Faunal and Floral Community
Responses to Contemporary Fire
Regimes in Eucalypt Forests of
Southeast Queensland
Diana Angelique Virkki
BSc (Hons) Griffith University
Griffith School of Environment
Griffith Sciences
Griffith University
Submitted in fulfilment of the requirements of the degree of
Doctor of Philosophy
February 2014
Abstract
As an ecological process fire plays a global role in structuring ecosystems and their
constituent fauna and flora. Fire is also regularly applied as a landscape management
tool and altered fire regimes affect global biodiversity. Fire can have a marked influence
on vegetation composition and structure with resultant flow on impacts on faunal
communities. However, faunal responses to fire are often quite varied and management
recommendations of appropriate fire regimes therefore generally include utilising a fire
mosaic approach to fire management. This thesis investigates the relationship between
variable fire regimes, vegetation composition, condition and structure, and grounddwelling vertebrate faunal communities.
The literature review (Chapter 1) revealed several gaps in current knowledge. This
included a lack of detailed knowledge on the effects of variable fire regimes, including
repeated management burns and fire mosaics, representative of multiple fire parameters
(time since fire, number of fires, fire interval and fire type), on ecosystem biota. This
was particularly evident for subtropical ecosystems. To analyse the effects of repeated
burns, fire exclusion and wildfire, this study targeted one of the longest running fire
experiments in Australia, at Bauple State Forest, where fire treatments have been
applied annually since 1952 and triennially since 1973. Additional dry eucalypt forest
sites at St Mary and Tiaro State Forests were included that represented variable fire
management practices. Detailed fauna trapping surveys and vegetation assessments
were completed on 35 plots across eight fire treatments at all three sites. Additionally, a
broader scale GIS analysis of fire history was done across the three forests with surveys
undertaken at an additional 74 sites allowing a comparison of reptile communities,
forest condition and structure among variable fire regimes at multiple scales.
To quantify the structural and compositional influences that fire has on vegetation
communities in subtropical systems, Chapter 3 involved a detailed analysis of the
communities within the three dry eucalypt forests. Fire treatments significantly altered
vegetation community diversity, structure and composition. Annual burning resulted in
reduced floral diversity among all strata and reduced structural heterogeneity in the
ground and shrub layers. Long unburned areas were more diverse but had lower canopy
heterogeneity. Wildfires did not significantly impact the vegetation community.
i
Consistent with previous research, this chapter found that frequent, repeated burning
can reduce the diversity and structural complexity of forests.
The effects of disturbance (fire and logging) on forest condition and forest variables
across the landscape were investigated in Chapter 4 using a biodiversity condition
assessment toolkit among 63 plots. Forest variables and overall condition were
correlated with historical disturbance patterns, derived from GIS, among Regional
Ecosystems (RE), including time since fire, number of fires, fire interval, fire type and
years since logging. The majority of forest variables were negatively affected by more
frequent (61.5% of variables), or recent fire disturbances (76.9% of variables). The most
pronounced negative impacts occurred for tree species richness, and two key habitat
attributes for fauna including coarse woody debris and litter cover, which were
maximised in long unburned areas highlighting the importance of maintaining long
unburned refuges in the landscape.
Chapter 5 investigated the response of reptile and anuran communities, surveyed at 35
trapping plots, to different fire regimes and habitat characteristics quantified in Chapter
3.
Reptiles were more vulnerable to fire impacts than anurans, particularly frequent
fires (annual burning) which reduced the abundance of several species; however,
species were generally not affected by wildfire. Species such as Lampropholis delicata
and Eulamprus martini favoured long unburned areas, however, other species, i.e.
Carlia p. pectoralis, preferred frequently and recently burned sites. Anurans, on the
other hand appeared to be resilient to fires, corroborating previous studies on this taxon
as they are able to retreat into moist microhabitats to survive. The ‘risk’ and ‘impact’ of
fires to individual species was interpreted using a conceptual model that was able to
categorise species as either tolerant or resilient to fire. These species-specific responses
are important considerations when planning fire management guidelines that advocate
frequent burning, as some species may be negatively affected.
In a parallel analysis, Chapter 6 assessed small mammal communities across the three
sites and their response to variable fire regimes and habitat parameters. Mammals
generally were not strongly influenced by fire but had highly variable responses which
made it difficult to determine the factors influencing either species richness or
abundance. It therefore appears likely that small mammal communities are not being
negatively affected by repeated burning practices or infrequent wildfire. However,
species were often correlated with habitat heterogeneity characteristics, highlighting the
ii
importance of maintaining a structurally diverse habitat that supports a variety of
mammal species. As such, the indirect effects of fires may lead to a reduction in ideal
habitat for certain mammals.
In Chapter 7, reptile communities were assessed across the landscape and correlated
with various fire parameters and forest variables from Chapter 4. Number of fires, time
since fire and fire type were key predictors for overall reptile abundance, as well as the
abundance of four of the seven common study species. Overall abundance was
negatively affected by number of fires and it also altered the overall reptile assemblage,
while at a species level Carlia spp. were negatively associated with time since fire. Fire
was generally a stronger predictor of reptile responses than habitat parameters with only
shrub cover found to be negatively associated with Carlia spp. A reduction in total
number of fires of some areas and increase in variability of burn intervals may support a
greater diversity and abundance of reptiles in parts of the landscape where the total
number of fires is high.
Fire heterogeneity was quantified within fire regimes across multiple scales in Chapter
8, using the variation in time since fire, number of fires, fire interval and unique
(combination) fire mosaic regimes derived from GIS. Heterogeneity at the patch, local
and site scales calculated from the Shannon-Wiener diversity index was weakly
correlated with reptile communities and the heterogeneity measures appeared to be a
poor surrogate of ‘fire diversity’. While this heterogeneity measure could not explain
patterns in reptile communities, it is important to note that it captures only one element
of landscape ‘pyrodiversity’. As other fire parameters including fire type revealed
significant responses amongst reptiles, the specific nature of any biodiversity response
to pyrodiversity appears linked to both scale and specific parameters used.
This thesis has demonstrated that aspects of the vegetation community as well as faunal
species, favoured different fire regimes, where some species and structural
characteristics preferred frequently burned areas while others (although more often than
not) preferred infrequently burned or long unburned areas. Support is provided for the
suggestion that a mix of frequently burned, infrequently burned and long unburned
areas across the landscape will maximise floral and faunal communities, and help meet
management goals of maintaining biodiversity. However, the scale of these unburned
refuges is an important question that requires more research.
iii
Statement of Originality
This work has not previously been submitted for a degree or diploma in any university.
To the best of my knowledge and belief, the thesis contains no material previously
published or written by another person except where due reference is made in the thesis
itself.
_________________________
Diana A. Virkki
iv
Table of Contents
Abstract
.....................................................................................................................i
Statement of Originality ...................................................................................................iv
Table of Contents ..............................................................................................................v
List of Figures ....................................................................................................................x
List of Tables .................................................................................................................xix
List of Appendices ........................................................................................................xxvi
Statement of Access.................................................................................................... xxvii
Acknowledgements ................................................................................................... xxviii
Chapter 1.
General introduction ....................................................................................1
1.1
Introduction ........................................................................................................ 1
1.2
Fire in Australia ................................................................................................. 3
1.2.1
Fire in southeast Queensland................................................................................. 4
1.3
The fire regime ................................................................................................... 6
1.4
Fire as a management tool ................................................................................. 7
1.4.1
Risk reduction ....................................................................................................... 8
1.4.2
Repeated burning practices ................................................................................... 8
1.4.3
Ecologically appropriate fire regimes ................................................................... 9
1.5
The impacts of a changing climate on fire regimes ......................................... 10
1.6
Fire and biodiversity ........................................................................................ 11
1.6.1
Fire-prone ecosystems ......................................................................................... 12
1.6.2
Fire and vegetation .............................................................................................. 13
1.6.3
Fire and fauna ...................................................................................................... 14
1.7
Fire heterogeneity ............................................................................................ 16
1.7.1
Scale .................................................................................................................... 16
1.7.2
Fire mosaics: landscape scale .............................................................................. 17
1.7.3
Fire patchiness: local scale .................................................................................. 18
1.8
Australian long-term fire experiments in a global context .............................. 19
1.9
Aims and significance of this study ................................................................. 22
1.10 Thesis outline ................................................................................................... 24
v
Chapter 2.
