Spatial and temporal patterns of forest fires in the Central Monte: relationships with regional climate

Abstract

Background Natural and anthropogenic wildfires burn large areas of arid and semi-arid forests with significant socioeconomic and environmental impacts. Fire regimes are controlled by climate, vegetation type, and anthropo-genic factors such as ignition sources and human-induced disturbances. Projections of climate and land-use change suggest that these controlling factors will change, altering fire regimes in the near future. In the southern Central Monte, Mendoza, Argentina, the factors that modulate the fire temporal and spatial variability are poorly understood. We reconstructed the fire history of southeast of Mendoza from 1984 to 2023 and investigated the relationships between fire extent and climate variability at seasonal and interannual scales. Burned areas were determined using Google Earth Engine by processing Landsat 5-TM, Landsat 7-ETM+ , and Landsat 8-OLI-TIRS sensor imagery. Results The region exhibited high spatial and temporal variability in fire occurrence, being a mosaic of areas with different fire histories and recovery times. Between 1985 and 2023, fire recurrence ranged from sites unburned to sites with up to 14 fires. The occurrence of large fires was strongly favored by a combination of a year with abundant spring-early summer precipitation, which favors fuel accumulation, followed by a year of low spring-early summer precipitation. Precipitation and burnt area showed a very pronounced 6-7 year cycle, suggesting a dominant climatic control on fire occurrence. Conclusions Fire distribution in southeastern Mendoza forests is not homogeneous, resulting in a mosaic of patches with different fire histories. This heterogeneity may be related to vegetation patterns and land use. The temporal variability of fires is strongly influenced by climate variability, which would promote fuel production and subsequent drying. Large fires are concentrated in periods of high interannual precipitation variability. Climate change scenarios predict an increase in temperature and precipitation variability in the region, suggesting future changes in fire dynamics. Our results contribute to the development of fire guidelines for southeastern Mendoza forests, focusing on periods of wet years followed by dry years that favor fire occurrence and spread.

Full text

Villagraetal. Ecological Processes (2024) 13:5
https://doi.org/10.1186/s13717-023-00481-6
RESEARCH
Spatial andtemporal patterns offorest res
intheCentral Monte: relationships withregional
climate
Pablo Eugenio Villagra1,2* , Erica Cesca1, Leandro Manuel Alvarez1, Silvia Delgado1 and Ricardo Villalba1
Abstract
Background Natural and anthropogenic wildfires burn large areas of arid and semi-arid forests with significant
socio-economic and environmental impacts. Fire regimes are controlled by climate, vegetation type, and anthropo-
genic factors such as ignition sources and human-induced disturbances. Projections of climate and land-use change
suggest that these controlling factors will change, altering fire regimes in the near future. In the southern Central
Monte, Mendoza, Argentina, the factors that modulate the fire temporal and spatial variability are poorly understood.
We reconstructed the fire history of southeast of Mendoza from 1984 to 2023 and investigated the relationships
between fire extent and climate variability at seasonal and interannual scales. Burned areas were determined using
Google Earth Engine by processing Landsat 5-TM, Landsat 7-ETM+ , and Landsat 8-OLI-TIRS sensor imagery.
Results The region exhibited high spatial and temporal variability in fire occurrence, being a mosaic of areas with dif-
ferent fire histories and recovery times. Between 1985 and 2023, fire recurrence ranged from sites unburned to sites
with up to 14 fires. The occurrence of large fires was strongly favored by a combination of a year with abundant
spring–early summer precipitation, which favors fuel accumulation, followed by a year of low spring–early summer
precipitation. Precipitation and burnt area showed a very pronounced 6–7 year cycle, suggesting a dominant climatic
control on fire occurrence.
Conclusions Fire distribution in southeastern Mendoza forests is not homogeneous, resulting in a mosaic of patches
with different fire histories. This heterogeneity may be related to vegetation patterns and land use. The temporal
variability of fires is strongly influenced by climate variability, which would promote fuel production and subsequent
drying. Large fires are concentrated in periods of high interannual precipitation variability. Climate change scenarios
predict an increase in temperature and precipitation variability in the region, suggesting future changes in fire
dynamics. Our results contribute to the development of fire guidelines for southeastern Mendoza forests, focusing
on periods of wet years followed by dry years that favor fire occurrence and spread.
Keywords Fire recurrence, Fire mapping, Recovery time, Climate, Dry forest, Wildfire
Background
Fire has been a frequent disturbance in numerous ecosys-
tems for millions of years, long before human activities
affected natural fire regimes. us, fire has been a very
important component in evolutionary processes and a
fundamental driver of the dynamics of many ecosystems
(Scott 2000; Korb etal. 2012; Giorgis etal. 2021). Recent
studies highlight the interactions between climate, vege-
tation, and human activities on fire dynamics, suggesting
Open Access
© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or
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to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this
licence, visit http://creativecommons.org/licenses/by/4.0/.
Ecological Processes
*Correspondence:
Pablo Eugenio Villagra
villagra@mendoza-conicet.gob.ar
1 Instituto Argentino de Nivología Glaciología y Ciencias Ambientales–
CONICET-CCT Mendoza, Avda Ruiz Leal S/N, 5500 Mendoza, Argentina
2 Facultad de Ciencias AgrariasUniversidad Nacional de Cuyo, Chacras de
Coria, M5528AHB Mendoza, Argentina

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Villagraetal. Ecological Processes (2024) 13:5
that increased biomass leads to an increase in these
events in regions with climatic constraints on fuel gen-
eration (Forkel etal. 2019). In some forests, fire is a vital
process of ecological dynamics, accelerating successional
phases, allowing the establishment of new species and
reducing intra- or interspecific competition (Defossé and
Urretavizcaya 2003; He etal. 2019). In other cases, how-
ever, wildfires can transform forest cover into degraded
shrublands or grasslands (White etal. 1985; Cesca etal.
2014; Archibald etal. 2018).
e fire regime of an area is controlled by the atmos-
pheric conditions at the time of fire occurrence, the long-
term climate, the type of fuel-producing vegetation, and
anthropogenic constraints related to ignition sources and
disturbance history (Pausas et al. 2004; Krawchuk and
Moritz 2011; Fischer etal. 2015; Archibald et al. 2018;
Mishra et al. 2023). Meteorological factors that deter-
mine fire occurrence are related to recent variations in
temperature, precipitation, humidity, and wind speed,
as well as the occurrence of lightning. Changes in these
climatic parameters, combined with fuel moisture levels
and the natural susceptibility or adaptation of vegetation
to ignition and flammability, influence fire spread (Pausas
etal. 2004; Archibald etal. 2018). Variations in weather
conditions determine the timing and average duration
of the fire season (Fischer etal. 2012, 2015), as well as
the amount of energy released during fires (Brooks and
Matchett 2006; Pricope and Binford 2012). On longer
time scales of centuries to millennia, climate largely
determines the characteristics and distribution of plant
communities, with seasonal to interannual variations in
climate controlling fuel accumulation and drying, igni-
tion, flammability, and associated fire behavior.
e Intergovernmental Panel on Climate Change
(IPCC 2021) has developed climate projections for
greenhouse gas emissions based on scenarios of future
economic, demographic, and technological growth
(Nakicenovic etal. 2000). In the western part of south-
ern South America, the projections show an increase in
temperature and consequently in climatic conditions
favorable to fire occurrence (IPCC 2021). For Argen-
tina, climatic models project an average warming of
1.5 and 4°C for the period 2080–2099, depending on
seasonality and emission scenarios, with an average
increase in annual temperatures of 2.5°C (IPCC 2021).
In addition, projections show a 5–30% increase in sum-
mer precipitation for the Chaco-Pampa plains, along
with a smaller increase in the length of the rainy sea-
son (Labraga and Villalba 2009). ese climatic changes
may affect agriculture, biodiversity, and aquatic habi-
tats, including changes in fire frequency and intensity.
Understanding the climatic factors that modulate fire
regimes will allow estimation and assessment of the
effects of temperature and precipitation increases on
fire regimes at local and regional scales (Flannigan etal.
2005).
e southeastern area of Mendoza Province, located
in the Central Monte desert, is characterized by highly
variable precipitation with annual totals between 300
and 400mm and a mean annual temperature of about
15 ºC under a predominant S-SE wind regime. e
vegetation is a mosaic of open forests, shrublands and
psammophilous grasslands (Villagra and Alvarez 2019),
and represents one of the areas with the highest fire
frequency in the Central Monte region (Villagra etal.
2009). Records from the National Fire Management
Plan show that fires in this area originate from anthro-
pogenic (negligent or intentional) and natural (thun-
derstorms) causes. Cesca etal. (2014) showed that fire
controls vegetation physiognomy, leading to a simplifi-
cation of vegetation structure and loss of woody layers.
Historical records suggest a close relationship between
fire and climatic conditions in the region. For example,
Loggio (1992) reported the burning of 560,000ha in Gen-
eral Alvear (southeastern Mendoza) during the particu-
larly dry spring–summer of 1986–1987, which followed
two years of above-average rainfall. e National Mete-
orological Service reported an increase in annual thun-
derstorms at the San Rafael weather station from 30.3 in
the 1961/1970s to 39.8 in 1971/80 and 46.1 in 1981/90.
Although these types of observations have been dis-
continued, recent work by Rasmussen etal. (2014) and
DiGangi etal. (2022) indicates that the area has one of
the highest November–January recurrence rates of light-
ning associated with convective storms in South Amer-
ica. Climate change models project a 10–30% increase
in precipitation in the region (Barros et al. 2014; IPCC
2021), particularly in summer associated with an increase
in convective storms. ese climate projections suggest
possible increases in fire occurrence and extension due
to (1) an increase in weather conditions favorable for fire
spread (heat and lack of rainfall); (2) an increase in fuel
availability; and (3) a larger number of lightning igni-
tions. erefore, we would expect a greater occurrence
of fires associated with warmer and drier periods leading
to greater flammability of fuels accumulated during pre-
vious relatively wet periods. Considering that the type of
fuel and its availability influence fire recurrence, differ-
ences in vegetation associated with spatial environmental
heterogeneity will also lead to a different fire recurrence
in the landscape at regional scale. is work is the first
attempt to characterize the fire regime in Prosopis wood-
lands by reconstructing the spatial and temporal vari-
ations of fires and establishing their relationships with
climatic variability in the Central Monte, Argentina.

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Villagraetal. Ecological Processes (2024) 13:5
Methods
Study area
e study area is located in the Monte biogeographical
province, specifically between the main watercourses of
the Diamante and Atuel rivers, in the southern sector
of the Central Monte, and covers 27.3 million hectares
(Fig. 1) (Rundel et al. 2007). e climate is semi-arid,
with a mean annual temperature of 15.4°C, total annual
precipitation of 400mm, and prevailing winds from the
S-SE (General Alvear weather station located at 35º S, 67º
39’ W, 465ma.s.l.; Gonzalez Loyarte etal. 2009). Sum-
mer rains are associated with convective thunderstorms
with high electrical activity and hail, although most of
the year experiences a pronounced water deficit. e
vegetation consists of a mosaic of various types of com-
munities: the forests, dominates by Prosopis flexuosa
and Geoffroea decorticans; the shrublands, dominated by
Larrea divaricata, L. cuneifolia, Condalia microphylla
and Atriplex lampa; and the psammophilous grassland,
dominated by Elionurus muticus and Hyalis argentea
(Tacchini etal. 2014). e recurrent fires caused changes
in the structure, shape, and community composition,
and controlled the transitions among vegetation types.
In addition, fires cause Prosopis flexuosa trees to show
Fig. 1 Fire recurrence during the study period. The green area on the map of Argentina represents the study area. The different colors represent
the number of fires between the seasons 1985–1986 and 2022–2023

Page 4 of 14
Villagraetal. Ecological Processes (2024) 13:5
numerous smaller stems. erefore, fire recurrence can
induce positive feedback that generates new stable states,
commonly dominated by shrubs or grasses (Cesca etal.
2014). Extensive cattle ranching is practiced in the area
for the commercial sale of their meat. is contrasts with
other woodlands in the region that follow a subsistence
economic model (Guevara etal. 1993, 2009).
Reconstruction ofre history andspatial distribution
ofburnt areas
We reconstructed the fire history of the area from 1984
to 2023. In the area, the growing season starts in October
and ends in April–May, and fires occur between August
and February, with the largest concentration in the spring
months (Fischer etal. 2012). erefore, the multi-tempo-
ral reconstruction of fires in the area was conducted by
separating events that occurred between July and June of
the following year (hereafter fire season).
To determine the surface area affected by the fire sea-
son, we used satellite imagery from the 1985–1986
through 2022–2023 fire seasons. Imagery processing was
performed using Google Earth Engine. is cloud-based
platform allows access and processing of widespread spa-
tial resources and satellite image collections, and to run
supervised classifications for fire detection (Gorelick
etal. 2017). We used the Landsat Simple Composite algo-
rithm to obtain each mosaic, a combination of the best
available scenes from Landsat 5-TM, Landsat 7-ETM+ ,
and Landsat 8-OLI-TIRS sensors (30 m spatial resolu-
tion) in terms of cloud cover and reflectance. For each
fire season, two mosaics were assembled, and the scenes
obtained between August and September and January
and March were compared, taking into account the fire
season. To detect fire in each mosaic, a combination of
false composite color bands was first used, which facili-
tated the identification of burned areas by shape, texture,
and hue (Roy and Boschetti 2009; Levin and Heimowitz
2012; Chen etal. 2022). In this way, it was possible to vis-
ually identify burnt areas for each season.
Before performing the classifications, we applied a
mask to exclude confusing areas, such as the productive
irrigated oases. en, supervised classifications (Random
Forest Algorithm) were applied, techniques previously
used in other regions of the world to identify burned
areas and to map dryland forests (Chuvieco et al. 2019;
Manzo-Delgado and Lopez-Garcia 2020; Guida-Johnson
etal. 2021). We chose the Random Forest algorithm to
perform the classifications because of its effectiveness,
predictive power, and accuracy (Breinman 2001). is
technique has been used to map tree cover and carbon
stocks in ecologically related landscapes such as the Espi-
nal (González-Roglich and Swenson 2016).
For supervised classifications, each mosaic was clas-
sified with training samples based on regions of inter-
est, which provide a representative description of the
total population, defining different spectral classes. e
size recommended for one region of interest is linked
to the characteristics of the technique used for the clas-
sification and the type of object classified (Foody and
Mathur 2004). Training samples were set for the types
of burnt and unburnt areas and distributed through-
out the image to collect homogeneous samples without
ignoring the natural variability of the system (Foody and
Mathur 2004). e regions of interest were redefined in
each image, excluding routes, firewalls, rivers, and cattle
trails, to avoid erroneous results in the final product of
the classifications.
e classification results were corroborated with the
fire reports and blueprints available from the Department
of Renewable Natural Resources (DRNR) of the Govern-
ment of Mendoza. e fire reports are spreadsheets filled
out by firefighters of the Provincial Plan for Fire Manage-
ment, where the areas affected by fire are registered using
GPS. e fire blueprints are prepared by the firefighters
using an expedited methodology that consists of walking
through the areas affected by the fire and delineating the
affected area on a map. Validation points based on high-
resolution imagery available in Google Earth were used
to create error matrices for each classification. We tested
25 fire seasons using this method and found an average
accuracy of 83.80% (Additional file1: TableS1). For these
fire seasons, we calculated overall accuracy, producer
and user accuracy for fire class, and quantity and assign-
ment disagreement (Congalton 1991; Pontius and Mil-
lions 2011; Warrens 2015). Overall accuracy is the most
precise descriptive statistic, representing the proportion
of correctly classified pixels (Congalton 1991). Producer
accuracy indicates the probability that a reference pixel
is correctly classified, while user accuracy estimates the
probability that the pixel on the map represents that
class on the ground (Congalton 1991). e disagreement
measure is an overall accuracy complement that can be
decomposed into quantity and allocation disagreement
(Warrens 2015).
Finally, we observed that the total area burned for the
1985–1986, 1986–1987, and 1987–1988 seasons was con-
sistent between Loggio (1992) and our satellite-derived
data. en, we added the area burned in the 1984–1985
season obtained by Loggio (1992) to the fire record, but
this data was not included in the statistical analysis.
A 39-year burned area record was developed from the
available information. We then created maps of areas
with different post-fire recovery periods, grouping fires
into 5-year periods, and fire recurrence (number of fires
during the period) using map algebra. Once the database

