landscapemetrics: an open‐source R tool to calculate landscape metrics

Quantifying landscape characteristics and linking them to ecological processes is one of the central goals of landscape ecology. Landscape metrics are a widely used tool for the analysis of patch‐based, discrete land‐cover classes. Existing software to calculate landscape metrics has several constraints, such as being limited to a single platform, not being open‐source or involving a complicated integration into large workflows. We present landscapemetrics, an open‐source R package that overcomes many constraints of existing landscape metric software. The package includes an extensive collection of commonly used landscape metrics in a tidy workflow. To facilitate the integration into large workflows, landscapemetrics is based on a well‐established spatial framework in R. This allows pre‐processing of land‐cover maps or further statistical analysis without importing and exporting the data from and to different software environments. Additionally, the package provides many utility functions to visualize, extract, and sample landscape metrics. Lastly, we provide building‐blocks to motivate the development and integration of new metrics in the future. We demonstrate the usage and advantages of landscapemetrics by analysing the influence of different sampling schemes on the estimation of landscape metrics. In so doing, we demonstrate the many advantages of the package, especially its easy integration into large workflows. These new developments should help with the integration of landscape analysis in ecological research, given that ecologists are increasingly using R for the statistical analysis, modelling and visualization of spatial data.     

rangeMapper: a platform for the study of macroecology of life‐history traits

Aim To introduce rangeMapper, an R package for the study of the macroecological patterns of life‐history traits, and demonstrate its capabilities using three case studies. The first case study addresses an important topic in conservation biology: biodiversity hotspots. Specifically, we investigate the congruence between global hotspots of three parameters that describe avian diversity: species richness, endemic species richness and relative body mass diversity. The second case study investigates a topic of relevance for macroecology: the inter‐specific relationship between range size and body size for avian assemblages, and how it varies geographically. The third case study tackles a methodological problem in macroecology: the influence of map resolution on statistical inference, i.e. the question of whether and how the relationship between species richness and body mass varies with map resolution. Innovation rangeMapper offers a tight integration of spatial and statistical tools for macroecological projects and it relies on a high‐performance database engine which makes it suitable for managing projects using a large number of species. rangeMapper's architecture follows closely the concepts described by Gaston et al. (2008 Journal of Biogeography, 35 , 483–500) and its flexibility allows for both complex data manipulation procedures and easy implementation of new functions. By choosing case studies to cover various technical and conceptual issues we demonstrate rangeMapper's capabilities to address a wide array of questions. Main conclusion rangeMapper ( http://cran.r‐project.org/package=rangeMapper ) is an open source front end software which can be used to address questions in both fundamental ecological research and conservation biology.     

hzar: hybrid zone analysis using an R software package

We present a new software package (hzar ) that provides functions for fitting molecular genetic and morphological data from hybrid zones to classic equilibrium cline models using the Metropolis–Hastings Markov chain Monte Carlo (MCMC) algorithm. The software applies likelihood functions appropriate for different types of data, including diploid and haploid genetic markers and quantitative morphological traits. The modular design allows flexibility in fitting cline models of varying complexity. To facilitate hypothesis testing, an autofit function is included that allows automated model selection from a set of nested cline models. Cline parameter values, such as cline centre and cline width, are estimated and may be compared statistically across clines. The package is written in the R language and is available through the Comprehensive R Archive Network (CRAN; http://cran.r-project.org/ ). Here, we describe hzar and demonstrate its use with a sample data set from a well‐studied hybrid zone in western Panama between white‐collared (Manacus candei) and golden‐collared manakins (M. vitellinus). Comparisons of our results with previously published results for this hybrid zone validate the hzar software. We extend analysis of this hybrid zone by fitting additional models to molecular data where appropriate.     

macroeco: reproducible ecological pattern analysis in Python

macroeco is a Python package that supports the analysis of empirical macroecological patterns and the comparison of these patterns to theoretical predictions. Here we describe the use of macroeco and the various functions that it contains. We also highlight a unique high‐level interface included with the package, MacroecoDesktop, that allows non‐programmers to access the functionality of macroeco. MacroecoDesktop takes simple text‐based metadata and parameter files as inputs and generates both tabular and graphical outputs, supporting users in creating reproducible workflows that follow the principles of simplicity, provenance, and automation. Both macroeco and MacroecoDesktop provide case studies for developers of analytically‐focused scientific software packages who wish to better support the reproducible use of their tools.     

Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses

Advances in tracking technology have led to an exponential increase in animal location data, greatly enhancing our ability to address interesting questions in movement ecology, but also presenting new challenges related to data management and analysis. Step‐selection functions (SSFs) are commonly used to link environmental covariates to animal location data collected at fine temporal resolution. SSFs are estimated by comparing observed steps connecting successive animal locations to random steps, using a likelihood equivalent of a Cox proportional hazards model. By using common statistical distributions to model step length and turn angle distributions, and including habitat‐ and movement‐related covariates (functions of distances between points, angular deviations), it is possible to make inference regarding habitat selection and movement processes or to control one process while investigating the other. The fitted model can also be used to estimate utilization distributions and mechanistic home ranges. Here, we present the R package amt (animal movement tools) that allows users to fit SSFs to data and to simulate space use of animals from fitted models. The amt package also provides tools for managing telemetry data. Using fisher (Pekania pennanti) data as a case study, we illustrate a four‐step approach to the analysis of animal movement data, consisting of data management, exploratory data analysis, fitting of models, and simulating from fitted models.     

phylin: an r package for phylogeographic interpolation

phylin is a package for the r programming environment which offers different methods to spatially interpolate genetic information from phylogeographic data. These interpolations can be used to predict the spatial occurrence of different lineages within a phylogeny using a modified method of kriging, which allows the usage of a genetic distance matrix to derive a model of spatial dependence. phylin improves the available methods to generate interpolated surfaces from a phylogenetic trees by assessing the autocorrelation structure of the genetic information, interpolating the genetic data based on a statistical model, estimating the uncertainty of the predictions and identifying lineage occurrence and contact zones probability without projection of pairwise genetic distances into mid‐points between sample locations. The package also includes methods to plot interpolation surfaces and provide summary tables from the generated data and models. We provide an example of the usefulness of this tool by inferring the spatial occurrence of distinct historical evolutionary lineages of the Lataste's viper (Vipera latastei Boscá, 1878) in the Iberian Peninsula and identifying potential contact areas. The maps of phylogenetic patterns obtained with these methods provide a spatial context to test hypotheses related to processes underlying the geographic distribution of genetic diversity and to inform conservation planning.     

A PANEL PERFORMANCE PROCEDURE IMPLEMENTED IN R

Monitoring performance is essential for the efficient use of a sensory panel both during training and while carrying out product assessments. We present a concise procedure to monitor panel performance based on classical statistical methods. The program includes tests for the ability to discriminate between products, repeatability of assessments, scale use, agreement between panelists and a principal component analysis map of panelist means across attributes. The algorithm is implemented in R – a state‐of‐the‐art, freely available statistical software package. The program output is summarized in graphs and tables. This easily applicable panel performance procedure is aimed at improving sensory practice, especially in areas where the use of highly complex systems is not feasible.     

germinator: a software package for high‐throughput scoring and curve fitting of Arabidopsis seed germination

Over the past few decades seed physiology research has contributed to many important scientific discoveries and has provided valuable tools for the production of high quality seeds. An important instrument for this type of research is the accurate quantification of germination; however gathering cumulative germination data is a very laborious task that is often prohibitive to the execution of large experiments. In this paper we present the germinator package: a simple, highly cost‐efficient and flexible procedure for high‐throughput automatic scoring and evaluation of germination that can be implemented without the use of complex robotics. The germinator package contains three modules: (i) design of experimental setup with various options to replicate and randomize samples; (ii) automatic scoring of germination based on the color contrast between the protruding radicle and seed coat on a single image; and (iii) curve fitting of cumulative germination data and the extraction, recap and visualization of the various germination parameters. The curve‐fitting module enables analysis of general cumulative germination data and can be used for all plant species. We show that the automatic scoring system works for Arabidopsis thaliana and Brassica spp. seeds, but is likely to be applicable to other species, as well. In this paper we show the accuracy, reproducibility and flexibility of the germinator package. We have successfully applied it to evaluate natural variation for salt tolerance in a large population of recombinant inbred lines and were able to identify several quantitative trait loci for salt tolerance. germinator is a low‐cost package that allows the monitoring of several thousands of germination tests, several times a day by a single person.     

