Forest Ecology and Management 329 (2014) 49–58

Contents lists available at ScienceDirect

Forest Ecology and Management

journal homepage: www.elsevier .com/locate / foreco

Drivers of lightning- and human-caused fire regimes in the Great Xing’anMountains

http://dx.doi.org/10.1016/j.foreco.2014.05.0470378-1127/� 2014 Elsevier B.V. All rights reserved.

⇑ Corresponding author at: State Key Laboratory of Vegetation and Environmen-tal Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.Tel.: +86 010 62838268; fax: +86 10 82595962.

E-mail address: gszhou@ibcas.ac.cn (G. Zhou).

Tianyu Hu a,c, Guangsheng Zhou a,b,⇑a State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, Chinab Chinese Academy of Meteorological Sciences, Beijing 100081, Chinac University of Chinese Academy of Sciences, Beijing 100049, China

a r t i c l e i n f o

Article history:Received 3 March 2014Received in revised form 19 May 2014Accepted 28 May 2014

Keywords:Great Xing’an MountainsLightning-caused fireHuman-caused fireDriving factorsSocial factorsPopulation

a b s t r a c t

Fire is a major disturbance in forest ecosystems. An understanding of the trends of forest fires and of themain factors driving ignition is important both for establishing effective fire prevention policies and forpredicting future changes. By analyzing climate, fuel, and social influences, we identified the majorfactors controlling changes in lightning- and human-caused fire regimes over the period 1967–2006 inthe Great Xing’an Mountains, which is the most fire-prone area of China. We found that both fire fre-quency and burned area of lightning-caused fires increased during this period, but human-caused firesshowed the opposite trend. The first lightning-caused fire in the spring fire season occurred earlier eachyear, but the first human-ignited fire was generally delayed. These changes imply that the occurrences oflightning- and human-caused fires were driven by different mechanisms. Climatic factors are the domi-nant drivers of lightning-caused fires, but not of human-caused fires. However, the driving factors behindthe first fire occurrence time in the spring fire season for the two ignition mechanisms were similar, i.e.,different types of accumulated energy. To avoid bias in projecting fire events, management policy shouldbe considered in areas in which human-caused fires dominate, e.g., the Great Xing’an Mountains.

� 2014 Elsevier B.V. All rights reserved.

1. Introduction

Forest fire plays an important role in shaping forest ecosystemsby affecting age-structures and landscape patterns, and alsothreatens public safety, property, and forest resources (Groganet al., 2000; Liu et al., 2005; Ryan and Williams, 2011). Fire regimesare defined by the frequency, size, seasonality, and intensity of fireswithin a given area (Flannigan et al., 2000). Currently, fire regimesare changing over most fire-prone area of the globe, demonstratingincreased fire frequency, larger burned areas, and longer fire sea-sons (Pausas, 2004; Kasischke and Turetsky, 2006; Westerlinget al., 2006; Littell et al., 2009; Senici et al., 2010; San-Miguel-Ayanz et al., 2012). Thus, identifying the main driving factorsbehind these changes has become crucial, in order to develop sus-tainable management strategies for biodiversity conservation andfor long-term forest productivity within a balanced forestecosystem.

Climatic factors and anthropogenic activities are considered asthe two major causes of fire regime changes. Climatic factors, suchas high temperature, low precipitation, strong wind, and long-termdrought have been long recognized as determinants of severe fireactivity (Vázquez and Moreno, 1993; Schulte et al., 2005; Taylorand Beaty, 2005; Xiao and Zhuang, 2007; Vasilakos et al., 2009;Pausas and Fernandez-Munoz, 2012). Climate factors also affectfuel loads through forest biomass production and litter decomposi-tion rates (Kitzberger et al., 1997; Clark et al., 2002). The timing ofthe fire season can be affected by early spring snowmelt(Westerling et al., 2006). Although climatic variables have beenfound to affect fire activity in many fire-prone areas, their degreeof impact might differ greatly depending on the geographicalregion and the ecosystem type. For instance, drought was reportedas a strongly influential factor on fire frequency in the boreal for-ests of eastern Canada (Carcaillet et al., 2001), whereas it has beenreported to show no correlation with fire frequency in the Pinusponderosa forests of California (Fry and Stephens, 2006).

Anthropogenic activities might also play a significant role in re-shaping fire regimes by altering the fuel characteristics. In the past,human activities have reduced fire severity and fire frequency byfragmenting landscapes and decreasing fuel loads through theprocesses of transforming forests into farmland and plantations

50 T. Hu, G. Zhou / Forest Ecology and Management 329 (2014) 49–58

(Granstrom and Niklasson, 2008; Bowman et al., 2011; Archibaldet al., 2012). Nowadays, tree planting and fire-suppressing activi-ties enhance fuel loads in some ecosystems and thus, increase fireactivity (Minnich, 1983, 2001; Cleland et al., 2004; Fule andLaughlin, 2007). However, the degree to which anthropogenicactivities affect fire activity is still under debate. Keeley andZedler (2009) indicated that the assertion that fire suppressionhas altered the fuel structure in ways that make landscapes morevulnerable to large fires is demonstrably false for southern Califor-nia. Furthermore, it is still debated whether fire suppression activ-ities and human ignition were the causes for fire regime change inthe boreal forests of Ontario, Canada (Miyanishi and Johnson,2001; Ward et al., 2001; Bridge et al., 2005; Martell and Sun, 2008).

