Spatial segregation of pines and oaks under different fire regimes in theSierra Madre Occidental

Andrew ParkGroupe de Recherche en Écologie Forestiére, Université du Québec à Montréal, C.P. 8888, succ. Centre-Ville,Montréal, H3C 3P8, Québec, Canada (e-mail: andrew.park@utoronto.ca; e-mail: anditopark@hotmail.com;phone: (416) 925 4935)

Received 29 March 2001; accepted in revised form 14 July 2002

Key words: Community segregation, Fire ecology, Neighborhood effects, Ripley’s K (t) analysis, Spatial patternanalysis

Abstract

Surface fire can modify spatial patterns and self-thinning in pine-oak ecosystems. Spatial pattern analyses wereused to compare pattern development and interspecific spatial interactions in trees and seedlings in five Madreanpine-oak stands with different recent fire histories. Interspecific and intraspecific patterns were compared in small(< 15 cm dbh) and large (< 15 cm dbh) diameter classes of the pines (Pinus durangensis, P. teocote, and P.leiophylla) and oaks (Quercus sideroxylla, Q. crassifolia, and Q. laeta) that collectively dominated the five stands.Numbers of juvenile trees in 2.5 × 2.5 m subplots were correlated with cumulative distances to adult trees. Smallpine and oak trees were intraspecifically clustered at all scales, irrespective of fire regime. Large pines werestrongly clustered only in stands with longer fire-free intervals, and patterns of large versus small pine trees wereregular or random in frequent fire stands. These patterns were consistent with fire-induced mortality of maturingtrees under frequent fire. Large and small pines were segregated from small oaks at short and long distances inone stand with a 32-year fire-free interval, implying that two or more dynamic factors had produced regularpatterns at different scales. Such regular spatial patterns at short distances were not seen in other stands. There-fore, there was little evidence for direct competition between oaks and pines. The results reported here are con-sistent with studies from other pine-oak ecosystems showing that different fire regime and site factors interact toinfluence stand development processes and relative dominance of pines and oaks. In some stands, the continuedabsence of fire could foster increasing tree densities and an intensification of local neighborhood effects, produc-ing segregation of pine and oak species at longer distances.

Introduction

Periodic natural disturbances such as fire can struc-ture spatial patterns among trees in forest ecosystems.For example, fire-induced mortality of Quercus lae-vis (turkey oak) in Florida sand hills tends to be con-centrated under large Pinus palustris (longleaf pine)trees, causing post-fire clumping of surviving smalloaks over short distances (Rebertus et al. 1989a). Theincreased segregation occurs because hot fires in py-rogenic litter of P. palustris kill smaller Q. laevisclose to the pines (Rebertus et al. 1989b). A contrast-ing process has been suggested to generate spatial

patterns in the pine-oak forests of the Sierra MadreOccidental. Fulé and Covington (1998) found thatoaks and pines over 1.3 m tall were more intraspe-cifically aggregated on fire-excluded sites than on asite that experienced frequent fire (Fulé and Coving-ton 1998). They proposed that frequent fires thinclumps of juvenile and pole-stage trees, creating morerandom adult distributions.

Pines and oaks appear to have different mecha-nisms for survival in ecosystems prone to frequentfire. Many pines resist fire through adaptations suchas thick bark and self pruning. By contrast, oaks arefire endurers; that is, adult and juvenile oaks are eas-

1Plant Ecology 169: 1–20, 2003.© 2003 Kluwer Academic Publishers. Printed in the Netherlands.

ily killed by fire but they persist by vigorous resprout-ing from roots (Barton 1999). In the Sierra MadreOccidental, southwest USA, Mediterranean Europeand Florida sandhills, long fire-free intervals appearto favor root-sprouting by oaks over the germinationof pine from seed (Platt et al. 1988; Fulé and Cov-ington 1994, 1997, 1998; Barton 1995, 1999; Zavalaet al. 2000). Vigorous sprouting of Quercus cambiiand Q. rhyzophylla was observed following a stand-replacing fire in the Sierra Madre Oriental (Esparzaand Pérez (1999), Esparza personal communication).Root sprouting oaks, such as Quercus hypoleucoides(silverleaf oak) rapidly recover their pre-fire abun-dance in Arizona canyons (Barton 1999). Quercuslaevis can gain dominance over pines in the Floridasandhills by continuous sprouting from roots (Reber-tus et al. 1993).

The effects of surface fires are distributed un-evenly in space (Rebertus et al. 1989a; Bunnell 1995)and time (Rebertus et al. 1993; Glitzenstein et al.1995). In the Sierra Madre Occidental, long fire-freeintervals or fire suppression appear to foster densepopulations of juvenile and adult trees. A greater pro-portion of these dense tree populations consists ofoaks and less fire adapted species such as Pseudot-suga menziessii and Abies durangensis (Fulé andCovington 1997). Season of burning and variabilityof fire-return intervals can affect the relative domi-nance of P. palustris and Q. laevis in Florida (Reber-tus et al. 1993; Glitzenstein et al. 1995). Site factors,such as gradients of dry season water stress, or varia-tions in soil and topography, affect the establishmentof drought-sensitive Quercus ilex and drought-toler-ant Pinus halapensis in Mediterranean forests (Zavalaet al. 2000).

Pines in the Sierra Madre Occidental reproduceexclusively from seed (Perry 1991), but Madreanoaks are capable of regenerating from prolific rootsprouts. (Fulé and Covington (1998), J. R. Bacon,personal communication). Madrean pines and oaksalso have, respectively, the resister and evader char-acteristics noted above (A. Park, personal observa-tion). They may therefore become segregated by fire-generated spatial mortality patterns or interspecificcompetition. Alternatively, spatially discrete micro-habitats may favor one or the other mechanisms ofregeneration. Finally, foresters in the Sierra Madrebelieve that the broad crowns of mature oaks limit theestablishment of juvenile pines.

Tests of spatial segregation have been used to in-vestigate competitive interactions (e.g., Duncan and

Stewart (1991) and Martens et al. (1997), Moeur(1997)), and the effects of fire (Rebertus et al. (1989a,1989b)). As noted above, differences in fire regimecan affect densities, spatial patterns, and therefore,interspecific interactions of pines and oaks. Here, Iattempt to clarify the role of different recent fire re-gimes in the development of intraspecific and inter-specific spatial patterns of pines and oaks in the SierraMadre Occidental. Five stands differing in the num-ber of fires that had occurred since 1880, and in thetime that had elapsed since the most recent fire, werechosen for study. Pine and oak populations were ex-pected to have different spatial distributions underdifferent fire regimes because of their contrastingmechanisms for persisting in fire-prone landscapes.Spatial patterns were also expected to differ betweenlarge and small trees because of size-specific differ-ences in their vulnerability to the effects of fire.Therefore, spatial pattern was analyzed at multiplespatial scales on two diameter classes of mature trees,and on juvenile trees less than 3 m in height to testthe following hypotheses:

• H1 Frequent fires will produce random or regu-lar spatial patterns amongst large (> 15 cm dbh)pines and oaks if fire-induced mortality (versuscompetitive self-thinning) disperses spatial pat-terns among trees as they mature.

