Phylogenetic diversity metrics for ecological communities: integrating species richness, abundance and evolutionary history

Phylogenetic information is increasingly being used to understand the assembly of biological communities and ecological processes. However, commonly used metrics of phylogenetic diversity (PD) do not incorporate information on the relative abundances of individuals within a community. In this study, we develop three indices of PD that explicitly consider species abundances. First, we present a metric of phylogenetic-abundance evenness that evaluates the relationship between the abundance and the distribution of terminal branch lengths. Second, we calculate an index of hierarchical imbalance of abundances at the clade level encapsulating the distribution of individuals across the nodes in the phylogeny. Third, we develop an index of abundance-weighted evolutionary distinctiveness and generate an entropic index of phylogenetic diversity that captures both information on evolutionary distances and phylogenetic tree topology, and also serves as a basis to evaluate species conservation value. These metrics offer measures of phylogenetic diversity incorporating different community attributes. We compare these new metrics to existing ones, and use them to explore diversity patterns in a typical California annual grassland plant community at the Jasper Ridge biological preserve.
Ecology Letters (2010) 13: 96–105     

Structural distance and evolutionary relationship of networks

Exploring common features and universal qualities shared by a particular class of networks in biological and other domains is one of the important aspects of evolutionary study. In an evolving system, evolutionary mechanism can cause functional changes that forces the system to adapt to new configurations of interaction pattern between the components of that system (e.g. gene duplication and mutation play a vital role for changing the connectivity structure in many biological networks. The evolutionary relation between two systems can be retraced by their structural differences). The eigenvalues of the normalized graph Laplacian not only capture the global properties of a network, but also local structures that are produced by graph evolutions (like motif duplication or joining). The spectrum of this operator carries many qualitative aspects of a graph. Given two networks of different sizes, we propose a method to quantify the topological distance between them based on the contrasting spectrum of normalized graph Laplacian. We find that network architectures are more similar within the same class compared to between classes. We also show that the evolutionary relationships can be retraced by the structural differences using our method. We analyze 43 metabolic networks from different species and mark the prominent separation of three groups: Bacteria, Archaea and Eukarya. This phenomenon is well captured in our findings that support the other cladistic results based on gene content and ribosomal RNA sequences. Our measure to quantify the structural distance between two networks is useful to elucidate evolutionary relationships.     

Strategies for cellular decision‐making

Stochasticity pervades life at the cellular level. Cells receive stochastic signals, perform detection and transduction with stochastic biochemistry, and grow and die in stochastic environments. Here we review progress in going from the molecular details to the information‐processing strategies cells use in their decision‐making. Such strategies are fundamentally influenced by stochasticity. We argue that the cellular decision‐making can only be probabilistic and occurs at three levels. First, cells must infer from noisy signals the probable current and anticipated future state of their environment. Second, they must weigh the costs and benefits of each potential response, given that future. Third, cells must decide in the presence of other, potentially competitive, decision‐makers. In this context, we discuss cooperative responses where some individuals can appear to sacrifice for the common good. We believe that decision‐making strategies will be conserved, with comparatively few strategies being implemented by different biochemical mechanisms in many organisms. Determining the strategy of a decision‐making network provides a potentially powerful coarse‐graining that links systems and evolutionary biology to understand biological design.     

Developing the genotype-to-phenotype relationship in evolutionary theory: A primer of developmental features

For decades, there have been repeated calls for more integration across evolutionary and developmental biology. However, critiques in the literature and recent funding initiatives suggest this integration remains incomplete. We suggest one way forward is to consider how we elaborate the most basic concept of development, the relationship between genotype and phenotype, in traditional models of evolutionary processes. For some questions, when more complex features of development are accounted for, predictions of evolutionary processes shift. We present a primer on concepts of development to clarify confusion in the literature and fuel new questions and approaches. The basic features of development involve expanding a base model of genotype-to-phenotype to include the genome, space, and time. A layer of complexity is added by incorporating developmental systems, including signal-response systems and networks of interactions. The developmental emergence of function, which captures developmental feedbacks and phenotypic performance, offers further model elaborations that explicitly link fitness with developmental systems. Finally, developmental features such as plasticity and developmental niche construction conceptualize the link between a developing phenotype and the external environment, allowing for a fuller inclusion of ecology in evolutionary models. Incorporating aspects of developmental complexity into evolutionary models also accommodates a more pluralistic focus on the causal importance of developmental systems, individual organisms, or agents in generating evolutionary patterns. Thus, by laying out existing concepts of development, and considering how they are used across different fields, we can gain clarity in existing debates around the extended evolutionary synthesis and pursue new directions in evolutionary developmental biology. Finally, we consider how nesting developmental features in traditional models of evolution can highlight areas of evolutionary biology that need more theoretical attention.     

