CSIRO PUBLISHING Australian Journal of Zoology, 2018, 66, 152–166 https://doi.org/10.1071/ZO18036

Resolving distribution and population fragmentation in two leaf-tailed of north-east Australia: key steps in the conservation of microendemic species

Lorenzo V. Bertola A, Megan Higgie A and Conrad J. Hoskin A,B

ACollege of Science and Engineering, James Cook University, Townsville, Qld 4811, Australia. BCorresponding author. Email: [email protected]

Abstract. North Queensland harbours many microendemic species. These species are of conservation concern due to their small and fragmented populations, coupled with threats such as fire and climate change. We aimed to resolve the distribution and population genetic structure in two localised leaf-tailed : P. gulbaru and P. amnicola. We conducted field surveys to better resolve distributions, used Species Distribution Models (SDMs) to assess the potential distribution, and then used the SDMs to target further surveys. We also sequenced all populations for a mitochondrial gene to assess population genetic structure. Our surveys found additional small, isolated populations of both species, including significant range extensions. SDMs revealed the climatic and non-climatic variables that best predict the distribution of these species. Targeted surveys based on the SDMs found P. gulbaru at an additional two sites but failed to find either species at other sites, suggesting that we have broadly resolved their distributions. Genetic analysis revealed population genetic structuring in both species, including deeply divergent mitochondrial lineages. Current and potential threats are overlain on these results to determine conservation listings and identify management actions. More broadly, this study highlights how targeted surveys, SDMs, and genetic data can rapidly increase our knowledge of microendemic species, and direct management.

Received 21 May 2018, accepted 4 October 2018, published online 2 November 2018

Introduction Borsboom et al. 2010; Couper and Hoskin 2013). The Some vertebrate species have remarkably small natural biogeographic insights and conservation challenges for these distributions. These species can be particularly interesting vertebrates are similar to those for ‘short-range endemic’ biogeographically, and are often of conservation significance invertebrates (Harvey et al. 2011). due to small and fragmented populations. Microendemic lizards Species may be highly localised and fragmented naturally and frogs characterise vertebrate diversity in the rainforests of due to long-term processes, or may have declined to small Queensland, in north-eastern Australia. Tropical and subtropical distributions in recent times due to human-associated impacts, rainforests have a broken distribution along the coastal or a combination of both. The small distributions of the mountains of Queensland and microendemic lizards and frogs Queensland rainforest lizards and frogs can generally be occur in these isolates (Moritz et al. 2005; Rosauer et al. 2017). considered to be natural – the result of long-term contraction As a broad-scale example, 13 leaf-tailed geckos species (Orraya, and fragmentation of rainforests over millions of years to mesic , Phyllurus) occur allopatrically across these rainforest refugia in the east-coast mountains (Moritz et al. 2005). These areas (Hoskin and Couper 2013). As a finer-scale example, 16 refugia have not been static in recent time, expanding and species of microhylid frogs (Cophixalus, Austrochaperina) contracting through the Pleistocene glacial cycles (Hugall et al. occur across the mountains of the largest of these areas, the 2002). To persist in an area, low-vagility rainforest-associated Wet Tropics (Hoskin 2004). Even small isolates harbour species had to survive through the globally dry glacial periods, considerable endemic diversity. For example, seven highly when Australian east-coast rainforests had their most restricted distinct rainforest-associated and frogs are restricted to known distribution (Hugall et al. 2002; Moritz et al. 2005; Bell the Melville Range, isolated from relatives in rainforest patches et al. 2010; Rosauer et al. 2017). In many areas from where to the north and south for millions of years (Hoskin 2014; rainforest largely disappeared, rainforest-associated and Hoskin and Couper 2014). Many microendemics are restricted frog lineages persisted in deeply piled rocky areas that acted to a single mountain or range, while others occur as distinct as mesic refugia (‘lithorefugia’: Couper and Hoskin 2008). subpopulations separated by areas of unsuitable habitat (e.g. In coastal Queensland these lithorefugia range from large

Journal compilation CSIRO 2018 www.publish.csiro.au/journals/ajz Leaf-tailed gecko distribution and phylogeography Australian Journal of Zoology 153 boulder-fields to small areas of piled boulders on slopes or skink Nangura spinosa, which has received considerable in individual gullies (Couper and Hoskin 2008). Therefore, attention in regard to distribution, phylogeographic structure, climate, topography and geology have all played key roles abundance and threats (e.g. Borsboom et al. 2010). in persistence of rainforest lineages. Competition and other Here we attempt to resolve the fine-scale distribution and species interactions also play a role in shaping distributions, population genetic structure of two highly localised leaf-tailed but resolving these interactions is complex (Cunningham et al. geckos in north-east Queensland. Leaf-tailed geckos, the 2016). carphodactyline clade comprising the genera Phyllurus, Although these species appear to have persisted in small Saltuarius and Orraya, exemplify the pattern of microendemism areas for millennia (Moritz et al. 2005; Hoskin et al. 2011; in moist refugia along the east coast of Australia (Couper et al. Rosauer et al. 2017), they are still of conservation concern. Taxa 2000; Moritz et al. 2005; Hoskin and Couper 2013). This is with small distributions are among the most vulnerable to particularly the case for the Phyllurus, which comprises extinction (MacArthur and Wilson 1967; Terborgh and Winter nine species, eight of which are highly localised and distributed 1980; Gilpin and Soule 1986; Harvey et al. 2011). Stochastic contiguously in rainforest isolates along the coastal ranges of events (e.g. fire or drought) can affect the entire species, whereas Queensland (Couper and Hoskin 2013; Wilson and Swan 2013). for widespread species these events may affect some populations Most Phyllurus occur in mid-east Queensland but two species only. For this reason, distribution area is one of the key factors occur to the north, on the northern side of a long-term in threatened species listing, even without any evidence of biogeographic barrier, the Burdekin Gap (Hoskin et al. 2003). population decline (e.g. Vulnerable D2: IUCN 2012). Small These two species, P. amnicola and P. gulbaru, occur in the distributions also offer little buffering from human-induced Townsville region, at the far southern end of the Australian change in the environment, such as clearing, invasive species, Wet Tropics (Fig. 1). The Wet Tropics is a fairly continuous novel disease and climate change. Even seemingly localised area of tropical rainforest along the mountains and associated threats such as illegal collecting or hunting, or changes in fire coastal lowlands between Townsville, Cairns and Cooktown. regime, may impact the entire population of a highly localised At its southern extent, in the Townsville region, rainforest is species. Therefore, threatened species categories are based very patchy and confined to uplands, gullies and rocky slopes, in largely around the combination of distributional area/population a matrix of open, fire-prone eucalyptus woodland with a grassy size and observed or projected decline due to an identified threat understorey. (e.g. IUCN 2012). was discovered in Patterson’s Gorge in Many of the highly localised Queensland rainforest lizards the southern Paluma Range in 2001 and was described in 2003 and frogs are listed as threatened species under federal (Hoskin et al. 2003). Surveys around the time of discovery (Environment Protection and Biodiversity Conservation Act, found the species at two sites within the gorge, but not in EPBC Act, 1999) and state (Nature Conservation Act, NCA, adjacent areas, and the species was assumed to be restricted to 1992) legislation, based on small distributional area in the rocky habitats of Patterson’s Gorge (Hoskin et al. 2003). combination with observed or projected threats such as disease, P. amnicola was discovered on Mt Elliot, a southern upland climate change, and changes in fire regime (Curtis 2012). outlier to the Wet Tropics region, in 1998 and was described in Illegal collecting is an additional, currently unquantified, threat 2000 (Couper et al. 2000). Surveys at the time of discovery, and to many of these species due to their rarity and spectacular in the few years following, found the species in two areas on appearance (e.g. leaf-tailed geckos) (Auliya et al. 2016). While Mt Elliot and the species was thus assumed to be restricted to many species are listed, many others have not been assessed or rocky habitats on Mt Elliot (Couper et al. 2000; Hoskin et al. are considered Data Deficient due to a lack of information. Data 2003). Both species were found to be restricted to rocky Deficient species, and indeed most of the listed species, have habitats associated with rainforest, particularly rainforest received very little targeted survey effort and are easily gullies and boulder slopes, with the geckos emerging from rock overlooked in general fauna surveys due to cryptic appearance crevices to hunt for invertebrates on rock surfaces at night and habits, and specific habitat requirements. Regardless of (Couper et al. 2000; Hoskin et al. 2003). Since the early 2000s whether species are listed or not, management is generally there have been no surveys targeting these species, with the limited by a lack of detailed information on fine-scale only additional records coming from the known sites and a distribution, population genetic structuring and connectivity, serendipitous recent discovery of a population of P. gulbaru and identification and quantification of threats. in Hervey Range, 3.5 km south of the closest population in Management of Queensland’s microendemic reptiles and Patterson’s Gorge (Murray and de Jong 2017). frogs requires the following broad steps: (1) resolve the Phyllurus gulbaru is listed as Critically Endangered at the distribution in detail, (2) identify any distinct lineages reflecting international (IUCN) and Australian (EPBC) levels, and listed historical isolation and/or local adaptation (Crandall et al. as Endangered by the Queensland Government (NCA). These 2000), (3) determine current isolation and connectivity between listings reflect the small and fragmented known range (two populations, (4) estimate population sizes, (5) resolve threats, disjunct areas of habitat in Patterson’s Gorge) and potential and (6) propose and test management actions. These steps have threats, including fire, illegal collecting, climate change, and been addressed for some high-profile species in Queensland, nearby urbanisation that may introduce threats such as invasive particularly birds and mammals (e.g. the northern bettong of species (Hoskin et al. 2003; Barnett et al. 2017). Fire has been the Wet Tropics: Laurance 1997; Pope 2000; Vernes and Pope particularly highlighted as a threat (Hoskin et al. 2003; Curtis 2006; Bateman et al. 2012) but rarely for highly localised reptile 2012), as the frequency, intensity and timing of fires have and frog species. An exception is the Critically Endangered changed in the last century due to burning for cattle grazing, 154 Australian Journal of Zoology L. V. Bertola et al.

RAINFOREST

P. gulbaru

P. amnicola

(a)

MH719329 HA S. cornutus MH719328 JF807328 MH719338 MH719340 BW MH719339 P. gulbaru

∗ .98/73 MH719334 PRIOR MH719335 SP THIS STUDY MH719337 MH719336 MH719333 MH719332 NP 1.00/84 MH719331 MH719330 MH719343 ∗ MH719342 HR MH719341 MH719344 MH719345 PI (b) 0 510 20 30 Km

P. amnicola PRIOR

MH719355 THIS STUDY MH719356 SM MH719357 MH719349 AC ∗ MH719352 CK MH719350 MH719351 AC

.78/NA MH719353 MH719354 CK MH719346 MH719347 WB .96/NA MH719348

(d) 0.01 (c) 036 1218 Km

Fig. 1. (a) The study area (grey) within Queensland, with all Phyllurus gulbaru and P. amnicola sites marked, and rainforest marked in green. (b) Close-up of the P. gulbaru distribution, with occurrence points before this study marked in blue and those from this study in red. Locality abbreviations: HA, Mt Halifax; BW, Bluewater Range; SP, South Patterson Gorge; NP, North Patterson Gorge; HR, Hervey’s Range; PI, The Pinnacles. (c) Close-up of the P. amnicola distribution, with occurrence points before this study shown in blue and those from this study shown in red. Locality abbreviations: SM, Saddle Mountain; CK, Cockatoo Creek; AC, Alligator Creek; WB, Western Boulder fields. (d) The BI consensus tree with support values from both BI and ML analyses (values shown as BI/ML). Nodes with BI posterior probability of 1.0 and ML bootstrap support of 100 are noted by an asterisk (*). NA represents a node not identified by ML analysis. Tip labels indicate GenBank accession numbers. Leaf-tailed gecko distribution and phylogeography Australian Journal of Zoology 155

