DOI: 10.5061/dryad.wdbrv15ww
title: Macroecological correlates of Darwinian shortfalls across terrestrial vertebrates

description: Most described species have not been explicitly included in phylogenetic
 trees—a problem named the Darwinian shortfall—due to a lack of molecular
 and/or morphological data, thus hampering the explicit incorporation of
 evolution into large-scale biodiversity analyses. We investigate potential
 drivers of the Darwinian shortfall in tetrapods, a group where at least
 one-third of described species still lack phylogenetic data, thus
 necessitating the imputation of their evolutionary relationships in
 fully-sampled phylogenies. We show that the number of preserved specimens
 in scientific collections is the main driver of phylogenetic knowledge
 accumulation, highlighting the major role of biological collections in
 unveiling novel biodiversity data and the importance of continued sampling
 efforts to reduce knowledge gaps. Additionally, large-bodied and
 wide-ranged species, as well as terrestrial and aquatic amphibians and
 reptiles, are phylogenetically better known. Therefore, future efforts
 should prioritize phylogenetic research on organisms that are
 narrow-ranged, small-bodied, and underrepresented in scientific
 collections, such as fossorial species. Addressing the Darwinian shortfall
 will be imperative for advancing our understanding of evolutionary drivers
 shaping biodiversity patterns and implementing comprehensive conservation
 strategies.
description: (a)   Phylogenetic assessment and species-level
 covariates We firstly identified which tetrapod species
 had their evolutionary relationships imputed in fully-sampled phylogenies
 available for the group (1–5), thus creating a binary response variable
 for 33,281 species included in such phylogenies. We excluded 228 marine
 species (according to the International Union for Conservation of Nature —
 IUCN), keeping only terrestrial vertebrates (<em>n</em> =
 33,053) in subsequent analyses. We then selected eight putative predictors
 to investigate how species’ biology, geography and biodiversity appeal,
 and socioeconomic-related factors affect the probability of species being
 imputed on phylogenies. Many predictors used here comes from the
 TetrapodTraits, a recent database for the world’s tetrapod including
 standardised species-level attributes (6). Below, we outline the
 attributes used as predictors, along with brief explanations of how they
 were computed. The year of species description was
 based on the original description publication date and was available for
 all but one species (Indotyphlops pushpakumara, an undescribed species
 included in Tonini’s phylogeny, 4). For body size, we used body mass
 information for birds, mammals, and reptiles as data coverage was on
 average higher than 95% (n = 24,761 out of 25,815 species). Conversely,
 body mass information was available for only 21.4% (<em>n</em>
 = 1,547) amphibian species, and therefore, we used body length, which
 coverage represented 97.9% (<em>n</em> = 7,083) species.
 Information on microhabitat use was available for 31,501 (95.3%) species,
 and it was converted into a continuous metric of verticality (7), with
 species being scored as: 0 = strictly fossorial, 0.25 = fossorial and
 aquatic/terrestrial, or fossorial and aquatic and terrestrial, 0.5 =
 aquatic/terrestrial, or fossorial and arboreal, or fossorial and
 aquatic/terrestrial and arboreal, 0.75 = terrestrial/aquatic and arboreal,
 or terrestrial and aquatic and arboreal, or terrestrial and aerial, and 1
 = strictly arboreal or aerial. Species-specific sources on body size and
 microhabitat are available in (6). Some TetrapodTraits
 attributes were derived as within-range predictors based on expert-based
 range maps for amphibians (8–10), birds (2,10), mammals (9–11), and
 reptiles (1,9,12) overlaid onto a 110×110 km cylindrical equal-area grid
 cell scheme. This grain size minimizes the false presences related to the
 use of expert range maps (13). Species range size was represented as the
 number of 110-km grid cells overlapped by range maps. Two other attributes
 corresponded to within-range averages of raster layers aggregated to the
 resolution of the grid cell scheme, namely: elevation, which was based on
 a 1km topographic layer (14) and roughly captures broad elevational
 patterns in a continuous format, without the discretization of species
 into lowland vs. highland areas, and human density (as inhabitants per
 km<sup>2</sup> at year 2017), derived from the HYDE 3.2
 database at a spatial resolution of 5 arc-min (15). Additionally, we
 obtained endemism richness, which represents a proxy for range rarity.
 This metric was computed as the sum of the inverse range sizes of all
 species per taxonomic class in a grid-cell (16), which we then used to
 calculate the median endemism richness for each species based on the cells
 they occur in. We extracted the number of preserved
 specimens per species deposited in biological collections worldwide using
 the function <em>occ_count</em> in the
 <em>rgbif</em> R package (17). For that, we created search
 queries containing valid species name plus their unique synonyms (i.e.,
 invalid names that can be tracked back to a single valid name) and setting
 the <em>basisOfRecord</em> argument to preserved specimen.