Delineating the southern extent of the Asian boreal forest, theGreat Xing’an Mountains have suffered frequent fire disturbanceslike other boreal forest areas. However, the fire regime in the GreatXing’an Mountains is quite different from other boreal forests, suchas those of Russian or Canada. Human ignition is one of the majorcauses of fire within this area, and lightning and anthropogenicactivities are also other significant factors that cause ignition (Huand Zhou, unpublished). Therefore, an understanding of the drivingfactors behind these two sources of ignition is essential for improv-ing sustainable forest management in the region. Many previousstudies have focused on lightning-caused ignition, but littleresearch has done on human-caused fires. As anthropogenic activ-ities intensify, human-caused fires are expected to exert similar, ifnot greater influence, compared with lightning-caused fires(Wotton et al., 2003; San-Miguel-Ayanz et al., 2012).

In this study, we identified lightning- and human-caused firesin the Great Xing’an Mountains during 1967–2006. We focusedour analyses on three critical elements of fire regime: fire occur-rence, burned area, and seasonality. Furthermore, we assembledinformation on meteorological variables, fuel supply, and socialfactors to identify the principal drivers associated with changesin fire regimes during the study period.

2. Materials and methods

2.1. Study area

The Great Xing’an Mountains (50�100–53�330N 121�120–127�000E) lie in Northeast China covering an area of approximately8.35 � 106 ha (Fig. 1). The regional climate is within the cool tem-perate zone with average annual temperature of �2 to �4 �C andannual precipitation of 350–500 mm. Altitude ranges from 300–1400 m. The dominant vegetation of the area is boreal coniferousforests. The dominant species include larch (Larix gmelinii), pine(Pinus sylvestris var. mongolica), birch (Betula platyphylla), shrubs(Vaccinium spp., Rhododendron dauricum and Ledum palustre), andgrass (Carex tristachya) forming landscapes with various foresttypes (Xu, 1998).

2.2. Fire data

The 40-year fire data set (1967–2006) of the Great Xing’anMountains was provided by the Heilongjiang Headquarters of For-est Fire Prevention. It includes the date and location of fire origin,fire size, date extinguished, vegetation type, ignition cause, and firesuppression information. From the records of cause of ignition, weclassified all fires into two ignition types: those caused by lightningand those caused by human activities, such as arson, cooking,smoking, trains, and power lines. Fires recorded as unknown(about 18.95%) were classified as caused by humans, because it islikely these fires were recorded as unknown by local forest manag-ers to avoid punishment when lightning was ruled out as the

possible cause. After converting the reported wildfire occurrencedates into day of year (DOY), we divided each year into two fireseasons, ‘‘spring fire season’’ (DOY 65 to 240) and ‘‘autumn fire sea-son’’ (DOY 255 to 305), based on the histogram of fire dates. Con-sequently, the fire season extended from DOY 65 to 305, and thenon-fire season extended from DOY 306-365/366 in the previousyear and from DOY 1-64 in the current year. The division of theyear into the fire and non-fire seasons was only used in the pro-cessing of meteorological data.

2.3. Meteorological data

The meteorological data were obtained from the database of theNational Meteorological Information Center of the China Meteoro-logical Administration. We selected all five stations within theGreat Xing’an Mountains region: Jiagedaqi, Huma, Xinlin, Tahe,and Mohe. All station data cover the period of the fire records,except Tahe and Xinlin, which commenced on January 1, 1972.Daily meteorological records include maximum, mean, and mini-mum temperature, precipitation, and relative humidity.

2.4. Driving factors

Precipitation, temperature, and a drought index were chosen asclimatic factors to explain lightning- and human-caused fires(Table 1). The Keetch–Byram Drought Index (KBDI), developedfor fire danger by Keetch and Byram (1968), was selected in thisresearch because it is used widely in the United States, and becauseit has been reported to perform better than the Palmer DroughtSeverity Index in predicting lightning-caused fires in the Great Xin-g’an Mountains (Jia et al., 2011). Jia et al. (2011) showed the coef-ficient of determination (R2) between annual counts of lightning-caused fires with PDSI and KBDI is respectively 0.09, 0.28 from1972 to 2005. Since the fire frequency and burned area are relatedto annual or fire-season meteorological conditions, annual and fire-season temporal scales data are calculated in this study.

It has been proposed that the relative role of fuel load, in deter-mining fire activity, changes in relation to the global productivitygradient (Pausas and Bradstock, 2007). The Great Xing’an Moun-tains lie within the cool temperate zone in which decompositionof ground litter is very slow. If there were no fuel treatment or dis-turbance to clear the litter, the surface fine fuel load would be suf-ficient to start a fire if there were ignition (Pausas and Bradstock,2007). As fire causes rapid consumption of fuel, and because theseverity of each fire event is rather unknown, the volume of con-sumed or remaining surface fuel load is unclear and difficult toestimate. Therefore, we substituted the consumed fuel load withaccumulated burned area as the fuel limitation factor for fire igni-tion. Considering that the net primary reproduction of boreal foresttakes about 10 years to recover from a fire disturbance (Hardenet al., 2000; Hicke et al., 2003), we use the 10-year moving accu-mulated burned area in our analysis.