fire management through high-frequency late-dry-season The preliminary environmental dataset included 28 burning, and due to the introduction of highly flammable variables, described in detail in Table S1 (Supplementary grasses. P. amnicola is not listed at the international, federal or Material). The environmental layers included: accuCLIM01 to state levels because rocky rainforest habitat (and hence, it is accuCLIM07 from Storlie et al.(2013), which are an improved assumed, the gecko) is widespread on Mt Elliot, and the entire version of the temperature BioClim layers for the Australian known distribution falls within a protected area. The species is Wet Tropics region; Bio08 to Bio19 from the BioClim dataset nonetheless also highly localised and potentially impacted by (Hutchinson et al. 2009); surfaces of elevation (DEM), slope, the same threats associated with small population size: illegal aspect, distance to stream and foliage projected cover (FPC) collecting, altered fire regimes, invasive species, and climate for the Australian Wet Tropics region from Storlie et al.(2013); change. surfaces of broad vegetation groups (BVG) for Queensland In this study, we aimed to resolve the geographic distribution at 1 : 2 million and 1 : 5 million scale (Neldner et al. 2017); and of these species and test for genetic structuring among surfaces of static rainforest refugia and preclearing rainforest populations. We conducted targeted surveys for these species cover (RFP) for the Australian Wet Tropics region (Graham in the southern Wet Tropics/Townsville region based on et al. 2010). expert knowledge. We then used the distribution data to resolve BVG layers were obtained in shapefile format from the the key environmental attributes determining the distribution Queensland Government Data portal and converted to raster of P. gulbaru and P. amnicola, and modelled the potential using ArcGIS 10.1 (ESRI 2012), while all other layers were distributions of both species. In doing this, we adopt the directly obtained in raster format by the eResearch group at hierarchical stepwise Species Distribution Model (SDM) James Cook University at a resolution of 250 m. All layers approach of van Gils et al.(2014) to model highly localised were clipped to the same extent using the ‘raster’ package species that have small numbers of unique occurrence points. (Hijmans et al. 2015) within R (R Development Core Team We then conducted further targeted surveys in key areas of 2013). Pearson’s correlation coefficient was estimated for the potential distributions predicted by the SDMs. Additionally, combinations of all 28 variables using the LayerStats function we sequenced a mitochondrial gene for individuals from all from the package ‘raster’ (Hijmans et al. 2015). The occurrence known populations of both species to assess historical isolation dataset for each species was produced using previously known and broad-scale genetic structuring within each species. From records from CJH’s personal database and records collected these results we derive management recommendations for during Round 1 of field surveys in this study. Presence records P. gulbaru and P. amnicola, and comment more broadly on were then overlaid onto environmental grids and one record the knowledge requirements for management of Queensland’s only per grid cell was retained. many microendemic reptile and frog species. Because recent studies have shown that using MAXENT with default parameters can result in poorly performing models (Shcheglovitova and Anderson 2013; Radosavljevic and Materials and methods Anderson 2014), and that species-specific tuning can increase Field surveys – Round 1 robustness to sampling bias (Anderson and Gonzalez 2011), Surveys were conducted between September 2015 and all models were fine-tuned using the R package ‘ENMeval’ September 2016. Survey sites were selected on Google Earth (Muscarella et al. 2014). Four feature class combinations by Conrad Hoskin, an expert on leaf-tailed geckos (e.g. Couper (Linear, L; Linear and Quadratic, LQ; Linear, Quadratic and et al. 2000; Hoskin et al. 2003; Couper and Hoskin 2013; Hoskin Hinge, LQH; Hinge, H) and regularisation multipliers from and Couper 2013). Surveys targeted areas of layered rock 0.5 to 5, with intervals of 0.25, were tested. The function associated with rainforest in the broad Townsville region. ENMevaluate calculates several evaluation metrics: area under Surveys were conducted on foot at night using head torches. the curve (AUC), minimum training presence omission rate GPS coordinates were recorded for each observed individual. (ORmin), 10% training omission rate (OR10) (Peterson 2011), and the Akaike Information Criterion corrected for small samples sizes (AICc) (Burnham and Anderson 2004; Warren Species distribution modelling and Seifert 2011). AUC has been widely adopted to evaluate We adopted a hierarchical, stepwise species distribution the discriminatory ability of SDMs, but recent studies have modelling approach (van Gils et al. 2014) using MAXENT shown its limitations, especially for presence-only methods 3.3.3k (Phillips et al. 2006) to build SDMs of the two study used to predict potential distributions (Jiménez-Valverde 2012). species, with the aim of estimating their full distribution and Thus, the best model was selected using AICc, as Warren and identifying areas to target future surveys (Raxworthy et al. 2003; Seifert (2011) found AICc to be the best-performing metric for Bourg et al. 2005). A presence-only method was selected small sample sizes. All SDMs adopted 10 000 pseudoabsence because, before this study, presence-only data were collected points, selected at random from a 75-km buffer around for this species, and because it is often difficult to distinguish occurrence points. The extent of the buffer was chosen following true absences from failed detection in cryptic and localised VanDerWal et al.(2009a) on the extent for pseudoabsence species. Furthermore, because these species have small and selection in the Australian Wet Tropics region. Targeted choice highly localised distributions, the number of maximum possible of pseudoabsences to correct for sampling bias was not unique presence points is low, and MAXENT is better suited implemented, as no common sampling bias could be identified than other widely used presence-only algorithms to deal with for all presence points. Sampling was conducted in different small sample sizes (Hernandez et al. 2006). environments (i.e. hilltops and mountaintops, rainforest gullies, 156 Australian Journal of Zoology L. V. Bertola et al. rocky outcrops) and at different distances from roads (<1kmto other areas around The Pinnacles). Methodology was otherwise 10 km from roads). as for the first round of surveys. Due to the small number of unique occurrence points available for each species, parsimony in the number of predictors Genetic sequencing and phylogenetic analysis fi was necessary to avoid over tting the model to the available A small tissue sample was taken from the tip of the tail, from up ’ records, which would reduce the model s ability to identify to four individuals per site, and preserved in 100% ethanol. novel areas of suitable habitat (Anderson and Gonzalez 2011). Genomic DNA was extracted using a modified version of the Following the hierarchical approach from van Gils et al.(2014) Salting Out method (Miller et al. 1988). The entire mitochondrial ‘ ’ ‘ we created three models for each species: a Climatic ,a Non- NADH dehydrogenase subunit 2 (ND2) gene was sequenced ’ ‘ ’ ® Climatic , and an All (overall combined) model. To do so, using published primers (Table 1) and GoTaq Flexi reagents the preliminary set of predictor variables was divided into two (Promega, Madison, WI, USA). For each 25 mL of PCR reaction, categories, climatic and non-climatic, and four subcategories: reagent concentrations and volumes were as follows: Colorless temperature, precipitation, topography and vegetation (Table S1). Flexi Buffer 1X, GoTaq® DNA polymerase 0.125 u, MgCl fi 2 Model parameters were ne-tuned for each subcategory of 1.5 mM, each dNTP 0.2 mM, forward primer 0.4 mM, reverse variables using ENMeval, and predictors with a permutation primer 0.4 mM and ~50 ng of genomic DNA. The following PCR importance of zero in the model with the lowest AICc were profile was adopted for both species: denaturation at 94C for removed from successive steps. The remaining predictors 1 min, then 30 cycles of 94C for 30 s, 58C for 30 s, 72C for were then joined according to their category, thus merging 1 min and final elongation at 72C for 3 min. PCR products were precipitation with temperature predictors (Climatic), and then cleaned with Sephadex® and sequenced in both directions topographic with vegetation predictors (Non-Climatic). Parameters at Macrogen Inc. (Seoul, Korea). All sequences have been for Climatic and Non-Climatic models were tuned with deposited in GenBank (MH719328–MH719357). ENMeval, and predictors with a permutation importance of Sequences were edited in Geneious 9 (http://www.geneious. zero removed. This created the Climatic and Non-Climatic com: Kearse et al. 2012) and aligned using ClustalW with ‘ ’ models. Finally, to create the All model, all remaining standard parameters (Thompson et al. 2002). The alignment predictors were combined, the best-performing parameters was conducted separately for P. gulbaru and P. amnicola and estimated with ENMeval, and predictors with a permutation visually inspected. The two alignments were then jointly importance of zero removed. ENMeval was run once more to aligned with a Saltuarius cornutus sequence from GenBank fi fi ne tune the nal model with the most parsimonious number (JF807328), with S. cornutus being a basal outgroup to both of predictors. species (Hoskin et al. 2003). Uncorrected p-distances were Permutation importance was adopted instead of percentage calculated in MEGA7 (Tamura et al. 2013). contribution to select variables because the latter is heuristically PartitionFinder was adopted to select the best-fit model of fi de ned and depends on the path taken by MAXENT to produce nucleotide evolution for phylogenetic analysis (Lanfear et al. fi the model, while the former depends only on the nal model 2012). For this purpose, ND2 was partitioned into its codon (Phillips et al. 2006). For the Climatic, Non-Climatic and positions, a necessary step for accurate phylogenetic analysis ’ fi All models, predictors with a Pearson s correlation coef cient (Lanfear et al. 2012). The predefined partitioning scheme higher than 0.7 and with the lower permutation importance comprised three partitions, representing the three codon value of the correlated pair were removed, and parameters positions of ND2. A user search was implemented, with linked tuned once more without the removed predictor using branch lengths. The Bayesian Information Criterion was ENMeval. Predictor and parameter selection was conducted adopted to select the best-fit model following Aho et al.(2014). separately for the two study species. This modelling approach PartitionFinder was run twice, constraining evaluated models was repeated on a reduced dataset including only occurrence to those available in RAxML (Stamatakis 2014) and MrBayes points available before this study. (Ronquist et al. 2012) respectively. Phylogenetic analysis was carried out using maximum – Field surveys Round 2 likelihood (ML) and Bayesian inference (BI) methods. ML Follow-up surveys were conducted between September 2016 analysis was performed with RAxML 8 (Stamatakis 2014), and April 2018, to survey additional areas predicted as suitable using the best-fit model inferred with PartitionFinder (GTR+G). by the SDMs. These surveys targeted areas identified as potential The autoMRE function to find the needed number of bootstrap suitable habitat in the SDMs that had previously received no, replicates was adopted, as this function was estimated to or limited, survey effort for these species (e.g. central Paluma perform the best by Pattengale et al.(2010). BI analysis Range, Cape Cleveland, Mt Stuart, central Hervey Range, and was carried out with MrBayes 3.2.6 (Ronquist et al. 2012).

