CSIRO PUBLISHING

International Journal of Wildland Fire, 2007, 16, 54–62 www.publish.csiro.au/journals/ijwf

First year survival of Pinus hartwegii followingprescribed burns at different intensities anddifferent seasons in central Mexico

Dante Arturo Rodríguez-TrejoA,C, Uriel Baruch Castro-SolisA,Marcelo Zepeda-BautistaA and Richard John CarrB

ADivisión de Ciencias Forestales, Universidad Autónoma Chapingo, Chapingo,Estado de México, CP 56230, México.

BNatural Resources Canada – Canadian Forest Service, Northern Forestry Centre,5320 – 122 Street Edmonton, AB T6H 3S5, Canada.

CCorresponding author. Email: dantearturo@yahoo.com

Abstract. In forests of young Pinus hartwegii Lindl. on the Ajusco volcano in southern Mexico City, central Mexico,a study was made of tree mortality resulting from low and high intensity prescribed burns. Low intensity burns (backingfire in early morning with high relative humidity) and high intensity burns (head fire at midday with low relative humidity)were conducted in two different seasons: 21 March and 29 May 2002. Five contiguous sites were selected, each consistingof open stands (300–700 trees ha−1) and closed stands (900–2500 trees ha−1). Two sites were used for morning burns, twofor afternoon burns, and the fifth remained an unburned control plot. Logistic regression was used to estimate probabilityof mortality 1 year after the burns as a function of fire season, fire intensity, stand density, and tree diameter at breastheight (dbh). Logistic regression was also used to estimate probability of infestation by bark beetles as a function of crownkill. A multiple linear regression model was used to show the effect of crown kill and tree height on live crown area.The probability of mortality was greatest in May high intensity burns, on closed stands and for low dbh trees. May burnshad the driest conditions, and closed stands had needle layers that produced fire smouldering. The root system heatingis a key mechanism influencing mortality of Pinus hartwegii in closed stands. The treatments creating greatest mortalityalso resulted in lower live crown areas. According to the multiple regression model, the lower the tree height and thehigher the crown kill, the lower the live crown area. The probability of infestation by bark beetles also increased withcrown kill.

Resumen. Se estudió la mortalidad de Pinus hartwegii Lindl. juveniles ante quemas prescritas de baja y alta intensidad,en el volcán Ajusco, México central. Las primeras fueron en contra de viento y pendiente, por la mañana, con alta humedadrelativa; las segundas a favor de viento y pendiente, a mediodía, con baja humedad. Se condujeron en dos épocas: marzo21 y mayo 29 de 2002. Fueron establecidas cinco parcelas contiguas, cada una con masas abiertas (300–700 árboles ha−1)y masas densas (900–2500 árboles ha−1). Dos parcelas fueron usadas para quemas por la mañana, dos para quemas por latarde, y una como testigo. Se empleó regresión logística para estimar la probabilidad de mortalidad a un año de las quemascomo función de época de quema, intensidad, densidad arbórea, y diámetro normal. También se usó regresión logísticapara estimar la probabilidad de infestación por insectos descortezadores como función del chamuscado de copa. Medianteregresión lineal múltiple se relacionó el chamuscado de copa y la altura del árbol con el área de copa viva. La mayorprobabilidad de mortalidad correspondió a la combinación: quemas en mayo, alta intensidad, masas densas y árboles dediámetro pequeño. Las quemas en mayo mostraron las condiciones más secas, y las masas densas tuvieron hojarasca comocombustible principal, que produce combustión sin llamas. El sistema de calentamiento de la raíz es un mecanismo claveque influencia la mortalidad de Pinus hartwegii en masas densas. Los tratamientos con mayor mortalidad exhibieronmenores áreas de copa viva. Se tuvo menor área de copa viva a menor altura del árbol y a mayor chamuscado de copa.La probabilidad de infestación por descortezadores aumentó con el chamuscado de copa.

Additional keywords: fire ecology, integral fire management, logistic regression, tree mortality.

Introduction

Fire regime refers to the long-term nature of fire in an ecosys-tem and the prominent effects of fire that characterise such anecosystem (Brown 2000). Gill (1975) proposed the term ‘fireregime’, integrating several temporal and spatial patterns of fire

occurrence in an area, plus their ecological effects. Fire regimesare generally defined according to frequency, severity, season,duration, extent, spatial distribution, depth of burn, and type offire (Gill 1975; Heinselman 1978; Kilgore 1981; Whelan 1995).Each characteristic of fire regime has ecological implications.

© IAWF 2007 10.1071/WF05061 1049-8001/07/010054

Survival of P. hartwegii 1 year after prescribed burn Int. J. Wildland Fire 55

For instance, the season of fire occurrence is related to differ-ences of physiological plant status (Trabaud 1987), which in turnmay affect tree survival; however, because of the variability ofspatial and temporal characteristics of fire and its effects, thedescriptions of fire regimes are broad (Whelan 1995). As exam-ples of fire regime classifications, Brown (2000) uses understory,stand replacement, mixed, and non-fire regimes. According toKeeley and Zedler (1998), the fire regimes may be combina-tions of (1) predictable (frequent fires)/unpredictable (very lowfrequency fires); and (2) stand-thinning/stand-replacing fires.