• H2 Small ( � 15 cm dbh but taller than 3 m)pines will be clustered over short distances inrecent/frequent fire stands because surface firecreates a patchwork of mineral-soil micrositesfavoring pine germination.

• H3 Regular spatial patterns between differentspecies of large versus small trees will occur atshort distances in all fire regimes if large treesinhibit the establishment of small trees throughcrown or root competition.

• H4 Regular spatial patterns will be observed be-tween large versus small pines, and large pinesversus small oaks if frequent fire enhances juve-nile tree mortality beneath large pines.

Specific predictions for spatial patterns that wouldbe observed in support or refutation of these hypoth-eses under different fire regimes are given in Table 1.

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Methods

Study sites and species

The five research sites were in a pine-oak forest co-managed by an ejido (a community with collectiveproperty rights over its forests, Taylor (2000)). Theywere located in the Sierra Madre Occidental betweenlatitudes 24°23� – 24°36� N and longitudes 105°37� –105°50� W (Figure 1) at altitudes of 2460–2560 ma.s.l. The area receives 800–1200 mm per year ofsummer monsoon rains. Frequent surface fires, over60 percent of which occur in the spring (Fulé and

Covington 1997, 1998) are widespread in the SierraMadre Occidental (Fulé and Covington (1996, 1999);Park 2001). Crown fires also occur, and these can beextensive in extreme Southern Oscillation years(Fernández and García-Gil 1998; Rentería Anima andDomínguez Moreno 1999; Fulé et al. 2000).

Study sites were chosen to represent a range of re-cent fire regimes from among 29 stands for which thedates of the most recent fires were known (Park2001). Three types of stand were identified: stands on20–50% slopes that had experienced recent, frequentfires (Recent Fire 1 and Recent Fire 2), (ii) standson slopes of 10–38% in which fires occurred 24 (In-

Table 1. Specific predictions for spatial patterns that would support or refute hypotheses.

Hypothesis Prediction if hypotheses supported Prediction if hypotheses refuted

H1 Large pines and oaks randomly distrib-

uted in recent/frequent fire stands.

Random or regular distribution in frequent

fire stands, but not in long fire-free interval

stands

Expect random or regular distributions in all

stands.

H2 Small pines more clumped in recent/

frequent fire stands than in stands with long

fire-free intervals.

Strong clumping of pines � 15 cm dbh in

recent/frequent fire stands, but not in long

fire interval stands.

Pines could be clumped in all stands, be

more clumped under long fire intervals, or

be distributed at random.

H3 Large and small trees of different spe-

cies adopt regular patterns at short distances

under crown or root competition.

Regular patterns observed between oaks and

pines, different oak and different pine spe-

cies under all fire regimes at short distances

Patterns between different species could be

clumped or random at short distances, or

regular patterns only in frequent fire stands

(see H5)

H4 Fire-enhanced mortality of small pines

and/or oaks beneath large pines.

Regular patterns between large pines and

small pines/oaks observed in frequent fire

stands only.

Random or clumped patterns between large

pines and small pines and/ or oaks in fre-

quent fire stands.

Figure 1. Map showing Durango within Mexico and relative positions of study sites.

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termediate Fire) and 42 years before sampling (OldFire 1), and (iii) a single stand (Old Fire 2) on0–12% slopes that experienced its most recent fire 32years before sampling (Table 2). Basal areas of ma-ture trees varied between 21 m2 ha−1 in Old Fire 1 to40 m2 ha−1 in Old Fire 2 (Table 2). All stands hadrecently received regeneration cuts under the Methodof Silvicultural Development silvicultural system(MDS, Rodriguez et al. (1993) and Instituto Nacionalde Estadistíca, G.e.I.I. (1997)). The regeneration cutis a commercial harvest that is supposed to improvethe light environment for natural pine regenerationwhile removing 38–64% of stand basal area (Park2001). Regeneration cuts applied to the five studysites had removed 5–13 m2 ha−1 of stand basal area.

The study sites contained 14–16 species of pines,oaks, junipers and Arbutus. However, in all stands,three pine and three oak species accounted for 76–93percent of all mature stems, and these were the onesused for spatial analysis. The pines, Pinus durangen-sis Mart., P. teocote Schl. et Cham.and P. leiophyllaSchl. et Cham. are Diploxylon or hard pines thatreach heights of 25–40 m (Perry 1991). They havecharacteristics of self-pruning, thick bark and non-se-rotinous cones that are shared by pines that experi-ence predictable surface and stand-thinning fires(Keeley and Zedler 1998). The oaks were Quercus si-deroxylla Humboldt and Bonpland, Q. crassifoliaHumboldt and Bonpland and Q. laeta Liebmann (Tre-lease 1924). Quercus sideroxylla is the most wide-spread oak in the region, reaches heights of 25 m ormore and often dominates lower slopes and valleybottom sites (Park 2001). Quercus laeta and Q. cras-sifolia are shorter trees with spreading crowns thattend to occupy dry upland sites. All three are capableof regenerating from root sprouts as well as seed (A.Park personal observation).

Field methods

A 0.36 ha plot was established in each of four studysites and one 0.25 ha. plot in the remaining site, thesmaller size of this plot being dictated by topographicrestrictions. Each plot was subdivided into 10 × 10 msubplots. In each plot, mature trees were mapped tothe nearest decimeter with reference to the corners ofthe 10 × 10 m subplots. Their final positions were ad-justed to plane after accounting for slope angle. Ma-ture pines were defined as trees greater than 3 m tall.Mature oaks were either over 3 m tall or greater than5 cm in diameter at breast height (dbh) if less than 3m in height. All other trees, including first year seed-lings, were counted as juveniles. Tree diameters weremeasured at a height of 1.3 m, or at 20 cm if the treewas a stump, and the presence of fire scars and char-coal staining was noted when these were present.Stem maps of juveniles were not be made becauselarge numbers of stems were present (up to 12,000juveniles ha−1 in frequent fire stands, see Table 3).Instead, they were sampled by counting juvenile treesless than 3 m tall within contiguous 2.5 × 2.5 m sub-plots.

