertainty

Understanding and acting upon risk is notably challenging, and navigating complexity with understandings developed for stable environments may inadvertently build a false sense of safety. Neglecting the potential for non-linear change or "black swan" events - highly impactful but uncommon occurrences - may lead to naive optimisation under assumed stability, exposing systems to extreme risks. For instance, loss aversion is seen as a cognitive bias in stable environments, but it can be an evolutionarily advantageous heuristic when complete destruction is possible. This paper advocates for better accounting of non-linear change in decision-making by leveraging insights from complex systems and psychological sciences, which help to identify blindspots in conventional decision-making and to develop risk mitigation plans that are interpreted contextually. In particular, we propose a framework using attractor landscapes to visualize and interpret complex system dynamics. In this context, attractors are states toward which systems naturally evolve, while tipping points - critical thresholds between attractors - can lead to profound, unexpected changes impacting a system's resilience and well-being. We present four generic attractor landscape types that provide a novel lens for viewing risks and opportunities, and serve as decision-making contexts. The main practical contribution is clarifying when to emphasize particular strategies - optimisation, risk mitigation, exploration, or stabilization - within this framework. Context-appropriate decision making should enhance system resilience and mitigate extreme risks.

Evolution repeats itself in replicate long-term studies in the wild

The extent to which evolution is repeatable remains debated. Here, we study changes over time in the frequency of cryptic color-pattern morphs in 10 replicate long-term field studies of a stick insect, each spanning at least a decade (across 30 years of total data). We find predictable "up-and-down" fluctuations in stripe frequency in all populations, representing repeatable evolutionary dynamics based on standing genetic variation. A field experiment demonstrates that these fluctuations involve negative frequency-dependent natural selection (NFDS). These fluctuations rely on demographic and selective variability that pushes populations away from equilibrium, such that they can reliably move back toward it via NFDS. Last, we show that the origin of new cryptic forms is associated with multiple structural genomic variants such that which mutations arise affects evolution at larger temporal scales. Thus, evolution from existing variation is predictable and repeatable, but mutation adds complexity even for traits evolving deterministically under natural selection.

Energy landscape analysis and time-series clustering analysis of patient state multistability related to rheumatoid arthritis drug treatment: The KURAMA cohort study

Rheumatoid arthritis causes joint inflammation due to immune abnormalities, resulting in joint pain and swelling. In recent years, there have been considerable advancements in the treatment of this disease. However, only approximately 60% of patients achieve remission. Patients with multifactorial diseases shift between states from day to day. Patients may remain in a good or poor state with few or no transitions, or they may switch between states frequently. The visualization of time-dependent state transitions, based on the evaluation axis of stable/unstable states, may provide useful information for achieving rheumatoid arthritis treatment goals. Energy landscape analysis can be used to quantitatively determine the stability/instability of each state in terms of energy. Time-series clustering is another method used to classify transitions into different groups to identify potential patterns within a time-series dataset. The objective of this study was to utilize energy landscape analysis and time-series clustering to evaluate multidimensional time-series data in terms of multistability. We profiled each patient's state transitions during treatment using energy landscape analysis and time-series clustering. Energy landscape analysis divided state transitions into two patterns: "good stability leading to remission" and "poor stability leading to treatment dead-end." The number of patients whose disease status improved increased markedly until approximately 6 months after treatment initiation and then plateaued after 1 year. Time-series clustering grouped patients into three clusters: "toward good stability," "toward poor stability," and "unstable." Patients in the "unstable" cluster are considered to have clinical courses that are difficult to predict; therefore, these patients should be treated with more care. Early disease detection and treatment initiation are important. The evaluation of state multistability enables us to understand a patient's current state in the context of overall state transitions related to rheumatoid arthritis drug treatment and to predict future state transitions.

Resilience assessment in complex natural systems

Ecological resilience is the capability of an ecosystem to maintain the same structure and function and avoid crossing catastrophic tipping points (i.e. undergoing irreversible regime shifts). While fundamental for management, concrete ways to estimate and interpret resilience in real ecosystems are still lacking. Here, we develop an empirical approach to estimate resilience based on the stochastic cusp model derived from catastrophe theory. The cusp model models tipping points derived from a cusp bifurcation. We extend cusp in order to identify the presence of stable and unstable states in complex natural systems. Our Cusp Resilience Assessment (CUSPRA) has three characteristics: (i) it provides estimates on how likely a system is to cross a tipping point (in the form of a cusp bifurcation) characterized by hysteresis, (ii) it assesses resilience in relation to multiple external drivers and (iii) it produces straightforward results for ecosystem-based management. We validate our approach using simulated data and demonstrate its application using empirical time series of an Atlantic cod population and marine ecosystems in the North Sea and the Mediterranean Sea. We show that Cusp Resilience Assessment is a powerful method to empirically estimate resilience in support of a sustainable management of our constantly adapting ecosystems under global climate change.

