icyyyraven/sdm_r_packages

A curated list of R packages used in species distribution modelling

A curated list of R packages for species distribution modelling

Introduction

A number of R packages have been developed for species distribution modelling. Most of these packages are on CRAN, and are mixed with the rest 12000 packages, making them difficult to find. Although CRAN Task Views are available for many disciplines, a list for the packages focusing on species distribution modelling is missing. Here, I list the packages that are related with species distribution modelling based on a thorough search on CRAN and github, and hope this list could reflect the recent advancement of this active research field.

Any contribution to this list is welcome. Feel free to fork to your own repository.

List of R Packages

• adehabitatHS :Analysis of Habitat Selection by Animals

• adespatial: Tools for the multiscale spatial analysis of multivariate data. Several methods are based on the use of a spatial weighting matrix and its eigenvector decomposition (Moran's Eigenvectors Maps, MEM).

• ALA4R:Atlas of Living Australia (ALA) Data and Resources in R. The Atlas of Living Australia (ALA) provides tools to enable users of biodiversity information to find, access, combine and visualise data on Australian plants and animals; these have been made available from http://ala.org.au/. ALA4R provides a subset of the tools to be directly used within R. It enables the R community to directly access data and resources hosted by the ALA.

• BIEN:Tools for Accessing the Botanical Information and Ecology Network Database. Provides Tools for Accessing the Botanical Information and Ecology Network Database. The BIEN database contains cleaned and standardized botanical data including occurrence, trait, plot and taxonomic data (See http://Bien.nceas.ucsb.edu/bien/ for more Information). This package provides functions that query the BIEN database by constructing and executing optimized SQL queries.

• biogeo:an R package for assessing and improving data quality of occurrence record datasets

• biomod2:Functions for species distribution modeling, calibration and evaluation, ensemble of models.

• bossMaps : Convert Binary Species Range Maps into Continuous Surfaces Based on Distance to Range Edge

• cocorresp:Fits predictive and symmetric co-correspondence analysis (CoCA) models to relate one data matrix to another data matrix. More specifically, CoCA maximises the weighted covariance between the weighted averaged species scores of one community and the weighted averaged species scores of another community. CoCA attempts to find patterns that are common to both communities.

• coenocliner:Simulate species occurrence and abundances (counts) along gradients.

• coenoflex: Simulates the composition of samples of vegetation according to gradient-based vegetation theory. Features a flexible algorithm incorporating competition and complex multi-gradient interaction.

• coexist: species coexistence modeling under asymmetric dispersal and fluctuating source-sink dynamics;testing the proportion of coexistence scenarios driven by neutral and niche processes

• comclim: Computes community climate statistics for volume and mismatch using species' climate niches either unscaled or scaled relative to a regional species pool. These statistics can be used to describe biogeographic patterns and infer community assembly processes. Includes a vignette outlining usage.

• CommEcol: Autosimilarity curves, standardization of spatial extent, dissimilarity indexes that overweight rare species, phylogenetic and functional (pairwise and multisample) dissimilarity indexes and nestedness for phylogenetic, functional and other diversity metrics. This should be a complement to available packages, particularly 'vegan'.

• ConR: Computation of Parameters Used in Preliminary Assessment of Conservation Status. Multi-species estimation of geographical range parameters for preliminary assessment of conservation status following Criterion B of the International Union for Conservation of Nature (IUCN, see http://www.iucnredlist.org).

• cooccur: Probabilistic Species Co-Occurrence Analysis in R. This R package applies the probabilistic model of species co-occurrence (Veech 2013) to a set of species distributed among a set of survey or sampling sites. The algorithm calculates the observed and expected frequencies of co-occurrence between each pair of species. The expected frequency is based on the distribution of each species being random and independent of the other species. The analysis returns the probabilities that a more extreme (either low or high) value of co-occurrence could have been obtained by chance. The package also includes functions for visualizing species co-occurrence results and preparing data for downstream analyses.