Fire history

Fire history was determined from wedges cut fromfire scarred live trees and complete sections takenfrom fire-scarred stumps (between 5 and 15 in eachplot). These were sanded with belt and orbital sand-ers using progressively finer grit papers, and finishedmanually with fine-grit emery cloth. Tree ring incre-ments were measured beneath a binocular microscopeconnected to a video monitor and a computerized datarecording program to record the data. Locations offire scars and possible false rings were noted. Skel-eton plots and graphs were prepared to establish thelocations of narrow increments used as markers in thecross-dating process. These were visually cross-dated

Table 2. Numbers and basal areas of pines, oaks, stumps and snags in the five study sites. Numbers and basal areas (shown in brackets) ha−1

are shown to compare the different sizes of plots. All pine and oak species counted in the stand are included.

Stand Pines ha−1 Oaks ha−1 Total ha−1 (inc. stump) Stumps ha−1 (Regen. Cut) Snags ha−1

Recent Fire 1 753 (21.17) 183 (2.59) 936 (23.76) 208 (7.99) 58 (3.18)

Recent Fire 2 772 (18.83) 203 (3.55) 975 (22.38) 133 (7.56) 17 (0.41)

Intermediate Fire 475 (21.39) 419 (11.27) 894 (32.66) 144 (6.78) 69 (2.85)

Old Fire 1 300 (13.61) 404 (7.63) 704 (21.24) 176 (6.86) 44 (3.54)

Old Fire 2 797 (27.95) 536 (12.02) 1333 (39.97) 419 (15.54) 31 (2.18)

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against each other, against increment cores from dom-inant trees in the same stand, and with reference tomaster series from sites near Topia, 200 km. north-west of my study area (Fulé and Covington 1997).Fire scar records from stumps were initially dated asfloating tree ring series using the COFECHA program(Grissino-Mayer and Holmes 1993). Cross-datingwas also checked using COFECHA. Mean fire inter-vals were calculated for the periods 1880 to 1999 andtheir statistical qualities analyzed using the FHX2 fireanalysis program (Grissino-Mayer 1995).

Statistical analysis

Ripley’s K(t) and K(12t) analyses (Ripley 1977; Dig-gle 1983; Upton and Fingleton 1985) were used totest hypotheses 1, 2, 3, and 4. These analyses weredone using computer software developed by Moeur(1993, 1997). The K(t) and K(12t) analyses comparethe distributions of all possible tree to tree distanceswithin a species (K(t)) or between two species(K(12t)) with their random expectation (assumed tofollow a Poisson distribution) to determine whetheran observed pattern is regular, random or clustered.The pattern is assessed over a range of discrete dis-tance classes (ti–k) that may be as long as half thelength of the shortest side of a plot. Distance classesti–k represent the radii of circles and K(t) is calculatedfor all inter-tree distances less than the radius of eachti. If the resulting pattern is random, K(t) = �t2, whichis simply the area of a circle with radius t.

Trees that lie outside the plot, and which are there-fore not counted, nevertheless influence the patternsof trees within the sampling area. For this reason anedge correction is applied to the K(t) equation (Diggle

1983; Haase 1995; Moeur 1997). The K(t) are thensubjected to a variance-stabilizing transformation:

Lti ���K�ti�/� � t (1)

This transformation has an expectation of zero un-der a Poisson distribution and takes values greaterthan or less than zero for clustered and regular distri-butions, respectively. To test for departure from ran-domness, the measured L(ti–n) are measured againstthe expectation of L(ti-n) under the null (Poisson) dis-tribution using confidence envelopes derived by ran-dom permutation of the actual data.

Dominant pines and oaks were divided into twosize classes for spatial analysis. Diameter classeswere those � 15 cm dbh and those > 15 cm dbh. Thisdivision point was selected to separate pole-sizedpines, which were obviously clustered in some stands,from older pines which generally appeared more iso-lated, and may have undergone self-thinning. Thesame division was applied to oaks to provide a com-mon basis for all analyses. The former breast heightdiameters of pine and oak stumps were estimated us-ing linear regression (Sokal and Rohlf (1981), r2 =0.97–0.99, p � 0.0001 for all species, n = 83–129).If fewer than twenty trees were present in any diam-eter class in a plot, the two classes were amalgam-ated because K(t) statistics may be less reliable whenmeasured on too few inter-tree distances (Fulé andCovington 1998).

Univariate L(t) analyses on individual diameterclasses were used to test Hypotheses 1 and 2. Hypoth-eses 3 and 4 were tested on trees using L12(t) on allpossible intraspecific and interspecific combinationsof the two diameter classes. Tests were conducted on

Table 3. Abundance of regenerating seedlings and saplings of dominant species in 0.36 and 0.25 ha plots, adjusted to numbers of plants perhectare for comparative purposes.

Species STAND

Recent Fire 1 Recent Fire 2 Intermediate Fire Old Fire 1 Old Fire 2

Pinus durangensis 3361 3150 178 168 194

Pinus teocote 994 1144 6 200 153

Pinus leiophylla – – 842 – 128

Quercus sideroxylla 839 369 2539 76 1639

Quercus crassifolia – 1489 8 1000 0

Quercus laeta 219 – – 924 3

Dead Pines 756 31 8 12 6

Other Species 951 3448 2136 1204 856

Totals 7120 9631 5717 3584 2978

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1 m distance classes (ti) ranging from 0–1 m to 29–30m in 0.36 ha plots and from 0–1 to 24–25 m distanceclasses in the 0.25 ha plot. Two sided confidence en-velopes were calculated at � = 0.05 for each distanceclass using 200 Monte-Carlo simulations in which thelargest and smallest 2.5% of values were discarded ateach t.

Hypothesis 4 was also tested on juvenile trees.Pearson correlation coefficients were calculated be-tween numbers of juveniles in 2.5 × 2.5 m subplotsand the cumulative distance from subplot centers tothe closest five adult trees in each diameter class. Toallow for edge effects, subplots from a 10 m bufferzone at the edge of each plot were excluded fromanalysis. Juvenile tree counts in subplots were as-sumed to be autocorrelated. Therefore, Clifford etal.’s (1989) modified t-test of association, as correctedby Dutilleul (1993), was used to test the significanceof the correlation coefficient. In place of the N-2 de-grees of freedom generally used to calculate the t-test,the modified t-test employs an estimate of “effectivesample size” that accounts for autocorrelation be-tween variables. Correlation coefficients and modifiedtests of significance were calculated using softwaredeveloped by Legendre (2000).