Exploring the impact of Anaplasma phagocytophilum on colonization resistance of Ixodes scapularis microbiota using network node manipulation

Upon ingestion from an infected host, tick-borne pathogens (TBPs) have to overcome colonization resistance, a defense mechanism by which tick microbiota prevent microbial invasions. Previous studies have shown that the pathogen Anaplasma phagocytophilum alters the microbiota composition of the nymphs of Ixodes scapularis, but its impact on tick colonization resistance remains unclear. We analyzed tick microbiome genetic data using published Illumina 16S rRNA sequences, assessing microbial diversity within ticks (alpha diversity) through species richness, evenness, and phylogenetic diversity. We compared microbial communities in ticks with and without infection with A. phagocytophilum (beta diversity) using the Bray-Curtis index. We also built co-occurrence networks and used node manipulation to study the impact of A. phagocytophilum on microbial assembly and network robustness, crucial for colonization resistance. We examined network robustness by altering its connectivity, observing changes in the largest connected component (LCC) and the average path length (APL). Our findings revealed that infection with A. phagocytophilum does not significantly alter the overall microbial diversity in ticks. Despite a decrease in the number of nodes and connections within the microbial networks of infected ticks, certain core microbes remained consistently interconnected, suggesting a functional role. The network of infected ticks showed a heightened vulnerability to node removal, with smaller LCC and longer APL, indicating reduced resilience compared to the network of uninfected ticks. Interestingly, adding nodes to the network of infected ticks led to an increase in LCC and a decrease in APL, suggesting a recovery in network robustness, a trend not observed in networks of uninfected ticks. This improvement in network robustness upon node addition hints that infection with A. phagocytophilum might lower ticks' resistance to colonization, potentially facilitating further microbial invasions. We conclude that the compromised colonization resistance observed in tick microbiota following infection with A. phagocytophilum may facilitate co-infection in natural tick populations.

A logic-incorporated gene regulatory network deciphers principles in cell fate decisions

Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.

Decreased Indian Ocean Dipole variability under prolonged greenhouse warming

The Indian Ocean Dipole (IOD) is a major climate variability mode that substantially influences weather extremes and climate patterns worldwide. However, the response of IOD variability to anthropogenic global warming remains highly uncertain. The latest IPCC Sixth Assessment Report concluded that human influences on IOD variability are not robustly detected in observations and twenty-first century climate-model projections. Here, using millennial-length climate simulations, we disentangle forced response and internal variability in IOD change and show that greenhouse warming robustly suppresses IOD variability. On a century time scale, internal variability overwhelms the forced change in IOD, leading to a widespread response in IOD variability. This masking effect is mainly caused by a remote influence of the El Niño-Southern Oscillation. However, on a millennial time scale, nearly all climate models show a long-term weakening trend in IOD variability by greenhouse warming. Our results provide compelling evidence for a human influence on the IOD.

Early warning indicators capture catastrophic transitions driven by explicit rates of environmental change

In response to external changes, ecosystems can undergo catastrophic transitions. Early warning indicators aim to predict such transitions based on the phenomenon of critical slowing down at bifurcation points found under a constant environment. When an explicit rate of environmental change is considered, catastrophic transitions can become distinct phenomena from bifurcations, and result from a delayed response to noncatastrophic bifurcations. We use a trophic metacommunity model where transitions in time series and bifurcations of the system are distinct phenomena. We calculate early warning indicators from the time series of the continually changing system and show that they predict not the bifurcation of the underlying system but the actual catastrophic transition driven by the explicit rate of change. Predictions based on the bifurcation structure could miss catastrophic transitions that can still be captured by early warning signals calculated from time series. Our results expand the repertoire of mechanistic models used to anticipate catastrophic transitions to nonequilibrium ecological systems exposed to a constant rate of environmental change.

