The probability of epidemic burnout in the stochastic SIR model with vital dynamics.

We present an approach to computing the probability of epidemic "burnout," i.e., the probability that a newly emergent pathogen will go extinct after a major epidemic. Our analysis is based on the standard stochastic formulation of the Susceptible-Infectious-Removed (SIR) epidemic model including host demography (births and deaths) and corresponds to the standard SIR ordinary differential equations (ODEs) in the infinite population limit. Exploiting a boundary layer approximation to the ODEs and a birth-death process approximation to the stochastic dynamics within the boundary layer, we derive convenient, fully analytical approximations for the burnout probability. We demonstrate-by comparing with computationally demanding individual-based stochastic simulations and with semi-analytical approximations derived previously-that our fully analytical approximations are highly accurate for biologically plausible parameters. We show that the probability of burnout always decreases with increased mean infectious period. However, for typical biological parameters, there is a relevant local minimum in the probability of persistence as a function of the basic reproduction number [Formula: see text]. For the shortest infectious periods, persistence is least likely if [Formula: see text]; for longer infectious periods, the minimum point decreases to [Formula: see text]. For typical acute immunizing infections in human populations of realistic size, our analysis of the SIR model shows that burnout is almost certain in a well-mixed population, implying that susceptible recruitment through births is insufficient on its own to explain disease persistence.

Quantifying heterogeneity to drug response in cancer-stroma kinetics.

A crucial challenge in medicine is choosing which drug (or combination) will be the most advantageous for a particular patient. Usually, drug response rates differ substantially, and the reasons for this response unpredictability remain ambiguous. Consequently, it is central to classify features that contribute to the observed drug response variability. Pancreatic cancer is one of the deadliest cancers with limited therapeutic achievements due to the massive presence of stroma that generates an environment that enables tumor growth, metastasis, and drug resistance. To understand the cancer-stroma cross talk within the tumor microenvironment and to develop personalized adjuvant therapies, there is a necessity for effective approaches that offer measurable data to monitor the effect of drugs at the single-cell level. Here, we develop a computational approach, based on cell imaging, that quantifies the cellular cross talk between pancreatic tumor cells (L3.6pl or AsPC1) and pancreatic stellate cells (PSCs), coordinating their kinetics in presence of the chemotherapeutic agent gemcitabine. We report significant heterogeneity in the organization of cellular interactions in response to the drug. For L3.6pl cells, gemcitabine sensibly decreases stroma-stroma interactions but increases stroma-cancer interactions, overall enhancing motility and crowding. In the AsPC1 case, gemcitabine promotes the interactions among tumor cells, but it does not affect stroma-cancer interplay, possibly suggesting a milder effect of the drug on cell dynamics.

Universal Poisson statistics of a passive tracer diffusing in dilute active suspensions.

The statistics of a passive tracer immersed in a suspension of active particles (swimmers) is derived from first principles by considering a perturbative expansion of the tracer interaction with the microscopic swimmer field. To first order in the swimmer density, the tracer statistics is shown to be exactly represented by a spatial Poisson process combined with independent tracer-swimmer scattering events, rigorously reducing the multiparticle dynamics to two-body interactions. The Poisson representation is valid in any dimension, for arbitrary interaction forces and for a large class of swimmer dynamics. The framework not only allows for the systematic calculation of the tracer statistics in various dynamical regimes but highlights in particular surprising universal features that are independent of the swimmer dynamics such as a time-independent velocity distribution.

Polymer folding through active processes recreates features of genome organization.

From proteins to chromosomes, polymers fold into specific conformations that control their biological function. Polymer folding has long been studied with equilibrium thermodynamics, yet intracellular organization and regulation involve energy-consuming, active processes. Signatures of activity have been measured in the context of chromatin motion, which shows spatial correlations and enhanced subdiffusion only in the presence of adenosine triphosphate. Moreover, chromatin motion varies with genomic coordinate, pointing toward a heterogeneous pattern of active processes along the sequence. How do such patterns of activity affect the conformation of a polymer such as chromatin? We address this question by combining analytical theory and simulations to study a polymer subjected to sequence-dependent correlated active forces. Our analysis shows that a local increase in activity (larger active forces) can cause the polymer backbone to bend and expand, while less active segments straighten out and condense. Our simulations further predict that modest activity differences can drive compartmentalization of the polymer consistent with the patterns observed in chromosome conformation capture experiments. Moreover, segments of the polymer that show correlated active (sub)diffusion attract each other through effective long-ranged harmonic interactions, whereas anticorrelations lead to effective repulsions. Thus, our theory offers nonequilibrium mechanisms for forming genomic compartments, which cannot be distinguished from affinity-based folding using structural data alone. As a first step toward exploring whether active mechanisms contribute to shaping genome conformations, we discuss a data-driven approach.

