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Project B1.1 ‘Managing fires in forested landscapes in southern Western

Australia’

FINAL REPORT

April 2010

Compiled by Roy Wittkuhn and Lachie McCaw

Western Australian Department of Environment & Conservation

Cover photo: View of part of the Project B1.1 study area looking south‐west from a rock outcrop on Nornalup Rd / Mountain Rd intersection. The photo shows London Block, part of the Bushfire CRC study area and a key part of the Walpole Fire Mosaic (WFM) trial. Taken shortly after one of the WFM burns, a mosaic of burnt/unburnt areas is evident.

Project B1.1 – Managing fires in forested landscapes in southern Western Australia – FINAL REPORT

Executive Summary Prescribed fire is widely used in forest landscapes in south-west Western Australia (SWA)

for a range of land management objectives including conservation of biodiversity, fuel reduction for mitigation of bushfires, and regeneration of areas after timber harvesting. Managers require knowledge of the ecological effects of fire in order to apply fire regimes that are consistent with these management objectives.

Research Project B1.1 investigated the response of a variety of plant and animal groups in open eucalypt forest and shrubland to a range of fire intervals over a period of 30 years.

Specific objectives of Project B1.1 were to:

1. Compile a fire history database in a geographic information system (GIS) based on fire records currently held by the Department of Environment and Conservation (DEC);

2. Conduct a space-for-time study using the fire history database and other GIS data layers in order to investigate the effect of contrasting fire regimes on various taxonomic groups in SWA; and

3. Apply the findings of this research to develop fire management guidelines for the use of prescribed fire in maintaining biodiversity in the species-rich forest/shrubland mosaic north-east of Walpole.

The fire history database provides important baseline information that has and will continue to have application to a wide variety of research tasks. Collaboration with Project B4.2 ‘Multi-Scale Patterns in Ecological Processes and Fire Regimes’ resulted in a ground-breaking paper that used the fire history database to demonstrate the effectiveness of prescribed burning at reducing the incidence and extent of wildfires for the Warren Region of SWA (Boer et al. 2009).

The space-for-time study that comprised the major research focus of Project B1.1 demonstrated that regimes of consecutive short (≤ 5 years), consecutive long (≥ 10 years), a very long (30 years) or mixed/moderate fire intervals did not result in altered community composition for vascular plants, ground-dwelling invertebrates, vertebrates or fungi. This novel whole-of-biodiversity approach provides evidence that the biota is highly resilient to a range of fire interval sequences, and could be applied to fire ecology studies in a broad range of ecosystems.

Challenges identified for future landscape-scale fire ecology work include integration of long-term experimental approaches with space-for-time studies, the importance of collecting and maintaining accurate spatial and temporal fire data, and understanding how community composition relates to environmental and spatial variables as well as fire history.

Major findings to come from Project B1.1 are as follows:

Finding 1: Fire regimes in forests and shrublands of the Warren Region of SWA can include intervals between fires of 3 to 30 years including consecutive short (≤ 5 y) intervals without adversely impacting a broad range of biota.

Finding 2: A program of strategic prescribed burning that breaks the connectedness of fuels >6 years old will reduce the likelihood of large-scale bushfires in the landscape of the Warren region.

Finding 3: Future research should investigate the relationship between species composition and time-since-fire versus fire regime effects.

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Table of Contents

Project B1.1 ‘Managing fires in forested landscapes in southern Western Australia’ .......................................................................................................................i

Executive Summary .....................................................................................................ii

Table of Contents ........................................................................................................iv

Acknowledgements .....................................................................................................vi

1. Introduction..........................................................................................................1

2. Fire History of the Warren Region ....................................................................3

2.1 Creation of a fire history database .................................................................3 2.2 Mapping temporal fire sequences ..................................................................4 2.3 Regional and temporal trends in fire history..................................................6 2.4 Retrospective measures of fire severity .........................................................7

3. Fire interval sequences and biodiversity............................................................9

3.1 Preface............................................................................................................9 3.2 Introduction....................................................................................................9 3.3 Study design and site characteristics............................................................10 3.4 Biological surveys........................................................................................12 3.5 Data analysis ................................................................................................12 3.6 Results..........................................................................................................13 3.7 Discussion ....................................................................................................15

4. Fire interval sequences in relation to plant juvenile period...........................17

4.1 Introduction..................................................................................................17 4.2 Methods........................................................................................................17 4.3 Results..........................................................................................................18 4.4 Discussion ....................................................................................................19

5. Synthesis of key findings from Project B1.1....................................................21

5.1 Key findings.................................................................................................21 5.1.1 Frequency of prescribed burning ...................................................................................21 5.1.2 Spatial patterning of prescribed burning........................................................................21 5.1.3 Future research ..............................................................................................................22

5.2 Value of fire history data to socio-ecological research................................22 5.3 Response of biodiversity to fire regime components in SWA.....................23 5.4 Challenges for landscape-scale fire ecology studies....................................24

5.4.1 Is time-since-fire a stronger driver of species composition than the fire regime?..........24 5.4.2 Longitudinal versus space-for-time studies ....................................................................24 5.4.3 Environmental variables and spatial autocorrelation ....................................................25

5.5 Implications for fire management ................................................................25

6. References...........................................................................................................27

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Acknowledgements

Many people contributed to the design, implementation and critical support of this project. In particular, Dr Richard Robinson, Dr Janet Farr, Mr Allan Wills, Mr Paul van Heurck, Mr Graeme Liddelow and Mr Ray Cranfield (DEC Science Division) and Dr Alan Andersen (CSIRO Sustainable Ecosystems) were heavily involved in the collection, analysis and interpretation of results.

A Steering Committee guided the design of the project in the early stages, and we acknowledge the contribution of Drs Neil Burrows and Ian Abbott (DEC Science Division), Ms Karlene Bain (DEC Frankland District Nature Conservation Coordinator), Mr Rick Sneeuwjagt, Mr Roger Armstrong and Ms Femina Metcalfe (DEC Fire Management Services) and Dr Pauline Grierson (University of Western Australia and Project Leader for Bushfire CRC Project B4.2).

A significant number of people assisted with field work and we thank them for long days (and sometimes nights), and acknowledge their dedication to the conservation of WA biota: Jennifer Hollis, Grant Phelan, Julie Fielder, Bob Smith, Kerry Ironside, Michael Voigt, Claire Dornan, Ryan Burrows, Bruce Ward, Jason Fletcher, Marika Maxwell, Carol Ebbett, Jamie Flett, Burak Pekin, Katrina Syme, Chris Vellios and Chloe Flaherty, as well as a number of students who volunteered their time to assist.

We thank: Tom Hamilton for his work on developing the fire history database and assisting with site selection; Verna Tunsell for data input; Lisa Wright and Deborah Harding for reference searches and Craig Carpenter for assistance with GIS and fire history information.

We would like to acknowledge staff of the DEC Frankland District (Walpole) for their support of the project and their assistance with some aspects of field work and safety considerations. Particular thanks to Amanda Pascoe, Katie McMahon-Stevens, Marion Tindale and others that manned the radios to take our scheduled calls, and Donna Virgo (nee Green), Ray Flanagan, Howard Manning, George Doust, Ted Middleton and others for imparting their local knowledge.

The Conservation Commission of Western Australia allowed access to sites vested under the draft management plan for the Walpole Wilderness and the DEC Animal Ethics Committee approved the surveys for vertebrate fauna.

Miller’s Basin, Kent River, situated within the study area of Project B1.1

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1. Introduction Fire-prone south-west Western Australia (SWA) is recognised as a global hotspot of

biodiversity (Myers et al. 2000) with a long (20–30 Ma BP) history of fire (Hopper 2003) including Aboriginal burning within the last 50 000 years (Hallam 1975; Abbott 2003). After 1826 European occupation disrupted patterns of Aboriginal burning leading to a period of extensive unplanned fires associated with land clearing for agriculture and commercial forest exploitation. Broadscale use of prescribed fire became part of forest management policy in the mid-1950s and since then has been widely used to reduce fuels, regenerate forests after timber harvesting and promote biodiversity conservation (Burrows 2008). However, the role and value of prescribed burning in biodiversity conservation remains contentious amongst scientists and the broader community (Clarke 2008; Reinhardt et al. 2008; Wittkuhn et al. 2008b).

