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P u b l i s h i n g Wildlife Research Volume 28, 2001 © CSIRO 2001 All enquiries and manuscripts should be directed to: Wildlife Research CSIRO Publishing PO Box 1139 (150 Oxford St) Collingwood, Vic. 3066, Australia Telephone: +61 3 9662 7622 Fax: +61 3 9662 7611 Email: [email protected] Published by CSIRO Publishing for CSIRO and the Australian Academy of Science www.publish.csiro.au/jour nals/wr Wildlife Research, 2001, 28, 555–564 The distribution and abundance of ground-dwelling mammals in relation to time since wildfire and vegetation structure in south-eastern Australia P. C. CatlingA, N. C. CoopsB and R. J. BurtA A B CSIRO Sustainable Ecosystems, GPO Box 284, Canberra, ACT 2601, Australia. CSIRO Forestry and Forest Products, Private Bag 10, Clayton South, Vic. 3169, Australia. Abstract. Vegetation undergoes a natural succession after wildfire. Following an initial flush of vegetation, when light and other resources become limiting, the stand structure rapidly reaches a maximum. As a result, vegetation structure does not form an even distribution over the landscape, but rather a patchwork pattern. The position and characteristics of a patch of habitat in the landscape may be critical in determining the faunal composition. In this paper a sequence of ‘habitat complexity scores’ (which describe vegetation structure independently of plant species) collected over 20 years following a wildfire was utilised to estimate vegetation structure in relation to time since fire. This information was compared with data collected over the same period on medium-sized and large grounddwelling mammals to examine the response of mammals to changes in vegetation structure. Models are presented of the response of ground-dwelling mammals to time since wildfire and to changes in habitat complexity scores, with predictions up to 25 years after wildfire. WeP.EtaCRfl.0eCcta0ot4lfi1nwg,ldNf.irCe.anCdovespgaentdiRo.nJst.Brucrtu eonmam aldistribution Introduction A temporal mosaic is created by changes in the structure of vegetation as it undergoes the natural progression from regrowth to senescence following disturbances such as fire, grazing or timber harvesting. As a result, vegetation structure does not form an even distribution over landscapes, but rather a patchwork pattern (McCarthy and Lindenmayer 1998; Catling and Coops 1999). There are likely to be many variations in patterns. Within one area many habitats can exist at once, due to the effect of terrain, species’ characteristics (Noble and Slatyer 1981), and variation superimposed with a variety of natural and human disturbances (Austin 1978; Braithwaite et al. 1993; Austin et al. 1996, 1997). For example, after wildfire in eucalypt forests there is a natural succession (Attiwill et al. 1994). Eucalypt seed will fall from the scorched crowns and seedlings will grow rapidly on the burned forest floor. Also, the heat of the fire will stimulate the germination of understorey species that have dropped seed or persisted in the soil since the last major fire, to form a dense stratum of shrubs through which the eucalypts emerge (Attiwill et al. 1994). Many mature trees may persist by putting out epicormic shoots. The eucalypts have reasonable crown space until full canopy closure, when light and other resources become limiting and the stand structure rapidly reaches a maximum (Jacobs 1955; Attiwill © CSIRO 2001 et al. 1994). This process of self-thinning, whilst ongoing, often commences at canopy closure about 10–15 years after establishment. Also at this time understorey development slows due to a lack of light and competition between individuals for moisture and other resources. Shrub species with regenerative properties stimulated by fires will show a marked increase in density soon after fire, followed by a progressive decline with time. Height and cover may show a similar pattern with time, but shrubberies may collapse, eventually leaving only a short or sparse understorey (Gill 1964; Catling 1991; Catling and Burt 1995) or, in the long term, a grassy understorey (Attiwill et al. 1994). The position and characteristics of a patch of habitat in the landscape may be critical in determining the faunal composition (Catling and Burt 1994, 1995; Braithwaite and Muller 1997). As with plant communities, the distribution of animals forms a patchwork across the landscape. The nature of this patchwork can be described, albeit in an abstract fashion, by time-since-fire functions (mathematical models to describe proportions of the landscape having different times since fire) (Gill and Catling, in press). The development of