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Habitat quality for arboreal marsupials dependsmainly on (1) the availability of food and (2) theavailability of nest sites. Some arboreal marsupialsare exclusively folivorous (koala and greater glider),but others may feed on gum, pollen, nectar andarthopods (Smith, 1982; Strahan, 1983). Braithwaiteet al. (1983, 1984) found that the abundance ofpossums and gliders in the Eden area was related toforest communities with one or more species ofpeppermint eucalypts (Eucalyptus radiata, Eucalyp-tus dives and Eucalyptus elata). These species wereshown to have high concentrations of foliage nutri-ents. These forests also grew mostly on high fertilitysoils derived from Devonian intrusive bedrock.Braithwaites hypothesis of nutrient limitation as acontrolling influence on arboreal marsupials has sincebeen supported by additional studies (Norton, 1988;Kavanagh and Lambert, 1990; Jones et al., 1994).Smith and Lindenmayer (1992) and Lindenmayeret al. (1990a, 1991b) showed that the abundance ofAcacia species and the amount of decorticating barkof eucalypts are also correlated with habitat qualityfor some arboreal marsupials. The gum of Acacia ispart of the diet of several marsupial species (e.g.Smith, 1982). The amount of decorticating bark hasbeen used to estimate the availability of arthopodsthat live under the bark, which are also eaten bysome marsupial species (Strahan, 1983; Kavanagh,1987).Most arboreal marsupials use natural cavities intrees to nest and are strongly dependent on theavailability of these natural cavities. The relationshipbetween natural cavities and arboreal marsupials hasbeen shown by Smith and Lindenmayer (1988,1992)and Lindenmayer et al. (1991~) in Victorian forests.Braithwaite et al. (1983) showed that the minimumdiameter at which a tree forms a hollow suitable foran arboreal marsupial is about 60-80 cm. Inions etal. (1989) found that no trees of less than 40-50 cmdiameter at breast height (DBH) contained hollowssuitable for possums in Western Australia. Smith andLindenmayer (1988, 1992) considered trees withDEH greater than 50 cm and with obvious hollowsas potential nest trees. Mackowski (1984) found thattrees (Eucalyptus pilularis) with DBH less than 1 mhave very fe w holes (one or none), while the numberof holes increases above this size. Lindenmayer et al.(1993a) showed a positive relationship between tree

diameter and the number of holes, fissures and hol-low branches. Different eucalypt species have differ-ent probabilities of developing holes ore differentabundance of holes (Lindenmayer et al.. 1991a,1993a; Bennett et al., 1994). Lindenmayer et al.(1991c, 1993a) also provided models predicting theoccupancy and the number of cavities of hollow-.bearing trees for eastern Victorian forests. However,hollow-bearing trees are only a small proportion ofthe total trees in a stand, and therefore numbers aredifficult to estimate and the application of thesemodels is problematic. Implications for forest man-agement of the availability of hollow-bearing treesare discussed in Lindenmayer et al. t 1990b) andSmith and Lindenmayer (1992).On the basis of these known resource require-ments for arboreal marsupials and the available data,we developed a statistical model to predict the pres-ence of arboreal marsupials (possums and gliders) inan area with a wide range of environmental condi-tions and high diversity of trees.

2. Methods2.1. Study area

The study area corresponds to the Eden conces-sion district, on the South Coast of New South Wales(Australia), approximately 405 000 ha. All forestsare dominated by eucalypt species and are harvestedmainly for woodpulp. Keith and Sanders (1990)provide a description of the vegetation of the area.The altitude ranges from near sea level to about 1100m. Bedrock types are also varied and include con-glomerates, sandstones, granites, granodiorite, tonali-ties and homfels. Details and a location map of thestudy area are given in Braithwaite (1983) andBraithwaite et al. (1984).2.2. Sampling

Data on animals and their habitat were collectedfrom clear-cut forest coupes, each between 5 and 30ha in size and dispersed over the study area. A 0.25ha sample plot was established around each tree inwhich an animal was found during logging. Addi-tional random plots within the coupes were estab-


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lished to obtain information on the forest structure inthe absence of arboreal marsupials. For the presentwork, only plots located in coupes where no animalswere found were accepted as indicative of the ab-sence of marsupials. Because the home range ofsome species is greater than the plot size, and toavoid problems of negative autocorrelation, plotswithout arboreal marsupials but located in coupeswhere there were animals were not accepted foranalysis. The total number of plots used was 471.In each plot, site characteristics (topographic posi-tion, bedrock type, slope, aspect, latitude, longitude,altitude) and diameter at saw cut of the trees (60-80cm above ground level) were measured. A total of533 leaf samples was obtained from 46 tree (euca-lypts) species over the study area. For each treespecies, between three and 33 samples were ob-tained, depending on the abundance of the tree in thestudy area. These samples were from mature fullyexpanded ieaves taken at random from the crown ofthe tree. The foliage concentration of nitrogen, phos-phorus, potassium and magnesium was determinedaccording to Lambert (1976).2.3. Numerical analysis

