tinker and knight 2001 - uwyo.edu

Ecological Modelling 141 (2001) 125–149

Temporal and spatial dynamics of coarse woody debris inharvested and unharvested lodgepole pine forests

Daniel B. Tinker *, Dennis H. KnightDepartment of Botany, Uni�ersity of Wyoming, Laramie, WY 82071, USA

Received 4 July 2000; received in revised form 15 February 2001; accepted 26 February 2001

Abstract

Coarse woody debris (CWD) biomass was measured and mapped in burned, clearcut, and intact lodgepole pineforests in two areas of the Rocky Mountains of Wyoming: the Medicine Bow National Forest (MBNF) andYellowstone National Park (YNP). In addition, the amount of CWD consumed or converted to charcoal by fire wasestimated in a recently burned stand in YNP. A spatially explicit simulation model (DEADWOOD) was then createdto simulate the effects of various clearcutting and fire regimes on CWD over a 1000-yr period. Approximately 8% ofdowned CWD were consumed during a single fire and an additional 8% was converted to charcoal. After 1000 yr ofsimulation, 100-yr fire-return intervals produced CWD that occupied more of the forest floor than did 200- or 300-yrintervals. The time required for 100% occupancy of the forest floor by CWD was 1125, 1350, and 1300 yr for 100-,200-, and 300-yr fire-return intervals, respectively. Simulations suggest that current harvest and post-harvest slashtreatment regimes will require at least four centuries longer for 100% of the forest floor to be occupied by CWD(1800–3600 yr) than under fire regimes. This may have important effects on soil characteristics. Only whenpost-harvest CWD slash was doubled over the current amounts did clearcutting leave sufficient CWD to maintainforest floor CWD within the historic range of variability for naturally developing post-fire stands. © 2001 ElsevierScience B.V. All rights reserved.

Keywords: Coarse woody debris; Lodgepole pine; Pinus contorta ; Fire ecology; Timber harvest; Yellowstone National Park;Simulation models; Clearcutting vs. natural fires; Forest floor coverage by CWD; CWD biomass created by disturbance; Lodgepolepine fire-return intervals; Post-harvest slash treatment; CWD effects on soil development; Rates of CWD production; Historic rangeof variability

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1. Introduction

Downed logs, large branches, stumps, andsnags are present as coarse woody debris (CWD)or potential CWD in naturally developing forests.This decomposing wood stores water and nutri-ents, contributes to structural complexity, andserves as habitat for a variety of organisms

* Corresponding author. Address: Department of Geo-sciences and Natural Resources Management, Western Caro-lina University, Stillwell 207-A, Cullowhee, NC 28723, USA.Tel.: +1-828-2273916; fax: +1-828-2277647.

E-mail address: [email protected] (D.B. Tinker).

0304-3800/01/$ - see front matter © 2001 Elsevier Science B.V. All rights reserved.

PII: S0304-3800(01)00269-1

D.B. Tinker, D.H. Knight / Ecological Modelling 141 (2001) 125–149126

(Maser et al., 1979; Grier et al., 1981; Davis et al.,1983; Harmon et al., 1986; Hansen et al., 1991;Harvey and Neuenschwander, 1991; Vogt et al.,1995; Sturtevant et al., 1997). Along with rootwood, forest floor CWD also is an important rawmaterial for the formation of soil organic matter(Harvey and Neuenschwander, 1991). CWD, andits spatial distribution through time, may play aprominent role in maintaining soil organic matterquality and quantity and, consequently, site pro-ductivity (Jurgensen et al., 1997). As logs, stumps,and woody roots decompose, different portions ofthe forest floor are affected at different times. Thisstudy tests the general hypothesis that the changesin mass and spatial arrangement of CWD throughtime probably are quite different in naturally de-veloping stands than in those affected by timberharvesting (Fig. 1).

There have been numerous studies of CWDamounts in various forest ecosystems (Harmon etal., 1986; Comeau and Kimmins, 1989; Arthurand Fahey, 1990; Wei et al., 1997), but very littleinformation exists on CWD spatial dynamics. Forexample, it is currently unknown how long ittakes until every square decimeter of the forestfloor has been covered by wood, which we callforest floor CWD occupancy rate. In this paper,we describe quantitatively both the temporal andspatial changes in CWD following crown fires andtimber harvesting in forests dominated by lodge-pole pine (Pinus contorta ssp. latifolia [Engelm. exWats.] Critchfield) in the Rocky Mountain region.More specifically, we tested two hypotheses: (1)the amount of time required for 100% of theforest floor to be covered by wood (logs, stumps,trees, snags) will be much longer under a clearcuttimber harvest regime than under natural fireregimes, regardless of fire-return interval; and (2)CWD will decrease over time as a result of regu-lar, repeated clearcut timber harvesting, comparedto a natural fire regime. We studied lodgepolepine forests because they are of considerable eco-nomic importance in many western US forests,and are the predominant vegetation type overmuch of Yellowstone National Park (YNP),where most stands have never been subjected towood removal.

There is considerable interest in whether or notclearcut timber harvesting more closely mimicsnatural disturbances such as fire than do othertimber harvest practices such as selective thinningor shelterwood cutting (Hammond, 1991; Keenanand Kimmins, 1993). Clearly, there is at least onemajor difference: clearcut timber harvesting re-moves most of the bolewood that would somedaybecome CWD (Jurgensen et al., 1997), while firesoften create large quantities of CWD after thestanding-dead trees fall (Spies et al., 1988). Forexample, lodgepole pine stands in British Colum-bia and Wyoming that had burned contained 2–5times more CWD than similar stands that hadbeen subjected to a single clearcut (Wei et al.,1997). Various studies have recommended thatspecific amounts of CWD should be left on a sitefollowing timber harvesting (Reinhardt et al.,1991; Graham et al., 1994). However, these esti-mates were based on measurements of CWD thatincluded inherited wood from the last stand-re-placing fire (Maser et al., 1979; Wei et al., 1997),a legacy that would decrease or disappear withrepeated clearcut timber harvesting (Sturtevant etal., 1997). CWD mass is augmented by trees thatdie of natural causes during stand development(Gore and Patterson, 1986; Franklin et al., 1987),another source of CWD that may be reduced oreliminated by clearcutting.

If timber harvesting is to be partially guided byCWD volume, then the temporal and spatial dy-namics of CWD should be better understood. Wehave attempted to provide a better understandingof CWD dynamics by developing a spatially-ex-plicit simulation model we call DEADWOOD.The model is largely parameterized with datacollected in YNP, where our study areas haveexperienced no harvesting, and the Medicine BowNational Forest (MBNF), where timber harvest-ing has been underway for more than a century.Several other models of CWD dynamics exist(e.g., Harmon et al., 1996; Bragg, 1997; McCarter,1997), but none consider the changes in spatialdistribution of CWD through time. With theemergence of ecosystem management on manypublic lands, knowledge about the historic rangeof variability (HRV) for a selected group of forestvariables is now considered important. DEAD-

D.B. Tinker, D.H. Knight / Ecological Modelling 141 (2001) 125–149 127

WOOD and similar models will be useful tools forthat purpose. By simulating different fire-returnintervals, an estimate of the HRV for CWD ispossible, within which forest managers might at-

tempt to maintain CWD amounts followingclearcutting. Simulation models such as DEAD-WOOD are the only alternative for estimating theHRV of most (if not all) ecosystem variables.

Fig. 1. Hypothetical trends of CWD biomass in lodgepole pine forests under (a) a natural fire regime, and (b) a 100-yr rotationclearcut harvest regime. Pre-disturbance CWD represents a baseline amount of CWD present in mature stands, where inputs arebalanced by decomposition. CWD created by each disturbance represents fire-killed snags for the fire regime and CWD (�7.5 cm)left as post-harvest slash. Note the absence of CWD added by the developing stand under the clearcut regime, since each successiveharvest removes the trees before natural mortality occurs. Figure adapted from Harmon et al. (1986).


