BackgroundThe dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time. However, the size of the datasets generated also poses some daunting challenges. In particular, Bayesian clustering algorithms based on pre-defined population genetics models such as the STRUCTURE or BAPS software may not be able to cope with this unprecedented amount of data. Thus, there is a need for less computer-intensive approaches. Multivariate analyses seem particularly appealing as they are specifically devoted to extracting information from large datasets. Unfortunately, currently available multivariate methods still lack some essential features needed to study the genetic structure of natural populations.ResultsWe introduce the Discriminant Analysis of Principal Components (DAPC), a multivariate method designed to identify and describe clusters of genetically related individuals. When group priors are lacking, DAPC uses sequential K-means and model selection to infer genetic clusters. Our approach allows extracting rich information from genetic data, providing assignment of individuals to groups, a visual assessment of between-population differentiation, and contribution of individual alleles to population structuring. We evaluate the performance of our method using simulated data, which were also analyzed using STRUCTURE as a benchmark. Additionally, we illustrate the method by analyzing microsatellite polymorphism in worldwide human populations and hemagglutinin gene sequence variation in seasonal influenza.ConclusionsAnalysis of simulated data revealed that our approach performs generally better than STRUCTURE at characterizing population subdivision. The tools implemented in DAPC for the identification of clusters and graphical representation of between-group structures allow to unravel complex population structures. Our approach is also faster than Bayesian clustering algorithms by several orders of magnitude, and may be applicable to a wider range of datasets.

Increasing attention is being devoted to taking landscape information into account in genetic studies. Among landscape variables, space is often considered as one of the most important. To reveal spatial patterns, a statistical method should be spatially explicit, that is, it should directly take spatial information into account as a component of the adjusted model or of the optimized criterion. In this paper we propose a new spatially explicit multivariate method, spatial principal component analysis (sPCA), to investigate the spatial pattern of genetic variability using allelic frequency data of individuals or populations. This analysis does not require data to meet Hardy-Weinberg expectations or linkage equilibrium to exist between loci. The sPCA yields scores summarizing both the genetic variability and the spatial structure among individuals (or populations). Global structures (patches, clines and intermediates) are disentangled from local ones (strong genetic differences between neighbors) and from random noise. Two statistical tests are proposed to detect the existence of both types of patterns. As an illustration, the results of principal component analysis (PCA) and sPCA are compared using simulated datasets and real georeferenced microsatellite data of Scandinavian brown bear individuals (Ursus arctos). sPCA performed better than PCA to reveal spatial genetic patterns. The proposed methodology is implemented in the adegenet package of the free software R.

Oli and Dobson proposed that the ratio between the magnitude and the onset of reproduction (F/ alpha ratio) allows one to predict the relative importance of vital rates on population growth rate in mammalian populations and provides a reliable measure of the ranking of mammalian species on the slow-fast continuum of life-history tactics. We show that the choice of the ratio F/ alpha is arbitrary and is not grounded in demographic theory. We estimate the position on the slow-fast continuum using the first axis of a principal components analysis of all life-history variables studied by Oli and Dobson and show that most individual vital rates perform as well as the F/ alpha ratio. Finally, we find, in agreement with previous studies, that the age of first reproduction is a reliable predictor of the ranking of mammalian populations along the slow-fast continuum and that both body mass and phylogeny markedly influence the generation time of mammalian species. We conclude that arbitrary ratios such as F/ alpha correlate with life-history types in mammals simply because life-history variables are highly correlated in response to allometric, phylogenetic, and environmental influences. We suggest that generation time is a reliable metric to measure life-history variation among mammalian populations and should be preferred to any arbitrary combination between vital rates.

Background Toxoplasma gondii is found worldwide, but distribution of its genotypes as well as clinical expression of human toxoplasmosis varies across the continents. Several studies in Europe, North America and South America argued for a role of genotypes in the clinical expression of human toxoplasmosis. Genetic data concerning T. gondii isolates from Africa are scarce and not sufficient to investigate the population structure, a fundamental analysis for a better understanding of distribution, circulation, and transmission.Methodology/Principal FindingsSeropositive animals originating from urban and rural areas in Gabon were analyzed for T. gondii isolation and genotyping. Sixty-eight isolates, including one mixed infection (69 strains), were obtained by bioassay in mice. Genotyping was performed using length polymorphism of 13 microsatellite markers located on 10 different chromosomes. Results were analyzed in terms of population structure by Bayesian statistical modeling, Neighbor-joining trees reconstruction based on genetic distances, F ST and linkage disequilibrium. A moderate genetic diversity was detected. Three haplogroups and one single genotype clustered 27 genotypes. The majority of strains belonged to one haplogroup corresponding to the worldwide Type III. The remaining strains were distributed into two haplogroups (Africa 1 and 3) and one single genotype. Mouse virulence at isolation was significantly different between haplogroups. Africa 1 haplogroup was the most virulent.Conclusion Africa 1 and 3 haplogroups were proposed as being new major haplogroups of T. gondii circulating in Africa. A possible link with strains circulating in South and Central America is discussed. Analysis of population structure demonstrated a local spread within a rural area and strain circulation between the main cities of the country. This circulation, favored by human activity could lead to genetic exchanges. For the first time, key epidemiological questions were addressed for the West African T. gondii population, using the high discriminatory power of microsatellite markers, thus creating a basis for further epidemiological and clinical investigations.

