Invasive cane toads might initiate cascades of direct and indirect effects in a terrestrial ecosystem
Understanding the impacts that invasive vertebrates have on terrestrial ecosystems extends primarily to invaders’ impacts on species with which they interact directly through mechanisms such as predation, competition and habitat modification. In addition to direct effects, invaders can also initiate ecological cascades via indirect population level effects on species with which they do not directly interact. However, evidence that invasive vertebrates initiate ecological cascades in terrestrial ecosystems remains scarce. Here, we ask whether the invasion of the cane toad, a vertebrate invader that is toxic to many of Australia’s vertebrate predators, has induced ecological cascades in a semi-arid rangeland. We compared activity of a large predatory lizard, the sand-goanna, and abundances of smaller lizards preyed upon by goannas in areas of high toad activity near toads’ dry season refuges and areas of low toad activity distant from toads’ dry season refuges. Consistent with the hypothesis that toad invasion has led to declines of native predators susceptible to poisoning, goanna activity was lower in areas of high toad activity. Consistent with the hypothesis that toad-induced goanna decline lead to increases in abundance the prey of goannas, smaller lizards were more abundant in areas of high toad activity. Structural equation modelling showed a positive correlation between goanna activity and distance from dry season refuge habitats used by toads. The abundances of small lizards was correlated negatively with goanna activity and distance from dry season refuges of toads. Our findings provide support for the notion that invasions by terrestrial vertebrates can trigger ecological cascades.
KeywordsCane toad Invasive species Rhinella marina Semi-arid Trophic cascade Varanus gouldii
The invasion of ecosystems by non-indigenous plant and animal species is a major driver of global environmental change and recognized as a serious threat to biodiversity (Vitousek and D’Antonio 1997; Mack et al. 2000). Research on the ecological consequences of biological invasions has focused largely on direct population-level impacts on single or specific guilds of native species through mechanisms such as predation (Risbey et al. 2000; Roy et al. 2012), competition (Corbin and D’Antonio 2004; Miller and Gorchov 2004) and habitat modification (Rodriguez 2006). However, population-level effects that invaders have on species they do not directly interact with (i.e., indirect effects) are often overlooked, even though there is evidence that such effects can manifest in the reorganization of recipient ecosystems (White et al. 2006).
Invaders are likely to induce cascades of indirect effects if they influence the abundance of strongly interactive species such as predators, pollinators and ecosystem engineers or substantially alter primary productivity or vegetation structure (Anderson and Rosemond 2007; Gooden et al. 2009; Letnic et al. 2009). In such circumstances, invaders can propagate ecological cascades whereby shifts in the abundance of one species indirectly affect the abundance and biomass of others (Terborgh and Estes 2010). Such ecological cascades may become evident temporally or spatially as alternating patterns in the abundances of species are affected directly and indirectly by invaders (Estes et al. 2011; Letnic et al. 2009). Ecological cascades initiated by invasive species is a relatively well documented phenomenon in aquatic systems (e.g. Simon and Townsend 2003; Baxter et al. 2004; Strayer 2010) with increasing evidence suggesting similar effects of invasive vertebrates on the terrestrial ecosystems of offshore islands (Roemer et al. 2002; Croll et al. 2005; Thoresen et al. 2017), but few studies have reported invasive species driving cascades in terrestrial ecosystems of mainland continents.
One reason for the scarcity of studies on the indirect impacts of vertebrate invaders in terrestrial ecosystems is that studies investigating biological invasions are difficult to plan. Hence, most studies reporting impacts of biological invasions are conducted post-invasion and typically evaluate the impacts of invasive vertebrates by manipulating their abundance or access to the species or ecosystem of interest. However, demonstrating that vertebrate invaders can have cascading effects in terrestrial ecosystems often requires conducting manipulative experiments at large spatial scales, which are logistically difficult (Parker et al. 1999). One way to advance knowledge of invasive vertebrates’ indirect impacts on ecosystems is to utilize “natural experiments” whereby the abundance of invaders varies in time or space in otherwise similar landscapes. Such studies can provide valuable insights into ecological processes at spatial and temporal scales that cannot be achieved through experimentation (Sax et al. 2005). In the semi-arid rangelands of northern Australia, spatial structuring of cane toad populations, whereby their activity is concentrated around dry season refuge habitats (Letnic et al. 2014, 2015), provides the opportunity to conduct a “large-scale” natural experiment to examine the role that introduced species have in structuring ecosystems.
The cane toad (Rhinella marina) is an anuran native to South America, that is currently invading northern and semi-arid regions of Australia (Florance et al. 2011). Cane toads contain toxins that are absent from Australian anurans. Consequently, most native Australian vertebrate predator species lack an evolutionary history of exposure to these toxins and many die after attacking or consuming toads (Shine 2010). Due to poisoning of individuals, populations of marsupial quolls, monitor lizards (i.e., goannas), freshwater crocodiles and some snake species have undergone marked declines following the arrival of toads (Shine 2010; Feit and Letnic 2015). Because cane toads have driven declines in predator populations, it follows that diminished levels of predation could lead to increases in abundance or survivorship of prey species in areas where cane toads have suppressed the abundances of predators (Doody et al. 2013, 2015).
Based on prior knowledge of cane toad biology, cane toads’ suppressive effects on varanid lizard populations (Doody et al. 2009, 2014, 2015) and varanid lizards’ predatory effects on populations of smaller lizard species (Olsson et al. 2005; Doody et al. 2012, 2015), we tested the following predictions: (1) toad activity should decrease with distance from AWP and be greater in the vicinity of dams where toads are abundant than tanks where toads are rare; (2) because encounter rates between toads and varanid lizards should decrease with distance from AWP, varanid lizard foraging activity should increase with distance from AWP and should be greater in the vicinity of tanks where toads are rare than dams where toads are abundant when distance from water is set to the mean distance; and (3) because of reduced predation by varanid lizards, the abundance of small lizards (Scincidae and Agamidae) should be greater in the vicinity of dams where toads are abundant than tanks where toads are rare.
We tested our predictions by comparing the abundance of cane toads, the foraging activity of the sand goanna (Varanus gouldii) (Fig. 1e, f) and the abundance of skinks and dragons along 12 km road transects in the vicinity of dams and tanks, respectively. We used structural equation modelling (SEM) to investigate the hypothesized direct and indirect relationships among the response variables. We also tested alternative hypotheses in our SEM based on prior knowledge that the abundances of goannas and smaller lizards are influenced by predation from mammalian predators (Olsson et al. 2005) and disturbances to vegetation by livestock grazing (James et al. 1999) and fire (Letnic et al. 2004).
Materials and methods
Study area and time
The study area has a mean annual rainfall of 580 mm, of which 96% falls in the wet season (November to April) and 4% in the dry season (May–October; Australian Bureau of Meteorology). The vegetation of the study area consists of open semi-arid savannah woodland with the dominant woody species of lancewood (Acacia shirleyi) and eucalypts (Eucalyptus leucophloia) and an understory dominated by grasses (Eriachne spp. and Sorghum spp.). We surveyed the foraging activity of sand goannas and the abundance of cane toads, skinks and dragons in April and November 2012, April and November 2013 and September 2014.
Cane toad abundance at AWP and along road transects
We estimated the abundance of toads in the direct vicinity of AWP by conducting nocturnal 4 m × 150 m strip transects radiating away from the AWP (n = 4 per AWP) using handheld 12 V spotlights with 25 W halogen bulbs. Cane toad abundance was calculated as the sum of individuals encountered along the four transects. We conducted a total of 42 cane toad counts at 31 AWP (ten dams and 21 tanks) (Online Resource 1); four dams and seven tanks were sampled twice during the study period with a minimum of 12 months between surveys.
