Southern Cross University
ePublications@SCU
School of Environment, Science and Engineering
Papers
School of Environment, Science and Engineering
1991
Data requirements for developing growth models
for tropical moist forests
Jerome K. Vanclay
Southern Cross University
Publication details
Vanclay, JK 1991, 'Data requirements for developing growth models for tropical moist forests', Commonwealth Forestry Review, vol. 70,
no. 4, pp. 248-271.
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Review
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nEvIEW: DATA REQUIREMENTIi FOR DEVELOPTNG
GROWTH MODEI-S FORTROPICAL
MOIST FORISTS
lercme K. Vanduf
Permandr emple ptols provide rhc basis to. grosth modeling.
yield predicrioo ind sLnaincd virtd managemenl.and rhc reliabilily of rhc dara is cruciat lo rhese and maov othe. asFcrs of
lorcsr manaSemenr.To oblain retiable data. ir is necesa.v lo
ensureconsistenlrrrndarJs and rhar a siJe ranAcotsranrj and sire
c(ndrtion\ are samphd r^inB horh nassivemoniroringand exp(n
mcntal ptots. Individuat rrces shoutd be numbcred.ma*ed and
mappcd. Rcmcasuremcnr f.cqueno should bc derermiftd lo faciti_
tale plot relaariod and cnsu.e rhat g.owrh is greaterthanmea\urc
menr eno6 Medsuremenr recods shoutd be unanbiguous and
R6srD6
Rcvue:Condirionspriatabt.s pour t_dYoturioo
dc modeb de croi,
ence Brur td foratskoDic.tes humides_
hs renains dc rtsrs pcrmancntsfournissnr ta basedc modatcs
dc croi$ance.de ta p.idicrion du rendemcnter dc t.organisation
du
rendemenrconrinu_h pracision ds donndd err indi\Dcnsabte
F)ur ccs dernicB er pour h.aucoul d.autrcsa\Dcds d( t.;xnknta
tion des lorars. Pour oht(nr des di,nneesprecisesrt taul ouc des
nomcs unitormessoicntparanrre cr ,{uc tei crpcrien.o -nri.r"nr
d ure amde seric d'ichantilons quanl aux condirioosdc siluarion
ou de sile. eo ulitisaol I observarionpassivecr des len.ins e,ocri
mentaur en mim( t(mps. txs arhcs
nanicut|eA doivent ctr.
.umarcres er clairencnr indiquis su. un plan. La friquencc des
contrdles dc mesurc doir,irre dcrcmine( atin dc dcilitcr tc\
chaogemenrsde reraid cr d'assurcrque ta croisane $il Dl[s
Srande que les eneuB de m.su.e. L.s r€gislrcs .le m6ure doivcnr
€l.e clai6 er si^.
*Depdhcd of Economis and Narurat
R.sourc.s. Royat vcrcrinary and Asri.ulrural
Udv€6nt- rtorald*.sEj57_DKrrn Frcd.ril\ttrrc_ Denmart.'
{,Cql
DATA REQUIREMENTS FOR DEVELOPING GRONTTII
I4 poclas [Ermanentes de moestr@ datr la base para 106modelc de crecimiento, la predi6ci6n de rcndidietrto y maoeF para un
rcodimierto soctedble, y la @nliabilidad en 106 dato6 es imprescindible tanto pam 6stc proFititoc como para otros asp€do6 del
mnnejo forestal. Pan obtener dato6 contrabl€s, es necesario Nsu
mf normas constatrts y que se muestrean una gama amplia de
@ndicion€s de siaioy de rcdal utilimdo m€todo6lrasivoc de moniroreo y pa@las exPerimenaales.l,os rftols individurrs debe. ser
nnmerados, marcadc y napeadc. Ii frecuencia de remedici6n
debe fijaF p6n facililar la reubicaci6n de 16 prelas y asegurar
qu€ el cr4imiento es mayor que lc emres de nedi.ntD- lG
archivos .le medici.6ndeben ser inequivocc y egmdo&
ha.odrdior
One way to assistthe contitrued survival of the topical moist foresr is to
manage ft fm commercial productiotr of timber and other lorcst products, so that it becomes valuable in the eyes of local communities. Trro
condilions are essential, but not sufficient lor its survival. Firstly to
eDsurethat harvesting leaves the forest in an ecologicaly and silviculturaly 'good" condition (vANcr-Ay, 19{b). Secondly, to eke out the
resource so that barvestitrg provides a continuing supply of timber and
other benefits (vANcr-ay, 191a). Growth models, when combined with
inventory, provide a reliable way to examine harvesting options, to
detemine the sustainable timber yield, and to examitre tbe impacts on
other values of the forest.
The first step in constructing a growth model is to obtain suitable
data. All too ofien. lhe modeling apprcach is dictated by limitations of
the data. Dara rcquirements ot maDy modelling approachesare similar
and allow a sel of minimum procedures to be established. The pIocedures discuss€d here relate to the requirements for development of
gowlh and yield models. Additional details may be necessaryif plots
are to s€rve olher Usessuch as ecological sfudies.
Although directed at tropical moist forestE this review is applicable
to data requiremetrts of gowth models generaly. Stem analys€sdo nor
pro\ride reliable growth data for many rree speciesin the tropical moisr
forest, so data must be obtained from remeasuremetrE otr permanent
sample plots (PSPS).KR MERand xozrowsKr (197927) rcported some
of the aromalies ot growth dtrgs in tropical tree specres.Some ever
green fi€es (e,g. Sl,r'?tezi.aspp.) may form dngs while dec-iduoustrces
25T
DATA REQUIREMENTSFORDEYELOPING GROWTII
(e.g. some Ftcrr spp.) may nor. Sone species (e.e. Hevea brczitie&is)
may folm seveml grov,th rings each year, whilc orher species (e.g.
Shorca rcbusrt) 'may Iorm only one ing but not necessarilyin the same
month each ycar. MAruArx (1981) presented a detailej review of rhe
possibiliti€s and problems of stem atralysisin tropical tree species.
Permanent plots can never be completely replaced by tempomry
plots even for species amenable to stem analysis, because o y psps
allow satisfactory statistical comparisons rvithitr and betweetr plots to
check lhe adequacy of models (srRAND, 1970), and only psps can prc
vide reliable and -onsisrent data on monality, clovn dymmics and
stand level variables (McouunN, 1984).
DEADMAN(199) recorded a number of observations conceminq data
tor grow r modelling: permanenl plots must cove. extremes ot si; aod
tearmenq periodic reviews of data collection policy are necessary;
quality of data collected is of extreme imponance; and documentation
should be complete, consistent and accnnte- aDLARD (190) emphasrzed lbree factors: relevarce, reliability and relationshiF. v Ncr,Ay
(1990a)stressedstadardi?ation of Focrdures, accu-arc measunerrenrs.
specific location (des€ription and map coordinates), clear obiecrives and
sufficietrt resources (funds alrd trained staff). crr.fls (1983) proyided a
comprehensive rcferetrce matrual for PSP establishment and maintetrarce in tempente regions, mo6t of which is relevant to the tropical
moist forest.
Data used for groMh rcsearch must be of a higher quality rhan that
used for point-in-lime inventodes For example, a diameter measurement of 5{)I 0.5 cm may seem precise, but if a remeasure indicates 5l i
0.5 c]n, the growth estimare will be 110.7 cm which is trot vew Drecise.
