1

Fauna species endemicity in Dry Sclerophyll bushland within the

Helidon Hills Region

M.J. Forrest

University of Queensland, Associate Degree of Applied Science.

Abstract. Fauna population dynamics change drastically after introduced fire

regimes, natural disasters and predation. Furthermore, changes in these factors alter

flora structure, composition and abundance. Planned fires help encourage plant

growth via re-seeding and reduce accumulated fuel loads. Natural processes such as

predation from introduced and native fauna species play a role in further reducing

population abundances after fire regimes. This paper discusses methods in obtaining

relevant data and reveals the current population statistics regarding total trapping

nights and the numbers of animals caught. A ‘habitat complexity score’ model sheet

was implemented to monitor the particular types of vegetation in the Helidon Hills

region where the animals were recorded.

Introduction

Dry sclerophyllous bushlands are susceptible to burning, logging and grazing practices which

in turn creates a patchy temporal mosaic which is not evenly dispersed across the landscape.

In other words it is the alteration of the vegetation structure levels after these events which

influence the vegetation regrowth phase to aging periods (Tolsma, et al. 2007).

Uncontrolled fire patterns and frequent predation can further disrupt the symbiotic

relationships between native fauna and flora species causing a reduction in rare flora endemic

to the region and in some cases the alteration of soil ph levels. This can contribute to erosion

and salinity. Many of these factors combined have a major impact on native fauna

reproduction levels and population dispersal (Nussbaumer, et al. 2012).

Continual human interference apart from natural bushfires and introduced fire regimes

contribute to the escalation and spread of patchy mosaic forests. In certain regions this can

affect the growth of flora by offering competitive advantages towards invasive species.

Characteristics prevalent with competition involve smothering understorey vegetation,

leaching moisture and nutrients from the area and reducing the light entering the understorey

layers caused by overgrown crown canopies (Burton 2005).

2

Flora community distribution represents changing animal abundance patterns that are found

in different frequencies throughout the monitored landscape (Elmouttie 2009).

To understand the complexities of animal population abundance and distribution across the

landscape it was necessary to utilise a ‘habitat complexity score’. This represented the

differing ground, understorey, flora crown compositions, litter (rocks and debris) and soil

moisture content of the trapping area landscape where the captured animals were recorded

(Machunter et al. 2009).

The abundance of animals was measured by counting the number of trapping nights either

having trapped an animal or without having trapped an animal. The total number of trapping

nights represents a longer time period which was divided by the total mean number of

animals actually caught during trapping (Forsyth et al. 2005).

To discover the Grand Mean or relative population abundance percentage in the monitored

areas from this meant applying the ANOVA technique which is discussed further in Results.

The purpose of acquiring population abundance data is mandatory since it not only estimates

numbers in relation to the period of recording, but also helps predict possible shifts in the

physical landscape and the impacts this would have on other biota in the region in 10 to 20

years time (CSIRO, n.d.).

Methods

Study Area

Helidon Hills is located to the South West of the Lockyer National Park covering 2,677 ha in

Queensland. The Vahlmer and Hagerdoorn properties within the Helidon Hills region were

utilised for trapping and recording data. Both properties are situated along Seventeen Mile

Road (Fig 1.). South East Queensland features a sub-humid to tropical climate and occasional

rain precipitation during the summer months. Lockyer National Park consists of dry

sclerophyllous forests consisting of different varieties of eucalypt and plant communities

dominating the landscape. Patches can be found near sandstone gorges, ridges and creek

systems (Department of National Parks, Recreation, Sport and Racing 2014).

Data Collection

Mammals

In August 2014, study sites consisted of six Pitfall lines PL1-PL3 and PL19-PL21, Elliott

lines EL1-EL8 and EL49-EL56, Cage lines CL1-CL8 and CL49-CL56, Passive soil plots

RL1 and RL7. These traps were used to record the fauna endemicity and dispersal patterns in

the Vahlmer and Hagedoorn regions.

3

The majority of traps were placed in a straight line with Elliott traps placed eight in row with

cage traps placed approximately in the middle or adjacent of each of the trap lines.

Pitfall lines consisted of 5 traps each placed 50 metres apart from each other towards the end

of the baited soil plot lines.

To record the tracks of medium to large sized mammal’s soil plot lines spanned the width of

the road in a 1 – 1.5 metre band. Along with the recorded data of captured animals via

trapping sites and animal tracks recorded from soil plots represents their total abundance

index within the monitored locations.

Vegetation

At each of the trapping lines a habitat complexity score representing the structural estimation

of habitat in the area was generated rating from 0 (non-existent or scarce) – 90% (highest

abundance).

The vegetation characteristics being analysed consisted of a) Tree canopy cover, b) Ground

vegetation cover, c) Shrub canopy cover, d) litter cover percentage (logs, rocks) and e)

moisture rating contents of sampled soil plot lines.