• CoordinateCleaner: Automated Cleaning of Occurrence Records from Biological Collections. Automated cleaning of geographic species occurrence records by automated flagging of problems common to biodiversity data from biological collections. Includes automated tests to easily flag (and exclude) records assigned to country or province centroid, the open ocean, the headquarters of the Global Biodiversity Information Facility, urban areas or the location of biodiversity institutions (museums, zoos, botanical gardens, universities). Furthermore identifies per species outlier coordinates, zero coordinates, identical latitude/longitude and invalid coordinates. Also implements an algorithm to identify data sets with a significant proportion of rounded coordinates. Especially suited for large data sets. See https://github.com/azizka/CoordinateCleaner/wiki for more details and tutorials.

• demoniche: demoniche carries out spatially-explicit demographic modelling. The model simulates stochastic and gradual niche changes, to investigate population dynamics and persistence in space and time. demoniche offers the following features:

• dismo: Species Distribution Modeling. Functions for species distribution modeling, that is, predicting entire geographic distributions form occurrences at a number of sites and the environment at these sites.

• downscale: Downscaling Species Occupancy. A set of functions that downscales species occupancy at coarse grain sizes to predict species occupancy at fine grain sizes.

• ecolottery: Coalescent-Based Simulation of Ecological Communities. Coalescent-Based Simulation of Ecological Communities as proposed by Munoz et al. (2017) doi:10.13140/RG.2.2.31737.26728. The package includes a tool for estimating parameters of community assembly by using Approximate Bayesian Computation.

• EcoSimR: Null Model Analysis for Ecological Data. Given a site by species interaction matrix, users can make inferences about species interactions by performance hypothesis comparing test statistics against a null distribution. The current package provides algorithms and metrics for niche-overlap, body size ratios and species co-occurrence. Users can also integrate their own algorithms and metrics within these frameworks or completely novel null models. Detailed explanations about the underlying assumptions of null model analysis in ecology can be found at http://ecosimr.org.

• ecospat: Collection of R functions and data sets for the support of spatial ecology analyses with a focus on pre, core and post modelling analyses of species distribution, niche quantification and community assembly. Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) and Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland. Read Di Cola et al. (2016) doi:10.1111/ecog.02671 for details.

• ENiRG: Ecological Niche in R and GRASS. A set of tools for the analysis of ecological niche of species and calculation of habitat suitability maps.

• ENMeval: Automated Runs and Evaluations of Ecological Niche Models. Automatically partitions data into evaluation bins, executes ecological niche models across a range of settings, and calculates a variety of evaluation statistics. Current version only implements ENMs with Maxent (Phillips et al. 2006)doi:10.1016/j.ecolmodel.2005.03.026.

• ENMTools: This package implements various tests, visualizations, and metrics for use with environmental niche models (ENMs) and species distribution models (SDMs).

• EnvNicheR: Niche Estimation. A plot overlying the niche of multiple species is obtained: 1) to determine the niche conditions which favor a higher species richness, 2) to create a box plot with the range of environmental variables of the species, 3) to obtain a list of species in an area of the niche selected by the user and, 4) to estimate niche overlap among the species.

• FactorsR: Identification of the Factors Affecting Species Richness. It identifies the factors significantly related to species richness, and their relative contribution, using multiple regressions and support vector machine models. It uses an output file of 'ModestR' (http://www.ipez.es/ModestR) with data of richness of the species and environmental variables in a cell size defined by the user. The residuals of the support vector machine model are shown on a map. Negative residuals may be potential areas with undiscovered and/or unregistered species, or areas with decreased species richness due to the negative effect of anthropogenic factors.

• fitdistrplus: Help to Fit of a Parametric Distribution to Non-Censored or Censored Data. Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME) and maximum goodness-of-fit estimation (MGE) methods (available only for non-censored data). Weighted versions of MLE, MME and QME are available.

• fuzzySim: fuzzySim is an R package for calculating fuzzy similarity in species occurrence patterns. It includes functions for data preparation, such as converting species lists (long format) to presence-absence tables (wide format), obtaining unique abbreviations of species names, or transposing (parts of) complex data frames; and sample data sets for providing practical examples.

• hSDM: hSDM is an R package for estimating parameters of hierarchical Bayesian species distribution models. Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.

• indicspecies: Relationship Between Species and Groups of Sites. Functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites. Also includes functions to measure species niche breadth using resource categories.

• iSDM: Functions for predicting and mapping potential and realized distributions of invasive species within the invaded range.