Difi€ritrt D.h Ne€ds
Samplc plots serve matry purpo6€s, but different Focedures are
required to satisfy efhcieDtly various needs of resource managers Some
infomation needs and coresponding sample plot procedures include:
. ResourceInventory ('What is the present nature and extent of the
resource?"): Typicaly a large number of plots (or point samples)
vill be rcquired to achieve the desired precision. precision can be
gahed by orietrting plots acrcs envfuonmentat gradients to maximize f,,ithin plot variation and thus reduce between Dlot varianceCo6t coDsiderafions usually dictate thal teml,onry inventory ptoa
(or point samples) are mo6t effrcient for r€source inventory (e-gMATrser rr. 1984, SC-HREUDER
?, ar. l9B7). Specializ€d techniques
DATA REQUIREMENIS FOR DEVEITOPINGGROWTII
251
for timber crdsing offer great efficiencies (e-9. 3-P sampling), but
may trot provide data suitable for itrput to yield forecasting systefrs.
. Continuous Fo,es. Inventory fot yield contol: lI yield regulation is
by volume contIol, it is important that permanent plots be reprcsentative and €stablished in various forest types and stand cond!
tiotrs itr proportion to their area (this system is often called Continuous Forest hveflory). As with resource hventory, Fecision is
gained by minimising betweeo plot variance. Plots should be
marked so that they catr be relocated for remqFunement, but
should rcmain itrcotrspicuous so that they receive mbiased management.
. Growth ModeuinC: The development of gowth models requires
data obtained from rhe remeasurement of PSPS.Tbe mo6t reliable
and flexible mode ing techniques require data in whicl the individual trees are identified. This rcquircs that a tre€s otr the PSP
arc permaneDtly tagged and uniquely numbercd. Irrespective of
the mode ing approach, unique numbering and tagging of trees is
the only sure way of detecting measureme errors- Growth nodelling also requires homogeneous plots, atrd this meatrsminimising
within plot variance: the ability of the PSPSro quantify the presetrt
r€source is irrelevatrt. Thus the sameplot seriescarmot be effrciently us€d for borh resource inventory and gtorth model develop
ment. If the growth model is to be used to investigate silviculturdl
and management allematives, the database must include experi
mental data vrith paired treatm€nt and control plots, both sith adequate isolation. In confast to continuous forest invcntory plots, it
rs trot necessaryfor PSPSto be represe ative or numedc{Iy proportioml to forest type areas, but it is essential that they sample
the full range of stand mnditioDs.
. Long Tem MorrianrinA of EnvironnEnral Crrdnge-'D wxNs and
FIEI-D(1978) descaibea series of plots to monilor subtle long telm
changesin a forest. Wlilst such studies are desirable, few organizations have the resourcesor nced to establish such plots on the same
scale as requir€d for growth studies. Such detailed plots should be
rcserved for special studies. For growth mode[ir& it is better to
sample the frrll rarye with conventional PSh thar to have a few
detailed 'Dawkins" plots- However, quantity is no substilute for
quality.
Permanent sample plots established to provide data for growth
modelling should be desiFed to satisfy this primary fted, and should
252
DATA I{EQUIREMENfS FOR DEVEIOPING GROWTII
not be comprcmised in order to satisfy secondary fteds. They need not
provide ef6cient resource invenrory data, as altemative sampling procedures can better fi lil that needModd Deeelopment
The hitial atrd most obvious rcquircment for data is during model
development when they are required for the constructior of the basic
funclions comprising the model.
Frc l: Polynomialapproximationof a nrndion with optbal aod sub-oprimal
sanpling
It can be shown mathematically, thai the relationship betweetr two
i?riables given by a series of poitrE known u/ithout e.ror, catr be
des.ribed by polynomials mGt accurately if the sample points are locat
ed along the trend line with intensity increasing towards the limits of
the region of interest. Figure I illustrates a cubic polynordal fitted to
seven points selected from a diameter increment functiotr (vANcl_Ay.
l99lb. speciesgroup l). Tbe ticks on the horizoDlat axis indicate a rcar
optimal sampling (A) which produced a very good approximation (sotid
lirc) to the original tunctiotr. A more a$iFdry sampling (o) resulred in
a paor approximation (dotted line tu Figure 1). ID establishing a staristical relationship, th€se points arc trot known with certainty, but vith
sorne eror, and the sampling intensity should reflect the variance.
Ext€nding lhis concept to a multi-dimensional s?ac€,it can be seen lhat
sampling should be mrried out acrossthe entirc rcsDome surface. with
DATA REQUIREMENTS FTORDEVELOPING GROWTH
a greater intensity at the extremities, a|rd in accordance with the vari
arce. Limited but reliable data at €ach exteme atrd at the mean arc
more usetrl than copious data clustercd about the mean (KrRKr-aND,
1985).
Such data should not otrly sample a range of stand and tree conditions, but mrlst also include remeasurements 1o emble detection of
chatrge,alrd mwt include a sufficient time period to averageany climatic variations, and to ensuIe that growth pattens are not obscured by
measuremetrterTor.
Valida.ion and Moniutring
To provide a convincing demonstration, data used to validate a model
should be excluded from its development atrd may comprise data dravm
ftom a different populatiotr. This ideal is not always attainable, and it is
common to partitiotr the available data into two sub6ets,one to be used
for development a|rd the other for validalion. It is inponant that the
subset used lor validatiotr should co aitr at least some data collected
over very long periods lo alow detection of pcsible subde bul cumulative erors in the model- Where tbe model is used to estimate some
optimurn stand conditiotr, it is advisable to obtaitr data to ascertain the
production from lhis estimated optimum condition.
Motritoring may be viewed as continuing validation of a model by
checking its operational predictions. It involves comparing projecled
and realised yields to identify any discreparcies- Such discreparcies
may arise due to changes in m2n'gement regime (especially logging
practices), decline in site productivity, inaccurate resource data, or corruptron of the validated gowth model. Monitoring the performanr€ of
models;s often neglede4 but is rccessary to eNure reliable forecasts.
Applicu,io,B Data
Applicarions data may comprise basic rcsource informatiotr us€d in
conjunctiotr with the gowth model to estimate future felds. Most operational resource invetrtory provides suitable data. Such data should
detail areas of each homogeneous foGst uoit, the speciescomposition,
stand condition, and the site productivity of each unit. MEAD (1982)
dissrssed ho*, to gawe the value of dala of this type- v Ncrny and
PRESToN(1989) gave an erample of the integrarion of inventory data
and a growth model to predict ields in tropical moist forest in oorth
Queensland-
254
DATA REQUREMENTS FOR DEYELOPING CIROWTII
Integra.ion of Dala
Resource data have been integrated since inventories began, but
rccently attempts have been made to defne principles and procedurcs
for efficient integration (e.g. LTJND,1986). Integratiotr simply implies
combining data obiained in different places, for different reasons, by
different agencies or at different times (vANcr-^y, 1990a). This may
nvolve combining regional inventories ro provide national or global
statrstics, finding correlarions between timber inventory and soil or
fauna survey data, or using growrh models derived from psp data to
enrapolate data from temporary inventory plots ro estimate sustainable
yields (e.9. vaNc'l.Ay and pREsroN,1989).
