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Rocky outcrops have a profound influence on the distribution and abundance of biodiversity worldwide (Anderson, Fralish & Baskin 1999; Porembski & Barthlott 2000). Such environments are well documented as being biological hotspots, and often support unique biotic communities and high levels of endemism (Poremski, Seine & Barthlott 1997). The importance of rocky environments is highlighted by the discovery of new endemic species. For example, eight endemic plant species were recently described from a dolomite ‘glade’ formation in Alabama (Allison & Stevens 2001). However, despite the apparent attention some rock outcrops have received, along with cliff face environments, these geological landforms rank among the most poorly surveyed ecosystems in the world (Larson, Matthes & Kelly 2000).
Granite inselbergs are one regularly occurring geological feature found in most vegetation types and climate zones throughout the world. They form isolated, insular habitats and are exposed at over 15% of all continental areas (Twidale & Romani 2005). Such a widespread distribution may explain why saxicolous species contribute substantially to the faunas of different continents (Howard & Hailey 1999). In the Serengeti grasslands, granite ‘kopjes’ support many endemic species, including reptiles (Trager & Mistry 2003), and in Africa and southern America, granite outcrops have been an important selective force in the evolution of mammal communities (Mares 1997). In Australia, granite inselbergs also have high conservation value. For example, in Western Australia granite inselbergs support a diverse range of specialized taxa, including approximately 2000 species of plants (Hopper, Brown & Marchant 1997), over 80 species of aquatic invertebrates (Bayly 1997), at least 30 species of mammals (Morris 2000), and at least 17 species of reptiles (Withers & Edward 1997; Wilson & Swan 2008). However, few studies have quantified the role granite outcrops play in conserving reptile diversity. This is surprising, considering inselbergs have been the subject of extensive botanical research in recent years (Hopper et al. 1997; Porembski & Barthlott 2000 and references therein).
Quantitative data on the distribution and abundance of species are essential in mitigating the impacts of human activities on biodiversity (Lindenmayer & Burgman 2005; Krebs 2008). This is applicable to granite landforms, which occur in human-modified landscapes throughout the world and are quarried for building and landscape gardening industries or utilized for intensive recreational activities, such as rock-climbing (Twidale 2000). Hence, the lack of ecological knowledge on biota associated with granite landforms has global implications for biodiversity conservation, but particularly in Australia, which contains more reptile taxa than any other continent and where endemic species are still being described from rocky environments (Horner 2007; Wilson & Swan 2008). It is evident from the literature that reptiles respond poorly to habitat fragmentation (Brown & Bennett 1995; Brown 2001; Mac Nally & Brown 2001; Driscoll 2004; Cunningham et al. 2007). However, despite the growing literature on the effects of habitat fragmentation on reptiles, no study has applied landscape theory to explain patterns of reptile diversity in granite landforms. Just how important are granite outcrops in conserving reptile diversity in modified landscapes and what are the factors responsible for influencing species diversity? Here, the cumulative effects emerging from a naturally patchy ecosystem occurring within human-induced fragmented landscapes is of primary interest.
We used a conceptual framework based on landscape ecology theory to investigate reptile responses in fragmented agricultural landscapes. Specifically, we investigate: (i) patch size theory, where patch size influences species richness and diversity (MacArthur & Wilson 1967; Rosenzweig 1995), (ii) matrix theory, where the type and condition of the surrounding landscape influences species richness and diversity within patches (Ricketts 2001; Lindenmayer & Franklin 2002), (iii) complexity theory, where habitat structural complexity influences species richness and diversity (MacArthur & MacArthur 1961), and (iv) hierarchy theory, where both local and landscape factors influence species distributions and diversity (Allen & Starr 1988; Mackey & Lindenmayer 2001). To explore the relevance of these theories in explaining patterns of reptile diversity, we measured a range of explanatory variables relating to: (i) landscape-level factors (e.g. geographical location, isolation and surrounding matrix condition), (ii) patch-scale factors (e.g. geomorphology, vegetation type, patch size, grazing intensity and outcrop structural complexity), and (iii) plot-scale attributes (e.g. percentage cover of vegetation strata, floristic groups, surface rock and fallen timber, etc.).
Based on these theories, we hypothesized that large, structurally complex outcrops in high-quality landscapes will support the greatest reptile diversity. However, relatively small, structurally simple outcrops in highly modified landscapes also may have important conservation value for some species. Although there was little evidence to suggest any of the outcrops in the study area had been damaged by reptile collectors, we hypothesized that other human-induced disturbances such as livestock grazing will have negative effects on reptile diversity. We believe that our work will provide much-needed new information on the ecological role of granite outcrops and will help land managers in developing strategic methods for managing granite landforms for reptile conservation in fragmented agricultural landscapes.
