ELECTRONIC SUPPLEMENTARY MATERIAL 1. SUPPORTING INFORMATION ON THE

CURRENT MEDFIRE VERSION

MEDITERRANEAN FIRE REGIME EFFECTS ON PINE-OAK FOREST LANDSCAPE MOSAICS UNDER

GLOBAL CHANGE IN NE SPAIN

European Journal of Forest Research

Assu GIL-TENA1,* – Núria AQUILUÉ1, 2 – Andrea DUANE1 – Miquel DE CÁCERES1,3 – Lluís BROTONS1,3,4

1. CEMFOR – CTFC, InForest Joint Research Unit, Solsona, 25280, Spain.

2. Université du Québec à Montréal, Centre d'Étude de la Forêt, H2X 3Y7, Canada

3. CREAF, Cerdanyola del Vallés, 08193, Spain.

4. CSIC, Cerdanyola del Vallés, 08193, Spain

* Corresponding author: assu.gil@ctfc.es

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ELECTRONIC SUPPLEMENTARY MATERIAL 1. SUPPORTING INFORMATION ON THE

CURRENT MEDFIRE VERSION

We listed below the new parameterizations performed in the current MEDFIRE version as well as other

supporting information.

Fuel age initialization

In MEDFIRE, Fuel Age at 2000 is an integer variable that indicates the years since the last fire for recently

burnt forests and shrublands and the age of unburnt forests. Fuel Age is based on 1) fire perimeter availability

(1980-1999; gathered from the Forest Fire Prevention Service of the Government of Catalonia) for burnt areas

and 2) computed from tree species site index curves (growth models) through top height (mean height of the 100

thickest stems on a hectare) for unburnt forests. The third and second Spanish National Forest Inventory (NFI;

Ministerio de Medio Ambiente 2006), respectively carried out in 1989-1990 and 2000-2001, have been used to

calibrate and validate the forest age throughout Catalonia for the main tree species modelled in MEDFIRE. Age

data from NFI plots has been interpolated through ordinary kriging.

Mean site index curves (Table 1.1 and Fig. 1.1) were used to obtain forest age from top height of NFI plots. The

site index curves used were the most suitable for each species in the region according to the information

available (Bravo et al. 2012).

Table 1.1. Mean site index curves used to obtain forest age (t in years) from top height data (Ho in m). For each

species, reference ages and the consequent top heights and lifetimes were input data to obtain forest age

according to the NFI top height data.

Species Site index equation Reference

P. halepensis Ho=15.215 * (1-e-0.02040 * (t-1.046))1/1.046 Montero et al. (2001)

P. nigra Ho=t2/16.884 + t * [(60/14) – 0.033 * 60 –

(16.884/60) + 0.033 * t)]

Palahí and Grau (2003)

P. pinea Ho=e5.5618 + (ln(15)-5.5618) * (t/100)^-0.184601 Piqué (2003)

P. sylvestris Ho= t2/18.6269+ t * [(100/18.5) – 0.03119 *

100 – (18.6269/100) + 0.03119 t)]

Palahí et al. (2004)

Q. ilex/ Q. suber Ho=20.7216/(1-(1-20.7216/10) * (80/t)1.4486) Sánchez-González et al. (2007)

Other Quercus

(Q. faginea)

Ho=e3.094 + (ln(7)-3.094) * (t/50)^-0.562 López-Senespleda et al. (2007)

Other conifers

(P. pinaster)

Ho=e3.66418-40.65083 (1/t) García-Abejón and Gómez-

Loranca (1989)

Other trees

(Castenaea sativa)

Ho=21.602 * (1-e-0.519 * (t/10))(1/0.988) Cabrera (1997), modified by

Beltrán et al. (2013)

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Fig. 1.1 Calibrated site index curves obtained from the NFI top height data

For each species, ordinary block kriging was used to interpolate age data throughout the species range (Forest

Land Cover Type distributions in 2000 used in the MEDFIRE model). The kriging resolution was 100 m and,

for each species, variograms were previously adjusted depending on visual fit whereas minimising the sum of

square error. Forest age was replaced by time since last fire when forests were placed within recent fire

perimeters (available since 1980). The variograms and krigings were obtained by means of the automap package

(Hiemstra et al. 2009) implemented in R (http://www.r-project.org).

