Drivers of forest fire occurrence in the cultural landscape of Central Europe

Abstract

Context
Wildfires in temperate Central Europe have traditionally been perceived as a mere consequence of human activity without any relevance to natural forest development, despite their documented frequent occurrence. As a result, knowledge about local fire ecology and patterns of wildfire occurrence in the landscape is lacking.

Objectives
We aimed to reveal the factors influencing the spatial distribution of forest fires in the Czech Republic as a model area for the broader region. Specifically, we aimed to (1) find out which factors influence the occurrence and frequency of the forest fires at the country scale and in a selected fire-prone region; (2) examine the relationship of lightning strikes and their polarity with wildfire incidence; (3) identify the conditions determining areas with naturally driven fire-prone conditions.

Methods
We took data on 15,985 wildfire records and explored their spatial distribution using GIS layers of human, topographic, climatic and vegetation composition factors. We analysed the data using GLM and hierarchical partitioning methods.

Results
Wildfire occurrence was controlled mostly by environmental factors whereas wildfire frequency was strongly driven by human factors. In the selected fire-prone region, the effect of environmental factors was even more pronounced and wildfire frequency was also driven, albeit marginally, by lightning strikes of positive polarity.

Conclusion
The pattern of wildfire occurrence in the Czech Republic was similar also to those from regions where wildfire is considered a natural part of local ecosystems. We identified the areas with natural fire-prone conditions which probably led to the development of local fire-adapted ecosystems.

Full text

RESEARCH ARTICLE
Drivers of forest fire occurrence in the cultural landscape
of Central Europe
Martin Ada
´mek .Zuzana Jankovska
´.Ve
ˇroslava Hadincova
´.
Emanuel Kula .Jan Wild
Received: 15 January 2018 / Accepted: 5 September 2018
ÓSpringer Nature B.V. 2018
Abstract
Context Wildfires in temperate Central Europe have
traditionally been perceived as a mere consequence of
human activity without any relevance to natural forest
development, despite their documented frequent
occurrence. As a result, knowledge about local fire
ecology and patterns of wildfire occurrence in the
landscape is lacking.
Objectives We aimed to reveal the factors influenc-
ing the spatial distribution of forest fires in the Czech
Republic as a model area for the broader region.
Specifically, we aimed to (1) find out which factors
influence the occurrence and frequency of the forest
fires at the country scale and in a selected fire-prone
region; (2) examine the relationship of lightning
strikes and their polarity with wildfire incidence; (3)
identify the conditions determining areas with natu-
rally driven fire-prone conditions.
Methods We took data on 15,985 wildfire records
and explored their spatial distribution using GIS layers
of human, topographic, climatic and vegetation com-
position factors. We analysed the data using GLM and
hierarchical partitioning methods.
Results Wildfire occurrence was controlled mostly
by environmental factors whereas wildfire frequency
was strongly driven by human factors. In the selected
fire-prone region, the effect of environmental factors
was even more pronounced and wildfire frequency
was also driven, albeit marginally, by lightning strikes
of positive polarity.
Conclusion The pattern of wildfire occurrence in the
Czech Republic was similar also to those from regions
Electronic supplementary material The online version of
this article (https://doi.org/10.1007/s10980-018-0712-2) con-
tains supplementary material, which is available to authorized
users.
M. Ada
´mek (&)V. Hadincova
´J. Wild
Institute of Botany, The Czech Academy of Sciences,
Za
´mek 1, 252 43 Pru
˚honice, Czech Republic
e-mail: martin.adamek@ibot.cas.cz
V. Hadincova
´
e-mail: veroslava.hadincova@ibot.cas.cz
J. Wild
e-mail: jan.wild@ibot.cas.cz
Z. Jankovska
´
The Forest Management Institute, Na
´br
ˇez
ˇnı
´1326,
250 01 Brandy
´s nad Labem, Czech Republic
e-mail: jankovska.zuzana@uhul.cz
E. Kula
Department of Forest Protection and Game Management,
Faculty of Forestry and Wood Technology, Zeme
ˇde
ˇlska
´3,
61300 Brno, Czech Republic
e-mail: kula@mendelu.cz
J. Wild
Faculty of Environmental Sciences, Czech University of
Life Sciences, Kamy
´cka
´129, 165 21 Prague, Czech
Republic
123
Landscape Ecol
https://doi.org/10.1007/s10980-018-0712-2(0123456789().,-volV)(0123456789().,-volV)

where wildfire is considered a natural part of local
ecosystems. We identified the areas with natural fire-
prone conditions which probably led to the develop-
ment of local fire-adapted ecosystems.
Keywords Wildfire Spatial pattern Temperate 
Pinus sylvestris Lightning strikes Polarity
Introduction
Fire is an important disturbance factor shaping forest
vegetation worldwide (Engelmark 1993; Skre et al.
1998; Pausas and Vallejo 1999; Podur et al. 2003). On
the Northern Hemisphere, fire is generally supposed to
be an integral part of natural dynamics of boreal
forests and Mediterranean ecosystems. In temperate
regions of Central Europe, by contrast, the role of fire
in the functioning of local forest ecosystems has been
traditionally marginalized, and wildfires have been
perceived just as harmful consequence of human
activity without any relevance to natural processes
(Clark and Merkt 1989; Ellenberg 1996; Tinner et al.
2005; Niklasson et al. 2010).
The reasons for such attitude to wildfire in Central
European forests can be relatively dense human
population and long-lasting land-use associated with
strong influence of natural vegetation and processes,
including the long tradition of fire suppression,
compared to e.g. Northern America or boreal Eurasia
(Angelstam and Kuuluvainen 2004). Another reason
can lie in typical fire behaviour, related to fire
adaptations of dominant tree species in temperate
and boreal Eurasia, which is represented by low
intensity surface fires (Rogers et al. 2015). These fires
are relatively easy to suppress and there is no need to
implement special prevention management such as
prescribed burning to reduce fuels. That is why this
topic had long been neglected by ecological studies
from this region, including the analysis of factors
influencing the wildfire incidence in the landscape.
However, wildfires have recently been recognized as
an important factor also in temperate regions of
Europe, namely in Alps and Carpathians (Delarze
et al. 1992; Tinner et al. 1999; Stahli et al. 2006;
Mu
¨ller et al. 2013; Valese et al. 2014; Feurdean et al.
2017) and in Pinus sylvestris forests of Lithuania,
Poland and the Czech Republic (Marozas et al. 2007;
Niklasson et al. 2010; Ada
´mek et al. 2015; Zin et al.
2015). Further investigations on the connection of fire
with local ecosystems are thus needed.
Lightning strikes are a major natural cause of
wildfires (Pyne et al. 1996). The proportion of
lighting-ignited fires varies substantially worldwide.
In the period of 2006–2010, lightning strikes ignited
7.3% of forest fires in Northern Europe, 0.5% in
Central Europe and 4.7% in Southern Europe, whereas
in Canada and the USA, the proportion of naturally
caused fires is about 48% (Cardille and Ventura 2001;
Ganteaume et al. 2013). This proportion, however,
depends on the local population density, as lightning-
caused fires prevail in remote areas with low popula-
tions (e.g. Flannigan et al. 2000). Different perspective
can be provided by comparing the spatial frequency of
lightning-ignited forest fires, which in some parts of
the USA and in European Alpine regions reaches up to
0.9 fires/(year 100 km
2
). In Mediterranean Europe, the
average density has been estimated to be 0.12 fires/
(year 100 km
2
). In the Czech Republic, the area of our
study, the average wildfire density (0.065 fires/(year
100 km
2
), is slightly lower than in W Siberia (0.075)
but slightly higher than in Northern European boreal
countries (0.039) (Granstro
¨m1993; Larjavaara et al.
2005; Kula and Jankovska
´2013;Mu
¨ller et al. 2013),
even though in the Czech Republic, there is a higher
proportion of mixed and broadleaved forests, which
are considered less fire-prone than coniferous forests
(Clark and Royall 1996; Moreira et al. 2001).
It is generally accepted that most lightning ignitions
in forests are caused by lightning with long continuing
current of returning strokes. However, because of poor
detection of this quality by current lightning-detection
systems, other related lightning characteristics like
stroke polarity, stroke multiplicity or current strength
are used instead (Pineda et al. 2014). Highly discussed
is the role of stroke polarity. Lightning strikes of
positive polarity are traditionally considered to ignite
fires more likely than negative ones, due to higher
current amplitudes, greater probability of a long
continuing current and less accompanying precipita-
tion (Flannigan and Wotton 1991; Larjavaara et al.
2005). However, the real importance of positive
lightnings on forest fire occurrence still remains
unclear, while recent studies provided contradicting
results on this topic (Larjavaara et al. 2005; Wotton
and Martell 2005; Pineda et al. 2014;Mu
¨ller and
Vacik 2017).
123
Landscape Ecol

Although forest fires are a natural phenomenon,
their main cause in populated landscapes is human
activity. In Europe, 97% of forest fires of known cause
within the period of 2006–2010 were directly or
indirectly caused by humans (Ganteaume et al. 2013).
Moreover, palaeoecological and fire-history studies
suggest that during the Holocene period, the frequency
of fire events was markedly positively influenced by
human presence in the landscape (Niklasson and
Granstro
¨m2000; Vannie
`re et al. 2008; Molinari et al.
2013; Bobek et al. 2017). Equally, the overwhelming
majority of recent forest fires in the Czech Republic is
caused by humans.
Besides ignition triggers, the distribution of wild-
fires in the landscape is also influenced by environ-
mental factors of both anthropogenic and natural
origin (Cardille and Ventura 2001; Yang et al. 2007;
Avila-Flores et al. 2010). Thus, human presence in the
landscape usually acts as an ignition trigger, while
environmental factors influence wildfire probability.
Such factors can be biotic, such as the vegetation cover
influencing the fuel type, load and inflammability, or
abiotic, such as the climate, topography or soil type
influencing fuel moisture and spreading of the fire
(Engelmark 1993; Cardille and Ventura 2001;Dı
´az-
Delgado et al. 2004). Anthropogenic factors influenc-
ing fire occurrence can be socio-economic, such as
population density or the rate of unemployment, as
well as socio-environmental, such as land use (Mor-
eira et al. 2001; Ganteaume et al. 2013). However, the
effect of all these factors on fire incidence varies
among habitat types and depends on the temporal and
spatial scale, as, for example, climatic variables
usually operate on a broader than regional scale (Yang
et al. 2007; Avila-Flores et al. 2010; Miranda et al.
2012).
Since the Central European landscape is influenced
by long-term human presence, human-ignited fires
could be an important factor shaping forest vegetation
throughout the Holocene period (Tinner et al. 2005).
Stable natural conditions increasing the fire-proneness
of a locality can promote the development of specific
fire-adapted vegetation even in temperate landscapes
(Ada
´mek et al. 2015). Knowledge of how the
environment affects patterns of wildfire occurrence
is therefore important for understanding the processes
influencing the development of the Central European
landscape. Moreover, this knowledge can be useful for
the purposes of nature conservation and for fire
prevention planning, especially at present, when the
fire risk in Europe is rising due to climate change
(Lindner et al. 2010).
In this study, we aimed to fill in the knowledge gap
in global fire ecology by revealing the rules of the
wildfire occurrence in the cultural landscape in humid
temperate climate, characterized by a long-term and
relatively dense human presence. As we are aware, it is
the first quantitative investigation of the influence of
human, biotic and abiotic factors on the spatial
distribution of recent forest wildfires in the Central
European region. Specifically, we aimed to find out:
(a) which factors influence the occurrence and
frequency of the forest fires on the country scale and
in a selected fire-prone region; (b) what is the role of
lightning strikes and their polarity in wildfire occur-
rence; and (c) which conditions determine naturally
fire-prone areas in the landscape.
Methods
Study area
We aimed to reveal the drivers of the occurrence of
forest fires operating at different geographical scales.
We therefore selected two model areas. The large
(country) scale was represented by the Czech Republic
(78,866 km
2
) situated in Central Europe, in the middle
of the temperate zone of the Northern Hemisphere
(Fig. 1). Its climate is mild with four seasons,
transitional between oceanic and continental, and
characterized by prevailing western winds, intensive
cyclonal activity and relative high precipitation. The
average temperature varies between ca. – 3 °C (Jan-
uary) and 17 °C (July), and average annual precipita-
tion across more than 60% of the country’s area is
600–800 mm (Tolasz 2007). The climate is, however,
considerably influenced by the relatively rapidly
changing elevation and relief. The elevation ranges
from 115 to 1603 m a.s.l. with a median of 430 m
a.s.l.. The prevailing relief type are hills and high-
lands. The average population density is 133 persons/
km
2
.
The naturally dominant vegetation formation in the
Czech Republic are mixed beech-fir forests transition-
ing towards broadleaved oak-dominated forest in the
lowlands and towards coniferous spruce-dominated
forests at higher altitudes (Chytry
´2012). However, as
123
Landscape Ecol

a result of intensive forestry management, practised
since the 19th century, the present forest composition
differs markedly from the natural state. Forests at
present cover 33.9% of the country and are mainly
composed of Picea abies (52%), Pinus sylvestris
(17%), Fagus sylvatica (7%), Quercus spp. (7%),
Larix decidua (4%), Betula pendula (3%), Abies alba
(1%). Other broad-leaved species (e.g. Carpinus
betulus,Acer spp., Fraxinus spp., Populus spp., Salix
spp., Tilia spp.) occupy ca. 8% of the forested area
(www.uhul.cz).
About 90% of the area of the Czech forests is
represented by commercial forests with intensive
forestry management, managed mostly by clear-cut-
ting system. More than 60% of the forested area is
state-owned (www.mzp.cz). The main threats for the
commercial forestry in the Czech Republic, in the
sense of the volume of salvage logging, are abiotic
factors, mainly the windstorms ([50%), followed by
droughts (5–13%), snow (2–12%) and frost (1–12%).
Biotic factors, mostly the insect outbreaks contribute
with 10–27%. Wildfire plays relatively minor role
with 1–7% of the volume of salvage logging (Ry-
chtecka
´and Urbancova
´2008) without greater socio-
economic and ecologic impact. It is related with rel-
atively efficient system of fire detection and suppres-
sion where all wildfires are suppressed. The use of fire
as a management tool in forests is restricted only to the
burning of harvest residues on clearcut areas while
Fig. 1 Localization of the study area in the European context
(country scale) and within the Czech Republic (selected region).
Maps show the number of wildfires/100 km
2
of forested area per
year and values of important drivers of wildfire occurrence in
municipality cadastres: Pinus sylvestris abundance, Mean
altitude; Population density
123
Landscape Ecol

prescribed fires to reduce forest fuels are not allowed
(Albers 2012).
In the period of 1992–2004, the average number of
forest fires in the country per year was 1230, with a
mean burned area of 0.49 ha/fire, median area of
0.025 ha/fire and the largest burned area of
400 ha/fire. The causes of fire were: unexplained
(29.9%), human-caused—mostly fire raising, smoking
and forestry management (68.7%), and lightning strike
(1.4%). The absolutely prevailing type of the fires was
ground fire (Kula and Jankovska
´2013). Present-day
climate-change scenarios for Central Europe predict
increasing frequency of droughts and wildfires (Lind-
ner et al. 2010; Trnka et al. 2015).
The regional scale was represented by an area of
4925 km
2
, located in the NW part of the Czech
Republic (Fig. 1). It was chosen due to its character-
istic and various natural conditions and markedly
frequent occurrence of wildfires (Kula and Jankovska
´
2013). We focused on this specific region to test the
drivers of wildfire occurrence on a narrower geo-
graphical scale. Such conditions enabled us to test the
role of lightnings as a potential natural ignition trigger
on finer scale, with regard to stroke polarity, using
precise data on the frequency of cloud-to-ground
lightning strikes. The region is characterized by a
relatively high forest cover with preserved natural and
semi-natural forests since the main part of the area is
situated in natural protected areas, including, for
example, Bohemian Switzerland National Park. The
topography and geology of the region is very diverse,
encompassing tertiary volcanic hills, quartzite moun-
tain ranges, sandstone rocky areas, river valleys and
tablelands. The elevation ranges between 115 m a.s.l.
(Elbe river valley) and 1012 m a.s.l. (Jes
ˇte
ˇd moun-
tain). A large part of the region is characterized by
sandstone bedrock with a typical rugged relief (‘‘rock
towns’’) with Pinus sylvestris as the dominant tree
species, forming there so-called ‘‘relic pinewoods’’.
Forests on volcanic bedrock are mainly composed of
broadleaved tree species such as Fagus sylvatica and
Quercus spp. Other parts of the regions are covered
mainly by forests dominated by Picea abies. The
semi-natural coniferous forests of this region have
recently been recognized as an extrazonal lowland
taiga that has probably been shaped by recurrent
wildfires over millennia (Chytry
´2012; Nova
´k et al.
2012; Ada
´mek et al. 2015).
Data on forest fire occurrence
In our analyses, we used a database of 15,985 forest
fires that occurred in the Czech Republic in the period
of 1992–2004. The dataset originated from the
administrative central evidence of fires of the General
Directorate of Fire Rescue Service of the Czech
Republic (GR
ˇHSZ C
ˇR). The data on forest fires in the
central evidence originate from forest owners whose
duty is to send the report of wildfires to the central
evidence every year. The database contains the records
of all events considered as wildfires by forest owners,
with burned area ranging from \1m
2
to 400 ha, with
the mean size of 0.49 ha and median size 0.025 ha.
The database was subsequently manually verified to
exclude non-forest fires (Kula and Jankovska
´2013).
All fire records were localized into 3474 cadastres
(corresponding to LAU2 units of the Nomenclature of
Territorial Units for Statistics of European Union)
from the total of 6251 existing in the Czech Republic.
Prior to our analyses, we excluded cadastres without
forest cover, military areas due to missing or inaccu-
rate data and the two largest cities, Prague and Brno.
The final dataset thus included 6097 cadastres on the
country scale and their subset of 330 on the regional
scale. The area of the cadastres analysed ranged from
0.25 to 214.9 km
2
(mean 12.4 km
2
, median 8.1 km
2
,
SD 13.6). Fire counts per cadastre over the period of
1992–2004, further referred as fire frequency ranged
from 0 to 191 (mean 2.5, median 1, SD 7).
Fire occurrence predictors
We computed the values of particular factors used to
explain the occurrence of wildfires in each cadastre
polygon using ArcGIS 10.1. software (www.esri.
com). For a complete list of factors used in our anal-
yses, see Table 1. The data source for human factors
such as the population density and the number of
accommodation facilities (a proxy for the rate of
tourism) in cadastres was the Czech Statistical Office
(www.csu.cz). Distance from the nearest city was
computed as the distance from the nearest settlement
with more than 50,000 inhabitants. The mean precip-
itation and temperature figures for each municipality
for the period 1992–2004 were computed from grid
data (cell size 500 m) on mean annual temperature and
sum of annual precipitation provided by the Czech
Hydrometeorological Office (CHMI; www.chmi.cz).
123
Landscape Ecol

The data on lightning strikes frequency came from the
Central European Lightning Detection Network
(CELDN) provided by the CHMI. The number of
cloud-to-ground (CG) lightning strikes, a potential
ignition trigger, was calculated from annual sums for
the period of 2002–2009. The only available data for
the whole country were the sums of all CG lightnings
in 77 districts of the Czech Republic. For the region of
the NW Czech Republic, we used sums of lightnings in
cadastres from the same period computed from grid-
ded data (1 91 km), where lightnings were divided
according to their polarity into negative (CG-) and
positive (CG?) lightnings. We used these categories
as separated factors because CG?lightnings are
claimed to be a stronger source of the wildfire ignition
than the more frequent CG-lightnings (Latham and
Williams 2001), although this is still disputed (e.g.
Flannigan and Wotton 1991; Nauslar 2014). Topo-
graphic factors such as mean altitude and the
ruggedness index (Riley et al. 1999) of each cadastre
were computed from the LiDAR Digital Elevation
Model (DEM) of the Czech Republic provided by the
Table 1 Factors used as
fire predictors and their
descriptive statistics
a
Factors excluded from
GLM analyses due to
collinearity
b
Betula pendula, B.
pubescens
c
Populus nigra, P. alba, P.
x canadensis
d
Tilia cordata, T.
platyphyllos, T. x vulgaris
Factors Country scale Regional scale
Min. Max. Mean SD Min. Max. Mean SD
Anthropic
Population (per km
2
) 0.9 2362 91.4 138 5.2 1486 94.6 153.8
N. of accomodation
(per km
2
)
0 28.1 0.08 0.4 0 2.3 0.1 0.2
Distance from city (km) 0 75 22.2 13.6 0 49.7 15.1 11.2
Climatic
Precipitation (mm) 434.8 1355 660.7 125 434.8 1090 718 125.3
Temperature (°C)
a
4.1 10 8.3 0.9 6.5 9.6 8.2 0.6
N. of all lightnings
(per km
2
)
17.2 51.2 27.6 5.3 NA NA NA NA
N. of CG-lightnings
(per km
2
)
NA NA NA NA 0 32.9 15.6 4.3
N. of CG?lightnings
(per km
2
)
NA NA NA NA 0 13.1 4.8 1.9
Topographic and soil
Altitude (m a.s.l.) 146.2 1145 412.4 144 146.2 606.7 348.1 84.1
Ruggednes index 0.3 22.4 4.7 2.9 1.1 22.4 6.9 3.3
Available water capacity 0.069 0.116 0.094 0.006 0.073 0.114 0.094 0.007
Forest composition (%)
Abies alba 0 33.1 0.5 1.2 0 0.8 0.1 0.1
Betula spp.
b
0 85 3.4 4.5 0 36.7 6 4.2
Carpinus betulus 0 47.2 1.9 3.8 0 22.8 2.2 3.3
Fagus sylvatica 0 65.4 3.5 6 0 57.5 5 6.1
Larix decidua 0 40 3.6 3.8 0 15.1 3.1 2.5
Picea abies 0 100 32.4 25.6 0 70.4 23.9 17.8
Pinus sylvestris 0 92.1 18 18.3 0 76 19.6 19.1
Populus spp.
c
0 87 2.2 7.1 0 80 1 4.9
Populus tremula 0 26 0.5 1.2 0 5.7 0.6 0.7
Quercus petraea 0 74.8 2.7 7.2 0 54.9 3.5 8.1
Quercus robur 0 100 9 12.1 0 76 12.6 12
Robinia pseudacacia 0 100 3.4 10.6 0 42.3 2.7 6.8
Tilia spp.
d
0 83.3 2.1 4.2 0 20 1.8 2.9
Forested area (ha) 0.25 167 9.1 11.4 0.25 167 10.9 14.5
123
Landscape Ecol