• jrich: Jack-Knife Support for Evolutionary Distinctiveness Indices I and W. These functions calculate the taxonomic measures presented in Miranda-Esquivel (2016). The package introduces Jack-knife resampling in evolutionary distinctiveness prioritization analysis, as a way to evaluate the support of the ranking in area prioritization, and the persistence of a given area in a conservation analysis. The algorithm is described in: Miranda-Esquivel, D (2016) doi:10.1007/978-3-319-22461-9_11.

• kissmig: a Keep It Simple Species Migration Model. Simulating species migration and range dynamics under stable or changing environmental conditions based on a simple, raster-based, stochastic migration model. Providing accessibility for considering species migration in niche-based species distribution models.

• KnowBR: Discriminating Well Surveyed Spatial Units from Exhaustive Biodiversity Databases.It uses species accumulation curves and diverse estimators to assess, at the same time, the levels of survey coverage in multiple geographic cells of a size defined by the user or polygons. It also enables the geographical depiction of observed species richness, survey effort and completeness values including a background with administrative areas.

• letsR: Tools for Data Handling and Analysis in Macroecology. R functions for handling, processing, and analyzing geographic data on species' distributions and environmental variables.

• mapr: Visualize Species Occurrence Data. Utilities for visualizing species occurrence data. Includes functions to visualize occurrence data from 'spocc', 'rgbif', and other packages. Mapping options included for base R plots, 'ggplot2', 'ggmap', 'leaflet' and 'GitHub' 'gists'.

• MaxentVariableSelection: Selecting the Best Set of Relevant Environmental Variables along with the Optimal Regularization Multiplier for Maxent Niche Modeling. Complex niche models show low performance in identifying the most important range-limiting environmental variables and in transferring habitat suitability to novel environmental conditions (Warren and Seifert, 2011 doi:10.1890/10-1171.1; Warren et al., 2014 doi:10.1111/ddi.12160). This package helps to identify the most important set of uncorrelated variables and to fine-tune Maxent's regularization multiplier. In combination, this allows to constrain complexity and increase performance of Maxent niche models (assessed by information criteria, such as AICc (Akaike, 1974 doi:10.1109/TAC.1974.1100705), and by the area under the receiver operating characteristic (AUC) (Fielding and Bell, 1997 doi:10.1017/S0376892997000088). Users of this package should be familiar with Maxent niche modelling.

• maxlike: Model Species Distributions by Estimating the Probability of Occurrence Using Presence-Only Data. Provides a likelihood-based approach to modeling species distributions using presence-only data. In contrast to the popular software program MAXENT, this approach yields estimates of the probability of occurrence, which is a natural descriptor of a species' distribution.

• maxnet: Fitting 'Maxent' Species Distribution Models with 'glmnet'. Procedures to fit species distributions models from occurrence records and environmental variables, using 'glmnet' for model fitting. Model structure is the same as for the 'Maxent' Java package, version 3.4.0, with the same feature types and regularization options. See the 'Maxent' website http://biodiversityinformatics.amnh.org/open_source/maxent for more details.

• MetaLandSim: Landscape and Range Expansion Simulation. Tools to generate random landscape graphs, evaluate species occurrence in dynamic landscapes, simulate future landscape occupation and evaluate range expansion when new empty patches are available (e.g. as a result of climate change).

• MIAmaxent: Maxent Distribution Model Selection. Tools for training, selecting, and evaluating maximum entropy (Maxent) distribution models. This package provides tools for user- controlled transformation of explanatory variables, selection of variables by nested model comparison, and flexible model evaluation and projection. It is based on the strict maximum likelihood interpretation of maximum entropy modelling.

• mobsim: Spatial Simulation and Scale-Dependent Analysis of Biodiversity Changes. Tools for the simulation, analysis and sampling of spatial biodiversity data (May et al. 2017) doi:10.1101/209502. In the simulation tools user define the numbers of species and individuals, the species abundance distribution and species aggregation. Functions for analysis include species rarefaction and accumulation curves, species-area relationships and the distance decay of similarity.

• modEvA: Model Evaluation and Analysis. Analyses species distribution models and evaluates their performance. It includes functions for performing variation partitioning, calculating several measures of model discrimination and calibration, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots.

