Population Decline in the Endangered Grassland Earless Dragon in Australia: Identification, Causes and Management

Home , Jacky dragon, Jerrabomberra (district), Majura (district), Tympanocryptis

Population decline in the endangered grassland earless dragon in Australia: Identification, causes and management

Wendy Jane Dimond B.Sc. (Massey University) M.Sc. (Massey University)

Institute for Applied Ecology Faculty of Applied Science University of Canberra Australia

A thesis submitted in fulfillment of the requirements of the Degree of Doctor of Philosophy at the University of Canberra. September 2010

Abstract

Identifying species under threat from extinction and what is driving their decline is vital to biodiversity conservation. Currently, lizard populations worldwide are under serious threat with widespread declines and predictions of multiple extinctions through climate change. No reptile extinction has been recorded in Australia since European settlement. Yet, emerging signs indicate that Australian reptiles are facing major threats from habitat fragmentation and other extinction forces. Contractions in the distribution of Australian reptiles are well documented with 25 percent of the country’s reptile fauna nominated by conservation agencies and individuals as warranting threatened status and requiring management. One of the most highly endangered lizards in the Australian Capital Territory (ACT), Tympanocryptis pinguicolla (grassland earless dragon), is facing range contraction from habitat loss and fragmentation, caused by agricultural and urban development. Tympanocryptis pinguicolla are of particular concern because they are native temperate grassland specialists, a habitat type of which only 0.5% of the pre1770 area in southeastern Australia remains in a seminatural condition. It is currently unclear if the remaining populations of T. pinguicolla are stable or if contraction is continuing.

In this thesis I report on the ecology of T. pinguicolla using long term mark recapture sampling at two locations: Jerrabomberra West and Majura, and resampling of ten former survey sites across the ACT. I compare historic population survey data to that collected for this thesis using a one tailed sign test to estimate declines in population sizes across the ACT. I also used an exponential growth state space model to estimate population trends at Jerrabomberra West and Majura. In addition multistage mark recapture (MSMR) models were used to estimate population sizes and survival rates of T. pinguicolla at these two locations. A gradual nonsignificant decline in population sizes of T. pinguicolla was observed across all sites from 1995 followed by a dramatic reduction (88%) from 2006 at the most densely populated site (Jerrabomberra West). Annual survival at that site was estimated to be low (0.017 to one year of age and 0.024 to adulthood) over the three years of the study. Of the ten sites at which T. pinguicolla were previously known to be present, four (40%) contained no trace of the lizards, despite extensive survey.

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To further understand what was driving these declines I conducted a Population Viability Analysis (PVA). Reproductive parameters for the PVA were informed using past survey data combined with data collected as part of this thesis work. Based on four discovered nests, clutch size was considered comparable to closely related agamids (5–7), with small (24 mm snout–vent length, 0.62 g) hatchlings and single clutches per year. In addition T. pinguicolla diet was examined using Schoener’s overlap index for differences between sites and electivity. Tympanocryptis pinguicolla feed on invertebrate prey, selecting for a small number of orders such as Coleoptera (beetles) and Lepidoptera (butterflies and moths, including larvae). Using this survival and reproductive data to build the PVA, I showed that the Jerrabomberra population had a very high probability (100%) of extinction within ten years and that the parameters most likely to be driving this decline were juvenile survival and fecundity. Taken together, these data suggest a regional decline in T. pinguicolla that places the species in grave jeopardy of extinction. The key extinction factors for T. pinguicolla are likely to include extreme drought conditions, coincident with over grazing and habitat fragmentation.

The power of monitoring grids (each comprised of 56 artificial shelter burrows) to detect T. pinguicolla was also assessed. I combined the use of zero inflated models with the proportion of traps occupied at a location to determine the number of shelter burrows required to have a given probability of detecting the species if it is present. Threevisit detection probabilities for T. pinguicolla were low at the beginning of the six week sampling period (February – March) (0.12 0.22) but reached levels (up to 0.5) comparable with the few other lizards for which detection probability have been estimated. In the situation where population density was at its lowest, 26 artificial burrows are needed to be checked for six weeks (18 checks) from February into March to have a 50% confidence of detecting the species if it is present. This rises to 167 burrows checked over the same time period for 99% confidence of detection. Once the established confidence rises above 60%, surveying over a longer time period results in a decrease in the trap days (number of artificial burrows multiplied by the number of checks). Given that the timing of the sampling period is one of the most important factors when trying to increase detection, moving the trapping to later in the summer may increase detection success.

Overall, these data suggest that this is a species for which the effects of habitat fragmentation and destruction and drought are a real threat. They are clearly still declining within the Canberra region, and if nothing changes, the effects of low fecundity and juvenile survival will drive the populations to extinction within ten years. With low population densities a large effort survey is required but this will be necessary if we want to inform the life history parameters around fecundity for which we currently have little data and we need most to focus on if we are to better model these populations.

Acknowledgements

It’s not the destination it is the journey, and what a journey it has been! I started my candidature single, incredibly homesick, knowing only two people in Australia and relatively broke. I have finished with an amazing husband, a new home, a fantastic group of likeminded friends and still relatively broke. In addition I have gained knowledge, not just about a species foreign to me, but on the ecology of a whole new continent! For all this I have a lot of people to thank. With five years of journeying there are many people who have touched my life and my research, please do not feel left out if my failing memory relieves me of your name, know you were part of it and I am appreciative.

Thank you Kia Ora

First to my supervisors Stephen Sarre and Will Osborne, thank you seems such an inadequate word to describe my gratitude for your support and encouragement. Even in the face of my, at times, clear ignorance you were kind and understanding. Thank you Will for your tireless hours in the field, you taught me more than just what my thesis entailed. Steve you helped open my eyes when all I could see were the details, I have learnt a lot from just following your example. If imitation is the sincerest form of flattery, I hope you are both flattered as I strive to emulate your skills in research and teaching. I’d also like to make a special thanks to Bernd Gruber and Murray Evans. Bernd, your advice with both statistics and computer modelling have really helped get me through, thank you for putting up with me both in Germany and Australia. Murray, your generosity in lending people, time, data and advice has been invaluable; I can only hope the benefit was mutual.

To all the members of the Institute for Applied Ecology (and AERG): Over the years I have worked with, or for, almost all of you and you have made my education a more well rounded one for it. In particular thanks to the other post graduate students, some who have shared a room with me over the years and had to put up with my crazy ramblings at the computer, you have made my stay here an experience not to forget. Thanks to Marion Hoehn, Don Fletcher, John Roe, Christy Davies, Ben Corey, Erika Alacs, Katarina Mikac, Alex Quinn, Martha Rees, Anna MacDonald,

Michael Jensen, David Wong, Carla Eisemberg, Kate Hodges, Larissa Schneider, Matt Young, Maria Boyle and Stewart Pittard. It has been an honour to share this journey with you. Congratulations Anett Richter, Frau Doktor , my cograssland researcher who helped teach me grasses I would never have learnt by myself. I would also like to thank those others who have given me advice and helped keep me in the real world particularly Niccy Aitken and Tony Buckmaster, I am grateful to call you friends. Lastly, but by no means leastly, the office staff, whom were much needed and appreciated arrivals in my candidature, especially Kerrie Aust, you all made the road much smoother.

None of this work would have been possible without all the help from my countless volunteers who tirelessly searched burrows, checked tubes and kept me company. Special thanks to Elliot Osborne for knowing what to do and getting on with it, even when the going got tough, your reliance at times was my rescue. Toni Stevens, Berlinda Bowler, Lesley Ishiyama and Sam Walker, it has been a pleasure working with all of you, I learnt a lot about giving direction but also on taking advice. A lot of data would never have been collected without you. Thanks must go to all the staff at ACT Parks Conservation and Lands who first led me and were later led by me, in and out of the grasslands over the last five years. To the IAE technical staff, my field work was much facilitated by your generous and practical advice and assistance. To James Dawson, Dave Hunter (both with NSW Department of Environment and Climate Change) and Art Langston (CSIRO) thank you all for your time, giving me both advice and data. For site access thanks to Department of Defence and Canberra Nature Parks staff and to the Margules family from Bonshaw, Louis was a great source of information and encouragement and will be sorely missed.

I would not be here if it weren’t for my family. I mentioned in my last thesis you were waiting for me to enter the ‘real world’, it seems somewhere along the line I realised this is my real world and without you I would never have made it. Thank you Grandad Dimond for first introducing me to the real world on our walks in Albany, Dad for never letting me stop asking why and Mum for always making home home when the real world got too much. Little bro, thank you for reminding me not all words need to have three syllables to be meaningful. It is to my grandparents, past and present that I dedicate this work.

Finally, there is one person above all who has put up with me through all the highs and lows and still loved me. John Couch has been supportive beyond belief. I don’t know if you understood what you were getting into when you met me but I could not be more grateful that coming to Australia brought me to you. It would not be an understatement to say that without you I would never have completed; you made the mountains look like mole hills but made sure I took seriously the important things. Thank you for the little things as well as the big, the cups of tea, the dinners, the wedding.

To everyone, like all things, it hasn’t all been very “choice bro” every step of the way but your kindnesses, encouragement and problem solving has helped me forget the not so choice parts and learn the lessons I needed.

Table of Contents

Abstract iii Acknowledgements ix Table of Contents xiii List of Figures xvi List of Tables xx

Chapter One Introduction 1 Thesis structure and scope 1 Background to this thesis 1 Habitat degradation, fragmentation and loss 2 Overexploitation 2 Species introductions 3 Climate change 3 Chains of extinction 4 Demographic stochasticity 5 Environmental stochasticity 6 Genetic stochasticity 6 Natural catastrophes 6 Tympanocryptis pinguicolla 8 Contributions of others to this thesis 11

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Chapter Two Back to the brink – population decline of the endangered grassland earless dragon (Tympanocryptis pinguicolla ) following its rediscovery 13 Abstract 13 Introduction 14 Materials and Methods 16 General methods 16 Population trend estimation 20 Survival 22 Population size and density 22 Results 23 Population size trends 23 Survival 26 Population size and density 26 Discussion 28 Recommendations for management 31 SUPPLEMENTAL TO CHAPTER ONE: S1. TRAPPING SUMMARY 33 SUPPLEMENTAL TO CHAPTER ONE: S2. SURVIVAL ANALYSIS 37

Chapter Three Dietary habits and reproductive biology of a declining dragon Tympanocryptis pinguicolla in the ACT, Australia. 43 Abstract 43 Introduction 44 Methods 46 Reproduction 48 Diet 48 Results 50 Reproduction 50 Diet 52 Discussion 57

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Chapter Four Diagnosing the decline of an endangered lizard ( Tympanocryptis pinguicolla ) using population viability analysis 63 Abstract 63 Introduction 64 Methods 67 Study site 67 Monitoring of Tympanocryptis pinguicolla 67 PVA model 69 Sensitivity analysis 72 Results 72 Population model and sensitivity 73 Discussion 77

Chapter Five Detecting the endangered grassland earless dragon in remnant grasslands 83 Abstract 83 Introduction 85 Methods 86 Monitoring protocol 86 Statistical analysis 87 Results 90 Discussion 96

Chapter Six Synopsis 101 Conclusions 104 Conservation Management and Future Research 105

References 107

List of Figures

Figure 1.1: Dorsal view of three different Tympanocryptis pinguicolla individuals captured and photographed on the same day (4/3/09). Note the three pale (or yellow) stripes from head to tail crossed by dark hexagonal bars. Grid is 1cm square. 9

Figure 2.1: Map of southeast Australia indicating extant (▲) and historical ( ▲) populations of Tympanocryptis pinguicolla (Robertson et al. 2000). 15

Figure 2.2: Map of the Canberra region showing the twenty three survey sites. 17

Figure 2.3: (a) Winter (June – January) total rainfall (mm) and (b) spring (November – January) mean daily maximum temperature anomalies at the Canberra Airport, ACT, Australia. Climate normals were calculated from 1961 to 1990. The grey line is the five year running mean. 18

Figure 2.4: Trap rate (individuals per 1000 trap days) over time (years) for all sites which have detected Tympanocryptis pinguicolla and been surveyed more than once. To show trend more clearly connecting lines were dashed where there was more than one year between surveys. (See Fig. 2.2 for site abbreviations). 23

Figure 2.5: Population trend () (±95% confidence interval) for Tympanocryptis pinguicolla populations at Majura Training Area (Majura) and Jerrabomberra Valley Grassland Reserve West (Jerrabomberra). Dotted line is equivalence area for a test of no trend in population capture rates. 24

Figure 2.6: Estimate of T. pinguicolla population size and density at Jerrabomberra Valley Grassland Reserve West, Canberra, Australia. (a) Average daily population size (±1 standard error) and (b) Population density in numbers per hectare (±1 standard error), over four trapping grids, in each trapping period. Juveniles Sub-adults Subadults Adults Adults. 26

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Figure S1: Population model schematic used to estimate probability of survival and transition for each stage and population viability in Tympanocryptis pinguicolla. 38

Figure S2: Adult Tympanocryptis pinguicolla survival compared between the two best mark recapture models. Grey bars are for the model Φp, P. and white bars for Φp, Pg, error bars are 95% confidence intervals. 40

Figure 3.1: Map of the Australian Capital Territory (ACT), Australia, showing the three Tympanocryptis pinguicolla study sites.1 Jerrabomberra Valley Grassland Reserve West; 2 Bonshaw; 3 Majura Training Area. 46

Figure 3.2: Percentage frequency of invertebrate orders in Tympanocryptis pinguicolla scats (grey bars) (n = 20) and collected in insect traps in the field (black bars) at Jerrabomberra Valley Grassland Reserve West from February to June 1999. 52