Results

Fire history

Fire return intervals ranged from 2 to 23 years be-tween 1880 and the date of the most recent fire (Ta-ble 4). A minimum of 6 years (Recent Fire1) and amaximum of 42 years (Old Fire 1) had elapsed be-

tween the most recent fire and the year of sampling.Recent Fire1 had experienced the most widespreadrecent fire, as shown by the large number of charcoalstained trees in this stand (Figure 2). Fire scars pen-etrating the cambium were much less frequent thancharcoal staining in Recent Fire 1, indicating that thewidespread 1993 fire was of low intensity. RecentFire 2 experienced more recorded fires than RecentFire 1 since 1880, but evidence of fire in this standwas more localized. In stands where longer fire-freeperiods had followed the most recent fire, few treeswith dateable scars were found.

Statistics derived from the two-parameter Weibulldistribution describe the probabilities of recurringfires (Table 4). The Weibull median interval is thetime interval during which the stand has a 50% prob-ability of burning once again. It is less influenced bylarge fire intervals than is the mean. The 100%Weibull hazard estimates the maximum fire-free in-terval before the stand is expected to burn again.Long 100% Weibull hazard intervals, such as thosefor Old Fire 1 and Old Fire 2 should be interpretedcautiously since skewed fire interval distributions can

Table 4. Fire return intervals at the five study sites for all recorded fires since 1880 (from Park- in review). Statistics were calculated for allfire years, including those represented by a single scar.

Stand Number of

fires

Time since

last fire*

FIRE INTERVALS (years)

Mean inter-

val (MFI)

Std. Dev. Min. Max. Weibull

median

Interval

100%

Weibull

hazard

Recent Fire 1 13 6 8.3 4.1 3 15 8.0 26.0

Recent Fire 2 14 13 7.5 4.0 2 16 7.1 28.5

Intermediate Fire 5 24 11.5 1.7 10 14 11.6 12.8

Old Fire 1 10 42 7.1 5.9 3 22 6.3 180.1

Old Fire 2 7 32 13.1 9.2 5 21 11.9 349.4

* Time since fire is reported as the interval from the last fire to the year of sampling.

Figure 2. Numbers of charcoal-stained (white) and fire-scarredtrees (black) found in four 0.36 ha plots and one 0.25 ha plot. Notethat a different scale has been used in Recent Fire 1 for clarity.

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bias estimates of the longest possible interval (Gris-sino-Mayer 1999). Distributions in Old Fire1 and OldFire 2 that featured a few long fire intervals among ageneral pattern of short ones led to 100% Weibullhazards 25 and 27 times greater than the mean returnintervals. In Old Fire 2, frequent fires between 1932–1949 (Figure 3) led to a low mean return interval de-spite this stand having the longest recent fire-free pe-riod.

K(t) and K(12t) analysis

The pine-oak stands in this study were characterizedby complex, often contrasting, sets of spatial patterns.The character of these patterns differed between spe-cies, size classes within species, and between sizeclasses of different species.

A synopsis of the patterns and their conformity tothe hypotheses is shown in Table 5. Univariate pat-terns used to test hypotheses 1 and 2 are reported inTable 6, and the bivariate patterns that tested hypoth-eses 3 and 4 are shown in Table 7. In interpretingconformity of a pattern to an hypothesis, both signif-icant deviation from randomness and the consistencyof that variation across distance classes must be con-sidered. Patterns that depart inconsistently or at onlya few points from random expectation should be in-terpreted more cautiously than patterns that are non-random across a wide range of contiguous distance

classes. The strength of departure from randomnesscan also be judged visually from graphs that super-impose Lt lines against 95% confidence envelopescalculated by Monte Carlo simulation.

Hypothesis 1 was supported by the strong cluster-ing of big P. durangensis and P. teocote over a widerange of ti in Old Fire 2 (Figure 4a). However, largetrees of these two species were also weakly clusteredat 2–7 m in Recent Fire1 (Figure 4b) and (P. du-rangensis only) in Recent Fire 2. Large pines wereclustered across a much greater range of distanceclasses in Old Fire 2 than in Recent Fire stands. InFigure 4a, clustered values for large P. durangensisin Old Fire 1 lie far above the upper boundary of the95% confidence envelope. The corresponding Lt pat-tern for large P. durangensis in Recent Fire 1 (Figure4b) shows only weak departure from random expec-tation.

Small pines were significantly clustered in mostdistance classes in all stands, irrespective of fire his-tory. This clustering was consistent (Table 6) and pro-nounced (Figures 4c,d), indicating that contagious re-cruitment from juvenile stages to pole-sized trees hasoccurred under a variety of fire regimes and topo-graphic/soil conditions. Hypothesis 2 was thereforenot supported, and factors independent of recent fireregime must have contributed to observed distribu-tions of pines � 15 cm dbh.

Figure 3. Fire scar records in the five study plots. Numbers over triangles represent the number of fire-scar samples on which dating wasbased. Stand codes: RF1 – Recent Fire 1, RF2 – Recent Fire 2, INTF – Intermediate Fire, OF1 – Old Fire 1, OF2 – Old Fire 2.

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Clustered patterns among small trees of all threeoak species indicated contagious recruitment similarto that observed in pines. In Recent Fire 2 small Q.crassifolia were clustered at all tI, while large treesof this species were randomly distributed at all scales.In Old Fire 1, Q. laeta and Q. crassifolia werestrongly clustered at all scales (Table 6).

There were no consistent spatial patterns betweenlarge oaks in those stands where they were suffi-ciently numerous to be tested. Patterns ranged fromregular (large Q. sideroxylla in Intermediate Fire, Fig-ure 4e) through completely random (large Q. crassi-folia in Recent Fire 2) to clustered (large Q. siderox-ylla in Old Fire 2, Figure 4f). These patterns mayhave been influenced by marked differences in oakpopulation densities in the various plots. The Q. sid-eroxylla in Recent Fire 1 were few in number, smalland weakly clustered at shorter distances. Large Q.sideroxylla were clustered from 4–16 m in Old Fire 2(Table 6).