Czech Office for Surveying, Mapping and Cadaster in
the form of the DMR 4G service, resampled to a grid
cell size of 20 m. The available water capacity of the
soil was computed from grid data of 500 m cell size
downloaded from European Soil Data Centre
(ESDAC) (http://esdac.jrc.ec.europa.eu), where it was
derived as the difference between the -33 kPa and
the -1500 kPa water content (expressed as volume
fraction) (Ballabio et al. 2016). The percentage
abundance of particular tree species in the forested
area of each cadastre was computed from grid data of
500 m cell size, provided by the Czech Forest Man-
agement Office (www.uhul.cz).
Data analyses
We performed our analyses on two geographical
scales to compare the drivers of fire occurrence for the
whole country and for a region selected for its specific
natural conditions. For the regional scale, we used
more precise lightning data divided into two factors
(CG-and CG?), summed for each cadastre. For both
spatial scales, we performed analyses with two dataset
types: (a) fire presence/absence data to reveal the
general pattern of fire occurrence and (b) fire counts [
0 data to reveal factors influencing the fire frequency.
The idea of this way of analysis is that one process is
causing the absence of fire, and at those sites where fire
is present, there is a second process influencing the
number of fires (Zuur et al. 2009). To check the
robustness of the analysis, we performed additional
analysis of the fire frequency including zero values.
On the country scale, we finally included 6097
cadastres using presence/absence data and 3461
cadastres using the fire counts data. In the regional-
scale analyses, we included 330 and 218 cadastres for
presence/absence and frequency data, respectively.
When the correlation of two factors exceeded the
arbitrary threshold of Spearman’s r
S
= 0.7, we only
retained the better interpretable factor for further
analyses. We thus excluded altitude from the country
scale analyses, which was critically correlated with
temperature and Picea abies abundance, and temper-
ature from both country scale and regional analyses,
which was critically correlated with precipitation. The
presence/absence data were analysed using general-
ized linear models (GLM) with binomial distribution
of errors; for counts data, we used GLM with a quasi-
Poisson distribution to account for overdispersion. In
the analyses, we incorporated all factors, including the
size of forested area [ha] as a covariable to be filtered
out. For all four analyses, we subsequently produced a
minimal adequate model containing all significant
factors and we computed pseudo-R
2
of particular
models to provide the proportion of explained vari-
ance of the model, using the most common approach:
(null deviance—residual deviance)/null deviance
(Zuur et al. 2009). We subsequently plotted residuals
of the model into the map of cadasters to check our
results for spatial autocorrelation.
To compare the relative importance of significant
factors (percentage of explained variance), we used
the hierarchical partitioning method using the R
package hier.part (Mac Nally and Walsh 2004). For
this comparison, we used a maximum of nine signif-
icant factors with the highest z/t values from each
analysis due to the inaccuracy of the hier.part method
with [9 factors included (Olea et al. 2010). The
significance of these factors was tested using the
randomization test method (rand.test) of the hier.part
package.
Within the selected NW region, we examined the
relationship of the frequency of CG-and CG?light-
nings with altitude and precipitation, using linear
regression. We additionally visualized the total num-
ber of fires in cadastres classified by the dominant tree
species (dominating and with abundance C30%),
related to the area of forest dominated by the given tree
species (number of fires/100 km
2
of forest). Similarly,
we visualized the frequency of fires across altitudinal
zones using average altitude values for each cadastre:
planar (146–210 m a.s.l.), colline (210–500 m a.s.l.),
submontane (500–800 m a.s.l.), montane
(800–1145 m a.s.l.). On the regional scale, we simi-
larly visualized the effect of geology and geomor-
phology on fire frequency. We distinguished four
landscape categories: areas with prevailing granodi-
orite bedrock (‘‘Granite’’); landscapes with volcanic
basalt hills (‘‘Basalt’’); sandstone rock towns with a
characteristic rugged relief formed by cliffs, rock
walls, pillars, canyons and narrow gorges (‘‘Rock
towns’’); relatively flat areas with sandstone bedrock
(‘‘Sandstone’’); and areas with prevailing loess or
loess-like loam sediments (‘‘Loess’’). Geological
areas were distinguished according to the geological
map of the Czech Republic 1:50,000 (www.geology.
cz), and sandstone ‘‘rock towns’’ were identified using
123
Landscape Ecol

the digital map of landscape typology of the Czech
Republic (Lo
¨w and Nova
´k2008).
Results
Country scale
The most fire-prone were forests dominated by Betula
spp. (110 fires/100 km
2
of forest of such composition),
Pinus sylvestris (38 fires/100 km
2
) and Quercus
petraea (33 fires/100 km
2
), and the least fire-prone
were forests dominated by Tilia spp. (2 fires/100 km
2
),
Populus spp. (11 fires/100 km
2
) and non-native
Robinia pseudoacacia (14 fires/100 km
2
). In the most
widespread forest type, dominated by Picea abies,
fires occurred with an intermediate frequency simi-
larly as in Quercus robur or Fagus sylvatica-domi-
nated forests—23 fires/100 km
2
(Fig. 2a). As for the
different altitudinal zones, the highest fire frequency
was in forests of the colline and planar zones and
decreased markedly towards higher altitudes (Fig. 2c).
The occurrence of forest fires in the Czech Republic
in the period of 1992–2004 in the sense of the presence
or absence of fire events depended more on environ-
mental than on human factors. The incidence of
wildfires increased with increasing abundance of
Picea abies (explained variance 14.4%), Pinus
sylvestris (10.4%), Betula spp. (0.3%) and Fagus
sylvatica; and with increasing Ruggedness index
(9.2%). Conversely, the incidence of wildfires
decreased with the abundance of Populus spp. and
with increasing precipitation (3.9%), which was
strongly correlated with altitude and temperature
(r
S
= 0.63 and r
S
=-0.73, respectively); the latter
factors were excluded from the analysis (see Data
analyses). Moreover, fire occurrence was influenced
also by the soil texture, namely it decreased with
increasing Available Water Capacity (7.8%). Some-
what weaker predictors of fire occurrence were human
factors. Fire occurrence increased with population
density (4.7%) and the density of accommodation
facilities. Conversely, it decreased with the distance
from the nearest large city ([50.000 inhabitants)
(1%).
The frequency of fires in cadastres, by contrast, was
driven mostly by population density (34.7%), and the
other human factors were significant as well. Fire
frequency was significantly influenced also by
environmental factors. It was positively influenced
by the abundance of Betula spp. (4.6%), Pinus
sylvestris (1.6%), Fagus sylvatica and Larix decidua,
by the frequency of cloud-to-ground lightning strikes
(4.2%), the Ruggedness index (3.8%) and negatively
by the soil available water capacity (2.6%), precipi-
tation (1.9%) and by the abundance of Tilia spp.
(1.4%) and other mostly deciduous tree species
(Table 2).
Fire-prone NW region
The narrower range of the environmental conditions of
the selected region in comparison with the country
scale harboured a lower diversity of forest types. The
most fire-prone forests were those dominated by
Betula spp. (238 fires/100 km
2
) followed by Pinus
sylvestris (68 fires/100 km
2
) and Picea abies (56 fires/
100 km
2
). The least fire-prone were forests dominated
by Populus tremula and Quercus petraea (no fires),
Fagus sylvatica (8 fires/100 km
2
) and non-native
Robinia pseudacacia (10 fires/100 km
2
). Q. robur
forests exhibited intermediate proneness to fires (32
fires/100 km
2
) (Fig. 2b). The highest fire frequency
occurred in areas with sandstone bedrock, especially
in sandstone ‘‘rock towns’’, where the frequency of
wildfires was almost double that of less rugged
sandstone areas. Less fire-prone forests were in areas
with granodiorite bedrock, in landscapes with volcanic
basalt hills and on loess sediments (Fig. 2d).
In the selected NW region, we found that pattern of
wildfire occurrence depends also on lightning strikes
occurrence. The frequency of wildfires was signifi-
cantly driven by the frequency of positive (CG?)
lightnings, while negative (CG-) lightnings did not
exhibit any significant effect. However, the signifi-
cance of the CG?lightning was not proved by the
hierarchical partitioning method, neither by additional
glm model including zero values (Online Resource 1),
thus its effect might not be principal. CG?lightnings,
which were about three times less frequent than
CG-(Table 1), occurred in the region more fre-
quently at lower altitudes (p value = \0.001,
R
2
= 0.113) and in places with low precipitation (p
value = \0.001, R
2
= 0.33). CG-lightnings, by
contrast, were slightly more frequent at higher
altitudes (p value = 0.013, R
2
= 0.014), but without
a significant relationship with precipitation (Fig. 3).
123
Landscape Ecol