Page 5 of 14
Villagraetal. Ecological Processes (2024) 13:5
was organized and the burned area for each season was
generated in a raster format, the map algebra was applied
using the tools available in GIS. In this way, the raster
combination of each year in successive layers allowed
us to identify the fire episodes that occurred during the
study period, the unburned areas, and the recurrence of
fires in the entire area (Collado and Echeverría 2005). In
order to identify dominant cycles of fire recurrence, the
burned area record for the period 1984–2023 was ana-
lyzed using Blackman–Tukey spectral analysis (Jenkins
and Watts 1968).
Relationships betweenre andclimate
Climate data were collected from various stations in
the vicinity of the study area. For precipitation, records
from the following stations were used: San Rafael Air-
port, Río Atuel, Ñacuñán (all in Mendoza Province), and
Santa Rosa (in La Pampa Province). Except for Rio Atuel,
where data were not available, the same station records
were used for temperature. For each individual record
to have the same weight in the regional record, we nor-
malized the temperature series to the 1975–2023 period
common to all stations. e deviations of the data of
each series were determined with respect to the mean
of the common period (1975–2023), and this value was
divided by the standard deviation of the series in the
common period. Finally, we obtained the regional record
by averaging the normalized deviations of the data from
all four-station series. For precipitation, we obtained
the percentages of precipitation relative to the average
precipitation during the same common period for each
period analyzed (year, season, or month). e regional
mean was obtained by averaging the values from all five
stations.
Relationships between annual burnt area and regional
variations in precipitation and temperature were deter-
mined using correlation matrices during the same com-
mon period 1984–2023. Prior to analysis, the burnt area
data were log-transformed and the normality of the
variables was analyzed. Since some of the series showed
non-normal distributions, relationships were determined
using Spearman correlations, which provide more robust
relationships for non-normal distributions (Zar 1984).
After identifying the monthly climate data most strongly
related to fire occurrence, we grouped them into seasonal
averages.
We used Superposed Epoch Analysis (SEA) to assess
the influence of climatic conditions in previous years and
the year of fire occurrence (Swetnam 1993). e six fire
seasons with the largest (1986–1987, 1993–1994, 2000–
2001, 2003–2004, 2013–2014, 2017–2018) and small-
est (1991–1992, 1996–1997, 2004–2005, 2011–2012,
2014–2015, 2015–2016) burned areas were selected for
analysis. Seasonal (November–December) precipitation
and (January–February) temperature records in a 6-year
window, starting 3years before and ending 2years after
the fires, were considered in the SEA analysis. Although
climate conditions in years t + 1 and t + 2 do not influence
fires in year t, their inclusion can help to detect climate
conditions following fire years (Grissino-Mayer 2001).
e deviations for each of the 6-year windows (one per
fire event) were superimposed and averaged to obtain the
climatic patterns associated with the largest and smallest
burned areas. Monte Carlo simulation techniques were
used to evaluate the statistical significance of the result-
ing climatic patterns. 1000 simulations based on random
sampling with replacement were performed to determine
the probability of occurrence associated with the mean
deviations of the resulting precipitation and temperature
patterns (Mooney and Duval 1993). e EVENT program
(version 6.02P) was used to perform the SEA (http://
www. ltrr. arizo na. edu/ softw are. html). In both analyses,
significant differences were estimated at p < 0.05.
Finally, using the facilities provided by Climate
Explorer (https:// clime xp. knmi. nl/ start. cgi), we deter-
mined the ERA5 spatial patterns of precipitation and
temperature anomalies over South America south of 10°
S associated with the six seasons with the largest (1986–
1987, 1993–1994, 2000–2001, 2003–2004, 2013–2014,
2017–2018) and smallest (1991–1992, 1996–1997, 2004–
2005, 2011–2012, 2014–2015, 2015–2016) burned areas.
Initially, climate anomalies were calculated for the entire
1984–2023 study period. However, because four of the
six largest burned areas occurred in the first 20years of
our record, anomalies were also calculated for the peri-
ods 1984–2003 and 2004–2023. is allowed us to assess
whether the relationships between climate variability and
fire occurrence were stable over time.
Results
Fire history
A large part (1,489,209 ha) of the analyzed area
(2,729,797ha) suffered at least one fire in the last 39years.
Considering that there are areas with high fire recur-
rence, the total area burned in the entire period 1985–
2023 is 3,133,883ha (Fig.1). e areas with the highest
fire recurrence were found in the central and southeast-
ern sectors, with more than 10 fire events between 1985
and 2023. e highest percentage of unburned area was
found in the southwest and northeast sectors (Fig.1). As
a result, the region is a mosaic of areas with different fire
recurrences (Fig.1) and post-fire recovery times (Fig.2).
e areas affected by fire showed a high interan-
nual variability, with seasons without fires or exceed-
ing 450,000 ha burnt. Within the period analyzed, 6
seasons were distinguished with fire events larger than

