Reversal of a cyanobacterial bloom in response to early warnings

FateNet: an integration of dynamical systems and deep learning for cell fate prediction

MOTIVATION

Understanding cellular decision-making, particularly its timing and impact on the biological system such as tissue health and function, is a fundamental challenge in biology and medicine. Existing methods for inferring fate decisions and cellular state dynamics from single-cell RNA sequencing data lack precision regarding decision points and broader tissue implications. Addressing this gap, we present FateNet, a computational approach integrating dynamical systems theory and deep learning to probe the cell decision-making process using scRNA-seq data.

RESULTS

By leveraging information about normal forms and scaling behavior near bifurcations common to many dynamical systems, FateNet predicts cell decision occurrence with higher accuracy than conventional methods and offers qualitative insights into the new state of the biological system. Also, through in-silico perturbation experiments, FateNet identifies key genes and pathways governing the differentiation process in hematopoiesis. Validated using different scRNA-seq data, FateNet emerges as a user-friendly and valuable tool for predicting critical points in biological processes, providing insights into complex trajectories.

AVAILABILITY AND IMPLEMENTATION

github.com/ThomasMBury/fatenet.

Phosphorus-driven regime shift from heterotrophic to autotrophic diazotrophs in a deep alpine lake

Biological nitrogen fixation plays a critical role in maintaining primary production, particularly in systematic nitrogen deficiency. However, little is known about the dynamics within diazotrophic community facing ongoing nutrient enrichment in freshwater lakes. Here, a consecutive five-year investigation on diazotrophic community was conducted in Lake Fuxian, an oligotrophic deep alpine lake on the trajectory to eutrophic state. Results showed a regime shift from heterotrophic to autotrophic diazotrophs induced by total phosphorus (TP) enrichment. Specifically, heterotrophic diazotrophs dominated the diazotrophic community when TP was lower than 21.8 μg/L, whereas heterotrophic diazotrophs or diazotrophic Cyanobacteria randomly dominated when TP ranged between 21.8 μg/L and 28.8 μg/L. When TP was higher than 28.8 μg/L, diazotrophic Cyanobacteria accounted for 60.4%-97.7% of the total N2-fixers, indicating diazotrophic biodiversity significantly declined under TP enrichment scenario. Moreover, the dominance of diazotrophic Cyanobacteria further facilitated phytoplankton growth, which strengthened positive feedback between phytoplankton and phosphorus under nitrogen deficiency conditions. This is the first report on the threshold-like state responses of freshwater diazotrophs to environmental drivers. Our study expands the knowledge of the diazotrophic dynamics in freshwater ecosystems and contributes quantitative evidence of ecological thresholds for future environmental policymaking.

Predicting discrete-time bifurcations with deep learning

Many natural and man-made systems are prone to critical transitions-abrupt and potentially devastating changes in dynamics. Deep learning classifiers can provide an early warning signal for critical transitions by learning generic features of bifurcations from large simulated training data sets. So far, classifiers have only been trained to predict continuous-time bifurcations, ignoring rich dynamics unique to discrete-time bifurcations. Here, we train a deep learning classifier to provide an early warning signal for the five local discrete-time bifurcations of codimension-one. We test the classifier on simulation data from discrete-time models used in physiology, economics and ecology, as well as experimental data of spontaneously beating chick-heart aggregates that undergo a period-doubling bifurcation. The classifier shows higher sensitivity and specificity than commonly used early warning signals under a wide range of noise intensities and rates of approach to the bifurcation. It also predicts the correct bifurcation in most cases, with particularly high accuracy for the period-doubling, Neimark-Sacker and fold bifurcations. Deep learning as a tool for bifurcation prediction is still in its nascence and has the potential to transform the way we monitor systems for critical transitions.

Early warnings for multi-stage transitions in dynamics on networks

Successfully anticipating sudden major changes in complex systems is a practical concern. Such complex systems often form a heterogeneous network, which may show multi-stage transitions in which some nodes experience a regime shift earlier than others as an environment gradually changes. Here we investigate early warning signals for networked systems undergoing a multi-stage transition. We found that knowledge of both the ongoing multi-stage transition and network structure enables us to calculate effective early warning signals for multi-stage transitions. Furthermore, we found that small subsets of nodes could anticipate transitions as well as or even better than using all the nodes. Even if we fix the network and dynamical system, no single best subset of nodes provides good early warning signals, and a good choice of sentinel nodes depends on the tipping direction and the current stage of the dynamics within a multi-stage transition, which we systematically characterize.

Long-range dependence and extreme values of precipitation, phosphorus load, and Cyanobacteria

Extreme daily values of precipitation (1939-2021), discharge (1991-2021), phosphorus (P) load (1994-2021), and phycocyanin, a pigment of Cyanobacteria (June 1-September 15 of 2008-2021) are clustered as multi-day events for Lake Mendota, Wisconsin. Long-range dependence, or memory, is the shortest for precipitation and the longest for phycocyanin. Extremes are clustered for all variates and those of P load and phycocyanin are most strongly clustered. Extremes of P load are predictable from extremes of precipitation, and precipitation and P load are correlated with later concentrations of phycocyanin. However, time delays from 1 to 60 d were found between P load extremes and the next extreme phycocyanin event within the same year of observation. Although most of the lake's P enters in extreme events, blooms of Cyanobacteria may be sustained by recycling and food web processes.