The five characteristics were individually added up according to the trapping site locations.

For example: the ground vegetation cover situated near the pitfall traps, cage traps and Elliott

traps was solely added up in each of the trap locations representing the total ground

vegetation cover in all of the monitored areas. This total was then divided by the number of

traps with caught animals. The number resulted in the average abundance of flora patterns in

the regions with animal presence (Fig. 3).

Fig.1. The location of the Vahlmer and Hagerdoorn sites in the Lockyer National Park.

4

Results

Changes in vegetation structure (habitat complexity scores) in relation to trap captures

The habitat complexity and average abundance represented by the Vahlmer and Hagedoorn

sites displays a varying degree of contrast between tree canopy covers, litter cover and

ground vegetation cover (Fig. 3a & 3b). There was more shrub canopy (43.6%) and ground

vegetation cover (60%) represented in Vahlmer as less tree canopies resulted in more light

penetrating the canopy towards the ground. This resulted in a predominant growth of

understorey vegetation (Canham et al. 1993).

More litter cover (34.2%) was evident in the Hagerdoorn site where higher population levels

of trees (50.3%) contributed to excess loads of fuel (Fig. 3b).

The sparse and dry landform representing Vahlmer could be contributed to past and current

fire regimes which have resulted in the spread of understorey species reliant on fire to

encourage re-seeding processes. It is possible logging practices are more prevalent in the

Vahlmer region which has reduced larger tree species abundance and replaced the landscape

with smaller flora varieties (Noss et al. 2006).

Fig. 3a. Vahlmer site representing frequency distributions of the measured habitat complexity

scores in August 2014.

0

10

20

30

40

50

60

70

1- Ground veg cover

2- Tree canopy cover

3- Shrub canopy cover

4- litter cover

Re

lati

ve f

req

ue

ncy

Co

ver

%

Trap

Vahlmer Habitat Complexity

Ground veg cover %

Tree canopy cover %

Shrub canopy cover %

Litter cover %

5

Fig. 3b. Hagedoorn site representing frequency distributions of the measured habitat

complexity scores in August 2014.

(Habitat complexity scores were situated around trap sites with captures. Refer to Appendix

A and B to verify average abundance results).

Faunal endemicity regarding trapping nights and capture records

Sixteen fauna species were recorded as captured. Identified species listed are the common

planigale, Planigale maculata; common garden skink, Lampropholis guichenoti; yellow-

footed antechinus, Antechinus flavipes; common dunnart, Sminthopsis murina; common

brushtail possum, Trichosurus vulpecula; fox, Vulpes vulpes; domestic or wild dog, Canis sp;

cat, felis catus; lace monitor, Varanus varius and Crow; Corvus.

Unidentified species represent four fauna consisting of a frog caught in the no. 2 pitfall trap

line in Hagedoorn and an unknown reptile (possibly lace monitor), insect and large macropod

sampled from the soil plot- lines located in Vahlmer. However, these species were still

counted in the capture records to represent overall endemicity.

0

10

20

30

40

50

60

1- Ground veg cover

2- Tree canopy cover

3- Shrub canopy cover

4- litter cover

5- Road/soil plot cover

Re

lati

ve f

req

ue

ncy

Co

ver

%

Trap

Hagedoorn Habitat Complexity

Ground veg cover %

Tree canopy cover %

Shrub canopy cover %

Litter cover %

Road/soil plot cover % (soil moisture)

6

It was clear Vahlmer represented the most fauna abundance in trap-line sections throughout

the selected sites. Altogether a total of 47 trapped animals consisting of three unknown

species and 10 identified species were recorded (Fig. 4a) page 7.

During the same period of sampling in Vahlmer, 8 animals representing 4 different species

were trapped in Hagedoorn (Fig. 4b) page 7.

Comparing the Habitat Complexity results with fauna endemicity has revealed an interesting

correlation between distribution patterns and abundance.

Vahlmer with high levels of ground vegetation cover and medium shrub canopy coverage

without a large presence of tree canopies seems to be the main preference for a larger

population abundance of animals (Garden et al. 2007).

The possibilities for this represent many variables. Fire regimes may have been present some

time ago in the Hagedoorn region which might have reduced the necessary levels of foliage

cover needed for animals to live and any available food resources for them to survive

adequately. Therefore, a transition towards appropriate habitat levels in the Vahlmer region

with available food sources may be necessary for most animals to survive (Creighton et al.

1997).

Predation levels are inevitable during fire regimes since species such as the fox Vulpes

vulpes, cat Felis catus, dog Canis sp. including raptors and particular snake species prefer to

prey upon animals which have been displaced from their habitat. If there is less of an

abundance of animals in a particular habitat after a fire regime predators will target areas

which contain more fauna diversity (Reaveley et al. 2009).