Hypothesis 3 was rejected for trees in 4 out of 5stands because regular distributions of pines versusoaks did not occur consistently at short distances (Ta-ble 7). An exception was the regular pattern for largeP. teocote versus small oaks in Old Fire 2 at 2–30 m(Figure 5a). Regular spatial patterns were also ob-served between small P. teocote and small P. du-rangensis versus small Q. sideroxylla at 14–30 m and4–13 m respectively (Figure 5b). However, large P.durangensis, which were clustered with P. teocote(Table 7), were distributed at random with respect to

small Q. sideroxylla at all distances. Also in opposi-tion to the expectations of Hypothesis 3, large andsmall P. teocote were significantly clustered with Q.laeta at distances greater than 4 m in Old Fire 1 (Fig-ure 5c). Clustered patterns of Q. laeta versus Q. cras-sifolia were also observed in Old Fire 1, suggestingsome habitat affinity between all three species. Largeversus small oaks were distributed at random in allstands at short distances, but large and small Q cras-sifolia were clustered between 21 and 30 m in RecentFire 2 (Table 7).

Hypothesis 4 (fire-induced mortality beneath largepines) was supported by the occurrence of regularpatterns between large and small pines at short dis-tances in recent fire stands. This tendency was mostpronounced in large P. durangensis versus small P.durangensis (Figure 5d) and small P. teocote (Ta-ble 7). Large versus small P. teocote did not displayregular distributions at short distances in Recent Fire1, but were significantly clustered from 19–23 m(Figure 5e). In contrast, large versus small P. du-rangensis were strongly clustered at 3–30 m in OldFire 2 (Figure 5f). Large versus small P. teocote werealso clustered at 1–10 m in the same stand (Table 7).

Large versus small P. leiophylla were distributedat random across most distance classes. Significantclustering in scattered distance classes in small ver-sus large P. leiophylla at 2 m in Intermediate Firecould have arisen fortuitously. Random distributionsof P. leiophylla versus all other species were also ob-served in Old Fire 2, implying that seed dispersal

Table 5. Synopsis of main results of hypothesis testing.

Hypothesis Result/comments

H1 Large pines and oaks randomly distributed in recent/frequent

fire stands.

Supported: large pines dispersed in Recent Fire 1 and 2 in com-parison to patterns in Old Fire 1 and 2. However but large pines

still clustered between 2–7 m. in recent fire stands.

H2 Small pines more clumped in recent/frequent fire stands than in

stands with long fire-free intervals.

Rejected: small pines clumped at almost every scale, irrespective of

stand.

H3 Large and small trees of different species adopt regular patterns

at short distances under crown or root competition.

Rejected, except in Old Fire 2 and Intermediate Fire. In Old

Fire 2, regular patterns developed between large P. teocote and

small Q. sideroxylla. In Intermediate Fire, juvenile oaks and pines

were less abundant close to small oaks.

H4 Fire-enhanced mortality of small pines and/or oaks beneath

large pines.

Supported for pines in Recent Fire 1 and Recent Fire 2 but notfor oaks. Dead juvenile pines in Recent Fire 1 located closer to

adults, but live juveniles generally located away from adults.

H5 Potential for intraspecific competition between seedlings greater

under frequent fire regimes.

Weakly supported. Weak to moderate segregation of pine and oak

nearest neighbours in Recent Fire 1, Intermediate Fire and Old Fire

2 only.

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patterns in this species occurred independently fromthe dispersal of other species.

Stem maps were used as aids in interpreting dif-ferences in spatial patterns in Old Fire 2 (Figure 6)

and Recent Fires 1 and 2 (Figure 7). These stem mapsshowed that four abundant species in Old Fire 2 wereconcentrated in different map quadrants. Small andlarge P. durangensis were concentrated in the north-

Table 6. Kt analysis of pines and oaks in five stands with different fire histories. Trees described as “B” were � 15 cm. dbh while thosedescribed as “Sm” were � 15 cm dbh; “St” represents stumps. Species codes are Pidu – P. durangensis, Pite – P. teocote, Pile – P. leio-phylla, Qusi – Q. sideroxylla, Qucr – Q. crassifolia and Qula – Q. laeta. Statistical signicance at the 95% level is shown as follows: ‰ –clustered distribution, ˆ – regular distribution, – – not significant.

Distance (m) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Recent Fire 1Pidu B (n = 33) – ‰ ‰ ‰ ‰ ‰ – ‰ – – – – – – – – – – – – – – – – – – – – – –

Pidu Sm (n = 100) ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Pite B (n = 20) – ‰ ‰ ‰ ‰ ‰ ‰ – – – – – – – – – – – – – – – – � � � � � � –

Pite Sm (n = 73) ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – – – – � � � � � � �

Qusi All (n = 32) ‰ ‰ ‰ – – – – ‰ ‰ – – – – – – – – – – – – – – – – – – – – –

Pinus spp. St (n = 54) ‰ ‰ – – ‰ ‰ ‰ – – – – � � – – – – – – – – – – – – – – – – –

Recent Fire 2Pidu B (n = 27) – ‰ ‰ ‰ ‰ – – – – – – – – – – – – – � � – � – – � � � � � �

Pidu Sm (n = 100) ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Pite All (n = 69) ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Qucr B (n = 20) – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

Qucr Sm (n = 45) ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Pinus spp. St (n = 42) ‰ ‰ – ‰ ‰ – – – – – – – – – – – – – – – – – – – – – – – – –

Intermediate FirePile B (n = 97) – – – ‰ ‰ ‰ ‰ ‰ – – ‰ – – ‰ ‰ – – – – – – – – – – – – – – –

Pile Sm (n = 49) – – – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Qusi B (n = 40) – – – – – – – – – – – – – – � � � � � – – � � – – – – – – –

Qusi Sm (n = 106) ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – – – – – –

Pinus spp. St (n = 48) – – – ‰ ‰ – – – – – – ‰ – – – – – – – – – – – – – – – – – –

Old Fire 1Pite B (n = 20) – ‰ ‰ ‰ – ‰ – – – – – – – – – – – – – ‰ – ‰ ‰ ‰ –

Pite Sm (n = 24) – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Qucr All (n = 42) ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Qula All (n = 59) ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Pinus spp. St (n = 23) – – – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Old Fire 2Pidu B (n = 22) – – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – – – – – – – – – – – – – –

Pidu Sm (n = 31) ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Pile B (n = 38) – – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Pile Sm (n = 62) ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – – – – – – – – �

Pite B (n = 38) – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Pite Sm (n = 20) ‰ – – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – – – –

Qusi B (n = 50) – – – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – – – – – – – – – – – –

Qusi Sm (n = 137) ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Pile St (n = 47) ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – – – – – – – – – – – – –

Pinus spp. St (n = 135) ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – – – – – – –

9

east quadrant (Figure 6a). Small Q. sideroxylla werestrongly clustered in the northwest quadrant (Figure6b). Large and small P. teocote were concentrated inthe south and east (Figure 6d). This contrasts withboth recent fire stands, where clusters of small treesappeared to occupy less space than those in Old Fire2, and large trees were distributed more evenly (Fig-ure 7).