 Synonym information was obtained from the IUCN taxonomy backbone using the
 <em>rl_synonyms</em> function in the
 <em>rredlist</em> R package (18). Data on these—and the
 spatially-based—predictors were available for more than 99.9% species.
 After excluding species with missing data in a least one predictor, our
 final dataset included 30,321 species: 6,526 amphibians, 9,369 birds,
 5,052 mammals, and 9,374 reptiles.  
 (b)  Statistical analyses We modelled the
 probability of a species being phylogenetically imputed (binary response
 variable: 0 = not imputed, 1 = imputed) as a function of eight putative
 predictors (fixed effects; see Box 1) while accounting for taxonomic
 relatedness (random effects) using generalised linear mixed-effects models
 (GLMM; 19) with a binomial error distribution and a logit link function
 (20). We included taxonomic family as a random variable in our models to
 minimize dependence issues among species. Families with less than three
 species (supplementary material, table S1) were removed to reduce
 instability in model estimates (21), which decreased to 30,178 the number
 of species to be modelled. We evaluated whether
 phylogenetic regression models were needed by examining the phylogenetic
 autocorrelation of GLMM residuals through Moran’s I correlograms computed
 across 14 distance classes (22). The phylogenetic correlograms were based
 on averaged results from 50 fully sampled phylogenies for each taxonomic
 class (1–5). For reptiles, we firstly constructed supertrees by combining
 Tonini’s and Colston’s phylogenies (1,4) using the function
 <em>tree.merger</em> in the R package
 <em>RRphylo</em> (23), which preserves branch length
 information in the combined trees (24). For the global models, we used
 only five trees due to computational limitations. Analyses of phylogenetic
 autocorrelation were performed using the R packages <em>phylobase
 </em>(25) and <em>phylosignal</em> (26).
 Prior to constructing the GLMMs, continuous predictors were
 log<sub>10</sub>-transformed if skewness or kurtosis were
 outside of the range of -2 and +2 (27), then centred and scaled
 (z-transformed) to allow direct comparisons of their effect sizes. We
 checked for multicollinearity among predictors using the Variation
 Inflation Factors (VIF), where strong multicollinearity is usually
 attributed to VIFs &gt; 5, indicating that variables should be removed
 from the analysis (28). Since none of our continuous variables had VIF
 &gt; 4, we kept them all in subsequent analyses (supplementary
 material, table S2). We modelled our binary response
 variable separately for amphibians, birds, mammals, and reptiles. Models
 were constructed<em> </em>globally as well as separately for
 each biogeographic realm (29), except for Oceania due to its low sample
 size (supplementary material, table S3). Species whose range overlapped
 realms by &gt;70% were assigned to realm-scale models. We inspected
 model fit using the R package <em>DHARMa </em>(30) and
 assessed explained variation by calculating the
 pseudo-R<sup>2</sup> with the R package
 <em>performance</em> (31). We used the package
 <em>lme4</em> (32) for fitting the mixed-effect models and
 <em>usdm</em> (33) for computing VIF values. All analyses were
 performed using R version 4.3.1 (34). See data accessibility for raw data
 and R-code. Lastly, we computed a measure of “Darwinian
 deficit” (35) per family and class to quantify the relative contribution
 of phylogenetically imputed relationships in representing the accumulated
 evolutionary history across taxa. This measure is based on Faith’s
 phylogenetic diversity (PD) metric (36) and was calculated as:
 <em>PD imputed species / (PD imputed species + PD non-imputed
 species)</em>. The Darwinian deficit ranges from 0 to 1 and informs
 the proportion of total PD (i.e., sum of branch lengths) that is
 attributed to imputed species in a sample (e.g., family). We obtained the
 Darwinian deficit per family across 100 fully-sampled phylogenies for each
 taxonomic class, using only families with at least one imputed species. We
 then inspected whether average values per family was influenced by the
 respective species richness.  
 <strong>References</strong>
 1.        Colston TJ, Kulkarni P, Jetz W, Pyron RA. Phylogenetic
 and spatial distribution of evolutionary diversification, isolation, and
 threat in turtles and crocodilians (non-avian archosauromorphs). BMC Evol
 Biol. 2020;20(81):1–16. 2.        Jetz W, Thomas GH,
 Joy JB, Hartmann K, Mooers AO. The global diversity of birds in space and
 time. Nature. 2012;491(7424):444–8. 3.        Jetz W,
 Pyron RA. The interplay of past diversification and evolutionary isolation
 with present imperilment across the amphibian tree of life. Nat Ecol Evol.
