1

To cite this paper:

Simpkins, C.A., Shuker, J.D., Lollback, G.W., Castley, J.G. and Hero, J.M. (2014).

Environmental variables associated with the distribution and occupancy of habitat specialist

tadpoles in naturally acidic, oligotrophic waterbodies. Austral Ecology 39, 95-105.

2

Title

Environmental variables associated with the distribution and occupancy of habitat specialist

tadpoles in naturally acidic, oligotrophic waterbodies

Authors

Clay Alan SimpkinsAC

, Jonathan D. ShukerA, Gregory W. Lollback

A, James Guy Castley

B

and Jean-Marc HeroA

A

Environmental Futures Centre, School of Environment, Griffith University, Parklands

Drive, Gold Coast campus, QLD 4222, Australia

B

International Centre for Ecotourism Research, School of Environment, Griffith University,

Parklands Drive, Gold Coast campus, QLD 4222, Australia

C

Corresponding author; E-mail: claysimpkins@hotmail.com

Phone Number: +61 07 555 28140 / +61 424 26 7171

Abstract

Environmental factors play an integral role, either directly or indirectly, in structuring faunal

assemblages. Water chemistry, predation, hydroperiod and competition influence tadpole

assemblages within waterbodies. We surveyed aquatic predators, habitat refugia, water height

and water chemistry variables (pH, salinity and turbidity) at 37 waterbodies over an intensive 22

day field survey to determine which environmental factors influence the relative abundance and

occupancy of two habitat specialist anuran tadpole species in naturally acidic, oligotrophic

waterbodies within eastern Australian wallum communities. The majority of tadpoles found were

of Litoria olongburensis (wallum sedge frog) and Crinia tinnula (wallum froglet) species, both

habitat specialists that are associated with wallum waterbodies and listed as Vulnerable under the

IUCN Red List. Tadpoles of two other species (Litoria fallax (eastern sedge frog), and Litoria

cooloolensis (cooloola sedge frog)) were recorded from two waterbodies. Tadpoles of Litoria

gracilenta (graceful treefrog) were recorded from one waterbody. Relative abundance and

occupancy of L. olongburensis tadpoles were associated with pH and water depth. Additionally,

L. olongburensis tadpole relative abundance was negatively associated with turbidity. Waterbody

occupancy by C. tinnula tadpoles was negatively associated with predatory fish and water depth

and positively associated with turbidity. Variables associated with relative abundance of C.

tinnula tadpoles were inconclusive and further survey work is required to identify these

environmental factors. Our results show that the ecology of specialist and non-specialist tadpole

species associated with „unique‟ (e.g. wallum) waterbodies is complex and species specific, with

specialist species likely dominating unique habitats.

Key Words

tadpole assemblage, acidic waterbody, acid frog, Litoria olongburensis, Crinia tinnula, tadpole,

wallum

Introduction

Environmental factors play an integral role, either directly or indirectly, in structuring faunal

assemblages (Krebs 2009). Different species will have varying tolerances to environmental

factors (e.g. tadpole intolerance to varying levels of water pH (reviewed in Pierce 1985); coral

intolerance to increasing temperature (Berkelmans & Willis 1999); eel intolerance to increased

salinity (Schofield & Nico 2009)), with organisms occurring outside of their tolerance range

often resulting in death or deformities (Gosner & Black 1957; Sadinski & Dunson 1992;

Schofield & Nico 2009). Therefore, the niches in which a species occurs within a species

distributional range will be restricted to areas where environmental variables are within the

species tolerance limits.

Habitat specialists will often occur within a limited number of habitats while generalist species

will occur across several habitat types (Krebs 2009). The absence of habitat specialist species

from multiple habitats is likely a result of intolerance to specific environmental factors or

competition restricting the species to niches where factors are ideal for survival. For example, in

Japan the abundance of grassland specialist grasshopper species was found to be negatively

influenced by the exotic vegetation species Eragrostis curvula (Yoshioka et al. 2010). This

relationship was hypothesised as being attributed to a decline in food availability or suitable

habitat following the introduction of the exotic plant species (Yoshioka et al. 2010). In some

environments non-habitat specialist and habitat specialist species are able to coexist within the

same habitat (Brown 1996). Alternatively, habitat specialist species and non-habitat specialist

species may not co-occur or be restricted in their abundance or occupancy within the

environment.

Within the amphibian taxa there are several factors that can structure amphibian tadpole

assemblages, including: intolerances to water chemistry variables (Smith et al. 2007; Wells

2007), hydroperiod (Snodgrass et al. 2000; Baber et al. 2004; Moreira et al. 2010), predation

(Hero et al. 1998; Hero et al. 2001; Vonesh et al. 2009) and competition (Wiltshire & Bull 1977;

Twomey et al. 2008).

Water chemistry and hydroperiod can influence amphibian assemblages, with numerous studies

focusing on either water chemistry tolerance limits under laboratory conditions or water

chemistry variables influencing amphibian assemblages. These variables include pH/acidity

(reviewed in Freda 1986; Freda & Taylor 1992; Persson et al. 2007; Barth & Wilson 2010)

salinity (Christy & Dickman 2002; Chinathamby et al. 2006; Rios-López 2008) and turbidity, as

represented by suspended sediment (Knutson et al. 2004). Hydroperiod can structure the tadpole

assemblage by excluding amphibian species with long-lived tadpole stages, as these species can

fail to metamorphose before desiccation occurs from temporary waterbodies (Welborn et al.

1996). Additionally, aquatic predators have also been suggested as a primary factor structuring

tadpole assemblages (Heyer et al. 1975; Hero et al. 2001; Vonesh et al. 2009) with predation risk

being determined by a number of factors, including the tadpole‟s vulnerability to the predator

assemblage or ability to avoid predators (e.g.. via refugia usage) (Kopp et al. 2006; Saidapur et

al. 2009). Competition may also structure tadpole communities by altering rates of tadpole

growth and development (Wilbur 1987; Mokany & Shine 2002; Twomey et al. 2008), potentially

leading to prolonged exposure to aquatic predators.

We surveyed a „unique‟ habitat (wallum heathland communities) across the distributional

range of an amphibian habitat specialist species (Litoria olongburensis) in an attempt to

determine how environmental variables influenced tadpoles of habitat specialist and non-

habitat specialist amphibian species within this „unique‟ environment. For the purpose of this

study, wallum heathland will be defined as vegetation communities including Banksia

woodland, sedgeland, heathland and Melaleuca swamps (Hines et al 1999, Griffith et al.

