Space and stochasticity in population dynamics.
Ontology highlight
ABSTRACT:
SUBMITTER: Ovaskainen O
PROVIDER: S-EPMC1568924 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Population fluctuations are generally attributed to the deterministic consequences of strong non-linear interactions among organisms, or the effects of random stochastic environmental variation superimposed upon the deterministic skeleton describing population change. Analysis of the population dynamics of the mussel Mytilus californianus taken in 16 plots over 18-years found no evidence that these processes explained observed strong fluctuations. Instead, population fluctuations arose because environmental stochasticity varied with abundance, which we term density-linked stochasticity. This phenomenon arises from biologically relevant mechanisms: recruitment variation and transmission of disturbance among neighboring individuals. Density-linked stochasticity is probably present frequently in populations, as it arises naturally from several general ecological processes, including stage structure variation with density, ontogenetic niche shifts, and local transmission of stochastic perturbations. More thoroughly characterizing and interpreting deviations from the mean behavior of a system will lead to better ecological prediction and improved insight into the important processes affecting populations and ecosystems.
| S-EPMC3781081 | biostudies-literature
Project description:Stochasticity alters the nonlinear dynamics of inherently cycling populations. The power spectrum can describe and explain the impacts of stochasticity. We fitted models to short observed time series of flour beetle populations in the frequency domain, then used a well fitting stochastic mechanistic model to generate detailed predictions of population spectra. Some predicted spectral peaks represent periodic phenomena induced or modified by stochasticity and were experimentally confirmed. For one experimental treatment, linearization theory explained that these peaks represent overcompensatory decay of deviations from deterministic oscillation. In another treatment, stochasticity caused frequent directional phase shifting around a cyclic attractor. This directional phase shifting was not explained by linearization theory and modified the periodicity of the system. If field systems exhibit directional phase shifting, then changing the intensity of demographic or environmental noise while holding constant the structure of the noise can change the main frequency of population fluctuations.
| S-EPMC1693752 | biostudies-literature
Project description:BACKGROUND:Members of the Culex pipiens complex (Cx. pipiens quinquefasciatus in Southern USA) play a critical role in the spillover of urban arboviruses such as West Nile virus or St. Louis encephalitis virus. Field studies have shown strong correlation between the periodicity of rainfall events and larval proliferation. However, mechanistic determinants driving this relationship are poorly understood. We hypothesize that rainfall events decrease strain from intraspecific competition through the associated reduction of immature density and the introduction of detritus. RESULTS:To address our hypothesis, we used laboratory competition experiments to inform a deterministic matrix projection model consisting of an age-structured larval matrix coupled with a stage-structured adult mosquito matrix. Rain events were simulated in a competition-based metabolic age model and compared to a null model including environmental variability. Variable rain delays in two-event simulations showed optimal proliferation occurring with rain delays between 16 and 21 days when including density-dependent effects. CONCLUSIONS:These results are comparable to the pattern observed in natural populations, indicating that Cx. quinquefasciatus proliferation rates can be modeled mechanistically as a density-dependent system. The empirical understanding of density-dependence as it relates to environmental stochasticity provides a theoretical platform for the study of larval dynamics and the impact of larval control in this medically relevant disease vector.
| S-EPMC5828118 | biostudies-literature
Project description:Underdominance systems can quickly spread through a population, but only when introduced in considerable numbers. This promises a gene drive mechanism that is less invasive than homing drives, potentially enabling new approaches in the fight against vector-borne diseases. If regional confinement can indeed be achieved, the decision-making process for a release would likely be much simpler compared to other, more invasive types of drives. The capacity of underdominance gene drive systems to spread in a target population without invading other populations is typically assessed via network models of panmictic demes linked by migration. However, it remains less clear how such systems would behave in more realistic population models where organisms move over a continuous landscape. Here, we use individual-based simulations to study the dynamics of several proposed underdominance systems in continuous-space. We find that all these systems can fail to persist in such environments, even after an initially successful establishment in the release area, confirming previous theoretical results from diffusion theory. At the same time, we find that a two-locus two-toxin-antidote system can invade connected demes through a narrow migration corridor. This suggests that the parameter space where underdominance systems can establish and persist in a release area while at the same time remaining confined to that area could be quite limited, depending on how a population is spatially structured. Overall, these results indicate that realistic spatial context must be considered when assessing strategies for the deployment of underdominance drives.