Microscopic mechanisms of cooperative communications within single nanocatalysts.

Catalysis is a method of accelerating chemical reactions that is critically important for fundamental research as well as for industrial applications. It has been recently discovered that catalytic reactions on metal nanoparticles exhibit cooperative effects. The mechanism of these observations, however, remains not well understood. In this work, we present a theoretical investigation on possible microscopic origin of cooperative communications in nanocatalysts. In our approach, the main role is played by positively charged holes on metal surfaces. A corresponding discrete-state stochastic model for the dynamics of holes is developed and explicitly solved. It is shown that the observed spatial correlation lengths are given by the average distances migrated by the holes before they disappear, while the temporal memory is determined by their lifetimes. Our theoretical approach is able to explain the universality of cooperative communications as well as the effect of external electric fields. Theoretical predictions are in agreement with experimental observations. The proposed theoretical framework quantitatively clarifies some important aspects of the microscopic mechanisms of heterogeneous catalysis.

Murburn posttranslational modifications of proteins: Cellular redox processes and murzyme-mediated metabolo-proteomics.

Murburn concept constitutes the thesis that diffusible reactive species or DRS are obligatorily involved in routine metabolic and physiological activities. Murzymes are defined as biomolecules/proteins that generate/modulate/sustain/utilize DRS. Murburn posttranslational modifications (PTMs) result because murburn/murzyme functionalism is integral to cellular existence. Cells must incorporate the inherently stochastic nature of operations mediated by DRS. Due to the earlier/inertial stigmatic perception that DRS are mere agents of chaos, several such outcomes were either understood as deterministic modulations sponsored by house-keeping enzymes or deemed as unregulated nonenzymatic events resulting out of "oxidative stress". In the current review, I dispel the myths around DRS-functions, and undertake systematic parsing and analyses of murburn modifications of proteins. Although it is impossible to demarcate all PTMs into the classical or murburn modalities, telltale signs of the latter are evident from the relative inaccessibility of the locus, non-specificities and mechanistic details. It is pointed out that while many murburn PTMs may be harmless, some others could have deleterious or beneficial physiological implications. Some details of reversible/irreversible modifications of amino acid residues and cofactors that may be subjected to phosphorylation, halogenation, glycosylation, alkylation/acetylation, hydroxylation/oxidation, etc. are listed, along with citations of select proteins where such modifications have been reported. The contexts of these modifications and their significance in (patho)physiology/aging and therapy are also presented. With more balanced explorations and statistically verified data, a definitive understanding of normal versus pathological contexts of murburn modifications would be obtainable in the future.

Mycorrhizal Symbiosis Increases Plant Phylogenetic Diversity and Regulates Community Assembly in Grasslands.

The intricate mechanisms controlling plant diversity and community composition are cornerstone of ecological understanding. Yet, the role of mycorrhizal symbiosis in influencing community composition has often been underestimated. Here, we use extensive species survey data from 1315 grassland sites in China to elucidate the influence of mycorrhizal symbiosis on plant phylogenetic diversity and community assembly. We show that increasing mycorrhizal symbiotic potential leads to greater phylogenetic dispersion within plant communities. Mycorrhizal species predominantly influence deterministic processes, suggesting a role in niche-based community assembly. Conversely, non-mycorrhizal species exert a stronger influence on stochastic processes, highlighting the importance of random events in shaping community structure. These results underscore the crucial but often hidden role of mycorrhizal symbiosis in driving plant community diversity and assembly. This study provides valuable insights into the mechanisms shaping ecological communities and the way for more informed conservation that acknowledges the complex interplay between symbiosis and community dynamics.

Divergent Assembly Processes of Phyllosphere and Rhizosphere Microbial Communities Along Environmental Gradient.