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

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

Evaluating the performance of temporal and spatial early warning statistics of algal blooms

Regime shifts have large consequences for ecosystems and the services they provide. However, understanding the potential for, causes of, proximity to, and thresholds for regime shifts in nearly all settings is difficult. Generic statistical indicators of resilience have been proposed and studied in a wide range of ecosystems as a method to detect when regime shifts are becoming more likely without direct knowledge of underlying system dynamics or thresholds. These early warning statistics (EWS) have been studied separately but there have been few examples that directly compare temporal and spatial EWS in ecosystem-scale empirical data. To test these methods, we collected high-frequency time series and high-resolution spatial data during a whole-lake fertilization experiment while also monitoring an adjacent reference lake. We calculated two common EWS, standard deviation and autocorrelation, in both time series and spatial data to evaluate their performance prior to the resulting algal bloom. We also applied the quickest detection method to generate binary alarms of resilience change from temporal EWS. One temporal EWS, rolling window standard deviation, provided advanced warning in most variables prior to the bloom, showing trends and between-lake patterns consistent with theory. In contrast, temporal autocorrelation and both measures of spatial EWS (spatial SD, Moran's  I) provided little or no warning. By compiling time series data from this and past experiments with and without nutrient additions, we were able to evaluate temporal EWS performance for both constant and changing resilience conditions. True positive alarm rates were 2.5-8.3 times higher for rolling window standard deviation when a lake was being pushed towards a bloom than the rate of false positives when it was not. For rolling window autocorrelation, alarm rates were much lower and no variable had a higher true positive than false positive alarm rate. Our findings suggest temporal EWS provide advanced warning of algal blooms and that this approach could help managers prepare for and/or minimize negative bloom impacts.

A whole-ecosystem experiment reveals flow-induced shifts in a stream community

The growing threat of abrupt and irreversible changes to the functioning of freshwater ecosystems compels robust measures of tipping point thresholds. To determine benthic cyanobacteria regime shifts in a potable water supply system in the tropical Andes, we conducted a whole ecosystem-scale experiment in which we systematically diverted 20 to 90% of streamflow and measured ecological responses. Benthic cyanobacteria greatly increased with a 60% flow reduction and this tipping point was related to water temperature and nitrate concentration increases, both known to boost algal productivity. We supplemented our experiment with a regional survey collecting > 1450 flow-benthic algal measurements at streams varying in water abstraction levels. We confirmed the tipping point flow value, albeit at a slightly lower threshold (40-50%). A global literature review broadly confirmed our results with a mean tipping point at 58% of flow reduction. Our study provides robust in situ demonstrations of regime shift thresholds in running waters with potentially strong implications for environmental flows management.

Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River

A data-driven approach to characterizing the risk of cyanobacteria-based harmful algal blooms (cyanoHABs) was undertaken for the Ohio River. Twenty-five years of river discharge data were used to develop Bayesian regression models that are currently applicable to 20 sites spread-out along the entire 1579 km of the river’s length. Two site-level prediction models were developed based on the antecedent flow conditions of the two blooms that occurred on the river in 2015 and 2019: one predicts if the current year will have a bloom (the occurrence model), and another predicts bloom persistence (the persistence model). Predictors for both models were based on time-lagged average flow exceedances and a site’s characteristic residence time under low flow conditions. Model results are presented in terms of probabilities of occurrence or persistence with uncertainty. Although the occurrence of the 2019 bloom was well predicted with the modeling approach, the limited number of events constrained formal model validation. However, as a measure of performance, leave-one-out cross validation returned low misclassification rates, suggesting that future years with flow time series like the previous bloom years will be correctly predicted and characterized for persistence potential. The prediction probabilities are served in real time as a component of a risk characterization tool/web application. In addition to presenting the model’s results, the tool was designed with visualization options for studying water quality trends among eight river sites currently collecting data that could be associated with or indicative of bloom conditions. The tool is made accessible to river water quality professionals to support risk communication to stakeholders, as well as serving as a real-time water data monitoring utility.

Vulnerable Waters are Essential to Watershed Resilience

Watershed resilience is the ability of a watershed to maintain its characteristic system state while concurrently resisting, adapting to, and reorganizing after hydrological (for example, drought, flooding) or biogeochemical (for example, excessive nutrient) disturbances. Vulnerable waters include non-floodplain wetlands and headwater streams, abundant watershed components representing the most distal extent of the freshwater aquatic network. Vulnerable waters are hydrologically dynamic and biogeochemically reactive aquatic systems, storing, processing, and releasing water and entrained (that is, dissolved and particulate) materials along expanding and contracting aquatic networks. The hydrological and biogeochemical functions emerging from these processes affect the magnitude, frequency, timing, duration, storage, and rate of change of material and energy fluxes among watershed components and to downstream waters, thereby maintaining watershed states and imparting watershed resilience. We present here a conceptual framework for understanding how vulnerable waters confer watershed resilience. We demonstrate how individual and cumulative vulnerable-water modifications (for example, reduced extent, altered connectivity) affect watershed-scale hydrological and biogeochemical disturbance response and recovery, which decreases watershed resilience and can trigger transitions across thresholds to alternative watershed states (for example, states conducive to increased flood frequency or nutrient concentrations). We subsequently describe how resilient watersheds require spatial heterogeneity and temporal variability in hydrological and biogeochemical interactions between terrestrial systems and down-gradient waters, which necessitates attention to the conservation and restoration of vulnerable waters and their downstream connectivity gradients. To conclude, we provide actionable principles for resilient watersheds and articulate research needs to further watershed resilience science and vulnerable-water management.