Table 1. Target DNA region and adopted primers for the two study species

Species Target Primer Source Sequence P. gulbaru ND2 L4437a (forward) Macey et al.(1997)50-AAGCTTTCGGGCCCATACC-30 H5934 (reverse) Macey et al.(1997)50-AGRGTGCCAATGTCTTTGTGRTT-30 P. amnicola ND2 M112F (forward) Sistrom et al.(2009)50-AAGCTTTCGGGGCCCATACC-30 Transfer RNA-Asn (reverse) Read et al.(2001)50-CTAAAATRTTRCGGGATCGAGGCC-30 Leaf-tailed gecko distribution and phylogeography Australian Journal of Zoology 157

The best-fit models identified by PartitionFinder were specified habitat at each of these localities and was generally at high for each partition: HKY+G, HKY, and HKY respectively for the densities within this habitat. The exception was the Western first, second and third codon positions of ND2. BI analysis was Boulders locality, where the species occurs at low density on run for 5 106 generations, sampling every 1000 generations, exposed boulder-field with little associated rainforest. with four chains and a temperature of 0.2. The default burn-in fraction of 25% was retained, and the consensus tree produced Species distribution modelling from the remaining trees. Stationary log-likelihood (lnL) scores In total, 14 unique occurrence points were used to model the and an average standard deviation of split frequencies lower distribution of P. gulbaru, and 13 points for P. amnicola (Fig. 1b, fi than 0.01 con rmed that enough generations were being applied c). Occurrence points for P. gulbaru did not include the Mt (Ronquist et al. 2012). Halifax site and one of the Pinnacles sites, as these were discovered in Round 2 of the surveys with the aid of the SDMs Results (see below). The preliminary environmental dataset included 28 variables, described in detail in Table S1. Following the – Field surveys Round 1 stepwise hierarchical selection of variables outlined in the Prior to this study, P. gulbaru was known only from south Methods, three final models were produced for each species: Patterson’s Gorge and north Patterson’s Gorge (blue symbols in Climatic, Non-Climatic, and All. Models for P. gulbaru retained Fig. 1b). In the first round of surveys we found the species at 4–6 variables, while models for P. amnicola retained 2–4 these localities, as well as at an additional three localities: variables (Table 2). Bluewater Range, Hervey Range, and The Pinnacles (red For P. gulbaru, variables included in the Climatic model symbols in Fig. 1b). The new localities were up to 10 km were mean diurnal range (AC02), mean temperature of coldest north (Bluewater) and 17 km south-east (The Pinnacles) of the quarter (BC11), precipitation of wettest month (BC13) and previously known range, greatly increasing the known precipitation seasonality (BC15), while for P. amnicola they distribution of P. gulbaru. P. gulbaru was found to be highly were temperature seasonality (AC04) and precipitation of localised at each of these localities. Assessment of habitat from wettest month (BC13). The Non-Climatic model for P. gulbaru satellite imagery (Google Earth) and on foot in the field show that included DEM, slope, distance to stream (SDist), preclearing the habitat required by these species, consisting of rainforest and rainforest (RFP), broad vegetation categories (BVG5) and piled rocky boulders, is currently fragmented. This is particularly foliage projected cover (FPC), while for P. amnicola the the case for the southern localities (Hervey Range and The Non-Climatic model included aspect, slope, BVG5 and Pinnacles), which are clearly separated from other localities FPC. Finally, the All model retained BC13, BC15, DEM and by unsuitable dry habitat. Despite being highly localised, FPC for P. gulbaru, and AC04, BC13, slope and aspect for P. gulbaru was generally present at high density in suitable P. amnicola. habitat at each site. The exception was The Pinnacles, where only The six final models had full AUC scores from 0.95 (e.g. two individuals were found during three thorough surveys of P. amnicola, Climatic) to 0.99 (e.g. P. gulbaru, All). the locality (totalling ~23 person-hours of survey effort). Regularisation multipliers and feature classes selected with Prior to this study, P. amnicola was known only from two ENMeval matched standard parameters by Phillips and Dudik areas on Mt Elliot: Alligator Creek and Western Boulders (2008) for the All model for both species and for the Climatic Fig. 1c). We found the species at these two localities, as well model for P. amnicola, whereas they differed for the remaining as at an additional locality on Mt Elliot (Cockatoo Creek). We three models, having higher regularisation multipliers and the also discovered a population of P. amnicola on the adjacent addition of hinge features for the P. gulbaru climatic model mountain, Saddle Mountain (Fig. 1c). New localities for (see Table 2). P. amnicola were relatively close to previously known ones For the three final models for each species, the logistic (~1–4 km) (Fig. 1c) but the population discovered on Saddle output was reclassified into suitability categories for ease of Mountain represents the first record of P. amnicola outside of interpretation (see Fig. 2). All models for P. gulbaru show Mt Elliot. P. amnicola was found to be restricted to rocky logistic values above 0.5 for localities where the species was

