Modelling Regeneration and Recruitment in a Tropical Rain Forest

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Modelling regeneration and recruitment in a tropical rain forest

Vanclay, Jerome K https://researchportal.scu.edu.au/discovery/delivery/61SCU_INST:ResearchRepository/1266831900002368?l#1367376290002368

Vanclay, J. K. (1992). Modelling regeneration and recruitment in a tropical rain forest. Canadian Journal of Forest Research, 22(1235), 1248–. https://researchportal.scu.edu.au/discovery/fulldisplay/alma991012820310002368/61SCU_INST:Research Repository

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Modelling regeneration and recruitment in a tropical rain forest

JenoMe K. VnNcr-ny Royal Veterinary and Agricultural University, Thorvaldsensvej 57, DK-1871 Frederiksberg C, Denmark ReceivedJuly 18, 1991 Accepted February 14, 1992

VANCLAY,J.K. 1992. Modelling regenerationand recruitment in a fopical rain forest. Can. J. For. Res. 22: 1235-1248. A two-stagemodel predicts the recruitment (i.e., the number of stemsreaching or exceeding l0 cm DBH) of the 100 species that account for 97Vo of all the recruitment observed on 217 permanent sample plots in ihe tropical rain forest of north Queensland. The first stage predicts the probability of the occurrence of any recruitment from stand blsal area and the presence of that speciesin the existing stand. These probabilities can be implemented stochastically,or deterministically by summing the probabilities and initiating recruitment on unity. The second stage indicates the expected amount of recruitment, given that it is known to occur, and employs stand basal area, the relative number of trees of that species in the stand, and site quality. This approach is easily implemented in growth models and planning systems.

VRNCLRY,J.K. 1992. Modelling regenerationand recruitment in a tropical rain forest. Can. J. For. Res. 22 : 1235-1248. Un modble i deux niveaux pr6dit le recrutement,c'est-i-dire le nombre de tiges de 10 cm ou plus au DHP, des 100 espdces qui comptent pour 97Vo de tout le recrutement tel qu'observ6 dans 217 parcelles 6chantillons permanentes dans la for6t ombrophile tropicale du nord du Queensland.Le premier niveau pr6dit la probabilit6 de recrutement d'une espdcei partir de la surface terribre du peuplement et de la pr6sence de cette espbce dans le peuplement existant. Ces probabiiit6s peuvenr Otre trait6es de fagon stochastique ou d6terministe en additionnant les probabilit6s et en commengant le recrutementlorsque la valeur de un est atteinte. Le second niveau indique la quantit6 attenduede recrutement,sachant qu'il y a du recrutement, et utilise la surface terridre du peuplement, le nombre relatif d'individus d'une espdcedans le peuplement et la qualit6 de station. Cette approche est facilement utilisable dans des modbles de croissanceet des systbmesde prise de d6cision. [Traduit par la r6daction]

Introduction Other approachesattempt to predict the number of stems Growth models may neglect the simulation of regeneration recruited as a function of stand condition. These vary from becauseof a lack of data or difficulty in modelling, or because the highly empirical to those that model some simple biolog- it is consideredunnecessary because silviculture involves clear- ical hypothesis.Letoumeau (1979) used an empirical approach felling and replanting. However, natural regeneration forms with 33 estimated parameters to predict numbers of stems, an essential component of selection harvesting systems used and accountedfor time between remeasuresin estimating size in rain-forest management,and long-term yield forecastsmust of recruits. Landford and Cunia (1977) predicted total number take account of the presenceand amount of this regeneration. of recruits (at 4 in. (10 cm) diameter) deterministically, but A review of the literature indicates two approachesto pre- the size and species stochastically. Both these models used dicting regeneration and recruitment. Regeneration models estimates of sapling density (numbers of stems in the 1, 2, predict the development of trees from seed or seedlings. As and 3 in. (2.5,5.1, and 7.6 cm) DBH size classes)in their suitable data for modelling regenerationare difficult to obtain, equations, and this estimate was assumedto remain constant many models predict recruitment rather than regeneration. through time; as saplings were recruited, new regeneration Recruitment models predict the number of stems reaching or was assumedto take their place. This limits the utility of the exceeding some specified nominal size limit (e.g., 1.3 m model for extrapolating inventory data, as such data (sapling height, 3 m height, 10 cm DBH over bark etc.). Recruitment density) may not be recorded during operational inventory. models may employ a static approach that predicts a constant Hann (1980) predicted recruitment with an exponential amount each year irrespective of stand condition, or may be function of site index, stand basal area,and basal area in the dynamic and respond to stand condition. smallest size class. His simulation cycle was 5 years, and this Static approachesto the prediction of recruitment assume ensureda realistic 5-year lag in the appearanceof recruitment that the amount of recruitment observed during the period of stimulated by the reduction in stand basal area following data collection reflects the long-term average, and that this logging. amount will not vary greatly between projection periods pre- Vanclay (1989a) predicted the total amount of recruitment dicted by the model. Such assumptions of static recruitment at 2O cm diameter in tropical moist forests in north'Queens- are common in stand table projection and matrix approaches. land as a linear function of stand basal area and site quality. Usher's (1966) matrix model for Scots pine (Pinus sylves- The composition of this recruitment was determined by pre- tris L.) predicted recruitment as a static proportion of the dicting the proportion in each of five species groups and number of trees in the larger size classes,and thus recruitment standardizingthe proportions. The proportion for each species increased as stand density increased. More realistic matrix group was predicted from the standbasal area,the site quality, approachesmay predict recruitment diminishing with increasing and the basal area of that species group (e.g., for the large, standdensity (e.9., Buongiorno and Michie 1980) or only after fast-growing species): the death of another tree (e.g., Bosch l97I). Although these approachesare rather empirical, they may provide useful esti- Pr : [1 + exp(2.407+ 0.005 608 BA - 0.011 05 Br mates of recruitment for stands that do not differ greatly from the source stands used for model development. - 0.004 64 BtSQ)l-t Printed in Canada / Imprim6 au Canada r236 where BA is total stand basal area(m2lha), Bi is the basal area regenerationis known to occur, the expected number of trees of the rth speciesgroup 7m2lha1,and SQ is the growth index. is determined using pseudorandom numbers, and it deter- These proportions were standardizedto ensure they summed mines the number of cohorts for that subplot. The number of to unity: speciespresent and the identity of these specieswere also Pi stochasticallydetermined. Height of regenerationis determin- p.' - 't - istically predicted. The model considers three categories of >P "best" regeneration: trees, comprising the two tallest trees per Botkin et al. (1972) establishedan ecological approachto plot regardless of species, the tallest tree of each additional modelling recruitment at 0.5 cm diameteron their 10 x 10 m species present, and the four tallest of any remaining trees. plots. They assumedthat a seed source was available for each Best trees were assumed to be advance growth of shade- of the major speciesconsidered by their model and compiled tolerant species if established 3 years prior to disturbance. a list of possible speciesfor the plot being modelled, on the Regeneration within each category was predicted indepen- basis of shade tolerance, growing season,and soil moisture. dently and differed considerably in composition (e.g., advance If the plot leaf area index (LAI) was less than a specified growth was more likely to comprise shade-tolerantspecies). threshold,60-75 cherry trees were recruited on the plot. If Some models simulate the growth of trees from seedlings "regeneration the plot LAI exceededthe first threshold, but was less than a to breast height within a separate model," or "understory secondlarger threshold,some (0-13) birches were recruited. operation" (Ek and Monserud I974a, 1974b; If the plot LAI exceeded both these thresholds, a random Dudek and Ek 1980). The approach is sufficiently flexible choice of the remaining suitable shade-tolerant species was that almost any size may be used as the criterion for recruit- made, and a random number (0, 1, or 2) of each species was ment into the main stand. Ek and Brodie (1975) simu- recruited. Shugart and West (1977) followed a similar approach, lated only suckers developing after logging (predicted from but identified the requirements of each species for mineral stand basal area before and after logging, site index, and soil or leaf litter, introduced stochastic elements of variable treatment), but many models simulate the development of weather and animal browsing, and also modelled sprouting regeneration throughout the development of the stand. Mod- from deadtrees. They recruited trees when they reachedbreast elling may start at any of a number of stages.Leak (1968) height. Similar successionmodels exist for subtropical rain modelled regenerationfrom the stageof flower development; forests in Australia (Shugart et al. 1980) and tropical rain Ek and Monserud (1974a, 1974b) modelled it from seedfall. forests in Central America (Doyle 1981). Reed (1980) fol- Germination could equally well be considered, and Vanclay "establishment" lowed an approach somewhat similar to that of Botkin et al. (1988) started with when the seedling had "off (1972), but introduced alternate seed years and years" survived its 1st year after germination. An advantage of the and imposed a maximum stocking of 2500 stems/ha,irrespec- approach is that it realistically models the time taken for tive of size, above which no recruitment could occur. regeneration to be recruited following a reduction in stand One of the difficulties in modelling recruitment is the great density due to logging. In contrast, recruitment models may variability in regeneration.Stand condition accountsfor some need to employ an explicit time lag. Recruitment models of this variation, as do periodicity of mast years and prevailing which employ an expression of stand density may lead to climate, but regenerationremains a rather stochastic process, overestimates of recruitment in the projection period imme- providing difficulties for efficient model estimation. Much of diately following logging. the variability associatedwith regeneration is due to the fact Vanclay (1988) predictedthe amountof established1-year- that during any period some regeneration may or may not old regeneration ina Callitris forest from stand basal area and occur, and that if the data are partitioned into a two-state site quality. Regeneration was modelled as cohorts repre- system, the ability to predict the amount of regeneration is senting height classesuntil it reached breast height, when it greatly enhanced.With a two-state approach, the first equa- was recruited to the main model. A maximum of 10 cohorts tion estimates the probability that some regeneration or were employed. Under ideal conditions (good sites with low recruitment will occur, and can be estimated using logistic stocking), these cohorts representedannual flushes of regen- regression with presence-absenceas the dependent variable. eration. Where regenerationexhibited slower growth and took The second stage is a conditional function to predict the more than 10 years to reach breast height, the most similar amount of recruitment, given that some is known to occur, cohorts were combined to ensure that the limit of 10 cohorts and can be estimated using ordinary linear regression. Ham- was not exceeded. ilton and Brickell (1983) gave an exampleof such a two-stage Ek and Monserud (1974a, 1974b) adopted a more detailed approach applied to the prediction of defective volume in approach to predict recruitment into their stochasticdistance- standing trees, which can be applied equally well to recruit- dependent individual tree model. The regeneration model ment modelling. used cohorts representing the number of stems for each Stageand Ferguson(1982) and Fergusonet al. (1986) used species and age in a number of subplots within the main a two-stage approach to predict recruitment in the pRocNosIS plot being simulated. A Monte Carlo approach selectedgood, model (Stage 1973; Wykoff et al. 1982; Wykoff 1986). They moderate, and poor seed years according to the observed used a stochastic procedure to predict the regeneration on frequency for each species.Seed and sprout production were 50 subplots, each 1/300 acre (about 0.001 ha), and these data estimated for each overstory tree as a function of its size and were aggregated into the main pRocNosrs model at 10 and the threshold age, and were distributed across the subplots 20 years after disturbance.They predicted the probability that according to the parent tree's position, height, and crown some regenerationwould occur using environmental variables width. Germination was predicted as a stochasticfunction of (habitat, slope, aspect, elevation), distance to seed source, microsite and canopy cover conditions. Each yeff, a germi- residual basal area, and time since disturbance. Given that nant or tree in the understory may die or survive and attain VANCLAY r237