General methodology ................................................................................25
2.1
Introduction ...................................................................................................... 25
2.2
Study area ........................................................................................................ 25
2.3
Study sites ........................................................................................................ 26
2.3.1
Bauple State Forest fire experiment .................................................................... 30
2.3.2
Tiaro State Forest ................................................................................................ 33
2.3.3
St Mary State Forest ............................................................................................ 34
2.4
Experimental design ........................................................................................ 34
2.4.1
The effects of repeated prescribed burning and wildfire ..................................... 34
2.4.2
Spatial analyses and fire regime study ................................................................ 36
Chapter 3.
3.1
Responses of eucalypt forests to frequent management burns and wildfire39
Introduction ...................................................................................................... 39
3.1.1
3.2
Aims and objectives ............................................................................................ 42
Methodology .................................................................................................... 43
3.2.1
Sampling sites ..................................................................................................... 43
3.2.2
Sampling protocol ............................................................................................... 43
3.2.3
Data assessment and statistical analyses ............................................................. 49
3.3
Results .............................................................................................................. 52
3.4
Discussion ........................................................................................................ 64
3.5
Conclusion ....................................................................................................... 72
3.5.1
Chapter 4.
Management implications ................................................................................... 72
The effects of disturbance history on forest condition and forest attributes
within dry eucalypt Regional Ecosystems ...............................................74
4.1
Introduction ...................................................................................................... 74
4.1.1
4.2
Aims and objectives ............................................................................................ 75
Methodology .................................................................................................... 76
4.2.1
Forest condition field assessments ...................................................................... 78
4.2.2
Condition scoring ................................................................................................ 80
4.2.3
Disturbance history ............................................................................................. 81
4.2.4
Data assessment and statistical analyses ............................................................. 82
4.3
Results .............................................................................................................. 84
4.3.1
Overall forest trends ............................................................................................ 87
vi
4.3.2
4.4
Trends within specific Regional Ecosystems ...................................................... 93
Discussion ...................................................................................................... 103
4.4.1
Specific Regional Ecosystem trends ................................................................. 104
4.4.2
Affected forest variables ................................................................................... 105
4.5
Conclusion and management implications .................................................... 109
Chapter 5.
Herpetofaunal responses to varying fire regimes and habitat structure in
dry eucalypt forests ................................................................................112
5.1
Introduction .................................................................................................... 112
5.1.1
5.2
Aims and objectives .......................................................................................... 114
Methodology .................................................................................................. 115
5.2.1
Sampling sites ................................................................................................... 115
5.2.2
Herpetofaunal surveys ....................................................................................... 116
5.2.3
Data assessment and statistical analyses ........................................................... 120
5.2.4
Interactive effects of fire treatment and seasonality .......................................... 122
5.2.5
Effects of time since fire and number of fires ................................................... 122
5.2.6
Determining the most important variables for herpetofauna ............................. 122
5.2.7
Variables explaining reptile composition .......................................................... 124
5.2.8
Influence of time since recent fire, fire patchiness and extent of burn at the
Bauple experiment ........................................................................................... 125
5.2.9
Reptiles and fine-scale microhabitat associations ............................................. 125
5.3
Results ............................................................................................................ 125
5.3.1
Reptiles .............................................................................................................. 125
5.3.2
Anurans ............................................................................................................. 129
5.3.3
Effects of fire treatment and seasonality on herpetofauna ................................ 131
5.3.4
Effects of time since fire and number of fires ................................................... 139
5.3.5
Determining the most important variables for herpetofauna ............................. 143
5.3.6
Variables explaining reptile composition .......................................................... 147
5.3.7
Influence of time since recent fire, fire patchiness and extent of burn at the
Bauple experiment ........................................................................................... 149
5.3.8
Reptiles and fine-scale microhabitat associations ............................................. 152
5.4
Discussion ...................................................................................................... 154
5.4.1
Reptiles .............................................................................................................. 155
5.4.2
Anurans ............................................................................................................. 159
5.4.3
Herpetofaunal resilience to fire and the importance of heterogeneity............... 160
5.5
Conclusion ..................................................................................................... 163
vii
Chapter 6.
Small mammal responses to varying fire regimes and habitat structure in
dry eucalypt forests ................................................................................166
6.1
Introduction .................................................................................................... 166
6.1.1
6.2
Aims and objectives .......................................................................................... 167
Methodology .................................................................................................. 168
6.2.1
Sampling sites ................................................................................................... 168
6.2.2
Trapping methods .............................................................................................. 168
6.2.3
Determinants of faunal communities ................................................................ 172
6.2.4
Data assessment and statistical analyses ........................................................... 173
6.2.5
Effects of time since fire, number of fires and interactive effects of fire treatment
and seasonality................................................................................................. 173
6.2.6
Determining the most important variables for mammals .................................. 174
6.2.7
Variables explaining faunal composition .......................................................... 176
6.2.8
Influence of time since recent fire, fire patchiness and extent of burn at the
Bauple experiment ........................................................................................... 176
6.3
Results ............................................................................................................ 176
6.3.1
Effects of time since fire, number of fires and interactive effects of fire treatment
and seasonality................................................................................................. 179
6.3.2
Determining the most important variables for fauna ......................................... 182
6.3.3
Variables explaining mammal composition ...................................................... 184
6.3.4
Influence of time since recent fire, fire patchiness and extent of burn at the
Bauple experiment ........................................................................................... 185
6.4
Discussion ...................................................................................................... 186
6.5
Conclusion ..................................................................................................... 190
Chapter 7.
Reptile responses to spatio-temporal fire history in subtropical eucalypt
forests .....................................................................................................191
7.1
Introduction .................................................................................................... 191
7.1.1
7.2
Aims and objectives .......................................................................................... 192
Materials and methods ................................................................................... 193
7.2.1
Study sites ......................................................................................................... 193
7.2.2
Reptile surveys .................................................................................................. 194
7.2.3
Predictor variables ............................................................................................. 196
7.2.4
Data assessment and statistical analysis ............................................................ 196
7.3
Results ............................................................................................................ 200
7.4
Discussion ...................................................................................................... 206
viii
7.5
Conclusion ..................................................................................................... 209
Chapter 8.
Testing the importance of fire heterogeneity for reptiles communities at
multiple spatial scales ............................................................................211
8.1
Introduction .................................................................................................... 211
8.1.1
8.2
Aims and objectives .......................................................................................... 212
Methodology .................................................................................................. 213
8.2.1
Sampling sites ................................................................................................... 213
8.2.2
Sampling protocol ............................................................................................. 214
8.2.3
Data assessment and statistical analysis ............................................................ 216
8.3
Results ............................................................................................................ 216
8.4
Discussion ...................................................................................................... 223
8.4.1
Community vs. species-specific responses ....................................................... 223
8.4.2
Scale issues........................................................................................................ 224
8.4.3
Non-heterogeneity relationships........................................................................ 225
8.4.4
Does ‘pyrodiversity beget biodiversity’? .......................................................... 226
8.4.5
Conclusion......................................................................................................... 226
Chapter 9.