Cane toads frequently travel along roads during dispersal periods (Brown et al. 2006). To document the distribution of toads with respect to distance from AWP, we conducted nocturnal surveys along low-use single lane dirt roads in a 4WD vehicle during a period when many toads had dispersed away from their dry season refuges at the end of the wet season in April 2012. Because of logistical constraints during field work, it was not possible to conduct road transects from all AWP. Hence, distance mediated effects were evaluated at a subset of dams (n = 4) and tanks (n = 5). We surveyed toad abundance over a total of 110 km at distances of up to 12 km from both reservoir types. The surveys were undertaken at a speed of 20 km/h and an observer noted with a GPS the location of all toads sighted. Toad activity was documented as number of toads per 500 m transect section.
Goanna activity indices
Sand goannas are difficult to survey using mark-recapture methods because they rarely enter traps (Letnic et al. 2004) and, in habitats with dense understory vegetation such as our study area, are difficult to sight and approach for the purposes of noosing, hand capture or visual surveys. Previous studies have used the occurrence of fresh goanna tracks and pits that goannas create whilst foraging to index goanna abundance (Paltridge 2002; Bird et al. 2014; Read and Scoleri 2014). Both indices have been validated against known abundances in other varanid species (Anson et al. 2014). Following these previous studies, we used two methods to index goanna activity, the occurrence of tracks (i.e. footprints and tail drag marks) crossing single-lane dirt roads and the occurrence of recent goanna foraging pits. Our track count index provided a measurement of goanna activity over a 24 h period, while the foraging pit index provided a cumulative measure of goanna activity for a period of approximately 1 month prior to our surveys. We conducted all monitoring under environmental conditions that favored lizard activity and ensured equal and high detection probabilities among track plots and surveys (Jessop et al. 2013a).
The track-based index of goanna activity was derived by scoring the occurrence (presence/absence) of tracks crossing 50 m track plots located along road transects radiating from dams and tanks. The transects were situated on low-use single lane dirt roads (Paltridge 2002; Read and Scoleri 2014). We surveyed 403 track plots over a total of 201.5 km (Online Resource 2). Each track plot consisted of a 50 m road section that was cleared of tracks on the day before the survey. Track plots were spaced 500 m apart and located between 0 and 12 km from the nearest AWP. We walked along each track plot and recorded the presence or absence of fresh goanna tracks (i.e., distinctive tail drags and claw imprints). As daily activity areas of sand goannas are unlikely to exceed an area of 200 m by 200 m (Green and King 1978), we are confident that each recorded track originated from a different individual.
The foraging-pit based index of goanna activity was derived by scoring the presence or absence of recent goanna foraging signs during 2 min active searches in the vicinity of each track plot (Jessop et al. 2013a). Whilst digging for fossorial prey, sand goannas leave characteristic ellipsoid foraging pits that often show deep scratch marks left by their strong forelimbs during excavation of the soil (Read and Scoleri 2014). We estimated the approximate age of foraging pits based on two criteria: the amount of leaf litter and other debris in the excavation and the coloration and texture of the excavated soil (initially darker and softer than the topsoil, gradually fading and hardening over the course of several weeks). To provide an indication of the age of foraging pits that we encountered, we excavated pits similar to goanna foraging pits and monitored them over a 2 month period. This allowed us to classify foraging pits into the two age classes of recent (i.e. younger than approximately 1 month) and old (i.e. older than 1 month). Only the presence of foraging pits younger than approximately 1 month was used for further analyses.
Small lizard abundance
To monitor the abundance of small lizards, we conducted 198 active diurnal searches (total search duration of 1980 min) following the methods of Lunney and Barker (1986) (Online Resource 3). During each of the surveys in April and November 2012 and 2013, we conducted 40 active diurnal searches (20 sites located near dams, 20 near tanks), during the survey in September 2014 we conducted 38 searches (20 sites located near dams, 18 near tanks). Active search sites comprised 1 ha (100 m × 100 m) plots and were spaced a minimum of 2 km apart and located along the same transects used to survey goanna activity. At each site, active searches were conducted simultaneously by two observers who portioned their search effort so that each observer restricted their search to a 50 × 100 m quadrat within each site. We recorded reptiles encountered on the ground, under logs, in litter, in grass and on stems and branches of trees. An observers’ experience bias was avoided by using random observer combinations for each survey. Each site was actively searched for 10 min (5 min per observer) and was conducted between 9:00 and 10:30 am. To prevent double counting, observers avoided walking the same paths twice. Sighted reptiles were identified to family level by their pattern and size and the microhabitat they were encountered in. All encountered skink species belonged to four genera (Carlia, Ctenotus, Lialis and Menetia), all encountered dragon species belonged to two genera (Amphibolurus and Diporiphora). The total number of individuals recorded during 10 min of active search was used as an index of the abundance of skinks and dragons at each active search site.
