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The importance of fine-scale savanna heterogeneity for
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reptiles and small mammals
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Authors: B. Price 1,2 , A.S. Kutt, 3 and C.A. McAlpine 1
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1. The University of Queensland, School of Geography, Planning and
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Environmental Management, Centre for Remote Sensing and Spatial
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Information Science, Brisbane, Australia 4072.
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2. Present and corresponding address: Forests and Parks Division, Department of
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Sustainability and Environment, 3/8 Nicholson St, East Melbourne 3002, Ph:
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+61 3 9637 9809, fax: +61 3 9637 8117, [email protected]
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3. CSIRO Sustainable Ecosystems, Tropical and Arid Systems, PMB PO,
Aitkenvale, Queensland, 4814 Australia.
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Abstract
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Tropical savannas are an important reservoir of global biodiversity. Australia’s
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extensive savannas, although still largely intact, are experiencing substantial declines in
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terrestrial biodiversity due to a variety of interrelated effects of altered fire regimes,
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grazing and increases in invasive species. These disturbance processes are spatially
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variable, combine to increase fine-scale landscape heterogeneity, but rarely result in
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well-defined patch boundaries. We quantified the importance of this heterogeneity for
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native reptile and small mammal species in a tropical savanna landscape of,
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Queensland, Australia. We used high resolution remote sensing imagery (IKONOS) to
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map habitat heterogeneity at a 4 m spatial resolution and at variable extents. We found
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that landscapes dominated by grass or bare ground had low reptile and small mammal
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diversity, while landscapes with a heterogeneous mix of grass, bare ground and trees
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had a high species diversity and relative abundance of most species. Landscape
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heterogeneity may increase reptile and small-mammal species richness by: i) increasing
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the variety and abundance foraging resources such as seeds and invertebrates; ii)
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providing cover from predators and high summer temperatures; and iii) increase
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functional connectivity and dispersal success. The importance of these resources and
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processes varies among individual species and at different spatial scales, reiterating the
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need to consider habitat requirements of multiple species in landscape management and
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conservation planning.
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Key words: remote sensing, habitat requirements, Australia, spatial-scale
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1. Introduction
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Tropical savannas are large, extensive biomes, characterised by gradual environmental
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variation and widespread ecological connectivity (Huntley and Walker, 1982).
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Climatically they are often highly seasonal (Williams et al., 1996). Water availability
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and soil nutrients control vegetation patterns (Scholes and Archer, 1997; Walker and
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Langridge, 1997), and superimposed on this, fire and grazing by livestock and/or native
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herbivores can create a spatially heterogeneous matrix both at landscape and regional
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scales (Bond et al., 2003a, b). These interacting patterns of climate, soils, fire, and
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grazing can have a significant effect on the spatial patterns of woody vegetation
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(Fensham et al., 2003; Fensham et al., 2009) and consequently on wildlife populations
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(Meik et al., 2002; Tassicker et al., 2006). Understanding the ecological patterns of
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these essentially non-equilibrium landscapes is important for their conservation
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management (Fuhlendorf and Engle, 2004).
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Australia’s tropical savannas are one largest intact biomes remaining in the world
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(Woinarski et al., 2007). Unlike many of the world’s temperate ecosystems and
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savannas, these landscapes are is in a relatively unmodified condition (Woinarski et al.,
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2007). Nevertheless, substantial decline of terrestrial biodiversity is occurring in these
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and other rangeland landscapes due to a variety of interrelated factors such as altered
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fire regimes, clearing for agriculture, grazing and increases in invasive species
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(Franklin, 1999). Disturbance processes within these landscapes can be temporally and
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spatially variable, increasing overall landscape heterogeneity but rarely resulting in
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well-defined patch boundaries, as might happen where mechanical tree clearing occurs
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(Pearson, 2002; Woinarski et al., 2005). Therefore, within the geographical spread of
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these tropical savannas, there is a fine-scale spatial and temporal gradient in the relative
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balance of trees and grasses, which in turn creates degrees of heterogeneity in the
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habitat resource (Whitehead et al., 2005; Williams et al., 1996). This variation can have
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a strong influence on patterns of wildlife abundance (Price et al., 2009). Despite this,
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there are few studies which consider the importance of landscape heterogeneity for
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wildlife populations living in Australia’s tropical savanna landscapes, or elsewhere in
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the world’s savannas.
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The broad patterns and determinants of savanna fauna distribution have generally
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studied more visible and mobile elements of the fauna such as birds (Kissling et al.,
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2008) and large mammals (Waltert et al., 2008). Distribution is strongly related to
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ground cover and woody vegetation structure, as influenced by fire, grazing or
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competitive interactions with other fauna (Kutt and Woinarski, 2007). The smaller, less
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mobile, elements of savanna fauna, such as reptiles and ground-dwelling mammals, are
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less well understood (Fischer et al., 2005b; Meik et al., 2002), and likely influenced by
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environmental conditions at local and landscape scales (Welsh et al., 2005). At the
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regional scale, reptiles have been shown to pattern strongly with geological substrate
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and broad climate gradients (Woinarski et al., 1999; Woinarski and Gambold, 1992).