• mopa: Species Distribution MOdeling with Pseudo-Absences. Tools for transferable species distribution modeling and pseudo-absence data generation allowing the straightforward design of relatively complex experiments with multiple factors affecting the uncertainty (variability) of SDM outputs (pseudo-absence sample, climate projection, modeling algorithm, etc.), and the quantification of the contribution of different factors to the final variability following the method described in Deque el al. (2010) doi:10.1007/s00382-011-1053-x. Multiple methods for pseudo-absence data generation can be applied, including the novel Three-step method as described in Iturbide et al. (2015) doi:10.1016/j.ecolmodel.2015.05.018. Additionally, a function for niche overlap calculation is provided, considering the metrics described in Warren et al. (2008) <10.1111/j.1558-5646.2008.00482.x> and in Pianka (1973) <10.1146/annurev.es.04.110173.000413>. (https://github.com/SantanderMetGroup/mopa/wiki)

• netassoc: Inference of Species Associations from Co-Occurrence Data. Infers species associations from community matrices. Uses local and (optional) regional-scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods.

• nicheROVER: (Niche) (R)egion and Niche (Over)lap Metrics for Multidimensional Ecological Niches. This package uses a probabilistic method to calculate niche regions and pairwise niche overlap using multidimensional niche indicator data (e.g., stable isotopes, environmental variables, etc.). The niche region is defined as the joint probability density function of the multidimensional niche indicators at a user-defined probability alpha (e.g., 95%). Uncertainty is accounted for in a Bayesian framework, and the method can be extended to three or more indicator dimensions. It provides directional estimates of niche overlap, accounts for species-specific distributions in multivariate niche space, and produces unique and consistent bivariate projections of the multivariate niche region. A forthcoming article by Swanson et al. (Ecology, 2014) provides a detailed description of the methodology. See the package vignette for a worked example using fish stable isotope data.

• nodiv: Compares the Distribution of Sister Clades Through a Phylogeny. An implementation of the nodiv algorithm, see Borregaard, M.K., Rahbek, C., Fjeldsaa, J., Parra, J.L., Whittaker, R.J. & Graham, C.H. 2014. Node-based analysis of species distributions. Methods in Ecology and Evolution 5(11): 1225-1235. doi:10.1111/2041-210X.12283. Package for phylogenetic analysis of species distributions. The main function goes through each node in the phylogeny, compares the distributions of the two descendant nodes, and compares the result to a null model. This highlights nodes where major distributional divergence have occurred. The distributional divergence for these nodes is mapped using the SOS statistic.

• paleobioDB: Download and Process Data from the Paleobiology Database. Includes 19 functions to wrap each endpoint of the PaleobioDB API, plus 8 functions to visualize and process the fossil data. The API documentation for the Paleobiology Database can be found in http://paleobiodb.org/data1.1/.

• pez: Phylogenetics for the Environmental Sciences Eco-phylogenetic and community phylogenetic analyses. Keeps community ecological and phylogenetic data matched up and comparable using 'comparative.comm' objects. Wrappers for common community phylogenetic indices ('pez.shape', 'pez.evenness', 'pez.dispersion', and 'pez.dissimilarity' metrics). Implementation of Cavender-Bares (2004) correlation of phylogenetic and ecological matrices ('fingerprint.regression'). Phylogenetic Generalised Linear Mixed Models (PGLMMs; 'pglmm') following Ives & Helmus (2011) and Rafferty & Ives (2013). Simulation of null assemblages, traits, and phylogenies ('scape', 'sim.meta.comm').

• phyloclim: Integrating Phylogenetics and Climatic Niche Modeling. Implements some methods in phyloclimatic modeling: estimation of ancestral climatic niches, age-range-correlation, niche equivalency test and background-similarity test.

• PresenceAbsence: Presence-Absence Model Evaluation. This package provides a set of functions useful when evaluating the results of presence-absence models. Package includes functions for calculating threshold dependent measures such as confusion matrices, pcc, sensitivity, specificity, and Kappa, and produces plots of each measure as the threshold is varied. It will calculate optimal threshold choice according to a choice of optimization criteria. It also includes functions to plot the threshold independent ROC curves along with the associated AUC (area under the curve).

