A comparison of individual‐based genetic distance metrics for landscape genetics

A major aim of landscape genetics is to understand how landscapes resist gene flow and thereby influence population genetic structure. An empirical understanding of this process provides a wealth of information that can be used to guide conservation and management of species in fragmented landscapes and also to predict how landscape change may affect population viability. Statistical approaches to infer the true model among competing alternatives are based on the strength of the relationship between pairwise genetic distances and landscape distances among sampled individuals in a population. A variety of methods have been devised to quantify individual genetic distances, but no study has yet compared their relative performance when used for model selection in landscape genetics. In this study, we used population genetic simulations to assess the accuracy of 16 individual‐based genetic distance metrics under varying sample sizes and degree of population genetic structure. We found most metrics performed well when sample size and genetic structure was high. However, it was much more challenging to infer the true model when sample size and genetic structure was low. Under these conditions, we found genetic distance metrics based on principal components analysis were the most accurate (although several other metrics performed similarly), but only when they were derived from multiple principal components axes (the optimal number varied depending on the degree of population genetic structure). Our results provide guidance for which genetic distance metrics maximize model selection accuracy and thereby better inform conservation and management decisions based upon landscape genetic analysis.     

FragmentationAnalyzer: An open‐source tool to analyze MS/MS fragmentation data

A thorough understanding of the fragmentation processes in MS/MS can be a powerful tool in assessing the resulting peptide and protein identifications. We here present the freely available, open‐source FragmentationAnalyzer tool ( http://fragmentation‐analyzer.googlecode.com ) that makes it straightforward to analyze large MS/MS data sets for specific types of identified peptides, using a common set of peptide properties. This enables the detection of fragmentation pattern nuances related to specific instruments or due to the presence of post‐translational modifications.     

PipeCraft: Flexible open‐source toolkit for bioinformatics analysis of custom high‐throughput amplicon sequencing data

High‐throughput sequencing methods have become a routine analysis tool in environmental sciences as well as in public and private sector. These methods provide vast amount of data, which need to be analysed in several steps. Although the bioinformatics may be applied using several public tools, many analytical pipelines allow too few options for the optimal analysis for more complicated or customized designs. Here, we introduce PipeCraft, a flexible and handy bioinformatics pipeline with a user‐friendly graphical interface that links several public tools for analysing amplicon sequencing data. Users are able to customize the pipeline by selecting the most suitable tools and options to process raw sequences from Illumina, Pacific Biosciences, Ion Torrent and Roche 454 sequencing platforms. We described the design and options of PipeCraft and evaluated its performance by analysing the data sets from three different sequencing platforms. We demonstrated that PipeCraft is able to process large data sets within 24 hr. The graphical user interface and the automated links between various bioinformatics tools enable easy customization of the workflow. All analytical steps and options are recorded in log files and are easily traceable.     

AmphiNom: an amphibian systematics tool

Large-scale comparative and systematic studies rely on the seamless merging of multiple datasets. However, taxonomic nomenclature is constantly being revised making it problematic to combine data from different resources or different years of publication, which use different synonyms. This is certainly true for amphibians, which have experienced a spike in taxonomic revisions in part as the result of the widespread use of DNA barcoding to resolve cryptic species delimitation issues and large-scale collaborative efforts to revise the entire amphibian tree. The ‘Amphibian Species of the World Online Reference’ (ASW) is one of the most widely used and most regularly updated databases for amphibian taxonomy, but existing R tools for querying synonyms such as ‘taxize’ do not include this resource. ‘AmphiNom’ is a tool suite written in the R programming language designed to facilitate batch-querying amphibian species names against the ASW database. This facilitates the merging of datasets that use different nomenclature and its functionality is easily integrated into customizable R workflows. Moreover, it allows direct querying of the ASW website using R and straightforward reporting of summary information on current amphibian systematics.     

Comet: An open‐source MS/MS sequence database search tool

Proteomics research routinely involves identifying peptides and proteins via MS/MS sequence database search. Thus the database search engine is an integral tool in many proteomics research groups. Here, we introduce the Comet search engine to the existing landscape of commercial and open‐source database search tools. Comet is open source, freely available, and based on one of the original sequence database search tools that has been widely used for many years.     

