Population Synchrony Research Articles

Average Measures of Phase and Synchrony in Inhomogeneous Populations of Circadian Oscillators

Circadian misalignment between the phase of molecular circadian oscillators and the external environment has been linked to adverse health effects, including cardiovascular disease, obesity, cancer, and psychiatric disorders. Desynchrony of the circadian clock within populations of oscillators has also been linked to these conditions. For this reason, we wish to develop a control strategy to shift molecular circadian phase while also controlling for population synchrony. Previous work has demonstrated that model predictive control can effectively solve this problem when synchrony is determined directly by measuring the phase of each cell in a homogeneous population. Such cell-specific phase measurements are rarely possible in vivo, and such homogeneity is not biologically plausible. For these reasons, we wish to design an observer that can accurately determine the mean phase of the population and derive a proxy for population synchrony. We show that a model parameterized with the average parameter set of the population can be used to sense phase from mean population expression levels, despite inaccuracies when sensing the phase of a single cell. Similarly, we are able to use the average amplitude of the population in comparison to the amplitude of the average population oscillator as a measure of synchrony within the population. Taken together, these two metrics, based on the average behavior of the cell, allow us to control the phase and synchrony of the population of cellular oscillators without measuring the phase of individual cells.

Spatial Scales of Population Synchrony in Predator-Prey Systems

Many species show synchronous fluctuations in population size over large geographical areas, which are likely to increase their regional extinction risk. Here we examine how the degree of spatial synchrony in population dynamics is affected by trophic interactions using a two-species predator-prey model with spatially correlated environmental noise. We show that the predator has a larger spatial scale of population synchrony than the prey if the population fluctuations of both species are mainly determined by the direct effect of stochastic environmental variations in the prey. This result implies that in ecosystems regulated from the bottom up, the spatial scale of synchrony of the predator population increases beyond the scale of the spatial autocorrelation in the environmental noise and in the prey fluctuations. Harvesting the prey increases the spatial scale of population synchrony of the predator, while harvesting the predator reduces the spatial scale of the population fluctuations of its prey. Hence, the development of sustainable harvesting strategies should also consider the impact on unharvested species at other trophic levels as well as human perturbations of ecosystems, whether the result of exploitation or an effect on dispersal processes, as they can affect food web structures and trophic interactions over large geographical areas.

Evidence that alpha blocking is due to increases in system-level oscillatory damping not neuronal population desynchronisation

The attenuation of the alpha rhythm following eyes-opening (alpha blocking) is among the most robust features of the human electroencephalogram with the prevailing view being that it is caused by changes in neuronal population synchrony. To further study the basis for this phenomenon we use theoretically motivated fixed-order Auto-Regressive Moving-Average (ARMA) time series modelling to study the oscillatory dynamics of spontaneous alpha-band electroencephalographic activity in eyes-open and eyes-closed conditions and its modulation by the NMDA antagonist ketamine. We find that the reduction in alpha-band power between eyes-closed and eyes-open states is explicable in terms of an increase in the damping of stochastically perturbed alpha-band relaxation oscillatory activity. These changes in damping are putatively modified by the antagonism of NMDA-mediated glutamatergic neurotransmission but are not directly driven by changes in input to cortex nor by reductions in the phase synchronisation of populations of near identical oscillators. These results not only provide a direct challenge to the dominant view of the role that thalamus and neuronal population de-/synchronisation have in the genesis and modulation of alpha electro-/magnetoencephalographic activity but also suggest potentially important physiological determinants underlying its dynamical control and regulation.

Next-generation neural mass and field modeling.

The Wilson-Cowan population model of neural activity has greatly influenced our understanding of the mechanisms for the generation of brain rhythms and the emergence of structured brain activity. As well as the many insights that have been obtained from its mathematical analysis, it is now widely used in the computational neuroscience community for building large-scale in silico brain networks that can incorporate the increasing amount of knowledge from the Human Connectome Project. Here, we consider a neural population model in the spirit of that originally developed by Wilson and Cowan, albeit with the added advantage that it can account for the phenomena of event-related synchronization and desynchronization. This derived mean-field model provides a dynamic description for the evolution of synchrony, as measured by the Kuramoto order parameter, in a large population of quadratic integrate-and-fire model neurons. As in the original Wilson-Cowan framework, the population firing rate is at the heart of our new model; however, in a significant departure from the sigmoidal firing rate function approach, the population firing rate is now obtained as a real-valued function of the complex-valued population synchrony measure. To highlight the usefulness of this next-generation Wilson-Cowan style model, we deploy it in a number of neurobiological contexts, providing understanding of the changes in power spectra observed in electro- and magnetoencephalography neuroimaging studies of motor cortex during movement, insights into patterns of functional connectivity observed during rest and their disruption by transcranial magnetic stimulation, and to describe wave propagation across cortex.