• RADanalysis: Normalization and Study of Rank Abundance Distributions. It has tools for normalization of rank abundance distributions (RAD) to a desired number of ranks using MaxRank Normalization method. RADs are commonly used in biology/ecology and mathematically equivalent to complementary cumulative distributions (CCDFs) which are used in physics, linguistics and sociology and more generally in data science.

• rangeBuilder: Occurrence Filtering, Geographic and Taxonomic Standardization and Generation of Species Range Polygons. Provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species distributions.

• rangeMapper: A Platform for the Study of Macro-Ecology of Life History Traits. Tools for easy generation of (life-history) traits maps based on species range (extent-of-occurrence) maps.

• raptr: Representative and Adequate Prioritization Toolkit in R. Biodiversity is in crisis. The overarching aim of conservation is to preserve biodiversity patterns and processes. To this end, protected areas are established to buffer species and preserve biodiversity processes. But resources are limited and so protected areas must be cost-effective. This package contains tools to generate plans for protected areas (prioritizations), using spatially explicit targets for biodiversity patterns and processes. To obtain solutions in a feasible amount of time, this package uses the commercial 'Gurobi' software package (obtained from http://www.gurobi.com/). For more information on using this package, see Hanson et al. (2017) doi:10.1111/2041-210X.12862.

• rbison: Interface to the 'USGS' 'BISON' 'API'. Interface to the 'USGS' 'BISON' (https://bison.usgs.gov/) 'API', a 'database' for species occurrence data. Data comes from species in the United States from participating data providers. You can get data via 'taxonomic' and location based queries. A simple function is provided to help visualize data.

• rCAT: Conservation Assessment Tools. A set of tools to help with species conservation assessments (Red List threat assessments). Includes tool for Extent of occurrence, Area of Occupancy, Minimum Enclosing Rectangle, a geographic Projection Wizard and Species batch processing.

• rebird: R Client for the eBird Database of Bird Observations. A programmatic client for the eBird database, including functions for searching for bird observations by geographic location (latitude, longitude), eBird hotspots, location identifiers, by notable sightings, by region, and by taxonomic name.

• red: IUCN Redlisting Tools. Includes algorithms to facilitate the assessment of extinction risk of species according to the IUCN (International Union for Conservation of Nature, see http://www.iucn.org for more information) red list criteria.

• redlistr: Tools for the IUCN Red List of Ecosystems and Species.A toolbox created by members of the International Union for Conservation of Nature (IUCN) Red List of Ecosystems Committee for Scientific Standards. Primarily, it is a set of tools suitable for calculating the metrics required for making assessments of species and ecosystems against the IUCN Red List of Threatened Species and the IUCN Red List of Ecosystems categories and criteria. See the IUCN website for detailed guidelines, the criteria, publications and other information.

• rfishbase: R Interface to 'FishBase'. A programmatic interface to http://www.fishbase.org, re-written based on an accompanying 'RESTful' API. Access tables describing over 30,000 species of fish, their biology, ecology, morphology, and more. This package also supports experimental access to http://www.sealifebase.org data, which contains nearly 200,000 species records for all types of aquatic life not covered by 'FishBase.'

• rgbif: Interface to the Global Biodiversity Information Facility API. A programmatic interface to the Web Service methods provided by the Global Biodiversity Information Facility.

• rinat: Access iNaturalist Data Through APIs. A programmatic interface to the API provided by the iNaturalist website http://inaturalist.org to download species occurrence data submitted by citizen scientists.

• RInSp: R Individual Specialization (RInSp).Functions to calculate several ecological indices of individual and population niche width (Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis' W, and Roughgarden's WIC/TNW) to assess individual specialization based on data of resource use. Resource use can be quantified by counts of categories, measures of mass/lenght or proportions. Monte Carlo resampling procedures are available for hypothesis testing against multinomial null models.

• rioja: Analysis of Quaternary Science Data. Functions for the analysis of Quaternary science data, including constrained clustering, WA, WAPLS, IKFA, MLRC and MAT transfer functions, and stratigraphic diagrams.

• rredlist: 'IUCN' Red List Client. 'IUCN' Red List (http://apiv3.iucnredlist.org/api/v3/docs) client. The 'IUCN' Red List is a global list of threatened and endangered species. Functions cover all of the Red List 'API' routes. An 'API' key is required.