Integrated inventory designs enable data from different inventories
to be meaningfully combined- This does not necessarily mean that
everything in eyery inventort/ musl be meariured. On the contrary, it is
better to do a few thitrgs well than to do a lot inadequately (vaNcr-ay,
199tu). Horyever, in desigtring a PSPsystem, ir is necesary to t e aware
of the information requircments of other res€archers atrd other disciplines, and ro consider how these rcqufuements can be effrciently
ac€ommodatedin PSP design. It is nor necessarythat all these requircments be satislied immediately, but Ether that the design accommodates these needs so that they can be phased in as required and when
feasible.
Plol S€b.lion.d
Era.bltulm€ra
Idealy, PSPSshould sample the geognphic range of the forest, and
enmmpass a broad rarge of forest qTres,site qualiry and topography. A
broad range of stand basal area and rree sizes should be samDled for
cach lree species. Plots sbould include stands whicb have be;n subiected to a range of silvicultural management, itrcluding extrcmes of
loggiq and reatmerr.
Growth of forests varies from year to year, fluctuations can be
extreme, alrd mortality tends to be clustered in both time and sDace
(ruRns, 1983). Thus shofl time periods can give rise ro biased growrb
estimat€s-Reliable growrh models ftquirc ISPS with long measurement
histories atrd adequat€ geogaphical distributions. Some of the Dlots
should be left unlogged over long periods to ensure tbe m6t exactitra
validationcIJRTrsand HytNK (1984) reconmeDded that new PSp installations
should be established only as pan ofa carefully planned series desipred
DATA REOUIREMENTS FOR DEVEITOPINCGRO1VTH
255
to give rcasonable coverage of some defined mnge of sire, geography,
stand condition and treatment. The primary obiective should be to prG
vide dara for defining response $ffaces; lhus studies should involve
many locations wilh minimal replication at each location- Satisfa€tory
growth models are dependent upon the availability of high{uality data
ftom a wide range of stand conditions and treatments. Bolh "passive
monitoring" data (i.e. suFey data itr undisturbed forest) and 'treatment
rcsponse" data (i.e. ftom paired treatmetrt and conEol plots) from
designedexpedments are rccessary.
Subjective location of PSPSmay eive rise to bias in the databas€ifthe
environment c$not be completely quandfied. It is prcferable to lo.ate
PSPSrandomly within a defned stratum of intenesl. This strzrtum may
be delined on the basis of standing volume, species compocition, soil
tJrpeor any other objective meaff- Care needs to be laken when eslablishing plots at the forest edge or along roads and frrebreaks, to avoid
bias (RENNoUJ, 1978; Fowr-ER and ARvANms, 1979). BRUCE(lgn)
gave a number of re€soDswhy research plots may give higher yields
than managed foresrs-Although some bias may be due to plot demarcation and management, much of this bias aris€s ftorn subjective location
of plots. The need for random location bas€d on a thoughtful stratifica
tion camot be overemphasizedTherc is some evidence that gains in precision c{n be achieved by
sampling more larg€ trees (e.& GERTNER,
198/), and it may be desirable
to establish some plots around subjectively seleded large trees. Such
subjective selection of plots may introduce bias, but rhis may be an
acceptable trade-off to reduce the vrriance associatedwith growth prediclions from large trees- To minimiz€ bias, these plots should constitute a small proportion of the total, and should be s€lectedwilhitr strata
bas€dotr site quality ald stand density (e.g. stand basal area).
Plots which are intended to be left utrmamged, for example to a|low
expression of density-dependent mortality and natual basal area in
dense stands, should be clearly marked and excluded ftom any logging
operalions. Such plots which receive s{recialmanagement (no logging or
more intensive treatment), should have adequate buffefi to eliminate
edge effects. The appropdale size of the buffer d€peads on tree size,
but should be wider than the mature tree height. Other plots intended
to receive routitre management, should be marked in such a way as to
be invisible to for€st workeG so as to ensure representative Featment.
sYNNc[r (lg79) argued rhat a plot should be "difficult to recognize for
those who do nor ktrow where it is, and easy to recognise for those who
do and are lookitrs for it"- Plots must have unambicuous addresses
2S
DATA REOUIREMENTS FOR DEVELOPING GROWIH
(DAwKrNsand FGLD, 1978). In some areas, plots mav suffer excessive
tranpling through bigh visitatioo. D^wxtrls;fld nEr-D(lq8) marked
plol locations clearly, but 50 m away from the plot, atrd used invisible
sleel markers at all four plot comel'
E{.rimeols
Although exf'erime al data are enensively used in developing plantation gro*'th models, it is morc coDmon to develop growth models for
mixed fmests solely from passive modtoring data. Although logging
and teatment of these forest standr is likely ro influence stard density,
other unlnown factors may also detemine stand density atrd compcitiotr in these plots.
Therc is a very real danger that attempts to desc.ribethe behaviou of
the stand as a Iirnction of stard density, for instance, wil be coDfounded
by the effects of site, pest and diseaseoccurence, and past historv. Box
(l%6) wamed lhat to 6nd out what happensto a system whe; you
interfere with it, you have to interfere with it, not josr passively observe
it. SNEDECoR
and crlcHRAN (19€0:356)discusseda study in which a survey revealed rhe unexpected rcsult rhal the apptication of farmyard
manure reduced the yield of potatoes by haff a toDne per hectarc. ID
contast, in controlle4 ratrdmi"ed experiments, matrure fucreasedthe
ield by rhree to six tonnes per hectare. The difference may be due to
the fact that tho6€ who had manure were livestoc& farmers wirh little
mtercst in growitrg potatoes, and those who were moet filful at growing potatoes had no manure. Calr we be sure that a similar oroblen in
our PSP data is trot rroubling our allempts to develop growth models
(e-g. statrd densiry and sire quality htencriotr)?
Passive monitoring data may indicate greatest growrh on the besr
sites with high standi4 basal area\ and little growth on poorer sites
wirh little basal area. A growth model constnrcled ftom such data could
suggest that greater increments acque in stands with greater competidon, as the effects of site quality and sraad deNity would be cotrfound_
ed. Thus a model constnrcled from such passive monitoring data yould
predict a rcduction in diameter indements following thinning, whilst a
model from experimental data (e.g. thinning studies) would show an
increasein diameter incremetrt.
CoDsideratiotr should be given ro establishing a series of plob itr
homogetreous tracts of eactr forcst type_ Some should be left at marimum stocljng to allow expression of densitydependenr mortality alrd
natural basal area, some should be logged and trcated as a managed
DATA REQUIREMENTS FOR DEYELOPING GROWTII
stand, and others should
open-gtown developme
exlneme treatmenis nay
essential to define pnperly
be heavily thiDred to altow expression of
and regeneratiotr- lt do€s trot matter th.t
never be applied itr practice; they remain
the r€sponsesurface for growth models.
Nmbcr of PlDls
The trumber of plots is usua y determircd by available resourcesThere is little point €stablishing more plots thaa catr be maintained. lt is
better to have few plots Foviditrg rcliable data, than many plots with
itradequate managemenl The number of plots rvill also be detemined
by the variability of the forest estate, and the treed to sample the ful
rarge of forest cotrditions. syNNoTr (1979) re€ommended 50 to 1m
rardomly located plots for each forest qrpe.
