Hierarchical partitioning (HP)
We conducted HP analyses to evaluate the relative importance of each
predictor together with the shared variation (Chevan & Sutherland,
1991). HP can overcome problems related to model-selection procedures,
which may fail to provide a valid means for ranking the relative
importance of the predictors (Whittingham et al., 2006; Mundry & Nuun,
2009). HP uses all combinations of predictors
(2N for N predictors) to determine the
amounts of variation explained by each predictor (Lai et al., 2022).
This is achieved by calculating the individual shared percentage of
variation explained by the model (semi-partialR 2) (Chevan & Sutherland, 1991). If a
predictor had a negative semi-partial R 2 due to
the strong and complex correlation among predictors (Peres-Neto et al.,
2006), we set the value to 0. We evaluated the statistical significance
in HP analyses based on 999 permutations (Lai et al., 2022). We applied
HP for each taxonomic group with only the relative measures (HPD, and
GDPc), only the absolute measures (HP and GDP), and both. We used the R
package rdacca.hp to estimate the relative importance of each
predictor in multivariate models through canonical analysis, without
limiting the number of predictors (Lai et al., 2022).