Structure-based identification of sensor species for anticipating critical transitions

Ecological systems can undergo sudden, catastrophic changes known as critical transitions. Anticipating these critical transitions remains challenging in systems with many species because the associated early warning signals can be weakly present or even absent in some species, depending on the system dynamics. Therefore, our limited knowledge of ecological dynamics may suggest that it is hard to identify those species in the system that display early warning signals. Here, we show that, in mutualistic ecological systems, it is possible to identify species that early anticipate critical transitions by knowing only the system structure-that is, the network topology of plant-animal interactions. Specifically, we leverage the mathematical theory of structural observability of dynamical systems to identify a minimum set of "sensor species," whose measurement guarantees that we can infer changes in the abundance of all other species. Importantly, such a minimum set of sensor species can be identified by using the system structure only. We analyzed the performance of such minimum sets of sensor species for detecting early warnings using a large dataset of empirical plant-pollinator and seed-dispersal networks. We found that species that are more likely to be sensors tend to anticipate earlier critical transitions than other species. Our results underscore how knowing the structure of multispecies systems can improve our ability to anticipate critical transitions.

Nano-Sized Polystyrene at 1 mg/L Concentrations Does Not Show Strong Disturbance on the Freshwater Microbial Community

In recent years, microplastics and nanoplastics have gained public attention, but their impacts on the freshwater microbial communities is rarely evaluated. In this study, the effects of 1 mg/L nano-sized polystyrene (nPS) and its modified forms (carboxyl-modified and amino-modified nPS) on the structures and functions of freshwater microbial community were determined. The nPS were found to slightly reduce the chlorophyll-a and increase the phycocyanin contents of freshwater microbial communities. Moreover, the richness of the microbial communities temporarily decreased during this process, while their diversity remained uninfluenced by treatment with nPS. Although the three tested nPS types were found to disturb the compositions of both the prokaryotic and eukaryotic communities to some degree, they did not affect the functions of freshwater bacterial communities significantly due to functional redundancy. Our study demonstrated that the ecotoxicities of the nPS itself were found to be lower than what is generally expected.

Assessing cyanobacterial frequency and abundance at surface waters near drinking water intakes across the United States

This study presents the first large-scale assessment of cyanobacterial frequency and abundance of surface water near drinking water intakes across the United States. Public water systems serve drinking water to nearly 90% of the United States population. Cyanobacteria and their toxins may degrade the quality of finished drinking water and can lead to negative health consequences. Satellite imagery can serve as a cost-effective and consistent monitoring technique for surface cyanobacterial blooms in source waters and can provide drinking water treatment operators information for managing their systems. This study uses satellite imagery from the European Space Agency's Ocean and Land Colour Instrument (OLCI) spanning June 2016 through April 2020. At 300-m spatial resolution, OLCI imagery can be used to monitor cyanobacteria in 685 drinking water sources across 285 lakes in 44 states, referred to here as resolvable drinking water sources. First, a subset of satellite data was compared to a subset of responses (n = 84) submitted as part of the U.S. Environmental Protection Agency's fourth Unregulated Contaminant Monitoring Rule (UCMR 4). These UCMR 4 qualitative responses included visual observations of algal bloom presence and absence near drinking water intakes from March 2018 through November 2019. Overall agreement between satellite imagery and UCMR 4 qualitative responses was 94% with a Kappa coefficient of 0.70. Next, temporal frequency of cyanobacterial blooms at all resolvable drinking water sources was assessed. In 2019, bloom frequency averaged 2% and peaked at 100%, where 100% indicated a bloom was always present at the source waters when satellite imagery was available. Monthly cyanobacterial abundances were used to assess short-term trends across all resolvable drinking water sources and effect size was computed to provide insight on the number of years of data that must be obtained to increase confidence in an observed change. Generally, 2016 through 2020 was an insufficient time period for confidently observing changes at these source waters; on average, a decade of satellite imagery would be required for observed environmental trends to outweigh variability in the data. However, five source waters did demonstrate a sustained short-term trend, with one increasing in cyanobacterial abundance from June 2016 to April 2020 and four decreasing.

Predicting algal blooms: Are we overlooking groundwater?

Significant advances in understanding and predicting freshwater algal bloom dynamics have emerged in response to both increased occurrence and financial burden of nuisance and harmful blooms. Several factors have been highlighted as key controls of bloom occurrence, including nutrient dynamics, local hydrology, climatic perturbations, watershed geomorphology, biogeochemistry, food-web control, and algal competition. However, a major research gap continues to be the degree to which groundwater inputs modulate microbial biomass production and food-web dynamics at the terrestrial-aquatic interface. We present a synthesis of groundwater related algal bloom literature, upon which we derive a foundational hypothesis: long residence times cause groundwater to be geochemically and biologically distinct from surface water, allowing groundwater inputs to modulate algal bloom dynamics (growth, decline, toxicity) through its control over in-stream water chemistry. Distinct groundwater chemistry can support or prevent algal blooms, depending on specific local conditions. We highlight three mechanisms that influence the impact of groundwater discharge on algal growth: 1) redox state of the subsurface, 2) extent of water-rock interactions, and 3) stability of groundwater discharge. We underscore that in testing hypotheses related to groundwater control over algal blooms, it is critical to understand how changes in land use, water management, and climate will influence groundwater dynamics and, thus, algal bloom probabilities. Given this challenge, we argue that advances in both modeling and data integration, including genomics data and integrated process-based models that capture groundwater dynamics, are needed to illuminate mechanistic controls and improve predictions of algal blooms.

