Making more sense of the order: A review of Canoco for Windows 4.5, PC‐ORD version 4 and SYN‐TAX 2000

Abstract. Personal computers of ever‐increasing speed have motivated programmers of multivariate software to adapt their programs to be run in Microsoft Windows and Macintosh platforms. Updated versions of these multivariate programs appear more and more frequently and are marketed intensively. In this review we provide a comparative analysis of the most recent versions of three analytical software packages –Canoco for Windows 4.5, PC‐ORD version 4 and SYN‐TAX 2000. The three packages share two characteristics. First, the most recent versions are now compatible with the most recent Windows platforms and should therefore be accessible for use by virtually all vegetation scientists. Second, they have capabilities for numerous multivariate techniques, although each package has some unique techniques. Thus, any one of the packages will have much to offer the user.     

Ecobat: An online resource to facilitate transparent,evidence‐based interpretation of bat activity data

Acoustic surveys of bats are one of the techniques most commonly used by ecological practitioners. The results are used in Ecological Impact Assessments to assess the likely impacts of future developments on species that are widely protected in law, and to monitor developments’ postconstruction. However, there is no standardized methodology for analyzing or interpreting these data, which can make the assessment of the ecological value of a site very subjective. Comparisons of sites and projects are therefore difficult for ecologists and decision‐makers, for example, when trying to identify the best location for a new road based on relative bat activity levels along alternative routes. Here, we present a new web‐based, data‐driven tool, Ecobat, which addresses the need for a more robust way of interpreting ecological data. Ecobat offers users an easy, standardized, and objective method for analyzing bat activity data. It allows ecological practitioners to compare bat activity data at regional and national scales and to generate a numerical indicator of the relative importance of a night's worth of bat activity. The tool is free and open‐source; because the underlying algorithms are already developed, it could easily be expanded to new geographical regions and species. Data donation is required to ensure the robustness of the analyses; we use a positive feedback mechanism to encourage ecological practitioners to share data by providing in return high quality, contextualized data analysis, and graphical visualizations for direct use in ecological reports.     

genodive version 3.0: Easy‐to‐use software for the analysis of genetic data of diploids and polyploids

genodive version 3.0 is a user‐friendly program for the analysis of population genetic data. This version presents a major update from the previous version and now offers a wide spectrum of different types of analyses. genodive has an intuitive graphical user interface that allows direct manipulation of the data through transformation, imputation of missing data, and exclusion and inclusion of individuals, population and/or loci. Furthermore, genodive seamlessly supports 15 different file formats for importing or exporting data from or to other programs. One major feature of genodive is that it supports both diploid and polyploid data, up to octaploidy (2n = 8x) for some analyses, but up to hexadecaploidy (2n = 16x) for other analyses. The different types of analyses offered by genodive include multiple statistics for estimating population differentiation (φ_ST, F_ST, F?_ST, G_ST, G?_ST, G??_ST, D_est, R_ST, ρ), analysis of molecular variance‐based K‐means clustering, Hardy–Weinberg equilibrium, hybrid index, population assignment, clone assignment, Mantel test, Spatial Autocorrelation, 23 ways of calculating genetic distances, and both principal components and principal coordinates analyses. A unique feature of genodive is that it can also open data sets with nongenetic variables, for example environmental data or geographical coordinates that can be included in the analysis. In addition, genodive makes it possible to run several external programs (lfmm , structure , instruct and vegan ) directly from its own user interface, avoiding the need for data reformatting and use of the command line. genodive is available for computers running Mac OS X 10.7 or higher and can be downloaded freely from: http://www.patrickmeirmans.com/software .     

Bio‐ORACLE: a global environmental dataset for marine species distribution modelling