a ‘habitat complexity score’ has provided a way of describing the structure of the forest and habitats of ground-dwelling mammals (Newsome and Catling 1979; Catling and Burt 1995). The score is independent of plant species and is based on the cover of the tree canopy, shrub understorey, ground herbage, cover of rocks and debris and 10.1071/WR00041 1035-3712/01/060555 556 P. C. Catling et al. general soil-moisture condition (Newsome and Catling 1979). Relationships have been demonstrated between variations in forest structure and mammal abundance, based on the habitat complexity scores, with some species positively related and others negatively (Catling and Burt 1995; Catling et al. 1998, 2000). Recently, a sequence of habitat complexity scores collected at Nadgee Nature Reserve over a 20-year period was utilised to estimate forest habitat complexity in relation to time since wildfire and to provide future predictions of the distribution of habitats following wildfire (Coops and Catling 2000). The frequency distribution of those habitat complexity scores were fitted using a binomial distribution and the fitted binomial parameters modelled to provide a relationship between the frequency of different habitats across the landscape and to the time since fire. In this paper we have used that historical sequence of habitat complexity scores and data on medium-sized and large ground-dwelling mammals to examine the response of mammals to changes in vegetation structure following wildfire and to predict the response of mammals up to 25 years after wildfire. Methods Study area Nadgee Nature Reserve Wilderness Area is a 20 000-ha reserve located in the south-eastern corner of New South Wales. It extends 22 km north from Cape Howe to Wonboyn Lake and inland up to 15 km (Fig.1). The climate is temperate and mild marine (Fox 1970). Heathlands extend along the coast, perched behind sea cliffs about 20–50 m high. There are heathlands on the coastal range at about 500 m above sea level that are more complex, with rocky outcrops, moist peaty soils and different vegetation. Dry sclerophyll forest of Eucalyptus sieberi and E. muellerana predominate between the coastal and upland heathlands. Small areas of wet sclerophyll forest, including E. longifolia with rainforest elements in the understorey, extend along watercourses and at the heads of wet gullies. There are thickets of Leptospermum spp. and Melaleuca spp. on the dunes and swales behind the beaches. Fig. 1. The location of Nadgee Nature Reserve Wilderness Area in south-eastern Australia. Fire history In the early 1900s fires were lit periodically by stockmen to promote regrowth for grazing and a wildfire burnt over half the reserve in 1933 (NSW NPWS 1979). Since the Area was proclaimed as a Nature Reserve in 1957 there have been two low-intensity fires of limited extent in 1963 and 1965 (Baird et al. 1994). More importantly, there have been two high-intensity wildfires, in December 1972 and November 1980, which severely burnt most of the reserve on each occasion (P. Catling, personal observation; Gilmour 1983). Overall, the primary management goal is conservation of the natural ecosystems. The only fires lit deliberately by management are of low intensity to reduce fuel loads near the northern reserve boundary with Wonboyn Village (P. Windle, NSW NPWS, personal communication). Data collection and the rest were in heathland and forest (n = 99); in the latter a habitat complexity score (Newsome and Catling 1979) was estimated for the structure of habitat. The score was based on five features: the percentage of (1) canopy cover; (2) shrub cover; (3) ground vegetation cover; (4) cover of litter, fallen logs and rocks; and (5) a moisture rating. Each feature was rated on a scale of 0–3 and the scores for the five features totalled to give the score. For example, in forest a score of 4 or 5 denotes a limited structure with few understorey shrubs and little ground cover, whereas a score of 9 or 10 is forest with a thick understorey and dense ground and litter cover. A score of 7 denotes moderate structural complexity. Habitat complexity scores were recorded on the 99 sites in December 1980 (one month after fire), 1986 and every two years since 1986 to 1998. In 1982 only a subset of the sites (n = 25) was recorded for habitat complexity. Vegetation In April 1972, study sites (n = 120) were established in the reserve as part of the study of the ecology of the dingo, Canis lupus dingo (Newsome et al. 1983). These sites were used later to study the responses of ground-dwelling mammals to wildfire (Newsome et al. 1975; Catling and Newsome 1981). Twenty-one sites were on beaches