Nudging cooperation among agents in an experimental social network.

We investigate the development of cooperative behavior in networks over time. In our controlled laboratory experiment, subjects can cooperate by sending costly messages that contain valuable information for the receiver or other subjects in the network. Any message sent can increase the chance that subjects find the information they are looking for and consequently their profit. We find that cooperation emerges spontaneously and remains stable over time. In an additional treatment, we provide a non-binding suggestion about who to contact at the beginning of the experiment. We find that subjects partially follow our recommendation, and this increases their own and others' profit. Despite the removal of suggestions, subjects build long-lasting relationships with the suggested contacts. Supplementary Information: The online version contains supplementary material available at 10.1007/s41109-023-00588-x.

Uni- and multivariate bias adjustment methods in Nordic catchments: Complexity and performance in a changing climate.

For climate-change impact studies at the catchment scale, meteorological variables are typically extracted from ensemble simulations provided by global and regional climate models, which are then downscaled and bias-adjusted for each study site. For bias adjustment, different statistical methods that re-scale climate model outputs have been suggested in the scientific literature. They range from simple univariate methods that adjust each meteorological variable individually, to more complex and more demanding multivariate methods that take existing relationships between meteorological variables into consideration. Over the past decade, several attempts have been made to evaluate such methods in various regions. There is, however, still no guidance for choosing appropriate bias adjustment methods for a study at hand. In particular, the question whether the benefits of potentially improved adjustments outweigh the cost of increased complexity, remains unanswered. This paper presents a comprehensive evaluation of the performance of two commonly used univariate and two multivariate bias adjustment methods in reproducing numerous univariate, multivariate and temporal features of precipitation and temperature series in different catchments in Sweden. The paper culminates in a discussion on trade-offs between the potential benefits (i.e., skills and added value) and disadvantages (complexity and computational demand) of each method to offer plausible, defensible and actionable insights from the standpoint of climate-change impact studies in high latitudes. We concluded that all selected bias adjustment methods generally improved the raw climate model simulations, but that not a single method consistently outperformed the other methods. There were, however, differences in the methods' performance for particular statistical features, indicating that other practical aspects such as computational time and heavy theoretical requirements should also be taken into consideration when choosing an appropriate bias adjustment method.

The geometry of evolved community matrix spectra.

Random matrix theory has been applied to food web stability for decades, implying elliptical eigenvalue spectra and that large food webs should be unstable. Here we allow feasible food webs to self-assemble within an evolutionary process, using simple Lotka-Volterra equations and several elementary interaction types. We show that, as complex food webs evolve under [Formula: see text] invasion attempts, the community matrix spectra become bi-modal, rather than falling onto elliptical geometries. Our results raise questions as to the applicability of random matrix theory to the analysis of food web steady states.

The Diurnal Path to Persistent Convective Self-Aggregation.

Clustering of tropical thunderstorms constitutes an important climate feedback because it influences the radiative balance. Convective self-aggregation (CSA) is a profound modeling paradigm for explaining the clustering of tropical oceanic thunderstorms. However, CSA is hampered in the realistic limit of fine model resolution when cold pools-dense air masses beneath thunderstorm clouds-are well-resolved. Studies on CSA usually assume the surface temperature to be constant, despite realistic surface temperatures varying significantly between night and day. Here we mimic the diurnal cycle in cloud-resolving numerical experiments by prescribing a surface temperature oscillation. Our simulations show that the diurnal cycle enables CSA at fine resolutions, and that the process is even accelerated by finer resolutions. We attribute these findings to vigorous combined cold pools emerging in symbiosis with mesoscale convective systems. Such cold pools suppress buoyancy in extended regions (∼100 km) and enable the formation of persistent dry patches. Our findings help clarify how the tropical cloud field forms sustained clusters under the diurnal forcing and may have implications for the origin of extreme thunderstorm rainfall and tropical cyclones.

Self-organized quantization and oscillations on continuous fixed-energy sandpiles.