Table 2. Summary of the three final Species Distribution Models for each species FC, best-fit feature class; RM, regularisation multiplier; AUC, area under the curve; AIC, Akaike Information Criterion; LQ, Linear and Quadratic feature classes; LQH, Linear, Quadratic and Hinge feature classes. Variables: see text for explanations

Species and SDM category Variables FC RM Full AUC Mean AUC AIC Phyllurus gulbaru Climatic AC02, BC11, BC13, BC15 LQH 2.25 0.98 0.98 261.14 Non-Climatic DEM, Slope, SDist, RFP, BVG5, FPC LQ 1.25 0.97 0.97 279.45 All BC13, BC15, DEM, FPC LQ 1 0.99 0.99 219.79 Phyllurus amnicola Climatic AC04, BC13 LQ 1 0.95 0.95 248.63 Non-Climatic Aspect, Slope, BVG5, FPC LQ 2.5 0.96 0.96 261.09 All AC04, BC13, Slope, Aspect LQ 1 0.98 0.98 246.03 158 Australian Journal of Zoology L. V. Bertola et al.

Phyllurus gulbaru Phyllurus amnicola (a)(b) Climatic

(c)(d) Non-climatic

N

(e)(f) All

Legend 0–0.5 0.5–0.6 0.6–0.7 0.7–0.8 0.8–0.9 01020 40 60 Km 0.9–1.0

Fig. 2. Logistic outputs for the three final models for each of the two study species. For ease of interpretation, the logistic maps produced by Maxent were reclassified into a scale of suitability levels (colours) presented in the legend. Training records are shown in the insets as circles (P. gulbaru) and diamonds (P. amnicola). Location IDs are as follows: BW, Bluewater; NP, north Patterson Gorge; SP, south Patterson Gorge; HR, Hervey Range; PI, Pinnacles; WB, Western Boulder fields; CK, Cockatoo Creek; AC, Alligator Creek; SM, Saddle Mountain. Leaf-tailed gecko distribution and phylogeography Australian Journal of Zoology 159 detected in the first round of surveys (i.e. the training records) of the species, with the Climatic model in particular lacking (Fig. 2a, c, e). The Climatic model (Fig. 2a) also identifies Mt resolution (Fig. S1, Supplementary Material). Elliot and Mt Stuart as areas with predicted probability of presence >0.5, while the Non-Climatic model (Fig. 2c) identifies – Mt Elliot and the eastern slopes of the Paluma Range as suitable Field surveys Round 2 areas for the species. The All model for P. gulbaru has a limited Follow-up surveys were based on the SDMs, targeting areas area of predicted suitability, primarily focussed around the identified as moderate to high predicted occurrence that had known locations (Fig. 2e). previously received no, or limited, survey effort for these For P. amnicola the three models are less variable, and they species. Targeted areas included the central Paluma Range all predict four main areas as suitable for this species: Mt Elliot, (north of known P. gulbaru records) for P. gulbaru, additional Saddle Mountain, Cape Cleveland, and the eastern slopes of the areas around The Pinnacles for P. gulbaru, Cape Cleveland Paluma Range (Fig. 2b, d, f). Of the three P. amnicola models, for P. amnicola, and Mt Stuart for either species (see Fig. 3). only the Non-Climatic model (Fig. 2d) has values of predicted The complete survey data (Round 1, Round 2, and all probability of presence >0.5 where the Western Boulders unsuccessful surveys for each species) are shown in Fig. 3. occurrence points are found. The follow-up surveys were successful in finding P. gulbaru SDMs obtained with the reduced dataset including only at two additional sites. The first was in the central Paluma occurrence records available prior to this study identified Range, at Mt Halifax. This location had probability of similar areas with a predicted probability of presence >0.5 for presence >0.5 only for the Climatic and Non-Climatic models P. amnicola. In contrast, for P. gulbaru these models included (Fig. 2a, c), and it fell outside of predicted areas in the All predicted suitable areas well outside the current known range SDM for P. gulbaru (Fig. 3). The second was an additional

Phyllurus gulbaru Phyllurus amnicola

N 0–0.5 0.5–0.6 0.6–0.7 0.7–0.8 0.8–0.9 0.9–1.0 P. gulbaru (Round 1) P. gulbaru (Round 2) P. amnicola (Round 1) Unsuccessful surveys