For each plot, basal area and number of trees ineach diameter class and for each tree species were

computed. The eight diameter classes used were (incm): < 10, 10-19.9, 20-29.9, 30-39.9, 40-59.9,60-79.9, 80-100, > 100.Because of the known variation in foliage nutrientconcentration due to different factors (tree age, leafposition on the crown, climate, soil type, etc.), thefoliage nutrient concentration was not used directly;instead, it was transformed to a factor. The values offoliage nutrient concentration were averaged for eachtree species. Then, all tree species were ranked from1 to 5 (from low to high concentration) to obtain aspecies foliage nutrient index. The ranking methodbased on four variables (four nutrients) was done bymeans of principal component analysis. The signifi-cance of this index is shown in Table 1. There weresignificant differences in nitrogen, phosphorus andpotassium values for the different levels of thisindex, but not for magnesium.For each tree species, a bark decorticating (shed-ding) index from 0 (e.g. stringybark eucalypts) to 2(some gum eucalypts) was assigned to indicate theamount of shedding bark of each species. Similarly,a hole index from 1 to 3 (1, low; 2, intermediate; 3,high) was assigned to indicate the proneness toproduce holes suitable for arboreal marsupials, basedon field experience. These indices were obtained byaveraging subjective estimates from three field biolo-gists.

12345ANOVA

MeanSDMeanSDMeanSDMeanSDMeanSD

8.85 d 0.430 e 3.379 d0.88 0.057 0.447

11.21 c 0.525 d 4.367 c1.72 0.087 0.652

12.32 c 0.689 c 5.843 b2.35 0.109 1.259

14.50 b 0.839 b 6.961 a1.16 0.090 1.164

17.53 a 1.038 a 6.858 a1.62 0.142 0.912*** **t ***

Table 1Mean and standard deviations (SD) of the foliage nutrient concentration (mg g - dry weight) for the five levels of the species foliagenutrient index (ANOVA and multiple comparison are also shown)Species foliage Nitrogen Phosphorus Potassium Magnesiumindex

2.2040.4541.7400.4261.7860.4441.4990.4311.7140.073NS

Significan ce of ANOVA: * * * P < 0.001; NS, not significant. In columns, means a with different letter are significantly different atP < 0.05.


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40

25 1

m All trees m Den trees

I 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

43

Diameter classFig. 1. Diameter class distribution of the overall trees sampled (total trees) and of the trees used by the arboreal marsupials (den trees).Labels of diameter classe s: l-4 refer to diameter classe s of 10 cm (i.e. < 10 cm, 10 to 19.9, etc.); 5-16 refer to diameter classe s of 20 cm(i.e. 40 to 59.9, 60 to 79.9, etc.). For all trees, trees with diameter greater than 100 are included in the diameter class 8 (100 to 119.9cm).

nities. They used 20 different species as den trees.The tree species most used were Eucalyptus cypel-locarpa, Eucalyptus fastigata and Eucalyptus vimi-nalis. Fig. 1 shows the size class distribution of alltrees (darker pattern) and den trees (lighter pattern)

in all plots together; 83% of the den trees, comparedwith 4.8% of the total trees, were greater than 80 cmin diameter.The most significant variable (i.e. explained mostdeviance) predicting the occurrence of arboreal mar-

Table 2Summary of the analysis of deviance for the occurence of arboreal marsupialsVariable Deviance d.f. Change in

devianceNull model 648.3 470For FNI < 2.5

null 446.5 329+ FNI 347.7 328 98.8 * * *+ topography 342.8 327 4.9 *+ soil nutrients 337.6 326 5.2 *+ hole index 328.1 325 9.5 * *+ bark index 322.0 324 6.1 *

For FNI > 2.5null 103.8 140+ large trees 92.9 139 10.9 * * *+ DR+DR 79.8 137 13.1 * *+ BA ratio 74.3 136 5.4 *

FNI, foliage nutrient index; DR, proportion of the number of sma ll trees; BA ratio, proportion of the basal area of sma ll trees.Responses curves are shown in Figs. 3-5. Significan ce of the change in deviance: * * l P < 0.001; * * P < 0.01; * P < 0.05. d.f., degreesof freedom.