Spatial covariation of competing species in a fluctuating environment.

Understanding how stochastic fluctuations in the environment influence population dynamics is crucial for sustainable management of biological diversity. However, because species do not live in isolation, this requires knowledge of how species interactions influence population dynamics. In addition, spatial processes play an important role in shaping population dynamics. It is therefore important to improve our understanding of how these different factors act together to shape patterns of abundance across space within and among species. Here, we present a new analytical model for understanding patterns of covariation in space between interacting species in a stochastic environment. We show that the correlation between two species in how they experience the same environmental conditions determines how correlated fluctuations in their densities would be in the absence of competition. In other words, without competition, synchrony between the species is driven by the environment, similar to the Moran effect within a species. Competition between the two species causes their abundances to become less positively or more negatively correlated. The same strength of competition has a greater negative effect on the correlation between species when one of them has a more variable growth rate than the other. In addition, dispersal or other movement weakens the effect of competition on the interspecific correlation. Finally, we show that movement increases the distance over which the species are (positively or negatively) correlated, an effect that is stronger when the species are competitors, and that there is a close connection between the spatial scaling of population synchrony within a species and between species. Our results show that the relationships between the different factors influencing interspecific correlations in abundance are not simple linear ones, but this model allows us to disentangle them and predict how they will affect population fluctuations in different situations.

Period-3 dominant phase synchronisation of Zelkova serrata: border-collision bifurcation observed in a plant population

The population synchrony of tree seed production has attracted widespread attention in agriculture, forestry and ecosystem management. Oaks usually show synchronisation of irregular or intermittent sequences of acorn production, which is termed ‘masting’. Tree crops such as citrus and pistachio show a clear two-year cycle (period-2) termed ‘alternate bearing’. We identified period-3 dominant phase synchronisation in a population of Zelkova serrata. As ‘period-3’ is known to provide evidence to imply chaos in nonlinear science, the observed period-3 phase synchronisation of Zelkova serrata is an attractive real-world phenomenon that warrants investigation in terms of nonlinear dynamics. Using the Hilbert transform, we proposed a procedure to determine the fractions of periods underlying the survey data and distinguished the on-year (high yield year) and the off-year (low yield year) of the masting. We quantified the effects of pollen coupling, common environmental noise and individual variability on the phase synchronisation and demonstrated how the period-3 synchronisation emerges through a border-collision bifurcation process. In this paper, we propose a model that can describe diverse behaviours of seed production observed in many different tree species by changing its parameters.

Temporal variation in spatial genetic structure during population outbreaks: Distinguishing among different potential drivers of spatial synchrony.

Spatial synchrony is a common characteristic of spatio‐temporal population dynamics across many taxa. While it is known that both dispersal and spatially autocorrelated environmental variation (i.e., the Moran effect) can synchronize populations, the relative contributions of each, and how they interact, are generally unknown. Distinguishing these mechanisms and their effects on synchrony can help us to better understand spatial population dynamics, design conservation and management strategies, and predict climate change impacts. Population genetic data can be used to tease apart these two processes as the spatio‐temporal genetic patterns they create are expected to be different. A challenge, however, is that genetic data are often collected at a single point in time, which may introduce context‐specific bias. Spatio‐temporal sampling strategies can be used to reduce bias and to improve our characterization of the drivers of spatial synchrony. Using spatio‐temporal analyses of genotypic data, our objective was to identify the relative support for these two mechanisms to the spatial synchrony in population dynamics of the irruptive forest insect pest, the spruce budworm (Choristoneura fumiferana), in Quebec (Canada). AMOVA, cluster analysis, isolation by distance, and sPCA were used to characterize spatio‐temporal genomic variation using 1,370 SBW larvae sampled over four years (2012–2015) and genotyped at 3,562 SNP loci. We found evidence of overall weak spatial genetic structure that decreased from 2012 to 2015 and a genetic diversity homogenization among the sites. We also found genetic evidence of a long‐distance dispersal event over >140 km. These results indicate that dispersal is the key mechanism involved in driving population synchrony of the outbreak. Early intervention management strategies that aim to control source populations have the potential to be effective through limiting dispersal. However, the timing of such interventions relative to outbreak progression is likely to influence their probability of success.