Prediction of depinning transitions in interface models using Gini and Kolkata indices

The intermittent dynamics of driven interfaces through disordered media and its subsequent depinning for large enough driving force is a common feature for a myriad of diverse systems, starting from mode-I fracture, vortex lines in superconductors, and magnetic domain walls to invading fluid in a porous medium, to name a few. In this work, we outline a framework that can give a precursory signal of the imminent depinning transition by monitoring the variations in sizes or the inequality of the intermittent responses of a system that are seen prior to the depinning point. In particular, we use measures traditionally used to quantify economic inequality, i.e., the Gini index and the Kolkata index, for the case of the unequal responses of precritical systems. The crossing point of these two indices serves as a precursor to imminent depinning. Given a scale-free size distribution of the responses, we calculate the expressions for these indices, evaluate their crossing points, and give a recipe for forecasting depinning transitions. We apply this method to the Edwards-Wilkinson, Kardar-Parisi-Zhang, and fiber bundle model interface with variable interaction strengths and quenched disorder. The results are applicable for any interface dynamics undergoing a depinning transition. The results also explain previously observed near-universal values of Gini and Kolkata indices in self-organized critical systems.

Balancing multiple management objectives as climate change transforms ecosystems

As climate change facilitates significant and persistent ecological transformations, managing ecosystems according to historical baseline conditions may no longer be feasible. The Resist-Accept-Direct (RAD) framework can guide climate-informed management interventions, but in its current implementations RAD has not yet fully accounted for potential tradeoffs between multiple - sometimes incompatible - ecological and societal goals. Key scientific challenges for informing climate-adapted ecosystem management include (i) advancing our predictive understanding of transformations and their socioecological impacts under novel climate conditions, and (ii) incorporating uncertainty around trajectories of ecological change and the potential success of RAD interventions into management decisions. To promote the implementation of RAD, practitioners can account for diverse objectives within just and equitable participatory decision-making processes.

A taxonomy of multiple stable states in complex ecological communities

Natural systems are built from multiple interconnected units, making their dynamics, functioning and fragility notoriously hard to predict. A fragility scenario of particular relevance concerns so-called regime shifts: abrupt transitions from healthy to degraded ecosystem states. An explanation for these shifts is that they arise as transitions between alternative stable states, a process that is well-understood in few-species models. However, how multistability upscales with system complexity remains a debated question. Here, we identify that four different multistability regimes generically emerge in models of species-rich communities and other archetypical complex biological systems assuming random interactions. Across the studied models, each regime consistently emerges under a specific interaction scheme and leaves a distinct set of fingerprints in terms of the number of observed states, their species richness and their response to perturbations. Our results help clarify the conditions and types of multistability that can be expected to occur in complex ecological communities.

Multistability and intermediate tipping of the Atlantic Ocean circulation

Tipping points (TP) in climate subsystems are usually thought to occur at a well-defined, critical forcing parameter threshold, via destabilization of the system state by a single, dominant positive feedback. However, coupling to other subsystems, additional feedbacks, and spatial heterogeneity may promote further small-amplitude, abrupt reorganizations of geophysical flows at forcing levels lower than the critical threshold. Using a primitive-equation ocean model, we simulate a collapse of the Atlantic Meridional Overturning Circulation (AMOC) due to increasing glacial melt. Considerably before the collapse, various abrupt, qualitative changes in AMOC variability occur. These intermediate tipping points (ITP) are transitions between multiple stable circulation states. Using 2.75 million years of model simulations, we uncover a very rugged stability landscape featuring parameter regions of up to nine coexisting stable states. The path to an AMOC collapse via a sequence of ITPs depends on the rate of change of the meltwater input. This challenges our ability to predict and define safe limits for TPs.

Predicting multiple observations in complex systems through low-dimensional embeddings

Forecasting all components in complex systems is an open and challenging task, possibly due to high dimensionality and undesirable predictors. We bridge this gap by proposing a data-driven and model-free framework, namely, feature-and-reconstructed manifold mapping (FRMM), which is a combination of feature embedding and delay embedding. For a high-dimensional dynamical system, FRMM finds its topologically equivalent manifolds with low dimensions from feature embedding and delay embedding and then sets the low-dimensional feature manifold as a generalized predictor to achieve predictions of all components. The substantial potential of FRMM is shown for both representative models and real-world data involving Indian monsoon, electroencephalogram (EEG) signals, foreign exchange market, and traffic speed in Los Angeles Country. FRMM overcomes the curse of dimensionality and finds a generalized predictor, and thus has potential for applications in many other real-world systems.

