Top-down effects of a large mammalian carnivore in arid Australia extend to epigeic arthropod assemblages

Journal of Arid Environments 165 (2019) 16–27

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Top-down effects of a large mammalian carnivore in arid Australia extend to epigeic arthropod assemblages

T

Peter Contos, Mike Letnic∗ Centre for Ecosystem Science, University of New South Wales, Sydney, 2052, Australia

ARTICLE INFO

ABSTRACT

Keywords: Apex predator Trophic cascade Mesopredator release hypothesis Insectivory Arthropod Scolopendridae Tenebrionidae

We compared abundances of terrestrial vertebrate insectivores, the rate of insectivory and composition of epigeic arthropod assemblages where an apex predator the dingo was common and rare on either side of the Dingo Barrier Fence (DBF) in Australia's Strzelecki Desert. Previous research in the region shows that suppression of dingoes initiates trophic cascades between dingoes-red foxes-small mammals and woody shrubs and between dingoes-kangaroos and grasses. Results show that terrestrial insectivores were more abundant and the rate of insectivory indexed as the rate of consumption of experimentally provisioned meal-worms was greater where dingoes were common. Overall abundance, diversity and taxon richness of arthropods was unaffected by dingo status. However, there were distinct differences in the composition of arthropod assemblages across the DBF. Scolopendridae, Acrididae and Lepismatidae were more abundant where dingoes were rare, while Tenebrionidae and Blattidae were more abundant where dingoes were common. Our results lend support to the idea that suppression of dingo populations can trigger ≥4 link trophic cascades that extend to arthropod assemblages. We hypothesize that dingo suppression engenders shifts in arthropod assemblages due to a decrease in the intensity of insectivory, changes in habitat structure and alteration of the predatory and competitive interactions between arthropod taxa.

1. Introduction Disruption to the interactions between apex predators and their prey and competitors can have dramatic consequences for the organisation of ecological communities (Letnic and Koch, 2010). Such disruptions can take the form of a trophic cascade, whereby the removal of a large carnivore has alternating positive and negative indirect effects on species in progressively lower trophic levels (Hairston et al., 1960). For example, in the absence of apex predators, populations of large herbivores typically irrupt. In turn, elevated levels of herbivory results in diminished biomass of plants palatable to large herbivores. A similar cascade of effects is predicted by the Mesopredator Release Hypothesis (MRH). According to the MRH reduced populations of large carnivores causes an increase in the abundance and predatory impact of mesopredators with concomitant declines in the abundances of the prey species of mesopredators (Crooks and Soulé, 1999). Most published examples of trophic cascades stemming from the removal of apex predators in terrestrial ecosystems describe 3 link cascades. However, in theory, apex predators effects could propagate to 4 or more trophic levels. By indirectly influencing vegetation and insectivorous small mammals it is conceivable that apex predators’ effects ∗

on ecosystems could extend to arthropods by influencing habitat availability and the intensity of insectivory, respectively. It is conceivable also that shifts in arthropod assemblages resulting from shifts in habitat availability and the intensity of insectivory could trigger cascades within arthropod assemblages by altering the balance of competitive and predatory interactions between arthropod species (Dunham, 2008; Potter et al., 2018; Silvey et al., 2015; Zhong et al., 2017). The dingo (Canis dingo) is mainland Australia's apex predator. Because they pose a threat to livestock, dingo populations are controlled across much of the continent by poisoning, shooting and exclusion fences. The dingo barrier fence (DBF) is an approximately 5000 km long dingo-proof fence that was constructed for the purpose of excluding dingoes from arid and semi-arid rangelands so that sheep in particular can be grazed with little threat from dingoes (McKnight, 1969). Dingoes are common to the north and west of the fence and rare to the south and east of the fence (Letnic et al., 2009b). Suppression of dingo populations in the area “inside” the dingo fence has resulted in the release of red fox and kangaroo populations from top-down population regulation (Florance et al., 2011; Letnic and Koch, 2010) and has triggered trophic cascades involving herbivorous kangaroos and grasses

Corresponding author. E-mail address: [email protected] (M. Letnic).

https://doi.org/10.1016/j.jaridenv.2019.03.002 Received 24 March 2018; Received in revised form 16 February 2019; Accepted 11 March 2019 Available online 20 March 2019 0140-1963/ © 2019 Published by Elsevier Ltd.

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Fig. 2. Map of the study region showing the sites (X) distributed across the DBF (▬). Areas where dingoes are common are indicated by grey shading and areas where they are rare are left white. Here, SRR indicates Strzelecki Regional Reserve.