Landscape fire can act both as an essential ecological disturbance and as a threatening process to the conservation of biodiversity (Bradstock et al. 1995; DeBano et al. 1998; Keith et al. 2002; Sugihara et al. 2006; Syphard et al. 2009). The frequency, intensity and seasonality of fire, the fire regime (sensu Gill 1975), can profoundly influence the distribution and abundance of organisms from a range of taxonomic groups (Gill & Bradstock 1995; Keith 1996; Bradstock et al. 1998; Andersen et al. 2005). Fire interval, the period between successive fires, is a key component of the fire regime. Organisms require a minimum fire interval to reach maturity and/or reproduce, thereby either surviving themselves or allowing offspring to re-colonise following fire (Whelan et al. 2002). Fire intervals that are too short in relation to life-histories may not provide sufficient time to rebuild populations that can withstand the next fire (Gill & Bradstock 1995; Keith 1996). Changes resulting from inappropriate fire intervals to the composition of vascular plants (Cary & Morrison 1995; Morrison et al. 1995; Bradstock et al. 1997; Watson & Wardell-Johnson 2004; Watson et al. 2009), invertebrates (York 2000; Andersen et al. 2005), vertebrates (Fox 1990; Woinarski et al. 2004) and fungi (Bastias et al. 2006; Anderson et al. 2007) have been reported across a range of Australian ecosystems. The decline of some Australian eucalypt forests and woodlands has also been linked to a shift to longer intervals between fires (Jurskis 2005; Close et al. 2009).

Opportunities to study the impacts of contrasting fire regimes on biota are often limited by the available fire history data. Due to the long history of fire use for fuel management in SWA, the Department of Environment & Conservation (DEC) has comprehensive records of the spatial and temporal extent of fire on public land. This fire history data is a resource that can be used for retrospective study of the influence of contemporary fire regimes on biodiversity which makes up a major component of Project B1.1 (McCaw et al. 2005; Wittkuhn et al. 2005; Wittkuhn et al. 2006a; Wittkuhn et al. 2009a).

The majority of studies investigating community differences due to contrasting fire regimes have one or more of the following limitations: (i) long-term manipulative studies take many years to provide results; (ii) experimental treatments compare frequent burning with long-unburnt sites which introduces the confounding variable of time-since-fire; (iii) a single group of taxa is usually studied; and (iv) studies are at relatively small scales, generally smaller than scales at which management decisions are made. Retrospective studies have some limitations (Strayer et al. 1986) but permit

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a rapid assessment of fire regime effects that would otherwise take decades to achieve.

This report summarises the findings from Bushfire CRC Project B1.1 ‘Managing fires in forested landscapes in southern Western Australia’. The main objectives of Project B1.1 were to:

1. Develop a fire history database in a geographic information system (GIS) based on fire records currently held by the DEC.

2. Devise a space-for-time study using the fire history database and other GIS data layers (e.g. vegetation complexes from Mattiske & Havel 1998) in order to investigate the effect of contrasting fire regimes on various taxonomic groups in SWA.

3. Use the results of these studies to devise fire management guidelines on the use of prescribed fire for maintaining biodiversity in the species-rich forest/shrubland mosaic north-east of Walpole.

This report presents the main findings in relation to these three objectives, culminating in a synthesis that provides an overview of the research findings, considerations for future work, and implications for fire management in the area.

Xanthosia rotundifolia & Kennedia coccinea flowering ~ 2 years post‐fire in jarrah forest within Project B1.1 study area.

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2. Fire History of the Warren Region This section describes the contemporary fire history of the Warren Region, an

administrative region that includes approximately 0.9 million ha of State forest and conservation estate managed by DEC. DEC and its predecessors, the Department of Conservation and Land Management (CALM) and the Forests Department, have undertaken prescribed burning at a landscape scale for more than five decades. In response to escalating unplanned fires that were becoming more difficult to control the Forests Department in 1953/54 adopted a policy of broad-scale prescribed burning, a decision considered momentous at the time (Wallace 1966). Fire history in parts of the region is well documented from around 1937 when mapping of areas in each fire season commenced. Maps use colour-codes to denote the fire type (wildfires, silvicultural burns or prescribed burns) and season-of-burn (McCaw et al. 2005; Hamilton et al. 2009).

Prior to 1995, all fire perimeters were captured on paper maps; these were later photographed to microfiche and archived. Since 1995, fire perimeters have been captured electronically into spatial databases. These datasets were stored separately with no simple way of determining the fire history of a given point in the landscape. The first task of Project B1.1 was therefore to digitize and combine the datasets into a single fire history database for the Warren Region.

2.1 Creation of a fire history database The Warren fire history database (FHD) was created from a number of sources held

by DEC including: (i) fire history images for the years 1937/38 to 1994/95; (ii) the Forest Management Information System (FMIS), a raster GIS, for the fire seasons 1988/89 to 2004/05; and (iii) vector data for the years 1998/99 to 2004/05. Fire data for each year were digitized as polygon shapefiles, and attributed information that was contained on the maps or in the database (Table 2.1). To complete the FHD, layers from all years were merged so that it was possible to determine all fires that occurred in a given area over time from 1937/38 to 2004/05. A detailed description of the methods used to create the FHD is given in Hamilton et al. (2009) and a summary (poster) is presented in Wittkuhn et al. (2005). The following papers are outputs relating to this objective of Project B1.1:

McCaw, L., Hamilton, T. & Rumley, C. (2005) Application of fire history records to contemporary management issues in south-west Australian forests. In: A Forest Conscienceness: Proceedings 6th National Conference of the Australian Forest History Society Inc, Augusta, Western Australia. (eds M. Calver, H. Bigler-Cole, G. Bolton, J. Dargavel, A. Gaynor, P. Horwitz, J. Mills & G. Wardell-Johnson), pp. 555-564. Millpress, Rotterdam.

Hamilton, T., Wittkuhn, R.S. & Carpenter, C. (2009) Creation of a fire history database for southwestern Australia: giving old maps new life in a Geographic Information System. Conservation Science Western Australia, 7(2), 429-450. Available at: http://www.dec.wa.gov.au/content/view/2321/1808/

http://www.dec.wa.gov.au/content/view/2321/1808/


Table 2.1. Attribute structure for the fire history database of the Warren Region, south-west Western Australia (Table reproduced from Hamilton et al. 2009).

Attribute

Description

Entries/values

Data type

Data length

Year Preceding calendar year of each fire season

1953, 1954, …., 2004 Number 4

Text 30

District

District in which fire was recorded (on fire history maps)

Blackwood, Donnelly, Frankland, Manjimup, Pemberton, Unknown, Walpole. (Note that the current Donnelly district encompasses the old Manjimup and Pemberton districts, and the current Frankland district encompasses the old Walpole district)

Date Prescribed burn last activity date or wildfire detection date

Various Date 6

Identifier Prescribed burn ID or wildfire serial number

Various Text 30

Name Name of prescribed burn or wildfire

Various Text 30

Firetype Type of fire PB – prescribed burn, WF – wildfire, UN - unknown Text 2

Season Season of prescribed burn AU – autumn, SP – spring, SU – summer, WI – winter, UN – unknown

Text 2

Ignition Ignition type for a prescribed burn

AC – aircraft, HB – hand burn, HC – helicopter Text 2

Cause Cause of a wildfire Accidental by other industry, accidental by recreational forest users, deliberate, escape from CALM prescribed burn, escape from other burning off, lightning.