Suppression of phosphorus release from sediment using lanthanum carbonate as amendment

The performance of lanthanum carbonate (LC) pertaining to the adsorption of phosphate (H_wPO₄^w-3) was investigated, and the possible adsorption mechanism was elucidated. The stabilization of H_wPO₄^w-3 adsorbed to LC was evaluated. The influence of LC addition on the upward transport of phosphorus (P) from sediment to overlying water (OL-W) was studied, and the adsorption performance of H_wPO₄^w-3 on the LC-amended sediment was explored. The results of this work indicated that LC performed well in the elimination of H_wPO₄^w-3 from water in the pH range of 4 to 11, and the commercial and self-prepared LC samples afforded the maximum H_wPO₄^w-3 adsorption capacities of 57.9 and 99.4 mg P/g, respectively, at pH 7. The presence of coexisting species including chloride, bicarbonate, and sulfate had a small influence on the H_wPO₄^w-3 adsorption onto LC. The main H_wPO₄^w-3 adsorption mechanism of LC at pH 7 was the ligand exchange reaction between carbonate and H_wPO₄^w-3 forming the inner-sphere La-phosphate complexation. The self-synthesized LC exhibited much higher H_wPO₄^w-3 adsorption performance than the commercial LC. The overwhelming majority (> 97.0%) of H_wPO₄^w-3 adsorbed to LC primarily existed in the form of muriatic acid-extractable P, which has relatively low re-releasing risk. The addition of LC into sediment could significantly prevent the release of P from the sediment solid into the OL-W, thereby leading to a lower concentration level of reactive soluble P (RSP) in the OL-W compared with no LC treatment. The addition of LC into sediment could greatly improve the H_wPO₄^w-3 uptake ability for the sediment, and the enhancement of H_wPO₄^w-3 adsorption onto the sediment by the added LC increased as the increase of the amendment dosage and the initial H_wPO₄^w-3 concentration. All results suggest that LC could serve as a promising amendment material for the control of sedimentary P release.

Detecting and distinguishing tipping points using spectral early warning signals

Theory and observation tell us that many complex systems exhibit tipping points—thresholds involving an abrupt and irreversible transition to a contrasting dynamical regime. Such events are commonly referred to as critical transitions. Current research seeks to develop early warning signals (EWS) of critical transitions that could help prevent undesirable events such as ecosystem collapse. However, conventional EWS do not indicate the type of transition, since they are based on the generic phenomena of critical slowing down. For instance, they may fail to distinguish the onset of oscillations (e.g. Hopf bifurcation) from a transition to a distant attractor (e.g. Fold bifurcation). Moreover, conventional EWS are less reliable in systems with density-dependent noise. Other EWS based on the power spectrum (spectral EWS) have been proposed, but they rely upon spectral reddening, which does not occur prior to critical transitions with an oscillatory component. Here, we use Ornstein–Uhlenbeck theory to derive analytic approximations for EWS prior to each type of local bifurcation, thereby creating new spectral EWS that provide greater sensitivity to transition proximity; higher robustness to density-dependent noise and bifurcation type; and clues to the type of approaching transition. We demonstrate the advantage of applying these spectral EWS in concert with conventional EWS using a population model, and show that they provide a characteristic signal prior to two different Hopf bifurcations in data from a predator–prey chemostat experiment. The ability to better infer and differentiate the nature of upcoming transitions in complex systems will help humanity manage critical transitions in the Anthropocene Era.

Forecasting resilience profiles of the run-up to regime shifts in nearly-one-dimensional systems

The forecasting of sudden, irreversible shifts in natural systems is a challenge of great importance, whose realization could allow pre-emptive action to be taken to avoid or mitigate catastrophic transitions, or to help systems adapt to them. In recent years, there have been many advances in the development of such early warning signals. However, much of the current toolbox is based around the tracking of statistical trends and therefore does not aim to estimate the future time scale of transitions or resilience loss. Metric-based indicators are also difficult to implement when systems have inherent oscillations which can dominate the indicator statistics. To resolve these gaps in the toolbox, we use additional system properties to fit parsimonious models to dynamics in order to predict transitions. Here, we consider nearly-one-dimensional systems-higher dimensional systems whose dynamics can be accurately captured by one-dimensional discrete time maps. We show how the nearly one-dimensional dynamics can be used to produce model-based indicators for critical transitions which produce forecasts of the resilience and the time of transitions in the system. A particularly promising feature of this approach is that it allows us to construct early warning signals even for critical transitions of chaotic systems. We demonstrate this approach on two model systems: of phosphorous recycling in a shallow lake, and of an overcompensatory fish population.

Long-term digital device-enabled monitoring of functional status: Implications for management of persons with Alzheimer's disease

INTRODUCTION

Informal caregiving is an essential element of health-care delivery. Little data describes how caregivers structure care recipients' lives and impact their functional status.

METHODS

We performed observational studies of community dwelling persons with dementia (PWD) to measure functional status by simultaneous assessment of physical activity (PA) and lifespace (LS). We present data from two caregiver/care-recipient dyads representing higher and average degrees of caregiver involvement.