lands because dingoes frequently attack and kill sheep (McKnight, 1969). The combination of an impenetrable fence and intensive population control of dingoes means that their population density is kept sufficiently low inside the fence (NSW) for sheep grazing to occur (McKnight, 1969). Dingoes are common outside the fence (QLD and SA) where population control is sporadic and opportunistic (Gordon and Letnic, 2016) (Fig. 2). This study took advantage of marked differences in dingo density by conducting experiments across the DBF in the Strzelecki desert, Australia. Two sites were sampled on each side of the fence (n = 2) (Fig. 2). Sites were matched for similarities in rainfall, topography and vegetation type and were situated on land used for conservation reserves (Sturt National Park, Strzelecki Regional Reserve) and for grazing cattle at low densities (Quinyambie Station, Winnathee Station) (0.1–2.85 cattle km2) (Gordon et al., 2015). The study region is topographically uniform and characterised by having sand dunes 3–8 m high running in a NE - SW direction. The vegetation community is classified as Sand Plain Mulga Shrubland (Keith, 2004) and is mostly composed of an understorey of short ephemeral grasses, forbs and herbs (of < 40 cm). The overstorey is sparsely distributed with trees and perennial shrubs, mostly Acacia aneura, Acacia ligulata, and Dodonaea viscosa. Innter-dune areas are mostly swales with clay soils supporting small forbs and shrubs. The study area has an arid climate with a mean annual rainfall ranging between 180 and 200 mm. However, sampling occurred following a prolonged period of high rainfall, which increased the ground cover of ephemeral and perennial herbs and forbs (such as Polycalymma stuartii and Blennodia sp.). The mean annual minimum temperature at the closest weather station (Tibooburra Airport, 29.43°S, 142.01°E) is 13 °C with a mean annual maximum of 27 °C. We tested whether the abundance of terrestrial vertebrate insectivores varied across the DBF by indexing their populations using pitfall and Elliott traps. Live-trapping of vertebrate insectivores was conducted on 14 occasions between November 2012 to March 2017 at all four study sites. Each site had nine grids with nine pitfall traps, spaced 20 m apart in a 3 × 3 grid, consisting of a 10 m aluminium flywire fence (20 cm height) positioned over a PVC pipe (16 cm diameter, 60 cm deep) (Letnic et al., 2009b). We also deployed 20 Elliott traps to increase the trapping effort for insectivores. Twenty traps were placed at 20 m intervals within a 4 × 5 m grid area and were baited using a mixture of peanut butter, oats and treacle. Each grid was spaced ∼1 km apart for independence of sampling and to ensure observations occurred over substantial spatial distances needed in this landscape-scale approach. Traps were checked at sunrise. Captured small mammals were identified, marked with an indelible marker pen to allow identification of recaptures within trap sessions and then released. The insectivores examined in this study included a varanid lizard, the sand

Fig. 1. A conceptual framework of the proposed positive (+) and negative (−) trophic interactions within the study system. The black solid arrows indicate “hypothesis 1” whereby the dingo releases insectivores through mesopredator suppression and corresponding shifts in insectivory drive arthropod assemblages (Letnic et al., 2011). The grey arrows indicate the proposed alternative “hypothesis 2”, whereby the dingo, through suppression of herbivores and release of granivorous mammals from predation (Gordon et al., 2017; Morris and Letnic, 2017), shifts vegetation communities. The varying effects of vegetation may then reverberate back up to alter arthropod communities. Where small mammals have been depleted, mesopredators may incorporate macro-invertebrates in their diet (Short et al., 1999). The blue eclipse contains the interactions examined in this study. Dashed lines indicate indirect interactions. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

(Morris and Letnic, 2017), and a trophic cascade involving an introduced mesopredator, the red fox and small mammals (Letnic et al., 2011) (Fig. 1). Exclusion of dingoes has also been linked to increases in the cover of woody shrubs due to a decrease in granivory and browsing by small mammals (Gordon et al., 2017a; Gordon and Letnic, 2016). Here, we take advantage of the natural experiment made possible by the DBF in Australia's Strzelecki Desert and ask if the suppression of dingo populations has influenced arthropod communities. We hypothesized that removal of dingoes could influence arthropod assemblages via two interaction pathways (Fig. 1). First, we hypothesized that lower abundances of small mammals and consequently rates of insectivory could influence the composition of arthropod assemblages (hypothesis 1). Second, we hypothesized that shifts in vegetation structure and composition could alter the suitability of habitat for arthropods and thus influence the composition of arthropod assemblages (hypothesis 2). To explore these hypotheses we: 1) compared the abundance of insectivores and the rate of insectivory where dingoes were common and rare; 2) contrasted the abundances and composition of arthropod assemblages where dingoes were common and rare. 2. Methods 2.1. Study site The DBF is a 5000 km long wire fence that was constructed between 1914 and 1917 to exclude dingoes from predominantly sheep-grazing 17

