DOI: 10.5061/dryad.5x69p8d9z
title: Global patterns of taxonomic uncertainty and its impacts on biodiversity research

description: Over two million species have been named so far, but many will be
 invalidated due to redundant descriptions. Undetected invalid species
 (i.e., synonyms) can impair inferences we make in biodiversity research
 and hamper the implementation of effective conservation strategies.
 However, the processes leading to the accumulation of invalid names remain
 largely unknown. Using multi-model inferences, we investigated the
 patterns and potential drivers of species- and assemblage-level variation
 in synonym counts across terrestrial vertebrates globally. We also
 explored how taxonomic uncertainty (i.e., instability in species
 identities) can affect latitudinal variation of diversification rates. The
 average number of synonyms was higher for species described earlier,
 better represented in scientific collections, with larger geographic
 ranges, occurring in temperate regions, and in areas of high biodiversity
 attention. In assemblage-level models, a higher average number of synonyms
 was associated with temperate regions harbouring more early-described
 species. Areas of high endemism richness showed fewer synonyms across
 amphibians and reptiles but had an inverse effect for birds and mammals.
 Other predictor-response relationships varied across taxonomic groups,
 biogeographical realm, and spatial grain. Assuming that more synonyms
 indicate more stable species that have been thoroughly studied and
 reviewed, high synonym numbers in temperate species and assemblages
 support claims of a potential latitudinal taxonomy gradient, where
 geographic variation in taxonomic practice could hinder the proper
 recognition of tropical species. We show that the accumulation of invalid
 names is not random and discuss how invalid hidden names can affect
 biodiversity inferences. A potential approach to address this problem
 would be developing a taxonomic uncertainty metric that could be
 incorporated into models (i.e., as weights to account for varying degrees
 of uncertainty during the fitting process). Our study provides an initial
 approximation and highlights the often-neglected issue of uncertainty and
 instability in species identities from a macroecological perspective.
description: <em>Synonym Data</em> We extracted
 the number of synonyms per species from the Catalogue of Life (CoL)
 Checklist (https://www.checklistbank.org/dataset/286246/download; Bánki
 <em>et al.</em>, 2024) on 20 Feb 2024. The taxonomic backbone
 of the CoL for amphibians, birds, and mammals was based on the Integrated
 Taxonomic Information System (ITIS; https://itis.gov/), while data for
 reptiles were from the Reptile Database (http://www.reptile-database.org;
 Uetz and Hošek, 2021). The CoL dataset included columns for rank and
 status, with the former informing if a scientific name represented, for
 example, a species or subspecies, and the latter indicating whether names
 were accepted, ambiguous, or unique synonyms. Using these columns, we
 filtered our dataset to include only accepted (i.e., valid) species and
 unique synonyms (i.e., invalid names). Consequently, currently valid
 subspecies were not analysed, as they are classified neither as full
 species nor as synonyms. However, we conducted a sensitivity analysis for
 cross-species models, treating valid subspecies as synonyms to assess the
 robustness of our main conclusions amid significant taxonomic
 changes.  <em>Taxonomic Data</em>
 We followed the taxonomy employed in fully-sampled phylogenies
 available for amphibians (Jetz and Pyron, 2018), turtles and crocodiles
 (Colston <em>et al.</em>, 2020), squamates (Tonini
 <em>et al.</em>, 2016), birds (Jetz <em>et
 al.</em>, 2012), and mammals (Upham, Esselstyn and Jetz, 2019) to
 incorporate species’ phylogenetic relationship into analyses and to take
 advantage of a recently available trait database for the world’s tetrapods
 (Moura <em>et al.</em>, 2024). While the taxonomies we
 followed included mostly species described before 2016, we highlight that
 taxa described after that may not have been targeted by revisionary work
 that could lead to the accumulation of synonyms. We
 merged the information from the CoL with data from the TetrapodTraits
 using fuzzy logic to check for and fix mismatches in species names between
 datasets due to spelling errors. For such, we used the
 <em>fuzzyjoin</em> package (Robinson, 2020) in software R
 version 4.3.1 (R Core Team, 2023). Checks were initially performed between
 valid species names in both datasets, then between valid species in
 TetrapodTraits with unique synonyms (i.e., names that can be tracked back
 to a single valid species) in the CoL database. After merging operations,
 we found synonym information for 31,175 (93.7%) species in TetrapodTraits.
