Supporting Information
Figure S1 – Modeled maps of abiotic environmental variables and plant functional diversity.
Environmental variables: (a) soil water holding capacity (WHC, %), (b) nitrogen nutrition
index (NNI, %), (c) phosphorus nutrition index (PNI, %), (d) fertility index (FGI = 1/3.(2.NNI +
PNI). Community weighted mean traits (CWM): (e) vegetative height (VH, cm), (f) leaf dry
matter concentration (LDMC, mg.g-1), (g) leaf nitrogen concentration (LNC, mg.g -1), (h) leaf
phosphorus concentration (LPC, mg.g-1). Functional divergence (FD, dimensionless 0-1): (i)
vegetative height (VH), (j) leaf dry matter concentration (LDMC), (k) leaf nitrogen
concentration (LNC), (l) leaf phosphorus concentration (LPC).
Maps were generated by interpolating General Linear Models of environmental variables and
traits (Table 2) using the land use map (Figure 2) and the Digital Elevation Model.
Figure S1
a
b
c
d
e
f
g
h
i
j
k
l
Appendix – Comparison of models of ecosystem properties and ecosystem services
Direct and indirect effects of landuse and abiotic factors on ecosystem properties
Alternative general linear models of ecosystem properties are presented in Table 1.
Models of green biomass including abiotic components (LU+abiotic) and functional traits
(TRAIT+ABIOTIC) explained 10% more variability (adjusted-R) and were much more
parsimonious (AIC) than the pure landuse model (LU) (Table 1). The LU+abiotic and
trait+abiotic models predicted less strong effects than the pure land use model for cessation
of fertilization in terraces (average 20% overprediction by LU vs trait+abiotic) and for summer
grasslands (underpredicted by up to 30% by the LU vs trait+abiotic model) (Figure S2a).
Effects of cessation of mowing whether in terraces (decreased production) or in old
grasslands (increased production) were robust across models. The introduction of abiotic
variability (LU+abiotic model) increased contrasts related to altitude within each of the broad
ensembles (terraces and grasslands), especially through its positive effects on WHC and
produced intermediate predictions between the LU and trait+abiotic models (data not shown).
The trait-based model (trait+abiotic) of Crude Protein Content improved model adjustment by
20% for a similar parsimony to the pure land use model (LU) (Table 1). LU overestimated
fodder quality improvement by mowing in Festuca grasslands (by 50%) and by fertilization in
terraces (by 20%), while underestimating quality on steep slopes (Figure S2b).
The trait-based model (trait+abiotic) of litter mass was of comparable statistical value (to the
pure landuse model (LU; there was no improved abiotic model) and reduced contrasts due to
cessation of mowing in old grasslands (by ca. 50%) and terraces (by ca. 30%) (Figure S2c).
Litter at higher elevation (steep slopes and summer grasslands) was underpredicted by the
LU vs. trait+abiotic model.
Soil carbon models were overall poorly predictive (Table 1), especially the pure landuse
model (<10% variance explained). LU failed to reflect decreasing stocks with altitude,
especially in grasslands with lower production (mown permanent grasslands and summer
grasslands) (Figure S2d). Among these, as well as in fertilized terraces, the LU+ abiotic
model predicted 10-20% greater stocks than the trait-based model (trait+abiotic) (data not
shown), with both models of equal predictive value
Landscape patterns in ecosystem service provision
Patterns of agronomic value were comparable between the pure landuse and the trait-based
models, although effects of fertilization in terraces and cessation of mowing in Festuca
grasslands were reduced by 20-30% when including traits (Figure S3a-b).
Overall the trait-based and pure landuse models produced similar patterns of cultural value
as contrasts in diversity of flowering dates with increasing altitude were overwhelmed by
landuse-based variation in species diversity and litter mass (Figure S3c-d).
Patterns of regulation value were similar across the trait-based and pure landuse models, but
the trait-based model moderated the positive effects of fertilization in terraces and of mowing
in Festuca grasslands by 20% (Figure S3e-f).
Figure S2 – Comparison of modeled ecosystem properties using a landuse only model (LU)
vs. the full model including direct abiotic effects and indirect effects through plant functional
diversity (trait+abiotic) (see Table 3). The maps present differences between models and
highlight areas where the LU model provides lower (blue shades) or higher (red shades)
estimates than the trait+abiotic model. Results were interpreted by examining differences
across models in their predictions of the effects of key transitions such as cessation of
fertilization in mown terraces or cessation of mowing in terraces or in Festuca grasslands.
Ecosystem properties: (a) green biomass production (g.m-2), (b) fodder crude protein content
(g.kg-1), (c) litter mass (g.m-2), (d) soil carbon concentration (%).
a
c
b
d
Figure S3 – Modeled ecosystem services using a land use only model (LU – left column) vs.
the full model including direct abiotic effects and indirect effects through plant functional
diversity (trait+abiotic – right column). Ecosystem services: (a)-(b) agronomic value, (c)-(d)
cultural value, (e)-(f) total ecosystem service value.
a
b
c
d
e
f
Figure S4 – Results from the Principal Components Analysis on Ecosystem Properties.
Principal component analysis
-5
0
5
0.2
**
*
*
*
*
-0.1
*
15
Unfertilized
+ unmown
terraces
5
*
**
* **
Crude Protein Content
* ***
*
FD Flowering Onset
*
Unfertilized
+ mown
terraces
0
0.0
0.1
Grazed summer
grasslands
-0.2
Factor 2 (23% variance)
*
10
10
-10
Litter Mass
*
**
*
*
Plant
Diversity
*
*
Soil Carbon
Unmown
**
*
*
* *
Festuca
Mown Festuca
**
grasslands
grasslands
Fertilised + mown *
*
terraces
Green Biomass
**
-0.4
*
*
*
-0.2
*
0.0
0.2
Factor 1 (58% variance)
0.4
-5
-15