2. Methods

2.1. Study areas

Study areas in YNP (44°N, 110°W) were lo-cated on subalpine plateaus and ranged in eleva-tion from 2200–2450 m. Stands that burned in1988 and 1996, and that were between 106–262 yrof age were sampled. In addition, unburnedstands of similar age and structure were sampledas close to each burned stand as possible. Standsin the MBNF (41°N, 106°W) were located ap-proximately 50 km west of Laramie, WY, atabout 2800 m elevation. The clearcuts were har-vested between 1991 and 1993. Six unharvestedstands of 98–244 yr in age were also sampled. Allstands were 5–15 ha in size.

2.2. Coarse woody debris biomass and spatialarrangement

In each stand, twenty-five 15.2-m (50-ft) tran-sects were sampled for downed woody biomassusing the planar intercept method developed byBrown (1974). This method requires that logs beclassified only as sound or rotten, but we classifiedeach log into one of the five decay classes iden-tified by Maser et al. (1979). Two or three 20×20-m grids also were established in each stand,and all downed CWD�7.5 cm, root crowns,seedlings, and standing-live and -dead trees weremapped in the field and later digitized using theARC/INFO Geographic Information System(ESRI Inc., 1995). In addition to downed wood,estimates of the biomass of root crowns, boles,branches, and foliage were made from allometricequations developed by Pearson et al. (1984) inthe MBNF.

2.3. Estimates of wood consumption andcon�ersion to charcoal

Simulations of additions and removals of CWDin lodgepole pine forests must include the amountthat is consumed or converted to charcoal duringan intense fire. Wood consumption was estimatedby summing: (1) estimates of wood volume con-verted to charcoal that was still present and mea-

surable and (2) estimates of wood volumecompletely consumed by the fire (Tinker andKnight, 2000). We attempted to estimate thebiomass of logs partially consumed by the fire.However, because the available methodsproved inaccurate, this component was not in-cluded in our final estimates. Our wood consump-tion estimates were made in only a single standwhich burned in YNP during 1996, rather than inthe numerous stands that burned in 1988, becausethe evidence used for estimating wood consump-tion disappears within a year or two followingfire.

Analysis of our digital maps of the CWD con-tained in the stand that burned during 1996 inYNP indicated that approximately 8% of downedCWD was completely consumed, and an addi-tional 8% was converted to charcoal. Therefore,during all burn simulations, 8% of all downedCWD was removed from the forest floor follow-ing a simulated burn. An additional 8% was sub-tracted from the subsequent estimates of biomass,but was not removed from the forest floor occu-pancy maps.

2.4. Model de�elopment

DEADWOOD is a spatially explicit simulationmodel composed of several process-based subrou-tines (Fig. 2). Input is from digital representationsof the 20×20-m plots of lodgepole pine standsdescribed above. Vector maps of the 20×20-mplots included polygons representing all woodwithin the sample area, including logs, stumps,and standing-live and -dead trees. For the logs,diameters at each end were noted, as well as thelengths and decay classes of each log. For stumpsand all trees, the diameters were recorded (Table1). The decay classes for stumps were added todigital map attribute tables. Input parameters, asdescribed below, are based either on empiricaldata collected during this study from YNP andthe MBNF, or on published values for the lodge-pole pine ecosystems of Wyoming (Fahey, 1983;Pearson et al., 1984). Values for input parametersand variables necessary to run the model simula-tions are included in Table 1. The model functionswithin the ARC/INFO (ESRI Inc., 1995) Geo-


Fig. 2. Flow chart for the DEADWOOD model. The log andstump decay submodel occurs before the treefall submodel sothat existing CWD will decay before newly-added CWD dur-ing each time-step. Similarly, the treefall submodel occursbefore the tree mortality submodel so that trees will not fallduring the time-step in which they die. The tree growthsubmodel occurs last so that trees that die during a giventime-step will not grow during that 10-yr period.

unburned stands in YNP was performed using amodified Clark and Evans (1954) method of

Table 1Model input parameters required for simulation with DEAD-WOOD

Forest floor maps – 20×20-m2 grid (400 m2)Logs, by decay class and size (diameter at both ends (cm)

and length (m))Stumps, by decay class and size (diameter at cut (cm))Standing trees, live and dead (diameter at breast height

(cm))

Seedling subroutineSeedling distribution pattern (e.g., random, clumped,

regular)Average seedling density (seedlings ha−1; Mean; SD), or

target seedling density range for specific simulationDiameter for 10-yr-old seedling (first 10-yr time-step)

Log and stump decomposition subroutinesDecay coefficients for each decay class for the tree species

of interestWood density (g m−3) for each decay classTime (yr) until complete disappearance for logs and

stumps (used to parameterize model)

Treefall subroutineTreefall rate (number of trees that fall per year, or

percentage of trees that fall per year in both young andmature stands)

Probabilities for treefall direction or azimuth (twelve30-degree azimuth classes) derived from analysis oflog/stump maps

Proportion of trees that snap-off at base, versusproportion of trees that tip-up

Tree diameter/height allometric relationshipStem taper values (mm taper per m of tree height) for all

size classes

Tree growth subroutineAverage annual tree-ring increment (mm yr−1) by size and

age class

Tree mortality subroutineSurvival/mortality proportions and rates for trees (number

of trees that die or survive per year, or percentage oftrees that die or survive per year), following differentdisturbances of interest.

DisturbancesFire-return interval for forest type of interest (yr)

Amount of wood removed (consumed or converted tocharcoal) by fire (mg ha−1)

Timber harvestsMinimum diameter of trees removed during each harvestAmount and distribution of post-harvest slash applied(mg ha−1; m2 from maps of sites)

graphic Information System. Program code waswritten exclusively using the ARC Macro Lan-guage (AML).

2.5. Model subroutines

To streamline model operation, troubleshoot-ing, and debugging, DEADWOOD was organizedin a modular form and is composed of six pro-cess-driven subroutines. The subroutines are de-scribed below, along with the respectiveparameter input values and sources.

2.5.1. Seedling subroutineThe seedling subroutine selects a random num-

ber of seedlings from a normal distribution anddistributes them throughout the simulation grid.A spatial analysis of the digital seedling maps of


Table 2Wood and decomposition characteristics of downed logs and stumps in decay classes I through VIa

Time on forest floor (yr)Decay class Decay coefficient (k)Wood bulk density (g cm−3)

0–20I 0.01980.41II 0.32 21–30 0.0174

31–60 0.0174III 0.2561–800.17 0.0124IV81–100 0.0124V 0.13�100 None0.05VI

a Bulk density values are adapted from Harmon and Sexton (1996) and decay coefficients are from Fahey (1983).

nearest neighbor analysis. Using the DISTANCEfunction in ARC/INFO, the mean distances be-tween all seedlings less than 10 yr of age werecalculated for seven unburned stands in YNP.The 10-yr-old maximum age was used assumingthat all seedlings 10 yr old or younger had estab-lished within the last 10 yr, the time-step intervalused by the model. The ratio of the observedmean distance to the expected mean distance rep-resents the index of departure from randomnessof the seedling establishment pattern: 1 is a ran-dom distribution, 0 is completely aggregated, and2.1419 is completely regular. The average ratio forunburned stands in YNP was 1.1; therefore, arandom establishment pattern was used in themodel.