Optimizing α for better statistical decisions: A case study involving the pace‐of‐life syndrome hypothesis

Setting optimal significance levels that minimize Type I and Type II errors allows for more transparent and well‐considered statistical decision making compared to the traditional α = 0.05 significance level. We use the optimal α approach to re‐assess conclusions reached by three recently published tests of the pace‐of‐life syndrome hypothesis, which attempts to unify occurrences of different physiological, behavioral, and life history characteristics under one theory, over different scales of biological organization. While some of the conclusions reached using optimal α were consistent to those previously reported using the traditional α = 0.05 threshold, opposing conclusions were also frequently reached. The optimal α approach reduced probabilities of Type I and Type II errors, and ensured statistical significance was associated with biological relevance. Biologists should seriously consider their choice of α when conducting null hypothesis significance tests, as there are serious disadvantages with consistent reliance on the traditional but arbitrary α = 0.05 significance level.     

trajr: An R package for characterisation of animal trajectories

Quantitative characterisation of the trajectories of moving animals is an important component of many behavioural and ecological studies, however methods are complicated and varied, and sometimes require well‐developed programming skills to implement. Here, we introduce trajr, an R package that serves to analyse animal paths, from unicellular organisms, through insects to whales. It makes a variety of statistical characterisations of trajectories, such as tortuosity, speed and changes in direction, available to biologists who may not have a background in programming. We discuss a range of indices that have been used by researchers, describe the package in detail, then use movement observations of whales and clearwing moths to demonstrate some of the capabilities of trajr. As an open‐source R package, trajr encourages open and reproducible research. It supports the implementation of additional methods by providing access to trajectory analysis “building blocks,” allows the full suite of R statistical analysis tools to be applied to trajectory analysis, and the source code can be independently validated.     

Efficient Bayesian analysis of occupancy models with logit link functions

Occupancy models (Ecology, 2002; 83: 2248) were developed to infer the probability that a species under investigation occupies a site. Bayesian analysis of these models can be undertaken using statistical packages such as WinBUGS, OpenBUGS, JAGS, and more recently Stan, however, since these packages were not developed specifically to fit occupancy models, one often experiences long run times when undertaking an analysis. Bayesian spatial single‐season occupancy models can also be fit using the R package stocc. The approach assumes that the detection and occupancy regression effects are modeled using probit link functions. The use of the logistic link function, however, is algebraically more tractable and allows one to easily interpret the coefficient effects of an estimated model by using odds ratios, which is not easily done for a probit link function for models that do not include spatial random effects. We develop a Gibbs sampler to obtain posterior samples from the posterior distribution of the parameters of various occupancy models (nonspatial and spatial) when logit link functions are used to model the regression effects of the detection and occupancy processes. We apply our methods to data extracted from the 2nd Southern African Bird Atlas Project to produce a species distribution map of the Cape weaver (Ploceus capensis) and helmeted guineafowl (Numida meleagris) for South Africa. We found that the Gibbs sampling algorithm developed produces posterior samples that are identical to those obtained when using JAGS and Stan and that in certain cases the posterior chains mix much faster than those obtained when using JAGS, stocc, and Stan. Our algorithms are implemented in the R package, Rcppocc. The software is freely available and stored on GitHub ( https://github.com/AllanClark/Rcppocc ).     

Analysis of interval‐grouped data in weed science: The binnednp Rcpp package

相似文献   

ResDisMapper: An r package for fine‐scale mapping of resistance to dispersal

Management of biological invasions and conservation activity in the fight against habitat fragmentation both require information on how ongoing dispersal of organisms is affected by the environment. However, there are few landscape genetic computer programs that map resistance to dispersal at small spatiotemporal scales. To facilitate such analyses, we present an r package named ResDisMapper for the mapping of resistance to dispersal at small spatiotemporal scales, without the need for prior knowledge on environmental features or intensive computation. Based on the concept of isolation by distance (IBD), ResDisMapper calculates resistance using deviations of each pair of samples from the general IBD trend (IBD residuals). The IBD residuals are projected onto the studied area, which allows construction and visualization of a fine‐scale map of resistance based on spatial accumulation of positive or negative IBD residuals. In this study, we tested ResDisMapper with both simulated and empirical data sets and compared its performance with two other popular landscape genetic programs. Overall, we found that ResDisMapper can map resistance with relatively high accuracy. The latest version of the package and associated documentation are available on Github ( https://github.com/takfung/ResDisMapper ).     