Figure 3.3: Percentage frequency of invertebrate orders in Tympanocryptis pinguicolla scats (grey bars) (n = 5) and collected in invertebrate pitfall traps in the field (black bars) at Jerrabomberra Valley Grassland Reserve West from October to December 2007. 52

Figure 3.4: Percentage frequency of invertebrate orders in Tympanocryptis pinguicolla scats (grey bars) (n = 3) and collected in invertebrate pitfall traps in the field (black bars) at Majura Training Area from October to December 2007. 53

Figure 3.5: Percentage frequency of invertebrate orders in Tympanocryptis pinguicolla scats (grey bars) (n = 7) and collected in invertebrate pitfall traps in the field (black bars) at Bonshaw from October to December 2007. 53

Figure 4.1: Population model schematic used to estimate probability of survival and transition for each stage and population viability in Tympanocryptis pinguicolla . 69

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Figure 4.2: Population size in 115 ha estimated from mark recapture (dotted line) of Tympanocryptis pinguicolla at Jerrabomberra Grassland Reserve West, compared to simulated mean (± 1 SE) (solid line) population trajectory predicted by the population model (a) Adult stage (b) Juvenile stage (c) Subadult stage. 73

Figure 4.3: P robability of extinction (and 95% confidence interval) over the next 20 years from the population viability analysis of Tympanocryptis pinguicolla in Jerrabomberra Grassland Reserve West. 74

Figure 4.4: Sensitivity (S x) of parameters used in the population model for Tympanocryptis pinguicolla in Jerrabomberra Grassland Reserve West. White bars are a +10%, and black bars are a 10%, change in the parameter. The parameter abbreviations are given in Table 4.1. 74

Figure 4.5: Mean time to extinction, with 95% confidence interval (dotted lines), as predicted by a PVA for Tympanocryptis pinguicolla at Jerrabomberra Grassland Reserve West with changes in (a) juvenile survival in winter (circles) and summer (diamonds) (b) fecundity. 75

Figure 5.1: Average daily maximum (dark bars) and minimum (grey bars) temperatures at the Canberra International Airport (February – March). Error bars are ± 1 standard error. (Data from Australian Bureau of Meteorology). 86

Figure 5.2: Average total weekly rainfall at the Canberra International Airport (February – March). Error bars are ± 1 standard error. (Data from Australian Bureau of Meteorology). 86

Figure 5.3: Estimated location wide occurrence for Tympanocryptis pinguicolla at Majura Training Area (dark bars) and Jerrabomberra Valley Grassland Reserve West (grey bars). Error bars are ± 1 standard error on model averaged estimates of occurrence. 91

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Figure 5.4: Probability of detection with a single burrow in one week (three checks) for Tympanocryptis pinguicolla at (a) Majura Training Area (b) Jerrabomberra Valley Grassland Reserve West over four years (2006 black, 2008 dark grey, 2007 light grey, 2009 lightest grey). Error bars are ± 1 standard error on model averaged estimates of detection. 92

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List of Tables

Table 2.1: Monthly survival estimates for each period a and stage a using the best

MSMR model S x*a, Pp*a, ψx*s . 25

Table S1: Locations, trapping methods and rates for Tympanocryptis pinguicolla between 1993 and 2009 in the Australian Capital Territory. * One site checked using a fibrescope. 32

Table S2: Comparison of Cormack Jolly Seber, survival and resighting models for adult Tympanocryptis pinguicolla only at Jerrabomberra Valley Grassland Reserve West, over four grids. 39

Table 3.1: Egg incubation data for four clutches of Tympanocryptis pinguicolla found in Canberra, Australia. 49

Table 3.2: Egg characteristics of four clutches of Tympanocryptis pinguicolla found in Canberra, Australia. 50

Table 3.3: Hatchling characteristics for four clutches of Tympanocryptis pinguicolla found in Canberra, Australia. 50

Table 3.4: Diet and niche breadth from Tympanocryptis pinguicolla scats at each of three sites: Jerrabomberra Valley Grassland Reserve West (Jerrabomberra West), Majura Training Area (Majura) and Bonshaw in Canberra, Australia. 54

Table 3.5: Schoener’s overlap index for the diet of Tympanocryptis pinguicolla between three sites in 2007: Jerrabomberra Valley Grassland Reserve West (Jerrabomberra West), Majura Training Area (Majura) and Bonshaw in Canberra, Australia. 55

Table 3.6: Preference values (=electivities) of potential foods items in the habitat of Tympanocryptis pinguicolla at each of three sites: Jerrabomberra Valley Grassland Reserve West (Jerrabomberra West), Majura Training Area (Majura) and Bonshaw in Canberra, Australia. (Positive preferences are in bold). 56

Table 4.1: Parameter values used during the model simulation of Tympanocryptis pinguicolla at Jerrabomberra Grassland Reserve West. 70

Table 4.2: Captures of Tympanocryptis pinguicolla at Jerrabomberra Grassland Reserve West, over all four grids at each site. 72

Table 5.1: Trapping success at Majura training Area (MTA) and Jerrabomberra Valley Grassland West (Jerrabomberra West) of Tympanocryptis pinguicolla over four to six grids and six weeks. 88

xxi Introduction

Introduction

THESIS STRUCTURE AND SCOPE

In this thesis, I report on the ecology of the endangered grassland earless dragon ( Tympanocryptis pinguicolla ), from the natural temperate grasslands of the Australian Capital Territory (ACT) and New South Wales (NSW), Australia. Once thought to be extinct in the ACT, the recent rediscovery of T. pinguicolla has led to questions about its conservation status. In this thesis, I present the results of field studies, designed to determine the key demographic elements of population size and trajectory for T. pinguicolla . The thesis is structured as four research chapters; each a standalone manuscript for publication that includes an Abstract, Introduction, Methods, Results and Discussion. Literature cited in each chapter is combined and presented as a single reference list. Included in the introductory chapter, is a brief overview of extinction processes and the importance of understanding the underlying cause when managing a species of conservation concern. I also introduce T. pinguicolla, and the ecological knowledge that provides the background to my research.

BACKGROUND TO THIS THESIS

Extinction of a species has occurred when either the last member of that species has died or when the remaining individuals are no longer able to reproduce (e.g. only one gender remaining) (Frankel et al. 1981). There are numerous examples of extinction (for examples see review by Ehrlich et al. 1983). Species conservation however relies on determining the causes of declines and extinctions (Caughley 1994) and not simply detecting and documenting the decline of a species. Extinction may result from either extrinsic (perturbations imposed from the outside) or

1 Introduction intrinsic (evolutionary changes) causes (Purvis et al. 2000b). Extrinsic causes include habitat destruction and degradation, overexploitation (overkill), species introductions and chains of extinction (Diamond 1989); and more recently, climate change (Gibbon et al. 2000; Westoby et al. 2006; Auld et al. 2009). These can swamp the effects of smaller scale intrinsic causes (Purvis et al. 2000b) and are an important conceptual tool for biodiversity conservation (Auld et al. 2009). Threatening processes can act alone or in combination to cause the extinction of a species through declines in abundance (Gibbon et al. 2000; Brook et al. 2008).

Habitat degradation, fragmentation and loss Habitat destruction is generally accepted as the leading cause of biodiversity loss worldwide (Sala et al. 2000; Auld et al. 2009). Habitat degradation reduces numbers of individual plants and animals able to occupy an area and decreases rates of reproduction and survival, while loss and fragmentation diminishes area, suitability and connectivity of habitats and restricts movement of species (Auld et al. 2009). Despite the growing concern over reptile population declines (Gibbon et al. 2000), the effects of habitat loss on reptiles have been understudied, particularly in Australia (Gardner et al. 2007).

Internationally, Australia has a relatively high rate of native vegetation clearance with the 1990 clearance rate for Australia being over half that of the Brazilian Amazonia (Glanznig 1995). In Australia, habitat loss is skewed towards landscapes with flat terrain in humid temperate and subtropical climates (Auld et al. 2009) with natural grasslands forming the most endangered community (Kirkpatrick et al. 1995). The latter are associated with a suite of threatened reptiles which include the striped legless lizard ( Delmar impar ), pygmy bluetongue lizard ( Tiliqua adelaidensis ), and the grassland earless dragon ( Tympanocryptis pinguicolla ) (Osborne et al. 1993a; Williams et al. 2001; Souter et al. 2007).

Overexploitation Overexploitation (or overkill (Diamond 1989)) can directly affect declines in abundance of species through harvesting (Brook et al. 2008). Harvests may be made for a number of reasons including food, medicine, trophy (e.g. skin, shell) and the pet trade. Lizards and snakes (e.g. tegu lizards ( Tupinambis spp .), monitor lizards ( Varanus spp .), pythons ( Python spp .), and iguanas

2 Introduction

(Iguana iguana )) have been used for meat and leather markets for many decades (Mieres et al. 2006) and turtles are commonly used in food (both eggs and adults) and the pet trade (Williams 1999). In Australia, harvesting of the saltwater crocodile ( Crocodylus porosus ), the freshwater crocodile ( Crocodylus johnstoni ) and the hawksbill turtle ( Eretmochelys imbricata ) resulted in serious declines in their populations (Tisdell et al. 2005).

Harvesting does not necessarily cause declines in populations. If managed, populations that show density dependent survival may be able to sustain harvesting (Dimond et al. 2007; Fordham et al. 2007). The challenge is to monitor populations in such a way as to inform harvesting levels for sustainability and identify species for which harvesting should not occur. Currently, with the exception of crocodilians, management programs are lacking for most exploited reptiles (Mieres et al. 2006).

Species introductions Species introductions have been made worldwide both deliberately and inadvertently and these have had dramatic consequences for native species on islands and on the mainland. Introduced species may affect native species by a number of mechanisms including habitat destruction (e.g. the spread of rubber vine ( Cryptostegia grandiflora ) in the riparian habitats of Australian native lizards (Valentine 2006)), predation (e.g. feral pig ( Sus scrofa ) predation on snakenecked turtles (Chelodina rugosa ) (Fordham et al. 2006)), and even poisoning (e.g. cane toads ( Bufo marinus ) poisoning Australian reptiles that have attempted to eat them (Smith et al. 2006)), to name just a few.

Biological invasions have been classified as a global change (Vitousek et al. 1997), with rates of invasion several orders of magnitude higher than prehistoric rates and every region on Earth affected (Ricciardi 2007).

Climate change Climate change is expected to threaten species by changing the temperatures available in their current distributions, making it hard for them to cope physiologically as well as allowing competitors and any diseases they bring to arrive from warmer zones (Westoby et al. 2006). Owing to the thermoregulatory requirements of reptiles, climate change is expected to have

3 Introduction significant effects on their populations. Some reptiles exhibit temperaturedependent sex determination whereby the sex ratio of the hatchlings is determined by nest temperatures during incubation (Gibbon et al. 2000). Given such sensitivity, global warming could alter sex ratios and thereby affect population demographics (Huey et al. 2008). Reptile activity in hot weather may result in body temperatures exceeding the critical thermal maximum, leading to death (Sinervo et al. 2010). Attempts to shelter from the heat (when temperatures rise) restricts foraging, constraining growth, maintenance, and reproduction, thereby reducing population growth rates and raising extinction risk (Sinervo et al. 2010). Twenty percent of lizard species are expected to go extinct worldwide by 2080 (Sinervo et al. 2010) as a direct result of predicted changes to climate.

Chains of extinction Chains of extinction also referred to as extinction cascades (Fischer et al. 2007) or a “dysfunction of biological interactions” (Auld et al. 2009), occur when the extinction of one species triggers the loss of one or more other species. This cascade occurs when species interactions such as predation, mutualism, competition and parasitism have flow on effects through an ecosystem (Diamond 1989). Cascades are not restricted to interactions solely between fauna but may also be as a result of changes in flora – fauna interactions (Fischer et al. 2007). Chains of extinction are particularly likely to occur when ‘keystone’ or ‘strongly interacting’ species are involved (Fischer et al. 2007), when species have highly specific dependencies on one another, or in systems governed by topdown regulation (Auld et al. 2009).

Fischer and Lindenmayer (2007) review losses and declines of species resulting from landscape modification and habitat fragmentation and conclude that extinction cascades are particularly likely to occur in highly modified landscapes, especially if keystone species or entire functional groups of species are lost. Extinctions as a result of a these event chains are usually traceable back to a cause as described above. However, there is growing consensus that recent extinctions can almost never be attributed to a single cause (Didham et al., 2005). Sinervos’ (2010) predictions of extinction for lizards as a result of climate change do not consider interactions between drivers e.g. competition between lizard species or the effects of global warmingcaused water shortages and as such are conservative estimates. Multiple agents of decline interact complementarily or synergistically during the final stages of extinction forming selfreinforcing

4 Introduction interactions that hasten the dynamics of extinction (Didham et al. 2005; Brook et al. 2008). This leads us from drivers of decline, when synergisms are small, to the smallpopulation paradigm when they are large (Brook et al. 2008). The smallpopulation paradigm, “which deals with the effect of smallness on the persistence of a population” and the declining population paradigm, which deals with the drivers of decline as discussed above (Caughley 1994), are becoming well integrated (Boyce 2002; Armstrong 2005; Brook et al. 2008). An understanding of one requires an understanding of the other.