Overall, the results of the analyses showed a similar
pattern to the broader country scale, but the effect of
environmental factors on the occurrence of wildfires in
the selected region was more pronounced. Fire
occurrence was driven only by forest composition,
with positive effect of the abundance of coniferous
species such as Pinus sylvestris (26.1%) and Picea
abies (12.3%) and negative effect of deciduous
Populus tremula (3.1%). Human factors were not
significant.
Similarly to the country scale, the frequency of
wildfires was driven the most by human factors like
population density (10.1%) and the density of accom-
modation facilities (9.6%) than by environmental
factors. The strongest environmental factor increasing
wildfire frequency was P. sylvestris abundance
(7.4%). The other significant factor was the Rugged-
ness index and the abundance of Betula spp. and
Quercus robur (Table 2).
The additional analysis of fire frequency with
included zero values provided comparable results to
the original analysis and thus largely confirmed its
results (Online Resource 1).
Discussion
Our results show that the spatial pattern of wildfire
occurrence in the cultural temperate Central European
landscape (represented by our model country, the
Fig. 2 Wildfire counts per 100 km
2
of forested area of given
characteristics. Wildfire frequency by tree dominant on the
country scale (a) and the regional scale (b); Wildfire frequency
on the country scale by altitudinal zone (c) and according to
geology/geomorphology on the regional scale (d). The prevalent
sandstone bedrock is divided into two categories: sandstone
‘‘rock towns’’ and other sandstone areas
123
Landscape Ecol

Czech Republic) is driven by a combination of human
and environmental biotic and abiotic factors. We
found that environmental factors mainly influence the
location of wildfires whereas human factors mostly
determine their frequency. Similar results comparing
the occurrence and frequency of forest fires have been
reported by Martı
´nez-Ferna
´ndez et al. (2013) from
Spain. This suggests that the frequency of wildfires in
environmentally conditioned fire-prone areas depends
mainly on the availability of ignition triggers, which in
the conditions of Central Europe are mostly of human
origin (Ganteaume et al. 2013; Kula and Jankovska
´
2013). However, wildfire frequency is also driven
naturally, even though to a lesser extent, by the
frequency of cloud-to-ground lightning strikes.
Table 2 Factors significantly influencing the spatial distribution of wildfires, at least at one scale level and explaining either fire
occurence (pres/abs) or fire frequency (Counts per cadaster over 1992–2004)
Factors Country scale Regional scale
Pres/abs Frequency Pres/Abs Frequency
z-val. I (%) t-val. I (%) z-val. I (%) t-val. I (%)
Human
Population (per km
2
) 8.87 4.70* 27.01 34.69* NS 6.17 10.11*
Accomodation (per km
2
) 2.65 5.22 NS 3.54 9.56
Distance from city -5.19 0.96* -4.4 NS 2.86 0.98
Climatic
Altitude NA NA NS NS
Precipitation -7.47 3.85* -8.16 1.86* NA NA
Lightnings (per km
2
) NS 4.76 4.20* NA NA
CG?lightnings
(per km
2
)
NA NA NS 2.37 0.4
Topographic and soil
Ruggednes index 5.91 9.18* 7.47 3.76* NS 4.33 4.26
Available water capacity -6.88 7.80* -5.54 2.62* NS NS
Forest composition (%)
Abies alba NS -2.33 NS 2.1
Betula spp. 3.96 0.33* 8.59 4.63* NS 3.11 2.33
Carpinus betulus NS -4.42 NS -2.6
Fagus sylvatica 3.2 3.85 NS NS
Larix decidua NS 4.23 NS 2.69
Picea abies 14.87 14.44* NS 3.77 12.25* 2.04
Pinus sylvestris 13.87 10.42* 6.84 1.60* 5.33 26.07* 3.51 7.40*
Populus spp. -2.08 -3.79 NS NS
Populus tremula NS -5.63 -2.4 3.11* NS
Quercus robur NS NS NS 3.24 1.18
Tilia spp. NS -6.02 1.43* NS NS
Forested area (ha) 20.9 48.32* 40.52 45.21* 6.2 58.57* 10.82 63.78*
Pseudo-R
2
0.24 0.55 0.36 0.59
Given are z/t values from GLM analyses of presence/absence and fire frequency data, and the proportion of explained variance [%] of
max. 9 selected factors from hierarchical partitioning analyses (I column) and pseudo-R
2
of the models. Plus and minus signs of z and
t values indicate a positive or negative effect on wildfire occurrence
NS Not significant, NA not available
* Indicate the factors significant in the randomization test
123
Landscape Ecol