Population and government policy were selected as the socialpredictors of human-caused fires. Different forest managementpolicies have different effects on residents’ activities, which is cru-cial with regard to human-caused fires. We selected governmentpolicy change because of the significant policy change followingthe Black Dragon Fire in 1987, which burned almost one eighthof the total study area. As there is lack of accurate official yearlypopulation data, the population trend was reconstructed from1967 to 2006. As reported by Liu et al. (2012a,b), the populationin this area grew from 30,000 to 116,000 during 1964–1968. Thiswas followed by a period of significant movement from 1969 to1986, when 787,000 moved in and 599,000 moved out. The popu-lation remained relatively steady during 1987–1997 with anannual growth rate of 2%. During 1998–2011, an annual growth

Fig. 1. The location of Great Xing’an Mountains in China.

Table 1Abbreviations and units of the driving factors used in this study.

Abbreviation Parameters Units

Meteorological factorsTAP Total annual precipitation mmTFP Total precipitation in fire season mmTNP Total precipitation in non-fire season mmMAT Mean annual temperature �CMFT Mean temperature during fire season �CXFT Mean maximum temperature during fire

season�C

KBDI Mean annual KBDI None (0–800)

KBDIf Mean KBDI during fire season None (0–800)

KBDIi KBDI at ignition day None (0–800)

APa Accumulate precipitation anomalyat ignition day

mm

GDDa Growing degree-days anomalyat ignition day

�C�day

Melt Timing of spring snowmelt, express using DOY Days

Fuel supplyABA Accumulated burned area, ten years moving

averageHa

Social factorsLocal government policy NoneLocal population Persons

T. Hu, G. Zhou / Forest Ecology and Management 329 (2014) 49–58 51

rate of �0.04% meant that the population declined slightly to about516,000 by the end of 2011.

Temperature and relative humidity drive fine fuel moisture atdaily time scales, which is an important factor in fire ignition.

Therefore, the KBDI, accumulated precipitation anomaly, andgrowing degree days anomaly (GDDa) of the ignition day wereselected as the first and last fire occurrence time predictors(Table 1), which represent the onset and end of the fire season.GDDa is evaluated with respect to the energy received by the sur-face compared with the mean level. High values of GDDa meanmore energy can be used for snowmelt during the early stages ofthe spring fire season which will induce an early snowmelt. Fur-thermore, the timing of spring snowmelt and the non-fire-seasonprecipitation were chosen to explain the first occurrence fire timein the spring fire season. The timing of spring snowmelt was calcu-lated using the degree-day method (Bengtsson, 1976). The degree-day factor in degree-day method varies with the ecosystem and noestimate is available for the Great Xing’an Mountains; therefore,we used Tang and Zhuang’s (2011) parameters derived for theAlaskan boreal forest.

2.5. Data analysis

Simple linear regression and second order polynomial regres-sion were implemented to detect the trends of both lightning-caused and human-caused fires. The Pearson correlation was usedto correlate fire regime characteristics with the explanatory vari-ables. Burned area (x) was converted by ln(1 + x) because it doesnot exhibit a normal distribution. We used the T-test on differencesbetween fire frequency and burned area before and after policychanges. We grouped significant correlated variables into threebasic sets: meteorological factors, fuel supply, and social factors.To compare the explanatory power of the different sets, we parti-tioned the variation of response table Y with respect to the twoexplanatory tables using the varpart function in vegan of R 3.0.2

52 T. Hu, G. Zhou / Forest Ecology and Management 329 (2014) 49–58

(R Develop Core Team, 2013). This application uses partial multipleregression analysis for partitioning fire frequency as anindependent variable. We calculated adjusted R2 values becausethis is the only unbiased method (Peres-Neto et al., 2006).

3. Results

3.1. Lightning- and human-caused fire regimes

Fig. 2 shows that lightning- and human-caused fire regimesexhibit different trends during 1967–2006. Fire frequency andburned area ignited by lightning show an increasing trend(P < 0.001 and P = 0.603, respectively), and the first fire occurrencetime (FFOT) in the spring fire season is advanced (P = 0.042). Incontrast, the fire frequency and burned area of human-caused firesexhibit decreasing trends (P = 0.005 and P = 0.029, respectively),

Fig. 2. Trends of lightning- and human-caused fire regimes during 1967–2006. FFOT: fiautumn fire season.

and the FFOT displays a statistically significant delay (P < 0.001).The last fire occurrence time (LFOT) for fires ignited by lightningand humans in the spring fire season are similar. The LFOT is inadvanced trend and shift into a delay trend. The FFOT and LFOTof lightning- and human-caused fires in the autumn fire seasonare steady during this period (Fig. 2). Lightning fires were exclu-sively a spring phenomenon until 1990.

3.2. Temporal variation of explanatory variables

Fig. 3 shows the temporal analysis of meteorological variables.It can be seen that mean annual temperature increased signifi-cantly during 1967–2006 (P < 0.001). Both annual and fire-seasonprecipitation fluctuate significantly from year to year and do notshow any significant trend (P = 0.146, P = 0.251, respectively). KBDIshows a significant U- shaped trend during the same period

rst fire occurrence time; LFOT: last fire occurrence time; S1: spring fire season; S2:

Fig. 3. Trends of driving factors in the Great Xing’an Mountains during 1967–2006. N: number of observations (see Table 1 for explanation of acronyms).