some height increment (function of cover, species,and age). height (1.3 m) or above buttressing)were numbered,tagged, When a tree reached breast height it was recruited into the and measured for diameter. The plots sample virgin, logged, main model. If a tree did not attain this height within a spec- and silviculturally treated forests. ified time (e.9., 25 yearsfor black spruce),it was assumedto Pairs of consecutiveremeasurements (i.e., all nonoverlapping have died. intervals) were selected from the data base and formatted to Monserud and Ek (1977) refined this approach, improving provide a data file suitable for analysis. Site quality (Vanclay the efficiency by reducing the number of cohorts to be mod- 1989b) could not be estimated for some plots, and the omis- elled. They assumedthat understory tree size was more rele- sion of these plots left 217 plots for the present study. Since vant than tree age and modelled the development of trees to plots were remeasured,these 217 plots provided 791 obser- 7.6 m height using five height cohorts of varying size, using vations of the incidence and amount of recruitment (at 10 cm movement ratios to predict upgrowth. The height increment DBH) for each species.The file also included details of stand of the mean tree was predicted from the potential height and site variables such as basal area, site quality, and soil type. increment (a function of height and site), overstory competi- Correct speciesidentity was recorded in the data baseusing tion index (a relative size-distanceindex), shade tolerance (a a three-character mnemonic. However, species identifica- function of speciesand height), and stand density. Monserud tion is often difficult in these forests, and routine resource and Ek determined that five cohorts were required to model inventory procedures record only the standard trade name the understory without compromising accuracy. Such detailed (StandardsAssociation of Australia 1983), using a set of approaches(Ek and Monserud 1974a, 1974b: Monserud and mnemonics known as the harvesting and marketing (H & M) Ek 1977) may not be warranted for yietd prediction models, codes. Although the H & M code retains the correct identity but may be relevant for models that analyze the silvicultural of most species, several members of a genus may share a alternatives for intensively managed stands. common code, as may members of more than one genus One difficulty with regeneration models is ensuring com- with similar timber characteristics.Some 100 noncommerical patibility with inventory data when the model is used for yield speciesand trees of undetermined identity may be labelled as prediction. Inventory data frequently sample only the larger miscellaneous.As the present study was to develop a recruit- stems (e.g., >10 cm DBH), and smaller stems may remain ment model for operational yield prediction purposes (e.g., unsampled.Thus there may be some censorshipof data. Such Vanclay and Preston 1989), it was appropriate to use the problems are more common for regeneration models (which H & M codes.The data file used in the present study included predict regeneration at very small sizes) than for recruitment 239 such H & M codes (including miscellaneous). models, which predict recruits at larger diameters (e.g., 10 cm). To avoid this censorship, it is necessaryeither for the inventory to provide a count of the smaller stems or for a model to Method predict the likely incidence of such stems from overstory The present data posed three difficulties: a range of plot "average" stocking. Augmenting such censored data with an sizes and measurementintervals, the large number of species small tree distribution for the forest type is preferable to using characteristic of tropical rain forests, and the variable nature the unadjustedcensored data (Randall et al. 1988). of recruitment. The data suggested an excessively hetero- Difficulties in obtaining uncensoreddata during an opera- scedasticpattern of recruitment, until partitioned for the two- tional inventory limit the utility of regeneration models for stage modelling approach, with one equation to predict the yield forecasting. Data concerning regeneration are often not probability that any recruitment of a given speciesoccurs and available, or may be unreliable owing to inability to reliably another to predict the amount, given that recruitment is known identify species of seedlings, whereas recruitment data are to occur. Once so partitioned, regressionanalyses were possible. always available from pennanent sample plots. The germi- The data file iniluded data diawn from a iange of piot sizes nation and initial survival of seedlings in the rain forest is and measurementintervals, both of which have an influence an uncertain phenomenon; vast numbers of individuals and on the likelihood of recruitment occurring. Longer measure- speciesmay genninate but never attain a significant size. The ment intervals have a higher probability of recruitment, and longer term survival and continued growth of these seedlings this can be accommodatedby weighting the regression anal- is much more under environmental control and thus recruit- ysis (e.g., Hamilton and Edwards 1976).The amount of recruitment is more predictable than regeneration. Thus modelling ment can easily be adjusted for plot size and measurement recruitment at some nominal size representsa viable altemative. interval by converting to stems per hectare per annum, but the influence of plot size on the probability of recruitment is more complex. Plot sizes varied from 0.03 to 0.5 ha; about half Data of them were half-acre (0.2023-ha) plots. The present study The present study concerns the tropical rain forests of north- assumesthat the probability of recruitment is determined pri- east Queensland.These forests have been managed for con- marily by stand condition (and measurement interval), and servation and timber production for more than 80 years (Just that the influence of plot size in the presentstudy is negligible. l99L), and prior to their recent inclusion on the World Heri- This assumption is tenable largely becausethe presenceof a tage List, provided a sustainedyield of 60 000 m3 of veneer species is a key explanatory variable in predictions, and it and sawlogs annually through conservative selection har- compensatesfor plot size. Recruitment of a species is more vesting (Preston and Vanclay 1988; Vanclay 1991c). The likely if that speciesis already present on the plot; a species Queensland Department of Forestry (1983) research pro- is more likely to be present on a bigger plot, and recruitment gramme provided a data base of 250 permanent sample plots of any species is more likely on a bigger plot. Thus this with a measurementhistory of up to 40 years (Vanclay 1990). assumption regarding plot size is reasonable given that pres- All trees exceeding 10 cm DBH (diameter over bark at breast ence of the species is one of the explanatory variables. 1238 CAN. J, FOR. RES. VOL.22.1992