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Over much of Australia, granite inselbergs are analogous to African kopjes as they are insular and surrounded by grassland plains. However, inselberg insularity in Australia has been exacerbated by excessive modification to the matrix through the development of crops and open pastures (Lindenmayer, Crane & Michael 2005). Small outcrops (less than 5 ha in size), are often overgrazed, cleared of native vegetation and harbour pest animals, such as the European rabbit Oryctolargus cuniculus (Mawson 2000) and invasive plants (Pigott 2000). In contrast, larger outcrops are generally less affected by human-induced activities and as such, are in better condition.
We studied an area within the South-western Slopes biogeographical region of New South Wales bordered by the towns of Tarcutta (0556007E, 6078043N) in the north, Albury (0494981E, 6008873N) in the south, Holbrook (0528452E, 6047058N) in the east and Walbundrie (0463118E, 6046968N) in the west. Intrusive granite formations are a dominant component of the regions’ geology (Twidale 2007). The predominant vegetation type on granite inselbergs in the region is temperate woodland (sensu Hobbs & Yates 2000); dominated by white box Eucalyptus albens, Blakely's red gum E. blakelyi, yellow box E. melliodora, red stringybark E. macrorhyncha and long-leaved box E. goniocalyx. Other outcrops with different soil profiles support communities dominated by currawang Acacia doratoxylon, tumbledown red gum Eucalyptus dealbata and white cypress pine Callitris glaucophylla.
We surveyed reptiles in 44 granite inselbergs located within 40 grazing landscapes and four areas managed for nature conservation. We surveyed along a gradient of patch sizes, ranging from small (< 1 ha, N = 7), medium (1–10 ha, N = 32) and large (> 10 ha, N = 5). The mean and median outcrop patch sizes were 11 and 4 ha, respectively, and the percentage cover of vegetation ranged from zero to 45%.
We classified inselbergs based on geomorphology (Campbell 1997; Twidale & Romani 2005) and recognized these four landform types (Fig. 1): (i) bornhardts – domical hills arising abruptly from the surrounding landscape, characterized by domes and expanses of exposed bedrock; (ii) nubbins – conical hills rising gently from the surrounding landscape, characterized by densely fractured jumbled rock piles; (iii) castle koppies – moderately fractured intrusions in gently undulating landscapes, characterized by orthogonally jointed pillars and domes; (iv) Corestone boulders (tors) – spatially dispersed rock masses in flat or gently undulating landscapes, characterized by fractured boulders, pillars and few surface rocks. Nubbins, castle koppies and corestone boulders are derived landforms representing varying stages in the erosion process of the original bornhardt (Twidale & Romani 2005). To sample all landforms in the study area, multiple inselbergs were surveyed on individual properties.
Figure 1. Granite inselberg landforms surveyed in the South-western Slopes of NSW; (a) bornhardt, (b) nubbin, (c) castle koppie, and (d) corestone boulders.
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We actively searched for reptiles between October 2006 and February 2007 on clear days above 25 °C and between 1000 and 1500 hours using an area-constrained (up to 4-ha grid centred over the outcrop) and time-constrained (2 h) protocol. For outcrops smaller than 4 ha, the interfacing landscape was not surveyed. We inspected all available habitats within each site including: beneath surface rocks, fallen timber, behind bark and within rock crevices. We did not damage rock exfoliations. Rather, where possible, crevices were inspected by reflecting sunlight off a mirror or with the aid of a torch. We also recorded reptiles found in the open either basking or foraging. Species were identified visually due to the difficulty of installing pitfall traps, noosing or hand-capturing animals. We believe the sampling method was adequate for detecting the majority of species in the focal area, although a few fossorial (Typhlopidae) species and nocturnal (Gekkonidae) individuals may have gone undetected.
measurement of covariates
To assess habitat suitability for reptiles, we recorded a range of categorical and continuous explanatory variables at multiple spatial scales. Landscape-scale attributes included distance to other outcrops and landscape context (i.e. whether outcrops were part of a range or formed isolated ‘satellites’). We classified outcrops in four landscape contrasts (sensu McIntyre & Hobbs 1999): (i) isolated in a ‘relictual’ matrix, that is, landscapes containing less than 2% of the original overstorey vegetation and supporting crops or annual pasture; (ii) isolated in a ‘fragmented’ matrix, that is, landscapes containing less than 10% of the original overstorey vegetation and supporting a mixture of native and exotic pasture; (ii) connected in a ‘variegated’ matrix, that is, landscapes containing abundant scattered paddock trees, roadside vegetation corridors or tree plantings, and (ii) connected in ‘continuous’ native vegetation. These were assessed visually from a GIS layer radiating 200 m and 500 m from the outcrop/matrix interface.