Fuel age is therefore a combination of forest age and time since fire for burnt forests and shrubs (Fig. 1.2).

Fig. 1.2 Fuel age in years in 2000

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Fire regime parameterization in climatically adverse and normal years

The classification of climatically adverse years in the period 1980-2000 as a function of cumulative soil water

deficit (CSWD) determined a new parameterization in the description of the Fire regime used in the Fire sub-

model. CSWD is the average between the cumulative soil water deficit of the current and preceding years and

was calculated from monthly climate data following a Thornthwaite-type approach (Thornthwaite and Mather,

1955) and accounting for the effects of slope and aspect on potential evapotranspiration and within-catchment

water redistribution. From the historical data, we computed the averaged annual CSWD for the entire study area.

A CSWD threshold of 270 mm classified climatically normal and adverse years in the 1980-2000 period (Fig.

1.3) and determined as climatically adverse years those with an annual area burnt greater than 25000 ha (i.e.

1986, 1994 and 1998, see also Regos et al. 2015).

Fig. 1.3 Classification of years as climatically normal or adverse as a function of CSWD values according to

historic data and climate projections until 2050 (A2 and B2 IPCC-SRES scenarios). The horizontal dashed line

marks the 270 mm threshold. Data from the years on the left of the vertical continuous line were used to

calibrate fire regime

Therefore, for climatically adverse and normal years two different distributions of annual area burnt (log normal

distribution) and fire size (power law distribution) were separately adjusted (Brotons et al. 2013). The

calibration was carried out using historical fire perimeters of fires larger than 50 ha (Table 1.2). Owing to the

stochastic behavior of the MEDFIRE model and the mathematical characteristics of the distribution of annual

area to burn (Table 1.2), we have defined the limits of the distributions to 100,000 ha, thus avoiding to have

unrealistic values of annual area to burnt.

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Table 1.2. Description of Fire regime parameters in the Fire sub-model. See more details on Fire sub-model

parameters in Brotons et al. (2013).

Variable Value Description

AnnualBurnDistNorm

AnnualBurnDistSevr

FireSizeDistNorm

FireSizeDistSevr

μ=7.92 σ=1.39

μ=9.35 σ=1.38

α=3.63 β=0.78

α=3.60 β=0.71

Distribution of annual area to burn for

normal and severe years

Distribution of fire sizes for normal and

severe years

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Probability of fire ignition

Local climate, land-use land-cover (LULC) spatial distribution, and human activities drive fire ignition in

Mediterranean landscapes (Moreira et al. 2011). A study over a 12-year period of the causality of fire ignition in

Catalonia reveled that human-related ignitions accounted for almost 80% of all the ignitions with an identified

cause (the proportion of ignitions with unknown cause was less than 12%) (González-Olabarria et al. 2012).

However, only a proportion of fire ignitions become fires large enough to scar the landscape.

Fire ignition probability and fire occurrence as functions of human-related and biophysical variables have been

modeled using a vast range of statistic methods depending on the model's goal and available data for model

fitting (Seidl et al. 2011). We aimed at building a spatially explicit predictive model of the probability of

human-caused wildfire occurrence in Catalonia based on biotic, abiotic, and human factors. We related fire

ignitions of fires whose area is greater than 50 ha to explanatory variables using a multivariate logistic

regression model.

The data set of fire ignitions (that gave rise to burnt extensions > 50 ha) occurred from 1987 to 2012 in

Catalonia (252 observations gathered from the Forest Fire Prevention Service of the Government of Catalonia)

was combined with the standard UTM 1×1 km grid. We defined the dependent variable of the logistic model as

the fire ignition occurrence by cell: 1 if at least there is an ignition within the cell and 0 otherwise. The subset of

1 km2 cells containing at least one ignition (250 cells) was completed with 5 time more cells of non-ignitions

randomly distributed over the space (Syphard et al. 2008). Regression models assume that the responses of

every single plot are independent. Although this assumption can hold in some cases, we acknowledge that the

spatial nature of the response variable suggest that neighboring plots may be influenced by the same factors at

different scales. In our case, values from 60% of the cells were randomly chosen for model fitting, while the

remaining 40% was reserved for independently testing the predictive capacity of the model (Cardille et al. 2001,

Martínez et al. 2009).