The underlying assembly processes of surface microbial communities are crucial for host plants and ecosystem functions. However, the relative importance of stochastic and deterministic processes in shaping epiphytic microbes remains poorly understood in both the phyllosphere and rhizosphere. Here, we compared the spatial variations in epiphytic microbial communities of two dominant grasses along a 1400 km transect on the Tibetan Plateau and assessed the assembly processes between the phyllosphere and rhizosphere. We found significant variations in epiphytic microbial community compositions between plant compartments and host species. Stochastic processes (drift and homogenizing dispersal) predominantly shaped microbial communities in both the phyllosphere and rhizosphere, with a greater contribution of stochastic processes in the phyllosphere. As environmental heterogeneity intensified, we found a transition from stochasticity to determinism in affecting the microbial assembly. This transition to homogeneous or variable selection depended on plant compartments and host species. Our study is among the first to compare the contribution of stochastic versus deterministic processes to epiphytic community assembly between the phyllosphere and rhizosphere on the Tibetan Plateau. These findings advance our knowledge of epiphytic microbial assembly and disentangle how host plants exploit the microbiome for improved performance and functioning in stressful alpine ecosystems.

Community assembly of organisms regulates soil microbial functional potential through dual mechanisms.

Unraveling the influence of community assembly processes on soil ecosystem functioning presents a major challenge in the field of theoretical ecology, as it has received limited attention. Here, we used a series of long-term experiments spanning over 25 years to explore the assembly processes of bacterial, fungal, protist, and nematode communities using high-throughput sequencing. We characterized the soil microbial functional potential by the abundance of microbial genes associated with carbon, nitrogen, phosphorus, and sulfur cycling using GeoChip-based functional gene profiling, and determined how the assembly processes of organism groups regulate soil microbial functional potential through community diversity and network stability. Our results indicated that balanced fertilization (NPK) treatment improved the stochastic assembly of bacterial, fungal, and protist communities compared to phosphorus-deficient fertilization (NK) treatment. However, there was a nonsignificant increase in the normalized stochasticity ratio of the nematode community in response to fertilization across sites. Our findings emphasized that soil environmental factors influenced the assembly processes of the biotic community, which regulated soil microbial functional potential through dual mechanisms. One mechanism indicated that the high phosphorus levels and low soil nutrient stoichiometry may increase the stochasticity of bacterial, fungal, and protist communities and the determinism of the nematode community under NPK treatment, ultimately enhancing soil microbial functional potential by reinforcing the network stability of the biotic community. The other mechanism indicated that the low phosphorus levels and high soil nutrient stoichiometry may increase the stochastic process of the bacterial community and the determinism of the fungal, protist, and nematode communities under NK treatment, thereby enhancing soil microbial functional potential by improving the ß-diversity of the biotic community. Taken together, these results provide valuable insights into the mechanisms underlying the assembly processes of the biotic community that regulate ecosystem functioning.

Local Community Assembly Mechanisms and the Size of Species Pool Jointly Explain the Beta Diversity of Soil Fungi.

Fungi play vital regulatory roles in terrestrial ecosystems. Local community assembly mechanisms, including deterministic and stochastic processes, as well as the size of regional species pools (gamma diversity), typically influence overall soil microbial community beta diversity patterns. However, there is limited evidence supporting their direct and indirect effects on beta diversity of different soil fungal functional groups in forest ecosystems. To address this gap, we collected 1606 soil samples from a 25-ha subtropical forest plot in southern China. Our goal was to determine the direct effects and indirect effects of regional species pools on the beta diversity of soil fungi, specifically arbuscular mycorrhizal (AM), ectomycorrhizal (EcM), plant-pathogenic, and saprotrophic fungi. We quantified the effects of soil properties, mycorrhizal tree abundances, and topographical factors on soil fungal diversity. The beta diversity of plant-pathogenic fungi was predominantly influenced by the size of the species pool. In contrast, the beta diversity of EcM fungi was primarily driven indirectly through community assembly processes. Neither of them had significant effects on the beta diversity of AM and saprotrophic fungi. Our results highlight that the direct and indirect effects of species pools on the beta diversity of soil functional groups of fungi can significantly differ even within a relatively small area. They also demonstrate the independent and combined effects of various factors in regulating the diversities of soil functional groups of fungi. Consequently, it is crucial to study the fungal community not only as a whole but also by considering different functional groups within the community.

Stability against fluctuations: a two-dimensional study of scaling, bifurcations and spontaneous symmetry breaking in stochastic models of synaptic plasticity.