RESULTS

We acquired >42,800 (subject 1); >41,300 (subject 2) PA data points and >154,500 (subject 1); >119,700 (subject 2) LS data points over 15 months of near continuous observation. PA and LS patterns provided insights into the caregiver's role in structuring the PWD's day-to-day function and change in function over time.

DISCUSSION

We show that device-enabled functional monitoring (FM) can successfully gather and display data at resolutions required for dementia care studies. Objective quantification of individual caregiver/care-recipient dyads provides opportunities to implement patient-centered care.

Tipping positive change

Tipping points exist in social, ecological and climate systems and those systems are increasingly causally intertwined in the Anthropocene. Climate change and biosphere degradation have advanced to the point where we are already triggering damaging environmental tipping points, and to avoid worse ones ahead will require finding and triggering positive tipping points towards sustainability in coupled social, ecological and technological systems. To help with that I outline how tipping points can occur in continuous dynamical systems and in networks, the causal interactions that can occur between tipping events across different types and scales of system-including the conditions required to trigger tipping cascades, the potential for early warning signals of tipping points, and how they could inform deliberate tipping of positive change. In particular, the same methods that can provide early warning of damaging environmental tipping points can be used to detect when a socio-technical or socio-ecological system is most sensitive to being deliberately tipped in a desirable direction. I provide some example targets for such deliberate tipping of positive change. This article is part of the theme issue 'Climate change and ecosystems: threats, opportunities and solutions'.

Stochastic disturbance regimes alter patterns of ecosystem variability and recovery

Altered ecosystem variability is an important ecological response to disturbance yet understanding of how various attributes of disturbance regimes affect ecosystem variability is limited. To improve the framework for understanding the disturbance regime attributes that affect ecosystem variability, we examine how the introduction of stochasticity to disturbance parameters (frequency, severity and extent) alters simulated recovery when compared to deterministic outcomes from a spatially explicit simulation model. We also examine the agreement between results from empirical studies and deterministic and stochastic configurations of the model. We find that stochasticity in disturbance frequency and spatial extent leads to the greatest increase in the variance of simulated dynamics, although stochastic severity also contributes to departures from the deterministic case. The incorporation of stochasticity in disturbance attributes improves agreement between empirical and simulated responses, with 71% of empirical responses correctly classified by stochastic configurations of the model as compared to 47% using the purely deterministic model. By comparison, only 2% of empirical responses were correctly classified by the deterministic model and misclassified by stochastic configurations of the model. These results indicate that stochasticity in the attributes of a disturbance regime alters the patterns and classification of ecosystem variability, suggesting altered recovery dynamics. Incorporating stochastic disturbance processes into models may thus be critical for anticipating the ecological resilience of ecosystems.

Effect of time series length and resolution on abundance- and trait-based early warning signals of population declines

Natural populations are increasingly threatened with collapse at the hands of anthropogenic effects. Predicting population collapse with the help of generic early warning signals (EWS) may provide a prospective tool for identifying species or populations at highest risk. However, pattern-to-process methods such as EWS have a multitude of challenges to overcome to be useful, including the low signal-to-noise ratio of ecological systems and the need for high quality time series data. The inclusion of trait dynamics with EWS has been proposed as a more robust tool to predict population collapse. However, the length and resolution of available time series are highly variable from one system to another, especially when generation time is considered. As yet, it remains unknown how this variability with regards to generation time will alter the efficacy of EWS. Here we take both a simulation- and experimental-based approach to assess the impacts of relative time series length and resolution on the forecasting ability of EWS. We show that EWS' performance decreases with decreasing time-series length. However, there was no evident decrease in EWS performance as resolution decreased. Our simulations suggest a relative time series length between 10 and five generations as a minimum requirement for accurate forecasting by abundance-based EWS. However, when trait information is included alongside abundance-based EWS, we find positive signals at lengths one-half of what was required without them. We suggest that, in systems where specific traits are known to affect demography, trait data should be monitored and included alongside abundance data to improve forecasting reliability.

Research questions to facilitate the future development of European long-term ecosystem research infrastructures: A horizon scanning exercise

Distributed environmental research infrastructures are important to support assessments of the effects of global change on landscapes, ecosystems and society. These infrastructures need to provide continuity to address long-term change, yet be flexible enough to respond to rapid societal and technological developments that modify research priorities. We used a horizon scanning exercise to identify and prioritize emerging research questions for the future development of ecosystem and socio-ecological research infrastructures in Europe. Twenty research questions covered topics related to (i) ecosystem structures and processes, (ii) the impacts of anthropogenic drivers on ecosystems, (iii) ecosystem services and socio-ecological systems and (iv), methods and research infrastructures. Several key priorities for the development of research infrastructures emerged. Addressing complex environmental issues requires the adoption of a whole-system approach, achieved through integration of biotic, abiotic and socio-economic measurements. Interoperability among different research infrastructures needs to be improved by developing standard measurements, harmonizing methods, and establishing capacities and tools for data integration, processing, storage and analysis. Future research infrastructures should support a range of methodological approaches including observation, experiments and modelling. They should also have flexibility to respond to new requirements, for example by adjusting the spatio-temporal design of measurements. When new methods are introduced, compatibility with important long-term data series must be ensured. Finally, indicators, tools, and transdisciplinary approaches to identify, quantify and value ecosystem services across spatial scales and domains need to be advanced.