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However, little is known about the influence of savanna landscape heterogeneity on the
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distribution and abundance of reptiles; though the few examples that exist indicate an
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association between patterns of tree size and distribution, and reptile abundance
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(Griffiths and Christian, 1996).
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To achieve biodiversity conservation outcomes in tropical savannas, it is necessary to
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understand habitat fauna relationships at appropriate spatial and temporal scales
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(Woinarski and Fisher, 2003). There is currently a lack of ecological studies that
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examine fine scale spatial heterogeneity in habitat and its influence on smaller
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vertebrates, especially in savanna landscapes. The choice of an appropriate scale of
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study must reflect the traits of the organisms of interest and the scale at which that
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organism uses resources. In addition, the definition of ‘habitat’ is important, given that
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many species may require more than one type of habitat over their entire life history or
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for the purposes of nesting or foraging (Fahrig, 2003; Law and Dickman, 1998; Wiens,
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1997). Habitat is a species-specific concept. To be effective for conservation and
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biodiversity management goals, mapping and spatial analysis of habitat must take into
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account the habitat requirements of multiple species (Fischer and Lindenmayer, 2006;
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Fischer et al., 2004). Both within and between taxonomic classes, there are vast
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differences in the requirements for different habitat components and the spatial
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arrangements of those components at a variety of spatial resolutions and extents.
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Habitat mapping traditionally classifies landscapes into discrete patches either of habitat
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and non-habitat (matrix) (Forman, 1995), or into patches of different classes of habitat
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(Dunn and Majer, 2007; Price et al., ; Wu and Loucks, 1995). However, these patches
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are classed as homogeneous in terms of within-patch resources and do not take into
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account the fine-scale resource heterogeneity relating to the composition and
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configuration of different vegetation elements within those patches. Many species
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require a range of different vegetation types for shelter and foraging purposes or may
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respond to environmental condition at a variety of scales (Fischer et al., 2004). As such,
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fine scale (<0.25 ha) variability within a vegetation community is likely to have an
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important influence on the distribution, abundance and diversity of fauna species.
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Modern high spatial resolution (down to 0.6 m) remote sensing is capable of capturing
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fine-scale heterogeneity in habitat structure. Coupled with spatial analysis techniques,
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remote sensing offers the opportunity to analyse the relationships between that
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heterogeneity and species’ distribution patterns and abundances to derive critical
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species-habitat relationships and measures of ecological function (Rollins et al., 2004;
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Turner et al., 2003).
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In this study, we addressed the question: how important is the fine-scale landscape
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heterogeneity for native reptile and small mammal species in a tropical savanna
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landscape of Queensland, northern Australia. We used high resolution remote sensing
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imagery (IKONOS) map habitat heterogeneity at a fine spatial grain and variable
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extents. We found that landscapes dominated by grass or bare ground had low reptile
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and small mammal diversity and abundance, while landscapes with a heterogeneous
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mix of grass, bare ground and trees had a high species diversity and abundance of most
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species. We also show that individual species respond to this heterogeneity differently
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and at different spatial scales, reiterating the need to consider habitat requirements of
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multiple species in conservation planning.
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1. Material and Methods
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2.1
Study area
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The Desert Uplands bioregion of Queensland, Australia (Figure 1) has a semi-arid
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climate with a mean annual rainfall of 350- 600 mm y-1. Vegetation consists
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predominantly of Acacia and Eucalyptus woodlands, ephemeral lake habitats and
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grasslands (Sattler and Williams, 1999). Open Eucalytpus woodlands (height < 15 m) on
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sandy soils are dominant (~85% of the region). However, within these woodlands, there
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is considerable spatial variation in tree density according to soil type, fire frequency,
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anthropogenic thinning and drought-related dieback.
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[Insert Figure 1 around here]
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Sites were located in a single regional ecosystem type (10.3.9, Sattler and Williams,
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1999), the extensive silver-leafed ironbark (Eucalyptus whitei) woodlands of the
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northern Prairie-Torrens Creek subregion of the Desert Uplands bioregion of north-
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eastern Australia. This subregion has a distinct vegetation, geology and geomorphology
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characteristic of low-fertility eucalypt savannas. Sites were stratified across four
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vegetation structural treatments: cleared (chain cleared and sown with buffel grass
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Cenchrus ciliaris), thinned (using a crocodile - a large, toothed metal roller dragged
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behind a bulldozer), intact with 30-45% cover, and intact with 45-06% cover.
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2.2.
