10.5061/DRYAD.BZKH18993
Chevalier, Mathieu
0000-0002-1170-5343
French Research Institute for Exploitation of the Sea
Broennimann, Olivier
University of Lausanne
Guisan, Antoine
University of Lausanne
Population abundance data and species range maps
Dryad
dataset
2021
FOS: Biological sciences
Swiss National Science Foundation
https://ror.org/00yjd3n13
CR23I2_162754
2022-07-28T00:00:00Z
2022-07-28T00:00:00Z
en
https://doi.org/10.1111/geb.13376
1702904199 bytes
7
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Aim – The abundant-center hypothesis (ACH) predicts a negative
relationship between species abundance and the distance to geographic
range center. Since its formulation, empirical tests of the ACH have
involved different settings (e.g. the distance to the ecological niche or
to the geographic range center), but studies found contrasting support for
this hypothesis. Here, we evaluate whether these discrepancies might stem
from differences regarding the context in which the ACH is tested
(geographical or environmental), how distances are measured, how species
envelopes are delineated, how the relationship is evaluated and which data
are used. Location – Americas. Time Period – 1800-2017. Major taxa studied
– mammal, bird, fish and tree seedlings. Methods – Using published
abundance data for 801 species, together with species range maps, we
tested the ACH using three distance metrics in both environmental and
geographical spaces with range and niche envelopes delineated using two
different algorithms, totaling 12 different settings. We then evaluated
the distance-abundance relationship using correlation coefficients
(traditional approach) and mixed-effect models to reduce the effect of
sampling noise on parameter estimates. Results – Similar to previous
studies, correlation coefficients indicated an absence of effect of
distance on abundance for all taxonomic groups and settings. In contrast,
mixed-effect models highlighted relationships of various strengths and
shapes, with a tendency for more theoretically-supported settings to
provide stronger support for the ACH. The relationships were however not
consistent across taxonomic groups and settings, and were sometimes even
opposite to ACH expectations. Main conclusions – We found mixed and
inconclusive results regarding the ACH. These results corroborate recent
findings, and suggest either that our ability to predict abundances from
the location of populations within geographical or environmental spaces is
low, or that the data used here have a poor signal-to-noise-ratio. The
latter calls for further testing on other datasets using the same range of
settings and methodological framework.
All the data used in this paper have either already been published (see
Dallas et al., 2017 and Osorio-Olvera et al., 2020) or are freely
available (range maps can be dowloaded
here https://www.fs.fed.us/nrs/atlas/littlefia/ for trees and
here https://www.iucnredlist.org/resources/spatial-data-download for
mammals, fish and birds; climatic data can be downloaded here
https://www.worldclim.org/data/worldclim21.html).
The R script showing how these data can be used to draw inferences is
available on github (https://github.com/Mathieu-Chevalier/ACH-GEB).
Requires R packages: raster, ade4, data.table,
dplyr, boa, rgeos, ks, tidymv, ggplot2,
robustbase, car, spatstat, ecospat, reshape2, mgcv, gratia, runjags, R2jags, qpcR, plyr, ggpubr, raptr, PBSmapping to load all RData files.