DOI: 10.5061/dryad.zgmsbccj2
title: Data and code for: Plants with higher dispersal capabilities follow ‘abundant-centre’ distributions but such patterns remain rare in animals

description: The ‘abundant-centre’ hypothesis posits that a species’ abundance is
 highest at its range centre and declines towards its range edge. Recently,
 the hypothesis has been much debated, with supporting empirical evidence
 remaining limited. Here, we provide the largest global test of the
 hypothesis to date, on 3,660 species using 5,703,589 abundance
 observations. We summarise species-level patterns and test the effects of
 dispersal-related species traits and phylogeny on abundance–distance
 relationships. Support for the hypothesis varied by taxonomic group, with
 abundant-centre patterns being more pronounced across all plants but
 non-significant when summarised across all animals. Dispersal did not
 explain abundance–distance relationships in animals but likely explains
 such patterns in non-woody plants. Phylogeny improved models of
 abundance–distance patterns for plants but not for animals. Despite this,
 controlling for phylogeny yielded non-significant group-level results for
 plants, suggesting that only certain plant groups may conform to
 abundant-centre patterns. Overall, we demonstrate that abundant-centre
 patterns are not a general ecological phenomenon; they tend to not apply
 to animals but can manifest in certain plant groups, depending on
 dispersal capabilities and evolutionary histories. Leveraging species’
 traits that account for dispersal can improve models of abundant-centre
 patterns across geographic space.
description: <strong>Literature searches</strong>
 We conducted a systematic literature search on
 23<sup>rd</sup> July 2021 by querying the ISI Web of Science
 database (apps.webofknowledge.com) with the following search string for an
 initial broad search: “(abundan* OR abundance-cent* OR abundant
 niche-cent* OR niche cent* OR abundant-centre hypothesis) AND (range OR
 geographic range OR range size OR range edge OR species distribution)”
 using the TITLE field. We retained all studies that 1) comprised
 peer-reviewed primary studies, 2) presented globally extensive abundance
 point observations across all taxonomic groups, 3) were published between
 1990-2020, 4) included extractable data relating to observed/estimated
 abundance counts and 5) were published in English, French or Spanish
 language. Examination of the returned studies (N = 818)
 revealed that some key literature was missing from our results. Therefore,
 we used the studies returned from our initial search as reference sources
 for additional key search terms to derive an optimised search string
 (using the <em>R</em> package ‘litsearchr’ (Grames
 <em>et al</em>. 2019)). Search terms were extracted from
 unique study titles, abstracts and tagged keywords (e.g., terms such as
 ‘range edge’, ‘abundance’ and ‘species range’). We built a keyword
 co-occurrence network and quantitatively assessed potential search terms
 using a 60% cumulative cut-off point (see Grames <em>et
 al</em>. 2019). Resulting search terms (N = 326) were grouped into
 either 1) the ‘species group’, 2) ‘geographic group’ or 3) ‘both groups’
 depending on whether the term referred to a species concept or geographic
 concept. Grouping refers to the string of search terms either side of the
 Boolean operator ‘AND’. We manually removed irrelevant search terms (N =
 268; e.g., ‘field sites’, ‘habitat patch’ and ‘statistically significant’)
 and retained the most relevant search terms (N = 58) which formed our
 optimised search string (Table S13). To verify whether our resulting
 search string was fully optimised, we cross-referenced four key articles
 that we expected to be included in the optimised search results (Virgós <em>et al</em>. 2011; Dixon <em>et al</em>. 2013; Baldanzi <em>et al</em>. 2013 and Dallas <em>et al</em>. 2017), all of which were included. We queried the WoS database using our optimised search string and obtained 531 studies. After screening of titles and abstracts, we retained 23 studies for data extraction. We supplemented our Web of Science literature search with the studies included within the foundational synthesis by Sagarin and Gaines (2002). We then conducted a snowball search of the literature that cited Sagarin and Gaines (2002) up until 31<sup>st</sup> December 2020. This resulted in a literature database of 1,109 studies of which we removed a set of 1,000 studies after screening titles and abstracts and an additional set of 95 studies after screening of full texts, leaving us with an additional 14 studies that were suitable for data extraction. <strong>Data extraction and processing</strong> From each study and for each species, we extracted raw abundance values and distance from the species’ geographic