Abiotic, human related, and LULC composition formed the set of potential explanatory variables for the fire

occurrence probability model (Table 1.3). The Land Cover Map of Catalonia at 100 m of resolution was

reclassified in 4 categories: Urban, Natural (Forest and Scrub), Crops (Agriculture, Crops, and Grass), and

Others. Two additional categories were defined: urban–rural interface and agriculture-forest interface following

González-Olabarria et al. (2011). Several backward stepwise regressions with different combinations of the

predictor variables were computed and compared in terms of AIC and finally a consistent model was chosen

because its good prediction accuracy [Area Under the Curve (AUC) of a Receiver Operating Characteristic

greater than 0.77] and lack of multi-collinearity problems [Variation Inflation Factors and pair-wise

correlations]. All the regression terms in the model (Formula 1.1) were significant at p<0.05 but Natural cover.

The predictive model for the probability of fire ignition is:

Formula 1.1)

logit ( Pignition∨non−ignition )= 6.5 -0.28·Temp -0.0099·Precip + 0.00035·Highw + 0.00020·Road + 0.00054·Rail

+ 0.58·Natural + 2.95·UrbNat + 2.73·AgroForest + 0.099·Temp×Natural

The negative effect of temperature once accounted for the interaction (Figure 1.4.) is due to the spatial extent

considered to calibrate the model which encompassed the whole Catalonia and for instance areas where fires

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larger than 50 ha do not usually occur despite the high summer temperatures [e.g. western part of Catalonia

(Lleida plateau) devoted to agriculture; see Figure 1 in the manuscript]. In the MEDFIRE model, the fire

ignition probability is updated whenever the dynamic explicative variables change (all but Highw, Road and

Rail).

Fig. 1.4 Probability of ignition as a function of temperature once accounting for the significant interaction with

Natural cover and setting constant the other predictors in Formula 1.1. at mean values. Only the response in the

range of temperatures according to the presence of Natural cover was shown (black dots)

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Table 1.3. Potential explicative variables for the logistic model, their significance in the univariate models (+), and selected (*) by the backward stepwise regression.

Group Factor Description Units Source p value+ in model

Abiotic

Elev Elevation m Digital Elevation Model

(DEM; Catalan Cartographic

Institute)

0  

Inland Distance to the sea m 0  

Temp Mean summer maximum temperature C Digital Climatic Atlas of

Catalonia (DCAC; Ninyerola

et al. 2005)

0.065 *

Precip Accumulated mean spring and summer precipitation mm 0 *

RadSol Potential summer solar radiation 10 KJ/(m2*day*micrometer) 0.005  

River Density of rivers and streams km/km²

Topographic map of

Catalonia (Catalan

Cartographic Institute)

0.449  

Human presence

Highw Density of highways km/km² 0.028 *

Road Density of secondary roads km/km² 0 *

Path Density of rural paths km/km² 0.104  

Rail Density of railways km/km² 0.040 *

Elec Density of electric lines km/km² 0.005  

Protect Percentage of protected areas per 1 km2 UTM

Cartography of protected

areas 0.356

LULC

Urban Urban ≥ 80% -

Land Cover Map of Catalonia

(Ibàñez et al. 2002; Burriel et

al. 2005; Ibàñez et al. 2010)

0.354  

Natural Forest + Shrub ≥ 80% - 0.001 *

Crop Agriculture + Crop + Grass ≥ 80% - 0  

Others Others ≥ 40% - 0.132  

UrbNat Urban ≥ 20% & Natural > 30% - 0 *

UrbCrop Urban ≥ 20% & Crop > 30% 0.23

AgroForest Natural > 20% & Crop > 20% & Urban < 20% - 0.006 *

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Post-fire regeneration transition probabilities

Unlike Brotons et al. (2013) that considered different transition matrices according to bioclimatic regions, in this study

we used only one transition matrix for the entire study area (Table 1.4) based on Rodrigo et al. (2004) since post-fire

regeneration in MEDFIRE is now constrained by the presence before the fire of the tree species within 1 km radius.