Feedback Regulation between Aquatic Microorganisms and the Bloom-Forming Cyanobacterium Microcystis aeruginosa

The frequency and intensity of cyanobacterial blooms are increasing worldwide. Interactions between toxic cyanobacteria and aquatic microorganisms need to be critically evaluated to understand microbial drivers and modulators of the blooms. In this study, we applied 16S/18S rRNA gene sequencing and metabolomics analyses to measure the microbial community composition and metabolic responses of the cyanobacterium Microcystis aeruginosa in a coculture system receiving dissolved inorganic nitrogen and phosphorus (DIP) close to representative concentrations in Lake Taihu, China. M. aeruginosa secreted alkaline phosphatase using a DIP source produced by moribund and decaying microorganisms when the P source was insufficient. During this process, M. aeruginosa accumulated several intermediates in energy metabolism pathways to provide energy for sustained high growth rates and increased intracellular sugars to enhance its competitive capacity and ability to defend itself against microbial attack. It also produced a variety of toxic substances, including microcystins, to inhibit metabolite formation via energy metabolism pathways of aquatic microorganisms, leading to a negative effect on bacterial and eukaryotic microbial richness and diversity. Overall, compared with the monoculture system, the growth of M. aeruginosa was accelerated in coculture, while the growth of some cooccurring microorganisms was inhibited, with the diversity and richness of eukaryotic microorganisms being more negatively impacted than those of prokaryotic microorganisms. These findings provide valuable information for clarifying how M. aeruginosa can potentially modulate its associations with other microorganisms, with ramifications for its dominance in aquatic ecosystems.IMPORTANCE We measured the microbial community composition and metabolic responses of Microcystis aeruginosa in a microcosm coculture system receiving dissolved inorganic nitrogen and phosphorus (DIP) close to the average concentrations in Lake Taihu. In the coculture system, DIP is depleted and the growth and production of aquatic microorganisms can be stressed by a lack of DIP availability. M. aeruginosa could accelerate its growth via interactions with specific cooccurring microorganisms and the accumulation of several intermediates in energy metabolism-related pathways. Furthermore, M. aeruginosa can decrease the carbohydrate metabolism of cooccurring aquatic microorganisms and thus disrupt microbial activities in the coculture. This also had a negative effect on bacterial and eukaryotic microbial richness and diversity. Microcystin was capable of decreasing the biomass of total phytoplankton in aquatic microcosms. Overall, compared to the monoculture, the growth of total aquatic microorganisms is inhibited, with the diversity and richness of eukaryotic microorganisms being more negatively impacted than those of prokaryotic microorganisms. The only exception is M. aeruginosa in the coculture system, whose growth was accelerated.

Time scale of resilience loss: Implications for managing critical transitions in water quality

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

Harnessing tipping points in complex ecological networks

Complex and nonlinear ecological networks can exhibit a tipping point at which a transition to a global extinction state occurs. Using real-world mutualistic networks of pollinators and plants as prototypical systems and taking into account biological constraints, we develop an ecologically feasible strategy to manage/control the tipping point by maintaining the abundance of a particular pollinator species at a constant level, which essentially removes the hysteresis associated with a tipping point. If conditions are changing so as to approach a tipping point, the management strategy we describe can prevent sudden drastic changes. Additionally, if the system has already moved past a tipping point, we show that a full recovery can occur for reasonable parameter changes only if there is active management of abundance, again due essentially to removal of the hysteresis. This recovery point in the aftermath of a tipping point can be predicted by a universal, two-dimensional reduced model.

The fungicide azoxystrobin promotes freshwater cyanobacterial dominance through altering competition

BACKGROUND

Sharp increases in food production worldwide are attributable to agricultural intensification aided by heavy use of agrochemicals. This massive use of pesticides and fertilizers in combination with global climate change has led to collateral damage in freshwater systems, notably an increase in the frequency of harmful cyanobacterial blooms (HCBs). The precise mechanisms and magnitude of effects that pesticides exert on HCBs formation and proliferation have received little research attention and are poorly constrained.

RESULTS

We found that azoxystrobin (AZ), a common strobilurin fungicide, can favor cyanobacterial growth through growth inhibition of eukaryotic competitors (Chlorophyta) and possibly by inhibiting cyanobacterial parasites (fungi) as well as pathogenic bacteria and viruses. Meta-transcriptomic analyses identified AZ-responsive genes and biochemical pathways in eukaryotic plankton and bacteria, potentially explaining the microbial effects of AZ.

CONCLUSIONS

Our study provides novel mechanistic insights into the intertwined effects of a fungicide and eutrophication on microbial planktonic communities and cyanobacterial blooms in a eutrophic freshwater ecosystem. This knowledge may prove useful in mitigating cyanobacteria blooms resulting from agricultural intensification.

Iron limitation effects on nitrogen-fixing organisms with possible implications for cyanobacterial blooms

Cyanobacteria-dominated harmful algal blooms are increasing in occurrence. Many of the taxa contributing to these blooms are capable of fixing atmospheric nitrogen and should be favored under conditions of low nitrogen availability. Yet, synthesizing nitrogenase, the enzyme responsible for nitrogen fixation, is energetically expensive and requires substantial concentrations of iron. Phosphorus addition to nitrogen poor streams should promote nitrogen fixation, but experimental results so far have been inconclusive, suggesting that other factors may be involved in controlling this process. With iron potentially limited in many streams, we examined the influence of phosphorus-iron colimitation on the community structure of nitrogen-fixing organisms. In stream microcosms, using microscopic and molecular sequence data, we observed: (i) the greatest abundance of heterocyst forming nitrogen-fixing cyanobacteria in low nitrogen treatments with high phosphorus and iron and (ii) greater abundance of non-photosynthetic nitrogen-fixing bacteria in treatments with nitrogen compared to those without it. We also found that comparisons between molecular results and those obtained from microscopic identification provided complementary information about cyanobacterial communities. Our investigation indicates the potential for phosphorus-iron colimitation of stream nitrogen-fixing organisms.