The seedling subroutine was different for non-disturbance and disturbance time-steps. Duringnon-disturbance conditions, the mean and stan-dard deviation (S.D.) of the number of seedlingsfound in unburned stands in YNP was used torandomly establish seedlings. We used a normalprobability distribution with a mean of 10.5 and aS.D. of 8.4 to select the number of seedlings toestablish each time-step. The equation used forthe normal probability distribution is:

P(x)=1

��2�e− (x−�)2/2�2, (1)

where P(x) is the probability that a randomlyselected number of seedlings (x) will occur giventhe mean (�) and S.D. (�). The appropriate num-ber of seedlings were then randomly distributedwithin the model grid by choosing x- and y-coor-dinates using the random-number generator inARC/INFO. If the random location for the

seedling occurred where existing wood occupiedthe grid, the random number sequence was re-peated until the seedling was established in anarea without wood (which is typical for lodgepolepine in our study area).

During time-steps when a clearcut occurred,seedlings were established by selecting a randomnumber of seedlings between 39 and 67. Thisrange of seedling density was used based on recentconversations with USFS personnel, who indi-cated that target tree densities are now approxi-mately 1375 trees ha−1 (Joe Harper, 1998,MBNF, personal communication). The seedlingswere then located randomly throughout the gridas described above. No further seedling establish-ment occurred during the clearcut simulations,thereby maintaining tree density. Seedling estab-lishment during burn simulations was similar toclearcut simulations in that a similar number ofseedlings was randomly established following thesimulated fire and maintained throughout the first40 yr after the fire. Given our objective of com-paring CWD dynamics following fire and clearcuttimber harvesting, it was necessary to comparestands of similar density. It is widely known thatpost-fire lodgepole seedling densities are quitevariable among stands (e.g., Tinker et al., 1994;Turner et al., 1997). However, our purpose was tosimulate CWD dynamics within similar standssubjected to different disturbances. Periodicseedling establishment resumed 40 yr post-fire tosimulate the maturation and subsequent seed pro-duction of the post-fire seedlings. Seedling recruit-ment was offset by mortality (described below)which moderated large changes in stand density.All newly established seedlings were assigned an


initial diameter of 2 cm at the completion of thefirst 10-yr time-step (Koch, 1987).

2.5.2. Log and stump decomposition subroutinesDecay class and wood bulk density data in map

attribute tables were used to simulate the decom-position of individual logs and stumps duringeach time-step, with different decay coefficients(k) for each decay class (Fahey, 1983). The modelwe used was

Yt=Y0e−kt, (2)

where Y0 is the initial wood density (g cm−3), Yt

is the wood density at time t, and k is the decayrate coefficient (Table 2). During each time-step,all logs and stumps from the digital maps wereselected by decay class and their bulk densitieswere reduced by the appropriate amounts. If thenewly calculated densities were below thethreshold density for a given decay class, thedecay class representing the next stage of decom-position was assigned to the log or stump andpassed to the next time-step. Threshold densitieswere modified from Busse (1994) for each of thefive decay classes of logs and stumps as describedabove. The densities and decay coefficients inTable 2 result in the complete decomposition of alog or stump in approximately 100 yr. This time-frame was based on estimates made by Fahey(1983) in the MBNF, but was shorter than the 150yr required for a lodgepole log to disappear esti-mated by Brown et al. (1998) in Colorado. Stoneet al. (1998) re-measured individual logs on theforest floor 65 yr after initial mass and densitymeasurements, finding that decay coefficients cal-culated using chronosequences (e.g., Fahey, 1983)produced values very similar to actual long-termrepeated measurements. In the model, logs andstumps that had decomposed to a density of lessthan 0.05 g cm−3 were assigned to decay class VI,an archival designation, and were removed fromthe current map and copied to a separate digitalmap used to store all of the decay class VI logs orstumps for subsequent forest floor occupancycalculations.

The assumption was made that all standing-dead trees (snags) that fall would contact theground and begin to decompose during the 10-yr

time-step in which they fell. The two-dimensionalconstraints of the model, along with limited infor-mation regarding residence time of logs elevatedoff the forest floor, made simulating the verticalstratification of downed logs impossible. This defi-ciency probably leads to more rapid decay thanactually occurs, but was consistent between allsimulations.

2.5.3. Treefall subroutineDuring each time-step, standing-dead trees that

fall are relocated to the forest floor as fallen logs.The treefall subroutine incorporated several as-pects of attrition of dead-standing trees, includingrate, fall-direction, and whether the tree snappedoff at the base, leaving a stump, or was tipped-up,which lifts the stump from the ground. The rateof attrition of dead-standing trees was not consid-ered during the clearcut simulations since allstanding trees, both live and dead, were removedduring the harvest. It was assumed that no mor-tality occurred between harvest rotations. Theassumption was made that dead trees probablystand longer in an intact stand because of protec-tion from the wind by surrounding live trees(Bartos and Amman, 1989; Mitchell and Preisler,1998). We therefore used a 50% attrition rate per10-yr time-step during a 30-yr period (3 time-steps) for stands that burned (Lyon, 1984), and aconsistent, slower rate (15%) of tree fall thereafterand for unburned and uncut stands (this parame-ter was tested during validation and sensitivityanalysis). Mitchell and Preisler (1998) found that50% of dead trees had fallen in unthinned standsof lodgepole pine after 9 yr, and 90% were downafter 14 yr following beetle kill in central Oregon.

During each 10-yr time-step, 15% of the treesthat were contained in an ARC/INFO table ofstanding-dead trees were randomly selected tofall. An analysis of field maps of burned andunburned stands in YNP revealed that approxi-mately 50% of the downed logs on the forest floorwere spatially associated with stumps still in theground, suggesting the trees had snapped offwhen they fell, and that the other 50% had tippedup, leaving no stumps in place. Stumps weretherefore added to the stump digital map for halfof the trees that fell. A table containing the spatial


information for the chosen trees was used togenerate the coordinates for drawing downedlogs.

To determine the azimuth along which eachtree would fall, an analysis of the digitized forestfloor maps was performed and the compass direc-tion of each downed log that was associated witha stump was determined. A one-sample Kol-mogorov–Smirnov test compared the observedcumulative azimuth values for the logs with atheoretical uniform distribution. The Kol-mogorov–Smirnov Z score was computed fromthe largest difference (in absolute value) between

the observed and theoretical cumulative distribu-tion function. This procedure tests whether theobservations could reasonably have come fromthe specified uniform distribution. The analysisindicated that the distribution of the logs was notuniform.

The measured log azimuths were combined intotwelve 30-degree azimuth classes and the propor-tions of logs in each class were calculated. Arandom number was used to select a probabilityfunction, developed from this actual distribution,to predict the direction (in degrees) a tree mightfall. Once the azimuth was determined, the newly-fallen log was drawn in ARC/INFO using theGENERATE function and trigonometric func-tions to calculate the x- and y-coordinates of eachcorner of the log polygon based on the locationand diameter of the dead-standing tree. Loglength (tree height) and taper were calculatedfrom morphometric relationships for lodgepolepine (Koch, 1987). The new digital log coveragewas then combined with the existing log coverageand all new logs were assigned an initial decayclass of I and an initial density of 0.41 g cm−3

(Busse, 1994).Because trees fell out of the simulation plot, but

not into the plot during simulations, output mapsare probably occupied by less CWD than wouldnormally occur. An examination of digital outputmaps from burn simulations suggested that thesouthwestern (lower-left) corner of the maps maynot have been occupied by CWD at the same rateas other areas of the plots (Fig. 3). This wasprobably due to the higher probability of treesfalling to the east or northeast, as a result ofpredominant southwesterly winds. The affectedarea was estimated to be approximately 10% ofthe total plot. Within the affected 10%, forestfloor occupancy by CWD was reduced by approx-imately 50%. This resulted in a 5% underestimateof the time required for CWD to cover the entireforest floor.