Spatial heterogeneity in climate change effects decouples the long-term dynamics of wild reindeer populations in the high Arctic.

The ‘Moran effect’ predicts that dynamics of populations of a species are synchronized over similar distances as their environmental drivers. Strong population synchrony reduces species viability, but spatial heterogeneity in density dependence, the environment, or its ecological responses may decouple dynamics in space, preventing extinctions. How such heterogeneity buffers impacts of global change on large‐scale population dynamics is not well studied. Here, we show that spatially autocorrelated fluctuations in annual winter weather synchronize wild reindeer dynamics across high‐Arctic Svalbard, while, paradoxically, spatial variation in winter climate trends contribute to diverging local population trajectories. Warmer summers have improved the carrying capacity and apparently led to increased total reindeer abundance. However, fluctuations in population size seem mainly driven by negative effects of stochastic winter rain‐on‐snow (ROS) events causing icing, with strongest effects at high densities. Count data for 10 reindeer populations 8–324 km apart suggested that density‐dependent ROS effects contributed to synchrony in population dynamics, mainly through spatially autocorrelated mortality. By comparing one coastal and one ‘continental’ reindeer population over four decades, we show that locally contrasting abundance trends can arise from spatial differences in climate change and responses to weather. The coastal population experienced a larger increase in ROS, and a stronger density‐dependent ROS effect on population growth rates, than the continental population. In contrast, the latter experienced stronger summer warming and showed the strongest positive response to summer temperatures. Accordingly, contrasting net effects of a recent climate regime shift—with increased ROS and harsher winters, yet higher summer temperatures and improved carrying capacity—led to negative and positive abundance trends in the coastal and continental population respectively. Thus, synchronized population fluctuations by climatic drivers can be buffered by spatial heterogeneity in the same drivers, as well as in the ecological responses, averaging out climate change effects at larger spatial scales.

Shifts in phenological mean and synchrony interact to shape competitive outcomes.

Climate change-induced phenological shifts are ubiquitous and have the potential to disrupt natural communities by changing the timing of species interactions. Shifts in first and/or mean phenological date are well documented, but recent studies indicate that shifts in synchrony (individual variation around these metrics) can be just as common. However, we know little about how both types of phenological shifts interact to affect species interactions and communities. Here, we experimentally manipulated the hatching phenologies of two competing species of larval amphibians to address this conceptual gap. Specifically, we manipulated the relative mean hatching time (early, same, or late relative to competitor) and population synchrony (high, medium, or low levels of variation around the mean) in a full 3×3 factorial design to measure independent and interactive effects of phenological mean and population phenological synchrony on competitive outcomes. Our results indicate that phenological synchrony within a population strongly influences intraspecific competition by changing the density of individuals and relative strength of early- vs. late-arriving individuals. Individuals from high-synchrony populations competed symmetrically, whereas individuals from low-synchrony populations competed asymmetrically. At the community scale, shifts in population phenological synchrony interact with shifts in phenological mean to affect key demographic rates (survival, biomass export, per capita mass, and emergence timing) strongly. Furthermore, changes in mean timing of species interactions altered phenological synchrony within a population at the next life stage, and phenological synchrony at one life stage altered the mean timing of the next life stage. Thus, shifts in phenological synchrony within populations cannot only alter species interactions, but species interactions in turn can also drive shifts in phenology.

Potential mechanisms related to the spatial synchrony of phytoplankton is dependent on the type of data

Spatial synchrony occurs when the temporal dynamics of local populations are correlated. Correlated environmental variations (Moran effect), dispersal, and trophic interactions are theoretically the main mechanisms underlying population synchrony. We estimated spatial synchrony for total biomass, community density, densities of genera, and phytoplankton classes in a reservoir. We tested the hypotheses that synchrony levels would be high because all sites were sampled in a single waterbody; the Moran effect would be the main synchronizing mechanism; and synchrony should increase with environmental specialization of taxa. We also conducted an exploratory analysis to identify the regions of the reservoir contributing most to synchrony. We found low levels of synchrony, indicating that local factors were more important in controlling the dynamics of the local phytoplankton populations. Environmental synchrony and geographic distance were not significantly correlated with the synchrony matrices derived from genera data; however, synchrony matrices of the most abundant phytoplankton classes were mainly correlated with environmental synchrony. These results indicate that the detection of the Moran effect depends on the type of data used. As predicted, our results indicated that the dynamics of specialist genera were more synchronized than that of generalist genera. Finally, the lower reach of the reservoir contributed most to synchrony.