Both very frequent fires over the same area and infrequent butvery severe fires, as a result of exclusion, deforest and degradethe environment. According to Pyne (2003), before the arrival ofhumans, fire regimens were determined by natural causes, butsince the appearance of man, they have been determined both bynatural causes, principally lightning, and anthropic causes.

The forests of Pinus hartwegii in Mexico are typical ofthe predictable stand-thinning fire regime (Rodríguez-Trejo andFulé 2003); however, even superficial fires have great variationin intensity, and their effects are different according to the sea-son. The effects are most severe when the meristems are at astage of active growth (Robbins and Myers 1992). Furthermore,knowledge of fire ecology and the impact of fire is necessary forthe integral management of fire, which merges fighting and pre-venting forest fires, the ecological use of fire, and an appropriateuse of fire by rural communities. The use of fire through pre-scribed burning with the objectives of conservation, restoration,forestry, or reduction of fire danger should maximise positiveimpacts and minimise negative impacts.

The use of fire in pine lands can promote the richness ofspecies (Swanson et al. 1990; Brown 2000; Keane et al. 2002),but fire should be applied with an intensity that does not generatesignificant mortality in the stand (except for those species char-acterised by stand-replacing fires). This is particularly importantin countries such as Mexico, where very frequent anthropic firescontribute to deforestation in various regions (Rodríguez-Trejoand Fulé 2003).

Based on the above, we hypothesise that in Mexican Pinushartwegii forests, with a greater intensity of fire with the advanceof the fire season (May) and in closed stands, the mortality anddamage will increase because of a higher proportion of needlesas fuel and a higher smouldering combustion, particularly insmall trees. Higher probabilities of infestation by bark beetleswith greater tree-damage levels (expressed as crown kill) arealso expected.

The objective of the present study was to evaluate survivaland damage in young Pinus hartwegii stands in central Mexico1 year after prescribed burns in different seasons and at differentintensities, considering the density of the stand (which in turninfluences the fuel complex), and the tree size.

MethodsStudy areaThe experiment was established on a slope of theAjusco volcanoin southern Mexico City, in lands of the communities of SanMiguel and Santo Tomás Ajusco, in an area with grassland anda forest of young Pinus hartwegii (1.5–8.5 m in height, 3.3 mmean height; 2–22 cm diameter at breast height [dbh], 8.3 cm

mean dbh). The climate in the zone is sub-humid and temperate,with the rainy season in summer, and less than 5% of precipita-tion falling during winter. The mean annual temperature is from5 to 12◦C, and the mean annual precipitation equals 1139 mm(García 1981).The fire season in the area of study is from Januaryto May, which corresponds to the dry season. The predominantgeological material consists of intermediary extrusive igneousrocks. The dominant soils are humic andosols, with lithosols assecondary soils, with medium texture.

Clandestine logging and cattle and sheep grazing have dis-turbed these stands. Although this type of forest is adapted tofire, grazing areas are associated with excessive fires, whichare detrimental to forest sustainability. Fire is used annually insome places to promote the regeneration of grasses to feed cattleor sheep. For example, Secretaría de Medio Ambiente Recur-sos Naturales y Pesca (1999) noted that in 1998, 121 ha wereaffected in Cumbres del Ajusco National Park, which representsmore than 20% of the protected area.

According to Martínez and colleagues (J. M. Martínez-Rodríguez, O. Rodríguez-Chávez, D. A. Rodríguez-Trejo,J. E. Morfín-Ríos, E. Alvarado-Celestino, unpublished data),the average fuel loads in the study site were 9.7 and 3.5 t ha−1

for needle litter and grasses in the closed stands, and 0.07 and11.1 t ha−1 in the open stands (Fig. 1). The total fuel loads werefrom 11.2 to 13.2 t ha−1.

Prescribed burn treatmentsFive adjacent rectangular plots were established, with elevationsbetween 3553 and 3626 m above sea level. Three plots had areasof 0.75 ha each, and two were of 0.6 ha each. The mean slopewas 55%, with a north-west aspect. The coordinates of the north-west vertex were 19◦12′58.8′′ N and 99◦16′11.7′′ W. Plots wereselected to contain both open stands and closed stands. Openstands were those that contained 300 to 700 trees ha−1, andclosed stands were those containing from 900 to 2500 trees ha−1.Approximately the upper half of the plots were open stands,and the lower half were closed stands. Two areas were burnedon 21 March 2002, in the middle of the fire season. The firsttreatment consisted of a low-intensity back fire in the earlymorning; the second treatment was a high-intensity head fire

Fig. 1. Variation in forest fuels on Ajusco Volcano, with grasses dominat-ing open stands, and needle litter dominating closed stands.