2.5.4. Tree growth subroutineTree diameter was incremented based on an

algorithm derived from a rangewide study ofRocky Mountain lodgepole pine by Koch andSchlieter (1991). They calculated average annual

Fig. 3. Maps of logs and stumps produced by DEADWOODsimulations assuming (a) a 200-yr fire-return interval, (b) 100%clearcut slash simulation (the observed amount), (c) the 50% ofobserved slash amounts after a clearcut, and (d) 200% theobserved amount of slash after a clearcut. All clearcut simula-tions were for the mid-range amounts described in Table 4.Percent occupancy of the forest floor for year 0 column is 7%,as shown above each map in the year 0 column. Percentoccupancy of the forest floor for the year 500 and year 1000simulations are likewise shown above each forest floor map.


tree ring increment by 10-yr age classes, findingthat tree growth begins to decline at around 20 yrof age and is greatly reduced around age 50,usually coinciding with stand canopy closure. Theequation for calculating annual radial incrementis:

Y=1.8841(X−0.611), (3)

where Y=annual ring width increment (mm) andX=age class, by decade (e.g., 1,2,3,…). Themodel assumes no variation in growing conditions(e.g., temperature, precipitation) between simula-tions or between time-steps. For DEADWOOD,three progressively slower growth rates were usedas the stand aged to simulate growth reduction innaturally developing lodgepole pine stands of av-erage density. Tree growth rates were constant foreach size class, and made no allowances for differ-ent stand densities. However, it should be notedthat stand densities were maintained within afairly narrow range; therefore, the growth rateassumption seems reasonable. The three annualrates were increased by a factor of 10 to accountfor growth during the entire 10-yr time-step. Ten-year increments used in the model were 12.34 mm10-yr−1 for trees up to 20 yr of age, 8.08 mm10-yr−1 for trees 21–50 yr of age, and 3.93 mm10-yr−1 for trees over the age of 50. The al-gorithms were tested during model developmentand produced results very similar to measure-ments of annual increments from Lotan andCritchfield (1990). In fact, simulated tree diame-ters nearly replicated their measurements for treesof 20 and 50 yr of age. Tree growth rates, asdescribed above, produced trees that were approx-imately 26 cm in diameter following 100 yr ofsimulated growth, which is close to the 22–25 cmrange that is considered acceptable after 100 yrfor lodgepole pine trees following clearcutting(Dave Carr, 1998, USFS, personal communica-tion). During simulations, tree diameters wereincremented by the appropriate amount in anARC/INFO table containing the spatial informa-tion for each tree. The table containing the incre-mented tree diameters was carried forward to thenext simulation time-step.

2.5.5. Tree mortality subroutineExcept for burned stands, we assumed that all

tree mortality resulted from chronic, individualtree death. We used life table data from Ives andRentz (1993) to model non-fire tree mortality.Their equation is:

Y=100.0−0.950X, (4)

where X=stand age in years and Y=% survival(r2=0.99). Chronic mortality may affect bothseedlings and mature trees and is often the resultof herbivory by rodents and insects, fungal patho-gens such as Armillaria sp., or other factors (Ivesand Rentz, 1993). Trees of all sizes and ages weretherefore assumed to have an equal chance ofdying during any time-step. Eq. (3) results in a10-yr mortality rate of 9.5%. Schmid and Mata(1993) found that 6.5% of all standing trees inunmanaged stands of lodgepole pine in Wyomingwere dead at any given time. The natural firesimulations assumed that all fires were of equaland sufficient severity to kill all trees within astand.

Trees were randomly chosen to die from thecurrent time-step tree table. If their diameter atthe time of death was �7.5 cm, they were movedto the standing-dead table and reclassified assnags. Because our simulations focused on onlyCWD�7.5 cm, seedlings and saplings �7.5 cmdiameter were removed completely at the time ofdeath.

2.6. Model e�aluation

To address model evaluation and validation, weadopted the approach of Rykiel (1996). Rykielsuggests that model validation should not be con-fused with finding the absolute ‘‘truth’’ regardinga natural system (e.g., Swartzman and Kaluzny,1987), and must be defined within specific valida-tion criteria, including (1) the purpose of themodel, (2) the performance criteria for the modelto be deemed acceptable for use, and (3) thecontext within which the model is to be used. Thespecific purpose of the DEADWOOD model is tosimulate CWD amounts and forest floor occu-pancy in lodgepole pine forests in the centralRocky Mountains following clearcut timber har-


Table 3Results of validation simulationsa

Map indices Simulated 200-yrInitial 100-yr Unburned stands measured in YNPstand stand

Minimum Maximum Mean (S.D.)

9.21 0.0Logs – decay class I 39.092.7 13.12 (14.96)5.22 0.03.1 29.07Logs – decay class II 12.46 (12.14)9.08 1.49 14.1Logs – decay class III 6.66 (5.33)0.47.03 2.242.4 13.98Logs – decay class IV 7.52 (5.24)

10.25 0.26Logs – decay class V 15.5914.7 8.65 (5.62)40.79 17.523.3 104.11Total logs 48.42 (31.72)0.33 0.0Stumps – decay classes I and II (avg.) 0.330.1 0.13 (0.14)0.48 0.110.5 1.06Stumps – decay classes III, IV, V (avg.) 0.69 (0.34)0.81 0.78Total stumps 1.080.6 0.82 (0.25)

1025 450675 650Stump density 558 (82)20.25 9.25 24.75 20.42 (6.15)Stump basal area 15.0

2.72 0.71.9 1.66Live trees 1.29 (0.38)2750 475Live tree density 13002650 875 (285)

68.0 17.547.5 42.5Live tree basal area 32.13 (9.53)0.94 0.0Snags 0.460.2 0.24 (0.18)

925 0425 525Snag density 250 (196)23.5 0.0Snag basal area 11.55.0 6.0 (4.5)45.24 19.5326.0 106.09Total area 50.76 (31.56)2.9 2.56 3.17Live tree: snag ratio 2.86 (0.2)6.23

a Map indices are reported for the initial 100-yr stand (actual measurements), simulated 200-yr stand, and mean values from fiveunburned stands measured in YNP. All units are m2 of forest floor coverage except for basal area, which is m2 ha−1, and for density,which are reported as stumps or stems ha−1. Values in bold indicate values not within the range of variability in our study areas.

vesting and natural fires. The performance criteriafor this model require that model output valuesfor simulated variables (described below) fallwithin the HRV of those variables, assumed to besimilar to the range among the five natural, undis-turbed stands of lodgepole pine measured inYNP. The context of the model, like its purpose,is that it be used within the confines of empiricaldata available for Rocky Mountain lodgepolepine forests and to estimate stand characteristicsthat are not measurable in a natural system. Un-measurable characteristics would include theamount and arrangement of CWD on the forestfloor following repeated clearcut timber harvest-ing, a condition that does not currently exist inthe central Rocky Mountains.

Ten replicate 100-yr validation simulations wereperformed using an early-to-mid successionallodgepole pine stand of approximately 105 yr ofage in YNP as the initial map. Maps created fromthe simulations were then compared to field maps

of stands of similar age and structure. Compari-sons were only made to unburned maps/stands inYNP of approximately 200 yr of age (modelsimulates a 100-yr-old stand for an additional 100yr), since many or most unburned stands in theMBNF have been salvaged by commercial orprivate firewood gatherers. This human influencereduced the amount of CWD on the forest floor,increased the number of stumps, decreased thenumber of snags, and possibly decreased the num-ber of live trees.