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Sixty 1-ha sites were sampled between May-June 2004 and re-surveyed between
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March-April 2005. Faunal sampling used a standardised 1-ha quadrat (Woinarski and
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Ash, 2002). Each site was sampled using a standard quadrat that comprised a nested
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trap and search array. This incorporates four pitfalls arranged in a ‘T’ configuration (30
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and 20 m of drift fence), six large (430 x 250 x 250 mm) funnel traps (2 per pitfall fence
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“arm”), and twenty small Elliott traps and two cage traps placed in a 50 x 50 m square.
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All Elliott and cage traps were baited with a mix of peanut butter, oats and honey and
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every second Elliott trap and all cage traps had dry dog food added. All traps were
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checked early morning, around midday and in the afternoon. In addition to the trapping,
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standard searches were undertaken. Three active searches were conducted at each site,
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generally one in the morning, one around midday, and one in the afternoon. Active
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searches involved 20 person-minutes turning logs and rocks, raking leaf litter and grass
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cover, peeling bark and shuffling through undergrowth. Active searches were restricted
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to the area bounded by the 50 x 50 m trap array. Two spotlight searches, each of 20
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person-minutes, were conducted at each site.
Fauna surveys
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Species detected only from scats, tracks or other signs given an abundance value of 1.
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Analyses used data pooled for each sampling event (2004 and 2005). All sites were
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located a minimum of 500 m from watering points to standardize the impact of grazing
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pressure across sites, and as far as practical from fence lines and roads. Sites were also
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located at least one kilometer apart wherever possible to maintain spatial independence.
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However, due to the small size of the thinned treatments, some sites were placed only
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500 m apart, and 200 m from water.
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To take into account both species richness and abundance, we calculated Shannon
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diversity index (H) for each faunal class (Shannon, 1948). We selected three mammal
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species and eleven reptile species to compare the importance of fine-scale habitat
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variability amongst species with different habitat preferences and behavioural attributes.
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Species were chosen using the following criteria; there was sufficient data to determine
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statistically significant patterns; species represented the range of taxonomic groups;
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where possible there were contrasting species within similar genera; and the species
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represented a range of life history pattern. (Selected species, scientific names and life
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history behaviours are summarised in Table 1).
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[Insert Table 1 around here]
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2.3
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Habitat variables were measured to act as site-scale variables following the methods
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outlined in and Eyre et al. (2006) and Neldner et al. (2005). Basal area was measured
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from two diagonal corners of the 5050 m quadrat for live and dead trees in three
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diameter categories per species (<5 cm, 5-20 cm, >20 cm), using a Bitterlich gauge, and
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averaged between corners. From this, an estimate of mean basal area for each tree and
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size class and total live and dead basal area was made. Foliage projective cover was
Vegetation surveys
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visually estimated for cover for six height classes (0-0.5; 0.5-1; 1-3; 3-5; 5-10; >10 m)
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with the 0-0.5 m height class representing ground vegetation and using seven cover
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classes (0; <5; 5-10; 10-25; 25-50; 50-75; >75%). Measures of percentage cover of bare
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earth, rock, litter, grass, sedges, herbs and forbs, and logs (>5 cm) were derived from 20
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0.5 m2 sub-quadrats in a regular grid within each 50 50 m quadrat. Cover is the
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mean cover score using all 20 quadrats. Total tree, shrub frequency is the number of 0.5
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m2 quadrats over which a tree or shrub (<1m) was recorded as present. Percentage cover
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and mean height of termite mounds were visually estimated. Visual estimates were
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made of cattle dung (0=none, 1=1 dropping, 2=more than 1).
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We conducted additional vegetation surveys coinciding with capture dates of the
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IKONOS imagery in January 2007. At 45 sites, 50 m 10 m belt transects were
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surveyed for foliage projected cover and shrub cover at one metre intervals. Ground
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cover was recorded in a similar manner categorised as bare soil, litter, woody debris,
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grass or low-lying shrub.
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2.4
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IKONOS imagery (4 m multispectral spatial resolution, 1 m panchromatic resolution)
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was acquired in January 2007. The imagery was used to map spatial variation in tree
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cover, bare soil and grass/low shrub cover (Figure 2). We were constrained by the non-
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availability of archival high resolution imagery data, hence it was not possible to
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coordinate image capture with the fauna surveys. Although tree cover may vary
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seasonally in savanna environments, our sites were located within low-moderate canopy
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cover where there was little recent mechanical tree clearing or thinning, thus we
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considered it reasonable to assume tree cover to be relatively stable over the period
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between image capture in early 2007 and fauna surveys in 2004 and 2005. Ground
Habitat mapping
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cover is obviously more seasonally variable. However, the IKONOS imagery was
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captured at the end of a long dry period and coincided with the commencement of
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summer storms when bare ground was at a seasonal high. Following geographic
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correction to ground-control points, we classified the IKONOS multi-spectral and
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panchromatic bands using the software Definiens Professional 5.0 (Definiens AG,
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2006). We segmented the images into objects such as individual trees, clumps of trees,
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grass cover and bare ground cover and then using a supervised classification method
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which designated a class to each object in the image as tree cover, grass cover or bare
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soil (Figure 2). Classification was derived from the panchromatic band, resulting in a 1
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m resolution classification (see Price et al., 2009 for further details).
