Figures Final Version

Landscape Ecology- DOI 10.1007/s10980-009-9438-5

Modelling and simulating mountain landscape reforestation using a socio-ecological framework- On-line Supplementary material

Annick Gibon1 • David Sheeren² • Claude Monteil² • Sylvie Ladet1 • Gérard Balent1

1 INRA, UMR 1201 DYNAFOR, BP 52627, 31326 Castanet Tolosan Cedex, France ² Université de Toulouse, INPT-ENSAT, UMR 1201 DYNAFOR, BP 32607, 31326 Castanet Tolosan Cedex

The appendix contains details about aspects of the study that could not be included in the paper for reasons of length.

1- Integrated landscape assessment

Ash-seed rain in the agricultural landscape Ash is an anemochorous species with high dispersal ability. From the map of all the old ash trees in the study area and the application of a circular buffer of 100 m around each tree (see the main body of the paper), all the study landscape appears to be concerned by potential ash-seed rain, except the lowest bottom units in the agricultural landscape (Fig. 1).

Fig. 1: Simulation of the territory under ash-seed rain in the Villelongue landscape (adapted from Julien 2006).

Legend: Each dot represents an individual tree as a source of seeds (a total of more than 3000 trees). The solid line represents the amalgamation of individual buffers simulating the seed dispersion area around each individual tree. Each buffer is an ellipsoid (100 m x 75 m) oriented according to the prevailing wind and the mountain slope. 2 – The first SMASH version

2.1. ABM structure and composition

Fig. 2. UML class diagram illustrating the main entities and relations in the SMASH model

AGENT RESOURCES

1 manage  1..* Management Actor entity

Indirect actor Direct actor 1..* Spatial Non-spatial entity entity

1 0..* Farmer Herd Batch Management Aggregate Cell 1..* strategy 1

Farming 1..* Cadastral 1..* Elementary Selective Retreat territory parcel parcel 1..* 1..* Farming Patrimonial Niche parcel mow  graze

lead 

Class Association Mutiplicity

Class name = concrete class = simple association 1 = only one = aggregation 0 ..1 = zero or one = composition 0 ..* = zero or more Class name = abstract class = inheritance 1 ..* = one or more

2. 2. Representation of the dynamics of the grassland system

Grass growth Grassland production at the parcel level used in SMASH is based on an empirical model of grass growth for Pyrenean grasslands established by Duru et al. (1998) and used by farmer advisory services in the region (Gibon et al., 1997). This model accounts for the variety of species in the grassland community and the plant nutrition conditions of the Pyrenean grasslands via three broad ‘grassland categories’, i.e. high, medium and low productivity grasslands (HP, MP and LP grasslands respectively). It enables estimation of grass growth as a function of average daily temperature, rainfall and potential evapotranspiration from a set of equations according to grassland category, soil, fertilisation practice and the grass production cycle. Due to mountain climate conditions, grass growth in the study area is considerably reduced in winter and was consequently not included in the model. To fit the grass growth model used in SMASH, we used a series of climate records from a local station of the French Meteorological Network (Ayros-Arbouix, Hautes-Pyrénées; data 1982-2004). We calculated an average daily value of grass growth for each 15 day-time step according to the grassland category and grass cycle, assuming a good soil water capacity and a balanced fertilisation regime. Figure 3 shows the resulting grass growth patterns over time for the first grass production cycle for a parcel at a 450-600 m altitude. The values for grass growth during 1rst cycle are adjusted for parcel altitude assuming there is 1°C decrease in daily average temperature for a 300 m increase of altitude, according to Duru et al. (1998). When an early light grazing is applied in meadows that are cut for hay, it is additionally assumed that the speed of growth during the following part of the 1st cycle is reduced by 10%. Grass production during the following production cycles is modelled in a similar way as a function of the parcel category and altitude, and the calendar period (e.g. for a MP grassland, 40 kg DM/ha/day until the end of June, 30 kg DM/ha/day from early July to the end of August, and then 20 kg DM/ha/day until mid-October). During these cycles, according to Duru et al.’s model, grass growth lasts for a maximum duration of two months, a break in grass growth being usually observed afterwards.

Fig. 3. Calibrated model of grassland production during the 1st grass production cycle as a function of grassland category for a parcel at 450-600m altitude (Cumulated grass production in Kg Dry Matter /ha)

0 Feb 1 Feb 2 Mar 1 Mar 2 Apr 1 Ap 2 May 1 May 2 Jun 1 Jun 2 Jul 1 Jul 2 Aug 1

HPG MPG LPG

Legend: HPG, MPG, LPG: high, medium and low productivity grasslands respectively.

Representation of management practices for each technical operation (TO) at the farm level and projected TO sequences at the farm parcel level As explained in the main body of the paper, the management practice for each TO type is represented in the form of a fixed calendar, which currently ignores most within-year adaptive rules. The hierarchical model established by Gibon et al. (1989) suggests representing the grassland management system at different hierarchical levels (Fig. 6 in the paper): (i) representing the links between the variety of grassland conditions and parcel management decision rules in the form of a set of a coupled grassland production category – projected TO sequence for each of the parcels in the system; (ii) representing the management practice for each TO at the farm level in the form of decision rules for their application in the parcel and year; (iii) creating a set of rules to ensure the overall organisation of each TO management practice and the projected TO sequences of every parcel at the whole farm level. The adaptive capacity of the system to face climate uncertainty relies on a nest of decision rules at each of these levels. In the first version of Smash, run in a fully deterministic way, the variety of grassland conditions and projected TO sequences is synthesized into broad ‘operational LU’ categories, which together account for the type of grassland productivity, the parcel altitude, and the projected TO sequence. Using this procedure, we distinguished nine categories of parcel for use in the simulation of the grassland system (Table 1). Operational categories are initialised in the ABM by attributing to each parcel an operational LU category based on its condition and TO sequence as collated in the field survey. A provisional calendar was then attributed to each type of grassland category for application of the TOs in the farming parcels during the course of the ABM simulation (Table 2).