2003) that contain soils that are often acidic (pH 3.4-5.4) and low in nitrogen (Griffith et al.

2008) and phosphorus (Groves 1981). Wallum communities provide ideal conditions in

determining how environmental factors are influencing habitat and non-habitat specialist

species due to the „unique‟ nature of the wallum communities (e.g. low pH and oligotrophic).

This study will aid in the understanding of how environmental factors are influencing the

distribution of habitat and non-habitat specialist amphibian species within „unique‟

environments whilst also providing insight into how environmental variables are influencing

amphibian species within naturally acidic, oligotrophic environments.

Methods

Study Site Selection and Sampling Design

Nine national parks and reserves where the habitat specialist species L. olongburensis are known

to occur were selected between the Great Sandy National Park, Queensland (26.014° S,

153.024° E), and Wooli, New South Wales (29.853° S, 153.263° E), in coastal wallum

vegetation communities on mainland Australia. Within this area there are three other habitat

specialist species (Crinia tinnula, Litoria cooloolensis and Litoria freycineti) and several non-

habitat specialist species (including, but not limited to, Litoria fallax, Litoria gracilenta and

Limnodynastes peronii). Eleven „survey transects‟ were established across the national parks and

reserves (one at each national park or reserve for all areas except Bundjalung National Park and

Yuraygir National Park which contained two survey transect lines) to select waterbodies (Figure

1). With the exception of areas where no national parks or protected areas occur (e.g. major

cities), the survey transects were established evenly across the known distributional range of L.

olongburensis (Meyer et al. 2006) (Figure 1). Wallum vegetation communities often consist of

several vegetation habitats (Ingram & Corben 1975, Hines et al. 1999). Therefore, survey

transects were placed using Queensland regional ecosystem and New South Wales vegetation

G.I.S. layers to ensure all vegetation communities were intersected at least once from one of the

survey transects. Additionally, historical locality records of L. olongburensis were used to ensure

each survey transect intersected a minimum of one waterbody where L. olongburensis had been

recorded. Survey transects spanned from one edge of the protected area to the opposite edge and,

therefore, varied in length. Anthropogenic disturbances (e.g. urban development) outside of

national parks may influence environmental factors like water depth/hydrology, nutrient input or

water chemistry (Meyer et al. 2006). Therefore, national parks and reserves were selected for

surveying in an attempt to minimize the influence of anthropogenic disturbances, which are

apparent throughout the range of L. olongburensis (Meyer et al. 2006).

Survey transects were then uploaded onto a Trimble® Juno™ SC unit for ground-based surveys

targeting waterbodies. A maximum of two days was allowed for surveying of each survey

transect. Waterbodies were sampled if the survey transect intersected the waterbody or if the

waterbody could be seen from the survey transect. Each waterbody had a „waterbody transect‟

that was perpendicular to the edge of the waterbody, and went from one edge of the waterbody to

the opposite edge. Where practical, the waterbody transect was placed through the deepest

section of the waterbody. Waterbody transects varied in length depending on the waterbody size,

with the shortest and longest waterbody transect being 48 m and 200 m, respectively.

Waterbodies that contained heterogenous vegetation and where transect length was greater than

50 m had waterbody transects placed in each vegetation type and, to reduce edge effects and

keep transects independent, a minimum of ten meters from the previous vegetation type. This

occurred at waterbodies in Bundjalung National Park and Yuraygir National Park.

All waterbody transects were surveyed over a 22 day period, in March 2010, to minimise the

influence of temporal variation. This time of year was chosen as peak activity calling for L.

olongburensis was predicted to occur in February and March (Hopkins 2003) and it was assumed

that tadpoles would be present from eggs deposited in February. It must be acknowledged that,

due to the timing of the survey, winter breeding species may have been missed during this study.

Dipnetting of aquatic fauna and measurements of water and vegetation characteristics were

undertaken at five evenly distributed sampling points along the waterbody transects. Sampling

point placement began 5 m from the waterbody transect edge. Diurnal dipnetting was conducted

using a circular net, with an aperture of approximately 30 cm in diameter and with mesh size less

than 0.5 mm to capture tadpoles and aquatic predators. In an attempt to sample the entire water

column three water column levels (bottom, middle and top) were dipnetted, with five sweeps at

each level. To ensure no recaptures occurred animals were not released until dip-netting had

been completed at each sampling point. It was assumed sampling points were sufficiently

spaced (minimum distance between sampling points was 9.5 m) to prevent recaptures between

successive sampling points.

Waterbody pH, salinity, turbidity, depth (as a representation of hydroperiod) and percent

vegetation cover were also measured. A handheld TPS Aqua-CPA Conductivity-TDS-pH-

Temperature Meter (version 1.2) was used to measure pH and salinity. Calibration occurred

between survey transects to ensure accurate measurements. A 2 point calibration curve was used

at 4.00 and 6.88 for pH calibration, while 2.00 ppt and 0.00 ppt were used for salinity calibration.

Turbidity was measured by placing a black and white marker into the bottom of a transparent

turbidity tube. Water was then added to the turbidity tube until the black and white marker could

not be seen when looking directly down into the tube. The height of the water within the

turbidity tube was then correlated to the turbidity value for the water height. Water for turbidity

measurements was sampled approximately 10cm below the waters surface. Water was sampled

as close to the surface as possible when water depth fell below 10cm. Water depth was measured

to the nearest centimetre in the centre of each sampling point. We compare waterbody size using

the waterbody transect lengths due to inadequate detail in available GIS layers, the large size of

the waterbodies and dense vegetation making it impractical to measure waterbody area during

the surveys.

Aquatic sedge and herb cover were visually estimated using an estimated 5m x 5m quadrat

centered on each sampling point. Percent cover was combined for each waterbody transect and

divided by the number of sampling points (5) to give representation of percent cover for each

waterbody. Aquatic sedge and herb cover was defined as the percentage of the quadrates

occupied by the vertical projection of foliage and were used as an estimate of habitat complexity.

Rainfall data between December 2009 – March 2010 were obtained from the Australian Bureau

of Meteorology Weather Stations located at Double Island Point Lighthouse (Weather Station #

040068), Beerburrum Forest Reserve (Weather Station # 040284), Byron Bay Cape Byron

Lighthouse (Weather Station # 058009) and Wooli Beach (Weather Station # 058080). The

rainfall averages for each month were obtained for all years that the weather stations had been in

service.