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[Insert Figure 2 around here]
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2.5 Explanatory variables
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Explanatory variables included local-scale variables and metrics measuring vegetation
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configuration and composition at the wider landscape context (Figure 2; for detailed
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summary see Appendix 1). Local scale variables were the field vegetation measures
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obtained for the 50 50 m quadrats. We reduced the list of local-scale variables from
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the available field data a priori to a subset of five variables per species following
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univariate generalised linear modelling and ranking according to Akaike’s Information
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Criterion (AIC) values (Akaike, 1973).
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Landscape-scale variables were derived from the imagery and measured at three
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different buffer extents around each site: 500 m, 1 km and 3 km. Metrics were derived
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from the classified image for the series of nested buffer distances, thereby allowing us
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to quantify the landscape context at a variety of spatial neighbourhoods. For each of the
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buffer extents, we calculated the ratio of grass cover to tree cover, the percentage of
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bare ground cover and the interspersion and juxtaposition index of tree cover, bare
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ground and grass cover. The interspersion and juxtaposition index provides a measure
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of intermixing of land cover types and as such measures heterogeneity within the buffer
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area (McGarigal and Ene, 2003). The interspersion and juxtaposition index as well as
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the percentage area covered by grass, trees and bare ground for each buffer were
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calculated using the Patch Analyst extension to ArcGIS (Girvetz and Greco, 2007)
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2.6 Statistical analysis
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We used a multivariate generalised linear modelling approach to compare the influence
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of local scale variables and landscape scale variables at different spatial extents to
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explain the diversity and relative abundance of reptiles and mammal species. The
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response variables were Shannon diversity of reptiles, Shannon diversity of mammals
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and the count of 11 individual reptile species and three mammal species (Table 1)
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High colinearity among explanatory variables can lead to high standard errors and
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difficulties in interpreting parameter estimates in generalised linear models (Graham,
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2003). Therefore, as a rule, we did not include pairs of explanatory variables with
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Spearman pairwise correlation coefficients > 0.5 in the same model. Where pairs of
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variables were highly correlated, we chose the variable that provided the most
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explanatory power for the response variable. As such we used slightly different final
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sets of explanatory variables for each response variable. As expected, landscape metrics
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that measure similar characteristics but at different spatial extents (e.g., grass/tree ratio
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at 500 m and grass/tree ratio at 1 km) were often highly correlated. We overcame this
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problem by creating multiple alternative models for each response variable, including
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variables from only one scale in each model (sensu Araujo and New, 2007; Schadt et
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al., 2002).
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All models were fitted using R version 2.8.0 (http://www.r-project.org). The Gaussian
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distribution was used to model Shannon’s diversity of mammals and reptiles. However,
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examination of the individual count (relative abundance) data revealed the data was
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zero-inflated, resulting in model over-dispersion (Zeileis et al., 2007). We subsequently
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applied a negative binomial model using the “glm.nb” function in the MASS package of
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R (Venables and Ripley, 2002).
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We compared the importance of each of the spatial extents (500 m, 1 km and 3 km) by
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ranking the generalised linear models for each scale level according to their AIC and
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Akaike weight (ωi) (Akaike, 1973). The Akaike weights represent the relative likelihood
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of a model (normalised to sum to 1), given the data and the full set of candidate models
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(Burnham and Anderson, 2002). We conducted a comparison of the support for the
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highest ranked model by determining the weight of evidence (as measured by the
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Akaike weight) in favour of Model i being the best K-L model compared to the
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alternative models. We using evidence ratios (Burnham and Anderson, 2002) to
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evaluate which model was the better model for each response variable. Evidence ratios
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were calculated as the ratio of the Akaike weights w2/w1 where w1 and w2 are the
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Akaike weights of Model 1 and Model 2 respectively.
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To compare the importance of each spatial extent and the influence of local-scale
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variables relative to landscape-scale variables, we calculated the independent effect size
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of explanatory variables using hierarchical partitioning analysis within the hier.part
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package in R (MacNally, 1996). Hierarchical partitioning analysis separates the
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percentage independent and joint contribution of each variable to the total explanatory
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power of the model (Chevan and Sutherland, 1991). The hier.part package also provided
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a coefficient of determination (adjusted R2) as a measure of goodness of fit for each
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model.
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To test for goodness of fit of the best approximating models for each species, we used a
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graphical method whereby the standardised residuals were plotted against the half-
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normal scores and overlaid with a simulated envelope. The model was considered a
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reasonable fit if the observed residuals followed an approximate straight line and fell
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within the envelope (Martin et al., 2005). Using R, we simulated 19 samples of n
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observations using the fitted model as if it were a true model. The minimum and
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maximum values of the n sets of order statistics provided the simulated envelope (Yang
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and Sun, 2006).