A database comprising a few plots each with many rcmea:tunements
violates statistical assumptiotrs of independence, and may requile special analysis techdques (wEsr €t dt 1 4, 1986). This violation becones
signilrcaat when the number of remeasuresis large relative to the number of plots. An altemative is to us€ partial replacement, abandoning
plots alter s€veral remeasures and establishing new ones (TENNENT
l 8). However, some plots must be rctained for long periods with
many rcmeasrries to allow convincing validarion.
Size rd Si4rc of Plors
A general guide to the choice of plot shape is to minimize the plot edge
to alea ratio, and the number of cnners. This leads to lhe choice of
poi samples, or circular, triangular, or four-sided plots according ro
the emphasis attached to comers and edges.Triangular plots are rarely
used, perhap6 because of the high edge to area ratio, and fourdded
plots are generally rcctangular (or square) to facilitate relocation of
comers and boundades.
Point samples (BEERSand Mru-ER,1961) have an advantage in being
defined by a single point and a basal arca fador (BAD, but they are
inconvenient when dealing with rccruitme , atrd qeate difficulties for
the developoent of distanceiependent models. Circular plots are also
defned by a stugle point and a radius, but the plot bolmdary ber.mes
more difficull to defire as the plot becomes large, as urlike polygonal
plots, sight lines cannot be established alory boundaries- As these plots
are defrcd by a siryle mafter (the ce rc), they may be morc difficult
lo relocate if the marker is damaged or removed. Rectangular plots are
258
DATA REQURET/ENIS FOR DEVELOPING GROV,ITH
more versatile. Plots marked by fow comer pegs may be less likely ro
be lo$ rhan cicular plots marked by only one f,eg. However, a morc
important rcasotr fm the choice of rectangul,ar plots is their straight
edges, few comers, and cotrvetrience. Square plots offer a theorctical
advantage of minimum edge to area ratioldeally, the plot size should be sufficicntiy small for the plot to bc
homogeoeous.al least wilh res?ect lo fore$ typ€ and site productivity,
and sufficiently large to provide a reprcsentative sample of rhe forcst
stand. If a distance depetrdent model is contemplated, rhe plot should
be suffrciently large to auo*' estimates of comperidon to be determined
for several trees on rhe plot. This leads to tte cooclusion thar larger
plols offer greatcr flexibility, and rhe authois exFrience suggeststhat
plols up to one hectare should be consider€d for growth modelling
studies in the lropical moist forest. Ecological studies may warrant even
larger plots.
c-uRrs atrd popE (1972) suggestedthat small plots may result in erratic estimates of stand andbutes trecauseof within stand clumpin& and
rccommended the us€ of large plots. payaNDEH (194) examined the
distribution of rrees within norrh American forests, and reported that
ur ike plantatiorls. natural forests mrely exhibit a regular spacitrg, bur
tend to have a random (in hardwoods) or slightty ctustered (conifers)
spalial distdbutiotr. HANN(1980) used plors varying in size f.om 03 ro
0.5 hectaresitr even aged stands, and 0.8 to 1.2 hectares in rmeven-aqed
slands. Plols smaller tbatr 0.8 hectares were available for the unevinaged stands, but were discarded by rrANN (1980) to avoid p.oblems
arisitrg from within stand clumping. syNNorT (1ql9) recommended
square plots one hectare in area, subdivided into 2j subDlots-wEsT€rdl
(1 8) touDd that 0.5 ha was tie praclicatupper limi( in tropicd noisr
for€sl for homogeneous plors, as physicrt and floristic discontinuiries
hampered the establishment of larger plots.
Unless circular plors or point samplesarc adopte4 tbe odentation of
lhe plots necds to be considered- This may be inconsequential for
square plots, but may tle significjnt wirh elongared rectangular plots.
Three possibilities exist. The plots may be randomly oriented, may be
oriented according lo the cardinal direction (e.g.long axis nonh-south),
m may be oriented according to topography. In view of the need for
plots which are homogeneous with regard to site productivity, the last
of these is likely to be preferable. It is suggestedthat wherever possible,
plots should bc oriented with their long axis perpendicular to the slopc,
or any other perceived gradient of sire producrivity to minimize within
plot variation- In contrast, for temporary invenrory plors, it is desirable
DATA REOUIREMENTS FCIR DEVETTOPING GROWTII
x9
to mfiimize urithin plot variatiotr so as to reduce the between plot vari
ation and thus the sampling error. PSPSfor growth model development
have a different goal, and thus within plor variation should tre
minimized.
Mesuement
Pr,occdorcs
Providing that PSPScontinue to provide useful information, existing
standards and p.ac€dure should be maintained to ensure udformityThe continuity of standards is caiticaf- However, when new plots are
esrablished or procedures for existing plots are revised, the following
requireme s should be accommodated (wHrrMoRE, 1989):
1. Divide each plot itrto subplots of maximum size 20 x 20 m. Mark
comeN permanently. Bdghtly coloured plastic pipe makes good
rot resistant pick€ts" but where elephants or primates are lilely to
d€stroy these, trencftes may have to be dug instead- Since logging
can completely d€stroy aI plot ma*s, buded steel mirkels may bc
used in co4utrctiotr witfi these2. Trces must be numbered ald permanendy marted. Never use the
same number rwice. Do not rc-use the number of a rree u/hich dics
bur give ingrowth trees trew numbers Use an embocs€daluminium
tag fastened with an aluminium nail half ddven home (to a|low for
growth). These nails need renewing about otrc€ every frve yearsNails should be below rhe merclantable sectio4 and should always
be on thc same side of the tree (e-g- northem or uphill) for easeof
relocation.
3- Make a map to show the position of every trce to the nearcst 1 m or
better. Work one sutrplot at a time- A maximum subplot size of Z) x
20 m makes mapping c€sy. Without a map confr$ions always oc.ur
b€€ausc of death. ingrowth or lGt number tags. Place measuring
tapes along two adiacent sides of the subplot, atrd estimate and map
coordinates of each tree while in the forest. REEDet al (1989) sWgestedan altemative using pentaFisms.
4. Specily the midmum grrth for the smallest tree to be induded- This
becomes important later when ingro{rths occur. For the sake of
clarity, note when surveys ofiginaly using imperial measure have
b€en cotrverted lo metric measure.For example, 12 itrch€s gifi = 9.7
c-Indiametet but rhe metric minimum is commo y 10 cm.
5. Since girrh can tre measured suffrciently accurately only if always
dotre at the same place, it is besr to paint a ring on each tlee f ith one
slraight edge- Painted rings need renewing every 3-4 years. In wet
2O
6.
?-
8.
9.
DATA R.EQUIREMENTSFOR DEVELOPING GROWIII
weather emulsion pai is easier to apply thatr oil-based paint. Some
paints (especially oil-based paints) may cause abnomal bark shed
and callus groMh (e.& Eucalyprus t racda.a a'I|d Ftindersia
pimenEliana in Queensland), so paints should be tested before
general use. A less accurate altemative is always to measure ginh a
fixed distance above or below the number-tag nail ff a nail is used as
the reference point the ginh should be measwed a standard distance
away from it (e.g. 2(150 cxn) becausesome speciesdevelop swollen
callus tissue aromd the trail itselt Make sure the girth measurened
point is well above all buttress€q for these may grow upwardsThe measudtrg tape must be of metal or fibr€glass because clorh
lapes stretch wten wet. All measudtrg tapes should periodicaly be
checked against a reliable standard.
