Habitat requirements of the endangered pygmy bluetongue lizard, Tiliqua adelaidensis

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available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/biocon

Habitat requirements of the endangered pygmy bluetongue lizard, Tiliqua adelaidensis Nicholas J. Soutera, C. Michael Bulla,*, Mark R. Lethbridgeb, Mark N. Hutchinsonc a

School of Biological Sciences, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia School of Geography, Population and Environmental Management, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia c South Australian Museum, North Terrace, Adelaide, SA 5001, Australia b

A R T I C L E I N F O

A B S T R A C T

Article history:

The pygmy bluetongue lizard, Tiliqua adelaidensis, occupies spider burrows as home sites. It

Received 31 May 2006

is an endangered species, known from only 19 small natural grassland sites in the mid-

Received in revised form

north of South Australia, all on privately owned land. Habitat requirements of the pygmy

13 September 2006

bluetongue lizard were investigated at four sites. Both within and between sites, lizards

Accepted 17 September 2006

were more likely to be found in areas with a greater number of deep spider burrows. Areas

Available online 3 November 2006

where lizards were not found tended to lack these burrows. Strong site similarities were found for a range of habitat parameters examined. Within these grasslands there was no

Keywords:

specific vegetation community associated with areas occupied by pygmy bluetongue liz-

Habitat requirements

ards. However there was a distinct vegetation community associated with an absence of

Spider burrows

lizards. Generally there was no difference in the abundance and diversity of ground dwell-

Natural grasslands

ing invertebrates between areas with and without lizards. As the only protected area of nat-

Reptiles

ural grassland within the known distribution, Mokota Conservation Park was assessed as a

Lizards

potential reintroduction site. It was found to be unsuitable due to a low number of deep spi-

Pygmy bluetongue lizard

der burrows and a vegetation community similar to that found in uninhabited areas of

South Australia

known lizard inhabited sites. Unless other conservation areas can be established, preservation of this lizard will rely on habitat management by private land holders. Community goodwill and informed advice to the land holders will be essential in this process.  2006 Elsevier Ltd. All rights reserved.

1.

Introduction

The success of any conservation program for a threatened species depends on a sound understanding of its habitat requirements. Loss, fragmentation and degradation of habitat are all major causes of species decline and extinction (Groombridge, 1992; Burgman and Lindenmayer, 1998; Primack, 1998). If the habitat that is critical for species persistence is understood, important areas can be identified and protected, and searches for further populations may be efficiently targeted. Similarly programs for reintroduction or translocation of

threatened species depend on identifying areas of suitable habitat (Griffith et al., 1989; Kleiman, 1989). Some reintroduction programs have failed when animals were released into unsuitable areas (Griffith et al., 1989; Carpenter et al., 1991; Short et al., 1992). Reviews of large numbers of animal release programs (Wolf et al., 1996; Fischer and Lindenmayer, 2000) have emphasized that success is more likely where translocations are to areas of high habitat quality for the introduced species. A number of lizard species that occupy grassland habitat are threatened by the invasion of woody bushes (Menke,

* Corresponding author. E-mail address: [email protected] (C.M. Bull). 0006-3207/$ - see front matter  2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2006.09.014

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2003), or the fragmentation of the habitat by human activities (Dorrough and Ash, 1999; McIntyre, 2003). The pygmy bluetongue lizard, Tiliqua adelaidensis, is an endangered reptile species with a restricted distribution in a region of South Australia that is referred to as the mid-north. It has only ever been found in natural grasslands (Milne, 1999) that, in South Australia, are dominated by native grass species but have an exotic plant component (Tre´mont and McIntyre, 1994). In South Australia, previously extensive natural grasslands have been fragmented and, by 1995, reduced to about 0.33% of their original distribution (Hyde, 1995). The pygmy bluetongue lizard is known from only 19 of over 100 surviving grassland remnants that have been searched (Milne, 1999; Souter, 2003). All known populations occur on private land, and the species is absent from the only grassland reserve within its distribution, Mokota Conservation Park (Fig. 1). The aim of this paper is to characterise the habitat requirements of the pygmy bluetongue lizard and then assess the suitability of Mokota as a potential site for reintroduction or translocation. We analysed three habitat components that we considered to be important for the lizard, vegetation, refuge burrows and food resources. Because it is found only in natural grasslands the pygmy bluetongue lizard may require a specific vegetation community. For refuge, the lizard occupies empty burrows of wolf spiders (Lycosidae) or trap door spiders (Mygalomorphae) (Milne, 1999). Deeper burrows are preferred. Milne (1999) found a mean depth of 23.4 cm (SE 0.64), and a minimum of 12 cm, for 128 burrows occupied by lizards. Lizards offered a choice of artificial holes chose 30 cm deep holes over shallower ones (Milne and Bull, 2000). Thus, the presence of suitably deep burrows is likely to be important in determining where lizards can live. The lizards eat ground dwelling invertebrates that move past their burrow entrance (Milne, 1999).