To identify any scale-related problems, we cal-culated the following indices on a 20×20-m gridand per hectare basis: area (m2) of logs, trees,snags, stumps; area of logs and stumps by decayclass (m2); density of trees, stumps, snags; basalarea (m2 for trees, stumps, snags); and live/deadtree ratio. Validation results generally fell withinthe range of variability as suggested by simula-tions for unburned stands in YNP (Table 3). Allestimates of forest floor occupancy by downed


logs and stumps of all decay classes were withinthe range of variability of unburned stands ofsimilar age and structure, and they were within1S.D. of the mean values for each index (Table 3).Estimates of live tree and snag basal area anddensity were slightly higher than actual unburnedstands measured in YNP, but they were still wellwithin reported values for lodgepole pine in theRocky Mountain region. This suggests that ourestimates of seedling establishment are higherthan actual rates in measured stands. An increasein seedling density could possibly result in in-creased coverage of the forest floor by survivingtrees, or later, logs. However, the live/dead treeratio was very close to the mean value for un-burned stands in YNP, indicating that seedlingestablishment and tree mortality subroutines areperforming satisfactorily relative to each otherand that they are maintaining stand density atmeasured levels.

To test model sensitivity, selected modelparameters were increased and decreased by 10%,both individually and in various combinations,and three replicate 100-yr simulations of eachchange were performed. The output variablesused as a measure of model behavior were identi-cal to those used for validation simulations. Theaverage values of several output variables were

compared to the original validation simulationoutput. The model was considered robust if out-put values were relatively insensitive (changed�10%) to small (�10%) changes in modelparameters (Swartzman and Kaluzny, 1987). Thesensitivity analysis indicated that the model out-put was relatively insensitive to 10% changes inparameter values, and most output valueschanged less than 10% when compared to normalparameters. The exceptions to this were decreasesin decay coefficients for decay class V logs anddecay classes IV and V stumps. In each case, a10% decrease in the decay coefficient resulted in agreater than 10% change in the respective totalareas. However, a close examination of densityvalues for logs and stumps at the end of the100-yr simulations revealed that running the simu-lations for an additional 10 yr would have re-sulted in the complete disappearance of the logsand stumps, similar to output produced by thenormal decay coefficient values. Therefore, we didnot consider these changes serious enough to war-rant further modification, as the actual simula-tions were run for �100 yr. No unusualsensitivity was identified in any of the other sub-routine parameters, including seedling establish-ment, tree mortality, treefall, or tree growth.

2.7. Simulations

All simulations, including validation and sensi-tivity analysis, were performed using data fromthe 105-yr-old stand described above as the initialtime-0 map. By using the same initial map for allsimulations, any changes in model output couldbe attributed to simulated forest processes ratherthan variability between initial stand maps. Thetime-step interval was 10 yr for all simulations.Model validation and sensitivity analysis simula-tions were run for 100 yr, and all other simula-tions of repeated clearcutting or natural fireregimes were 1000 yr in duration. Five replicatesimulations were performed for each treatment(Table 4) and the results were averaged. Thisnumber of replicates was chosen based on therange of results for three replicate simulationsused in the sensitivity analysis. New digital mapswere created for logs and stumps at each 10-yr

Table 4Burn and clearcut simulations with DEADWOOD. For slashleft behind after clearcuts, the lowest (low) and highest (high)amounts are given

Simulated Simulated slash biomass afterfire-return clearcuttingintervals

100-yr return 50% slash application – low; 6.0 m2

interval (8.1 mg ha−1)50% slash application – high; 14.1 m2

(18.9 mg ha−1)200-yr return 100% slash application – low; 11.4 m2

interval (15.3 mg ha−1)100% slash application – high; 26.8 m2

(35.9 mg ha−1)300-yr return 200% slash application – low; 22.8 m2

(30.6 mg ha−1)interval200% slash application – high; 53.6 m2

(71.8 mg ha−1)


time-step. However, diameters for all live treesand snags were maintained in text tables that wereused by ARC/INFO to create the necessary treeand snag digital maps for post hoc analysis.

2.7.1. Burn simulationsFire-return intervals of 100, 200, and 300 yr

were simulated (Table 4). The 100-yr interval waschosen to parallel the 100-yr clearcut rotationsdescribed below, so that direct comparisons couldbe made. The 200- and 300-yr intervals werechosen to represent a range within the estimatednatural fire-cycle described by Romme and De-spain (1989) for YNP. When a fire occurred, alllive trees �7.5 cm were moved to the dead-stand-ing snag table. It was assumed that all trees �7.5cm were completely consumed by the fire. Toaccount for wood consumption in larger sizeclasses, log and stump maps were reduced in areaby an amount equivalent to 8% biomass con-sumption based on a regression model developedto predict CWD biomass (mg ha−1) from the areaof the forest floor covered by wood. The initialmodel was

Biomass=1.42 (cover)−2.95 (r2=0.82). (5)

However, this model was not useful for areasoccupied by logs of less than �1.5 m2 becausethe resulting biomass estimation was negative. Anew regression model was developed, forcing theline through the origin, creating a y-intercept of 0.The new model used to calculate biomass was

Biomass=1.34 (cover)+0 (r2=0.82). (6)

Values for forest floor coverage by wood (m2)were converted to biomass using the regressionequations, and reduced by 8% to account forwood consumption as described earlier. Only 8%was removed at this stage of the simulation be-cause the remainder of the �16% of total woodburned by fire was still present as charcoal. Thecharcoal on logs still occupied forest floor area,but was not considered CWD biomass and was,therefore, removed later in the simulations. Theadjusted biomass estimates were converted backto area measurements, and logs and stumps wererandomly removed from the respective map untilthe adjusted burned values were reached. Seedling

establishment and treefall occurred following firesas described previously. Normal post-fire standdevelopment and dynamics resumed at year-40and continued until the next burn simulation.

2.7.2. Clearcut simulationsClearcut timber harvesting was simulated at

100-yr intervals for 1000 yr (Table 4). It wasassumed that future clearcut timber harvests onthe MBNF will occur at the current �100-yrrotation interval and that harvest techniques andpost-harvest slash treatments will remain constantduring the 1000-yr simulation period (USDA,1985). The 100-yr interval was chosen because itfalls within the range of harvest rotation times forlodgepole pine in the MBNF (90–120 yr; USDA,1985). All live trees �17.8 cm diameter and alldead-standing trees �20.3 cm diameter were re-moved during each simulated harvest (USDA,1985). All other standing trees less than the mini-mum diameters listed above, yet �7.5 cm diame-ter, were added to the forest floor as CWD.

Additional downed CWD was added to theforest floor based on analyses of digital maps ofsound, downed CWD �7.5 cm drawn fromclearcut stands in the MBNF (hereafter referredto as slash). We found a wide range of CWDslash cover on the cut-over stands (11.4–26.8 m2).Five different maps of sound CWD were chosenthat spanned the range of CWD measured in theclearcuts, and were used as five slash ‘‘treatments’’(T1–T5) for purposes of simulation. During asimulation of one of the slash treatments, thesame slash map was applied to the site at 100-yrintervals (following each simulated clearcut) tomaintain a constant application of CWD, but theslash map was rotated to a random azimuth foreach clearcut event. This was to simulate logsscattered by roller-chopping and to avoid dupli-cated coverage of the forest floor. Later, the fiveCWD slash treatment maps were used in addi-tional simulations. One scenario applied each mapto the site twice (rotated differently each time),while another scenario added approximately one-half of the amount of slash shown on each map,thereby simulating a twofold increase of slashapplication (200%; 22.8–53.6 m2) and a 50% re-duction (6.0–14.1 m2) of slash application relative


Fig. 4. One thousand-year simulation results of 100-, 200-, and 300-yr fire-return intervals. Values for each 100-yr period representthe amount of the forest floor directly affected by wood (includes CWD and live- and dead-standing trees) as predicted by theDEADWOOD model. All values are averages of five replicate simulations for each treatment. Percent occupancy is the percent ofthe 400 m2 plots occupied by wood. The horizontal line represents 50% occupancy of the forest floor by wood. Error bars represent�1S.D.

to the original five maps (100%). This designresulted in 15 different slash treatment/applicationcombinations (Table 4).

