An efficient method for stochastic simulation of biological populations in continuous time

Priority effects and the macroevolutionary dynamics of biodiversity

Priority effects can play a fundamental role in the assembly of ecological communities, but how they shape the dynamics of biodiversity over macroevolutionary timescales remains unclear. Here we develop and analyse a metacommunity model combining local priority effects with niche evolution, speciation and extinction. We show that by promoting the persistence of rare species, local priority effects cause the evolution of higher metacommunity diversity as well as major disparities in richness among evolutionary lineages. However, we also show how classic macroevolutionary patterns of niche incumbency-whereby rates of regional diversification and invasion slow down as ecological niches are filled-do not depend on local priority effects, arising even when invading species continuously displace residents. Together, these results clarify the connection between local priority effects and the filling of ecological niche space, and reveal how the impact of species arrival order on competition fundamentally shapes the generation and maintenance of biodiversity.

Analytical treatment for cyclic three-state dynamics on static networks

Whenever a dynamical process unfolds on static networks, the dynamical state of any focal individual will be exclusively influenced by directly connected neighbors, rather than by those unconnected ones, hence the arising of the dynamical correlation problem, where mean-field-based methods fail to capture the scenario. The dynamic correlation coupling problem has always been an important and difficult problem in the theoretical field of physics. The explicit analytical expressions and the decoupling methods often play a key role in the development of corresponding field. In this paper, we study the cyclic three-state dynamics on static networks, which include a wide class of dynamical processes, for example, the cyclic Lotka-Volterra model, the directed migration model, the susceptible-infected-recovered-susceptible epidemic model, and the predator-prey with empty sites model. We derive the explicit analytical solutions of the propagating size and the threshold curve surface for the four different dynamics. We compare the results on static networks with those on annealed networks and made an interesting discovery: for the symmetrical dynamical model (the cyclic Lotka-Volterra model and the directed migration model, where the three states are of rotational symmetry), the macroscopic behaviors of the dynamical processes on static networks are the same as those on annealed networks; while the outcomes of the dynamical processes on static networks are different with, and more complicated than, those on annealed networks for asymmetric dynamical model (the susceptible-infected-recovered-susceptible epidemic model and the predator-prey with empty sites model). We also compare the results forecasted by our theoretical method with those by Monte Carlo simulations and find good agreement between the results obtained by the two methods.

Reducing Spreading Processes on Networks to Markov Population Models

Stochastic processes on complex networks, where each node is in one of several compartments, and neighboring nodes interact with each other, can be used to describe a variety of real-world spreading phenomena. However, computational analysis of such processes is hindered by the enormous size of their underlying state space.

In this work, we demonstrate that lumping can be used to reduce any epidemic model to a Markov Population Model (MPM). Therefore, we propose a novel lumping scheme based on a partitioning of the nodes. By imposing different types of counting abstractions, we obtain coarse-grained Markov models with a natural MPM representation that approximate the original systems. This makes it possible to transfer the rich pool of approximation techniques developed for MPMs to the computational analysis of complex networks’ dynamics.

We present numerical examples to investigate the relationship between the accuracy of the MPMs, the size of the lumped state space, and the type of counting abstraction.

The evolution of plasmid stability: Are infectious transmission and compensatory evolution competing evolutionary trajectories?

Conjugative plasmids are widespread and play an important role in bacterial evolution by accelerating adaptation through horizontal gene transfer. However, explaining the long-term stability of plasmids remains challenging because segregational loss and the costs of plasmid carriage should drive the loss of plasmids though purifying selection. Theoretical and experimental studies suggest two key evolutionary routes to plasmid stability: First, the evolution of high conjugation rates would allow plasmids to survive through horizontal transmission as infectious agents, and second, compensatory evolution to ameliorate the cost of plasmid carriage can weaken purifying selection against plasmids. How these two evolutionary strategies for plasmid stability interact is unclear. Here, we summarise the literature on the evolution of plasmid stability and then use individual based modelling to investigate the evolutionary interplay between the evolution of plasmid conjugation rate and cost amelioration. We find that, individually, both strategies promote plasmid stability, and that they act together to increase the likelihood of plasmid survival. However, due to the inherent costs of increasing conjugation rate, particularly where conjugation is unlikely to be successful, our model predicts that amelioration is the more likely long-term solution to evolving stable bacteria-plasmid associations. Our model therefore suggests that bacteria-plasmid relationships should evolve towards lower plasmid costs that may forestall the evolution of highly conjugative, 'infectious' plasmids.

