Hidden patterns of phylogenetic non‐stationarity overwhelm comparative analyses of niche conservatism and divergence

Aim Despite the importance of the niche concept in ecological and evolutionary theory, there are still many discussions about its definition and operational evaluation, especially when dealing with niche divergence and conservatism in an explicit phylogenetic context. Here we evaluate patterns of niche evolution in 67 New World Carnivora species, measured using Hellinger distances based on MAXENT models of species distribution. We show how inferences on niche conservatism or divergence depend on the way phylogenetic patterns are analysed using matrix comparison techniques. Innovation Initially we used the simplest approach of Mantel tests to compare Hellinger distances ( N ) derived from MAXENT and phylogenetic distances ( P ) among species. Then we extended the Mantel test to generate a multivariate correlogram, in which phylogenetic patterns are analysed at multiple levels in the phylogeny and can reveal nonlinearity in the relationship between divergence and time. Finally, we proposed a new approach to generate ‘local’ (or ‘specific’) leverages of components for Mantel correlation, evaluating the non‐stationarity in the relationship between N and P for each species. This new approach was used to show if some lineages are more prone to niche shift or conservatism than others. Main conclusions Standard Mantel tests indicated a poor correspondence between N and P matrices, discarding the idea of niche conservatism for Carnivora, but the correlogram supports that closely related species tend to be more similar than expected by chance. Moreover, the variance among Hellinger distances between pairs of closely phylogenetically related species is much larger than for the entire clade. Phylogenetic non‐stationarity analysis shows that in some Carnivora families the niche tends to divergence (Mustelidae and Canidae), whereas in others it tends to conservatism (Procyonidae and Mustelidae) at short phylogenetic distances. Our analyses clearly show that misleading results may appear if niche divergence is analysed only by simple matrix correlations not taking into account complex patterns of phylogenetic nonlinearity and non‐stationarity.     

Correlation analysis of dissimilarity matrices

Distance-based methods have been a valuable tool for ecologists for decades. Indirectly, distance-based ordination and cluster analysis, in particular, have been widely practiced as they allow the visualization of a multivariate data set in a few dimensions. The explicitly distance-based Mantel test and multiple regression on distance matrices (MRM) add hypothesis testing to the toolbox. One concern for ecologists wishing to use these methods lies in deciding whether to combine data vectors into a compound multivariate dissimilarity to analyze them individually. For Euclidean distances on scaled data, the correlation of a pair of multivariate distance matrices can be calculated from the correlations between the two sets of individual distance matrices if one set is orthogonal, demonstrating a clear link between individual and compound distances. The choice between Mantel and MRM should be driven by ecological hypotheses rather than mathematical concerns. The relationship between individual and compound distance matrices also provides a means for calculating the maximum possible value of the Mantel statistic, which can be considerably less than 1 for a given analysis. These relationships are demonstrated with simulated data. Although these mathematical relationships are only strictly true for Euclidean distances when one set of variables is orthogonal, simulations show that they are approximately true for weakly correlated variables and Bray–Curtis dissimilarities.     

Spatial distribution and environmental correlates of Australian snubfin and Indo-Pacific humpback dolphins

We present data on the spatial distribution of Australian snubfin and humpback dolphins using boat-based line transect surveys in three adjacent bays located in the Far Northern Section of the Great Barrier Reef Marine Park, northeast Queensland. We used Geographic Information Systems (GIS), and both randomization and Mantel tests to examine the relationship between the spatial distribution of the dolphins and three simple, readily quantified, environmental variables: distance to land, distance to river mouth, and water depth. Mantel tests allowed us to make clear inferences about the correlation of the species' distributions with environmental variables, while taking into account spatial autocorrelation and intercorrelation among variables. Randomization tests indicated snubfin and humpback dolphins occur closer to land than would be expected at random. Two-sample randomization tests indicated snubfin dolphins were found closer to river mouths than were humpback dolphins. Taking spatial autocorrelation into account, Mantel tests indicated all environmental variables were correlated with the spatial distribution of snubfin and humpback dolphins. Interspecific differences in spatial distribution appeared to be related to proximity to river mouths. Preference by snubfin and humpback dolphins for nearshore, estuarine waters is likely related to the productivity of these tropical coastal areas. This spatial analysis suggests that existing protected areas in this region may not include the most critical habitats for snubfin and humpback dolphins. The techniques used here shown relationships between the spatial distribution of the dolphins and environmental features that should facilitate their management and conservation.     