Since errors occur itr recording girths, measure every tree twice inde
pendently. ff the s€cond reading difIe6, mate a third one. l_oose
bark (except on species with corky or flaky bark), epiphytes and
dimbers must b€ removed from the line of measureme . Tbe booker should always repcat the measurement for the measurer to confirm.
lf, despite pre{autions at initial sun ey, a bultre*s grows up into the
line of measurement, the line (atrd prcferabty the reference oail if
any) will need to be moved frfiher up the bole although this is
clearly not satisfactory, becauseit alteE the girth measurement base
Ior that tree. Measure at both the old and new heigh6, and rerord
that a change in measurement height has occurred
For trees with more than one trunk at the height of measurement
decide the pulpose of suwey and, if necessary,crcate s€parate tree
number and ginh recods.
w}.tto
Me$ule
Plot location should be described and its coordinates Oadrude and
longilude or other grid coordiDates of the south-west comer, correct lo
the nearest secotrd or better) and mientation (dircction of long ads)
should be recorded Topographic featules (altitude, dope, aspect, distarce to ddge), climate (rainfal amount and distribution). indicator
platrrs. and soii physical characteristics (depth. texture) sboujd also be
recorded- Unifomity of site should also be ass€ssed.These variables
treed only be recorded ar plot establishment, and ar occasioDal
Factors which may causefluctuations in the observed go{rth include
DATA REQUIREMENTS FOR DEVELOPING GROWTII
drought, heavy seed croF, p€st populations, dis€aseoutbreaks atrd fire
damage. Such information may assist the validation of modcls and
guide rhe hterpretation of oudieB in regression analysis. Thus thc
occurrence of such events should be rccorded on ihe plot measue
record.
sl Norr (199) observed that it is impo6sible to measure a|l sizesand
species, and is irelevant to mqrsure all seedlitrgs and saplings since
mo6t will die- He recorrmended that a[ tre€s (of a[ species including
uselessstems) exceeding 10 cm d.b.h. (diameter breast high over bark)
be measured.This is necessaryto enable estimates of stand basal area.
an important variable in pledic-titrg tree growrh and stand dynamics.
Limits as sma|l as 3 cm have been adopted itr natural conifercus forests
(e.9. ARNEY,1985; vANcLAy, 1988), but may be impractical in tropical
moist forests. Reliable ingrowth data require thal the measurement
limil is lcss than the desired ingrowth size (e.9. for recruiment at 10 c-rn
d.b.h., measure all trees excecding 7 crn). Subplots within the main plot
may be used to record data on slems smaller thatr this measurement
limit.
Tree diameter and status (alive/deadffeled and cause) should be
recorded at every measu.c. Every Uee present at the previous measurc
should be accounted for. Height, crov/n panmetets atrd estimated
defect should tte recorded at establishment, and periodicaly at remeasurcments.Diameters should be measured at 1.3 metres (from the
ground on the uphi side of the tree) or above buttress, and should bc
measuled perpendicular to the axis of the tree (cAnRoN, 1968:17).The
vantage point for height measurement should be carefully chosen ro
alllow good visibility and a sighthg angle of around 45 degrees (RoMEsBURGand MoHAr, 1990). Natural mortality should be clearly distin
guished from loggitrg, treatment and other removals. Trees which
apFar lo6t should be ma*ed as such, and should not be attributed to
death udess there is eddence to support thisThe measure crew must check doubtfi items and make swe that the
cufient measurementsare correct. They should record that such checks
have been made. Decrements and olhet anomalies itr the data should
not be altered once the measurc cr€w h?s departed the plot. Although
these data may at times look unrealistic, "massaging" the darabas€ 10
alter these data may cause significant lo6s of hformatiotr. At besr, this
practice may result itr unredisticaly low estimates of standard eror
associatedwith any functions developed, At worst, it may exclude the
oppo.trmity to iDvestigate some originaly utrsuspected event (e-g.
weather patterns and climate change) or unfores€eDtopic- There may
2M
DATA REQUIREMENTS FOR DEVEI-OPING GROWTII
ttc good rea$ns to "massage" or omir data from some specific anatysis,
but the maitr databas€should never be attered.
The quality and co6t of data available for ana\rsis may be improved
sub6tantially through the use of electronic data recorden (BELrz and
KEIIH, l9B0; FNs atrd RUsr, 1987; LEECHer a/. 1989;wooD, 1990). Etectronic hand-held devices enable basic checks of the itrDut dala to elimioalesimpleerrors (e.g.lranspGition) at lheir source.instantvalidation,
comparisons with previons measwe, and sDeedy transfer of data to
databas€.They can ensurc thaa the measurer do€s not progrcss to the
ncxt trce oI plot until all trecessaryvariables have b€en recordedIn summary, the following variables shoutd be measured:
. At the idtial enuneration (and occasionaly rcmeariured :rs new
technology improves the precision that can be attained):
plot location, dimensions atrd area,
- tree speciesand coorditrates.
- ropography (altitude, aspect,sloF, relative position on slope),
- forest type,
- floristics (a|l spccieson plot and relative abundance),
- physical soil characteristics (depth, texrure. colour, parcnt
material), and atr
- itrdic{tiotr of the uniformity of the site;
. At the frrst measurc, immediately after logging, and pedodica y
(e-9. cvery semnd or third measure):
- sulficient t ee heights fm the determination of site
Foductivity
(or data necessary for altemalive estimates of site Droducriviry),
- merchantable heights and defect assessmentsof all srems
(including noFcommercial species,as utilizatiotr standards may
change wirh time),
- caown characteristics (domitrarce, size, density, pcitio4 elc.),
and
- basal area counts at each tree (utrnecessaryif tree coordinates
are recorded);
. At every measure, ass€ssall stems (includiDg trotr-commercial,
every stem from previous measue must be reconciled) for:
diameter (over bark, brcast high or above buttess) and height
m measure poml'
- validity (to indicate defecls at measure
lnint and anonirlous
but correct incremetrts),
- sratus (aliye, dead, logged, treated), a'!d
- tree coordinates (recruits only);
DATA REQUIREMENIS
FOR DEVELOPING CROWTH
. As necessary,rccord the occrrrence of:
- logging, treatment and other management activities, and rhe
Fescription used,
- scars and othcr damage which may affect measurements ot
growrh,
meteorological phenomena (drought. flood,etc.),
mast years (heavy secd crops),
- pests, diseases,fire, or
- any other a:pect which may affecl growth.
Wheo to Rem€s[I€
Theorelically. the frequency with which plots should be remeasured is
inlluenced by two factors: ease of re-locating and identirying trees, and
the rate of growth oi change relative to the measurement eror. Remeasurements mrxit be sufficiently frequent to emure that the locatiotr of
the plot and/or identities of stems are not losq in some forest typ€s this
may be as frcquent as every two years, Conversely. co6t effrcietrcies
demand that rcmeasurements shoutd trot be unn€c€ssarily frcquent.
The incaement of stems should be substantially geaaer than the error
associated with the measuremetrt if the remeasue is to be useful:
shorter inrervals lead to excessive variance in aegession functions.
