10.5061/DRYAD.931ZCRJH4
Sporbert, Maria
0000-0001-7994-8491
Martin Luther University Halle-Wittenberg
Keil, Petr
German Center for Integrative Biodiversity Research
Seidler, Gunnar
Martin Luther University Halle-Wittenberg
Bruelheide, Helge
Martin Luther University Halle-Wittenberg
Jandt, Ute
0000-0002-3177-3669
Martin Luther University Halle-Wittenberg
Aćić, Svetlana
University of Belgrade
Biurrun, Idoia
0000-0002-1454-0433
University of the Basque Country
Campos, Juan Antonio
University of the Basque Country
Čarni, Andraž
University of Nova Gorica
Chytrý, Milan
0000-0002-8122-3075
Masaryk University
Custerevska, Renata
UKIM, Skopje
Dengler, Jürgen
Zurich University of Applied Sciences
Golub, Valentin
Russian Academy of Sciences
Jansen, Florian
University of Rostock
Kuzemko, Anna
National Academy of Sciences of Ukraine
Lenoir, Jonathan
University of Picardie Jules Verne
Marcenò, Corrado
University of the Basque Country
Moeslund, Jesper Erenskjold
Biodiversity and Conservation, Rønde
Pérez-Haase, Aaron
University of Barcelona
Rūsiņa, Solvita
University of Latvia
Šilc, Urban
University of Ljubljana
Tsiripidis, Ioannis
Aristotle University of Thessaloniki
Vandvik, Vigdis
University of Bergen
Vassilev, Kiril
Bulgarian Academy of Sciences
Virtanen, Risto
University of Oulu
Welk, Erik
0000-0001-7994-8491
Martin Luther University Halle-Wittenberg
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
2020
Graduiertenförderung Sachsen-Anhalt (scholarship to Maria Sporbert),
with additional support through institutional funds of Martin Luther
University Halle-Wittenberg*
2021-06-18T00:00:00Z
2021-06-18T00:00:00Z
en
123746678 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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).
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)