At the (outcrop) patch scale, we measured a range of variables (Table 1). Structural complexity was assessed visually by estimating rock structural classes, based on volume, to the nearest 5%. The structural classes were: (i) low-lying expanses of embedded rock, termed ‘pavements’; (ii) objects less than 0·5 m3, (corresponding with the kinds of objects that could feasibly be rolled over), termed ‘rocks’; (iii) objects ranging between 0·5 m3 to 2 m3, termed ‘boulders’; (iv) ‘pillars’ ranged between 2 m3 to 5 m3; (v) ‘blocks’ ranged between 5 to 10 m3; and (vi) ‘domes’ exceeded 10 m3. We tallied structural classes for each outcrop and calculated a complexity score ranging from one – simple outcrop (dominated by one or two structural classes) to five – complex outcrop (outcrops containing combinations of five or six structural classes). Mid-range values corresponded to outcrops with varying combinations of between two and five structural classes.
Table 1. Outcrop patch-scale variables used in linear regression models to explain overall reptile abundance, species richness and diversity on granite inselbergs in the South-western Slopes of New South Wales. *See text for detailed descriptions
|Explanatory variable||Variable type||Description|
| Elevation (metres above sea level)||Continuous||The highest point on the outcrop obtained from a Global Positioning System|
| Aspect||Categorical||Measured using a compass|
| Slope||Continuous||Measured using a clinometer|
| Landform type||Categorical||Corestone, nubbin, koppie, bornhardt|
| Outcrop complexity*||Categorical||Score ranging from 1 to 5|
| Outcrop patch size (ha)||Continuous||Calculated from a GIS aerial layer|
| Rock class cover*||Continuous||Visually estimated to the nearest 5%|
| Vegetation type||Categorical||Eucalypt woodland, Acacia doratoxylon, Callitris sp. woodland|
| Vegetation structure||Categorical||Old growth, regrowth, including seedling regrowth and multi-stemmed regrowth, cleared|
| Vegetation remnant patch size (ha) ||Continuous||Calculated from a GIS aerial layer.|
| Grazing intensity||Categorical||Heavy – set stocking; light – seasonal or rotational grazing; none – no livestock grazing|
At the plot-scale, we randomly established a 100 × 100 m (1 ha) grid and visually estimated such continuous variables as: (i) percentage cover of overstorey and understorey vegetation layers, (ii) percentage ground cover (e.g. exotic/native forbs, exotic/native grass, leaf litter, fine woody debris, bare earth, lichen and rock cover), (iii) large logs/fallen trees (density and estimate of volume), and (iv) the number of trees, tree stems, shrubs, dead trees and tree stumps.
We explored relationships between explanatory variables and overall abundance, species richness and diversity using Generalized Linear Models (GLMs) (McCullagh & Nelder 1989). For count data, we used Poisson regression and linear logistic regression for presence or absence data (rarer species). We accounted for extra variation due to clustering in count data by estimating a dispersion parameter and using variance ratio statistics for inference. We assessed dependence between observations within farms by fitting area (farm) as a random effect in a linear mixed model framework. The resulting correlations were low (0·26 for species abundance, 0·3 for species richness and 0·28 for species diversity), and therefore, we dealt with random effects only in generalized linear regression models. We rescaled explanatory variables by transforming the original data by taking natural logarithms where necessary. We constructed models first by fitting explanatory variables separately, ignoring all other variables, and secondly, by jointly considering all likely candidate variables. In the case of multiple regressions, we selected ‘best’ models using Akaike and Schwartz Information Criteria (AIC and SIC, respectively) for all possible regressions.
To measure species diversity, we used two indices; the Shannon–Wiener diversity (Shannon index) and the Simpson diversity index. When indices were congruent, we reported the most significant value.
In Shannon's diversity index, log Pi, where Pi is the proportion of species ‘i’. For Simpson's index, , where the parameters were the same as described for the Shannon–Wiener index.
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We have quantified the role granite inselbergs play in conserving reptile diversity in fragmented, agricultural landscapes. We have also illustrated that conceptual landscape ecology theories, traditionally applied to fragmented remnant vegetation, can contribute to a broader understanding of reptile diversity patterns. These concepts, which include patch size, matrix condition and habitat structural complexity, are useful in studying the biota associated with otherwise often overlooked rock-dominated environments such as granite inselbergs. We believe that additional investigations are required to disentangle the effects of patch size, habitat complexity and landform. Furthermore, additional experiments are needed to investigate the influence of canopy-shading on the ecology of saxicolous reptiles, and to further draw attention to the importance of such ‘forgotten’ habitats in the future persistence of reptiles in modified landscapes worldwide.