Atmospheric self-organization and activator-inhibitor dynamics in biology provide examples of checkerboardlike spatiotemporal organization. We study a simple model for local activation-inhibition processes. Our model, first introduced in the context of atmospheric moisture dynamics, is a continuous-energy and non-Abelian version of the fixed-energy sandpile model. Each lattice site is populated by a nonnegative real number, its energy. Upon each timestep all sites with energy exceeding a unit threshold redistribute their energy at equal parts to their nearest neighbors. The limit cycle dynamics gives rise to a complex phase diagram in dependence on the mean energy µ: For low µ, all dynamics ceases after few redistribution events. For large µ, the dynamics is well-described as a diffusion process, where the order parameter, spatial variance σ, is removed. States at intermediate µ are dominated by checkerboardlike period-two phases which are however interspersed by much more complex phases of far longer periods. Phases are separated by discontinuous jumps in σ or ∂_{µ}σ-akin to first- and higher-order phase transitions. Overall, the energy landscape is dominated by few energy levels which occur as sharp spikes in the single-site density of states and are robust to noise.

Differential gene regulation in the anterior cingulate cortex and superior temporal cortex in schizophrenia: A molecular network approach.

The closely connected anterior cingulate cortex (ACC) and superior temporal cortex (STC) are important for higher cognitive functions. Both brain regions are disturbed in schizophrenia, i.e., functional and structural alterations have been reported. This postmortem investigation in brains from patients with schizophrenia and controls compared gene expression in the left ACC and left STC. Most differentially expressed genes were unique to each brain region, but some clusters of genes were equally dysregulated in both, giving rise to a more general disease-specific pattern of gene regulation. The data was used to construct a molecular network of the genes identically expressed in both regions as primary nodes and the metabolically connected genes as secondary nodes. The network analysis identified downregulated clusters of immune-associated gene products and upregulated clusters belonging to the ubiquitin-proteasome system. These findings could help to identify new potential therapeutic targets for future approaches.

Circling in on Convective Self-Aggregation.

In radiative-convective equilibrium simulations, convective self-aggregation (CSA) is the spontaneous organization into segregated cloudy and cloud-free regions. Evidence exists for how CSA is stabilized, but how it arises favorably on large domains is not settled. Using large-eddy simulations, we link the spatial organization emerging from the interaction of cold pools (CPs) to CSA. We systematically weaken simulated rain evaporation to reduce maximal CP radii, R max , and find reducing R max causes CSA to occur earlier. We further identify a typical rain cell generation time and a minimum radius, R min , around a given rain cell, within which the formation of subsequent rain cells is suppressed. Incorporating R min and R max , we propose a toy model that captures how CSA arises earlier on large domains: when two CPs of radii r i , r j ∈ [ R min , R max ] collide, they form a new convective event. These findings imply that interactions between CPs may explain the initial stages of CSA.

Particle-Based Tracking of Cold Pool Gust Fronts.

The gust fronts of convective cold pools (CPs) are increasingly recognized as loci of enhanced triggering for subsequent convective cells. It has so far been difficult to track these gust fronts in high-resolution data, such as large eddy simulations (LES)-rendering mechanistic analysis of CP interaction incomplete. Here, a simple tracking method is defined, tested, and applied, which uses horizontal advection and a condition on horizontal divergence, to emit tracers at the perimeter of surface precipitation patches. Tracers are then reliably transported to the gust front, yielding closed bands marking the CP boundary. The method thereby allows analysis of the dynamics also along the gust front, which allows to identify point-like loci of pronounced updrafts. The tracking works well for a single idealized CP and reliably tracks a population of CPs in a midlatitude diurnal cycle. As the method uniquely links CPs and their tracers to a specific parent precipitation cell, it may be useful for the analysis of interactions in evolving CP populations.

Mechanical Forcing of Convection by Cold Pools: Collisions and Energy Scaling.