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goanna (Varanus gouldii) and three small mammals, the crest-tailed mulgara (Dasycercus cristicauda), dunnart species (Sminthopsis spp.) and the dusky hopping mouse (Notomys fuscus). Varanus gouldii and the marsupial dasyurids, Dasycercus spp. and Sminthopsis spp. are primarily insectivores that consume a wide variety of invertebrate prey (Losos and Greene, 1988; Morton et al., 1983). The rodent, N. fuscus is often classified as an omnivore (Murray et al., 1999). However, for the purposes of our study we classified N. fuscus as an insectivore because previous studies have shown that they consume arthropod prey (Murray et al., 1999) and because we found that they consumed meal worms (see below). We indexed the intensity of insectivory in areas where dingoes were common or rare using foraging trays containing mealworms (Tenebrio molitor). We chose mealworms as they were commercially available and are commonly used in field experiments investigating insectivore foraging strategies (Haythornthwaite and Dickman, 2000; Schmaljohann and Dierschke, 2005). On each trapping grid, we placed three foraging trays (30 cm long, 15 cm wide, 5 cm deep) 20 m apart. Each tray was buried flush with the ground. Because small mammals in arid Australia are nocturnal, and to avoid the meal worms perishing during the heat of the day, we provisioned the trays with mealworms at sunset. 10 mealworms were mixed through a 1 L matrix of sieved sand. The number of mealworms remaining was recorded at sunrise and the trays replenished with mealworms and sand. We recorded insectivory for three consecutive nights in July and two consecutive nights in November 2016 at each site. The rate of insectivory on each sampling occasions was expressed as the mean of the percentage of the mealworms consumed from the original 10 mealworms stocked on each night. At each foraging tray we used a Reconyx™ (Hyperfire HC500) infrared camera to determine which insectivores were consuming mealworms. We secured cameras to a metal post 10 cm from the ground and at a 5–10° angle towards the ground to ensure foraging trays were within the range of the camera. We calculated the “hit-rate” of insectivores by expressing the presence of a species at a foraging tray on a given night as a percentage of the total number of visits by insectivores to a foraging tray throughout the study. We expected that the consumption of mealworms would increase with the density of insectivores. A positive linear relationship between insectivory rates and insectivore density would suggest that the insectivores examined in this study were contributing to the observed rates of insectivory. We used a Generalised Linear Model (GLM) to assess differences in capture rates of insectivores across the DBF with treatment and time as the fixed factors, site as a random factor and “time x treatment” as the interaction term. we chose a negative binomial distribution for these analyses as it accounted for over-dispersion in the model and the large number of zeroes associated with ecological abundance data (Linden and Mantyniemi, 2011). We used the “manyglm” function from the “mvabund” package (Wang et al., 2012) in the statistical programme R (R Core Team, 2016) to construct the GLMs. We tested for differences in observed rates of insectivory across the DBF using a Linear Mixed-Effects Model (LME) with grid as a replicate, treatment and time as the fixed factors, site as a random factor, and “time x treatment” as the interaction term. Significance for each term in all LMEs was assessed using likelihood ratio tests. we constructed LMEs using the “lmer” function from the “lme4” package (Bates et al., 2014) in R (R Core Team, 2016). We assessed the accuracy of my insectivory index using a linear regression with the “lm” function from the “base stats” package in R (R Core Team, 2016). At each grid, we deployed six pitfalls consisting of a 2 m plywood drift fence positioned over a bucket (30 cm diameter, 40 cm deep). Combined with the PVC pitfalls, the buckets increased the trapping effort for arthropods but not insectivores as the buckets were ineffective at catching insectivores due to the buckets’ shallowness. We conducted arthropod surveys in November 2016 and March 2017 and included

trapping at eight grids at Strzelecki Regional Reserve (dingo common) and Sturt National Park (dingo rare), seven grids at Winnathee Station (dingo rare) and six grids at Quinyambie Station (dingo common) for two nights in November and three nights in March. This amounted to a total of 10 trapping nights for each treatment and 14 replicates (grids) for the dingo common treatment (n = 14) and 15 for the dingo rare (n = 15). At sunrise we collected all arthropods present in the PVC and bucket pitfalls and placed them in a 75% ethanol mixture. Arthropod specimens were later sorted to Family level using published keys (CSIRO, 1991). Family level was chosen for the analyses as arthropod Families within Orders have an incredibly diverse range of ecological niches, thus allowing a stronger examination of ecological interactions. We conducted vegetation surveys at each grid in both sampling sessions in order to determine if the abundance of arthropod Families was correlated with environmental variables. We measured shrub cover with a bitterlich gauge (75 cm long, 15 cm cross-bar) following the method of Friedel and Chewings (1988) and the percentage ground cover of forbs, grasses and bare ground using the step-point method Letnic et al. (2009a). We tested for differences in arthropod assemblages and composition across the DBF via two approaches. Firstly, we used LMEs with treatment, time and “time x treatment” as the fixed factors, site as the random factor and set abundance (individual specimens), Family richness and diversity (Shannon-Weiner index H′) as the response variables. We repeated this analysis but set the abundances of specific arthropod Families as the response variables with a negative binomial distribution. Family abundances that did not pass Levene's test of normality (Acrididae, Tenebrionidae, and Lepismatidae) were appropriately transformed to satisfy the requirements of statistical testing for normality. All 31 Families of arthropods were tested for significant differences across the DBF, only the results of Families that showed significant responses were included in the results. For the LMEs, we used the “lmer” function from the “lme4” package (Bates et al., 2014) in the statistical programme R (R Core Team, 2016). We justified the sampling effort by composing a species-accumulation curve of Families found within each treatment and checking that the curve reached an asymptote (levelled-off) (Colwell et al., 2004). We made species-accumulation curves using the “specaccum” function from the “vegan” package (Oksanen et al., 2007) in R (R Core Team, 2016). Differences in mean minimum temperatures between both treatments over the sampling periods were analysed using a Welch's t-test with the “t.test” function from the “base stats” package in R (R Core Team, 2016). We used a Canonical Analysis of Principal Components (CAP) to model changes in arthropod assemblages across the DBF against environmental variables. CAP produces a multivariate cloud of points which visually depicts differences in assemblages. This procedure was able to draw axes through the multivariate cloud that had the strongest correlation with a set of habitat variables. Variables with Pearson correlation coefficients > 0.5 were considered to contribute to the observed differences and were then plotted onto the multivariate cloud as vectors. The length and direction of each vector indicated the strength and sign of the relationship between that variable and the CAP axes. We repeated this procedure using Family variables in order to show which Families contributed most to the assemblages and to indicate which environmental variables were correlated with the abundances of those Families. We used a zero-adjusted Bray-Curtis dissimilarity matrix and square-root transformed abundances to down-weight the contribution of dominant Families. The environmental variables included in this model were: insectivore abundance (average at each grid per night), forb, shrub, sand and grass cover. we conducted these analyses using PRIMER (v. 6.1.15) with the PERMANOVA+ (v. 1.0.5) add on package (Anderson et al., 2008).