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Finally, we tested for spatial autocorrelation in the Pearson residuals of the best
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approximating model for each response variable using spline correlograms produced
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using the ‘spline.correlog’ function in the ‘ncf’ R package with 1000 permutations
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(Bjornstad, 2008). This function uses a modified nonparametric spatial covariance
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function to produce a generalized estimate of spatial dependency as a continuous
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function of distance and a bootstrap algorithm to estimate the 95% confidence region
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(Bjornstad and Falck, 2001).
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3. Results
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3.1 Importance of the landscape context
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The results of generalised linear modelling and hierarchical partitioning revealed that
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landscape heterogeneity variables had a strong influence on both mammal and reptile
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abundance and diversity. Across all species, this influence was stronger than the local-
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scale habitat variables (Figure 3). The evidence ratios comparing the alternative models
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at different landscape extents for each response variable (Table 2) show that for most
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species there was little difference in the performance of models with varying landscape
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extents. This indicates that while the landscape context is important for the majority of
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the study reptile and mammal species, there was little sensitivity to increasing the
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landscape extent within the range of 1-3 km.
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[Insert Figure 3 around here]
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[Insert Table 2 around here]
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3.2 Mammals
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At the local-scale, the ranking of independent effects shows that the most influential
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variables for mammals were log cover at the ground layer, large live trees (basal area
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for tree with dbh of 5-20 cm), and foliage projected cover at different heights,
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depending on species (Figure 3). Model averaging of the generalised linear models
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revealed that Tachyglossus aculeatus and Pseudomys delicatulus responded positively
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to taller and older woody vegetation (basal area and FPC), with a negative response to
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logs and lower woody vegetation cover (Figure 4). Aepyprymnus rufescens, however,
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preferred sites with more logs, fewer small trees and higher foliage projected cover at
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0.5 m above ground level. This species also responded negatively to disturbance from
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clearing and thinning.
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[Insert Figure 4 around here]
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The independent effect of landscape-scale variables on mammals was generally highly
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ranked (Figure 3). In particular, the percentage of bare ground in the landscape was
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consistently ranked as one of the top three variables to negatively influence mammal
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abundance. Also the interspersion juxtaposition index had a strong positive influence.
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Generalised linear models also showed that at the landscape-scale, all mammal species
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responded positively to the fine-scale heterogeneity in habitat elements within the
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landscape (Figure 4). There was a general preference for greater vegetation (woody and
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grass) cover and avoidance of open areas as indicated by the negative relationship of all
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species to the percentage of bare ground and the grass-tree ratio. The negative response
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to the grass-tree ratio indicates a preference for woody vegetation cover compared to
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open grassy vegetation cover.
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3.3 Reptiles
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Our results revealed greater variation in the responses of reptile species. The ranking of
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independent effects showed that for local-scale variables, the number of termite mounds
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(negative effect), basal area of dead trees (positive effect) and foliage projected cover
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below 10 m (mixed effect but mostly positive) were most influential (Figure 3). At this
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scale, the results of the generalised linear models showed that responses varied
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considerably from species to species (Figure 4). Due to idiosyncratic differences in
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species’ behavioural traits, it was difficult to group responses by activity type (nocturnal
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or diurnal), body size or preferred substrate.
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Independent effects of landscape-scale variables were high for all reptile responses
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(Figure 3). The results of model averaging showed that at the landscape scale, the
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arboreal Oedura castelnaui, and terrestrial Proablepharus tenuis, Ctenotus robustus,
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and Heteronotia binoei responded positively to percentage of bare ground cover and to
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the grass-tree ratio (Figure 4). This result suggests a preference for habitats with high
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tree cover with a bare ground cover. The remainder of the reptiles had a negative
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response to the percentage of bare ground, with terrestrial Carlia munda and
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Diplodactylus steindachneri responding strongly negatively to the grass tree ratio,
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indicated a preference for more closed woodland. All individual reptile species
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responded positively to the interspersion/juxtaposition index, although interestingly the
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response of Shannon diversity of reptiles to this index was negative.
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3.4. Spatial autocorrelation and model fit
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There was low spatial autocorrelation in the raw data of the small mammal and reptile
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species abundance. The generalised linear model of both reptiles and mammals diversity
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adequately accounted for any spatial autocorrelation with spatial correlation values for
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model residuals being much closer to zero and evenly distributed across the range of
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distances. Similarly, the 95% confidence intervals did not cross the X-axis, hence the
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assumption of spatial independence of the model residuals was not violated. The half-
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normal plots revealed that all of the best approximating generalised linear models
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provided a reasonable fit.
