Paststudieshave extensivelycharacterizedchamise
and otherchaparral
species.Severalstudiesexaminedfuel
characteristics
of one or more chaparral species (Countryman and
Philpot1970,Nord and Countryman
ChamiseChaparralDead
Fuel FractionIs Not Reliably
Predictedby Age
1972, Wakimoto 1977, Rundel and
Parsons 1979), but none of these
studiesquantitativelydescribeda rela-
tion of age to fraction dead. Other
chaparralstudiesprovideage-related
information
concerning
the dynamics
of the stand speciescomposition
Timothy E. PaysenandJackD. Cohen,• PacificSouthwest
ForestandRangeExperiment
Station, USDA ForestService,
Riverside,CA 92507.
ABSTRACT.Fire managers
of southern
California
chaparral
oftenassume
thatthe
amountof deadmaterialin chaparral
shrubsis closelyrelatedto canopyage.
Analysisof chamise
(Adenostomafasciculatum),sampled
from southern
Californiashrublands,
indicates
thattheratio
of deadto live components
is not related
reliablyto ageof shrubcanopy.Further
statistical
description
of thedataindicates
thatfractions
ofdeadmaterial
greater
than
0.40arerare.Theresults
provide
grounds
for seriously
questioning
currentassumptionsaboutthestrongrelationship
between
ageandfractiondeadin southern
Californiachaparral
fuels.
(Hanes 1971, Patric and Hanes 1964,
Stohlgrenet al. 1984),but the fraction
of thedeadvegetative
fuelwasnotde-
of other species.This age-dynamic
chaparralfuel modelhasbeendistributed to field personnel through
classeson prescribedburningand fire
behavior
in California.
scribed. Several authors believe that
the fraction of dead vegetationincreaseswith age, but did not present
quantitative studies (Philpot 1974,
Green1981,Hanes1974).
Also, the
southern California chaparralfuel
model option (SCAL) in the FIRECAST (Cohen 1986) computerprogram is based on the dynamic fuel
model.
FIRECAST
The literature did not contain the
information
needed to resolve our
questionaboutageand fractiondead,
so we conducteda field study that
sampledthe dead vegetativefraction
of chamisechaparralwith regardto
canopyage.Thisarticlereportsthe resuitsof thatfieldstudy.
is a fire behavior
predictor generallyavailableto fire
managersfrom variousfire agencies
throughthe computersystemof the
CaliforniaOffice of EmergencySer-
METHODS
vices.
Althoughthe conceptof a relationship between age of chaparraland
fractionof deadfuel hasbeenwidely
acceptedby fire managers,contradictory observationsduring prescribed
Naturalresource
managers
of burns
and wildfires compelledus to
southernCaliforniashrublands
(chap- questionit. Our initial field observaarral)addressthe planningaspects
of
tions of the fraction dead in various
wildlandfire by usingthe conceptof
age classesof chaparralindicateda
age classmanagement.Wildland fire
high degreeof variabilityand genermanagersassumethat the dead vege- allylesstotaldeadfuelthanpredicted.
tativefuel is the mostimportantchar- We initiallyturnedour attentionto the
acteristic
thatinfluencesfire intensity. literaturefor guidancein determining
And, they assumethat the older the
whethera relationshipbetweenage
Chamisewas chosenfor this study
on the basis of three considerations:
WestJ. Appl. For. 5(4):00-00, October1990.
vegetativecanopy, the greater is the
fraction of dead fuel. These two as-
(1) chamisechaparraldatastronglyinfluencedthe age/fractiondead relationshipbelievedto exist;(2) Hanes
(1971) indicated that chamisewas the
most common chaparral speciesin
southern California, and (3) Coun-
trymanand Philpot(1970)quantified
fuel information,includingthe fraction dead, for a 33-year-oldstandof
chamisethat couldserveas onepoint
for comparison.
Two typesof samplingprocedures
and fraction dead exists.
were followed.
