North-Kimberley Landscape Conservation Initiative

2013–14 Monitoring, Evaluation, Research & Improvement Report ACKNOWLEDGEMENTS The Department of Parks and Wildlife acknowledges the Traditional Owner groups who have enabled staff access to their country to conduct both operational land management activities and the field monitoring program under the Landscape Conservation Initiative. Parks and Wildlife acknowledges the traditional custodians and Native Title holders of Balanggarra, Dambimangari, Wilinggin and Wunambal Gaambera lands and thank them for their support and direct contributions to this program, as well as the contributions that their own Healthy Country programs have made to conservation outcomes in the North-Kimberley.

Parks and Wildlife acknowledge the land management and research programs done by Australian Wildlife Conservancy in the central and north Kimberley. The contributions they have made to fire management, feral animal management and biodiversity conservation are appreciated.

The authors thank the large number of Parks and Wildlife staff involved in the development, implementation and analysis of the LCI reporting program and on-ground threat management programs. This includes regional staff: Richard Fairman, Lindsay Baker, John Hayward, Greg Goonack, Richard Tunnicliffe, Paul Hyndes, Allan Thomson, Philip de Bruyn, David Chemello, Tracy Sonneman, Ed Hatherley, Nathan Connor, Jai Latham, Daryl Moncrieff and Luke Bentley; and Perth-based staff: Lesley Gibson, Norm McKenzie, Greg Keighery, Ricky van Dongen, Bart Huntley, Jane Chapman, Graeme Behn, Janine Kinloch and Georgina Pitt.

Parks and Wildlife acknowledge the Department of Agriculture and Food for their contribution to large feral herbivore and weed management, and the authors thank Mick Everitt for supplying data from the Judas donkey program.

Cover photographs North-Kimberley coastline with rainforest patch, palm forest at Mitchell Plateau and golden-backed tree rat by David Bettini.

©Western Australia, May 2016

Department of Parks and Wildlife, PO Box 942, Kununurra WA 6743

Citation Corey, B., Radford, I., Carnes, K. and Moncrieff, A. (2016) North-Kimberley Landscape Conservation Initiative: 2013– 14 Monitoring, Evaluation, Research & Improvement Report. Department of Parks and Wildlife, Kununurra, WA.

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SUMMARY Context. Despite its relatively intact landscapes, northern Australia has been experiencing worrying biodiversity declines, with strong evidence that these herald an imminent wave of extinctions; similar to those experienced in southern Australia. Biodiversity values are threatened by altered fire regimes, introduced and domestic herbivores, feral predators and prey, and invasive plants. The North-Kimberley has been far less affected by these declines, and is considered a stronghold for biodiversity. Aims. The Landscape Conservation Initiative (LCI) was established under the Western Australian government’s Kimberley Science and Conservation Strategy and aims to retain and enhance current natural biodiversity and landscape values in the North-Kimberley through partnerships with land owners and managers. Methods. Threats from fire and invasive animals and plants are being managed across > 65,000 km2 by the Department of Parks and Wildlife, Native Title groups, conservation NGOs, pastoralists and other government departments. A monitoring program focused on three biodiversity indictors has been established to measure management effectiveness: (1) rainforest patch extent and condition; (2) small-medium sized mammal diversity and abundance; and (3) vegetation and habitat condition. The premise being that these should remain stable or improve if the aforementioned threats are being managed adequately. Key results. Rainforest patches remained stable from 2012 to 2014, with no evidence of degradation. Overall, small mammal diversity and abundance has been retained, but there are areas of concern; most notably the King Edward, and Laterite regions of the Mitchell Plateau, and the Orchid Creek region in Drysdale River National Park. These areas have very low diversity (≤ 1 species) and abundance (< 5 % trap success) – similar to reference sites on Mount Elizabeth station (an active pastoral station). There is evidence to suggest that vegetation cover is increasing. Increased fire management has made great inroads in terms of arresting the boom and bust fire regimes that dominated the North-Kimberley prior to 2008. Late season fires have been reduced by nearly 50 %, resulting in a 55 % increase in vegetation patchiness (for ≥ 3 year old vegetation). However, the extent of this long unburnt vegetation has not increased. Large feral herbivore control is yet to make a measurable reduction in landscape impacts. Weed management has reduced the extent of grader grass (Themeda quadrivalvis) by > 50 % in the Mitchell River area; however data recording and data management needs to be improved across other areas and for other species so the full extent of management efforts can be captured and evaluated. Conclusion. Monitoring has demonstrated that increased investment in threat management in the North- Kimberley has achieved some measurable environmental outcomes and positive benefits for biodiversity. Equally as important, it has also highlighted areas where management needs to be fine-tuned. Finally, it has helped to shed light on the causal mechanisms of biodiversity declines across northern Australia, in particular small mammals and the role that fire and large introduced herbivores play in the decline and persistence of small mammals. Implications. Monitoring suggests that: (1) the size, frequency and intensity of fires still needs to be reduced – in particular fire frequency; current research indicates this would have the added benefit of reducing the impacts of feral cats, which are more efficient hunters in more open habitats; (2) the impacts of large feral herbivores still need to be reduced; and (3) better monitoring of weed management actions is required so that efforts can be evaluated.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... 1 SUMMARY ...... 2 TABLE OF CONTENTS ...... 3 1. BACKGROUND ...... 4 2. CONTEXT AND APPROACH ...... 5 3. RESULTS AND DISCUSSION ...... 6 3.1 LANDSCAPE OUTCOMES ...... 6 Biodiversity indicators – at a glance ...... 6 3.1.1 Rainforest patch extent and condition ...... 7 3.1.2 Mammal diversity and abundance ...... 9 3.1.3 Vegetation and habitat condition ...... 12 3.1.4 Rainfall trends ...... 17 3.2 MANAGEMENT ACTION EFFECTIVENESS ...... 18 Management of fire, feral animals and weeds – at a glance ...... 18 3.2.1 Managing inappropriate fire regimes ...... 19 3.2.2 Managing the impacts of invasive animals ...... 26 3.2.3 Managing the impacts of invasive plants ...... 32 3.3 IMPLICATIONS FOR MANAGEMENT ...... 36 3.3.1 LANDSCAPE OUTCOMES ...... 36 3.3.2 MANAGEMENT ACTION EFFECTIVENESS ...... 37 4. REFERENCES ...... 39

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1. BACKGROUND Northern Australia is considered relatively intact in comparison with other landscapes in Australia. Despite this, scientists and land and wildlife managers are concerned by the rapid decline of biodiversity across northern Australia, particularly of small to medium sized mammals (Fitzsimmons et al. 2010; Woinarski et al. 2010; 2011; Ziembicki et al. 2013) and granivorous birds (Franklin 1999; Franklin et al. 2005; Murphy et al. 2010; Woinarksi and Legge 2013). There are strong indications that current declines herald an imminent wave of extinctions, similar to those experienced in other regions of Australia (Johnson 2006; McKenzie et al. 2007; Fisher et al. 2014; Woinarski et al. 2015), unless threatening processes can be better understood and mitigated.

Biodiversity values across northern Australia are threatened by a range of pervasive and interacting processes: altered fire regimes (Andersen et al. 2005; Legge et al. 2008; Griffiths et al. 2015; Lawes et al. 2015; Radford et al. 2015), introduced and domestic herbivores (Legge et al. 2011a; van Doorn et al. 2015), feral predators and prey (Johnson 2006; O’Donnell et al. 2010; Woinarksi et al. 2011; Frank et al. 2014; McGregor et al. 2015a; Radford and Fairman 2015) and invasive plants (Rossiter et al. 2009; Setterfield et al. 2010).

The Kimberley has been far less affected by these declines, and is widely seen as one of the most ecologically important regions in Australia; boasting spectacular and varied landscapes that are home to diverse and unique assemblages of plants and animals. The North-Kimberley bioregion (Fig. 1) in particular, has been listed as a National Biodiversity Hotspot and is considered a stronghold for wildlife (McKenzie et al. 2007; Start et al. 2007; Burbidge et al. 2008; Start et al. 2012; Corey et al. 2013; Radford et al. 2014; Radford et al. 2015; Turpin 2015; Olds et al. in press).

The Landscape Conservation Initiative (LCI) was established under the Western Australian Government’s Kimberley Science and Conservation Strategy and aims to retain and enhance current natural biodiversity and landscape values in the North-Kimberley. The LCI is managed by the Department of Parks and Wildlife with some collaboration between Native Title and Indigenous ranger groups, conservation NGOs and pastoralists in a cross- tenure approach to protect biodiversity values in the North-Kimberley. This is being achieved by managing: (1) inappropriate fire regimes; (2) the impacts of feral animals; and (3) the impacts of invasive plants across an area of more than 65,000 km2 that includes State managed conservation reserves, pastoral lands, lands held under Native Title and declared as Indigenous Protected Areas, as well as private conservation areas (Fig. 1).

Fig. 1. The North-Kimberley Landscape Conservation Initiative project area showing main land tenures and areas managed for conservation.

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The aims of LCI management (and rationale behind these) are described in detail by Corey et al. (2013), but broadly seek to reduce the extent and intensity of fires and the occurrence and hence impacts of large feral herbivores and invasive plants. A monitoring, evaluation, research and improvement (MERI) program was established to measure and report on progress towards managing these threats (Shedley et al. 2012). Indicators for measuring management effectiveness and reporting on biodiversity condition at a landscape scale are: (1) rainforest patch extent and condition; (2) small-medium sized mammal diversity and abundance; and (3) vegetation and habitat condition. These elements of the landscape are acutely sensitive to the aforementioned threats (see Corey et al. 2013), and as such provide a barometer of change. This monitoring also follows increased calls for agencies involved in implementing conservation actions to be more accountable in terms of tangible biodiversity-driven outcomes (e.g. Lindenmayer et al. 2012; Legge 2015).

Plate 1. Rainforest patches (top left), native vegetation condition (top right) and small-medium sized mammals (bottom, photo: David Bettini) are being used to measure management effectiveness in the North-Kimberley.

2. CONTEXT AND APPROACH The LCI project area (65,194 km2 in total) contains a mix of State-managed conservation reserves, unallocated Crown land, pastoral leases (including those managed principally for conservation), Commonwealth managed lands, and Aboriginal Lands Trust estate. Native Title has been determined over nearly the entire area, with much of it recognised as Exclusive Possession Native Title. Indigenous Protected Areas (IPAs) have also been declared over large areas, including Indigenous-managed pastoral leases (Fig. 1). The LCI area is delineated by the Mitchell sub-region of the North-Kimberley IBRA (Interim Biogeographic Regionalisation for Australia) region, with the addition of Drysdale River National Park to the east and the King Leopold Ranges Conservation Park (north of Gibb River Road) to the south (Fig. 1).