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Fig. 3. Mean abundances of the four insectivores examined in this study between November 2012 and March 2017. Error bars represent ± 1 standard error (SE).

3. Results 3.1. Insectivore abundance and the rate of insectivory Three-out-of-the-four terrestrial vertebrate insectivores examined in this study were more abundant in the presence of the dingo. Capture rates of Varanus gouldii were consistently greater where dingoes were common (χ2 = 11, P = 0.003), as were capture rates for Sminthopsis spp. (χ2 = 9.12, P = 0.01) and Notomys fuscus (χ2 = 88.86, P < 0.001) (Fig. 3). Only Dasycercus cristicauda abundance did not differ significantly across the DBF (χ2 = 1.41, P = 0.49) (Fig. 3). Notomys fuscus abundance varied over time (χ2 = 53.44, P = 0.003), with no other species showing a time or interaction effect (Appendix A). There was a positive linear relationship between the intensity of insectivory (% mealworm take) and the number of insectivores present where the foraging trays were deployed (F1,126 = 57.8, P < 0.001) (Fig. 4a). The consumption rate of mealworms increased with the density of insectivores, thus providing evidence that the insectivores examined in this study were responsible for the observed rates of insectivory. There was no interaction between time and treatment for observed rates of insectivory (χ2 = 2.29, P = 0.13). Insectivory was markedly more intense where dingoes were common during both sampling periods (χ2 = 27.37, P < 0.001). In the July sampling period, the rate of insectivory on experimentally provisioned mealworms was 714% greater at sites where dingoes common than sites where dingoes were rare (Fig. 4b). Similarly, in the November sampling period the rate of insectivory was 233% greater at sites where dingoes were common than sites where dingoes were rare (Fig. 4b). There was a main effect of time (χ2 = 9.38, P = 0.003) and was most likely due to the 21.5% increase in the intensity of insectivory on the inside of the DBF between July and November which corresponded with an increase in the abundance of N. fuscus at these sites (Fig. 3).

Fig. 4. (a) Scatterplot showing the relationship between insectivore densities and insectivory. (b) Mean ( ± 1 standard error) mealworm take on either side of the dingo fence during July and November 2016.

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Fig. 5. Mean ( ± SE) abundance (a) Family richness (b) and diversity (H′) (c) of epigeic arthropods across the DBF.

Outside the fence in July, mammalian insectivores foraged at 60% of trays deployed, with N. fuscus the only insectivore foraging in a tray (100%). Inside the fence in July, insectivores foraged at < 2% of trays deployed, with N. fuscus, again, the only insectivore to visit a tray. Outside the fence in November, insectivores foraged at 46% of trays deployed, with N. fuscus foraging at 95% of these and D. cristicauda foraging at the remaining 5%. Inside the fence in November, no insectivores were examined foraging in trays, however several N. fuscus were captured on camera moving past the trays. Avian insectivores were recorded foraging in trays but at minimal rates (< 1%).

separated assemblages according to the presence of insectivores vs. shrub cover (Fig. 7c). CAP axis 2 had a canonical correlation of δ = 0.85 and separated assemblages according to forb cover (Fig. 7c, Appendix B). Acrididae abundance was correlated negatively with the abundance of insectivores and this Family characterised the dingo rare treatment (Fig. 7a). Tenebrionidae abundance was correlated negatively with shrub cover and was typical of the dingo common treatment. Blattidae and Lycosidae abundance contributed to the dingo common treatment and positively correlated with forb cover (Fig. 7a, Appendix B). Despite the markedly different temperatures between the two trapping periods, arthropod assemblages in March 2017 reflected those in November 2016 as there was also a distinct difference in assemblages 0 0 ʹHQM = 0.77067, P = 0.001) (Fig. 7b). on each side of the DBF (QM Assemblage composition in March 2017 was correlated with the abundance of insectivores, shrub cover, forb cover and sand cover 0 0 (QM ʹHQM = 1.27, P = 0.005, m = 4) (Fig. 7d). CAP axis 1 had a canonical correlation of δ = 0.94 and separated assemblages according to insectivores and sand vs. shrub cover (Fig. 7d). CAP axis 2 had a canonical correlation of δ = 0.57 and, like November, separated assemblages according to forb cover (Fig. 7d, Appendix B). Scolopendridae abundance negatively correlated with insectivores, Lepismatidae abundance positively correlated with shrub cover, and both Families characterised the dingo rare treatment (Fig. 7b). Again, Tenebrionidae were typical of the dingo common treatment and their abundance correlated negatively with shrub cover and correlated positively with sand (Fig. 7b, Appendix B).

