Anne-Béatrice Dufour

Revealing cryptic spatial patterns in genetic variability by a new multivariate method

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.

Community ecology in the age of multivariate multiscale spatial analysis

Species spatial distributions are the result of population demography, behavioral traits, and species interactions in spatially heterogeneous environmental conditions. Hence the composition of species assemblages is an integrative response variable, and its variability can be explained by the complex interplay among several structuring factors. The thorough analysis of spatial variation in species assemblages may help infer processes shaping ecological communities. We suggest that ecological studies would benefit from the combined use of the classical statistical models of community composition data, such as constrained or unconstrained multivariate analyses of site-by-species abundance tables, with rapidly emerging and diversifying methods of spatial pattern analysis. Doing so allows one to deal with spatially explicit ecological models of beta diversity in a biogeographic context through the multiscale analysis of spatial patterns in original species data tables, including spatial characterization of fitted or residual variation from environmental models. We summarize here the recent progress for specifying spatial features through spatial weighting matrices and spatial eigenfunctions in order to define spatially constrained or scale-explicit multivariate analyses. Through a worked example on tropical tree communities, we also show the potential of the overall approach to identify significant residual spatial patterns that could arise from the omission of important unmeasured explanatory variables or processes.

Frequency-dependent maintenance of left handedness in humans.

The percentage (10-13%) of left handedness in humans has apparently not changed since the Neolithic. Left handedness is heritable and appears to be repeatedly associated with some reduced fitness components; the persistence of left handedness implies that left handers have a fitness advantage in some situations. We propose that left handers have a frequency-dependent advantage in fights and for that reason a fitness advantage. To test this hypothesis, left handedness frequencies in the general population and in sporting individuals (both students and the sporting elite) have been compared, as sporting performance is likely to be a good indicator of fighting abilities. The higher proportion of left-handed individuals in interactive sports (reflecting some fighting elements), reaching 50% in some sports categories, but not in noninteractive sports, is consistent with the fighting hypothesis. The greater frequency of left handedness in males than in females is also consistent with this hypothesis, as male-male fights are universally more frequent than other combinations. The frequency-dependent advantage in fights of left handers might explain the stability of left handedness.

Genetic markers in the playground of multivariate analysis

Multivariate analyses such as principal component analysis were among the first statistical methods employed to extract information from genetic markers. From their early applications to current innovations, these approaches have proven to be efficient for the analysis of the genetic variability in various contexts such as human genetics, conservation and adaptation studies. However, because multivariate analysis is a wide and diversified area of statistics, choosing a method appropriate to both the data and to the question being asked can be difficult. Moreover, some particularities of genetic markers need to be taken into account when using multivariate methods. As a consequence, multivariate analyses are often used as black boxes, which results in frequent mistakes in the literature. In this review, we provide a critical analysis of the application of multivariate methods to genetic markers, using a general framework that unifies all these methods for the sake of clarity. First, we focus on some common mistakes in these applications and ways to avoid these pitfalls. We then detail the most critical particularities of allele frequencies that demand adaptations of multivariate methods, and we propose solutions to the subsequent problems. Finally, we tackle several questions of interest in which multivariate analysis has a great role to play, such as the study of the typological coherence of different genetic markers, or the investigation of spatial genetic patterns.

EIGENANALYSIS OF SELECTION RATIOS FROM ANIMAL RADIO‐TRACKING DATA

The development of methods to analyze habitat selection when resources are defined by several categories (e.g., vegetation types) is a topical issue in radio-tracking studies. The White and Garrott statistic, an extension of the widely used test of Neu et al., can be used to determine whether habitat selection is significant. As well, Manlys selection ratio, a particularly useful measure of resource selectivity by resource users, allows detection of the most strongly selected habitat types. However, when both the number of animals and types of habitat are large, the biologist often has to deal with an excessively large number of measures. In this paper we present a new method, the eigenanalysis of selection ratios, that generalizes these two common methods within the framework of eigenanalyses. This method undertakes an additive linear partitioning of the White and Garrott statistic, so that the difference between habitat use and availability is maximized on the first factorial axes. The eigenanalysis of selection ratios is therefore optimal in habitat selection studies. Although we primarily consider the case where the habitat availability is the same for all animals (design II), we also extend this analysis to the case where the habitat availability varies from one animal to another (design III). An application of this method is provided using radio-tracking data collected on 17 squirrels in five habitat types. The results indicate variability in habitat selection, with two groups of animals displaying two patterns of preference. This difference between the two groups is explained by the patch structure of the study area. Because this method is mainly exploratory, and therefore does not rely on any distributional assumption, we recommend its use in studies of habitat selection.

One year variability of peak heights, heterozygous balance and inter-locus balance for the DNA positive control of AmpFℓSTR© Identifiler© STR kit

Multivariate analyses of 205 positive control experiments in an AmpFℓSTR© Identifiler© STR kit were used to analyze the factors affecting peak heights at 16 loci. Peak heights were found to be highly correlated between loci and there was evidence for a difference in sensitivity of the two genetic analyzers in the blue channel. Heterozygous balance response at 10 loci was found to behave as a random variable following a beta-distribution with typical median values of 90%, without locus or genetic analyzer effect. Inter-locus balance at 16 loci was influenced by the blue channel effect and a temporal switch of unexplained origin. The implications of these results for the choice of minimum threshold values in quality control are discussed.