Disentangling reporting and disease transmission

Second-order statistics such as the variance and autocorrelation can be useful indicators of the stability of randomly perturbed systems, in some cases providing early warning of an impending, dramatic change in the system’s dynamics. One specific application area of interest is the surveillance of infectious diseases. In the context of disease (re-)emergence, a goal could be to have an indicator that is informative of whether the system is approaching the epidemic threshold, a point beyond which a major outbreak becomes possible. Prior work in this area has provided some proof of this principle but has not analytically treated the effect of imperfect observation on the behavior of indicators. This work provides expected values for several moments of the number of reported cases, where reported cases follow a binomial or negative binomial distribution with a mean based on the number of deaths in a birth-death-immigration process over some reporting interval. The normalized second factorial moment and the decay time of the number of reported cases are two indicators that are insensitive to the reporting probability. Simulation is used to show how this insensitivity could be used to distinguish a trend of increased reporting from a trend of increased transmission. The simulation study also illustrates both the high variance of estimates and the possibility of reducing the variance by averaging over an ensemble of estimates from multiple time series.

Early warning signals for critical transitions in cardiopulmonary health, related to air pollution in an urban Chinese population

Respiratory, and cardio-cerebrovascular health-related diseases significantly threaten human health and together with air pollution form a complex pathophysiological system. Other complex biological systems show that increased variance and autocorrelations in time series may act as valid early warning signals for critical transitions. On population level, we determined the likelihood that increased variance and autocorrelation of hospital visit on cardiopulmonary disease preceded critical transitions in population health by human-pollution interactions. We investigated long-term hospital visits from a hospital in Nanjing City, China during 2006-2016 for the most important cardiopulmonary diseases likely to be influenced by air pollution: cerebrovascular accident disease (CVAD), coronary artery disease (CAD), chronic obstructive pulmonary disease (COPD), lung cancer disease (LCD), and the grouped categories of respiratory system disease (RESD) and cardio-cerebrovascular system disease (CCD). The time series of standard deviations (SDs) and autocorrelation at-lag-1 (AR-1) were studied as potential Early-Warning Indicators (EWIs) of transitions in population health. Elevated SDs provided an early warning for critical transitions in visit for LCD and overall CCD and CVAD, for the period of 2012-2013, after which a real transition of increased visit occurred for these disease categories. Statistical testing showed that these SDs were significantly increased (p < 0.1). The long-term air pollution together with intermittent pollution episodes may have triggered critical transitions in population health for cardiopulmonary disease. It is recommended to consider significant increases in variability in time series of relevant system parameters, such as visit, as early warning signs for future transitions in populations' health states.

Temporal and spatial distribution of Microcystis biomass and genotype in bloom areas of Lake Taihu

Cyanobacterial blooms as a global environmental issue are of public health concern. In this study, we investigated the spatial (10 sites) and temporal (June, August and October) variations in: 1) their biomass based on chlorophyll-a (chl-a) concentration, 2) their toxic genotype based on gene copy ratio of mcyJ to cpcBA, and 3) their cpcBA genotype composition of Microcystis during cyanobacterial bloom in Lake Taihu. While spatial-temporal variations were found in chl-a and mcyJ/cpcBA ratio, only spatial variation was observed in cpcBA genotype composition. Samples from northwestern part had a higher chl-a, but mcyJ/cpcBA ratio didn't vary among the sites. High chl-a was observed in August, while mcyJ/cpcBA ratio and genotypic richness increased with time. The spatial variations in chl-a and mcyJ/cpcBA ratio and temporal variation in cpcBA genotype were correlated negatively with dissolved N and positively with dissolved P. Spatial distribution of Microcystis biomass was positively correlated with nitrite and P excluding October, but no correlation was found for spatial distribution of mcyJ/cpcBA ratio and cpcBA genotype. Spatial distribution of toxic and cpcBA genotypes may result from horizontal transport of Microcystis colonies, while spatial variation in Microcystis biomass was probably controlled by both nutrient-mediated growth and horizontal transport of Microcystis. The temporal variation in Microcystis biomass, toxic genotype and cpcBA genotype composition were related to nutrient levels, but cause-and-effect relationships require further study.