Biotic drivers of wildfire incidence
Our findings regarding the susceptibility of coniferous
forests to fire, especially those dominated by Pinus
sylvestris, is in agreement with other ecological
studies claiming P. sylvestris as a fire-adapted and
simultaneously fire-attracting species due its easily
flammable, resiny litter and sparse canopy that enables
the ground layer to dry out (Agee 1998; Angelstam
1998; Gromtsev 2002; Lecomte et al. 2005). Betula
spp. also markedly indicated the higher probability of
forest fires. This might be due to several reasons.
Betula is a pioneer tree species that typically colonizes
burnt areas (Huotari et al. 2008; Reyes and Casal
2012), so an explanation might be the abundance of
birch is actually a consequence of previous fire
occurrence. However, wildfires are usually not as
extensive to explain the prevalence of birch in the
region, and, moreover, almost all burnt areas have
been reforested by tree species that are economically
more valuable. Betula spp. often accompanies Pinus
sylvestris in nutrient-poor conditions and possesses
highly flammable bark that remains on the forest
ground after decaying of old trunks and branches. In
the case of a fire, this could increase the likelihood of
its spreading. Wildfires, to some extent, also occurred
in forests dominated by deciduous species like Fagus
sylvatica or Quercus spp. This result supports the
theory that also temperate oak and beech forests are
associated with the occurrence of wildfires (Abrams
1992; Brose et al. 2013; Ascoli et al. 2015). The least
fire-prone were forests dominated by Populus spp. and
Tilia spp. (Fig. 2a, b), which grow in relatively moist
conditions of flood plains and shady scree slopes,
respectively (Chytry
´2012).
Abiotic drivers of wildfire incidence
The occurrence of wildfires depended on abiotic
environmental factors such as the relief, climate and
altitude. Although we did not use altitude as a
predictor in the country scale analysis, since it highly
correlated with precipitation and temperature, its
effect was clearly evident in our comparison of fire
frequency, which was markedly lower at higher
altitudes (Fig. 2c). This result is in accordance with
numerous other studies which have found a negative
effect of altitude and precipitation on wildfire inci-
dence (e.g. Engelmark 1993; Pew and Larsen 2001;
Futao et al. 2016). However, in drier climatic condi-
tions, the effect of these factors can be the opposite, as
fuel availability increases with increasing precipita-
tion values (Martı
´nez-Ferna
´ndez et al. 2013).
In our study, the occurrence and frequency of
wildfires increased with increasing ruggedness of the
relief, which is consistent with the results of similar
studies (Kalabokidis et al. 2002; Ganteaume et al.
2013). In rugged landscapes there are more fire-prone
sites than on flat land, such as south-oriented slopes
and convex rock tops with shallow soils drying out
more easily (Angelstam 1998; Mouillot et al. 2003).
Fig. 3 Frequency of cloud-to-ground lightning strikes of
negative (CG-) and positive (CG?) polarity in the NW region,
related to altitude (a) and precipitation (b). aCG-:R
2
= 0.014;
p value = 0.013; CG?:R
2
= 0.113, p value = \0.001;
bCG-:R
2
= 0.004, p value = 0.131; CG?:R
2
= 0.33, p
value = \0.001
123
Landscape Ecol

Our previous study (Ada
´mek et al. 2015) has found
that the most fire-prone sites in rugged sandstone
landscape of ‘‘rock towns’’ are steep SW-facing slopes
and elevated rock plateaus. Additionally, such pro-
truding, convex sites attract lightning strikes, the main
natural ignition trigger (Engelmark 1993; Vogt 2011).
The frequency of cloud-to-ground lightning strikes
turned out to have a significant positive effect on
wildfire frequency also in more populated landscapes,
although they provably cause only about 1.4% of
forest fires in the Czech Republic (Kula and Jankovska
´
2013). Using more precise data on the frequency of
lightning strikes, we found wildfire frequency to be
driven, albeit marginally, by positive (CG?) light-
nings. CG?lightnings, in contrast to negative (CG-)
lightnings, occurred more frequently in the areas with
lower altitudes and precipitation (Fig. 3). CG?light-
nings are claimed to be a stronger ignition trigger than
CG-lightnings, even though they are less frequent.
This has been explained by their larger magnitude,
temperature, higher probability of a long continuing
current (Latham and Williams 2001;Mu
¨ller and Vacik
2017) and by the fact that they more often accompany
convective or so-called ‘‘good weather’’ thunder-
storms, which last a short time and bring little rainfall.
This is in accordance with our results where
CG?lightnings are associated with lower precipita-
tions. CG-lightnings, by contrast, occur more fre-
quently with frontal thunderstorms accompanied by
higher rainfall (Larjavaara et al. 2005). This result
indicates a possible interconnection between the
occurrence of a fire-adapted lowland pine taiga in
the region and the frequency of CG?lightning strikes
as a natural ignition trigger.
Wildfire incidence also depended on the physical
characteristics of soil, namely the available water
capacity of (AWC) which influenced wildfire occur-
rence negatively. AWC largely depends on the soil
texture with the lowest values on sandy and gravel
soils (Saxton and Rawls 2006; Ballabio et al. 2016).
This result thus probably reflects the fire-prone
conditions of drainable soils. A positive effect of
coarse soils on the incidence of wildfires was also
found by Cardille and Ventura (2001).
The frequency of wildfires in the NW region
strikingly differed depending on geological condi-
tions. It was higher on sandstone bedrock, where the
pine-dominated lowland taiga occurs (Chytry
´2012;
Nova
´k et al. 2012), and lower on more fertile basalt
and loess bedrock with a higher cover of broadleaved
forests. However, the most fire-prone areas of the
region were sandstone rock towns (Fig. 2d), which is
related with the high abundance of Pinus sylvestris,
touristic attractiveness and ruggedness of the
landscape.
The pattern of this pine region resembles, for
example, the Pitch Pine (Pinus rigida) barrens of New
Jersey and adjacent regions (North Eastern USA)
which occur on acidic and drainable soils within
humid temperate climate and are highly fire-depen-
dent (Boerner 1981; Landis et al. 2005). The occur-
rence of fire-prone pinewood regions in temperate
climate, related with drainable and nutrient-poor
acidic soils, thus applies globally.
Human drivers of wildfire incidence
In our study, population density together with distance
from the nearest large city facilities and density of
accommodation, as a proxy for tourism intensity,
turned out to be important drivers of wildfire occur-
rence similarly as in other studies explaining a large
proportion of wildfire ignitions by the human factors
(Cardille and Ventura 2001; Pew and Larsen 2001;
Zumbrunnen et al. 2012; Ganteaume et al. 2013;
Martı
´nez-Ferna
´ndez et al. 2013; Futao et al. 2016).
However, in our results these factors influenced
mainly the fire frequency whereas their effect on fire
occurrence was less pronounced. These results thus
contradict the idea that human factors can more or less
obscure the effects of environmental factors such as
the climate or topography (Flatley et al. 2011;
Zumbrunnen et al. 2012). In our study, by contrast,
environmental factors did have an apparent effect on
fire occurrence despite relatively high population
density of the Czech Republic.
Conclusions
The pattern of wildfire distribution in the Czech
Republic follows similar rules as in other regions of
the world, even those where wildfire is considered part
of the natural dynamics of local ecosystems. In the
densely populated cultural landscape of Central
Europe, the distribution of wildfires, not surprisingly,
depends strongly on human factors. However, people
act mainly as a ubiquitous ignition trigger whereas
123
Landscape Ecol