Forced mechanical lifting through cold pool gust fronts can trigger new convection and, as previous work highlights, is enhanced when cold pools collide. However, as shown by conceptual models, the organization of the convective cloud field emerging from two versus three colliding cold pools differs strongly. In idealized dry large-eddy simulations we therefore compare collisions between two and three cold pools. The triggering likelihood is quantified in terms of the cumulative vertical mass flux of boundary layer air and the instantaneous updraft strength, generated at the cold pool gust fronts. We find that cold pool expansion can be well described by initial potential energy alone. Cold pool expansion monotonically slows but shows an abrupt transition between an axisymmetric and a broken-symmetric state mirrored by a sudden drop in expansion speed. We characterize these two dynamic regimes by two distinct power law exponents and explain the transition by the onset of "lobe-and-cleft" instabilities at the cold pool head. Two-cold pool collisions produce the strongest instantaneous updrafts in the lower boundary layer, which we expect to be important in environments with strong convective inhibition. Three-cold pool collisions generate weaker but deeper updrafts and the strongest cumulative mass flux and are thus predicted to induce the largest midlevel moistening, which has been identified as a precursor for the transition from shallow to deep convection. Combined, our findings may help decipher the role of cold pools in spatially organizing convection and precipitation.

Theory of invasion extinction dynamics in minimal food webs.

When food webs are exposed to species invasion, secondary extinction cascades may be set off. Although much work has gone into characterizing the structure of food webs, systematic predictions on their evolutionary dynamics are still scarce. Here we present a theoretical framework that predicts extinctions in terms of an alternating sequence of two basic processes: resource depletion by or competitive exclusion between consumers. We first propose a conceptual invasion extinction model (IEM) involving random fitness coefficients. We bolster this IEM by an analytical, recursive procedure for calculating idealized extinction cascades after any species addition and simulate the long-time evolution. Our procedure describes minimal food webs where each species interacts with only a single resource through the generalized Lotka-Volterra equations. For such food webs ex- tinction cascades are determined uniquely and the system always relaxes to a stable steady state. The dynamics and scale invariant species life time resemble the behavior of the IEM, and correctly predict an upper limit for trophic levels as observed in the field.

Noise-induced polarization switching in complex networks.

The combination of bistability and noise is ubiquitous in complex systems, from biology to social interactions, and has important implications for their functioning and resilience. Here we use a simple three-state dynamical process, in which nodes go from one pole to another through an intermediate state, to show that noise can induce polarization switching in bistable systems if dynamical correlations are significant. In large, fully connected networks, where dynamical correlations can be neglected, increasing noise yields a collapse of bistability to an unpolarized configuration where the three possible states of the nodes are equally likely. In contrast, increased noise induces abrupt and irreversible polarization switching in sparsely connected networks. In multiplexes, where each layer can have a different polarization tendency, one layer is dominant and progressively imposes its polarization state on the other, offsetting or promoting the ability of noise to switch its polarization. Overall, we show that the interplay of noise and dynamical correlations can yield discontinuous transitions between extremes, which cannot be explained by a simple mean-field description.

Existence and construction of large stable food webs.

Ecological diversity is ubiquitous despite the restrictions imposed by competitive exclusion and apparent competition. To explain the observed richness of species in a given habitat, food-web theory has explored nonlinear functional responses, self-interaction, or spatial structure and dispersal-model ingredients that have proven to promote stability and diversity. We return instead here to classical Lotka-Volterra equations, where species-species interaction is characterized by a simple product and spatial restrictions are ignored. We quantify how this idealization imposes constraints on coexistence and diversity for many species. To this end, we introduce the concept of free and controlled species and use this to demonstrate how stable food webs can be constructed by the sequential addition of species. The resulting food webs can reach dozens of species and generally yield nonrandom degree distributions in accordance with the constraints imposed through the assembly process. Our model thus serves as a formal starting point for the study of sustainable interaction patterns between species.

Exploring the Link between Nucleosome Occupancy and DNA Methylation.