Understanding change in benthic marine systems

BACKGROUND

The unprecedented influence of human activities on natural ecosystems in the 21st century has resulted in increasingly frequent large-scale changes in ecological communities. This has heightened interest in understanding such changes and effective means to manage them. Accurate interpretation of state changes is challenging because of difficulties translating theory to empirical study, and most theory emphasizes systems near equilibrium, which may not be relevant in rapidly changing environments.

SCOPE

We review concepts of long-transient stages and phase shifts between stable community states, both smooth, continuous and discontinuous shifts, and the relationships among them. Three principal challenges emerge when applying these concepts. The first is how to interpret observed change in communities - distinguishing multiple stable states from long transients, or reversible shifts in the phase portrait of single attractor systems. The second is how to quantify the magnitudes of three sources of variability that cause switches between community states: (1) 'noise' in species' abundances, (2) 'wiggle' in system parameters and (3) trends in parameters that affect the topography of the basin of attraction. The third challenge is how variability of the system shapes evidence used to interpret community changes. We outline a novel approach using critical length scales to potentially address these challenges. These concepts are highlighted by a review of recent examples involving macroalgae as key players in marine benthic ecosystems.

CONCLUSIONS

Real-world examples show three or more stable configurations of ecological communities may exist for a given set of parameters, and transient stages may persist for long periods necessitating their respective consideration. The characteristic length scale (CLS) is a useful metric that uniquely identifies a community 'basin of attraction', enabling phase shifts to be distinguished from long transients. Variabilities of CLSs and time series data may likewise provide proactive management measures to mitigate phase shifts and loss of ecosystem services. Continued challenges remain in distinguishing continuous from discontinuous phase shifts because their respective dynamics lack unique signatures.

Understanding the Risk of Sexual Reoffending in Adult Men: A Network-Based Model

The predominant approach to understand dynamic risk factors of sexual reoffending has been referred to as the Propensities Model (Thornton, 2016). According to this model, dynamic risk factors can be conceptualized as latent constructs whose change alters the risk of sexual reoffending. Despite its strengths and contributions to research, this model does not offer answers to the question of how dynamic risk factors contribute to the risk of sexual reoffending, or of how sustained change in risk might take place. In this paper we introduce the Network-Based Model of Risk of Sexual Reoffending (NBM-RSR), which addresses several limitations and constraints of the Propensities Model and offers empirically testable propositions regarding the nature and development of the risk of sexual reoffending. The NBM-RSR considers risk of sexual reoffending to involve a self-sustaining network of causally connected dynamic risk factors. Consistent with this, an increased risk of sexual reoffending is characterized through a network that contains more and stronger interconnected dynamic risk factors with a higher strength. Sustained change in risk of sexual reoffending occurs when activity in the network exceeds a critical point resulting in a new self-sustaining network. Propositions based on the NBM-RSR are introduced and translated into testable hypotheses. These propositions revolve around (a) risk of sexual reoffending resulting from the construction of a network of causally connected dynamic risk factors, (b) network stability, sudden changes, and critical transitions, and (c) dynamic risk factors' relative influence on risk of sexual reoffending.

Interactions between water quality and microbes in epiphytic biofilm and superficial sediment of lake in trophic agriculture area