Ground-dwelling mammals Soil plots were placed at each of the 120 study sites and used to record the tracks of medium-sized and large mammals (Morrison 1981; Triggs 1996) to provide an index of their abundance (Catling and Burt 1994). Soil plots are 1-m-wide bands of raked soil and sand aligned Effect of wildfire and vegetation structure on mammal distribution Table 1. Abundance ratings for ground-dwelling mammals Data are derived from Catling and Burt (1994, 1997). Values are the percentage of plot-nights with tracks Species Scarce Long-nosed potoroo Bandicoot Common brushtail possum Common ringtail possum Common wombat Large wallaby Eastern grey kangaroo Dingo Fox Cat <0.5 <2 <2 <1 <2 <5 <2 <5 <5 <2 Abundance rating Low Medium High 0.5 –1.5 2–5 2–5 1–2 2–5 5–10 2–5 5–10 5–15 2–5 >3 >10 >10 ≤3 >15 >20 >10 >20 >40 >15 1.5–3 5–10 5–10 2–3 5–15 10–20 5–10 10–20 15–40 5–15 across vehicle tracks. They were read for animal tracks, then re-raked each morning for three consecutive days. If strong winds or rain ruined the plots for the identification of tracks, the plots were re-prepared and read as soon as possible, until three nights of results had accumulated. Samples were taken at least twice per year. From the abundance data for each species a rating of scarce, low, medium or high abundance was used (Table 1). Analysis In order to convert the raw observations to a relative rank for each species the recorded observations from the 99 sites were pooled for each habitat score for each of the 9 observation dates, resulting in a variable that could be modelled and compared consistently through time (percentage of plot-nights with tracks). Multiple regression was used to determine the relationship between the observed abundance of each species (percentage of plot-nights with tracks) and (a) the observed habitat complexity score and (b) the time (in years) since the fire. An optimum model was selected based on the highest coefficient of determination (r2) and significance of the model. A stepwise regression approach was used to examine the response of fauna to time since fire and to changes in vegetation structure, utilising (a) simple linear representations of time since fire and (b) a quadratic (polynomial) transformation of time since fire. The assumption behind the use of a polynomial transformation was based on observations of eucalypt stand development (see Introduction). Using a polynomial transformation best fits the transition in forest structure from initially simple, to complex and then slowly simplifying. As habitat complexity score is an ordinal (ranked) variable it was introduced to the modelling procedure as a categorical variable, with each habitat complexity score requiring an additional parameter (or degree of freedom) in the model. Stepwise regression analysis was used in case there were highly significant correlations between the time since fire and habitat complexity score, which may possibly occur in situations where the forest was completely removed by fire, resulting in a strong correlation between the complexity of the regrowth and the time since the major fire. In most cases this was unlikely to be the case, with most forest stands left after the fire with some varying stand structure. Results Changes in vegetation structure (habitat complexity scores) with time since fire From the changes in the habitat complexity scores recorded since the 1980 fire it was apparent that the forest habitat was becoming more complex over time. In 1980, immediately 557 after the vegetation had been removed or affected by fire, most sites had a very simple structure (average score 4.1) and there were very few plots with scores of 7 and above (Fig. 2). By Year 6 after fire (1986), about half the sites had an observed score of 7 and below, with the remainder above (average score 6.7). By 1998 (Year 18 after fire), most sites recorded a score of 7 and above (average score 7.7). Changes in the features of the habitat complexity score with time since wildfire The temporal changes in the components of the habitat complexity score demonstrate in which strata of the forest the major changes in structure took place (Fig. 3). Moisture rating was scored but is not presented because it did not vary at a site over time or between sites. This was due to the static nature of the component; i.e. a site located near a watercourse or in a swamp in 1980 (resulting in a higher moisture score) remained so throughout the monitoring period. Overall, for all features (canopy, shrub, ground and litter cover) there was a general shift in the number of sites with low cover scores to high cover scores from just after the fire (1980) to 18 years later (1998) (Fig. 3). For canopy cover and shrub cover, by 1992 the number of sites in each category had stabilised, indicating that the tree canopy and the shrub density in the understorey had reached maximum cover (Fig. 3a, b). For canopy cover, by 1992 (12 years after fire) most sites were in the 30–70% cover category (Fig. 3a), and for shrub cover, in the >70% cover category (Fig. 3b). There has been little change in the number of sites in each category for canopy cover or shrub cover since 1992. However, ground cover stabilised much sooner at about 6 years after fire (1986), as indicated by the number of sites in each category that shifted from zero cover immediately after the fire to categories 30–70% and >70% by 1986 (Fig. 3c). Since then, ground cover has declined in many sites, with a threefold increase in the number of sites in the category <30% cover from 1986 to 1998. Litter cover was stable until 1988 and then many sites increased in litter cover. After 1988 the number of sites in category 0 declined and shifted to category <30% cover, and then by 1996 many of those had increased in litter cover and shifted to 30–70% cover. By 1998 over half the sites were in category 30–70% cover (Fig. 3d). To summarise Fig. 3, tree cover and shrub cover reached a maximum 12 years after wildfire and have varied little since. Ground cover reached a maximum very quickly (by 6 years after fire). After Year 12, when shrub and canopy cover reached their maximum, and light reaching the lower strata was at its lowest, a decrease in ground cover resulted. Litter cover was low and stable for about 8 years, but increased since as shrub and tree cover reached their maximum. Litter cover was still increasing, which would help to restrict the amount of ground cover also. 558 P. C. Catling et al. Fig. 2. Frequency distributions of the measured habitat complexity scores from 1980 to 1998 in Nadgee Nature Reserve (from Coops and Catling 2000). The solid line is the predicted binomial response. Models of faunal response to time since wildfire and changes in vegetation structure Ten mammal species were studied: long-nosed potoroo, Potorous tridactylus; common brushtail possum, Trichosurus vulpecula; common ringtail possum, Pseudocheirus peregrinus; common wombat, Vombatus ursinus; eastern grey kangaroo, Macropus giganteus; dingo, Canis lupus dingo, or their crosses with domestic dogs, Canis lupus familiaris; cat, Felis catus; and fox, Vulpes vulpes. Some species were grouped because their tracks either could not be, or were not, identified to species. For example, the tracks of southern brown bandicoot, Isoodon obesulus, and longnosed bandicoot, Perameles nasuta, could not be distinguished so they were grouped as ‘bandicoot’. A second group was ‘large wallaby’ (red-necked wallaby, Macropus rufogriseus, and swamp wallaby, Wallabia bicolor). Potoroo Overall, potoroos were scarce to low in abundance throughout the study (Table 1; Fig. 4a). Both habitat complexity score and the time since fire were significant variables in explaining the abundance of potoroos (Table 2). They were scarce or absent from some habitats immediately after the fire and gradually increased across all habitat complexity scores. Their abundance can be related primarily to time since fire, with a strong linear response to this variable over the monitoring period. There was also a significant response to habitat complexity score, with a Effect of wildfire and vegetation structure on mammal distribution Bandicoot a) Canopy cover 100% 80% 60% 40% 20% 0% 1980 559 1986 1988 1990 1992 1994 1996 1998 b) Shrub cover 100% 80% Table 2 indicates a highly significant positive relationship between abundance of bandicoots and habitat complexity score. This model explains 54% of the variation in abundance of bandicoots using habitat alone. Fig. 4b indicates scarce abundance of bandicoots at low habitat scores (1–3) with a steady increase in abundance from habitat scores of 4 onwards, reaching an asymptote at medium-to-high levels at habitat scores of 8 and above. Prediction The predicted abundance of bandicoots was not affected by the time since fire; their abundance was solely related to habitat complexity, with medium-to-high numbers of bandicoots likely to be observed in forests with high complexity. 