Number of species to model only when the regressionanalysis was constrained to a single Of the 239 H & M species groups occurring on the per- explanatory variable. Their best results were obtained using manent sample plots, 213 were observed to occur as recruit- the basal area of trees larger than the current tree as the ment on one or more occasions.However, the contribution'of explanatory variable. The cluster analysis was weighted by individual speciesto the total recruitment varied greatly. Sixty the inverse of the significance level of slope parameter, and speciesaccounted for 90Vo,80 speciesaccounted for 95Vo,and it provided 20 clusters from 110 species-typeequations. The 100 species accounted for 97Vo of all recruitment observed. amount of data assigned to each cluster varied greatly, and The remaining 3Voof recruitment comprised 113 species,all the outcome was subjectively adjusted to provide the final of which offered insufficient data for meaningful analysesof grouping. The adequacyof final groups was testedby fitting a regenerationcharacteristics. Accordingly, only the more prev- multiparameter linear function and examining the total (across alent 100 species were included in the recruitment model. clusters) residual sums of squares,on the assumption that a These species were those that were observed as recruitment better grouping would result in a better fit. Whilst the method on nine or more occasions;they included a reasonablenumber did provide a grouping of similar elements,it did not provide of commerical and noncommerical speciesand light-demanding a unique solution. and shade-tolerantspecies, and they should provide a reason- Leech et al. (1991) used a Behrens-Fisher analogue of able representation of the forest. Simulating the recruitment Hotelling's T2 to group 27 speciesfor fitting volume equa- of these 100 species should provide sufficient precision for tions. They used a polynomial equation to predict tree volume yield prediction purposes,provided that simulated ecological (V) from tree diameter (D) for tree i: consequencesare interpreted carefully. Two options exist for recruitment of the remaining % = Fo,+ pr;D + Fz,Dz + ... * \niD" 113 species:they may be aggregatedwith the miscellaneous group, or they may be ignored. Both options have disadvan- Then, using the vector of coefficients tages.The less abundant speciesmay have ecological characteristics unlike those dominating the miscellaneousgroup, and ui = [For,F,i, Fz,, ..., F,'i] aggregating these could lead to bias. Similarly, ignoring this recruitment also leads to bias. In the present study, they were Hotelling's T2 between two speciesi and j can be defined as ignored since they represent such a small component of the total recruitment. d'u = (ui - u)'S-r (ui - u)

Aggregating species where S-l is the combined covariance matrix of regression It is impractical to develop recruitment models for each of coefficients for species i and "t. By calculating all possible these 100 individual tree speciesbecause of the large number combinations, a symmetric matrix with zero diagonal ele- of functions that would be required and becausethe paucity ments can be formed. Principal coordinate analysis (Gower of data for many speciesinhibits the development of reliable 1966) was used to group specieson the basis of this matrix. relationships. Thus for efficient estimation of recruitment, it Leech et al. (1991) concluded that the technique should only is desirable to aggregate these species into several groups. be used when the order of the polynomial and the sign of This reduces the number of functions required to a more the highest term are the same for each of the two indi- manageablenumber, and avoids the requirement for specific vidual speciesequations. The method was also computation- equations for specieswith few data. Such groupings need not ally intensive. form the basis for growth modelling, as simulation models Vanclay (I99lb) devised an objective means to aggregate can retain the individual identity of all species(Vanclay and 237 speciesinto 41 groups to enable efficient estimation of Preston 1989), but are necessaryfor the estimation of incre- diameter increment functions for a growth model of tropical ment, mortality, and recruitment functions. Ideally, species rain forests in north Queensland.This approach involved the should be grouped on a priori grounds, and tests performed following: to justify the validity of such groupings. This may be possible (1) Ranking species in order of increasing number of in temperate forests where there are few species with well- observations. documented ecological characteristics, but is unrealistic for (2) Assigning the species of highest rank as the founding the 100 species in the present study. Taxonomy (family or speciesof group 1. genus) may not provide a good guide to the regeneration (3) For each speciesin decreasingorder of rank, conducting strategy,and other methods (e.9., Swaine and Whitmore 1988) pairwise F-tests with all founding speciesof higher rank. based on successionalstatus, seed morphology, etc. may be If the incoming speciesexhibited a significantly different rather subjective. Regenerationis dependentupon stand density (P < 0.01) increment pattern from all existing founding and other factors, so a grouping basedon averagerecruitment species,it became the founding speciesof a new group. may be specific to the set used. Not only is it difficult to Species not significantly different from all founding resolve which speciesto combine, but it is not clear how many speciesremained ungrouped. groups are required. (4) After identifying the founding species, comparing the Meldahl et al. (1985), Leech et al. (1991), and Vanclay ungrouped species, in order of rank, with all existing (I99Ib) have examined proceduresto resolve thesequestions. groups, and combining each with the most similar group. Meldahl et al. (1985) argued that the grouping should reflect Similarity was determined as the grouping which led to the dynamics of growth, and this could be best expressed the smallestincrease in residual sum of squareswhen the through the coefficients of a regression equation on diameter incoming species was incorporated in the group. These increment. They attempted cluster analysis on these coeffi- comparisons were made with the whole group, not just cients, but found that reasonable results could be obtained with the founding species. VANCLAY r239