The first was a multi-
sumptions
formthe basisfor management of chaparralfuel by age class
(HunterandPhilpot1982).The goalof
managementby age classis to keep
S o u t h e r n
chaparralstandsyoung enough to
prevent accumulationsof dead fuel
that resultin unmanageable,
highly
destructive wildland fires (Rogers
El_
'
1982).
Chaparralfire managers
havebeen
usinga mathematical
modelthat provides various fuel descriptors, including the fraction of fuel that is
dead, as a function of canopyage
(Rothermeland Philpot1973,Philpot
1974).Althoughthis modelis largely
basedon chamisechaparral(Adenostomafasciculatum)
data, it is generally
appliedto chaparralstandscomposed
•
r•
_
....
CaI i fo;;:r:i!i.n
i aMojave•Nev•lda
..?'
Desert
...;./ r a n s.v.::•"'"
•"
•
..'.,',?,l:',"•':.:.::?:.':,+",..,.....q
n
Pa c i f i c
o oe a
ß
'........ ':.':':.?Z:,:
"e '•"'"'"":";;""
....
•"Z
•
'l,
•
.-.+ n.
• 'R.
•'.."•.In '.•"."a
"."i.'•"vC'.:i'."-'i:':•.
..:',..
e •o•oraao
;,:::..:,.,..
:::','a
'
Coastalandinlandintensive
samples
xNow at the Southeastern
ForestExperi-
Thirty-one
shrub
sample
Moxico
ment Station, USDA Forest Service, De-
partment of Agriculture, Macon, GA
31020.
Figure1. Symbols
indicate
thelocations
of samplesitesof chamise
fractiondead.
WJAFS(4)1990 127
Canopyages,for the sitessampled,
rangedfrom9 to 100years.The samplinglocations
werechosenarbitrarily
with respectto severalfactors:(1) the
availabilityof chamiseas the dominant shrubspeciesin the area, (2) our
abilityto identifycanopyagefromage
classmaps, (3) the representation
of
1
A
0.8
therangeofknowncanopy
ages(cur-
0.6
++
0.4
rent fire history maps limit this to
1-100 years),(4) the representation
of
the geographic
and ecological
(based
mostlyon climateat this stageof the
procedure)range of southern Californiachamise
sites,and(5)ourability
+
0.2
to access the site for shrub collection.
o
22
44
66
88
With the use of vegetationmaps,fire
history maps (for age class),dimate
zonemaps,androadmapsfor accessibility, a generallocationwas chosen
throughan iterativeprocess
until all of
110
1
B
0.6
the above criteria were met. General
locations were never chosen from on-
0.6
0.4
siteinspections.
Using the intensive, site-specific,
+
samplingprocedure,a sampleof size
ten was taken from each of two dif-
ferent southernCaliforniachaparral
sites.The generallocationswere arbitrarilyselected
as in the multisiteprocedure,but with two major exceptions:(1) the chamise-dominated
sites
representedcanopy ages of 30-39
years sincethe last fire, and (2) the
generallocationsrepresenteda gen-
0.2
o
o
22
44
66
66
i
c
0.8
eral climatic contrast between the
coastal zones and the interior moun-
tains. Within the selectedgenerallocations,specificsiteswere randomly
0.6
selected.
0.4
0.2
0
22
Individual shrubs, the sampling
units for both samplingprocedures,
were randomlyselected.The random
shrub was selectedby choosinga
random directionand distance(within
44
66
68
110
CANOPY AGE (years)
Figure 2. Scatterplots of the 31-shrubsample(A) and intensivesampleplots at the
coastaland interior mountains(B) and North Mountain (C) displaysampleresultsfor
chamisefractiondeadby canopyage.
site procedure that sampled individual chamiseshrubsextensively
across
southern
California.
The
secondproceduresampledthreespecific southern
California
sites inten-
The multisiteproceduresampled31
chamise
southern
shrubs from 15 different
California locations within
areasvegetatedby chaparralcommunity series(Paysenet al. 1980,1982).