3.2. Arthropod assemblages We caught a total of 1422 arthropod specimens over the two sampling efforts from 12 Orders and 31 Families (Appendix B). The abundance (χ2 = 0.02, P = 0.88), Family richness (χ2 = 3.75, P = 0.07) and diversity (H’) (χ2 = 1.14, P = 0.29) of epigeic arthropods did not differ across the DBF (Fig. 5). Abundance (χ2 = 14.55, P < 0.001) and Family richness (χ2 = 47.10, P < 0.001) decreased from November to March. Diversity did not vary across sampling times (χ2 = 0.05, P = 0.82) (Fig. 5c, Appendix B). Although temperature may influence arthropod activity, patterns of arthropod abundance, Family richness and diversity across the DBF were consistent between November and March despite the average minimum temperature being 6 °C warmer on the outside of the DBF in November (t = 2.89, df = 6, P = 0.03) (dingo rare = 15.4 °C, dingo common = 21.5 °C). Average minimum temperatures did not differ across the DBF in March (t = 1.06, df = 10, P = 0.31) (dingo rare = 20.3°C, dingo common = 21.5 °C). Of the 31 arthropod Families analysed, the abundances of five showed significant responses to the presence or absence of the dingo (Fig. 6). The Acrididae Family showed an interaction effect (χ2 = 6.91, P = 0.01) and were more abundant where dingoes were rare (χ2 = 20.05, P < 0.001). Although observed, no Acrididae specimens were captured outside the fence in November or March. Lepismatidae were approximately 12 times more abundant where dingoes were rare as opposed to common (χ2 = 24.50, P < 0.001) and were more abundant in March as opposed to November (χ2 = 6.48, P = 0.014) (Fig. 6). Scolopendridae were approximately four times more abundant inside the DBF as opposed to outside (χ2 = 8.13, P = 0.006). The Tenebrionidae Family were more abundant where dingoes were common (χ2 = 75.27, P < 0.001) and in March as opposed to November (χ2 = 11.37, P = 0.001) (Fig. 7). Blattidae abundance showed an interaction between treatment and time (χ2 = 4.18, P = 0.05), and were more abundant where dingoes were common (χ2 = 6.78, P = 0.001) (Fig. 6, Appendix B). In November 2016, arthropod assemblages inside and outside the 0 0 DBF were distinctly different (QM ʹHQM = 0.82011, P = 0.001) (Fig. 7a). Assemblages composition was correlated with insectivore 0 0 abundance, shrub cover, and forb cover (QM ʹHQM = 2.01, P = 0.001, m = 4) (Fig. 7c). CAP axis 1 had a canonical correlation of δ = 0.90 and

4. Discussion Our results provide evidence that the presence or absence of a terrestrial apex predator may alter ecosystems to such a degree that two distinct arthropod assemblages can form in adjacent landscapes. In line with hypothesis 1 (Fig. 1), insectivores were more abundant where dingoes were common which in turn increased the intensity of insectivory (indexed by the rate of mealworm consumption) by up to 55.5%. Although the total abundance, diversity and species richness of arthropods did not differ across the DBF, the abundance of some arthropod Families were dramatically different on either side of the DBF. Taken collectively, these findings lend support to the idea that suppression of dingo populations can trigger ≥4 link trophic cascades. Below we discuss hypothesized interaction pathways through which the removal of dingoes could influence arthropod assemblages. Although we did not directly measure the abundance of dingoes or introduced mesopredators (red foxes and feral cats) on either side of the DBF, there is a large body of evidence, including from our study area, showing that where dingo populations are suppressed, the abundances of introduced mesopredators increases (Brook et al., 2012; Gordon et al., 2017b; Hradsky et al., 2017; Letnic et al., 2011) and in so doing drives declines in the abundance of the prey species of mesopredators 20

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Fig. 6. The mean abundances ( ± SE) of the five arthropod Families that showed significant responses across the DBF in both November 2016 (a) and March 2017 (b). Tenebrionidae beetles (mostly Helea spp.) were the most abundant Family caught within these five Families.

(Gordon et al., 2017b; Greenville et al., 2017; Letnic et al., 2011). Our findings that the small mammals Sminthopsis spp. N. fuscus and lizard V. gouldii were less abundant on the side of the DBF where dingoes were rare and mesopredators abundant are consistent with previous studies and the MRH (Crooks and Soulé, 1999; Gordon et al., 2017b; Letnic et al., 2009a; Read and Scoleri, 2015). Only D. cristicauda showed no statistical association with the dingo, but that may be an artefact of this insectivore being a recent invader of our study system that at the time of our study had only colonized one of our four study sites (Letnic et al., 2016). The marked differences in insectivore abundances, intensity of insectivory and composition of arthropod assemblages across the DBF that we report in this study support hypothesis 1 (Fig. 1) and point towards there being a four-level trophic cascade involving dingoes-red

foxes-mammalian insectivores and arthropods. In accordance with hypothesis 1, the depauperate insectivore guild inside the DBF resulted in reduced rates of insectivory which we indexed as the rate of predation on the mealworms which we deployed, although it is important to note that the rodent N. fuscus was the most important consumer of meal worms. Furthermore, it is pertinent to note also that red foxes are also consumers of arthropods (Short et al., 1999) but occurred at much lower densities than the other insectivores examined in this study, and thus were likely to have little impact on invertebrate assemblages. Despite the dramatic differences in abundances of insectivores and the associated intensity of insectivory, the overall abundance, Family richness and diversity of arthropods did not differ across the DBF. As we sampled a broad range of arthropod taxa, it is likely that the increased abundances of some taxa were negated by decreases in abundance of Fig. 7. CAP ordinations showing which arthropod families contributed to the differences in assemblage composition across the DBF in (a) November 2016 and (b) March 2016 and which habitat variables correlated with the composition of arthropod assemblages in (c) November 2016 and (d) March 2016. The blue circle is placed arbitrarily to the underlying plot. The length and direction of each vector (blue line) shows the strength and sign of the relationship between that variable and the CAP axes. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