Aim The oceans harbour a great diversity of organisms whose distribution and ecological preferences are often poorly understood. Species distribution modelling (SDM) could improve our knowledge and inform marine ecosystem management and conservation. Although marine environmental data are available from various sources, there are currently no user‐friendly, high‐resolution global datasets designed for SDM applications. This study aims to fill this gap by assembling a comprehensive, uniform, high‐resolution and readily usable package of global environmental rasters. Location Global, marine. Methods We compiled global coverage data, e.g. satellite‐based and in situ measured data, representing various aspects of the marine environment relevant for species distributions. Rasters were assembled at a resolution of 5 arcmin (c. 9.2 km) and a uniform landmask was applied. The utility of the dataset was evaluated by maximum entropy SDM of the invasive seaweed Codium fragile ssp. fragile. Results We present Bio‐ORACLE (ocean rasters for analysis of climate and environment), a global dataset consisting of 23 geophysical, biotic and climate rasters. This user‐friendly data package for marine species distribution modelling is available for download at http://www.bio‐oracle.ugent.be . The high predictive power of the distribution model of C. fragile ssp. fragile clearly illustrates the potential of the data package for SDM of shallow‐water marine organisms. Main conclusions The availability of this global environmental data package has the potential to stimulate marine SDM. The high predictive success of the presence‐only model of a notorious invasive seaweed shows that the information contained in Bio‐ORACLE can be informative about marine distributions and permits building highly accurate species distribution models.     

POY version 5: phylogenetic analysis using dynamic homologies under multiple optimality criteria

We present POY version 5, an open source program for the phylogenetic analysis of diverse data types including qualitative, aligned sequences, unaligned sequences, genomic data, and user‐defined sequences. In addition to the maximum‐parsimony optimality criterion supported by POY4, POY5 supports several types of maximum likelihood as well as posterior probability. To make these analyses feasible, new heuristic search algorithms and parallelization options have been implemented for all criteria.     

Late‐onset retinal degeneration pathology due to mutations in CTRP5 is mediated through HTRA1

Late‐onset retinal degeneration (L‐ORD) is an autosomal dominant macular degeneration characterized by the formation of sub‐retinal pigment epithelium (RPE) deposits and neuroretinal atrophy. L‐ORD results from mutations in the C1q‐tumor necrosis factor‐5 protein (CTRP5), encoded by the CTRP5/C1QTNF5 gene. To understand the mechanism underlying L‐ORD pathology, we used a human cDNA library yeast two‐hybrid screen to identify interacting partners of CTRP5. Additionally, we analyzed the Bruch's membrane/choroid (BM‐Ch) from wild‐type (Wt), heterozygous S163R Ctrp5 mutation knock‐in (Ctrp5^S163R/wt), and homozygous knock‐in (Ctrp5^S163R/S163R) mice using mass spectrometry. Both approaches showed an association between CTRP5 and HTRA1 via its C‐terminal PDZ‐binding motif, stimulation of the HTRA1 protease activity by CTRP5, and CTRP5 serving as an HTRA1 substrate. The S163R‐CTRP5 protein also binds to HTRA1 but is resistant to HTRA1‐mediated cleavage. Immunohistochemistry and proteomic analysis showed significant accumulation of CTRP5 and HTRA1 in BM‐Ch of Ctrp5^S163R/S163R and Ctrp5^S163R/wt mice compared with Wt. Additional extracellular matrix (ECM) components that are HTRA1 substrates also accumulated in these mice. These results implicate HTRA1 and its interaction with CTRP5 in L‐ORD pathology.     

The pairwise approach to analysing species co‐occurrence

The analysis of species co‐occurrence patterns continues to be a main pursuit of ecologists, primarily because the coexistence of species is fundamentally important in evaluating various theories, principles and concepts. Examples include community assembly, equilibrium versus non‐equilibrium organization of communities, resource partitioning and ecological character displacement, the local–regional species diversity relationship, and the metacommunity concept. Traditionally, co‐occurrence has been measured and tested at the level of an entire species presence–absence matrix wherein various algorithms are used to randomize matrices and produce statistical null distributions of metrics that quantify structure in the matrix. This approach implicitly recognizes a presence–absence matrix as having some real ecological identity (e.g. a set of species exhibiting nestedness among a set of islands) in addition to being a unit of statistical analysis. An emerging alternative is to test for non‐random co‐occurrence between paired species. The pairwise approach does not analyse matrix‐level structure and thus views a species pair as the fundamental unit of co‐occurrence. Inferring process from pattern is very difficult in analyses of co‐occurrence; however, the pairwise approach may make this task easier by simplifying the analysis and resulting inferences to associations between paired species.     