| S-EPMC8284921 | biostudies-literature
Project description:Conventional bacterial growth studies rely on large bacterial populations without considering the individual cells. Individual cells, however, can exhibit marked behavioral heterogeneity. Here, we present experimental observations on the colonial growth of 220 individual cells of Salmonella enterica serotype Typhimurium using time-lapse microscopy videos. We found a highly heterogeneous behavior. Some cells did not grow, showing filamentation or lysis before division. Cells that were able to grow and form microcolonies showed highly diverse growth dynamics. The quality of the videos allowed for counting the cells over time and estimating the kinetic parameters lag time (?) and maximum specific growth rate (?max) for each microcolony originating from a single cell. To interpret the observations, the variability of the kinetic parameters was characterized using appropriate probability distributions and introduced to a stochastic model that allows for taking into account heterogeneity using Monte Carlo simulation. The model provides stochastic growth curves demonstrating that growth of single cells or small microbial populations is a pool of events each one of which has its own probability to occur. Simulations of the model illustrated how the apparent variability in population growth gradually decreases with increasing initial population size (N(0)). For bacterial populations with N(0) of >100 cells, the variability is almost eliminated and the system seems to behave deterministically, even though the underlying law is stochastic. We also used the model to demonstrate the effect of the presence and extent of a nongrowing population fraction on the stochastic growth of bacterial populations.
| S-EPMC3623257 | biostudies-literature
Project description:Stem cell differentiation underlies many fundamental processes such as development, tissue growth and regeneration, as well as disease progression. Understanding how stem cell differentiation is controlled in mixed cell populations is an important step in developing quantitative models of cell population dynamics. Here we focus on quantifying the role of cell-cell interactions in determining stem cell fate. Toward this, we monitor stem cell differentiation in adherent cultures on micropatterns and collect statistical cell fate data. Results show high cell fate variability and a bimodal probability distribution of stem cell fraction on small (80-140 μm diameter) micropatterns. On larger (225-500 μm diameter) micropatterns, the variability is also high but the distribution of the stem cell fraction becomes unimodal. Using a stochastic model, we analyze the differentiation dynamics and quantitatively determine the differentiation probability as a function of stem cell fraction. Results indicate that stem cells can interact and sense cellular composition in their immediate neighborhood and adjust their differentiation probability accordingly. Blocking epithelial cadherin (E-cadherin) can diminish this cell-cell contact mediated sensing. For larger micropatterns, cell motility adds a spatial dimension to the picture. Taken together, we find stochasticity and cell-cell interactions are important factors in determining cell fate in mixed cell populations.
| S-EPMC4521170 | biostudies-literature
Project description:BackgroundRespiratory viral infections are a leading cause of mortality worldwide. As many as 40% of patients hospitalized with influenza-like illness are reported to be infected with more than one type of virus. However, it is not clear whether these infections are more severe than single viral infections. Mathematical models can be used to help us understand the dynamics of respiratory viral coinfections and their impact on the severity of the illness. Most models of viral infections use ordinary differential equations (ODE) that reproduce the average behavior of the infection, however, they might be inaccurate in predicting certain events because of the stochastic nature of viral replication cycle. Stochastic simulations of single virus infections have shown that there is an extinction probability that depends on the size of the initial viral inoculum and parameters that describe virus-cell interactions. Thus the coinfection dynamics predicted by the ODE might be difficult to observe in reality.ResultsIn this work, a continuous-time Markov chain (CTMC) model is formulated to investigate probabilistic outcomes of coinfections. This CTMC model is based on our previous coinfection model, expressed in terms of a system of ordinary differential equations. Using the Gillespie method for stochastic simulation, we examine whether stochastic effects early in the infection can alter which virus dominates the infection.ConclusionsWe derive extinction probabilities for each virus individually as well as for the infection as a whole. We find that unlike the prediction of the ODE model, for similar initial growth rates stochasticity allows for a slower growing virus to out-compete a faster growing virus.