Information on the LCI project area, threat management programs and monitoring site selection and methodology, including fire scar analysis are covered in detail by Corey et al. (2013). Similarly, information on the conservation and land management programs carried out by other groups that are independent, but complementary to the LCI program are provided elsewhere (e.g. Wunambal Gaambera Aboriginal Corporation 2010; Balanggarra Aboriginal Corporation 2011; Dambimangari Aboriginal Corporation 2012; Wilinggin Aboriginal Corporation 2012; Legge et al. 2015a). Reporting under the MERI program only includes outcomes from Department-led activities, or those undertaken as fee-for-service arrangements. The exception to this is the fire scar analysis of prescribed burning carried out within the LCI area. For the purpose of this report it would be counter-productive to dissect the LCI area into smaller compartments because (1) the report is about regional, gross fire patterns; (2) multiple boundary intersects create artificial fire pattern metrics; and (3) the units of analysis need to be large relative to the historical size of late dry season fires. Thus, the fire metrics reported here represent the efforts of many groups to manage fire: EcoFire (coordinated by the Australian Wildlife Conservancy) and the North Kimberley Fire Abatement Project, coordinated by the Kimberley Land Council with the Balanggarra, Dambimangari, and Wunggurr Rangers, and the Wunambal Gaambera Aboriginal Corporation and its Uunguu Rangers.

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3. RESULTS AND DISCUSSION 3.1 LANDSCAPE OUTCOMES Biodiversity indicators – at a glance

Table 1. Landscape Conservation Initiative outcome performance reporting: Landscape health indicators across the North-Kimberley.

Condition monitoring Monitoring target Outcome Indicator trend† Rainforest patch extent retained in extent and condition OCM 1a OT1a

Extent to which monitored rainforest Stable or increasing trend in Mean rainforest patch size remained STABLE areas maintain their size rainforest patch size stable from 2012 to 2014 (Fig. 2). OCM1b OT1b Extent to which monitored rainforest Maintenance of vegetation density Vegetation cover within rainforest areas maintain their vegetation and/or cover with no evidence of patches was maintained from 2012 to STABLE condition (structure) degradation by grazing or burning, 2014 (Table 3), with no evidence of or invasion of annual plant species degradation. Small-medium mammal species (< 5 kg) diversity and abundance retained OCM2a OT2a Changes in numbers of species detected Stable or increasing trend in Mammal diversity has (on average) number of small mammal species remained stable (Fig. 4), but is very STABLE detected low at Orchid Creek (Drysdale River National Park) and King Edward monitoring clusters (Fig. 3, 5). OCM2b OT2b Specific species presence and absence Trends in average abundance of Mammal abundance has (on average) rates monitored species stable or remained stable (Fig. 6), but is very STABLE increasing low at Orchid Creek, King Edward and Laterite monitoring clusters (Fig. 3, 7). Vegetation and habitat condition retained, enhanced or re-established OCM3a OT3a Fine-scale vegetation condition across Stable or increasing trend in on- Vegetation condition varied between six major habitat types ground measures of cover for biotic habitat types (Table 4) and between STABLE vegetation strata within habitat years for shrub and perennial grass types cover (Table 5; Fig. 9), but with no consistent trend over time. OCM3b OT3b Habitat structural condition across six Stable or increasing trend in on- Partially decomposed leaf litter depth major habitat types ground measures of cover for varied between habitat types, but STABLE abiotic structural habitat with no consistent trend over time components within habitat types (Table 4, 5, Fig. 10, 11). OCM3c OT3c Landscape-scale vegetation condition Stable or increasing trend in Vegetation cover has increased across across six major habitat types remotely sensed Landsat all habitat types except rainforest, INCREASE vegetation cover within habitat which has remained stable (Fig. 13, 2). types

†Key INCREASE Increasing trend; indicator is increasing (improving) STABLE No trend; indicator remains unchanged, but stable DECREASE Decreasing trend; indicator is deteriorating (getting worse)

Plate 2. Bachsten Creek is a stronghold for small-medium sized mammals in the North-Kimberley.

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3.1.1 Rainforest patch extent and condition 3.1.1.1 Stable or increasing trend in rainforest patch size (OT1a) From 2012 to 2014 the Rapideye imagery analysis identified no significant loss in either mean or total rainforest extent (Mann-Whitney U Test: U = 3885.5; P = 0.231; Fig. 2; see also van Dongen et al. 2015). Non-significant changes in mean and total extent (Fig. 2) were related to fluctuations in semi-deciduous tree cover and the timing of Rapideye imagery capture between 2012 and 2014 (van Dongen et al. 2015). Although North-Kimberley rainforest canopy cover fluctuates less than that of other savanna vegetation (see section 3.1.3), rainforest patches contain semi-deciduous species that lose leaves towards the end of the dry season (Kenneally et al. 1991). Efforts should be made in future years to standardise the time of season when images are captured and analysed for changes in extent, to ensure that any changes can be interpreted correctly.

(a) 5 (b) 400

4 300 3 200 2

Total extent (ha) extent Total 100 1 Mean patch extent (ha) extent patch Mean 0 0 2012 2014 2012 2014

Fig. 2. (a) Mean and (b) total extent of 94 rainforest patches from 2012 to 2014 in the Landscape Conservation Initiative area measured via Rapideye satellite imagery. Error bars are + 1 standard error.

3.1.1.2 Maintenance of vegetation density/cover (OT1b) Rainforest vegetation structure at on-ground monitoring sites (Table 2) differed among localities for shrub, litter, log and exposed rock cover, but there was no evidence of change from 2011 to 2014 (Table 3). Over this period, rainforest patches were characterised by consistently closed tree and shrub canopies (69 % and 34 % projected ground cover respectively), high understorey rockiness (25 %) with high leaf litter cover (69 %) and very low perennial grass cover (2.2 %; Table 4). There was little evidence of degradation of rainforest patches. Although there was localised damage by cattle in some patches (e.g. tracks and resting spots under the canopy) only understory damage had occurred, and was insufficient to cause increased light penetration to the forest floor, and result in perennial grass growth.

Table 2. On-ground monitoring sites across habitat types within the Landscape Conservation area, where vegetation and habitat attributes are measured and small-medium sized mammals are trapped.

Number of sites within habitat type Monitoring Main Monitoring Sandstone Sandstone Volcanic Laterite Total cluster tenure frequency Riparian Rainforest scree woodland woodland forest Northern† MRNP/UCL Annual 2 2 2 2 2 3 13 Mitchell Plateau† MRNP/UCL Annual 2 2 1 3 3 - 11 Laterite UCL Annual - - 2 2 3 - 7 Yalgi UCL/ALT Annual 1 1 1 1 1 2 7 King Edward UCL Annual 2 2 2 1 2 - 9 Mt. Hart† KLRCP Annual 1 1 2 2 - - 6 Silent Grove† KLRCP Annual 2 1 2 3 - - 8 Mt. Trafalgar† PRNP Biennial - - 5 - - 3 8 Cascade Ck. † PRNP Biennial 2 3 0 2 - 1 8 Mt. Elizabeth Pastoral Biennial 1 3 4 - - - 8 Bachsten Ck. UCL Biennial 3 3 - 2 - - 8 Orchid Ck. DRNP Biennial 2 - 2 3 - 1 8 Total 18 18 23 21 11 10 101

† Denotes clusters with a comparative baseline measure for small-medium sized mammals. MRNP, Mitchell River National Park; UCL, unallocated Crown Land; ALT, Aboriginal Lands Trust; KLRCP, King Leopold Ranges Conservation Park, PRNP, Prince Regent National Park; DRNP, Drysdale River National Park.

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Table 3. Analysis of Variance (ANOVA) F values for rainforest vegetation structural attribute differences between location and all years of monitoring (2011 to 2014). Values in bold denote statistically significant differences. Year of monitoring as a factor is nested within locality due to non-independence of repeatedly measured monitoring sites.

Habitat attribute Locality Year (locality) Degrees of freedom 5, 9 10, 9 Prism Basal area (m2.ha-1) 2.78 0.36 Bitterlich Tree cover (%) 0.69 0.16 Shrub cover (%) 3.53* 0.09 Transect Perennial grass (%) 0.28 0.30 Annual grass (%) - - Forbs (%) 2.09 1.35 Litter (%) 5.60* 0.73 Logs (%) 8.18* 0.90 Exposed rock (%) 5.87 ** 0.99 Bare ground (%) 1.92 2.29 Termite mounds (%) - - Sub-shrubs (%) 1.16 0.78 Shrub (%) 2.73 1.11 Tree (%) 0.69 0.16

* P < 0.05, ** P < 0.01 - None recorded

3.1.1.3 Rainforest patches – summary and management recommendations Although data for monitored rainforest patches suggests stability of these monitoring targets (OT1a, b), it is important that any evidence of cattle damage at rainforest patches is fed back into the threat management planning process. For instance, on Parks and Wildlife-managed lands, through the aerial shoot program planning process, and on non-Department managed lands through the appropriate land manager or Healthy Country planning team. Similarly, incursions of priority weed species (see section 3.2.3) should also be reported.

Plate 3. Rainforest patch at Camp Creek Conservation Reserve (left) and Surveyors Pool (right).

Plate 4. Surveyors Pool in Mitchell River National Park during the wet season.

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3.1.2 Mammal diversity and abundance 3.1.2.1 Stable or increasing trend in number of small mammal species (OT2a) A total of 49 sites were surveyed in 2013 and 43 in 2014 (see Table 2); in addition another eight sites on Mount Elizabeth Station were visited opportunistically in 2013 (Table 2; Fig. 3). The number of different small mammal species (small mammal diversity) averaged across all monitoring sites has not changed from historical surveys (one-

way ANOVA [year as factor]: F 3, 27 = 1.031; P = 0.395; Fig. 4). Mammal diversity did differ between geographic clusters of sites (ANOVA on ranks: H 10 = 25.016; P = 0.005; Fig. 5). Post-hoc tests revealed that Orchid Creek and King Edward clusters (Fig. 3) had very low diversity in 2013 and 2014 (Fig. 5). Similarly, mammal diversity was also very low at Mount Elizabeth Station (mean diversity = 0.50; range = 0–1).

Fig. 3. On-ground monitoring clusters for small-medium sized mammals and vegetation in the Landscape Conservation Initiative area. Note: different coloured monitoring sites denote different geographic clusters (italicised labels).

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1 Mammal diversity Mammal 14 46 41 49 43 0 2003-04 2011 2012 2013 2014 Fig. 4. Mammal diversity (number of different species) averaged across all monitoring sites in the Landscape Conservation Initiative project area relative to baseline surveys (2003-04). Note: bars are means (+ 1 standard error) and values within bars are the number of sites trapped.

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6 Baseline 2011 2012 2013 2014 5 4 3 2

Mammal diversity Mammal 1 0

Fig. 5. Mammal diversity (number of different species) averaged across geographic monitoring clusters in the Landscape Conservation Initiative project area relative to baseline surveys (2003-04). Note: bars are means (+ 1 standard error); there is no equivalent historical baseline data on mammal diversity for sites at Laterite, Yalgi, King Edward, Bachsten Ck. or Orchid Ck.

Plate 5. Department of Parks and Wildlife staff and Uunguu Rangers plan monitoring site set up at Mitchell River ranger stations (left); Kade Malay with a northern quoll from a rainforest patch at Lone Dingo monitoring site (right).

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3.1.2.2 Trends in abundance of monitored species stable or increasing (OT2b) Mammal abundance averaged across monitoring sites (Fig. 3), has not changed from historical surveys (ANOVA

[year as factor]: F 3, 27 = 0.674; P = 0.576; Fig. 6). Mammal abundance did differ between geographic clusters (ANOVA: F 8, 20 = 16.719; P < 0.001; Fig. 7). Orchid Creek, King Edward and Laterite clusters (Fig. 3) had very low abundance in 2013 and 2014 (Fig. 7).

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Mammal abundanceMammal 2 14 46 41 49 43 0 2003-04 2011 2012 2013 2014

Fig. 6. Mammal abundance (trap success) averaged across all monitoring sites in the Landscape Conservation Initiative project area relative to baseline surveys (2003-04). Note: bars are means (+ 1 standard error) and values within bars are the number of sites trapped.

30 Baseline 2011 2012 2013 2014 25 20 15 10 5 Mammal abundanceMammal 0

Fig. 7. Mammal abundance (trap success) averaged across geographic monitoring clusters in the Landscape Conservation Initiative project area relative to baseline surveys (2003-04). Note: bars are means (+ 1 standard error); there is no equivalent historical baseline data on mammal abundance for sites at Laterite, Yalgi, King Edward, Bachsten Ck. or Orchid Ck.

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3.1.2.3 Small mammals – summary and management recommendations Overall mammal abundance and diversity has remained high in the LCI region (Fig. 4, 6). Most subregions (clusters) had mammal abundance and diversity well above thresholds of concern (Fig. 5, 7), with no evidence of collapse like elsewhere in northern Australia (e.g. Kakadu, Woinarksi et al. 2010). However, diversity and abundance continues to remain very low at Laterite and King Edward clusters and was also low at Orchid Creek (Fig. 5, 7). This is low relative to other monitoring sites in the North-Kimberley and elsewhere in northern Australia (e.g. Woinarksi et al. 2010; Gillespie et al. 2015). Low diversity and abundance has been attributed to increased size of both early and late fire, and fire frequency, as well as feral herbivores (see section 3.2.2.5; see also Radford et al. 2015). Black-footed tree rats remain unaccounted for in the monitoring program in 2013 and 2014; despite the deployment of both arboreal remote cameras and nest boxes at monitoring sites (see Fig. 8).

Fig. 8. (a) Scaly-tailed possum (Wyulda squamicaudata), (b) sugar glider (Petaurus breviceps spp.), (c) golden-backed tree rat (Mesembriomys macrourus), (d) northern quoll (Dasyurus hallucatus). Photos are from arboreal-mounted cameras at monitoring sites.

The Department is now involved in an ARC Linkage Project which will examine fire, tree hollows and the Kimberley’s declining arboreal mammals. The project will address the issue of whether the scarcity of hollow-bearing trees, that act as den sites, is contributing to the decline of these species, and whether management actions, such as controlling bushfires and providing next boxes, can increase the abundance of these mammals. In the meantime, current evidence suggests fire size needs to be reduced below 100 ha (see section 3.2.1.6) and habitat destruction by feral herbivores needs to be carefully managed (see section 3.2.2.5) if small-medium sized mammals are to be retained in the North-Kimberley.

3.1.3 Vegetation and habitat condition 3.1.3.1 Stable or increasing trend in vegetative cover (OT3a) Vegetation and habitat attributes varied among rainforest, laterite forest, sandstone woodland, volcanic woodland, sandstone scree and riparian habitat types (Table 3). However, only three of these attributes varied between years of the study, indicating the possibility of a trend in condition (Table 4).

Table 4. Mean vegetation and habitat attribute values at monitoring sites for the different vegetation types.

Habitat type Habitat attribute Sandstone Sandstone Sandstone Volcanic Volcanic Volcanic Laterite Rainforest riparian scree woodland riparian scree woodland Prism Basal area (m2.ha-1) 11.2 12.7 6.0 3.6 4.9 5.6 4.8 4.6 Bitterlich Tree cover (%) 32.2 68.6 21.3 11.5 20.6 19.7 20.1 19.3 Shrub cover (%) 6.1 34.1 9.4 10.3 6.9 8.1 5.1 6.6 Transect Perennial grass (%) 26.8 2.2 47.3 14.9 19.8 56.9 66.3 40.2 Annual grass (%) 2.7 0.0 3.6 5.9 3.1 1.9 0.0 1.0 Forbs (%) 1.7 1.6 3.0 1.0 1.9 1.0 1.6 1.9 Leaf litter (%) 36.2 68.3 25.8 16.1 28.4 18.5 10.1 24.6 Logs (%) 0.53 1.32 0.09 0.30 0.42 0.14 < 0.01 0.20 Exposed rock (%) 12.8 24.8 6.6 54.4 23.9 2.7 16.6 17.2 Bare ground (%) 17.8 2.1 13.5 7.0 22.4 20.1 2.7 13.8 Termite mounds (%) 0.12 < 0.01 < 0.01 0.04 0.02 0.02 < 0.01 0.15 Sub-shrubs (%) 3.7 9.2 2.5 2.4 3.1 2.2 4.2 3.1 Shrub (%) 7.4 33.1 10.1 11.9 5.7 6.8 6.1 3.6 Tree (%) 42.1 68.6 34.0 15.6 25.8 28.3 17.4 23.9 Points Litter depth (mm) 10.1 19.8 12.1 5.3 6.2 4.7 11.8 6.2 Fragmented litter (mm) 2.4 4.6 1.8 1.7 1.0 0.8 1.3 1.1 Soil crust (mm) < 0.01 1.56 0.06 0.06 0.02 0.47 0.36 0.14 Worm cast (mm) < 0.01 0.42 0.08 0.11 0.04 < 0.01 < 0.01 0.01

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Table 5. ANOVA F values for differences between habitat attributes for all the major vegetation types and year of monitoring (2011 to 2014). Values in bold denote statistically significant differences. Year of monitoring as a factor is nested within locality due to non-independence of repeatedly measured monitoring sites.

Habitat attribute Vegetation type Year (vegetation type) Degrees of freedom 7, 236 24, 236 Prism Basal area (m2.ha-1) 23.25*** 0.54 Bitterlich Tree cover (%) 48.12*** 0.32 Shrub cover (%) 23.73*** 0.45 Transect Perennial grass (%) 20.72*** 1.57* Annual grass (%) 1.63 0.79 Forbs (%) 0.95 1.07 Litter (%) 20.76*** 1.04 Logs (%) 9.10*** 0.45 Exposed rock (%) 31.29*** 0.58 Bare ground (%) 4.26*** 1.21 Termite mounds (%) 1.29 0.57 Sub-shrubs (transect) (%) 4.03*** 1.03 Shrub (transect) (%) 22.78*** 1.66* Tree (transect) (%) 16.55*** 0.33 Points Litter depth (mm) 6.49*** 1.50 Fragmented litter (mm) 4.57*** 2.72*** Soil crust (mm) 0.93 1.23 Worm cast (mm) 1.61 0.48 Pegs Erosion (mm) 2.52 (0.018) 0.64 (0.850)

* P < 0.05, ** P < 0.01, *** P < 0.001

There were changes in perennial grass and shrub cover among years (Table 5). However, these changes did not indicate a downward trend; rather these attributes fluctuated in no consistent trend (Fig. 9). Perennial grass cover was generally lower in 2012 and 2014 at volcanic scree, volcanic woodland and sandstone riparian sites, while laterite sites had higher cover values in 2014 and volcanic riparian sites had high cover during 2012 (Fig. 9a). Shrub cover also varied inconsistently from 2011 to 2014, with laterite sites showing higher values in 2011 and 2014, sandstone riparian increasing in cover in 2014, volcanic vegetation having reduced cover in 2014 and other vegetation types remaining the same (Fig. 9b).

There was no difference in annual grass (Sorghum spp.) cover among habitat types or between years of monitoring (Table 4, 5). Shrub cover varied between years; however this was inconsistent between years, rather than there being a trend in condition (Table 4, 5, Fig. 8b). Year to year variability in ground-layer vegetation is probably related to year to year variation in fire extent at the time of monitoring (see Fig. 17) and annual variation in wet season rainfall (Fig. 14). Further analysis of annual variability among these vegetation attributes will be undertaken once five years of annual data are available, allowing more rigorous statistical investigation of these changes.

There was variation in vegetation structure among monitoring clusters (Table 6), but change among clusters between years was only detected for bare ground (Table 6; Fig. 10). These differences did not appear to represent a consistent trend. Instead, variation appears to be related to variations in fire occurrence. For instance higher levels of bare ground at Laterite, Silent Grove and Mount Hart clusters (see Fig. 3) in 2012 and 2014 (Fig. 10) are associated with extensive fires and removal of ground cover at in those areas.

Plate 6. Livistonia eastonii at the Mitchell Plateau. Photo: David Bettini.

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(a) 100 (b) 100 Lat Lat Rf Rf 80 SR SR SS SS SW 10 SW 60 VR VR VS VS VW VW

40 (%) cover shrub

10 1

20 Log Perennial grass cover (%) cover grass Perennial

0 0 2011 2012 2013 2014 2011 2012 2013 2014 Fig. 9. (a) Mean perennial grass cover at monitoring sites by vegetation type; (b) mean shrub cover at monitoring sites by vegetation type. Note

the y axis in Fig. 8b is on a log10 scale to allow better discrimination among vegetation types. Habitat types are: Lat, Laterite; Rf, rainforest; SR, sandstone riparian; SS, sandstone scree; SW, sandstone woodland; VR, volcanic riparian; VS, volcanic scree; VW, volcanic woodland.

Table 6. ANOVA F values for differences between localities (geographic clusters of sites) and year of monitoring for habitat attributes. Year of monitoring is nested within locality due to non-independence of repeatedly measured monitoring sites. Values in bold denote statistically significant differences. Year of monitoring as a factor is nested within locality due to non-independence of repeatedly measured monitoring sites.

