Population abundance data and species range maps
Cite this dataset
Chevalier, Mathieu; Broennimann, Olivier; Guisan, Antoine (2022). Population abundance data and species range maps [Dataset]. Dryad. https://doi.org/10.5061/dryad.bzkh18993
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.
Swiss National Science Foundation, Award: CR23I2_162754