Near promoters, both nucleosomes and CpG sites form characteristic spatial patterns. Previously, nucleosome depleted regions were observed upstream of transcription start sites and nucleosome occupancy was reported to correlate both with CpG density and the level of CpG methylation. Several studies imply a causal link where CpG methylation might induce nucleosome formation, whereas others argue the opposite, i.e., that nucleosome occupancy might influence CpG methylation. Correlations are indeed evident between nucleosomes, CpG density and CpG methylation-at least near promoter sites. It is however less established whether there is an immediate causal relation between nucleosome occupancy and the presence of CpG sites-or if nucleosome occupancy could be influenced by other factors. In this work, we test for such causality in human genomes by analyzing the three quantities both near and away from promoter sites. For data from the human genome we compare promoter regions with given CpG densities with genomic regions without promoters but of similar CpG densities. We find the observed correlation between nucleosome occupancy and CpG density, respectively CpG methylation, to be specific to promoter regions. In other regions along the genome nucleosome occupancy is statistically independent of the positioning of CpGs or their methylation levels. Anti-correlation between CpG density and methylation level is however similarly strong in both regions. On promoters, nucleosome occupancy is more strongly affected by the level of gene expression than CpG density or CpG methylation-calling into question any direct causal relation between nucleosome occupancy and CpG organization. Rather, our results suggest that for organisms with cytosine methylation nucleosome occupancy might be primarily linked to gene expression, with no strong impact on methylation.

Expert Game experiment predicts emergence of trust in professional communication networks.

Strong social capital is increasingly recognized as an organizational advantage. Better knowledge sharing and reduced transaction costs increase work efficiency. To mimic the formation of the associated communication network, we propose the Expert Game, where each individual must find a specific expert and receive her help. Participants act in an impersonal environment and under time constraints that provide short-term incentives for noncooperative behavior. Despite these constraints, we observe cooperation between individuals and the self-organization of a sustained trust network, which facilitates efficient communication channels with increased information flow. We build a behavioral model that explains the experimental dynamics. Analysis of the model reveals an exploitation protection mechanism and measurable social capital, which quantitatively describe the economic utility of trust.

Stabilization of epigenetic states of CpG islands by local cooperation.

DNA methylation of CpG sites is an important epigenetic mark in mammals. Active promoters are often associated with unmethylated CpG sites, whereas methylated CpG sites correlate with silenced promoters. Methylation of CpG sites must be generally described as a dynamical process that is mediated by methylation enzymes, such as DNMT1 and DNMT3a/b. However, there are several models of how CpG sites can be protected from methylation and thereby remain unmethylated. In this paper we examine the combination of both: the positive feedbacks of DNA methylation and a short range counterpart which in turn protects-and thereby maintains-the unmethylated state. The emergent dynamics is provided by collaborative, re-enforcing feedbacks in favor of methylated CpG islands and cooperative protection of one CpG site by another in favor of unmethylated CpG sites. Our results suggest that this synthesis of mechanisms provides equally robust maintenance of both the unmethylated and methylated states of CpG islands.

DNA methylation in human epigenomes depends on local topology of CpG sites.

In vertebrates, methylation of cytosine at CpG sequences is implicated in stable and heritable patterns of gene expression. The classical model for inheritance, in which individual CpG sites are independent, provides no explanation for the observed non-random patterns of methylation. We first investigate the exact topology of CpG clustering in the human genome associated to CpG islands. Then, by pooling genomic CpG clusters on the basis of short distances between CpGs within and long distances outside clusters, we show a strong dependence of methylation on the number and density of CpG organization. CpG clusters with fewer, or less densely spaced, CpGs are predominantly hyper-methylated, while larger clusters are predominantly hypo-methylated. Intermediate clusters, however, are either hyper- or hypo-methylated but are rarely found in intermediate methylation states. We develop a model for spatially-dependent collaboration between CpGs, where methylated CpGs recruit methylation enzymes that can act on CpGs over an extended local region, while unmethylated CpGs recruit demethylation enzymes that act more strongly on nearby CpGs. This model can reproduce the effects of CpG clustering on methylation and produces stable and heritable alternative methylation states of CpG clusters, thus providing a coherent model for methylation inheritance and methylation patterning.