Population spatial synchrony enhanced by periodicity and low detuning with environmental forcing.

Explaining why fluctuations in abundances of spatially disjunct populations often are correlated through time is a major goal of population ecologists. We address two hypotheses receiving little to no testing in wild populations: (i) that population cycling facilitates synchronization given weak coupling among populations, and (ii) that the ability of periodic external forces to synchronize oscillating populations is a function of the mismatch in timescales (detuning) between the force and the population. Here, we apply new analytical methods to field survey data on gypsy moth outbreaks. We report that at timescales associated with gypsy moth outbreaks, spatial synchrony increased with population periodicity via phase locking. The extent to which synchrony in temperature and precipitation influenced population synchrony was associated with the degree of mismatch in dominant timescales of oscillation. Our study provides new empirical methods and rare empirical evidence that population cycling and low detuning can promote population spatial synchrony.

Diel population and functional synchrony of microbial communities on coral reefs

On coral reefs, microorganisms are essential for recycling nutrients to primary producers through the remineralization of benthic-derived organic matter. Diel investigations of reef processes are required to holistically understand the functional roles of microbial players in these ecosystems. Here we report a metagenomic analysis characterizing microbial communities in the water column overlying 16 remote forereef sites over a diel cycle. Our results show that microbial community composition is more dissimilar between day and night samples collected from the same site than between day or night samples collected across geographically distant reefs. Diel community differentiation is largely driven by the flux of Psychrobacter sp., which is two-orders of magnitude more abundant during the day. Nighttime communities are enriched with species of Roseobacter, Halomonas, and Alteromonas encoding a greater variety of pathways for carbohydrate catabolism, further illustrating temporal patterns of energetic provisioning between different marine microbes. Dynamic diel fluctuations of microbial populations could also support the efficient trophic transfer of energy posited in coral reef food webs.

Similarity in seasonal flow regimes, not regional environmental classifications explain synchrony in brown trout population dynamics in France

Abstract Investigating the variables driving synchrony in populations and the spatial scales at which they operate is worthwhile for both a basic understanding of population dynamics and for management. Various environmental classifications have been developed to group sites according to predefined similarities in abiotic variables (with spatial and/or temporal components) and to explain population dynamics in running waters. These classifications may be useful to managers and decision makers, as studies have shown relationships between time‐dependent population variability and environmental classifications on different spatial scales. We investigated whether synchrony among brown trout (Salmo trutta) populations could be explained by three regional (hydro‐ecoregions, hydrological units, and natural flow regime classification) and one local environmental classification and their associated variables. We first analysed whether densities of two size classes (0+ and >0+ fish) varied over a large spatial scale (i.e. 112 sites spread throughout France sampled for >11 years). We then determined whether the degree of synchrony matched the spatial patterns defined by the environmental classifications. Finally, we investigated whether variables such as river distance between sites, hydrology, and temperature were associated with patterns of synchrony. Overall, relationships observed between synchronies in environmental variables and trout population densities were weak. Synchrony was best explained by the classification based on hydrological units, defined as discrete physical units that provided boundaries with real biological significance for river assemblages (12% for 0+ density and 9% for >0+ density and total densities). However, when considering environmental variables alone, synchrony in trout populations was mainly associated (from 11% to 23%) with seasonal flow variables such as high flows during reproductive and emergence periods. Temperature and river distance did not explain synchrony between trout populations. High flows during emergence appear to be a key driver of brown trout population dynamics. Floods during the reproductive period could explain synchrony among brown trout populations, although the effect was weaker than that of high flows during the emergence period. We concluded that existing physical, morphological, and biogeographical classifications at regional spatial scales were not sufficiently integrative to account for environmental drivers (such as flow variables) that were most associated with the spatiotemporal patterns of synchrony in trout populations. Flow explained synchrony better than classifications did. Further investigations should focus on the spatial patterns of flow variations during the emergence and reproductive periods to derive geographical areas that best capture synchrony patterns in trout dynamics. This approach could be extended to other freshwater fish species for which climatic drivers are important, and may help to explain the distributional changes of exotic species, the propensity of populations to fluctuate synchronously and the scale of synchrony patterns under different scenarios of climate change, or the threat of extinction of native species if the Moran effect is high.

Coordination of rapid cholinergic and dopaminergic signaling in striatum during spontaneous movement.