range centroids (in km). Corresponding authors were contacted via email correspondence if data were not publicly available. Where data were not publicly available and the corresponding author was unable to provide the data, we extracted abundance and distance data from appropriate figures within the published articles using the web-based tool WebPlotDigitizer version 4.5 (Rohatgi 2021; https://automeris.io/WebPlotDigitizer). If abundance data were available but distance values were not, global range maps were obtained in shapefile formats for each species and range centroids were calculated. We obtained global range shapefiles for terrestrial mammals from the IUCN Red List (IUCN 2021; https://www.iucnredlist.org) and for birds from the BirdLife Data Zone version 2020.1 (BirdLife International and the Handbook of the Birds of the World 2020; http://datazone.birdlife.org/home), thus accounting for the entire ranges for migratory and non-migratory species. IUCN range maps have been criticized due to oversimplification of species’ ranges derived from sampling bias (Herkt <em>et al</em>. 2017), but represent the most comprehensive spatial data set available for our study species. If global polygon range maps for particularly under-sampled taxonomic groups were unavailable, e.g., invertebrates and some plant species (N spp. = 8), we downloaded species occurrence point data from the Global Biodiversity Information Facility (GBIF; https://www.gbif.org). Occurrence data were then cleaned using the ‘CoordinateCleaner’ R package (Zizka <em>et al</em>. 2019) and manual checks were performed to remove any remaining outliers (Zizka <em>et al</em>. 2020; Panter <em>et al</em>. 2020). We calculated minimum convex hulls for terrestrial species in an attempt to not overestimate their global range sizes by accounting for unsuitable terrestrial environments, which we interpreted as proxies for global species ranges and calculated range centroids using QGIS 3.14.16 (QGIS.org 2022). Then, we calculated the geodesic distances on a sphere (km) between the sampling sites with associated abundance values to obtain the distance to the species’ range centroid, using the WGS84 co-ordinate reference system. Abundance and distance values were log<sub>10</sub>-transformed prior to statistical analyses to account for scaling inconsistencies. Species with unresolved species-level taxonomies (106) as well as species with fewer than five observations were omitted from the analysis. We calculated Spearman Rank Correlation Coefficients (<em>r<sub>s</sub></em>) between log<sub>10</sub>(abundance) and log<sub>10</sub>(distance) values (Fig. 2). Negative <em>r<sub>s</sub></em> values are consistent with an ‘abundant-centre’ distribution (see Fig. 2). <strong>Scale effects on abundance–distance relationships</strong> We used three measurements to attempt to explore the effect of scale on ‘abundant-centre’ patterns: 1) we calculated the study extent (km), i.e., the spatial extent at which the study was conducted at encompassing the total study area between sampling locations in the data. This was measured using both latitudinal and longitudinal measurements. Initially, we used four categorical levels: ‘Local’ ≤ 250 km, ‘Landscape’ &gt; 251-500 km, ‘Regional’ &gt; 501-1500 km and ‘Continental’ &gt; 1,501 km. 2) We calculated the grain (km<sup>2</sup>), i.e., the spatial scale at which data were collected, which is important because the area of the base unit defines the spatial scale of the study (Field <em>et al</em>. 2009), and variation in grain may be reflected in population abundance estimates (see Caten <em>et al</em>. 2022). Grain was extracted from each study by taking the base unit area for each sampling technique, e.g., sampling units measured in km<sup>2</sup> (Kallimanis & Koutsias 2012). 3) We calculated study focus (km<sup>2</sup>), defined as the spatial scale at which data were analysed. Often, abundance estimates from individual sampling sites are averaged across larger sampling areas, e.g. a protected area sampled using a number of line transects and multiple sampling points along each transect, with abundance values averaged across all of the sampling points to produce an estimate for each transect. In most cases grain and focus remained the same for each study, e.g. when abundance data were recorded in the form of points within a species’ geographic range. Grain and focus values were log<sub>10</sub> transformed due to the large variation in the range of these values. We then plotted the distribution of the log<sub>10</sub>-transformed values on separate histograms and visualised the natural breaks in the data. Using these we binned both grain and focus into two new categorical levels: ‘small’ and ‘large’ (-10 to -3 and -3 to 3 on log<sub>10</sub> scale, respectively). We decided to drop extent from our