In Caughley’s (1994) seminal paper on the smallpopulation paradigm he admits his characterisation of this paradigm involved a “jerky list” of disparate ideas, however the overarching concept was that of stochasticity. Small populations are at the mercy of stochastic processes i.e. events that are random or by chance. Four elements of stochasticity that can affect small populations detrimentally are generally recognised, these include demographic stochasticity, genetic stochasticity, environmental stochasticity and natural catastrophes (Shaffer 1987; Caughley 1994). Understanding how such stochastic processes influence the deterministic population dynamics of single and metapopulations remains a focus of contemporary ecological research (Wiegand et al. 2001; Bonsall et al. 2004; Waddell et al. 2010).

Demographic stochasticity Demographic stochasticity is the random variation in birth and death rates that leads to population size fluctuations (Caughley 1994; Engen et al. 1998). Even though it occurs in all populations its affects are most alarming in small populations where a single dramatic or small, consecutive downturns in population size, can lead to extinction (Lande 1993).

In small populations demographic variance can pose a higher risk to extinction than environmental stochasticity, especially random fluctuations in the proportion of males and females and the way they pair for reproduction (Legendre et al. 1999; Melbourne et al. 2008). The effect of demographic stochasticity in small populations can be seen in the declining perennial plant Primula veris , where it caused deviation of morph ratios from unity such that in very small populations complete morph loss was encountered (Kéry et al. 2003). In addition, the role of demographic noise on the dynamics of metapopulations has been explored. For example

5 Introduction in hostparasitoid metapopulations demographic stochasticity has a destabilizing influence on population dynamics by disrupting the parasitoids' ability to regulate the host, which then increases unchecked (or at least to carrying capacity) (Wilson et al. 1997; Bonsall et al. 2004).

Environmental stochasticity Environmental stochasticity refers to the variation in population growth rates imposed by changes in the environment and is in addition to demographic stochasticity, which reflects the variation in individuals fates (May 1973). Year to year fluctuations in the environment will have much the same effect on large and small populations (Caughley 1994). Although the commonly held view is that environmental variance increases the likelihood of population extinction, this has more recently been challenged, especially in relation to longlived plants (Higgins et al. 2000; Garcia 2003; García 2008). For example Higgins et al. (2000) argue that environmental stochasticity in the form of variance in recruitment rates can promote persistence in plants that can store reproductive potential between generations. Efford (2001) argued that this result was an artifact from the structural relationship between the mean and standard deviation of a lognormal distribution in their simulation. In the longlived herb Borderea chouardii population growth rates stayed near equilibrium under environmental stochasticity (Garcia 2003). Moreover, the form or degree of correlation in the environmental noise can affect the dynamics of populations (Ripa et al. 1996; Vasseur et al. 2004; Ruokolainen et al. 2009)

Genetic stochasticity Genetic stochasticity is the loss of genetic variation by chance random genetic drift, inbreeding and the resulting accumulation of deleterious mutations (Shaffer 1981). Extinction risk is increased by inbreeding depression and loss of genetic diversity and both occur across a wide range of taxa in the wild as well as the laboratory (Frankham 2005). In addition, individuals with more heterozygosity are fitter than individuals of the same cohort with less heterozygosity (Reed et al. 2003).

Natural catastrophes Natural catastrophes include such things as floods, fires and droughts and are often considered a special case of environmental stochasticity (Caughley 1994). However, they differ because they

6 Introduction have a relatively large effect on population persistence in comparison to the normal year to year fluctuations in population size (Lindenmayer et al. 2005). In addition they can occur on large scales and at random intervals (Shaffer 1987), which can make their prediction more difficult. However some catastrophes such as fire and drought may be accounted for when predicting population existence. For example, in a reintroduction model created for the greater bilby (Macrotis lagotis) , drought was predicted using meteorological data for the period 18881991, the incidence of fire was then linked with intermittent large rains (Southgate et al. 1995). In this example the probability of extinction was very sensitive to frequency of big rain and drought events, whereas altering the magnitude of effect of these events had less impact on the population (Southgate et al. 1995)

In ten wild vertebrate populations that all declined to extinction yeartoyear rates of decline and yeartoyear variability increased as the timetoextinction decreased, evidencing dynamics akin to those theoretically proposed to occur in extinction vortices (Fagan et al. 2006). The term extinction vortex was coined to describe the synchronous effects of inbreeding depression, genetic drift and chance stochastic events on small populations (Gilpin et al. 1986).

In many situations, a species is present in small populations as a result of declines that may have occurred in the past so that an understanding of both what drove the decline and what could further drive small populations to extinction is important. In contrast to Caughley’s (1994) assertion that PVAs were solely the tool of the small population paradigm, they are clearly a tool which crosses over both paradigms (Beissinger 2002; Boyce 2002; Norris 2004; Armstrong 2005; Lindenmayer et al. 2005). The focus of PVAs has moved from only estimating extinction probability to also assessing management scenarios designed to deal with decline drivers, part of the declining population paradigm (Fieberg et al. 2000). For example a PVA was used to recommend management via herbivore exclusion to manage Euphorbia clivicola , a threatened succulent, which is part of the overexploitation issue described as part of the declining population paradigm (Pfab et al. 2000).

7 Introduction

Tympanocryptis pinguicolla

Grassland earless dragons ( T. pinguicolla ) are small, cryptic, insectivorous lizards in the family Agamidae. The genus is found across Australia with the most widespread species being T. lineata (Cogger 1992). Tympanocryptis pinguicolla was first described in 1948 (Mitchell 1948) as T. lineata pinguicolla and had a broad distribution from southern Victoria to Bathurst in New South Wales (Osborne et al. 1993a; Robertson et al. 2000). Tympanocryptis are characterised by the absence of external ear structures and functioning tympanum (ear drum) and the loss of a phalange on the fifth toe of the rear foot (Greer 1989; Cogger 1992). Tympanocryptis pinguicolla closely resemble several other members of the genus, particularly T. lineatea . The dorsal colour pattern consists of dark brown blocks of colour bisected by a longitudinal vertebral pale stripe and two dorsolateral palecoloured stripes that run from the head to tail (Fig. 1.1). The pattern of the blocks is unique to each individual and doesn’t change throughout its life so these markings can be used for individual identification (Dimond unpublished data) (Fig. 1.1).

Tympanocryptis pinguicolla are among the most endangered lizards in Australia and have faced extensive range contractions nationally. Despite repeated sightings, surveys since 1967 have failed to find T. pinguicolla in the state of Victoria, where it is now possibly extinct (Swan 2000) while it was also thought to be extinct in the Australian Capital Territory (ACT) until its rediscovery in 1991 (Osborne et al. 1993a). Currently the species is restricted to native grassland habitat in the ACT in the Majura and Jerrabomberra valleys, and, in New South Wales, at Queanbeyan Nature Reserve and at several sites near Cooma (ACT Government 2004; Nelson 2004) (Fig. 2.1). Since their rediscovery in 1991 the distribution of T. pinguicolla throughout the ACT and into NSW has been investigated through presence/absence surveys and short term monitoring (Langston 1996; Nelson et al. 1996; Nelson 1997; Nelson et al. 1998; Evans et al. 2002)

Increased concern for the conservation status of T. pinguicolla occurred after the subspecies T. lineata pinguicolla was elevated to full species in 1999 following the use of allozyme electrophoresis and multivariate morphological techniques to examine the T. lineata species

8 Introduction group (Smith et al. 1999) The species is now listed as endangered under the Commonwealth Environment Protection and Biodiversity Conservation Act 1999 (the EPBC Act) (Department of the Environment 2010) because of contractions in its distribution. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species (Australasian Reptile & Amphibian Specialist Group 1996) has listed T. pinguicolla as vulnerable but notes also that revision of its status is required. Tympanocryptis pinguicolla is also listed as endangered in New South Wales and the Australian Capital Territory and critically endangered in the state of Victoria (Robertson et al. 2000; Swan 2000; Nelson 2004).

9 Introduction

Although habitat preference and thermal ecology have been studied for this species (Langston 1996; Nelson 2004), little is known of its population dynamics. Currently no information is available on hatching success, survival or population sizes and there have only been two reported discoveries of egg clutches and incubation burrows in this species (Langston 1996). Habitat loss and fragmentation, caused by agricultural and urban development have been considered the main reasons for the range contraction seen in this species (Robertson et al. 2000), especially as T. pinguicolla are native temperate grassland specialists and do not occur in grasslands that have been significantly modified or degraded (Langston 1996; Stevens 2006). Only 0.5% of natural temperate grasslands in southeastern Australia remain in a seminatural condition (Kirkpatrick et al. 1995). Recently, reserve status has been conferred on several large areas of natural temperate grassland that contain populations of T. pinguicolla in the Jerrabomberra Valley in the ACT . However, it is unclear if populations within these areas are stable or if contraction is continuing. Degradation of natural temperate grasslands may be occurring in these areas owing to: rock removal, irrigation, changed fire regimes, changed grazing regimes, invasion of weeds and introduced animals (Brereton et al. 1993; Cogger et al. 1993; Osborne et al. 1993a; Melville et al. 2007).

To address these concerns, I investigate the status of the populations across the ACT. In chapter 3 I summarise the available ecological data on reproduction and diet from both previous studies and that collected during fieldwork carried out for my thesis. I investigate population decline with the use of a Population Viability Analysis in chapter 4. Finally, in chapter 5, I use zero inflated models with the proportion of traps occupied at a location to estimate effort required in future surveys for this species. Although every effort was made to reduce repetition, these chapters are set out as manuscripts and as such some reiteration has been unavoidable.

Chapter 2 Dimond W J, Osborne W S, Evans, M, Gruber, B and Sarre, S D. Back to the brink – population decline of the endangered grassland earless dragon ( Tympanocryptis pinguicolla ) following its rediscovery. Presented in thesis as submitted for publication in the Journal of Applied Ecology.

10 Introduction

Chapter 3 To be submitted for publication as: Dimond W J, Richter A, Langston A, Sarre, S D and Osborne W S. Dietary habits and reproductive biology of a declining dragon Tympanocryptis pinguicolla in the ACT, Australia.

Chapter 4 To be submitted for publication as: Dimond W J, Gruber, B, Osborne W S and Sarre, S D. Diagnosing the decline of an endangered lizard ( Tympanocryptis pinguicolla ) using population viability analysis.

Chapter 5 To be submitted for publication as: Dimond W J, Osborne W S and Sarre, S D. Detecting the endangered grassland earless dragon in remnant grasslands.

Contributions of others to this thesis

The work in this thesis includes manuscripts for which I am the main but not the sole author. Will Osborne and Stephen Sarre were included as coauthors in acknowledgement of their intellectual and physical contributions to the research. These contributions took the form of advice and input on project design and draft manuscripts, and provision of funding and other resources. Murray Evans (ACT Parks Conservation and Lands) made available T. pinguicolla survey data collected prior to my involvement. Murray and Bernd Gruber both offered advice on survey design. In addition Bernd provided assistance with statistical analysis. Anett Richter and Art Langston kindly provided unpublished data and comment on a manuscript. I took the lead in all work, I designed the research, undertook the field work, analysed the data and wrote the manuscripts

11 Introduction

12 Population Decline

Back to the brink – population decline of the endangered grassland earless dragon ( Tympanocryptis pinguicolla ) following its rediscovery

ABSTRACT

1. Lizard populations are under serious threat with widespread declines and predictions of multiple extinctions through climate change.

2. We assessed the stability of populations of the endangered grassland earless dragons (Tympanocryptis pinguicolla) conducted after its rediscovery in the Australian Capital Territories using capture rates at 23 sites and survival and population size estimation at one intensively studied site .

3. We show a gradual nonsignificant decline in population size across all sites from 1995 followed by a dramatic reduction (88%) from 2006 at the most densely populated site.

4. Using markrecapturerelease approaches, we estimate annual survival at that site to be low (0.017 to one year of age and 0.024 to adulthood) over the three years of the study.

5. Taken together, these data suggest a regional decline among T. pinguicolla populations that place the species in grave jeopardy of becoming the first confirmed reptile extinction in Australia since European settlement. The key extinction factors are likely to include extreme drought conditions, coincident with over grazing and habitat fragmentation.

6. Synthesis and applications. Our data show the value in continuous monitoring of at risk species in identifying declines before it is too late and in providing strong baselines with which to identify causes of decline through rigorous experimentation.

13 Population Decline

INTRODUCTION

No reptile extinction has been recorded in Australia since European settlement (Caughley et al. 1996; Department of the Environment 2010). This superficially strong record masks emerging signs that Australian reptiles face major threats from habitat fragmentation and other extinction forces (Smith et al. 1996; Driscoll 2004). Contractions in the distribution of Australian reptiles are well documented (Sarre et al. 1995; Dorrough et al. 1999; Díaz et al. 2000; Fitzgerald et al. 2002; Jellinek et al. 2004; Driscoll et al. 2005; Brown et al. 2008; Smith et al. 2009) and climatic changes are likely to exacerbate these reductions (Sinervo et al. 2010) pushing vulnerable species to extinction. At present, 25 percent of the country’s reptile fauna have been nominated by conservation agencies and individuals as warranting threatened status and requiring management (Cogger et al. 1993), and 54 of those species and subspecies are formally listed as vulnerable, endangered, or critically endangered (Department of the Environment 2010).

The accurate estimation of population size trends is central to identifying species at risk of extinction. Such estimations can be extremely challenging, particularly when populations are small or cryptic (McCulloch et al. 2001; Melbourne et al. 2004; Humbert et al. 2009). An undiagnosed decline may lead to inappropriate management resulting in a failure to prevent extinction. The key issue is gathering sufficient quality data to obtain the precision necessary to detect a true decline (Fagan et al. 2010) and the demographic and ecological information necessary to be able to identify agents of decline (Hone et al. 2005). The difficulty of doing so is magnified by the smaller population sizes often associated with endangered species. Moreover, the nature of research funding in many countries, including Australia, makes maintaining a cohesive program of population monitoring extremely difficult. The net result being that many conservation decisions are made on disparate and less than adequate data.