Habitat attribute Locality Year (Locality) Degrees of freedom 12, 230 26, 230 Prism Basal area (m2.ha-1) 3.09*** 0.27 Bitterlich Tree cover (%) 3.49*** 0.42 Shrub cover (%) 2.55** 0.16 Transect Perennial grass (%) 2.26** 1.15 Annual grass (%) 5.09*** 0.76 Forbs (%) 2.35** 0.89 Litter (%) 9.54*** 1.24 Logs (%) 6.19*** 0.86 Exposed rock (%) 1.79 0.24 Bare ground (%) 1.97* 2.04** Termite mounds (%) 0.94 0.53 Sub-shrubs (transect) (%) 1.61 1.34 Shrub (transect) (%) 2.58** 0.75 Tree (transect) (%) 3.50*** 0.55

* P < 0.05, ** P < 0.01, *** P < 0.001

70 2011 2012 2013 2014 60 50 40 30 20 10 Change in coverage (%) coverage in Change 0

Fig. 10. Change in lack of vegetation cover among monitoring clusters across years of monitoring.

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3.1.3.2 Stable or increasing trend in condition of abiotic habitat components (OT3b) Litter depth and fragmented litter depth varied between vegetation types (Table 5) with highest values in rainforest and lowest values at volcanic riparian sites (Table 4; Fig. 11). Very little evidence of significant soil crusts or worm cast layers were found and these did not vary between habitats (though highest values of both were observed at rainforest sites; Table 4). There was no consistent trend over time in litter depth or other ground layer attributes (Table 5; Fig. 11). Increases in litter depth were observed from 2012 to 2014 in laterite, rainforest and sandstone scree vegetation, while volcanic scree experienced some decline (Fig. 11). As with vegetation attributes, variability in litter depth and a lack of directional trend probably indicates year to year variability in fires and rainfall, not a decline in site condition. This will be further investigated in future reports.

10 Lat Rf 8 SR SS SW 6 VR VS 4 VW Litter depth (mm) depth Litter 2

0 2012 2013 2014 Fig. 11. Mean fragmented litter layer depth (mm) at monitoring sites within each vegetation type across monitoring years. Habitat types are: Lat, Laterite; Rf, rainforest; SR, sandstone riparian; SS, sandstone scree; SW, sandstone woodland; VR, volcanic riparian; VS, volcanic scree; VW, volcanic woodland. Note that litter depth was not measured in 2011.

3.1.3.3 Stable or increasing trend in Landsat vegetation cover Time series modelling and cumulative sum (CUSUM) control charts were used to assess changes in vegetation cover across vegetation types (Fig. 12) from Landsat imagery (see van Dongen et al. 2015; Fig. 13). Increases (beyond control limits) in vegetation cover were observed for sandstone scree, sandstone woodland, volcanic woodland, riparian and laterite forest habitat types, but not for rainforest patches (van Dongen et al. 2015; Fig. 13). Whether or not this change can be attributed to management actions (i.e. increased management of fire from 2008 onwards) is yet to be determined, however early exploratory analyses suggest both rainfall and fire regimes – including prescribed fire – play an important role in driving changes in vegetation cover (B. Corey and R. van Dongen, unpublished data). Rainforest patches showed no increases (Fig. 13c, d) in cover which is consistent with the results in section 3.1.1.

Fig. 12. Major vegetation types used in the stratification and selection of LCI monitoring sites and for monitoring of remotely sensed Landsat vegetation cover.

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Fig. 13. Habitat-specific model (red lines) and original Landsat time series of vegetation cover values (black lines) and CUSUM control charts for: Laterite (a and b); rainforest (c and d); sandstone woodland (e and f); riparian (g and h); sandstone scree (I and j); and volcanic woodland (k and l) habitat types. Note: values are means of 50 plots within each habitat type; blue vertical line in 2008 denotes change in fire management (see section 3.2.1) and dashed horizontal red lines are control limits set at three standard deviations (see van Dongen et al. 2015).

Plate 7. Cocky apple (Planchonia careya) (left); spinifex (Triodia sp.) on rocky scree (middle) and cycads (Cycas sp.) at the Mitchell Plateau.

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3.1.3.4 Vegetation condition – summary and management recommendations Although variation was found for some vegetation cover and habitat attributes between years of monitoring (e.g. perennial grass and shrub cover, and fragmented litter depth), the significance of these in terms of long-term trends are not yet apparent due to the small number of years the monitoring program has been running. However, some year to year variation is expected in savanna landscapes which frequently have ground layer vegetation and litter consumed by fire, and where annual wet season rainfall can vary considerably between years (Fig. 14). Future reports will be in a position to analyse longer term trends, when five and six years of vegetation and habitat attribute data are available. These analyses should be a high priority.

Landsat time series analysis and CUSUM suggests an increase in regional vegetation cover since the implementation of the LCI program. However more analysis is needed to determine the influences of methodology (e.g. number of images used in analysis) and any impacts upon ‘increases’ in cover, as well as the influences of both fire and rainfall, before inferences about management can be made. Increases in vegetation cover due to large-scale conservation management actions (e.g. prescribed fire) have not previously been documented. Fire scars mapped from Landsat imagery would also be highly beneficial when analysing future monitoring data. These have a finer resolution (30 m) compared to currently used methods (NAFI, 250 m) and go back further in time (1987 onwards for Landsat, vs. 2000 onwards for NAFI); this would allow for a longer term picture of what drives vegetation cover. It is important to rule out environmental influences, so that any changes can be correctly attributed to management (or otherwise).

3.1.4 Rainfall trends Wet season rainfall preceding 2013 and 2014 calendar years varied from below to above average across the LCI area (Fig. 14).

3000 3000 3000 Kalumburu Theda Station Doongan Station 2500 2500 2500

2000 2000 2000

1500 1500 1500

1000 1000 1000

Wet season rainfall (mm) rainfall season Wet 500 500 500

0 0 0 1998-99 2000-01 2002-03 2004-05 2006-07 2008-09 2010-11 2012-13 1998-99 2000-01 2002-03 2004-05 2006-07 2008-09 2010-11 2012-13 1998-99 2000-01 2002-03 2004-05 2006-07 2008-09 2010-11 2012-13 3000 3000 3000 Drysdale River Station Mt. Elizabeth Station Charnley River 2500 2500 2500

2000 2000 2000

1500 1500 1500

1000 1000 1000

500 500 500 Wet season rainfall (mm) rainfall season Wet

0 0 0 1998-99 2000-01 2002-03 2004-05 2006-07 2008-09 2010-11 2012-13 1998-99 2000-01 2002-03 2004-05 2006-07 2008-09 2010-11 2012-13 1998-99 2000-01 2002-03 2004-05 2006-07 2008-09 2010-11 2012-13

Fig. 14. Wet season rainfall records from Bureau of Meteorology stations in the Landscape Conservation Initiative project area. The dashed line represents long-term averages.

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3.2 MANAGEMENT ACTION EFFECTIVENESS Management of fire, feral animals and weeds – at a glance

Table 7. Landscape Conservation Initiative management effectiveness in the North-Kimberley.

Management action effectiveness Target Management action Outcome target achieved† Managing inappropriate fire regimes MA1a MAT1a Reduce proportion of the Increasing trend in the proportion Since 2008, the area unburnt has remained stable (Fig. PARTLY sub-region burnt on an of the sub-region (area of 15, 16), but has not decreased. ACHIEVED annual basis vegetation) unburnt in a single year. MA1b MAT1b Reduce proportion of Increasing trend in proportion of Since 2008, the proportion of area burnt by early dry ACHIEVED fires in late dry season fires that burnt in the early dry season fires has increased by > 50 % (Fig. 15, 16, 17). season. MA1c MAT1c Reduce proportion of Decrease the mean distance Since 2008, distances from burnt to nearby unburnt ACHIEVED large aerial extent fires between unburnt patches. vegetation (≥ 3 years of age) have decreased by 45 %, from 9 to 5 km (Fig. 18, 19, Table 8). MA1d MAT1d Increase habitat value An increasing trend in the Since 2008, the proportion of older vegetation (≥ 3 years through effective fire proportion of vegetation in older of age) has remained stable (Fig. 20, 21). Although the PARTLY management age classes (> 3 years old). total area of long-unburnt vegetation has not increased, ACHIEVED the number of patches has increased, thereby improving biodiversity access to this habitat (Fig. 21). Managing the impacts of introduced animals on the landscape Cattle MAT2a Increase habitat value Reduce or maintain impacts from Cattle impacts varied between geographic areas and across the sub-region feral cattle to no or low levels of years, but there was no overall downward trend in cattle through management of observable impact across impacts (Fig. 24). This could be a result of the ad-hoc NOT the landscape impacts of monitoring sites nature of some culls in earlier years and planned culls not ACHIEVED cattle going ahead in 2013 and 2014. This suggests that reductions to cattle numbers are insufficient or sustained enough for reductions in impacts to be achieved. Other target species MAT2b Reduce the detrimental Maintain other target species at Donkeys have been maintained at low numbers via the impacts of donkeys, low numbers Department of Agriculture and Food’s Judas donkey horses and pigs sufficient program (Table 10) as well as the Department of Parks ACHIEVED to retain natural and Wildlife’s aerial shoot program (Table 9). Horses and conservation values of pigs have been maintained at low numbers via the the sub-region Department’s aerial cull program (Table 9). Feral predators and prey MAT2c Mitigate the impacts of Undertake and/or support applied The Department has been involved in, and supported feral cats and cane toads research that will help in research that has provided land managers with advice for on biodiversity values mitigating the impacts of cats and mitigating the impacts of feral cat predation on small ACHIEVED within the sub-region cane toads on at risk species mammals (Table 11). Similarly, the Department has been involved in and continues to support research that will assist in mitigating the impacts of cane toads on quolls and goannas (Table 11). Managing the impacts of invasive plants on the landscape MA3a MAT3a Eradicate high priority Area occupied by high priority Work to eradicate rubbervine (Cryptostegia grandiflora) species1, and/or species1 and/or priority species2 in and gamba grass (Andropogon gayanus) has begun (Table PARTLY infestations of priority priority locations3 decreasing to 12). ACHIEVED species2 where they zero occur in priority locations3 MA3b MAT3b Reduce the extent of A decreasing trend in the area Grader grass (Themeda quadrivalvis) infestations have PARTLY priority species2 in occupied by priority3 species been reduced by > 50 % within the Mitchell River work ACHIEVED priority locations3 zone (Fig. 27, 28).

†Key ACHIEVED Management actions are meeting objective PARTLY ACHIEVED Management actions have met part, but not all, of objective; further actions are required NOT ACHIEVED Management has not yet met objective 1, 2, 3 See section 3.2.3 for definitions of priority species and locations

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3.2.1 Managing inappropriate fire regimes 3.2.1.1 Total area burnt (MAT1a) During 2013 and 2014, 45.0 % and 44.6 % of the LCI area burnt respectively (Fig. 15). Since increased management of fire began in 2008, the total area burnt has remained relatively stable (26.4–45.0 %) compared to the boom and bust cycle that dominated annual fire regimes prior to 2008 (19.4–65.1 %; Fig. 15). However, the total area burnt has not changed with increased fire management (ANOVA: F 2, 12 = 0.616; P = 0.556; Fig. 16). This mirrors results from savanna burning programs elsewhere that suggest the total area burnt cannot be changed by management (e.g. van Wilgen et al. 2004; Russell-Smith et al. 2013; Legge et al. 2015b; Murphy et al. 2015). Rather, total area burnt may be predetermined by factors such as rainfall and soil, leaving only the timing of fire and fire scar size under human control.