Text 50

Burnpurpose

Purpose of a prescribed burn

Advance burn, biodiversity conservation, community/strategic protection, fire research (scientific), hardwood silviculture, hazard reduction (protection), nature conservation, regeneration/slash burn, silvicultural, strategic buffers, tops disposal burn, tourism and recreation, under pine canopy

Text

30

Comments Comments relating to capture/validation of data and quality of fire history images

Various Text 80

Confidence

Confidence in accuracy of captured data

High – no uncertainty, Medium – minor uncertainty in boundary or part of boundary, Low – significant uncertainty in boundary, Very Low – major uncertainty in boundary.

Text

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Area Area of captured fire (m2) Various Number 16

Perimeter Perimeter of captured fire (m) Various Number 16

Hectares Area of captured fire (ha) Various Number 16

2.2 Mapping temporal fire sequences Temporal fire sequences refer to the ordered sequence of fire variables through

time, such as fire intervals or fire seasons (Wittkuhn et al. 2009a). They provide more information than a simple measure of fire frequency (number of fires per unit time) or average inter-fire interval. This is important biologically because taxa respond to sequences of events. For example, the repetition of two or more short fire intervals can reduce the abundance of obligate seeding plants (Cary & Morrison 1995; Bradstock et al. 1997).

We used the Warren FHD to derive temporal sequences of fire intervals and seasons using a classification scheme described by Wittkuhn & Hamilton (2006), Wittkuhn et al. (2009a) and Wittkuhn & Hamilton (in press). Logical test functions were used to

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assign integer classifications to fire intervals (short, moderate and long) and fire types/seasons (wildfires and prescribed burns in different seasons). Integer classifications were joined together to form a sequence of numbers representing the order of either fire intervals or fire seasons in reverse time sequence (Fig 2.1). This allowed sequences to be mapped in a GIS environment so that spatial dimensions formed by overlapping polygons were readily observed, and the temporal sequence of fire data within each polygon interpreted across the landscape (Wittkuhn et al. 2009a). This is useful for identifying spatio-temporal patterns, and for identifying potential study sites for fire regime studies (Wittkuhn & Hamilton in press).

The following papers are outputs relating to this objective of Project B1.1:

Wittkuhn, R. & Hamilton, T. (2006) Mapping fire regimes of Western Australia in a GIS - viewing temporal data in a spatial context. Third International Fire Ecology and Management Congress, San Diego, California, 13-17 November 2006. Association for Fire Ecology.

Wittkuhn, R.S., Hamilton, T. & McCaw, L. (2009) Fire interval sequences to aid in site selection for biodiversity studies: mapping the fire regime. Proceedings of the Royal Society of Queensland (Bushfire 2006 Conference Special Edition), 115, 101-111.

Wittkuhn, R.S. & Hamilton, T. (in press) Using fire history data to map temporal sequences of fire intervals and seasons. Fire Ecology 6 (2).

Fig. 2.1. Calculation of fire interval sequences for polygons contained in the GIS fire history database. For the two polygons, actual fire years are shown. From this, the real fire intervals are calculated and then classified into interval types, either as short (≤ 5 years, designated by ‘1’), medium (6 – 9 years, designated by ‘2’) or long (≥ 10 years, designated by ‘3’). Interval types are joined together into a single whole number (shown inside the polygon boundary). This ‘fire interval sequence’ is contained in its own column in the fire history database, and can be used to map temporal information across the landscape. Where a fire interval sequence contains a ‘0’ (always at the end of the sequence) this indicates that fewer than the maximum number of fire intervals for the entire dataset are contained in this polygon. Figure reproduced from Wittkuhn et al. (2009a).

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2.3 Regional and temporal trends in fire history Trends in fire history for the Warren Region were investigated by Hamilton et al.

(2009) and in collaboration with researchers from the University of Western Australia (Bushfire CRC Project B4.2 – Multi-Scale Patterns in Ecological Processes and Fire Regimes) (Boer et al. 2009).

Trends in the annual area burnt by fire type show that regimes in the Warren Region are dominated by prescribed burning (Fig. 2.2). Total area burnt shows an increasing trend from 1953/54 until the mid-1980s, followed by a general reduction in area until 2004/05. In 2002/03, the area of wildfires was far greater than at any time in the preceding years (Fig. 2.2), and this strongly influences the fuel age distribution (Fig. 2.3).

0

20

40

60

80

100

120

140

160

1953

1956

1959

1962

1965

1968

1971

1974

1977

1980

1983

1986

1989

1992

1995

1998

2001

2004

Year

Area (x 1000 ha)

Unknown

Wildfire

Prescribedburn

Fig. 2.2. Annual area burnt by fire type (wildfire, prescribed burn, or unknown origin) for the Warren Region of Western Australia, 1953/54–2004/05. Figure reproduced from Hamilton et al. (2009).

0

20

40

60

80

100

120

140

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Year last burnt

Area (x 1000 ha)

Fig. 2.3. Fuel age distribution for the Warren Region (clipped to DEC-managed estate), showing the total area of land for the year last burnt (black bars) and the simplest negative exponential fitted model to year of last burn (light grey bars). Figure reproduced from Hamilton et al. (2009).

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Using the Warren Region FHD (1953/54–2004/05), Boer et al. (2009) investigated the impact of prescribed burning on the incidence and extent of unplanned fires. This is one of few empirical studies on the effectiveness of prescribed burning for mitigating the size and incidence of wildfires at a regional level. Key findings of the paper were:

At a regional level, prescribed burns have ~ 6-year inhibitory effect on the extent of wildfires.

The incidence of large unplanned fires was significantly less than the long-term average for the region when the annual extent of prescribed fire was at a maximum and significantly more when the annual extent of prescribed fire was at a minimum.

Since the 1960s, the length of time sites remain unburned by wildfire has approximately doubled to ~ 9-years.

Over the observation period (1953/54 – 2004/05), on average 82% of the annual area burnt was due to planned fires.

The patterning of young (< 6-years) and old (> 6-years) fuel age patches have changed over time. The connectedness of old fuel patches was a significant determinant of the annual extent of area burnt by wildfires, while the percentage of area with young fuels was less important. This highlights that the strategic placement of prescribed burns to break up old fuels is most important to reduce the size of wildfires.

The following papers are outputs relating to this objective of Project B1.1:

Boer, M.M., Sadler, R.J., Wittkuhn, R.S., McCaw, L. & Grierson, P.F. (2009) Long-term impacts of prescribed burning on regional extent and incidence of wildfires - evidence from fifty years of active fire management in SW Australian forests. Forest Ecology and Management, 259, 132-142.

Hamilton, T., Wittkuhn, R.S. & Carpenter, C. (2009) Creation of a fire history database for southwestern Australia: giving old maps new life in a Geographic Information System. Conservation Science Western Australia, 7 (2), 429-450. Available at: http://www.dec.wa.gov.au/content/view/2321/1808/

2.4 Retrospective measures of fire severity Two undergraduate students received Bushfire CRC scholarships to undertake

research projects with Project B1.1. Ms Chloe Flaherty and Mr Ryan Burrows, both from The University of Western Australia, were selected in 2006 and 2007 respectively to investigate retrospective measures of fire severity. Chloe compiled a literature review of methodologies that used biological indicators for fire severity measures, and trialled the use of several methods on a subset of five sites (Flaherty 2006). Ryan consolidated this work in the following summer, developing a fire severity score for each forest site used in Project B1.1 (Burrows 2007).

The reports by Chloe and Ryan are available from the Bushfire CRC, or from Roy Wittkuhn ([email protected]), and are listed below.

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http://www.dec.wa.gov.au/content/view/2321/1808/

mailto:[email protected]


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Flaherty, C. (2006) Retrospective measures of fire intensity for forested landscapes in southwestern Australia. Unpublished report. Western Australian Department of Environment & Conservation and Bushfire CRC.

Burrows, R. (2007) Retrospective measures of fire intensity using epicormic sprouting and sapling frequency in the southern jarrah forest of southwestern Australia. Unpublished report. Western Australian Department of Environment & Conservation and Bushfire CRC.