analyses due to uneven sample sizes: data for only three groups (birds, mammals and plants) global 2,717 species vs. local 146 species. We also dropped focus from our analyses because the natural break categorical bins were identical to those for grain. Grain was subsequently omitted from the statistical analyses due to its strong correlation with animal species group and plant functional group variables, and thus could not be included within the same models. <strong>Compilation of dispersal-related species traits and geographic variables</strong> We compiled six dispersal-related species traits for animal and eight traits for plant species to examine their effects on abundance–distance relationships (Table 1; Table S2). Traits were selected based on the morphological and/or ecological characteristics of the study species and included: for animals 1) taxonomic group (categorical), 2) body size (continuous), 3) invasiveness (binary 1,0) and 4) feeding guild (categorical); and for plants: 1) functional group (categorical), 2) mean plant height (m), 3) seed mass (mg), 4) invasiveness (binary 1,0), 5) life span (categorical) and 6) life form (categorical) (see Table 1 for an overview and Table S3 for justifications for the inclusion of each species trait/geographic variable). To explore spatial patterns within global abundance–distance relationships, we also compiled geographic data for species-level range sizes (km<sup>2</sup>) and absolute latitudes (°). Due to small sample sizes (N = 9 species), invertebrates were dropped prior to statistical analysis. To examine the effects of body size, we compiled body mass (g) data for mammals and birds, and snout-vent lengths (SVL; cm) for freshwater and reef fishes to produce the trait variable ‘body size’. For plants, we used mean plant height (m) as a proxy for body size. Mean plant height was used instead of maximum plant height as these were the only data available for our selected species, and notable effects of plant height on species abundance patterns would be reflected in either measurement. Where plant height and seed mass data were unavailable, we supplemented our data with gap-filled measurements from Bruelheide <em>et al</em>. (2018) and Kattge <em>et al</em>. (2020) which were estimated using Bayesian Hierarchical Probabilistic Matrix Factorization (BHPMF; Schrodt <em>et al</em>. 2015). Trait data for plant functional groups were sourced from the BiolFlor database (Kühn <em>et al</em>. 2004) and the corresponding levels ‘dwarf shrub’ (N = 11 species) and ‘subshrub’ (N = 4 species) were merged into the level ‘shrub’ to produce four distinct categorical levels: ‘grasses’, ‘herbs’, ‘shrubs’ and ‘trees’. Plant life-form data followed the classification of Raunkiær (1934) but due to small sample sizes for ‘chamaephytes’ (woody plants with perennating buds borne close to the soil surface), we merged these with the ‘phanerophytes’ (woody perennial plants with buds at a distance from the surface, such as trees and shrubs). Invasiveness was assessed using a binary approach (1 = invasive and 0 = non-invasive) according to the Invasive Species Specialist Group’s Global Invasive Species Database (ISSG GISD; http://www.iucngisd.org/gisd/). For both animal and plant species, absolute latitude (°) was calculated as the absolute value of the range centroid. The following species traits/geographic variables were log<sub>10</sub>-transformed prior to analysis: body size (cm; g), mean plant height (m), seed mass (mg) and range size (km<sup>2</sup>) to account for right-skew within the data. We tested for, but did not find, collinearity between continuous explanatory variables using a correlation threshold value ≥ 0.70 in the R package ‘hmisc’ (Harrell Jr 2022) and visualised this using the ‘pheatmap’ package (Kolde 2019) (Fig. S1). For full metholodogical details, see the "Methods" section of the associated manuscript.
description: Data sets are provided as comma separated values (.csv) files and
 the code is provided as a R File format for the software R.
description: ***animalia_v2.csv*** Data set containing animal abundance-distance
 correlations, original sources and dispersal-related species traits.
 Animal sample size = 3,060 species. Binomial = Genera and species names
 for taxon studied (one species per row). Genus = genera name. Species =
 species name. Study = original study where species specific abundance data
 were extracted from. Year = publication year. AbundCent.test = 1,0 binary
 variable confirming whether the study included specifically tested the
 abundant-centre hypothesis. Data.Source = original dataset where abundance
 data were sourced (note some studies used third-party data). DOI.title =
 Digital Object Identifier for the included studies. Environment =
 biogeographic realm of the study species, e.g., terrestrial or marine.
