Generalized Linear Models outperform commonly used canonical analysis in estimating spatial structure of presence/absence data.

ABSTRACT:

Background

Ecological communities tend to be spatially structured due to environmental gradients and/or spatially contagious processes such as growth, dispersion and species interactions. Data transformation followed by usage of algorithms such as Redundancy Analysis (RDA) is a fairly common approach in studies searching for spatial structure in ecological communities, despite recent suggestions advocating the use of Generalized Linear Models (GLMs). Here, we compared the performance of GLMs and RDA in describing spatial structure in ecological community composition data. We simulated realistic presence/absence data typical of many β-diversity studies. For model selection we used standard methods commonly used in most studies involving RDA and GLMs.

Methods

We simulat

SUBMITTER: Carlos-Junior LA 

PROVIDER: S-EPMC7474884 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature