New robust weighted averaging- and model-based methods for assessing trait-environment relationships.
- Subject Areas
- Ecology, Statistics, Climate Change Biology
- community assembly, ecological community, environmental gradients, fourth-corner approach, Whittaker Siskiyou Mountains data, generalized linear mixed models, weighted averaging, hierarchical models, species niche centroid, Community Weighted Means
- © 2018 ter Braak
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. New robust weighted averaging- and model-based methods for assessing trait-environment relationships. PeerJ Preprints 6:e27439v1 https://doi.org/10.7287/peerj.preprints.27439v1
Statistical analysis of trait-environment association is challenging owing to the lack of a common observation unit: Community weighted mean regression (CWM) uses site points, multilevel models use species points, and the fourth corner correlation uses all species-site combinations. This situation invites the development of new methods capable of using all observation-levels. To this end, new multilevel and weighted averaging-based regression methods are proposed.
Compared to existing methods, the new multilevel method has additional site-related random effects that are unrelated to the observed environment; they represent the unknowns in the environment that interact with the trait. The new weighted averaging method combines site-level CWM with a species-level regression of Species Niche Centroids (SNC) on to the trait. The regressions are weighted by Hill's effective number (N2) of occurrences of each species and the N2 -diversity of a site, and are subsequently combined in a sequential test procedure known as the max-test.
Using the test statistics of these new methods, the permutation-based max test provides strong statistical evidence for trait-environment association in a plant community dataset, where existing methods show (very) weak evidence. The powers of the two new methods were similar in a simulation study based on this dataset.
Both methods can be extended i) to account for phylogeny and spatial autocorrelation and ii) to select functional traits and environmental variables from a greater set of variables.
This is a preprint submission to PeerJ Preprints. It has been submitted to a peer reviewed journal; it is in first version. Comments and suggestions are welcome to improve it.