70 LDS 60 EDS 50

40

30

20

10 Proportion of LCI area burnt (%) burnt area ofLCI Proportion 0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Fig. 15. Fire seasonality in the LCI project area from 2000 to 2014, derived from MODIS imagery. The first (lighter) dashed line represents the transition from a management approach that focused on the creation of blocks and buffers aimed to compartmentalise the landscape (2000– 2007), to the introduction of patch mosaic burning (2008–2010), which continued from 2011–2014 under the LCI.

50 LDS EDS 40 19.2 30 36.2 23.7 20

24.3 10 11.5 13.3 Proportion of LCI area burnt (%) burnt ofarea LCI Proportion 0 Before Transition After

Fig. 16. Fire seasonality and total area burnt within the LCI project area. Note: 2000–07, before; 2008–10, transition; 2011–2014, after (LCI).

3.2.1.2 Early and late season fires (MAT1b) During 2013 early fires burnt 19.0 % of the LCI area and late fires burnt 25.9 % (Fig. 15); in 2014 28.3 % of the area burnt early whereas only 16.3 % burnt late (Fig. 15). During 2013, late fires accounted for more than half of the annual fire scar, making this the worst year since the LCI program began (Fig. 15). Overall however, late fires have

decreased since the LCI project commenced (two-way ANOVA: management: F 2, 24 = 0.693; P = 0.510; season: F 1, 24 = 6.882; P = 0.015; management x season: F 2, 24 = 6.493; P = 0.006; Fig. 16, 17). Increased management has resulted in a doubling of early fires (prescribed fire) and a concurrent halving in late fires (Fig. 16).

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Plate 9. An early dry season landscape mosaic produced by prescribed burning at Silent Grove in the King Leopold Ranges Conservation Park.

Fig. 17. Contrast in the spatial pattern of early season (green) and late season (red) fire extent within the LCI project area.

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3.2.1.3 Distances from burnt to unburnt vegetation (MAT1c) Annual trends in distances from burnt to nearby unburnt vegetation (a measure of landscape patchiness) are shown in Fig. 18. In 2013, both the mean and maximum distances from burnt to nearby unburnt vegetation increased, particularly to the three year old age class (Fig. 18b). Given the extent of late fires in that year (Fig. 15, 17), which burn more extensively than early fires, this increase in not surprising. However, the average mean and maximum distances from burnt vegetation to nearby unburnt vegetation has generally decreased with better management of fire (Table 8; Fig. 19).

(a) 5 20 All unburnt vegetation

4 16

3 12

2 8

1 4 Mean Mean distance to unburnt vegetation (km) vegetation unburnt to distance Mean Maximum Maximum distance to unburnt vegetation (km) vegetation unburnt to distance Maximum 0 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

(b) 6 30

5 25

4 20

3 15 ≥ 3 year old unburnt vegetation 2 10

1 5 Mean Mean distance to unburnt vegetation (km) vegetation unburnt to distance Mean Maximum Maximum distance to unburnt vegetation (km) vegetation unburnt to distance Maximum 0 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

(c) 10 50

8 40

6 30

4 20 ≥ 5 year old unburnt vegetation

2 10 Mean Mean distance to unburnt vegetation (km) vegetation unburnt to distance Mean Maximum Maximum distance to unburnt vegetation (km) vegetation unburnt to distance Maximum 0 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Fig. 18. The mean (dark green line on left axis) and maximum (orange line on right axis) distances from burnt areas to: (a) the nearest unburnt vegetation (> 20 ha in size); (b) vegetation that is three years of age (> 20 ha in size); and (c) vegetation that is five years of age (> 20 ha in size). The first (lighter) dashed line represents the transition from a management approach that focused on the creation of blocks and buffers aimed to compartmentalise the landscape (2000–2007), to the introduction of patch mosaic burning (2008–2010), which continued from 2011–2014.

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(a) 4 20

3 15

2 10

1 5 Mean Distance to unburnt vegetation (km) vegetation unburnt to Distance

Distance to unburnt vegetation (km) vegetation unburnt to Distance Max 0 0 Before Transition After

(b) 6 30

5 25

4 20

3 15

2 10

1 5 Mean Distance to unburnt vegetation (km) vegetation unburnt to Distance Distance to unburnt vegetation (km) vegetation unburnt to Distance Max 0 0 Before Transition After

(c) 10 50

8 40

6 30

4 20

2 10 Mean Distance to unburnt vegetation (km) vegetation unburnt to Distance Distance to unburnt vegetation (km) vegetation unburnt to Distance Max 0 0 Before Transition After

Fig. 19. Mean and maximum distances from burnt to nearby unburnt vegetation that is: (a) of all ages; (b) three years or older; and (c) five years or older within the Landscape Conservation Initiative project area. Note: 2000–07, before; 2008–10, transition; 2011–2014, after (LCI).

Table 8. Summary of test of means for distances between burnt and nearby unburnt vegetation.

Distance from burnt vegetation to Test Statistic Multiple comparison All unburnt vegetation

Mean ANOVA F 2, 12 = 6.003; P = 0.016* Before vs. after Maximum ANOVA F 2, 12 = 9.175; P = 0.004* Before vs. transition; before vs. after Vegetation ≥ 3 years of age

Mean ANOVA F 2, 10 = 14.102; P = 0.001* Before vs. transition; before vs. after Maximum ANOVA on ranks H 2 = 2.760; P = 0.273 Non-significant Vegetation ≥ 5 years of age

Mean ANOVA F 2, 8 = 12.420; P = 0.004* Before vs. after; transition vs. after Maximum ANOVA on ranks H 2 = 6.041; P = 0.039* Before vs. after

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3.2.1.4 Extent of old-growth vegetation (MAT1d) Vegetation that is < 1 to 2 years of age dominated the LCI area in both 2013 and 2014, comprising 83 % and 86 % of all unburnt vegetation respectively (Fig. 20). These figures did not differ from long-term averages (Fig. 20). Older growth vegetation in both 2013 and 2014 (≥ 3 years of age) was much more patchily distributed across the landscape than the long-term average, even though the total area of long-unburnt vegetation has not changed (Fig. 20).

There has been no change in the extent of old growth vegetation with increased fire management (two-way

ANOVA: management: F 2, 57 = 0.0454; P = 0.956; Fig. 21; age class: F 5, 57 = 34.861; P < 0.001; Fig. 21; management x age class: F 10, 57 = 0.265; P = 0.986). There has however been an increase in the average number of patches (> 20 ha) that make up these age classes (two-way ANOVA: management: F 2, 57 = 14.018; P < 0.001; Fig. 21; age class: F 5, 57 = 15.268; P < 0.001; Fig. 21; management x age class: F 10, 57 = 0.330; P = 0.969). 50 2500 2013 2014 40 2000 Long-term 30 1500

20 1000

10 500 Proportion of LCI area (%) area LCI of Proportion

0 0 ha) 20 (> patches of Number < 1 1 2 3 4 5 +

Vegetation age (years) Fig. 20. The extent of vegetation age (measured as time since last fire) in the Landscape Conservation Initiative project area, on the left axis. The average number of patches (> 20 ha in size) for each age class is presented on the right axis. Note: Long-term averages are from 2000–2014 and errors are ± 1 standard error.

60 3000 Before 50 Transition 2500 After 40 2000 30 1500 20 1000 10 500 Proportion of LCI area (%) area LCI of Proportion 0 0 ha) 20 (> patches of Number < 1 1 2 3 4 5 +

Vegetation age (years) Fig. 21. The extent of vegetation age (measured as time since last fire) in the Landscape conservation Initiative area, on the left axis. The average number of patches (> 20 ha in size) for each age class is presented on the right axis. Values are means for the following time periods: 2000–2007 (before); 2008–2010 (transition); 2011–2014 (after [LCI]); error bars are ± 1 standard error.

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3.2.1.5 Prescribed fire – summary and management recommendations Recent fire management has been able to arrest the boom and bust cycles that dominated fire regimes in the North- Kimberley (Fig. 15), by increasing early (prescribed) fire which has resulted in a concurrent reduction in late fires (Fig. 16). However, the total area burnt has largely remained unchanged despite this management effort (Fig. 16). Similar analyses of fire management practices and annual fire scars in South African savannas (van Wilgen et al. 2004) as well as savannas in the central Kimberley (Legge et al. 2015b) and Arnhem Land (Russell-Smith et al. 2013) have found a similar pattern. This suggests that the total area burnt may be relatively insensitive to management. Managers have much more control over when fire is applied to the landscape through early prescribed burning, and thus the severity (and hence impacts) of this fire. If fire is applied under the right conditions, then fires will remain small, cool and patchily distributed across the landscape. Suppression is another avenue for reducing the total area burnt, but much of the North-Kimberley is very remote with no vehicle access, making such undertakings very difficult and costly. On the other hand, AWC and some ranger groups in Arnhem Land do employ remote indirect firefighting techniques to good effect. Better access via established networks of tracks on pastoral leases as well as being based ‘on country’, rather than in regional centres like Broome or Kununurra enables these groups to do this. Nonetheless, such approaches may need to be considered by Parks and Wildlife in future years as a means for both reducing the total area burnt and protecting older aged vegetation (see below).

While older growth vegetation is now more patchily distributed across the landscape, meaning it is less likely to burn in a single fire event, its overall extent has not increased (Fig. 21). This suggests fire is still too frequent. Given its well documented importance to a suite of nationally threatened fauna (e.g. Radford et al. 2015; Legge et al. 2015c; Lawes et al. 2015), long unburnt vegetation needs to be viewed as a biological asset and managed accordingly. This means identifying these areas in the fire planning process and devising ways to protect them, or burning them under very mild conditions to ensure these assets are broken-up, but largely retained. Also fire managers should reduce the amount of vegetation in the two year age class that is targeted for burning, and aim to get it through to at least three years of age.

During 2013 and 2014 there were no fewer than five different land managers delivering prescribed burning programs across the North-Kimberley. As a consequence, one might expect to see a continued improvement against fire targets. This was not the case, and suggests some of these programs may need to be better coordinated across the region. Given this, and the fact that there are some targets that continue to remain unmet (Table 7), then it would be prudent to examine how varying operational approaches to prescribed burning affects these fires metrics; in particular those relating to the extent and availability of long-unburnt vegetation (see also Legge et al. 2015b).

