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
Details
Serval ID
serval:BIB_C293D24D2ED3
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
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
Journal
Global Ecology and Biogeography
ISSN
1466-822X
1466-8238
1466-8238
Publication state
Published
Issued date
30/08/2021
Peer-reviewed
Oui
Editor
Schrodt Franziska
Volume
30
Number
11
Pages
2211-2228
Language
english
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.
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.
Keywords
Ecology, Ecology, Evolution, Behavior and Systematics, Global and Planetary Change
Web of science
Open Access
Yes
Funding(s)
Swiss National Science Foundation / CR23I2_162754
Create date
04/10/2021 10:36
Last modification date
26/10/2021 6:13