Page 6 of 14
Villagraetal. Ecological Processes (2024) 13:5
140,000 ha: 1986–1987, 1993–1994, 2000–2001, 2003–
2004, 2013–2014 and 2017–2018. ere were no two
consecutive years with large fire events (Fig.3).
Relationships betweenre andclimate
Relationships between interannual variation in total
burned area and climatic factors showed that fire extent
was inversely related to total precipitation during the
current growing season and positively related to precipi-
tation during the previous growing season (Figs.3, 4). In
contrast, the burned area was positively related to tem-
perature during the growing season and inversely related
to temperature during the previous year (Figs.3, 4). e
strongest relationship between burnt area and precipita-
tion during the period 1984–2023 was observed for the
period November–December during the season of the
fire events (r = − 0.52, p < 0.05; Table 1). e strongest
relationship between burnt area and precipitation during
the season preceding the fire occurred when considering
the late winter–spring period (August–October, r = 0.54;
p = 0.001; Table 1). Temperatures during the current
summer were positively correlated with total burnt area,
reaching statistical significance in February (r = 0.39;
p = 0.02) (Fig. 4). e mean temperature of the period
Fig. 2 Areas with different post-fire recovery times in southeastern Mendoza. The green zone in the location map represents the study area. The
different colors represent the recovery time in years since the last fire

Page 7 of 14
Villagraetal. Ecological Processes (2024) 13:5
January–February was the most strongly correlated with
the burnt area (Table1). e years with the largest burnt
area showed higher than average temperatures in Janu-
ary–February (Fig. 3). In contrast, spring temperatures
were not correlated with burned area. Temperatures
during the previous year were negatively correlated with
total burned area (Fig.4; Table1).
Spectral analysis showed that both November–Decem-
ber precipitation and burned area showed a very pro-
nounced 6–7year cycle (Fig. 5). We observed that the
six seasons with the most extensive fires coincided with
November–December precipitation 60% below the mean.
However, not all seasons with precipitation 60% below
the November–December mean resulted in fire occur-
rence (Fig.3). In fact, similar conditions were recorded in
other seasons (e.g., 1988–1989, 2001–2002, 2005–2006,
2010–2011, 2019–2020). Similarly, not all years with
abundant rainfall and low temperatures lead to large fires
in the following year.
Superposed Epoch Analysis (SEA) showed that the
years with the largest area burned were associated with
above average precipitation in the previous growing sea-
son, but higher temperatures and significantly lower pre-
cipitation in the year of the fire. e years with the fewest
hectares burned showed a pattern consistent with high
precipitation and low temperatures in the year of fire
(Fig.6).
e spatial patterns of precipitation and temperature
anomalies associated with the six years with the largest
and smallest areas burned during the period 1984–2023
showed that fire seasons were associated with Novem-
ber–December precipitation deficits along a territo-
rial band over Argentina, extending from the Andean
Piedmont at 35°S to the Atlantic coast at 39°S (Fig.7a).
e years with largest burned areas showed positive but
weaker temperature anomalies in January and February
over the same territory (not shown). Over the study area,
negative November–December precipitation anoma-
lies associated with large fires range from 12 to 24 mm
with respect to the average (Fig.7a). In contrast, positive
November–December precipitation anomalies between 6
and 12 mm were recorded during the seasons with small-
est burned areas (Fig.7b). A similar analysis, considering
separately the periods 1984–2003 and 2004–2023, clearly
showed that the spatial patterns of November–Decem-
ber precipitation anomalies were comparatively differ-
ent between the two intervals. During the earlier period,
Fig. 3 Burnt surface area and the regional climate of southeastern
Mendoza for the 1984 to 2023 period. Relationship between annual
and the November–December rainfall (top), measured
as a percentage of the mean for that period. Relationship
between the surface burnt area and the deviation of the mean
annual and January–February temperature (bottom)
Fig. 4 Variations of the Spearman correlation coefficient
between burned area and monthly mean temperature (top)
and between area burned and anomalies in monthly total
precipitation (bottom) from July of the previous year to June
of the current year. To facilitate visualization, the relationships are
also shown in smoothed form using a 6-month spline filter interval

