DOI 10.1111/geb.13376 Aim
The abundant centre hypothesis (ACH) predicts a negative relationship between species abundance and the distance to the geographical range centre. Since its formulation, empirical tests of the ACH have involved different settings (e.g. the distance to the ecological niche or to the geographical range centre), 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
The Americas.
Time period
1800–2017.
Major taxa studied
Mammals, birds, 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, totalling 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.
abundance, Bayesian inference, centroid, convex hull, distance, ellipsoids, kernel density, margins, mixed-effect models, nonlinearity
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