Calculations and statistical analysis
The effect of mixing litter on each response variable was assessed by
computing, for each pair of replicates, the deviation between observed
(O) and expected (E) values: (O-E)/E. The relative mixture effect (RME;
Wardle et al., 1997) was calculated as the deviation between the values
in the mixture (O) and the average values of the two single species (E).
To detect any species-specific effects of the mixture, the relative
individual performance (RIP; Zhou et al., 2020) was calculated as the
deviation between the values of mass loss and fungal variables of a
species in the mixture (O) and the values of the same single species
(E). RME and RIP were considered significantly different from zero
(i.e., nonadditive) when the mean was bigger than its 95% confidence
limits (CL), i.e., when the 95% CL did not overlap 0 in the graphs,
synergistic if the deviation was positive or antagonistic if the
deviation was negative (Ball et al., 2008).
To provide a measure of the effect of the mixture on colonisation by
each of the biota associated with leaf litter, a ‘RME/RIP decomposers’
and a ‘RME detritivores’ was calculated as above, with the same weight
for each of the three fungal variables and of the three shredder
variables, respectively. The effect of the mixture across exposure
scenarios was assessed with a ‘global RME/RIP’, calculated with the same
weight for each of the three exposures to overcome the different number
of observations among exposure scenarios. To capture the effect of
mixing litter on the process of decomposition, defined as ‘all
biological processes contributing to organic matter mass loss and
transformation’ (Gessner et al., 2010), an integrated measure containing
the effects on mass loss and associated biota - ‘RME/RIP processing’ -
was calculated as above with the same weight for mass loss, decomposers,
and detritivores.
To examine all sources of variation of the values of the response
variables, a mixed model analysis of variance (GLM ANOVA) was used to
assess the effect of: 1) mixture [are observed values similar to
expected ones?]; 2) exposure scenario [are observed and expected
values similar among the three exposure scenarios?]; 3) interaction
1×2 [does the effect of mixture depend on exposure scenario?]. Due
to the different number of sampling occasions among exposures, ANOVA was
calculated with type III SS (Shaw & Mitchell-Olds, 2003). In case of a
significant interaction, one-way ANOVA was carried out as above to
detect the effect of the levels of each factor. Because the identity of
the sampling days varied among exposures, time was set as a random
factor. Tukey’s HSD test was used for pairwise comparisons after a
significant exposure scenario effect (Zar, 2010). All data was tested
for parametrical assumptions; heteroscedastic variables (Levene’s test)
were transformed with the square root (counts) or with the natural
logarithm (all other variables) (Zar, 2010). The statistical analyses
were performed with the software STATISTICA 13.0 with the level of
significance set at p =0.05.