Granite outcrop near Suez Rd, northern end of Willmott Block, Project B1.1 study area


3. Fire interval sequences and biodiversity

3.1 Preface The text in this section forms a summary of a paper currently in preparation. Any

citations in regard to this section of the report should be made to the following paper (conduct a web-search or contact Roy Wittkuhn1 for the status of this paper).

Wittkuhn, R.S., McCaw, L., Wills, A.J., Robinson, R., Andersen, A.N., van Heurck, P., Farr, J., Liddelow, G., Cranfield, R. (in preparation) Variation in fire interval sequences has no detectable effects on species richness or community composition in fire-prone landscapes of south-west Western Australia. For submission to Journal of Applied Ecology.

3.2 Introduction Contemporary fire regimes in south-west Western Australia (SWA) are deliberately

managed for fuel reduction in order to minimize severe wildfires and to achieve land management objectives such as forest regeneration and biodiversity conservation (McCaw & Burrows 1989; Boer et al. 2009). Since the mid 1950s prescribed fire has been widely used in SWA as a management tool on public lands to reduce fuels, regenerate forests after timber harvesting and in recent decades to promote biodiversity conservation objectives (Burrows 2008). While significant knowledge of fire ecology in SWA has accumulated in recent decades (Bell et al. 1984; Bell et al. 1989; Abbott & Burrows 2003; Burrows 2008), an integrated study of the effects of contrasting fire regimes at a ‘whole of biodiversity’ level has not previously been attempted. Nor has this been done within the extremes of contemporary fire regimes, which may provide an appraisal of the effectiveness of prescribed burning to maintain species diversity at a landscape scale.

Fire interval, the period between successive fires, is a key component of the fire regime. Fire intervals that are too short in relation to life-histories may not provide sufficient time to rebuild populations that can withstand the next fire (Gill & Bradstock 1995; Keith 1996). At the other extreme of fire frequency, changes to species composition and abundance have been reported in ecosystems where contemporary fire intervals exceed historical norms, such as in North American forests (Stephenson 1999; Brockway et al. 2005; Kaufmann et al. 2007), and tropical and sub-tropical savannas (Bowman 2000). The decline of some Australian eucalypt forests and woodlands has also been linked to a shift to longer intervals between fires (Jurskis 2005; Close et al. 2009).

Using accurate contemporary fire history data for SWA (Hamilton et al. 2009), we asked the following questions with the aim of determining the resilience of biological communities to contrasting fire regimes at various spatial scales. (1) What impacts do the different fire interval sequences have on species richness and composition (i.e. what is the degree of community resilience, sensu Holling 1973)? (2) Are fire regime responses different for forest and shrubland biota, and how can fire intervals be managed to account for this? Results from this study are discussed in terms of the dual management objectives of wildfire mitigation and biodiversity conservation in the species-rich landscape of SWA.

1 [email protected]

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mailto:[email protected]


3.3 Study design and site characteristics The 50 000 ha study area was situated approximately 40 km northeast of Walpole in

Mount Roe National Park within the Warren bioregion of the Southwest Botanical Province (Fig. 3.1). The landscape is gently undulating and consists of open sclerophyll eucalypt forest and woodland on lateritic uplands interspersed with low-lying seasonally-inundated shrublands and sedgelands that vary considerably in structure and floristic composition. Climate of the area is mediterranean, with warm, dry summers (December to February) and mild, wet winters (June to August). Average annual rainfall ranges 900–1100 mm across the study area.

We used the vegetation complex mapping of Mattiske and Havel (1998) to stratify the study area into two broad structural types represented in each of the fire regime polygons:

Fig. 3.1. Location of the ~50 000 ha study area in southwestern Australia for determination of community response to contrasting fire interval sequences. Figure reproduced from Wittkuhn et al. (in prep.).

(1) Shrubland, which occurred in seasonally-inundated lowlands of the Caldyanup complex, and were dominated by monocotyledons (especially Cyperaceae spp.) and small shrubs (< 1 m tall) predominantly from the families Myrtaceae, Papillionaceae and Proteaceae. Trees (< 8 m tall) of Melaleuca preissiana (common), jarrah (Eucalyptus marginata, uncommon), and Nuytsia floribunda (uncommon) were present at some sites.

(2) Forest, dominated by jarrah and marri (Corymbia calophylla) to 20 m tall, with a mid-storey of Banksia grandis, Persoonia longifolia and P. elliptica, and arborescent monocots (Xanthorrhoea preissi and Kingia australis). Forests have a species-rich understorey. Forest sites included examples of two Collis (CO) complexes which differ slightly in soil type and vegetation structure: COy1, described as being on lateritic and yellow duplex soils, and COp1 on shallow gritty yellow duplex soils, usually without laterite (Havel & Mattiske 2000).

The entire study area was burnt by wildfire or prescribed burns in the fire season of 2002/03; hence all sites chosen had the same time-since-fire (Table 3.1). Using GIS, we investigated sequences of fire intervals by simplifying actual fire intervals into

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three groups: short (≤ 5 years), moderate (6–9 years) and long (≥ 10 years) (Wittkuhn et al. 2009a; Wittkuhn & Hamilton in press) and summarized in Section 2.2. We identified polygons with successive short fire intervals (SS-regime), and polygons with successive long fire intervals (LL-regimes). All other polygons were considered a ‘mixed’ or ‘moderate’ (M) regime, except for one polygon with a single fire interval of 30-years (burnt in 1972 and 2002). This interval is one of the longest for the study area, and is referred to as a ‘very long’ (VL) regime (Table 3.1).

Thirty sites were identified using GIS by intersecting fire histories, vegetation complexes and digitised aerial photography, and verified by field visits. We used an unbalanced design that provided a measure of the variation across the study area as well as maximized the availability of sites with the extreme fire regimes. Sixteen sites were located in forest: four with SS-, three with LL-, seven with M- and two with VL-regimes (Table 3.1). Fourteen sites were located in shrubland: four with SS-, three with LL- and seven with M-regimes (Table 3.1). There were no suitable shrubland sites with a VL-regime.

Table 3.1. Attributes of the study sites used for determining community responses to contrasting fire interval sequences.‘VC’ = vegetation complex (CA = Caldyanup which

represents the seasonally inundated shrubland/sedgeland in drainage basins; COy1 = jarrah forest on lateritic uplands; COp1 = jarrah forest on shallow gritty sands). ‘Fire interval

sequence’ and its ‘Abbreviation’ are classifications based on the ‘Actual fire intervals’ which are presented in reverse time series (most recent to least recent). Table reproduced from

Wittkuhn et al. (in prep.).

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3.4 Biological surveys The retrospective study of fire regimes undertaken for Project B1.1 maintained a

constant time-since-fire across sites, investigated a range of taxa, and examined the effects of fire regimes at a landscape scale (~ 50 000 ha). Large, replicated sampling grids were used to examine the extent to which different sequences of past fire intervals influence richness, abundance and composition of a wide range of taxonomic groups (vascular plants, macrofungi, ground-dwelling invertebrates and vertebrates).

Data were collected on plots designed for monitoring forest ecosystem health after logging – referred to as the FORESTCHECK

1 study. These 2 ha plots allow for all groups of organisms to be surveyed at a single site (Fig. 3.2). A summary of survey techniques is presented here; refer to Wittkuhn et al. (in prep.) for more information.

Vascular plants were surveyed using four 30 × 30 m quadrats at each site (Fig. 3.2). Species lists were compiled separately for each quadrat, and abundance was estimated using a modified Braun-Blanquet classification (Mueller-Dombois & Ellenberg 1974).

Invertebrates were surveyed using ten pitfall traps (90 mm diameter × 110 mm deep) placed 10 m apart along a straight-line transect (Fig. 3.2). Traps remained open for ten days and four surveys were conducted over two years. We confined our analysis to ants and beetles and combined the four surveys by summing species abundances at the site level.