9.1
Summary and recommendations ............................................................. 228
Summary ........................................................................................................ 228
9.1.1
Does frequent burning reduce floral or faunal diversity? .................................. 229
9.1.2
Do infrequent wildfires reduce floral or faunal diversity? ................................ 230
9.1.3
Does the spatio-temporal variability in fire management practices increase floral
and faunal diversity?........................................................................................ 231
9.2
How well do fire management practices and disturbances conform to current
theory?................................................................................................... 232
9.3
Conservation outcomes .................................................................................. 235
9.4
Management implications .............................................................................. 235
9.4.1
Recommended adjustment to current fire management in dry eucalypt forests of
southeast Queensland ...................................................................................... 237
9.5
Future implications ........................................................................................ 239
9.6
Future directions ............................................................................................ 240
References
................................................................................................................242
Appendices
................................................................................................................278
ix
List of Figures
Figure 2.1. Study sites including Tiaro, Bauple and St Mary State Forests showing study region
(inset map) in Queensland, Australia, and displaying Regional Ecosystems (RE). When multiple
REs were listed for any given area, the first RE listed is displayed. Dominant REs (Table 2.2)
are shown with *. ........................................................................................................................ 27
Figure 2.2. Study plots within the three study sites, St Mary, Tiaro and Bauple State Forest,
showing the long-term fire experiment treatments in Bauple State Forest and additional fire
treatments within St Mary and Tiaro State Forests. Treatment names represent: AB = annually
burned, LU = long unburned, TB = triennially burned, WF = wildfire, SM01 = St Mary last
burned 2001, SMWF = St Mary wildfire, T01 = Tiaro last burned 2001 and T03 = Tiaro last
burned 2003................................................................................................................................. 32
Figure 2.3. Study sites in southeast Queensland, including St Mary and Tiaro State Forests, and
Bauple State Forest long-term fire experiment, showing 74 study plots and fire regime groups
(1-15), representing number of fires-fire interval-time since fire, where VL = very low, L = low,
M = medium, H = high and VH = very high (see Table 2.6 for value ranges). Location of study
region within Queensland, Australia, is shown in inset map. ..................................................... 37
Figure 3.1. Subplot (40 m × 40 m) showing tree size classes measured within certain distances
from the midline, including recruitment (1-10 cm DBH), established (10-30 cm DBH) and
mature (>30 cm DBH) positioned on the western end of the larger plot area (100 m × 40 m)... 46
Figure 3.2. Example of measuring distances for vegetation communities to calculate
heterogeneity along a 40 m transect. ........................................................................................... 47
Figure 3.3. Changes in vegetation variables within the annually burned treatment over time at
~12 months post-fire (mean ± S.E. across plots), showing ground cover (%) of a) bare ground,
b) litter, c) shrub and d) grass...................................................................................................... 54
Figure 3.4. Changes in vegetation variables within the annually burned treatment over time at
~one month post-fire (mean ± S.E. across plots), showing a) bare ground cover (%) and b) shrub
layer heterogeneity (H’). ............................................................................................................. 54
Figure 3.5. Significant relationships (with Bonferroni corrections) with structural components
among treatments and over time, showing mean ± S.E. a) basal area (per m2) among treatments,
b) canopy cover (%) among treatments and c) CWD over time also showing treatments (with
treatments alternating black and grey for ease of interpretation). ............................................... 56
x
Figure 3.6. Significant (with Bonferroni corrections) relationships with species richness among
treatments and over time, showing mean ± S.E. species richness of a) ground layer (dominant
species) among treatments (with treatments alternating black and grey for ease of interpretation)
and over time, b) shrub layer over time, c) shrub layer among treatments and d) canopy layer
among treatments. ....................................................................................................................... 59
Figure 3.7. Relationship with total heterogeneity among treatments (with treatments alternating
black and grey for ease of interpretation) and over time, showing mean ± S.E. heterogeneity of
a) shrub layer and b) canopy layer. ............................................................................................. 60
Figure 3.8. Significant (with Bonferroni corrections) relationships with ground cover variables
among treatments (with treatments alternating black and grey for ease of interpretation) and
over time, showing mean ± S.E. cover (%) of a) bare ground, and b) grass, and variables with
significant (P<0.05) main effects only showing c) shrub cover (%) over time, d) litter cover (%)
over time and e) litter cover (%) among treatment. .................................................................... 61
Figure 3.9. Differences in a) proportion burned and b) burn patch heterogeneity between AB
and TB treatments within different seasons, showing means ± S.E. ........................................... 62
Figure 3.11. MDS derived from Bray-Curtis similarity showing the distribution of each study
plot across all surveys based on structural composition of woody plants from tree species DBH
within three size classes (1: 1-10 cm - recruitment, 2: >10-30 cm - established, and 3: >30 cm mature), with plot treatments identified and the vector overlay displaying species and the size
class (1, 2, 3) with a Pearson correlation >0.5. ........................................................................... 64
Figure 4.1. Study plots representing Regional Ecosystems (RE) within the three study sites of St
Mary State Forest, Tiaro State Forest and the Bauple State Forest long-term fire experiment,
where REs represent: 12.9-10-17b = spotted gum woodland, 12.9-10.19 = ironbark woodland,
12.5.7 = spotted gum association, 12.9-10.2 = spotted gum open-forest and 12.5.4 = myrtle
woodland (Table 4.1). ................................................................................................................. 77
Figure 4.2. BioCondition assessment methodology showing a) study plot layout, including 100
× 50 m plot, 50 × 20 m subplot, 50 × 10 m subplot, five 1 × 1 m quadrats and 100 m transect,
and b) example of canopy and shrub assessment along 100 m transect. Methods adapted from
Eyre et al. (2011)......................................................................................................................... 80
Figure 4.3. Relationship between fire type and mean ± S.E. overall forest condition score from
all study plots, where WF = wildfire, PB = prescribed burn, TD = top disposal and LU = long
unburned. Red dashes represent the cut-off to the highest condition class for forest condition
(>0.80). Significant differences from post-hoc tests are shown with lettering, where the same
xi
letter represents a similarity between fire types, different letters represent a significant
difference (P<0.05) and no letters represent no significant difference with any other fire type. 87
Figure 4.4. Significant relationships (P<0.05) of forest variables with number of fires (log
scale), showing line of best fit for linear or quadratic polynomial relationships with a) tree
canopy cover (%), b) litter cover (%), c) grass species richness, d) shrub canopy cover (%) and
e) woody debris length (m/ha). Circles represent raw data and dashed lines represent 95%
confidence intervals. ................................................................................................................... 88
Figure 4.5. Significant relationships (P<0.05) of forest variables with time since fire (log scale),
showing line of best fit for linear or quadratic polynomial relationships with a) tree canopy
cover (%) and b) litter cover (%), c) tree species richness, d) grass species richness, e) shrub
canopy cover (%) and f) woody debris (m/ha). Circles represent raw data and dashed lines
represent 95% confidence intervals............................................................................................. 89
Figure 4.6. Forest variables with significant differences among fire type from Kruskal-Wallis
tests (P<0.05), showing mean ± S.E. of a) tree species richness, b) woody debris (m/ha), c) tree
canopy cover (%), d) weed cover (%), e) forb species richness and f) shrub species richness.
