DOI: 10.5061/dryad.931zcrjh4
title: Data from: Testing macroecological abundance patterns: the relationship between local abundance and range size, range position and climatic suitability among European vascular plants

description: Aim: A fundamental question in macroecology centres around understanding
 the relationship between species’ local abundance and their distribution
 in geographic and climatic space (i.e. the multi-dimensional climatic
 space or climatic niche). Here, we tested three macroecological hypotheses
 that link local abundance to the following range properties: (1) the
 abundance-range size relationship, (2) the abundance-range centre
 relationship, and (3) the abundance-suitability relationship. Location:
 Europe Taxon: Vascular plants Methods: Distribution range maps were
 extracted from the Chorological Database to derive information on the
 range and niche sizes of 517 European vascular plant species. To estimate
 local abundance, we assessed samples from 744,513 vegetation plots in the
 European Vegetation Archive, where local species’ abundance is available
 as plant cover per plot. We then calculated the ‘centrality’, i.e. the
 distance between the location of the abundance observation and each
 species’ range centre in geographic and climatic space. The climatic
 suitability of plot locations was estimated using coarse-grain species
 distribution models (SDMs). The relationships between centrality or
 climatic suitability with abundance were tested using linear models and
 quantile regression. We summarized the overall trend across species’
 regression slopes from linear models and quantile regression using a
 meta-analytical approach. Results: We did not detect any positive
 relationships between a species’ mean local abundance and the size of its
 geographic range or climatic niche. Contrasting yet significant
 correlations were detected between abundance and centrality or climatic
 suitability among species. Main conclusions: Our results do not provide
 unequivocal support for any of the relationships tested, demonstrating
 that determining properties of species’ distributions at large grains and
 extents might be of limited use for predicting local abundance, including
 current SDM approaches. We conclude that environmental factors influencing
 individual performance and local abundance are likely to differ from those
 factors driving plant species’ distribution at coarse resolution and broad
 geographic extents.
description: <b>Geographic ranges and Climatic
 niches</b> We used existing data on the
 geographic ranges of 517 European vascular plant species from the
 Chorological Database Halle (CDH; E. Welk et al., unpublished
 data). Species’ range information was processed to coarse-grain raster
 layers of 2.5-min resolution, which corresponded to grid cells covering
 approximately 15 km² each across Central Europe. The multi-dimensional
 climatic space (or climatic niche) of each geographic range was determined
 using principal components analysis (PCA) of 19 bioclimatic variables from
 WorldClim (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005) at
 2.5-min cell resolution. <b>Local abundance in
 vegetation plots</b> Local abundance (=cover)
 values for a total of 744,513 vegetation plots were obtained from the
 European Vegetation Archive (EVA; Chytrý et al., 2016) for the 517 study
 species in October 2015. Cover-abundance values compiled in EVA that were
 based on different scales (e.g. Domin, 1928; Braun-Blanquet, 1951) were
 transformed to a common percentage scale (van der Maarel, 1979). When more
 than one plot per species was present in a 2.5-min raster cell, we
 calculated mean values of abundance (%).
 <b>Distance from centre of the geographic range or climatic
 niche</b> To determine the centroids of each
 species’ geographic range and climatic niche, all grid cells in which a
 species was indicated as present in the CDH database were considered.
 Geographic range centroids were calculated as the arithmetic mean of
 spatial central coordinates of grid cells over the species’ CDH geographic
 range. To determine species’ niche centroids, the multivariate climatic
 space was translated into two-dimensional space (using PCA), and species’
 geographic occurrences were projected into this climatic niche space.
 Niche centroids were determined as the arithmetic mean of PCA-coordinates
 of the respective species’ raster cell values.<b>
 </b>Geographic distance (in kilometres) from each respective EVA
 vegetation plot to the respective species’ CDH range centre was determined
 using Haversine great circle geographic distance. We calculated
 Mahalanobis distance to the climatic niche centroid as a measure in
 climatic space. Mahalanobis distance is considered as a good proxy for
 marginality since it takes into account the covariance structure of the
 data (Osorio‐Olvera, Soberón, & Falconi, 2019; Osorio‐Olvera et
 al., 2020). For each species’ vegetation plot position, the distance to
 range or niche centroid was divided by the species-specific maximum
 distance to the range or niche centroid
 (distance/distance<sub>max</sub>).
 <b>Coarse-grain climatic suitability</b>
 We used species distribution modelling (SDM) to obtain spatial
 estimates of climatic suitability within each species’ geographic
 range. SDMs estimate spatial predictions of environmental suitability from
 0 (not suitable) to 1 (most suitable). The methods we applied are ‘bioclim’
 (similarity method), ‘multivariate
 adaptive regression splines’ (mars) (statistical modelling), ‘random
 forest’ (rf) and ‘support vector machine’ (svm) (machine learning
 methods).
description: <strong>Fieldname; Description</strong>
 Species; Species full name X_Geo; latitude
 position of the 2.5-min raster cell in geographic space
 Y_Geo; longitude position of the 2.5-min raster cell in
 geographic space Dist_Geo_scaled; distance of
 vegetation plot to range centroid (divided by the species-specific maximum
 distance to the range centroid) Dist_Pca_scaled;
 distance of vegetation plot to niche centroid (divided by the
 species-specific maximum distance to the  niche centroid)
 rf; climatic suitability predicted from model ‘random forest’
 (rf) svm; climatic suitability predicted from
 model ‘support vector machine’ (svm) mars; climatic
 suitability predicted from model ‘multivariate adaptive regression
 splines’ (mars) bioclim; climatic suitability predicted
 from model ‘bioclim’ Cover_perc; species mean cover
 value per 2.5-min grid cell (calculated over all plots within one 2.5-min
 grid cell) Cover_perc_min; species minimum cover value
 per 2.5-min grid cell (calculated over all plots within one 2.5-min
 grid cell) Cover_perc_max; species maximum cover value
 per 2.5-min grid cell (calculated over all plots within one 2.5-min
 grid cell)