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Phylogenetic community structure when competition and environmental filtering determine abundances
Author(s) -
Freilich Mara A.,
Connolly Sean R.
Publication year - 2015
Publication title -
global ecology and biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.164
H-Index - 152
eISSN - 1466-8238
pISSN - 1466-822X
DOI - 10.1111/geb.12367
Subject(s) - abundance (ecology) , competition (biology) , ecology , community , relative species abundance , taxon , phylogenetic tree , community structure , similarity (geometry) , null model , niche , biology , macroecology , species richness , ecosystem , computer science , biochemistry , artificial intelligence , image (mathematics) , gene
Aim Ecologists have long been concerned with understanding how local communities are assembled from the pool of species present in the broader biogeographical region. Although there has been considerable interest in the use of measures of species relatedness as tools to detect community assembly process, empirical and simulation studies have produced mixed results on the effectiveness of the technique. Here, we ask how well the most commonly used metrics of such community phylogenetic patterns detect the operation of filtering and competition in simulated communities where filtering and competition determine species abundances as well as co‐occurrence patterns. Location Simulated local communities assembled from a simulated regional species pool. Methods We simulate the evolution of niche traits for a regional species pool on a phylogeny, and then simulate the assembly of a local community from this regional species pool using a L otka– V olterra model. We then test whether the net relatedness index ( NRI ) or the nearest taxon index ( NTI ) can detect the assembly process. We compare the performance of abundance‐weighted ( NRI AW and NTI AW ) and occurrence‐based ( NRI O and NTI O ) versions of the metrics along a gradient of local community size as a percentage of the regional pool. Results We find that abundance weighting can substantially increase the power to detect assembly processes. Moreover, clustering and over‐dispersion are, in fact, most detectable when assembly processes act mainly on abundance rather than occurrence. Where they differed, NTI AW tended to outperform NRI AW at detecting limiting‐similarity competition. NRI AW outperformed NTI AW at detecting filtering except when filtering was very strong compared with limiting‐similarity competition. Main conclusions Our findings imply that phylogenetic information is more likely to yield information about community assembly when abundance information is incorporated, and local communities contain a relatively large fraction of the regional species pool.