Peterborough

Terowie Terowie

Hallett

Mt Bryan

Hallett Trees Hallett

Mokota School

Burra

Fig. 1 – Location of samples sites (triangles and bold script) in the mid north of South Australia. Towns are indicated by circles and non-bold script.

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The species may be excluded from areas where suitable prey are scarce. We also included in analyses a number of physical properties of the sites and the soils. We compared components of the habitat between areas inhabited by lizards and adjacent areas where lizards were absent. We also compared different sites occupied by the lizards. The overall aim was to identify any component of the environment that may be important in predicting the suitability of a particular site to maintain a population of the pygmy bluetongue lizard.

2.

Methods

2.1.

Site descriptions

Five sites from within the distribution of the pygmy bluetongue lizard were sampled (Fig. 1). Four of these sites support populations of the pygmy bluetongue lizard in varying densities (Milne, 1999), but the species has not been detected from the fifth site, Mokota, even though it has natural grasslands and is located in the geographic range of the lizard. The School site, near Burra, is on the northern side of a low hill with an altitude of 520–580 m. It has been grazed by sheep, goats and cattle. The soil had a silty loam A horizon over either a B horizon of grey loam with a slight amount of clay, a highly calcareous Bk horizon or rock. The soil is described by the principle profile form (Northcote, 1979) of Um5.61. The Hallett site, on the northern side of a low hill and with an altitude of 630–650 m, has been lightly grazed by sheep. It has a red duplex soil with a loam A horizon over either a B horizon of medium/heavy (>50%) clay, rock, or a highly calcareous Bk horizon. The soil is described by the principle profile form of either, Dr2.13 (for profiles with a B horizon) or Um1.43 (for profiles without a B horizon). Hallett Trees, 7 km east of the Hallett site, is also on a low hill, altitude of 590–610 m, grazed by sheep. The soil has a silty loam A horizon over either rock or a highly calcareous Bk horizon, and is described by the principle profile form, Um5.61. The Terowie site, located between Peterborough and Terowie, is the most northern population of the pygmy bluetongue lizard currently known. It is at an altitude of 590–620 m and is grazed by sheep. The soil has a sandy loam A horizon over rock, and is described by the principle profile form, Uc1.43. Mokota Conservation Park, located east of Mt Bryan, has an altitude of 610–620 m. The park contains 455 ha of natural grassland that was lightly grazed by sheep before 2000. It has a red duplex soil with a sandy loam A horizon over either rock or a B horizon of medium/heavy (>50%) clay. When present the B horizon lies over either rock or a highly calcareous Bk horizon. The soil has the principle profile form of either Dr2.13 (for profiles with a B horizon) or Um1.43 (for profiles without a B horizon).

2.2.

Survey methods at the four lizard occupied sites

We located spider burrows within each of the four lizard populations and inspected them for lizard occupancy using an Olympus IF8D4X2-10L optic fibrescope and portable light

