Sustainability of culture-driven population dynamics

We consider models of the interactions between human population dynamics and cultural evolution, asking whether they predict sustainable or unsustainable patterns of growth. Phenomenological models predict either unsustainable population growth or stabilization in the near future. The latter prediction, however, is based on extrapolation of current demographic trends and does not take into account causal processes of demographic and cultural dynamics. Most existing causal models assume (or derive from simplified models of the economy) a positive feedback between cultural evolution and demographic growth, and predict unlimited growth in both culture and population. We augment these models taking into account that: (1) cultural transmission is not perfect, i.e., culture can be lost; (2) culture does not always promote population growth. We show that taking these factors into account can cause radically different model behavior, such as population extinction rather than stability, and extinction rather than growth. We conclude that all models agree that a population capable of maintaining a large amount of culture, including a powerful technology, runs a high risk of being unsustainable. We suggest that future work must address more explicitly both the dynamics of resource consumption and the cultural evolution of beliefs implicated in reproductive behavior (e.g., ideas about the preferred family size) and in resource use (e.g., environmentalist stances).     

The reproductive value in distributed optimal control models

We show that in a large class of distributed optimal control models (DOCM), where population is described by a McKendrick type equation with an endogenous number of newborns, the reproductive value of Fisher shows up as part of the shadow price of the population. Depending on the objective function, the reproductive value may be negative. Moreover, we show results of the reproductive value for changing vital rates. To motivate and demonstrate the general framework, we provide examples in health economics, epidemiology, and population biology.     

Sequence-related amplified polymorphism (SRAP) of wild emmer wheat (Triticum dicoccoides) in Israel and its ecological association

Genetic diversity and population structure of 15 wild emmer wheat (Triticum dicoccoides) populations from Israel were detected by 30 sequence-related amplified polymorphism (SRAP) primer pairs. Two hundred and forty four fragments out of 438 were polymorphic. The proportion of polymorphic loci (P), the genetic diversity (He), and Shannon's information index were 0.557, 0.198, and 0.295, respectively. The population Amirim had the highest genetic variation, whereas the population of Tabigha had the lowest genetic variation. The hierarchical analysis of molecular variance (AMOVA) revealed that most of the variation was presented within populations. The value of genetic distance (D) between the populations varied from 0.027 to 0.165 with an average of 0.079, and the estimates of genetic distance were geographically independent based on the Mantel test (r = 0.105, P = 0.168). A total of 30 significant (P < 0.05) correlations were detected between 14 SRAP loci and 12 ecogeographic factors.     

Microsatellite diversity and population genetic structure of yellowcheek, Elopichthys bambusa (Cyprinidae) in the Yangtze River

Yellowcheek carp (Elopichthys bambusa) is the only species of genus Elopichthys. It is widely distributed in Chinese freshwaters but currently its populations have declined to threatening level. We examined the genetic diversity and population structure of E. bambusa in the Yangtze River basin. A total of nine polymorphic microsatellite markers were employed to study five populations occurring in middle and lower reaches of the river. The results revealed low-to-moderate genetic diversity. The number of alleles per locus varied between 3 and 8 with an average of 4.8. Observed heterozygosity ranged from 0.15 to a maximum of 1.00. Significant deviations (P < 0.01) from Hardy–Weinberg equilibrium were observed for all the tested locus-population combinations with clear heterozygosity deficits. AMOVA indicated that majority of the variance lies within populations (93.81%) than among the populations (7.05%). Pairwise F_ST and unbiased genetic distance pointed out significant differentiation among the samples from populations with different connections to the Yangtze River. In the UPGMA dendrogram, clustering pattern of populations indicated that most of the populations are reproductively isolated due to anthropogenic interventions. Clustering of PYL and DTL populations shows ongoing gene flow through the mainstream. The recent hydrological alterations and overfishing are major factors shaping the current genetic structure. These results can be helpful for effective management and sustainable conservation of E. bambusa populations.     