Statistical Analysis

Water depth, water chemistry factors and all species of aquatic predators obtained from

dipnetting were combined for each waterbody transect. One waterbody located next to an

estuarine creek system contained an extreme mean salinity level of 420 parts per million and had

recently been disturbed by fire, and was removed from the analysis. Mean values of water depth

and water chemistry parameters were estimated from the values at the five sampling points in

each waterbody. Tadpole species were considered present from a waterbody transect if a species

was recorded from one of the five sampling points. Tadpole numbers were added together for

each waterbody transect for each species for relative abundance analysis. Tadpoles used for

analysis were of Gosner Stage 25 or later due to difficulties identifying tadpoles in earlier Gosner

Stages. Fish from the species Gambusia holbrooki (mosquito fish), Rhadinocentrus ornatus

(ornate rainbow fish), Hypseleotris galii (firetail gudgeon) and Nannoperca oxleyana (oxleyan

pygmy perch) were grouped into the „predatory fish‟ category to estimate relative fish abundance

for each waterbody transect. Predatory aquatic invertebrates (Belostomatidae (giant water bugs)

and Aeshnidae (dragonfly families)) were excluded from analysis as they were only detected in

two waterbodies.

Spearman Rank Correlation tests performed in IBM SPSS Statistics Version 19 (SPSS, Inc.,

2009, Chicago) showed no pairs of variables had correlation coefficient values greater than or

equal to 0.7 (Table 1), the threshold value considered to represent high correlation in other

studies (Babbitt et al. 2003; Garden et al. 2007). Accordingly, all variables were included in

analyses.

Models focusing on the influence of environmental variables on abundance and occupancy of L.

olongburensis and C. tinnula were constructed a priori. Generalised linear models were used to

assess the importance of environmental variables on relative abundance models (using a Poisson

link function) and occupancy models (using a binomial link function). We used a generalized

„rule of thumb‟ of n/3 (where n = number of waterbodies sampled) to obtain the maximum

number of predictor variables to use in each model (Crawley 2007). Predictor variables included

in L. olongburensis and C. tinnula abundance and occupancy models included vegetation cover,

predatory fish abundance, pH, salinity, turbidity and depth. Predation levels can be influenced by

the availability of aquatic refuge (e.g. vegetation cover) (Babbitt & Tanner 1998; Kopp et al.

2006). Additionally, it has been proposed that water clarity may impact on predation success

(Spieler 2003). Therefore, interaction models between these three variables were included in

analysis. Tadpoles of other species were excluded from the models due to the low number of

waterbodies in which they were detected. Factors that relate to physiological tolerances often

produce a unimodal distribution (e.g. Austin 1999). To detect any unimodal responses, some

models included quadratic terms for pH and depth.

Generalised linear models using Akaike‟s Information Criterion (AICc to adjust for small sample

size (n =37)) were performed in the freeware statistical package R (R Core Development Team,

2011) to determine model ranking and selection. The „best‟ model was the model with the lowest

AICc value (Burnham & Anderson 2002). To determine the ranking of the models, Δi values

were calculated, where higher Δi values indicated models with poorer fit to the given data

(Burnham & Anderson 2002; Johnson & Omland 2004). If a model had a Δi ≤ 2, then there was

considerable evidence that the model could be the “best” model, given the data (Johnson &

Omland 2004). If a model had a Δi 2-4 then there was considered to be moderate evidence that

the model could be the “best” model, given the data. Akaike Weights (wi) enable greater

interpretation of the relative likelihood of a model given the data (Burnham & Anderson 2002;

Johnson & Omland 2004). Therefore, each model was assigned a wi, which was used to

determine the “probability that model i is the best model for the observed data, given the

candidate set of models” (Johnson & Omland 2004). The closer the wi was to 1, the closer the

model for the given data (Burnham & Anderson 2002). To determine the relative importance of

variables within models where Δi < 4 the wi values were summed from all models where Δi < 4

and where the variable of interest occurred (Grueber et al. 2011). The closer the variable of

interest was to 1 the higher the importance of the variable. Twenty models were chosen to model

L. olongburensis and C. tinnula tadpole relative abundance and occupancy.

Quantile regressions (Cade & Noon 2003; Lancaster & Belyea 2006) were performed in the

statistical program R (R Core Development Team, 2011) using the quantreg package (version

4.62) in an attempt to determine the optimal range for factors having a unimodal distribution that

were related to L. olongburensis tadpole relative abundance. The number of waterbodies sampled

limited the upper and lower quantiles that could be fitted to the data. Therefore, the 0.85 quantile

was used to predict the upper limits for relative abundance with 95% confidence intervals. The

0.65 quantile was also plotted to determine shape consistency with the 0.85 quantile. A Gaussian

bell-shaped response was assumed for each model.

Results

Waterbody Characteristics and Rainfall Conditions

A total of 37 waterbody transects were surveyed. Five tadpole species were encountered

throughout the survey: L. olongburensis, C. tinnula, L. gracilenta, L. fallax and

L. cooloolensis. Waterbody transects surveyed had mean pH ranging between 3.00 – 5.04,

salinity ranging between 3.32-108.3 parts per million (ppm), turbidity ranging between 0 – 40

nephelometric turbidity units (NTU), mean water depth ranging between 6.2 – 36.2 cm and

relative fish abundance between 0 – 67. Tadpoles of L. olongburensis were recorded from 11

waterbodies where the maximum number of individual L. olongburensis tadpoles caught for an

individual waterbody transect was nine. Waterbodies containing L. olongburensis tadpoles had a

mean pH ranging from 3.40 – 4.34, salinity between 35.82 - 93.72 ppm, turbidity between 0 –

20.4 NTU, mean water depth ranging between 10.2 – 30 cm and relative fish abundance between

0 – 4 individuals. Tadpoles of C. tinnula were recorded from 14 waterbody transects where the

maximum number of individual C. tinnula tadpoles caught for an individual waterbody transect

was 44. Waterbodies with C. tinnula tadpoles had mean pH ranging between 3.35 – 4.84, salinity

between 35.82 – 99.10 ppm, turbidity between 0 - 26.2 NTU, mean water depth ranging between

7.2 – 27.4 cm and relative fish abundance between 0 – 4 individuals. Gambusia holbrooki

occurred at four waterbody transects. Tadpoles of L. olongburensis and C. tinnula were not

recorded in waterbodies with G. holbrooki. Both L. fallax and L. cooloolensis were recorded

from two separate waterbodies. Litoria gracilenta occurred at one waterbody. Tadpoles of L.

fallax were not recorded with L. cooloolensis, L. olongburensis or C. tinnula tadpoles. Tadpoles

of L. cooloolensis and C. tinnula were found co-occurring in three waterbodies.