This approach overcomes many of the difficulties associ- B ackho usi a banc roft ii, and B I ep haro cary a inv o luc r i g er a may ated with the alternativesdiscussed above and is computation- dominate some rain-forest standsand were also considered as ally efficient. Instead of a comparison of all possible pairs, potential indicator species. initial comparisons are made between species with a lot of Empirical screening of species for possible indicators data, reliable parameter estimates, and homogeneous vari- involved the compilation of a correlation matrix for all species ance. Species with not much data are only later compared in the data, showing the correlation between occurrence in with one of these major groups. It also avoids Leech et al.'s the existing stand and occurrence of recruitment. The amount (1991) need to arbitrarily select a subsetof the more numerous of recruitment was not considered, as given that recruitment species to define the groups. This selection is by no means is known to occur, the amount is determined largely by stand intuitive, as in Vanclay's (l99lb) study, the species that basal area and site quality, and additional variables contribute ranked 186 with only 13 observations initiated a new group. little more to the model. The correlation matrix indicated a This approach provided an objective basis for aggregating large number of species pairs with significant correlations. species, but there is, unfortunately, no guarantee that the Twenty-five species, some having the greatest number of outcome is optimal. However, it provided an efficient, objec- significant entries, and some the highest correlations, were tive, and repeatable means to combine many species into a selectedfor further screeningusing regressionanalysis. Many manageable number of groups for modelling the diameter of these specieswere highly correlated with basic stand vari- increment of tropical rain forests.Vanclay (1991c) also used ables (e.9., pioneer and gap-colonizing species indicate low a variation on this approach to aggregate species to predict basal area) and were not significant when included in regres- mortality using logistic equations that were fitted by sion analyses with stand variables such as stand basal area maximum likelihood estimation and compared using the like- and site quality. Eight specieswere found to be significant in lihood ratio test. two or more logistic regressionsof recruitment occurrenceon basal area, site quality, and species occurrence, but did not Explanatory variables contribute significantly to regressionson the grouped data. Becausethe model is to be used for projecting operational inventory data for yield predictions, all driving variables Results should be readily obtainable in routine resource assessment. Basic stand variables such as site quality and stand basal area Probability of recruitment are obvious candidates for explanatory variables. The pres- Preliminary analyses for several species with lots of data ence of the species in the existing stand should also be an suggestedthat most of the variation in occurrence could be important variable. However, it is not necessarythat a species explained by stand basal area and by the presenceor absence exist in or near a stand for regeneration to occur. Many rain- of that species in the stand. Thus these two variables were forest species have efficient means of dispersal (Stocker used as explanatory variables in comparing speciesfor aggre- 1983), and occurrence in the immediate vicinity is not a pre- gation. Comparisons used Vanclay's (l99Ic) method (pair- = requisite for regeneration of all species.Stand basal area and wise likelitrobd ratio tests X2) and the model the presence or relative abundanceof the species were used p = lL + exp(-(Fo + prpRES + p2BA))l-t to aggregatespecies into groups, but additional variables were considered for the final group equations. where P is the annual probability of recruitment (at 10 cm Many of the plots used in the present study were logged or DBH) for that species, BA is stand basal area (all stems silviculturally treated, and the time since such disturbance >10 cm DBH), and PRES is a binary (0, 1) variable indicating may provide a useful explanatory variable. Analyses indicated the presence(1) or absence(0) of that speciesin the (>10 cm that time since the last logging explained only a little of the DBH) stand. The individual tree data were fitted using max- variation observed in the data, but that time since the last imum likelihood estimation weighted by the inverse of the silvicultural treatment offered greater potential. Unfortu- measurementinterval. nately, time since treatment was highly correlated with stand This analysis indicated five groups (Appendix), based on basal area, and both variables could not be used in pairwise comparisons of the equation fitted to the 791 obser- fitting models to the data without causing instability in the vations on each of 100 species.Of these 79 IO0 observations, estimated parameters. Stand basal area was preferred as an 4586 confirmed the presence of recruitment for a particular explanatory variable, particularly as silvicultural treatment species. Following grouping, the inclusion of additional was applied mainly in experiments and few areas were treated explanatory variables was investigated. Soil parent material, operationally. time since treatment, and the logarithm of stand basal area Other species were also screenedas possible indicators of were found to be significant. Thus the final equation was favourable or unfavourable conditions for recruitment. Such P = + exp(-(Fo + pTPRES p2BA p3log(BA) species need not have a direct antagonistic or synergistic tll [1 + + effect on the developing regeneration, but may merely indi- + p4SOIL + psTR))l-t cate suitable stand conditions not reflected in the basic stand variables. Candidatesfor such indicator specieswere selected where P, BA, and PRES are as defined above, TR is the both subjectively using ecological principles, and empirically treatment response (TR = t a'/9, where r is years since the last using a comprehensive screening process. Subjective selec- silvicultural treatment), and SOIL is a binary variable that tion identified groups of species indicative of environmental takes the value one on soils derived from basic volcanic and conditions. Thus palms were chosen as indicators of moist coarse granite parent materials, and,zero otherwise. The treat- sites, and sclerophyllous species (Acacia, Casuarina, Euca- ment response term (TR) provides for a maximum response lyptus, Melaleuca spp.) were chosen as indicators of marginal 9 years after silvicultural treatment. The model was fitted to rain forest sites. Other species including Agathis species, the individual tree data using maximum likelihood estimation 1240 CAN. J. FOR. RES. VOL.22, 1992

TesI-E 1. Parameterestimates for eq. I

Estimate for parameter Species No. of group observations P. pTPRES FzBA Frloe(BA) B4sorl- FsTR I 791 _6.769**x 1.896*** -0.027 84 1.1570 0.2073*** 0.2188**r< 2 7 9r0 _4.726*** 2.289**x -0.047 03x*r, 0.3960 0.2073' 0.2188" 3 126s6 _6.075{,r,* 2.491*** _0.06977>F** 0.6880 0.2073' 0.2188" 4 49 833 _8.476*tfx 2.808*** -0.03024x* 0.8707*x 0.2073" 0.2188" ) 7 9r0 _6.881xr,* 3.782*x* -0.03031 0.52t3 0.2073' 0.2188'

Note: ***, P < 0.001; **, P < 0.01. oThese parameters are all identical with pa.r md Fs.r.

Tnsle 2. Parameterestimates for eq. 2

Estimate for parameter Species No. of group observations tu Frlog(BA) B2log(RNO+0.2) F,SQ F+SOIL I 742 4.971*** _0.5460*{

NorE: ***, P < 0.001; **, P < 0.01. oThese parameters are all identical with po,r. weighted by the inverse of the measurementinterval to com- where N is the number (trees/traper year) of recruits (at 10 cm pensate for the differing measurementintervals and provide DBH), BA is the stand basal area (m'/ha), and RNO is the annual probabilities of recruitment. relative number (0 < RNO ( 1) of that speciesin the (>10 cm Some of the parameter estimates in Table 1 are not signif- DBH) stand. icant at the usual levels of significance, but have been retained This analysis indicated eight species groups. Following in the model provided that the sign and magnitude of the grouping, the inclusion of additional explanatory variables estimate is consistent with parameters for other species was investigated. Site quality and soil type were significant, groups, and that parameter estimates for at least one species and the final model was group were significant at the usual levels. The parameter estimatesfor p3 log(BA) were associatedwith large standard I2l log(N) : Fo + pllog(BA) + p2log(RNO + 0.2) error estimates, bul collectively were significant (likelihood ratio test statistic X' = 14 on 5 df, P - 0.016) and provided a + fuSQ+ p4SoIL more realistic response.The inclusion in the model of both where N, BA, RNO are as previously defined, SQ is site BA and log(BA) ensuresa low probability of recruitment for quality estimated using Vanclay's (1989b) growth index, and low stand basal area, which is consistent with the available SOIL is a binary (0, 1) variable, which takes the value one data (Table 3), and predicts a maximum within the range of on alluvial and fine-grained ("Tully") granite soils zero the data used in the present study (Fig. 1). and elsewhere. Parameterestimates are given in Table 2, and the Amount of recruitment relationship is illustrated in Fig. 2. No indicator speciescon- A subsetof the data basecomprising 4586 records provided tributed significantly to this relationship. Some parametersin estimates of the amount of recruitment, given that it was Table 2 are not statistically significant (i.e., P > 0.05), but known to occur. Preliminary analyses of the more abundant were retained in the model provided that the corresponding species indicated that stand basal area and relative abundance term was significant (P < 0.001) for other speciesgroups, and of the speciesin the (>10 cm DBH) stand explained most of that the sign and magnitude of the parameter estimate were the variation. The curvilinear response provided by the loga- logical and comparable to those of other groups. rithmic transformation of relative abundance gave a slightly Analysis of the present data has been complicated by the better fit than a linear model and ensured more conservative different plot sizes and remeasurement intervals. To provide extrapolations for the several species for which the present a consistent basis for analysis, probabilities of recruitment data spanned only a small part of the possible range of values. have been adjusted to annual probabilities by weighting by Species were aggregated using Vanclay's (I99lb) method- the inverse of the remeasurement interval, and amounts of ology (pairwise F-tests) and the equation recruitment have been standardized to stems per hectare per year. Both these adjustments are necessary for variance stabi- log(N) - P0 + p1log(BA) + p2log(RNO + 0.2) lization, but result in a double correction that underestimates VANCLAY r24r

