Download Wildlife Research - CSIRO Publishing

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. (1994).
‘The Burning Continent. Forest Ecosystems and Fire Management
in Australia.’ (Institute of Public Affairs: Australia.)
Austin, M. P. (1978). Vegetation. In ‘Land Use on the South Coast of
New South Wales’. (Eds M. P. Austin and K. D. Cocks.) pp. 44–66.
(CSIRO: Melbourne.)
Austin, M. P., Pausas, J. G., and Nicholls, A. O. (1996). Patterns of tree
species richness in relation to environment in south-eastern New
South Wales, Australia. Australian Journal of Ecology 21, 154–164.
Austin, M. P., Pausas, J. G., and Noble, I. R. (1997). Modelling
environmental and temporal niches of eucalypts. In ‘Eucalyptus
Ecology: from Individuals to Ecosystems’. (Eds J. E. Williams and
J. C. Z. Woinarski.) pp. 129–150. (Cambridge University Press:
Cambridge, UK.)
Baird, I., Catling, P. C., and Ive, J. R. (1994). Prescribed burning for
wildlife management at Nadgee Nature Reserve, Australia.
International Journal of Wildland Fire 4, 107–121.
Braithwaite, L. W., Turner, J., and Kelly, J. (1984). Studies on the
arboreal marsupial fauna of eucalypt forests being harvested for
wood pulp at Eden, New South Wales. III. Relationships between
faunal densities, eucalypt occurrence and foliage nutrients, and soil
parent materials. Australian Wildlife Research 11, 41–48.
Braithwaite, L. W., Belbin, L., Ive, J., and Austin, M. (1993). Land use
allocation and biological conservation in the Batemans Bay forests
of New South Wales. Australian Forestry 56, 4–21.
Braithwaite, R. W. (1991). Fauna and habitat surveys as ecological
pathfinders. In ‘Nature Conservation: Cost Effective Biological
Surveys and Data Analysis’. (Eds C. Margules and M. P. Austin.)
pp. 23–28. (CSIRO: Australia.)
Braithwaite, R. W., and Muller, W. J. (1997). Rainfall, groundwater and
refuges: predicting extinctions of Australian tropical mammal
species. Australian Journal of Ecology 22, 57–67.
564
P. C. Catling et al.
Catling, P. C. (1986). Rattus lutreolus, the colonizer of heathland after
fire in the absence of Pseudomys species? Australian Wildlife
Research 13, 127–139.
Catling, P. C. (1991). Ecological effects of prescribed burning practices
on the mammals of south-eastern Australia. In ‘Conservation of
Australia’s Forest Fauna’. (Ed. D. Lunney.) pp. 353–363. (Royal
Zoological Society of New South Wales: Sydney.)
Catling, P. C., and Burt, R. J. (1994). Studies of the ground-dwelling
mammals of eucalypt forests in south-eastern New South Wales
– the species, their abundance and distribution. Wildlife Research
21, 219–239.
Catling, P. C., and Burt, R. J. (1995). Studies of the ground-dwelling
mammals of eucalypt forests in south-eastern New South Wales: the
effect of habitat variables on distribution and abundance. Wildlife
Research 22, 271–288.
Catling, P. C., and Burt, R. J. (1997). Studies of the ground-dwelling
mammals of eucalypt forests in north-eastern New South Wales
– the species, their abundance and distribution. Wildlife Research
24, 1–19.
Catling, P. C., and Coops, N. C. (1999). Prediction of the distribution
and abundance of small mammals in eucalypt forests of southeastern Australia from airborne videography. Wildlife Research 26,
641–650.
Catling, P. C., and Newsome, A. E. (1981). Responses of the Australian
vertebrate fauna to fire: an evolutionary approach. In ‘Fire and the
Australian Biota’. (Eds R. H. Groves, I. R. Noble and A. M. Gill.)
pp. 273–310. (Australian Academy of Science: Canberra.)
Catling, P. C., Burt, R. J., and Forrester, R. I. (1998). Models of the
distribution and abundance of ground-dwelling mammals in the
eucalypt forests of south-eastern New South Wales. Wildlife
Research 25, 449–466.
Catling, P. C., Burt, R. J., and Forrester, R. I. (2000). Models of the
distribution and abundance of ground-dwelling mammals in the
eucalypt forests of north-eastern New South Wales in relation to
habitat variables. Wildlife Research 27, 639–654.
Coops, N. C., and Catling, P. C. (1997). Utilising airborne multispectral
videography to predict habitat complexity in eucalypt forests for
wildlife management. Wildlife Research 24, 691–703.
Coops, N. C., and Catling, P. C. (2000). Estimating forest habitat
complexity in relation to time since fire. Austral Ecology 25,
344–351.
Corbett, L. K. (1995). ‘The Dingo in Australia and Asia.’ (University of
New South Wales Press: Sydney.)
Fox, A. (1970). Development plan, 1970–75: Nature Reserve No. 6
– Nadgee. New South Wales National Parks and Wildlife Service
Report.
