Figure 4:
Tea mass loss and decomposition metrics showed little variation
in response to environmental factors. Points represent plot-level
averages of a) relative mass loss in the two litter types,b) decomposition rate constant k , and c)stabilisation factor S . Trend lines and ribbons show mean and
95% confidence intervals of predicted linear relationships,
respectively. Slope, intercept and p- values were extracted from
individual sub-models within the Structural Equation Model (see Figure
5).
Environmental variation explained only a small amount of variation in
our observed decomposition rate constant (k ) and stabilisation
rate (S ). However, we observed a slightly higher explanatory
power for microclimate (indirect vegetation/soil influence) compared to
plant trait or soil-related explanatory variables (direct
vegetation/soil influence; Figures 4b,c). Warmer plots had higherS (p < 0.01) and slightly (non-significant)
higher k (p = 0.08), and S was slightly lower in
moist soils (p = 0.15). However, neither leaf traits nor soil
characteristics explained variation in decomposition metrics (Figures
4b,c). Also, no spatial patterns were visible for either decomposition
variable (Figure S4).
Overall, our hypothesised system of topography, vegetation and soils,
and microclimate explained little variation in decomposition, reflected
in a low overall model fit of the SEM (Fisher’s C = 769.5,df = 106, p < 0.001; Table S3). Explanatory
power was highest for leaf economic traits (R2= 0.45) and soil parameters (pH / texture: R2 =
0.28; nutrients: R2 = 0.27;
GDD0: R2 = 0.38) but low for
decomposition variables (Green Tea mass loss:R2 = 0.16; Rooibos Tea mass loss:R2 = 0.05; k :R2 = 0.08; S :R2 = 0.16; Figure 5). Moreover, SEM
relationships between variables besides decomposition only partly
reflected our expectations (Figures 1,5). Elevation and solar radiation
were the most influential topographic predictors. Higher elevation
significantly predicted lower vegetation cover, more acquisitive leaf
traits, lower soil nutrient content, and lower soil temperatures (allp < 0.001), as well as higher silt and lower sand
content in the soil (p < 0.05). Higher solar radiation
was related to higher soil moisture (p < 0.01, with
solar radiation being highly negatively correlated with slope (Pearson’s
r = -0.93)), as well as to cooler soils, smaller plants, and more
acquisitive leaf traits (all p < 0.05). In addition,
higher TWI predicted higher soil pH and higher silt rather than sand
content (p < 0.01) and higher soil nutrient
concentration (p < 0.05). TPI did not predict any
variation in vegetation, soil or microclimate variables (Figure 5). Only
one of the tested relationships between vegetation and microclimate was
significant, as higher vegetation cover predicted lower soil
temperatures (p < 0.001). Soil texture did not predict
any variation in soil moisture (Figure 5).