T. Hu, G. Zhou / Forest Ecology and Management 329 (2014) 49–58 53

(P < 0.001), but the time of spring snowmelt does not demonstrateany significant trend (P = 0.716). The 10-year moving average ofaccumulated burned area reveals a significant decreasing trendthroughout the period (P < 0.001), especially during 1997–2002.The fire season variables show similar patterns to the annualvariables, except for total precipitation in the non-fire season,which increased markedly.

3.3. Correlation between driving factors and fire regime

The regimes of lightning- and human-caused fires respond toeach annual driving factor in various ways, as shown in Table 2.The lightning-caused fire frequency shows significant negativetemporal relationships with annual/fire-season precipitation andaccumulated burned area, but it is positively correlated with KBDI.Human-caused fires show significant negative relationships withtotal non-fire-season precipitation and mean temperature (fireseason and annual). There is no significant relationship between

maximum fire-season temperature and either lightning- orhuman-caused fires. The lightning-caused burned area is corre-lated only with fire-season KBDI, while the human-caused burnedarea is related to total non-fire-season precipitation and meantemperature (fire season and annual).

After the Black Dragon Fire of 1987, local forest managementauthorities adopted strict polices to reduce losses to fire. Thischange of policy led to a significant decrease in the frequency ofhuman-caused fires and burned area, but did not have any effecton lightning-caused fires (Fig. 4).

During the study period, the population steadily increased untilthe turn of the 21st century (Fig. 5c) and it is insignificantly corre-lated with human-caused fires (Fig. 5a). The frequency of human-caused fires is significantly positively correlated with populationduring the sub-period 1967–1982, but there is no significant corre-lation during the remainder of the period (Fig. 5a). The human-caused burned area does not show any significant correlation withpopulation either during the entire period or during the two sub-

Table 2Pearson’s correlation coefficient between fire frequency, area burned, and drivingfactors in the Great Xing’an Mountains during 1967–2006.

Factors Fire frequency Burned area

Lightning-caused

Human-caused

Lightning-caused

Human-caused

TAP �0.445** �0.143 �0.222 �0.117TFP �0.483** �0.110 �0.227 �0.112TNP 0.054 �0.567** �0.23 �0.318*

MAT 0.173 �0.359* �0.043 �0.474**

MFT 0.323 �0.372* 0.114 �0.387*

XFT 0.255 �0.289 0.08 �0.269KBDI 0.396* 0.132 0.322 0.128KBDIf 0.499** 0.123 0.362* 0.112ABA �0.376* 0.288 �0.088 0.337

* Correlation is significant at the 0.05 level (two-tailed).** Correlation is significant at the 0.01 level (two-tailed).

Fig. 4. T-test for the number of fires and burned area in the Great Xing’anMountains under different policies. Low restriction: normal policy before 1987(1967–1987); high restriction: stricter policy after 1987 (1988–2006); **Differenceis significant at the 0.01 level (two-tailed).

54 T. Hu, G. Zhou / Forest Ecology and Management 329 (2014) 49–58

periods (Fig. 5b); however, the human-caused burned area doesshow a significant correlation with human-caused fire frequency(Fig. 5d).

The timing of the autumn fire season did not change muchduring the entire period. We focused our analysis on the spring-fire-season. Table 3 presents factors that control the length andthe timing of the lightning- and human-caused fire seasons. TheGDDa on the first ignition day negatively affects the FFOT of thetwo ignition types; i.e., the larger GDDa reflects the earlier com-mencement of the fire season. The lightning-caused FFOT is signif-icantly related to the timing of snowmelt, but it is not affected bythe total non-fire-season precipitation. However, the FFOT ofhuman-caused fires is significantly related to total non-fire-seasonprecipitation, but it is not affected by the timing of snowmelt. TheLFOTs of the two ignition types are driven by both the KBDI and theanomaly of accumulated precipitation of the ignition day. Droughtand low precipitation are associated with a longer spring fireseason.

3.4. Contribution of each factor

Based on the correlation analysis, we grouped all driving factorsinto three categories (meteorology, fuel supply, and social factors)and analyzed each group’s contribution to the fire frequency. Thesefactors can explain 54% and 42% of the variances of the lightning-and human-caused fire frequencies, respectively, as shown inFig. 6. Meteorological factors explain 43% of the variance of light-

ning-caused fires, whereas fuel supply explains alone none of thevariance of lightning-caused fires; however, the interactionbetween meteorological factors and fuel supply does explain 11%of the variance. Meteorological factors and social policy explainonly 5% and 6% of the variances of the human-caused fire frequen-cies, respectively, but their interactive effect explains 31% of thevariance.

4. Discussion

4.1. Response of fire occurrence to explanatory variables

Lightning- and human-caused fires do not share any similaritiesregarding the response of their fire occurrence to all explanatoryvariables. Lightning ignitions respond primarily to climate factors,of which drought is recognized as the most important (Keetch andByram, 1968; Balling et al., 1992; Xiao and Zhuang, 2007).Although lightning ignitions are significantly negatively correlatedwith accumulated burned area (Table 2), which represents the lim-itation of available fuel supply, the meteorological factors are deci-sive for lightning-caused fires (Fig. 6). The mean ratio of ABA towhole area is 17.23% (the range 0.87–38.37%). Almost 1/5 of thewhole area was affected by fire before 1987. The lightning-causedfire locations were found to be spatially clustered in study area (Liuet al., 2012a,b), the burned area will be resistant to another ignitionduring the recover cycle. Although there was no significant corre-lation between ABA and other meteorological factors whichaffected fire frequency, the ABA did not show effect alone to thefire frequency during the study period according to the analysisof contribution.