Significant differences from post-hoc tests are shown with lettering, where the same letter
represents a similarity between fire types, different letters represent a significant difference
(P<0.05) and no letters represent no significant difference with any other fire type. ................. 91
Figure 4.7. Significant relationships (P<0.05) of forest variables with years since logging,
showing line of best fit for quadratic polynomial relationships with a) tree species richness, b)
shrub species richness, c) grass species richness, d) forb species richness and e) woody debris
(m/ha). Circles represent raw data and dashed lines represent 95% confidence intervals. ......... 92
Figure 4.8. Principal Components Analysis of forest variables displayed as a bubble plot
representing number of fires at all plots. The fire and logging variables that were highly
correlated with PC axes are overlaid. .......................................................................................... 93
Figure 4.9. Significant relationships (P<0.05) with forest variables and number of fires (log
scale) within spotted gum woodland (RE 12.9-10.17b), showing line of best fit for linear or
quadratic polynomial relationships with a) litter cover (%), b) weed cover (%), c) recruitment,
d) shrub canopy cover (%), e) tree species richness and f) woody debris (m/ha). Circles
represent raw data, black dashed lines represent 95% confidence intervals and red dashed line
represents benchmark value for RE. ........................................................................................... 94
Figure 4.10. Significant relationships (P<0.05) with forest variables and time since fire (log
scale) within spotted gum woodland (RE 12.9-10.17b), showing line of best fit for linear or
quadratic polynomial relationships with a) tree canopy cover (%), b) weed cover (%), c) shrub
xii
canopy cover (%), d) tree species richness and e) woody debris (m/ha). Circles represent raw
data, black dashed lines represent 95% confidence intervals and red dashed line represents
benchmark value for RE.............................................................................................................. 96
Figure 4.11. Significant relationships (P<0.05) with forest variables and fire interval (log scale)
within spotted gum woodland (RE 12.9-10.17b), showing line of best fit for linear or quadratic
polynomial relationships with a) weed cover (%), b) litter cover (%), c) recruitment, d) shrub
canopy cover (%), e) tree species richness and f) woody debris (m/ha). Circles represent raw
data, black dashed lines represent 95% confidence intervals and red dashed line represents
benchmark value for RE.............................................................................................................. 97
Figure 4.12. Forest variables from within spotted gum woodland (RE 12.9-10.17b) with
significant differences among fire type from Kruskal-Wallis tests (P<0.05), showing mean ±
S.E. of of a) tree species richness, b) canopy cover (%) (benchmark value is 56 %), c) woody
debris (m/ha) and d) shrub canopy cover (%). Red dashed line represents benchmark value for
RE. No top disposal burns occurred within this RE. Significant differences from post-hoc tests
are shown with lettering, where the same letter represents a similarity between fire types,
different letters represent a significant difference (P<0.05) and no letters represent no significant
difference with any other fire type. ............................................................................................. 98
Figure 4.13. Principal Components Analysis of forest variables within spotted gum woodland
(RE 12.9-10.17b) displayed as a bubble plot representing number of fires at all plots. Values are
representing number of fires at each plot and the fire and logging variables that were highly
correlated with PC axes are overlaid. .......................................................................................... 99
Figure 4.14. Significant relationships (P<0.05) with forest variables and number of fires within
ironbark woodland (RE 12.9-10.19), showing line of best fit for quadratic polynomial
relationships with a) woody debris (m/ha), b) tree canopy cover (%) (benchmark value is 55 %)
and c) weed cover (%). Circles represent raw data, black dashed lines represent 95% confidence
intervals and red dashed line represents benchmark value for RE. ........................................... 100
Figure 4.15. Significant relationships (P<0.05) with forest variables and fire parameters within
ironbark woodland (RE 12.9-10.19), showing line of best fit for quadratic polynomial
relationships with fire interval and a) woody debris (m/ha) and b) tree canopy cover (%)
(benchmark value is 55 %), c) weed cover (%) and d) fire type with mean ± S.E. of weed cover
(%). Circles represent raw data, black dashed lines represent 95% confidence intervals and red
dashed line represents benchmark value for RE. Significant differences for fire type from posthoc tests are shown with lettering, where the same letter represents a similarity between fire
types, different letters represent a significant difference (P<0.05) and no letters represent no
significant difference with any other fire type. ......................................................................... 101
xiii
Figure 4.16. Significant relationships (P<0.05) with forest variables and years since logging
within ironbark woodland (RE 12.9-10.19), showing line of best fit for linear or quadratic
polynomial relationships with a) shrub canopy cover (%), b) forb species richness and c) shrub
species richness (benchmark value is 7 species). Circles represent raw data, black dashed lines
represent 95% confidence intervals and red dashed line represents benchmark value for RE.. 102
Figure 4.17. Significant relationships (P<0.05) for forest variables within spotted gum
association (RE 12.5.7), showing line of best fit for linear or quadratic polynomial relationships
for a) time since fire and native perennial grass cover (%), b) time since fire and weed cover
(%), c) years since logging and native perennial grass cover (%), and d) years since logging and
weed cover (%). Circles represent raw data, black dashed lines represent 95% confidence
intervals and red dashed line represents benchmark value for RE. ........................................... 103
Figure 5.1. Trapping grid array (40 m × 100 m) at each plot and active search strip transects.
Vegetation surveying transect on the left hand side is also shown (1 × 100 m). ...................... 117
Figure 5.2. Species accumulation curves for reptiles from a) pitfall trapping, where time =
survey nights across all plots (equivalent to 140 trap nights each) and b) active searches, where
time = each active search across all plots (equivalent to 17.5 person hours each). Species
richness represents the total number of accumulated species across all plots. .......................... 128
Figure 5.4. Mean herpetofaunal abundance and richness (± S.E.) with a significant (P<0.05)
treatment effect, showing a) reptiles (active), b) native anuran abundance and c) anuran
richness. Significant differences from LSD post-hoc tests are shown with lettering, where the
same letter represents a similarity between treatments, different letters represent a significant
difference (P<0.05) and no letters represent no significant difference with any other treatment.
................................................................................................................................................... 132
Figure 5.5. Mean reptile species abundances (± S.E.) with a significant (P<0.05) treatment
effect, showing a) Eulamprus martini (active), b) Lampropholis amicula (active), c)
Lampropholis amicula (pitfall) and d) Lygisaurus foliorum (pitfall). Significant differences from
LSD post-hoc tests are shown with lettering, where the same letter represents a similarity
between treatments, different letters represent a significant difference (P<0.05) and no letters
represent no significant difference with any other treatment. ................................................... 134
Figure 5.6. Mean Limnodynastes peronii abundance (± S.E.) with a significant (P<0.05)
treatment effect. Significant differences from LSD post-hoc tests are shown with lettering,
where the same letter represents a similarity between treatments, different letters represent a
significant difference (P<0.05) and no letters represent no significant difference with any other
treatment.................................................................................................................................... 135
xiv
Figure 5.7. Mean herpetofaunal abundances (± S.E.) with significant (P<0.05) interactive effects
of treatment × season, showing seasons separate with a) reptiles (pitfall) and b) total anurans.
Significant differences from LSD post-hoc tests are shown with lettering, where the same letter
represents a similarity between treatments, different letters represent a significant difference
(P<0.05) and no letters represent no significant difference with any other treatment. Note: each
graph has a different scale on the y-axis. .................................................................................. 136
Figure 5.8. Mean reptile abundances (± S.E.) with significant (P<0.05) interactive effects of
treatment × season, showing seasons separate with a) Carlia p. pectoralis (pitfall) and b) C. p.
pectoralis (active). Significant differences from LSD post-hoc tests are shown with lettering,
where the same letter represents a similarity between treatments, different letters represent a
significant difference (P<0.05) and no letters represent no significant difference with any other
treatment. Note: each graph has a different scale on the y-axis. ............................................... 137
Figure 5.10. Mean Rhinella marina abundance (± S.E.) with significant (P<0.05) interactive
effects of treatment × season, showing seasons separate. Significant differences from LSD posthoc tests are shown with lettering, where the same letter represents a similarity between
treatments, different letters represent a significant difference (P<0.05) and no letters represent
no significant difference with any other treatment. Note: each graph has a different scale on the
y-axis. ........................................................................................................................................ 139
Figure 5.11. Scatter plots of herpetofaunal relationships with time since fire (back transformed
ln scale), showing line of best fit (linear or quadratic) and r2 for significantly correlated (P<0.05)
abundances for a) reptiles (pitfall), b) Cryptoblepharus pulcher (pitfall), c) Lampropholis
amicula (pitfall), d) L. delicata (pitfall), e) Lygisaurus foliorum (pitfall) and f) Eulamprus
martini (active). Circles represent raw data and dashed lines are showing 95% confidence
intervals. .................................................................................................................................... 140
Figure 5.12. Scatter plot of Limnodynastes terraereginae abundance with time since fire (back
transformed ln scale), showing line of best fit (linear) and r2 for significantly correlated (P<0.05)
abundances. Circles represent raw data and dashed lines are showing 95% confidence intervals.