Interplay between dopaminergic and cholinergic neuromodulation in the striatum is crucial for movement control, with prominent models proposing pro-kinetic and anti-kinetic effects of dopamine and acetylcholine release, respectively. However, the natural, movement-related signals of striatum cholinergic neurons and their relationship to simultaneous variations in dopamine signaling are unknown. Here, functional optical recordings in mice were used to establish rapid cholinergic signals in dorsal striatum during spontaneous movements. Bursts across the cholinergic population occurred at transitions between movement states and were marked by widespread network synchronization which diminished during sustained locomotion. Simultaneous cholinergic and dopaminergic recordings revealed distinct but coordinated sub-second signals, suggesting a new model where cholinergic population synchrony signals rapid changes in movement states while dopamine signals the drive to enact or sustain those states.

Spatial synchrony in sub-arctic geometrid moth outbreaks reflects dispersal in larval and adult life cycle stages.

Spatial synchrony in population dynamics can be caused by dispersal or spatially correlated variation in environmental factors like weather (Moran effect). Distinguishing between these mechanisms is challenging for natural populations, and the study of dispersal-induced synchrony in particular has been dominated by theoretical modelling and laboratory experiments. The goal of the present study was to evaluate the evidence for dispersal as a cause of meso-scale (distances of tens of kilometres) spatial synchrony in natural populations of the two cyclic geometrid moths Epirrita autumnata and Operophtera brumata in sub-arctic mountain birch forest in northern Norway. To infer the role of dispersal in geometrid synchrony, we applied three complementary approaches, namely estimating the effect of design-based dispersal barriers (open sea) on synchrony, comparing the strength of synchrony between E.autumnata (winged adults) and the less dispersive O.brumata (wingless adult females), and relating the directionality (anisotropy) of synchrony to the predominant wind directions during spring, when geometrid larvae engage in windborne dispersal (ballooning). The estimated effect of dispersal barriers on synchrony was almost three times stronger for the less dispersive O.brumata than E.autumnata. Inter-site synchrony was also weakest for O.brumata at all spatial lags. Both observations argue for adult dispersal as an important synchronizing mechanism at the spatial scales considered. Further, synchrony in both moth species showed distinct anisotropy and was most spatially extensive parallel to the east-west axis, coinciding closely to the overall dominant wind direction. This argues for a synchronizing effect of windborne larval dispersal. Congruent with most extensive dispersal along the east-west axis, E.autumnata also showed evidence for a travelling wave moving southwards at a speed of 50-80km/year. Our results suggest that dispersal processes can leave clear signatures in both the strength and directionality of synchrony in field populations, and highlight wind-driven dispersal as promising avenue for further research on spatial synchrony in natural insect populations.

Ecological drift and competitive interactions predict unique patterns in temporal fluctuations of population size.

Recent studies have highlighted the importance of higher-order competitive interactions in stabilizing population dynamics in multi-species communities. But how does the structure of competitive hierarchies affect population dynamics and extinction processes? We tackled this important question by using spatially explicit simulations of ecological drift (10 species in a homogeneous landscape of 64 patches) in which birth rates were influenced by interspecific competition. Specifically, we examined how transitive (linear pecking orders) and intransitive (pecking orders with loops) competitive hierarchies affected extinction rates and population dynamics in simulated communities through time. In comparison to a pure neutral model, an ecological drift model including transitive competition increased extinction rates, caused synchronous density-dependent population fluctuations, and generated a white-noise distribution of population sizes. In contrast, the drift model with intransitive competitive interactions decreased extinctions rates, caused asynchronous (compensatory) density-dependent population fluctuations, and generated a brown noise distribution of population sizes. We also explored the effect on community stability of more complex patterns of competitive interactions in which pairwise competitive relationships were assigned probabilistically. These probabilistic competition models also generated density-dependent trajectories and a brown noise distribution of population sizes. However, extinction rates and the degree of population synchrony were comparable to those observed in purely neutral communities. Collectively, our results confirm that intransitive competition has a strong and stabilizing effect on local populations in species-poor communities. This effect wanes with increasing species richness. Empirical assemblages characterized by brown spectral noise, density-dependent regulation, and asynchronous (compensatory) population fluctuations may indicate a signature of intransitive competitive interactions.

Next-generation neural field model: The evolution of synchrony within patterns and waves.