Vertebrates (excluding birds) were surveyed only on forest sites as previous trapping has yielded low capture rates in shrublands (G. Liddelow, unpublished data). Fifteen wire cage traps (20 × 20 × 45 cm) and 15 pitfall traps (20 L buckets with a 5 m flywire drift fence; Fig. 3.2) were opened simultaneously over four nights and checked daily for animal captures. Four trapping sessions were conducted over two years and surveys were combined for data analysis.

Macrofungi were surveyed on a subset of ten forest plots (Table 3.1), but no surveys were attempted in shrubland plots as fruiting is known to be poor possibly due to winter inundation (R. Robinson, unpublished data). At each site, abundance of macrofungi sporophores were surveyed on two belt transects (200 m long and 2 m wide; Fig. 3.2) and four surveys were conducted over two years. Data were summed across the four surveys for each site.

3.5 Data analysis One-way ANOVA was used to test for differences between mean species richness in

forest and shrubland, and between fire regimes within each vegetation type after assumptions of normality and constant variance were upheld. Variation in community composition was investigated for each taxonomic group using non-metric multidimensional scaling (nMDS) (Kruskal 1964).

We present the composition of biota in relation to three factors: fire interval sequences; vegetation type (forest versus shrubland); and forest type (comparison between the two forest complexes). Differences in species composition between vegetation complexes and fire interval sequences were tested using the PERMANOVA procedure on similarity matrices for each biotic group.

1 http://www.dec.wa.gov.au/science-and-research/landscape-research/forestcheck-index-page.html

http://www.dec.wa.gov.au/science-and-research/landscape-research/forestcheck-index-page.html


Fig. 3.2. The systematic 2 ha plot layout for biological surveys. Fungi transects and vertebrate traps (wire cages and pitfall buckets) were not used on shrubland sites. Figure reproduced from Wittkuhn et al. (in prep.).

3.6 Results There were 403 vascular plant species, 112 ant species, 378 beetle morphospecies,

37 vertebrate fauna species (seven native mammals, three introduced mammals, 15 lizards, two snakes, and ten frogs) and 395 macrofungi species recorded in this study.

Species richness for all groups did not differ between sites with contrasting fire interval sequences, and this was the case for both forest and shrubland (Table 3.2). Although not significant, the richness of plant species was higher in sites that experienced a 30-year interval prior to the last fire (VL-regime sites) than all other groups (Table 3.2). There was no significant difference in species composition of sites in the contrasting fire regime groups for both forest and shrubland when data for all biotic groups were analysed by PERMANOVA and represented by nMDS (Fig. 3.3). Notwithstanding the influence of M-regime sites on the ordinations in Figure 3.3, there is no evidence of SS-sites clustering apart from LL- or VL-sites for any of the taxonomic groups in either vegetation type.

Table 3.2. Mean species richness (± SEM) for taxonomic groups at sites with short-short (SS), mixed/moderate (M), long-long (LL) and very long (VL) fire interval sequences in forest and shrubland. P-values are given from one-way ANOVA between fire interval sequences. Table

reproduced from Wittkuhn et al. (in prep.) Fire Forest Shrubland regime Plants Ants Beetles Vertebrates Fungi Plants Ants Beetles SS (n=4) 82±4 24±2 35±4 11±1 142±15 76±11 16±2 27±6 M (n=7) 91±7 25±1 34±4 13±1 135±17 68±9 16±2 30±3 LL (n=3) 91±7 26±2 32±6 12±1 150±9‡ 81±6 18±2 28±5 VL (n=2) 101±6 25±1 33±1 12±3 - - - - All sites† 90±4 25±1 34±2 12±1 141±8 73±5 16±1 29±2 P-value 0.561 0.751 0.979 0.290 0.783 0.635 0.790 0.923 † n = 16 for forest; n = 14 for shrubland

‡Fungi LL sites are a combination of two VL sites (sites 27 and 28) and one LL site (site 26).

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Mean species richness per site was significantly higher in forest than shrubland for vascular plants (P = 0.0124) and ants (P < 0.0001), but there was no significant difference in beetle richness between the two vegetation types (P = 0.1135; Table 3.2). The nMDS ordinations demonstrated a clear distinction between forest and shrubland sites in terms of the composition of vascular plant, ant and beetle species (Fig. 3.3).

Composition of vascular plants exhibited a weak tendency for the two forest complexes to group separately (Fig. 3.3) with a marginally significant PERMANOVA P-value of 0.067. All other taxonomic groups showed no significant differences in species composition between the two forest complexes (Fig. 3.3).

The composition of plants, ants and beetles was more variable among shrubland than forest sites, and this is noticeable from the spread of points in the nMDS ordinations (Fig. 3.3).

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3.7 Discussion Our data show that varying fire intervals had no persistent effect on the richness and

composition of biota associated with forests and shrublands of the Warren bioregion, and therefore demonstrate a high degree of resilience (sensu Holling 1973) in relation to fire. It appears that a long-term history of varied fire regimes from pre-human to more recent times has pre-conditioned the biota to persist across a range of fire intervals varying from long (at least 30 years) to short (≤ 5 years) fire intervals. Such variability occurs in the short term at individual sites. For example, our SS-sites have experienced both moderate (6–9 years) and long (≥ 10 years) intervals at some stage in the recent past, including their most recent fire interval. This can be an important factor determining community composition, with shrubland communities in south-eastern Australia showing compositional differences depending on the length of the most recent fire interval (Cary & Morrison 1995). We suggest that the variability that has occurred in our sites makes an important contribution to observed resilience, whereas repeated short intervals over the longer term would likely lead to substantial ecological change.

Many other studies across a range of organisms and ecosytems have similarly showed little or no effect of different fire interval sequences on species richness (Bradstock et al. 1997; Hanula & Wade 2003; Watson & Wardell-Johnson 2004; Woinarski et al. 2004). However, our results contrast with those of other studies showing that short fire intervals can alter community composition. For example, short fire intervals can influence plant species abundances in shrubby woodlands and open forests of eastern Australia (Cary & Morrison 1995; Morrison et al. 1995; Bradstock et al. 1997; Watson & Wardell-Johnson 2004). Other studies likewise suggest short fire intervals to have a strong impact on fungi (Bastias et al. 2006; Anderson et al. 2007; Bastias et al. 2009) and habitat characteristics that influence species composition of vertebrates (Catling 1991; Woinarski et al. 2004) and invertebrates (Andersen 1991; Vanderwoude et al. 1997; York 1999a, b, 2000; Orgeas & Andersen 2001; Andersen et al. 2003; Hanula & Wade 2003; Andersen et al. 2006). We suggest that substantial ecological change occurred in most of these studies because short fire intervals were maintained over an extended period of time, rather than being part of a variable regime.

A key research question arising from this work is whether time-since-fire is a stronger driver of species composition than is previous fire history. For shrubby woodlands and open forests of eastern Australia, both time-since-fire and fire frequency have been shown to influence vascular plant communities, though in different ways (Morrison et al. 1995; Watson & Wardell-Johnson 2004). The concept of biota showing a distinct compositional pattern in response to ecological variables that are re-set by the occurrence of fire (e.g. Fox 1990; Parr et al. 2004) invokes theories of ‘ecological memory’ (Peterson 2002). It also has critically important implications for conservation management, which could focus on the maintenance of an appropriate mosaic of time-since-fire across the landscape.

The fact that two consecutive fire intervals < 6 years does not lead to persistent changes in community composition also has important implications for resolving trade-offs between managing fuels for wildfire control on one hand, and managing for biodiversity on the other, given that the effects of prescribed burning on wildfire incidence and extent persists for up to 6 years (Boer et al. 2009). Thus, it is possible to maintain the age of vegetation and fuel at < 6 years across parts of the landscape,

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and therefore limit the spread of wildfire, without deleterious impacts on a range of biota. Our results did not demonstrate any significant beneficial or detrimental ecological effects from the long or very long fire intervals, except that the two forest sites with a VL-regime demonstrated elevated plant species richness compared with other sites. This suggests that the organisms in this landscape are well adapted to tolerate significant variation in fire intervals.