 Our dataset initially included 33,281 species: 7,238 amphibians, 9,993
 birds, 5,911 mammals, and 10,139 reptiles. However, we removed 228 marine
 species (as per the International Union for Conservation of Nature — IUCN)
 and focused solely on terrestrial vertebrates (n = 33,049). 
 <em>Species-level Covariates</em>
 Attributes related to species biology included body size and
 habitat use. For body size, we used body mass data for birds, mammals, and
 reptiles, which had on average a data coverage exceeding 95% (n = 24,758
 out of 25,811 species), and body length for amphibians, which covered
 97.9% (n = 7,083) of species. Habitat use data were obtained for 31,497
 species (95.3%), being converted into a continuous metric of verticality
 (Oliveira and Scheffers, 2019), with species scored as: 0 = strictly
 fossorial, 0.25 = fossorial and aquatic/ terrestrial , 0.5 = aquatic, or
 terrestrial, or aquatic/ terrestrial, or fossorial/ terrestrial/ arboreal,
 or fossorial/ terrestrial / aquatic/ arboreal, 0.75 = terrestrial and
 arboreal, or aquatic and arboreal, or terrestrial and aquatic/ arboreal,
 or terrestrial and aerial, or terrestrial and arboreal/ aerial, and 1 =
 strictly arboreal or aerial. Sources on body size and microhabitat are
 available in Moura <em>et al.</em> (2024).
 Attributes related to taxonomic practice included year of
 description, taxonomic activity, and knowledge of species’ evolutionary
 relationships. Species’ description years were based on the original
 publication date and was available for all but one species (the squamate
 <em>Indotyphlops pushpakumara</em>, a nomen nudum included in
 Tonini’s phylogeny; Tonini <em>et al.</em>, 2016). To inform
 taxonomic activity per species, we selected all taxa described within the
 same family and year, and then tallied the total number of unique
 taxonomists. Because the per-family number of taxonomists is expected to
 increase with family richness, we divided the number of taxonomists by the
 number of species described per year in a given family (Moura and Jetz,
 2021). We acknowledge that many authors contributing to species
 descriptions are not necessarily taxonomists, with recent rise in
 authorship reflecting changes in taxonomic practice rather than an
 increase in taxonomic capacity (Boero, 2001; Rodrigues et al., 2010;
 Bebber et al., 2014). Despite this potential bias, the number of authors
 remains a useful proxy for taxonomic activity in beta taxonomic studies,
 highlighting the growing collaboration between taxonomists and researchers
 from various fields (Grieneisen et al., 2014; Poulin and Presswell, 2016).
 Lastly, we used a binary variable to identify tetrapod species with
 evolutionary relationships imputed (0 = not imputed, 1 = imputed) in
 fully-sampled phylogenies (Jetz <em>et al.</em>, 2012; Tonini
 <em>et al.</em>, 2016; Jetz and Pyron, 2018; Upham, Esselstyn
 and Jetz, 2019; Colston <em>et al.</em>, 2020).
 Some predictors analysed here were extracted from Moura
 <em>et al.</em> (2024) and represent within-range attributes
 derived from spatial intersections between expert-based range maps for
 amphibians (Jetz, McPherson and Guralnick, 2012; IUCN, 2021; Moura et al.,
 2024), birds (Jetz et al., 2012; Jetz, McPherson and Guralnick, 2012),
 mammals (Jetz, McPherson and Guralnick, 2012; IUCN, 2021; Marsh et al.,
 2022) and reptiles (Roll et al., 2017; Colston et al., 2020; IUCN, 2021),
 environmental, and socioeconomic layers using Lambert’s cylindrical
 equal-area grid of 110×110 km. This spatial resolution has been suggested
 to minimises comission errors related to the use of expert range maps
 (Hurlbert and Jetz, 2007). We used the following spatially based
 attributes: (i) range size, computed as the number of 110×110 km grid
 cells overlapped by the species’ range, (ii) latitude, based on the
 centroid of each species’ range map, (iii) elevation, as the average
 within-range elevation, and (iv) on-ground accessibility, measured as the
 average within-range time to travel to cities with more than 50,000
 inhabitants (ETA50k). These four attributes were available for 33,037
 (99.6 %) species. To evaluate endemism richness, we
 used the spatial intersections of species’ range maps across the 110×110
 km grid cells (available in Moura <em>et al.</em>, 2024) to
 compute the sum of the inverse range sizes of all species per taxonomic
 class within a grid cell (Kier and Barthlott, 2001), which we then used to
 calculate the median value for each species based on the grid cells they
 occupy. Endemism richness was available for 33,898 (96 %) species. We
 obtained the number of preserved specimens deposited in biological
 collections per species based on the Global Biodiversity Information
 Facility (GBIF) database — data available for 32,985 (99.8%) species.