 2018;2:850–8. 4.        Tonini JFR, Beard KH, Ferreira
 RB, Jetz W, Pyron RA. Fully-sampled phylogenies of squamates reveal
 evolutionary patterns in threat status. Biol Conserv.
 2016;204:23–31. 5.        Upham NS, Esselstyn JA, Jetz
 W. Inferring the mammal tree: Species-level sets of phylogenies for
 questions in ecology, evolution, and conservation. PLoS Biol.
 2019;17(12):1–44. 6.        Moura MR, Ceron K, Guedes
 JJM, Chen-Zhao R, Sica Y V, Hart J, et al. A phylogeny-informed
 characterisation of global tetrapod traits addresses data gaps and biases.
 PLoS Biol. 2024;In press. 7.        Oliveira BF,
 Scheffers BR. Vertical stratification influences global patterns of
 biodiversity. Ecography (Cop). 2019;42(2):249–249.
 8.        González-del-Pliego P, Freckleton RP, Edwards DP, Koo
 MS, Scheffers BR, Pyron RA, et al. Phylogenetic and Trait-Based Prediction
 of Extinction Risk for Data-Deficient Amphibians. Curr Biol.
 2019;29(9):1557–63. 9.        IUCN. The IUCN Red List
 of Threatened Species. Version 2021.3 [Internet]. 2021 [cited 2019 Mar
 19]. Available from: https://www.iucnredlist.org
 10.      Jetz W, McPherson JM, Guralnick RP. Integrating
 biodiversity distribution knowledge: Toward a global map of life. Trends
 Ecol Evol. 2012;27(3):151–9. 11.      Marsh CJ, Sica Y
 V., Burgin CJ, Dorman WA, Anderson RC, del Toro Mijares I, et al. Expert
 range maps of global mammal distributions harmonised to three taxonomic
 authorities. J Biogeogr. 2022;49(5):979–92. 12.     
 Roll U, Feldman A, Novosolov M, Allison A, Bauer AM, Bernard R, et al. The
 global distribution of tetrapods reveals a need for targeted reptile
 conservation. Nat Ecol Evol. 2017;1:1677–1682. 13.     
 Hurlbert AH, Jetz W. Species richness, hotspots, and the scale dependence
 of range maps in ecology and conservation. Proc Natl Acad Sci U S A.
 2007;104(33):13384–9. 14.      Amatulli G, Domisch S,
 Tuanmu MN, Parmentier B, Ranipeta A, Malczyk J, et al. A suite of global,
 cross-scale topographic variables for environmental and biodiversity
 modeling. Sci Data. 2018;5(1):1–15. 15.      Goldewijk
 KK, Beusen A, Doelman J, Stehfest E. Anthropogenic land use estimates for
 the Holocene - HYDE 3.2. Earth Syst Sci Data. 2017;9(2):927–53.
 16.      Kier G, Barthlott W. Measuring and mapping endemism and
 species richness: A new methodological approach and its application on the
 flora of Africa. Biodivers Conserv. 2001;10(9):1513–29.
 17.      Chamberlain S, Barve V, Mcglinn D, Oldoni D, Desmet P,
 Geffert L, et al. rgbif: Interface to the Global Biodiversity Information
 Facility API. R package version 3.7.8. [Internet]. 2023 [cited 2023 Nov
 13]. Available from: https://cran.r-project.org/package=rgbif
 18.      Chamberlain S. rredlist: “IUCN” Red List Client. R
 package version 0.7.0. [Internet]. 2020. p. 23. Available from:
 https://cran.r-project.org/package=rredlist 19.     
 Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM. Statistics for Biology
 and Health. Springer; 2009. 574 p. 20.      Bolker BM,
 Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, et al.
 Generalized linear mixed models: a practical guide for ecology and
 evolution. Trends Ecol Evol. 2009;24(3):127–35.
 21.      Harrison XA, Donaldson L, Correa-Cano ME, Evans J,
 Fisher DN, Goodwin CED, et al. A brief introduction to mixed effects
 modelling and multi-model inference in ecology. PeerJ.
 2018;2018(5):1–32. 22.      Revell LJ. Phylogenetic
 signal and linear regression on species data. Methods Ecol Evol.
 2010;1:319–29. 23.      Castiglione S, Melchionna M,
 Profico A, Sansalone G, Modafferi M, Mondanaro A, et al. Human face‐off: a
 new method for mapping evolutionary rates on three‐dimensional digital
 models. Palaeontology. 2022;65(1):1–10. 24.     
 Castiglione S, Serio C, Mondanaro A, Melchionna M, Raia P. Fast production
 of large, time calibrated, informal supertrees with tree.merger.
