APPENDIX S1: Detailed methods of measurement and calculation for the growing season length,
the nutrient index and the disturbance intensity
1. Estimation of the growing season length (GSL)
This estimation was done at the scale of the whole of continental France (Borgy et al.
(submitted) give further details on this index and its estimation).
Monthly means of air temperature (°C) and monthly sums of rainfall (mm) are provided by the 1
km resolution gridded data set of MeteoFrance named AURELHY (Analyse Utilisant le RELief
pour l’HYdrométéorologie; Bénichou and Le Breton 1987). Potential Evapotranspiration (mm) is
calculated following the method of Piedallu & Gegout (2008). Soil water holding capacity –
which includes both gravitational and capillary water – is derived from the 1/1,000,000-scale
Soil Geographical Database of France, following the methodology of Le Bas et al. (1997) and
using the pedotransfer functions from Al Majou et al. (2008). A one bucket model is
implemented to estimate the dynamics of the Available Soil Water content (AW, m) (Laio et al.
2001) and to derive GSLtw.
All climate variables (i.e. Temperature, Rainfall and Evapotranspiration) are spatially
interpolated at the 5-km grid cell resolution and temporally interpolated at a daily time step. For
each pixel, the Available Soil Water content (AW) of day n equalled AW of day (n-1), plus
precipitation and minus potential evapotranspiration. AW was bound between 0 and the soil
holding water capacity. The model was run for 10 years with the same climate forcing to
estimate the yearly time course of AW. Growing Season Length (GSLtw) corresponded to the
number of days in the year for which (i) mean daily temperature was above 5°C (Kikuzawa &
1
Onoda 2013) and (ii) the ratio AW/soil holding water capacity was above 0.2. This was chosen
as an acceptable compromise for different types of soil (Laio et al. 2001).
2. Nutrient limitation indices
These indices were determined from above-ground live biomass data and chemical analyses
conducted on this biomass. As for the trait measurements, this biomass was harvested at the
spring peak of biomass production which was estimated to happen in each site when the
dominant species were in the peak of their flowering period. Assessing the nutrient index at
spring peak of biomass was a way to minimize the effect of other limiting factors for growth
(e.g. drought or other environmental stresses) than nutrient availability. Nitrogen and phosphorus
concentrations were determined from oven-dried (70oC) ground material (0.5 mm), all species
together. The nitrogen nutrition index (NNI; Eqn 1) was determined as the ratio between the
actual nitrogen concentration of the aboveground biomass determined by the Dumas method
using a C/N analyzer (Thermo, USA) and the critical concentration indicating the minimum N
content required for the maximum biomass production, as proposed by Lemaire & Gastal (1997):
NNI = (%Nmeasured / %Noptimum) * 100
eqn. 1
with %Noptimum = 4.8*(AGBmass)-0.32. The constant terms (4.8 and 0.32) to calculate %Noptimum
were proposed as general values to be used in such grasslands as they were found to be largely
similar among temperate C3 grasses and other C3 species, including legumes (Duru & Ducrocq
1997; Lemaire & Gastal 1997; Gastal et al. 2015). NNI thus represented a community-level (all
species were considered together) nitrogen limitation index and all values below 1 indicated an
actual nitrogen limitation for growth.
2
The phosphorus nutrition index (PNI) was similarly determined, calculating the ratio
between the actual Olsen phosphorus concentrate on of the aboveground biomass and the
minimum concentration allowing maximum growth. However, contrary to the case of nitrogen,
there is no general estimation of this optimal phosphorus concentration because this value
depends on the observed nitrogen concentration (Duru & Ducrocq 1997; Jouany et al. 2004).
Consequently, the optimal phosphorus concentration for biomass production of a vegetation
cannot be determined independently of its nitrogen concentration. We thus calculated the PNI
based on the measured aboveground nitrogen concentration (Eqn 2; Duru & Ducrocq 1997;
Jouany et al. 2004):
PNI = (%Pmeasured / %Poptimum) * 100
eqn. 2
with %Poptimum = 0.15 + 0.065*%Nmeasured. This relation between Poptimum and Nmeasured is linear
for the range of nitrogen and phosphorus concentrations observed in grasslands (Jouany et al.
