Erratum: Information theory broadens the spectrum of molecular ecology and evolution: (Trends in Ecology and Evolution 32:12, p:948–963, 2017) (Trends in Ecology & Evolution (2017) 32(12) (948–963), (S0169534717302550), (10.1016/j.tree.2017.09.012))

In Sherwin et al. [1], several corrections are required, having been noticed when assisting other researchers to use the methods. In the main text, in Figure III in Box 2, nine subscripts were incorrect (reversing localities 1 and 2). The correct figure is shown below. [Figure presented] On pages 5 and 6 of the supplement, it should be explained that [Formula presented], that is, the averaging happens before conversion to the D scale (see Equations 10 and 11 of Jost [2]). Similarly, on page 8 of the supplement, [Formula presented]. On Page 6 of the supplement, the equation from Dewar et al. [3] is incorrectly transcribed; the correct equation is: [Formula presented] The authors and publisher apologise for any confusion

Testing for Network and Spatial Autocorrelation

Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in data sampled from the nodes of a network, motivated by social network applications. We demonstrate that our proposed statistic for categorical variables can both be used in the spatial and network setting

Foraging patterns of acorn woodpeckers (Melanerpes formicivorus) on valley oak (Quercus lobata Née) in two California oak savanna-woodlands

Landscape characteristics and social behavior can affect the foraging patterns of seed-dependent animals. We examine the movement of acorns from valley oak (Quercus lobata) trees to granaries maintained by acorn woodpeckers (Melanerpes formicivorus) in two California oak savanna-woodlands differing in the distribution of Q. lobata within each site. In 2004, we sampled Q. lobata acorns from 16 granaries at Sedgwick Reserve in Santa Barbara County and 18 granaries at Hastings Reserve in Monterey County. Sedgwick has lower site-wide density of Q. lobata than Hastings as well as different frequencies of other Quercus species common to both sites. We found acorn woodpeckers foraged from fewer Q. lobata seed source trees (Kg = 4.1 ± 0.5) at Sedgwick than at Hastings (Kg = 7.6 ± 0.6) and from fewer effective seed sources (Nem* = 2.00 and 5.78, respectively). The differences between sites are due to a greater number of incidental seed sources used per granary at Hastings than at Sedgwick. We also found very low levels of seed source sharing between adjacent granaries, indicating that territoriality is strong at both sites and that each social group forages on its own subset of trees. We discovered an interesting spatial pattern in the location of granaries. At Sedgwick, acorn woodpeckers situated their granaries within areas of higher-than-average tree density, while at Hastings, they placed them within areas of lower-than-average tree density, with the outcome that granaries at the two sites were located in areas of similar valley oak density. Our results illustrate that landscape characteristics might influence the number of trees visited by acorn woodpeckers and the locations of territories, while woodpecker social behavior, such as territoriality, shapes which trees are visited and whether they are shared with other social groups

The influence of habitat structure on genetic differentiation in red fox populations in north-eastern Poland

The red fox (Vulpes vulpes) has the widest global distribution among terrestrial carnivore species, occupying most of the Northern Hemisphere in its native range. Because it carries diseases that can be transmitted to humans and domestic animals, it is important to gather information about their movements and dispersal in their natural habitat but it is difficult to do so at a broad scale with trapping and telemetry. In this study, we have described the genetic diversity and structure of red fox populations in six areas of north-eastern Poland, based on samples collected from 2002–2003. We tested 22 microsatellite loci isolated from the dog and the red fox genome to select a panel of nine polymorphic loci suitable for this study. Genetic differentiation between the six studied populations was low to moderate and analysis in Structure revealed a panmictic population in the region. Spatial autocorrelation among all individuals showed a pattern of decreasing relatedness with increasing distance and this was not significantly negative until 93 km, indicating a pattern of isolation-by-distance over a large area. However, there was no correlation between genetic distance and either Euclidean distance or least-cost path distance at the population level. There was a significant relationship between genetic distance and the proportion of large forests and water along the Euclidean distances. These types of habitats may influence dispersal paths taken by red foxes, which is useful information in terms of wildlife disease management

Ecological implications of a flower size/number trade-off in tropical forest trees

Peer reviewedPublisher PD

Measuring differentiation among populations at different levels of genetic integration

<p>Abstract</p> <p>Background</p> <p>Most genetic studies of population differentiation are based on gene-pool frequencies. Population differences for gene associations that show up as deviations from Hardy-Weinberg proportions (homologous association) or gametic disequilibria (non-homologous association) are disregarded. Thus little is known about patterns of population differentiation at higher levels of genetic integration nor the causal forces.</p> <p>Results</p> <p>To fill this gap, a conceptual approach to the description and analysis of patterns of genetic differentiation at arbitrary levels of genetic integration (single or multiple loci, varying degrees of ploidy) is introduced. Measurement of differentiation is based on the measure Δ of genetic distance between populations, which is in turn based on an elementary genic difference between individuals at any given level of genetic integration. It is proven that Δ does not decrease when the level of genetic integration is increased, with equality if the gene associations at the higher level follow the same function in both populations (e.g. equal inbreeding coefficients, no association between loci). The pattern of differentiation is described using the matrix of pairwise genetic distances Δ and the differentiation snail based on the symmetric population differentiation Δ_<it>SD</it>. A measure of covariation compares patterns between levels. To show the significance of the observed differentiation among possible gene associations, a special permutation analysis is proposed. Applying this approach to published genetic data on oak, the differentiation is found to increase considerably from lower to higher levels of integration, revealing variation in the forms of gene association among populations.</p> <p>Conclusion</p> <p>This new approach to the analysis of genetic differentiation among populations demonstrates that the consideration of gene associations within populations adds a new quality to studies on population differentiation that is overlooked when viewing only gene-pools.</p