 Specifically, we used the function <em>occ_count</em> in the
 <em>rgbif</em> R package (Chamberlain <em>et
 al.</em>, 2023), creating search queries with valid species names
 plus their unique synonyms and setting the
 <em>basisOfRecord</em> argument to preserved specimens. After
 excluding species with missing data in the response or predictor
 variables, our final dataset had a total of 28,802 terrestrial
 vertebrates: 6,440 amphibians, 9,183 birds, 4,158 mammals, and 9,021
 reptiles.  
 <em>Assemblage-level Covariates</em>
 For assemblage-level analyses, we modelled the average number of
 synonyms per grid cell for each taxonomic class separately across three
 spatial grains (110, 220, and 440 km). As predictors, we used latitude, as
 well as median values per grid cell for elevation (Amatulli <em>et
 al.</em>, 2018), ETA50K (Nelson <em>et al.</em>, 2019),
 endemism richness, and year of description. For the number of preserved
 specimens per assemblage, we downloaded all data on preserved tetrapod
 species available on GBIF (GBIF.org, 2024) on 03 Apr 2024, selecting only
 records with available coordinates and not flagged as suspicious
 (potentially erroneous) by GBIF. In total, we downloaded 12,485,651
 records; however, after data cleaning procedures (i.e., removing duplicate
 records), the number of observations was reduced to 6,288,920.
 We computed three additional predictors for the assemblage-level
 analyses: species richness, representing the number of species whose range
 intersected each grid cell;  median per capita gross domestic product at 5
 arc-min resolution, weighted by the country area covering each grid cell
 (Kummu, Taka and Guillaume, 2018), which represents a proxy for financial
 resources potentially available for biodiversity research; and the number
 of biodiversity institutions — i.e., herbaria, museums, and universities —
 per grid cell, based on the dataset available in the R package
 <em>CoordinateCleaner</em> (Zizka <em>et al.</em>,
 2019). <em>Determinants of Synonym Count
 Variation </em> We modelled synonym counts per
 species (cross-species analyses) and the average number of synonyms (i.e.,
 total number of synonyms divided by the total number of species) per grid
 cell (assemblage-level analyses) separately for amphibians, reptiles,
 birds, and mammals. Our two response variables were modelled as a function
 of biological, geographical, taxonomical, and socioeconomic-related
 factors (see Figure 1). In the cross-species analyses,
 we modelled synonym counts through generalised linear models (GLMs) using
 a negative binomial distribution to account for overdispersion (Lindén and
 Mäntyniemi, 2011; Stoklosa, Blakey and Hui, 2022). We assessed whether
 phylogenetic regression models were necessary by examining the
 phylogenetic autocorrelation of GLM residuals, constructing Moran’s
 <em>I</em> correlograms with coefficients calculated at 14
 distance classes (Revell, 2010). For this, we used the most recent
 phylogenies available for major tetrapod groups: amphibians (Jetz and
 Pyron, 2018), turtles and crocodiles (Colston <em>et al.</em>,
 2020), squamates (Tonini <em>et al.</em>, 2016), birds (Jetz
 <em>et al.</em>, 2012), and mammals (Upham, Esselstyn and
 Jetz, 2019). We selected 50 fully sampled phylogenies, computing Moran’s
 <em>I</em> correlations for each tree and then averaging the
 results (except for global models, where due to computation constraints,
 we limited calculations to five trees). For reptiles, we first combined
 Tonini’s <em>et al.</em> and Colston’s <em>et
 al.</em> phylogenies using the R function
 <em>tree.merger</em> in the package
 <em>RRphylo</em> v. 2.7.0 (Castiglione, Serio, <em>et
 al.</em>, 2022), which preserves branch length information in the
 combined trees (Castiglione, Melchionna, <em>et al.</em>,