ARNEY'S(1985) model of temperate coniferous fotest used only dara
from measure interyals of 4 years or more, and vaNcr-Ay's (1991b)
growth model for tropical moist forest used data spanning approximately 5 years. In the tropics! an interval of two to five years may be
apFopriate for thc remeaswement of PSPSAs aDnual ircrements are genenlly r€qufued, measurements should
be raken on the anniversary of the prcvious measure whercver possible, especialy for annual or bienDial measurements.Always try to mea
swe during the sameseason,as trees in the seasoral tropics may exhibit
marked seasonal lluctuations in gfuth due to changes in rylem water
lension (UEBERMAN,1982). Remeasurements should avoid periods of
rapid change (e.9. bark shed, rapid groMh), should aim to measure
during dolmalrcy wherc it occurs, and should try to replicate enyirmmenlal cotrditions at the previous measure (e-g- avoid remeasudng
immediately after raitr if the previous measule was after a long dry
spell)- Remeasurementsshould also be taken at the time of (preferably
both before and aft€r) logging (or silvicuftural treatment), and as soon
as possible after wildfre, cyclotre or other distubance. Ktrowledge of
impending logging or treatment is requircd. particularly if plot bound-
2g
DATA REQUIREMEITITSFOR DEVELOPING GROWTII
aries are corcealed, so that the necessarymeasurescan be arlanged- It
may be desirable to mark plots with buried steel pegs prior to logging to
insure against the lGs of plot markers.
Admirturrrdoo
Adminisrrarive and officc procedures associatedwith maintaining psp
measurement rccords are often neglected and can be a major causeof
lo6s of informadon. Aspects to consider include the d€sign of field
forms, copying alrd storing the completed forns, and transferring rbe
data to computer.
Folms should be desigtred specificaly for the pupose of psp meast[€ment, and every column should be clearly marked with fie data to
be recorded The form should have no tedundant fields, artrd stafr
should become accutitomed to completing every field. Every folm
should indicare plot identity, date, page trumber (e.& page I of 3) and
the name of the assessingofiicer, and this should be completed belorc
deparling lbe plot. Fo]ms should be compleled using i sbarp dark
pencil (e.g. a 0.5 mm clutch pencil with 2B l€ad), and no alterations
should be made after departing tbe plot- Forms should not be transcaibe4 as this inviles tEnsposition and other erro$. Alry duplicate
copies required should be photocopied (where photocopiers are nor
available, tmnscriptions should be clearly marked as such, and should
be carefuly checked by a third pelson). Forms should be fled s€curcly
and unmbiguously. preferably with one plot per folder, with the forms
arranged in chronological order- It is a wise precaution io have a copy
of the data stored in a remote location (e.g. districr and head of6ce)Details from th€ previous measure should be aiailable during plot
remeasures.This can be achieved by pdnting rcmessure forms by com
puter and includitrg details of previous measuremetrts. Allematively,
thts can be achieved by photocopying parts of th€ previous measure
otrto the new measure sheer, or by downloading previous dala onto an
electronic data recorder.
As data entry derects many illegible characteq erroB and omissiotrs,
data should be entered onto computer as soon as Eacticable after collectioD. whilst the Deasure cre\L still rec€ll some details o[ the olor.
Dala should be verified (i.e. re€ntered and compared) by a different
operator to detect any erroB itr data entry. Electronic data recoders
offer several advaDtages,including such validation at the point of data
collection, where checks can be made. Validation programs should
check the data for fmher errors alid omissioDs, and $rmary repons
DATA REQUIREMENTS FOR DEVELOPING CROWTH
should be produc€d for the information of assessingoffrcers a|rd forest
managers. Copies of the data should be made and siored itr secure
remote locations.
Obvious errols and omissions itr the computer data file should be
amended, but the t€mptatiotr to "massage" the data so that it all lmks
consistetrt should be avoided- The data on the comDuter must accurately reflecl lhe lield neasurements. An atromalous measuremeot may oT
may not be due to measurement erlor in the field, and the database
administratois adjustmenr rcmains a guess rather than a fact. Any
alteration inserted by the database admfuistratm sbould be clearly indicated as such (in the 'validity" field), atrd these allerations should be
kept to a mitrimum. It is much saf€r to let users edit their own copy of
the data as necessaryfor: theia own analyses, than to aher the master
copvAn effective system requires a considerable commilmetrt itr staff and
resourcesto itritiate and maintaitr the PSPS,and this conmitment must
be on-goitrg- Sufiicient rcsources and trafued staff are €ssetrtial, or the
quality and utility oI data will deteriorare.
Rerypr|isd
Periodic reappraisal of data collection policy and practice is trec€ssary
to emurc that the data being colected are futfitling current and perceived future needs. 'The quality of data is of extreme impofiance.
Compelent, weu Dotivaled and supervised field crews are needed lor
measurement, and the control of all research plots should b€ vested in a
single high level authority" (DEADM N, 1979). Reaprpraisalsshould
addresss?ecificslly two questions conceming data quality: .Is the specified qualiq/ adequate for current atrd perceived future needs?" and'Is
the s?ecfied quality being attained?"
The need to sample exremes of forest cotrdition has already been
discussed-However, the concept of what is extreme chang€sover time.
Thus it is necessaryto €onsider if the extremes being sampled are suffrcient, and if trot, new plots should be established. The co6t of data collectiotr a|rd hardling is high, so plots should be abatrdoned when tro
Ionger us€fuI. The decision to terminate a plot with a lotrg mcasurement
history should nor be taketr lightly, as rhese plots will be most valuabte
for validation. However, it is inevitable rhat natural (aad human) pertuftatioN (e-g. lightning srrike, landslip, iffect or tungar aMck) will
extensively modily some plots. Sucb plots may no lotrger provide us€fuI
tree growth data, but may provide useful rccruitment atrd olher
26
DATA REQUIREMENTS FOR DEVELOPING GROWTIT
ecological data.
Perceptiotr of future requ ements wiU change over time, so the data
colleclion policy should be periodically amended to conform with rhese
pelceived needs. These ametrdments may require the termination of
some plots. establishmetrt of othefi, addition of new variables to be
measuaed,or the deletion of othels. However, changesin measuremcnt
procedwes (esp€cially deletion of variables) should not be undertakeo
lightly; stable, consistent measurement procedures are essential for
gro*th research. There sbould be substanrial and contitrued resistance
to changing the plor measurcment system (Mcoutrl_^N. 1984).
Permanent sample plots should satisfy the data requiremcnts for
growrh models ten and more years hence. In order to provide for this
next generation of growth models, it is approFiate to appraise caitically
the ulility of the present PSPS.and to establish new plots specificily
dire{ted at collecting data for such luture growth models. Such a series
of "elite" plots should sample the range of forest conditions (and
include thinning studies). but should be established in limited trumbcrs
so thar appmpdate care and attention can be given to detail and accuracy. The emphasiswirh these plots must be quality, not quantiry.
Pmblems wiah Ertulirg D{tr
The gleatest problem facing many agencies is that the data necessary
for growth model developmenl are not available. Plots may not have
been established, may have been neglecled or abandoned. and measure
recods may have trcetr lost. As thcre is little rhat can be done to salvage such lost data. it is imperative that care and aflention are devoted
to exisling PSPS and their measure records. Oth€r problems which
s€verelyrcsl.icl tbe utiliry of dala hclude unreliablt measurements,
cha.ges to procedures, and misraken or undetemined species
identities.
