Ecology of Infectious Disease: Forging an Alliance

The Ecology of Infectious Diseases (EID) program is a joint National Science Foundation–National Institutes of Health initiative to produce predictive understanding of disease dynamics, with a focus on diseases with an environmental component. The interdisciplinary research projects funded by this program take advantage of the wide range of theoretical and methodological advances developed over the past 30 years. The challenge for disease ecology is to unravel these systems, discover how complex they truly are, and to determine if they can be predicted and controlled using targeted environmental, public health, or medical interventions. Between 1999 and 2005, a total of 42 research awards were made under the EID program. EID projects have had affects on policy in two areas: adoption of novel interventions on a local scale and use of models by government agencies for the purpose of allocating public health resources. The past 6 years have been an exciting time for the field of disease ecology; we expect the coming years to be even more exciting and productive. As US federal government employees writing an article as part of our official duties, copyright of all publications is retained by the US government. The views expressed here by Samuel M. Scheiner and Joshua P. Rosenthal are those of the authors and do not represent official views or policies of the National Science Foundation, the National Institutes of Health, or the United States Government.     

Myxomycetes:Biology,Systematics,Biogeography,and Ecology

正黏菌是丰富多彩的生物多样性世界提供给人类的一个独特类群。Myxomycetes:Biology,Systematics,Biogeography,and Ecology是迄今为止全方位、多视角展示黏菌生物学的第一本巨著。由美国阿肯色大学Steven L.Stephenson教授等活跃在当今黏菌各研究领域的,以及来自全球11个国家的29位国际知名黏菌学者共同完成。全书按照领域和专题分为13章,由不同作者分别编写,中国工程院院士、《菌物研究》主编、吉林农业大学李玉教授及其团队成员工琦教授和刘朴教授是"第六章黏菌生理学和生物化学"的作者。     

The Impact of Ecology and Biogeography on Legume Diversity,Endemism, and Phylogeny in the Caribbean Region: A New Direction in Historical Biogeography

The legume family is so well represented in the Caribbean that if a preserve was needed somewhere on earth to harbor all of the primary lineages in this family, the flora of just Cuba would suffice. Molecular phylogenetic, biogeographic, and evolutionary rates analysis all suggest that legume diversity and endemism in the Caribbean are mostly of recent origin and are likely a function of the abundance of seasonally dry tropical forests (SDTFs) throughout the neotropics. Legumes have a strong ecological affinity for SDTFs, and the Caribbean basin is well covered by this forest type. Rate-variable molecular clock analysis suggests that the majority of worldwide island lineages of legumes have ages of much less than 30 Ma. Singular historical events invoking land bridges or mobile continental plates are thus not needed to explain Caribbean legume diversity and endemism. The Greater Antilles are large islands located close to the American continent. They are therefore expected to fairly represent the diverse continental lineages of legumes. Yet, they are distant enough to be dispersal limited. As such, island lineages can speciate and diversify over evolutionary time unimpeded by high rates of immigration from the mainland. Vicariance and other standard phylogenetic methods of historical biogeography are likely to be replaced by those of ecological and island biogeography. This is because model selection approaches derived from the neutral concept of isolation by distance will be able to quantify patterns of alpha and beta diversity and detect niche assembly and phylogenetic niche conservatism within and among metacommunities that are hypothesized to constrain phylogeny.     

Biogeography of Human Infectious Diseases: A Global Historical Analysis

Objectives

Human pathogen richness and prevalence vary widely across the globe, yet we know little about whether global patterns found in other taxa also predict diversity in this important group of organisms. This study (a) assesses the relative importance of temperature, precipitation, habitat diversity, and population density on the global distributions of human pathogens and (b) evaluates the species-area predictions of island biogeography for human pathogen distributions on oceanic islands.

Methods

Historical data were used in order to minimize the influence of differential access to modern health care on pathogen prevalence. The database includes coded data (pathogen, environmental and cultural) for a worldwide sample of 186 non-industrial cultures, including 37 on islands. Prevalence levels for 10 pathogens were combined into a pathogen prevalence index, and OLS regression was used to model the environmental determinants of the prevalence index and number of pathogens.

Results

Pathogens (number and prevalence index) showed the expected latitudinal gradient, but predictors varied by latitude. Pathogens increased with temperature in high-latitude zones, while mean annual precipitation was a more important predictor in low-latitude zones. Other environmental factors associated with more pathogens included seasonal dry extremes, frost-free climates, and human population density outside the tropics. Islands showed the expected species-area relationship for all but the smallest islands, and the relationship was not mediated by habitat diversity. Although geographic distributions of free-living and parasitic taxa typically have different determinants, these data show that variables that influence the distribution of free-living organisms also shape the global distribution of human pathogens. Understanding the cause of these distributions is potentially important, since geographical variation in human pathogens has an important influence on global disparities in human welfare.     