2004). As for NNI, PNI represent a community-level phosphorus limitation index and all values
below 1 indicated an actual phosphorus limitation for growth. When legumes consist of more
than 20% of a communities biomass, PNI can be underestimated (Jouany et al. 2004) but this
situation never occurred in our communities. The nutrient limitation index (NI; Eqn 3) is a
synthetic variable composed of NNI and PNI, thus representing a more general, communitylevel, nutrient limitation index (Dumont et al. 2012):
NI 
2 NNI  PNI
3
eqn. 3
3
3. Disturbance intensity
The disturbance intensity (gg-1y-1; Eqn 4) was measured as the proportion of biomass
removed annually by grazing (Bgrazing) or mowing (Bmowing), relative to the total biomass
produced (Btotal).
Disturbance intensity 
Bgrazing  Bmowing
Btotal
eqn. 4
The total biomass production was estimated in every plot of each site where 3-5 cages of 0.25-1
m2 (the number and size of the cages varied from site to site) were randomly placed in each plot
to exclude grazing animals and avoid mowing. From these cages we measured the maximal
standing biomass produced and extrapolated this value to the remaining area of the plot. The
biomass removed by mowing (Bmowing), when applicable, could be directly measured. The
biomass removed by grazing animals (Bgrazing) necessitated the use of the following formula:
A
Bgrazing   ingi  Di
eqn. 5
i 1
Where A is the number of individual animal grazing on a given plot, Di is the number of days
where the individual i grazed and ingi is the estimated quantity of daily ingested biomass by the
individual i, depending on the type of animal (bovine, ovine or equine) and its weight (see INRA
2007 for the determination of ingi).
4
REFERENCES
Le Bas, C., King, D. & Daroussin, J. (1997) Geo-information for sustainable land management.
ITC-ISSS (ed Enschede), Pays-Bas.
Bénichou, P. & Le Breton, O. (1987) Prise en compte de la topographie pour la cartographie des
champs pluviométriques statistiques. La Météorologie, 7, 23–34.
Dumont, B., Rossignol, N., Loucougaray, G., Carrère, P., Chadoeuf, J., Fleurance, G., Bonis, A.,
Farruggia, A., Gaucherand, S., Ginane, C., Louault, F., Marion, B., Mesléard, F. &
Yavercovski, N. (2012) When does grazing generate stable vegetation patterns in temperate
patures? Agriculture, Ecosystems & Environment, 153, 50–56.
Duru, M. & Ducrocq, H. (1997) A nitrogen and phosphorus herbage nutrient index as a tool for
assessing the effect of N and P supply on the dry matter yield of permanent pastures.
Nutrient cycling in agroecosystems, 47, 59–69.
Gastal, F., Lemaire, G., Durand, J.-L. & Louarn, G. (2015) Quantifying crop responses to
nitrogen and avenues to improve nitrogen-use efficiency. Crop physiology, 2nd ed (eds
V.O. Sadras & D.F. Calderini), pp. 161–206. Academic Press, Oxford.
INRA. (2007) Alimentation Des Bovins, Ovins et Caprins (ed J Agabriel). Quae, Versailles.
Jouany, C., Cruz, P., Petibon, P. & Duru, M. (2004) Diagnosing phosphorus status of natural
grassland in the presence of white clover. European Journal of Agronomy, 21, 273–285.
Kikuzawa, K. & Onoda, Y. (2013) Mechanisms underlying global temperature‐related patterns
in leaf longevity. Global Ecology and Biogeography, 22, 982–993.
Laio, F., Porporato, A., Ridolfi, L. & Rodriguez-Iturbe, I. (2001) Plants in water-controlled
ecosystems: Active role in hydrologic processes and response to water stress: II.
Probabilistic soil moisture dynamics. Advances in Water Resources, 24, 707–723.
Lemaire, G. & Gastal, F. (1997) N uptake and distribution in plant canopies. Diagnosis of the
nitrogen status in the crops (ed G. Lemaire), pp. 3–43. Springer-Verlag, Berlin, DE.
Al Majou, H., Bruand, A., Duval, O., Le Bas, C. & Vautier, A. (2008) Prediction of soil water
retention properties after stratification by combining texture, bulk density and the type of
horizon. Soil Use and Management, 24, 383.
Piedallu, C. & Gegout, J. (2008) Efficient assessment of topographic solar radiation to improve
plant distribution models. Agricultural and Forest Meteorology, 148, 1696.
5