Analysis of the spatial pattern of arthropod fauna of jarrah (Eucalyptus marginata) foliage using a Mantel correlogram

Abstract The results of a two year study of foliage arthropods in Eucalyptus marginata canopies by Abbott et al. led them to conclude that distance was unimportant in explaining the similarity of foliage arthropod faunas. However, that conclusion was based on an analysis that did not account for the lack of independence between sample points. Re-analysis of their data using a Mantel correlogram detected significant spatial pattern in resemblance between canopy faunas, consistent with a patch size of about 20km.     

Combining spatial and phylogenetic eigenvector filtering in trait analysis

Aim To analyse the effects of simultaneously using spatial and phylogenetic information in removing spatial autocorrelation of residuals within a multiple regression framework of trait analysis. Location Switzerland, Europe. Methods We used an eigenvector filtering approach to analyse the relationship between spatial distribution of a trait (flowering phenology) and environmental covariates in a multiple regression framework. Eigenvector filters were calculated from ordinations of distance matrices. Distance matrices were either based on pure spatial information, pure phylogenetic information or spatially structured phylogenetic information. In the multiple regression, those filters were selected which best reduced Moran's I coefficient of residual autocorrelation. These were added as covariates to a regression model of environmental variables explaining trait distribution. Results The simultaneous provision of spatial and phylogenetic information was effectively able to remove residual autocorrelation in the analysis. Adding phylogenetic information was superior to adding purely spatial information. Applying filters showed altered results, i.e. different environmental predictors were seen to be significant. Nevertheless, mean annual temperature and calcareous substrate remained the most important predictors to explain the onset of flowering in Switzerland; namely, the warmer the temperature and the more calcareous the substrate, the earlier the onset of flowering. A sequential approach, i.e. first removing the phylogenetic signal from traits and then applying a spatial analysis, did not provide more information or yield less autocorrelation than simple or purely spatial models. Main conclusions The combination of spatial and spatio‐phylogenetic information is recommended in the analysis of trait distribution data in a multiple regression framework. This approach is an efficient means for reducing residual autocorrelation and for testing the robustness of results, including the indication of incomplete parameterizations, and can facilitate ecological interpretation.     

Environmental and spatial influences on benthic community composition in wooded headwater streams in Zoar Valley, New York, USA

This study determined the relative influences of environmental variation versus spatial autocorrelation on benthic macroinvertebrate community composition of temperate headwater streams. We enumerated fauna in riffle zones of 23 separate (i.e., not tributaries to each other) but closely grouped from first- to third-order woodland streams surrounding Zoar Valley Canyon, western New York State, USA, during spring, summer, and fall of 2006. Watershed geomorphology (stream order, catchment area, and forest cover) and semi-quantitative habitat characteristics, the latter of which were incorporated into a Qualitative Habitat Evaluation Index (QHEI), were also recorded for each stream. Non-metric Multidimensional Scaling (NMDS) ordination was used to establish patterns of biotic similarity among these streams. Matrices of biological and environmental Euclidean distances were constructed for all between-stream pairings. Additionally, a between-stream spatial matrix was constructed based on global position system coordinates of sampling sites. Non-significant partial Mantel coefficients indicated that biological distances were uncorrelated with spatial distances both among all 23 study streams and among 12 first-order streams only. In contrast, biological distances were positively correlated with environmental distances (r _M = 0.375 and 0.289 for all streams and for first-order streams only, respectively; P values < 0.05). Environmental and spatial distances were uncorrelated (partial Mantel P values > 0.05), indicating that the measured environmental characteristics were not spatially structured. Each of the geomorphological and habitat variables was statistically associated with NMDS community composition axes (stepwise multiple regression, one-factor MANOVA). These results suggest that environmental filters and niche-based species sorting may operate here between separate streams, and that study sites appear to be effectively insular in their biota despite close physical proximity. Handling editor: Sonja Stendera     

Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data

The Mantel test is widely used to test the linear or monotonic independence of the elements in two distance matrices. It is one of the few appropriate tests when the hypothesis under study can only be formulated in terms of distances; this is often the case with genetic data. In particular, the Mantel test has been widely used to test for spatial relationship between genetic data and spatial layout of the sampling locations. We describe the domain of application of the Mantel test and derived forms. Formula development demonstrates that the sum-of-squares (SS) partitioned in Mantel tests and regression on distance matrices differs from the SS partitioned in linear correlation, regression and canonical analysis. Numerical simulations show that in tests of significance of the relationship between simple variables and multivariate data tables, the power of linear correlation, regression and canonical analysis is far greater than that of the Mantel test and derived forms, meaning that the former methods are much more likely than the latter to detect a relationship when one is present in the data. Examples of difference in power are given for the detection of spatial gradients. Furthermore, the Mantel test does not correctly estimate the proportion of the original data variation explained by spatial structures. The Mantel test should not be used as a general method for the investigation of linear relationships or spatial structures in univariate or multivariate data. Its use should be restricted to tests of hypotheses that can only be formulated in terms of distances.     

Environment, dispersal and patterns of species similarity

Aim The aim of this paper is to evaluate the combined effects of geographical distance and environmental distance on patterns of species similarity (similarity in species composition between sites), and to identify factors affecting the rate of decay in species similarity with each type of distance. Location Israel. Methods Data on species composition of land snails and land birds were recorded in 27 sites of 1 × 1 km scattered across a rainfall gradient in Israel. Matrices of similarity in species composition between all pairs of sites were computed and analysed with respect to corresponding matrices of geographical distance and rainfall distance (defined as the difference in mean annual rainfall between sites, and used as a measure of environmental distance). Mantel tests were applied to determine the correlation between species similarity and each type of distance. Factors affecting the decay in species similarity were investigated by comparing different subsets of the data using randomization tests. Results Both rainfall distance and geographical distance had negative effects on species similarity. The effect of rainfall distance was statistically significant even after controlling for differences in geographical distance, and vice versa. The per‐unit effect of rainfall distance on species similarity decreased with increasing geographical distance, indicating that the two types of distances interacted in determining the similarity in species composition. Snails showed a higher rate of decay in species similarity with geographical distance than birds, and large snails showed a higher rate of decay than small snails, which are better passive dispersers. The per‐unit effects of both rainfall distance and geographical distance on species similarity were higher in the desert region than in the Mediterranean region. Analyses focusing on a grain size of 10 × 10 m showed a lower similarity in species composition and a lower rate of decay in species similarity with rainfall distance than analyses carried out at a grain size of 1 × 1 km. Main conclusions Patterns of similarity in species composition are influenced by the combined effects of environmental variation, the position of the area along environmental gradients, the dispersal properties of the component species, and the scale (both spatial extent and grain size) at which the patterns are examined.     

地理距离及环境差异对云南元江干热河谷植物群落beta多样性的影响

beta多样性反映了群落间物种组成的差异, 是生物多样性研究的热点之一。本研究通过对云南元江干热河谷41个植物群落样方进行调查, 用Jaccard相异系数表征物种beta多样性, 用样方之间的最近谱系距离(mean nearest taxon distance, MNTD)及平均谱系距离(mean pairwise distance, MPD)表征谱系beta多样性, 采用基于距离矩阵的多元回归和方差分解方法, 探讨了该区域干热河谷典型植物群落的物种beta多样性和谱系beta多样性与样方间环境差异(主要是气候)及地理距离之间的关系。结果表明: (1)群落间的地理距离和年平均温度差异对干热河谷植物群落的物种beta多样性和谱系beta多样性有显著影响; (2)地理距离对物种beta多样性和MNTD的影响最大; 地理距离和年平均温度差异对MPD的影响均较大; (3)样方间年平均温度与年平均降水量的差异和地理距离能够解释群落间beta多样性及谱系beta多样性11-13%的变异。以上结果表明, 生态位分化和扩散限制对该地区植物群落的beta多样性均有显著影响, 其中扩散限制的影响可能更大。此外, 人类活动等其他因素也很可能对元江干热河谷的群落组成具有非常重要的影响。     