................................................................................................................................................... 141
Figure 5.13. Scatter plots of reptile relationships with number of fires (back transformed ln
scale), showing line of best fit (linear or quadratic) and r2 for significantly correlated (P<0.05)
abundances for a) reptiles (pitfall), b) reptiles (active), c) Lampropholis amicula (pitfall), d) L.
delicata (pitfall), e) L. delicata (active), f) Carlia p. pectoralis (pitfall) and g) C. p. pectoralis
(active). Circles represent raw data and dashed lines are showing 95% confidence intervals. . 142
Figure 5.14. Scatter plots of significant (P<0.05) anuran relationships with number of fires
(back transformed ln scale), showing line of best fit (linear or quadratic) and r 2 for a) anuran
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abundance, b) anuran richness, c) Rhinella marina and d) Limnodynastes tasmaniensis. Circles
represent raw data and dashed lines are showing 95% confidence intervals. ........................... 143
Figure 5.16. Principal Coordinates Analysis based on Bray-Curtis distance measures of reptile
composition across plots from different fire treatments showing patterns for high correlations
with PCO axes and a) species and b) habitat and fire variables. ............................................... 148
Figure 5.17. Herpetofauna with significant (P<0.05) relationships with fire patchiness within
AB and TB and time since recent fire as identified from LMM models, showing proportion
burned (%) and a) Lampropholis amicula (active) and b) L. delicata (pitfall), and burn patch
heterogeneity and c) Carlia p. pectoralis (active). See Figure 4.18 for graphs with time since
recent fire. ................................................................................................................................. 150
Figure 5.18. Reptiles with significant (P<0.05) relationships with time since recent fire (months)
at the Bauple fire experiment, showing season and mean ± S.E. of a) Lampropholis amicula
(active), b) L. delicata (pitfall), c) Carlia p. pectoralis (active), d) reptile abundance (pitfall), e)
Lygisaurus foliorum (pitfall), f) reptile richness and g) Oedura tryoni (active). ...................... 151
Figure 5.19. Anurans with significant (P<0.05) relationships with time since fire, showing
seasons and mean ± S.E. of a) total abundance b) native abundance, c) richness and d) Rhinella
marina abundance. .................................................................................................................... 152
Figure 5.20. Reptile species’ relationships with significant (P<0.05) microhabitats from active
searches during spring 2012 and winter 2013 (St Mary and Tiaro), showing mean ± S.E. of
habitat attribute cover (%) at locations of reptile detections for each species (n=593), shown for
a) ground vegetation, b) litter cover, c) coarse woody debris (CWD) and d) shrub canopy
(presence/absence). As shrub canopy is based on presence/absence data, this represents the
proportion of reptile occurrences under shrub cover, i.e. 0=no reptiles under canopy and
1=reptiles always under canopy. Significant differences from LSD post-hoc tests are shown
with lettering, where the same letter represents a similarity in species, different letters represent
a significant difference (P<0.05) and no letters represent no significant difference with any other
species. ...................................................................................................................................... 153
Figure 5.21. Principal Coordinates Analysis based on a Bray-Curtis distance measure of reptile
species abundance among treatments from active searches during spring 2012 (all sites) and
winter 2013 (St Mary and Tiaro), showing microhabitat variables that were highly correlated
with PCO axes overlaid. ............................................................................................................ 154
Figure 5.18. Conceptual diagram of herpetofaunal resilience to fire regimes (time since fire and
number of fires) in dry eucalypt forest, with species assigned along a gradient of resilience (low
to high, grey dotted line) of negative impacts from fire regimes based on observed responses,
xvi
and predicted impacts and risk based on this. Fire regime scales are representative of those for
subtropical southeast Queensland dry eucalypt forests, where the number of fires scale
represents: low = ~10-50+ year intervals and high = ~annual burning, and the time since fire
scale represents: low = ~<1 years and high = ~10+ years. Potential risk is identified from
specific habitat requirements found and a likelihood that an impact may occur. * Species with
unknown relationships are those that showed no significant relationships with time since fire
and number of fires in the current study and therefore may have variable risk or impact. ....... 161
Figure 6.1. Trapping grid array (100 × 40 m) at each plot. Vegetation surveying transect on the
left hand side is also shown (1 × 100 m) as traps on this side were placed 5 m from the edge to
avoid trampling within the vegetation transect. ........................................................................ 170
Figure 6.3. Significant relationships (P<0.05) for fire treatment with Mus musculus (with both
seasons together) showing mean ± S.E. Mus musculus abundance. Significant differences from
LSD post-hoc tests are shown with lettering, where the same letter represents a similarity
between treatments, different letters represent a significant difference (P<0.05) and no letters
represent no significant difference with any other treatment. ................................................... 180
Figure 6.4. Significant relationships for mammals with fire treatment × season interactive
effects, showing seasons separate with mean ± S.E. of a) mammal abundance, b) richness and c)
Antechinus f. flavipes abundance (note: c) graphs have a different scale on the y-axis).
Significant differences from LSD post-hoc tests are shown with lettering, where the same letter
represents a similarity between treatments, different letters represent a significant difference
(P<0.05) and no letters represent no significant difference with any other treatment............... 181
Figure 6.5. Principal Coordinates Analysis based on Bray-Curtis distance measures of mammal
composition across plots from different fire treatments showing high correlations with PCO
axes and a) mammal species and b) fire and habitat variables. ................................................. 184
Figure 6.6. Significant (P<0.05) mammal relationships from annually burned and triennially
burned sites, showing a) mammal richness and proportion burned, b) mammal richness and time
since fire, c) Mus musculus abundance among treatments and time since fire and d) Sminthopsis
murina abundance over time since fire within both treatments. ............................................... 186
Figure 7.1. Vegetation plot area and reptile survey transect, showing (a) 50 × 20 m plot for
coarse woody debris (CWD) surveys, 1 × 1 m quadrats for ground substrate cover surveys and
100 m transect midline used for canopy cover and reptile surveys, and (b) example of assessing
shrub and tree canopy cover where each distance measure of total length of cover is recorded.
................................................................................................................................................... 195
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Figure 7.2. Species accumulation curve representing mean number of species (± S.E.)
encountered per survey (i.e. hour) within each fire regime group. ........................................... 200
Figure 7.3. Scatter plots showing important relationships with predictor variables and reptiles
determined from negative binomial Generalised Linear Mixed Models, displaying a) overall
reptile abundance and number of fires (back transformed ln scale), b) Carlia spp. abundance and
time since fire (back transformed ln scale) and c) Carlia spp. abundance and shrub cover (%).
Circles represent raw data, unbroken line represents linear line of best fit and dashed line is
showing the 95% confidence interval. ...................................................................................... 203
Figure 7.4. Mean reptile species’ abundances (number of occurrences per two hours) ± S.E.