Correlations

Correlations between juvenile tree counts and the cu-mulative distance to the five closest trees in each di-ameter class were generally low (from 0.00 to +0.34and −0.28, Table 8). Almost all correlations had TypeI error probabilities higher than � = 0.05 because au-tocorrelation had reduced effective degrees of free-dom. However, the distribution of negative and posi-

Figure 4. Example plots of distance vs. L(t) that compare and contrast intraspecific spatial patterns. Solid line is L(t) while dotted linesrepresent upper and lower 2.5% limits of the 95% confidence interval.

10

tive correlations (Table 8) was consistent with spatialpatterns in large versus small adult trees that sup-ported Hypothesis 4.

In Recent Fire 1, plots with more dead juvenilepines tended to be closer to adult pines. Live juvenilepines in both recent fire stands tended to be locatedfurther away from adult trees, or had no specific spa-tial associations. In Intermediate Fire and Old Fire 1,

correlations showed that juvenile pines were growingsomewhat apart from adult pines. In contrast, juvenilepines tended to grow closer to adult pines of all spe-cies in Old Fire 2.

Juvenile Q. sideroxylla were more numerous atgreater distances from adult pines in several cases,but displayed no tendency to grow closer to adultoaks. By contrast, Quercus crassifolia showed a gen-

Figure 5. Example plots of distance vs. L(12t) that compare and contrast interspecific and between-diameter-class spatial patterns in differentstands. Solid line is L(12t) while dotted lines represent upper and lower 2.5% limits of the 95% confidence interval.

11

Table 7. Bivariate K(12t) analyses of species and diameter classes in five stands with different fire histories. Trees described as “B” were �

15 cm. dbh while those described as “Sm” were � 15 cm dbh. Species codes are Pidu – P. durangensis, Pite – P. teocote, Pile – P. leio-phylla, Qusi – Q. sideroxylla, Qucr – Q. crassifolia and Qula – Q. laeta. Statistical signicance at the 95% level is shown as follows: ‰ –clustered distribution, � – regular distribution, – – not significant.

Distance (m) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Recent Fire 1Pidu B vs Pidu Sm – – � � � � � � � � � � � � � � � – – – – – – – – – – – – –Pidu B vs Pite Sm – – – – – – � � � � � � � � � � � – – – – – – – – – – – – –Pidu Sm vs Pite B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pite B vs Pite Sm – – – – – – – – – – – – – – – – – – ‰ ‰ ‰ ‰ ‰ – – – – – – –Pite B vs Pidu B – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – – – ‰ ‰ – – – – – – – – – – – – – – –Pite Sm vs Pidu Sm ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – – – – – – – – – – – – – – – – – –Pidu B vs Qusi All – – – – � � – � – – – – – – – – – – – – – – – – – – – – – –Pidu Sm vs Qusi All – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pite B vs Qusi All – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pite Sm vs Qusi All – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

Recent Fire 2Pidu B vs Pidu Sm – � � – – � � � � � � – – – – – – – – – – – – – ‰ ‰ ‰ ‰ ‰ ‰

Pidu B vs Qucr B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pidu B vs Qucr Sm – – – – – – – – – – – – – – – – – – – – – – – – – ‰ ‰ ‰ ‰ ‰

Pidu Sm vs Qucr B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pidu Sm vs Qucr Sm – – – – – – – – – – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – – – – –Pite All vs Pidu B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pite Sm vs Pidu B – – – � � � � � � – – – – – – – – – – – – – – – – – – – – –Pite Sm vs Pidu Sm ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – – – – – – – – – – – – –Pite Sm vs Qucr B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pite Sm vs Qucr Sm – – – – – – – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – – – – – – – –Qucr B vs Qucr Sm – – – – – – – – – – – – – – – – – – – – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Intermediate FirePile B vs Pile Sm – ‰ – – – – – – – – – – – – – – – – – – – – – – – – ‰ ‰ ‰ ‰

Pile Sm vs Qusi B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pile B vs Qusi Sm – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pile B vs Qusi B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pile Sm vs Qusi Sm – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Qusi Sm vs Qusi B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

Old Fire 1Pite B vs PiteSm – – – – – – – – – – – – – – – – – – – – – – – – –Pite B vs Qucr All – – – – – – – – – – – – – – – – – – – – – – – – –Pite Sm vs Qucr All – – – – – – – – – – – – – – – – – – – – ‰ ‰ ‰ ‰ ‰

Pite B vs Qula All – – – – – – – – – – ‰ ‰ – – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Pite Sm vs Qula Sm – – – – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Qucr All vs Qula All – – – – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Old Fire 2Pidu B vs Pidu Sm – – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Pidu B vs PileB – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pidu B vs Pile Sm – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pidu Sm vs PileB – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pidu Sm vs Pile Sm – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pidu B vs Pite B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pidu B vs Pite Sm – – – – – – – – – – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – ‰ ‰ –Pidu Sm vs Pite B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pidu Sm vs Pite Sm – ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰

Pidu B vs Qusi B ‰ – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pidu B vs Qusi Sm – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pidu Sm vs Qusi B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pidu Sm vs Qusi Sm – – – � – � � � � � � � � – – – – – – – – – – – – – – – – –Pile B vs Pile Sm – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pile B vs Qusi B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pile B vs Qusi Sm – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pile Sm vs QusiB – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pile Sm vs Qusi Sm – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pite B vs Pite Sm ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ ‰ – – – – – – – – – – – – – – – – – – – –Pite B vs Pile B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pite B vs Pile B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pite B vs Pile Sm – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pite Sm vs Pile B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pite Sm vs PileSm – – – ‰ – – – – – – – – – – – – – – – – – – – – – – – – – –Pite B vs Qusi B – � – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pite B vs Qusi Sm – � � � � � � � � � � � � � � � � � � � � � � � � � � � � �Pite Sm vs Qusi B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pite Sm vs Qusi Sm – – – – – – – – – – – – � � � � � � � � � � � � � � � � � �Qusi Sm vs Qusi B – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

12

eral tendency to grow closer to adult conspecifics andadult P. teocote in Old Fire 1. Juvenile Q. crassifoliaalso tended to be associated with adult Q. laeta in OldFire 1, but Q. laeta juveniles were negatively corre-lated with both Q. crassifolia and adult conspecifics.Juvenile Q. sideroxylla tended to grow away fromsmall conspecific adults and P. leiophylla in Interme-diate Fire. Juvenile P. leiophylla displayed the sametendency in Intermediate Fire, but the opposite ten-dency in Old Fire 2.