Genetic structure of the indigenous populations of Siberia

This study explores the genetic structure of Siberian indigenous populations on the basis of standard blood group and protein markers and DNA variable number of tandem repeats (VNTR) variation. Four analytical methods were utilized in this study: Harpending and Jenkin's R-matrix; Harpending and Ward's method of correlating genetic heterozygosity (H) to the distance from the centroid of the gene frequency array (r_ii); spatial autocorrelation, and Mantel tests. Because of the underlying assumptions of the various methods, the numbers of populations used in the analyses varied from 15 to 62. Since spatial autocorrelation is based upon separate correlations between alleles, a larger number of standard blood markers and populations were used. Fewest Siberian populations have been sampled for VNTRs, thus, only a limited comparison was possible. The four analytical procedures employed in this study yielded complementary results suggestive of the effects of unique historical events, evolutionary forces, and geography on the distribution of alleles in Siberian indigenous populations. The principal components analysis of the R-matrix demonstrated the presence of populational clusters that reflect their phylogenetic relationship. Mantel comparisons of matrices indicate that an intimate relationship exists between geography, languages, and genetics of Siberian populations. Spatial autocorrelation patterns reflect the isolation-by-distance model of Malecot and the possible effects of long-distance migration. Am J Phys Anthropol 104:177–192, 1997. © 1997 Wiley-Liss, Inc.     

Genome-wide association to fine-scale ecological heterogeneity within a continuous population of Biscutella laevigata (Brassicaceae)

Gene flow, drift and selection can be detected through different signatures across the genome and the landscape. Genetic discontinuities along with their correlation to environmental features can be used to tease out isolation-by-distance and isolation-by-time from processes related to selection. Using spatial statistics (spatial autocorrelation methods, canonical correspondence analysis and partial Mantel tests) dealing with genome-wide amplified fragment length polymorphism (AFLP) under unlikely Hardy-Weinberg assumptions, this study investigates 124 individuals within a continuous population of the autopolyploid Biscutella laevigata (Brassicaceae). Fine-scale spatial genetic structure was strong and the mosaic-like distribution of AFLP genotypes was consistently associated with habitat factors, even when controlled for geographical distances. The use of multivariate analyses enabled separation of the factors responsible for the repartition of the genetic variance and revealed a composite effect of isolation by distance, phenological divergence and local adaptation to habitats characterised by different solar radiation regimes. These results suggest that the immigrant inviability barrier facilitated the maintenance of adapted subpopulations to distinct environmental conditions at the local scale.     

Genetic relationships among some new cat populations sampled in Europe: A spatial autocorrelation analysis

The allelic frequencies of nine Mendelizing genetic characteristics that control coat colour, tabby and length and some skeletal abnormalities have been studied in four feral domestic cat populations, two in the north of Catalonia (Girona and Roses & L’Estartit, northeastern Spain) and two Adriatic Italian populations (Rimini and Venice). Using different genetic and multivariate analyses (Nei’s and Cavalli-Sforza and Edwards’s genetic distances, phenograms and cladograms using different algorithms, strict consensus trees, canonical population, principal coordinates and nonmetric multidimensional scaling analyses), I show the genetic relationships between these populations and other Western European cat populations previously studied. In the Western European area comprising Catalonia, Italy, France and Great Britain, I found significant spatial structure for thet ^b, l andW alleles and for the average correlogram for the seven alleles studied as a whole using a spatial autocorrelation analysis. The genetic distance matrices between these European cat populations also showed a significant correlation with the geographical distance between these populations using Mantel’s test. These analyses showed that in each of these countries, local cat populations have characteristic genetic profiles which were different to neighbouring populations in nearby countries. At least in this area of Western Europe, the geographical distances between cat populations (although the gene flow can be relatively high) is an important factor which can explain differences in allele frequencies between these populations.     