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source (Olympus KLS-131) (Milne and Bull, 2000) to determine approximate edges to the local distribution. Then, at each site, we established four transects of 100–200 m to cross those distribution edges, starting within the known local distribution and extending at least 50 m beyond the edge. We set two transects at each site up the hill slope, and two across the hill slope. Transects were located to avoid obviously unfavorable habitat such as woodlands, exotic grasslands or cultivated fields. Transects were either perpendicular to each other, or parallel and spaced at least 20 m apart. We surveyed each transect three times: spring 1999 (20 October–25 November); autumn 2000 (3 April–9 May); and spring 2000 (14 November–6 December). In the first survey, at each 10 m along each transect, we inspected all detected spider holes within a 4 m diameter circle and recorded those occupied by lizards. We also inspected additional spider holes encountered along each transect. In the second and third surveys we inspected all holes within a 2 m wide band along each transect. We recorded between 1 and 18 lizards along each transect on each survey (averages: School 12.0; Hallett 2.7; Hallett Trees 5.7; Terowie 6.7). Direct comparisons and variance measures are not valid because of differing transect lengths among sites, and different survey methods between the first and later surveys. From these surveys we defined three categories of ‘‘lizard area’’. The inhabited area was the continuous section along each transect from within the local distribution to the last sampling point where lizards were recorded in at least two of the surveys. The marginal area was an extension along the transect to include sampling points where we recorded lizards only once. The uninhabited area was the portion of the transect where lizards were never found. We located boundaries between ‘‘lizard areas’’ at the nearest 5 m point along the transect, beyond the position of the last lizard encountered. At the same time we measured a number of physical and biological parameters along each transect, and compared values among different ‘‘lizard area’’ categories. These parameters are described below.

2.3.

Vegetation

We surveyed vegetation in 1 · 1 m quadrats at 10 m intervals along each transect in the four lizard occupied sites, in October–November 1999 (total 151 quadrats). We identified plant taxa to the lowest level practicable (usually to species). We estimated percentage cover of vegetation within each quadrat by the point quadrat method (Greg-Smith, 1983; Kershaw and Looney, 1983). A needle was lowered vertically to the ground 10 times at 10 cm intervals across the midline of the quadrat, and we recorded the presence of vegetation, bare ground, litter, rock, moss or lichen at each point. Plant taxa encountered by the needle, above ground level, were recorded to give an estimate of aerial cover.

2.4.

Burrow depth

We recorded the location and depth of each burrow detected, during each of the last two transect surveys. Depth was measured from the length of optic fibrescope cable taken to reach

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the bottom. For occupied burrows we followed Milne (1999) and accounted for the lizard body length by adding 10 cm to the depth to the lizard head. Holes deeper than the length of the optic fibrescope cable were recorded as >77 cm. We divide holes into four depth classes based on their perceived suitability for lizard occupation: 0–12 cm, unsuitable; 12–23.3 cm, marginal; 23.4–30 cm, suitable; and >30 cm, highly suitable. The density (number/m2) of spider burrows in each depth class was calculated for the 24 combined samples from each of the inhabited, marginal and uninhabited areas (3), at each site (4), for each survey (2).

2.5.

Invertebrates

We set a 70 mm diameter pitfall trap for diurnal invertebrates each 10 m along one transect at Hallett and Terowie, and along two transects at the School site and Hallett trees (total 52 traps). Pitfall traps were half filled with 70% ethanol, closed at night, and opened on six days in October 2000. We identified spiders to family and insects to order, except Hymenoptera which we divided into Formicidae (ants) and other Hymenopterans. Although pitfall traps do not sample all invertebrates, they selectively sample the ground active component that makes up most of the lizard prey.

2.6.

Slope and altitude

We estimated slope at each 10 m point along each transect using a Leica TC600 theodolite, down-loaded into Liscad (Listech, 1999). Slope was measured as percent gradient. We determined altitude at the same points using a Trimble 4000 SE GPS unit.

2.7.

Soil depth and penetrability

We surveyed soil depth in August 2000 when winter rains had made the soil pliable. At 10 m intervals along each transect, using a 1 m long auger with a 5 cm diameter by 13 cm long bit, we determined maximum penetrable soil depth when either rock was struck or the soil became too hard. If neither occurred at the auger limit, we recorded a soil depth of 1 m. We extracted soil cores to describe the soil horizon and to measure horizon depth, then replaced the cores. We measured surface penetrability at each sampling point using a Soil test CL-700 pocket penetrometer.

2.8. Independent analyses: vegetation, spider burrow depth and invertebrates We performed classification and ordination analyses on each of three multivariate data sets. For vegetation, the variables were the presence or absence of each plant taxon within each of the 151 sampling quadrats. For burrow depths we used the density of burrows in each depth class (log(x + 1) transformed) in each of the 24 sampling units. The profile association measure, which takes into account the relationship between depth classes was calculated prior to classification and ordination (Faith et al., 1985). For invertebrates the variables were abundance of each taxon in each of 52 pitfall trap samples.