100m) froman access
point. If the resuitingvectordid notlocatean appropriatechamisesiteand shrub,the processwasrepeatedfromthelocationof
the unusable sample location. The
sampledshrubwas cut, bagged,and
removedfrom the sitefor analysisat
the USDA
Forest Service Fire Labora-
tory, in Riverside,CA.
The shrub analysis followed the
procedures
usedby Countrymanand
Philpot(1970).Shrubmaterial,foliage,
and stemslessthan 76 mm, wasseparatedinto livingand deadcategories.
The materialwas then put into containersfor oven-dryingovera 24-hour
sively, with a greater number of
shrubs taken from each site than was
the casefor the first procedure.The
intensivesampleswere used to provide insightinto the kind of withinsitevariabilitythat mightbe foundin
southern California standsof chamise,
and to providea comparison
for evaluating the multi-site data. Figure 1
identifies
locations
samplesweretaken.
128
WJAF 5(4)1990
where
shrub
Table1. Summary
statistics
for all chamisesampledatasetsof fractiondead.
Data set
Statistic
Median
Mean
S.D.
Samplesize
3'1-shrub
Coastal mtns.
North Mtn.
North Mtn.
sample
age 3'1yr
age 36 yr
age 33 yr
age 55 yr
0.228
0.223
0. '120
0.249
0.237
0.092
0.259
0.259
0.053
0.259
0.244
0.088
0.303
0.286
0. '107
31
10
Interior
10
mtns.
16
16
Table2. Sampleestimates
andbootstrap
summary
statistics
for the mean,standard
deviation,
and median,for the 31-shrubdataset.The summary
statistics
were run on 500 bootstrap
the actual data, and Table 2 presents
estimates(0') of each statistic.
timates. The small values for the stan-
Summarystatistics
Statistic estimates
Mean
dard errors of the mean, median, and
S.D.
Median
0•
0.233
0.118
0.228
Mean (0*) 2
0.230
0.117
0.220
S.E. (0*)
0.021
0.020
Bias (mean (0*) - 0),
-0.003
indicatesthe sampleestimateof the statistic
of interest.
0.022
-0.001
-0.008
O*represents
a bootstrap
estimate
of the statistic
of interest.
period at 95øC.The dried vegetation
was then weighed to determine its
summarystatistics
of the bootstrapes-
RESULTS
the standard
deviation
for the boot-
strapsample,as well as the low bias
values,imply that the actual31-shrub
sampleis not significantly
influenced
by outliersor unevennessin the data.
Similarly,the bootstrapresultssuggestthat the Kendall'stau measureof
correlation
providesa reasonable
estimate of the relationshipbetweenthe
fraction dead and the age. Figure 3
summarizes
the actual and the boot-
The plot of the 31-shrub,multisite strapestimateof Kendall'stau. A posAs a complement
to thetwo sample data revealslittle, if any, relationship itive tau between 0.0 and 1.0 indicates
sets described above, data were inbetween the fraction dead and age
a positive relationshipbetween the
cludedfromthe originalCountryman (Figure2). Inspectionof the site-spe- fractiondead and age. The resulting
cific data sets shows that the varitau of 0.225, while statisticallydifand Philpot (1970) chamise fuels
ferent from zero (with a two-tailed
abilityfound on a specificsiteis simstudy, and from our resamplingof
ilar to that found in the multisite data,
probabilityof 0.078),indicatesa very
their site. Countryman and Philpot
and the rangeof the specificsitedata weak association. The values for the
sampled 16 chamiseshrubsfrom a
single site whose vegetativecanopy is comparableto that of the multisite bias and the standard error of tau indata.
dicate that the Kendall's tau estimate
age was 33 years old. Twenty-two
The
results
of
the
bootstrap
proceis
reasonable
for the 31-shrubsample.
years later, we sampled16 chamise
dure suggestthat unevennessand
Furtherdescriptionof the multisite
shrubs from their original site (the
outlyingvaluesin the 31-shrub,multishrubsample,withoutregardto age,
North Mountainsamplesin Figure1)
influindicatesa strong clusteringof the
whosecanopyage was then 55 years site sampledo not significantly
old.
ence the estimates of the mean, medata.Figure4, a histogram
of the frequencyof occurrence
of the fraction
To establisha quantitativemeasure dian, and the standard deviation.
dead, showsthe 31-shrubsampledata
of
of the relationshipbetweenfraction Table1 presentssummarystatistics
deadand agein our multisitesample,
we used a nonparametric
coefficient,
mass.