56 Int. J. Wildland Fire D. A. Rodríguez-Trejo et al.

during high danger hours. The same treatment was applied tothe next two plots on 29 May 2002, during the peak of the fireseason (Fig. 2). An unburned control was also included. Dur-ing the prescribed burns, a total of 24 observations of the firebehavior and atmospheric conditions were taken from the fourplots, and are synthesised in Table 1. The rate of spread wasmeasured in 24 randomly placed and separate 10-m long exper-imental units. Flame length was estimated visually with the helpof graduated 3-m long wooden rulers. The atmospheric variableswere measured with pocket wind meters (wind speed), slingpsychrometers (temperature, relative humidity), and compasses(wind direction). Light fuels moisture content was estimatedwith Sackett tables, modified by Burgan and Cohen (unpub-lished, referred to by Rothermel 1983), using the followinginformation: relative humidity, dry bulb temperature, month,fuels exposed or not exposed, time of the day, aspect and slope.

Fire intensity was estimated from flame length using the mod-els of Byram (1959) and Alexander (1982). In Eqns 1 and 2, Lis the flame length (m), and I is the fire intensity (kW m−1).

L = 0.0775 I0.46 (1)

I = exp((ln L − ln 0.0775)/0.46) (2)

The May treatments and those applied after midday (highintensity) showed greater calorific intensities and rates of spread,

Unburnedcontrol

Marchlowintensity

Marchhighintensity

Mayhighintensity

Maylowintensity

Black line Firebreak

Lowdensity

N

Highdensity

Up slope

Downslope

Fig. 2. Diagram of the prescribed burn plots. Each of the three plots onthe left have a surface area of 0.75 ha. Each of the two plots on the right havea surface area of 0.6 ha.

Table 1. Characteristics of the applied fire treatmentsLFMC, light fuels moisture content; Mrl, Myl, low intensity prescribed burns in March and May, respectively; Mrh, Myh, high intensity prescribed burns in

March and May, respectively

Treatment Density Time Temperature Relative Wind speed Wind LFMC Flame Rate of spread Fire intensity(h) (◦C) humidity (%) (km h−1) direction (%) length (m) (m min−1) (kW m−1)

Mrl Open 0851 7–18 49–70 <3.2–12.8 N, E 10–14 0.20–1.00 0.10–1.00 8.0–260.0Closed 12–15 0.10–0.40 0.04–0.08 1.7–35.0A

Mrh Open 1245 16–18 30–49 <6.4–20 N 8–10 0.50–6.00 0.20–5.00 58.0–12 770.0Closed 9–11 0.10–0.40 ≤0.19 1.7–35.0A

Myl Open 0915 12–18 20–25 <2.5–4 N 6–7 0.20–1.00 0.20–1.00 8.0–260.0Closed 8–9 0.20–0.40 ≤0.17 8.0–35.0A

Myh Open 1255 16–19 12–18 <3–20 N 3–4 0.50–8.00 0.30–80.00 58.0–23 867.0Closed 5–6 0.25–0.50 ≤0.34 13.0–58.0A

AEstimates of fire intensity based on flame length in high-density stands, with needle litter as main fuel, do not include below-ground heat pulse.

given the accumulated drought in the former case, and the lowerrelative humidity and stronger winds in the latter, in additionto the burning technique employed (back fire for low intensityprescribed burns, head fire for high intensity prescribed burns;Table 1, Fig. 3).

Canadian Forest Fire Weather Index (FWI) System compo-nent values for the study area were estimated for the afternoonsof the burns. The FWI System, an empirical sub-system of the

Fig. 3. March low intensity prescribed burning (a), and May high intensityprescribed burning (b).

Survival of P. hartwegii 1 year after prescribed burn Int. J. Wildland Fire 57

Canadian Forest Fire Danger Rating System, uses noon weatherobservations to estimate mid-afternoon (peak) burning condi-tions. The FWI System was originally developed through studiesin jack pine (Pinus banksiana) forest. The FWI System consistsof three fuel moisture codes and three fire behavior indexes (VanWagner 1987).

Noon weather observations on the burn dates from Tlalpan,portable meteorological instruments at the study site, and grid-ded FWI System products provided by the Northern ForestryCentre for Secretaría de Medio Ambiente y Recursos Naturales,were used to estimate FWI System values. Using the Spatial FireManagement System (SFMS), FWI System grids were rerun for1 March through 31 May 2002, allowing stabilisation of fuelmoisture codes.Temperature and humidity were adjusted for gridcell elevation.The temperature adjustment uses the United StatesStandard Lapse Rate (−6.5◦C km−1). The humidity adjustmentuses the adjusted temperature and an assumption that the mix-ing ratio of water vapor to dry air remains constant over smallvertical distances. The grid cells, which are 2-km squares, weresampled at the site coordinates to obtain fuel moisture codesand weather parameters. Weather station information forTlalpanand the study site were added to the weather station database, andweather observations from the grids, Tlalpan, and the portableinstruments were added to the weather database. Wind speedtaken from the portable meteorological kit was at 1.5 m aboveground level. Because the FWI System requires wind speedsat the 10-m level, wind speed at 10 m was estimated using the1.5-m speed, stand density, stand height estimates, and a roughmethod of wind adjustment in pine canopies (Alexander 1998).A doubling of the wind speed from 10 to 20 km h−1 on bothburn days was used for the low density, 3–8 m tree heights at theinstrument location.