Detection of individual ploidy levels with genotyping‐by‐sequencing (GBS) analysis

Ploidy levels sometimes vary among individuals or populations, particularly in plants. When such variation exists, accurate determination of cytotype can inform studies of ecology or trait variation and is required for population genetic analyses. Here, we propose and evaluate a statistical approach for distinguishing low‐level ploidy variants (e.g. diploids, triploids and tetraploids) based on genotyping‐by‐sequencing (GBS) data. The method infers cytotypes based on observed heterozygosity and the ratio of DNA sequences containing different alleles at thousands of heterozygous SNPs (i.e. allelic ratios). Whereas the method does not require prior information on ploidy, a reference set of samples with known ploidy can be included in the analysis if it is available. We explore the power and limitations of this method using simulated data sets and GBS data from natural populations of aspen (Populus tremuloides) known to include both diploid and triploid individuals. The proposed method was able to reliably discriminate among diploids, triploids and tetraploids in simulated data sets, and this was true for different levels of genetic diversity, inbreeding and population structure. Power and accuracy were minimally affected by low coverage (i.e. 2×), but did sometimes suffer when simulated mixtures of diploids, autotetraploids and allotetraploids were analysed. Cytotype assignments based on the proposed method closely matched those from previous microsatellite and flow cytometry data when applied to GBS data from aspen. An R package (gbs2ploidy) implementing the proposed method is available from CRAN.     

Assessing group differences in biodiversity by simultaneously testing a user‐defined selection of diversity indices

Comparing diversities between groups is a task biologists are frequently faced with, for example in ecological field trials or when dealing with metagenomics data. However, researchers often waver about which measure of diversity to choose as there is a multitude of approaches available. As Jost (2008, Molecular Ecology, 17 , 4015) has pointed out, widely used measures such as the Shannon or Simpson index have undesirable properties which make them hard to compare and interpret. Many of the problems associated with the use of these ‘raw’ indices can be corrected by transforming them into ‘true’ diversity measures. We introduce a technique that allows the comparison of two or more groups of observations and simultaneously tests a user‐defined selection of a number of ‘true’ diversity measures. This procedure yields multiplicity‐adjusted P‐values according to the method of Westfall and Young (1993, Resampling‐Based Multiple Testing: Examples and Methods for p‐Value Adjustment, 49, 941), which ensures that the rate of false positives (type I error) does not rise when the number of groups and/or diversity indices is extended. Software is available in the R package ‘simboot’.     

MODISTools – downloading and processing MODIS remotely sensed data in R

Remotely sensed data – available at medium to high resolution across global spatial and temporal scales – are a valuable resource for ecologists. In particular, products from NASA's MODerate‐resolution Imaging Spectroradiometer (MODIS), providing twice‐daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This automation reduces the risk of human error, and the researcher effort required compared to manual per‐location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta‐analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between species richness across multiple higher taxa observed at 526 sites in temperate forests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time‐series value, temporal mean, and temporal variability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R² values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub ( https://github.com/seantuck12/MODISTools ).     

Assessing the genetic diversity in Argopecten nucleus (Bivalvia: Pectinidae), a functional hermaphrodite species with extremely low population density and self‐fertilization: Effect of null alleles

Argopecten nucleus is a functional hermaphroditic pectinid species that exhibits self‐fertilization, whose natural populations have usually very low densities. In the present study, the genetic diversity of a wild population from Neguanje Bay, Santa Marta (Colombia), was estimated using microsatellite markers, and the effect of the presence of null alleles on this estimation was assessed. A total of 8 microsatellite markers were developed, the first described for this species, and their amplification conditions were standardized. They were used to determine the genotype of 48 wild individuals from Naguanje Bay, and 1,010 individuals derived from the offspring of 38 directed crosses. For each locus, the frequencies of the identified alleles, including null alleles, were estimated using the statistical package Micro‐Checker, and the parental genotypes were confirmed using segregation analysis. Three to 8 alleles per locus with frequencies from 0.001 to 0.632 were detected. The frequencies of null alleles ranged from 0.10 to 0.45, with Ho from 0.0 to 0.79, and He from 0.53 to 0.80. All loci were in H‐W disequilibrium. The null allele frequencies values were high, with lower estimations using segregation analysis than estimated using Micro‐Checker. The present results show high levels of population genetic diversity and indicate that null alleles were not the only cause of deviation from H‐W equilibrium in all loci, suggesting that the wild population under study presents signs of inbreeding and Wahlund effect.