In contrast, our results show that there is little connectionbetween human-caused fires and drought (Table 2). The expectednumber of human-caused ignitions in a region is strongly drivenby the moisture content of the fine surface fuel (Martell et al.,1987, 1989; Wotton et al., 2003), which is not directly associatedwith drought. Lightning-caused ignitions, on the other hand, aremost strongly influenced by moisture in the organic layer, wherelightning ignitions can smolder and be held over (Anderson,2002; Wotton and Martell, 2005). As moisture in the organic layeris likely to decrease during periods of drought, lightning-causedfires respond more sensitively to slowly varying drought than dohuman-caused fires, which are more readily affected by rapidlychanging moisture content of the fine surface fuels.

Our study shows that non-fire-season total precipitation, tem-perature, social policy, and population drive human-caused firesin the Great Xing’an Mountains. Non-fire-season precipitationincreases fine fuel moisture in the early stages of the spring fireseason through snowmelt and hence, reduces the probability ofthe occurrence of human-caused fires. Many studies have sug-gested that a temperature increase reduces dead fuel moistureand, hence, enhances the occurrence of fires (e.g., Meehl andTebaldi, 2004; Arienti et al., 2009). However, our study suggeststhat an increase in temperature restrains human-caused fire occur-rence. The ignition of human-caused fires is usually because ofnegligence. High temperatures result in stricter forest manage-ment and supervision, which improves the residents’ awarenessof fire safety. Because of this social policy enforcement, the occur-rence of human-caused fires is reduced when temperaturesincrease. This is particularly true in our analysis for the period fol-lowing the Black Dragon Fire. The positive effect of populationincrease on the occurrence of human-caused fires is not evidentthroughout the entire study period, which is unlike the findingsof work in southern California (Keeley and Fotheringham, 2001),but similar to the situation in Missouri (Guyette et al., 2002),where the positive correlation was limited by other factors. The

Table 3Pearson’s correlation coefficients of the spring fire season’s FFOT and LFOT to thedriving factors in the Great Xing’an Mountains during 1967–2006.

Factors FFOT LFOT

Lightning-caused

Human-caused

Lightning-caused

Human-caused

Melt 0.364* 0.267TNP 0.101 0.498**

KBDIi 0.284 0.146 0.388* 0.568**

APa 0.043 �0.155 �0.511** �0.638**

GDDa �0.523** �0.697** �0.186 �0.033

* Correlation is significant at the 0.05 level (two-tailed).** Correlation is significant at the 0.01 level (two-tailed).

Fig. 5. Relationship between population and (a) human-caused fire frequency and (b) human-caused burned area. (c) Reconstructed population from 1967–2006.(d) Relationship between human-caused fire frequency and burned area. Triangular and round dots represent fire frequency or burned area before and after 1982,respectively.

T. Hu, G. Zhou / Forest Ecology and Management 329 (2014) 49–58 55

positive correlation occurs only in the early stages when therewere large inward and outward migrations. Although the popula-tion was at a high level in the later stages, its large size did notenhance human-caused fire activity under the strict managementpolicy and weaker population movement.

4.2. Response of burned area to explanatory variables

Burned areas caused by lightning- and human-caused firesresponded differently to the selected driving factors. The mean KBDIin fire season is the only factor that affects the lightning-causedburned area; this result is also supported by the work of others(Collins et al., 2006; Littell et al., 2009). Our result indicates thatthe human-caused burned area is related to fire frequency(Fig. 4d). Stocks et al. (2002) have suggested that a large total burnedarea represents several large fires. Whether a fire becomes a run-away, depends on several factors including weather conditions, fuel

supply, and level of suppression. Therefore, many fires within thesame period will increase the probability of a run-away fire. Low fireactivity is the reason for the reduction of the human-caused burnedarea under the stricter social policy.

4.3. Response of fire seasonality to weather variables

Lightning- and human-caused FFOTs in the spring fire seasonare correlated with same meteorological factors, i.e., accumulatedenergy. Although lightning- and human-caused FFOTs are affectedby the time of snowmelt (Westerling et al., 2006) and total non-fire-season precipitation, respectively, other factors require consid-eration. Total non-fire-season precipitation determines the surfacefuel moisture at the early stages of the spring fire season, and thesurface fuel moisture dropping to the ignition condition is becauseof desorption and absorption processes, which are driven byenergy and moisture. The timing of snowmelt is the result of accu-mulated energy and total non-fire-season precipitation. Both light-ning- and human-caused LFOTs in the spring fire season arecontrolled by drought. Drought keeps surface fuel in an ignitablecondition during late stages of the spring fire season, whereasenergy accumulation has little influence on the LFOTs at this stage.

4.4. Implications for prediction

Fuel and social policy are important factors to the lightning-and human-caused fires respectively, in addition to meteorologicalfactors. However, they are typically ignored when predicting forestfires under projection scenarios. Most predictions of future fireregime based on Global Circulation Models only consider thechanges in meteorological factors (Stocks et al., 1998; de Grootet al., 2003, 2013; Brown et al., 2004; Fried et al., 2004;

Fig. 6. Contribution of meteorological factors, fuel supply, and social factors to lightning- and human-caused fire occurrences.