The average rainfall for the months between December 2009 – March 2010 differed among

weather stations, with above average rainfall being recorded for the Beerburrum Forest Reserve

(+219mm) and Byron Bay Cape Byron Lighthouse (+202.2mm) weather stations and below

average rainfall for the Double Island Point Lighthouse (-210.9mm) and Wooli Beach (-87.9mm)

weather stations.

Abundance

Three models for L. olongburensis relative abundance had a Δi < 2. The weighting of the best

models for L. olongburensis tadpole relative abundance was 99.8%, suggesting that the other

models compared poorly (Table 2). The best model contained pH, depth and turbidity, with all

variables having strong relative importance (Table 3). Turbidity was the only factor within the

best model that was negatively associated with the relative abundance of L. olongburensis

tadpoles (Table 2). The other two models contained all variables measured. Other variables

within these models had lower relative variable importance when compared with pH, depth and

turbidity (Table 3).

Unlike L. olongburensis, C. tinnula tadpole relative abundance was best explained (weighting of

100%), by the model with all predictor factors (Table 2).

Occupancy

Two models for L. olongburensis tadpole occupancy had a Δi < 2 while only one model had a Δi

between 2-4. The combined weighting of the models with a Δi < 2 for L. olongburensis

occupancy was 77.3%, suggesting that the other models compared poorly given the data. Models

with a Δi < 2 contained pH and depth, with pH having the highest relative variable importance

(Table 3). Both variables were unimodel in their influence on L. olongburensis occupancy (Table

2).

Three models for C. tinnula tadpole occupancy had a Δi < 2 while seven models had a Δi

between 2-4. The combined weighting for models with a Δi < 2 for C. tinnula tadpole occupancy

was 54.7%. These best models contained predatory fish, turbidity and depth. Predatory fish and

depth had the highest relative variable importance when compared with other variables (Table 3).

Predatory fish and depth were negatively associated with C. tinnula tadpole occupancy, while

turbidity was positively associated with C. tinnula tadpole occupancy (Table 2).

Optimal Range

Determining an optimal range for factors influencing L. olongburensis tadpole abundance is

difficult due to large 95% confidence intervals in the maximum (0.85 quantile) abundance

models. However, quantile regressions showed a unimodal response between L. olongburensis

tadpole relative abundance and water depth and pH (Figure 2 and 3). Confidence intervals for pH

are large for the 0.85 quantile and are lowest towards the lower and upper pH ranges.

Additionally, confidence intervals are smallest towards the deeper water depths for the 0.85

quantile and largest towards shallower water depths.

The Gaussian distribution fits well for the 0.85 and 0.65 pH quantiles, with p < 0.001 for all co-

efficient values. For water depth quantiles only Depth and Depth2

for the 0.85 quantile fitted a

Gaussian distribution, with co-efficient values having p < 0.05 (Table 4).

Discussion

We present the first quantitative assessment of environmental variables influencing the relative

abundance and occupancy across the entire distributional range of a habitat specialist tadpole

species. As expected, waterbodies within this range were dominated by tadpoles of two habitat

specialist species (L. olongburensis and C. tinnula), with the abundance and occupancy of these

species associated with either water chemistry variables or predatory fish. Additionally, tadpoles

of the non-habitat specialist species L. fallax and the habitat specialist species L. cooloolensis

were recorded from two independent waterbodies. The low occupancy recorded for L.

cooloolensis is likely due to surveys only being conducted within a proportion of L. cooloolensis

distributional range. Furthermore, the low accounts of the non-habitat specialist species L.

gracilenta could be explained by low detection probabilities of encountering this species or

intolerance to an environmental variable within the unique surveyed habitat. The low occupancy

of L. fallax tadpoles is likely explained by this species inability to successfully metamorphose in

acidic water (pH = 3.5) (Meyer 2004).

Variables influencing abundance and occupancy of two habitat specialist species

Waterbody pH and water depth were associated with the relative abundance and occupancy of L.

olongburensis tadpoles. While L. olongburensis tadpole pH tolerance has never been tested, two

other „acid‟ frogs (C. tinnula and L. cooloolensis) have successfully metamorphosed when

exposed to pH water of 3.5, 4.5 and 6.5 (Meyer 2004). It is expected that L. olongburensis

tadpoles would also be able to metamorphose when exposed to these pH levels as tadpoles of L.

olongburensis co-occurred with C. tinnula and L. cooloolensis tadpoles within this study.

Large confidence intervals and the quantile ranges extending over a majority of the response

variables tested (pH and depth) give a guide to the optimal range for L. olongburensis tadpole

abundance. Despite large confidence intervals, the 0.85 and 0.65 quantile for pH showed a

unimodal response, suggesting that pH is influencing L. olongburensis as a quadratic, nonlinear

response. The unimodal relationship observed suggests that competition, predation or a

physiological intolerance is limiting this species at the upper or lower bounds of the unimodal

relationship. Acidity intolerance has been recorded for numerous amphibian species, where death

occurs to tadpoles outside of their pH tolerance range (reviews in Pierce 1985 & Freda 1986;

Meyer 2004). Therefore, physiological intolerance of L. olongburensis tadpoles to pH levels

occurring at the lower limits of the pH range within this study is probable. However,

experimental tests on L. olongburensis tadpole tolerance using pH levels lower than those used

by Meyer (2004) are required to confirm this conclusion. Towards the upper limits of the pH

quantile regressions it is possible that competition from „non-acid‟ frog species (e.g. L. fallax)

may influence the abundance of L. olongburensis tadpoles. This is difficult to confirm in this

study as only two waterbodies recorded a potential competitor (L. fallax) and pH within

waterbodies surveyed did not exceed 5.21. It is unlikely that „non-acid‟ frog species would

successfully compete and exclude or reduce L. olongburensis tadpoles in the lower pH ranges as

„non-acid‟ frog tadpoles (e.g, L. fallax and Crinia parinsignifera) have 100% hatchling mortality

or 0% hatching success when exposed to pH levels of 3.5 (Meyer 2004).