Engineered nanoparticles interact with nutrients to intensify eutrophication in a wetland ecosystem experiment

Despite the rapid rise in diversity and quantities of engineered nanomaterials produced, the impacts of these emerging contaminants on the structure and function of ecosystems have received little attention from ecologists. Moreover, little is known about how manufactured nanomaterials may interact with nutrient pollution in altering ecosystem productivity, despite the recognition that eutrophication is the primary water quality issue in freshwater ecosystems worldwide. In this study, we asked two main questions: (1) To what extent do manufactured nanoparticles affect the biomass and productivity of primary producers in wetland ecosystems? (2) How are these impacts mediated by nutrient pollution? To address these questions, we examined the impacts of a citrate-coated gold nanoparticle (AuNPs) and of a commercial pesticide containing Cu(OH)₂ nanoparticles (CuNPs) on aquatic primary producers under both ambient and enriched nutrient conditions. Wetland mesocosms were exposed repeatedly with low concentrations of nanoparticles and nutrients over the course of a 9-month experiment in an effort to replicate realistic field exposure scenarios. In the absence of nutrient enrichment, there were no persistent effects of AuNPs or CuNPs on primary producers or ecosystem productivity. However, when combined with nutrient enrichment, both NPs intensified eutrophication. When either of these NPs were added in combination with nutrients, algal blooms persisted for >50 d longer than in the nutrient-only treatment. In the AuNP treatment, this shift from clear waters to turbid waters led to large declines in both macrophyte growth and rates of ecosystem gross primary productivity (average reduction of 52% ± 6% and 92% ± 5%, respectively) during the summer. Our results suggest that nutrient status greatly influences the ecosystem-scale impact of two emerging contaminants and that synthetic chemicals may be playing an under-appreciated role in the global trends of increasing eutrophication. We provide evidence here that chronic exposure to Au and Cu(OH)₂ nanoparticles at low concentrations can intensify eutrophication of wetlands and promote the occurrence of algal blooms.

Experimental evidence of spatial signatures of approaching regime shifts in macroalgal canopies

Developing early warning signals to predict regime shifts in ecosystems is a central issue in current ecological research. While there are many studies addressing temporal early warning indicators, research into spatial indicators is far behind, with field experiments even more rare. Here, we tested the performance of spatial early warning signals in an intertidal macroalgal system, where removal of algal canopies pushed the system toward a tipping point (corresponding to approximately 75% of canopy loss), marking the transition between a canopy- to a turf-dominated state. We performed a two-year experiment where spatial early warning indicators were assessed in transects where the canopy was differentially removed (from 0 to 100%). Unlike Moran correlation coefficient at lag-1, spatial variance, skewness, and spatial spectra at low frequency increased along the gradient of canopy degradation and dropped, or did not show any further increase beyond the transition point from a canopy- to a turf-dominated state (100% canopy removal). Our study provides direct evidence of the suitability of spatial early warning signals to anticipate regime shifts in natural ecosystems, emphasizing the importance of field experiments as a powerful tool to establish causal relationships between environmental stressors and early warning indicators.

Indicators of transitions in biological systems

In the face of global biodiversity declines, predicting the fate of biological systems is a key goal in ecology. One popular approach is the search for early warning signals (EWSs) based on alternative stable states theory. In this review, we cover the theory behind nonlinearity in dynamic systems and techniques to detect the loss of resilience that can indicate state transitions. We describe the research done on generic abundance-based signals of instability that are derived from the phenomenon of critical slowing down, which represent the genesis of EWSs research. We highlight some of the issues facing the detection of such signals in biological systems - which are inherently complex and show low signal-to-noise ratios. We then document research on alternative signals of instability, including measuring shifts in spatial autocorrelation and trait dynamics, and discuss potential future directions for EWSs research based on detailed demographic and phenotypic data. We set EWSs research in the greater field of predictive ecology and weigh up the costs and benefits of simplicity vs. complexity in predictive models, and how the available data should steer the development of future methods. Finally, we identify some key unanswered questions that, if solved, could improve the applicability of these methods.

Critical dynamics in population vaccinating behavior

Complex adaptive systems exhibit characteristic dynamics near tipping points such as critical slowing down (declining resilience to perturbations). We studied Twitter and Google search data about measles from California and the United States before and after the 2014–2015 Disneyland, California measles outbreak. We find critical slowing down starting a few years before the outbreak. However, population response to the outbreak causes resilience to increase afterward. A mathematical model of measles transmission and population vaccine sentiment predicts the same patterns. Crucially, critical slowing down begins long before a system actually reaches a tipping point. Thus, it may be possible to develop analytical tools to detect populations at heightened risk of a future episode of widespread vaccine refusal.

Vaccine refusal can lead to renewed outbreaks of previously eliminated diseases and even delay global eradication. Vaccinating decisions exemplify a complex, coupled system where vaccinating behavior and disease dynamics influence one another. Such systems often exhibit critical phenomena—special dynamics close to a tipping point leading to a new dynamical regime. For instance, critical slowing down (declining rate of recovery from small perturbations) may emerge as a tipping point is approached. Here, we collected and geocoded tweets about measles–mumps–rubella vaccine and classified their sentiment using machine-learning algorithms. We also extracted data on measles-related Google searches. We find critical slowing down in the data at the level of California and the United States in the years before and after the 2014–2015 Disneyland, California measles outbreak. Critical slowing down starts growing appreciably several years before the Disneyland outbreak as vaccine uptake declines and the population approaches the tipping point. However, due to the adaptive nature of coupled behavior–disease systems, the population responds to the outbreak by moving away from the tipping point, causing “critical speeding up” whereby resilience to perturbations increases. A mathematical model of measles transmission and vaccine sentiment predicts the same qualitative patterns in the neighborhood of a tipping point to greatly reduced vaccine uptake and large epidemics. These results support the hypothesis that population vaccinating behavior near the disease elimination threshold is a critical phenomenon. Developing new analytical tools to detect these patterns in digital social data might help us identify populations at heightened risk of widespread vaccine refusal.