The self-reported Montgomery-Åsberg depression rating scale is a useful evaluative tool in major depressive disorder

Abstract Background The use of Patient-reported Outcomes (PROs) as secondary endpoints in the development of new antidepressants has grown in recent years. The objective of this study was to assess the psychometric properties of the 9-item, patient-administered version of the Montgomery-Åsberg Depression Rating Scale (MADRS-S). Methods Data from a multicentre, double-blind, 8-week, randomised controlled trial of 278 outpatients diagnosed with Major Depressive Disorder were used to evaluate the validity, reliability and sensitivity to change of the MADRS-S using psychometric methods. A Receiver Operating Characteristic (ROC) curve was plotted to identify the most appropriate threshold to define perceived remission. Results No missing values were found at the item level, indicating good acceptability of the scale. The construct validity was satisfactory: all items contributed to a common underlying concept, as expected. The correlation between MADRS-S and physicians' MADRS was moderate (r = 0.54, p Conclusion Taking account of patient's perceptions of the severity of their own symptoms along with the psychometric properties of the MADRS-S enable its use for evaluative purposes in the development of new antidepressant drugs.</p

Craniometric Data Supports Demic Diffusion Model for the Spread of Agriculture into Europe

BACKGROUND:The spread of agriculture into Europe and the ancestry of the first European farmers have been subjects of debate and controversy among geneticists, archaeologists, linguists and anthropologists. Debates have centred on the extent to which the transition was associated with the active migration of people as opposed to the diffusion of cultural practices. Recent studies have shown that patterns of human cranial shape variation can be employed as a reliable proxy for the neutral genetic relationships of human populations. METHODOLOGY/PRINCIPAL FINDINGS:Here, we employ measurements of Mesolithic (hunter-gatherers) and Neolithic (farmers) crania from Southwest Asia and Europe to test several alternative population dispersal and hunter-farmer gene-flow models. We base our alternative hypothetical models on a null evolutionary model of isolation-by-geographic and temporal distance. Partial Mantel tests were used to assess the congruence between craniometric distance and each of the geographic model matrices, while controlling for temporal distance. Our results demonstrate that the craniometric data fit a model of continuous dispersal of people (and their genes) from Southwest Asia to Europe significantly better than a null model of cultural diffusion. CONCLUSIONS/SIGNIFICANCE:Therefore, this study does not support the assertion that farming in Europe solely involved the adoption of technologies and ideas from Southwest Asia by indigenous Mesolithic hunter-gatherers. Moreover, the results highlight the utility of craniometric data for assessing patterns of past population dispersal and gene flow

High Differentiation among Eight Villages in a Secluded Area of Sardinia Revealed by Genome-Wide High Density SNPs Analysis

To better design association studies for complex traits in isolated populations it's important to understand how history and isolation moulded the genetic features of different communities. Population isolates should not “a priori” be considered homogeneous, even if the communities are not distant and part of a small region. We studied a particular area of Sardinia called Ogliastra, characterized by the presence of several distinct villages that display different history, immigration events and population size. Cultural and geographic isolation characterized the history of these communities. We determined LD parameters in 8 villages and defined population structure through high density SNPs (about 360 K) on 360 unrelated people (45 selected samples from each village). These isolates showed differences in LD values and LD map length. Five of these villages show high LD values probably due to their reduced population size and extreme isolation. High genetic differentiation among villages was detected. Moreover population structure analysis revealed a high correlation between genetic and geographic distances. Our study indicates that history, geography and biodemography have influenced the genetic features of Ogliastra communities producing differences in LD and population structure. All these data demonstrate that we can consider each village an isolate with specific characteristics. We suggest that, in order to optimize the study design of complex traits, a thorough characterization of genetic features is useful to identify the presence of sub-populations and stratification within genetic isolates

Manifold Learning for Human Population Structure Studies

The dimension of the population genetics data produced by next-generation sequencing platforms is extremely high. However, the “intrinsic dimensionality” of sequence data, which determines the structure of populations, is much lower. This motivates us to use locally linear embedding (LLE) which projects high dimensional genomic data into low dimensional, neighborhood preserving embedding, as a general framework for population structure and historical inference. To facilitate application of the LLE to population genetic analysis, we systematically investigate several important properties of the LLE and reveal the connection between the LLE and principal component analysis (PCA). Identifying a set of markers and genomic regions which could be used for population structure analysis will provide invaluable information for population genetics and association studies. In addition to identifying the LLE-correlated or PCA-correlated structure informative marker, we have developed a new statistic that integrates genomic information content in a genomic region for collectively studying its association with the population structure and LASSO algorithm to search such regions across the genomes. We applied the developed methodologies to a low coverage pilot dataset in the 1000 Genomes Project and a PHASE III Mexico dataset of the HapMap. We observed that 25.1%, 44.9% and 21.4% of the common variants and 89.2%, 92.4% and 75.1% of the rare variants were the LLE-correlated markers in CEU, YRI and ASI, respectively. This showed that rare variants, which are often private to specific populations, have much higher power to identify population substructure than common variants. The preliminary results demonstrated that next generation sequencing offers a rich resources and LLE provide a powerful tool for population structure analysis