60% Brushtail possum 40% 20% 0% 1980 1986 1988 1990 1992 1994 1996 1998 1986 1988 1990 1992 1994 1996 1998 c) Ground cover 100% 80% 60% 40% 20% 0% 1980 d) Litter cover Fig. 4c and Table 2 indicate that the abundance of brushtail possums responded little to time since fire and that the abundance can solely be attributed to spatial variations in forest habitat complexity. Habitat complexity score was the sole significant variable, explaining 43% of the variation in abundance of possums. The model was also highly significant, with a standard error of 3.5%. The relationship to habitat complexity score was complicated, with no brushtail possums at low scores but a steady increase in their abundance as scores increased from 4 to 8. The highest abundance of possums can be expected at the highest recorded habitat complexity score (10). Prediction The abundance of brushtail possums is expected to be unaffected by time since fire. The increase in their distribution and abundance is based on an increase in habitats of high structural complexity. 100% Ringtail possum 80% 60% 40% 20% 0% 1980 1986 1988 1990 1992 1994 1996 1998 Fig. 3. Histograms of changes in the percentage of sites in each score category for the features of the habitat complexity score in relation to time since wildfire. Black = score 3 (>70% cover), white = score 2 (30–70% cover), light grey = score 1 (<30% cover), dark grey = 0. slight increase in expected abundance as habitat score increased. The model showed a decrease in abundance at medium habitat complexity scores (scores of 3–5). Prediction After 5 years the model predicted an increase in abundance in all habitats and, by 25 years, for potoroos to be in high abundance in all habitats. Fig. 4d and Table 2 indicate that the abundance of ringtail possums responded little to time since fire and that the abundance can solely be attributed to spatial variations in forest habitat complexity. Habitat complexity score was the sole significant variable, explaining 35% of the variation in abundance of possums. The model was also significant, with a standard error of 2.0%. As with the brushtail possum, the relationship to habitat complexity score was complicated, with no ringtail possums at low habitat complexity scores, but a steady increase in their abundance as habitat complexity scores increased from 4 to 9 and then a slight fall at score 10. The greatest abundance of ringtail possums can be expected at the larger habitat complexity scores and especially score 9. Prediction Abundance of ringtail possums was unaffected by time since fire. The increase in their distribution and abundance was based on an increase in habitats of high structural complexity. (e) Wombat (h) Dingo (d) Ringtail possum (g) Kangaroo (i) Cat (f) Wallaby (c) Brushtail possum Fig. 4. Quadratic models of the distribution and abundance of medium-sized and large ground-dwelling mammals in relation to time since fire and changes in vegetation structure as measured by habitat complexity scores. Axes are as follows: x-axis = habitat complexity score (1–10); y-axis = time since fire (1–25); z-axis = abundance (% of soil plots with tracks). (b) Bandicoot (a) Potoroo 560 P. C. Catling et al. Effect of wildfire and vegetation structure on mammal distribution Table 2. The significant variables for the models of grounddwelling mammals in relation to time since fire and habitat complexity scores TSF, time since fire; HCS, habitat complexity score; n.s., not significant Species Variables R2 s.e. d.f. P Potoroo Bandicoot Brushtail possum Ringtail possum Wombat Wallaby Kangaroo Dingo Fox Cat HCS, TSF HCS HCS 0.53 0.54 0.43 1.2 5.9 3.5 47 48 48 <0.001 <0.001 <0.001 HCS 0.35 2.0 48 <0.01 HCS, TSF, TSF2 HCS, TSF TSF, TSF2 TSF, TSF2 0.57 0.65 0.18 0.16 4.6 5.7 3.0 4.8 46 47 55 55 HCS, TSF 0.61 3.7 47 <0.001 <0.001 <0.01 <0.01 n.s. <0.001 Wombat Fig. 4e and Table 2 show a complicated relationship between the abundance of wombats and the complexity of habitat and time since fire. Habitat complexity scores and time since fire are significant variables in the model as well as a quadratic form of the response concerning time since fire. As a result, the model shows an inverse quadratic response to time since fire, implying that the abundance of wombats was high immediately after the fire, then decreased over the next decade before starting to increase in the past 15 years. The relationship between habitat complexity scores and wombat abundance was strong and linear. Although there were variations in abundance with time since fire, wombats consistently were in greatest abundance at the highest habitat score. The decline in abundance of wombats with time since fire may be related to predation by dingoes (see Discussion). Fig. 4e indicates that the abundance of wombats 25 years after fire was similar in the areas of greater habitat complexity score and was equivalent to their immediate post-fire abundance. Prediction Wombat abundance will continue to increase in the reserve across all habitat