 2022). We inspected model residuals using the R package
 <em>DHARMa</em> v. 0.4.6 (Hartig, 2022) and assessed model fit
 by calculating Nagelkerke’s pseudo-R<sup>2</sup> with the R
 package <em>performance</em> v. 0.10.2 (Lüdecke <em>et
 al.</em>, 2021). Analyses of phylogenetic autocorrelation were
 performed using the R packages <em>phylobase</em> (Bolker
 <em>et al.</em>, 2020) and <em>phylosignal</em>
 (Keck <em>et al.</em>, 2016). In the
 assemblage-level analyses, we initially modelled the average number of
 synonyms per grid cell using non-spatial GLMs with a Gaussian error
 distribution and an identity link function. We then tested for spatial
 autocorrelation (SAC) in model residuals using Moran’s
 <em>I</em> coefficients based on 14 distance classes (Dormann
 <em>et al.</em>, 2007; Revell, 2010). Upon detecting SAC in
 the residuals of the non-spatial GLMs (Supplementary Fig. S1), we
 proceeded with spatial GLMs. To account for SAC, we first calculated
 ‘residuals autocovariates’ (RAC), which were then included as covariates
 in the spatial GLMs, similar to an error-type SAR model (Meyer
 <em>et al.</em>, 2015). Specifically, we used the
 <em>autocov_dist</em> function in the R package
 <em>spdep</em> v. 1.2.7 (Bivand, 2022) to compute RACs based
 on the queen’s contiguity neighbourhood structure (a list of neighbourhood
 cells for each grid cell) and the residuals from the non-spatial model
 (see the R-code for details). After fitting the spatial models, we
 re-evaluated SAC in the residuals, which showed a substantial reduction
 compared to the non-spatial models (Supplementary Fig. S2). We inspected
 model residuals using the package <em>DHARMa</em> and assessed
 model fit through the coefficient of determination
 R<sup>2</sup> statistic. Continuous
 predictors were log<sub>10</sub>-transformed if their skewness
 or kurtosis fell outside the range of -2 to +2 (George and Mallery, 2010),
 and then centred and scaled (<em>z</em>-transformed) to allow
 direct comparisons of effect sizes. We checked for multicollinearity among
 predictors using Variation Inflation Factors (VIFs), where strong
 multicollinearity is usually attributed to VIFs &gt; 10, suggesting
 that variables should be removed from the analysis (Kutner <em>et
 al.</em>, 2005). As none of our continuous variables had VIFs
 &gt; 5, all were retained for subsequent analyses (Supplementary
 Tables S1–2). We modelled synonym counts globally as well as separately
 for each biogeographic realm (Dinerstein <em>et al.</em>,
 2017), excluding Oceania due to its low sample size (Supplementary Table
 S3 ). Species whose ranges overlapped with realms by more than 70% were
 assigned to realm-scale models, which ensures that only species with a
 strong association to a specific realm are included in realm-specific
 analyses. <em>Influence of Synonym Counts on
 Diversification Rates</em> We explored the
 potential impacts of taxonomic uncertainty on eco-evolutionary research
 through an exercise based on latitudinal variation in recent speciation
 rates, measured by the diversification rate statistic (Jetz <em>et
 al.</em>, 2012) using the R package <em>picante</em> v.
 1.8.2 (Kembel <em>et al.</em>, 2010). Initially, we identified
 cases where the estimated number of synonyms
 (N<sub>est.</sub>) differed significantly from the observed
 number of synonyms (N<sub>obs.</sub>) based on global
 cross-species models. Species with N<sub>est.</sub> &gt;
 N<sub>obs.</sub> (negative residuals) were classified as
 needing to “gain” synonyms, leading to lumping events. Conversely, species
 with N<sub>est.</sub> &lt; N<sub>obs.</sub>
 (positive residuals) were flagged as needing to “lose” synonyms, prompting
 splitting events by elevating synonyms to full species status.