Juvenile Pinus teocote tended to be more numer-ous in plots that were close to adult oaks in recent

fire stands. Juvenile P. durangensis were also associ-ated with short distances to adult Q. sideroxylla inRecent Fire 1. In Old fire 2, no such correlations wereobserved, and in Old Fire 1, P. teocote was more nu-merous away from adult Q. laeta, and was not asso-ciated with adult Q. crassifolia. Taken together, theseobservations suggest that fire regime, species compo-sition and site characteristics interact to produce con-trasting intraspecific and interspecific interactions be-tween juvenile and mature trees.

Figure 6. Stem maps for dominant pine and oak species in RF1 (A/B), and RF2 (C/D). Codes: black circles = small trees; open circles =large trees; triangles in B = Q. laeta.

13

Discussion

Spatial pattern analysis revealed patterns that sup-ported Hypothesis1 (random distribution of largepines and oaks), and spatial correlations providedweak support for Hypothesis 4 (enhanced mortalityunder large pines; see Table 1 and Table 5). Hypoth-eses 2 (small pines more clumped in recent firestands) and hypothesis 3 (root competition betweendifferent species) were rejected. Significant clusteringamong small pines in every stand, and regular pat-terns of pines versus oaks in Old Fire 2 implied eco-

logical causes not tested in hypotheses 2 and 3. Theability to infer the causes of the observed patterns waslimited, however, by the unreplicated treatments andretrospective nature of the analysis. Interpretations inthis study should therefore be considered most rele-vant to the unique combinations of stand history andspecies composition represented by the sites (see Plattet al. (1988)). Results also cannot be extended beyondthe maximum scale of measurement. Nonetheless,scales of analysis used in this study accord with re-sults of other temperate forest studies that report sig-nificant spatial interactions up to about 30 m (Bon-

Figure 7. Stem maps for dominant pine and oak species in DF2. Codes: black circles = small trees; open circles = large trees.

14

Tabl

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15

nicksen and Stone 1981; Kenkel 1988; Platt andRathbun 1993; Biondi et al. 1994; Peterson andSquiers 1995).

Univariate spatial pattern analysis supported a rolefor fire in determining differences in tree distributionsin recent fire versus old fire stands. The stronger clus-tering of small and large pines in old fire stands com-pared to the small fire stands suggested that differentfire regimes had modified juvenile survival, thinningof maturing pine cohorts, and interspecific interac-tions among adult trees. Dispersed patterns of largepines in frequent fire stands versus strongly clusteredpatterns in Old Fire 2 were consistent with fire-in-duced mortality of maturing trees under frequent fire(Hypothesis 1).

Regular distributions of large versus small pines infrequent fire stands supported the possibility that py-rogenic litter enhances young tree mortality beneathlarge pines (Hypothesis 4). The tendency for dead andliving juvenile pines to be located, respectively, closerto and further away from adult pines in Recent Fire 1was also consistent with the expectations of Hypothe-sis 4. Living P. teocote juveniles tended to be closerto adult Q. sideroxylla, implying that fire tempera-tures could be cooler beneath oaks, as is the case forQ. laevis in Florida (Platt et al. 1991). Juvenile oakswere not consistently less abundant in plots that werecloser to mature pines, however, in contrast to juve-nile oak distributions in P. palustris-Q. laevis stands(Rebertus et al. 1989a, 1989b).

Small pines and oaks were clustered over all ex-cept the shortest distance classes in every stand. Dif-ferences in fire regime do not, therefore, provide acomplete explanation for the distribution patterns ofsmall trees (Hypothesis 2). However, different fire re-gimes may have affected the distribution of largetrees. Clustering among large pines and large oaks inOld Fire 2 may have arisen because infrequent firesallowed more young trees to survive into larger sizeclasses than in frequent fire stands. This result agreeswith Fulé and Covington (1998) finding of greater in-traspecific tree aggregation at sites without a recenthistory of frequent fires.

There was no evidence that high tree densities inOld Fire 2 had led to increased mortality because fewstanding dead trees were present (Table 2). Mortalityrates of adult trees in pine-oak ecosystems are gener-ally low (Biondi 1996; Platt et al. 1988). If fire re-mains infrequent, tree densities may therefore con-tinue to increase in old fire stands and IntermediateFire. These conditions would encourage stands to

shift towards conditions characterized by slowgrowth, stagnating nutrient cycles, and increasedcrown fire hazard, as seen in ponderosa pine standsin the US southwest (Covington and Moore 1994;Covington et al. 1997; Fulé and Covington 1997).

Proportions of adult oaks and juveniles werehigher in stands with longer fire-free intervals. Long-term research in P. palustris-Q. laevis ecosystemsshows that irregular fire intervals offer a “survivalwindow” allowing oaks to grow out of the small sizeclasses that suffer high mortality rates during frequentfires (Rebertus et al. 1993). Fire return intervals weremore variable in old fire than in frequent fire stands,suggesting that Q. sideroxylla populations may besimilarly favored by longer, less predictable fire in-tervals.

Fire variability may also help to explain the near-random patterns among large pines in recent firestands. Although 1–3 year interval fires in P. palus-tris stands cause mortality among juvenile pines(Rebertus et al. 1993), few P. palustris � 10 cm dbhare killed by fire (Platt and Rathbun 1993). In mystudy sites, fire scars generally affected no more than1.5 m of the bowl of any fire-scarred tree. Charcoalstaining was also three times more common than firescarring in Recent Fire 1 (Figure 2). These observa-tions imply that fire-induced mortality of small (pole-sized) trees is rare in these stands, and that only oc-casional intense fires could thin clusters of pole-sizedtrees sufficiently to produce random patterns. Suchfires occur in ponderosa pine ecosystems (Fischer andBradley 1987; Barnes et al. 1998), and in Mexicoduring Southern Oscillation extremes (Fulé et al.2000; Fernández and García-Gil 1998). Stochasticvariations in timing and intensity of fires may there-fore be integral to maintaining the open character ofwarm-temperate pine and pine-oak ecosystems.