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others, resulting in arthropod communities that may be equal in size and diversity, yet starkly different in their composition. Although previous studies have shown insectivorous birds and bats consistently reduce arthropod abundances (Mooney et al., 2010), the role of terrestrial insectivores is less clear due to a lack of literature. However, our results generally align with those of Dunham (2008), who showed the exclusion of terrestrial insectivores from an African forest floor had varying effects on epigeic arthropod taxa. Our CAP analyses revealed profound separation between arthropod communities inside and outside the DBF. The abundance of insectivores, and presumably, an associated increase in predation pressure (insectivory), was consistently associated with differences in arthropod assemblages and negatively correlated with the abundances of two Families. In November 2016, the abundance of grasshoppers (Acrididae) was correlated negatively with that of insectivores, and contrary to previous studies (Fielding and Brusven, 1993), did not correlate with grass or any other habitat variable. Dietary studies show that orthopterans are important prey of the insectivores present in our study area (Losos and Greene, 1988; Morton et al., 1983), and provide support for the notion that a decrease in insectivore abundance in the absence of dingoes (hypothesis 1) may have contributed to the disparity in Acrididae abundances that we observed across the dingo fence. However, grasshopper abundance was also positively correlated with shrub cover. Thus we hypothesize that a combination of the two aforementioned interaction pathways may be working in tandem to dictate their distribution (Fig. 1) (Greenville et al., 2014). In March 2017, the abundance of large predatory centipedes (Scolopendridae) was correlated negatively with insectivore abundance and did not respond to any other variable in the model. Consistent with our findings, Smith et al. (2017) demonstrated that Scolopendrids increased in abundances upon experimental removal of insectivores. Our results also generally confirm the hypothesis proposed by Read and Scoleri (2015), who speculated that suppression of V. gouldii populations by invasive mesopredators would release centipedes from predation. Consequently, we hypothesize that elevated abundance of Scolopendrids at sites “inside” the DBF where dingoes were rare was due to release from predation by insectivores, particularly V. gouldii (Fig. 1). Changes to vegetation communities can often drive changes in arthropod assemblages through bottom-up processes (Kwok et al., 2016). Previous studies from our study area provide evidence that removal of dingoes is linked to a decrease in grass cover and an increase in the cover of woody shrubs due to an increase in grazing activity by large herbivores and a decrease in the consumptive effects of small mammals, respectively (Gordon et al., 2017a; Morris and Letnic, 2017). In accord with the idea that shifts in vegetation structure associated with dingo control may have influenced arthropod assemblages (hypothesis 2), our results revealed correlations between shrub cover and the abundances of beetles from the family Tenebrionidae and Thysanurans from the family Lepismatidae. Specifically, Tenebrionidae showed a negative correlation with shrub cover while Lepismatidae were correlated positively with shrub cover. Although the abundances of Tenebrionidae and Lepismatidae were correlated with habitat changes that have been linked to trophic cascades resulting from dingo suppression (Gordon et al., 2017a; Gordon and Letnic, 2016), it is likely that arthropod taxa were also influenced by variation in habitat variables unrelated to the presence or absence of dingoes top-down effects. For example, the abundance of Blattidae was correlated with forb cover. Previous studies in the region provide little evidence that forb cover is associated with dingo control (Letnic et al., 2009; Morris and Letnic, 2017). However, previous studies show that forb cover is strongly influenced by rainfall history (Letnic, 2004) and

