  Variance partitioning methods, which are built upon multivariate statistics,
have been widely applied in different taxa and habitats in community ecology.
Here, I performed a literature review on the development and application of the
methods, and then discussed the limitation of available methods and the
difficulties involved in sampling schemes. The central goal of the work is then
to propose some potential practical methods that might help to overcome
different issues of traditional least-square-based regression modeling. A
variety of regression models has been considered for comparison. In initial
simulations, I identified that generalized additive model (GAM) has the highest
accuracy to predict variation components. Therefore, I argued that other
advanced regression techniques, including the GAM and related models, could be
utilized in variation partitioning for better quantifying the aggregation
scenarios of species distribution.