Fig. 7. The p-value and correlation coefficient between fire counts and burned area at different temporal scales. Dashed horizontal line represents p = 0.05. The x axis is thetemporal scale. 1 stands for one year’s fire counts and burned area, e.g. 1967, 1968 and so on; 2 stands for two years’, e.g. 1967–1968, 1968–1969 and so on.

56 T. Hu, G. Zhou / Forest Ecology and Management 329 (2014) 49–58

Flannigan et al., 2013), and take less consideration of changes infuel and social policy. Our results show that these meteorologicalpredictions might be relevant in predicting lightning-caused fires,but would not be appropriate for human-caused fires. Fire manage-ment policy is an important factor in minimizing and altering theeffects of high temperature on the ignition of human-caused fires.Human-caused fires would be overestimated without consideringfire management policy. As the accumulation energy is well corre-lated with lightning- and human-caused FFOTs, fire managersshould use an energy accumulation index and other fire weatherindices to predict the date of onset of the fire season and thus,implement a proper fire management plan.

4.5. Caveat

There are two limitations in this study. First, the analysis oftemporal trends neglected the potential for serial autocorrelationand the correlations between variables were not de-trended. Thismay amplify the strength of correlation between some variablesand slope of some temporal trends. Second, burned area as a

regime indicator was a repeat of fire frequency when the temporalscale is yearly. The burned area caused by humans showed a signif-icant relationship with fire frequency at any temporal scale duringthe study period (Fig. 7). But lightning-caused fires showed adifferent pattern than human-caused fires. When the temporalscale is up to 11 and less than 27 years, the burned area was inde-pendent of fire frequency. Perhaps the cycle of some of the extremeclimatic events, such as long-term drought which would increasethe mean burned area and change the relationship between firefrequency and burned area, coincided with the temporal scale11–27 years.

5. Conclusions

The lightning- and human-caused fire regimes have changedsignificantly during 1967–2006 in the Great Xing’an Mountains.Lightning-caused fire is primarily controlled by meteorological fac-tors, especially drought, as is the case for most global fire-proneareas. However, the driving factors of human-caused fires are dif-ferent; meteorological and social factors have not played major

T. Hu, G. Zhou / Forest Ecology and Management 329 (2014) 49–58 57

roles. Whether high population leads to high frequency of human-caused fires depends on the population migration and degree ofenforcement of social policy. If we only consider the change inmeteorological conditions when predicting future fire regimes,the results will be biased. To predict fire regimes better, we shouldconsider the fuel and social policies in future studies.

Acknowledgments

This research was jointly supported by the National BasicResearch Program of China (2010CB951303) and the Special Fundfor Meteorological Scientific Research in the Public Interest(GYHY200706021).

References

Anderson, K., 2002. A model to predict lightning-caused fire occurrences. Int. J.Wildland Fire 11, 163–172.

Archibald, S., Staver, A.C., Levin, S.A., 2012. Evolution of human-driven fire regimesin Africa. P. Natl. Acad. Sci. USA 109, 847–852.

Arienti, M.C., Cumming, S.G., Krawchuk, M.A., Boutin, S., 2009. Road networkdensity correlated with increased lightning fire incidence in the Canadianwestern boreal forest. Int. J. Wildland Fire 18, 970–982.

Balling, R.C., Meyer, G.A., Wells, S.G., 1992. Climate change in Yellowstone-national-park – is the drought-related risk of wildfires increasing. Climatic Change 22,35–45.

Bengtsson, L., 1976. Snowmelt estimated from energy budget studies. Nord. Hydrol.7, 3–18.

Bowman, D.M.J.S., Balch, J., Artaxo, P., Bond, W.J., Cochrane, M.A., D’Antonio, C.M.,DeFries, R., Johnston, F.H., Keeley, J.E., Krawchuk, M.A., Kull, C.A., Mack, M.,Moritz, M.A., Pyne, S., Roos, C.I., Scott, A.C., Sodhi, N.S., Swetnam, T.W., 2011. Thehuman dimension of fire regimes on Earth. J. Biogeogr. 38, 2223–2236.

Bridge, S.R.J., Miyanishi, K., Johnson, E.A., 2005. A critical evaluation of firesuppression effects in the boreal forest of Ontario. Forest Sci. 51, 41–50.

Brown, T.J., Hall, B.L., Westerling, A.L., 2004. The impact of twenty-first centuryclimate change on wildland fire danger in the western United States: Anapplications perspective. Climatic Change 62, 365–388.

Carcaillet, C., Bergeron, Y., Richard, P.J.H., Frechette, B., Gauthier, S., Prairie, Y.T.,2001. Change of fire frequency in the eastern Canadian boreal forests during theHolocene: does vegetation composition or climate trigger the fire regime? J.Ecol. 89, 930–946.

Clark, J.S., Grimm, E.C., Donovan, J.J., Fritz, S.C., Engstrom, D.R., Almendinger, J.E.,2002. Drought cycles and landscape responses to past aridity on prairies of theNorthern Great Plains, USA. Ecology 83, 595–601.

Cleland, D.T., Crow, T.R., Saunders, S.C., Dickmann, D.I., Maclean, A.L., Jordan, J.K.,Watson, R.L., Sloan, A.M., Brosofske, K.D., 2004. Characterizing historical andmodern fire regimes in Michigan (USA): a landscape ecosystem approach.Landscape Ecol. 19, 311–325.