Stochastic models of synaptic plasticity must confront the corrosive influence of fluctuations in synaptic strength on patterns of synaptic connectivity. To solve this problem, we have proposed that synapses act as filters, integrating plasticity induction signals and expressing changes in synaptic strength only upon reaching filter threshold. Our earlier analytical study calculated the lifetimes of quasi-stable patterns of synaptic connectivity with synaptic filtering. We showed that the plasticity step size in a stochastic model of spike-timing-dependent plasticity (STDP) acts as a temperature-like parameter, exhibiting a critical value below which neuronal structure formation occurs. The filter threshold scales this temperature-like parameter downwards, cooling the dynamics and enhancing stability. A key step in this calculation was a resetting approximation, essentially reducing the dynamics to one-dimensional processes. Here, we revisit our earlier study to examine this resetting approximation, with the aim of understanding in detail why it works so well by comparing it, and a simpler approximation, to the system's full dynamics consisting of various embedded two-dimensional processes without resetting. Comparing the full system to the simpler approximation, to our original resetting approximation, and to a one-afferent system, we show that their equilibrium distributions of synaptic strengths and critical plasticity step sizes are all qualitatively similar, and increasingly quantitatively similar as the filter threshold increases. This increasing similarity is due to the decorrelation in changes in synaptic strength between different afferents caused by our STDP model, and the amplification of this decorrelation with larger synaptic filters.

Microbial difference and its influencing factors in ice-covered lakes on the three poles.

Most lakes in the world are permanently or seasonally covered with ice. However, little is known about the distribution of microbes and their influencing factors in ice-covered lakes worldwide. Here we analyzed the microbial community composition in the waters of 14 ice-covered lakes in the Hoh Xil region of northern Qing-Tibetan Plateau (QTP), and conducted a meta-analysis by integrating published microbial community data of ice-covered lakes in the tripolar regions (the Arctic, Antarctica and QTP). The results showed that there were significant differences in microbial diversity, community composition and distribution patterns in the ice-covered tripolar lakes. Microbial diversity and richness were lower in the ice-covered QTP lakes (including the studied lakes in the Hoh Xil region) than those in the Arctic and Antarctica. In the ice-covered lakes of Hoh Xil, prokaryotes are mainly involved in S-metabolic processes, making them more adaptable to extreme environmental conditions. In contrast, prokaryotes in the ice-covered lakes of the Arctic and Antarctica were predominantly involved in carbon/nitrogen metabolic processes. Deterministic (salinity and nutrients) and stochastic processes (dispersal limitation, homogenizing dispersal and drift) jointly determine the geographical distribution patterns of microorganisms in ice-covered lakes, with stochastic processes dominating. These results expand the understanding of microbial diversity, distribution patterns, and metabolic processes in polar ice-covered lakes.

Stochastic transmission in epidemiological models.

Recent empirical evidence suggests that the transmission coefficient in susceptible-exposed-infected-removed-like (SEIR-like) models evolves with time, presenting random patterns, and some stylized facts, such as mean-reversion and jumps. To address such observations we propose the use of jump-diffusion stochastic processes to parameterize the transmission coefficient in an SEIR-like model that accounts for death and time-dependent parameters. We provide a detailed theoretical analysis of the proposed model proving the existence and uniqueness of solutions as well as studying its asymptotic behavior. We also compare the proposed model with some variations possibly including jumps. The forecast performance of the considered models, using reported COVID-19 infections from New York City, is then tested in different scenarios. Despite the simplicity of the epidemiological model, by considering stochastic transmission, the forecasted scenarios were fairly accurate.

Single-particle diffusional fingerprinting: A machine-learning framework for quantitative analysis of heterogeneous diffusion.

Single-particle tracking (SPT) is a key tool for quantitative analysis of dynamic biological processes and has provided unprecedented insights into a wide range of systems such as receptor localization, enzyme propulsion, bacteria motility, and drug nanocarrier delivery. The inherently complex diffusion in such biological systems can vary drastically both in time and across systems, consequently imposing considerable analytical challenges, and currently requires an a priori knowledge of the system. Here we introduce a method for SPT data analysis, processing, and classification, which we term "diffusional fingerprinting." This method allows for dissecting the features that underlie diffusional behavior and establishing molecular identity, regardless of the underlying diffusion type. The method operates by isolating 17 descriptive features for each observed motion trajectory and generating a diffusional map of all features for each type of particle. Precise classification of the diffusing particle identity is then obtained by training a simple logistic regression model. A linear discriminant analysis generates a feature ranking that outputs the main differences among diffusional features, providing key mechanistic insights. Fingerprinting operates by both training on and predicting experimental data, without the need for pretraining on simulated data. We found this approach to work across a wide range of simulated and experimentally diverse systems, such as tracked lipases on fat substrates, transcription factors diffusing in cells, and nanoparticles diffusing in mucus. This flexibility ultimately supports diffusional fingerprinting's utility as a universal paradigm for SPT diffusional analysis and prediction.