| S-EPMC6469119 | biostudies-literature
Project description:Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multimolecular structures embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computer simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However, it is often difficult to reconcile conflicting computational results that use different approaches to describe the same phenomenon. To address this issue systematically, we have defined a series of computational test cases ranging from very simple to moderately complex, varying key features of dimensionality, reaction type, reaction speed, crowding, and cell size. We then quantified how explicit spatial and/or stochastic implementations alter outcomes, even when all methods use the same reaction network, rates, and concentrations. For simple cases, we generally find minor differences in solutions of the same problem. However, we observe increasing discordance as the effects of localization, dimensionality reduction, and irreversible enzymatic reactions are combined. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision making by researchers developing new models. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.
| S-EPMC8120688 | biostudies-literature
Project description:Spatio-temporal changes in genetic structure among populations provide crucial information on the dynamics of secondary spread for introduced marine species. However, temporal components have rarely been taken into consideration when studying the population genetics of non-indigenous species. This study analysed the genetic structure of Styela plicata, a solitary ascidian introduced in harbours and marinas of tropical and temperate waters, across spatial and temporal scales. A fragment of the mitochondrial gene Cytochrome Oxidase subunit I (COI) was sequenced from 395 individuals collected at 9 harbours along the NW Mediterranean coast and adjacent Atlantic waters (> 1,200 km range) at two time points 5 years apart (2009 and 2014). The levels of gene diversity were relatively low for all 9 locations in both years. Analyses of genetic differentiation and distribution of molecular variance revealed strong genetic structure, with significant differences among many populations, but no significant differences among years. A weak and marginally significant correlation between geographic distance and gene differentiation was found. Our results revealed spatial structure and temporal genetic homogeneity in S. plicata, suggesting a limited role of recurrent, vessel-mediated transport of organisms among small to medium-size harbours. Our study area is representative of many highly urbanized coasts with dense harbours. In these environments, the episodic chance arrival of colonisers appears to determine the genetic structure of harbour populations and the genetic composition of these early colonising individuals persists in the respective harbours, at least over moderate time frames (five years) that encompass ca. 20 generations of S. plicata.
| S-EPMC4924124 | biostudies-literature
Project description:There is increasing recognition that stochasticity involved in gene regulatory processes may help cells enhance the signal or synchronize expression for a group of genes. Thus the validity of the traditional deterministic approach to modeling the foregoing processes cannot be without exception. In this study, we identify a frequently encountered situation, i.e., the biofilm, which has in the past been persistently investigated with intracellular deterministic models in the literature. We show in this paper circumstances in which use of the intracellular deterministic model appears distinctly inappropriate. In Enterococcus faecalis, the horizontal gene transfer of plasmid spreads drug resistance. The induction of conjugation in planktonic and biofilm circumstances is examined here with stochastic as well as deterministic models. The stochastic model is formulated with the Chemical Master Equation (CME) for planktonic cells and Reaction-Diffusion Master Equation (RDME) for biofilm. The results show that although the deterministic model works well for the perfectly-mixed planktonic circumstance, it fails to predict the averaged behavior in the biofilm, a behavior that has come to be known as stochastic focusing. A notable finding from this work is that the interception of antagonistic feedback loops to signaling, accentuates stochastic focusing. Moreover, interestingly, increasing particle number of a control variable could lead to an even larger deviation. Intracellular stochasticity plays an important role in biofilm and we surmise by implications from the model, that cell populations may use it to minimize the influence from environmental fluctuation.
| S-EPMC3827321 | biostudies-literature