Epiphytic and superficial sediment biofilm-dwelling microbial communities play a pivotal role in water quality regulation and biogeochemical cycling in shallow lakes. However, the interactions are far from clear between water physicochemical parameters and microbial community on aquatic plants and in surface sediments of lake in trophic agriculture area. This study employed Illumina sequencing, Partial Least Squares Path Modeling (PLS-PM), and physico-chemical analytical methods to explore the interactions between water quality and microbes (bacteria and eukaryotes) in three substrates of trophic shallow Lake Cyohoha North, Rwanda. The Lake Cyohoha was significantly polluted with total phosphorus (TP), total nitrogen (TN), nitrate nitrogen (NO3-N), and ammonia nitrogen (NH3-N) in the wet season compared to the dry season. PLS-PM revealed a strong positive correlation (+0.9301) between land use types and physico-chemical variables in the rainy season. In three substrates of the trophic lake, Proteobacteria, Cyanobacteria, Firmicutes, and Actinobacteria were dominant phyla in the bacterial communities, and Rotifers, Platyhelminthes, Gastrotricha, and Ascomycota dominated in microeukaryotic communities. As revealed by null and neutral models, stochastic processes predominantly governed the assembly of bacterial and microeukaryotic communities in biofilms and surface sediments. Network analysis revealed that the microbial interconnections in Ceratophyllum demersum were more stable and complex compared to those in Eichhornia crassipes and sediments. Co-occurrence network analysis (|r| > 0.7, p < 0.05) revealed that there were complex interactions among physicochemical parameters and microbes in epiphytic and sediment biofilms, and many keystone microbes on three substrates played important role in nutrients removal, food web and microbial community stable. These findings emphasize that eutrophic water influence the structure, composition, and interactions of microbes in epiphytic and surface sediment biofilms, and provided new insights into the interconnections between water quality and microbial community in presentative substrates in tropical lacustrine ecosystems in agriculturally polluted areas. The study provides useful information for water quality protection and aquatic plants restoration for policy making and catchment management.

Predation and spatial connectivity interact to shape ecosystem resilience to an ongoing regime shift

Ecosystem regime shifts can have severe ecological and economic consequences, making it a top priority to understand how to make systems more resilient. Theory predicts that spatial connectivity and the local environment interact to shape resilience, but empirical studies are scarce. Here, we use >7000 fish samplings from the Baltic Sea coast to test this prediction in an ongoing, spatially propagating shift in dominance from predatory fish to an opportunistic mesopredator, with cascading effects throughout the food web. After controlling for the influence of other drivers (including increasing mesopredator densities), we find that predatory fish habitat connectivity increases resilience to the shift, but only when densities of fish-eating top predators (seals, cormorants) are low. Resilience also increases with temperature, likely through boosted predatory fish growth and recruitment. These findings confirm theoretical predictions that spatial connectivity and the local environment can together shape resilience to regime shifts.

Anticipating regime shifts by mixing early warning signals from different nodes

Real systems showing regime shifts, such as ecosystems, are often composed of many dynamical elements interacting on a network. Various early warning signals have been proposed for anticipating regime shifts from observed data. However, it is unclear how one should combine early warning signals from different nodes for better performance. Based on theory of stochastic differential equations, we propose a method to optimize the node set from which to construct an early warning signal. The proposed method takes into account that uncertainty as well as the magnitude of the signal affects its predictive performance, that a large magnitude or small uncertainty of the signal in one situation does not imply the signal's high performance, and that combining early warning signals from different nodes is often but not always beneficial. The method performs well particularly when different nodes are subjected to different amounts of dynamical noise and stress.

Critical transitions in the Amazon forest system

The possibility that the Amazon forest system could soon reach a tipping point, inducing large-scale collapse, has raised global concern1-3. For 65 million years, Amazonian forests remained relatively resilient to climatic variability. Now, the region is increasingly exposed to unprecedented stress from warming temperatures, extreme droughts, deforestation and fires, even in central and remote parts of the system1. Long existing feedbacks between the forest and environmental conditions are being replaced by novel feedbacks that modify ecosystem resilience, increasing the risk of critical transition. Here we analyse existing evidence for five major drivers of water stress on Amazonian forests, as well as potential critical thresholds of those drivers that, if crossed, could trigger local, regional or even biome-wide forest collapse. By combining spatial information on various disturbances, we estimate that by 2050, 10% to 47% of Amazonian forests will be exposed to compounding disturbances that may trigger unexpected ecosystem transitions and potentially exacerbate regional climate change. Using examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories, involving different feedbacks and environmental conditions. We discuss how the inherent complexity of the Amazon adds uncertainty about future dynamics, but also reveals opportunities for action. Keeping the Amazon forest resilient in the Anthropocene will depend on a combination of local efforts to end deforestation and degradation and to expand restoration, with global efforts to stop greenhouse gas emissions.