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4. Discussion
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The study represents one of the first to quantify the importance of fine-scale savanna
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heterogeneity for small native animals. Numerous studies have quantified the effect of
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habitat loss and fragmentation on wildlife populations in fragmented landscapes (see for
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example Bender et al., 1998; Fahrig, 2003; Lindenmayer et al., 2007; Stephens et al.,
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2003). In fact, the dominant and most widely applied paradigm of conservation biology
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and landscape ecology is based on the patch-matrix-corridor model of landscape
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structure (Fischer and Lindenmayer, 2006). However, in savanna biomes, landscape
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boundaries are often diffuse and difficult to differentiate. The tree-grass continuum is
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widely used in savanna ecology to characterise the structure of savanna landscapes (Joy
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Belsky, 1991). Our study extended the conceptual approach developed by Price et al.
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(2009) by quantifying the influence of the spatial variation in tree cover, grass cover and
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bare ground along this continuum for small reptiles and mammals.
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The pattern of both local-scale habitat heterogeneity and landscape-scale heterogeneity
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is of particular importance for reptiles and ground dwelling mammals, which are less
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mobile and have more restricted home range or habitat requirements (Recher et al.,
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2009). We have demonstrated that the configuration and composition of habitat at the
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landscape context scale is of importance for mammals and reptiles as well as more
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mobile birds species (Price et al., 2009). However, in general, landscape scale variables
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had a much greater influence on the bird species than on reptiles and mammals. At a
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local-scale, the presence of a particular substrate (Garden et al., 2007), ground cover
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(Monamy and Fox, 2000) or tree type (Griffiths, 1999) have been shown to be
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important for reptiles and small mammals. Subtle gradients of distribution and
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abundance can occur across small landscape areas (Woinarski and Gambold, 1992).
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Nevertheless, landscape effects, such as vegetation configuration, grazing pattern, fire
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history can have a profound influence on the presence or absence of mammals and
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reptiles across environments seemingly suitable for a particular species (Fischer et al.,
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2005a; McKenzie et al., 2007). This study demonstrated a general pattern across all
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species of a negative response to large amounts of bare ground at the landscape scale,
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with a preference for high interspersion of grass, tree and bare ground at a fine
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resolution across the landscape. In tropical savanna environments, changes in the spatial
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heterogeneity of trees, grass/shrubs and bare ground due to, for example, fire and
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grazing disturbance, is recognised as critical to understanding the functioning of
389
rangeland ecosystems (Fuhlendorf and Engle, 2004; Ludwig and Tongway, 1995). This
390
is especially so where there is evidence of broad-scale decline in taxa such as small
391
mammals (Woinarski et al., 2001).
392
Despite the complexity of many of the models and the integration of landscape and site
393
based explanatory terms, most of the models exhibited a strong connection to individual
394
species ecology and life history. The rufous bettong Aepyprymnus rufescens is a small
395
terrestrial marsupial that shelters in grass nests or hollow logs, and feeds on tubers,
396
grass roots and truffles (McIlwee and Johnson, 1998). This species is commonly
397
associated with mosaic landscapes (ie recently burnt/unburnt), as it needs a combination
398
of cover and disturbed environments with new vegetation growth (Kavanagh and
399
Stanton, 2005; Pope et al., 2005). Similarly the echidna Tachyglossus aculeatus is an
400
ant and termite feeding specialist associated with open woodland environments that
401
provide an ample food source (Wilkinson et al., 1998), and the relationship with tree
402
cover seem sensible. This species is absent in predominantly treeless environments such
403
as the extensive Mitchell grasslands (Fisher, 2001). The model for the delicate mouse
404
Pseudomys delicatulus is less explicable, as this small rodent is commonly associated
18
405
with open environments, with low ground cover. Perhaps in this instance the
406
explanatory model is weak, except that there is a link to the juxtaposition index, and this
407
species is certainly persistent in sites that have a mixture of grazing and fire history
408
(Braithwaite and Brady, 1993; Kutt and Woinarski, 2007), which would create
409
heterogeneous environments.
410
The reptiles tested were predominantly from the skink and gecko families (Scincidae
411
and Gekkonidae) the former being predominantly diurnal and the latter nocturnal. The
412
nocturnal species were all geckos and represented a mixture of terrestrial litter
413
sheltering (Heteronotia), burrowing (Diplodactylus) and arboreal species (Oedura). The
414
association of the arboreal Oedura with tree cover is understandable and a well
415
established relationship (Fisher and Kutt, 2007) as is Heteronotia binoei with increasing
416
bare ground as this species is a widespread generalist and disturbance tolerant
417
(Woinarski and Ash, 2002). The Diplodactylus gecko is a termite feeder that
418
occasionally uses fallen timber to shelter, which supports the association with
419
increasing woody vegetation variables. However this species is considered a neutral or
420
disturbance tolerant species (James, 2003; Landsberg et al., 1997) and there was a
421
positive association with the clearing variable.