The grassland earless dragon, Tympanocryptis pinguicolla (Agamidae), is a species for which little is known and yet the conservation imperative is grave. The dragon itself once had a broad distribution that included known populations in southern Victoria (McCoy 1890; Mitchell 1948), and the tablelands in southern New South Wales as far north as Bathurst (Osborne et al. 1993b) (Fig. 2.1). Within the ACT, historic records suggest that the lizard was locally common before

14 Population Decline

1970 (Robertson et al. 2000) but was not sighted between 1970 and 1991 and considered absent until rediscovered near Canberra in 1991 (Osborne et al. 1993a). The species is a habitat specialist, restricted in distribution to the natural temperate grassland habitats in southeastern Australia, and heavily dependent on arthropod burrows within those grasslands (McCoy 1890; Stevens et al. 2010). The grasslands themselves have suffered wholesale clearing or modification such that over 95% of the preEuropean estate is now lost or irreversibly modified (Kirkpatrick et al. 1995).

T. pinguicolla persists in only two small regions of remnant grassland; one in the Australian Capital Territory (ACT) and adjacent New South Wales region between Canberra and Queanbeyan, and the other in the Southern Tablelands in the Monaro region between Cooma and Nimmitabel (Fig. 2.1; Robertson et al. 2000). These two regions are separated by over 100km, and the populations within them are phylogenetically distinct and likely to be separate evolutionary significant units or even species (Scott et al. 2000; Melville et al. 2007). The vulnerability of these phylogenetic forms is further exacerbated by a patchy and highly fragmented distribution in populations that are of unknown size. The species is classified as endangered nationally (under the Environment Protection and Biodiversity Conservation Act 1999) and at a state level (ACT, New South Wales and Victoria) (Robertson et al. 2000) and recognised as Vulnerable on the International Union for the Conservation of Nature (IUCN) Red List of Threatened Animals.

Much of the remaining habitat of T. pinguicolla is under threat of development given its proximity to the rapidly expanding urban centre of Canberra and thus an accurate assessment of the status of this species is required if the species and its habitat is to be managed effectively. Here, we report on surveys of T. pinguicolla populations in the Canberra region spanning 17 years and provide an account of population and distribution change over that period. We demonstrate that, T. pinguicolla has experienced a longterm decline in population size punctuated by recent dramatic collapses and lay the foundation for future conservation actions for this species.

15 Population Decline

500 km

Figure 2.1 : Map of southeast Australia indicating extant (▲) and historical ( ▲) populations of Tympanocryptis pinguicolla (Robertson et al. 2000) .

MATERIALS AND METHODS

General methods

We report on research at 23 native grassland sites in the ACT region from September 1993 to March 2009 (Table S1; Fig. 2.2). Each of these locations supports Austrodanthonia – Austrostipa native grasslands, and all have been used for livestock grazing. These locations vary in their exposure to cultivation from never cultivated to most recently cultivated over thirty years ago. Generally these sites are characterised by open structured tussock grasslands with little or no trees and shrubs (Osborne et al. 1993a; Osborne et al. 1993b), limited or no fertilisation or pasture improvement, and comprising low rises in well drained areas (Osborne et al. 1993a). All

16 Population Decline sites are within 13 km of the Canberra International Airport and climate records from the airport (Bureau of Meteorology) show that since 2000 this area has been subject to low winter – spring (June – January) rainfall compared to the climate normals (Fig. 2.3a) and rising mean maximum spring (November – January) temperatures on cloudless days (Fig. 2.3b).

Pitfalls or artificial shelters have been used to search for T. pinguicolla at these 23 locations (Table S1). Pitfalls consisted of a sleeve of PVC piping 9cm in diameter and 12cm deep, with a dirt base, covered by a 200 by 200 mm metal shelter mounted on wire legs with an 80 mm high, 500 mm long aluminium drift fence placed across the sleeve in some surveys or removed altogether in others. Artificial shelter tubes (tubes) were made of a piece of capped PVC drain pipe (31mm diameter x 142mm deep), lined with brown paint and sand, slipped inside an outer sleeve and shaded by a 200 x 200 mm canvas or metal roof. Both types of traps were dug into the ground flush with the surface.

Trapping arrays comprised four main configurations (Table S1): (1) Linear transects ranged in length from 450m to just over a kilometre and from one to eleven transects per site with traps placed either 5m or 10m apart and occasionally made up of alternating trap types (pitfall and tube). (2) Group of eight pitfall traps arranged in four pairs (with a 400 by 400mm cover) 5m apart in a square, with one or two groups of eight at each site; (3) Group of four tubes arranged on a 14.2m diagonal, with between two and 134 groups placed at each site; (4) Grid configurations, which took one of two forms. A single grid consisting of 19 by 11 pitfall traps at 5m intervals covering 0.45 ha was placed at one site (used 19931996) and a standardised grid system (20022009) which comprised tubes placed in a seven by eight grid spaced at 10m intervals and covering 0.42 ha. Grids were separated by no less than 100m.

17 Population Decline

16 10

6 12

4Km

21 17 4 11

8 18

7 20 19 14 9 15 3 13

1 2 23

Figure 2.2: Map of the Canberra region showing the twenty three survey sites. 1 Jerrabomberra West (Jerrabomberra Valley Grassland Reserve West); 2 Jerrabomberra East (Jerrabomberra Valley Grassland Reserve East); 3 Bonshaw; 4 Pialligo; 5 Majura (Majura Training Area); 6 Airport (Canberra International Airport); 7 AMTECH (Advanced Manufacturing Technology Estate); 8 Belconnen Naval Base; 9 Callum Brae; 10 Campbell Park; 11 Crace; 12 Malcolm Vale; 13 Urambi Hills NP; 14 Australian Geo Site Office; 15 Therapeutic Goods Association; 16 Avonley; 17 Fairbairn; 18 Dundee; 19 Cookanalla; 20 Fyshwick; 21 Mulanggari Grassland Reserve, 22 Weston 23 Queanbeyan Nature Reserve.

18 Population Decline a

19 Population Decline

Capturemarkrelease methods were employed in all lizard surveys with marking by toe clipping (1994 to 1996), nontoxic sharpie pen, or photographic pattern recognition. Tympanocryptis pinguicolla have dark crossbars located between three pale longitudinal stripes which run the length of the dorsal surface (Wilson et al. 2003). This pattern of bars and stripes is unique to each individual and does not change over time. We used a six point coding system based on the pattern of crossbars and stripes (Fig 1.1) (code available on request) to facilitate identification. A blind trial of 83 reference photos against 140 test photos confirmed this approach as highly accurate (134, 97%, correct).

All individuals were sexed, weighed (g) and the snout vent length (SVL) and tail length (mm) measured. We defined adults by the size of the smallest gravid female (all individuals >48.3mm SVL) identified over all captures, and juveniles by the size of the smallest individual captured during the breeding season (i.e. the period just before hatching, October – November) (in summer, individuals <38.3mm SVL). All individuals between those two size classes were categorised as sub adults and unable to breed.

Sex was assigned by assessing hemipenal swelling at the base of the tail and by inspection of the cloaca to identify the ends of the hemipenes, visible as red dots at either side of the vent in males. This method was validated through a blind test of eleven museum specimens that were subsequently dissected to confirm sex. Ten of the eleven specimens were identified correctly with the incorrect specimen being a juvenile. Consequently, we report sex here for adults only.

Population trend estimation

Given the diversity of trapping regimes used over the years and anecdotal evidence suggesting that lizards avoid traditional bucket pitfall traps after being captured once, we standardised all surveys to a common unit of trapping rate calculated as the number of unique individuals per 1000 trap days. Average trap rates between 1994 and 2001 and between 2002 and 2009, at each site, were compared using a onetailed sign test where pluses and minuses were assigned to trapping rates between the two periods. Unless otherwise stated, we used SPSS 17 for the analyses reported in this study.

20 Population Decline

To further investigate population trends we established intensive trapping regimes at two populations, Majura 20022009, and Jerrabomberra West 20062009 ([5] and [1] Fig. 2.2). The data for these two sites share a common trap type and trapping grid array and were monitored contemporaneously from 2006 to 2009. We estimated population trends using an exponential growth state space (EGSS) model (Dennis et al. 2006; Humbert et al. 2009). This approach assumes both observation error and environmental process noise, and can accommodate missing data. The EGSS model is written as: dX (t) = (ln λ)dt + dB (t)

Y(ti) = X(ti ) + Fi

(Equations 3 & 4: Humbert et al. 2009). Where X(t) is the unobserved logabundance of the population (assumed to be a stochastic process) at time t and Y(t) is the estimated or observed 2 2 value of X(t) . Here dB (t) ~ normal ( 0,σ dt ) and Fi ~ normal ( 0,τ ) . The term dB(t) is a random perturbation representing the process noise (environmental variability), and Fi represents the observation error ( τ2), assumed to have no auto or crosscorrelations. We used the EGSS model with restricted maximum likelihood (REML) estimates. The quantity = ln λ is the expected change of X(t) in one time unit; it is our trend parameter such that > 0 is increasing, < 0 is decreasing and = 0 is constant. An asymptotic 100(1α) % confidence interval is calculated as

±zα/2 SE(), where z α/2 is the 100(1(α/2))th percentile of the standard normal distribution, and SE() is: SE() = √Var() Trend analysis was performed with R 2.10.1 for Windows.

We used an equivalence test based on confidence intervals to test for a significant trend (). This tests whether the true trend is negligible as opposed to nonzero. An equivalence region ( bl, b u) is defined as that region which includes all values of the trend parameter that are considered negligible (Dixon et al. 2005). We used the bounds suggested by Dixon et al (2005) that correspond to a doubling or halving time of 10 years (0.0694, 0.0693) for populations of shorter lived species with large annual fluctuations in abundance. There are no degrees of freedom involved in the Dixon/Pechman equivalence test, as applied to our trend model. Instead, we used a normal distribution for the test statistic:

21 Population Decline

Ho: ≤ bl test statistic: z = ( – bl)/√Var( )

H1: > bl Pvalue is area to the right of z in a standard normal distribution

Ho: ≥ bu test statistic: z = ( – bu)/√Var( )

H1: > bu Pvalue is area to the left of z in a standard normal distribution

We conclude that trend is negligible (that is, ≤ bl or ≥ bu) if both Pvalues are less than 0.05 or the confidence interval for the trends lie entirely within the equivalence region (Schuirmann 1987).

Survival

We estimated annual survival from 20062008 at one site, Jerrabomberra West, which ultilised one trap type (artificial burrow) to avoid trap shy behaviour noted when using bucket pitfall traps. We used the Multistage mark recapture (MSMR) models (White et al. 2006) in program MARK (White et al. 1999). Capture sessions were conducted when the dragons are most active (February to midMarch and October to midNovember) and therefore most susceptible to encountering artificial burrows. Five trapping periods were conducted between February 2006 and March 2008. We looked for effects on survival of time (p), period type (or season) (x) and stage (s) (Fig. S1). We calculated monthly survival for each stage and period from the best MSMR model. Further details of these analyses are provided in the Supporting Information (Appendix S2).

Population size and density

We estimated the population size of adults, subadults and juveniles on any given day, over the four trapping grids at Jerrabomberra West. We used the best multistrata model, S x*a, Pp*a, ψx*s to obtain an estimate of resighting probability ( P) for each period (Appendix S2). The estimated population size (Nˆ ) was given by n/P where n was the average number of individuals seen each day of the trapping period.

To estimate density, we took the daily population estimates and employed White and Shenk’s

(2001) formulation to estimate density given Nˆ and the proportion of time radio tracked

22 Population Decline

individuals spent in traps ( pi). Radio tracking data were collected during the first period of trapping (summer 2006) and occurred while traps were open (Stevens et al. 2010). Data were collected at each radiotracked sighting identifying where the individual was caught i.e. in a natural burrow, tussock or artificial burrow. Thus pi = gi/Gi, where pi is the probability of a location being in an artificial burrow for animal i, estimated by the number of total locations ( Gi) divided into the number of locations found in artificial burrows ( gi) (White et al. 2001). The mean

( p) and it’s variance [Vˆra (p)] of the pi’s can be used to correct the population estimate (Nˆ ) and its variance [V ˆra (Nˆ )] to obtain an unbiased estimate as:

Nˆp D = , A with variance estimated as:

Nˆ 2V ˆra ( p) + p 2V ˆra (Nˆ ) V ˆra (Dˆ ) = , A2 assuming that Nˆ and p are independent (White et al. 2001).

RESULTS

Population size trends

Between 1997 and 1999 ten of the 23 sites were found to contain T. pinguicolla . Dramatic changes in trap rates occurred between 1997 and 1999 in populations at the Airport, AMTECH, Callum Brae, Campbell Park, Malcolm Vale, and Jerrabomberra East with all showing a substantial decrease over those years (Fig. 2.4). These populations do not appear to have recovered from those declines with several (Airport, AMTECH, Callum Brae and Jerrabomberra East) no longer, or only just, detectable by the late 2000’s. Conversely, populations at Bonshaw, Jerrabomberra West and Majura showed no clear trend during this initial monitoring. Overall the majority of populations exhibited decreases in trap rates over the long term, while two populations (Queanbeyan Nature Reserve and Jerrabomberra West) experienced at least one large population spike somewhere between the years 2000 and 2006. Bonshaw was the only site that appeared to maintain a stable population across the 16 years of trapping.