Modelling niche and neutral dynamics: on the ecological interpretation of variation partitioning results

Many studies have attempted to disentangle the effects of neutral and niche‐mediated processes on community composition using partial Mantel tests and variance partitioning on dissimilarity matrices. Recently, doubts about the reliability of these methods have emerged. Here we explore how the results are affected by three confounding factors that may affect ecological data to different degrees: spatial autocorrelation of the environmental variables, length of the compositional gradient, and sampling noise. We document that the statistical hypotheses tested in these methods may or may not correspond to the ecological hypotheses of interest. A major discrepancy emerges if a large proportion of sampling units in the analysed dataset share no species, in which case compositional dissimilarities saturate to a fixed maximum value although explanatory dissimilarities do not. With increasing dissimilarity saturation, the explanatory power of regression models decrease, which may lead to the erroneous conclusion that the ecological processes represented by the explanatory variables are not operating. A survey of recent literature suggests that there is a general lack of awareness of this problem, although it appears to affect > 10% of relevant studies. Our simulations show that if dissimilarity saturation is due to a long ecological gradient, extended dissimilarities essentially solve the problem for any degree of saturation. Using distances from a hybrid multidimensional scaling alleviates the saturation problem when the degree of saturation is < 60%. However, neither correction method can provide a solution to problems caused by insufficient sampling. How the presence of multiple explanatory gradients in combination with sampling noise affects overall analysis performance remains to be clarified.     

Spatial Dependency of Vegetation–Environment Linkages in an Anthropogenically Influenced Wetland Ecosystem

Management and restoration of vegetation patterns in ecosystems depends on an understanding of allogenic environmental factors that organize species assemblages and autogenic processes linked to assemblages. However, our ability to make strong inferences about vegetation–environment linkages in field studies is often limited due to correlations among environmental variables, spatial autocorrelation, and scale dependency of observations. This is particularly true in large, heterogeneous ecosystems such as the Everglades. Here, an extensive canal-and-levee system has modified historical fire regimes and hydropatterns while contributing large inputs of surface-water phosphorus (P), nitrogen (N) and cations such as sodium (Na). Some of these anthropogenic influences have been implicated as factors leading to the shift of sawgrass (Cladium jamaicense Crantz) and slough communities to an assemblage of weedy species such as cattail (Typha domingensis Pers.). To untangle the independent effect of multiple variables, we used a spatially explicit, multivariate approach to identify linkages among spatial patterns, environmental factors, and vegetation composition along a 10-km gradient of anthropogenic influence in the Everglades, an area immediately downstream from canal inflow structures. Clusters of plots were stratified among three zones (Impacted, Transition, and Reference), a design that allowed us to contrast vegetation–environment linkages and spatial patterns at multiple scales and degrees of ecosystem alteration. Along the 10-km gradient, partial Mantel tests showed that nutrients (phosphorus, nitrogen, and potassium) and hydropattern (frequency of dryness) were independently linked to patterns in fine-scale vegetation composition, but phosphorus was the only environmental variable linked to patterns of coarse-scale composition. Regardless of scale, the effect of distance from canal inflows accounted for variation in vegetation that could not be explained by other variables. A significant residual effect of spatial proximity among sampling locations also was detected and was highly suggestive of dispersal or other spatial determinants of vegetation pattern. However, this pure spatial effect was significantly stronger in the Transition and Impacted zones than in the Reference zone—fine-scale environmental variables explained all of the spatial structure in vegetation in the Reference zone. A further examination of spatial patterns in vegetation by using Mantel correlograms revealed significant heterogeneity at fine, local scales in the Reference zone, but this pattern progressively degraded toward homogeneity among closely neighboring locations in the Impacted zone. However, the fine-scale vegetation pattern in the Reference zone was hierarchically nested at a broader scale and yielded a similar coarse pattern across the landscape, whereas the coarse pattern in the Transition and Impacted zones was relatively heterogeneous and fragmented. Collectively, these results indicate that allogenic spatial and environmental factors related to the canal system have disrupted the coupling between pattern and process by altering fine-scale vegetation–environment linkages and spatial patterns characteristic of the natural Everglades ecosystem.     