2.8. Map analysis

Following the simulations, analyses of forestfloor coverage were performed on maps represent-ing 100-yr intervals (i.e., 100, 200 yr, etc.) in thesimulation period (Fig. 3). For each map, theamount of the forest floor occupied by logs andstumps (by decay class), trees, and snags wascalculated in ARC/INFO. Rates of forest floorcoverage by wood (mg ha−1 yr−1) for each 100-yr interval were also calculated. Using the areameasurements, CWD biomass was estimated us-ing the regression model described above. Outputvalues of forest floor occupancy (m2 and percent)and biomass (mg ha−1) for the five replications ofeach simulation were averaged. Simulation outputvalues were analyzed using Analysis of Variance(ANOVA) to detect significant differences amongtreatments means. Tukey’s post hoc multiple-comparison tests were done to identify wheresignificant differences occurred. Analyses wereperformed using the SPSS for Windows software

package (SPSS Inc., 1995). All alpha levels were0.05.

3. Results

3.1. Forest floor occupancy by coarse woodydebris

3.1.1. Burn simulationsWith the 100-yr return interval, CWD covered

90% (360 m2) of the forest floor by year 1000,while CWD from the 200- and 300-yr returnintervals covered 78% (311 m2) and 75% (300 m2),respectively (Fig. 4). CWD covered significantlymore forest floor with the 100-yr fire-return inter-val than the 200- or 300-yr intervals during sevenof the 10 simulation periods (P�0.05; Fig. 4).The 200-yr interval differed significantly from the300-yr interval only at simulation year 300 (P�0.001). The rate of forest floor occupancy byCWD was significantly less for the 200-yr interval(6% (25 m2) 100-yr−1) than for 100- or 300-yrintervals (8% (32 and 31 m2) 100-yr−1), respec-tively (P�0.05; Fig. 5).


3.1.2. Clearcut/slash simulationsEach of the five measured post-harvest slash

amounts was simulated at three different propor-tions of the amount observed after actualclearcuts: 100%, or the amount observed; 50%, orhalf the amount observed; and 200%, or doublethe amount observed (5 amounts×3 propor-tions=15 different simulations; see Table 4 forfull description of simulations). No significantdifferences were found either between or withinany of the 15 simulations through the first 200 yr

(P�0.05; Fig. 6). By year 500, and continuingthrough year 1000, forest floor CWD coveragewas significantly different among the 50%, 100%,and 200% simulations, regardless of the initialamount, with the 100% simulation greater thanthe 50% simulation, and the 200% simulationhigher than either the 50% and 100% simulations(P�0.05; Fig. 6).

For the 100% slash simulations, by year 1000(10 harvest rotations), CWD produced by thehighest slash amount occupied 60% (240 m2) of

Fig. 5. Rates of forest floor occupancy (m2 100-yr−1) for the three simulated fire-return intervals and for each of the three clearcutsimulations. Numbers 1–5 along the horizontal axis refer to five different CWD slash amounts that were simulated. 100, 200 and300 on the horizontal axis refers to fire-return intervals for burn simulations. Error bars represent �1S.D. Non-overlapping errorbars indicate a significant difference between the treatments.

Fig. 6. Comparison of 1000-yr simulation results of three of the five slash amounts at three levels (lowest, middle, and highest; Table4). Simulation 1 (a) was 11.4 m2 at 100%; simulation 3 (b) was 18.4 m2 at 100%; and simulation 5 (c) was 26.8 m2 at 100%. Thesolid line indicates that 50% of the observed slash amount was used for the simulation; the dashed line indicates that the observedamount of slash was used (100%); and the dotted line is for the simulation that assumed 200% of the observed amount of slash.Values for each 100-yr period represent the amount of the forest floor directly covered by wood (includes CWD and live- anddead-standing trees) as predicted by DEADWOOD. All values are averages of five replicate simulations for each treatment. Percentoccupancy is the percent of the 400 m2 sample grid covered by wood. The dotted horizontal line represents 50% occupancy of theforest floor by wood. Error bars indicate �1S.D.


Fig. 6.


the forest floor, while the lowest slash amountoccupied only 39% (155 m2; Fig. 7(a)). The lowestand highest of the five CWD slash amounts dif-fered significantly in the amount of CWD pro-duced beginning at year 300 (P�0.01), and allfive treatment amounts were significantly differentby year 1000 (P�0.05; Fig. 7(a)).

After 1000 yr (10 harvest rotations) of simula-tion with 50% of the highest slash amount, CWDcovered only 44% (175 m2) of the forest floor, andwas significantly higher than 50% of the lowestamount (31%, 126 m2; Fig. 7(b)). By year 700,and continuing through year 1000, 50% of the twolow slash amounts (1 and 2) differed significantlyfrom all other treatments (P�0.05), but not fromeach other (Fig. 7(b)). Similarly, amounts 3 and 4also differed significantly (P�0.05) from all othersimulations by year 700, but were not significantlydifferent from each other (Fig. 7(b)). As expected,the highest slash amount produced CWD thatcovered a significantly greater amount of theforest floor than the other four lesser treatmentsbeginning in year 500, and continued to be great-est throughout the remainder of the simulation(P�0.05; Fig. 7(b)).

After 1000 yr of simulated clearcutting (10 har-vest rotations) at double the amount of slashobserved (200%), the lowest amounts of CWDcovered 54% (215 m2) of the forest floor, while thehighest amounts covered 93% (373 m2; Fig. 7(c))of the forest floor. The lowest slash amount at the200% simulation occupied more forest floor thanthe highest amount of the simulation when theobserved slash amounts were reduced by half(Fig. 7(b) and (c)). No significant differences inCWD forest floor cover appeared until year 300(Fig. 7(c)). At this point, the two lowest simula-tions (1 and 2 in Table 4) covered significantly lessforest floor than the other three (P�0.05). Begin-ning in year 500, simulations 3 and 4 had asignificantly higher forest floor cover than the two

lowest treatments (1 and 2; P�0.05), but signifi-cantly less forest floor coverage than the highesttreatment (P�0.05; Fig. 7(c)). Doubling thehighest amounts of slash produced significantlyhigher forest floor cover than the other four treat-ments, beginning in year 400 (P�0.01), and re-mained higher for the remainder of thesimulation.

The range of forest floor CWD cover rates forthe 50% application was 5.9–10.9 m2 (1.5–3%)100-yr−1; 9.2–18.9 m2 (2–5%) 100-yr−1 for the100% application, and 15.8–33.5 m2 (4–8%) 100-yr−1 for the 200% application (Fig. 5). Both the50% and 100% slash simulations produced CWDat significantly lower rates than any of the fire-re-turn intervals (Fig. 5). Notably, only the threehighest treatments in the 200% simulation pro-duced CWD at a rate that was not significantlydifferent from the burn simulations, and thehighest slash amount of the 200% CWD simula-tion had a higher CWD coverage rate than the200-yr fire-return interval (Fig. 5).

3.2. Complete forest floor occupancy by wood (%)

Because of the stochastic nature of the modeland the associated difficulty in randomly locatingtrees and logs to cover 100% of the forest floor, a50% occupancy time for forest floor occupancywas initially estimated from the 1000-yr simula-tions. The time required to cover the final 1% or2% of the forest floor could take an inordinateamount of time relative to forest floor coveragefor the first 98% because of the random nature oftree and log placement. The three different fire-re-turn interval simulations produced CWD andtrees that occupied 50% of the forest floor within450–530 yr (Fig. 4). For clearcuts simulated at100% observed CWD slash amounts, only thethree highest slash amounts (3–5) produced suffi-cient wood to cover 50% of the forest floor within

Fig. 7. Comparison of 1000-yr simulation results of simulations using the observed amount of slash (100%, (top), 50% the observedamount of slash (middle), and 200% the observed amount of slash (bottom) for all five levels of slash biomass (T1–T5; see Table4). Values for each 100-yr period represent the amount of the forest floor directly covered by wood (includes CWD and live- anddead-standing trees) as predicted by DEADWOOD. All values are averages of five replicate simulations for each amount of slash.Percent occupancy is the percent of the 400 m2 sample grid occupied by wood. The horizontal line indicates 50% occupancy of theforest floor by wood. Error bars are �1S.D.