0.15

.Eb0 = o I c 'EE o.r E EI 6 o € fi o.os E. ql, EI EI

Standbasal area (mzAa)

FIc. l. Predicted probability of recruitment with SOIL = 0, TR = 0, and PRES = I (solid lines) or PRES = 0 (dotted lines).

€60 e, E g t)

ci c) E 5 e 940 o C! e o E cl G') E20

020 100 Stand basal sea (m2fta)

Ftc. 2. Predicted amount of recruitment, given that some occurs with SQ = 7, SOIL = 0, and RNO = O.2 (solid lines) or RNO = 0 (dotted lines).

the expected recruitment. Thus the parameters p6 in Table 2 comparisons are especially important in multistage models have been adjusted to provide unbiased estimates of recruit- such as the present one. Table 3 contrastspredictions with the ment (Table 3). Figure 3 illustrates predictions from the raw data used in fitting the model. Comparisons with inde- product ofeqs. I and2. pendent data will be made when these data become available. Table 3 indicates the total recruitment observed (stems/tra) Discussion and the recruitment predicted by the model (i.e., probability x Checking predictions amount x years). There is generally a good correspondence, One of the best ways to gauge the quality of a model is to except for two cells. Speciesgroup 2-2 (viz group 2 for eq. 1 compare model predictions with raw data; both with the data and group 2 for eq,.2), with 80 m,zlhabasal area, indicates a used in fitting the model and with independent data. Such big discrepancy due to the recruitment of 731 stemsftraon one r242 CAN. J. FOR. RES. VOL.22. 1992

Tasle 3. Comparison of observed (O) and predicted (P) recruitment (stems/tra)by initial stand basal area

Initial stand basal area (m2lha) Group Total identityo 0-9 10-19 20-29 30-39 4049 50-59 60-69 70-79 80-89 recruitment

1-1(1) o 93 885 1 031 833 645 240 169 82 28 4 007 P 101 I 008 | 514 I 202 885 633 234 t32 36 5 745 2-2(r) o t27r 3 648 3 266 2743 810 r63 30 t4 747 12 692 P t44t 3 724 2 020 r 471 627 167 37 10 3 9 499 2-3 (2) o 393 2758 1 852 I 807 242 52 t4 10 0 7 r28 P 43t 2252 1 57r I 007 321 98 3I 8 3 5 724 2-s (4) o 499 2 801 3 655 I 909 809 87 44 35 r28 9 967 P t3r7 4 480 3 954 22r5 9s8 280 65 27 l1 13306 2-6 (3) o 8r7 2 886 2 r83 775 r43 1ll l0 0 0 6 925 P 814 2326 1 801 745 264 130 15 I2 3 6 110 3-1(s) o 224 | r32 496 248 170 7 l0 0 0 2 288 P 249 874 560 267 tt4 43 9 3 0 2 lr8 3-2(r) o ro4 436 203 74 20 0 0 0 0 837 P 105 232 t42 44 18 6 I 0 0 549 3-4 (2\ o r49 769 607 473 105 20 2 0 0 2 124 P 185 930 639 349 l3l 44 ll 3 1 2 292 3-s(1) o 33 113 61 0 10 0 0 0 0 217 P 100 206 II9 54 20 l0 3 0 0 513 3-8 (7) o 473 | 382 1 041 490 t82 49 5 0 0 3 622 P 380 I 385 I ot7 440 t57 65 I2 4 I 3 461 4-1(1) o 49 64 46 104 35 10 10 0 0 317 P 10 52 74 6I 40 13 3 6 0 259 4-2(r4) o 82 725 749 477 230 108 89 56 27 2 543 P r02 566 73r 5t7 358 206 48 33 8 2 570 4-3(1) o 0020 r47 l5 0 0 5 0 187 P 944 38 48 52 20 6 6 I 225 4-4(s) o 13 373 409 329 183 91 t27 48 6 | 579 P 21, r7l 269 228 r99 r26 47 22 9 l 091 4-s (2) o 78 95 230 334 3t2 27 25 2l 6 I t28 P 21 95 132 r07 l0l 42 20 17 6 541 4-7 (2) o 040 34 108 r02 80 106 92 0 560 P 26 156 130 95 68 43 22 t9 I 559 4-8 (38) o 274 1 485 1 983 | 709 99s 4r7 144 t07 t9 7 t35 P 256 I 803 2 r38 l 730 I l9l 857 223 r27 25 8 349 s-l (1) o 122 350 LO7 40 t02 22 0 0 0 743 P 74 299 204 103 66 50 26 7 0 830 s-2(r) o 9 222 122 76 20 34 2 0 7 493 P 19 tt4 r7t 97 100 67 t2 8 4 591 s-4(3) o 22r 988 | 679 885 930 255 l19 t7 7 5 l0l P 423 | 271 | 442 929 667 267 69 72 l0 5 150 VANCLAY r243

Tesle 3 (concluded)

Initial stand basal area (m2ftra) Group Total identityo 0-9 10-19 20-29 30-39 4049 50-59 60-69 7{F-79 80-89 recruitment s-7(3) o 161 595 558 135 128 t73 463 4l 0 2 254 P83447 628 337 r94 148 104 5l I 1,992 s-8(2) o25 22 43 126 58 116 54 5 0 448 P20 93 164 215 r44 r36 36 16 0 824 All (100) o 5091 21 768 20 374 13 82r 6244 2063 t424 533 976 72 295 P 6190 22527 19 457 12260 6673 3448 1033 584 124 72 298 No. cases 53 200 187 140 101 5l 28 22 9 791 oGroup identity indicates the coefficients for eqs. I and2, respectively. Thus 3-4 indicates group 3 for eq. I and group 4 for eq. 2. The number of species is given in parentheses.