 In the lumping exercise, we collapsed branches of 10%, 15%, and
 20% of species with the highest negative residual values, recomputing
 diversification rates for each proportion across ‘new’ phylogenies and
 calculating delta values based on current phylogenetic knowledge. This
 exercise involves removing the selected species from the phylogeny,
 mimicking their lumping with their respective sister species. In the
 splitting exercise, we selected 10%, 15%, and 20% of species with the
 highest positive residual values and added different proportions of
 synonyms (20%, 30%, and 40% of synonyms randomly chosen from the pool of
 synonyms among selected species) to the most recent phylogenies, thus
 reflecting distinct future scenarios of synonymisation rates. These
 synonyms were placed as a monophyletic clade within the current valid
 species to which they belong. If more than one synonym was added for the
 same species, the first synonym was placed at a position sampled by a
 uniform distribution of the valid species’ branch length, and subsequent
 synonyms were placed randomly on sampled branches descending from the most
 recent common ancestor between the valid species and its synonyms. This
 procedure simulated the uncertainty about the phylogenetic relationships
 of synonyms. Again, we calculated delta values based on current
 phylogenetic knowledge. We performed an additional analysis in the
 splitting exercise, simulating under the Yule model to test its effect on
 branch lengths and the estimation of diversification rates, compared with
 a uniform distribution. We bound species on phylogenies
 using the R packages <em>phytools</em> v. 1.2.0 (Revell, 2012)
 and <em>ape</em> v. 5.6.2 (Paradis and Schliep, 2019). Both
 lumping and splitting procedures were repeated for 100 trees. For each
 scenario, we computed the average species diversification rate across
 trees per taxonomic class and plotted these rates by latitude using the R
 package <em>ggplot2</em> v. 3.4.0 (Wickham, 2016). This
 exercise allowed us to explore how speciation rates might be perceived
 under distinct scenarios of taxonomic knowledge.
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description: # Global patterns of taxonomic uncertainty and its impacts on biodiversity
 research
 [https://doi.org/10.5061/dryad.5x69p8d9z](https://doi.org/10.5061/dryad.5x69p8d9z) **There are 10 files associated with this paper:** (1) our raw data, containing some predictors used in cross-species models (AdditionalVars.csv); (2) marine taxa as per the IUCN (marine_spp_IUCN.csv); (3) synonym information for currently valid species stored in a long format, including their description dates (Tetrapoda_BetaTaxonomy.csv); (4) phylogenetic trees for amphibians, birds, mammals, and reptiles stored in a zip file (phylo_trees.zip); (5) outputs from phylogenetic autocorrelation analyses (phylocorr_outs.zip); (6-7) spatial data (including predictor and response variables) needed for the assemblage-level analyses and mapping spatial variation in synonym counts (SpatialDataset.Rdata & gridShps.Rdata); (8) the R code to replicate the analyses performed in this work (Rcode_DarwinianShortfalls.R); (9) additional R scripts needed for the exercise on diversification rates (diversification_scripts.zip); (10) the supporting information (figures and tables) associated with our study. **Description of the data and file structure** (*1*) AdditionalVars.csv: contains 5 columns, each one explained below: \- Scientific.Name: Scientific name of each species included in the most fully-sampled phylogenies for tetrapods. \- Synonyms: Known unique synonyms for each species according to the Catalogue of Life checklist. \- N_synonyms: Number of unique synonyms for each species. \- N_authors_per_taxa: Number of authors in the taxonomic family in the year of the species description (see main text for detailed explanation). \- N_specimens: Number of preserved specimens per species according to the GBIF database. (*2*) marine_spp_IUCN.csv; contains 2 columns (see below) and represents species classified as marine by the International Union for Conservation of Nature (IUCN). This data is used to remove marine tetrapods, keeping only terrestrial vertebrates in subsequent analyses. \- scientificName: scientific name of each tetrapod species classified as marine by the IUCN. \- authority: authors of species description. (*3*) Tetrapoda_BetaTaxonomy.csv: this file is derived from the Catalogue of Life checklist and contains 6 columns, each one explained below: \- parent_species: Scientific name of each species included in the most fully-sampled phylogenies for tetrapods. \- scientificName: The scientific name of a taxon. \- status: Informs whether a given scientific name represents a accepted (valid) species or a synonym (invalid). \- rank: Informs whether a given scientific name represents a species, subspecies, or infraspecific name. \- class: Informs the taxonomic class that each species belong to. \- year: Informs the year of description of each scientific name, when available. (*4*) phylo_trees.zip: contains 4 files, which represent 100 fully-sampled phylogenies for amphibians, birds, mammals and reptiles. All species in each phylogenies matches the species in