To investigate the alternative hypotheses that habitat disturbance by cattle or predation by dingoes or feral cats were factors influencing the abundance of goannas and/or smaller lizards, we recorded the presence of tracks of cattle, dingoes, and feral cats at each tracking plot. An index of cattle activity for each track plot and each active search site was expressed as the percentage of track plots with fresh tracks within a 1.5 km radius. To account for the wide-ranging habitat of dingoes, dingo activity was expressed as mean values obtained for each sub-site. Cat activity was omitted from the analyses owing to the low activity of cats (only 3.8% of the track plots contained cat tracks).
The reduction of vegetation coverage by fire is known to influence the abundance of sand goannas and smaller lizard species (Letnic et al. 2004; Bird et al. 2014). To investigate whether differences in the fire history could explain differences in the abundance of goannas, skinks and dragons we obtained data on the fire history of each active search site from the North Australian Fire Information.
Cane toad abundance at AWP and along road transects
We analyzed differences in cane toad density in the direct vicinity of AWP using a generalized linear mixed model (GLMM) with a Poisson distribution and a log link function. To account for multiple sampling between years, AWP identity was included as random factor. We analyzed differences in cane toad density along 12 km road transects radiating from the two different reservoir types using a generalized additive model (GAM) with a Poisson distribution and a log link function. To account for the nested structure of the data, we included the identity of each transect and the year in which the survey was conducted in as random factors. All GLMM and GAM analyses were performed in R Version 3.0.3 using the ‘glmm’ and ‘mgcv’ libraries.
Goanna activity and small lizard abundance along transects
We combined the track and foraging pit indices of goanna activity in our analysis and defined a plot as indicating recent goanna activity if goanna tracks and/or recent foraging pits were present. Because our road surveys revealed a decline of cane toad abundance at distances of up to 3 km from dams, followed by a steady count to distances up to 12 km (see “Results” section), we divided our 12 km transects into two sections (i.e. < 3 and > 3 km). For the initial analysis of goanna activity and small lizard abundance along transects we compared the averaged goanna activity and lizard abundance of each transect section between individual transects using GLMM with a normal distribution and log link function. Type of nearest AWP, distance to AWP and the interaction of type and distance were included as fixed factors in the models. To account for the nested structure of the data, we included the identity of each transect and the year in which the survey was conducted in as random factors.
Because our expectation in this study system was that even though the biomass of lizards (i.e., as small ectotherms) would be relatively high, the distribution of individuals is expected to be very patchy. This reflects well known observations, that semi-arid lizards, as consequences of sensitivity to heterogeneity in structural habitat resources (e.g. ground vegetation cover, course woody debris) and relatively small home ranges, can be extremely variable in spatial occurrence (Letnic et al. 2004). Our survey design thus considered that an increased number of plots sampled once, rather than fewer plots sampled repeatedly, would permit better encounter rates and less variation in lizard detection in an otherwise very large study area. We however acknowledge, that as a potential trade-off of this approach, our measurements of naive count data could not account for imperfect detection (Guillera-Arroita et al. 2014). Ideally, if time had permitted, we would have performed repeated count surveys on a large number of sites to allow use of potentially more robust count estimation methods (Royle and Nichols 2003).