Page 8 of 14
Villagraetal. Ecological Processes (2024) 13:5
precipitation deficits associated with years of extensive
fire (> 140,000 ha) were much more pronounced over the
study area, with precipitation reductions of more than 30
mm (Fig.7c). In contrast, spatial precipitation deficits in
the more recent period were very small or absent in the
study area (Fig.7d). e analysis of the regional precipi-
tation series showed that during the period 1984–2003,
the November–December precipitation was more abun-
dant (110% of the mean), but also much more variable
(SD 57%). In contrast, during the period 2004–2023, pre-
cipitation was much lower (88% of the mean) and less
variable between years (38%, Fig.7e).
Discussion
Fire history
e interannual variability in the extent of fires in the
southern sector of Central Monte is large, with almost no
fires in some years and exceeding 600,000ha burned in
others. In the last four decades, half of the total area of
more than 2,700,000ha has burned at least once, includ-
ing sectors that have experienced up to 14 fires during
this period. Collado and Echeverría (2005) and Fischer
etal. (2012), using a combination of remote sensing and
GIS techniques to assess fire variability, suggest that eco-
system dynamics in semi-arid regions of Argentina are
strongly controlled by fire, as has been observed in vari-
ous forests around the world (Turner etal. 2011; Pricope
and Binford 2012; Ibarra-Montoya and Huerta-Martinez
2016; Manzo-Delgado and Lopez-Garcia 2020).
e existence of highly coherent common oscillations
between interannual variations in burned areas and
November–December precipitation (Fig.5) suggest that
regional climate variability is an important driver of fire
in Central Monte, as has been documented by numer-
ous authors for different ecosystems around the world
(Veblen etal. 1999; Zhang etal. 2010; Turner etal. 2011;
Ibarra-Montoya and Huerta-Martínez 2016; Manzo-
Delgado and López-García 2020). However, spatial het-
erogeneity in the distribution and recurrence of fires did
not appear to be random, as would be expected when
climate is the only factor regulating fire probability. is
was demonstrated by the frequent occurrence of fires
in certain areas and the lack of fires in others (Figs. 1,
2). is suggests that other variables, including physi-
cal (i.e., topography), biological (i.e., vegetation type),
and/or anthropogenic (i.e., land use) factors simultane-
ously influence fire regimes (Whelan 1995; Fischer etal.
2012; Archibald etal. 2018). For example, the area with
the highest fire return is largely dominated by grasses,
with scattered emergent woody plants (Prosopis flexuosa,
Geoffroea decorticans, Larrea spp.) at low densities (Vil-
lagra etal. 2009; Tacchini etal. 2014). Grasslands seem
to favor the occurrence and spread of fires, as has been
Table 1 Correlations between burnt area and the sum of
precipitations by periods, and between burnt area and the mean
temperature by period
The correlations in bold are signicant by p < 0.05. r = correlation coecient and
p = signicance value. N = 39. Empty cells indicate no data
Period Precipitation Temperature
r p r p
Fire season Jul–Jun − 0.25 0.14 0.09 0.59
Jul–Dec − 0.34 0.04 − 0.03 0.84
Jan–Jun − 0.05 0.76 0.23 0.17
Nov–Dec − 0.49 0.003 – –
Oct–Dec − 0.40 0.01 – –
Sep–Nov – – − 0.25 0.14
Dec–Feb 0.40 0.01
Jan–Feb 0.42 0.01
Jan–Mar 0.41 0.01
Season previous to fire Jul–Jun 0.30 0.07 − 0.40 0.01
Jul–Dec 0.54 0.001 − 0.37 0.02
Jan–Jun − 0.001 0.99 − 0.32 0.05
Oct–Dec − 0.40 0.01 – –
Nov–Dec − 0.49 0.003 – –
Nov–Jan − 0.50 0.002 – –
Jan–Feb – – − 0.20 0.22
Dec–Feb – – − 0.26 0,11
Jan–Mar – – − 0.19 0.25
Fig. 5 Blackman–Tukey (BTM) power spectra of interannual
variations in burnt area and Nov–Dec precipitation in the study area
estimated over the interval 1984–2023 (a). The coherency spectrum
between these two records is shown in b. Records are highly
coherent at 6–8-year wavelengths (light blue box)

Page 9 of 14
Villagraetal. Ecological Processes (2024) 13:5
observed in other semi-arid regions (Verhoeven et al.
2020). An interesting aspect to be analyzed in future
research is the existence of possible positive feedbacks
between fire and vegetation that may contribute to the
maintenance of grasses in a relatively stable stage, as has
been observed in Monte Austral (Rostagno etal. 2006).
In this sense, high fire recurrence modifies the den-
sity and growth morphology of Prosopis flexuosa (the
dominant tree species in the area), creating a landscape
dominated by shorter individuals with numerous smaller
stems, a loss of diametric classes > 20cm basal diameter,
concurrent with an increase in the grass layer (Cesca
2013; Cesca etal. 2014). Could this change in community
structure favor fire recurrence by creating positive feed-
back between vegetation and fire?
Our study showed that the spatial variability of fire
creates a mosaic of patches with different post-fire
recovery times and fire recurrences. is fire-induced
heterogeneity could increase diversity at the regional
scale, affecting forest and livestock productivity in the
area (Scholes and Walker 1993; Huston 1998; Zinck
etal. 2010; Cesca etal. 2014; Burkle etal. 2015). How-
ever, the positive effect of fire on landscape diversity
appears to be associated with fires of low severity and
small size (Zinck etal. 2010; Miller and Safford 2020),
while landscape diversity decreases in areas with high
severity or recurrent extended fires (Mahood and Balch
2019; Zinck etal. 2010; Moghli etal. 2022). In contrast,
the effect of fire on local diversity is variable and cannot
be clearly established at smaller spatial scales (Giorgis
et al. 2021). However, frequent and severe fires have
been observed to reduce alpha diversity, particularly of
woody species (Mahood and Balch 2019). Besides, fire
recurrence has been observed to reduce many ecosys-
tem services and ecosystem multifunctionality, effects
that can be buffered in areas with long post-fire recov-
ery times (Moghli etal. 2022). Future research should
investigate the causes of the irregular shape and size of
fires. In this sense, firefighting techniques developed by
the Provincial Fire Management Plan are likely to influ-
ence the duration and size of fires because, as observed
in many cases, firebreaks stop fire spread (personal
communication from Mendoza firefighters). is sug-
gests that fire management tasks could modify the
spatial heterogeneity of the landscape. us, the evalu-
ation of the relationships between the size, severity and
frequency of fire events in the study area could help to
model the spatial heterogeneity generated by fires on
vegetation structure and diversity, useful information
for management strategies in a region where the main
economic activities are supported by grass and forest
productivity.
Fig. 6 Regional January–February temperature anomalies (in °C) and November–December precipitation anomalies (in percent of mean)
for 3 years before and 2 years after the fire (year 0). Deviations on the left correspond to the 6 years with the largest area burned, while those
on the right correspond to the 6 years with the smallest area burned in the interval 1984–2023. The bars with asterisks indicate significant
differences (p < 0.05) from the means obtained in 1000 Monte Carlo simulations based on the same number of years as the events (Mooney
and Duval 1993). The dashed lines correspond to the 95% confidence interval