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We calculated the Bray–Curtis (Czekanowski) association measure (Belbin, 1993) for presence/absence data for vegetation, and for raw invertebrate abundance. For all three analyses, samples were classified using the flexible UPGMA (unweighted-pair groups method) procedure in PATN (Belbin, 1993). UPGMA is an hierarchical agglomerative technique where all samples begin as single objects and are agglomerated into larger clusters based on similarities. Default values were used for all settings in UPGMA (with the space distortion parameter b = 0.1) as recommended by Belbin (1993). Samples were also ordinated using semi-strong hybrid multidimensional scaling (SSH) in the PATN software package (Belbin, 1993). Ordination takes a complex data set and produces a graphical summary of the data in a specified number of dimensions. Ordination techniques arrange samples in multidimensional space such that points that are close together correspond to samples similar in composition of the measured variables (Jongman et al., 1995). We chose three dimensions and conducted 500 random starts to reduce the chance of the algorithm being trapped in local optima (Faith, 1990). We chose the ratio-ordinal cut value after examination of the histogram of association measures, as described in Belbin (1993). If the stress of the ordination was greater than 0.15 (a ‘‘fair’’ result (Belbin, 1993)) we increased the number of dimensions and repeated the ordination until the stress was lower than 0.15 or the addition of extra dimensions did little to reduce the stress. For each of the three data sets (vegetation, burrow depth, invertebrates) we used the Principal Axis Correlation procedure (PCC) in PATN to examine the relationship between the ordination and other environmental variables. PCC is a multiple-linear regression program designed to determine how well a set of attributes can be fitted to an ordination space (Belbin, 1993). Attributes subject to PCC were ‘‘lizard area’’ (ranked: inhabited, 1; marginal, 2 and uninhabited, 3), longitude, latitude, slope, altitude, soil penetrability (as a ranked measure: 1, 64.75 kg/cm2 and 2, >4.75 kg/cm2), A horizon depth, maximum penetrable soil depth, aerial cover and ground cover. For the PCC analysis of burrow depth, we used mean habitat variables for each ‘‘lizard area’’ (except for penetrability where we used the median value). We tested the PCC correlation coefficients for statistical significance using Monte Carlo Attributes to Ordination (MCOA) in PATN over 100 randomisations (Faith and Norris, 1989). As 10 correlations were carried out at the same time, the significance level of the test was adjusted using the Bonferroni procedure to a new significance level of (0.05/10 =) 0.005. However due to the size of the data sets used, PATN could not calculate more than 100 MCAO randomisations, with 0.01 the lowest significance level available. Thus the significance level was set to 0.01 and only correlations with an r value greater than 0.50 were evaluated.

2.9.

Suitability of Mokota

We established two 200 m transects at Mokota in an area of high spider burrow density. We sampled environmental, vegetation and spider burrow data along these two transects (22 quadrats), and sampled invertebrates along one transect, as

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described above. The three SSH ordinations of vegetation, spider burrow depth and invertebrates were repeated with the addition of samples from Mokota to determine how these samples related to those from sites with lizards. ANOSIM analysis was also repeated with the addition of samples from Mokota.

3.

Results

3.1.

Vegetation

We recorded 66 plant taxa (listed in Souter, 2003) from the 151 quadrats sampled at the four lizard occupied sites. Thirty-two were endemic, 31 exotic, and three could not be identified. Sixteen of the 66 taxa were grasses, 47 were forbs, herbs or shrubs, two were lilies and one was a geophyte. The predominant endemics were spear grasses (Austrostipa spp.), identified in 133 quadrats, wallaby grasses (Danthonia spp., 101 quadrats), wingless bluebush (Maireana enchylaenoides, 74 quadrats) and brush wire-grass (Aristida behriana, 74 quadrats). The most common exotic species were thread iris (Gynandriris setifolia) and onion grass (Romulearosea, each in 131 quadrats), wild oats (Avena barbata, 112 quadrats), rats tail fescue (Vulpia myuros f. myuros, 111 quadrats) and storks bills (Erodium botrys and E. brachycarpum, 98 quadrats). Mean ground cover by vegetation varied from 62% to 74% among sites. Mean aerial vegetation cover varied from 5% at Terowie to 30% at Hallett. UPGMA classification of the vegetation samples produced seven clusters (Table 1). There was some delineation among groups according to ‘‘lizard area’’. Group A and group E were dominated by uninhabited area samples, whilst group C was dominated by marginal and uninhabited area samples. There was no tendency for inhabited area samples to dominate any of the groups. Sample groups split largely according to site. Groups A and C had 39 of the 44 Hallett samples, Group B had 19 of the 22 samples from Hallett trees, Groups D and E contained 42 of the 47 Terowie samples, and group F had 19 of the 20 School site samples. SSH ordination of the vegetation data yielded a four dimensional model (stress = 0.145). Inhabited and marginal area samples were spread evenly through the centre of the ordination space (Fig. 2). Although one group of uninhabited area samples formed a distinct cluster at the positive end of Axis 2 (Fig. 2a, d, and e), other uninhabited area samples were spread across the ordination space. There was no clear separation of samples according to ‘‘lizard area’’. Samples formed stronger clustering according to site than according to ‘‘lizard area’’ along all SSH Axes (Fig. 3). PCC indicated significant correlations in the analysis of vegetation data, for nine of the 10 ‘‘lizard area’’/environmental parameters (Table 2). Whilst the correlations for ‘‘lizard area’’, slope, penetrability, maximum soil depth and ground cover were all significant, they all had r values less than 0.50. Vectors showed a strong geographical gradient (longitude, latitude) which followed the separation of sample groupings (Fig. 3). The altitude vector followed a gradient of samples from lower altitude sites, such as the School Site, to higher altitude sites such as Hallett. Aerial vegetation cover closely followed this gradient and was most likely a reflection of the geographical gradient as the