Interpolated precipitation was set to 0.0 on days when nosignificant amounts fell at nearby stations and when precipitationwas not reported at the study site (for about 4 days before eachburn date). Several millimeters of rain likely fell on 18 May2002, as nearby stations reported several hours of rain totallingnearly 10 mm.The FWI System component values at the weatherstations were then calculated between the date of the last probableprecipitation events and the burn dates.

A year after applying the May treatments, six sampling unitswere randomly determined in each plot, measuring 100 m2 each(three for closed stands and three for open stands), for a totalof 30 sampling units, which included a total of 322 trees. Thenumber of trees per sampling unit was from three to seven foropen stands, and from nine to 25 for closed stands.The followingdata were obtained from each site: number of live or dead trees(a tree was considered alive if it had green foliage present, with-out considering the amount), height (determined with graduatedtelescopic ruler), dbh (with diametric tape), diameter (to esti-mate area of live crown) and length of live crown (with a metrictape), and number of trees with presence of bark beetles (withone or more resin masses per tree). The intensity of infestationper tree was not assessed. The diameter of the crown prior to theapplication of the treatments was also considered.

Statistical analysisWith the data from 258 trees, a logistic model (Hosmer andLemeshow 2000) was developed for survival. In Eqn 3, P is the

probability of occurrence for the dependent binomial variable(mortality), α1 is the intercept, β1 is a constant associated withthe independent burning season variable X1 (1 for May, 0 forMarch), β2 is the constant associated with the independent fireintensity variable X2 (1 for high intensity, 0 for low intensity),β3 is the constant associated with the independent tree densityvariable X3 (1 for closed stands, 0 for open stands), and β4 isthe constant associated with the independent dbh variable X4(expressed as a continuous variable, in cm).

P = 1/(1 + exp − (α1 + β1X1 + β2X2 + β3X3 + β4X4)) (3)

The stand density was used as an indicator of the type of fuelpresent, given that pine needles are dominant in the areas of highdensity, whereas grasses dominate in areas of low density.

A logistic model was employed to estimate the probability ofinfestation by bark beetles (PI) as a function of crown scorch(X1) from a sample of 179 trees (Eqn 4):

PI = 1/(1 + exp − (α1 + β1X1)) (4)

The statistical analysis was conducted with the ‘logistic’ proce-dure of SAS v.8.0 for microcomputers (SAS Institute, Cary, NC,USA).

The live crown area and the percentage of vertical live crown(equal to 100 minus crown kill), were analyzed with a t-test, com-paring each treatment with the respective control (closed or openstands). The same was done for the percentage of trees affectedby beetles, but in this case, the percentage was standardised withthe arc sin function. For the comparisons, the procedure used was‘t-test’ of SAS v.8.0 for microcomputers.

To predict live crown area, we used a multiple linear regres-sion model by first obtaining the Pearson’s regression coeffi-cients among live crown area (dependent variable), and treeheight, dbh, diameter at the base of bole, crown kill, and survival(independent variables). The dependent variables to be includedin the model were selected by the magnitude of their coefficientwith live crown area, and had to be independent of other vari-ables to prevent multicolinearity. The residuals of every selectedindependent variable were also checked for heteroscedasticity.This was performed with the SAS v.8.0 procedures ‘proc corr’and ‘proc reg’.

Results and discussionMortality and effects on crownThe May high intensity burns on closed stands exhibited thehighest mortality (Fig. 4). Also, Fig. 5 shows the mortal-ity by dbh, season, and fire intensity to help visually clarifythe dataset. The regression model result (Eqn 5) was signifi-cant (χ2 = 132.6307, P < 0.0001), and the significance of theirdependent variables is exhibited in Table 2. The association ofpredicted probabilities and observed responses are 90.3% con-cordant, 0.3% tied, and 9.4% discordant. This model shows thatmortality was greatest in the May burns, closed stands, and highintensity burns, and that the lower the dbh of the trees, the higherthe mortality. In this way, a 1-cm dbh tree growing at high den-sity in a stand burned in May at high intensity has a mortalityprobability of 0.97. In comparison, a tree with the same diam-eter, but growing at low density in a stand burned in March at

58 Int. J. Wildland Fire D. A. Rodríguez-Trejo et al.

a a

aa

b

a a a a

a

0

20

40

60

80

100

120

C Mr LI Mr HI My LI My HI

Fire treatments

Sur

viva

l (%

)

CS

OS

Fig. 4. Pinus hartwegii survival, 1 year after the fire in several firetreatments: C = control, Mr LI = March low intensity, Mr HI = Marchhigh intensity, My LI = May low intensity, My HI = May high intensity,OS = open stands (300–700 ha−1), CS = closed stands (900–2500 ha−1).The error bars represent standard deviation, and the letters correspond toleast significant difference.