complexity scores, but their abundance will be greatest at greater habitat complexity scores. Large wallaby Large wallabies were in medium abundance immediately after the fire and steadily decreased in abundance since then, with low and scarce abundance in all habitat types 20 years after fire. The model predicted habitat complexity score and time since fire as significant variables in wallaby distribution, with a linear response to time since fire (Table 2). The model was the most significant of all the presented models, with the largest correlation coefficient (r2 = 0.65). This result implies that large wallabies have a strong positive relationship with habitat complexity score, their greatest abundance occurring immediately after fire in 561 the high-habitat-score areas of the reserve, but declining to scarcity immediately after fire in areas of low habitat scores. This implies a need for cover from predation immediately after the fire, but that predation played a major role in their decline in open habitats and with time (see Discussion). The decline in abundance of large wallabies was consistent across all habitat areas from the time of fire, so that they were scarce in habitat complexity scores of 5 at around 18 years and in forests of score 10 at 25 years. Prediction The model predicted a gradual decline in abundance of wallabies from low abundance to scarce in the average to very complex habitats by Year 20. By 25 years after fire they will be scarce. Kangaroo Kangaroos were in low abundance throughout the monitoring period (Table 1; Fig. 4g). Fig. 4g indicates that they were in scarce and low abundance immediately after the fire, with a slight increase in numbers to medium abundance about 12 years after fire. There has been a modelled decline in abundance since then. The model indicates no significant relationship with habitat complexity score. It also predicts the disappearance of kangaroos from the reserve from about 20 years after fire. Although the model was significant at the 0.05 level, it was significant at lower levels than most of the models presented. Prediction After 15 years the abundance of kangaroos will reach a peak (although their abundance is still low) and by 25 years after fire the model predicted an absence of kangaroos from all habitats regardless of the structural complexity. Dingo Table 2 indicates a significant relationship between dingo abundance and a polynomial response to time since fire; this model was similar to that for kangaroo. There was no significant relationship with habitat complexity scores, indicating that abundance was solely related to time since fire. The model indicated that the abundance of dingoes peaked at about 12 years after the fire and slowly decreased subsequently. The model predicts dingoes at a low abundance in the future. Prediction The model predicted a decline in abundance of dingoes in all habitats by Year 15, such that by Year 25 they would be scarce or would have disappeared from all habitats (Fig. 4h). Fox There was no significant relationship between habitat complexity score or time since fire and the abundance of foxes. Habitat complexity score was weakly, but not significantly, related to fox abundance. As a result, a null model of fox abundance was as good as any other, implying that their abundance was related to variables other than fire disturbance and forest structural complexity. 562 Prediction The abundance of foxes could not be predicted on the basis of forest structure or time since fire. Cat Cats were in medium abundance before the fire and fell to low abundance immediately after the 1980 fire. Habitat complexity scores and the time since fire were significant variables in explaining the abundance of cats (Table 2), with a prediction of most cats at 25 years after fire in areas of medium to high habitat complexity scores. The cat population gradually increased to medium abundance since the fire, reaching a peak high abundance in some habitats at 25 years after fire (Fig. 4i). There was a linear response to time since fire. In the early years post-fire, the abundance of cats was low in the least complex habitats and at low to medium abundance in the others. By 15 years after fire, cats appeared in some habitats of lower complexity and had reached high abundance in the more complex habitats. Prediction The model predicts an increase in abundance in all habitats by 15 years after fire; by 25 years they should be in medium to high abundance in all habitats. Discussion We have developed statistical models from an extensive data set collected over many years and involving two surrogates – time since fire and habitat complexity – and abundance data for ground-dwelling mammals. Predictive methods based on surrogate measurements can be a valuable tool in investigating species distribution and abundance, and can be particularly useful when considering the impact of events such as disturbance or climate change. The use of surrogate measurements relies on the assumption that a model can be created linking the surrogate and the target (RAC 1993). Vegetation type is a surrogate often used to predict the distribution and abundance of arboreal fauna (e.g. Braithwaite et al. 1984; Kavanagh 1984). However, vegetation type is not a good surrogate for ground-dwelling mammals (Catling and Burt 1994, 1997), whereas structural complexity has proven to be a good surrogate in many cases (Fox and Fox 1981; Catling and Burt 1995; Catling et al. 1998). The development of a habitat complexity score provided an index of habitats of ground-dwelling mammals that was independent of plant species, and related mainly to changes in the biomass of vegetation (Newsome and Catling 1979). In the forests of eastern New South Wales the abundance of ground-dwelling mammals varies considerably, due to differences in habitat complexity (Catling and Burt 1995; Catling et al. 1998, 2000). Some species are positively related and others negatively related to habitat complexity, and the features of habitat complexity (tree canopy cover, shrub cover, ground cover, litter cover and wetness) are significant in many models (Catling et al. 1998, 2000). In this paper we have fitted the two variables as nominal (habitat complexity score) and either linear or quadratic P. C. Catling et al. (time since fire), using multiple linear regression. Obviously, other modelling procedures were available, including the use of generalised linear modelling and decision tree analysis. Analysis of the input data indicated that multiple linear regression was the simplest approach and we have no a priori indications that any other approach offers additional benefits. All of these approaches, however, fail to utilise the fact that the data set comprised repeat measurements in time of the same experimental units. As a result, there was possibly a correlation between successive yearly observations, particularly for vegetation assessments. Time series analysis therefore may offer some added interpretation of the data set. In this study three patterns of response by the grounddwelling mammals were identified: increasers, decreasers and those that showed no trend. Different responses can be expected due to the temporal component and to different changes in the features of the habitat complexity score. The response for the increasers (potoroo, bandicoot, ringtail and brushtail possums, wombat and cat) took two forms: those that responded both to time since fire and the increase in the availability of habitats of high structural complexity; and those that responded to the increase in habitats of high structural complexity only. A similar response was found for small mammals after fire on heathland and in adjacent forest at Nadgee, where their abundance and species richness increased as habitats aged and grew in complexity (Catling 1986). Models developed in other studies for the increasers (particularly potoroo, bandicoot and cat) also reflect their preference for undisturbed forests that are structurally complex, with a dense shrub and ground cover (NSW NPWS 1994; Catling et al. 2000). For brushtail possums, models from other studies reflect their preference for undisturbed forests that were structurally less complex with a relatively open understorey, but with some shrub cover and low litter cover (Lindenmayer et al. 1990; NSW NPWS 1994; Catling et al. 1998, 2000). The models for wombat were particularly interesting. Wombats survived the fire well in their burrows and increased in abundance after fire (Newsome et al. 1975). Similarly, they survived well in a clear-felled forest burnt afterwards and continued to occupy their ranges and breed (McIlroy 1973). Results from other studies reflect the preference of wombats for riverine communities with an open grassy understorey and low shrub and litter cover, and their requirements for burrowing sites on slopes above creeks and gullies (McIlroy 1995; Catling et al. 1998, 2000). The decline in wombat abundance observed in our study at about 10 years after fire was most likely due to predation by dingoes. The occurrence of wombat in the diet of dingoes increased from 1–3% in the first 5 years after fire to 14–40% in Years 7–9 after fire (Catling and Burt, unpublished). After that time, dingo abundance fell (see below) and wombat abundance increased. Effect