Standbasalarea(mz^a)

Ftc. 3. Expected average annual recruitment for major species groups (with PRES = 1, RNO = 0.2, SQ = 7, SOIL = 0, TR = 0).

plot (0.04 ha) during a single interval (5.8 years). Although Figure 3 illustrates the expected annual recruitment in a anomalous,there seemedno good reason to reject this datum. form that allows confirmation by local field staff with a "feel" Ano^therdiscrepancy occurred for species group 2-5, with for the forest. Table 3 allows predictions to be compared 4 m"lha basal area, for which a large amount of recruitment with the untransformed observations. Recruitment is a rather was predicted but none was observed on a single plot over unpredictable process, and specific predictions (i.e., indi- two consecutive time periods. This discrepancy occurred for vidual plot, single occasion) remain poor, but stand-level an enrichment planting study in which natural regeneration trends can be predicted wit! greater confidence. The good- was cut to enhance the growth of the planted trees (not nessof fit at ttre sknd level r2 - I - I, (y - j> t> (y - y), included in the present data). The predictions give a reason- applied to the 198 cells in Table 3, is 88Vo. able indication of the recruitment that might have eventuated in the absence of this treatment. Since no recruitment was Correlation of groupings observed,these two caseswere used only in fitting the model This study has employed two independentgrouping sfrategies. for probability of recruitment, and not in fitting the model for Table 4 demonstratesthat this was in fact necessary.There is amount of recruitment. Apart from these two major discrep- no evidence of any correlation between the two groupings ancies, predictions seemedreasonable. (expectednonempty cells : 25, actual -- 22,Xi = 4.23, P = O.4). t244 CAN. J. FOR.RES. VOL.22. 1992

TRU-E 4. Species composition of groups for predicting probability and amount of recruitment

Group I Group 2 Group 3 Group 4 Group 5 Total

Group I MISO CMH, EVD, NBD NHQ CNN NTQ, SST Group2 MSWO SLQ BSH,BTD, CLL WAS L7 COL, FKJ, HKA PAL, QWN, RLI RSS,TBH, WBH WHZ, YEV Group3 BSL, NRAO WAL 3 Group 4 CLO, NSOO BKR, HYW, IBS BRT, NSS l0 PTM, RCD RDT Group5 ALB, BWD ROO LAN, SMP 7 QMP,'QSA Group6 HMW, PKA 3 wBso Group7 BBN, SVB BTM,OTYW 5 WCD Group8 BRO,BSO, NEV NRW,O RBN,O BRY,"BUA 47 PPW,SSW, TRQ and 36 spp. WCB

Total l0 t6 63 10 100 aFounding species of group. See Appendix for identity of species codes.

The size of Table 4 cannot be reduced without combining Conclusion the founding species that exhibit significantly different The two-stage approach is suited to modelling recruitment recruitment patterns. There is no evidence that the groupings of the many species in the tropical rain forests of north for the prediction of diameter increment (Vanclay l99lb) or Queensland. The first stage predicts the probability of the for (Vanclay mortality 1991c) are indicative of recruitment occurrence of any recruitment from stand basal area and the pattern. groupings Clearly, optimal may vary according to presenceof that speciesin the existing stand.The secondstage objectives. indicates the expected amount of recruitment, given that it is Implementation known to occur, and employs stand basal area, the relative Application of the model requires an estimate of the pres- number of trees of that speciesin the stand, and site quality. ence and relative abundanceof each speciesin the stand.This Several species and groups of specieswere screenedas pos- poses no problem for short-term predictions, but offers two sible indicator species, but contributed no improvement to alternatives for long-term simulations. Should the presence the model. and abundance derive from the composition of the original Acknowledgements inventory data or from the current composition of the simulated stand? Composition of natural stands may vary consid- This work was completed whilst the author was with the erably over time. Flushes of pioneer species following Queensland Forest Service. The author gratefully acknowl- disturbance may be short-lived, and the turnover rate of edges the efforts of the many Forest Service staff who have species may be high even in undisturbed stands (e.g., Poore been involved in the management of the permanent sample 1968). However, it is impossible that predicted recruitment plots and of the resulting data base. would always be correct, and using simulated composition Bosch,C.A. 1971.Redwoods: a populationmodel. Science (Wash- could further bias predictions. Conversely, if an inventory ington,D.C.), 172:345-349. were performed soon after disturbance, it may include some Botkin, D.8., Janak,J.F., and Wallis, J.R. 1972.Some ecological pioneer speciesthat would not be expected to persist long in consequencesof a computermodel of forest growth. J. Ecol. 60: the stand. Thus the best compromise may be to use the relative 849-872. abundance of species in the original stand, on the condition Buongiorno,J., and Michie, B.R. 1980.A matrix modelof uneven- that the species remains in the simulated stand. This solution agedforest management.For. Sci. 26: 60ffi25. is intuitive and cautious and seemsto give good results. The Doyle,T.W. 1981.The role of disturbancein the gapdynamics of a model has been implemented to allow the user to choose montanerain forest: an applicationof a tropical forest succession model./n Forestsuccession: between these various options (presenceof speciesin original conceptsand applications.Edited by D.C. West,H.H. Shugart,and D.B. Botkin. Springer-Verlag,New inventoried stand, in the current simulated stand, or the con- York. pp. 56-73. ditional compromise) and to encourage users to conduct sen- Dudek,A., andEk, A.R. 1980.A bibliographyof worldwideliterature sitivity analyses. Similarly, users can elect to invoke on individual tree basedforest standgrowth models.Department stochastic or deterministic simulation and are encouraged to of ForestResources, University of Minnesot4 Minneapolis.Staff investigate both options. Pap.Ser. 12. VANCLAY 1245