These factors are represented in (Fig. 4a & Fig. 4b). Vahlmer during the sampling period

collected an estimated total of 25 predators consisting of 4 different species monitored on soil

plots. Most of the common species were the dog Canis sp. with 18 monitored, the lace

monitor Varanus varius with 4 monitored, fox Vulpes vulpes with 2 and the feral cat felis

catus with only 1 sampled. The amount of tracks monitored on soil plots could represent

avoidance techniques and opportunistic patterns.

Hagedoorn did not possess any known species of predator which have been recorded in

Vahlmer during the same period apart from 1 sampled crow Corvis.

There appeared to be nearly twice the number of common brushtail possums Trichosurus

vulpecula. Nine were found in Vahlmer compared to Hagedoorn which had five. It was

discovered the species may prefer to be in areas with sufficient ground and shrub canopy

cover with a substantial amount of tree canopy foliage. This could be in relation to the animal

relying on lower vegetation cover to evade predators while in the process of searching for

available food sources in close proximity to the ground (Bennett et al.1999).

7

Fig. 4a. Vahlmer capture records. A total of 47 animals were trapped consisting of three

unknown species and ten identified species.

Fig. 4b. Hagedoorn capture records. A total of 8 animals were trapped consisting of four

different species. Altogether the grand total of fauna in both the Vahlmar and Hagedoorn

regions represented 55 animals.

Relative abundance determined by trapping nights.

Since field work sampling occurred during a short period of time there was not enough data

to determine the real percentage of overall abundance and the revealing evidence of

environmental factors influencing endemicity. It was necessary to gain an overall estimation

of fauna abundance by implementing a trapping night’s table (Fig. 5) page 8.

The purpose of including both the half-trap and full- trap nights is to give an estimation of

increased abundance and absence over a wider analysis period.

Regardless of whether traps have captured or not captured an animal, both the half- trap and

full-trap nights in the Vahlmer and Hagedoorn regions were counted. This represented a

grand total of 588.5 total trap nights.

8

Fig. 5. Vahlmer and Hagedoorn trapping tables. Presented data represents half and full

trapping nights over a short allocated time frame.

Relative abundance using the Grand Mean (ANOVA) test.

The overall relative population abundance was estimated using the ANOVA technique to

discover x= the Grand Mean.

To achieve this ratio animals from both sites were separated into their species sub-groups and

totalled representing 55 animals altogether.

This figure was divided by the grand total of trapping nights consisting of 588.5 trap nights

presented as an equation on page 9.

9

x= 6+38+2+2+1+6 = 55

588.5 Nt (Number trap nights).

x= 10.7% relative abundance.

The resulting figure presents a relatively low abundance of different fauna species located

throughout the trapping sites at Vahlmer and Hagedoorn.

However the population abundance figure will shift either to a higher or lower range in the

future depending on continual environmental factors and human influences such as logging

and fire regimes (Department of the Environment, Water, Heritage and the Arts 2008).

During the monitoring and recording period on the field it was evident via the scorched

canopies and trunks of trees in particular parts of Helidon Hills that a previous fire regime

had taken place. This undoubtedly would have affected the overall endemicity in the affected

areas resulting in decreasing flora and fauna abundance. This would have also influenced the

slowing down periods of re-generation and lifecycle processes (Kenny et al. 2004).

Discussion

Monitoring fauna abundance in relation to fire regimes and ecological factors has taken place

in the Helidon Hills region in the past few years as part of the Wildlife Technologies Program

instigated by the University of Queensland.

The process has enabled abundance data for fauna in relation to time since fire to be

manipulated into statistical models of measurement.

Developing models prove useful in determining ecological factors contributing to population

abundance and the range of possibilities involved in the process such as climate change and

human related influence on the land.

Continual monitoring in the region can reveal more informative data on abundance including

gathering historical data on factors affecting endemicity in the region more than ten years

ago. This approach can level down the range of possible factors affecting endemicity and can

link the surrogate (animal population affected) to the more likely target (environmental

factor) affecting abundance during the current period of time (Collier et al. 2008).

The habitat complexity score has revealed that the majority of animals captured are mainly

influenced by the biomass/abundance of particular vegetation when certain aspects of flora

abundance seemed to be scarce elsewhere. Abundance of particular flora species can

influence the outcome of animals which rely on these sources to survive. Predators will often

relocate to areas where prey animals can be found (Yarrow 2009).

There is a positive and negative relation to habitat complexity in regards to species preference

and the following features of ground cover, tree canopy cover, shrub cover, road plot

moistness and litter cover percentages (Catling et al. 2001).

10

In this paper the variable models developed were the habitat complexity score graph and the

ANOVA technique which displayed the overall relative abundance by dividing the total

rounded fauna population in both the Vahlmer and Hagedoorn sites to the total trapping

nights of both regions.