among differing fire types, for Carlia spp. (triangles), Lygisaurus foliorum (diamonds),
Eulamprus martini (circles) and Lampropholis delicata (squares) where WF = wildfire, PB =
prescribed burn, TD = top disposal and LU = long unburned. Significant differences (P<0.05)
among fire types for each species are represented by letters, where the same letter = similarities
among fire types, differing letters = a significant difference in abundance among fire types and
no letter = the fire type was not significantly different to any other type (P>0.05). ................. 204
Figure 7.5. Principal Coordinates Analysis based on Bray-Curtis distance measures of reptile
composition, displaying an overlay of highly correlated a) habitat and fire variables (Pearson
correlation>0.3) with plots represented as fire type and b) reptile species with plots shown as
number of fires (Pearson correlation>0.5). ............................................................................... 205
Figure 8.1. Representation of scales where heterogeneity calculations were measured based on
fire history parameters within unique blocks (mapped fire polygons), showing plots (red circles)
and buffers within the study sites (Tiaro State Forest shown here) in order to represent a) fine
scale: 500 m buffer around each plot, b) local scale: unique fire regime block +200 m buffer and
c) site (regional) scale: each forest. ........................................................................................... 215
Figure 8.2. Scatter plots showing significant (P<0.05) relationships with reptiles and fire
heterogeneity variables, using specific correlations to illustrate the general pattern found with
certain heterogeneity parameters at different spatial scales, including: a) reptile species richness
and mean fire interval H’ at the fine scale, b) Lampropholis delicata abundance and unique fire
mosaic H’ at the local scale, c) Eulamprus martini abundance and mean fire interval H’ at the
site scale, and d) reptile abundance and number of fires heterogeneity at the site scale. Circles
represent raw data. .................................................................................................................... 220
Figure 8.3. Principal Coordinates Analysis based on Bray-Curtis distance measures of reptile
composition across plots showing patterns on site mosaic heterogeneity and with variables
highly correlated with PCO axes overlaid, including a) fire heterogeneity where A = interval H’,
B = time since fire H’, C = number of fires H’ and D = mosaic H’, and b) reptile species. ..... 222
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List of Tables
Table 1.1. Current long-term fire experiments in Australia. ....................................................... 21
Table 2.1. Percentage of open eucalypt forests in Queensland represented in different land
tenures (adapted from Montreal Process Implementation Group for Australia 2008). ............... 26
Table 2.2. Dominant Regional Ecosystem (RE) types found in Bauple, St Mary and Tiaro State
Forests and their dominant vegetation species (DERM 2010) showing proportional extent across
the study sites. ............................................................................................................................. 28
Table 2.3. Fire treatments within the Bauple State Forest fire experiment. ................................ 31
Table 2.4. Fire history of comparative areas used for repeated fire use study within St Mary and
Tiaro. Treatments represent: T01 = Tiaro last burned 2001, T03 = Tiaro last burned 2003, SM01
= St Mary last burned 2001 and SMWF = St Mary wildfire....................................................... 35
Table 2.5. Timing of sampling of vegetation and fauna within trapping plots across Bauple, St
Mary and Tiaro State Forests, where AB = annually burned, TB = triennially burned, WF =
wildfire and LU = long unburned. St Mary site treatments and Tiaro site treatments were
surveyed together. ....................................................................................................................... 36
Table 2.6. Description of fifteen fire regime groups used in the selection of replicate survey
plots (n=74) within polygons of >16 ha, where VL = very low, L = low, M = medium, H = high
and VH = very high. .................................................................................................................... 38
Table 2.7. Timing of sampling of vegetation and reptile surveys across Bauple, St Mary and
Tiaro State Forests....................................................................................................................... 38
Table 3.1 Timing of vegetation surveys at three study sites showing time since fire for each
survey period and application of ongoing management burns. Replicates/time is showing the
number of repeat surveys over time and treatment codes represent: AB = annual burn,
TB = triennial burn, WF = wildfire in 2006, LU = long unburned, SMWF = St Mary wildfire in
2006, SM01 = St Mary last burned in 2001, T01 = Tiaro last burned in 2001 and T03 = Tiaro
last burned in 2003. “●” represents post-fire patchiness surveys and “×” represents when fire
treatments took place at the Bauple experiment. ......................................................................... 44
Table 3.2. Dependent vegetation composition and structural variables quantified at each site and
independent fire variables used for comparison, with variables used in analyses that were not
highly correlated (Spearman correlation ≥ |0.7|) with other variables shown by *. .................... 45
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Table 3.3. One-way ANOVA outputs comparing vegetation variables with similar time since
fire dates over years within the annual burn plots, showing variables that were significantly
different among years (P<0.05) and specific years that were significantly different (< or >)
(P<0.05) from LSD post-hoc tests............................................................................................... 53
Table 3.4. Results of LMM showing comparison of vegetation variables among fire treatments,
over time, as well as a treatment × time interaction, displaying significance values followed by
the test statistic (F statistic) in parentheses with significant relationships shown in bold (using α
based on sequential Bonferroni corrections shown in columns) (d.f. (treatment)=7; d.f. (time)=4;
d.f. (treatment × time)=8). ........................................................................................................... 55
Table 3.5. Significant comparisons among treatments and over time based on LSD post-hoc
tests from LMM’s on vegetation variables (with α based on sequential Bonferroni corrections
done for each variable using each pairwise comparison). Interaction terms are relating to:
Treatment (time) = differing times within each treatment and Time(treatment) = differing
treatments within each time. Significant differences (with Bonferroni corrections) are shown by
> or < and NS = not significant or not considered if an interaction term is significant. Times
represent: 1 = Aug-Sept ’10, 2 = Aug ’11, 3 = Oct-Nov ’11, 4 = Oct ’12 and 5 = June-July ’13.
..................................................................................................................................................... 57
Table 3.6. Results of LMM showing comparison of burn patch heterogeneity and proportion
burned among fire treatments, seasons, as well as a treatment × season interaction, displaying
significance (P) values, where * indicates significant values (P<0.05). ..................................... 62
Table 3.7. Permanova outputs comparing tree structural composition based on three size classes
(recruitment: 1-10 cm, established: >10-30 cm, and mature: >30 cm) among treatments and over
time.............................................................................................................................................. 63
Table 4.1. Regional Ecosystems (RE) represented across study plots in St Mary, Tiaro and
Bauple, showing the number of study plots in each RE (see Appendix 1 for full RE
descriptions). ............................................................................................................................... 78
Table 4.2. Vegetation and habitat variables measured for BioCondition assessments as well as
calculated landscape metrics, based on Eyre et al. (2011). ......................................................... 79
Table 4.3. Mean (± S.E.) of forest variables across all plots within each Regional Ecosystem,
showing benchmark values (BM). REs represent: 12.9-10.19 = ironbark woodland, 12.9-10-17b
= spotted gum woodland, 12.5.7 = spotted gum association, 12.9-10.2 = spotted gum openforest and 12.5.4 = myrtle woodland........................................................................................... 85
Table 4.4. Summary of disturbance variables significantly influencing (P<0.05) forest condition
variables from within each Regional Ecosystem as well as overall (plots from all REs together),
xx
showing significant variables as NF = number of fires (ln transformed), TSF = time since fire
(ln transformed), FI = fire interval (ln transformed), FT = fire type and L = years since logging.