Food Web Assembly Rules for Generalized Lotka-Volterra Equations.

In food webs, many interacting species coexist despite the restrictions imposed by the competitive exclusion principle and apparent competition. For the generalized Lotka-Volterra equations, sustainable coexistence necessitates nonzero determinant of the interaction matrix. Here we show that this requirement is equivalent to demanding that each species be part of a non-overlapping pairing, which substantially constrains the food web structure. We demonstrate that a stable food web can always be obtained if a non-overlapping pairing exists. If it does not, the matrix rank can be used to quantify the lack of niches, corresponding to unpaired species. For the species richness at each trophic level, we derive the food web assembly rules, which specify sustainable combinations. In neighboring levels, these rules allow the higher level to avert competitive exclusion at the lower, thereby incorporating apparent competition. In agreement with data, the assembly rules predict high species numbers at intermediate levels and thinning at the top and bottom. Using comprehensive food web data, we demonstrate how omnivores or parasites with hosts at multiple trophic levels can loosen the constraints and help obtain coexistence in food webs. Hence, omnivory may be the glue that keeps communities intact even under extinction or ecological release of species.

Phage and bacteria support mutual diversity in a narrowing staircase of coexistence.

The competitive exclusion principle states that phage diversity M should not exceed bacterial diversity N. By analyzing the steady-state solutions of multistrain equations, we find a new constraint: the diversity N of bacteria living on the same resources is constrained to be M or M+1 in terms of the diversity of their phage predators. We quantify how the parameter space of coexistence exponentially decreases with diversity. For diversity to grow, an open or evolving ecosystem needs to climb a narrowing 'diversity staircase' by alternatingly adding new bacteria and phages. The unfolding coevolutionary arms race will typically favor high growth rate, but a phage that infects two bacterial strains differently can occasionally eliminate the fastest growing bacteria. This context-dependent fitness allows abrupt resetting of the 'Red-Queen's race' and constrains the local diversity.

Collaboration between CpG sites is needed for stable somatic inheritance of DNA methylation states.

Inheritance of 5-methyl cytosine modification of CpG (CG/CG) DNA sequences is needed to maintain early developmental decisions in vertebrates. The standard inheritance model treats CpGs as independent, with methylated CpGs maintained by efficient methylation of hemimethylated CpGs produced after DNA replication, and unmethylated CpGs maintained by an absence of de novo methylation. By stochastic simulations of CpG islands over multiple cell cycles and systematic sampling of reaction parameters, we show that the standard model is inconsistent with many experimental observations. In contrast, dynamic collaboration between CpGs can provide strong error-tolerant somatic inheritance of both hypermethylated and hypomethylated states of a cluster of CpGs, reproducing observed stable bimodal methylation patterns. Known recruitment of methylating enzymes by methylated CpGs could provide the necessary collaboration, but we predict that recruitment of demethylating enzymes by unmethylated CpGs strengthens inheritance and allows CpG islands to remain hypomethylated within a sea of hypermethylation.

Communication dynamics in finite capacity social networks.

In communication networks, structure and dynamics are tightly coupled. The structure controls the flow of information and is itself shaped by the dynamical process of information exchanged between nodes. In order to reconcile structure and dynamics, a generic model, based on the local interaction between nodes, is considered for the communication in large social networks. In agreement with data from a large human organization, we show that the flow is non-Markovian and controlled by the temporal limitations of individuals. We confirm the versatility of our model by predicting simultaneously the degree-dependent node activity, the balance between information input and output of nodes, and the degree distribution. Finally, we quantify the limitations to network analysis when it is based on data sampled over a finite period of time.