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Table 1 – Results of flexible UPGMA groupings by ‘‘lizard area’’ and site for vegetation and invertebrates, and by ‘‘lizard area’’, site and survey season for burrow depth Vegetation Group

Lizard area

Burrow depth Site

Group

Lizard area

Invertebrates

Site

Season

Group

Lizard area

Site

A

4 Inhabited 1 Marginal 26 Uninhabited

30 Hallett 1 Terowie

A

1 Inhabited 1 Marginal

2 Hallett

2 Autumn

A

8 Inhabited 5 Marginal 6 Uninhabited

14 School 1 Hallett 4 Hallett trees

B

10 Inhabited 2 Marginal 10 Uninhabited

1 School 2 Hallett 19 Hallett trees

B

1 Inhabited

1 School

1 Autumn

B

2 Inhabited 1 Marginal 1 Uninhabited

1 School 3 Hallett trees

C

1 Inhabited 3 Marginal 9 Uninhabited

9 Hallett 1 Hallett trees 3 Terowie

C

1 Inhabited 1 Marginal

2 Hallett trees

1 Autumn 1 Spring

C

5 Inhabited 4 Marginal 5 Uninhabited

5 5 3 1

D

19 Inhabited 8 Marginal 10 Uninhabited

1 Hallett 36 Terowie

D

1 Inhabited

1 Hallett

1 Spring

D

1 Marginal 4 Uninhabited

5 Hallett

E

7 Uninhabited

1 Hallett trees 6 Terowie

E

2 Inhabited 5 Marginal 8 Uninhabited

5 2 3 5

School Hallett Hallett trees Terowie

7 Autumn 8 Spring

E

2 Inhabited 2 Marginal 2 Uninhabited

6 Terowie

F

20 Inhabited 9 Marginal 11 Uninhabited 1 Marginal

39 School 1 Hallett trees

F

2 Inhabited 1 Marginal

1 Hallett 1 Hallett trees 1 Terowie

1 Autumn 2 Spring

F

1 Marginal 3 Uninhabited

4 Terowie

G

School Hallett Hallett trees Terowie

1 Terowie

Note that the letter designating each group does not imply equivalence across the three measured sets of variables.

vector consistently pointed towards the more densely covered Hallett samples.

3.2.

samples according to site. This is best seen along Axis 1 where site groups increased in altitude from the School Site on the left to Hallett on the right (Fig. 4d and e).

Burrow depth 3.3.