Fig. 7.


the 1000-yr simulation period (requiring 800–1000 yr to do so; Fig. 7(a)). None of the five slashamounts simulated at 50% observed rates pro-duced enough wood to cover 50% of the forestfloor during the 1000-yr simulation (Fig. 7(b)). Onthe other hand, all five slash amounts at the 200%or double observed rates resulted in 50% coverageof the forest floor within 450–930 yr (Fig. 7(c)).The highest observed slash amount, when simu-lated at 200% the observed amounts, covered 50%of the forest floor in a similar time as the 100-yrfire interval (Figs. 4 and 7(c)).

Estimates of the time required for 100% of theforest floor to be affected by wood were not madeby simply doubling the 50% coverage times be-cause of the presence of inherited wood during theearly harvest simulations. Instead, the simulatedrates of forest floor coverage produced byDEADWOOD for each burn (Fig. 4) and clearcut(Fig. 7) treatment were averaged, and then usedto project 100% occupancy. The 100-yr fire-returninterval simulation produced enough wood tocompletely cover the forest floor more quickly(�1125 yr) than any other burn or clearcut treat-ment, except the simulations where the post-har-vest slash was assumed to be 200% of the highestobserved amount (Fig. 8). The 200- and 300-yrreturn intervals covered 100% of the forest floorin 1350 and 1300 yr, respectively (Fig. 8). Theobserved post-harvest slash simulations tookmuch longer, and required from 1800 (highestamounts) to 3600 yr (lowest amounts) to com-pletely cover the forest floor (Fig. 8). When theseslash amounts were doubled, the times requiredwith regular clearcutting to cover 100% of theforest floor were reduced by 700 and 1450 yr forthe highest and lowest amounts to 1100 and 2150yr, respectively (Fig. 10). However, when the fiveslash amounts were applied at only 50% of theamount observed, the time required to completelycover the forest floor increased to 3000 (highesttreatment) and 5600 yr (lowest treatment), respec-tively (Fig. 8).

3.3. Coarse woody debris biomass

Since CWD biomass inputs were calculatedfrom forest floor area occupied by CWD, the

relative proportions of biomass resulting from the15 simulations are very similar to our results forforest floor occupancy (Figs. 5 and 9). CWDbiomass input ranges were 6–13 mg ha−1 100-yr−1 when assuming 50% of the observed slashamount, 10–24 mg ha−1 100-yr−1 when assum-ing the observed amount, and 19–44 mg ha−1

100-yr−1 when assuming 200% of the observedamount. The burn simulations produced CWDbiomass input rates ranging from 33–42 mg ha−1

100-yr−1 (Fig. 9). All three burn simulations andthe three highest slash amounts simulated at 200%the observed amounts produced significantlygreater inputs of CWD biomass to the forest floorthan the 100% or the 50% slash levels (P�0.05;Fig. 9). The 100 and 300-yr fire-return intervalsproduced significantly higher rates of CWDbiomass addition than the 200-yr return interval.

4. Discussion

DEADWOOD simulated changes in theamount and spatial distribution of CWD follow-ing 100 yr of stand development that were com-parable to actual stand measurements that areavailable (Table 3). Log length (tree height) esti-mates obtained through dimensional relationshipsbetween diameter at breast height and stem taper(Koch, 1987) reflected tree heights within the mea-sured range of variability for lodgepole pine.There are more accurate methods to model treeheight and growth (e.g., Cieszewski and Bella,1993), but such methods require input parametersrelating individual stem dynamics and eco-physio-logical responses. The DEADWOOD model doesnot incorporate this level of detail but our esti-mates are within the range of variability reportedin the literature (Table 3).

The stochastic elements of DEADWOOD pro-duce different results among replications. How-ever, replication values were not vastly differentand the S.D. of the average values usually wasless than 5% of the mean (2–11%), indicating thatthe model runs consistently. As stated earlier,many physiological process-based parameters areimplicitly lumped in the model, reflecting ourintention to simulate CWD dynamics rather than

D.B

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inker,D

.H.

Knight/

Ecological

Modelling

141(2001)

125–

149143Fig. 8. DEADWOOD simulation results used to predict the amount of time required to cover 100% of the forest floor with CWD. For each of the four graphs, the

vertical line delineates simulated results (0–1000 yr) from projected values. Projected values were made by calculating the average annual cover rate for each simulationduring a 1000-yr period, and then applying that calculated rate until 100% forest floor occupancy was reached.


stand development. Modifications that wouldmost improve model output and its predictivepower probably are a better accounting of thevariability in site characteristics and stand densi-ties. Without information on soil texture, nutrientavailability, precipitation, and other variables, theassumption of consistent growing conditionsamong simulations is not realistic. Additionalrefinements to algorithms such as tree growth andmortality rates based on site characteristics mightimprove the model as well. Accounting for sitedifferences with DEADWOOD probably wouldbe difficult. Climatic variability was not intro-duced into any simulations. However, Peterson(1990) studied growth trends in lodgepole pinestands in the Sierra Nevada, but could not findany clear relationship between climatic conditionsand long-term growth trends.

In its present state, DEADWOOD does notaccount for successional changes in tree speciescomposition within a simulated stand, but this

does not present a problem when simulatingclearcut timber harvesting for a pioneer tree likelodgepole pine. Notably, lodgepole pine often isseral to a forest dominated by subalpine fir (Abieslasiocarpa (Hook.) Nutt.) or Engelmann spruce(Picea engelmanii [Parry ex Engelm.]). A changein species composition would undoubtedly changeCWD dynamics. A shift in species compositionwould require species-specific adjustments for im-portant processes such as seedling establishment,growth, mortality, treefall, and decay rates.Treefall rates used in the model are higher thanrates found by Morrison and Raphael (1993) inSierra Nevada lodgepole pine stands. However,most of the lodgepole pine stands used for theirstudy were in riparian areas, a site conditionunlike those measured and simulated for thisstudy.

When forest floor occupancy rate by CWD iscalculated, no allowance for fragmentation ofdowned woody material is made by DEAD-

Fig. 9. Rates of CWD biomass accumulation (mg ha−1 100-yr−1) for the three simulated fire-return intervals and for all fivesimulations within each of the three levels of assumed slash CWD. Error bars indicate �1S.D. Non-overlapping error bars indicatea significant difference between the treatments.


WOOD. This creates unrealistic conditions perti-nent to forest floor structure and decomposition(Stone et al., 1998), but it probably does not havea large influence in the actual calculations of areaoccupied by CWD. In other words, a singledowned log that is 2 m in length occupies thesame area of the forest floor as two log pieces thatare 1 m long and of equal diameter. Also notrepresented in DEADWOOD is vertical structureof downed logs.

Forest floor occupancy times were reduced byapproximately 5% due to the absence of treesfalling into the plot. Often referred to as ‘‘wrap-ping’’, this is a common problem with manyspatial models and typically affects models ofsmall extent, such as DEADWOOD, more thanlandscape-scale models of greater area. It mightbe possible to avoid difficulties of this type byreducing the map analysis window, or by mappinga larger plot in the field and using a smallersub-sample of the study area for analysis. Oursimulation results suggest that the time for CWDto completely cover the forest floor is centurieslonger after clearcut timber harvesting than afternatural fires. Moreover, the slash that remainsafter clearcutting is mechanically distributedthroughout the plots. Therefore, increasing theestimates of forest floor coverage during burnsimulations by an additional 5% would only ac-centuate the differences between burning andclearcutting.