natural environmental factors determine the suscepti-
bility of habitats to being ignited. The main natural
conditions that increase the likelihood of wildfires are:
a rugged relief at lower altitudes, drainable soils with a
prevalence of coniferous forests, especially of Pinus
sylvestris, and mixed with Betula spp. If sufficient
ignition triggers are available, be it of human or
natural origin, such conditions can in the long term
lead to the development of fire-adapted ecosystems,
even in relatively humid climate. Natural conditions,
including occurrence of positive cloud-to-ground
lightning strikes as a potential natural ignition trigger
determine the susceptibility of habitats in the sand-
stone landscapes of NW Bohemia to fire. This region is
a good example of a naturally conditioned fire-prone
area within temperate Central Europe where fire-
adapted vegetation is shaped also by lightning-ignited
wildfires.
Acknowledgements This research was supported by the
Czech Science Foundation (Grant 14-22658S) and by the
Academy of Sciences of the Czech Republic (long-term
research development project RVO67985939). We further
thank Martin Weiser for the help with data analyses, Martin
Kopecky
´for the help with GIS and Frederick Rooks for
language advice.
References
Abrams MD (1992) Fire and the development of oak forests.
Bioscience 42:346–353
Ada
´mek M, Bobek P, Hadincova
´V, Wild J, Kopecky
´M (2015)
Forest fires within a temperate landscape: a decadal and
millennial perspective from a sandstone region in Central
Europe. For Ecol Manag 336:81–90
Agee JK (1998) Fire and pine ecosystems. In: Richardson DM
(ed) Ecology and biogeography of Pinus. Cambridge
University Press, Cambridge
Albers J (2012) Comparative analysis of the forest fire situation
in Central-Eastern Europe, Master Thesis. BOKU, Vienna,
Austria
Angelstam PK (1998) Maintaining and restoring biodiversity in
European boreal forests by developing natural disturbance
regimes. J Veg Sci 9:593–602
Angelstam P, Kuuluvainen T (2004) Boreal forest disturbance
regimes, successional dynamics and landscape struc-
tures—a European perspective. Ecol Bull 51:117–136
Ascoli D, Vacchiano G, Maringer J, Bovio G, Conedera M
(2015) The synchronicity of masting and intermediate
severity fire effects favors beech recruitment. For Ecol
Manag 353:126–135
Avila-Flores D, Pompa-Garcia M, Antonio-Nemiga X, Rodri-
guez-Trejo DA, Vargas-Perez E, Santillan-Perez J (2010)
Driving factors for forest fire occurrence in Durango State
of Mexico: a geospatial perspective. Chin Geogr Sci
20:491–497
Ballabio C, Panagos P, Monatanarella L (2016) Mapping topsoil
physical properties at European scale using the LUCAS
database. Geoderma 261:110–123
Bobek P, Svobodova
´HS, Werchan B, S
ˇvarcova
´MG, Kunes
ˇP
(2017) Human-induced changes in fire regime and subse-
quent alteration of the sandstone landscape of Northern
Bohemia (Czech Republic). The Holocene 28:427–443
Boerner REJ (1981) Forest structure dynamics following wild-
fire and prescribed burning in the New Jersey pine barrens.
Am Midl Nat 105:321–333
Brose PH, Dey DC, Phillips RJ, Waldrop T (2013) A meta-
analysis of the fire-oak hypothesis: does prescribed burning
promote oak reproduction in Eastern North America? For
Sci 59:322–334
Cardille JA, Ventura SJ (2001) Environmental and social factors
influencing wildfires in the Upper Midwest, United States.
Ecol Appl 11:111–127
Chytry
´M (2012) Vegetation of the Czech Republic: diversity,
ecology, history and dynamics. Preslia 84:427–504
Clark J, Merkt J (1989) Post-glacial fire, vegetation, and human
history on the northern alpine forelands, south-western
Germany. J Ecol 77:897–925
Clark JS, Royall PD (1996) The role of fire during climate
change in an eastern deciduous forest at Devil’s Bathtub,
New York. Ecology 77:2148–2166
Delarze R, Caldelari D, Hainard P (1992) Effects of fire on forest
dynamics in southern Switzerland. J Veg Sci 3:55–60
Dı
´az-Delgado R, Lloret F, Pons X (2004) Spatial patterns of fire
occurrence in Catalonia, NE, Spain. Landscape Ecol
19:731–745
Ellenberg H (1996) Vegetation Mitteleuropas mit den Alpen in
o
¨kologischer, dynamischer und historischer Sicht. No title.
Ulmer, Stuttgart
Engelmark O (1993) Early post-fire tree regeneration in a Picea-
Vaccinium Forest in Northern Sweden. J Veg Sci
4:791–794
Feurdean A, Florescu G, Vannie
`re B, Tant¸a
˘u I, O’Hara RB,
Pfeiffer M, Hutchinson SM, Gałka M, Moskal-del Hoyo M,
Hickler T (2017) Fire has been an important driver of forest
dynamics in the Carpathian Mountains during the Holo-
cene. For Ecol Manag 389:15–26
Flannigan M, Stocks B, Wotton B (2000) Climate change and
forest fires. Sci Total Environ 262:221–229
Flannigan MD, Wotton BM (1991) Lightning-ignited forest
fires in northwestern Ontario. Can J For Res 21:277–287
Flatley WT, Lafon CW, Grissino-Mayer HD (2011) Climatic
and topographic controls on patterns of fire in the southern
and central Appalachian Mountains. Landscape Ecol, USA
Futao G, Su Z, Wang G, Sun L, Lin F, Liu A (2016) Wildfire
ignition in the forests of southeast China: identifying dri-
vers and spatial distribution to predict wildfire likelihood.
Appl Geogr 66:12–21
Ganteaume A, Camia A, Jappiot M, San-Miguel-Ayanz J, Long-
Fournel M, Lampin C (2013) A review of the main driving
factors of forest fire ignition over Europe. Environ Manag
51:651–662
Granstro
¨m A (1993) Spatial and temporal variation in lightning
ignitions in Sweden. J Veg Sci 4:737–744
123
Landscape Ecol