Insights for Integrated Conservation from Attitudes of People toward Protected Areas Near Hwange National Park, Zimbabwe

Increase in human settlements at the edge of protected areas (PAs) is perceived as a major threat to conservation of biodiversity. Although it is crucial to integrate the interests of surrounding communities into PA management, key drivers of changes in local populations and the effects of conservation on local livelihoods and perceptions remain poorly understood. We assessed population changes from 1990 to 2010 in 9 villages located between 2 PAs with different management policies (access to natural resources or not). We conducted semi-directive interviews at the household level (n =217) to document reasons for settlement in the area and villagers attitudes toward the PAs. We examined drivers of these attitudes relative to household typology, feelings about conservation, and concerns for the future with mixed linear models. Population increased by 61% from 2000 to 2010, a period of political and economic crisis in Zimbabwe. Forty-seven percent of immigrants were attracted by the area; others had been resettled from other villages or were returning to family lands. Attitudes toward PAs were generally positive, but immigrants attracted by the area and who used resources within the PA with fewer restrictions expressed more negative attitudes toward PAs. Household location, losses due to wild animals, and restrictions on access to natural resources were the main drivers of this negative attitude. Profit-seeking migrants did not expect these constraints and were particularly concerned with local overpopulation and access to natural resources. To avoid socio-ecological traps near PAs (i.e., unforeseen reduced adaptive capacity) integrated conservation should address mismatches between management policy and local expectations. This requires accounting for endogenous processes, for example, local socio-ecological dynamics and values that shape the coexistence between humans and wildlife.

Short-term effects of using verbal instructions and demonstration at the beginning of learning a complex skill in figure skating

This study examined whether providing verbal instructions plus demonstration and task repetition facilitates the early acquisition of a sport skill for which learners had a prior knowledge of the individual motor components. After one demonstration of the task by an expert, 18 novice skaters practiced a figure skating jump during a 15-min. period. Subjects were randomly assigned to one of 3 groups: a group provided with a verbal instruction that specified the subgoals of the task (Subgoals group), a group provided with a verbal instruction that used a metaphor (Metaphoric group), and a group not receiving any specific instruction during training (Control group). Subjects were filmed prior to and immediately following the practice session. Analysis indicated that the modifications of performance were related to the demonstration and the subsequent task repetitions only. Providing additional verbal instructions generated no effect. Therefore, guiding the learner toward a solution to the task problem by means of verbal instruction seems to be ineffective if done too early in the course of learning.

Once upon Multivariate Analyses: When They Tell Several Stories about Biological Evolution

Geometric morphometrics aims to characterize of the geometry of complex traits. It is therefore by essence multivariate. The most popular methods to investigate patterns of differentiation in this context are (1) the Principal Component Analysis (PCA), which is an eigenvalue decomposition of the total variance-covariance matrix among all specimens; (2) the Canonical Variate Analysis (CVA, a.k.a. linear discriminant analysis (LDA) for more than two groups), which aims at separating the groups by maximizing the between-group to within-group variance ratio; (3) the between-group PCA (bgPCA) which investigates patterns of between-group variation, without standardizing by the within-group variance. Standardizing within-group variance, as performed in the CVA, distorts the relationships among groups, an effect that is particularly strong if the variance is similarly oriented in a comparable way in all groups. Such shared direction of main morphological variance may occur and have a biological meaning, for instance corresponding to the most frequent standing genetic variation in a population. Here we undertake a case study of the evolution of house mouse molar shape across various islands, based on the real dataset and simulations. We investigated how patterns of main variance influence the depiction of among-group differentiation according to the interpretation of the PCA, bgPCA and CVA. Without arguing about a method performing ‘better’ than another, it rather emerges that working on the total or between-group variance (PCA and bgPCA) will tend to put the focus on the role of direction of main variance as line of least resistance to evolution. Standardizing by the within-group variance (CVA), by dampening the expression of this line of least resistance, has the potential to reveal other relevant patterns of differentiation that may otherwise be blurred.

Relationships of motor performance of adult men to anthropometry: A multivariate analysis

Relationships between 12 anthropometric characteristics and motor performance, as measured by various fitness tests, were considered in a sample of 165 Flemish adults observed at age 30 years. In addition to a bivariate correlation study, a canonical correlation analysis was carried out. More than 72% of the variance was shared by the first three canonical variables. The first canonical variable can be explained as a general size function. Static and functional strength are clearly related to this function. The second canonical variable can be interpreted as a size‐fatness function. Nearly all motor tests are projected on the second composite, indicating that in adult men, subcutaneous fatness is negatively related to physical fitness. By means of a biplot of the first two canonical variables, interrelationships between body dimensions and motor performance are clarified. A combination of the first two functions seems to provide information about physique.