Kendall's tau, as a measure of correlation.
We chose not to use the usual
correlationand regressiontechniques
From 31 data points:
because there was no reason to believe
tau=.225
From bootstrapof 500 replications(n=31)
mean(tau*)=.214
that the restrictions
imposedby these
techniques(for example,linearityin
std. dev.(tau*)=.153
bias(tau)=mean(tau*)-t
the case of the linear correlation coeffi-
cient, r) were appropriate.Kendall's
tau respondsonly to monotonicity,
and thereforedoes not demand specificfunctionalrelationships
(suchas
linearity)of the data.
Becauseof the small samplesize,
we performeda bootstrapresampling
procedure (Efron 1982, Efron and
Gong1983)on the 31-shrubsampleto
checkthe internalconsistency
of the
data as it might affectthe measureof
correlation. The resampling comprised500 iterationsof resampling
with replacement
from the 31 pairsof
fractiondead/agevalues.The size of
each samplewas 31. For each iteration, an estimate of Kendall's tau was
calculated; to summarize these estimates, mean and standard errors of
the tau estimates
lated.
were
then
au
=.214-.225=-.016
*bootstrap estimates
8O
•
64
LU
32
LU
U.
16
calcu-
For the samemultisitesample,we
calculatedsummarystatisticson the
fraction dead data to generally de-
-0.5
-0.2
0.1
0.4
0.7
TAU
scribe the occurrence of the dead fuel
componentfor southernCalifornia
chamisechaparral.The fractiondead
was describedusing the bootstrap
procedureon the samplemean, median, standarddeviation, and quantiles(10ththrough90thpercenttie).
Figure3. A histogram
of the500bootstrap
estimates
of Kendall'stau. Thevalueof tau
calculated from the actual field data (n = 31) is 0.225. From bootstrap estimatesof 500
replications,
mean(tau*)= 0.214;S.D. (tau*)= 0.153;bias(tau)= mean(tau*)- tau =
0.214 - 0.225 = -.011. (Tau* indicatesthe bootstrapestimateof tau.)
WJAF5(4)1990 129
rehableage esbmateexistsfor other
chaparralspecies.
Our resultsshowa symmetricdistribution
12
of chamise
fraction
dead
valuesarounda relativelylow valuein
the fraction dead scale. This distribu-
tion suggeststhat 80% of the time we
will find stands of chamise with frac-
tion deadvalueslessthan or equalto
0.31--regardlessof canopyage.
RECOMMENDATIONS
Southern California vegetation
managersrequiringan estimateof the
fraction
o
0.5
FRACTION
DEAD
Figure 4. Histogramshowingthe frequencydistributionof the actual31 chamisefrac-
this distribution, the mean is 0.233
and the median is 0.228 (from Table
1). The closeness
of the meanand median values indicates that the data dis-
suitsof this studybring into serious
questionthe efficacyof ageclassmanagementin chamisechaparralif that
management
is basedon an expected
age-fractiondead relationshipas a
predictorof potentialfire behavioror
CONCLUSIONS
Our data indicatethat a general
fractiondead/agerelationship
is very
other effects.
weak for southern California chamise
chaparral(Adenostoma
fasciculatum)-tribution is somewhatsymmetrical. at leastwithin the agerange(9 to 100
This concurs with the low measure of
years)sampled.Thus,ageof chamise
fractiondead/agecorrelation
provided canopydoesnot providea reliableesby Kendall'stau.
