Bayesian multilevel modelling of compositional data for simultaneous inferences on Allele compositions and inbreeding coefficients

Sample data from a number of sub-populations are often investigated in order to integrate the findings of different research studies on a particular area. In case of compositional samples, like the allele frequencies collected at a single locus in different surveys, the data are independent multinomial vectors. Each multinomial distribution depends on a specific probability vector, that is, the unknown relative composition of the sub-population. A Bayesian hierarchy approach is proposed here to model the variability of the sub-composition vectors around a common mean with possibly different scales. The common mean can be seen as the relative composition of the aggregated population. Scale parameters are well known in Biology as the Wright's inbreeding coefficients. The method presented here extends some previous work by assuming less prior knowledge on the subject and constraints on the model. A relatively simple Monte Carlo algorithm is described to perform joint inferences on general and local compositions and inbreeding coefficients. The method is applied on two case studies. The first one is based on DNA samples from ten Italian regions at the loci TH01 and FES, obtained from a database currently used for forensic identification, in which inbreeding assessments can be crucial. The second application is based on a set of colour-blind sample rates in North-East Indian populations collected by Choudhury (1994). The Author found some controversial results from the classical test for comparing proportions. A clearer picture, instead, is obtained by the current Bayesian approach.     

Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder

Aim Species distribution models (SDMs) or, more specifically, ecological niche models (ENMs) are a useful and rapidly proliferating tool in ecology and global change biology. ENMs attempt to capture associations between a species and its environment and are often used to draw biological inferences, to predict potential occurrences in unoccupied regions and to forecast future distributions under environmental change. The accuracy of ENMs, however, hinges critically on the quality of occurrence data. ENMs often use haphazardly collected data rather than data collected across the full spectrum of existing environmental conditions. Moreover, it remains unclear how processes affecting ENM predictions operate at different spatial scales. The scale (i.e. grain size) of analysis may be dictated more by the sampling regime than by biologically meaningful processes. The aim of our study is to jointly quantify how issues relating to region and scale affect ENM predictions using an economically important and ecologically damaging invasive species, the Argentine ant (Linepithema humile). Location California, USA. Methods We analysed the relationship between sampling sufficiency, regional differences in environmental parameter space and cell size of analysis and resampling environmental layers using two independently collected sets of presence/absence data. Differences in variable importance were determined using model averaging and logistic regression. Model accuracy was measured with area under the curve (AUC) and Cohen's kappa. Results We first demonstrate that insufficient sampling of environmental parameter space can cause large errors in predicted distributions and biological interpretation. Models performed best when they were parametrized with data that sufficiently sampled environmental parameter space. Second, we show that altering the spatial grain of analysis changes the relative importance of different environmental variables. These changes apparently result from how environmental constraints and the sampling distributions of environmental variables change with spatial grain. Conclusions These findings have clear relevance for biological inference. Taken together, our results illustrate potentially general limitations for ENMs, especially when such models are used to predict species occurrences in novel environments. We offer basic methodological and conceptual guidelines for appropriate sampling and scale matching.     

拟似然非线性模型中的置信域：几何法

对拟似然非线性模型在欧氏内积实间建立了修改的Ｂａｔｅｓ＆Ｗａｔｔｓ几何结构，基于此几何结构，导出了参数和子集系数的与统计曲率有关的三种近似置信域，进一步推广和发展了Ｈａｍｉｌｔｏｎ ｅｔ ａｌ．（１９８２）。Ｈａｍｉｌｔｏｎ（１９８６）和Ｗｅｉ（１９９４，１９９８）等人的相应结果。     

Evolutionary dynamics of giant viruses and their virophages

Giant viruses contain large genomes, encode many proteins atypical for viruses, replicate in large viral factories, and tend to infect protists. The giant virus replication factories can in turn be infected by so called virophages, which are smaller viruses that negatively impact giant virus replication. An example is Mimiviruses that infect the protist Acanthamoeba and that are themselves infected by the virophage Sputnik. This study examines the evolutionary dynamics of this system, using mathematical models. While the models suggest that the virophage population will evolve to increasing degrees of giant virus inhibition, it further suggests that this renders the virophage population prone to extinction due to dynamic instabilities over wide parameter ranges. Implications and conditions required to avoid extinction are discussed. Another interesting result is that virophage presence can fundamentally alter the evolutionary course of the giant virus. While the giant virus is predicted to evolve toward increasing its basic reproductive ratio in the absence of the virophage, the opposite is true in its presence. Therefore, virophages can not only benefit the host population directly by inhibiting the giant viruses but also indirectly by causing giant viruses to evolve toward weaker phenotypes. Experimental tests for this model are suggested.     

Linking recruitment to trophic factors: revisiting the Beverton--Holt recruitment model from a life history and multispecies perspective

The Beverton--Holt recruitment model can be derived from arguments about evolution of life history traits related to foraging and predation risk, along with spatially localized and temporarily competitive relationships in the habitats where juvenile fish forage and face predation risk while foraging. This derivation explicitly represents two key biotic factors, food supply (I) and predator abundance (R), which appear as a risk ratio (R/I) that facilitates modelling of changes in trophic circumstances and analysis of historical data. The same general recruitment relationship is expected whether the juvenile life history is simple or involves a complex sequence of stanzas; in the complex case, the Beverton--Holt parameters represent weighted averages or integrals of risk ratios over the stanzas. The relationship should also apply in settings where there is complex, mesoscale variation in habitat and predation risk, provided that animals sense this variation and move about so as to achieve similar survival at all mesoscale rearing sites. The model predicts that changes in food and predation risk can be amplified violently in settings where juvenile survival rate is low, producing large changes in recruitment rates over time.