Plate 9. Prescribed fire lit under appropriate conditions produce smaller fire scars (above left) with internal patches that remain unburnt (above right), and are of benefit to biodiversity. Conversely, later fires burn hotter (below left) and produce extensive fire scars that leave no unburnt vegetation (below right); food and shelter for wildlife is completely removed and predation by feral predators increases.

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3.2.1.6 Fire and biodiversity indicators There were strong relationships between vegetation age (a function of fire frequency) and vegetation patchiness for 2013 and 2014 monitoring data (Fig. 22, 23; see also Corey et al. 2013). Similarly, Radford et al. (2015) found strong negative associations of mammal abundance and diversity with frequency of late dry season fires, the spatial extent of recently burnt habitat (post-fire age < 1 year within a 3 km radius of monitoring sites). Shrub cover was positively related to mammal abundance and diversity, and availability of rock crevices, ground vegetation cover and the spatial extent of four year old unburnt vegetation were all positively associated with at least some of the individual mammal species modelled (Radford et al. 2015).

The take home message for fire managers is that a high frequency of intense late dry season fires as well as extensive, recently burnt vegetation are bad for small mammals. It is important to note that this includes early fires (those that burn between January and June), which can still be damaging if they are too big. Fire mosaics that reduce large scale and intense fires, while retaining vegetation that is four years of age and older, will clearly benefit mammals. This is also supported by recent work from Kapalga in the Northern Territory which suggests that increased fire frequency directly impacts small mammal survival and recruitment (Griffiths and Brook 2015; Griffiths et al. 2015), and also from Kakadu National Park, which recommends that fires not exceed 100 ha, otherwise mammals will be ‘burnt-out’ out of the landscape (Lawes et al. 2015). Fires also need to be cool enough so that shelter (e.g. hollow logs, large trees with hollows, patches of perennial grass) are retained (Radford et al. 2015), to mitigate the impacts of predation by feral cats (McGregor et al. 2014). This requires fire managers to burn under the mildest of conditions.

With more data on other biological indicators (rainforests and vegetation), similar recommendations can be made regarding these components of the landscape in future reports. Nonetheless, small mammals are considered one of the most sensitive components of savanna landscapes, so framing fire management strategies around them should convey benefits to vegetation generally due to the shelter requirements of mammals (Radford et al. 2015).

(a) 20 (b) 5

4 15 3 10 2 5 1 Mammal diversity Mammal Mammal abundance Mammal 0 0 Bad Good Bad Good Fire history Fire history

Fig. 22. The abundance (a) and diversity (b) of small-medium sized mammals at trapping quadrats sampled from 2011 to 2014, for quadrats with ‘bad’ and ‘good’ fire histories. Bars show means and standard errors. MODIS fire scar imagery for a ten year period prior to sampling was used to attribute quadrats as having ‘bad’ (i.e. burnt more frequently than average) or ‘good’ (i.e. burnt less frequently than average) fire histories prior to each sampling event.

(a) 14 (b) 4 12 10 3 8 2 6 4 1

2 diversity Mammal Mammal abundanceMammal 0 0 Less More Less More Vegetation patchiness Vegetation patchiness

Fig. 23. The abundance (a) and diversity (b) of small-medium sized mammals at trapping quadrats sampled from 2011 to 2014, for quadrats with ‘less’ and ‘more’ patchy fires. Bars show means and standard errors. MODIS fire scar imagery was used to determine the number of different aged vegetation patches within a 3 km radius of quadrats, and less patchy sites have fewer different vegetation patches than average, whilst more patchy sites have more.

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3.2.2 Managing the impacts of invasive animals 3.2.2.1 Reduce or maintain no to low observable impacts from feral cattle (MAT2a) Indices of cattle damage to vegetation at on-ground monitoring sites (Fig. 3; see section 3.2.2.5) were grouped within each monitoring cluster to ascertain if any measurable improvements in habitat condition had been achieved as a result of aerial culls (Table 9; Fig. 24). Impacts varied between areas and years, but there were no overall downward trends in observable impacts (Fig. 24). This is likely to be the result of management actions (aerial culls) varying considerably across the LCI area, largely due to factors outside of the Departments control. For instance a number of culls have had to be cancelled or considerably reduced in area because long-term strategic agreements could not be reached with the relevant land managers. This reduced ability to undertake culls or mustering will continue to reduce the effectiveness of these actions in reducing impacts of cattle and other large herbivores on ground layer vegetation and therefore threatened biodiversity (e.g. small mammals and granivorous birds).

1 High stocking density (unmanaged)

H 0.8 2011 2012 2013 2014 0.6 Low-medium stocking density (managed) 0.4 M 0.2 Cattle impact index impact Cattle L 0

Fig. 24. Cattle impacts at monitoring clusters within the Landscape Conservation Initiative project area. Note: values are means of all sites within each geographic cluster and bars are + 1 standard error. The lighter shaded area represents average cattle impacts (± 1 standard deviation) across eight sites on a low-medium density stocked station (Mount Elizabeth Station), while the darker shaded area represents average cattle impacts (± 1 standard deviation) across eight sites on cattle station with much higher stocking rates (Carson River Station). Note: cattle impacts are scored on an index of 0–1; H, high impacts; M, moderate impacts; L, low impacts (see section 3.2.2.5); there were no impacts measured at Bachsten Creek, Cascade Creek or Mount Trafalgar.

Plate 10. Aerial culling operations at Mitchell Plateau (left) and a large scrub bull near Mitchell River campground (right).

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3.2.2.2 Maintain other target species at low numbers (MAT2b) Large feral herbivore control operations in 2013 and 2014 included a combination of aerial culls (carried out by Parks and Wildlife [Table 9] and DAFWA [Table 10]), and musters by both neighbouring pastoralists and contract musterers on behalf of pastoralists (Table 9).

Table 9. Summary of large invasive herbivore management operations within the Landscape Conservation Initiative area in 2013 and 2014. Note: figures do not include animals (wandering stock) mustered by neighbouring pastoralists prior to aerial shoots.

Animals removed Location Method Cattle Donkey Horse Pig Cat Total 2013 Mitchell River† Aerial cull 1,867 0 0 12 0 1,879 Theda Station Aerial cull 1,801 0 64 5 0 1,870 Doongan Station Aerial cull 396 0 0 0 0 396 Carson River Station Muster 1,726 0 0 0 0 1,726 King Leopold Ranges Conservation Park Muster 395 0 0 0 0 395 Sub-total 6,185 0 64 17 0 6,256 2014 Carson River Station Aerial cull 1,603 10 159 0 2 1,774 Theda Station Aerial cull 1,173 0 48 0 0 1,221 Drysdale River National Park Aerial cull 575 25 14 0 1 615 Mitchell River† Aerial cull 1,071 0 1 0 1 1,073 Doongan Station Aerial cull 137 0 0 0 0 137 King Leopold Ranges Conservation Park Aerial cull 611 0 0 14 0 625 King Leopold Ranges Conservation Park Muster 620 0 0 0 0 620 Sub-total 5,790 35 222 14 4 6,065 Total 11,975 35 286 31 4 12,3221

†includes Parks and Wildlife managed conservation reserves (Mitchell River National Park, Lawley River National Park, Prince Regent National Park, Camp Creek Conservation Park and Laterite Conservation Park, and surrounding unallocated Crown Land

The Department of Agriculture and Food (DAFWA) continued to operate the Judas donkey program within the LCI area and across the Kimberley (Table 10), which continues to maintain very low numbers of donkeys following a sustained culling program that removed more than 550,000 animals across the Kimberley. Pigs and horses were maintained at low numbers (Table 9). Similarly un-collared donkeys were shot when encountered by Parks and Wildlife aerial marksmen (Table 9). Within some aerial culls, designated flights are allocated to searching for pigs (as well as signs of pigs).

Table 10. Number of donkeys removed within and surrounding the LCI area by DAFWA from 2013 to 2014 as part of the Judas Program.

Property/Area 2013 2014 Carson River Station 18 8 Charnley River Station 7 1 Defence Reserve (Yampi) 8 15 Doongan Station 0 0 Drysdale River Station 6 17 Drysdale River National Park 308 123 Ellenbrae Station 27 9 Forrest River Reserve 39 19 Gibb River Station 26 14 Home Valley Station 15 8 Kalumburu 35 3 Kunmunya 0 0 Leopold Downs Station 0 0 Mitchell Plateau 0 0 Mount House Station 0 2 Mount Barnett Station 24 24 Mount Elizabeth Station 63 41 King Leopold Ranges Conservation Park (ex Mount Hart Station) 79 2 Napier Downs Station 0 0 Pantijan 101 14 Prince Regent National Park 7 0 Theda Station 7 0 Total 763 300

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3.2.2.3 Mitigating the impacts of feral predators and prey (MAT2c) The Department has been directly involved in, or financially supported, research seeking to understand and manage the impacts of feral cats (Table 11; plate 11). This work – coordinated by the Australian Wildlife Conservancy – is providing the first empirical evidence of the devastating impacts that cats have on small mammals and testing innovative methods for mitigating these impacts.

The Department has also been involved in and supported research by the University of Sydney (assisted by the Balanggarra Rangers) that could help mitigate the impacts of cane toads on goannas, through taste aversion trials (Table 11; plate 11). Similar research with northern quolls at the Mitchell Plateau, with assistance from the Wunambal Gaambera Aboriginal Corporation, is in its infancy.

Table 11. Summary of feral predator and prey research supported by the Department of Parks and Wildlife through the Kimberley Science and Conservation Strategy.