 Palaeontology. 2022;(e12588):1–11. 25.      Bolker B,
 Butler M, Cowan P, Vienne D de, Eddelbuettel D, Holder M, et al.
 phylobase: Base Package for Phylogenetic Structures and Comparative Data
 [Internet]. 2020. Available from:
 https://cran.r-project.org/package=phylobase 26.     
 Keck F, Rimet F, Bouchez A, Franc A. Phylosignal: An R package to measure,
 test, and explore the phylogenetic signal. Ecol Evol.
 2016;6(9):2774–80. 27.      George D, Mallery P. SPSS
 for Windows Step by Step: A Simple Guide and Reference, 17.0 update. 10th
 ed. Boston: Allyn & Bacon; 2010. 386 p.
 28.      Kutner MH, Nachtsheim CJ, Neter J, Li W. Applied Linear
 Statistical Models. 5th ed. New York: McGraw-Hill Irwin; 2005. 1396
 p. 29.      Dinerstein E, Olson D, Joshi A, Vynne C,
 Burgess ND, Wikramanayake E, et al. An Ecoregion-Based Approach to
 Protecting Half the Terrestrial Realm. Bioscience. 2017
 Jun;67(6):534–45. 30.      Hartig F. DHARMa: Residual
 Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models
 [Internet]. 2022. Available from:
 https://cran.r-project.org/package=DHARMa 31.     
 Lüdecke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D. performance: An
 R Package for Assessment, Comparison and Testing of Statistical Models. J
 Open Source Softw. 2021;6(60):3139. 32.      Bates D,
 Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using
 lme4. J Stat Softw. 2015;67(1). 33.      Naimi B, Hamm
 NAS, Groen TA, Skidmore AK, Toxopeus AG. Where is positional uncertainty a
 problem for species distribution modelling? Ecography (Cop).
 2014;37(2):191–203. 34.      R Core Team. R Foundation
 for Statistical Computing, Vienna, Austria. 2023. R: A Language and
 Environment for Statistical Computing. Available from:
 https://www.r-project.org/ 35.      Nakamura G, Richter
 A, Soares BE. FishPhyloMaker: An R package to generate phylogenies for
 ray-finned fishes. Ecol Inform. 2021;66:101481.
 36.      Faith DP. Conservation evaluation and phylogenetic
 diversity. Biol Conserv. 1992;61(1):1–10.
description: # README: Macroecological correlates of Darwinian shortfalls across
 terrestrial vertebrates
 [https://doi.org/10.5061/dryad.wdbrv15ww](https://doi.org/10.5061/dryad.wdbrv15ww) There are five main documents associated with this paper: (*1*) our raw data (**AdditionalVars.csv**); (*2*) marine taxa as per the IUCN (**marine_spp_IUCN.csv**); (*3*) phylogenetic trees for amphibians, birds, mammals, and reptiles stored in a zip file (**phylo_trees.zip**); (*4*) outputs from phylogenetic autocorrelation analyses (**phylocorr_outs.zip**); and (*5*) the R code (**Rcode_DarwinianShortfalls.R**). ## Description of the data and file structure (*1*) **AdditionalVars.csv**; contains 3 columns, each one explained below: * Scientific.Name: scientific name of each species included in the most fully-sampled phylogenies for tetrapods. - sequenced: Informs if there is any genetic data available for each species (data obtained from Smid 2022; DOI: 10.1111/jbi.14491) * GBIF_occ: Total number of preserved specimens housed in scientific collection for each species based on the Global Biodiversity Information Facility (GBIF). (*2*) **marine_spp_IUCN.csv**; this file 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.  1. scientificName: scientific name of each tetrapod species classified as marine by the IUCN. 2. authority: authors of species description. (*3*) **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 and for computing Darwinian deficits across taxonomic families and classes. 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 (RcodeDarwinianShortfalls.R) provided along with the other files. (*4*) **phylocorr_outs.zip**; contains 28 Rdata files, which represent the results of the phylogenetic autocorrelation analyses across each class-realm combination. Since 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. (*5*) **Rcode_DarwinianShortfalls.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 in commented by the authors and intuitively structured, including needed packages and their respective versions. ## Code/Software All analyses were performed using the software R version 4.3.1.  The R-code has 7 steps: 1\. Load and understand the data, then explore the response and predictor variables. 2\. Make barplots showing the proportion of phylogenetically imputed species per family. 3\. Prepare variables that will be used, check for multicollinearity and standardise. 4\. Model the binary response using generalised linear mixed-effects models (GLMMs). 5\. Check whether GLMM residuals show phylogenetic autocorrelation. 6\. Create a plot with model coefficients and CI intervals. 7\. Compute Darwinian deficits across taxonomic families and classes. ## Usage notes Software R is required to open RcodeDarwinianShortfalls.R 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 AdditionalVars.csv and marine_spp_IUCN.csv