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0.0 I I I I1 2 3 4 5

Foliage Nutrient IndexFig. 2. Observed response of arboreal marsupials to site foliagenutrient index (FNI). Each point represents the frequency ofoccurrence for observations in a range of 0.25 units of FNI. Thevertical line indicates the FNI value o f 2.5, i.e. the level where themodel was split (see text). Different symbols refer to the numberof observations.

supials was site FNI. Along that nutrient gradient,there was a strong positive relationship with theprobability of occurrence at low nutrient levels, untila certain level of FNI is reached (FNI = 2.5; Fig. 2).This pattern suggested that the presence of arborealmarsupials along each segment of the gradient mightbe governed by different parameters. Therefore, thedata were split in two parts (FNI < 2.5 and FNI >2.51, and the statistical modelling was done for eachpart (Table 2). The split model was later comparedwith the model using the full data set, and a signifi-cant increase in explained deviance was obtainedusing the split mode1 (Table 3).At low values of FNI (i.e. FNI < 2.5), the signifi-cant variables were FNI, topography, soil nutrientindex, hole index and bark index. The occurrence of

Fig. 3. Predicted probability of occurrence of arboreal marsupialsin sites with FNI less than 2.5 (see Fig. 2), in relation to FNI (al,bark index (b) and hole index (cl. These predictions correspond toforest on ridge or slopes with low or intermediate soil nutrientlevels. Dotted lines refer to 95% confidence interval.

I/ I -,1.5 2.0 2.5

Foliage Nutrient Index

I0.0 1 / *Ii

0.0 0.5 1.0 1.5 2.0Bark Index

(cl 1.0

0.0 1 1.5 2.0 2.5 3.0Hole Index


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Table 3Summary of the analysis of deviance comparing the model splitby the FNI (Table 2 and Figs. 2-5) with the model using the fulldata set (not shown)Model Deviance d.f. Change in

devianceNull model 648.3 470Full model 440.9 465 207.3 * * *Split model 396.3 460 44.7 * * *The change in deviance is significant at P < 0.001. d.f., degreesof freedom.

arboreal marsupials showed a positive relationshipwith FNI, hole index and bark index (Fig. 3). Thebark index showed large variation in predicting theoccurrence of arboreal marsupials at intermediateand high levels (Fig. 3(b)). Forest on flat areas orgullies showed a higher probability of occurrence forarboreals than forest on slopes or ridges (Fig. 4).Forest located on high fertile soils also showedhigher occurrence of arboreal marsupials. The influ-ence of soil nutrients is more important on slopesand ridges than on flats and gullies.At high values of FNI, the occurrence of marsupi-als is usually high, but some variation was found(Fig. 2). The main variables to explain that variationwere the number of trees with diameter greater than60 cm, and the proportion of the number and basalarea of small trees (diameter < 20 cm). None of

1 2Topography

Fig. 4. Predicted probability of occurrence of arboreal m arsupialsin sites with FNI less than 2.5 (see Fig. 2) in different topographicpositions (1, ridges or slopes; 2, flats or gullies) and different soilnutrient levels (1, low or intermediate; 3, high). All other signifi-cant variables in the model were set at mean values. Vertical linesrefer to 95% confidence interval.

(a) 1.0

0 10 20 30Number of large trees

(b) 1.0

0.0 L

\\

I 1 I I0.0 0.2 0.4 0.6 0.8 1.0

Proportion of sma ll treesFig. 5. Predicted probability of occurrence of arboreal marsupialsin sites with FNI greater than 2.5 (see Fig. 2) in relation to (a) thenumber of large trees (trees with diameter greater than 60 cm),and (b) the proportion of sma ll trees (trees with diameter less than20 cm). Dotted lines refer to 95% confidence interval.

these variables were correlated with FNI. The num-ber of large trees and the proportion of basal area ofsmall trees showed a positive relationship with thepresence of arboreal marsupials (Fig. 5(a)). The pro-portion of small trees with respect to the total treesshowed a curve with a maximum at intermediatevalues (Fig. 5(b)). The occurrence of fauna declineswhen there are fe w small trees in the forest. It alsodeclines, and more strongly, when there is a veryhigh proportion of small trees with respect to thetotal, although the variation is relatively high at this


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end of the gradient. At intermediate values, theoccurrence remains high.When the residuals of the overall model wereinspected, a possible outl ier was observed. One site

had a very large adjusted residual, but the coefficientof sensitivity and the potent ial influence were notvery large. In this site a commom brushtail possumwas observed, while the prediction was an absenceof marsupials. This could be because we only haveten observations of this species in the data set, andprobably the model cannot predict this species accu-rately as it typically inhabits open woodlands ratherthan forests (Strahan, 1983). The model was fi ttedagain without this site, and the coefficients obtainedwere very similar to the previous model. So, theorig inal model was accepted.

Although the model presented here is statisticallysignif icant, a measure of success is needed. The errorrate of the split model (overall error for presence orabsence) was 18.47%. The percentage of presencespredicted correctly was 83.96 and the percentage ofabsences predicted correctly was 79.54. The modelprovides an adequate method to predict the habitatquality for arboreal marsupials in the study area.

