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: [email protected]
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.
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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.
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