Ignoring non‐English‐language studies may bias ecological meta‐analyses

Meta‐analysis plays a crucial role in syntheses of quantitative evidence in ecology and biodiversity conservation. The reliability of estimates in meta‐analyses strongly depends on unbiased sampling of primary studies. Although earlier studies have explored potential biases in ecological meta‐analyses, biases in reported statistical results and associated study characteristics published in different languages have never been tested in environmental sciences. We address this knowledge gap by systematically searching published meta‐analyses and comparing effect‐size estimates between English‐ and Japanese‐language studies included in existing meta‐analyses. Of the 40 published ecological meta‐analysis articles authored by those affiliated to Japanese institutions, we find that three meta‐analysis articles searched for studies in the two languages and involved sufficient numbers of English‐ and Japanese‐language studies, resulting in four eligible meta‐analyses (i.e., four meta‐analyses conducted in the three meta‐analysis articles). In two of the four, effect sizes differ significantly between the English‐ and Japanese‐language studies included in the meta‐analyses, causing considerable changes in overall mean effect sizes and even their direction when Japanese‐language studies are excluded. The observed differences in effect sizes are likely attributable to systematic differences in reported statistical results and associated study characteristics, particularly taxa and ecosystems, between English‐ and Japanese‐language studies. Despite being based on a small sample size, our findings suggest that ignoring non‐English‐language studies may bias outcomes of ecological meta‐analyses, due to systematic differences in study characteristics and effect‐size estimates between English‐ and non‐English languages. We provide a list of actions that meta‐analysts could take in the future to reduce the risk of language bias.     

geoknife: reproducible web‐processing of large gridded datasets

Geoprocessing of large gridded data according to overlap with irregular landscape features is common to many large‐scale ecological analyses. The geoknife R package was created to facilitate reproducible analyses of gridded datasets found on the U.S. Geological Survey Geo Data Portal web application or elsewhere, using a web‐enabled workflow that eliminates the need to download and store large datasets that are reliably hosted on the Internet. The package provides access to several data subset and summarization algorithms that are available on remote web processing servers. Outputs from geoknife include spatial and temporal data subsets, spatially‐averaged time series values filtered by user‐specified areas of interest, and categorical coverage fractions for various land‐use types.     

EDENetworks: A user‐friendly software to build and analyse networks in biogeography,ecology and population genetics

The recent application of graph‐based network theory analysis to biogeography, community ecology and population genetics has created a need for user‐friendly software, which would allow a wider accessibility to and adaptation of these methods. EDENetworks aims to fill this void by providing an easy‐to‐use interface for the whole analysis pipeline of ecological and evolutionary networks starting from matrices of species distributions, genotypes, bacterial OTUs or populations characterized genetically. The user can choose between several different ecological distance metrics, such as Bray‐Curtis or Sorensen distance, or population genetic metrics such as F_ST or Goldstein distances, to turn the raw data into a distance/dissimilarity matrix. This matrix is then transformed into a network by manual or automatic thresholding based on percolation theory or by building the minimum spanning tree. The networks can be visualized along with auxiliary data and analysed with various metrics such as degree, clustering coefficient, assortativity and betweenness centrality. The statistical significance of the results can be estimated either by resampling the original biological data or by null models based on permutations of the data.     

Co‐occurrence is not evidence of ecological interactions

There is a rich amount of information in co‐occurrence (presence–absence) data that could be used to understand community assembly. This proposition first envisioned by Forbes (1907) and then Diamond (1975) prompted the development of numerous modelling approaches (e.g. null model analysis, co‐occurrence networks and, more recently, joint species distribution models). Both theory and experimental evidence support the idea that ecological interactions may affect co‐occurrence, but it remains unclear to what extent the signal of interaction can be captured in observational data. It is now time to step back from the statistical developments and critically assess whether co‐occurrence data are really a proxy for ecological interactions. In this paper, we present a series of arguments based on probability, sampling, food web and coexistence theories supporting that significant spatial associations between species (or lack thereof) is a poor proxy for ecological interactions. We discuss appropriate interpretations of co‐occurrence, along with potential avenues to extract as much information as possible from such data.     

The evolution of large‐scale body size clines in Plethodon salamanders: evidence of heat‐balance or species‐specific artifact?