010

2030

4050

6070

8090

100

1–5 6–10 11–15 16–20

dbh category (cm)

Mor

talit

y (%

)

My HI

My LI

Mr HI

Mr LI

Fig. 5. Pinus hartwegii mortality, by diameter at breast height (dbh)category and fire treatment. Densities were combined. My HI = Mayhigh intensity, My LI = May low intensity, Mr HI = March high intensity,Mr LI = March low intensity. Error bars represent standard deviation.

Table 2. Significance of the parameters of the logistic regression modelto predict probability of Pinus hartwegii mortality

Parameter d.f. Estimate Standard error Chi square P

Intercept 1 −2.3145 0.8526 7.3698 0.0066Season 1 2.2619 0.4166 29.4781 <0.0001Intensity 1 2.0528 0.4344 22.3266 <0.0001Density 1 2.0343 0.6586 9.5422 0.0020Diameter 1 −0.4119 0.0732 31.6688 <0.0001

low intensity, has a mortality probability of only 0.06, and sucha value is reduced to 0.0002 for a tree in this last condition butwith a dbh of 15 cm.

P = 1/(1 + exp − (−2.3145 + 2.2619 X1 + 2.0528 X2

+ 2.0343 X3 − 0.4119 X4)) (5)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20Diameter at breast height (cm)

Pro

babi

lity

of m

orta

lity

My HI CSMy HI OSMy LI CSMy LI OSMr HI CSMr HI OSMr LI CSMr LI OS

Fig. 6. Probability of Pinus hartwegii mortality for different fire sea-son and fire intensity treatments, tree density and diameter at breastheight. My = May, Mr = March, HI = high intensity, LI = low intensity,CS = closed stand, OS = open stand.

Table 3. Odds ratio estimates for the parameters of the logisticregression model to predict probability of Pinus hartwegii mortality

Effect Point estimate 95% Wald confidence limits

Season 9.601 4.243–21.724Intensity 7.790 3.324–18.253Density 7.647 2.103–27.800Diameter 0.662 0.574–0.700

A graphic expression of the model for all the treatments andfor a range of dbh is shown in Fig. 6. Four groups are formedin Fig. 6, from highest to lowest probability of mortality for thefirst year after the application of fire:

(1) May high intensity on closed stands;(2) May high intensity on open stands, May low intensity on

closed stands, and March high intensity on closed stands;(3) May low intensity on open stands, March high intensity on

open stands, March low intensity on closed stands;(4) March low intensity on open stands.

The odds ratios for mortality are almost 10 times higher inMay in comparison with March, almost eight times higher forhigh intensity in comparison with low intensity, and also almosteight times higher for closed stands in comparison with openstands. The odds increase by ∼0.66 for each centimeter less indbh (Table 3).

During 2002, the dry season included May, which in asso-ciation with the elevated temperatures of the season (Gobiernode la Ciudad de México 2006), produced greater fire intensitycompared with March treatments (Table 1), and higher mortalityin the stand. Most importantly, the Drought Code, Initial SpreadIndex, and Fire Weather Index values for the study site were

Survival of P. hartwegii 1 year after prescribed burn Int. J. Wildland Fire 59

Table 4. Fire Weather Index System component values for the experimental site calculated on both burning datesMeteorological data were collected at noon at the study site. BUI, buildup index; DMC, duff moisture code; DSR, daily severity index;

FFMC, fine fuel moisture code; FWI, fire weather index; ISI, initial spread index

Date Temperature Relative Wind speed Precipitation FFMC DMC Drought ISI BUI FWI DSR(◦C) humidity (%) (km h−1) (mm) code

21 March 15.6 47.00 20.00 0 85.6 43.4 555.5 6.3 72.6 19.3 5.129 May 18.3 30.00 20.00 0 87.5 42.6 619.2 8.2 72.7 23.3 7.2

10 to 20% higher for 29 May than for 21 March (Table 4), whichsupports the observation of higher mortality after the May burn.

The FWI indicates frontal fire intensity, with high valuesindicating great fire intensity and possible crowning. The FWIroughly correlates with flame length (Van Wagner 1987),whereas burn depth is related to mid-level and deep-level fuelmoisture, which are indicated respectively by the Duff Mois-ture Code (DMC) and Drought Code (DC). The Buildup Indexindicates the available fuel load (Van Wagner 1987).