01020 40 60 Km

Fig. 3. Summary of all surveyed locations for both species, both successful and unsuccessful, displayed over the All model for each species. Occurrence records from the first round of surveys (specified in the legend) were used to train the models. Round 2 surveys were conducted following the SDMs and one additional location was found (P. gulbaru, HA). All surveys shown specifically targeted Phyllurus geckos, including the unsuccessful surveys ( symbols). A successful survey for one species was an unsuccessful survey for the other species because only one species was found. Location IDs are as follows: HA, Mt Halifax; BW, Bluewater; NP, north Patterson Gorge; SP, south Patterson Gorge; HR, Hervey Range; PI, Pinnacles; MS, Mt Stuart; WB, Western Boulder fields; CK, Cockatoo Creek; AC, Alligator Creek; SM, Saddle Mountain; CC, Cape Cleveland. 160 Australian Journal of Zoology L. V. Bertola et al. site in The Pinnacles area. This location had probability of (Moritz et al. 2005; Rosauer et al. 2017). These narrow endemics presence >0.6 for all three models (Fig. 3). No other locations are biogeographically interesting because they provide were found for either species. information on rainforest stability, lineage persistence, and speciation through time (Moritz et al. 2005, 2009; Hoskin et al. Genetic population structure 2011). These species are also of conservation interest due to their small distributions and increasing concern over threats The final nucleotide alignment comprised 1041 base pairs. The such as altered burning practices (e.g. Hoskin et al. 2003) and sequencing products included in the analyses had unambiguous climate change (e.g. Williams et al. 2003; Fordham et al. single peaks and the ND2 region for all individuals translated 2016). A primary consideration with these narrowly restricted into amino acids. For all P. amnicola samples, a three-base- endemics is resolving just how narrow their distributions pair deletion was present in ND2 at position 953–955 of the are, and whether population genetic structuring is present. This alignment, resulting in a loss of one amino acid with no changes information is central to decisions on management, such as to other amino acids. Frequencies of bases A, C, G, T within ND2 species conservation listing, because range size and population were 0.3, 0.34, 0.11 and 0.25 respectively. The bias against subdivision are two primary criteria in species listing (e.g. G matched mitochondrial base frequency patterns in other IUCN 2012). vertebrates (Kocher et al. 1989). The BI and ML analysis Queensland rainforest herpetofauna is typical of many consensus trees had nearly identical topologies, so the BI groups globally in that many species have received little survey consensus tree is presented here with BI posterior probabilities attention since their original description, so their distribution and ML bootstrap values (see Fig. 1d). and population structuring remain poorly resolved. This means Within P. gulbaru, sampling localities showed monophyly that many would be considered Data Deficient in threatened with generally high support values (Fig. 1d). The Pinnacles species listing, which inhibits conservation recognition and locality was highly divergent (uncorrected p-distance = 6.5%) formulation of management actions. Further, poor distribution from all other localities of P. gulbaru, with maximum support knowledge limits our ability to assess potential threats because values for both ML and BI (Table 3; Fig. 1d). Interestingly, it is hard to estimate, infer, or project the level of threat from despite the geographical proximity among the remaining factors such as fire regime changes, climate change or invasive localities, sequences from each were monophyletic. However, species when only part of a species’ distribution is known. sequence divergence among these localities was low (uncorrected Although microendemics, by definition, have small ranges, p-distance = 0.6%) (Table 3; Fig. 1d). The P. amnicola resolving their fine-scale distribution may have significant sequences from Mt Elliot and Saddle Mountain were each conservation outcomes, both in terms of determining threatened monophyletic, and mean uncorrected p-distance between Saddle species status and devising management actions. We used field Mountain and Mt Elliot (Alligator Creek, Cockatoo Creek, surveys, species distribution modelling, and phylogenetics to Western Boulders) samples was 2.9% (Table 3; Fig. 1d). better resolve the distribution and population genetic structure Population structuring on Mt Elliot was limited, with low of two representative rainforest reptiles. divergence of the Western Boulders samples but no clear The first round of field surveys was based on expert opinion. structure among the Alligator Creek and Cockatoo Creek This resulted in the discovery of multiple previously unknown samples (Table 3; Fig. 1d). The mean uncorrected p-distance populations of P. gulbaru and P. amnicola (Fig. 3). Expert between P. amnicola localities at Mt Elliot was 0.7% (Table 3). opinion was based on considerable prior field survey experience for leaf-tailed geckos, and on close scrutiny of topography, Discussion vegetation, and substrate on Google Earth. These surveys The rainforest fragments along the east coast of Queensland targeted previously unsurveyed areas around the known are characterised by highly localised reptile and frog species distributions that appeared to have significant amounts of

Table 3. Genetic divergence of mtDNA gene between localities within Phyllurus gulbaru and Phyllurus amnicola Divergences are uncorrected p-distances. Location IDs are as follows: HA, Mt Halifax; BW, Bluewater; NP, north Patterson Gorge; SP, south Patterson Gorge; HR, Hervey Range; PI, Pinnacles; AC, Alligator Creek; CK, Cockatoo Creek; WB, Western Boulder fields; SM, Saddle Mountain