REs represent: 12.9-10-17b = spotted gum woodland, 12.9-10.19 = ironbark woodland, 12.5.7 =
spotted gum association. See Figures 4.3 – 4.17 for graphs depicting the nature of each specific
relationship. ................................................................................................................................. 86
Table 4.5. Summary of relationships between disturbance variables and forest variables across
all Regional Ecosystems where the figures represent the percentage of total variables (n=13)
that demonstrated each relationship. The total percentage of variables that was correlated with
each disturbance variable is also shown. General patterns are represented, i.e. when a parabolic
relationship was mostly positively increasing, it is counted as a positive relationship. .............. 86
Table 5.1 Timing of fauna surveys at three study sites showing application of ongoing
management burn treatments and time since fire for each survey period, where grey shading is
indicating when a survey was done within each treatment. Treatment codes represent: AB =
annual burn, TB = triennial burn, WF = wildfire (Bauple), LU = long unburned, SMWF = St
Mary wildfire, SM01 = St Mary last burned in 2001, T01 = Tiaro last burned in 2001 and T03 =
Tiaro last burned in 2003. “●” is showing post-fire patchiness surveys and “×” is showing when
fire treatments took place at the Bauple experiment. ................................................................ 116
Table 5.2. Herpetofauna variables as well as uncorrelated (Spearman rank correlation ≤|0.5|)
habitat, fire and weather variables quantified at each site for analyses of faunal responses. .... 119
Table 5.3. Total captures of reptile species from pitfall traps over the four survey periods at each
treatment, showing the mean ± S.E. abundance among plots for each species, total number of
captures and Shannon-Wiener diversity indices for each treatment (H’). Species that were also
detected using active searches are shown by an ×..................................................................... 126
Table 5.4. Total observations of reptile species from active searches over the four survey
periods at each treatment, showing the mean ± S.E. abundance among plots for each species,
total captures and species, Shannon-Wiener diversity indices for each treatment (H’) and if
species were also detected using pitfall traps, denoted by an ×. ............................................... 127
Table 5.5. Total captures of anuran species from pitfall traps over the four survey periods at
each treatment within Bauple, St Mary and Tiaro, showing the mean ± S.E. for each species,
total captures and species, and Shannon-Wiener diversity indices for each treatment (H’). .... 130
Table 5.6. Faunal comparison among fire treatment and season using Linear Mixed Models
showing abundance and richness of herpetofauna, where significance values (α=0.05) are
displayed followed by the test statistic (F statistic) in parentheses and significant relationships
(P<0.05) shown in bold. Degrees of freedom for each factor are: treatment = 7, season = 1,
xxi
treatment × season = 7. The nature of effects analysed using LSD post-hoc tests are depicted in
Figures 5.2-5.6. ......................................................................................................................... 131
Table 5.7. Faunal comparison among fire treatment and season using Linear Mixed Models
showing species-specific abundances of herpetofauna, where significance values (α=0.05) are
displayed followed by the test statistic (F statistic) in parentheses and significant relationships
(P<0.05) shown in bold. Degrees of freedom for each factor are: treatment = 7, season = 1,
treatment × season = 7. The nature of effects analysed using LSD post-hoc tests are depicted in
Figures 5.2-5.6. ......................................................................................................................... 133
Table 5.8. Herpetofauna with a significant (P<0.05) season main effect showing the mean and
standard error within winter and spring. ................................................................................... 135
Table 5.9. Model-averaged coefficients ± confidence intervals of explanatory habitat, fire and
weather variables from negative binomial Generalised Linear Mixed Models on anuran
abundance, richness and species-specific abundances. Bold values indicate where coefficient
confidence intervals do not overlap zero................................................................................... 146
Table 5.10. Significant (P=0.001) fire and habitat variables (normalised) best correlated with
reptile composition. ................................................................................................................... 147
Table 5.11. Significant (P<0.05) relationships from Linear Mixed Models of herpetofauna
among AB and TB comparing fire treatment, time since fire, and relationships with burn patch
heterogeneity and proportion burned. ....................................................................................... 149
Table 6.1 Timing of fauna surveys at three study sites showing application of ongoing
management burn treatments and time since fire for each survey period, where grey shading is
indicating when a survey was done within each treatment. Treatment codes represent: AB =
annual burn, TB = triennial burn, WF = wildfire (Bauple), LU = long unburned, SMWF = St
Mary wildfire, SM01 = St Mary last burned in 2001, T01 = Tiaro last burned in 2001 and T03 =
Tiaro last burned in 2003. “●” is showing post-fire patchiness surveys and “×” is showing when
fire treatments took place at the Bauple experiment. ................................................................ 169
Table 6.2. Mammal, habitat, fire and weather variables quantified at each site for analyses of
faunal responses. ....................................................................................................................... 172
Table 6.3. Total captures of mammal species over the four survey periods at each treatment,
showing the mean ± S.E. (across all treatments) for each species, total individuals captured
(indiv.), recaptures (recap.) and the capture rate for each species based on the trap types they
were detected in, Shannon-Wiener diversity indices (H’) for each treatment and the methods in
which they were detected, where S = Sherman trap, H = hair funnel, P = pitfall trap and C =
cage trap. ................................................................................................................................... 177
xxii
Table 6.4. Mammal comparison among fire treatment and season using Linear Mixed Models
showing abundance, richness and species-specific abundances, displaying significance values
(α=0.05) followed by the test statistic (F statistic) in parentheses and significant relationships
(P<0.05) shown in bold (d.f. (treatment)=7, 27; d.f. (season)=1, 27; d.f. (treatment × season)=7,
27). ............................................................................................................................................ 180
Table 6.5. Model-averaged coefficients ± confidence intervals of explanatory habitat, fire
(treatment) and weather variables from negative binomial Generalised Linear Mixed Models on
mammal abundance, richness and species-specific abundances. .............................................. 183
Table 6.6. Significant (P<0.05) relationships from LMM’s of mammals among AB and TB
comparing fire treatment, time since recent fire, and relationships with proportion burned. ... 185
Table 7.1. Predictor variables showing all fire parameters, habitat variables and weather
variables for analysis of responses by reptile communities. ..................................................... 197
Table 7.2. Mean values (± S.E.) for fire parameters across three study sites for the period
between 1985 and 2012, showing fire types (most recent fire) occurring within each site. ..... 198
Table 7.3. Model-averaged coefficients ± confidence intervals of predictor habitat, fire and
weather variables from negative binomial Generalised Linear Mixed Models on reptile
abundance, richness and species-specific abundances. All models are from those with number of
fires, except that for Carlia spp. showing outputs from time since fire model. Fire type codes
represent: PB = prescribed burn, TD = top disposal burn and WF = wildfire. ......................... 202
Table 8.1. Significant (P<0.05) relationships with reptile abundance, species richness and
species-specific abundances and fire heterogeneity (H’) variables from simple linear regression.
................................................................................................................................................... 218
Table 8.2. Summary of significant (P<0.05) relationships with reptiles and fire heterogeneity
variables from simple linear regression, showing the number of dependent variables (reptile
abundance, richness or species-specific abundances) that were significantly correlated with each
fire parameter heterogeneity index at three spatial scales (F = fine, L = local and S = site) and
the nature of the relationship. .................................................................................................... 219
Table 8.3. Variables that best explain the variation in reptile composition (P=0.001) with
BioEnv correlations (Rho) presented. ....................................................................................... 221
Table 1. Description of management issues associated with fire management guidelines
provided by Queensland Herbarium (2013) for dominant dry eucalypt forest Regional
Ecosystems (RE) in the study sites. .......................................................................................... 278
xxiii
Table 2. Fire history of Tiaro State Forest showing the major fire affected areas, the most recent
fire, and the percentage overlap of previous fires, where * represents the most recent fire and
shading indicates the areas used for location of trapping plots, based on the comparability with
treatments at Bauple SF, i.e. long unburned. A mix of locations across the entire forest was used
in the broad-scale fire history analyses of Chapters 4, 7 and 8. ................................................ 284
Table 3. Fire history of St Mary State Forest showing the major fire affected areas, the most
recent fire, and the percentage overlap of previous fires, where * represents the most recent fire
and shading indicates the areas used for location of vegetation and trapping plots for Chapter 3,
5 and 6. A mix of locations across the entire forest was used in the broad-scale fire history
analyses of Chapters 4, 7 and 8. ................................................................................................ 285
Table 4. Significantly correlated (P<0.05) patchiness, species richness and heterogeneity
variables showing Spearman correlations >0.5. Variables removed from further analyses due to
high correlations are shown in grey. H’ represents heterogeneity. ........................................... 286
Table 5. Summary of woody plants in each treatment using relative density of each species
(plants/m2) measured from DBH subplots (40 × 40 m) within each plot, showing life form of
each species (life form: CT = canopy tree, ST = subcanopy tree, S = shrub and W = weed),
frequency of occurrence (F) among plots (%), mean prevalence (P) per plot based on the
DAFOR scale* (Agea et al. 2007; Kent 2011) and dominant size class (S) showing the size
class for each species that had the highest density (plants/m2) (size class: 1 = small: 10-100 mm
DBH; 2 = medium: >100-300 mm DBH; 3 = large: >300 mm DBH). Table is displaying data
from the first survey at each site only (Aug-Sept ’10 at Bauple and Oct-Nov ’11 at St Mary and
Tiaro). ........................................................................................................................................ 287
Table 6. Summary of ground cover vegetation recorded from transect measurements showing
percentage cover of species within each treatment based on occurrence within ground cover
vegetation communities along vegetation transects (three × 40 m transects per plot), showing
life form of each species (life form: F = forb, G = grass, S = shrub and W = weed). Table is
displaying data from the last survey at each site only (June-July ‘13). ..................................... 289
Table 7. Summary of structural variables across the three study sites recorded from DBH
subplots (40 × 40 m), transect measurements (three × 40 m transects per plot) and ground cover
quadrats (5 × 1 m2 per plot) at each study plot showing the mean ± S.E. within each treatment
across all survey periods. The highest value for each variable is shown in bold. ..................... 291
Table 8. Percentage occupancy of reptile species across plots from pitfall traps within each
treatment at Bauple, St Mary and Tiaro, showing the mean ± S.E. occupancy for each species
and total occupancy among all plots. Species also detected using active searches are denoted by
an ×. ........................................................................................................................................... 292
xxiv
Table 9. Percentage occupancy of reptile species across plots from active searches within each
treatment at Bauple, St Mary and Tiaro, showing the mean ± S.E. occupancy for each species
and total occupancy among all plots. Species also detected using pitfall traps are denoted by an
×. ............................................................................................................................................... 293
Table 10. Percentage occupancy of anuran species across plots from pitfall traps within each
treatment at Bauple, St Mary and Tiaro, showing the mean ± S.E. occupancy for each species
and total occupancy among all plots. ........................................................................................ 294
Table 11. Percentage occupancy of mammal species across plots within each treatment at
Bauple, St Mary and Tiaro, showing the mean ± S.E. occupancy for each species, total
occupancy among all plots and the methods in which they were detected, where S = Sherman
trap, H = hair funnel, P = pitfall trap and C = cage trap. ........................................................... 295
Table 12. Percentage occupancy of reptile species across plots at St Mary, Tiaro and Bauple
State Forests within fire regime groups from two survey periods, with fire regimes represented
as fire frequency- fire interval- time since fire, where VL = very low, L = low, M = medium, H
= high and VH = very high (see Chapter 2: 2.4.2 Table 2.6). ................................................... 296
xxv
List of Appendices
Appendix 1. Regional Ecosystem (RE) descriptions of dominant forest types occurring
across the study sites St Mary, Tiaro and Bauple State Forests representing dry eucalypt
forest. Sourced from Queensland Herbarium (2013). .................................................. 278
Appendix 2. Fire history of study sites sourced from Forest Products, QPWS and
DEEDI spatial maps. .................................................................................................... 284
Appendix 3. Spearman rank correlations of vegetation variables (Chapter 3). ............ 286
Appendix 4. Summary tables of vegetation variables (Chapter 3). .............................. 287
Appendix 5. Herpetofaunal plot occupancy (Chapter 5). ............................................. 292
Appendix 6. Small mammal plot occupancy (Chapter 6). ........................................... 295
Appendix 7. Reptile plot occupancy (Chapter 7). ........................................................ 296
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Statement of Access
This thesis is submitted by Diana A Virkki in fulfilment of the requirements for the
Degree of Doctorate of Philosophy in the Griffith School of Environment, Griffith
University.