Opening the black box: an open‐source release of Maxent

This software note announces a new open‐source release of the Maxent software for modeling species distributions from occurrence records and environmental data, and describes a new R package for fitting such models. The new release (ver. 3.4.0) will be hosted online by the American Museum of Natural History, along with future versions. It contains small functional changes, most notably use of a complementary log‐log (cloglog) transform to produce an estimate of occurrence probability. The cloglog transform derives from the recently‐published interpretation of Maxent as an inhomogeneous Poisson process (IPP), giving it a stronger theoretical justification than the logistic transform which it replaces by default. In addition, the new R package, maxnet, fits Maxent models using the glmnet package for regularized generalized linear models. We discuss the implications of the IPP formulation in terms of model inputs and outputs, treating occurrence records as points rather than grid cells and interpreting the exponential Maxent model (raw output) as as an estimate of relative abundance. With these two open‐source developments, we invite others to freely use and contribute to the software.     

Sampling strategy optimization to increase statistical power in landscape genomics: A simulation‐based approach

An increasing number of studies are using landscape genomics to investigate local adaptation in wild and domestic populations. Implementation of this approach requires the sampling phase to consider the complexity of environmental settings and the burden of logistical constraints. These important aspects are often underestimated in the literature dedicated to sampling strategies. In this study, we computed simulated genomic data sets to run against actual environmental data in order to trial landscape genomics experiments under distinct sampling strategies. These strategies differed by design approach (to enhance environmental and/or geographical representativeness at study sites), number of sampling locations and sample sizes. We then evaluated how these elements affected statistical performances (power and false discoveries) under two antithetical demographic scenarios. Our results highlight the importance of selecting an appropriate sample size, which should be modified based on the demographic characteristics of the studied population. For species with limited dispersal, sample sizes above 200 units are generally sufficient to detect most adaptive signals, while in random mating populations this threshold should be increased to 400 units. Furthermore, we describe a design approach that maximizes both environmental and geographical representativeness of sampling sites and show how it systematically outperforms random or regular sampling schemes. Finally, we show that although having more sampling locations (between 40 and 50 sites) increase statistical power and reduce false discovery rate, similar results can be achieved with a moderate number of sites (20 sites). Overall, this study provides valuable guidelines for optimizing sampling strategies for landscape genomics experiments.     

基于理想生态系统模式与三维景观指数的徽派村落空间特征解析——以呈坎为例

徽派村落是中国传统村落的典型代表,蕴含了丰富的地域文化和生态内涵,是中国古代人与自然和谐相处的典范,其研究与保护在生态学、地理学、建筑学和美学等领域具有重要的科学价值.本文以呈坎村为例,综合风水理论、心理场理论等,从生态学视角提出理想人居环境的理想生态系统模式,并引入三维景观指数,对徽派村落空间结构进行解析,定量分析村落理想生态系统各景观要素组合结构的生态功能体现.结果表明: 呈坎村立体结构为“山-林-屋-水-林”的景观空间组合模式,形成了以人为中心、自然景观立体环绕的圈层结构,符合理想人居模式.传统基于投影距离的方法造成了景观指数偏差(如对面积与边长的低估).平均斑块面积三维指数比二维指数高6.7%;林地的面积比重三维指数比二维指数高1.0%,其他景观类型的面积比重均有所下降,其中建设用地和耕地等人工斑块的面积比重下降最多.面积与周长指标被低估,形状指标和多样性指标被高估,导致景观格局分析中自然斑块的优势度被低估、人工斑块的优势度被高估.三维景观指数表明,呈坎村落生态系统中各自然要素及组合体现出了较好的生态功能,关键要素和景观生态系统具有较高的稳定性、连接度和聚集性;定量印证了以呈坎为代表的徽派村落是理想的生态系统.     

NetLogoR: a package to build and run spatially explicit agent‐based models in R

NetLogoR is an R package to build and run spatially explicit agent‐based models (SE‐ABMs) using the R language. SE‐ABMs are models that simulate the fate of entities at the individual level within a spatial context and where patterns emerge at the population level. NetLogoR follows the same framework as the NetLogo software (Wilensky 1999). Rather than a call function to use the NetLogo software, NetLogoR is a translation into the R language of the structure and functions of NetLogo. Models built with NetLogoR are written in R language and are run on the R platform; no other software or language has to be involved. NetLogoR provides new R classes to define model agent objects and functions to implement spatially explicit agent‐based models in the R environment. Users of this package benefit from the fast and easy coding provided by the highly developed NetLogo framework, coupled with the versatility, power and massive resources of the R language.     