Resilience and high compositional variability reflect the complex response of river waters to global drivers: The Eastern Siberian River Chemistry database

The chemical composition of river waters represents an important matter of investigation to understand environment modifications in response to climate changes and global warming. Prolonged dry periods, heavy flood events, degradation of the lands and ice thawing, modify the chemical composition of river waters influencing the drivers governing the complex dynamics of river catchments where everything comes together. In this framework, Compositional Data Analysis (CoDA) offers methods in which the complex structure of the river water composition and the interrelationships among the various components are put into the proper context for their statistical analysis. In this research, we propose a new CoDA approach combining the robust Mahalanobis distance (D) calculus of ilr-transformed chemical variables and the perturbation difference, both with respect to a pristine compositional benchmark. The aim was to trace the change in the chemical composition of the Eastern Siberian River Chemistry Database where degradation of the permafrost for global warming produces important effects on natural waters. The findings indicate complex multiplicative laws and feedback mechanisms governing solutes in Eastern Siberian rivers, with high values of D found where permafrost is more discontinuous. Perturbations clearly discriminate chemical components more resilient to stresses induced by global changes (Ca2+, Mg2+ and HCO3-) from those whose variability is not maintained under control (Cl-, Na+, SO42-). These outcomes open up a new scenario in searching for spatiotemporal resilience metrics to reveal rivers response to environmental changes.

Retrospective analysis of the pelagic ecosystem of the Western Mediterranean Sea: Drivers, changes and effects

In the Western Mediterranean Sea, forage fishes have changed in abundance, body condition, growth, reproduction, and distribution in the last decades. Different hypotheses have been proposed to explain these changes, including increase in fishing mortality; changes in environmental conditions affecting species fitness, and planktonic productivity and quality; recovery of top predators; and increase in competitors. We investigated the main drivers and changes of the pelagic ecosystem and their effects using an ecosystem-based modelling approach. Specifically, we (1) quantified the potential historical contribution of various drivers of change, (2) investigated changes in temporal trends and spatial distributions of main ecosystem components, and (3) identified ecological consequences of these changes in top predator and competitors, their fisheries and ecosystem traits during 2000-2020. We updated an established Ecopath food-web model representing the Spanish and French Mediterranean sub-areas (GSA06 and GSA07) in 2000 with recent available data. We applied the temporal dynamic Ecosim module, and tested historical time series of fishing effort, fishing mortality and environmental factors as potential drivers. Observed biomass and landings of key species were used to validate model projections. A spatial-temporal Ecospace model was developed to project species distribution changes. Results showed historical biomass and catch changes driven by a combination of high fishing pressure and environmental change (i.e. increase in temperature and salinity, and decline in primary productivity). Small pelagic fish showed significant temporal changes and predicted shifts in their distributions, following a latitudinal gradient. Predators and competitors showed changes as well, displaying heterogeneous spatial patterns, while fisheries landings declined. Overall, results matched observations (e.g., decline of sardine, fluctuations of anchovy and increases in bluefin tuna) and illustrated the need to complement traditional assessments with integrative frameworks to move towards an ecosystem-based approach in the Mediterranean. They also highlighted important knowledge gaps to guide future research in the region.

Climate-driven invasion and incipient warnings of kelp ecosystem collapse

Climate change is progressively redistributing species towards the Earth's poles, indicating widespread potential for ecosystem collapse. Detecting early-warning-signals and enacting adaptation measures is therefore a key imperative for humanity. However, detecting early-warning signals has remained elusive and has focused on exceptionally high-frequency and/ or long-term time-series, which are generally unattainable for most ecosystems that are under-sampled and already impacted by warming. Here, we show that a catastrophic phase-shift in kelp ecosystems, caused by range-extension of an overgrazing sea urchin, also propagates poleward. Critically, we show that incipient spatial-pattern-formations of kelp overgrazing are detectable well-in-advance of collapse along temperate reefs in the ocean warming hotspot of south-eastern Australia. Demonstrating poleward progression of collapse over 15 years, these early-warning 'incipient barrens' are now widespread along 500 km of coast with projections indicating that half of all kelp beds within this range-extension region will collapse by ~2030. Overgrazing was positively associated with deep boulder-reefs, yet negatively associated with predatory lobsters and subordinate abalone competitors, which have both been intensively fished. Climate-driven collapse of ecosystems is occurring; however, by looking equatorward, space-for-time substitutions can enable practical detection of early-warning spatial-pattern-formations, allowing local climate adaptation measures to be enacted in advance.