422
The fossorial species Lerista chordae and Menetia greyii are all associated with positive
423
measures of litter cover, tree cover and mid-canopy and shrub cover, and these factors
424
generally have a high contribution to the parameter estimates. All of these species are
425
very small bodied (<2 g) and shelter in dense litter around the bases of trees and shrubs
426
and are susceptible to effects of fire and heavy cattle grazing (Amey et al., 2005;
427
Caughley, 1985; Woinarski and Ash, 2002). Medium bodied diurnal skinks such as
428
Ctenotus hebetior and Carlia munda are only moderately tolerant of changes in ground
19
429
cover (Kutt and Woinarski, 2007) and both were generally associated with sites factors
430
of intermediate cover, and a greater density of tree cover. The large bodied skink
431
Ctenotus robustus requires moderate to high ground cover to persist in local landscapes
432
(Landsberg et al., 1997) and there was a strong positive association with groundcover
433
<0.5m, though at a landscape scale the association with a more open woodland
434
structure. This highlights the site-landscape scale nexus; despite a particular habitat
435
preference (e.g. high ground cover) for a species based on their known ecology (e.g.
436
thermal preferences), landscape scale patterns may be quite different (ie more open
437
rather than closed woodland structure). Overall for most reptile species, there was a
438
strong positive association with the landscape heterogeneity index, and the relationship
439
between landscape heterogeneity (that is maximisation in variation of environmental
440
conditions) has been demonstrated for both temperate (Fischer and Lindenmayer, 2005)
441
and tropical systems (Woinarski and Gambold, 1992).
442
5. Conclusions
443
There was significant variation in the response to habitat variables amongst species,
444
which highlights both the difficulties and necessity of a multiple species approach to
445
management for conservation (Fischer and Lindenmayer, 2006; Fischer et al., 2004;
446
Price et al., 2009). This study has important implications for biodiversity conservation.
447
Landscape context as well as varying individual species habitat requirements will need
448
to be considered in order to achieve biodiversity goals. Several previous studies have
449
recognised that habitat must be defined and managed for on a species by species basis
450
(Fahrig, 2003; Fischer and Lindenmayer, 2006; Price et al., 2009). Any definition of
451
habitat should also include the spatial configuration of habitat elements at an
20
452
appropriate local scale, which may differ with taxa, and also within the broader
453
landscape context.
454
6. Acknowledgements
455
This study was funded by the Australian Research Council (ARC) Discovery Project
456
DP0667029: “Beyond discrete landscape metrics: spatial analysis tools and surface
457
textural measures for quantifying gradients in landscape structure”. We are grateful for
458
the help of numerous landholders in granting us access to their properties for the survey:
459
the Bodes (Woura Park and Timaru) and Haydons (Penrice). Michiala Bowen (The
460
University of Queensland) assisted greatly with vegetation surveys and Figure design.
461
Eric Vanderduys and Justin Perry (CSIRO Sustainable Ecosystems) provided valuable
462
assistance with the fauna surveys, which was funded by CSIRO Sustainable Ecosystems
463
and the Australian Government Natural Heritage Trust.
464
21
465
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Tables
Table 1: Behavioural traits of individual reptile and mammal species
Family
Reptiles
Agamidae
Species
Activity
Thermal
Diet
Amphibolurus nobbi
Diurnal
Heliothermic
Insects, small arthropods
15
Gekkonidae
Diplodactylus steindachneri
Nocturnal
Thigmothermic
2
Gekkonidae
Heteronotia binoei
nocturnal
Thigmothermic
Gekkonidae
Oedura castelnaui
Nocturnal
Thigmothermic
Termites, insects, small
arthropods
Termites, insects, small
arthropods
Insects, small arthropods,
other geckos
Gekkonidae
Rhynchoedura ornata
Nocturnal
Thigmothermic
Scincidae
Scincidae
Carlia munda
Ctenotus hebetior
Diurnal
Diurnal
Heliothermic
Heliothermic
Scincidae
Ctenotus robustus
Diurnal
Heliothermic
Scincidae
Lerista chordae
Nocturnal and
diurnal
Scincidae
Menetia greyii
Scincidae
Termites, insects, small
arthropods
Insects, small arthropods
Insects, small arthropods
Weight (g)
2
15
2
2
5
20
Thigmothermic
Insects, small arthropods,
other skinks, small fleshy
fruits
Insects, small arthropods
Diurnal
Heliothermic
Insects, small arthropods
<1
Proablepharus tenuis
Diurnal
Heliothermic
Insects, small arthropods
<1
Pseudomys delicatulus
delicate mouse
Nocturnal
Endothermic
Seed
9
Potoroidae
Aepyprymnus rufescens
rufous bettong
Nocturnal to
crepuscular
Endothermic
Grasses, seeds, herbs,
root tubers, fungi
3000
Tachyglossidae
Tachyglossus aculeatus
short-beaked echidna
Nocturnal and
diurnal
Endothermic
Ants and termites
invertebrates
4000
Mammals
Muridae
2
Forage and shelter substrate
Terrestrial to scansorial, utilising trunks and lower
branches to bask or sleep.
Terrestrial, generally using spider holes and fallen
timber to shelter during the day.