On Combining Family‐Based and Population‐Based Case–Control Data in Association Studies

Summary Combining data collected from different sources can potentially enhance statistical efficiency in estimating effects of environmental or genetic factors or gene–environment interactions. However, combining data across studies becomes complicated when data are collected under different study designs, such as family‐based and unrelated individual‐based case–control design. In this article, we describe likelihood‐based approaches that permit the joint estimation of covariate effects on disease risk under study designs that include cases, relatives of cases, and unrelated individuals. Our methods accommodate familial residual correlation and a variety of ascertainment schemes. Extensive simulation experiments demonstrate that the proposed methods for estimation and inference perform well in realistic settings. Efficiencies of different designs are contrasted in the simulation. We applied the methods to data from the Colorectal Cancer Family Registry.     

Estimation of Plant Demographic Parameters from Stage‐Structured Censuses

Summary This article presents some statistical methods for estimating the parameters of a population dynamics model for annual plants. The model takes account of reproduction, immigration, seed survival in a seed bank, and plant growth. The data consist of the number of plants in several developmental stages that were measured in a number of populations for a few consecutive years; they are incomplete since seeds could not be counted. It is assumed that there are no measurement errors or that measurement errors are binomial and not frequent. Some statistical methods are developed within the framework of estimating equations or Bayesian inference. These methods are applied to oilseed rape data.     

Association Tests for a Censored Quantitative Trait and Candidate Genes in Structured Populations with Multilevel Genetic Relatedness

Summary Several statistical methods for detecting associations between quantitative traits and candidate genes in structured populations have been developed for fully observed phenotypes. However, many experiments are concerned with failure‐time phenotypes, which are usually subject to censoring. In this article, we propose statistical methods for detecting associations between a censored quantitative trait and candidate genes in structured populations with complex multiple levels of genetic relatedness among sampled individuals. The proposed methods correct for continuous population stratification using both population structure variables as covariates and the frailty terms attributable to kinship. The relationship between the time‐at‐onset data and genotypic scores at a candidate marker is modeled via a parametric Weibull frailty accelerated failure time (AFT) model as well as a semiparametric frailty AFT model, where the baseline survival function is flexibly modeled as a mixture of Polya trees centered around a family of Weibull distributions. For both parametric and semiparametric models, the frailties are modeled via an intrinsic Gaussian conditional autoregressive prior distribution with the kinship matrix being the adjacency matrix connecting subjects. Simulation studies and applications to the Arabidopsis thaliana line flowering time data sets demonstrated the advantage of the new proposals over existing approaches.     

Leishmaniadonovani s.l.: Evaluation of the proliferation potential of promastigotes using CFSE staining and flow cytometry

Leishmania infantum causes visceral leishmaniasis in all countries in the Mediterranean basin. It uses Phlebotomine sandflies as vectors where the promastigote stage develops, reproduces and becomes infective. Therefore the reproductive power of the promastigotes determines the inoculum size of the isolate. Ten Leishmania strains from Cyprus: two Leishmania donovani and eight L. infantum were used to study the proliferation capacity of the promastigotes. Population increase during a 6-day culture period was assessed quantitatively, by haematocytometer enumeration, and qualitatively by following the division history of each population during the same period by CFSE staining and flow cytometry. The strains exhibited different proliferation rates with L. infantum showing higher multiplication rates than L. donovani. These differences may represent their fitness capabilities and their ability to synchronize the multiplication activity of individual members in the population for the production of a sizeable inoculum in time for the vector’s blood meal.     

Modeling epidemics dynamics on heterogenous networks

The dynamics of the SIS process on heterogenous networks, where different local communities are connected by airlines, is studied. We suggest a new modeling technique for travelers movement, in which the movement does not affect the demographic parameters characterizing the metapopulation. A solution to the deterministic reaction-diffusion equations that emerges from this model on a general network is presented. A typical example of a heterogenous network, the star structure, is studied in detail both analytically and using agent-based simulations. The interplay between demographic stochasticity, spatial heterogeneity and the infection dynamics is shown to produce some counterintuitive effects. In particular it was found that, while movement always increases the chance of an outbreak, it may decrease the steady-state fraction of sick individuals. The importance of the modeling technique in estimating the outcomes of a vaccination campaign is demonstrated.