There was little evidence for interspecific crown orroot competition between large and small trees(Hypothesis 3). Exceptions were the regular patternsat short distances of large and small P. teocote and P.durangensis versus small Q. sideroxylla in Old Fire2. Regular spatial patterns of P. teocote versus Q. si-deroxylla also occurred at distances of up to 30 m inOld Fire 2, suggesting that two or more dynamic fac-tors may have produced regular patterns at differentscales. Regularity at short distances in Old Fire 2 mayhave been produced by the combined effects of crownand root competition. Niche partitioning and/or dis-persal limitation are more likely, however, to have

16

produced regularity across longer distance classes inOld Fire 2.

Different fire regimes, abetted by different site en-vironments, may have influenced the scale and qual-ity of interactions within and between species in OldFire 2. Spatially explicit Markov models, (Frelich etal. 1998) show that 2–5 species exerting strong neigh-borhood effects can segregate into spatially distinctcommunities, even starting from random distributionsin a uniform environment. Patches of high and lowfire intensity (Rebertus et al. 1989a; Waldrop andBrose 1999; Platt et al. 1991) could have imposedsmall-scale patchworks of exposed mineral soil, re-sidual litter, and live and dead trees (especially juve-niles) across frequent fire stands. These patterns, im-posed repeatedly and frequently, may have producedthe discrete clusters of small trees, intermingling ofspecies, and dispersed adult tree patterns seen in Fig-ure 6. In contrast, deep, extensive litter layers thatbuild up in the absence of fire would limit successfulpine germination (Cain and Shelton 1998; Farmer1997; van der Wall and Joyner 1998). The few seedsthat did germinate under these circumstances wouldlikely do so near to adult trees because most seeds fallat short distances from the parent tree (Rudis et al.1978; Farmer 1997; Lanner 1998), perhaps leading toa concentration of the local species neighborhood.

Increased neighborhood strength among small Q.sideroxylla may have developed because more rootsprouts survived in the more variable fire regime. InFlorida sandhills, small Q. laevis produce moresprouts than do large ones (Rebertus et al. 1993). Asimilar tendency in Madrean oaks might explain thelarge clusters of small trees in Old Fire 2, and the ab-sence of spatial interactions between large and smallQ. sideroxylla in Old Fire 2 and Intermediate fire.

The spatial relationships and abundance patternsreported here support results from other pine-oak ec-osystems where fire interacts with site characteristicsto influence the quality of species relationships. Inparticular, strong clustering of large and small treesin Old Fire 2, and the dominance of oaks in Interme-diate Fire and old fire stands support observations thatinfrequent fires favor oak accession and denser treepopulations overall. In the pine-oak stands studiedhere, this generalization is complicated by the occur-rence of different spatial patterns in different species/size-class combinations, which themselves may havebeen modified by site character.

Fire frequency is partly a function of site characterand regional climate. Therefore these variables tend

to be confounded. A general model proposed to ex-plain the role of regional climate predicts that estab-lishment will be limited by available soil moisture onxeric sites, but that light will be the limiting resourcein mesic environments (Holmgren et al. 1997; Calla-way and Walker 1997). This model is supported inMediterranean pine-oak systems, where drought-tol-erant P. halapensis and shade-tolerant Q. ilex respec-tively dominate dry and wet ends of a regionaldrought gradient (Zavala et al. 2000). Barton (1992,1993) found that the germination and survival of pinespecies, including P. leiophylla and P. ponderosa,were better in shaded microsites beneath nurse treesin Arizona’s Chiricahua Mountains. Pinus resinosaand P. strobus have also been found to establish pref-erentially beneath large Quercus robur (Kellman andKading 1992). In my study, the tendency for P. teo-cote juveniles to be closer to oaks in recent fire standscould reflect cooler fire temperatures beneath oaks orthe creation of more favorable soil moisture condi-tions in the generally coarse soils of recent fire stands.By contrast, the tendency of both juvenile P. leio-phylla and Q. sideroxylla to be segregated from smallQ. sideroxylla trees in Intermediate Fire may indicateroot competition from dense clusters of small oaktrees in this stand. Root competition from adult con-specifics is cited as restricting the distribution of Pi-nus palustris juveniles at distances beyond the radiiof adult pine crowns (Brockway and Outcalt 1998),and as a competitive mechanism between large andsmall ponderosa pine in the Gus Pearson NaturalArea, Arizona (Biondi 1996). Where fire has permit-ted tree densities to increase, root competition, detect-able by spatial pattern analysis, may be more likely.

Two qualitative models have been proposed tosupport the generalization that oaks experience a rela-tive advantage where fire intervals are longer (Barton1999; Rebertus et al. 1993). In both models, pine es-tablishment from seed is favored under intermediatefire frequencies. In Barton’s model, derived from ob-servations in mixed stands of Pinus engelmanii, P.leiophylla, Quercus hypoleucoides, Q. arizonica, Q.emoryi and Q. rugosa in Arizona, oaks are favored atvery short and longer fire intervals. This speculationmay be true for the extensive sprouting of Quercuscambii and Q. rhyzophylla after a rare stand-replac-ing fire in the Sierra Madre Oriental (Esparza andPérez 1999). In Rebertus and colleagues’ model, P.palustris is favored by intermediate fires and low var-iance of the fire-return interval, but oaks were only

17

favored under long return intervals or shorter averageintervals with high variance.

In my study, fire frequency and variability ap-peared to be related to site. Fires were less frequentin low-ling sites with fine-textured soils. Oak domi-nance may therefore be inherent in Madrean pine-oakstands with this combination of fire regime and sitequality (see also Barton (1999)). On the other hand,dominance by pines, especially P. durangensis and P.teocote, may be assured on more steeply-sloping siteswith coarse soils as long as frequent fires continue.Understanding the interplay between fire regimes, sitecharacteristics and species composition in pine-oakstands is therefore likely to be an essential precondi-tion to the institution of improved forestry manage-ment in Madrean forests.

Acknowledgements

I am grateful to the directorate and staff of ForestConservation and Development Unit No. 4, and to thepeople of a Durango ejido for permission to studytheir forest, and for accommodation and friendshipduring my fieldwork. Thanks also to Nick Moss, whoably assisted in gathering the field data. In Durango,Lidia Orrante and her family provided friendship,company and good food. Jeff Bacon of Juarez Uni-versity identified tricky oak specimens and providedinsights into life and research in Mexico. I owe manythanks to Pete Fulé and Richard Joos, Dr WilliamPlatt (editor of Plant Ecology), and two anonymousreviewers for reviewing and greatly improving earlierdrafts of this manuscript. Field work was partiallysupported by a Canada-Latin America Research LinksFellowship from the International Development Re-search Center, and a University of Toronto Open Fel-lowship.

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