Aim Location Methods Key results Conclusions and implications Experimental evidence that feral cats cause local extirpation of small mammals in Australia’s tropical savannas (Frank et al. 2014) To determine the effect Wongalara Established two Rat populations persisted in First study to provide direct evidence that of predation by cat Wildlife enclosures and the predator proof enclosure, cats are capable of extirpating small populations on the Sanctuary, cats were allowed but declined to extinction in mammals. Supports the notion that demography of small Northern access to one but the predator-accessible predation by feral cats is contributing to mammals. Territory. not the other. enclosure. declines of small mammals in northern Small mammals Australia. The conservation of small were monitored mammals in northern Australia may within both require intensive control of cat enclosures. populations, including large cat-free enclosures. Landscape management of fire and grazing regimes alters the fine-scale habitat utilisation by feral cats (McGregor et al. 2014) To determine habitat Mornington GPS collars were Cats selected areas with open Intense fires and grazing by introduced selection by feral cats in Wildlife attached to cats grass cover, including heavily- herbivores create conditions that are relation to fire, grazing Sanctuary, to track grazed areas. They strongly favoured by cats because hunting success and small-mammal central movements. selected areas recently burnt is improved. Impacts can be reduced by abundance. Kimberley. by intense fires, but only in maximising grass cover, minimising the habitats with a higher incidence of intense fires, and reducing abundance of small mammals. grazing pressure. Feral cats are better killers in open habitats, revealed by animal-borne video (McGregor et al. 2015a) To determine how Mornington Attached collar- Hunting success was Habitat structure influences the impacts habitat structure affects Wildlife mounted video dependent on microhabitat of feral cats on their prey. Maintaining the hunting success of Sanctuary, cameras to feral structure surrounding prey complex vegetation cover (though feral cats. central cats and populations, increasing from management of fire and grazing) can Kimberley. measured 17 % in habitats with more reduce the impacts feral cats have on variation in cover to 70 % in open areas. small threatened mammals. hunting success among different habitats. Density and home range of feral cats in north-western Australia (McGregor et al. 2015b) To provide estimates of Mornington Transects of Cats occurred in densities of Feral cat densities were low and home density and home range Wildlife infrared cameras 0.18 cats km–2 (range = 0.09– ranges were large compared to feral cats size of feral cats and Sanctuary, were used to 0.34 km–2). Density was not elsewhere in Australia. Low densities test whether these are central determine cat related to livestock grazing or mean direct control will be difficult. affected by livestock Kimberley. densities and GPS abundance of small mammals. Instead, land-management practices that grazing, small mammal collars were Home ranges of males were reduce the impacts of cats should be abundance and other attached to cats twice the size of females (855 investigated. environmental factors. to track vs. 397 ha). There was little movements. overlap in the home ranges of cats of the same sex. Ecological immunization: in situ training of free-ranging predatory lizards reduces their vulnerability to invasive toxic prey (Ward-Fear et al. 2016) To evaluate conditioned Oombulgurri, Used radio- Within 110 days of toad Releasing small toads in advance of the taste aversion of free- east telemetry to invasion, all but one of the main invasion front may offer a logistically ranging goannas as a Kimberley Monitor the untrained goannas died, simple and feasible way to buffer the means for mitigating survival of ‘toad- whereas more than half of the impact of invasive toads on apex the impacts of cane trained’ and ‘un- toad-trained goannas were still predators. toads trained’ goannas alive following invasion by cane toad front

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Plate 11. A pale field rat fitted with a radio collar (a); radio-tracking a feral cat along a fire scar from a helicopter (b); a cat’s eye view (c); a cat, with prey, captured on a remote camera trap (d). Photos are copyright protected and supplied by the Australian Wildlife Conservancy.

Plate 12. Radio-tracking floodplain monitors at Oombulgurri. Photos: Georgia Ward-Fear.

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3.2.2.4 Invasive animals – summary and management recommendations Cattle Given the strong negative relationship between observable cattle damage to ground layer vegetation and threatened mammals (see section 3.2.2.5), it is vital that these impacts be reduced if biodiversity outcomes are to remain a key target for this program. Every effort should therefore be made to reduce cattle numbers in as much of the LCI area as possible so that an overall reduction in large herbivore impacts can be achieved and sustained. This may be easier said than done given the divergent views on where and how best to manage cattle in the North-Kimberley. For instance, some Indigenous communities adjacent to conservation reserves rely on ‘killer’ animals for fresh meat. Similarly, there are pastoral leases operating within the region (and bordering conservation reserves), and the Departments ability to influence cattle management there is very limited; particularly given the current high value of export cattle and the increased push for pastoral intensification (Commonwealth of Australia 2015).

The best solution to this conundrum may be to develop a regional large herbivore management strategy in conjunction with other North-Kimberley land managers, which identifies biodiversity assets, land-use needs and aspirations, as well as management scenarios and solutions. Specific agreements could then be developed with individual neighbours and land managers, such as strategic fencing in conjunction with mustering and aerial culls that lead to longer-term solutions for reducing the impacts of cattle. Ideally, the impact thresholds developed by Corey et al. (in prep.) should be linked with data on cattle numbers and densities, which are then linked back to animal removal programs, enabling managers to estimate the financial and biodiversity costs of implementing management actions (see Corey 2015).

Because the aerial shoot program has grown in terms of the areas that it operates over, the Department now undertakes large feral herbivore management over areas not sufficiently captured by the on-ground monitoring program. Given the scale over which management activities take place and the remoteness of these areas, a rapid assessment approach is needed. It is recommended that 6–12 aerial photo monitoring sites (see Corey et al. 2013) are installed in each management zone and these be visited annually when undertaking culling activities. Given the resources allocated to the aerial shoot program (Corey 2015) it is important to ensure that any actions that utilise scant conservation dollars deliver measurable benefits for biodiversity.

Other target species Donkeys, pigs and horses occur in reasonably low densities in the North-Kimberley. The DAFWA run Judas donkey program continues to maintain low donkey numbers (Table 10). There is merit in collaborating with other land managers in the North- Kimberley, perhaps through the North Kimberley Land Conservation District Committee (NKLCDC) to map the locations of culled pigs and pig damage, to monitoring pig numbers over the entire North-Kimberley.

Plate 13. Feral pigs (above left) cause immense damage to billabongs (and other sensitive habitats) in northern Australia (above right), and must be maintained at current low numbers in the North-Kimberley.

Feral predators and prey Recent research suggests that it may be difficult to directly manage the impact of feral cats owing to their apparent low densities (McGregor et al. 2015b), and that the best way to mitigate the impacts is to better manage fire regimes and large feral herbivores. Impacts can be reduced by maximising grass cover, minimising the incidence of intense fires and reducing grazing pressure (McGregor et al. 2015a). Indeed these are the main premises of the LCI program.

The Department will be trialling the lethal cat bait Hisstory in 2016 in parts of the North-Kimberley. Successful reductions in cat populations using similar bait (Curiosity) have been achieved on Christmas, French and Dirk Hartog Islands (Johnson et al. 2011). The Department is currently supporting trials to train native wildlife to avoid cane toads and assess the impacts on non-target species, and to test the logistical feasibility of applying these techniques on a broader scale.

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3.2.2.5 Feral cattle and biodiversity indicators Both mammal abundance and diversity is higher at sites that have less than average impacts from feral cattle (Mann-Whitney U Test: abundance: U = 1237.5; P < 0.001; diversity: U = 1388.5; P < 0.001; Fig. 25; see also Radford et al. 2015). Mammal diversity declines by a third (approx. one species) at monitoring sites with more cattle impacts (Fig. 25a), while abundance decreases by > 50 % (Fig. 25b).

(a) 4 (b) 14 12 3 10 8 2 6 1 4

Mammal diversity Mammal 2 Mammal abundance Mammal 0 0 More Less More Less Feral cattle impacts Feral cattle impacts Fig. 25. Mammal diversity (a) and abundance (b) is higher at sites that have less than average impacts from cattle. The abundance (a) and diversity (b) of small- medium sized mammals at trapping quadrats sampled from 2011 to 2014, for quadrats with ‘more’ and ‘less’ impacts to vegetation from feral cattle. Bars show means and standard errors. Indices of cattle damage (as per Corey et al. 2013) are used to measure impacts of cattle at monitoring quadrats, and sites with ‘more’ impacts have more observable cattle damage than average, whereas those with ‘less’ have less.

Corey et al. (in prep.) used small-medium sized mammal monitoring data (see section 3.1.2) and site based measurements of large feral herbivore impacts (see section 3.2.2) to examine the relationships between threatened small-medium sized mammals and cattle, and establish impact thresholds to base future management decisions regarding cattle management, and to assist in determining management effectiveness.

Monitoring sites with no cattle impacts had an average of three different species compared to just one at sites with high impacts (Fig. 26a). Similarly, mammal abundance declined by 80 % at sites with high cattle impacts (Fig. 26b). Even moderate levels of cattle disturbance were associated with a substantial decline in mammal abundance and diversity (Fig. 26). Impacts from cattle need to be maintained below low levels of observable damage to conserve threatened mammals. This suggests that if the aims of feral cattle aerial culls are to retain or improve levels of mammal abundance and diversity, then observable impacts must be maintained below low levels of impact (see MAT2a).

(a) 4 (b) 12 10 3 8 2 6 4 1

Mammal diversity Mammal 2 Mammal abundanceMammal 0 0 None Low Moderate High None Low Moderate High

Observable impacts from cattle Observable impacts from cattle Fig. 26. Small-medium sized mammal diversity (a) and abundance (b) declines across cattle impact categories.

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3.2.3 Managing the impacts of invasive plants Weed management activities are prioritised primarily according to level of potential impact on environmental values and secondarily on size of infestation and the feasibility of either eradication or containment (Table 12). For instance, any species listed as a Weed of National Significance (WoNS) that is found in the Kimberley is automatically a priority for control (e.g. gamba grass, parkinsonia, prickly acacia, rubbervine, bellyache bush, Mimosa). Other species not listed at a National level but which have demonstrably had a high level of environmental impact elsewhere in northern Australia, particularly in terms of altering fuel loads and fire regimes, are a high priority under the LCI (e.g. grader grass [Themeda quadrivalvis]). Contained populations where there is a very good opportunity to achieve complete eradication are also a priority – for example Hyptis (Hyptis suaveolens) found on priority Kimberley islands (e.g. Lyons et al. 2014). Some weed species are almost ubiquitous across the landscape due to factors such as seed spread by birds (e.g. stinking passionfruit vine [Passiflora foetida]). This species is a priority for longer term control initiatives such as the investigation of biological control agents (Table 13; see also Webber et al. 2014).Weed management activities carried out under the LCI during 2013 and 2014 are summarised in Table 13.

Table 12. Summary of Landscape Conservation Initiative weed management prioritisation process.

Rank Rationale Species/areas Objective High Those that pose the highest threat to biodiversity Rubbervine and gamba grass Eradication priority values and/or where eradication is a feasible option. species This includes species that occur outside of the LCI project area, and where the aim is to prevent the establishment of these species within the North- Kimberley Priority Those that pose a threat to biodiversity values Grader grass, hyptis, stinking Reduction in size of infestation species and/or where reduction is a feasible option passionflower, bellyache bush, and hence impacts chinee apple and taro Priority High biodiversity value assets and vector nodes such Threatened and Priority Ecological Localised eradication or locations as road side verges, campgrounds and picnic areas Communities and identified priority reduction in size of infestations Kimberley Islands; road side verges, and hence impacts campgrounds and picnic areas

CyberTracker software (see Ansell and Koenig 2011) was trialled for mapping and recording weed management information within the Mitchell River work zone (Plate 14; Fig. 27, 28). The software is run on ruggedized PDAs (personal data assistant) and enables users to record standardised and geo-referenced information on the size and density of infestations (see Fig. 28). This will enable mangers to track progress in meeting weed management objectives (Table 7, 12).

Plate 14. A sequence of data screens (left) used to record weed management data on a hand-held PDA (right).

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Table 13. Summary of weed management programs carried out under the Landscape Conservation Initiative program during 2013 and 2014. Note: WoNS, Weed of National Significance.