Ek, A.R., and Brodie, J.D. 1975. A preliminary analysis of short- Reed, K.L. 1980. An ecological approachto modelling the growth of rotation aspen management.Can. J. For. Res. 5: 245-258. forest trees.For. Sci. 26:33-5O. Ek, A.R., and Monserud, R.A. 1974a. Trials with program FoREsr: Shugart,H.H., and West, D.C. 1977. Development of an Appalachian growth and reproduction simulation for mixed species even- or deciduousforest successionmodel and its application to assessment uneven-aged forest stands. In Growth models for tree and stand of impact of the chestnut blight. J. Environ. Manage. 5: 161-180. simulation: Proceedings, IUFRO Working Party S4.0 1-4 Meetings, Shugart,H.H., Hopkins,M.S., Burgess,I.P.,and Mortlock, A.T. 1980. 1973. Edited by J. Fries. Department of Forest Yield Research, The development of a succession model for subtropical rain- Royal College of Forestry, Stockholm. Res. Note 30. pp. 56-69. forest and its application to assessthe effects of timber harvest Ek, A.R., and Monserud, R.A. 1974b. FoREST:A computer model for at Wiangaree State Forest, New South Wales. J. Environ. Manage. simulating the growth and reproduction of mixed species forest ll.:243-265. stands.Univ. Wis. Madison Coll. Agric. Life Sci. Res. Rep. R2635. Stage, A.R. 1973. Prognosis model for stand development. USDA Ferguson,D.E., Stage,A.R., and Boyd, R.J. 1986. Predicting regen- For. Serv. Res. Pap. INT-137. eration in the grand fir - cedar - hemlock ecosystem of the northern Stage, A.R., and Ferguson, D.E. 1982. Regenerationmodelling as a rocky mountains. For. Sci. Monogr. 26. component of forest successionsimulation. 1n Forest Succession Gower, J.C. 1966. Some distanceproperties of latent roots and vector and Stand Development Research in the Northwest. Proceedings methods in multivariate analysis. Biometrika, 12: 325-338. of a Symposium at Oregon State University, Corvallis, 26 Mar. Hamilton, D.A., and Brickell, J.E. 1983. Modelling methods for a 1981. Edited by J.E. Means. Forest ResearchLaboratory, Oregon two-state system with continuous responses.Can. J. For. Res. 13: State University, Corvallis. pp. 24-30. tttT-rlzt. StandardsAssociation of Austalia. 1983.Nomenclature of Australian Hamilton, D.A., and Edwards, B.M., 1976.Modelling the probability timbers. Australian Standard 2543-1983. Standards Association of of individual tree mortaliry. USDA For. Serv. Res. pap. INT-185. Australia, North Sydney, New South Wales. Hann, D.W. 1980. Development and evaluation of an even- and Stocker, G.C. 1983. Aspects of the dynamics of rainforests in north- uneven-aged ponderosa pine I Aizona fescue stand simulator. eastAustralia. Ph.D. thesis,University of New England, Armidale, USDA For. Serv. Res. Pap. INT-267. New South Wales. Just, T.E. 1991. Management of tropical rainforests in north Queens- Swaine, M.D., and Whitmore, T.C. 1988. On the definition of eco- land. In Forest Management in Australia, Proceedings of Confer- logical speciesgroups in nopical rain forests.Vegetatio, 75: 81-86. ence of Institute of Foresters of Australia 1987, Perth. Edited by Usher, M.B. 1966. A matrix approachto the managementof renew- F.H. McKinnell, E.R. Hopkins and J.E.D. Fox. Surrey Beatty, able resources,with special referenceto selection forests. J. Appl. Chipping Norton, New South Wales. pp.228-239. Ecol.3: 355-367. Landford, B.L., and Cunia, T. 1977. Stand projection by simulation Vanclay, J.K. 1988. A stand growth model for cypress pine. In Mod- using CFI data. In Inventories on successive occasions. Papers elling Trees, Stands and Forests. Proceedings of a Workshop, presented at the XVI IUFRO World Congress, 20 June - Z July Aug. 1985, University of Melbourne. Edited by J.W. Leech, 1976, Oslo. Edited by P. Schmidt-Haas.Swiss Federal Institute of R.E. McMurtrie, P.W. West, et al. School of Forestry, University Forest Research,Birmensdorf. Rep. l7l. pp. 29-52. of Melbourne. Bull. 5. pp. 310-332. Leak, W.B. 1968. Birch regeneration:a stochasticmodel. USDA For. Vanclay, J.K. 1989a. A growth model for north Queensland Serv. Res. Note NE-85. rainforests.For. Ecol. Manage. 27 : 245-27 l. Leech, J.W., Correll, R., and Aung Kyaw Myint. 1991. Use of Vanclay, J.K. 1989b. Site productivity assessmentin rainforests: an Hotelling's T2 and principal coordinate analysis to assistin aggre- objective approach using indicator species. In Growth and yield in gating species for volume table construction. For. Ecol. Manage. topical mixed/moist forests. Proceedings of the Seminar, 20- 40:279-288. 24 June 1988, Kuala Lumpur, Malaysia. Edited by Wan Razali Letourneau, L. 1979. Updating continuous forest inventory by simu- Mohd, H.T. Chan, and S. Appanah. Forest ResearchInstitute of lation. In Forest resource inventories. Proceedings of SAF/TUFRO Malaysia, Kepong. pp. 225-241. Workshop at Colorado State University, 23-26 July 1979, Fort Vanclay, J.K. 1990. Effects of selection logging on rainforest produc- Collins. Edited by W.E. Frayer. Department of Forestry and Wood tivity. Aust. For. 53:2OO-214. Sciences,Colorado State University, Fort Collins. pp. 341-353. Vanclay, J.K. l99la. Ecological sensitivity of Australian rainforests Meldahl, R.S., Eriksson, M., and Thomas, C.E. 1985. A method for to selective logging. Aust. J. Ecol. 16:54I-542. grouping species- forest type combinations for the development Vanclay, J.K. l99lb. Aggregating ftee species to develop diameter of growth models for mixed species stands. 1lr Proceedings of the increment equations for tropical rainforests. For. Ecol. Manage.42; 3rd Biennial Southern silvicultural ResearchConference, 7-8 Nov. 143-168. t984, Atlanta, Ga. Edited by E. Shoulders. U.S. For. Serv. South. Vanclay, J.K. l99lc. Mortality functions for north Queensland For. Exp. Stn. Gen. Tech. Rep. SO-54.pp. 422-428. rainforests.J. Trop. For. Sci. 4(l). In press. Monserud, R.A., and Ek, A.R. 1977. Prediction of understorey tree Vanclay, J.K., and Preston,R.A. 1989. Sustainabletimber harvesting height growth in northem hardwood stands.For. Sci. 23:39I-400. in the rainforests of northern Queensland. In Forest Planning for Poore, M.E.D. 1968. Snrdiesin Malaysian rainforest. I. The forest on People. Proceedings of l3th Biennial Conference of the Institute the Triassic sedimentsin Janka Forest Reserve.J. Ecol. 56: 143-196. of Foresters of Australia, 18-22 Sept. 1989, Leura, New South Preston, R.A., and Vanclay, J.K. 1988. Calculation of timber yields Wales. Institute of Forestersof Australia, Sydney. pp. l8l-191. from north Queensland rainforests. Queensland Department of Wykoff, W.R. 1986. Supplement to the user's guide for the stand Forestry, Brisbane. Tech. Pap. 47. prognosis model-version 5.0. USDA For. Serv. 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Appendix The following list indicates the affinities of species included in the present study. The list indicates the H & M code, botanical and common names, and the group to which the species has been assigned for the prediction of probability and amount of recruitment. A superscnpt a following the group identity indicates that the species was the founding species for the group. The amount of data used in the present study is also indicated, both as the number of occasions on which recruitment was recorded, and the total amount of recruitment observed on all occasions (stems/tra).Some species are denoted with a dash, which indicates that the species sharesthe same H & M code as another species,and data for the two specieshave been combined. The speciesgroups reflect similarity of recruitment patterns,and do not necessarilyhave any other ecological significance. The group numbering reflects the amount of data available for the founding speciesof the group, and in no way implies any commercial or silvicultural preference. In the interests of brevity, some varieties and subspecieshave been omitted from this list. The species presented are those actually representedin the data. Some H & M codes are also applied to other speciesnot present in the data base.

Total H&M Probability Amount No. of recruits code Botanical name Common name group group observations (stemsAra)

ALB Prunus turneriana Almondbark 2574 789.9 BBN Castanospermumaustrale Black bean 4725 583.7 BKR Commersonia bartramia Brown kurrajong 44 9 86.3 BLA Sloanea australis ssp.parviflora Blush alder 4833 229.6 BLC Planchonella xerocarpa Blush coondoo 48t2 54.4 BLO Bleasdalea bleasdalei Blush silky oak 4826 209.2 BLO Opisthiolepis heterophylla Blush silky oak 48 BLW Beilschmiedia obtusifulia Blush walnut 48a rt9.9 BLW Endiandra sp. (RFK 19) Blush walnut 48 BOC Brackenridgea nitida ssp. australiana Brown ochna 4823 148.8 BRC Canarium baileyanum Brown cudgerie 4820 rt3.4 BRO Darlingia darlingiana Brown silky oak 3897 992.3 BRP Podocarpus elntus Brown pine 481l 7t.2 BRP Podocarpus grayi Brown pine 48 BRQ Elaeocarpus coorangooloo Brown quandong 4823 116.8 BRQ Elaeocarpus ruminatus Brown quandong 48 BRT Argyrodendron polyandrum Brown tulip oak 5498 1684.8 BRT Argyrodendron trifoliolatum Brown tulip oak 54 BRY Brombya platynema Brombya 5a8 t7 128.8 BSH Sy4tgium cormiflorum Bumpy satinash 42r6 93.0 BSL Acacia aulacocarpa Brown salwood 2393 3600.4 BSO Musgravea heterophylla Briar silky oak 3854 763.3 BTD Glochidion ferdinandi Buftonwood 42t2 81.0 BTD Glochidion harveyanum Buttonwood 42 BTD Glochidion sunuilranum Buttonwood 42 BTM Castanospora alphandii Brown tamarind 57" t riz.z BUA Apodytes brachystylis Buff alder 5847 324.4 BWD Litsea bindoniana Bollywood 25166 2544.6 BWD Litsea leefeana Bollywood 25 CHS Syrygium luehmannii Cherry satinash 48l3 74.6 CLL Cryptocarya cinnamomifolia Cinnamon laurel 42l8 185.9 CLO Carnarvonia araliifolfa Caledonian oak 3454 607.7 CHM Alangium villosum Canary muskheart 3l35 387.9 CNN Aleurites moluccana Candlenut 5l67 999.5 COL Cryptocarya sp. (RFK 2153) Coconut laurel 42t2 74.6 COW Endiandra dichrophylla Coach walnut 4830 186.8 COW Endinndra montana Coach walnut 48 COW Endiandra tooram Coach walnut 48 EUQ Elaeocarpus eumundi Eumundi quandong 48t2 q5.g EVD Euodia elleryana Evodia 3l36 412.5 FIG Ficus spp. Figwood 4828 2r2.4 FKI Brachychiton acerifulius Flame kurrajong 4220 t54.9 GCB Sloanea macbrydei Grey carabeen 482l 178.3 HKA Flindersia ifflaiana Hickory ash 4222 329.6 HMW Alstonia muellerana Hard milkwood 26r2r 2606.6 HYW Endiandra pubens Hatry walnut 44 9 128.7 IBS Polyscins australiana Ivory basswood 4433 230.6 LL Cryptocarya angulata Ivory laurel 48t7 96.2 VANCLAY 1247