A new dynamic null model for phylogenetic community structure

Phylogenies are increasingly applied to identify the mechanisms structuring ecological communities but progress has been hindered by a reliance on statistical null models that ignore the historical process of community assembly. Here, we address this, and develop a dynamic null model of assembly by allopatric speciation, colonisation and local extinction. Incorporating these processes fundamentally alters the structure of communities expected due to chance, with speciation leading to phylogenetic overdispersion compared to a classical statistical null model assuming equal probabilities of community membership. Applying this method to bird and primate communities in South America we show that patterns of phylogenetic overdispersion – often attributed to negative biotic interactions – are instead consistent with a species neutral model of allopatric speciation, colonisation and local extinction. Our findings provide a new null expectation for phylogenetic community patterns and highlight the importance of explicitly accounting for the dynamic history of assembly when testing the mechanisms governing community structure.

The impact of heterogeneous transmission on the establishment and spread of antimalarial drug resistance

Despite the important insights gained by extending the classical models of malaria, other factors, such as immunity, heterogeneous biting, and differential patterns of drug use have not been fully explored due to the complexity of modeling multiple simultaneous malaria infections competing within a host. Understanding these factors is important for understanding how to control the spread of drug resistance to artemisinin which is just emerging in Southeast Asia. The emergence of resistance plays out at the population level, but is the result of competition within individuals for transmission events. Most studies of drug resistance evolution have focused on transmission between hosts and ignored the role of within-host competition due to the inherent complexity of modeling at multiple scales. To embed within-host competition in the model, we used an agent-based framework that was developed to understand how deviations from the classical assumptions of the Ross-MacDonald type models, which have been well-described and analyzed, impact the dynamics of disease. While structured to be a stochastic analog to classical Ross-Macdonald type models, the model is nonetheless based on individuals, and thus aspects of within-host competition can be explored. We use this framework to explore the role of heterogeneous biting and transmission on the establishment and spread of resistance in a population. We find that heterogeneous transmission slows the establishment of resistance in a population, but once resistance is established, it speeds the spread of resistance through the population. These results are due to the skewed distribution of biting which makes onward transmission a low probability and suggests that targeting the "core" group of individuals that provide the vast majority of bites could significantly slow the spread of resistance.

Largenet2: an object-oriented programming library for simulating large adaptive networks

SUMMARY

The largenet2 C++ library provides an infrastructure for the simulation of large dynamic and adaptive networks with discrete node and link states.

AVAILABILITY

The library is released as free software. It is available at http://biond.github.com/largenet2. Largenet2 is licensed under the Creative Commons Attribution-NonCommercial 3.0 Unported License.

CONTACT

gerd@biond.org

Female choosiness leads to the evolution of individually distinctive males

Individual recognition is a taxonomically widespread ability that underlies a diverse suite of behaviors including the identification of individual nest-mates, agonistic opponents, and mating partners. However, as yet relatively little is known about the circumstances under which the requisite signal diversity can evolve. Here, we develop a model describing a novel mechanism of individual identity evolution via sexual selection. Females choose among a subset of males, but can select the most attractive male only when he bears a unique identity signal. This mimics a species in which mate assessment and choice are temporally separate, such as when females observe males in direct conflict and must subsequently locate the winner. When females in our model are choosy at least 10% of the time, diversity at individuality signaling loci evolves as a by-product of selection on male attractiveness more rapidly than does diversity at equivalent loci evolving only under neutral processes. Even at lower discrimination rates, drifting signal diversity gives the female choice mechanism sufficient traction to drive up average male attractiveness. The mechanism we describe here can significantly increase signal diversity at even low rates of discrimination by females.