Data for the developmeDr of growth models may be de.ived from
PSPSwhich were esrablishedfor purpoGesother than growth modelling_
Such plots may sample a restricted range of stand conditions, omitting
very poor and exceptionally produclive sites, and avoiding extremes of
stocking. Thus thes€ data may nor Fovide an efficietrt means to esti
mate responsesurfacesby regression equations to predicl the behaviour
of the forcst under various conditions. Records concemine the establishmenlof many PSPSare sketchyor unavailabte.and tbe reasonsfor
the placement of these plots are frequently not clear. Some plots may
have been randomly or haphazrrdly locared in defined stata, but
DATA REQUIREMENTS FOR DEVELTOPTNG
GROWTH
257
others may have becn subjectively located. Alry departure from a strarified mndom approach in establishing these plots requires some soulsearchingon the pan of the modellet in considedng the lrocsible effects
of personal bias in choosing plot locations, particularly where site
quality camot be rcliably quanrilied.
PSPSshould receive representative managemcnt (loggin& treatment,
etc.), except for experimcnts which sample extreme srand conditiotrs.
This may bc assuredwhere plots are marked with subtenanean or other
invisible markers, but intentional or uninteDrional bias itr loggitrg, rreatment and other management may become sigdfrcant wben the plot is
visible, Such managementbias may nor be a problem where it is reflected in the stand structure (e-g- removal of rrees), but the effects of difrerences in logging damage and climber cutting may be more insidious.
However. such differences are litely to be greater fm research plots
than for passive monitoring plots. Differential management should tle
reflected in stand structure, but tests of some PSR esrablished for 50
ycars in Ouee6land failed to detect differeNes betweetr PSps and
temporary plots establishedadjacent to them.
Growth in topical forests is often highly variabl€, and rhis vadaliotr
may be andbuted, at least in part, ro factors such as wearher, seed
years, pest populations, diseaseoutbrcaks, 6rc damage, etc. Such information may be useftrl for interpretiDg apparently anomalous dara
detected during analyses, but is infrequently recorded and rarely
transfered to the database. Evaluation of site productivity is a major
obctade in predicting ields from tropical moist forest, and development of a method for reliable site evaluation, aDd acquisition of rhe
necessarydata, should be a pdorityHo{r serious are these deficiencies so often presetrt in dara available
for growth modelling? It is impocsible to predicr what dif6culties these
atrd othe. deficiercies may intloduce, until the data are acrually used in
eamest. No data sel catr be perfect, but many will be fomd to co ain
deficiercies ihat will frustrate future a[alyses. Ahhough plot temeasurement may aplrar to the measure cTew to be uDrewarding, co ection and management of PSP data is vital to for infomed lorcst EanagementCord|rior
PSPSprovide the basis for growth modening, yield predictior and sustained yield managemeDt, a|rd the reliability of the data is crucial to
these and maDy other aspectsof forest rnanagemenl. To obtain reliable
264
DATA REQUIREME}NS FOR DEVEI'PING
GROWTH
data, it
-
is necessaryto ens[e that
standards arc consistent,
a wide range of stand and sitc conditions are sampled,
both passivemonitoring and experimental plots are used,
trees are numbered, marked and mapped,
- rcmsFurement frequency etrables plot relocation and glowth
greatet than measurement error. and
- measurement rccords are mambiguous and s€crrre.
Acl(trowledgencrls
Part of lhis work was researched urhile the author was a visiling
academic at the Oxford Forestry Institutc. The author wishes to thank
the OFI and the Queensland Forest Service for Foviding this opportu,
nity. Philip Adfard of the OFI, Niel Henry and Johr Rudder of the
QFS, Jerry Leech of the South Australian Woods and Forests Departmenl, and Geoff Wood of the Austnlian National Universitv Drovided
helpful comment on the draft manuscripr.
Reft|€occa
ADLARD, P. G. (1990), Easing the pathway between field and fle. BrrL
RectLAgrorL Genbloux 8. 2.
ARrEy, J. D. (1985). A modeling stmtegy fm the growrh proi€ction of
managedstands. [email protected]. Fdr Res-l5,5ll-518.
BEEFS,T. W. and MtrrER, C. I. (1964). Point sampling: research results.
theory and applicatiotr. Purdue Univ.. Research Bull. No- 786BELxz, R- C. and KErrH, G. C (r98O). Electronic t€chnologt' speeds
f,\esa sitrvey. Soutll -1.Appl For.4,3, t15-rr8.
Box, G. E- P. (1966). Use and abuse of rcgession. Technometrics8 4,
625-5D.
BRUG, D. (lqt). Yield differences between rcsearch plots and man
agedforcsts. l. For. 15, 1, l+17.
CARRoN,L T. (1 8)- An Outline of Forest Mcnsuration {,ith Special
Refercnce to Australia. A.N.U, Press,Crnberra.
CuRTrs,R. O- (1S83). Procedues for esrablishing and mainraining permanent plots for silvicullwal and yield r€s€arch. USDA For. Serv. Getr.
Teah. Rep. PNW-155.
CitRTrE R. O- and HyrNK, D. M- (1984). Data for growth a|rd ield
models. In D. D. van Hoose. and N. van Pelt (Compilers), Proceedings
- Growth and yield and other metrsurational tricks: A regional
DATA REQUIREMENIS FORDEVELOPING GROWTIT
2fi
technical coDfererce. Loga4 Urah, Nov e7 1984. USDA For. Serv.
Gen. Te€h. Rep. INT-193.
CuRTIs,R. O. and PorE, R. B- (f y/2). Some considerations in desier of
groMh studies and associaled intentories of the Westem Nallonal
Forests. Draft of Intemal Report, USDA For. S€ra. pac. Northwest.
For. and Range Exp. Sta-, Portland, Oreg.
DAv(Drs, H. C. atrd FTELD,D- R. B. (19?8). A long term suveiltance
system for British woodland vegetation. Corirnotw For- Insftt., Occas.
Pap. 1., Ort.nd.
DEcDM N, M. W. (recorder), (1979). Session 2 otr crowrh atrd yield
Prediction. In D. A. E iott (ed.) Technicat Working croup Meeting on
Mensuratiotr for Management Planning of Exotic For€st plantations"
2{ Oct 198, N.Z For. Sefl. For- Res. IDstit-, F.RI. St/mp. No. 2()FrNs,L. and Rusr, M. (lgfD. Crmparative c6t of wing an electronic
dala reco.deratrd field forw.Wesr.l. AppL For. Z. t,28iO.
FoqIlR, G. W. and ARvaNms, L, G. (lqrg). Aspects of statistical bias
due to lhe forest edge: fixcd area circular plots. CurL J_ Fo.. Res.g,
383-389.
GERaNER,G. (198?). A Focedure to evaluate sampling schemes for
improving already calibrated models- For€rr Sci 3, J,632-643.
HA 1{, D. W. (1980). Development and evaluation of an even- and
rmeven-aged ponderosa pine/Arizone fescue stard simulator. USDA
For. Serv. Res. Pap. INT-267.