Unified Shape and External Load State Estimation for Continuum Robots.

Continuum robots navigate narrow, winding passageways while safely and compliantly interacting with their environments. Sensing the robot's shape under these conditions is often done indirectly, using a few coarsely distributed (e.g. strain or position) sensors combined with the robot's mechanics-based model. More recently, given high-fidelity shape data, external interaction loads along the robot have been estimated by solving an inverse problem on the mechanics model of the robot. In this paper, we argue that since shape and force are fundamentally coupled, they should be estimated simultaneously in a statistically principled approach. We accomplish this by applying continuous-time batch estimation directly to the arclength domain. A general continuum robot model serves as a statistical prior which is fused with discrete, noisy measurements taken along the robot's backbone. The result is a continuous posterior containing both shape and load functions of arclength, as well as their uncertainties. We first test the approach with a Cosserat rod, i.e. the underlying modeling framework that is the basis for a variety of continuum robots. We verify our approach numerically using distributed loads with various sensor combinations. Next, we experimentally validate shape and external load errors for highly concentrated force distributions (point loads). Finally, we apply the approach to a tendon-actuated continuum robot demonstrating applicability to more complex actuated robots.

The combination of multiple environmental stressors strongly alters microbial community assembly in aquatic ecosystems.

Microorganisms play a critical role in maintaining the delicate balance of ecosystem services. However, the assembly processes that shape microbial communities are vulnerable to a range of environmental stressors, such as climate change, eutrophication, and the use of herbicides. Despite the importance of these stressors, little is known about their cumulative impacts on microbial community assembly in aquatic ecosystems. To address this knowledge gap, we established 48 mesocosm experiments that simulated shallow lake ecosystems and subjected them to warming (including continuous warming (W) and heat waves (H)), glyphosate-based herbicides (G), and nutrient loading (E). Our study revealed that in the control group, both deterministic and stochastic processes codominated the assembly of microbial communities in water, whereas in sediment, the processes were primarily stochastic. Interestingly, the effects of multiple stress factors on assembly in these two habitats were completely opposite. Specifically, stressors promoted the dominance of stochastic processes in water but increased the importance of deterministic processes in sediment. Furthermore, warming amplified the effects of herbicides but exerted an opposite and stronger influence on assembly compared to nutrients, emphasizing the complexity of these mechanisms and the significance of considering multiple stressors. The interaction of some factors significantly affected assembly (p < 0.05), with the effects of WEG being most pronounced in water. Both water and sediment exhibited homogeneous assembly of microbial communities (mean NTI >0), but the phylogenetic clustering of microbial communities in water was more closely related (NTI >2). Our research revealed the response model of microbial community assembly in aquatic ecosystems to multiple environmental stresses, such as agricultural pollution, climate change, and eutrophication, and indicated that microbial community changes in sediment may be an important predictor of lake ecosystem development. This provides scientific evidence that better environmental management can reduce impacts on aquatic ecosystems under the threat of future warming.

Functional Formulation of Quantum Theory of a Scalar Field in a Metric with Lorentzian and Euclidean Signatures.

We study the Schrödinger equation in quantum field theory (QFT) in its functional formulation. In this approach, quantum correlation functions can be expressed as classical expectation values over (complex) stochastic processes. We obtain a stochastic representation of the Schrödinger time evolution on Wentzel-Kramers-Brillouin (WKB) states by means of the Wiener integral. We discuss QFT in a flat expanding metric and in de Sitter space-time. We calculate the evolution kernel in an expanding flat metric in the real-time formulation. We discuss a field interaction in pseudoRiemannian and Riemannian metrics showing that an inversion of the signature leads to some substantial simplifications of the singularity problems in QFT.

Stochastic modeling of Dalbulus maidis, vector of maize diseases.