With regard to our results, a shorter fire-returninterval produced more CWD after 1000 yr thanlonger intervals in lodgepole pine forests (Fig. 4).This probably occurred because tree growth ratesdecline with age and most of the basal area oftrees grown by the model during any given simu-lation is accumulated during the first 100 yr. Forexample, 100-yr-old trees were approximately 24cm in diameter, while 200- and 300-yr-old treeshad only increased to near 28 and 32 cm indiameter, respectively. Regular inputs of 24-cmdiameter logs to the forest floor every 100 yrresulted, therefore, in more forest floor coverageby wood than less-frequent inputs of slightlylarger trees every 200 or 300 yr (Fig. 4). Notably,the average rate of CWD input was not signifi-cantly different between the 100- and 300-yr inter-

vals (Fig. 5). This could be attributed to thehigher amounts of CWD input to the forest floorwhen 300-yr-old stands burn balancing the lowerinput rates which occurred during the 300-yr non-fire periods.

CWD on the forest floor was consistentlyhigher during the first few hundred years for bothfire and clearcut simulations (Figs. 4–6) becauseof the large amount of inherited wood presentprior to the first simulated disturbance. This wasparticularly true for clearcut simulations, whereCWD biomass and cover were very similar duringsimulation years 100–200 (Fig. 6). However, ashypothesized, once the inherited wood had de-composed, slash treatment after harvesting be-came more important. Results from simulationsof CWD amounts using the HARVEST model(Harmon et al., 1996) showed a similar decline inCWD following timber harvesting in the 1920s.Sturtevant et al. (1997) measured CWD abun-dance and structure in a balsam–fir forest inNewfoundland and found that if harvest rotationtimes coincided with periods of lowest CWD,CWD amounts decreased through time. They alsofound higher structural diversity contributed bylarge dead and downed logs in old-growth foreststhan stands that had been clearcut 50–60 yrearlier.

Not surprisingly, there were significant differ-ences among all 15 clearcut simulations. The di-vergence through time in forest floor CWDcoverage among the simulations emphasizes thecumulative effect of reduced amounts of CWDinput after repeated harvests. The biomass inputrates of CWD estimated every 100 yr for thedifferent post-harvest slash treatments were equiv-alent to the actual amounts of slash we measuredin stands subjected to a single timber harvest (Fig.9). Given the approximate residence time of 100yr for logs on the forest floor in Wyoming lodge-pole pine forests (Fahey, 1983), the amounts pre-dicted by the model for each 100-yr period weresimilar to actual single-harvest measurements.

All three burn simulations produced sufficientwood to completely cover the forest floor within1400 yr (Fig. 8). However, only by doubling theamount of slash applied during the clearcut simu-lations did the clearcut simulations cover 100% of


Fig. 10. Horizontal bars indicate the range of years required to occupy 100% of the forest floor by each fire and clearcut slashsimulation (100%, 50% and 200%), as predicted by DEADWOOD. The range of values for the burn simulations (vertical dottedlines) is considered to be the HRV for CWD cover of the forest floor in naturally-developing stands. The time required for completeforest floor cover under the natural fire regimes ranged from 1125–1350 yr; 100% clearcut slash simulations required 1800–3600 yrto completely cover the forest floor; 50% clearcut slash simulations required 3000–5600 yr; and 200% clearcut slash simulations onlyrequired 1100–2150 yr. The shorter forest floor CWD occupancy time required by the 200% clearcut slash simulations fall withinthe HRV for CWD coverage of the forest floor as predicted by the natural fire simulations.

the forest floor with wood within a similaramount of time, and only then for the threehighest slash amounts (simulations 3–5; Fig. 8).None of the currently observed amounts of slash(100%) would leave enough wood to mimic CWDdynamics by natural fires (Fig. 8). More impor-tantly, even a 50% reduction in CWD left on sitefollowing clearcutting may require several thou-sand more years than 100% slash treatments tocompletely cover the forest floor with wood (Fig.8). While no other data exist to corroborate theamount of wood consumed during severe, naturalfires, the seemingly low amount of wood con-sumed seems reasonable for the following reasons:first, the fire which burned the stand that wassampled was an intense crown fire (Phil Perkins,YNP, personal communication). Fires of highintensity often consume a smaller amount ofwood than fires of lesser intensity (Hall, 1991);second, there was a large amount of CWD on the

ground prior to the fire, which probably provideda maximum amount of potential downed woodfor consumption.

If forest managers are attempting to useclearcutting as a harvest technique that mimicsnatural fire in the kind of forest we studied, it isimportant to leave more CWD as slash on the sitethan current amounts. Obviously, broadcast burn-ing or pile-and-burn slash treatments would leaveless CWD on site than other treatments. Only bydoubling the amount of slash left behind wouldthe CWD be within the HRV of CWD inputsfrom fires (Fig. 10). This could be due, in part, tothe absence of any inherited wood subsequent tothe initial harvest. Stands subjected to naturalfires without harvesting, on the other hand, pro-duce regular and larger inputs of CWD. Bragg(1997) used the forest vegetation simulator (FVS)model in conjunction with a riparian CWD re-cruitment model to simulate CWD inputs follow-


ing disturbance over a 300-yr period in theBridger-Teton National Forest in northwesternWyoming. His results showed that CWD deliveryto stream channels was significantly reduced fol-lowing clearcut timber harvesting but that it in-creased following simulated spruce beetleoutbreaks. Beukema et al. (1997) also found thatCWD�30 cm diameter increased by 90–160 mgha−1 following fire simulations using the FireEffects Model Extension of the FVS.

Single measurements of CWD in unmanaged ornaturally-developing forests are useful for estab-lishing baseline information, but they fail to cap-ture the dynamics of CWD in space and time.Downed log biomass and dead-standing treesvary temporally and spatially as new stands de-velop repeatedly after a series of disturbancesspread out over hundreds of years. DEADWOODprovided estimates of both the amount and distri-bution of CWD as affected by clearcut timberharvesting and natural fire (Fig. 3). Further, whileour simulation results from DEADWOOD werequalitatively similar to other models describedearlier that simulate CWD amounts (e.g., Har-mon et al., 1996; Beukema et al., 1997; Bragg,1997), none of the other models evaluate forestfloor CWD dynamics in a spatially explicit man-ner, making direct comparisons difficult.

For lodgepole pine forests in Wyoming, ourresults suggest that natural fires may create up tofour times more CWD during a 100-yr periodthan current post-harvest slash treatments in theMBNF (Fig. 9), regardless of fire-return interval.However, clearcutting may mimic the HRV forCWD during a sequence of natural fires if ap-proximately twice the amount of CWD (30–70mg ha−1) is left on site than currently remainsfollowing clearcutting (15–35 mg ha−1), therebyapproximating the biomass and forest floor cover-age of CWD following intense natural fires (20–45 mg ha−1). Deficiencies in the amount of CWDslash as small as 5 mg ha−1 100-yr−1 may resultin a cumulative reduction in forest floor occu-pancy by CWD after several hundred years ofrepeated clearcutting, which could affect biologi-cal diversity and soil characteristics.

Acknowledgements

We wish to thank Mark Harmon and WilliamPulliam for their suggestions during the earlyphases of model development. We also thankSharon Stewart for her assistance with the modelprogramming; and Kris Johnson, Donna Ehle,Sharon Stewart, David Melkonian, Mark Lyfordand Sally Tinker for assistance with data collec-tion. We extend our appreciation to Mike Sandersand Dave Carr of MBNF, and Dave Phillips,Kathleen O’leary, and John Varley of YNP, fortheir help and cooperation during this study. Thisresearch was supported by grants from the Uni-versity of Wyoming/National Park Service Re-search Center and the US Department ofAgriculture (NRI 96-35101-3244). Mark Harmon,James Agee, and two anonymous reviewers pro-vided very helpful reviews of earlier drafts of themanuscript.

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