 Kingdom = taxonomic kingdom. Group = taxonomic group for animal species,
 e.g., birds, freshwater fishes, mammals or reef fishes. coef =
 abundance-distance correlation coefficient (based on Spearman Rank
 Correlations). t = Spearman Rank test statistic. p = Spearman Rank
 P-value. extent = original study extent, e.g., continental/global or
 local.regional (see Methods). n = abundance sample size for each species.
 invasive = 1,0 binary variable confirming whether the species is
 considered invasive in parts of its range or not (see Methods).
 feeding.guild = animal feeding guild, e.g., carnivore, omnivore or
 herbivore. mean.latitude = mean latitude across a species range, later
 transformed into absolute latitude (see associated R code). grain =
 categorical grain size of the original study (see Methods). log.body.mass
 = body size variable log10 transformed (g for all animal groups excluding
 fishes; svl cm for fishes) (see Methods). log.range size = log10
 transformed range size estimates derived from the IUCN Red List (km2) (see
 Methods). ## Sharing/Access information Data was derived from the
 following sources: Chaiyes et al. (2020). Ecosphere. 10.1002/ecs2.3134
 Dallas et al. (2017) Ecology Letters. 10.1111/ele.12860 Feldman et al.
 (2015) Global Ecology and Biogeography. 10.1111/geb.12323 Freeman &
 Beehler (2018) Journal of Biogeography. 10.1111/jbi.13370
 Martinez-Guiterrez et al. (2017) Diversity and Distributions.
 10.1111/ddi.12662 Santini et al. (2018) Ecography. 10.1111/ecog.04027
 Shalom et al. (2020) Journal of Biogeography. 10.1111/jbi.13920 Wen et al.
 (2020) Journal of Biogeography. 10.1111/jbi.14025 ***plantae_v2*** Data
 set containing plant abundance-distance correlations, original sources and
 dispersal-related species traits. Plant sample size = 600 species.
 Binomial = Genera and species names for taxon studied (one species per
 row). Genus = genera name. Species = species name. Study = original study
 where species specific abundance data were extracted from. Year =
 publication year. AbundCent.test = 1,0 binary variable confirming whether
 the study included specifically tested the abundant-centre hypothesis.
 Data.Source = original dataset where abundance data were sourced (note
 some studies used third-party data). DOI.title = Digital Object Identifier
 for the included studies. Environment = biogeographic realm of the study
 species, e.g., terrestrial or marine. Kingdom = taxonomic kingdom. Group =
 higher taxonomic group, e.g., plants. coef = abundance-distance
 correlation coefficient (based on Spearman Rank Correlations). t =
 Spearman Rank test statistic. p = Spearman Rank P-value. extent = original
 study extent, e.g., continental/global or local.regional (see Methods). n
 = abundance sample size for each species. invasive = 1,0 binary variable
 confirming whether the species is considered invasive in parts of its
 range or not (see Methods). functional.group = functional group variable,
 e.g., trees, shrubs, herbs and grasses. feeding.guild = animal feeding
 guild, e.g., carnivore, omnivore or herbivore. life.form = plant life
 forms based on the classification system of Raunkiær (1934), e.g.,
 phanerophyte, hemicryptophyte, geophyte and therophyte. life.span = plant
 life span, e.g., annual, annual/biennial, biennial, biennial/perennial and
 perennial. mean.latitude = mean latitude across a species range, later
 transformed into absolute latitude (see associated R code). grain =
 categorical grain size of the original study (see Methods). log.range size
 = log10 transformed range size estimates (see Methods).
 lofe.mean.plant.height.m = log10 transformed mean plant height (m)
 estimate (see Methods). log.seed.mass.mg = log10 transformed seed mass
 (mg) estimates (see Methods). ## Sharing/Access information Data was
 derived from the following sources: Baer & Maron (2019) Journal of
 Ecology. 10.1111/1365-2745.13086 Dallas et al. (2017) Ecology Letters.
 10.1111/ele.12860 Dixon et al. (2012) Molecular Ecology. 10.1111/mec.12207
 Gao et al. (2017) Ecosphere. 10.1002/ecs2.1737 McMinn et al. (2016)
 Journal of Biogeography. 10.1111/jbi.12879 Phiri et al. (2015) Polar
 Biology. 10.1007/s00300-015-1749-1 Sporbert et al. (2020) Journal of
 Biogeography. 10.1111/jbi.13926 ## Code/Software This data set should be
 used to run the accompanying R code "all analyses together - version
 4 - SK.R".