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Data from: Testing macroecological abundance patterns: the relationship between local abundance and range size, range position and climatic suitability among European vascular plants

Citation

Sporbert, Maria et al. (2021), Data from: Testing macroecological abundance patterns: the relationship between local abundance and range size, range position and climatic suitability among European vascular plants, Dryad, Dataset, https://doi.org/10.5061/dryad.931zcrjh4

Abstract

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.

Methods

Geographic ranges and Climatic niches

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.

Local abundance in vegetation plots

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 (%).

Distance from centre of the geographic range or climatic niche

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. 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/distancemax).

Coarse-grain climatic suitability

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).

Usage Notes

Fieldname; Description

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)

Funding

Graduiertenförderung Sachsen-Anhalt (scholarship to Maria Sporbert), with additional support through institutional funds of Martin Luther University Halle-Wittenberg