The FWI System predicts fire characteristics at peak burningtime in late afternoon based on local noon weather conditions.The FWI System values would not directly apply to morningburns, although methods exist to calculate hourly fine fuel mois-ture code values and thus hourly FWI (Van Wagner 1977) usinghourly weather observations or diurnal curves. Also, the FWISystem values apply to flat terrain; consequently, downslopeburn intensity is overestimated and upslope burn intensity isunderestimated. The Canadian Forest Fire Behavior Prediction(FBP) System includes fuel and topographic effects, which couldbe useful for evaluating fire intensity in this Pinus hartwegiienvironment, which appears similar to British Columbia’s drysouthern interior.

The method used to determine the FWI System values hasseveral potential sources of error, including sampling of grid cellparameters interpolated from weather stations several km away,and the use of weather instruments within the stand. Accuracycould be improved by maintaining on-site weather stations forseveral weeks before prescribed burns. The DC and DMC havebeen estimated in lodgepole pine forests by destructive samplingof the forest floor litter (Lawson and Dalrymple 1996; Lawsonet al. 1997), although application to ponderosa or similar pineforests has not been established. Although the FWI System isused across Canada and in other world regions, correlation ofcalculated values with regional fire conditions normally requirescalibration (e.g. de Groot et al. 2005).

McHugh and Kolb (2003) documented higher mortality atgreater fire intensity, indicating mortality rates at 3 years ofPinus ponderosa Douglas ex. Laws. with 5–90 cm diameterof 32.4–13.9%, respectively, in forest fires with intensities of188–4132 kW m−1, and of 18% in prescribed burns with intensi-ties of 44 to 234 kW m−1. In that study, 76–95% of the mortalityoccurred during the first year. Furthermore, in seedlings of Pinushartwegii, the prescribed burns on flat terrain implied a sur-vival of 84.3%, which was reduced to 8.1% in sloped terrain,owing to the greater intensity of the fire (Velázquez-Martínezet al. 1986). Also, Regelbrugge and Conard (1993) found, withthe use of logistic regression, that the probability of mortality

decreased with increasing tree dbh and height, and increasedwith increasing height of stem-bark char.

The season in which fire occurs is also important, because ofthe physiological status of the trees and the differences in fireintensity that may be reached during the different seasons. After10 years of follow-up in South-western Colorado, the mortalityof Pinus ponderosa after spring and summer (growing season)burns was ∼2.5 times higher than mortality of fall (dormant sea-son) burns, despite similar crown damage (Harrington 1993).However, studying the same species, Thies et al. (2005) foundthat the mortality of trees was higher after fall burns in com-parison with that after spring burns, because the fall burns wereinherently more severe than spring burns in Oregon.

The tendencies of mortality at different densities obtained inthe present study were related to the predominance of needlelitter in closed stands and grasses in open stands. Regardingthis fuels pattern, Fulé and Covington (1994) reported for Pinusponderosa forests that litter and duff depths can be estimatedfrom measurements of stand density. Also, duff depth increaseswith tree diameter and decreases with distance from the tree inPinus ponderosa forests (Ryan and Frandsen 1991).

In the present study there appeared to be greater mortal-ity in closed stands because of deeper needle layers in whichfire smouldered for a longer time, causing greater root kill. Forinstance, Ryan and Frandsen (1991) found that smoulderingcombustion consumed 98% of the duff beneath mature Pinusponderosa trees. In open stands, this below-ground smoulderingmay have a lesser effect on roots and tree mortality. On the con-trary, surface fire intensity is higher in open stands, and crownscorch may be related to survival, considering that the mortalityis higher in high tree density areas for both cases. Also below-ground smouldering is dependent on the forest floor dryness,and the fine fuels (needles) had lower moisture content in Maythan in March (Tables 1 and 4), corresponding to the highermortalities found in May when compared with those in March.

Hartford and Frandsen (1992) demonstrate the trend of theheat pulse release.They reported for Larix occidentalis Nutt. andAbies lasiocarpa (Hook.) Nutt. forests that the burn of slash fuelover moist duff produced significant flame lengths, but minimalheating in the mineral soil. Hartford and Frandsen also reportedthat the burn of litter over dry duff produced long duration heat-ing with very high duff consumption and lethal temperatures atthe mineral soil surface. These researchers also mentioned thatfire effects on ecosystem components are not apparent whenjudged only by surface fire intensity.

Another example of the above can be observed in the naturalregeneration of Pinus palustris Mill. in the south-eastern United

60 Int. J. Wildland Fire D. A. Rodríguez-Trejo et al.

States. The Pinus palustris are present in the center of gaps,with a band around the perimeter of the gap. In such bands, theseedlings are absent owing to competition with fine roots of adulttrees, shade, and mainly from the accumulation of pine needlesacting as a fuel on this border. In comparison with the grasses, theneedles produce greater calorific intensities as they burn, thuskilling the seedlings (Brockway and Outcalt 1998). On the otherhand, Miller et al. (1998) also reported greater post-fire mortal-ities for Pinus contorta Dougl. ex Loud. at higher densities.