Phylodynamics of Infectious Disease Epidemics

We present a formalism for unifying the inference of population size from genetic sequences and mathematical models of infectious disease in populations. Virus phylogenies have been used in many recent studies to infer properties of epidemics. These approaches rely on coalescent models that may not be appropriate for infectious diseases. We account for phylogenetic patterns of viruses in susceptible–infected (SI), susceptible–infected–susceptible (SIS), and susceptible–infected–recovered (SIR) models of infectious disease, and our approach may be a viable alternative to demographic models used to reconstruct epidemic dynamics. The method allows epidemiological parameters, such as the reproductive number, to be estimated directly from viral sequence data. We also describe patterns of phylogenetic clustering that are often construed as arising from a short chain of transmissions. Our model reproduces the moments of the distribution of phylogenetic cluster sizes and may therefore serve as a null hypothesis for cluster sizes under simple epidemiological models. We examine a small cross-sectional sample of human immunodeficiency (HIV)-1 sequences collected in the United States and compare our results to standard estimates of effective population size. Estimated prevalence is consistent with estimates of effective population size and the known history of the HIV epidemic. While our model accurately estimates prevalence during exponential growth, we find that periods of decline are harder to identify.COALESCENT theory has found wide applications for inference of viral phylogenies (Nee et al. 1996; Rosenberg and Nordborg 2002; Drummond et al. 2005) and estimation of epidemic prevalence (Yusim et al. 2001; Robbins et al. 2003; Wilson et al. 2005), yet there have been few attempts to formally integrate coalescent theory with standard epidemiological models (Pybus et al. 2001; Goodreau 2006). While epidemiological models such as susceptible–infected–recovered (SIR) consider the dynamics of an entire population going forward in time, the coalescent theory operates on a small sample of an infected subpopulation and models the merging of lineages backward in time until a common ancestor has been reached. The original coalescent theory was based on a population of constant size with discrete generations (Kingman 1982a,b). Numerous extensions have been made for populations with overlapping generations in continuous time, exponential or logistic growth (Griffiths and Tavare 1994), and stochastically varying size (Kaj and Krone 2003). However, infectious disease epidemics are a special case of a variable size population, often characterized by early explosive growth followed by decline that leads to extinction or an endemic steady state.If superinfection is rare and if mutation is fast relative to epidemic growth, each lineage in a phylogenetic tree corresponds to a single infected individual with its own unique viral population. An infection event viewed in reverse time is equivalent to the coalescence of two lineages and every transmission of the virus between hosts can generate a new branch in the phylogeny of consensus viral isolates from infected individuals. Recently diverged sequences should represent transmissions in the recent past, and branches close to the root of a tree should represent transmissions from long ago. Consequently, branching patterns provide information about the frequency of transmissions over time (Wilson et al. 2005). The correspondence between transmission and phylogenetic branching is easiest to detect for viruses such as human immunodeficiency virus (HIV) and hepatitis C virus that have a high mutation rate relative to dispersal. Underlying SIR dynamics also apply to other pathogens, although in some cases it may be more difficult to reconstruct the transmission history.We examined the properties of viral phylogenies generated by the most common epidemiological models based on ordinary differential equations (ODEs). We are able to fit epidemiological models to a reconstructed phylogeny for sampled viral sequence data and make inferences regarding the size of the corresponding infected population. Our solution takes the form of an ODE analogous to those used to track epidemic prevalence and thereby provides a convenient link between commonly used epidemiological models and phylodynamics. Virtually all coalescent theory to date has been expressed in terms of integer-valued stochastic processes. Our motivation for using differential equations to describe the coalescent process is a desire to formalize a link with standard epidemiological models that are also expressed in terms of differential equations.We use our method to calculate the distribution of coalescent times for samples of viral sequences, fit SIR models to a viral phylogeny, and calculate median time to the most recent common ancestor (MRCA) of the sample. Our method also provides equations that describe the time evolution of the cluster size distribution (CSD)—the distribution of the number of descendants of a lineage over time. Clusters of related virus are often interpreted as epidemiologically linked. For example, clusters of acute HIV infections may represent short transmission chains between high-risk individuals (Yerly et al. 2001; Hue et al. 2005; Pao et al. 2005; Goodreau 2006; Brenner et al. 2007; Drumright and Frost 2008; Lewis et al. 2008). Because our model reproduces the moments of the cluster size distribution, it can be used to predict the level of clustering as a function of epidemiological conditions. The moments could be directly compared to empirical values or they could be used to reconstruct the entire CSD, whereupon standard statistical tests could be used for comparing distributions.Although our equations describe the macroscopic properties of the population distribution of cluster sizes, we generalize our method to the case of a small cross-sectional sample of sequences. This allows us to develop a likelihood-based approach to fitting SIR models to observed sequences.By considering variable degrees of incidence and the size of the infected population, our solution sheds light on the relationship between coalescent rates and epidemic dynamics. Coalescent rates are low near peak prevalence, but higher when there is a large ratio of incidence to prevalence. This can occur early on, when the epidemic is entering its expansion phase, as well as late if the epidemic has multiple periods of growth.