UPGMA classification of spider hole depth samples indicated six groups with strong associations with ‘‘lizard area’’ (Table 1). The six groups formed into two major groupings (A, B, C, D) (E, F). The smaller grouping (A–D) contained inhabited and marginal area samples from Hallett, Hallett trees and the School site. The larger grouping (E–F) included samples from all sites. All uninhabited area samples were found within one group (E). Some samples from the same site and ‘‘lizard area’’ were found in different clusters in different seasons, but there were no broad grouping according to season. SSH ordination of the burrow depth data yielded a three dimensional solution (stress 0.175). This stress is above that recommended by Belbin (0.15), but increasing to four dimensions did not reduce the stress (0.175). All uninhabited area samples formed a group in the centre of the ordination space, whilst marginal area samples tended to be more spread out (Fig. 4a–c). The majority of inhabited area samples were widely dispersed through the ordination space but mostly separated along Axis 3 from the marginal and uninhabited area samples (Fig. 4c). Samples from Terowie and the School site tended to cluster strongly in the centre of the ordination whilst samples from the remaining two sites were more dispersed (Fig. 4d–f). The majority of the disparate inhabited area samples were from Hallett, Hallett Trees and the School Site. PCC indicated a significant correlation of the ordination with altitude (Table 2). The altitude vector delineated groups of

Invertebrates

We identified 21 invertebrate taxonomic groups from the four sites. Ants were the most common, found in all of the 52 pitfall traps. Other common groups included flies (Diptera, 45 traps), wolf spiders (Lycosidae, 35 traps) and bees and wasps (other Hymenoptera, 45 traps). Locusts (Orthoptera) were the most abundant group. We collected 7461 of them, the majority (7435) from Terowie. Ants were the next most abundant (1068 specimens), followed by flies (183 specimens). UPGMA classification identified six major groups at the time of sampling (Table 1). The first division of the dendrogram (groups E–F) separated 10 of the 11 Terowie samples from all other samples. Within the second, larger division (groups A–D) there was little clustering according to either ‘‘lizard area’’ or site, although group A contained 14 of the 20 samples from the School site. Each group contained samples from inhabited or marginal areas and samples from uninhabited areas. SSH ordination produced a two dimensional model (stress = 0.090). There was some aggregation of inhabited area samples in the centre of the ordination plot, but samples did not form distinct groups according to ‘‘lizard area’’ at the time of sampling (Fig. 5a). Samples did form site specific groups (Fig. 5b). The Terowie samples formed the most distinct group, well separated from the other sites. This difference may not have been sustained if we had sampled at other

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Acov

-1

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Fig. 2 – SSH ordination plots of natural grassland vegetation samples displayed according to ‘‘lizard area’’, on axes (a) 1 vs 2, (b) 1 vs 3, (c) 1 vs 4, (d) 2 vs 3, (e) 2 vs 4 and (f) 3 vs 4. Closed circles = inhabited areas; open squares = marginal areas; open diamonds = uninhabited areas. PCC vectors: Lo = longitude; La = latitude; Alt = altitude; Acov = aerial cover.

times. Four of the 10 ‘‘lizard area’’/environmental parameters were significantly correlated with the ordination plot according to the principle axis correlation procedure although only

longitude, latitude and ground cover had correlation coefficients greater than 0.5 (Table 2). Both the geographical gradient and ground cover gradient delineated sample groups.

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-2 -2

-1

0

Lo

Acov

Acov

-1

0

1

2

Axis 1

-2 -2

-1

0

1

2

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Fig. 3 – SSH ordination plots of natural grassland vegetation samples displayed according to site, on axes (a) 1 vs 2, (b) 1 vs 3, (c) 1 vs 4, (d) 2 vs 3, (e) 2 vs 4 and (f) 3 vs 4. Open triangle = School site; closed circle = Hallett site; open diamond = Hallett Trees; cross = Terowie. PCC vectors as in Fig 2.

3.4.

Suitability of Mokota

We recorded 50 plant taxa from the 22 sampling quadrats in Mokota, 22 endemic, 24 exotic and four could not be identi-

fied. Twelve were grasses, 34 were either forbs, herbs or shrubs, two were lilies, one was a geophyte and one was a sedge. The predominant native species were iron grass (Lomandra spp., 17 quadrats), wallaby grass (Danthonia spp., 16

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Table 2 – PCC correlation coefficients (r) and MCAO significance of ‘‘lizard area’’/environmental parameters correlated against the SSH ordination of vegetation, burrow depth and invertebrate samples (* indicates a significant correlation according to 100 Monte Carlo randomisations at the p < 0.05 level, ** indicates a significant correlation at the p < 0.01 level) Parameter Lizard area (ranked) Longitude Latitude Altitude Slope Penetrability (categorical) A horizon depth Maximum soil depth Aerial vegetation cover Ground vegetation cover