Our study found that the smaller the dbh, the greater the mor-tality, with the effect tending to be more pronounced in closedstands (Fig. 6). Similarly, in Pinus ponderosa, McHugh andKolb (2003) found that 50% of the mortality in trees of 5–90 cmdbh occurred in trees with a diameter of 10 cm or less. Further-more, in Pinus ponderosa, the superficial fires of low intensity,high intensity and very high intensity (crown fires), respectively,killed 0.8, 9.2 and 47.3% of the trees with 10–18 cm dbh, and21.3, 57.5 and 73.7% of those with heights of up to 1.4 m (Wrightand Bailey 1982).

A tendency was observed in which the trees in sites withgreater density or those burned at high intensity had a lowerproportion of live crown as a result of fire damage, and conse-quently had lower vigor. In the May treatment with high intensityfire, trees were found that had lost 100% of their crown, but theterminal buds survived and resprouted during the growth season.The probability of these trees surviving is low, as McHugh andKolb (2003) found with crown damage of 85% or more causingdeath in Pinus ponderosa.

The percentage of live vertical crown, for almost all of thetreatments, had statistically significant reductions in comparisonwith their controls. The only exception was March low intensityburns in closed stands (49.6%, with 53.6% for the closed standcontrol; t = 0.81, P = 0.4229).The higher values for the percent-age of live vertical crown were present in the March low intensityburns in open stands (82.1%, with 91.3% for the open stand con-trol; t = 2.47, P = 0.0202), and the lowest value correspondedto the May high intensity burns in closed stands (36.5%, with53.6% for the closed stand control; t = 4.19, P = 0.0002).

The proportion of crown kill is a good indicator of potentialmortality in pines (Van Wagner 1973). This author generated acrown kill model that is broadly used. Based on a wide literaturereview on ponderosa pine, Fowler and Sieg (2004) reported that80–95% of crown scorch volume would indicate a probabilityof mortality exceeding 0.80 within 2 or 3 years. Also, Ryan andReinhardt (1988) found that the lower the dbh and the higher thecrown scorch in Pinus ponderosa, the higher the probability ofmortality.

Beverly and Martell (2003) indicated that after applying pre-scribed burns in forests of Pinus strobus L., there was less than25% damage in crowns in 79% of the surviving stand, and only4% of the stand had greater than 75% damage in the crown,which suggests a minimal potential mortality as a result of theprescribed burns. Lussier et al. (2002) noted that in suppressedtrees, the highest mortality could be related to the low initialfoliar area, little vigor, and to the greater competition from thehigher density stand.

Only three treatment combinations, all from the two groupsthat had higher mortalities, showed significant reductions intheir live crown areas when compared with their controls: May

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 10 20 30 40 50 60 70 80 90 100

Crown kill (%)

Pro

babi

lity

of in

fest

atio

n

Fig. 7. Probability of infestation of Pinus hartwegii by bark beetles as afunction of crown kill.

high intensity in closed stands (2.05 m2, and 5.26 m2 for theclosed stand control; t = 4.28, P = 0.0001), May low inten-sity in closed stands (2.56 m2, and 5.26 m2 for the control;t = 3.86, P = 0.0003), and March high intensity in closed stands(2.97 m2, with 5.26 m2 for the control; t = 3.38, P = 0.013). Theaverage live crown area per tree at both densities tended to begreater in the control and in the March low intensity prescribedburn. Robbins and Myers (1992) noted that crowns were morevulnerable to damage during the growth stage than during thatof repose, and in the present study growth was reinitiated by thebeginning of spring.

A model was derived to predict post-fire live crown area basedon tree height and crown kill (Eqn 6, P = 0.0001, R2 = 0.64,adjusted R2 = 0.60).

ca = 1.77441 + 1.34615(h) − 0.06052(ck) (6)

Such a model shows that in all the fire treatments, the lower thetree height (h) and the higher the crown kill (ck), the lower thelive crown area (ca). Smaller trees are generally more susceptibleto fire damage, and the greater crown kills are related to higherfire intensities.

Effects of pestsThe bark beetle infestation logistic model was significant:χ2 = 15.6984, P < 0.0001; for the intercept, χ2 = 26.9993,P < 0.0001; for the independent variable crown kill, χ2 =13.7535, P = 0.0002. The association of predicted probabilitiesand observed responses showed the model was 63.5% concor-dant, 8.3% tied, and 28.2% discordant. This indicates the higherthe crown kill, the higher the probability of damage by bark bee-tles (Fig. 7, Eqn 7). The odds ratio was equal to 1.032, with the95% Wald confidence limits from 1.015 to 1.049,

PI = 1/(1 + exp − (−2.4584 + 0.0315 X1)). (7)

Similarly, in Pinus ponderosa trees, the intensity of crown scorchmay affect the colonisation attempts of Ips spp. and Dendroc-tonus spp. (Wallin et al. 2003).According to Kelsey and Gladwin(2003), greater susceptibility is related to the production ofethanol, given that in Pinus ponderosa, the trees that had beenseverely affected by fire produced 15 to 53 times more ethanol inthe phloem and sapwood respectively, and this product attractsbark beetles.