Collins, B.M., Omi, P.N., Chapman, P.L., 2006. Regional relationships betweenclimate and wildfire-burned area in the Interior West, USA. Can. J. Forest Res.36, 699–709.

de Groot, W.J., Bothwell, P.M., Carlsson, D.H., Logan, K.A., 2003. Simulating theeffects of future fire regimes on western Canadian boreal forests. J. Veg. Sci. 14,355–364.

de Groot, W.J., Flannigan, M.D., Cantin, A.S., 2013. Climate change impacts on futureboreal fire regimes. Forest Ecol. Manag. 294, 35–44.

Flannigan, M.D., Stocks, B.J., Wotton, B.M., 2000. Climate change and forest fires. Sci.Total Environ. 262, 221–229.

Flannigan, M., Cantin, A.S., de Groot, W.J., Wotton, M., Newbery, A., Gowman, L.M.,2013. Global wildland fire season severity in the 21st century. Forest Ecol.Manag. 294, 54–61.

Fried, J.S., Torn, M.S., Mills, E., 2004. The impact of climate change on wildfireseverity: a regional forecast for northern California. Climatic Change 64, 169–191.

Fry, D.L., Stephens, S.L., 2006. Influence of humans and climate on the fire history ofa ponderosa pine-mixed conifer forest in the southeastern Klamath Mountains,California. Forest Ecol. Manag. 223, 428–438.

Fule, P.Z., Laughlin, D.C., 2007. Wildland fire effects on forest structure over analtitudinal gradient, Grand Canyon National Park, USA. J. Appl. Ecol. 44, 136–146.

Granstrom, A., Niklasson, M., 2008. Potentials and limitations for human controlover historic fire regimes in the boreal forest. Philos. Trans. Roy. Soc. B 363,2353–2358.

Grogan, P., Bruns, T.D., Chapin, F.S., 2000. Fire effects on ecosystem nitrogen cyclingin a Californian bishop pine forest. Oecologia 122, 537–544.

Guyette, R.P., Muzika, R.M., Dey, D.C., 2002. Dynamics of an anthropogenic fireregime. Ecosystems 5, 472–486.

Harden, J.W., Trumbore, S.E., Stocks, B.J., Hirsch, A., Gower, S.T., O’Neill, K.P.,Kasischke, E.S., 2000. The role of fire in the boreal carbon budget. Global ChangeBiol. 6, 174–184.

Hicke, J.A., Asner, G.P., Kasischke, E.S., French, N.H.F., Randerson, J.T., James Collatz,G., Stocks, B.J., Tucker, C.J., Los, S.O., Field, C.B., 2003. Postfire response of NorthAmerican boreal forest net primary productivity analyzed with satelliteobservations. Global Change Biol. 9, 1145–1157.

Jia, B., Zhou, G., Yu, W., Fang, D., 2011. Characteristics of lightning fire indaxing’anling forest region from 1972 to 2005 and its relationships withdrought index. Sci. Silvae Sin. 47, 99–105.

Kasischke, E.S., Turetsky, M.R., 2006. Recent changes in the fire regime across theNorth American boreal region – Spatial and temporal patterns of burning acrossCanada and Alaska. Geophys. Res. Lett. 33, L09703.

Keeley, J.E., Fotheringham, C.J., 2001. Historic fire regime in Southern CaliforniaShrublands. Conserv. Biol. 15, 1536–1548.

Keeley, J.E., Zedler, P.H., 2009. Large, high-intensity fire events in southernCalifornia shrublands: debunking the fine-grain age patch model. Ecol. Appl.19, 69–94.

Keetch, J.J., Byram, G.M., 1968. A drought index for forest fire control. Forest Service,Southeastern Forest Experiment Station. US Department of Agriculture.

Kitzberger, T., Veblen, T.T., Villalba, R., 1997. Climatic influences on fire regimesalong a rain forest-to-xeric woodland gradient in northern Patagonia,Argentina. J. Biogeogr. 24, 35–47.

Littell, J.S., McKenzie, D., Peterson, D.L., Westerling, A.L., 2009. Climate andwildfire area burned in western U.S. ecoprovinces, 1916–2003. Ecol. Appl. 19,1003–1021.

Liu, H., Randerson, J.T., Lindfors, J., Chapin, F.S., 2005. Changes in the surface energybudget after fire in boreal ecosystems of interior Alaska: an annual perspective.J. Geophys. Res.-Atmos. 110, D13101.

Liu, Q., Gao, P., Wang, Z., 2012a. Great Xing’an Mountains: the relationship betweenpopulation growth and economic development. Statist. Consult. 6, 17–19.

Liu, Z.H., Yang, J., Chang, Y., Weisberg, P.J., He, H.S., 2012b. Spatial patterns anddrivers of fire occurrence and its future trend under climate change in a borealforest of Northeast China. Global Change Biol. 18, 2041–2056.

Martell, D.L., Sun, H., 2008. The impact of fire suppression, vegetation, and weatheron the area burned by lightning-caused forest fires in Ontario. Can. J. Forest Res.38, 1547–1563.

Martell, D.L., Otukol, S., Stocks, B.J., 1987. A logistic model for predicting dailypeople-caused forest fire occurrence in Ontario. Can. J. Forest Res. 17, 394–401.