Total H&M Probability Amount No. of recruits code Botanical name Common name group group observations (stemsftra)

KML Mallotus mollissimus Kamala 4 8 75 581.0 KML Mallotus philippensis Kamala 4 8 KML Mallotus polyadenos Kamala 4 8 KML Rockinghamia angustifulia Kamala 4 8 KRS Syzygium kuranda Kuranda satinash 4 8 62 uor.o LAN Acronychia acidula Lemon apen 4 5 62 1028.5 MCB Xanthophyllum octandrum Macintyre's boxwood 4 8 28 r79.7 MIS Miscellaneous Miscellaneous la la 474 3r32.4 MSW Flindersia pimenteliana Maple silkwood 2a 2a 233 r2780.7 MWN Endiandra sp. aff. E. muelleri Rose walnut 4 8 28 21o.4 NBD Omalanthus populifolius Native bleedingheart 3 I 28 552.7 NEV Euodia vitiflora Northern evodia 3 8 44 339.6 NHQ Elaeocarpus sericopetalus Hard guandong 4 I 30 3r7.4 NKP Agathis atropurpurea Queensland kauri pine 4 8 3l 247.5 NKP Agathis robusta Queenslandkauri pine 4 8 NLL Cryptocarya hypospodia Northern laurel 4 8 22 t20.5 NRA Alphitonia whitei Red ash 2 3a r64 3528.1 NRW Endiandra cowleyana Rose walnut 4a 8 42 233.3 NRW Endiandra hypotephra Rose walnut 4 8 NSB Citronella smythii Silky beech 4 8 26 169.0 NSO Cardwellia sublimis Northern silky oak 3a 4a 150 1557.2 NSS Daphnandra repandula Sassafras ) 4 156 r774.6 NSS Doryphora aromatica Sassafras 5 4 NTG Myristica insipida Nutmeg 4 8 33 r76.8 NTQ Elaeocarpusfoveolatus Northern quandong 3 I 24 352.7 PAL Gillbeea adenopetala Pink alder 4 2 2I 160.6 PKA Alphitonia petriei Pink ash 2 6 93 2170.5 PLM Archontophoenix alexandrae Piccabeenpalm 4 8 ll 85.8 PLM Licuala ramsayi Fan palm 4 8 PPW Cinnamomum laubatii Pepperwood 3 8 27 183.1 PTM Jagera discolor Pink tamarind 4 4 61 670.0 PTM Jagera pseudorhus Pink tamarind 4 4 PTM Sarcotoechia lanceolata Pink tamarind 4 4 PTM Toechima erythrocarpum Pink tamarind 4 4 QMP Flindersia brayleyana Queenslandmaple 2 5a G zlu.+ QSA Flindersia bourjotiana Silver ash 2 5 229 4117.5 QWN Endiandra palmerstonii Queenslandwalnut 4 2 9 45.4 RAL Caldcluvia australiensis Rose alder 4 8 11 68.8 RBN Blepharocarya involucrigera Rose butternut 4 84 29 26t.8 RCD Toona australis Red cedar 4 4 44 1009.2 RDT Argyrodendron peralatum Red tulip oak 5 4 9l 2109.2 RDT Argyrodendron sp. (RFK 2139) Red tulip oak 5 4 RLL Cryptocarya mackinnoniana Rusty laurel 4 2 66 479.5 RMP Crytocarya rigida Rose maple 4 8 l0 63.5 ROO Darlingia ferruginea Rose silky oak 3 5 27 303.9 ROO Placospermum coriaceum Rose silky oak 3 5 RPS Syzygium endophloium Rolypoly satinash 4 8 19 107.4 RSS Syzygium johnsonii Rose satinash 4 2 12 7t.l SBN Archidendron vaillantii Salmon bean 4 8 l1 70.5 SBS Polyscias elegans Silver basswood 4 8 68 873.r SHT Halfurdia scleroryla Saffronheart 4 8 15 ro2.3 SLQ Elaeocarpus grandis Silver guandong 3 2 62 928.0 SMP Flindersia laevicarpa Scented maple 4 5 10 126.1 SNW Endiandra sankeyana Sankey's walnut 4 8 t2 84.3 SST Dendrocnide photinophylla Shining-leaved stingingtree 3 I 48 693.8 SSW Flindersia acuminata Silver silkwood 3 8 44 463.6 STP Canarium australianum Scrub turpentine 4 8 t0 69.7 STP Canarium muelleri Scrub turpentine 4 8 STS Ceratopetalumsuccirubrum Satin sycamore 4 8 46 358.4 SVB Casearia dallachii Silver birch 4 7 ll 144.9 SYN Synima cordierorum Synima 4 8 t7 106.1 TBH Tetrasynandra laxiflora Tetra beech 4 2 18 138.8 TBH Tetrasynandra pubescens Tetra beech 4 2 TRQ Elaeocarpus largiflorens Tropical quandong 3 8 57 515.0 r248 CAN. J. FOR. RES. VOL.22.1992

Total H&M Probability Amount No. of recruits code Botanical name Common name group group observations (stemVha)

TST Franciscodendron laurifulium Tulip sterculia 4 8 58 486.8 TYW hnthorylum veneficum Thorny yellowwood 5 7 53 830.0 WAL Polyosma alangiacea White alder 4 3 13 186.6 WAS Acronychia acronychioides White aspen 5 2 55 492.6 WAS Acronychia vestita White aspen 5 2 WBH Gmelina fasciculiflora White beech 4 2 11 52.8 WBS Polyscias murrayi White basswood 2 6a 95 2217.2 WCB Sloanea langii White carabeen 3 8 60 506.3 WCD Melia azedarach var. australasica White cedar 5 7 t7 726.4 WHO Stenocarpus sinuatus White silky oak 4 8 10 70.0 WHZ Symplocos cochinchinensis White hazelwood 4 2 34 306.5 YEV Euodia bonwickii Yellow evodia 4 2 4l 479.9 YEV Euodia xanthoryloides Yellow evodia 4 2 YWN Beilschmiedia bancroftii Yellow walnut 4 8 l3 67.6