Page 10 of 14
Villagraetal. Ecological Processes (2024) 13:5
Relationships betweenre andclimate
e three approaches used here supported a strong rela-
tionship between the occurrence of extensive fires and
climate in southeastern Mendoza. At seasonal scale, fires
were largely favored by the combination of abundant
spring–early summer precipitation in the previous grow-
ing season, followed by below-average spring–early
summer precipitation in the current fire year. We pro-
pose that wet spring–early summer in the previous year
would favor the accumulation of fuels that increase their
Fig. 7 Spatial association of November–December precipitation anomalies with: a the six years with largest burned areas during the period
1984–2023; b the 6 years with the smallest burned areas during the same period; c the years with the largest (> 140,000 ha) burned areas
for the period 1984–2003; and d the years with the largest burned areas for the period 2004–2023. The black square represents the study area. Inset
(e): precipitation anomalies (percentage of the long-term mean) for both periods. Standard deviations (SD) are also shown for both periods

Page 11 of 14
Villagraetal. Ecological Processes (2024) 13:5
flammability during the subsequent dry-hot growing
season. is coordinated process should be critical in
arid environments where biomass production is lower
in most years. Our results supported previously docu-
mented climate-fire relationships in arid to semi-arid
regions, suggesting that the occurrence of large fires
depends on the accumulation of fuels such as desiccated
grasses, which are more susceptible to fire in dry years
(Swetnam and Betancourt 1998; Brooks and Matchett
2006; Pausas and Bradstock 2007; Pricope and Binford
2012; Verhoeven etal. 2020). Fischer etal. (2012, 2015)
noted that the highest density of fires in the central semi-
arid region of Argentina overlaps with our study area.
ese authors found that fires are concentrated during
the winter–spring transition, when water deficit peaks
due to the increase in temperature after winter and the
delay in summer rains. Verhoeven etal. (2020) also high-
lighted the importance of pre-fire year rainfall in Austral-
ia’s drylands. However, they emphasized the importance
of rainfall two years before the fire, while area burned
was negatively related to rainfall in the previous year.
In contrast, Turner et al. (2008) reported that burned
area in semi-arid northern Australia was related to rain-
fall in the previous year. e semi-arid climatic regimes
in both southeastern Mendoza and northern Australia
may explain the consistency of our results with those of
Turner etal. (2008). In semi-arid environments, a rainy
season may provide sufficient water inputs for abundant
fuel production, whereas in arid areas, biomass accumu-
lation may require more time. e degree of water deficit
and the duration of the dry season are essential factors
that determine the amount and state of fuel available for
ignition. Llorens and Frank (2003) also document large
fires in Prosopis caldenia forests at the end of the 1990s
associated with more abundant precipitation, which in
turn increased the amount of small fuel production.
At the interannual scale, spectral analysis revealed
the presence of pronounced 6–7 year oscillations in
regional November–December precipitation over the
last 40years. ese cycles were highly coherent with the
occurrence of extensive fires in southeastern Mendoza
every 6–7 years (Fig. 5), suggesting that variations in
precipitation during the fire season are the main driver
of interannual variations in total burned area. However,
the spatial relationships between climatic conditions and
fires do not appear to be stable over time. For example,
the spatial relationships between November–Decem-
ber precipitation and fires > 140,000 ha were stronger
and more geographically extensive during the period
1984–2003 (Fig.7). Interestingly, 4 of the 6 largest fires
recorded since 1984 occurred during this period. Dur-
ing this time interval, precipitation was 30% higher than
in the more recent 2004–2023 period (Fig.7e), allowing
for greater fuel production and accumulation. Similarly,
November–December precipitation was 1.5 SD more
variable in the 1984–2003 period than in the 2004–2023
period (Fig.7e), favoring not only greater fuel production
in years of abundant precipitation, but also intensify-
ing fuel drying during dry-hot fire seasons. Our results
suggest that fire occurrence in the Monte is not only
dependent on dry conditions during fire seasons but is
also exacerbated by greater interannual variability in
precipitation. Periods with higher interannual variability
would favor not only the production of fuel in relatively
wet years, but also strong desiccation in dry years, induc-
ing the ignition and spread of fires.
According to the national fire management plan, the
proportions of fires started by natural (lighting) and
human (including unintentional and deliberate) ignition
sources were similar in proportions during the period
2000 and 2010 in the study area (Cesca 2013). In any case,
ignition sources seem to be abundant across the region
(Rasmussen etal. 2014; DiGangi etal. 2022) and should
play a minor role as control factors of the temporal vari-
ability of fire occurrence. Defossé and Urretavizcaya
(2003) pointed out that once ignited, the evolution of the
fire depends on the quantity and characteristics (flam-
mability, size) of the fuel, relative humidity, temperature,
wind, slope, and exposure. Fischer etal. (2015) observed
that fire duration and extent depend on biomass regu-
lation, mainly in shrublands and agricultural lands,
whereas wildfires largely depend on fuel conditions and
the presence of degraded forests (i.e., grasslands). More
research is needed to understand the complex interac-
tions between climate, fuel availability and fire events.
Our results contribute to understanding the role of
weather factors but an integrated analysis of the complex
interactions among climate, fuel, and other involved fac-
tors is recommended for planning and management in
semi-arid areas, since fires usually occur on a regional
scale, with ecological consequences that transcend juris-
dictional boundaries (Avitabile etal. 2013).
One of the factors that could be important in con-
trolling fire occurrence is the previous occurrence of
large fires (e.g., 1986–1987, 1993–1994, 2000–2001 and
2017–2018) and this could explain the 6–7 year cycle
of burnt area observed in the spectral analysis. In these
cases, fire consumes most of the fuel and two to seven
growth seasons are needed to recover the fuel declin-
ing the probability of occurrence and the distance of fire
spread. In example, after a large fire (1986–1987), at least
two humid seasons (e.g., 1988–1989, 1990–1991) would
be necessary to create the amount of fuel for the next
event (e.g., 1993–1994). erefore, our results supported
the idea that large fire events suppress the probability of
fire occurrence in the coming years. Further research is