Spatial Distribution of Algal Assemblages in a Temperate Lowland Peat Bog

Samples of phytobenthos were collected during three different seasons in 2005 along a linear transect of a lowland peat bog at various spatial scales (10 cm, 1 m, 10 m) to investigate the seasonal dynamics, diversity, and factors influencing the spatial patterns of microalgal communities. Non‐metric multidimensional scaling (NMDS), similarity percentage (SIMPER) analyses, ANOSIM, Mantel tests and diversity indices were used to analyze the data. Seasonal dynamics were exhibited by an increase in diversity, and a decrease in dominance from May to October, with significant differences in species composition. Mantel tests showed the significant influence of distance, microhabitat type, and conductivity on maintaining the similarity of species composition on scales of 1 m and 10 m. The small‐scale processes (colonization and niche differentiation), microhabitat type, geographic distance and conductivity were found to be the main factors influencing the distribution of algal assemblages. We conclude that these factors are related to winter disturbance, and the consequent colonization and subsequent niche differentiation. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Geographic variation of flower traits in Narcissus papyraceus (Amaryllidaceae): do pollinators matter?

Aim The geographic clinal variation of traits in organisms can indicate the possible causes of phenotypic evolution. We studied the correlates of flower trait variation in populations of a style‐dimorphic plant, Narcissus papyraceus Ker‐Gawl., within a region of high biogeographical significance, the Strait of Gibraltar. This species shows a geographic gradient in the style‐morph ratio, suggested to be driven by pollinator shifts. We tested whether parallel geographic variation of perianth traits also exists, concomitant with vegetative trait variation or genetic similarity of plant populations. Location The Strait of Gibraltar region (SG hereafter, including both south‐western Iberian Peninsula and north‐western Morocco). Methods We used univariate and multivariate analyses of flower and vegetative traits in 23 populations. We applied Mantel tests and partial Mantel correlations on vegetative and flower traits and geographic locations of populations to test for spatial effects. We used Moran’s autocorrelation analyses to explore the spatial structure within the range, and performed the analyses with and without the Moroccan samples to test for the effects of the SG on spatial patterns. Amplified fragment length polymorphism data were used to estimate the genetic distance between populations and to ascertain its relationship with morphometric distance. Results There was high variation between and within populations in both flower and vegetative traits. Mantel correlations between geographic and morphometric distances were not significant, but the exclusion of Moroccan populations revealed some distance effect. Partial Mantel correlation did not detect a significant correlation between flower and vegetative morphometric distances after controlling for geographic distance. There were opposite trends in spatial autocorrelograms of flower and vegetative traits. The genetic distance between pairs of populations was directly correlated with geographic distance; however, flower morphometric and genetic distances were not significantly correlated. Main conclusions The SG had some influence on phenotypes, although the causes remain to be determined. The opposite trend of variation in flower and vegetative traits, and the lack of correlation between genetic distance and dissimilarity of flower phenotypes favour the hypothesis of pollinator‐mediated selection on flower morphology, although this may affect only particular traits and populations rather than overall phenotypes. Although stochastic population processes may have a small effect, other factors may account for the high flower variation within and between populations.     

Effects of landscape and history on diversification of a montane, stream-breeding amphibian

Aim The aim of this study was to understand the roles of landscape features in shaping patterns of contemporary and historical genetic diversification among populations of the Andean tree frog (Hypsiboas andinus) across spatial scales. Location Andes mountains, north‐western Argentina, South America. Methods Mitochondrial DNA control region sequences were utilized to assess genetic differentiation among populations and calculate population pair‐wise genetic distances. Three models of movement, namely traditional straight‐line distance and two effective distances based on habitat classification, were examined to determine which of these explained the most variation in pair‐wise population genetic differentiation. The two habitat classifications were based on digital vegetation and hydrology layers that were generated from a 90‐m resolution digital elevation model (DEM) and known relationships between elevation and habitat. Mantel tests were conducted to test for correlations between geographic and genetic distance matrices and to estimate the percentage variation explained by each type of geographic distance. To investigate the location of possible barriers to gene flow, we used Monmonier’s maximum difference algorithm as implemented in barrier 2.2. Results At both geographic scales, effective distances explained more variation in genetic differentiation than did straight‐line distance. The least‐cost distances based on the simple classification performed better than the more detailed habitat classification. We controlled for the effects of historical range fragmentation determined from previous nested clade analyses, and therefore evaluated the effect of different distances on the genetic variation attributable to more recent factors. Effective distances identified populations that were highly divergent as a result of isolation in unsuitable habitats. The proposed locations of barriers to gene flow identified using Monmonier’s maximum difference algorithm corresponded well with earlier analyses and supported findings from our partial Mantel tests. Main conclusions Our results indicate that landscape features have been important in both historical and contemporary genetic structuring of populations of H. andinus at both large and small spatial scales. A landscape genetic perspective offers novel insights not provided by traditional phylogeographic studies: (1) effective distances can better explain patterns of differentiation in populations, especially in heterogeneous landscapes where barriers to dispersal may be common; and (2) least‐cost path analysis can help to identify corridors of movement between populations that are biologically more realistic.     