of wildfire and vegetation structure on mammal distribution The decreasers (kangaroos, wallabies and dingo) responded primarily to time since fire and were most likely linked. Only large wallabies responded to the availability of structurally complex habitats, but that was immediately after the fire only. Their decline in open habitats and with time since fire is attributed to predation by dingoes. Macropodids were always a significant component in the diet of dingoes at Nadgee (Newsome et al. 1983; Catling and Burt, unpublished) and in other areas of temperate Australia (Corbett 1995). The response models for the decreasers reflect their preference for open and structurally simple habitats. In the first few years after fire, habitats were structurally simple with an open understorey (Coops and Catling 2000); consequently, the highest abundances of large wallabies, kangaroos and dingoes were recorded 6–9 years after fire. However, abundance declined sharply after that because the availability of structurally simple habitats continued to decline rapidly (Coops and Catling 2000) and there was severe predation by dingoes on the macropodids. Maximum canopy and shrub cover were reached about 12 years after fire. The models from other studies for eastern grey kangaroo reflect their preference for forests that are structurally simple, with an open grassy understorey and low shrub, ground and litter cover (Catling et al. 1998, 2000). Kangaroos require some shelter from predation by dingoes (Newsome et al. 1983), but they require open grassy areas to provide food (Poole 1995). Similarly, for dingoes the most favoured habitat was the ecotone between grassy valleys and eucalypt forests (Newsome and Catling 1979). The models from other studies for dingoes reflect their higher abundance or mobility in spring, and their preference for drier forests with some shrub cover but little ground cover (Catling et al. 1998, 2000). The availability and type of prey have a major influence on the distribution and abundance of dingoes (Corbett 1995). There was no prediction from our models for the fox. Foxes occur virtually everywhere in southern New South Wales and they display a lack of habitat preference (Jarman 1986; Phillips and Catling 1991; this study). Their abundance is influenced by factors other than structural complexity (Catling and Burt 1995) and so models from other studies in New South Wales vary considerably. In south-eastern New South Wales there were no structural variables in the model (Catling et al. 1998). In north-eastern New South Wales the model reflects their higher abundance and mobility in autumn, and their preference for drier forests that are structurally simple, with good shrub cover and average ground cover but low litter cover (Catling et al. 2000). Due to the large sampling effort needed to gather reliable abundance data for fauna, a landscape approach for animal studies has been slow to develop (Braithwaite 1991). Habitat complexity scores derived from forest structure provide descriptions of habitat and facilitate the prediction of faunal distribution and abundance (Catling and Burt 1995; 563 Catling and Coops 1999). Coops and Catling (1997) utilised airborne videographic imagery to accurately predict habitat complexity scores for small ground-dwelling fauna. From these predictions habitat complexity maps were developed as an initial stratification of the landscape into regions indicating the distribution and abundance of several small mammals (Catling and Coops 1999). The models developed here could be used in conjunction with videography to indicate the distribution and abundance of medium-sized and large ground-dwelling mammals at a landscape scale. Acknowledgments Thanks to the National Parks and Wildlife Service of New South Wales for granting access to Nadgee Nature Reserve. We acknowledge Dr Lisa Ganio (Oregon State University), whose statistical knowledge, advice, pragmatism and interest in this research were greatly appreciated. This work is a component of a large project involving CSIRO Sustainable Ecosystems (formerly CSIRO Wildlife and Ecology), NSW National Parks and Wildlife Service, CSIRO Earth Observation Centre (EOC) and CSIRO Forestry and Forest Products. We thank those organisations for their support and funding. Part of this research was undertaken within the CSIRO Multi-Divisional Project 31 on the Spatial Prediction of Forest Productivity. References Attiwill, P., Florence, R., Hurditch, W. E., and Hurditch, W. J. 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Manuscript received 25 May 2000; accepted 22 January 2001 http://www.publish.csiro.au/journals/wr