Survival of P. hartwegii 1 year after prescribed burn Int. J. Wildland Fire 61

a

ab

b

0

10

20

30

40

50

60

70

80

�40 40–60 �60

Crown scorch (%)

Tre

es a

ffect

ed b

y ba

rk b

eetle

s (%

)

Fig. 8. Percentage of trees with bark beetles by crown scorch category.Error bars correspond to standard deviation, and letters represent leastsignificant difference.

Grouping by crown kill categories, the trees with greater than60% crown kill post fire showed a greater incidence of pests(Fig. 8). In the control plot, crown kill rate from beetle infestationwas only 0.8%.

The mortality of the trees was not only due to the direct dam-age by fire, but probably also due to the subsequent attack byinsects, of which there are few studies worldwide according toRasmussen et al. (1996). The effect on the stand due to barkbeetles tended to be greater in closed stands with high intensityfire, probably because of the higher damage levels.

Regarding the proportion of trees with beetles present, threehigh-density plots exhibited differences in relation to their con-trols: the May high intensity prescribed burn on closed standshad the greatest value of infestation (88.9%, with 1.7% for theclosed stand control; t = −7.63, P = 0.0016), followed by Marchhigh intensity burn in closed stands (58.6%, with 1.7% for thecontrol; t = −3.08, P = 0.0369), and by the May low inten-sity burn in closed stands (43.4%, with 1.7% for the control;t = −6.84, P = 0.0024). The remaining treatments had no dif-ferences in comparison with their controls, because of the samereason stated in the previous paragraph.

Similarly, Pérez-Chávez (1981) reported that for Pinus leio-phylla Schl. et Cham., the probability of damage by bark beetleswas greater after fire. Espinoza and Muñoz (1988) found a rela-tionship between the surface affected during 1 year and the effectof Dendroctonus mexicanus Hopk. the following year, in forestsof Pinus leiophylla Dougl. and of Pinus montezumae Lamb. inthe State of Mexico. Dendroctonus adjunctus Blf. infests treesthat are suppressed, dominated, or over-mature, and those thathave been damaged by fires (Cibrián-Tovar et al. 1996).

Conclusions

The results of the present work suggest measured use of pre-scribed fire to reduce tree mortality and maintain vigor in Pinushartwegii in regions similar to the study area. It is best to conductprescribed burns in the beginning of the fire season, and no laterthan the middle of it (March). Also, it is preferable to use low

intensity fire rather than high intensity fire. We also suggest theuse of the FWI System to plan prescribed burns.

The root system heating is a key mechanism in Pinushartwegii mortality in closed stands, so the use of prescribedburns on areas with varied tree density may cause a differentialeffect, for higher mortality is expected in denser areas. Also,low intensity treatments and low crown kill seem to be associ-ated with lower probabilities of bark beetle infestation. The useof fire in this way may help reduce the fire danger in comparisonwith unburned areas and provide other associated benefits.

The results of the present work also provide a comparisonamong the effects on tree mortality, infestation by pests, andresidual crown of prescribed burning to those of forest fires(emulated with high intensity prescribed burns).

It is recommended that more studies be conducted on Pinushartwegii and other Mexican pine species, including other burn-ing seasons and older and taller trees. Another recommendationis to conduct studies more specific to the relationship betweenfire and pests, root mortality, and to work preferably on largerexperimental areas. This would also enable collection of a longweather record at the experimental sites to allow correlation ofFWI System components with burning conditions in regionaltree species. Classification of Mexican fuel types in the FBPSystem context coupled with ongoing improvements to the FBPSystem would provide a more accurate correlation between fireintensity and pine mortality in Mexico.

AcknowledgementsWe thank the IJWF Associate Editor and the referees who gave invalu-able critical observations to the present work. We thank the CONACYTinstallation project no. I35626-B, and the University of Chapingo for fund-ing the ‘Proyecto Ajusco’ (to whom this research belongs); also thanks toCONAFOR, CORENADER, and the Mexico City Government for theirauthorisation and help to conduct the prescribed burns. We are in debt tothe San Miguel and Santo Tomás Ajusco communities for allowing us toconduct this work in their lands. We are grateful for the invaluable help withfield work provided by Mr Gerardo Mendoza Ángeles, and for the FWI gridsampling performed by Peter Englefield at the Northern Forestry Centre.Finally, we thank Brenda Laishley, Bill de Groot, and Michael Brady fromNatural Resources Canada – Canadian Forest Service, for their invaluablereview of the document; and Natural Resources Canada – Canadian ForestService for covering publication costs.

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