Additionally, the resilience demonstrated by all taxonomic groups to a range of fire interval sequences means that occasional shorter intervals that may result from unplanned fires affecting recently-burnt sites are unlikely to have serious adverse consequences for biodiversity. Further research should focus on quantifying the range of spatial scales at which mosaics of young and old fuels should be implemented for effective species conservation and wildfire mitigation (Fernandes & Botelho 2003; Penman et al. 2007; Boer et al. 2009; Whelan 2009).

There was a clear distinction between forest and shrubland sites in terms of species composition of vascular plants and ground-dwelling invertebrates, which was strongly evident from the ordinations (Fig. 3.3). By demonstrating no significant influence of common fire interval sequences on the richness or composition of biota in either of these two vegetation types, fire management can be integrated at the landscape scale where fire regimes can be planned which apply to both vegetation types. While this is desirable from an efficiency perspective, we also caution that the shrubland is composed of several ecotones which may have different fire requirements for biodiversity conservation.

The threatened endemic marsupial Setonix brachyurus

(Quokka) was surveyed on two forest sites during fieldwork for

Project B1.1


4. Fire interval sequences in relation to plant juvenile period

4.1 Introduction Plant juvenile period is defined as the time taken for a plant species to reach

reproductive maturity, either from germination (primary juvenile period) or from resprouting after a disturbance such as fire (secondary juvenile period). The juvenile period has been proposed as guide to determining appropriate fire interval sequences for the conservation of vascular plant species. Gill & Nicholls (1989) proposed minimum fire intervals that are twice the juvenile period of the most fire-sensitive species as conservative guidelines for maintenance of floristic diversity. Burrows and co-workers (Burrows & Friend 1998; Burrows et al. 2008) devised a similar framework for SWA by compiling a database on juvenile periods for a range of taxa over wetter and drier parts of the region.

The rationale of using plant juvenile periods to determine fire intervals for fire management is that the most fire-sensitive species will be the first to become extinct locally should fire intervals become too narrow. Project B1.1 investigated successive short (≤ 5 year) fire intervals, providing the opportunity to test the effectiveness of using the plant juvenile period for determining landscape-scale fire management decisions. We hypothesize that there will be fewer species and/or a lower abundance of species with longer juvenile periods in the sites experiencing successive short fire interval sequences (SS-regime) than in sites with successive long (LL), very long (VL) or mixed/moderate (M) regimes.

4.2 Methods All plants surveyed in Section 3 were classified as seeders or resprouters, and where

the data were available, assigned to one of six plant juvenile periods according to data held on the NatureMap website (DEC 2007 – 2009). In this database, juvenile period is defined as the time at which 50% of the population of a species has reached flowering age (Burrows et al. 2008). We categorized each species as having one of six juvenile periods: (i) ≤ 12 months, (ii) 13–24 months, (iii) 25–36 months, (iv) 37–48 months, (v) 49–60 months or (vi) > 60 months. The database on juvenile periods was not complete for all species identified in our study. Hence, for shrubland, we used 134 of the 268 species and 183 of 246 species for forest.

The mean number of species and individuals in each juvenile period group was calculated separately for forest and shrubland. For forest, comparisons by way of separate one-way ANOVAs were conducted between the two forest complexes as well as between fire interval sequences. For shrubland, one-way ANOVA was used to investigate differences between fire interval sequences. Data were checked for normality of residuals and constant variance prior to analysis, and transformed by a square-root or logarithm procedure where appropriate. Community composition of forest and shrubland sites was investigated using nMDS with each species classified according to juvenile period, resulting in six variables (described above). We summed individuals within each juvenile period group (across species) to investigate the pattern based on total individuals in the sites (log-transformed). We also ran the analysis on the number of species that were classified into the respective groups (untransformed). For both analyses, Bray-Curtis dissimilarity measures were used.

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We used PERMANOVA to test for differences between the two forest complexes and between fire regimes for forest sites (2-way PERMANOVA: vegetation complex and fire regime) and shrubland sites (one-way PERMANOVA: fire regime).

4.3 Results Forest and shrubland flora consisted of ~ 60% sprouters and 40% non-sprouters,

and these proportions did not differ with respect to fire regime in either vegetation type (ANOVA P > 0.3).

Regardless of vegetation type, the greatest proportion of species and individuals had juvenile periods of 13–24 months, and few species or individuals had juvenile periods > 36 months (Fig. 4.1). No significant differences were found between the fire regimes for mean number of species or individuals in each juvenile period group (Fig. 4.1).

Three species had juvenile periods 49–60 months: Melaleuca lateritia (60 mo), Melaleuca viminea (60 mo) and Acacia pentadenia (54 mo), and only one had a juvenile period > 60 months (Hakea falcata, 72 mo). Hakea falcata was found in both forest and shrubland, A. pentadenia was restricted to forest and the Melaleuca species to shrubland.

05

101520

253035

4045

≤ 12 13-24 25-36 37-48 49-60 > 60

Juvenile period (months)

Nu

mb

er

of s

pe

cie

s

0

5

10

15

20

25

≤ 12 13-24 25-36 37-48 49-60 > 60

Juvenile period (months)

Nu

mb

er

of s

pe

cie

s

Fig. 4.1. Mean number (± SEM) of vascular plant species classified by juvenile period within the fire regime groups for forest (left) and shrubland (right). = short-short (SS), = mixed/moderate (M), = long-long (LL) and = very long (VL). No significant differences exist between the number of species found in fire regimes within each juvenile period group.

Multivariate analysis of the composition of juvenile period groups showed no significant differences between fire regimes for both vegetation types, both at the level of individuals and species (PERMANOVA P > 0.69 for all tests in Fig. 4.2). For forests, the nMDS for number of individuals in the juvenile periods suggested a broad distinction between two groups, which was not related to vegetation complex (PERMANOVA P = 0.1916) or fire regime (P = 0.6982). Instead, this grouping appeared due to the distribution of Acacia pentadenia, an obligate seeder with soil-stored seed. At a finer level, sites 9 and 11 formed a third group which was delineated by a higher abundance of Hakea falcata. Hakea falcata was found only in four shrubland sites (three M- and one LL-site).

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Fig. 4.2. nMDS of plots based on vascular flora that has been classified by length of the juvenile period for (a) number of species in forest sites, (b) number of individuals in forest sites (log-transformed), (c) number of species in shrubland sites, and (d) number of individuals in shrubland sites (log-transformed). Labels represent fire regime and site number: M = mixed/moderate; SS = short-short; LL = long-long; VL = very long). The two forest complexes are shown: = COy1; = COp1.

4.4 Discussion The hypothesis that plots with SS-regimes may retain fewer species or individuals

with a long juvenile period (sensu Gill & Nicholls 1989) was not supported by our analysis for either forest or shrubland. Data were highly skewed, with ~90% having a primary juvenile period ≤ 3 years, and only four species with juvenile periods > 4 years. Sites grouped according to intrinsic environmental factors that presumably provided suitable conditions for those species with long juvenile periods, rather than grouped by fire interval sequences. For forests, it was the occurrence of Acacia pentadenia (juvenile period 54 mo) that split the ordination of sites into two groups, and the occurrence of Hakea falcata (juvenile period 72 mo) in greater numbers that isolated sites 9 and 11 from the others (Fig. 4.2b). For shrublands, lack of data on plant juvenile periods may have prevented adequate hypothesis testing. Only 50% of the species surveyed in shrubland had corresponding juvenile period data on the NatureMap website, compared with 74% for forests.