Cooperation in Microbial Populations: Theory and Experimental Model Systems

Cooperative behavior, the costly provision of benefits to others, is common across all domains of life. This review article discusses cooperative behavior in the microbial world, mediated by the exchange of extracellular products called public goods. We focus on model species for which the production of a public good and the related growth disadvantage for the producing cells are well described. To unveil the biological and ecological factors promoting the emergence and stability of cooperative traits we take an interdisciplinary perspective and review insights gained from both mathematical models and well-controlled experimental model systems. Ecologically, we include crucial aspects of the microbial life cycle into our analysis and particularly consider population structures where ensembles of local communities (subpopulations) continuously emerge, grow, and disappear again. Biologically, we explicitly consider the synthesis and regulation of public good production. The discussion of the theoretical approaches includes general evolutionary concepts, population dynamics, and evolutionary game theory. As a specific but generic biological example, we consider populations of Pseudomonas putida and its regulation and use of pyoverdines, iron scavenging molecules, as public goods. The review closes with an overview on cooperation in spatially extended systems and also provides a critical assessment of the insights gained from the experimental and theoretical studies discussed. Current challenges and important new research opportunities are discussed, including the biochemical regulation of public goods, more realistic ecological scenarios resembling native environments, cell-to-cell signaling, and multispecies communities.     

Disturbance, interspecific interaction and diversity in metapopulations

Metapopulation diversity patterns depend on the relations among the timescales of local biological interactions (predation, competition), the rates of dispersal among local populations and the patterns of disturbance. We investigate these relationships using a family of simple non-linear Markov chain models. We consider three models for interspecific competition; if the species are identified with early and late successional species, the models describe the facilitation, inhibition and tolerance models of ecological succession. By adding a third competing species we also compare transitive competitive hierarchies and intransitive competitive networks. Finally, we examine the effects of predation in mediating coexistence among competing prey species. In each model we find circumstances in which biotic or abiotic disturbance can increase both local and regional diversity, but those circumstances depend on the various timescales in the model in ways that arc neither obvious nor trivial.     

Individual cell-based models of the spatial-temporal organization of multicellular systems--achievements and limitations.

Computational approaches of multicellular assemblies have reached a stage where they may contribute to unveil the processes that underlie the organization of tissues and multicellular aggregates. In this article, we briefly review and present some new results on a number of 3D lattice free individual cell-based mathematical models of epithelial cell populations. The models we consider here are parameterized by bio-physical and cell-biological quantities on the level of an individual cell. Eventually, they aim at predicting the dynamics of the biological processes on the tissue level. We focus on a number of systems, the growth of cell populations in vitro, and the spatial-temporal organization of regenerative tissues. For selected examples we compare different model approaches and show that the qualitative results are robust with respect to many model details. Hence, for the qualitative features and largely for the quantitative features many model details do not matter as long as characteristic biological features and mechanisms are correctly represented. For a quantitative prediction, the control of the bio-physical and cell-biological parameters on the molecular scale has to be known. At this point, slide-based cytometry may contribute. It permits to track the fate of cells and other tissue subunits in time and validated the organization processes predicted by the mathematical models.     

Introgression of resistance genes between populations: a model study of insecticide resistance in Bemisia tabaci

Introgression is a key process in conservation biology, genetic modification of (crop) species and in the evolutionary ecology of many species. Here we consider the case of introgression of insecticide resistance in the whitefly, Bemisia tabaci. B. tabaci is a species complex consisting of a range of biotypes, known to have a high degree of inter-biotype reproductive isolation. In areas where insecticide resistant and susceptible biotypes of B. tabaci coexist, introgression of the resistance gene will have considerable consequences for whitefly control. Using a stochastic branching process model we calculate the relative importance of life-history traits in determining the probability of introgression given that a hybridization event has occurred. We show that a fitness cost expressed through the average number of eggs laid, has the largest effect on the introgression probability as compared to fitness costs expressed through other life-history parameters. These results change when we consider a reproductive isolation mechanism, for which we show that the fitness cost expressed through the male survival and mating probability have the largest effect on the probability of introgression.     

分子进化研究：Ⅱ.5SrRNA序列的分形与分子进化的关系

本文根据分形理论的原理和方法,在对现行的计算核酸序列分维的方法进行修改的基础上,对各类生物的80余种5SrRNA序列的分维进行了计算,并结合耗散结构理论就其分维与分子进化的关系问题进行了研究和探讨。作者认为,5SrRNA序列的分维与其分子进化间的关系是一种复杂的非线性关系,在分子进化的过程中,序列的分维表现为随机涨落。     

Stochasticity Versus Determinism: Consequences for Realistic Gene Regulatory Network Modelling and Evolution