KTRXL ND, A- (1985). Data needs and .ecetrt developmetrts in rcsouce
managemetrt and planning. Theme p'aper, Session4C, Making the besr
use of investmetrt resources. 12tt C-oDmonwealth Forcstw CoDferenceVicroria BC, CrnadaKRAMER,P- J. and KozrowsK, T. T. (1Cr9). physiotogt of Woody
Prn s- Academic Press,New yo*.
LEEcrr, J. W., SurroN, M. W. and ARorER, G. R. (1989). Recordiry
field measurementsotr Hwky Hunter miqocomputeG . Aust For. 52,i,
6&73.
LTEBERMAN,D. (1983). Seasonality and phenology b a dry tropical
foresr in Ghana. J- Ecol
,79t-8m.
LITND, H. c- 0986). A primer on integrating resouce inventodes.
USDA For. S€rv. cer Tech. ReD. WO-49.
MCQUILLAN,A. G. (1984). crowth aDd yield modelting: {'here do we
go to fronr here? ln D. D. vaIr Hoo6er and N. vatr peh (compilers) pro
ceedings- Growth and yield and other mensurarional tricks: A recional
lechnical conterence. Logatr. Urah. No. G7 tS84. USDA For-"Serv.
Gen. Tecl. Rep. INT-193.
ZN
DATA REQUIR.EMENTSFOR DEVELOPING GROWTH
MARTAUX,A. (1981). Past efforts in measuring age and amual gros'th
in tropical trees. ln F. H. Bormarm and G- BerlF @e) Age a
Growth RoE of Tropic. Trees: New Direcnons Ior n€r€drcrl Proc. of
Workhop on Age and Growth rate Determinalion for Tropical Trees,
held at llarvard Forest, Petersham"Ma., 1-3 AFil 1980.Yale Univ. SchFor- and Elv- Studies. Bultetin 94.
MArrs, L G-, HETHERTNGToN,
J. C- and Kass B, J. Y. (1984). Sampling
with panial replaceme - alite'attlle rciew. Cornnmnw- FoL Rev.63,
3,194:2M.
ME^D, D. A. (1982). Evaluating the quality of dara used for resource
plannilg- Itr T- C-ocorar and W- Heii (eds) koc. of Working Party
53.&.01 Planning and Control of Forest Operations, XVII rumO
World Crngress G17 Sept 1981, Kyoto, Japan, l-ife Sci. and Ag. Exp.
Sta-,Univ of Maine ar Orona, Misc. Rep.264.
PAYANDETL
B. (1974). Spatial pattem of trees in the major fore.st types
of northem Odtario. CdrLl. FoL Re\ 1, 1.8-14.
REED,D. D., l-Ecnry, H. O. atrd BuRToN, A. J. (1989). A simple procedure for mapping tree locations iD forest statrds. Forest ScL S,3,
(s74z
RENNoII: K. (198). Top height its definition and estimalion. Cornnmn*l- For Rev. Sl.3-215:219.
RoMESBURG,
H. C. and MoHAI, P. (1990). Improving the pre€isiotr of
tree height estimates.CrrL L For. Res.m, D-4G1250.
SCHREUDER,
H. T., B^Ny RD,S. G. and BRrNx, G. E. (fger). C-omparison of three sampling methods in estimating stard lMramete6 for a
tropical foresl Fo.. gcol Matmge.2l,1l9-1n.
SNEDECoR,
G. w. and CocHR^N, w. G- (1980)- St4tistical Methodt,Th
ed- Iowa Udv- Pr€ss.
STRAND,L. (1Cl0). Regession problems in yield lable constructiotr.
Marld- No^ke Skoglo,soksv. tt, s,495-505.
SyNNor'r, T. J. (1979). A manual of Frmanetrt plot procedures for
tropicaf rain forcsts. Tropical Forcstt Papers 14. Commonwealth
For€stry Institute, fford.
TENNENT,R- B. (1988). A New Zraland data collection procedure for
growrh modelitrg- In J. W. Irech, R. E. McMurtde, P. W. West, R. D.
Spencer and B- M. Spencer (eds) Modellitrg Trees, Stands and For€sts.
Proceedings of a Workshop itr Augost 1985 al rhe University of Melboume. School of Forcstry, University of Melboune, Bu etin No. 5,
i2-tm_
VANcr-Ay, J. L (1988). A stand gro*th model for crpre.rspine. ln J- W.
Irech, R. E. McMurtrie, P. W. West, R. D. Spencer a|rd B. M. Spencer
DATAREQUIREMEITITSFORDEVEI.OPINGGROWTII
NI
(edt Modeling Tre€\ Srands atrd Forests. proceedings of a Workhop
in August 1985 at the UDivcrsity of Melboume- School of Foresfiv.
Univenity of Melboume, Bullctin No. 5,3tG322
VAItIcr,Ay,J. K. (190a). Integrated rcsource monitorinA: an Australian
penpective ot crrrent lreods and future nccds. In bbbat Natural
Resowce Monitoring and Ass€ssments:preparing for the 21st century,
Proceedings of the intemational conference and workshoo- Z-30
September1 9, Venice,Iraly, d5Go58.
V^Ncr-Ay, J. K. (190b). Effecrs of selection loggin8 on rainforest producliviry. Aust. For. 5t 3,21D214.
VANcr-Ay, J. K. (1991a). Research Deeds for sustainable forest
resources.In N. coodberg, M- BoDell and D. Berlz ken (eds) Tropical
Rainforest Research in Aust alia: koceedings of a workshop heid in
Tovrwille, Australia, +6 May, 19m. ltrsr ute of Tropical Rainforest
Studies, Tolvnsville, Australi4 133-143.
VaNcI-Ay, J- K. (1991b). Aggregaring fiee speciesto develop diamerer
mcrement equatiotrs for Eopical rainforests. For. Ecol- Manage_,(Z,
143-168.
\/ANcr-ay, J. K. and PREsroN,R. A. (19ti9). SustaiMble timber harvest_
rng in the rainforests of nonhem eueeDsland. h Forcst ptsninT
fo.
People, Pn]rf,'edings of 13rh biennial conference of the tnstitute of
Forcsters of Australia,I-eura, NSW, 1&22 S€premb€r 1989, r8l 191.
WEsr, P. W., RATKowsKy, D. A. and DAvrs, A. W. (1984). problems of
hypothesis leslitrg ot regressionswith multiplc measuremeos trom indi
vidual sampling udts. For- EcoL Monage. T,Nt-724.
WEsr, P. W., Davrs, A W. and Rafiowsr(y, D- A. (1986)-Approaches
to rcgression anarysiswith multiple measurementsftom individual samplitng tmits. L StutkL Comp. Simul X, 14y175WEsr, P. W., SToc-r<ER,
G. C. and UNwrN, c- L- (1988). Etrvironmenral
rclationship6 atrd flaristic and structual change in some unlogged t opi_
cd rafuforest plots of nonh Queenslatrd. proc Ecot. Soc. AusL lS.
494).
WrxrMoRE. T. C- (1989). cuidelines to avoid rcmeasurement Droblems
itr permanent sample pfols in aropical raitr lorests. Biorropi(, Zl- J,
282:253.
WooD, G- B. (1990)- Choo6ing a porlable dara recorder. AusL For. St
J, r73 181-