Structural equation modelling
Because our initial analysis indicated significant differences in cane toad abundance, recent goanna activity and small lizard abundance between transects in the vicinity of tanks and dams (see “Results” section), we used piecewise SEM to further test hypotheses based on a priori knowledge of interactions hypothesized to occur between cane toads, goannas and smaller lizard species (Grace 2006). We constructed our a priori SEM model based on trophic cascade theory and prior knowledge of factors impacting the abundance of small terrestrial lizards. As opposed to classical SEM, where covariance matrices are used, piecewise SEM uses localized estimates to deduce direct and indirect effect pathways (Grace 2006; Colman et al. 2014). This approach allows the modelling of data that do not meet the assumptions of classic SEM and the incorporation of exogenous factors such as spatial dependence (Pasanen-Mortensen et al. 2013; Colman et al. 2014). Localized estimates within the SEM were fitted using a GLMM (Poisson log-link function; skink and dragon models) or LMM (goanna model). To account for the nested structure of the data we included the identity of each transect as a random factor. For the GLMM, an observation level random effect was included to account for overdispersion (Harrison 2014).
Our initial models were parameterized with values obtained for each active search site for the variables goanna activity (percentage of plots with recent goanna tracks and/or foraging pits within 1.5 km of the active search site), cattle activity (percentage of plots with cattle tracks within 1.5 km of the active search site) and the number of months since the last fire as well as with mean values obtained for each sub-site for dingo activity (percentage of plots with dingo tracks) to account for the wide-ranging habitat of dingoes. Because the impact of cane toads on goannas was negatively correlated with increasing distance from dams whereas increasing distance from tanks was not correlated with goanna abundance (see “Results” section), we used the distance from the nearest dam to each of the active search sites as a proxy for the impact of cane toads on goannas. We used a backwards step-wise elimination process for model simplification whereby the most non-significant predictor variables were sequentially deleted until all interaction were significant. The most parsimonious model was then selected using Akaike’s Information Criterion for small sample sizes (AICc) as that with the lowest AICc value (Burnham and Anderson 2002). Standardized path coefficients were calculated by normalizing data to fall within one standard deviation of a mean centered on zero and the amount of variance explained by each ‘piece’ of the SEM (i.e., the goanna, skink and dragon models) was assessed using marginal R2 values. The overall fit of the SEM was assessed using a Fisher C test and associated p value. The model is a good representation of the data if the Fisher C p-value is > 0.05. All SEM analyses were performed in R Version 3.0.3 using the ‘piecewiseSEM’ library.
Interaction pathways between variables were determined by applying a priori knowledge, which resulted in the following set of hypothesized pathways (Fig. 5a): (1) Cane toad activity should negatively affect goannas owing to lethal ingestion; (2) the foraging activity of goannas should negatively affect the abundance of skinks and dragons (Olsson et al. 2005); (3) because of selective predation pressure, goanna activity should have a stronger impact on skinks than dragons (Sutherland 2011); (4) we used distance to the nearest dam as a proxy for the impact of cane toads because the impact of cane toads on goannas was negatively correlated with distance from dams (see “Results” section); (5) dingo activity should negatively affect goanna activity owing to predation (Paltridge 2002); (6) habitat modification resulting from grazing by livestock can have detrimental effects on both the abundance of goannas and of smaller lizard species such as skinks and dragons (James 2003); (7) time since fire was hypothesized to positively affect populations of goannas (Bird et al. 2014) and to negatively affect skinks and dragons (Letnic et al. 2004).
Cane toad abundance at AWP and along road transects
Goanna activity along transects
Small lizard abundance along transects
We encountered 6.0 times more skinks during 10-min active searches along transects in the vicinity of dams where toads were abundant (0.13 ± 0.02) than tanks where toads were rare (0.02 ± 0.01, F1,198 = 31.48, p < 0.001). In both the vicinity of dams and tanks, skink abundance was greater within the < 3 km transect sections (dams: 0.19 ± 0.03, tanks: 0.07 ± 0.02) than the > 3 km transect sections (dams: 0.10 ± 0.01, tanks: 0.01 ± 0.004, F1,198 = 17.61, p < 0.01, Fig. 4b).
We encountered 2.0 times more dragons during 10-min active searches along transects in the vicinity of dams where toads were abundant (0.06 ± 0.01) than tanks where toads were rare (0.03 ± 0.001, F1,198 = 6.18, p < 0.05). Dragon abundance did not differ between the < 3 and > 3 km transect sections within transects from dams or tanks, respectively (F1,198 = 1.71, p = 0.19, Fig. 4c).