Phyllurus gulbaru Phyllurus amnicola HA BW NP SP HR PI AC CK WB SM P. gulbaru HA – 0.004 0.007 0.005 0.005 0.062 BW – 0.006 0.003 0.005 0.065 NP – 0.008 0.007 0.067 SP – 0.006 0.066 HR – 0.066 PI – P. amnicola AC – 0.003 0.010 0.029 CK – 0.008 0.029 WB – 0.026 SM – Leaf-tailed gecko distribution and phylogeography Australian Journal of Zoology 161 suitable habitat buffered by topography (i.e. gullies, slopes, barriers to current and historic gene flow because these aspect) or rock to enable persistence over millennia (i.e. Phyllurus geckos are restricted to rocky rainforest habitat ‘lithorefugia’: Couper and Hoskin 2008). Round 1 of surveys and likely have very limited dispersal capabilities away from greatly increased the number of known sites, particularly for suitable microhabitat. P. gulbaru. This increase in number and spread of occurrence Phylogenetics can provide insights into population genetic records for P. gulbaru (Fig. 1b) helped improve the resolution structuring, and thus past and present isolation. In general, the of SDMs (Fig. 2 versus Fig. S1). The increase in occurrence genetic data supported the presence of barriers to gene flow records made little difference for the P. amnicola SDMs (Fig. 2 in both species. All P. gulbaru sites were monophyletic, but versus Fig. S1), most likely because the new records were genetic divergence was generally low (Fig. 1; Table 3). The generally close to the recordsavailable beforethis study (Fig. 1c), exception was the Pinnacles population, which has an average and sampled similar environmental conditions. divergence of ~6.5% from all other P. gulbaru populations All three final SDMs for each species (i.e. Climatic, Non- (Fig. 1; Table 3). This level of divergence is on par with that Climatic and All models) were then used to target additional between recognised subspecies of Phyllurus ossa (Couper and survey efforts (Round 2). For both species, the SDMs predicted Hoskin 2013). This suggests that the Pinnacles population is into the range of the other species (see Figs 2 and 3). In currently isolated and has probably been isolated for at least particular, the P. amnicola SDM predicted north through the hundreds of thousands of years, based on coarse molecular clock distribution of P. gulbaru. This overprediction is interesting estimates for protein coding mtDNA genes (e.g. Moritz et al. because it suggests ecological similarity between these two 2000; Borsboom et al. 2010). There was very little genetic species. This similarity would probably result in competition structuring between P. amnicola sites on Mt Elliot (up to 12 km if the species co-occurred, and this likely explains (along with straight-line apart), but ~2.9% divergence between Mt Elliot dispersal limitations) why none of the nine Phyllurus species and Saddle Mountain sites (Fig. 1; Table 3). This suggests that occur sympatrically (Hoskin et al. 2003). The ‘Climatic’ and the gap in suitable habitat between these two mountains, ‘Non-Climatic’ SDMs for P. gulbaru suggested that small estimated currently at ~2 km straight-line, has been in place areas of suitable habitat exist just to the north of the known for a considerable period. Although we have only used mtDNA sites in the southern Paluma Range (Fig. 2e). A follow-up sequence divergence in this study, mtDNA divergence is survey in this area indeed found a localised population at Mt generally correlated with nDNA divergence when assessing Halifax (Fig. 3). It is likely that other small populations occur historical population structuring (e.g. Singhal et al. 2018). in this area. Surveys to the north of Mt Halifax in the central Genome-wide nuclear markers such as single nucleotide and northern Paluma Range failed to find any Phyllurus geckos polymorphisms (SNPs) would be required to resolve patterns (Fig. 3) but did find the northern leaf-tailed gecko (Saltuarius of population connectivity in more recent times (Gagnaire cornutus) at most sites. The ‘All’ SDM for P. amnicola identified et al. 2015). Cape Cleveland and Mt Stuart as areas with high probability of presence, but Round 2 of surveys at these sites failed to find any Phyllurus geckos (see Figs 2f and 3). The habitat at Cape Species distribution modelling of narrow endemics Cleveland, which was surveyed multiple times, appeared highly This study shows how Species Distribution Models (SDMs) suitable. The absence of leaf-tailed geckos on Cape Cleveland can provide useful information for the conservation of probably reflects severe contraction or loss of rainforest habitat microendemics. As covered above, for both species, models at this site historically, such as during the last glacial maxima generated from a moderate number of unique occurrence when rainforest distribution in the Wet Tropics was much localities gave informative estimates of the range of both reduced (VanDerWal et al. 2009b). species, identified additional areas to survey, found additional We believe we have now resolved the breadth of distribution populations, identified likely gaps in suitable habitat of both species. More undiscovered populations are likely (in congruence with genetic structuring and field surveys), present in between known ones (i.e. in the areas of high suitability and gave insights into ecological similarity between the two in Fig. 3), but negative surveys around the current known sites species. suggest that we now know the main extent of both species’ The SDMs for both species also show areas of modelled distributions. Ground observations during field surveys, visual habitat where the species do not occur, which reveals assessment of satellite imagery, and SDMs show that gaps of limitations in the modelling. As covered above, reasons why unsuitable habitat are currently present between known the species are restricted to a subset of predicted locations populations. In some cases these appear to be narrow gaps include habitat barriers and dispersal limitations, aspects of of rainforest lacking suitable rocky substrate, whereas in microhabitat suitability that the models do not include (e.g. other cases the barriers are more substantial, lacking rainforest depth of layered rock or rock type), and competition. altogether. For P. gulbaru, the Pinnacles and Hervey Range Competition is important in determining distributions but is populations are separated from other populations by barriers of complex and not easily incorporated into SDMs. Ecological unsuitable dry forest habitat 12 km and 3 km wide, respectively similarity between Phyllurus geckos is discussed above but (Fig. 1). P. amnicola populations on Mt Elliot fall within an competition with Saltuarius leaf-tailed geckos may also extensive interconnected patch of suitable habitat, but a narrow play a role in shaping distributions. S. cornutus is common gap in suitable habitat (<2 km) exists between Mt Elliot and throughout the Wet Tropics, with the most southerly records the Saddle Mountain population (Fig. 2). Although these gaps being at the northern P. gulbaru sites (Mt Halifax, Bluewater may not sound particularly large, they are probably significant Range, Patterson’s Gorge). We found S. cornutus and P. gulbaru 162 Australian Journal of Zoology L. V. Bertola et al. in close proximity at two of these sites but S. cornutus was at their unusual appearance and their rarity in the wild and low density and typically on trees (versus P. gulbaru on rocks). in collections. Field surveys conducted in 2013 detected a However, S. cornutus is often found on rocks elsewhere in the substantial drop in population density of P. amnicola along a Wet Tropics and may be a reason why P. gulbaru is not found section of Alligator Creek (CJH, pers. obs.) before poaching further north on the Paluma Range (where S. cornutus is was confirmed by the online advertisement of individuals for common) and, more broadly, why Phyllurus geckos are absent sale by a collector in the USA. The poacher apparently obtained from the Wet Tropics. specific site details from a post on the internet that outlined It is worth noting that the addition of a modest number access to the site and where to find the geckos, and probably of occurrence points by the targeted surveys in this study also from the original description paper. This reinforces the substantially improved the resolution of the Climatic SDM, push to keep site details for rare and desirable species secret or particularly for P. gulbaru (Fig. 2 versus Fig. S1). However, vague, even in description papers (Lindenmayer and Scheele it is important to reiterate that deriving ecologically sensible 2017). models for microendemic species needs to account for the An additional potential threat to both species is competition inherently small number of occurrence points. SDMs produced from the invasive gecko Hemidactylus frenatus (Hoskin 2011). from small numbers of unique occurrences must be interpreted This species is invading natural habitats in the Townsville with caution because of their tendency to overfit and their region, and occurs as high-density breeding populations in higher sensitivity to sampling bias. Tuning of regularisation some forest habitats of the Townsville region (Barnett et al. multipliers and feature classes can reduce these effects, and 2017). H. frenatus has not yet been detected at any Phyllurus should thus become common practice (Anderson and Gonzalez sites but will presumably spread to some of these with time. 2011; Shcheglovitova and Anderson 2013; Radosavljevic and Continued monitoring is required to assess the degree to which Anderson 2014). As Galante et al.(2018) recently showed, they will invade these sites and the potential competitive tuning of parameters and model selection can reduce the effect impacts on Phyllurus. of overfitting and sampling bias by selecting fewer parameters and providing more general and ecologically sensible models. In particular, information criteria such as AICc seem to perform Management recommendations well for model selection in data-poor species (Warren and We assess management for each species by first identifying Seifert 2011; Galante et al. 2018). evolutionarily significant units (ESUs) within each species, then identifying potential management actions for threats outlined above, and finally assessing whether the current conservation Potential threats to P. gulbaru and P. amnicola status is appropriate based on the results herein. Crandall et al. With the distributions largely resolved, a better assessment of (2000) argued that the concept of ESUs should incorporate both potential threats and their likely influence is possible. Although genetic and phenotypic data so that management preserves these species appear to have persisted in these localised areas adaptive diversity across the range of a species. Their criteria for millions of years (Couper et al. 2000; Hoskin et al. 2003), for determining ESUs involves assessing historic and current novel human-induced threats are present. Changes to fire gene flow (e.g. mtDNA divergence for historic genetic flow; regimes has been highlighted as a potential threat to rainforest divergence at microsatellites or SNPs for current gene flow) areas in the Townsville region and other parts of tropical and assessing divergence in phenotypic traits (e.g. morphology, Australia (e.g. Russell-Smith and Stanton 2002; Hoskin et al. habitat, physiology) (Crandall et al. 2000). In determining 2003). Fire is a natural part of this region but the frequency, ESUs in P. gulbaru and P. amnicola we reject historic gene intensity, and timing of fires has changed in the last century due flow where we find at least moderate mtDNA divergence. We to burning for cattle grazing, fire management through high- did not assess current gene flow, genetically, but did assess frequency burning, late dry-season burning, and the introduction the potential for current gene flow during field surveys and of highly flammable grasses. In particular, guinea grass modelling. We reject current gene flow where the field surveys (Megathyrsus maximus) and molasses grass (Melinis minutiflora) and SDMs suggest substantial current habitat barriers. In terms flourish on the interface between rainforest and open woodland of phenotypic divergence, we do not present morphological in this region, creating high fuel loads around rainforest patches data in this paper but summarise differences in morphology (Russell-Smith and Stanton 2002). These higher fuel loads, in that have been identified between populations (Hoskin, unpubl. conjunction with inappropriate fire regimes, particularly late data). dry-season burning, may be incrementally reducing rainforest Phyllurus gulbaru is more widespread than previously area and connectivity in this region (Hoskin et al. 2003). thought; however, it still has a localised and highly fragmented Increased extreme events such as prolonged drought and distribution, with each population restricted to a small area of heatwaves are projected as climate change progresses (Mann suitable habitat (Fig. 1b). The Pinnacles population is an ESU et al. 2017), potentially increasing impacts on fire-sensitive that should be managed separately from other populations. This habitats in this region. population is highly divergent for mtDNA (6.5% uncorrected Poaching represents another significant threat. P. amnicola p-distance) and currently isolated by a substantial habitat barrier individuals have been poached from Mt Elliot in recent years, (Figs 1, 2). It is also morphologically distinct in terms of body as revealed by their possession and sale on the internet in the proportions, tail shape, and aspects of scalation (Hoskin, unpubl. USA and Germany. Australian leaf-tailed geckos (Phyllurus, data). These genetic and phenotypic differences are of a similar Saltuarius and Orraya) are highly desired by collectors due to or greater magnitude than seen between subspecies of P. ossa Leaf-tailed gecko distribution and phylogeography Australian Journal of Zoology 163