• rvertnet: Search 'Vertnet', a 'Database' of Vertebrate Specimen Records. Retrieve, map and summarize data from the 'VertNet.org' archives (http://vertnet.org/). Functions allow searching by many parameters, including 'taxonomic' names, places, and dates. In addition, there is an interface for conducting spatially delimited searches, and another for requesting large 'datasets' via email.

• sads: Maximum Likelihood Models for Species Abundance Distributions. Maximum likelihood tools to fit and compare models of species abundance distributions and of species rank-abundance distributions.

• sdm:Species Distribution Modelling. An extensible framework for developing species distribution models using individual and community-based approaches, generate ensembles of models, evaluate the models, and predict species potential distributions in space and time. For more information, please check the following paper: Naimi, B., Araujo, M.B. (2016) doi:10.1111/ecog.01881.

• SDMPlay: Species Distribution Modelling Playground. Functions provided by this pedagogic package allow to compute models with two popular machine learning approaches, BRT (Boosted Regression Trees) and MaxEnt (Maximum Entropy) applied on sets of marine biological and environmental data. They include the possibility of managing the main parameters for the construction of the models. Classic tools to evaluate model performance are provided (Area Under the Curve, omission rate and confusion matrix, map standard deviation) and are completed with tools to perform null models. The biological dataset includes original occurrences of two species of the class Echinoidea (sea urchins) present on the Kerguelen Plateau and that show contrasted ecological niches. The environmental dataset includes the corresponding statistics for 15 abiotic and biotic descriptors summarized for the Kerguelen Plateau and for different periods in a raster format. The package can be used for practicals to teach and learn the basics of species distribution modelling. Maps of potential distribution can be produced based on the example data included in the package, which brings prior observations of the influence of spatial and temporal heterogeneities on modelling performances. The user can also provide his own datasets to use the modelling functions.

• sdmpredictors: Species Distribution Modelling Predictor Datasets. Terrestrial and marine predictors for species distribution modelling from multiple sources, including WorldClim http://www.worldclim.org/,, ENVIREM http://envirem.github.io/, Bio-ORACLE http://bio-oracle.org/ and MARSPEC http://www.marspec.org/.

• SDMTools: Species Distribution Modelling Tools: Tools for processing data associated with species distribution modelling exercises. This packages provides a set of tools for post processing the outcomes of species distribution modeling exercises. It includes novel methods for comparing models and tracking changes in distributions through time. It further includes methods for visualizing outcomes, selecting thresholds, calculating measures of accuracy and landscape fragmentation statistics, etc.. This package was made possible in part by financial support from the Australian Research Council & ARC Research Network for Earth System Science.

• sdmvspecies: Create Virtual Species for Species Distribution Modelling. A software package help user to create virtual species for species distribution modelling. It includes several methods to help user to create virtual species distribution map. Those maps can be used for Species Distribution Modelling (SDM) study. SDM use environmental data for sites of occurrence of a species to predict all the sites where the environmental conditions are suitable for the species to persist, and may be expected to occur.

• SiMRiv: Simulating Multistate Movements in River/Heterogeneous Landscapes. Provides functions to generate and analyze spatially-explicit individual-based multistate movements in rivers, heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as mechanistic null models (Potts & Lewis 2014, doi:10.1098/rspb.2014.0231) to test a variety of 'Movement Ecology' hypotheses (Nathan et al. 2008, doi:10.1073/pnas.0800375105), including landscape effects (e.g. resources, infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk, dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species spread, and population dynamics.

• spacodiR: Spatial and Phylogenetic Analysis of Community Diversity. SPACoDi is primarily designed to characterise the structure and phylogenetic diversity of communities using abundance or presence-absence data of species among community plots.

• SPECIES: Statistical package for species richness estimation SPECIES is an R package for estimation of species richness or diversity.

• SPEDInstabR: Estimation of the Relative Importance of Factors Affecting Species Distribution Based on Stability Concept. From output files obtained from the software 'ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.

• sperich: Auxiliary Functions to Estimate Centers of Biodiversity. Provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.