Terrestrial, utilising rocks, fallen timber or bark to
shelter and lay eggs.
Arboreal, sheltering under bark and in hollows of
rough-barked trees during the day and foraging on the
lower portion of trees and occasionally on the ground.
Terrestrial, favouring sandy soils, generally using
spider holes to shelter during the day.
Terrestrial, utilising logs, grass cover, litter to shelter.
Terrestrial, utilising logs, or deep burrows or shelter.
Prefers open ground cover.
Terrestrial, utilising logs, or burrows or shelter. Prefers
higher ground cover.
Fossorial to burrowing, hiding in deep litter and soil,
often under logs or leaf litter at the base of trees.
Occasionally terrestrial movement and foraging.
Fossorial to terrestrial, utilising logs, grass cover, litter
to shelter and feed.
Fossorial to terrestrial, utilising logs, grass cover, litter
to shelter and feed.
Terrestrial, utilising deep burrows, rocks, logs to
shelter during the day. Prefers open area with low
ground cover.
Terrestrial, utilising shallow scrapes, logs and grass
nests in dense grass to shelter during the day. Forages
in a range of dense to open habitat.
Terrestrial, feeding on wide variety of invertebrates
from logs, mounds, shrubs, litter patches. Highly
catholic in habitat preferences.
32
Table 2: AIC and R-squared values value for the most parsimonious model at each
scale level for each species
Species
Shannon diversity mammals
Rufous bettong
Delicate mouse
Short-beaked echidna
Shannon diversity reptiles
Amphibolurus nobbi
Carlia munda
Ctenotus hebetior
Ctenotus robustus
Diplodactylus steindachneri
Rhynchoedura ornata
Proablepharus tenuis
Oedura castelnaui
Menetia greyii
Lerista chordae
Heteronotia binoei
AIC 500m
46.67
69.13
91.91
51.21
21.73
164.26
102.78
211.66
152.12
132.07
123.86
121.30
95.96
223.62
111.38
196.57
AIC 1k
48.51
69.71
91.96
50.89
22.2
163.87
103.16
216.36
153.97
135.72
131.55
121.74
95.96
222.62
105.46
195.95
AIC 3k
51.24
67.31
93.04
53.53
17.99
166.27
112.07
202.16
153.21
144.13
133.80
121.9
94.99
223.64
105.27
196.57
Evidence ratio, best to
next best model
2.51
2.48
1.03
1.18
6.49
1.21
1.21
115.84
1.72
6.19
46.71
1.24
1.62
1.65
1.10
1.36
R-squared value for best
model (hier-part)
0.387
0.326
0.188
0.277
0.442
0.347
0.464
0.594
0.37
0.257
0.412
0.282
0.542
0.375
0.452
0.333
33
Figure Captions
Figure 1. Map of the study area in the Desert Uplands bioregion and field survey site
locations (inset).
Figure 2: Schematic showing the spatial heterogeneity in savanna vegetation at the
site-scale and at the landscape-scale and explanatory variables measured at each
scale.
Figure 3: Ranking of explanatory variables by independent effect for each response
variable. Each square represents the frequency each explanatory variable occurred in the
top three explanatory variables for the Shannon’s diversity and individual mammal and
reptile response variables. (+) indicates a positive effect, while (-) indicates a negative
effect of the average parameter estimate. Gray shading represents mammals, while
black shading represents reptiles.
Figure 4: Bar graphs showing: a) average parameter estimates of explanatory variables
derived from the averaging of generalised linear models; and b) independent effect
resulting from hierarchical partitioning for each response variable. Variables are
grouped according to: ground layer habitat attributes, tree basal area (units cm DBH),
tree foliage projected cover (%), disturbance (cattle dung and tree clearing), and
measures of landscape heterogeneity.
34
Figure 1.
35
Figure 2.
36
Figure 3.
37
Figure 4.
38
Appendices
Table A1: Explanatory variables used in generalised linear modelling
Local scale (50 50m quadrats)
Landscape scale (at 0.5, 1, 2 and 3km
neighbourhoods)
Total basal area dead trees (TOTDEAD)
Percentage of bare ground area in a patch (pl_b)
Basal area of live trees less than 5m in height (LIVE_L5)
Grass tree ratio (gt)
Basal area of live trees 5 to 20m in height (LIVE5_20)
Interspersion and juxtaposition index (iji_all)
% Bare earth cover (BARE)
% Grassy vegetation cover (GRVEG)
% Litter (LITT)
% Forbs (FORB)
% Log cover (LOGS)
Foliage projected cover 0-0.5m (FPC5-10)
Foliage projected cover 0.5-1m (FPC5-10)
Foliage projected cover 1-3m (FPC5-10)
Foliage projected cover 5-10m (FPC5-10)
Foliage projected cover over 10m (FPC>10)
Number of termite mounds (TERMITE)
Amount of cattle dung (DUNG)
Clearing treatment (TREATMENT)
39