Using a robust multi‐settings inference framework on published datasets still reveals limited support for the abundant centre hypothesis: More testing needed on other datasets. Issue 11 (30th August 2021)

• Record Type: Journal Article
• Authors: Chevalier, Mathieu; Broennimann, Olivier; Guisan, Antoine
• Editors: Schrodt, Franziska
• Is Part Of: Global ecology & biogeography. Volume 30:Issue 11(2021)
• Page Start: 2211
• Page End: 2228
• Publication Date: 2021-08-30
• Subjects: abundance -- Bayesian inference -- centroid -- convex hull -- distance -- ellipsoids -- kernel density -- margins -- mixed‐effect models -- nonlinearity; Ecology -- Periodicals; Biogeography -- Periodicals; Biodiversity -- Periodicals; Macroevolution -- Periodicals; 577
• DOI: 10.1111/geb.13376
• Languages: English
• ISSNs: 1466-822X

Abstract

Abstract: 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.