• spocc: Interface to Species Occurrence Data Sources. A programmatic interface to many species occurrence data sources, including Global Biodiversity Information Facility ('GBIF'), 'USGSs' Biodiversity Information Serving Our Nation ('BISON'), 'iNaturalist', Berkeley 'Ecoinformatics' Engine, 'eBird', 'AntWeb', Integrated Digitized 'Biocollections' ('iDigBio'), 'VertNet', Ocean 'Biogeographic' Information System ('OBIS'), and Atlas of Living Australia ('ALA'). Includes functionality for retrieving species occurrence data, and combining those data.

• spThin: Functions for Spatial Thinning of Species. Occurrence Records for Use in Ecological Models spThin is a set of functions that can be used to spatially thin species occurrence data. The resulting thinned data can be used in ecological modeling, such as ecological niche modeling.

• SSDM: Stacked Species Distribution Modelling. Allows to map species richness and endemism based on stacked species distribution models (SSDM). Individuals SDMs can be created using a single or multiple algorithms (ensemble SDMs). For each species, an SDM can yield a habitat suitability map, a binary map, a between-algorithm variance map, and can assess variable importance, algorithm accuracy, and between- algorithm correlation. Methods to stack individual SDMs include summing individual probabilities and thresholding then summing. Thresholding can be based on a specific evaluation metric or by drawing repeatedly from a Bernoulli distribution. The SSDM package also provides a user-friendly interface.

• subniche: Within Outlying Mean Indexes: Refining the OMI Analysis. Complementary indexes calculation to the Outlying Mean Index analysis to explore niche shift of a community and biological constraint within an Euclidean space, with graphical displays.

• Traitspace: A Predictive Model for Trait Based Community Assembly of Plant Species. Implements a predictive model of community assembly called 'Traitspace' (Laughlin et al. 2012, Ecology Letters). Traitspace is a hierarchical Bayesian model that translates the theory of trait-based environmental filtering into a statistical model that incorporates intraspecific trait variation to predict the relative abundances and the distributions of species. The package includes functions to plot the predicted and the observed values. It also includes functions to compare the predicted values against the observed values using a variety of different distance measures and to implement permutation tests to test their statistical significance.

• untb: Ecological Drift under the UNTB. A collection of utilities for biodiversity data. Includes the simulation of ecological drift under Hubbell's Unified Neutral Theory of Biodiversity, and the calculation of various diagnostics such as Preston curves. Now includes functionality provided by Francois Munoz and Andrea Manica.

• usdm: Uncertainty Analysis for Species Distribution Models. This is a framework that aims to provide methods and tools for assessing the impact of different sources of uncertainties (e.g.positional uncertainty) on performance of species distribution models (SDMs).)

• vegdata: Access Vegetation Databases and Treat Taxonomy. Handling of vegetation data from different sources ( Turboveg http://www.synbiosys.alterra.nl/turboveg/; the German national repository http://www.vegetweb.de and others. Taxonomic harmonization (given appropriate taxonomic lists, e.g. the German taxonomic standard list "GermanSL", http://germansl.infinitenature.org).

• velociraptr: Fossil Analysis.Functions for downloading, reshaping, culling, cleaning, and analyzing fossil data from the Paleobiology Database https://paleobiodb.org.

• wallace: A Modular Platform for Reproducible Modeling of Species Niches and Distributions.The 'shiny' application 'wallace' is a modular platform for reproducible modeling of species niches and distributions. 'wallace' guides users through a complete analysis, from the acquisition of species occurrence and environmental data to visualizing model predictions on an interactive map, thus bundling complex workflows into a single, streamlined interface.

• zetadiv: Functions to Compute Compositional Turnover Using Zeta Diversity.Functions to compute compositional turnover using zeta-diversity, the number of species shared by multiple assemblages. The package includes functions to compute zeta-diversity for a specific number of assemblages and to compute zeta-diversity for a range of numbers of assemblages. It also includes functions to explain how zeta-diversity varies with distance and with differences in environmental variables between assemblages, using generalised linear models, linear models with negative constraints, generalised additive models,shape constrained additive models, and I-splines.

• zoon: Reproducible, Accessible & Shareable Species Distribution Modelling. Reproducible and remixable species distribution modelling. The package reads user submitted modules from an online repository, runs full SDM workflows and returns output that is fully reproducible.