A major goal in macroecology is to determine how body size varies geographically, and explain why such patterns exist. Recently, a grid‐cell assemblage analysis found significant body size trends with latitude and temperature in Plethodon salamanders, and support for the heat‐balance hypothesis as a possible explanation for these trends. Here we demonstrate that the heat‐balance hypothesis is unlikely to have generated this pattern, and that there is no overall body size trend with temperature in Plethodon. Using data from 3155 local Plethodon assemblages, we find no support for body size clines with latitude, and no relationship between body size and temperature. We also found that body size did not covary with elevation, in contrast to what was predicted by heat‐balance. We then examined the various scenarios under which body size clines across grid‐cell assemblages could evolve via heat‐balance, and found that none were tenable in light of the existing data. Instead, a single, widely distributed species was responsible for the pattern across grid‐cell assemblages. Finally, we examined why phylogenetic eigenvector regression does not account for phylogenetic non‐independence among taxa, and should not be used to account for shared evolutionary history in assembly‐level analyses. Assemblage‐level patterns are a useful means of assessing biogeographic trends, and are an important complement to within‐species and cross‐species patterns. However, while the use of grid‐cell assemblage approaches from digital databases is expedient, their results must be examined critically, and whenever possible, compared with data obtained from local species assemblages (particularly for ecological mechanisms that operate at the level of individuals). Finally, our results emphasize the importance of using corroborative data to evaluate alternative hypotheses, so that potential mechanisms that explain bioegeographic patterns are properly assigned.     

EstimateS turns 20: statistical estimation of species richness and shared species from samples,with non‐parametric extrapolation

EstimateS offers statistical tools for analyzing and comparing the diversity and composition of species assemblages, based on sampling data. The latest version computes a wide range of biodiversity statistics for both sample‐based and individual‐based data, including analytical rarefaction and non‐parametric extrapolation, estimators of asymptotic species richness, diversity indices, Hill numbers, and (for sample‐based data) measures of compositional similarity among assemblages. In the first 20 yr of its existence, EstimateS has been downloaded more than 70 000 times by users in 140 countries, who have cited it in 5000 publications in studies of taxa from microbes to mammals in every biome.     

Ecological,historical and evolutionary determinants of modularity in weighted seed‐dispersal networks

Modularity is a recurrent and important property of bipartite ecological networks. Although well‐resolved ecological networks describe interaction frequencies between species pairs, modularity of bipartite networks has been analysed only on the basis of binary presence–absence data. We employ a new algorithm to detect modularity in weighted bipartite networks in a global analysis of avian seed‐dispersal networks. We define roles of species, such as connector values, for weighted and binary networks and associate them with avian species traits and phylogeny. The weighted, but not binary, analysis identified a positive relationship between climatic seasonality and modularity, whereas past climate stability and phylogenetic signal were only weakly related to modularity. Connector values were associated with foraging behaviour and were phylogenetically conserved. The weighted modularity analysis demonstrates the dominating impact of ecological factors on the structure of seed‐dispersal networks, but also underscores the relevance of evolutionary history in shaping species roles in ecological communities.     

Comparison of regression methods for spatially‐autocorrelated count data on regularly‐ and irregularly‐spaced locations

It has long been known that insufficient consideration of spatial autocorrelation leads to unreliable hypothesis‐tests and inaccurate parameter estimates. Yet, ecologists are confronted with a confusing array of methods to account for spatial autocorrelation. Although Beale et al. (2010) provided guidance for continuous data on regular grids, researchers still need advice for other types of data in more flexible spatial contexts. In this paper, we extend Beale et al. (2010)‘s work to count data on both regularly‐ and irregularly‐spaced plots, the latter being commonly encountered in ecological studies. Through a simulation‐based approach, we assessed the accuracy and the type I errors of two frequentist and two Bayesian ready‐to‐use methods in the family of generalized mixed models, with distance‐based or neighbourhood‐based correlated random effects. In addition, we tested whether the methods are robust to spatial non‐stationarity, and over‐ and under‐dispersion – both typical features of species distribution count data which violate standard regression assumptions. In the simplest of our simulated datasets, the two frequentist methods gave inflated type I errors, while the two Bayesian methods provided satisfying results. When facing real‐world complexities, the distance‐based Bayesian method (MCMC with Langevin–Hastings updates) performed best of all. We hope that, in the light of our results, ecological researchers will feel more comfortable including spatial autocorrelation in their analyses of count data.