Scientific.Name. These trees are needed for checking phylogenetic autocorrelation in model residuals. Data for amphibians, birds, and mammals are available on nexus format, while data for reptiles is stored on Rdata format. Nexus files are primarily designed for use with specialised software, but they are plain text files and can be opened and edited using standard text editors. Here, we open and explore both nexus and Rdata files with R software using the R code (Rcode_BetaTaxonomy.R) provided along with the other files. (*5*) phylocorr_outs.zip: contains 28 Rdata files, which represent the results of the phylogenetic autocorrelation analyses across each class-realm combination. As these analyses may take too long to run on personal computers, we have made these outputs available so that readers can load them to understand the data structure and recreate the related plots. Rdata files can be read with the software R. (*6*) SpatialDataset.Rdata: represents datasets containing predictors and response variables for assemblage-level analyses in 3 spatial grains (110, 220, and 440 km). Each dataset have 14 columns, each one explained below: \- TreeTaxon: Informs the taxonomic class of each species, which matches the species included in the TetrapodTraits dataset, and therefore, the more recent fully-sampled phylogenies for tetrapods (see Methods). \- Cell_Id110: Grid cell's identifiers. \- Long: Longitude of each grid cell (centroid). \- Lat: Latitude of each grid cell (centroid). \- nSpp: Species richness, representing the number of species whose range intersected each grid cell. \- Syn_counts: Total number of synonyms per grid cell. \- AvgSynCounts: Average number of synonyms per grid cell (total synonym counts divided by species richness). \- EndRichness: Endemism richness as the sum of the inverse of species range sizes per grid cell. \- DiscYear: Median description year based on all species per grid cell. \- N_specimens: The number of preserved specimens per grid cell based on data from GBIF. \- elev: Median elevation value per grid cell. \- ETA50K: Median time to travel to cities with more than 50,000 inhabitants per grid cell. \- GDP: Median per capita gross domestic product. \- N_biodInst: The number of biodiversity institutions — i.e., herbaria, museums, and universities — per grid cell (*7*) gridShps.Rdata: represents the grid cell shapefiles at 110, 220, and 440 km spatial resolutions. Each shape file contains grid cell's unique identifiers (Cell_Id), coordinate data (Long and Lat) and a geometry column, needed for mapping. (*8*) Rcode_BetaTaxonomy.R: this file contains the R code used to perform all analyses of our work as well as for creating the images on the main text and supplementary material. The script is commented by the authors and intuitively structured, including needed packages and their respective versions. (*9*) diversification_scripts.zip: contains 10 additional R scripts needed for running the diversification rates exercise across each taxonomic class. You need to reate a new folder named 'diversification_scripts', placing the downloaded scripts on it. This folder should be created within the 'diversification' folder, which will be automatically created in step 15 of the Rcode_BetaTaxonomy.R scrip. (*10*) Supporting_information.pdf: contains 16 supplementary figures and five supplementary tables associated with our study. **Code/Software** All analyses were performed using the software R version 4.3.1. The R-code has 18 steps: 1\. Explore the beta taxonomy of terrestrial vertebrates. 2\. Load and understand cross-species data, then explore the response and predictor variables. 3\. Prepare cross-species variables that will be used, check for multicollinearity and standardise. 4\. Model synonym counts per species using generalised linear models (GLMs). 5\. Check whether GLM residuals show phylogenetic autocorrelation. 6\. Create a plot with model coefficients and CI intervals. 7\. Prepare assemblage-level variables that will be used, check for multicollinearity and standardise. 8\. Plot the average number of synonyms per grid cell 9\. Model the average number of synonyms per assemblage using generalised linear models (GLMs). 10\. Check whether GLM residuals from assemblage models show spatial autocorrelation. 11\. Compute 'residuals autocovariates' to account for spatial autocorrelation. 12\. Model assemblage-level data using spatial GLMs (SAR-type models). 13\. Check spatial autocorrelation of residuals from spatial SAR-type models. 14\. Create a plot with model coefficients from SAR-type models. 15\. Impact of synonyms on diversification rates across regions and taxa. 16\. Plot results from the impacts of synonyms on diversification rates. 17\. Impacts of synonyms on diversification rates using a Yule process. 18\. Sensitivity analysis -- adding currently valid subspecies as synonyms. **Usage notes** Software R is required to open the Rcode_BetaTaxonomy.R file as well as for reading phylogenetic trees available in the phylo_trees.zip file (note that you will need a ZIP extractor to extract the phylogenies first) and the Rdata files within phylocorr_outs.zip (you also need to extract the files first to your working directory/folder). Microsoft Excel can be used to view any .csv file.