4. DiscussionThese results are consistent with the hypothesis

that leaf quali ty is a major determinant of the distri-bution of arboreal marsupials in eucalypt forests(Braithwaite et al. , 1983, 1984; Cork, 1992). Asimilar trend was observed by Majer et al. (1992)studying fol iage arthropods in eucalypt forests. Cork(1992) has shown that the concentration of nutrientsin eucalypts leaves is inversely related to phenols(e.g. tannins), which are toxic for folivorous mam-mals. With our data, we cannot distinguish whetherthe arboreal marsupials respond directly to concen-tration of nutrients, or to the concentration of thesesecondary compounds. Our results also suggest thatthere are other factors than leaf quality which areimportant in predicting the occurrence of arborealmarsupials. The model shows that there is a thresh-old of fol iage nutrients below which the most impor-tant factors are related to availability of food (i.e.nutrients, bark index). In this range of fol iage nutri-ents, topography and hole index are also significant,

but the effect is less than for foliage nutrients, barkindex and soil nutrients (Figs. 3 and 4). Above thethreshold. the factors that control the habitat qualityare exclusively related to forest structure (Fig. 5). Asimilar threshold effect has been found in a study ofthe greater g lider in the north coastal forest of NewSouth Wales (Cork et al. , 1994j4).

The importance of topography for the occurrenceof arboreal marsupials has been suggested previously(Lunney, 1987: Lindenmayer et al.. 1990~. X99lb).We found more arboreal animals in forest on flats orgull ies than on slopes or ridges as Lunney (1987)and Lindenmayer et al. (199Oc) found. Lindenmayeret al. (1991b) found a negative relationship withslope. The effect of topography can be interpreted indifferent ways. Gullies are more protected from fire.and this allows more stable populations of arborealfauna. In the study area, gullies offer greater fforisticdiversity (Austin et al. , 1996) and, furthermore,greater diversity of foraging resources are availab le(Kavanagh, 1984). The energetic cost that an animalneeds for foraging in flat forest or gullies is lowerthan in forest located on slopes. We cannot distinguish between these hypotheses.

Another parameter that we found to be importantfor predicting the occurrence of arboreal mammalswas the bark decorticating index. Lindenmayer et al.(1990a, 1991b, 1993a) showed that the amount utdecorticating bark was an important factor IO predictthe occurrence of arboreal marsupials, the occurrenceand abundance of sugar glider, and the abundance ofLeadbeaters possum, in Eutdyp~us regnms forests.In all these cases. as well ah in the present study.bark index is assumed o be a surrogate measureoithe availability of arthropods that live underneath hebark.

The importance of the availability of potentialnest trees (PNT, i.e. trees large enough to develop ahole suitable for nesting arboreal mammals) o pre-dict marsupial distribution has been shown by Smithand Lindenmayer (1988. 19922).Bennett et al. (19941and Lindenmayer et al. (1991a) also showed thatdifferent eucalypt specieshave different susceptibil-ity to hole formation. Our results are consistent withthesestudies; .e. the occurrence of arboreal marsupi-als increaseswith the number of PNT. Our measureof PNT includes the number of large trees (Fig. 3)and the hole index (basedon large trees, and assum-


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(e.g. high number of large trees for den sites). Aconflict arises because forests with high fertility arealso those preferred for harvesting due to their higherproductivity. Therefore, the high-quality areas forarboreal marsupials are the same as those identifiedas of maximum utility for timber production. In thiscontext, Braithwaite et al. (1993) found a poor repre-sentation of forest with highly fertile soils in pre-served forests (e.g. national parks, flora reserves) incontrast with its representation in timber productionforests.

5. ConclusionsThe main factor predicting the occurrence of ar-boreal marsupials is the availability of food. Theavailability of food is estimated by the quality ofleaves (foliage nutrients and its correlates), theamount of decorticating bark (availability of arthro-pods) and the soil nutrients. When the food is not alimiting factor, forest structure determines habitatquality for arboreal marsupials. In this case, thenumber of large trees (potential nest trees) and the

proportion of small trees to the total are the mostimportant parameters.

AcknowkdgementsThis work has been partially financed by theMinisterio de Education y Ciencia (The SpanishGovernment) with a postdoctoral fellowship to thefirst author. We thank S.J. Cork and A.O. Nichollsfor their valuable comments as the work progressed,and M. Clayton and E.M. Cawsey for their help in

sampling and data base management (respectively).Useful comments on the manuscript were providedby S.J. Cork, D.B. Lindemnayer, A.O. Nicholls andtwo anonymous reviewers.

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1995-Pausas-FEM-modelling-Habitat-Quality

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