23 Population Decline was more was Tympanocryptis re re were linesclearlyconnectingwherethere dashed e e (years) detected sites for all have which te abbreviations). abbreviations). te Trap rate tim overdays)(individualsTrap rate 1000 trap per and been thanmoreonce. been mosurveyed To and show trend than one year onesisurveys.year between 2.2than for Fig. (See Figure 2.4: Figure pinguicolla

24 Population Decline

Average trap rates decreased at seven of the nine sites for which surveys had been conducted in both periods giving a marginally nonsignificant result ( P = 0.0898). In contrast, the trapping rates at the intensively studied sites of Majura (n = 7; 20032009) and Jerrabomberra West (n=4; 20062009) provided a clear indication of substantial declines for both populations of = 0.18 (95% CI 0.64 to 0.29) and = 0.79 (95% CI 1.41 to 0.17) respectively. Large confidence intervals, arising from extremely low capture rates, in the Majura trend means that we are unable to say with certainty that the trend is significantly different from the negligible region (i.e. around

0) ( P = 0.323 and P = 0.148, for bl and bu respectively) (Fig. 2.5) but it is clear that the trapping rate at the Jerrabomberra West site has declined significantly and is well below the negligible region ( P < 0.001 and P ~ 0, for bl and bu respectively) (Fig. 2.5).

25 Population Decline

Survival

The best survival models for the multistage analysis showed that while survival did not differ between years, it did vary with period type (Table S2). Overall, monthly survival was lowest in spring (0.43) and highest in winter (0.86) (Table 2.1). Juveniles were present only in the summer and winter periods with survival between adults and juveniles comparable in winter (82% adults; 86% juveniles) but 35% higher for adults in summer (Table 2.1). Further details are provided in Appendix S1.

Table 2.1: Monthly survival estimates for each period a and stage a using the best MSMR model

Sx*a, Pp*a, ψx*s .

Stage Period Adults Juveniles Subadults 0.74 0.48 Summer (0.53 – 0.91) (0.36 – 0.59) 0.82 0.86 Winter (0.71 – 0.89) (0.80 – 0.91) 0.43 0.43 Spring (0.28 – 0.55) (0.28 – 0.55) 0.78 0.78 New Year (0.64 – 0.87) (0.64 – 0.87) aSee figure S1 for explanation.

Population size and density

Average daily population size and density estimates revealed a decline in numbers for all stages over the three years of the study at Jerrabomberra West (Fig. 2.6). In the summer period of 2006 there were approximately 3.5 juveniles for every adult, this dropped to approximately 0.5 juveniles for every adult in the following year (Fig. 2.6a). Juveniles were the only stage to show a slight increase at the end of the study (summer 2008) reaching a ratio of approximately 5:1 juveniles to adults. As expected from the high recruitment of juveniles in 2006, the number of

26 Population Decline adults showed a slight increase from summer 2006 to spring 2007. This same recruitment facilitated increase did not occur in spring 2008 and the numbers of adults continued to decrease into summer 2008. Subadults are considered part of the population only in the spring period (Fig. S1) and showed a decline over the two springs sampled in this study to almost none present in spring 2007. a 20

6 Population Density (per Ha) (per Density Population 4

0 Summer 2006 Spring 2006 Summer 2007 Spring 2007 Summer 2008 Period b 140

120

100

40 Predicted daily population size population daily Predicted 20

0 Summer 2006 Spring 2006 Summer 2007 Spring 2007 Summer 2008 Period Figure 2.6: Estimate of T. pinguicolla population size and density at Jerrabomberra Valley Grassland Reserve West, Canberra, Australia. (a) Average daily population size (±1 standard error) and (b) Population density in numbers per hectare (±1 standard error), over four trapping

Sub-adults Adults grids, in each trapping period. Juveniles Subadults Adults.

27 Population Decline

DISCUSSION

Our data demonstrate that T. pinguicolla has experienced a dramatic decline in the ACT between the time of its rediscovery in 1993 and 2009. Over that time, average trapping rates have decreased across all known sites from eight individuals per 1000 trap nights to less than two. Our analyses of trends in two of the most intensely studied populations show this situation to be severe. The extent of the decline is most evident in the largest known population (Jerrabomberra West) where density collapsed from 19.8 individuals per ha in 2006 to 2.4 in 2008. In addition to these unambiguous declines in abundance, presence/absence surveys conducted since 2007 show that T. pinguicolla are no longer detectable at three (30%) of the ten sites formerly known to contain populations of T. pinguicolla suggesting that at least three populations are close to, or have even gone to, extinction. In all but one (Bonshaw) remaining sites, where T. pinguicolla are still present, trapping rates are substantially lower than those rates observed in previous surveys. Taken together, these data suggest that this species has experienced a decline across its known distribution in the Canberra area that is both sharp and substantial and threatens its very existence.

It is clear this species is in decline, but the causes of that decline are not straight forward. Declines may be closely related to grassland management across sites and, in a proximate sense, as a result of lack of ground cover (reduced height and density of plants particularly the tussock grasses), which have been caused by drought and exacerbated by overgrazing. At Majura overgrazing reached damaging levels earlier than in the other lowland grasslands (ACT Government 2010), in addition the airport undergoes regular mowing, Campbell Park was severely overgrazed by 2007 and AMTECH and Callum Brae has increased in weediness (WD and WO pers. obs.). The decline at Jerrabomberra West has been more rapid than at Majura and the data indicate that there are two patterns in trap rates among sites. A dramatic expansion between 1997 and 2006 followed by a precipitous decline to very low numbers as seen at Jerrabomberra West and Queanbeyan Nature Reserve and moderate traps rates in the past punctuated with a decline to low, almost nondetectable numbers in more recent surveys as seen at Majura. Regardless of the pattern, declines at so many sites suggest that some regional

28 Population Decline phenomenon is also having an affect with the most likely being climate variables such as rainfall and temperature.

The correlated nature of the declines in T. pinguicolla populations and the extreme drought conditions experienced in southern Australia (Ummenhofer et al. 2009) suggest that rainfall and temperature have been key drivers of the observed decline. In particular we can see that in the Canberra region winter/spring rainfall has been below normal levels since 2006 and was below normal for three of the five years before that (Fig. 2.3a). In addition, the spring period has experienced higher than normal temperatures in eight of the last ten years since 2000 (Fig. 2.3b). As an ectothermic animal T. pinguicolla survival and reproduction is likely to be highly affected by both temperature and rainfall. Winter and spring rainfall have been shown to be positively associated with reproductive output in Uta stansburiana and several Ctenotus species (Hoddenbach et al. 1968; James 1991). In addition rainfall preceding the reproductive period has been suggested to influence the timing of reproduction (Bradshaw et al. 1991), number of clutches produced per female each year (Knapp et al. 2006), clutch size (Worthington 1982; Patterson 1991), egg size (Jordan et al. 2002) and recruitment of lizards (Mayhew 1966; Dickman et al. 1999). Dry conditions have been shown to be an ultimate factor in forcing a lizard population to abandon reproduction altogether (Nagy 1973). Temperature may affect reproduction by modifying time of activity, as when temperatures are high activity can be reduced to prevent overheating (Adolph et al. 1993). A reduction in activity during spring may reduce encounters with members of the opposite sex and hence opportunities for mating. In addition activity has been shown to be positively related to total annual fecundity (Adolph et al. 1993). Changes in temperature may also influence the population sex ratio in species with Temperaturedependent Sex Determination (Mitchell et al. 2010) by skewing sex ratios, although it is unknown if T. pinguicolla exhibits such a mode of sex determination. Finally, variation in precipitation coupled with temperature may lead to dramatic changes in plant and insect numbers (Hunter et al. 2001; Staley et al. 2007) which in turn may limit the resources available to T. pinguicolla and cause fluctuations in population size and density (Ballinger 1977; Germano et al. 2005).

29 Population Decline

Our analysis showed very low survival rates, with survival in the first year to adulthood 0.017 and annual adult survival 0.024; however, it did not differ between years during the decline suggesting that actual survival did not change. This may reflect the reduced accuracy caused by the smaller numbers of individuals captured which would make differences between years harder to detect. Nevertheless, our data do not support the proposition that decreasing survival over years was a key proximate cause of the observed population decline. Changes in survival often result from seasonality which can affect many aspects of a lizard’s biology and in turn affect its life history and demography (LemosEspinal et al. 2003). Our data show that survival among adult and juvenile T. pinguicolla is highest during the winter period when most individuals enter torpor and movement decreases significantly (Stevens et al. 2010), and is lowest for adults during the spring period when breeding and egg laying is occurring and summer, post hatching, for juveniles. This pattern is similar to that seen in the primarily annual Australian, spinifex inhabiting agamid lizard Amphibolurus fordi where most adults died in January after reproducing, when they were one year of age (Cogger 1978).

Although we do not show a reduction in survival across years, survival rates in T. pinguicolla do appear suppressed. In most lizards, mortality among juveniles exceeds annual mortality among adult animals (Rogovin et al. 2004), and in stable age populations annual juvenile survival for lizards is expected to be 13% lower (on average) than adults (Pike et al. 2008). Clearly the Jerrabomberra West population is not stable, as evidenced by the closer to 30% difference between annual adult (0.024) and hatchling to one year of age (0.017) survival. While, this could be an artefact of juveniles dispersing to new areas and consequently having lower recapture rates (Pike et al. 2008), our population recapture rates were higher or similar for juveniles as for adults. The average annual adult survival for a range of 20 species of lizards was found to be around 0.4 (Pike et al. 2008), which is higher than for our population (0.024). However, these values were for populations that were near stable in size unlike those likely to be seen in declining populations of reptiles (Berglind 2000; McCoy et al. 2004; Le Galliard et al. 2005).

It is possible the collapse that we have seen is a short term fluctuation in population size typical of such a shortlived, rapidly maturing species and is not indicative of a more permanent or even terminal decline. Such cyclical population fluctuations have been observed in a small number of

30 Population Decline

Sri Lankan agamids with population minima observed at the turn of the year in three species of Calotes (Erdelen 1988) and the Mexican knobscaled lizard ( Xenosaurus newmanorum ) was shown to exhibit variation in life history traits at both seasonal and annual timescales (Lemos Espinal et al. 2003). In another study, longer term fluctuations were found in populations of scrub lizards ( Sceloporus woodi ), where large decreases and increases were found over 1015 years (McCoy et al. 2004). Nevertheless in all cases the proportions of different age/size classes stayed fairly constant between years and seasons, this was not the case in T. pinguicolla . Taken together, our data suggest a regional decline among T. pinguicolla populations that place the species in grave jeopardy of becoming the first confirmed reptile extinction in Australia since European settlement.

Recommendations for management

Our analysis of population trends in T. pinguicolla enables a refinement of the threatened status of this species. The IUCN red list specifies a reduction in population size of 80% over 10 years or three generations in the past, future or a combination of these as one criterion by which species can be listed as critically endangered (Hoffmann et al. 2008). The large decline in trapping rates we show in less than ten years leads us to believe that a reduction in population size of this magnitude is likely in at least two of the populations that have been monitored in the ACT, and may well be a feature of all ACT populations. Although it was known that T. pinguicolla inhabited small fragmented populations across its range (Robertson et al. 2000), there has been no effort to determine trends in these populations. We suggest that given the large number of small, historic populations that a system for regular monitoring of more sites might be applicable for this species. Humbert et al (2009) found that the robustness of the EGSS model to the absence of more than half of the counts in time series implies that in some cases improved estimates of trend and its variance may be obtained by skipping some consecutive years in a monitoring program and putting the money saved into extending the time series or improving estimates for each year that data are collected. This could also result in extended sampling across more populations by using a staggered sampling method across years and sites.

31 Population Decline

Declines to small numbers can be detrimental to the conservation of this species, because unlike in previous extreme droughts, the grasslands are small and isolated so the opportunities for populations to move out of refugia and recolonise where extinctions have occurred will be few. Without source populations of lizards, isolated blocks of habitat may lose this species during adverse environmental times. In addition bottlenecks and fragmentation are likely to reduce genetic diversity over time, which would make the species less able to adapt to future evolutionary change (Franklin et al. 1998) Given climate change predictions we can expect an increase in frequency and severity of droughts (Karoly et al. 2003), which are all too likely to drive this species to extinction. Recently it has been shown that climate change has been the cause of 4% of local lizard extinctions worldwide and by 2080 could reach 39% (Sinervo et al. 2010). Management with the aim to conserve this species will have umbrella effects for other species in grasslands especially if management focuses on habitat rehabilitation (in the face of drought and overgrazing) and conservation (through gazetting grassland areas as reserves).

Identification of population decline is an important step in any conservation programme and we show here how it can be done using a range of methods on even limited data. The next step is to diagnose the cause(s) of decline, or alternatively, factors limiting growth (Martin et al. 2007). In the case of T. pinguicolla the drought that southeastern Australia is currently undergoing (Ummenhofer et al. 2009) coincides with the population decline and could be impacting T. pinguicolla through reduced food availability and/or grass cover. The drastic reduction in the number of juvenile lizards suggests that factors limiting reproduction may deserve more attention than they have in the past. Nevertheless, rigorous evaluation of the causes of decline and factors limiting survival need to be performed with current experiments under way to test hypotheses related to, food availability, and grass cover.