Martell, D.L., Bevilacqua, E., Stocks, B.J., 1989. Modelling seasonal variation in dailypeople-caused forest fire occurrence. Can. J. Forest Res. 19, 1555–1563.

Meehl, G.A., Tebaldi, C., 2004. More intense, more frequent, and longer lasting heatwaves in the 21st century. Science 305, 994–997.

Minnich, R.A., 1983. Fire Mosaics in Southern-California and Northern BajaCalifornia. Science 219, 1287–1294.

Minnich, R.A., 2001. An integrated model of two fire regimes. Conserv. Biol. 15,1549–1553.

Miyanishi, K., Johnson, E.A., 2001. Comment – a re-examination of the effects of firesuppression in the boreal forest. Can. J. Forest Res. 31, 1462–1466.

Pausas, J.G., 2004. Changes in fire and climate in the eastern Iberian Peninsula(Mediterranean basin). Climatic Change 63, 337–350.

Pausas, J.G., Bradstock, R.A., 2007. Fire persistence traits of plants along aproductivity and disturbance gradient in mediterranean shrublands of south-east Australia. Global Ecol. Biogeogr. 16, 330–340.

Pausas, J.G., Fernandez-Munoz, S., 2012. Fire regime changes in the WesternMediterranean Basin: from fuel-limited to drought-driven fire regime. ClimaticChange 110, 215–226.

Peres-Neto, P.R., Legendre, P., Dray, S., Borcard, D., 2006. Variation partitioning ofspecies data matrices: estimation and comparison of fractions. Ecology 87,2614–2625.

R Core Team, 2013. R: A language and environment for statistical computing. In. RFoundation for Statistical Computing, Vienna, Austria.

Ryan, C.M., Williams, M., 2011. How does fire intensity and frequency affectmiombo woodland tree populations and biomass? Ecol. Appl. 21, 48–60.

San-Miguel-Ayanz, J., Rodrigues, M., Oliveira, S., Pacheco, C., Moreira, F., Duguy, B.,Camia, A., 2012. Land Cover Change and Fire Regime in the EuropeanMediterranean Region. In: Moreira, F., Arianoutsou, M., Corona, P., De lasHeras, J. (Eds.), Manag for Ecosyst. Springer, Netherlands, pp. 21–43.

Schulte, L.A., Mladenoff, D.J., Burrows, S.N., Sickley, T.A., Nordheim, E.V., 2005.Spatial controls of Pre-Euro-American wind and fire disturbance in NorthernWisconsin (USA) forest landscapes. Ecosystems 8, 73–94.

Senici, D., Chen, H.Y.H., Bergeron, Y., Cyr, D., 2010. Spatiotemporal variations of firefrequency in central boreal forest. Ecosystems 13, 1227–1238.

Stocks, B.J., Fosberg, M.A., Lynham, T.J., Mearns, L., Wotton, B.M., Yang, Q., Jin, J.Z.,Lawrence, K., Hartley, G.R., Mason, J.A., McKenney, D.W., 1998. Climate changeand forest fire potential in Russian and Canadian boreal forests. Climatic Change38, 1–13.

Stocks, B.J., Mason, J.A., Todd, J.B., Bosch, E.M., Wotton, B.M., Amiro, B.D., Flannigan,M.D., Hirsch, K.G., Logan, K.A., Martell, D.L., Skinner, W.R., 2002. Large forestfires in Canada, 1959–1997. J Geophys Res-Atmos 107, FFR 5-1-FFR 5–12.

Tang, J., Zhuang, Q., 2011. Modeling soil thermal and hydrological dynamics andchanges of growing season in Alaskan terrestrial ecosystems. Climatic Change107, 481–510.

Taylor, A.H., Beaty, R.M., 2005. Climatic influences on fire regimes in the northernSierra Nevada Mountains, Lake Tahoe Basin, Nevada, USA. J. Biogeogr. 32, 425–438.

Vasilakos, C., Kalabokidis, K., Hatzopoulos, J., Matsinos, I., 2009. Identifying wildlandfire ignition factors through sensitivity analysis of a neural network. Nat.Hazards 50, 125–143.

58 T. Hu, G. Zhou / Forest Ecology and Management 329 (2014) 49–58

Vázquez, A., Moreno, J., 1993. Sensitivity of fire occurrence to meteorologicalvariables in Mediterranean and Atlantic areas of Spain. Landscape Urban Plan24, 129–142.

Ward, P.C., Tithecott, A.G., Wotton, B.M., 2001. Reply – a re-examination of theeffects of fire suppression in the boreal forest. Can. J. Forest Res. 31, 1467–1480.

Westerling, A.L., Hidalgo, H.G., Cayan, D.R., Swetnam, T.W., 2006. Warming andearlier spring increase western U.S. Forest Wildfire Activity. Science 313, 940–943.

Wotton, B.M., Martell, D.L., 2005. A lightning fire occurrence model for Ontario. Can.J. Forest Res. 35, 1389–1401.

Wotton, B.M., Martell, D.L., Logan, K.A., 2003. Climate change and people-causedforest fire occurrence in Ontario. Climatic Change 60, 275–295.

Xiao, J.F., Zhuang, Q.L., 2007. Drought effects on large fire activity in Canadian andAlaskan forests. Environ. Res. Lett. 2, 044003.

Xu, H.C., 1998. Forest in Great Xing’ An Mountains of China. Science Press, Beijing.