Gene regulation is one important mechanism in producing observed phenotypes and heterogeneity. Consequently, the study of gene regulatory network (GRN) architecture, function and evolution now forms a major part of modern biology. However, it is impossible to experimentally observe the evolution of GRNs on the timescales on which living species evolve. In silico evolution provides an approach to studying the long-term evolution of GRNs, but many models have either considered network architecture from non-adaptive evolution, or evolution to non-biological objectives. Here, we address a number of important modelling and biological questions about the evolution of GRNs to the realistic goal of biomass production. Can different commonly used simulation paradigms, in particular deterministic and stochastic Boolean networks, with and without basal gene expression, be used to compare adaptive with non-adaptive evolution of GRNs? Are these paradigms together with this goal sufficient to generate a range of solutions? Will the interaction between a biological goal and evolutionary dynamics produce trade-offs between growth and mutational robustness? We show that stochastic basal gene expression forces shrinkage of genomes due to energetic constraints and is a prerequisite for some solutions. In systems that are able to evolve rates of basal expression, two optima, one with and one without basal expression, are observed. Simulation paradigms without basal expression generate bloated networks with non-functional elements. Further, a range of functional solutions was observed under identical conditions only in stochastic networks. Moreover, there are trade-offs between efficiency and yield, indicating an inherent intertwining of fitness and evolutionary dynamics.     

AgentCell: a digital single-cell assay for bacterial chemotaxis

MOTIVATION: In recent years, single-cell biology has focused on the relationship between the stochastic nature of molecular interactions and variability of cellular behavior. To describe this relationship, it is necessary to develop new computational approaches at the single-cell level. RESULTS: We have developed AgentCell, a model using agent-based technology to study the relationship between stochastic intracellular processes and behavior of individual cells. As a test-bed for our approach we use bacterial chemotaxis, one of the best characterized biological systems. In this model, each bacterium is an agent equipped with its own chemotaxis network, motors and flagella. Swimming cells are free to move in a 3D environment. Digital chemotaxis assays reproduce experimental data obtained from both single cells and bacterial populations.     

Principles of evolutionary learning design for a stochastic neural network

The authors develop principles for evolutionary learning typical of biological systems and demonstrate how these principles can be realized with a formal stochastic network.     

The island rule and a research agenda for studying ecogeographical patterns

We are currently experiencing a resurgence of interest in ecogeographical rules, which describe general trends in morphology and related traits along geographical gradients. In order to develop a more comprehensive understanding of the generality and underlying causal mechanisms for these patterns, we recommend a new, more integrated research agenda. In particular, we recommend studies that simultaneously consider different clines in morphology, geographical ranges and diversity as intricately related phenomena; all being ecological, evolutionary and biogeographical responses of organisms to selection regimes that vary non-randomly over space and time, and among species with different ecological and evolutionary histories.     

A Scalable Computational Framework for Establishing Long-Term Behavior of Stochastic Reaction Networks

Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics. We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values. The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs, well-known for their tractability. It is notably shown that the computational complexity is often linear in the number of species. We illustrate the validity, the efficiency and the wide applicability of our results on several reaction networks arising in biochemistry, systems biology, epidemiology and ecology. The biological implications of the results as well as an example of a non-ergodic biological network are also discussed.     

Mobility unevenness in rock–paper–scissors models

We investigate a tritrophic system whose cyclic dominance is modelled by the rock–paper–scissors game. We consider that organisms of one or two species are affected by movement limitations, which unbalances the cyclic spatial game. Performing stochastic simulations, we show that mobility unevenness controls the population dynamics. In the case of one slow species, the predominant species depends on the level of mobility restriction, with the slow species being preponderant if the mobility limitations are substantial. If two species face mobility limitations, our outcomes show that being higher dispersive does not constitute an advantage in terms of population growth. On the contrary, if organisms move with higher mobility, they expose themselves to enemies more frequently, being more vulnerable to being eliminated. Finally, our findings show that biodiversity benefits in regions where species are slowed. Biodiversity loss for high mobility organisms, common to cyclic systems, may be avoided with coexistence probability being higher for robust mobility limitations. Our results may help biologists understand the dynamics of unbalanced spatial systems where organisms’ dispersal is fundamental to biodiversity conservation.     

Evolution and development — the nematode vulva as a case study

To understand how morphological characters change during evolution, we need insight into the evolution of developmental processes. Comparative developmental approaches that make use of our fundamental understanding of development in certain model organisms have been initiated for different animal systems and flowering plants. Nematodes provide a useful experimental system with which to investigate the genetic and molecular alterations underlying evolutionary changes of cell fate specification in development, by comparing different species to the genetic model system Caenorhabditis elegans. In this review, I will first discuss the different types of evolutionary alterations seen at the cellular level by focusing mainly on the analysis of vulva development in different species. The observed alterations involve changes in cell lineage, cell migration and cell death, as well as induction and cell competence. I then describe a genetic approach in the nematode Pristionchus pacificus that might identify those genetic and molecular processes that cause evolutionary changes of cell fate specification.