Cities of the Anthropocene: urban sustainability in an eco-evolutionary perspective

Cities across the globe are driving systemic change in social and ecological systems by accelerating the rates of interactions and intensifying the links between human activities and Earth's ecosystems, thereby expanding the scale and influence of human activities on fundamental processes that sustain life. Increasing evidence shows that cities not only alter biodiversity, they change the genetic makeup of many populations, including animals, plants, fungi and microorganisms. Urban-driven rapid evolution in species traits might have significant effects on socially relevant ecosystem functions such as nutrient cycling, pollination, water and air purification and food production. Despite increasing evidence that cities are causing rapid evolutionary change, current urban sustainability strategies often overlook these dynamics. The dominant perspectives that guide these strategies are essentially static, focusing on preserving biodiversity in its present state or restoring it to pre-urban conditions. This paper provides a systemic overview of the socio-eco-evolutionary transition associated with global urbanization. Using examples of observed changes in species traits that play a significant role in maintaining ecosystem function and resilience, I propose that these evolutionary changes significantly impact urban sustainability. Incorporating an eco-evolutionary perspective into urban sustainability science and planning is crucial for effectively reimagining the cities of the Anthropocene. This article is part of the theme issue 'Evolution and sustainability: gathering the strands for an Anthropocene synthesis'.

Diversity-induced trivialization and resilience of neural dynamics

Heterogeneity is omnipresent across all living systems. Diversity enriches the dynamical repertoire of these systems but remains challenging to reconcile with their manifest robustness and dynamical persistence over time, a fundamental feature called resilience. To better understand the mechanism underlying resilience in neural circuits, we considered a nonlinear network model, extracting the relationship between excitability heterogeneity and resilience. To measure resilience, we quantified the number of stationary states of this network, and how they are affected by various control parameters. We analyzed both analytically and numerically gradient and non-gradient systems modeled as non-linear sparse neural networks evolving over long time scales. Our analysis shows that neuronal heterogeneity quenches the number of stationary states while decreasing the susceptibility to bifurcations: a phenomenon known as trivialization. Heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in network size and connection probability by quenching the system's dynamic volatility.

Landscape quantifies the intermediate state and transition dynamics in ecological networks

Understanding the ecological mechanisms associated with the collapse and restoration is especially critical in promoting harmonious coexistence between humans and nature. So far, it remains challenging to elucidate the mechanisms of stochastic dynamical transitions for ecological systems. Using an example of plant-pollinator network, we quantified the energy landscape of ecological system. The landscape displays multiple attractors characterizing the high, low and intermediate abundance stable states. Interestingly, we detected the intermediate states under pollinator decline, and demonstrated the indispensable role of the intermediate state in state transitions. From the landscape, we define the barrier height (BH) as a global quantity to evaluate the transition feasibility. We propose that the BH can serve as a new early-warning signal (EWS) for upcoming catastrophic breakdown, which provides an earlier and more accurate warning signal than traditional metrics based on time series. Our results promote developing better management strategies to achieve environmental sustainability.

Heterogeneous associations of gut microbiota with Crohn's disease activity

The multi-factorial involvement of gut microbiota with Crohn's disease (CD) necessitates robust analysis to uncover possible associations with particular microbes. CD has been linked to specific bacteria, but reported associations vary widely across studies. This inconsistency may result from heterogeneous associations across individual patients, resulting in no apparent or only weak relationships with the means of bacterial abundances. We investigated the relationship between bacterial relative abundances and disease activity in a longitudinal cohort of CD patients (n = 57) and healthy controls (n = 15). We applied quantile regression, a statistical technique that allows investigation of possible relationships outside the mean response. We found several significant and mostly negative associations with CD, especially in lower quantiles of relative abundance on family or genus level. Associations found by quantile regression deviated from the mean response in relative abundances of Coriobacteriaceae, Pasteurellaceae, Peptostreptococcaceae, Prevotellaceae, and Ruminococcaceae. For the family Streptococcaceae we found a significant elevation in relative abundance for patients experiencing an exacerbation relative to those who remained without self-reported symptoms or measurable inflammation. Our analysis suggests that specific bacterial families are related to CD and exacerbation, but associations vary between patients due to heterogeneity in disease course, medication history, therapy response, gut microbiota composition and historical contingency. Our study underscores that microbial diversity is reduced in the gut of CD patients, but suggests that the process of diversity loss is rather irregular with respect to specific taxonomic groups. This novel insight may advance our ecological understanding of this complex disease.