Table 1.4. Post-fire transition probabilities for dynamic land cover types (in %). The probability of forest species to

remain the same or to change to another species after fire were rescaled from Rodrigo et al. (2004) for monospecific

stands.

Pre-fire \ Post-fire (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

(1) Pinus halepensis 82 0 0 0 0 5 5 0 0 8

(2) Pinus nigra 2 0 0 0 0 23 27 0 0 48

(3) Pinus pinea 0 0 2 0 12 0 0 9 0 77

(4) Pinus sylvestris 0 0 0 0 0 13 67 0 0 20

(5) Quercus suber 0 0 0 0 99 0 0 0 0 1

(6) Quercus ilex 3 0 0 0 0 71 6 0 0 20

(7) Other Quercus spp. 0 0 0 0 0 3 93 0 0 4

(8) Other conifers 0 0 0 0 10 0 0 73 0 17

(9) Other trees 0 0 0 0 0 0 0 0 100 0

(10) Shrubland 0 0 0 0 0 0 0 0 0 100

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Probability of afforestation

Annual probability of afforestation has been modelled as a function of shrubland age according to the time since last fire

or fuel age (Sturtevant et al. 2014), amount of neighboring forest in reproductive age, climate variables and topography

(Acácio et al. 2009) (Table 1.5). The 1993 and 2009 versions of the Land Cover Map of Catalonia (LCMC; Ibàñez et al.

2002; Burriel et al. 2005; Ibàñez et al. 2010) were used to model afforestation of unburnt shrubland among LCMC

versions.

Forest reproductive age was set to 15 years according to the main tree species in the Catalonia (Tapias et al. 2001).

Unburnt shrubland age was set to 20 years in 1989 (date in which the forest age was available) owing to the abundance of

resprouter species in Mediterranean EU which corresponds to an average fire recurrence interval of this range (Keeley

1986) and also matches fire return intervals in the Mediterranean EU (e.g. Portugal; Fernandes et al 2012).

Table 1.5. Data sources and details on the explicative variables for the logistic model assessing probability of

afforestation. Fire perimeters were gathered from the Forest Fire Prevention Service of the Government of Catalonia.

Factor Description Units Source

Slope º DEM

Temp Mean summer maximum temperature 0.1°C

DCACPrecip Accumulated mean spring and summer precipitation 0.1 mm

RadSol Potential summer solar radiation 10 KJ/(m2*day*micrometer)

ForNeigh Forest ≥15 years in a 150m radius

1993 LCMC and

Second NFI

(1989-1990)

TSFshrub Age of shrubland Years

1993 LCMC and

fire perimeters

We considered a minimum comparable resolution among the LCMC versions of 10 m and, therefore, afforestation was

modelled at this scale in the central 100 m pixels coincident with the MEDFIRE 100 m grid. Predictor variables were

obtained at 100 m resolution.

The model was calibrated with 60% of the dataset and tested non linear relationships of all variables and the interaction

between Temp and Precip. A backward stepwise regression was performed. Due to the local scale of the afforestation

process and the spatial resolution of MEDFIRE, only significant factors at p≤0.05 were retained in the final model

(Formula 1.2) and a fair model prediction accuracy was guaranteed (AUC of 0.72 in the validation dataset).

Formula 1.2)

logit (Pafforestation|non-afforestation) = -11.62 + 2.951·ForNeigh -0.9559·ForNeigh^2 + 0.081·Temp -0.00013·Temp^2 +

0.0015·Precip -0.000000068·Precip^2 -0.033·Slope + 0.00035·Slope ^2 -0.0039·RadSol +0.00000074·RadSol^2+

0.37·TSFshrub -0.011·TSFshrub^2 -0.0000033·Temp×Precip

Annual probability of afforestation was rescaled from Formula 2 following the time elapsed among LCMC (16 years).

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Acknowledgements

We thank the Forest Fire Prevention Service of the Government of Catalonia for providing data on fire perimeters and

ignitions. Miquel Ninyerola and Meritxell Batalla (UAB) generate spatially explicit climatic predictions from data

provided by the Spanish Meteorological Agency and the Spanish Ministry of Marine and Rural Environment within the

MONTES-Consolider project. We thank Mario Beltrán for his valuable help in age initialization.

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