Neural field models are commonly used to describe wave propagation and bump attractors at a tissue level in the brain. Although motivated by biology, these models are phenomenological in nature. They are built on the assumption that the neural tissue operates in a near synchronous regime, and hence, cannot account for changes in the underlying synchrony of patterns. It is customary to use spiking neural network models when examining within population synchronization. Unfortunately, these high-dimensional models are notoriously hard to obtain insight from. In this paper, we consider a network of θ-neurons, which has recently been shown to admit an exact mean-field description in the absence of a spatial component. We show that the inclusion of space and a realistic synapse model leads to a reduced model that has many of the features of a standard neural field model coupled to a further dynamical equation that describes the evolution of network synchrony. Both Turing instability analysis and numerical continuation software are used to explore the existence and stability of spatiotemporal patterns in the system. In particular, we show that this new model can support states above and beyond those seen in a standard neural field model. These states are typified by structures within bumps and waves showing the dynamic evolution of population synchrony.

Intertidal clams exhibit population synchrony across spatial and temporal scales.

Long‐term datasets can be particularly useful for parsing out factors influencing populations, yet few studies have utilized continuous datasets to quantify population dynamics in bivalve molluscs. We used dynamic factor analysis on a clam biomass dataset spanning 28 yr and five distinct regions in the southern Salish Sea to determine (1) if native intertidal clam populations exhibit synchrony and (2) what environmental covariates may be correlated with these population trends. Once covariates were accounted for, the model with the most data support included three predominant trends to describe multidecadal change in clam biomass. Intraspecific synchrony was highest among Saxidomus gigantea and Leukoma staminea populations, with no clear evidence of covariance in Clinocardium nuttallii. Specifically, we quantified a pronounced decadal decline in L. staminea and an increase in S. gigantea biomass on most beaches. No beaches showed synchrony in trends across all three species, indicating that species‐specific trends (regardless of location) were more common than beach‐specific trends (regardless of species). Seven environmental covariates were evaluated in their capacity to explain variability in annual mean biomass. Of these, the North Pacific Gyre Oscillation lagged 4 yr prior to the observation year was most supported by the data in the best fitting model, implying that 4 yr old clam biomass is partially determined by oceanographic processes affecting larval clams. Although results suggest large‐scale density‐independent factors play a role in venerid clam population dynamics, it is also likely local factors account for variability not explained by our model.

Spatial synchrony of zooplankton during the impoundment of amazonic reservoir

The escalating demand for energy and the strong political and economic pressure to explore the Amazon’s hydroelectric potential has led to the increase of hydroelectric reservoirs in the region. Reservoirs are artificial ecosystems that modify the structure of biological populations. Evaluating the spatial synchrony of populations and environmental variables in the development of reservoirs may reveal determinant factors and processes operating in these ecosystems. This may help understand the impacts, optimize monitoring systems and develop strategies to improve and maintain the ecological integrity (or stability) of these environments. In this study, we evaluated the spatial synchrony in zooplankton populations and limnological environmental variables during the implementation of a hydroelectric reservoir in the Amazon region, on the border of the states of Pará and Amapá. Synchrony was determined by mean cross-correlation between the pairs of points for each variable, followed by Mantel test to investigate its influencing factors. We observed spatial synchronous values for both population and limnological environmental variables. However, we could not identify the mechanisms that are operating, indicating that more than one factor may be influencing the ecosystem dynamics. Population synchrony increased after impoundment. Population synchrony may be associated with some limnological variables, such as the ones related to the amounts of nutrients, whereas environmental synchrony can be related to extrinsic factors, such as climatic variation, not tested this study. In order to increase the understanding of the mechanisms operating over population and environmental dynamics, we suggest that, besides evaluating limnological environmental variables and spatial synchrony, analyses should include meteorological and landscape data.

Modeling driver cells in developing neuronal networks.

Spontaneous emergence of synchronized population activity is a characteristic feature of developing brain circuits. Recent experiments in the developing neo-cortex showed the existence of driver cells able to impact the synchronization dynamics when single-handedly stimulated. We have developed a spiking network model capable to reproduce the experimental results, thus identifying two classes of driver cells: functional hubs and low functionally connected (LC) neurons. The functional hubs arranged in a clique orchestrated the synchronization build-up, while the LC drivers were lately or not at all recruited in the synchronization process. Notwithstanding, they were able to alter the network state when stimulated by modifying the temporal activation of the functional clique or even its composition. LC drivers can lead either to higher population synchrony or even to the arrest of population dynamics, upon stimulation. Noticeably, some LC driver can display both effects depending on the received stimulus. We show that in the model the presence of inhibitory neurons together with the assumption that younger cells are more excitable and less connected is crucial for the emergence of LC drivers. These results provide a further understanding of the structural-functional mechanisms underlying synchronized firings in developing circuits possibly related to the coordinated activity of cell assemblies in the adult brain.