The 0.85 quantile for water depth showed a unimodal response, suggesting that water depth is

influencing L. olongburensis in a quadratic and not a linear response. Quantile regression models

for L. olongburensis tadpoles and water depth showed that tadpole relative abundance will

increase with water depth until water depth reaches approximately 22cm, after which tadpole

relative abundance will decrease. Water depth was used as an indicator of hydroperiod, with

deeper waterbodies representing waterbodies with longer hydroperiods. The reduced number of

L. olongburensis tadpoles in deeper waterbodies is likely explained by increased predatory fish,

which need a more permanent hydroperiod for population persistence, or a change in the

predator assemblage. Predatory fish were not in the best models explaining L. olongburensis

tadpole abundance or occupancy. However, predatory fish had the highest correlation coefficient

with depth when compared with other measured variables. Additionally, predatory fish only co-

occurred with L. olongburensis tadpoles in two waterbodies, suggesting coexistence between

predatory fish and L. olongburensis tadpoles is low or abundance. Aquatic fauna assemblages

(Welborn et al. 1996; Babbitt et al. 2003; Jocqué et al. 2007; Richter-Boix et al. 2007; Fernandes

et al. 2010) and an increase in predator size (Welborn et al. 1996; Richter-Boix et al. 2007) are

associated with increased hydroperiod or water depth. Therefore, an increase in

hydroperiod/depth may result in the number of L. olongburensis tadpoles being reduced due to

inadequate anti-predator strategies to larger predators (e.g. tadpole size may be an inadequate

anti-predator strategy to predators with large mouth-gapes) occurring in deeper sections of a

waterbody. Alternatively, deeper waterbodies may have reduced the probability of detecting

tadpoles due to an increase in the volume of water. Decreasing hydroperiod has been shown to

induce early metamorphosis (Loman 1999; Lane & Mahony 2002; Loman 2002; Márquez-

García et al. 2010) and tadpoles of L. olongburensis may have metamorphosed in shallower

waterbodies in an attempt to metamorphose before waterbody desiccation. Alternatively,

shallower waterbodies may never have contained tadpoles of L. olongburensis. Water height has

been found to influence oviposition-site selection (Goldberg et al. 2006) and adult L.

olongburensis may have chosen deeper waterbodies with a lower desiccation risk to lay their

eggs.

Turbidity was positively associated with C. tinnula tadpole occupancy and negatively associated

with L. olongburensis tadpole abundance. Natural organic acids (NOA) produce dark, tannin-

stained water (Barth & Wilson 2010) and may have been the cause of turbidity. Alternatively,

turbidity may have been caused by suspended sediment (Walling 1977). NOA has been

attributed to an increase in growth and locomotive performance in Limnodynastes peronii

tadpoles (Barth & Wilson 2010) and other protective mechanisms in various aquatic species

(Gonzalez et al. 2002; Wood et al. 2003) when exposed to acidic conditions. Similar, positive,

processes could be occurring with C. tinnula tadpoles. Alternatively, reduced tadpole numbers

have been observed with an increased turbidity in previous surveys (Knutson et al. 2004;

Schmutzer et al. 2008), with several hypotheses suggested as to the cause of the decline,

including suffocation of tadpole eggs (hypothesised from suffocation of fish eggs in water with

high turbidity) or reduced ability to forage (Schmutzer et al. 2008). Similar, negative processes

could be occurring with L. olongburensis tadpoles.

There were several waterbody transects that failed to detect L. olongburensis tadpoles despite

being within the optimal pH and water depth ranges. The adult surveys that coincided with our

surveys found a majority of waterbodies to have L. olongburensis adults present (Shuker

unpublished data). The absence of tadpoles from these waterbodies suggests that eggs may have

been deposited but did not survive long enough to hatch, or tadpoles were in such low densities

that they were not detected. As mentioned previously, females have the ability to choose their

oviposition site (Goldberg et al. 2006) and females may have chosen alternative spawning sites

due to an unsuitable environmental factor. Alternatively, tadpoles occurring in larger, deeper

waterbodies may have been missed due to an increase in the sampling area in larger waterbodies

and tadpoles potentially being dispersed over a larger area.

Predatory fish were negatively associated with C. tinnula tadpole occupancy. Predatory fish

have the ability to exclude tadpole species that lack adequate anti-predator defences from

waterbodies (Kats et al. 1988; Hero et al. 1998, 2001; Gillespie 2001). Species that lack efficient

anti-predatory fish defences (e.g. unpalatability (Kats et al. 1988; Hero et al. 2001) or reduced

mobility (Woodward 1983)) may therefore be confined to waterbodies with ephemeral

hydroperiods where fish are often excluded due to waterbody desiccation (Heyer et al. 1975).

Therefore, the negative relationship between predatory fish and C. tinnula tadpole occupancy is

likely explained by lack of efficient anti-predatory fish strategies.

The model that best explained C. tinnula relative abundance was the model with all variables,

suggesting there are complex interactions occurring between all variables and C. tinnula tadpole

relative abundance. Further survey work and experimentation are required to determine the key

factors influencing C. tinnula tadpole relative abundance.

Habitat specialists and non-habitat specialist species within „unique‟ environments

Our data indicate that within „unique‟ environments habitat specialist species will dominate the

tadpole assemblages, with non-habitat specialist species being restricted in both presence and

abundance. It is indicated (based on our data from „unique‟ wallum habitats) that habitat

specialist tadpole species will be predominantly influenced by only a few variables (e.g. pH,

depth and turbidity all had a relative variable importance of one for L. olongburensis relative

abundance while predatory fish and depth had the highest relative variable importance for C.

tinnula occupancy). Furthermore, our study indicates that some of these variables are likely to

have a unimodel influence on the abundance of habitat specialist species. It can therefore be

recommended that quadratic variables be used in data analysis when undertaking similar studies

to provide an indication of upper, lower and optimal limits of environmental variables

influencing habitat specialist species distribution.

Non-habitat specialist tadpole species are likely to be absent or low in abundance within „unique‟

habitats, as tadpoles of the non-habitat specialist species L. fallax and L. gracilenta were low in

both occupancy and abundance within our study. While habitat specialist species may not utilize

all environmental niches present within a „unique‟ habitat, giving the potential for other species

to occupy these niches (Brown 1996), our results provide justification for the hypothesis that

non-habitat specialist species can be reduced in „unique‟ environments due to environmental

variable intolerances or competition from habitat specialist species.

Acknowledgements

We would like to thank the Griffith School of Environment and FKP Ltd for providing project

funding. We also thank Sonya Clegg, Katrin Lowe, Donna Treby, Diana Virkki, James Bone,

Diana Kiss, two anonymous reviewers and the associate editor and editor of Austral Ecology for

feedback on the manuscript. Griffith Animal Ethics number ENV/17/08, New South Wales

Department of Environment and Climate Change Scientific License number S21717 and

Queensland Department of Environment and Resource Management permit number

WITK05620308 were obtained for this study.

References

Austin M.P. (1999) The potential contribution of vegetation ecology to biodiversity research.

Ecography 22, 465-484.

Babbitt K.J. & Tanner G.W. (1998) Effects of cover and predator size on survival and

development of Rana utricularia tadpoles. Oecologia, 114.