Structural equation modelling
In accord with our a priori predictions, the results of this study demonstrate that: (1) cane toad abundance in the late wet season decreased with distance from AWP and their numbers were greater in the vicinity of dams than tanks when distance from AWP was held constant; (2) goanna activity increased with distance from dams and, when distance to water was set to mean distance, goanna activity was greater in the vicinity of tanks where toads were rare than dams where toads were abundant; (3) the abundance of skinks and dragons was greater in the vicinity of dams where toads were abundant than tanks where toads were rare and skink abundance of was negatively correlated with distance from AWP. In comparison to distance to dams (our proxy for the abundance and impact of cane toads), cattle activity, time since last fire and mammalian predator activity were weak predictors of goanna activity and skink abundance. However, cattle activity was the best predictor of dragon abundance. Taken together, these findings provide support for the idea that the invasion of cane toads has propagated an ecological cascade whereby cane toad induced declines of goanna populations near toads’ dry season refuges have facilitated increased abundances of small lizards. More generally, our results provide support for the notion that terrestrial invaders may trigger ecological cascades that become evident as alternating spatial patterns in the abundances of species negatively and positively affected by invaders.
A short-coming of our study is that we did not experimentally manipulate cane toad abundance but instead relied upon natural differences in cane toad activity resulting from the type of reservoir utilized at AWP and distance from AWP (Letnic et al. 2014). Because natural systems are intrinsically variable, it remains possible that confounding factors such as differences in grazing pressure, vegetation type, geomorphology, fire history and the activity of mammalian predators may have contributed to the differences we observed (Underwood 1990). During the design of our study, we attempted to control for variation in these variables in two ways. First, we selected study sites with little variation in underlying geology, vegetation type, land use and fire history. Second, at each of our study sites we measured indices of cattle grazing activity, mammalian predator activity and time since last fire, and included these variables as alternative hypotheses to explain goanna activity and lizard abundances in our SEM. None of these variables explained as much variation in goanna activity or skink abundance as the direct or indirect effects of our proxy for cane toad abundance and impact, distance from dam.
Our results are consistent with the hypothesis that higher encounter rates between toads and goannas in the direct vicinity of dams was the driver of the reduction in goanna activity near dams. In turn, lower rates of predation by goannas near dams has allowed for increased abundances of small lizards. This hypothesis is supported by earlier studies showing that cane toads are more abundant at dams than tanks (Feit et al. 2015), that goanna populations have declined following the invasion of cane toads (Griffiths and McKay 2007, Doody et al. 2009) and that suppression of goanna populations can drive increases in the abundances of skinks and dragons (Olsson et al. 2005; Doody et al. 2013; Read and Scoleri 2014). Moreover, the stronger negative correlation between goanna and skink activity as opposed to that between goannas and dragons is consistent with previous studies showing that sand goannas consume skinks more frequently than dragons (Losos and Greene 1988; Sutherland 2011). Nevertheless, our results indicate that, in addition to goanna activity, factors not measured in this study could influence skink abundance along road transects in our study system. The strength of the indirect path coefficient of the effect of distance to dams on skink abundance mediated by goanna activity is − 0.62 (i.e. 0.70 × − 0.88), approximately half the strength of the direct pathway between dams on skinks (1.33), suggesting that goannas explain about half of the response of skinks to increasing distance to dams. This could reflect an underestimation of goanna foraging by the activity indices used in this study or factors affecting skink activity not measured during our surveys. We therefore caution that controlled experiments are required to confirm or refute our cane toad induced ecological cascade hypothesis.