Vegetation r

Burrow depth r

Invertebrates r

0.439** 0.800** 0.851** 0.843** 0.399** 0.332** 0.190 0.279** 0.600** 0.342**

0.467 0.330 0.239 0.780** 0.302 0.455 0.304 0.519 0.424 0.335

0.117 0.762** 0.828** 0.460** 0.314 0.129 0.152 0.219 0.406 0.504**

quadrats), brush wire-grass (A. behriana, 16 quadrats) and wingless bluebush (M. enchylaenoides, 13 quadrats). The most common exotic species were thread iris (G. setifolia, 20 quadrats), onion grass (R. rosea, 20 quadrats), rats tail fescue (V. myuros f. myuros, 20 quadrats), silvery hair grass (Aira sp., 19 quadrats) and hop clover (Trifolium campestre, 38 quadrats). Mean (±SE) aerial vegetation cover was 50 ± 5%, and mean (±SE) ground vegetation cover was 78 ± 3%, both higher than the four sites with lizards. SSH ordination of vegetation samples yielded a four dimensional model (stress = 0.154). A separation between a large group of uninhabited area samples from the loose aggregation of inhabited and marginal area samples was evident along Axis 1 (Fig. 6a). Samples from Mokota were located in this group and were clustered with samples from Hallett (Fig. 6b). We measured 90 spider burrows from Mokota in autumn 2000, the deepest of which was 22 cm (Table 3), and a further 88 in spring 2000, the deepest being 28 cm. Only three out of 178 burrows were deeper than the mean lizard inhabited depth in occupied sites. SSH ordination of spider burrow depth class samples yielded a four dimensional model (stress = 0.174). The pattern of samples when displayed according to ‘‘lizard area’’ was the same as reported for the ordination without Mokota (Fig. 7a). The samples from Mokota were located in the group of uninhabited area samples at the centre of the ordination (Fig. 7b). Twelve broad taxonomic groups of invertebrates were collected from Mokota. The majority of invertebrates collected were ants (Hymenoptera) and flies (Diptera). SSH ordination of invertebrate abundance samples yielded a four dimensional model (stress = 0.121). Mokota samples were located to the bottom left of the ordination plot (Fig. 8).

4.

Discussion

4.1.

Habitat requirements

Spider hole depth was the major factor associated with areas inhabited by lizards at each site. Previous research (Milne and Bull, 2000) showed the lizards preferred deep over shallow burrows, presumably to reduce predation risk from snakes and avian raptors, to provide cooler refuges

over summer, and to avoid grassfires. Pygmy bluetongue lizards were found in areas of natural grassland with a supply of suitably deep spider burrows. This association between lizard inhabited areas and spider hole depth resulted primarily because uninhabited areas had few or no holes in the depth classes 23.4–30 cm and >30 cm, those most suitable for lizards. Marginal areas for lizards had more holes in these classes, whilst inhabited areas had still more. This trend was consistent across all four sites where lizards were found, and where 25–50% of deeper holes were occupied by lizards (Souter, 2003). The study supports Milne’s (1999) observation that lizards are reliant on a supply of deep spider burrows for survival, and shows that lizards are confined to areas that contain an adequate supply of suitable burrows. Some suitably deep burrows that were recorded as unoccupied may have been recently abandoned by their previous occupant. For example male pygmy bluetongues are known to move between burrows in spring searching for mates (Milne, 1999). Deep burrows may also be abandoned if the edges deteriorate (Souter, 2003), or if the occupant is taken by a predator. Alternatively low occupancy rate of suitable burrows may simply result from a low population density. The finding that not all suitable burrows were occupied, reflects similar results from Milne (1999). The analysis in the current study did not detect any specific association of grassland plants, or of ground dwelling invertebrates, that separated areas that were or were not inhabited by pygmy bluetongue lizards in local population sites. Furthermore, there were greater differences among occupied sites in plants and invertebrates, than there were between inhabited and uninhabited regions within sites. The lizards did not appear to rely on a particular community of natural grassland plants for suitable habitat. Each occupied site had its own, largely unique vegetation community and cover of aerial and ground vegetation. An affinity of vegetation samples according to soil type suggests that soil type may influence the vegetation community. Note that this analysis was restricted to grassland ecosystems, so we are not claiming that broader vegetation patterns are irrelevant to the distribution of the lizards. Although no specific vegetation association was found to be related to lizard occupancy within these grasslands,