timateof the fractiondeadof vegetaMean percentlies,developedfrom
tive fuel. Since the current method of
the bootstrapprocedure,were usedto
usingageto estimatethe fractiondead
establisha cumulativefrequencydisis basedon chamisecharacteristics,
no
tribution
and other
not recommended. Further, the re-
tion dead data.
distributed around a central value. For
dead of chamise
chaparral speciesshould obtain an
averagefraction dead estimatefrom
on-sitesampling.The useof a canopy
agemap (e.g., a fire historymap)as a
tool for predicting fraction dead in
chamiseand otherchaparralstandsis
o
for fraction dead, which
The lackof an age/fraction
deadrelationshipfor chamisechaparraldoes
not negatethe factthata fractiondead
exists.The lack of an agerelationship
indicatesthat otherinteractingfactors
suchas yearlyclimatevariability,insects, and diseasecould be responsible for the fraction of dead fuel. Be-
causewe sampledover a relatively
allowsestimationof the frequencies
of
fractionsdead relative to particular
values.For example,asseenin Figure
5, 50% of the fractionsdead fall below
approximately0.22, and 80% of the
fractions dead fall below approximately 0.31. Fractions dead approaching0.50and greaterare rare.
An inspectionof all datasets,summarized in Table 1, using box and
whisker plots (Figure 6) indicatesa
10o
80,
prevailing tendencyof the fraction
dead data to cluster around
a central
value.With boxand whiskerplotswe
cancomparethe samplesetswith regard to dispersion,centraltendency,
and skewness(Emersonand Strenio
1983).Inspectionrevealsthat the me-
m
60
,r,
""
,r,
40
dian value for each data set is central
to the rangevalues,andcentralto the
values that delimit the central 50% of
the data.Theoverlapof valueswithin
the central 50% of the data for each
data set, and containment of all data
set medians within the 95% confidence limits for the North Mountain
(age55 years)median,givesthe impressionthat all sampleswere from a
singlepopulation.This overlapalso
suggeststhat site, at the stand level
(asopposedto the shrublevel),is not
a dominantfactorin the development
of fraction dead.
130 WJAF5(4)1990
o
o.o
0.1
0.2
FRACTION
0.3
0.4
DEAD
Figure5. Bootstrapestimateof the cumulativefrequencydistributionof chamisefraction deadfor the 31-shrubsample.
COrn,FrRYMAN,C. M., AND C. W. PHmPOr.1970.
Physicalcharacteristics
of chamiseas a wildland fuel. USDA For. Serv.Res.Pap.PSW-66.
16 p.
EFRON,
B. 1982.Thejackknife,thebootstrap
and
other resamplingplans. Soc.Indus. Appl.
Math. Philadelphia.92 p.
EFRON,B., ANDG. GONe;.1983.A leisurelylook
at the bootstrap,the jackknife,and cross-validation. Am. Stat. 37(1):36-48.
0.8
EMERSON,
J. D., ANDJ. S•,•Nuo. 1983.Boxplots
0.8
and batchcomparison.P. 58-96 in Understanding
robustandexploratory
dataanalysis,
D. C. Houglin,F. Mosteller,and J. W. Tukey
0.6
0.6
(eds.).Wiley, New York. 447p.
GREEN,L. R. 1981.Burningby prescriptionin
0.4
0.4
0.2
0.2
0.0
0.0
chaparral.USDAFor. Serv.Gen.Tech.Rep.
PSW-51.36p.
PlANms,T. L. 1971. Successionafter fire in the
chaparral of southern California. Ecol.
Monogr.41(1):27-52.
PlANms,
T. L. 1974.The vegetationcalledchap-
arral.p. 1-5 in Proc.syrup.onlivingwiththe
chaparral.SierraClub, SanFrancisco.
HUNTS.
R, S.C., ANDC. W. PHILPtrr.1982.Fire be-
havior and managementin Mediterranean-
typeecosystems:
a summary
andsynthesis.