Babbitt K.J., Baber M.J. & Tarr T.L. (2003) Patterns of larval amphibian distribution along a

wetland hydroperiod gradient. Can. J. Zoolog.81, 1539-1552.

Baber M.J., Fleishman E., Babbitt K.J. & Tarr T.L. (2004) The relationship between wetland

hydroperiod and nestedness patterns in assemblages of larval amphibians and predatory

macroinvertebrates. Oikos 107, 16-27.

Barth B.J. & Wilson R.S. (2010) Life in acid: interactive effects of pH and natural organic acids

on growth, development and locomotor performance of larval striped marsh frogs

(Limnodynastes peronii). J. Exp. Biol. 213, 1293-1300.

Berkelmans R. & Willis B.L. (1999) Seasonal and local spatial patterns in the upper thermal

limits of corals on the inshore Central Great Barrier Reef. Coral Reefs 18, 219-228.

Brown, J.S. (1996) Coevolution and community organization in three habitats. Oikos 75, 193-

206.

Burnham K.P. & Anderson D.R. (2002) Model Selection and Multimodel Inference: A Practical

Information-Theoretic Approach. Springer, New York.

Cade B.S. & Noon B.R. (2003) A gentle introduction to quantile regression for ecologists. Front.

Ecol. Environ. 1, 412-420.

Chinathamby K., Reina R.D., Bailey P.C.E. & Lees B.K. (2006) Effects of salinity on the

survival, growth and development of tadpoles of the brown tree frog, Litoria ewingii. Aust.

J. Zool. 54, 97-105.

Christy M.T. & Dickman C.R. (2002) Effects of salinity on tadpoles of the green and golden bell

frog (Litoria aurea). Amphibia-Reptilia 23, 1-11.

Crawley M.J. (2007) The R Book. Wiley, England.

Fernandes I.M., Machado F.A. & Penha J. (2010) Spatial pattern of a fish assemblage in a

seasonal tropical wetland: effects of habitat, herbaceous plant biomass, water depth, and

distance from species sources. Neotrop. Ichthyol. 8, 289-298.

Freda J. (1986) The influence of acidic pond water on amphibians: A review . Water Air Soil

Poll. 30, 439-450.

Freda J. & Taylor D.H. (1992) Behavioral response of amphibian larvae to acidic water. J.

Herpetol. 26, 429-433.

Garden J.G., McAlpine C.A., Possingham H.P. & Jones D.N. (2007) Habitat structure is more

important than vegetation composition for local-level management of native terrestrial

reptile and small mammal species living in urban remnants: A case study from Brisbane,

Australia. Austral Ecol. 32, 669-685.

Gillespie G.R. (2001) The role of introduced trout in the decline of the spotted tree frog (Litoria

Spenceri) in south-eastern Australia. Biol. Conserv. 100, 187-198.

Goldberg F.J., Quinzio S. & Vaira M. (2006) Oviposition-site selection by the toad

Melanophryniscus rubriventris in an unpredictable environment in Argentina. Can. J.

Zoolog. 84, 699-705.

Gonzalez R.J., Wilson R.W., Wood C.M., Patrick M.L. & Val A.L. (2002) Diverse strategies for

ion regulation in fish collected from the ion-poor, acidic Rio Negro. Physiol. Biochem. Zool.

75, 37-47.

Gosner K.L. & Black I.H. (1957) The effects of acidity on the development and hatching of New

Jersey Frogs. Ecology 38, 256-262.

Griffith S.J., Bale C., Adam P. & Wilson R. (2003) Wallum and related vegetation on the NSW

North Coast: description and phytosociological analysis. Cunninghamia 8, 202-252.

Griffith S.J., Bale C. & Adam, P. (2008) Environmental correlates of coastal heathland and allied

vegetation. Aust. J. Bot. 56, 512-526.

Groves, R.H. (1981) Heathland soils and their fertility status. pg 143-149 in Specht, R.L.

Ecosystems of the World 9B: Heathlands and Related Shrublands; Analytical Studies.

Elsevier Scientific Publishing Company, Amsterdam.

Grueber C.E., Nakagawa S., Laws R.J. & Jamieson I.G. (2011) Multimodel inference in ecology

and evolution: challenges and solutions. J. Evolution. Biol. 24, 699-711.

Hero J.-M., Gascon C. & Magnusson W.E. (1998) Direct and indirect effects of predation on

tadpole community structure in the Amazon rainforest. Aust. J. Ecol. 23, 474-482.

Hero J.-M., Magnusson W.E., Rocha C.F.D. & Catterall C.P. (2001) Antipredator defenses

influence the distribution of amphibian prey species in the Central Amazon rain forest.

Biotropica 33, 131-141.

Heyer W.R., McDiarmid R.W. & Weigmann D.L. (1975) Tadpoles, predation and pond habitats

in the tropics. Biotropica 7, 100-111.

Hines H., Mahony M. & McDonald K. (1999) An assessment of frog declines in wet subtropical

Australia. Pp. 44- 63 in Campbell, A. (ed.) Declines and Disappearances of Australian

Frogs. Environment Australia, Canberra.

Hopkins, M.C. (2003) Temporal and spatial distribution of breeding activity in an 'acid' frog

community at Tugun, south east Queensland. Honours Thesis, Griffith University.

Ingram G.J. & Corben C.J. (1975) The frog fauna of North Stradbroke Island, with comments on

the “acid” frogs of the wallum. Proc. R. Soc. Queensl. 86, 49-54.

Jocqué M., Graham T. & Brendonck L. (2007) Local structuring factors of invertebrate

communities in ephemeral freshwater rock pools and the influence of more permanent water

bodies in the region. Hydrobiologia 592, 271-280.

Johnson J.B. & Omland K.S. (2004) Model selection in ecology and evolution. Trends Ecol.

Evol. 19, 101-8.

Kats L.B., Petranka J.W. & Sih A. (1988) Antipredator defenses and the persistence of

amphibian larvae with fishes. Ecology 69, 1865-1870.

Knutson M.G., Richardson W.B., Reineke D.M., Gray B.R., Parmelee J.R. & Weick S.E. (2004)

Agricultural ponds support amphibian populations. Ecol. Appl. 14, 669-684.

Kopp K., Wachlevski M. & Eterovick P.C. (2006) Environmental complexity reduces tadpole

predation by water bugs. Can. J. Zoolog. 84, 136-140.

Krebs C.J. (2009) Ecology: Sixth Edition. Benjamin Cummings, United States of America.

Lancaster J. & Belyea L.R. (2006) Defining the limits to local density: alternative views of

abundance-environment relationships. Freshwater Biol. 51, 783-796.

Lane M.J. & Mahony S.J. (2002) Larval anurans with synchronous and asynchronous

development periods: contrasting responses to water reduction and predator presence. J.

Anim. Ecol. 71, 780-792.

Loman J. (1999) Early metamorphosis in common frog Rana temporaria tadpoles at risk of

drying: an experimental demonstration. Amphibia-Reptilia 20, 421-430.

Loman J. (2002) Temperature, genetic and hydroperiod effects on metamorphosis of brown frogs

Rana arvalis and R. temporaria in the field. J. Zoo. 258, 115-129.

Meyer E. (2004) Acid adaptation and mechanisms for softwater acid tolerance in larvae of

anuran species native to the “Wallum” of east Australia. PhD Thesis, University of

Queensland.

Meyer E., Hero J-M., Shoo L. & Lewis B. (2006) National recovery plan for the

wallum sedgefrog and other wallum-dependent frog species. Report to Department

of the Environment and Water Resources, Canberra. Queensland Parks and Wildlife

Service, Brisbane.

Mokany A. & Shine R. (2002) Competition between tadpoles and mosquito larvae. Oecologia

135, 615-620.

Moreira L.F.B., Machado I.F., Garcia T.V. & Maltchik L. (2010) Factors influencing anuran

distribution in coastal dune wetlands in southern Brazil. J. Nat. Hist. 44, 1493-1507.

Márquez-García M., Correa-Solís M. & Méndez M.A. (2010) Life-history trait variation in

tadpoles of the warty toad in response to pond drying. J. Zoo. 281, 105-111.

Persson M., Räsänen K., Laurila A. & Merilä J. (2007) Maternally determined adaptation to

acidity in Rana arvalis: are laboratory and field estimates of embryonic stress tolerance

congruent? Can. J. Zoolog. 85, 832-838.

Pierce B.A. (1985) Acid tolerance in amphibians. BioScience 35, 239-243.

Richter-Boix A., Llorente G.A. & Montori A. (2007) A comparative study of predator-induced

phenotype in tadpoles across a pond permanency gradient. Hydrobiologia 583, 43-56.

Rios-López N. (2008) Effects of increased salinity on tadpoles of two anurans from a Caribbean

coastal wetland in relation to their natural abundance. Amphibia-Reptilia 29, 7-18.

Sadinski W.A. & Dunson W.J. (1992) A multilevel study of effects of low pH on amphibians of

temporary ponds. J. Herpetol. 26, 413-422.

Saidapur S.K., Veeranagoudar D.K., Hiragond N.C. & Shanbhag B.A. (2009) Mechanism of

predator–prey detection and behavioral responses in some anuran tadpoles. Chemoecology

19.

Schmutzer A.C., Gray M.J., Burton E.C. & Miller D.L. (2008) Impacts of cattle on amphibian

larvae and the aquatic environment. Freshwater Biol. 53, 2613-2625.

Schofield P.J. & Nico L.G. (2009) Salinity tolerance of non-native Asian swamp eels (Teleostei:

Synbranchidae) in Florida, USA: comparison of three populations and implications for

dispersal. Environ. Biol. Fish. 85, 51-59.

Smith M.J., Schreiber E.S.G., Scroggie M.P., Kohout M., Ough K., Potts J., et al. (2007)

Associations between anuran tadpoles and salinity in a landscape mosaic of wetlands

impacted by secondary salinisation. Freshwater Biol. 52, 75-84.

Snodgrass J.W., Bryan Jr. A.L. & Burger J. (2000) Development of expectations of larval

amphibian assemblage structure in southeastern depression wetlands. Ecol. Appl. 10, 1219-

1229.

Spieler, M. (2003). Risk of predation affects aggregation size: a study with tadpoles of

Phrynomantis microps (Anura: Microhylidae). Anim. Behav.65, 179-184.

Twomey E., Morales V. & Summers K. (2008) Evaluating condition-specific and asymmetric

competition in a species-distribution context. Oikos 117, 1175-1184.

Vonesh J.R., Kraus J.M., Rosenberg J.S. & Chase J.M. (2009) Predator effects on aquatic

community assembly: disentangling the roles of habitat selection and post-colonization

processes. Oikos 118, 1219-1229.

Walling D.E. (1977) Assessing the accuracy of suspended sediment rating curves for a small

basin. Water Resour. Res. 13, 531-538.

Welborn G.A., Skelly D.K. & Werner E.E. (1996) Mechanisms creating community structure

across a freshwater habitat gradient. Annu. Rev. Ecol. Syst. 27, 337-363.

Wells K.D. (2007) The Ecology and Behavior of Amphibians. The University of Chicago Press,

United States of America.

Wilbur H.M. (1987) Regulation of structure in complex systems: experimental temporary pond

communities. Ecology 68, 1437-1452.

Wiltshire D.J. & Bull C.M. (1977) Potential competitive interactions between larvae of

Pseudophryne bibroni and P. Semimarmorata (Anura: Leptodactylidae). Aust. J. Zool. 25,

449-454.

Wood C.M., Matsuo A.Y.O., Wilson R.W., Gonzalez R.J., Patrick M.L., Playle R.C., et al.

(2003) Protection by natural blackwater against disturbances in ion fluxes caused by low pH

exposure in freshwater stingrays endemic to the Rio Negro. Physiol. Biochem. Zool.76, 12-

27.

Woodward B.D. (1983) Predator-prey interactions and breeding-pond use of temporary-pond

species in a desert anuran community. Ecology 64, 1549-1555.

Yoshioka A., Kadova T., Suda S. & Washitari, I. (2010) Impacts of weeping lovegrass

(Eragrostis curvula) invasion on native grasshoppers: responses of habitat generalist and

specialist species. Biol. Invasions 12, 521-539.

Table 1: Comparison of habitat characteristics for surveyed waterbodies in wallum habitats of

eastern Australia. Spearman correlation coefficients (SCC) were compared for 37 waterbody

transects. None of the variables were considered highly correlated (SCC ≥ 0.7).

Predatory

Fish

pH Salinity Depth Turbidity

pH 0.290 - - - -

Salinity -0.331 -0.281 - - -

Depth 0.576 0.178 0.016 - -

Turbidity -0.168 -0.357 0.426 -0.042 -

Vegetation

Cover

0.025 -0.108 -0.110 0.206 -0.086

Table 2: Comparison of waterbody characteristics associated with the relative abundance and

occupancy of L. olongburensis or C. tinnula tadpoles in eastern Australia. Aikiki models with Δi

values less than 4 are presented. + indicates a positive relationship while – indicates a negative

relationship to L. olongburensis or C. tinnula tadpole relative abundance or occupancy. Variables

with a 2 indicate a unimodal distribution with L. olongburensis or C. tinnula tadpole relative

abundance or occupancy.