I, the undersigned, the author of this thesis, understand that Griffith University will
make it available for use within the University Library and by microfilm or other
means, allow access to users in other approved libraries. All users consulting this thesis
will have to sign the following statement:
“In consulting this thesis I agree not to copy or closely paraphrase it in whole or in part,
without the written consent of the author and to make proper written acknowledgment
for any assistance which I have obtained from it”.
Beyond this I do not wish to place any restrictions on access to this thesis.
…………………………………..
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Signature
Date
Diana Virkki
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Acknowledgements
I have received an immense amount of support and assistance over the years from countless
spectacular people, and I can’t thank each and every one of you enough. Firstly, thank you to
my amazing supervisors, in particular Dr Guy Castley – you are one in a million. You go above
and beyond and I wouldn’t be where I am today without your support and brilliant mind. I also
thank Dr Tom Lewis for your endless support and encouragement, as well as Cuong Tran who
closely collaborated with this work and provided immeasurable assistance. It has been a
pleasure working with you all and I thank you from the bottom of my heart.
I thank all of my volunteers for giving up your time to assist this project, especially those who
attended my bucket hole and trench digging boot camps: Sam Appleton, Shane Norman Patrick
Howard, Jen and Clint Walker, Jo Lammersdorf, David Guest, Sara-Jane Griffiths, Luke Allen,
Tiegan Howell, Kain Wong, Matt Davies, John Little, Brian Lazell, Michael Lowe, Mel
McGregor, Graeme White, Mark Vollmerhausen and family, Marina Richardson, Juliet
Musgrave, Adam Day, Kelly Weir, Lauren Bailey, Cameron Bishop, Carlos Torrente, Debra
Dolby, Andy Dalton, Rebecca Craythorn, Hsi Yong, Teresa Steinmeyer, Lucy Halliday, Laura
Ryan, Rick Dickson-Battye, Lisa Gould, Janika Hakoluoto and Mallory Booth. Thanks to Bob
Tomkins for your repeat visits and for your scones, Ernie Ryder for bringing along your
immense knowledge of Australian flora and Col Bowman for being an amazing backup
volunteer and all the support. A massive thank you to Jean Paffenhoff for spending four months
with me as a trainee – your assistance in the field was beyond measure and I hope you enjoyed
your time in the Australian bush.
Thank you to the staff at the Gympie NPRSR and DAFF offices, for the ongoing support,
provision of forestry maps, storage space and satellite phone, specifically Peter Leeson, Andrew
Young, Darren Rogers, Garry King, Peter Lusk, Brian Tighe, and particularly Scott Swift with
additional field assistance. Additionally, thanks to Tim Robson (QPWS) for providing initial
GIS fire history layers and Rowena Thomas for chasing up the recent fires.
Thank you to RFS and NPRSR for the ongoing maintenance and continuation of the fire
experiment at Bauple. The experiment is a gem; don’t let the fire go out. Thank you for letting
me partake in the burns and complete my fire training! Special thanks to Murray Abel (Origin
Energy) for kindly letting me tag along during the fire training course.
Thanks to the SEQ Fire and Biodiversity Consortium for providing funding for this project and
the ongoing support over the years. I really appreciate it. Thanks also to the Wildlife
Preservation Society of Australia for providing additional funding.
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Special thanks go to Annie Kelly (Qld Herbarium) for providing assistance and expertise with
BioCondition assessments and benchmark creation. Thanks also to Valerie Debuse for
reviewing the vegetation chapter. Michael Arthur – thank you for your statistical genius and
humouring me when we both knew no one could help me anymore. A massive thank you goes
to Kirsty Sullivan for undertaking the hair ID analyses.
Thank you to those who provided me with generously discounted accommodation and a home
away from home: Claudia at the Theebine Hotel; Dave Keable for so generously allowing me to
‘housesit’ in your amazing cottage and for field assistance; Jan and Greg Littlejohn, and puppy
Jessie at the Quilt farmstay for providing me with constant support and friendship; Carol Morris
and the sorely missed legion of puppies, cats, fish, chickens and cows – I appreciate everything
you did for me, including lending me your car.
There are a few people who I wouldn’t be here without the help of – as I would be stuck in the
mud somewhere in the middle of the forest! Thank you to those who took the time to rescue my
car, particularly Marc Brommett and crew from the Macadamia House, Dave Osbourne and
Mark Hunt (DEEDI/DAFF), and Col Bowman and Dave Keable.
Thank you to the wonderful Amanda Dawson and Seanan Wild, for always readily agreeing to
come out on field trips and being an amazing support. Thanks also to Kat Dawkins for all the
support and friendship even though you are on the other side of the world now. A heartfelt
thanks goes to Matti Virkki for being an incredibly dedicated volunteer and little bro.
To the amazing group of RHD students that I had the pleasure of spending time with – thank
you for being there and making our office worth spending every day at. Special thanks to Chris
Henderson for being an amazing ear to my rants and keeping me going. I must also thank James
Bone for valuable assistance in the field, and I really appreciate the coffee rituals – thank you
for keeping me smiling. Donna Treby, I am so grateful I was able to share this experience with
you. I can’t thank you enough for taking the time to review my chapters and providing me with
immense support.
An enormous thank you goes to my incredible partner, Ryan Pearson. Your support and
motivation has helped me make it to the end and I am so grateful for you. I must also thank the
entire Pearson family for the continuous love and motivation, and the roof over our heads – I am
more than thankful. And lastly, thank you to my family for the love and support.
This study was completed under the Scientific Purposes Permit WISP08414510 (DERM).
Ethics approval was obtained from Griffith University Animal Ethics Committee: Ethics
approval number ENV/09/08 to Dr Guy Castley.
Much love goes to the fauna of SEQ, but particularly those residing in Bauple State Forest, for
allowing me to spend time with you, burn you and catch you! This thesis is for you.
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