Integrating small-scale landscape elements into land use/cover: The impact on landscape metrics’ values

Over the last 30 years the use and misuse of landscape metrics has been widely studied. However, there has been less attention on incorporating small-scale landscape elements into landscape analysis. Data type used in the analysis can be either vector or raster, while the raster format is more widely used. However, using large-scale topographical vector databases has several advantages – they cover whole countries with very detailed and accurate topographical data. Despite the high level of detail, their amount in Mb is small, which allows simultaneously to analyse large areas. The peculiarity of vector data is that small-scale landscape elements are mapped as point elements or lines. For calculating landscape metrics, the integration of these features and LULC (land use/cover) polygons is needed. In the current study we investigated how integration of point and linear elements into polygon layers affects the values of landscape metrics. Adding line buffers influenced metrics’ values more than adding point elements. The ensemble of point and linear objects is similar to linear objects. Our study revealed that integrating small-scale landscape elements into land use/cover layers by using buffers gives more realistic values if the buffer size is in compliance with the size of the phenomena in the real world and suitable landscape metrics are chosen. However, the metrics that responded to adding small-scale landscape elements in correspondence with their real world impact on landscape metric values might not always be the best ecological indicators in terms of small-scale landscape elements. Another issue is that values of landscape metrics depend directly on the number of classes determined in the data specification, and on the data model. If the number of mappable point and linear objects changes, or the data model of the linear objects changes, the values of landscape metrics differ.     

phylosignal: an R package to measure,test, and explore the phylogenetic signal

Phylogenetic signal is the tendency for closely related species to display similar trait values as a consequence of their phylogenetic proximity. Ecologists and evolutionary biologists are becoming increasingly interested in studying the phylogenetic signal and the processes which drive patterns of trait values in the phylogeny. Here, we present a new R package, phylosignal which provides a collection of tools to explore the phylogenetic signal for continuous biological traits. These tools are mainly based on the concept of autocorrelation and have been first developed in the field of spatial statistics. To illustrate the use of the package, we analyze the phylogenetic signal in pollution sensitivity for 17 species of diatoms.     

基于景观镶嵌度指数的森林破碎化模式动态——以美国俄勒冈州为例

有效的景观模式特征评价及其变动预测是合理调控和管理森林景观、维持景观安全格局的基本前提.利用3期美国国家土地覆盖数据库(1992、2001和2006年),采用景观镶嵌度指数与马尔科夫模型相结合的方法,分析了美国俄勒冈州的森林破碎化模式及森林与其他土地利用类型空间交互特征的变化.结果表明: 景观镶嵌度模型中,开发主导的景观镶嵌类型(D)转变为单一的开发类型(DD)的概率最大,为0.319,说明城市化是推动区域景观格局变化的主要动力;森林安全度模型中,主要为农业和开发景观镶嵌类型(ad)的森林损失率最高,表明在城市与农业占主导的景观上森林被吞噬的可能性最大;稳态分布表明,森林破碎化趋势日益加剧,到稳定状态时森林占总区域的面积比例不到50%,空间分布趋向于混合型的景观格局.景观镶嵌度模型2006年模拟值与实际值Kappa系数达到0.82,模型精度较高;森林安全度模型Kappa系数为0.21,模型精度较差.     

EcoGenetics: An R package for the management and exploratory analysis of spatial data in landscape genetics

The integration of ecology and genetics has become established in recent decades, in hand with the development of new technologies, whose implementation is allowing an improvement of the tools used for data analysis. In a landscape genetics context, integrative management of population information from different sources can make spatial studies involving phenotypic, genotypic and environmental data simpler, more accessible and faster. Tools for exploratory analysis of autocorrelation can help to uncover the spatial genetic structure of populations and generate appropriate hypotheses in searching for possible causes and consequences of their spatial processes. This study presents EcoGenetics, an R package with tools for multisource management and exploratory analysis in landscape genetics.     