Hakea falcata is a serotinous non-sprouter and has the longest juvenile period of all species in our study. It therefore represents the most fire-sensitive species in terms of fire interval sequences (Gill & Nicholls 1989). However, H. falcata was surveyed at SS-sites in forest, albeit at low numbers. In shrubland, H. falcata had limited distribution, including absence from all four SS-sites and two of the three LL-sites. We suggest that the persistence of H. falcata and other species with long juvenile periods (e.g. A. pentadenia) at forest sites subject to the SS-regime could be a result of prescribed fires being patchy and of non-lethal intensity due to low levels of fuel

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accumulation (Gould et al. 2007) and mild burning conditions in spring. In addition, the observation of juvenile period for H. falcata on the NatureMap website is from the Avon wheatbelt of southern WA which is ~400 km north of our study area and significantly drier. Where a species is widespread geographically, its juvenile period is longer at the drier end of its range (Burrows et al. 2008). Hence, H. falcata in our study area may have a shorter juvenile period than recorded in the database, and may be another reason for its persistence on SS-sites. Acacia pentadenia accumulates an abundant soil seed store, and its hard-seededness would buffer it from repeated fires at short intervals. The absence of H. falcata from shrubland sites with a SS-regime warrants further investigation, though it appears to have limited distribution in this vegetation complex.

Hakea falcata (Proteaceae), recorded during fieldwork for Project B1.1

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5. Synthesis of key findings from Project B1.1 In this section, we first summarize the key findings from Project B1.1 and then

address four main areas of fire ecology and management that are relevant to southern Western Australia, but also have implications for fire research and management in a broader context:

1. Value of fire history data to socio-ecological research

2. Response of biodiversity to fire interval sequences in SWA

3. Challenges for landscape-scale fire ecology studies

4. Implications for fire management

5.1 Key findings

5.1.1 Frequency of prescribed burning

Finding: Fire regimes in forests and shrublands of the Warren Region of SWA can include intervals between fires of 3 to 30 years without adversely impacting a broad range of biota.

While no differences in species composition were demonstrated from this study as a result of different fire interval regimes, the impact of a particular fire interval regime for three or more successive cycles is unknown. Previous work has demonstrated that no single regime is ideal for all species (see review papers in Abbott & Burrows 2003), and therefore a precautionary approach of maintaining a range of fire intervals for a patch of forest is recommended.

This range is quite wide, and while based on biological data from Project B1.1, also fits with fuel accumulation and wildfire hazard data (Gould et al. 2007).

Fire intervals in the range 3–30 years are unlikely to significantly influence community composition in forests and shrublands of the Southern Hilly Terrain Landscape Conservation Unit (see Mattiske & Havel 1998 for details on Landscape Conservation Units), and therefore provides the range suggested for prescribed burning.

5.1.2 Spatial patterning of prescribed burning

Finding: A program of strategic prescribed burning that breaks the connectedness of fuels >6 years old will reduce the likelihood of large-scale bushfires in the landscapes of the Warren region.

Results of the collaboration between UWA and DEC determined that the percentage of the landscape existing in young (≤ 6 years) fuel ages was less important than the connectedness of old (> 6 years) fuel ages on explaining wildfire size (Boer et al. 2009).

By maintaining young fuels around older fuels, the probability of a large wildfire occurring is reduced. Results from Wittkuhn et al. (in prep.) and Sections 3 and 4 demonstrate that maintaining a patchwork of young fuel ages is not likely to be detrimental to biodiversity, even if two short fire intervals (≤ 5 years) are required to reduce connectedness of old fuels.

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5.1.3 Future research

Finding: Future research should investigate the relationship between species composition and time-since-fire versus fire regime effects.

This study investigated sites with the same time-since-fire but contrasting fire histories. Given that trends in species richness and composition were not detected in relation to fire histories, it is suggested that a range of times-since-fire across the landscape may be a stronger driver of species diversity and composition than fire regime per se (Wittkuhn et al. in prep.).

The Walpole Fire Mosaic study (described in section 5.4.1) may address this hypothesis, though a carefully-designed study that sets out to test this question explicitly would be useful.

5.2 Value of fire history data to socio-ecological research Research conducted under Projects B1.1 (presented here) and B4.2 has

demonstrated the value of accurate fire history data to socio-ecological research. Project B1.1 has delivered a number of peer-reviewed papers and conference presentations and posters describing and investigating the fire history of the Warren Region (McCaw et al. 2005; Wittkuhn et al. 2005; Wittkuhn & Hamilton 2006; Wittkuhn et al. 2006a; Wittkuhn et al. 2006b; Wittkuhn & McCaw 2007; Wittkuhn et al. 2008a; Wittkuhn et al. 2008b; Boer et al. 2009; Hamilton et al. 2009; Wittkuhn et al. 2009a; Wittkuhn et al. 2009b; Wittkuhn & Hamilton in press). Additionally, a series of papers describing the results of ecological responses to contrasting fire interval sequences (described in Sections 3 & 4) that used fire history information to determine the study design, will be published from this work.

Work completed through Project B1.1 to compile fire history data and establish a network of replicated sampling areas in the field has also facilitated supplementary research beyond the scope of the initial project plan. This opportunity was used to good effect by Burak Pekin, a PhD candidate with the University of Western Australia Ecosystems Research Group, who investigated relationships between climate, fire frequency and forest structure and biomass in eucalypt forests. His field studies utilized the 16 plots established in forest ecosystems as part of Project B1.1. The main findings of this work were that increased fire and drought shift tree species composition towards more fire-resistant species and result in denser stands of smaller trees, and that total stand biomass declines with increasing aridity but is unrelated to fire frequency (Pekin et al. 2009).

While long-term studies that implement fire treatments are the best source for determining the effects of contrasting fire regimes (e.g. York 1999a; Penman et al. 2008a), these studies are constrained by the range of original treatments imposed and the scale of plot sizes which cannot fully represent the range of factors operating at the landscape scale. Hence, fire history data incorporated in a GIS and attributed with as much information as possible allows the development of retrospective, space-for-time studies to investigate fire ecology and management questions.

Project B1.1 has compiled fire history data for a landscape where prescribed fire has accounted for more than 80% of the area burnt in the past five decades. A related project conducted by Bushfire CRC PhD student Alison O’Donnell has examined fire history in the semi-arid Lake Johnson area of the southern WA Goldfields where most fires are ignited by lightning and there is minimal intervention through prescribed fire

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or fire suppression (O’Donnell et al. 2010; O’Donnell et al. in review). The landscape of the Lake Johnson area is a mosaic of eucalypt woodlands and shrublands interspersed with large salt lakes and isolated outcropping ranges of igneous and metamorphic (greenstone and banded ironstone) rocks. While the Walpole and Lake Johnson areas are superficially dissimilar because of differences in annual rainfall (900 mm and 250 mm respectively) there is scope to make useful comparisons about fire regimes and their ecological effects. This could include comparison of:

Age class distributions and fire return periods for major vegetation types;

Occurrence and size of unplanned fires, particularly extensive fires that burn for more than a few days;

Climate and weather factors associated with extensive fires;

The importance of natural landscape features (rock outcrops, lakes) as barriers to fire spread and as refugia for plants and animals with specific fire regime requirements.

Fire history information compiled for the Warren region has also been made available to French scientists from Cemagref1 for use in a comparative study of fire mosaics in Mediterranean ecosystems in France and SWA. Mr Nicolas Faivre, a PhD student from Cemagref, has applied a methodology developed for French ecosystems to quantify the characteristics of fire mosaics in parts of the study area used for Project B1.1, and to examine the response of vascular plants to different fire regimes. Nicolas completed 2 months of field work in late spring 2009 assisted by Bushfire CRC vacation student Kelly Paterson.

5.3 Response of biodiversity to fire regime components in SWA

Results of this project suggest that biota of SWA are very resilient to a range of fire interval sequences (Wittkuhn et al. in prep., summarized in Section 3). Similarly, when vascular plants are investigated in relation to life history characteristics (juvenile periods, as described in Section 4), no effects of contrasting fire regimes are found. These results suggest that fire management in SWA, in terms of planning fire interval sequences, can vary widely without significant risk to the composition and richness of biota. Importantly, the occurrence of successive short (≤ 5 year) intervals does not significantly change community composition relative to the other fire regimes. However, the study does not investigate regimes of three or more successive short fire intervals, which may lead to community change in the long-term.