We developed a simple linear stochastic model for Dalbulus maidis dependent exclusively on temperature, whose parameters were determined from published field and laboratory studies performed at different temperatures. This model takes into account the principal stages and events of the life cycle of this pest, which is vector of maize diseases. We implemented the effect of distributed delays or Linear Chain Trick (LCT) considering a fixed number of sub-stages for egg and nymph stages of Dalbulus maidis in order to accurately represent what is observed in nature. A sensitivity analysis allows us to observe that the speed of the dynamics is sensitive to changes in the development rates, but not to the longevity of each stage or the fecundity, which almost exclusively affect insect abundance. We used our model to study its predictive and explanatory capacity considering a published experiment as a case study. Although the simulation results show a behavior qualitatively equivalent to that observed in the experimental results it is not possible to explain accurately the magnitude, nor the times in which the maximum abundances of second-generation nymphs and adults are reached. Therefore, we evaluated three possible scenarios for the insect that allow us to glimpse some of the advantages of having a computational model in order to find out what processes, taken into account in the model, may explain the differences observed between published experimental results and model results. The three proposed scenarios, based on variations in the parameterized rates of the model, can satisfactorily explain the experimental observations. We observed that in order to better simulate the experimental results it is not necessary to modify fecundity or mortality rates. However, it is necessary to accelerate the average development rates of our model by 20 to 40 %, compatible with extreme values of the rates close to the upper edges of the confidence bands of our parameterization rate curves, according to insects with faster development rates already reported in literature.

Light controls alpine meadow community assembly during succession by affecting species recruitment from the seed bank.

Some research indicates that soil seed banks can promote species coexistence through storage effects. However, the seed bank mechanism that maintains plant assembly and its role in degraded grassland restoration are still not clear. We collected seed bank samples from early, mid and late secondary successional stages of an abandoned subalpine meadow on the Tibetan Plateau, and samples from each stage were exposed to full (i.e., natural), mid, and low light treatments in the field to represent light availability at the bottom/understory (soil surface) of a plant community in the early, mid and late stages of succession, respectively. Species richness, seed density, species composition, and community weighted mean values (CWMs) of seed mass of the species whose seeds germinated in soil samples were evaluated. In response to the light treatments, species richness increased significantly with increased light only for the late successional stage, seed density increased significantly with increased light only in the early and mid successional stages, and seed mass decreased significantly with increased light only in the mid and late successional stages. Species composition differed significantly among the light treatments only in the late successional stage. For the successional series, species richness and seed mass of the species that germinated increased significantly with succession only under mid and full light treatments. Seed density decreased significantly with succession in each light treatment. Species composition differed significantly between the early- and late stage and between the mid and late stage in each light treatment. Both the abiotic (light) and biotic (seed mass) factors influence seed bank recruitment to the plant community. Regeneration of small-seeded species in the seed bank was inhibited under low light in the late successional stage. The balance of stochastic and deterministic processes along a successional gradient was determined by regeneration from the seed bank depending on light intensity change. Differences in seed response to light intensity change largely determined plant community assembly. Our findings should help in the development of effective conservation and restoration strategies.

Persistent decadal differences in plant communities assembled under contrasting climate conditions.

Plant community assembly outcomes can be contingent upon establishment year (year effects) due to variations in the environment. Stochastic events such as interannual variability in climate, particularly in the first year of community assembly, contribute to unpredictable community outcomes over the short term, but less is known about whether year effects produce transient or persistent states on a decadal timescale. To test for short-term (5-year) and persistent (decadal) effects of establishment year climate on community assembly outcomes, we restored prairie in an agricultural field using the same methods in four different years (2010, 2012, 2014, and 2016) that captured a wide range of initial (planting) year climate conditions. Species composition was measured for 5 years in all four restored prairies and for 9 and 11 years in the two oldest restored prairies established under average precipitation and extreme drought conditions. The composition of the four assembled communities showed large and significant differences in the first year of restoration, followed by dynamic change over time along a similar trajectory due to a temporary flush of annual volunteer species. Sown perennial species eventually came to dominate all communities, but communities remained distinct from each other in year five. Precipitation in June and July of the establishment year explained short-term coarse community metrics (i.e., species richness and grass/forb cover), with wet establishment years resulting in a higher cover of grasses and dry establishment years resulting in a higher cover of forbs in restored communities. Short-term differences in community composition, species richness, and grass/forb cover in restorations established under average precipitation and drought conditions persisted for 9-11 years, with low interannual variability in the composition of each prairie over the long term, indicating persistently different states on a decadal timescale. Thus, year effects resulting from stochastic variation in climate can have decadal effects on community assembly outcomes.