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multivariate analysis identified a combination of plant taxa that was associated with a subset of uninhabited sites. This may suggest that we have identified a vegetation community

that did not support lizards. This trend resulted largely from the data from Hallett, a sparsely populated site with a majority of uninhabited area samples, which also had a distinct

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Table 3 – Number of spider holes found in each depth class of significance for lizards at Mokota in autumn and spring 2000 0–12 cm Autumn Spring

57 56

12–23.3 cm 33 29

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vegetation community. The low number of inhabited samples from this site may have biased the analysis. Inhabited samples from Hallett were more similar to the uninhabited samples from Hallett than inhabited samples from other sites. Areas inhabited by lizards were not associated with a specific ground dwelling invertebrate fauna, rather each site

had its own distinct community at the time of sampling. This suggests that the pygmy bluetongue lizard is not reliant on a particular invertebrate community as prey. This conclusion is not surprising given that the pygmy bluetongue is a generalist sit-and-wait predator (Milne, 1999). As a generalist it is able to consume a variety of prey and being a sit-andwait predator, and a reptile, it is likely to have very low energy requirements. It is unclear whether there is any competition for prey between the lizards and the spiders that build the burrows. Soil surface resistance, measured in this study as penetrability has been shown to be important in burrow site selection for some burrowing arachnids (Lamoral, 1978; Bradley, 1986; Polis and McCormick, 1986) but not others (Bradley, 1996). However soil penetrability was not found to be an important factor in describing lizard distribution.

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4.2.

Suitability of Mokota Conservation Park

The area of Mokota Conservation Park surveyed did not appear to provide suitable natural habitat for pygmy bluetongue lizards. This is primarily due to the lack of suitably deep spider burrows. Mokota is also most similar in environmental characteristics to Hallett, the site that consistently had the fewest lizards and large uninhabited areas. Mokota and Hallett share the same soil type and have similar vegetation communities. Thus the translocation of lizards to Mokota is not recommended, because lizards are unlikely to find suitable refuge burrows, and because Mokota has closer vegetation similarity to a site with a very low density of lizards, than to sites with denser lizard populations.

4.3.

Implications for conservation management

In the short-term, there are now no conservation reserves within the known geographical range of the pygmy bluetongue lizard that have a suitable combination of natural habitat requirements to support a viable lizard population. Mokota Conservation Park is the only suitably located reserve, and the results of this study suggest it is inappropriate for the lizard. A strategy that might sustain a lizard population at Mokota might involve the provision of artificial burrows, which lizards have been shown to accept (Milne et al., 2003). However maintaining those burrows would be a long-term and expensive commitment. An alternative strategy is to collaborate with private landholders to maintain existing population sites, as they have done over many years, to allow the

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persistence of populations. An important component of this collaboration will be to maintain conditions that encourage burrow construction by lycosid and mygalomorph spiders. A number of potential threats to the populations include ploughing (which would destroy the burrows), and overgrazing (where sheep may trample and destroy burrow entrances). Other studies of grazed lands in Australia have reported major impacts of sheep grazing on the soil dwelling invertebrate fauna (Abensperg-Traun et al., 1996). Pesticide spraying, grassfires, and poaching by collectors will still threaten individual populations, but additional holes can be added to augment population numbers (Souter et al., 2004). Ultimately this strategy depends on the goodwill of the local farming community and in the conservation managers providing informed advice about the consequences of different management options. Similar strategies involving landholder sooperation are already in place in other conservation programs (Kleijn and van Zuijlen, 2004). A longer-term strategy may be the purchase of appropriate privately owned land that supports native grasslands and an abundance of deep spider burrows, to set aside for the conservation of the pygmy bluetongue lizard. The persistence of this species ultimately depends on the persistence of the spiders that construct the burrows that they use. Like many endangered species conservation management for one taxon will be most effective if we can preserve all of the species in the ecosystem.

Acknowledgements This research was funded by a National Heritage Trust Grant (Project No. 6496). Research carried out in Mokota Conservation Park was conducted under NPWS permit number U24314 1. We thank Tim Milne, Sylvia Clarke, Tim Croft, Richard Williams, Plant Biodiversity Centre staff, Colin Rivers, Rob Couzner, Steve Fildes, Malcolm Wright and Duncan Mackay for advice and help. Chris and Jane Parker, Brian Brookes, Barry Clapp and John Agnew from the Burra Community School are thanked for allowing us access to their properties.

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