Our study did not examine the role of fire intensity as a component of fire regimes, although we recognize that fire intensity and other physical characteristics of fire can be important in determining responses of plants and animals. Mapped fire history records do not provide any direct information about fire characteristics, other than those than can be inferred from the season or date of the fire and whether the fire was planned or unplanned. Satellite imagery has been available since the mid 1970’s and with appropriate processing and enhancement can provide information about the effect of fire on vegetation, notably the severity of crown damage in forests. It was beyond the scope of this study to retrospectively analyse fire intensity at each of the

1 Cemagref - The Institute for Research in Science and Technology for the Environment. http://www.cemagref.fr/actualites

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http://www.cemagref.fr/actualites


30 sampling grids. However, work by Bushfire CRC vacation students Chloe Flaherty and Ryan Burrows does provide information about the relative severity of fire at each forest site following the 2002/03 fires (Flaherty 2006; Burrows 2007). Given the dominant forest trees (jarrah and marri) in the study area are tolerant of fire and can survive a wide range of fire intensity, it is likely that the effects of past fire intensity will be expressed as subtle differences in stand structure and the condition of individual trees, particularly stem scarring and crown health.

5.4 Challenges for landscape-scale fire ecology studies Project B1.1 has identified a number of knowledge gaps that should form key

research projects to better inform land management agencies which are outlined below:

5.4.1 Is time-since-fire a stronger driver of species composition than the fire regime?

Results from Wittkuhn et al. (in prep.) and summarized in Section 3 of this report suggest that the fire interval sequence had no significant effects on the composition of the ecological community. All sites had the same time-since-fire, which may account for the similarity in species composition and richness. Hence, the major question to arise from this work is whether time-since-fire is a stronger driver of species composition than the fire regime per se. Research that investigates a mosaic of times-since-fire across the landscape and spans a range of fire histories may indicate their relative contributions to species diversity at the landscape scale.

A major research project, the Walpole Fire Mosaic study, has been initiated in SWA in the same area used by Wittkuhn et al. (in prep.) that is introducing frequent fire to a ~ 4000 ha study area with the aim to create fine-grain mosaics of various seral stages (Burrows & Wardell-Johnson 2004). This does not mean that the entire 4000 ha is burnt frequently. Rather, the introduction of fire every 2–3 years would likely burn a fraction of the study area, creating multiple seral stages as the experiment progresses through time. The hypotheses to be tested are that frequent introduction of fire will (1) maintain and promote a fine-grained habitat mosaic incorporating a range of interlocking seral stages, (2) that this mosaic will promote biodiversity, and (3) that the mosaic will reduce the severity and impact of wildfires on biota and ecosystems (Burrows & Wardell-Johnson 2004). The results from this project can be compared with those from Wittkuhn et al. (in prep.) as they use the same general study area, vegetation types and surveying methods. Hence, it will be possible to investigate the relative importance of fire regime and time-since-fire on biodiversity.

5.4.2 Longitudinal versus space-for-time studies The studies described in this report represent space-for-time studies using

retrospective data sources. Longitudinal studies that test specific hypotheses and are long-term are preferred methods for collecting ecological data. However, once established, these studies are difficult to manipulate to answer alternate questions that may arise from the study. Many existing studies are also of limited spatial scale while the outcomes from them are applied to landscape-scale management decisions. Thus, there is a place for space-for-time studies in fire ecology research, and as mentioned above, the collation of accurate fire history records are essential to enable these studies.

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Linking of space-for-time with longitudinal studies provides an appraisal of the effectiveness of both designs to answer particular questions. The Walpole Fire Mosaic study, described in Section 5.4.1, is a longitudinal experiment that will be able to assess some of the conclusions presented by Wittkuhn et al. (in prep.) and described in Sections 3 and 4.

5.4.3 Environmental variables and spatial autocorrelation Fire plays a complex role in maintaining, changing and dictating species

composition. Assessing the role of fire on species composition is necessarily undertaken across several sites or plots, which introduces both spatial and environmental variables that may influence trends in the data. Spatial autocorrelation refers to the similarity of a measured variable (e.g. species composition) between pairs of sites as being more (positive autocorrelation) or less (negative autocorrelation) than expected for randomly associated pairs of observations (Legendre 1993). Spatial autocorrelation is likely to influence fire studies that use many plots over large areas.

Data from Project B1.1 is being used to investigate the relative contribution of fire, environmental and spatial variables on the variation in assemblage data (RS Wittkuhn & BK Pekin unpublished data). A range of environmental variables have been collected and several fire variables, including the severity of the last fire (Flaherty 2006; Burrows 2007), have been derived from the fire histories of the sites which will be incorporated into a variation partitioning model (Borcard et al. 1992). Outcomes from this work will determine the proportion of variation explained by independent components: pure spatial, pure environmental, pure fire, as well as interactions between these factors and undetermined (residual) components. The analysis will also determine which environmental variables best explain the distribution of the different taxonomic groups (vascular plants, ants, beetles, vertebrates and fungi). This research will be novel in its determination of the factors influencing community composition in a fire-prone environment, and we hope to publish in a high-ranking peer-reviewed journal. Early indications from data analysis suggest that fire and spatial variables play a very small role in determining community composition, and that environmental variables are stronger drivers.

5.5 Implications for fire management In this sub-section, we bring together the results of Projects B1.1 and B4.2 (in the

form of the paper by Boer et al. 2009) to discuss the implications for fire management in SWA. Much of this section is summarized from Wittkuhn et al. (in prep.).

Using a 52 year fire history record for the Warren region of SWA, Boer et al. (2009) demonstrated a significant negative relationship between the extent of prescribed burning and unplanned fires and identified that the inhibitory effect of prescribed fires could persist for up to six years, which is consistent with documented patterns of fuel dynamics in forests and shrublands (Gould et al. 2007). The results from Project B1.1 suggest that it is possible to maintain the age of vegetation and fuel at ≤ 6 years across parts of the landscape without deleterious impacts on a range of biota. However, Boer et al. (2009) showed that the connectedness of old (> 6 year) fuel patches was a significant determinant of the annual extent of area burnt by wildfire in the region, while the percentage area with young (≤ 6 year) fuels was less important. Rather than the creation of large areas with young fuels, this highlights the

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importance of conducting a strategic prescribed burning program to break up the connectedness of older fuels in the landscape, which is a more achievable objective.

Additionally, the resilience demonstrated by all taxonomic groups to a range of fire interval sequences means that occasional shorter intervals that may result from unplanned fires affecting recently-burnt sites are unlikely to have serious adverse consequences for biodiversity. Forest and shrubland fuels are unlikely to re-burn within 2–3 years except under severe weather conditions (Gould et al. 2007; McCaw et al. 2008), and even then the sparse and discontinuous nature of the fuel typically results in a mosaic of unburnt patches which provide localised refuges for invertebrates and smaller vertebrates and a source of propagules for re-establishment of plants. This is in contrast with long unburnt fuels, where unplanned fires under dry summer conditions can have severe impacts on vegetation, habitat elements and soil, resulting in very few patches remaining unburnt (Finney et al. 2005; Burrows 2008). However, we caution that resilience to two successive short fire intervals does not necessarily imply resilience to such intervals over the longer term and that knowledge of the cumulative effects of three or more fires at short fire intervals is primarily derived from small scale experimental studies which cannot fully represent the range of factors operating at the landscape scale (e.g. York 1999a; Burrows & Wardell-Johnson 2003; Department of Sustainability and Environment 2003; Penman et al. 2008b). Further research should focus on quantifying the range of spatial scales at which mosaics of young and old fuels should be implemented for effective species conservation and wildfire mitigation, which is a major objective of the Walpole Fire Mosaic study described in Section 5.4.1 (Burrows & Wardell-Johnson 2004). This understanding will become critical if predictions of increased fire danger and more extreme fire events due to climate change become a reality (Hughes 2003; Lucas et al. 2007).

Nuytsia floribunda (Western Australian Christmas Tree) flowering 1‐year after a prescribed burn on the border of the study area.

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