Model AICc Δi wi

Litoria olongburensis tadpole relative

abundance

pH – pH2 + Depth – Depth

2 – Turbidity

pH - pH2 + Salinity + Depth - Depth

2 -

Turbidity - % Cover

pH - pH2 + Salinity + Depth - Depth

2 -

Turbidity - Predatory Fish - % Cover

126.2

126.9

127.5

0

0.64

1.30

0.444

0.322

0.232

Litoria olongburensis tadpole occupancy

pH – pH2

43.2 0 0.463

pH – pH2 + Depth – Depth

2 44.5 1.34 0.237

Depth – Depth2 46.9 3.69 0.073

Crinia tinnula tadpole relative abundance

pH - pH2 - Salinity + Depth - Depth

2 +

Turbidity - Predatory Fish - % Cover

472.4 0 1

Crinia tinnula tadpole occupancy

(-) Predatory Fish

(-) Depth

(-) Predatory Fish + Turbidity

48.1

48.8

49.9

0

0.67

1.78

0.257

0.184

0.106

Depth – Depth2

Turbidity

Predatory Fish * Depth

Salinity

Predatory Fish * Turbidity

(-) % Cover

(-) % Cover – Predatory Fish + Turbidity

50.2

51.7

51.8

51.9

51.9

52

52.1

2.10

3.52

3.66

3.77

3.8

3.84

3.99

0.09

0.044

0.041

0.039

0.038

0.038

0.035

Table 3: Relative importance of waterbody characteristics associated with the relative abundance

and occupancy of L. olongburensis or C. tinnula tadpoles in eastern Australia. Model averaged

coefficients and relative importance of each environmental predictor for models where Δi < 4 for

L. olongburensis relative abundance and occupancy and C. tinnula occupancy are displayed.

Relative variable importance values rounded to the nearest second decimal place.

Variable Estimate S.E. † Confidence

Interval

Rel. var.

imp.‡

Litoria olongburensis

abundance

pH 59.5 16.5 26.02, 92.994 1

pH2 -7.47 2.08 -11.7, -3.247 1

Depth 0.4 0.127 -0.143, 0.663 1

Depth2 -0.01 -0.003 -0.018, -0.004 1

Turbidity

% Cover

Salinity

Predatory Fish

-0.026

-0.017

0.0243

-0.137

-0.03

0.009

0.012

-0.096

-0.087, 0.034

-0.036, 0.002

-0.0002, 0.049

-0.334, 0.06

1

0.55

0.55

0.23

Litoria olongburensis

occupancy

pH 54.689 34.155 -14.24, 123.62 0.76

pH2 -7.33 4.158 -15.76, 1.1 0.72

Depth 0.625 0.393 -0.167, 1.418 0.36

Depth2 -0.018 0.009 -0.037, -0.004 0.33

Crinia tinnula occupancy

Predatory Fish

Depth

Depth2

Turbidity

Salinity

% Cover

Predatory Fish * Turbidity

Predatory Fish * Depth

-0.395

-0.013

-0.006

0.031

0.01

-0.008

-0.027

0.022

0.525

0.166

0.007

0.038

0.016

0.015

0.054

0.041

-1.459, 0.67

-0.346, 0.319

-0.02, 0.007

-0.046, 0.108

-0.023, 0.043

-0.038, 0.022

-0.137, 0.082

-0.061, 0.104

0.48

0.38

0.11

0.28

0.07

0.07

0.04

0.04 † Standard Error

‡ Relative variable importance

Table 4: Coefficients of the 0.85 and 0.65 regression quantiles where the independent factors

were mean pH and mean water depth. Litoria olongburensis tadpoles were the dependant factor

within the regression quantile models.

Quantile values for Litoria olongburensis and pH

Parameter Estimate S.E.† t p-value

0.85 quantile

Intercept

pH

pH2

-29.74462

15.81659

-1.96724

7.03260

3.40438

0.40236

-4.22953

4.64595

-4.88921

0.00017

0.00005

0.00002

0.65 quantile

Intercept

pH

pH2

-29.28921

15.09734

-1.84790

12.66667

6.11008

0.71455

-2.31231

2.47089

-2.58611

0.02695

0.01865

0.01416

Quantile values for Litoria olongburensis and water depth

Parameter Estimate SE† t p-value

0.85 quantile

Intercept

Depth

Depth2

-1.66692

0.35474

-0.00853

1.05192

0.08616

0.00167

-1.58465

4.11721

-5.11025

0.12230

0.00023

0.00001

0.65 quantile

Intercept

Depth

Depth2

-0.79584

0.15177

-0.00378

0.50358

0.08050

0.00186

-1.58036

1.88529

-2.02956

0.12328

0.06796

0.05029 † Standard Error

Figure 1: Localities of survey sites, with numbers representing the following localities: 1 –

Cooloola Section of the Great Sandy National Park; 2- Noosa National Park; 3 – Mooloolah

National Park; 4 – Beerwah Scientific Reserve; 5 – Tyagarah Nature Reserve; 6 – Lennox

Heads; 7 – Bunjalung National Park; 8 – Yuragir National Park (North); 9 – Yuragir National

Park (South). Black dots represent Litoria olongburensis record localities from EPA/QPWS,

NSWDEC, the Australian Museum, Queensland Museum, South Australian Museum, and

various biologists (Meyer et al. 2006). Solid lines represent Australian coastline and the

Queensland / New South Wales state border. Map of Australia shows enlarged area within the

rectangle, with solid lines representing the Australian coastline and the Australian state and

territory borders.

Figure 2: „Jitter‟ plots for quantile regressions of (a) the 0.85 quantiles and (b) the 0.65 quantiles

(solid line) and the 95% confidence intervals (dotted lines) for mean waterbody pH and relative

abundance of Litoria olongburensis tadpoles. Circles represent waterbody transects.

Figure 3: „Jitter‟ plots for quantile regressions of the (a) the 0.85quantiles and (b) the 0.65

quantiles (solid line) and the 95% confidence intervals (dotted lines) for mean waterbody depth

and relative abundance of Litoria olongburensis tadpoles. Circles represent waterbody transects.

1

Figure 1 2

3

4

5

Figure 2 6

7

Figure 3 8

9