32 Population Decline S1

West, 4tubes Groups of Groups

0 0 ffice; AMTECH,ffice; 7.88 7.88 0.93 3.33 3.33 4.87 4.78 Transects Transects nd*Reserve) One with tubes with

tubes Grids with with Grids

assland Reserve East;assland Reserve TGA,

0 0 0 0 nds nds records, Department NSW of 4.46 4.46 25.3 25.3 14.88 80.36 8 8 pitfalls Groups of Groups between 1993 andAustralian 2009between inthe

2.35 2.35 3.91 2.87 pitfalls Grids with with Grids

que que individualsdays. 1000trap per (Jerrabomberra

0 0 8 6.25 6.25 7.91 7.45 1.43 1.43 4.85 1.85 (1996), Nelson and Dimond, (1996), (2004) thisstudy. Transects Transects with pitfalls with ton ton , Majura AGSO, Area; AustralianTraining O Geo Site ternational Mulanggari Airport; Mulanggari, Grassla Tympanocryptispinguicolla

Estate; Jerrabomberra East, Jerrabomberra ValleyJerrabomberraEast,JerrabomberraEstate; Gr .compiled (Data fromConservation ACTParks andLa PTERTRAPPING S1. SUMMARY ONE: Location Location West Jerrabomberra West Jerrabomberra West Jerrabomberra Callum(AGSO) Brae AGSO Airport AMTECH NavalBelconnen Base Callum Brae CampbellPark Crace EastJerrabomberra Bonshaw Majura Weston West Jerrabomberra

Trapping rate was was as of Trappingrate number calculated theuni Survey Survey JanFeb Months SepFeb SepFeb FebApr FebApr JanMay JanMay FebMar trapping Locations, rates methodsand for

1994 1994 1995 1996 Year Survey Survey 19931994 19931994 SUPPLEMENTALCHA TO S1: Table CapitalTerritory. Valley Jerrabomberra GrasslandReserve West; Majura Manufacturing Advanced Technology Association;Therapeutic Goods Canberra Airport,In using sitefibrescope a checked Water Environmentand records, Climate LangsChange

33 Population Decline S1

0 0 6 0 0 0 15 10 4.93 3.33 2.45 4.01 4.76 17.86 20.37 4 tubes 4tubes Groups of Groups

0 0 0 0 0 0 1.89 22.8 22.8 1.89 1.89 0.25 3.51 3.51 0.78 0.78 2.78 0.71 2.86 Transects Transects with tubes with

tubes tubes Grids with with Grids

8pitfalls Groups of Groups

pitfalls Grids with with Grids

0 0 0 0.2 1.53 1.59 1.59 5.95 5.95 0.78 1.69 3.55 with with pitfalls Transects Transects

mVale Location Location Airport AMTECH Callum Brae Campbell Park Majura TGA West Jerrabomberra AMTECH Avonley Callum Brae Campbell Park Cookanalla Dundee EastJerrabomberra Fairbairn Bonshaw Malcol Majura Pialligo Airport Fyshwick West/Callum Jerrabomberra Brae Majura Bonshaw Nature Queanbeyan Reserve

Survey Survey Months FebMar FebMar FebMar FebMar FebMar FebMar FebApril

1997 1997 1998 1999 1999 2000 Year Year Survey Survey

34 Population Decline S1

0 0 0 0.29 4 tubes 4tubes Groups of Groups

0 0 12 12 6.54 6.54 51.7 31.31 Transects Transects with tubeswith

0 6.7 0.6 5.36 5.36 1.56 2.73 0.89 6.25 6.98 6.98 4.12 1.79 2.46 51.59 16.74 tubes tubes Grids with with Grids

8 pitfalls 8pitfalls Groups of Groups

pitfalls pitfalls Grids with with Grids

Transects Transects with pitfalls with Location Location Crace Vale Malcolm Mulanggari Naval Belconnen Base* Majura Majura AMTECH Majura West Jerrabomberra Majura Majura West Jerrabomberra NatureReserve Queanbeyan Majura West Jerrabomberra Majura West Jerrabomberra NatureReserve Queanbeyan Campbell Park CallumBrae Bonshaw Majura West Jerrabomberra

Survey Survey Months Months FebMar FebMar FebMar FebMar FebMar FebMar FebMar OctNov FebMar OctNov OctNov MarApr MarApr FebMay AprMay AprMay MarMay MarMay FebApril FebApril 2001 2002 2003 2004 2005 2006 2007 Year Year Survey Survey

35 Population Decline S1

4 tubes 4tubes Groups of Groups

0 3.3 Transects Transects with tubeswith

0 0 0 0 4.02 4.02 0.89 7.14 0.45 0.45 2.01 1.34 4.46 tubes tubes Grids with with Grids

8 pitfalls 8pitfalls Groups of Groups

pitfalls pitfalls Grids with with Grids

Transects Transects with pitfalls with Location Location CallumBrae Campbell Park Bonshaw Majura West Jerrabomberra NatureReserve Queanbeyan AMTECH CallumBrae East Jerrabomberra Bonshaw Majura West Jerrabomberra NatureReserve Queanbeyan

Survey Survey Months Months FebApr FebApr FebMar FebMar

2008 2008 2009 Year Year Survey Survey

36 Population Decline S2

SUPPLEMENTAL TO CHAPTER ONE: S2. SURVIVAL ANALYSIS

The CormackJollySeber (CJS) model estimates survival as the probability of surviving between successive surveys given that temporary and/or permanent emigration is >0 (Horton et al. 2008), and capture probability as the probability of encountering an individual at a particular time. The Multistage mark recapture (MSMR) model estimates survival (the probability of surviving between successive surveys in a particular stratum), capture probability (the probability of encountering a live animal at a particular time in a particular stratum) and transition probability (the probability that an individual moves between stratum) (White et al. 2006). Likely candidate models (as opposed to all possible combinations of the parameters) were created based on our knowledge of the system and factors that were both likely to affect the population and that were supported by the sparseness of the data (Fig. S1) (Burnham et al. 2001). We selected the most parsimonious models based on Akaike’s information criterion corrected for bias (AICc). The notation of Lebreton et al. (1992) is used for model specification. The bootstrap approach for goodnessoffit implemented in MARK (White et al. 1999) was used to investigate the fit of models. The inverse logit link function was used to calculate actual parameters (local survival rates and recapture rates) from untransformed model parameters.

METHODS

Capture sessions were conducted when the dragons are most active (February to midMarch and October to midNovember) therefore most susceptible to encountering artificial burrows. Five trapping periods were conducted between February 2006 and March 2008 consisting of 18 – 20 trapping occasions.

Differences between sexes We used the CJS model on the full complement of adults only (103 individuals) to assess an assumption of no difference in survival between sexes. In that analysis, individuals were recorded as detected when first encountered as an adult (SVL > 48.3mm). The adult only CJS analysis modelled sources of heterogeneity by considering the effects of sex (g) and time periods (p) on adult survival and recapture probabilities. We created capture histories from all 98 encounter occasions across the three years recognising nine (five capture, four noncapture) time periods,

37 Population Decline S2 within and between trapping and resulting in the assessment of six models (Table S1). Of these, two models were tested for goodness of fit: Φ p, Pg and Φ g, Pg; where Φ shows factors affecting survival and P shows factors affecting resighting (Lebreton et al. 1992). We then compared simpler models by removing the effects of sex and time periods so that (.) indicated no difference between time periods or sex.

Annual survival All 220 individuals were used in the MSMR model, with the lizards produced at the site recorded as first sighted during the summer trapping period. Specifically, we modelled sources of heterogeneity by considering the effects of stage (s), where stage refers to adult, subadult or juvenile; time periods (p); and period type (x), where the nine periods were combined into four period types referred to as summer (FebruaryMarch), winter (MarchOctober), spring (October – November) and new year (NovemberFebruary). To test for a difference between adult and sub adult survival and recapture probabilities we combined adults and subadults for some models, in our notation we referred to this as age (a). A candidate model set of eight models was fitted to the encounter histories. Survival (S) and recapture ( P) were allowed to vary with periodtype, time period, stage and age and interactions of period type with age and stage (x*s) and (x*a) and time period with age (p*a). Survival and recapture models were not allowed to vary with time period as an interactive effect with stage (p*s) because this model required too many parameters to be estimated for the available data. Transition parameters (ψ) were set to vary by the interactive model of period type and stage. This provided the only biologically reasonable model for transitioning given our population outline (Fig. S1). In every model, all but nine transition parameters were fixed to zero because individuals could not move to a smaller stage. Seven transition parameters were set to one; all individuals in the summer and spring periods stay in their stage, in the winter and new year periods all adults stay adults and in the new year period all subadults become adults. The only unknown transitions in our model are from juveniles to sub adults and juveniles to adults, which were estimated in the winter period (Fig. S1). We first compared candidate models for resighting using the most complex survival model S p*a as recommended by Lebreton et al (1992). We then compared the survival models using the best resighting model. The global model for this analysis was S p*a, Pp*a, ψx*s .

38 Population Decline S2 , , SAny , survival , survival of population Awin , transition probability probability , transition SAJ , survival of sub adults in spring; spring; S in adults sub of survival ,

, survival of adults in summer; S summer; in of adults , survival SAspr A sum A , survival of juveniles in winter; winter; T in juveniles , survival of ; F, fecundity F,; J win J lity of survival and transition for litytransition for eachsurvival and and of stage , survival of adults in new year; S new year; inadults survivalof , Any Parameters used in population model: model: S population in Parameters used a . , survival of juveniles in summer; S summer; in juveniles of , survival J sum J , transition probability of juveniles to sub adults sub to juveniles of probability ,transition JSA , survival of adults in spring; spring; S in adults survivalof , Asp Population model schematic used to used schematic Population model probabi estimate Tympanocryptis pinguicollaTympanocryptis viabilityin adults in winter; S adults in Figure Figure S1: of sub adults to juveniles T juveniles to ofadults sub S innew year; subadults survival of

39 Population Decline S2

RESULTS

We found no difference in survival between sexes but some differences in recapture probability (Table S2). Goodness of fit tests based on parametric bootstrap simulations and the ratio of observed to expected deviances, indicated that the models, Φ P, Pg and Φ g, Pg, fit the adult encounter data well with underdispersion (i.e. less variation than expected by chance) detected (GOF test, ĉ = 0.68 and 0.48 respectively). We followed the recommendation of not adjusting ĉ when it is less than one (i.e. leave ĉ=1) (Burnham et al. 2002; Stenhouse et al. 2005). In addition, a sensitivity test on the models with fluctuations in ĉ (0.5 – 1.5) resulted in minimal change in model rankings.

Table S2: Comparison of Cormack Jolly Seber, survival and resighting models for adult Tympanocryptis pinguicolla only at Jerrabomberra Valley Grassland Reserve West, over four grids. Model a Kb AIC c d w e Φ P c i i P g 11 1305.1 0.0 0.96 P . 10 1311.5 6.4 0.04 . g 3 1345.3 40.2 0.00 g g 4 1346.3 41.2 0.00 g . 3 1349.2 44.1 0.00 . . 2 1349.3 44.2 0.00 aFactors affecting survival (Φ) and recapture ( P) probability: g, sex; P, time period. bNumber of parameters in model. cAkaike’s information criterion. d Delta AIC c indicating difference in AIC c value form that of the best model. eAkaike weight indicating relative support for the model

Comparison of the survival models for adults showed P on its own to be the best model (Table S2), meaning survival was equal for the two sexes but changed with time period. However, comparison of the recapture models showed that g was the best model indicating no difference in resighting probability over time periods but some differences between sexes. The second best model did not include sex in either the survival or recapture models and was much lower

40 Population Decline S2

weighted than the best model (4%, Table S2). The difference in resighting models made little difference to survival (Fig. S2) suggesting that the exclusion of the parameter of adult sex in the final model would have little effect on the survival estimates.

1.2

0.8

0.6 Adult Survival Adult 0.4

0.2

0 Summer 2006 Winter 2006 Spring 2006 New Year 2006 Summer 2007 Winter 2007 Spring 2007 New Year 2007 Summer 2008 Time period

The goodness of fit test for the multistage, full data analysis indicated that the global model,

Sp*a, Pp*a, ψx*s, fitted the encounter data well with some underdispersion detected (GOF test, ĉ = 0.68). As in the first analysis, we left ĉ unadjusted (Burnham et al. 2002). The best resighting model was the global model p*a, indicating that resighting changed over time (over all time periods and years) and did not differ between sub adults and adults but did between these and juveniles. Overall, recapture rates were low ranging from 0.170.45. Recaptures increased over time (i.e. with a decrease in individuals captured). Juvenile recaptures were slightly higher than adult recaptures for the summer periods (when juveniles were present and trapped) until summer 2008, when the adult recapture rate was slightly higher (0.30 vs 0.29).

The best survival models for the multistage analysis showed that while survival did not differ between years, it did vary with period type (Table S3). There is also some indication that there was a difference in survival between juveniles and adults (including sub adults). However, this

41 Population Decline S2 difference was only slight given that the change in AIC between these two best models is less than three (White et al. 1999; Burnham et al. 2002).

Table S3: Comparisons of multistage survival models for all Tympanocryptis pinguicolla at Jerrabomberra Valley Grassland Reserve West, over four grids using the best resighting model (p*a) a and transition model (x*s) a. b c c c c Survival Model K AIC c i wi x*a 15 3399.6 0.0 0.56 x 13 3400.8 1.1 0.32 x*s 17 3403.7 4.0 0.07 p 18 3405.3 5.7 0.03 p *a 23 3408.5 8.9 0.01 s 11 3469.1 69.5 0.00 . 10 3470.1 70.5 0.00 a 12 3470.7 71.1 0.00 aSee text for description. bFactors affecting survival (S): p, time period; a, age; s, stage; x, period type. cAs for Table S2.

42 Diet and Reproduction 3

Dietary habits and reproductive biology of a declining dragon Tympanocryptis pinguicolla in the ACT, Australia.