Fox, B. J., and Fox, M. D. (1981). A comparison of vegetation
classifications as descriptors of small mammal habitat preference.
In ‘Vegetation Classification in Australia’. (Eds A. N. Gillison and
D. J. Anderson.) pp. 166–180. (CSIRO and Australian National
University Press: Canberra.)
Gill, A. M. (1964). Soil–vegetation relationships near Kinglake West,
Victoria. M.Sc. Thesis, Botany Department, University of
Melbourne.
Gill, A. M., and Catling, P. C. (in press). Fire regimes and biodiversity
of forested landscapes of Southern Australia. In ‘Flammable
Australia’. (Ed. R. Bradstock, J. Williams and A. M. Gill.)
(Cambridge University Press.)
Gilmore, P. (1983). A survey of the vegetation of Nadgee Nature
Reserve. New South Wales National Parks and Wildlife Service
Report.
Jacobs, M. R. (1955). ‘Growth Habits of the Eucalypts.’
(Commonwealth Government Printer: Canberra.)
Jarman, P. (1986). The red fox an exotic, large predator. In ‘The
Ecology of Exotic Animals and Plants, Some Australian Case
Histories’. (Ed. R. L. Kitching.) pp. 43–61. (John Wiley and Sons:
Brisbane.)
Kavanagh, R. P. (1984). Seasonal changes in habitat use by gliders and
possums in south-eastern New South Wales. In ‘Possums and
Gliders’. (Eds A. P. Smith and I. D. Hume.) pp. 527–543.
(Australian Mammal Society: Sydney.)
Lindenmayer, D. B., Norton, T. W., and Tanton, M. T. (1990).
Differences between wildfire and clearfelling on the structure of
montane ash forests of Victoria and their implications for fauna
dependent on tree hollows. Australian Forestry 53, 61–68.
McCarthy, M. A., and Lindenmayer, D. B. (1998). Multi-aged mountain
ash forest, wildlife conservation and timber harvesting. Forest
Ecology and Management 104, 43–56.
McIlroy, J. C. (1973). Aspects of the ecology of the common wombat,
Vombatus ursinus (Shaw, 1800). Ph.D. Thesis, Australian National
University, Canberra.
McIlroy, J. C. (1995). Common wombat. In ‘The Mammals of
Australia’. (Ed. R. Strahan.) pp. 204–205. (Reed Books: Sydney.)
Morrison, R. G. B. (1981). ‘A Field Guide to the Tracks and Traces of
Australian Animals.’ (Rigby: Adelaide.)
NSW NPWS (1979). Nadgee Nature Reserve. Provisional Fire
Management Plan. Eden District.
NSW NPWS (1994). Results of vertebrate fauna surveys of north-east
NSW forests. North East Forests Biodiversity Study Report No. 3a.
Vol. 1, Site and transect based methods. Vol. 2, Incidental records.
New South Wales National Parks and Wildlife Service.
Newsome, A. E., McIlroy, J., and Catling, P. C. (1975). The effects of
an extensive wildfire on populations of twenty ground vertebrates in
south-east Australia. Proceedings of the Ecological Society of
Australia 9, 107–123.
Newsome, A. E., and Catling, P. C. (1979). Habitat preferences of
mammals inhabiting heathlands of warm temperate coastal,
montane and alpine regions of southeastern Australia. In
‘Ecosystems of the World. Vol. 9A. Heathlands and Related
Shrublands of the World’. (Ed. R. L. Specht.) pp. 301–316.
(Elsevier Scientific Publishing Co.: Amsterdam.)
Newsome, A. E., Catling, P. C., and Corbett, L. K. (1983). The feeding
ecology of the dingo. II. Dietary and numerical relationships with
fluctuating prey populations in south-eastern Australia. Australian
Journal of Ecology 8, 345–366.
Noble, I. R., and Slatyer, R. O. (1981). Concepts and models of
succession in vascular plant communities subject to recurrent fire.
In ‘Fire and the Australian Biota’. (Eds A. M. Gill, R. H. Groves and
I. R. Noble.) pp. 311–335. (Australian Academy of Science:
Canberra.)
Phillips, M., and Catling, P. C. (1991). Home range and activity patterns
of red foxes in Nadgee Nature Reserve. Wildlife Research 18,
677–686.
Poole, W. E. (1995). Eastern grey kangaroo. In ‘The Mammals of
Australia’. (Ed. R. Strahan.) pp. 335–338. (Reed Books: Sydney.)
RAC (1993). The use of surrogate measurements for determining
patterns of species distribution and abundance. Resource
Assessment Commission, Research Paper No. 8.
Triggs, B. (1996). ‘Tracks, Scats and Other Traces. A Field Guide to
Australian Mammals.’ (Oxford University Press: Melbourne.)
Manuscript received 25 May 2000; accepted 22 January 2001
http://www.publish.csiro.au/journals/wr