P.
520-522in Proc.syrup.on dynamics
andmanagementof Mediterranean-type
ecosystems.
USDAFor.Serv.Gen.Tech.Rep.PSW-58.
NORD,E. C., ANDC. M. COUNTRYMAN.
1972.Fire
Sample
relations.P. 88-97 in Proc.syrup.on wildland
shrubs--theirbiologyand utilization.USDA
For. Serv. Gen. Tech. Rep. INT-1.
PATiUC,
J. H., ANDTSDL. •.
1964.Chaparral
suCcessionin a San Gabriel mountain area of
Fiõure6. Boxandwhiskersplotsfor all dataon chamisefractiondeaddiscussed
in this
paper.Thelenõthof theboxapproximates
theinterquartileranõeof thedata.The cross
bar showsthe median,with notchesapproximatinõ95% confidencelimits for the median. Ninety-ninepercentof the data pointsfall within the ranõeindicatedby the
"whiskers." Outliers are indicatedby asterisks.
California.Ecology
45(2):353-360.
PAYSEN,
T. E., rr At. 1980.A vegetation
dassificationsystemappliedto southernCalifornia.
USDA For. Serv. Gen. Tech. Rep. PSW-45.
33 p.
PA¾sWN,
T. E., J. A. DœP,S¾,
AND C. E. CONIReD.
1982.Vegetation
classification
system
forusein
California:its conceptualbasis.USDA For.
short time period (1984-85), we
cannotbe certainasto how persistent
the value of centralclusteringand the
for example, we assumea level of
fractiondead (basedupon a canopy
age map) and that assumptionis not
distribution
correct, then observed fire behavior
of fraction dead are. Our
descriptions of the fraction dead
shouldbe viewedasthe sampledcondition and subjectto change.
Important new understanding
about southern California chaparral
would include how the fraction dead
is producedand how longit takesfor
significantfractionsdead to accumulate. This information
would indicate
for how longsampleestimates
of fraction dead would be viable.
Giventhe lackof any consistent
relationshipbetweenage and fraction
dead in chamise,we as fire professionalsneedto reexamineourperceptionsof the way fractiondeadaffects
fire behaviorin these stands--especiallyif thatunderstanding
isbasedon
field observation of fire behavior. If,
should not fit our expectations--if
there is a necessary
relationshipbetween fire behavior and fraction dead.
If, however, the fire behavior does fit
our expectations
under thesecircumstances,then perhapsfire behavioris
not as heavilydependenton the fraction dead as we have been led to believe. We are forced to conclude that
fire behavior variations
in chamise
chaparralare more complexthan can
be describedby a singlefuel characteristiclikethefractionof deadvegetation.
LiTERATURE
[]
user's manual.
Francisco.
ROGERS,M.J. 1982. Fire management in
southernCalifornia.P. 496-501in Proc.syrup.
on dynamicsand management
of Mediterranean-typeecosystems.
USDA For. Serv.Gen.
Tech.Rep.PSW-58.
RcrrrmRM•r,R. C., ANDC. W. •.
USDA
Gen.Tech.Rep.PSW-90.11p.
For. Serv.
1973.Fire
in wildland management,predictingchanges
in chaparralflammability.J. For.71:164-169.
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fasciculatum)alonga fire-inducedage gradient.J.
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T. J., D. J. PARSONS,
ANDP. •/.
RUNDEr.
1984.Population
structureof Adenostomafasciculatum
in maturestandsof chamise
chaparral
in the southernSierraNevada,California.Oecologia64:87-91.
WAKIMO?O,
R. R. 1977.Chaparralgrowthand
fuel assessment in southern
CITED
Corms,J. D. 1986.Estimatingfirebehaviorwith
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Serv.Gen.Tech.Rep. PSW-63.14 p.
PHna,o?,
C. W. 1974.The changingroleof fireon
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P.
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USDA For. Serv.
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WJAFS(4)1990 131