Table 1. Representation of the variety of grassland conditions and provisional TO sequence at the farm parcel level in the form of ‘operational LU’ types in the first version of SMASH.

Grassland Parcel altitudinal Operational LU TO sequence productivity category category category Category P1 HP < 600 m MMM(G) P2 MP or LP < 600 m MG P3 MP < 600 m MMG HP From 600 to P4 g MMG(G) 1000m HP From 600 to P5 g M(M)G(G) 1000m MP From 600 to P6 g MG(G) 1000m MP or LP From 600 to P7 G(G)G 1000m P8 MP or LP > 1000m MG(G) P9 MP or LP > 1000m GGG

Legend: The sequence of letters in column 2 refers to the annual sequence of mowing (‘M’) and herd grazing operations ( ‘g‘ for early grazing of 1st cycle growth in meadows and ‘G’ for all other types). The grazing operations in brackets are optional and refer to adaptive behaviour to face climate uncertainty.

Table 2. ABM representation of the provisional calendar for TO in the parcel as a function of its ‘operational LU’ category.

Type of TO Provisional calendar for TOs Parcel sequence Jan. Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec. type year round 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

P1 MMM(G) M M M (G)

P2 MG M G

P3 MMG M M G

P4 g MMG(G) g M M G + (G)

P5 g M(M)G(G) g M (M) G + (G)

P6 g MG(G) g M G + (G)

P7 G(G)G G (G) G

P8 MG(G) M G + (G)

P9 GGG G G G 2.3. Sequence of simulated actions in a 1-year cycle

Table 3: Sequence of simulated actions in a 1-year cycle

Period in the Simulated actions annual cycle Beginning of  Each farmer defines his/her projected annual sequence of TOs according to his/her the year management strategy.  Dynamics variables are re-initialized (e.g. the quantity of fodder available on each parcel, the quantity of fodder removed by mowing and grazing over the year). Each basic 15-  The food requirements of each farmer’s herd are updated according to a fixed day step over Livestock Unit Grazing Day (LUGD), i.e. a unit of animal demand equal to a fixed the whole year number of Kg of Dry Matter (DM) per day.  The TOs of hay cutting and herd grazing are carried out according to the previously defined year-round action plan.  The quantity of fodder available on the grasslands is updated according to the grass production model. End of the year  The annual grazing pressure is computed for each elementary parcel that comprises the farming parcels.  LCC due to the reforestation process at the elementary parcel level and the LUC at the farm parcel level are assessed according to the set of transition rules.

2.4. Future representation of the impact of climate uncertainty on the grassland system Rules attached to adaptive management with regard to climate uncertainty are not yet included in the model. Climate uncertainty will be represented according to usual practice in ComMod models (e.g. Etienne 2003; Bah et al. 2006.): it will be simulated as stochastic variations in climate conditions between years for the simulated seasons, calibrated from a series of climate-years collated at the local meteorological station. Their impacts on the grassland systems will be simulated by means of changes applied in the values of some parameters according to the conditions in a given year (e.g. restriction of 10 or 20% of the growth rate for a ‘bad’ spring, or 20 % in the hay cutting speed for a ‘rainy’ summer).

References Bah A., Touré I., Le Page C., Ickowicz A, and Diop AT (2006) An agent-based model to understand the multiple uses of land and resources around drillings in Sahel. Math Comp Model 44:513-534 Balent G (1991) Construction of a reference frame for studying the changes in species composition in grassland. Opt Medit 15:73-81. Duru M, Balent G, Gibon A et al (1998) Fonctionnement et dynamique des prairies permanentes. Exemple des Pyrénées centrales. Fourrages 153:97-113 Etienne M (2003) SYLVOPAST: a multiple target role-playing game to assess negotiation processes in sylvopastoral management planning. http://jasss.soc.surrey.ac.uk/6/2/1.html Gibon A (ed.), 1997 Les prairies permanentes de milieu océanique et de montagne humide: Outils de diagnostic agro-écologique et guide pour leur utilisation (authors : Balent G, Duru M, Gibon A, Magda D, Theau JP). INRA Toulouse and Groupe Régional Fourrages Midi-Pyrénées Toulouse, France, 51 pp Gibon A, Lardon S, Rellier JP (1989) The heterogeneity of grassland fields as a limiting factor in the organization of forage systems. Development of a simulation tool of harvest management in the Central Pyrenees. INRA-Etudes et Recherches sur les Systèmes Agraires et le Développement 16:105-117 Julien MP, 2006. Processus de colonisation des prairies permanentes par le frêne (Fraxinus excelsior L.) et conséquences sur la biodiversité.PhD Thesis, Université Toulouse III - Paul Sabatier, France, 254 pp