(Couper and Hoskin 2013) and the Pinnacles population is being (Hoskin, unpubl. data). The sites on Mt Elliot should be described as a subspecies (Hoskin, unpubl. data). The remaining managed as a set of patchy, but at least partially connected, populations of P. gulbaru should be considered a series of subpopulations, with the aim of maintaining connectivity and isolated populations within a single ESU. The mtDNA gene flow. monophyly of each population, albeit with minimal divergence Fire appears to be less of a threat to P. amnicola because the (Fig. 1), suggests at least recent isolation, as supported by the sites on Mt Elliot and Saddle Mountain are generally moister highly localised nature of suitable habitat observed during and more buffered by extensive rocky areas than P. gulbaru field surveys. This is particularly the case for the Hervey Range sites. Nonetheless, efforts to reduce fire incursion of rainforest population, which is the most divergent and isolated in that are required for P. amnicola and other highly localised ESU. However, we have not detected morphological differences rainforest endemics of Mt Elliot (e.g. the Mt Elliot nursery-frog among these populations (Hoskin, unpubl. data). Although all (Cophixalus mcdonaldi), the Mt Elliot sunskink (Lampropholis one ESU, these populations still need to be managed separately elliotensis), the Mt Elliot crayfish (Euastacus bindal), and because each occupies an isolated area of habitat that could be the Mt Elliot assassin spider (Austrarchaea hoskini). As for subject to different threats. Understanding connectivity within P. gulbaru sites, management efforts for P. amnicola could this ESU, through more detailed population genetic analyses, involve reducing fire frequency, limiting late dry-season may be useful for future management decisions. burning, and strategic burning to protect specific areas. As Fire is a threat to the more southerly populations, particularly mentioned above, illegal collection of P. amnicola is known South Patterson’s Gorge, Hervey Range and The Pinnacles to have occurred. Stopping poaching is difficult but should (Fig. 1b). All of these sites consist of small areas of rocky, involve limiting site information to protected area managers relatively dry, rainforest habitat surrounded by open, fire-prone only and installing hidden cameras in P. amnicola habitat to vegetation (including high fuel loads of guinea grass). Late monitor visitation. dry-season fires, which occur regularly in this area, have Phyllurus amnicola fits the IUCN (2012) Endangered incrementally reduced the rainforest of at least two of these criteria in terms of extent of occurrence (B1), area of occupancy sites over the last 15 years (CJH, pers. obs.). Active fire (B2), and small number of locations (Mt Elliot and Saddle management is required in these areas to protect the rainforest Mountain) (B2a), but fulfilling a B category listing is dependent fragments and facilitate expansion into adjacent rocky habitat on an observed, estimated or projected decline in habitat area near P. gulbaru sites. This could involve reducing fire frequency or number of mature individuals (B2b) (IUCN 2012). Poaching and conducting controlled burning early in the dry season to is a known threat but whether the observed reduction in adults protect these areas from uncontrolled fires later in the dry at one site due to poaching has significantly impacted the season. Poaching is another potential threat and site details species, or whether poaching is a continuing threat, is unknown. should be limited to protected area managers only. Fire is another potential threat but currently fire does not Phyllurus gulbaru should retain its current listing of appear to be impacting the known sites. A research priority is Endangered (under both IUCN 2012 and Queensland NCA monitoring whether H. frenatus will invade P. amnicola habitat 1992). For example, it fits the IUCN (2012) Endangered listing and assessing potential competitive impacts on Phyllurus.We (B1, B2, a, b) due to its small extent of occurrence and area recommend that P. amnicola remain unlisted, pending further of occupancy, severely fragmented populations, and observed research on potential threats. and projected reduction in area of suitable habitat from inappropriate burning at southern sites. At the federal level, Conflicts of interest the species is currently listed as Critically Endangered (EPBC fl Act 1999). Given the increase in distributional area and The authors declare no con icts of interest. number of locations/populations in this study, P. gulbaru no longer fulfils a Critically Endangered listing and we suggest Acknowledgements that this be reviewed to consider an Endangered EPBC listing. This research was conducted under Queensland Environment and Heritage Description of The Pinnacles population as a subspecies Protection permits(WISP16444615, WITK16444715, WITK13653913)and (Hoskin, unpubl. data) would then require individual assessment James Cook University Ethics (A2225). This research did not receive of conservation status. A research priority will be estimating any specific funding. We thank Stephen Zozaya, Anders Zimny, Leah Carr, population size at The Pinnacles, where only four individuals Jenny Cocciardi, Jaimie Hopkins and Tobias Baumgartner for help during have been observed at two sites over five targeted surveys. field surveys; Diana Pazmino and Jaimie Hopkins for help in the laboratory; Phyllurus amnicola has a highly localised distribution, and Collin Storlie for help in obtaining environmental layers and modelling. being restricted to Mt Elliot and Saddle Mountain. The species We also thank local protected area managers Kenneth McMahon, Leigh is widespread and probably fairly connected across Mt Elliot, Benson and Marty McLaughlin for site access information and general but appears to be more localised on Saddle Mountain. The discussions. Mt Elliot and Saddle Mountain populations should be managed independently as separate ESUs. This is due to mtDNA References divergence (2.9% uncorrected p-distance), suggesting historical Aho, K., Derryberry, D., and Peterson, T. (2014). Model selection for isolation, and the observed probable barrier of unsuitable habitat ecologists: the worldviews of AIC and BIC. Ecology 95, 631–636. between the two mountains, suggesting no current gene flow. doi:10.1890/13-1452.1 Additionally, the Saddle Mountain adults are ~25% smaller Anderson, R. P., and Gonzalez, I. (2011). Species-specific tuning increases (for snout–vent length) than those at all sites on Mt Elliot robustness to sampling bias in models of species distributions: an 164 Australian Journal of Zoology L. V. Bertola et al.

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