It is evident the gathered results in this paper only reflect the current trend affecting

population abundance in Helidon Hills. For this reason repeat observations and sampling will

have to be conducted in the future to analyse any decrease or increase in fauna and flora

population dynamics. Due to the rapidly changing diversity of the landscape it may prove

difficult to repeat the exact same quantifiable measurements that had taken place to gather

this data.

It is recommended other models of measuring diversity should be consorted if there proves to

be no relevant data to obtain over a period of time in the future.

Incorporating time series analysis measurements may prove useful in obtaining more

proficient, informative and relevant data (Catling et al. 2001).

The adopted models revealed the different responses displayed by the possum, feral dog and

lace monitor for a preference of undisturbed habitats featuring medium to dense shrub and

ground vegetation cover including low litter cover as represented in Vahlmer.

There was no prediction for the animals scarce in abundance such as the planigale, skink,

yellow-footed antechinus, common dunnart, fox, un-named insect and macropod, feral cat,

frog or crow since it would take a longer period of trapping nights to verify any distinct

change in their behaviour patterns.

The problem which was prevalent when sampling data at the research sites was the time and

team effort required to accumulate enough population abundance data to be used to form a

general hypothesis. In this case it may be necessary to incorporate other methods of analysis

by using satellite imagery of the property’s landscape to gain an accurate prediction of habitat

complexity scores to make measuring fauna abundance out on the field less time-consuming

and more focus driven (Parks and Wildlife Service 2014).

It is vital to present this research information to clients who need to utilise the data to better

understand the ecological processes affecting the stakeholders land. To ensure bio-diversity

in the adjoining Helidon Hills region is not affected by the client’s actions it is necessary to

set a number of recommendations for clients to implement accordingly into their work

schedule. Agricultural practices involving crop production, herbicide and pesticide use may

affect regional diversity in Helidon Hills from particles being spread by wind gusts (Strategic

Planning & Development Policy Committee 2012).

11

Livestock grazing, logging and inappropriate fire regimes may further displace native flora

and fauna if continued over a longer period of time (Environment Protection and Biodiversity

Conservation Act 1999).

Clients should collaborate with researchers and share information regarding periods of

agricultural practices taking place. This so an appropriate limitation on logging production

yield and livestock grazing in the Vahlmer and Hagedoorn areas during specific times can

take place. A reduction in the use of chemical applications during certain periods of time to

limit the likelihood of particles being transferred by wind to segments of the national park is

another step to be considered by stakeholders.

Acknowledgements

Thanks go to Luke Leung (Wildlife Technologies program course co-ordinator), Sonya

Fardell (Technical Support), Justin Hechinger and Melanie Mills (Research undergraduate

students) for their statistical knowledge and support during the data collection period and to

the Vahlmer and Hagedoorn residents for allowing research teams access to the sites on their

properties in the Lockyer National Park region. The Queensland Parks and Wildlife Service

have provided me with useful information on the effect of fire regimes on fauna and flora

biodiversity.

Additional information and advice was gathered prior to conducting the fauna and flora

abundance analysis regarding population statistics from Dr. Peter Elsworth (Experimentalist

for the Robert Wicks Pest Animal Research Centre, Biosecurity Queensland, Department of

Agriculture, Fisheries and Forestry).

12

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Appendices

APPENDIX A. Hagerdoorn Habitat Complexity.

*Both the Vahlmar and Hagedoorn tables on this page and the following page listing the

figures in the average abundance columns represent the bar graph results on pages 4

and 5 in the RESULTS section of the report.

Pitfall 1 Pitfall 2 Gage Trap 1

Cage Trap 2

Cage Trap 3

Road soil plot

TOTAL AVERAGE ABUNDANCE

Ground veg cover

15 25 25 40 15 0 120 120/6=20

Tree canopy cover

50 70 60 60 60 2 302 302/6=50.3

Shrub canopy cover

15 38 19 20 18 0 110 110/6=18.3

Litter cover

50 40 40 55 20 0 205 205/6=34.2

16

APPENDIX B. Vahlmer Habitat Complexity.

Ground veg cover

Tree Canopy cover

Shrub canopy cover

Litter cover

Elliott trap line 49

70 40 40 20

Elliott trap line 53

80 40 80 10

Pitfall trap 19 80 50 20 40

Pitfall trap 21 60 50 20 5 Cage Trap 49 70 40 40 20

Cage Trap 50 50 30 50 20 Cage Trap 51 30 30 20 10

Cage Trap 52 50 30 70 10

Cage Trap 53 80 40 80 10 Cage Trap 54 90 40 60 50

Road soil plot 0 20 0 0 TOTAL 660 410 480 195

AVERAGE ABUNDANCE

660/11=60 410/11= 37.3

480/11= 43.6 195/11= 17.7

17

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