Page 12 of 14
Villagraetal. Ecological Processes (2024) 13:5
needed to understand the mutual regulation between fuel
availability and fire events.
Climate change models project an increase in the
occurrence of fire weather conditions under both mod-
erate and high levels of global warming, future scenarios
that are of great concern to society (IPCC 2021). How-
ever, some authors suggested that the global trend in
burned area is decreasing or not increasing significantly,
as the effects of warmer temperatures and drought are
offset by increased humidity, population growth, and
changes in land use (Doerr and Santin 2016; Arora and
Melton 2018; Forkel etal. 2019). us, the global trend
is the result of the balance of compensating trends of
controls occurring at the regional scale. Our results were
consistent with climatic conditions being one of the main
drivers of fire occurrence in the Central Monte, south-
east of Mendoza. Interannual climatic variability appears
to be the main factor associated with large fire events.
Model simulations for the twenty-first century predict an
increase in the amount and temporal variability of pre-
cipitation, warmer temperatures, and a high frequency of
thunderstorms for the Central Monte (Labraga and Vil-
lalba 2009). e increase in precipitation may lead to an
increase in biomass production, which, combined with
more frequent drought events, will increase fuel avail-
ability, a limited fire resource in arid lands (Forkel etal.
2019). e highest frequency of fires in Central Monte
occurs in areas with total annual precipitation between
200 and 400 mm, where wet and dry years frequently
alternate (Villagra etal. 2009). In addition, the increase
in the number of electrical storms would increase the
number of ignition sources, although this factor is not
important for the expansion of fires in the study area. In
addition, human activities also affect the extent and shape
of burned areas through firefighting techniques, the
influence of roads, and land-use changes. e diversity
of factors influencing fire suggests that future changes in
fire dynamics are complex and not straightforward.
e knowledge provided by our study is important
for developing guidelines for fire decision-makers in the
southeastern forests of Mendoza. Particular attention
should be paid to wet years followed by dry years. In
this sense, preventive and contingency resources should
be ready, focusing on firewall maintenance and personal
availability.
Conclusions
Most of the study area in the southern Central Monte has
been affected by at least one wildfire in the last 39years.
e distribution of fires is spatially heterogeneous and
temporally variable, resulting in a mosaic of patches with
different recurrence and recovery times. e spatial het-
erogeneity is related to vegetation patterns and land use.
e temporal variability of fires is strongly influenced by
climate variability, since fires are favored by the combi-
nation of abundant spring–early summer precipitation in
the previous growing season, followed by below-average
spring–early summer precipitation in the current fire
year. ese conditions could increase fuel production
and result in subsequent drying. Consequently, the larg-
est fires are concentrated in periods of high interannual
precipitation variability. Climate change scenarios indi-
cate an increase in temperature, precipitation variability,
and storm occurrence in the region, suggesting future
changes in fire dynamics induced by increased fuel pro-
duction and more frequent electrical storms. Our results
contribute to the development of fire guidelines to deci-
sion-makers in southeastern Mendoza forests, focusing
on periods of wet years followed by dry years that favor
fire occurrence and spread.
Abbreviations
GIS Geographical Information System
GPS Global Positioning System
SEA Superposed Epoch Analysis
SD Standard deviation
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s13717- 023- 00481-6.
Additional le1: TableS1. Accuracy indexes from error matrixes based
on high-resolution images available from Google Earth.
Acknowledgements
We thank Gualberto Zalazar, Anahí Miner, Alberto Ripalta, Carlos Cesca, Sergio
Londero, Alberto Ripalta, Agustina Aranda and Hugo Debandi for their col-
laboration at different stages of the project. We thank Anabela Bonada, for
improving the English version of the manuscript. We thank to the Department
of Renewable Natural Resources (DRNR) of the Government of Mendoza and
the Provincial Plan for Fire Management for providing us with the fire blue-
prints and collaborating in the corroboration of the first fire maps.
Author contributions
PEV and EC developed the ideas for this manuscript, contributed to the
design, to the analysis and interpretation of data, to draft and revise the
manuscript. EC, LMA and SD conducted the remote sensing and geographic
information systems analysis. RV contributed to the analysis and interpretation
of data and to revise the manuscript. All authors contributed to the writing of
the manuscript. All authors read and approved the final manuscript.
Funding
This research was supported by Consejo Nacional de Investigaciones
Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo, Ministerio de
Ciencia, Tecnología e Innovación, Argentina (PITES 03 and Red Bosque-Clima
contribution No. 1).
Availability of data and materials
The datasets used and/or analyzed during the current study are available from
the corresponding author on reasonable request.

Page 13 of 14
Villagraetal. Ecological Processes (2024) 13:5
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Received: 29 September 2023 Accepted: 26 December 2023
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