Using simulations to evaluate Mantel‐based methods for assessing landscape resistance to gene flow

Mantel‐based tests have been the primary analytical methods for understanding how landscape features influence observed spatial genetic structure. Simulation studies examining Mantel‐based approaches have highlighted major challenges associated with the use of such tests and fueled debate on when the Mantel test is appropriate for landscape genetics studies. We aim to provide some clarity in this debate using spatially explicit, individual‐based, genetic simulations to examine the effects of the following on the performance of Mantel‐based methods: (1) landscape configuration, (2) spatial genetic nonequilibrium, (3) nonlinear relationships between genetic and cost distances, and (4) correlation among cost distances derived from competing resistance models. Under most conditions, Mantel‐based methods performed poorly. Causal modeling identified the true model only 22% of the time. Using relative support and simple Mantel r values boosted performance to approximately 50%. Across all methods, performance increased when landscapes were more fragmented, spatial genetic equilibrium was reached, and the relationship between cost distance and genetic distance was linearized. Performance depended on cost distance correlations among resistance models rather than cell‐wise resistance correlations. Given these results, we suggest that the use of Mantel tests with linearized relationships is appropriate for discriminating among resistance models that have cost distance correlations <0.85 with each other for causal modeling, or <0.95 for relative support or simple Mantel r. Because most alternative parameterizations of resistance for the same landscape variable will result in highly correlated cost distances, the use of Mantel test‐based methods to fine‐tune resistance values will often not be effective.     

phorest: a web‐based tool for comparative analyses of expressed sequence tag data

Comparative analysis of expressed sequence tags is becoming an important tool in molecular ecology for comparing gene expression in organisms grown in certain environments. Additionally, expressed sequence tag database information can be used for the construction of DNA microarrays and for the detection of single nucleotide polymorphisms. For such applications, we present phorest , a web‐based tool for managing, analysing and comparing various collections of expressed sequence tags. It is written in PHP (PHP: Hypertext Preprocessor) and runs on UNIX, Microsoft Windows and Macintosh (Mac OS X) platforms.     

Performance of partial statistics in individual‐based landscape genetics

Individual‐based landscape genetic methods have become increasingly popular for quantifying fine‐scale landscape influences on gene flow. One complication for individual‐based methods is that gene flow and landscape variables are often correlated with geography. Partial statistics, particularly Mantel tests, are often employed to control for these inherent correlations by removing the effects of geography while simultaneously correlating measures of genetic differentiation and landscape variables of interest. Concerns about the reliability of Mantel tests prompted this study, in which we use simulated landscapes to evaluate the performance of partial Mantel tests and two ordination methods, distance‐based redundancy analysis (dbRDA) and redundancy analysis (RDA), for detecting isolation by distance (IBD) and isolation by landscape resistance (IBR). Specifically, we described the effects of suitable habitat amount, fragmentation and resistance strength on metrics of accuracy (frequency of correct results, type I/II errors and strength of IBR according to underlying landscape and resistance strength) for each test using realistic individual‐based gene flow simulations. Mantel tests were very effective for detecting IBD, but exhibited higher error rates when detecting IBR. Ordination methods were overall more accurate in detecting IBR, but had high type I errors compared to partial Mantel tests. Thus, no one test outperformed another completely. A combination of statistical tests, for example partial Mantel tests to detect IBD paired with appropriate ordination techniques for IBR detection, provides the best characterization of fine‐scale landscape genetic structure. Realistic simulations of empirical data sets will further increase power to distinguish among putative mechanisms of differentiation.     

funrar: An R package to characterize functional rarity

Emphasis has been put in recent ecological research on investigating phylogenetic, functional and taxonomic facets of biological diversity. While a flourishing number of indices have been proposed for assessing functional diversity, surprisingly few options are available to characterize functional rarity. Functional rarity can play a key role in community and ecosystem dynamics. We introduce here the funrar R package to quantify functional rarity based on species trait differences and species frequencies at local and regional scales. Because of the increasing availability of big datasets in macroecology and biogeography, we optimized funrar to work with large datasets of thousands of species and sites. We illustrate the use of the package to investigate the functional rarity of North and Central American mammals.     

Shifts in reciprocal river‐riparian arthropod fluxes along an urban‐rural landscape gradient

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