Gromtsev A (2002) Natural disturbance dynamics in the boreal
forests of European Russia: a review. Silva Fenn 36:41–55
Huotari N, Tillman-Sutela E, Pasanen J, Kubin E (2008) Ash-
fertilization improves germination and early establishment
of birch (Betula pubescens Ehrh.) seedlings on a cut-away
peatland. For Ecol Manag 255:2870–2875
Kalabokidis KD, Konstantinidis P, Vasilakos C (2002) GIS
analysis of physical and human impact on wildfire patterns.
In: Viegas DX (ed) Proceedings of IV international con-
ference on forest fire research. 2002 wildlife fire safety
summit, Luso-Coimbra, Port, 18–23 November 2002.
Millpress, Rotterdam, pp 1–13
Kula E, Jankovska
´Z (2013) Forest fires and their causes in the
Czech Republic (1992–2004). J For Sci 59:41–53
Landis RM, Gurevitch J, Fox GA, Fang W, Taub DR (2005)
Variation in recruitment and early demography in Pinus
rigida following crown fire in the pine barrens of Long
Island, New York. J Ecol 93:607–617
Larjavaara M, Kuuluvainen T, Rita H (2005) Spatial distribution
of lightning-ignited forest fires in Finland. For Ecol Manag
208:177–188
Latham D, Williams E (2001) Lightning and forest fires. In:
Johnson EA, Miyanishi K (eds) Forest fires: behavior and
ecological effects. Academic Press, San Diego,
pp 375–418
Lecomte N, Simard M, Bergeron Y, Larouche A, Asnong H,
Richard PJH (2005) Effects of fire severity and initial tree
composition on understorey vegetation dynamics in a
boreal landscape inferred from chronosequence and pale-
oecological data. J Veg Sci 16:665–674
Lindner M, Maroschek M, Netherer S, Kremer A, Barbati A,
Garcia-Gonzalo J, Seidl R, Delzon S, Corona P, Kolstro
¨m
M, Lexer MJ, Marchetti M (2010) Climate change impacts,
adaptive capacity, and vulnerability of European forest
ecosystems. For Ecol Manag 259:698–709
Lo
¨w J, Nova
´k J (2008) Typologicke
´c
ˇlene
ˇnı
´krajin C
ˇeske
´
republiky—typological subdivision of landscapes in the
Czech Republic. Urban a u
´zemnı
´Rozv XI:19–23
Mac Nally R, Walsh CJ (2004) Hierarchical partitioning public-
domain software. Biodivers Conserv 13:659–660
Marozas V, Racinskas J, Bartkevicius E (2007) Dynamics of
ground vegetation after surface fires in hemiboreal Pinus
sylvestris forests. For Ecol Manag 250:47–55
Martı
´nez-Ferna
´ndez J, Chuvieco E, Koutsias N (2013) Modelling
long-term fire occurrence factors in Spain by accounting for
local variations with geographically weighted regression. Nat
Hazards Earth Syst Sci 13:311–327
Miranda BR, Sturtevant BR, Stewart SI, Hammer RB (2012)
Spatial and temporal drivers of wildfire occurrence in the
context of rural development in northern Wisconsin, USA.
Int J Wildl Fire 21:141–154
Molinari C, Lehsten V, Bradshaw RHW, Power MJ, Harmand P,
Arneth A, Kaplan JO, Vannie
`re B, Sykes MT (2013)
Exploring potential drivers of European biomass burning
over the Holocene: a data-model analysis. Glob Ecol
Biogeogr 22:1248–1260
Moreira F, Rego FC, Ferreira PG (2001) Temporal (1958–1995)
pattern of change in a cultural landscape of northwestern
Portugal: implications for fire occurrence. Landscape Ecol
16:557–567
Mouillot F, Ratte J-P, Joffre R, Moreno JM, Rambal S (2003)
Some determinants of the spatio-temporal fire cycle in a
mediterranean landscape (Corsica, France). Landscape
Ecol 18:665–674
Mu
¨ller MM, Vacik H (2017) Characteristics of lightnings
igniting forest fires in Austria. Agric For Meteorol
240–241:26–34
Mu
¨ller MM, Vacik H, Diendorfer G, Arpaci A, Formayer H,
Gossow H (2013) Analysis of lightning-induced forest fires
in Austria. Theor Appl Climatol 111:183–193
Nauslar, NJ (2014) Examining the lightning polarity of lightning
caused wildfires. In: 23rd international lightning detection
conference, Tucson, AZ, 18–19 Mar
Niklasson M, Granstro
¨m A (2000) Numbers and sizes of fires:
long-term spatially explicit fire history in a Swedish boreal
landscape. Ecology 81:1484–1499
Niklasson M, Zin E, Zielonka T, Feijen M (2010) A 350-year
tree-ring fire record from Bialowieza Primeval Forest,
Poland: implications for Central European lowland fire
history. J Ecol 98(6):1319–1329
Nova
´k J, Sadlo J, Svobodova-Svitavska H (2012) Unusual
vegetation stability in a lowland pine forest area (Doksy
region, Czech Republic). Holocene 22:947–955
Olea PP, Mateo-Toma
´s P, de Frutos A (2010) Estimating and
modelling bias of the hierarchical partitioning public-do-
main software: implications in environmental management
and conservation. PLoS ONE 5:1–7
Pausas JG, Vallejo VR (1999) The role of fire in European
Mediterranean Ecosystems. In: Chuvieco E (ed) Remote
sensing of large wildfires in the European Mediterranean
basin. Springer, Berlin, pp 3–16
Pew K, Larsen CP (2001) GIS analysis of spatial and temporal
patterns of human-caused wildfires in the temperate rain
forest of Vancouver Island, Canada. For Ecol Manag
140:1–18
Pineda N, Montanya
`J, van der Velde OA (2014) Characteristics
of lightning related to wildfire ignitions in Catalonia.
Atmos Res 135–136:380–387
Podur J, Martell DL, Csillag F (2003) Spatial patterns of light-
ning-caused forest fires in Ontario, 1976-1998. Ecol
Modell 164:1–20
Pyne SJ, Andrews PL, Laven RD (1996) Introduction to wild-
land fire. Wiley, New York
Reyes O, Casal M (2012) Effects of forest fire ash on germi-
nation and early growth of four pinus species. Plant Ecol
175:81–89
Riley SJ, DeGloria SD, Elliot R (1999) A terrain ruggedness
index that quantifies topographic heterogeneity. Int J Sci
5:23–27
Rogers BM, Soja AJ, Goulden ML, Randerson JT (2015)
Influence of tree species on continental differences in
boreal fires and climate feedbacks. Nat Geosci 8:228–234
Rychtecka
´P, Urban
ˇcova
´N (2008) S
ˇkodlivı
´c
ˇinitele
´lesa v letech
1996-2006/Harmful factors of the forest in the period of
1996-2006. Lesnicka
´pra
´ce 6/08
Saxton KE, Rawls WJ (2006) Soil water characteristic estimates
by texture and organic matter for hydrologic solutions. Soil
Sci Soc Am J 70:1569
Skre O, Wielgolanski FE, Moe B (1998) Biomass and chemical
composition of common forest plants in response to fire in
western Norway. J Veg Sci 9:501–510
123
Landscape Ecol

Stahli M, Finsinger W, Tinner W, Allgower B (2006) Wildfire
history and fire ecology of the Swiss National Park (Central
Alps): new evidence from charcoal, pollen and plant
macrofossils. The Holocene 16:805–817
Tinner W, Conedera M, Ammann B, Lotter AF (2005) Fire
ecology north and south of the Alps since the last ice age.
The Holocene 8:1214–1226
Tinner W, Hubschmid P, Wehrli M, Ammann B, Conedera M
(1999) Long-term forest fire ecology and dynamics in
southern Switzerland. J Ecol 87:273–289
Tolasz R (2007) Atlas podnebı
´C
ˇeska/climate atlas of Czechia.
CHMU
´a Univerzita Palacke
´ho, Olomouc
Trnka M, Bra
´zdil R, Moz
ˇny
´M, S
ˇte
ˇpa
´nek P, Dobrovolny
´P,
Zahradnı
´c
ˇek P, Balek J, Semera
´dova
´D, Dubrovsky
´M,
Hlavinka P, Eitzinger J, Wardlow B, Svoboda M, Hayes M,
Z
ˇalud Z (2015) Soil moisture trends in the Czech Republic
between 1961 and 2012. Int J Climatol 35:3733–3747
Valese E, Conedera M, Held AC, Ascoli D (2014) Fire, humans
and landscape in the European Alpine region during the
Holocene. Anthropocene 6:63–74
Vannie
`re B, Colombaroli D, Chapron E, Leroux A, Tinner W,
Magny M (2008) Climate versus human-driven fire
regimes in Mediterranean landscapes: the Holocene record
of Lago dell’Accesa (Tuscany, Italy). Quat Sci Rev
27:1181–1196
Vogt BJ (2011) Exploring cloud-to-ground lightning earth
highpoint attachment geography by peak current. Earth
Interact 15:1–16
Wotton BM, Martell DL (2005) A lightning fire occurrence
model for Ontario. Can J For Res 35:1389–1401
Yang J, He HS, Shifley SR, Gustafson EJ (2007) Spatial patterns
of modern period human-caused fire occurrence in the
Missouri Ozark Highlands. For Sci 53:1–15
Zin E, Drobyshev I, Bernacki D, Niklasson M (2015) Den-
drochronological reconstruction reveals a mixed-intensity
fire regime in Pinus sylvestris-dominated stands of
Białowie_
za Forest, Belarus and Poland. J Veg Sci
26:934–945
Zumbrunnen T, Mene
´ndez P, Bugmann H, Conedera M, Gimmi
U, Bu
¨rgi M (2012) Human impacts on fire occurrence: a
case study of hundred years of forest fires in a dry alpine
valley in Switzerland. Reg Environ Chang 12:935–949
Zuur A, Ieno EN, Walker N, Saveliev AA, Smith GM (2009)
Mixed effects models and extensions in ecology with R.
Springer, New York
123
Landscape Ecol