Map Location/s Species Activity Partner/s Comments reference 2013 Mitchell River work zone A Grader grass (Themeda Weed Wunambal Gaambera quadrivalvis, WoNS), control Aboriginal Corporation hyptis (Hyptis (Uunguu Rangers) suaveolens) and stinking passionflower (Passiflora foetida) Mount Hart (King B Taro (Colocasia Weed Wilinggin Aboriginal Obscured by I (see Leopold Ranges CP) esculenta) control Corporation (Wunggurr below) on the map Rangers) El Questro station C Gamba grass Weed DAFWA, Wunggur Rangers, Not within the LCI area (Andropogon gayanus, control El Questro station but the infestation is WoNS) small and has the potential to invade LCI area. Willare (Fitzroy River) D Rubbervine (Cryptostegia Weed Rangelands NRM, Aquilia Not within the LCI area grandiflora, WoNS) control Project but the infestation has the potential to invade LCI area. 2014 Kununurra, Lake Argyle E P. foetida Research CSIRO Research into biological and Koolan Island control options (Webber et al. 2014). Mitchell River work zone A T. quadrivalvis, H. Weed Wunambal Gaambera suaveolens and P. foetida control Aboriginal Corporation (Uunguu Rangers) Carson River station F T. quadrivalvis Weed Kalumburu Aboriginal mapping Corporation Mount Hart (King B C. esculenta Weed Wilinggin Aboriginal Leopold Ranges CP) control Corporation (Wunggurr Rangers) El Questro station D A. gayanus Weed DAFWA, Wunggur Rangers, Not within the LCI area control El Questro station but the infestation is small and has the potential to invade LCI area. Gibb River station G T. quadrivalvis Weed Wunggur Rangers Work carried out (mound springs) control entirely by Wunggur Rangers as FFS Old Mitchell River H T. quadrivalvis Weed All North Helicopters Aerial spraying homestead control Mount Hart (King I T. quadrivalvis Weed Australian Wildlife Aerial spraying; work Leopold Ranges CP) control Conservancy carried out entirely by AWC as FFS Willare (Fitzroy River) D C. grandiflora Weed Rangelands NRM, Aquilia Not within the LCI area control Project but the infestation is has the potential to invade LCI area. Sir Graham Moore Island J H. suaveolens Weed Balanggarra Rangers mapping Wyndham, King River K Bellyache bush (Jatropha Weed Balanggarra Rangers Not within the LCI area and Kurunjie track gossypifolia, WoNS) mapping but infestations are small and helps Balanggarra Rangers meet Healthy Country objectives. Oombulgurri L Chinnee apple (Ziziphus Weed Balanggarra Rangers Not within the LCI area mauritiana, WoNS), H. mapping but infestations are suaveolens, C. and small and helps grandiflora control Balanggarra Rangers meet Healthy Country objectives.

FFS, Fee for service

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3.2.3.1 Eradicate high priority species, or priority species in priority areas (MAT3a) Although outside of the LCI area, the Department has been involved in the eradication of a small outbreak of Gamba grass (Andropogan gayanus) on El Questro Station (Table 13; Fig. 27) with property managers from El Questro, DAFWA and the Wunggurr Rangers (Chemello 2015). This species has the potential to spread extensively across savanna landscapes and fundamentally alter fire regimes (Setterfield et al. 2010), and as such is a high priority for eradication. The Department also contributes funds to the eradication of rubbervine at Willare (Fig. 27).

Fig. 27. Locations of Department of Parks and Wildlife funded weed management programs carried under the Landscape Conservation Initiative.

3.2.3.2 Decreasing trend in area occurred by priority species (MAT3b) Grader grass infestations within the Mitchell River work zone (see Fig. 27) have been a major focus of weed management programs. The size of individual infestations has been reduced; there are less large (≥ 100 m) infestations and more small (≤ 5 m) infestations (Fig. 28a), and as a consequence the total area of all infestations has decreased (Fig. 28c).

(a) 100 (b) 120 (c) 20 2013 2013 2014 80 100 2014 15 80 60 60 10 40 40

Total area (ha) area Total 5 20 20 Number of infestations of Number Number of infestations of Number 0 0 0 ≤ 5 m 20 m 50 m 100 m ≤ 5 % 5–25 % 25–75 % ≥75 % 2013 2014 Size class Density (coverage) Year Fig. 28. (a) Size distributions of grader grass (Themeda quadrivalvis) infestations within the Mitchell River work zone; density distributions (b) and (c) total area of those infestations in 2013 and 2014.

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Plate 15. Department of Parks and Wildlife staff and Uunguu Rangers controlling grader grass at Munurru (King Edward River campground).

Plate 16. Grader grass at Gibb River mound spring (Threatened Ecological Community) that has been sprayed by the Wunggurr Rangers.

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3.2.3.3 Invasive plants – Summary and management recommendations Although considerable management effort has been invested in weed management across the North-Kimberley (Table 13; Fig 27), it is hard to gauge the success of the majority of these activities. This is largely due to a lack of coordinated mapping and the relevant capture of standardised management data.

Trials with CyberTracker software on ruggedized PDAs (see plate 14) in the Mitchell River work zone (Fig. 28) have demonstrated the utility of this approach in terms of being able to capture, map and evaluate the outcomes of management efforts (Fig. 27). This approach is used widely by Indigenous land managers across northern Australia, and to good effect (Ansell and Koenig 2011; North Australian Indigenous Land and Sea Management Alliance 2014). If management efforts are to be captured and fully evaluated then standardised methods such as CyberTracker need to be used.

3.3 IMPLICATIONS FOR MANAGEMENT 3.3.1 LANDSCAPE OUTCOMES

3.3.1.1 Rainforest patches Monitoring indicates that rainforest patches appear to be stable (Fig. 2), but the timing of future image captures needs to be considered so that any phenological changes do not influence patch size estimates, and changes (or otherwise) can be attributed correctly and in context.

3.3.1.2 Small-medium sized mammals Monitoring indicates that mammal diversity and abundance remains relatively stable across the LCI area (Fig. 4, 5) but is very low in the King Edward, Laterite and Orchid Creek regions (Fig. 6, 7). These regions correspond with higher cattle impacts and increased fire frequency (see Fig. 29) which have been demonstrated to be detrimental for small mammals (Legge et al. 2011; Radford et al. 2015; Griffiths et al. 2015; Corey et al. in prep.). Efforts need to be made to improve threat management in these regions.

3.3.1.3 Vegetation and habitat condition On-ground measures of native vegetation and habitat condition suggest overall stability of these indicators, but influences from fire and rainfall are (unsurprisingly) becoming apparent (Table 5, 6; Fig. 9, 10, 11). Further analyses of monitoring data to tease apart any influences of environmental variables and management should be a priority. Similarly, time series analyses of Landsat aerial imagery suggests vegetation cover is increasing across the area of focus (Fig. 13). Further analysis of this monitoring data to tease apart any influences of environmental variables and management should also be a priority.

Plate 16. Small-medium sized mammals that have declined across northern Australia are well-represented across most LCI monitoring sites. There are areas however, where abundance and diversity is very low and these require greater management attention (see section 3.1.2.3).

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Fig. 29. Fire frequency (from 2000 to 2014) and cattle impacts in the Landscape Conservation Initiative area. Red denotes areas that burn every year, while green denotes areas that burn less frequently. Cattle impact categories follow Corey et al. (in prep.).

3.3.2 MANAGEMENT ACTION EFFECTIVENESS 3.3.2.1 Managing inappropriate fire regimes Monitoring of annual fire scars indicates that fire management has been able to arrest the boom and bust cycles that dominated fire regimes in the North-Kimberley prior to 2008 (Fig. 15, 17) and increase vegetation patchiness (Fig. 19). However, both the total area burnt (Fig. 16) and the extent of older growth vegetation (Fig. 21) remains unchanged. These results mirror those from savanna landscapes elsewhere (e.g. van Wilgen et al. 2004; Russell- Smith et al. Legge et al. 2015b; Murphy et al. 2015), and suggest these attributes may be relatively insensitive to management.

This raises the question as to whether some of these should be discarded as management targets, and instead focus solely on reducing the extent of individual fires, reducing fire frequency, and increasing the availability of unburnt patches within the landscape? Shifting fire seasonality from late to early dry season creates small patches of unburnt vegetation within fire scars that are not always detected by analysis due to constraints imposed by imagery (e.g. 250 m pixel size). The net effect of this shift means that it should be easier for animals that rely on un- burnt vegetation to persist in their pre-fire ranging areas (Legge et al. 2015b). However, Murphy et al. (2015) have demonstrated that the extent of fire sensitive vegetation can be increased through prescribed burning; suggesting that fire managers in the North-Kimberley should continue to attempt to increase the extent of long-unburnt vegetation.

Given the importance of fire frequency, fire seasonality and fire extent to a host of nationally significant species, further analyses to examine how varying operational approaches to prescribed burning affects these fires metrics; in particular those relating to the extent and availability of long-unburnt vegetation are warranted (see also Legge et al. 2015b).

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In 2013, the number of land managers delivering prescribed burning programs across the North-Kimberley increased. The annual fire scars for that year (Fig. 17) suggests that coordination between all of the fire management programs could be improved. Furthermore, a detailed analysis of fire pattern metrics as well as biodiversity outcomes delivered by each program would be timely, and could help shed light on how varying operational approaches to fire management could be used to benefit biodiversity.

3.3.2.2 Managing the impacts of invasive animals Monitoring of on-ground measures of cattle disturbance to vegetation indicates no downward trend in the impacts of this threat (Fig. 24). Impacts are still too high in the King Edward, Laterite, Orchid Creek and Yalgi areas (Fig. 29). These areas are also subject to higher fire frequency (Fig. 29), which has been demonstrated to amplify the negative impacts of feral cats (McGregor et al. 2014; 2015a; Leahy et al. 2015). Efforts need to be made to manage large feral herbivores in these areas. However, some of these areas are outside the Departments influence and culls have been more sporadically applied to southern and inland areas since the initiation of LCI, often due to factors beyond the Department’s control. This suggests that tailored and perhaps more collaborative approaches to large feral herbivore management may be required in future.

3.3.2.3 Managing the impacts of invasive plants The biggest hurdle to reducing the impact of weeds has been the lack of coordinated mapping and recording of management activities. This makes is very difficult to track progress against management targets, despite significant investment in on-ground actions (Table 13). Where the Department has used the appropriate technology to do this (e.g. Plate 14), it is possible to demonstrate what management efforts have achieved (Fig. 28). CyberTracker technology, in conjunction with GIS programs, provides a quick and easy way to collect this information in a standardised format and this approach needs to be used across the LCI area by both the Department and groups working under Fee for Service arrangements. This may require key staff to undertake basic CyberTracker training as well as GIS and general mapping training so that the weed management work undertaken in the LCI area can be evaluated and used to inform future management.

Plate 18. Processing small mammals at a monitoring site near Surveyors Pool in the Mitchell River National Park.

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