thus it is conceivable that forb cover and possibly the abundance of Blattidae could have been influenced by between site variation in rainfall. Thus far in our discussion, we have primarily considered how dingo control could influence arthropod assemblages by influencing the intensity of predation by vertebrate insectivores and habitat structure. However, it is conceivable that changes in the intensity of insectivory and habitat structure in the presence/absence of dingoes could influence arthropod assemblages by triggering shifts in the strength of interactions between invertebrate taxa (Dunham, 2008; Silvey et al., 2015; Zhong et al., 2017). For example, Silvey et al. (2015) observed an increase in the numbers of a large scorpion (Urodacus yaschenkoi) in the absence of insectivores, resulting in cascading effects to the scorpions’ spider prey. Like U. yaschenkoi, Scolopendrids are large, dominant predators and can be labelled as secondary mesopredators (Silvey et al., 2015). In accordance with the MRH, the release of Scolopendrids that we report inside the DBF may potentially have cascading effects on their primarily arthropod prey. Such cascades could be demonstrated by manipulating the abundance of Scolopendrids. Because many arthropods are litter feeders, it is also conceivable that shifts in arthropod assemblages could influence litter-breakdown and soil processes. Through grazing, macro-detrivores mobilize nutrients such as calcium, nitrates and organic carbon from decaying plant and fungi tissue (Pramanik et al., 2001). In this vein, we hypothesize that the dramatic difference in abundances of Tenebrionids across the DBF could have cascading effects on soil nutrients. Historically, it was believed that terrestrial large carnivores have restricted ecosystem effects that rarely extend beyond indirect effects on vegetation. However, there is now a growing body of evidence that suggests this view may be simplistic and that large carnivores have pervasive effects on ecosystem structure and function (Estes et al., 2011). Our findings cement this view as we found that the presence of the dingo altered an ecosystem function (insectivory) and was linked to a shift in the composition of arthropod assemblages. Indeed, our findings further the idea that the ecological communities on the inside and outside of the DBF are so starkly different that they can be described as separate “ecological universes” (Newsome et al., 2001). As terrestrial food-webs are often highly complex and “entangled” (Montoya et al., 2006), it may be simplistic to assume that just one or the other hypothesized pathways that we propose (Fig. 1) is solely responsible for driving the abundances of certain arthropod taxa (Fig. 1). Indeed, it is likely that multiple processes that are both related and unrelated to dingo population suppression are working in combination to shape the very different arthropod assemblages that exist on either side of the DBF. We caution also, that our results are correlative and highlight that experimental studies which manipulate the abundance of dingoes are required to confirm or refute the hypotheses we present in this study. Furthermore, targeted trapping for arthropods alone may be required as trap predation from insectivores within the pitfall traps may have biased our perception of arthropod assemblage composition. Nonetheless, our results provide evidence that the effects of large carnivores may extend to influence the intensity of insectivory and composition of epigeic arthropod communities. Acknowledgements This research was funded by the Australian Research Council. Thanks to the Ogilvy family, Greg Connors and NSW National parks and Wildlife Service for facilitating access to the study sites. Thanks to Ben Feit, Anna Feit, Christopher Gordon, Charlotte Mills and the many volunteers who assisted with field work.

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jaridenv.2019.03.002. 22

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Appendix A Table 1

Likelihood ratio chi-squared values (χ2), degrees of freedom (df) and P-values (P) from the generalised linear models of insectivore abundances. Significance codes: 0 “***”, 0.001 “**”, 0.01 “*”, 0.05 “.”, 0.1 ““, 1 Response Variable

Term

df

χ2

P

V.gouldii Captures

Treatment Time Treatment Treatment Time Treatment Treatment Time Treatment Treatment Time Treatment

1 13 13 1 13 13 1 13 13 1 13 13

11.00 21.47 0.00 1.415 6.402 0.001 9.122 0.395 0.971 88.86 53.44 18.14

0.003* 0.123 0.948 0.487 0.395 0.463 0.011* 0.395 0.971 < 0.001*** 0.003* 0.281

D.cristicauda Captures Sminthopsis spp. Captures N.fuscus Captures

x Time x Time x Time x Time

Table 2

Likelihood ratio chi-squared values (χ2), degrees of freedom (df) and P-values (P) from the linear mixed-effects model for observational insectivory. Significance codes: 0 “***”, 0.001 “**”, 0.01 “*”, 0.05 “.”, 0.1 ““, 1 Response Variable

Term

df

χ2

P

Observational Insectivory

Treatment Time Treatment x Time

1 1 1

27.366 9.383 2.286

9.196e-04*** 0.003* 0.133

Appendix B Table 3

The Families caught throughout the study period. A tick (✓) indicates that that Family was caught in a pitfall trap within that sampling month and treatment with a blank space meaning that Family was not caught. The number of individual specimens for each Family is noted next to each tick. November Order Araneae

Blattodea Coleoptera

Hemiptera

Hymenoptera Lepidoptera Mantodea Orthoptera Scolopendromorpha Scorpiones Scutigeromorpha Thysanura

Family Lycosidae Miturgidae Nemesiidae Oxyopidae Sparassidae Thomisidae Zodariidae Blattidae Bolboceratidae Carabidae Coccinellidae Curculionidae Elateridae Scarabaeidae Tenebrionidae Trogidae Miridae Pentatomidae Reduviidae Scutelleridae Mutillidae Pompilidae Unknown Mantidae Acrididae Gryllidae Tettigoniidae Scolopendridae Buthidae Scorpionidae Scutigeridae Lepismatidae

March

Dingo Common ✓ (76) ✓ (25) ✓ (16) ✓ (9)

Dingo Rare ✓(42) ✓ (69) ✓ (1) ✓ (13)

✓ (4) ✓ (1) ✓ (24) ✓ ✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

(90) (2) (7) (1) (4) (107) (5)

✓ (2) ✓ ✓ ✓ ✓

(1) (1) (2) (1)

✓ (8) ✓ (3) ✓ (1) ✓ (23)

(3) (5) (1) (170) (1) (11) (2) (1) (10) (10) (3) (3) (1) (7) (4) (3) (3) (1) (29) (15) (1) (6)

✓ (1) ✓ (1) ✓ (44)

✓ (3)

23

Dingo Common ✓ (23) ✓ (6)

Dingo Rare ✓ (45) ✓ (8)

✓ (5)

✓ (10) ✓ (2)

✓ (7)

✓ (5)

✓ (26)

✓ (8)

✓ (1) ✓ (8)

✓ ✓ ✓ ✓ ✓

✓ (159) ✓ (6)

(2) (24) (1) (41) (5)

✓ (1) ✓ (18) ✓ (1)

✓ (2)

✓ ✓ ✓ ✓ ✓

(1) (1) (1) (4) (8)

✓ (2)

✓ (14)

✓ (1)

✓ (5)

✓ (7)

✓ (84)

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Fig. 8. A species-accumulation curve for the cumulative number of Families caught in pitfalls across grids in the dingo common treatment. Levelling-off of the curve indicates addition of extra grid replicates would most likely not increase the amount of arthropod Families caught in pitfalls.