ABSTRACT

Conservation planning requires knowledge of the life history of the species of concern, and a lack of this knowledge can be an impediment to endangered species conservation. Detailed studies of declining taxa can clarify reasons for their vulnerability to extinction and guide their management. We obtained data on life history traits monitoring efforts on declining populations of the threatened grassland earless dragon Tympanocryptis pinguicolla from grasslands of the Australian Capital Territory (ACT). We collected research and observations spanning 16 years of surveying, from 1995 to 2007. Clutch size, based on four discovered nests, was considered comparable to closely related agamids (5–7), with small (24 mm snout–vent length, 0.62 g) hatchlings and single clutches per year. Tympanocryptis pinguicolla feed on invertebrate prey, selecting for a small number of orders such as Coleoptera (beetles) and Lepidoptera (butterflies and moths, including larvae). This high level of selection in food and low recruitment rate are both characteristics of species that are highly vulnerable to extinction.

Diet and Reproduction

INTRODUCTION

The ‘evil quartet’ (habitat loss, over exploitation, introduced species and chains of extinctions) are commonly accepted as the main processes driving extinction (Diamond 1984; Diamond 1989; Gibbon et al. 2000; Purvis et al. 2000a). More recently global climate change has been added to this list (Westoby et al. 2006; Auld et al. 2009). In addition, a lack of adequate ecological data upon which meaningful action should be based has also been identified as an important threat to the conservation of species and biodiversity (Mares 1986; Tear et al. 1995). This lack of data has made it difficult to generalise about the driving forces of extinction (Brook et al. 2008) and, given the difficulty of detecting individuals at low densities, a continuation of monitoring populations (or species) until they become extinct has occurred infrequently (but see Fagan et al. 2006).

When individual species are the focus, it is essential that informed approaches to conservation planning and management consider specific characteristics of life history traits such as reproduction and diet (Caughley 1994). This is especially important given the lack of generality in factors that predispose a species towards endangerment (Fitzgerald et al. 2004; Henle et al. 2004a). These data provide key information for estimating population viability (Morris et al. 2002), understanding the evolution of lifehistory patterns (Vitt et al. 1982; Henle 1991) and assisting in conservation management (Milne et al. 2002). Unfortunately, in most situations involving endangered or threatened species, such characteristics are seldom readily available because they are time consuming and expensive to obtain, and the situation is often made more difficult because of the endangered status of the organism itself. A lack of longterm data is a major impediment to effective conservation decision making (Caughley et al. 1996; Mace et al. 1996). In Australia, 25% of the reptile fauna has been nominated by conservation agencies and individuals as warranting threatened status and requiring management (Cogger et al. 1993). Of those taxa, 54 are formally listed as vulnerable, endangered or critically endangered (Department of Environment and Heritage 2009).

The gathering of demographic information sufficient for wellinformed conservation decision making can be very difficult for reptiles because their clandestine nature, comparatively large home ranges, low population densities, and rareness of congregational behavior, makes them

Diet and Reproduction particularly difficult to study in the field (Gibbon et al. 2000). In addition, the natural history of most Australian species remains poorly known with reliable life history studies reported for less than ten of the 700 extant species of reptiles in 1993 (Cogger et al. 1993). As recently as 2001 it was noted that although numerous medium or longterm fieldbased studies of lizard life histories had contributed to our understanding of the evolution of lizard life history, such research has been largely based on North American phrynosomatid species (Wapstra et al. 2001). Few reptiles of conservation significance in Australia have been examined sufficiently for a detailed knowledge of their demography and behaviour to be available (Webb et al. 2002) although notable exceptions include: Tiliqua adelaidensis (Milne et al. 2000; Milne et al. 2002; Milne et al. 2003; Souter et al. 2007; Fenner et al. 2008); Oedura reticulata (Kitchener et al. 1988; Sarre 1995; Hoehn et al. 2007) Hoplocephalus bungaroides (Webb et al. 1998; Webb et al. 2002) and Hoplocephalus stephensii (Fitzgerald et al. 2004; Fitzgerald et al. 2005).

Tympanocryptis pinguicolla (grassland earless dragon) is an Australian endemic of the globally distributed Agamidae family. Its genus, Tympanocryptis, is also endemic to Australia and comprises eight species of small and primarily terrestrial lizards, characterised by the absence of external ear structures and functioning tympanum (ear drum) (Cogger 1992; Wilson et al. 2003). T. pinguicolla is believed to have once had a broad distribution within its preferred natural grassland environment from southwestern Victoria (VIC) to the Central Tablelands of New South Wales (NSW) (Osborne et al. 1993a; Robertson et al. 2000). The range of the species has declined extensively, and remaining populations are now found only in remnant natural temperate grasslands in the Australian Capital Territory (ACT) (ACT Government 1997) and the Monaro region of the Southern Tablelands of NSW (Chapter 2, Robertson et al. 2000). Despite many surveys and some unconfirmed sightings (Department of Sustainability and Environment 2003), the species has not been captured in Victoria since 1967 (Robertson et al. 2000).

Owing to its reduced distribution (Chapter 2), and the disturbance pressures imposed by agriculture and urban development, T. pinguicolla have been declared vulnerable nationally (Cogger et al. 1993), endangered in the ACT and NSW and critically endangered in Victoria (Robertson et al. 2000; ACT Government 2004). In areas where there have been recent surveys of historic sites there is evidence that the species has become locally extinct, and at other sites that

Diet and Reproduction have been monitored in the ACT region individual populations appear to be in very serious decline (Chapter 2). A better understanding of the life history, basic biology and ecology of this species is a high priority for research as an underpinning to securing this species (Cogger et al. 1993; Robertson et al. 2000). While there is very little published information on the ecology of T. pinguicolla , there are several unpublished studies including research on thermal ecology (Nelson 2004), habitat use and home range (Langston 1996; Benson 1999; Stevens et al. 2010), diet (Benson 1999), conservation genetics (Scott et al. 2000; Melville et al. 2007) and population monitoring (Chapter 2; ACT Government 1997; Evans et al. 2002).

In this paper, we focus on the key reproductive and life history characteristics of the species including oviposition sites, clutch sizes, and diet. The reproductive output of females is a critical parameter in determining the likely or maximum rate of increase of a species of concern (Nunney 1991) and resource availability can in turn affect fecundity (e.g. Boutin et al. 2006). Species that exhibit ecological specialisation, such as species that rely extensively on a particular prey species, and can rarely switch prey types to use alternative species if the preferred prey becomes scarce, face an elevated risk of extinction (e.g. Webb et al. 1998; Suarez et al. 2002). Therefore identifying prey species and level of specialisation is valuable in identifying causes of decline for T. pinguicolla . Furthermore, the ACT is currently experiencing a lengthy drought (Ummenhofer et al. 2009) which may have influenced the diet of T. pinguicolla and in fact may have been a player in the decline, particularly if prey abundance declined or fecundity was reduced (see Chapter 2). The current work underpins the development of demographic models of this species that will specifically aim to identify the vulnerable life history stages and traits for this species clearly at high risk of extinction.

METHODS

The research was conducted periodically from 1993 to 2009 at three sites in the ACT: The Majura Training Area (Majura), Jerrabomberra Valley Grassland Reserve West (Jerrabomberra West) and Bonshaw (Fig. 3.1). These sites were selected as they support habitat thought to be preferred by T. pinguicolla including open structured tussock grasslands with little or no trees and shrubs (Osborne et al. 1993a; Osborne et al. 1993b; Smith 1994; Department of

Diet and Reproduction

Sustainability and Environment 2003), limited or no fertilisation or pasture improvement, and are generally located on low rises in well drained areas (Osborne et al. 1993a; Smith 1994; Benson 1999). Majura and Jerrabomberra West are dominated by Austrodanthonia Austrostipa grassland, a floristic association of natural temperate grassland, which is a native ecological community that is dominated by native species of perennial tussock grasses and a diversity of native herbaceous plants (forbs) (ACT Government 2004). The community is naturally treeless and in the ACT it occurs where tree growth is limited by cold air drainage, generally below 625 m above sea level (ACT Government 2004). Grasslands at Bonshaw comprise equal proportions of natural temperate grassland and native pasture, where native pasture is characterised by a high cover of native grasses, but very low to no forb diversity and a low cover of exotic species (ACT Government 2004).

Civic

Jerrabomberra

NSW

Diet and Reproduction

Majura and Bonshaw are both managed by the Australian Defence Force while Jerrabomberra West was private leasehold, subjected to light sheep grazing, until its recent acquisition as a nature reserve (see Stevens et al. 2010 for a detailed description). Majura is used solely for military training but the lowland temperate grassland was surrounded by a macropod exclusion fence in 2008 as a management action to reduce the impacts of grazing by kangaroos while the Bonshaw site, which is under a pastoral lease, is subject to low level grazing by merino sheep and kangaroos.

Reproduction

T. pinguicolla is particularly cryptic in its egg laying habits so information on this important trait was collected opportunistically. In the present study, all females captured were assessed for gravidity by palpation and visual inspection. However, we were unable to accurately determine number of eggs from palpation, therefore we recorded females as gravid or not gravid only. For one of those females who was captured repeatedly during Oct 2006, we were able to calculate the Relative Clutch Mass (RCM) to be 49%, as the proportion of original body mass lost after parturition (i.e. (pre partum body mass – post partum mass)/postpartum mass (Doughty 1997)). A small number of nest discoveries were made by chance during routine monitoring of T. pinguicolla . Egg dimensions, clutch size and mass were recorded for all clutches and those collected from traps were artificially incubated between 2428ºC for 5070 days.

Diet

Prey availability data were obtained from Benson (1999) at Jerrabomberra West in 1999 and collected at Jerrabomberra West, Majura and Bonshaw in 2007 (present study). Benson (1999) sampled ground dwelling invertebrates by using 120 by 120 mm sticky traps anchored in the centre, level to the ground. Five traps each were placed randomly in short and long tussock areas with a flat, rectangular (300 by 200 mm), green, metal shelter set about 200 mm above each trap. These sticky traps were replaced fortnightly from the beginning of February to the end of June (Benson 1999). Invertebrates collected in 2007 were sampled at each site using 40 pit fall traps containing Propylene Glycol and of a capacity (5ml; diameter 15mm) considered sufficient to

Diet and Reproduction catch arthropods of a size edible by T. pinguicolla . The vials were arranged in a crossshaped array with 10 vials on each arm aligned North, West, South and East, spaced at 1m intervals and checked fortnightly for six weeks between October and December.

The T. pinguicolla scats were collected from individuals captured during concurrent mark recapture trapping in 1999 (Benson 1999) and 2007 at all sites (this study). The scats were cleaned and sorted and the body parts from each scat identified to order by use of a microscope, identification keys and reference collection.

Diet information was obtained prior to the current drought (Benson 1999) and after many years of drought (2007; current study) so diet similarity among sites and between years at Jerrabomberra West was investigated using Schoener’s overlap index (Schoener 1970) (C);

Cxy = 1 – 0.5 ∑ │ pxi – pyi │

Where; pxi and pyi are the proportions by number of prey type i in the diets of groups x and y (sites or years), respectively. This index ranges from 0 (no dietary overlap) to 1 (complete dietary overlap). Schoener’s index values above 0.6 are usually considered to be biologically significant (Wallace 1981).

Preference for each food type was determined by calculating “electivity” values defined by Ivlev (1961): Electivity= [ (ir ) − p(i)]/[ (ir ) + p(i)] where r(i) is the proportion of the food type i in the diet and p(i) is the proportion of the food type i in the environment. The result is a metric ranging from negative one (food considered strongly avoided), through zero (food taken in proportion to its availability in the environment) to one (food considered strongly preferred) (Lechowicz 1982). Food classes representing less than 0.5% of both the diet and field proportions were considered to be insufficient to demonstrate selectivity and so proportions were assigned equal values in the analysis (no selectivity) by making the field proportion equal to the dietary proportion. All invertebrate orders found in the diet were found in the field. Preferences were calculated for each site separately.

Diet and Reproduction

RESULTS

Reproduction

In total, four clutches of the grassland earless dragon were discovered between November and January in 1994, 2006 and 2008. One clutch was observed in what appeared to be a shallow scrape (Clutch two, Tables 3.1, 3.2 and 3.3). The scrape was angled at 2530 degrees from the horizontal and measured 67mm long, 24mm wide and 16mm high and had a flat floor and rounded ceiling. The day after egg laying the scrape was found to be filled in, and covered with soil and fine gravel particles. The remaining three clutches were found in traps, one buried in the loose soil used to fill in a modified pitfall trap (Clutch one; Table 3.1) and two in artificial shelter tubes (Clutches three and four; Table 3.1), one of which had been back filled, and eggs found in the unfilled tube showed signs of desiccation. The three clutches found in traps were successfully incubated in laboratory incubators.