Genetic spatial autocorrelation can readily detect sex-biased dispersal

Sex-biased dispersal is expected to generate differences in the fine-scale genetic structure of males and females. Therefore, spatial analyses of multilocus genotypes may offer a powerful approach for detecting sex-biased dispersal in natural populations. However, the effects of sex-biased dispersal on fine-scale genetic structure have not been explored. We used simulations and multilocus spatial autocorrelation analysis to investigate how sex-biased dispersal influences fine-scale genetic structure. We evaluated three statistical tests for detecting sex-biased dispersal: bootstrap confidence intervals about autocorrelation r values and recently developed heterogeneity tests at the distance class and whole correlogram levels. Even modest sex bias in dispersal resulted in significantly different fine-scale spatial autocorrelation patterns between the sexes. This was particularly evident when dispersal was strongly restricted in the less-dispersing sex (mean distance <200 m), when differences between the sexes were readily detected over short distances. All tests had high power to detect sex-biased dispersal with large sample sizes (n ≥ 250). However, there was variation in type I error rates among the tests, for which we offer specific recommendations. We found congruence between simulation predictions and empirical data from the agile antechinus, a species that exhibits male-biased dispersal, confirming the power of individual-based genetic analysis to provide insights into asymmetries in male and female dispersal. Our key recommendations for using multilocus spatial autocorrelation analyses to test for sex-biased dispersal are: (i) maximize sample size, not locus number; (ii) concentrate sampling within the scale of positive structure; (iii) evaluate several distance class sizes; (iv) use appropriate methods when combining data from multiple populations; (v) compare the appropriate groups of individuals.     

MODELING BRAIN EVOLUTION FROM BEHAVIOR: A PERMUTATIONAL REGRESSION APPROACH

This paper has two complementary purposes: first, to present a method to perform multiple regression on distance matrices, with permutation testing appropriate for path-length matrices representing evolutionary trees, and then, to apply this method to study the joint evolution of brain, behavior and other characteristics in marsupials. To understand the computation method, consider that the dependent matrix is unfolded as a vector y; similarly, consider X to be a table containing the independent matrices, also unfolded as vectors. A multiple regression is computed to express y as a function of X. The parameters of this regression (R² and partial regression coefficients) are tested by permutations, as follows. When the dependent matrix variable y represents a simple distance or similarity matrix, permutations are performed in the same manner as the Mantel permutational test. When it is an ultrametric matrix representing a dendrogram, we use the double-permutation method (Lapointe and Legendre 1990, 1991). When it is a path-length matrix representing an additive tree (cladogram), we use the triple-permutation method (Lapointe and Legendre 1992). The independent matrix variables in X are kept fixed with respect to one another during the permutations. Selection of predictors can be accomplished by forward selection, backward elimination, or a stepwise procedure. A phylogenetic tree, derived from marsupial brain morphology data (28 species), is compared to trees depicting the evolution of diet, sociability, locomotion, and habitat in these animals, as well as their taxonomy and geographical relationships. A model is derived in which brain evolution can be predicted from taxonomy, diet, sociability and locomotion (R² = 0.75). A new tree, derived from the “predicted” data, shows a lot of similarity to the brain evolution tree. The meaning of the taxonomy, diet, sociability, and locomotion predictors are discussed and conclusions are drawn about the evolution of brain and behavior in marsupials.