Biodiversity loss is a major challenge. Over the past century, the average rate of vertebrate extinction has been about 100-fold higher than the estimated background rate and population declines continue to increase globally. Birth and death rates determine the pace of population increase or decline, thus driving the expansion or extinction of a species. Design of species conservation policies hence depends on demographic data (e.g., for extinction risk assessments or estimation of harvesting quotas). However, an overview of the accessible data, even for better known taxa, is lacking. Here, we present the Demographic Species Knowledge Index, which classifies the available information for 32,144 (97%) of extant described mammals, birds, reptiles, and amphibians. We show that only 1.3% of the tetrapod species have comprehensive information on birth and death rates. We found no demographic measures, not even crude ones such as maximum life span or typical litter/clutch size, for 65% of threatened tetrapods. More field studies are needed; however, some progress can be made by digitalizing existing knowledge, by imputing data from related species with similar life histories, and by using information from captive populations. We show that data from zoos and aquariums in the Species360 network can significantly improve knowledge for an almost eightfold gain. Assessing the landscape of limited demographic knowledge is essential to prioritize ways to fill data gaps. Such information is urgently needed to implement management strategies to conserve at-risk taxa and to discover new unifying concepts and evolutionary relationships across thousands of tetrapod species.

The investigation of genetic clusters in natural populations is an ubiquitous problem in a range of fields relying on the analysis of genetic data, such as molecular ecology, conservation biology and microbiology. Typically, genetic clusters are defined as distinct panmictic populations, or parental groups in the context of hybridisation. Two types of methods have been developed for identifying such clusters: model‐based methods, which are usually computer‐intensive but yield results which can be interpreted in the light of an explicit population genetic model, and geometric approaches, which are less interpretable but remarkably faster.Here, we introduce snapclust, a fast maximum‐likelihood solution to the genetic clustering problem, which allies the advantages of both model‐based and geometric approaches. Our method relies on maximising the likelihood of a fixed number of panmictic populations, using a combination of geometric approach and fast likelihood optimisation, using the Expectation‐Maximisation (EM) algorithm. It can be used for assigning genotypes to populations and optionally identify various types of hybrids between two parental populations. Several goodness‐of‐fit statistics can also be used to guide the choice of the retained number of clusters.Using extensive simulations, we show that snapclust performs comparably to current gold standards for genetic clustering as well as hybrid detection, with some advantages for identifying hybrids after several backcrosses, while being orders of magnitude faster than other model‐based methods. We also illustrate how snapclust can be used for identifying the optimal number of clusters, and subsequently assign individuals to various hybrid classes simulated from an empirical microsatellite dataset. snapclust is implemented in the package adegenet for the free software R, and is therefore easily integrated into existing pipelines for genetic data analysis. It can be applied to any kind of co‐dominant markers, and can easily be extended to more complex models including, for instance, varying ploidy levels. Given its flexibility and computer‐efficiency, it provides a useful complement to the existing toolbox for the study of genetic diversity in natural populations.

Phylogenetic comparative methods have long considered phylogenetic signal as a source of statistical bias in the correlative analysis of biological traits. However, the main life-history strategies existing in a set of taxa are often combinations of life history traits that are inherently phylogenetically structured. In this paper, we present a method for identifying evolutionary strategies from large sets of biological traits, using phylogeny as a source of meaningful historical and ecological information. Our methodology extends a multivariate method developed for the analysis of spatial patterns, and relies on finding combinations of traits that are phylogenetically autocorrelated. Using extensive simulations, we show that our method efficiently uncovers phylogenetic structures with respect to various tree topologies, and remains powerful in cases where a large majority of traits are not phylogenetically structured. Our methodology is illustrated using empirical data, and implemented in the adephylo package for the free software R.

Attempts to control predator numbers through spatially restricted culling typically faces a compensation process via immigration from surrounding source populations. To extend control effort to avoid this issue is in most instances impractical, both logistically and financially. Evidence-based strategy is therefore required to improve management practices. In close collaboration with local managers and hunters, we manipulated culling effort on red fox (Vulpes vulpes) over 5-6 years in 5 areas measuring 246 AE 53 km 2 . We estimated fox density in late February each year by spotlight counts with distance sampling and estimated reproductive performance by post-mortem examination of culled foxes. We then used mixed modeling to assess how culling rate (defined as foxes killed/foxes available) affected fox population growth from year to year, accounting for compensatory feedbacks. We found a strong compensatory density feedback acting through immigration, allowing red fox populations to resist high culling rates. Culling appeared ineffective at reducing late winter densities to below 25-32% of the estimated carrying capacity. On average, an annual culling rate equivalent to about 45% of the pre-breeding population was required to maintain density at 1 fox/ km 2 , given a carrying capacity of 1.5 foxes/km 2 , although there was considerable variation among sites. The required culling rate dropped to 25% if the culling could be performed during winter, after the fox dispersal period. In contrast, culling during the pre-dispersal breeding period was totally compensated for through immigration by the following February. Concentrating culling during the winter could improve the ability of practitioners to control year-to-year trends in fox numbers, taking into account site-specific carrying capacity. A winter strategy would also reduce the number of animals killed and hence the ethical and logistical costs of fox control, given limited financial and human resources. Our study illustrates how collaboration between local practitioners and scientists can make large-scale replicated management experiments achievable, leading to mutually approved guidelines. Ó 2015 The Wildlife Society.