The relative importance of deterministic processes versus chance is one of the most important questions in science. We analyze the success of variance partitioning methods used to explain variation in β-diversity and partition it into environmental, spatial, and spatially structured environmental components. We test the hypotheses that 1) the number of environmental descriptors in a study would be positively correlated with the percentage of β-diversity explained by the environment, and that the environment would explain more variation in β-diversity than spatial or shared factors in VP analyses, 2) increasing the complexity of environmental descriptors would help account for more of the total variation in β-diversity, and 3) studies based on functional groups would account for more of the total variation in β-diversity than studies based on taxonomic data. Results show that the amount of unexplained β-diversity is on average 65.6%. There was no evidence showing that the number of environmental descriptors, increased complexity of environmental descriptors, or utilizing functional diversity allowed researchers to account for more variation in β-diversity. We review the characteristics of studies that account for a large percentage of variation in β-diversity as well as explanations for studies that accounted for little variation in β-diversity.
College and Department
Life Sciences; Biology
BYU ScholarsArchive Citation
Lamb, Kevin Vieira, "Analyzing Metacommunity Models with Statistical Variance Partitioning: A Review and Meta-Analysis" (2020). Theses and Dissertations. 9248.
community ecology, variance partitioning, environmental, spatial, stochastic, deterministic, meta-analysis