Fig. 9. A species-accumulation curve for the cumulative number of Families caught at grids in the dingo rare treatment. Levelling-off of the curve indicates that my sampling effort was sufficient.

Table 4

Likelihood ratio chi-squared values (χ2), degrees of freedom (df) and P-values (P) from the linear-mixed effects models on arthropod abundance, Family richness and diversity. Significance codes: 0 “***”, 0.001 “**”, 0.01 “*”, 0.05 “.”, 0.1 ““, 1 Response Variable

Term

df

χ2

P

Abundance

Treatment Time Treatment x Time Treatment Time Treatment x Time Treatment Time Treatment x Time

1 1 1 1 1 1 1 1 1

0.022 14.545 0.275 3.752 47.102 0.277 1.142 0.051 0.011

0.884 < 0.001*** 0.602 0.066 < 0.001*** 0.601 0.290 0.822 0.918

Family Richness Diversity (H′)

Table 5

Likelihood ratio chi-squared values (χ2), degrees of freedom (df) and P-values (P) from the linear mixed-effects models of arthropod Family abundance across the DBF. Significance codes: 0 “***”, 0.001 “**”, 0.01 “*”, 0.05 “.”, 0.1 ““, 1 Family

Term

df

χ2

P

Acrididae

Treatment Time Treatment x Time Treatment

1 1 1 1

20.046 6.941 6.941 24.492

< 0.001*** 0.011* 0.011* < 0.001***

Lepismatidae

24

(continued on next page)

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Table 5 (continued) Family

Term Time Treatment Treatment Time Treatment Treatment Time Treatment Treatment Time Treatment

Scolopendridae Tenebrionidae Blattidae

x Time x Time x Time x Time

df

χ2

P

1 1 1 1 1 1 1 1 1 1 1

6.475 2.370 8.130 1.618 2.432 75.266 11.365 1.519 6.784 4.177 4.177

0.014* 0.130 0.006* 0.209 0.125 < 0.001*** 0.001** 0.223 0.012* 0.046 0.046

Table 6

The results of the November arthropod data CAP analysis showing how much each CAP axis contributed to the differences in assemblages (Correlation, Correlation Squared). Eigenvalue

Correlation

Correlation Squared

1 2 3 4

0.8995 0.8455 0.6068 0.347

0.8091 0.7149 0.3682 0.1204

Table 7

Results from the November data CAP analysis showing which variables contributed most to the assemblage differences. Variables that had the highest positive or negative relation to each CAP contributed most to that CAP. Environmental Variable

CAP 1

CAP 2

CAP 3

CAP 4

Sand Insectivores Shrub Grass Forb

0.124 −0.646 0.742 −0.100 0.079

0.176 −0.108 −0.234 −0.074 0.947

0.057 −0.599 −0.571 −0.495 −0.258

−0.956 −0.047 0.070 −0.223 0.172

Table 8

Family responses to each CAP from the November arthropod data. Here, a negative sign indicates that Family negatively correlated with the environmental variables that defined that CAP. Values above 0 indicate a positive correlation.

CAP CAP CAP CAP

1 2 3 4

Tenebrionidae

Blattidae

Lycosidae

Miturgidae

Acrididae

Lepismatidae

−0.6473 0.358187 0.13315 0.0413

−0.25637 0.449107 0.305488 0.072374

−0.1868 0.66439 0.199748 0.0121

0.51448 0.39457 −0.30689 0.210867

0.742667 0.137906 0.023569 0.110785

0.511389 −0.5022 0.315156 0.223191

Table 9

The results of the March arthropod data CAP analysis showing how much each CAP axis contributed to the differences in assemblages (Correlation, Correlation Squared). Eigenvalue

Correlation

Correlation Squared

1 2 3 4

0.9408 0.5745 0.2287 0.0285

0.8851 0.3301 0.0523 0.0008

25

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Table 10

Results from the March data CAP analysis showing which variables contributed most to the assemblage differences. Variables that had the highest positive or negative relation to each CAP contributed most to that CAP. Environmental Variable

CAP 1

CAP 2

CAP 3

CAP 4

Shrub Insectivores Sand Grass Forb

−0.570 0.686 0.451 0.010 −0.004

0.086 0.172 −0.140 −0.317 0.918

0.330 −0.216 0.755 −0.521 −0.055

0.680 0.417 0.213 0.558 0.084

Table 11

Family responses to each CAP from the March arthropod data. Here, a negative sign indicates that Family negatively correlated with the environmental variables that defined that CAP. Values above 0 indicate a positive correlation.

CAP CAP CAP CAP

1 2 3 4

Tenebrionidae

Trogidae

Scolopendridae

Elateridae

Lepismatidae

0.758762 −0.21009 −0.23689 −0.10764

0.242894 −0.54621 0.280626 0.017895

−0.59923 −0.1738 −0.32063 0.002916

−0.27698 −0.49959 −0.28936 −0.4593

−0.69515 0.140063 0.217307 −0.15677

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