Central to our hypothesis that cane toads’ impacts on lizard assemblages decrease with distance from dams is the idea that cane toads are largely restricted to refuge sites with water during the dry season and disperse away from refuges during the wet season (Letnic et al. 2014). Thus, during the wet season, encounters between cane toads and goannas could occur anywhere in the landscape, while in the dry season goannas would most likely encounter toads within 500 m of permanent water (Florance et al. 2011). However, during the wet season, the density of cane toads and hence likelihood of a goanna encountering a cane toad should decrease with distance from refuges because as they disperse they are in effect diffusing away from a point source (Florance et al. 2011; Tingley et al. 2013). We contend then that the patterns in goanna and small lizard abundance we report are legacy effects that reflect higher encounter rates between goannas near (< 3 km) sources of permanent water during both the wet season and dry season.
Our SEM analysis suggests that distance to the nearest dam, and thus the population density and impact of cane toads, was not the only factor influencing the foraging activity of goannas and the abundance of small lizards in our study. Goanna activity was also negatively correlated with the activity of dingoes and, contrary to our expectations, positively correlated with cattle activity. In contrast to increasing distance to dams, however, the activity of cattle and dingoes had only weak effects on the foraging activity of goannas in our SEM. Similarly, habitat disturbance by cattle and the reduction of vegetation coverage by fire had weak effects on lizard abundance in our SEM. Nevertheless, the correlation between the abundance of skinks and goanna activity was stronger than the correlation between their abundance and cattle activity or the time since last fire.
We acknowledge that because our count estimates of lizard abundances did not consider imperfect detection, the large difference reported for lizard abundances between tanks and dams may be overestimated. Such a consequence could arise if goanna predation also induced increased lizard anti-predator behavior that could affect their detection probability and our capacity to observe accurately lizards during count surveys. For example, if small lizards in high-goanna density areas are exposed to greater predation risk and respond through adjustments in daily activity patterns or microhabitat use, or variation in cryptic or flight initiation behavior this could affect our capacity to accurately count them (Vanhooydonck and Van Damme 2003; Cooper and Wilson 2007; Cooper and Frederick 2009). Whilst this is an important methodological consideration, reduced lizard abundance due to the respective contributions of direct mortality or increased anti-predator behavior, nevertheless represents the two key processes through which predators limit prey populations (Preisser et al. 2005). Further study is needed to better understand how goanna predation influences small lizard abundance. Such studies could include those that use a survey design that considers repeated lizard plot counts to enable abundance estimation analyses that consider imperfect detection (e.g. Royal–Nichols abundance type occupancy models; Royal and Nichols 2003), or explicitly set out to detect non-consumptive predation influences (e.g. predator induced spatial avoidance using 2-species occupancy models; Robinson et al. 2014). These studies alongside others that also directly quantify variation in small lizard anti-predator behavior across spatial gradients of goanna predation risk would greatly aid understanding of toad induced trophic consequences in this system.
Direct population level impacts of cane toads have not been restricted to a single goanna species but a multitude of native predators in terrestrial and aquatic ecosystems including other monitor lizards, crocodiles, snakes and quolls (Shine 2010; Feit and Letnic 2015). Doody et al. (2006, 2009, 2013, 2015) demonstrated that cane toad-induced decline of goannas can have cascading effects on species not predicted to be directly affected by cane toads such as small lizards, tree snakes, freshwater turtles and grain-eating birds in riparian systems in northern Australia. Cane toads have also been reported to have direct suppressive effects on their invertebrate prey, and to compete with nesting birds for burrows (Boland 2004; Greenlees et al. 2007; Feit et al. 2015). Given the extent of their reported direct impacts, we contend that it is likely that cane toads have also had indirect impacts on species which have strong interactions with species that have declined following the toad invasion. Indeed, given the multitude of possible indirect interaction pathways they could potentially disrupt (Doody et al. 2009, 2013, 2015; Feit et al. 2015), we suspect that the invasion of cane toads has affected a much greater range of taxa than has thus far been reported.
Funding was provided by the Hermon Slade Foundation and Mazda Foundation. We thank Frogwatch, Parks & Wildlife NT and the managers of Dungowan and Camfield for their support. A. Feit provided valuable comments on earlier drafts of the manuscript. Animal census procedures were conducted under the Northern Territory Parks and Wildlife Commission permit 44073, approved by the Animal Care and Ethics Committees of the University of New South Wales (12/103A) and Western Sydney University (A9776).
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