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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: [email protected]
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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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