Table 3.1: Egg incubation data for four clutches of Tympanocryptis pinguicolla found in Canberra, Australia. Clutch Site Date Date Hatched Estimated Incubation # of # Discovered Discovered (n) Incubation Temp eggs hatched Period (days) (°C) 1 Jerrabomberra 29 Nov 1994 4 Feb 1995 (4) 6870 24 5 5 West 6 Feb 1995 (1) 2 Jerrabomberra 12 Dec 1994 28 Feb 1995 7984 5 4 West (3) 3 March 1995 (1) 3 Jerrabomberra 19 Oct 2006 10 Dec 2007 5354 26 7 4 West (2) 11 Dec 2007 (2) 4 Bonshaw 21 Jan 2008 11 March 2008 50 2628 5 1

Diet and Reproduction

Table 3.2: Egg characteristics of four clutches of Tympanocryptis pinguicolla found in Canberra, Australia. Clutch Mean Long Axis Length (mm) Mean Short Axis Length Combined Mass (n) (mm) (n) (g) (n) 1 14.4 (4) 9.4 (5) 3.87 (5) 2 3 13.1 (3) 8.3 (3) 3.24 (7) 4 14.1 (3) 8.42 (5) 2.59 (5)

Table 3.3: Hatchling characteristics for four clutches of Tympanocryptis pinguicolla found in Canberra, Australia. Clutch Mean Snout Vent Length (mm) (n) Mean Tail Length (mm) (n) Mean Mass (g) (n) 1 24.6 (5) 31.2 (5) 0.66 (5) 2 25.33 (3) 31.3 (3) 0.65 (3) 3 22.8 (4) 28.1(4) 0.61 (4) 4 22.3 (1) 32.9 (1) 0.57 (1)

Incubation period for all eggs to hatch averaged 64.5 days (SE = 6.8; Table 3.1), with clutch size being between five and seven eggs (Table 3.1). Owing to the unknown laying date of the first nad last nests in the sample and low sample size (four nests), we were unable to determine the impacts of different incubation temperatures. Mean clutch weight was 3.2 g (Table 3.2) with eggs discovered in traps comprising a mean length of 13.9mm and diameter at midpoint of 8.7mm (Table 3.2). Mean snout vent length and weight of the newly hatched individuals was 23.8mm and 0.62g respectively (Table 3.3).

In total, fifteen gravid females were identified in the period September to January. Snoutvent length for gravid females in 2006 and 2007 ranged from 48.3 – 64.8mm and weight ranged from 4.18 8.91g.

We infer from the mark recapture data that females can breed within their first year; four individuals were caught as juveniles (SVL < 38.3mm) in February and by October the same year were gravid. In addition four females captured as adults (SVL > 48.3mm) in Feb (i.e. already a

Diet and Reproduction minimum of 1 year old) were recaptured, clearly gravid, in October the same year. Therefore females are capable of breeding in their second year. No females were captured gravid in two consecutive years, although this nondetection does not mean that this is not possible.

Diet

Ground dwelling invertebrates from ten orders were collected during field surveys at Jerrabomberra, Majura and Bonshaw. Of these, seven orders were recorded as prey items in scat analyses at Jerrabomberra in 1999 (Benson 1999), four at Jerrabomberra and Majura in 2007 and five orders as prey items at Bonshaw (Figures 3.2, 3.3, 3.4 and 3.5). At Jerrabomberra, 215 and 235 prey items were recorded in 20 and five scats (mean = 10.75 and 47 items per scat) in 1999 (Benson 1999) and 2007 respectively; at Majura 75 prey items were recorded in three scats (mean = 25 items per scat) and at Bonshaw 540 prey items were recorded in seven scats (mean = 77 items per scat). At both Majura and Bonshaw, Coleoptera (beetles) represented the greatest proportion of total prey items found within the scats with very few other orders found (Table 3.4). However, at Jerrabomberra, Hymenoptera (ants) were found in highest numbers in scats in 1999 (Benson 1999) with Coleoptera and Hymenoptera found in very similar, high numbers (115 and 117 respectively, Table 3.4).

Diet and Reproduction

100

40 % frequency % 20

a a a e a r ia ra da te er ea ar e fied p pt po opter an ipter att o e D il Ar Bl Isopt h Hemi pido Orthoptera C Col Le Unidenti Hymenoptera Invertebrate orders

40 % frequency % 20

a a a e a a d r ra d ter ea ter ie p pte tera pte p po tif o an p o r Di Iso ilo en ole emipter A rth Blattaria H O Ch nid C Lepidoptera U Hymeno Invertebrate Orders Figure 3.3: Percentage frequency of invertebrate orders in Tympanocryptis pinguicolla scats (grey bars) (n = 5) and collected in invertebrate pitfall traps in the field (black bars) at Jerrabomberra Valley Grassland Reserve West from October to December 2007.

Diet and Reproduction

100

40 % frequency % 20

ra a e a ra a a a d e ra ra ri d e t te e er te a ifi p pter nea t tt t op o a p op a n d Di h i Ar t Bl Isopter ide men Hemipt Or Chilopo Coleo Lep Un Hy

Invertebrate orders Figure 3.4: Percentage frequency of invertebrate orders in Tympanocryptis pinguicolla scats (grey bars) (n = 3) and collected in invertebrate pitfall traps in the field (black bars) at Majura Training Area from October to December 2007.

100

40 % frequency % 20

e a a era ra ra a r ri da t te e tera o tta op ipt ane ipte nop m r D la ilop e le idop A B Isoptera h He p Orthoptera C Co Le Unidentified Hym Invertebrate orders

Diet and Reproduction

4 7 0 0 0 2 1 1 0 0 0 Total found found which which scats in in scats particular particular number of of number prey wasprey type Grassland Bonshaw 2007 Bonshaw 7 0 0 0 2 1 0 0 7 0 11 11 519 540 examined examined in in scatsall Total prey items found items

2 3 0 1 1 0 0 0 0 0 2 Total which which scats in in scats prey type prey particular particular was found found was number of of number Majura 2007 Majura 7 7 0 1 5 0 0 0 0 0 3 62 75 3050% examined examined in all in scats Total prey Totalprey plant plant matter items found found items

5 5 4 1 0 0 0 1 0 0 0 scats at each of three sites: at scats each Valley Jerrabomberra Total which

scats in in scats prey prey type particular particular was found found was number of of number 2007 2007 2 2 0 0 0 1 0 0 0 5 Area (Majura) and Area Australia.inCanberra, Bonshaw 117 115 235 Jerrabomberra WestJerrabomberra 5stones examined examined in all scatsall in Total prey Totalprey items found found items

0 4 2 0 4 1 6 3 0 20 20 12 Tympanocryptis pinguicolla Total which which scats in in scats prey type prey particular particular was found found was number of of number 0 0 2 0 4 1 6 3 0 16 45 20 138 215 examined examined in all scatsall in Total prey Totalprey items found found items Jerrabomberra West 1999WestJerrabomberra Diet and Dietand niche breadth from Prey taxa Prey Order Hymenoptera Coleoptera Hemiptera Lepidoptera Araneae Diptera Orthoptera Blattaria Isoptera Unidentified of Total number scats analysed of Total number material prey itemsprey in scats Noninvertebrate Reserve West West),MajuraReserve (Jerrabomberra Training Table 3.4: Table

Diet and Reproduction

Schoener’s overlap index was just below the critical level of overlap (0.6) between 1999 and 2007 at Jerrabomberra West (0.58). This was reflected in the electivity values which showed fairly similar electivities across orders except for Coleoptera, Lepidoptera (butterflies and moths including larvae) and Isoptera (termites) (Table 3.6). Coleoptera and Isoptera in particular were highly preferred in one year and avoided in the other. In 2007 T. pinguicolla had similar diets between Bonshaw and Majura (0.84) and even Jerrabomberra West and Majura were very close to the critical level of overlap at 0.6 (Table 3.5). At all three sites T. pinguicolla showed a strong preference for Coleoptera and only at Bonshaw did the lizards have a preference for any other order; Orthoptera (grasshoppers) (0.96); which was eaten in proportion to its availability at Majura (0) and avoided at Jerrabomberra West (0.4) (Table 3.6) where diet overlap was shown to be the lowest (0.5, Table 3.5). Although Hymenoptera (mainly ants) were the second highest proportion of food items in scats at Jerrabomberra and Majura and close to second at Bonshaw (Fig.s 3.23.5) they were only eaten in close proportion to their availability at Jerrabomberra and in much lower proportions to their availability at Majura and Bonshaw (Table 3.6).

Jerrabomberra Majura Bonshaw West Jerrabomberra West 1 0.58 0.5 Majura 1 0.84 Bonshaw 1

Diet and Reproduction

Jerrabomberra West Jerrabomberra West Order 1999 2007 Majura 2007 Bonshaw 2007 Hymenoptera 0.16 0.12 0.65 0.95 Coleoptera 0.65 0.83 0.87 0.91 Hemiptera 1.00 0.19 1.00 0.00 Lepidoptera 0.87 1.00 0.52 1.00 Araneae 0.75 1.00 0.71 1.00 Diptera 1.00 1.00 1.00 0.49 Orthoptera 0.46 0.40 0.00 0.96 Blattaria 0.04 0.00 0.00 0.37 Isoptera 1.00 0.00 0.00 0.00 Chilopoda 0.00 0.00 0.00 0.00

DISCUSSION

Our observations indicate that T. pinguicolla can lay a single clutch of around five to seven eggs a year and are strictly insectivorous, selecting for only two or three orders – particularly Coleoptera (beetles). Diet, clutch, egg and hatchling body sizes are basic components of life history and are important data for the management of endangered species under the threats imposed by small population size and habitat disturbance. Our findings expand considerably the available information on the feeding habits and reproductive traits of T. pinguicolla . In fact there has been only one previous report of the field diet of T. pinguicolla , and this was conducted at a single site at Majura in the ACT (Benson 1999). There is no published information on clutch size in this species and only three published reports on clutch size for this genus are available (for T. tetraporophora and T. lineata ) (Müller 1998; Greer et al. 1999; Heard et al. 2004). Our data on diet and clutch size thus enable us to examine the role that dietary and/or reproductive characteristics may have had on the recent and dramatic decline in populations of T. pinguicolla in recent years (Chapter 2).

Diet and Reproduction

Given the observed decline in numbers of juveniles and concomitant lack of recruitment to the Jerrabomberra West population of T. pinguicolla (Chapter 2), it is very likely that a major driver of the decline is related to poor recruitment into the population. Recruitment may be depressed by reductions in clutch size or in the numbers of clutches laid, failure of eggs to hatch or poor survival of the juveniles directly following hatching. Of the four clutches we found three contained five eggs and one seven (Table 3.4). There was no indication, across all years, that any female was gravid more than once in a season (Dimond unpublished data) suggesting that this species lays only a single clutch per year. This is a situation that is characteristic of agamids, which tend to lay single, small clutches annually (Table 2, Greer 1989; Amey et al. 2000). Other studies also indicate that most T. pinguicolla live only for one to one and a half years providing just a single opportunity for a female to breed during her lifetime (Smith 1994; Nelson 2004). A reduction in clutches laid or clutch size may be as a result of decreased resource availability, when below average rainfall occurs (Whitford et al. 1977). However, of our clutches the largest (seven eggs) was found during the middle of the “big dry” whilst clutches of five were found at the very beginning of this drought (Ummenhofer et al. 2009). Neither the clutch nor egg size varied across years for the specie Ctenophorus pictus , with distinct differences in climatic conditions (Niejalke 2006) and a number of studies have failed to link climatic variables or resource availability to clutch or egg size variation (Bauwens et al. 1987; Jones et al. 1987; Taylor et al. 1992; Censky 1995). To elucidate the effects of rainfall and resource availability on T. pinguicolla , it will be necessary to monitor invertebrate availability in relation to changes in population size, demographic structure, and body condition.

Searches of the grassland during the period when gravid females were being detected failed to reveal nests (WD pers. obs.), probably owing to the fact that the nests are a filled in with soil and debris and camouflaged after laying is complete (e.g. Table 3.1, Clutch two). However, the laying of eggs in the artificial tubes (a feature also observed on several occasions by L. Nelson pers. comm.) indicates that eggs are likely to be placed in naturally occurring invertebrate burrows that at least are similar in size to the artificial tubes. The use of arthropod burrows or naturally occurring cracks in the soil for refuge sites is not uncommon in small lizards (Souter et al. 2007) and has been recorded for T. pinguicolla near Melbourne (McCoy 1890) and for other species of

Diet and Reproduction

Tympanocryptis (Starr et al. 2006), although very few species have been shown to actually nest in arthropod burrows (Milne et al. 2002). To what extent T. pinguicolla are obligatory users of arthropod burrows for oviposition is not known. It is of interest that no test scrapes (excavations made by female dragons that are inspecting potential nest sites) has ever observed during the ten years that we have been working in these grasslands. We suggest that this may be because most females are simply laying their eggs in the available arthropod burrows these burrows are ready made and may require inspection to see that they are suitable, but little excavation. The discovery of at least one lizard digging at the base of a tussock (although perhaps expanding the size of an existing burrow) and of one clutch buried in loose soil in a small pitfall trap supports the hypothesis that the use of arthropod burrows for oviposition is not necessarily obligate.

It has been shown for a number of lizards that females will not lay eggs unless soil moisture is adequate (Stamps 1976; Socci et al. 2005), that eggs fail without correct soil moisture conditions (Brown et al. 2006), and in Anolis aeneus females are able to retain shelled oviducal eggs during periods of drought (Stamps 1976). Given the ongoing drought conditions in southeast Australia (Ummenhofer et al. 2009), it is possible that some or all of these events are occurring and may be an explanation for the low numbers of juveniles entering the population. Future monitoring should focus on gravid females and their nests to try and assess whether these events could be driving the decline of the population.

The results of this study support the expectation that T. pinguicolla are primarily insectivorous. The agamid (dragon) family have been shown to be largely insectivorous with a range of specialist and generalist feeders (Pianka 1971); iguanian (including agamid) diets in general contain large proportions of ants, other Hymenopterans and Coleoptera (Vitt et al. 2003). T. pinguicolla had fairly similar diets at the three sites and even between the two years at the same site as indicated by the overlap indices. The least overlapping populations were Bonshaw compared to Jerrabomberra West in 2007, where overall Bonshaw had a higher number of prey orders taken and showed a marked preference towards Orthoptera, a preference not seen at any other site. This may simply reflect differences in the proportions of native pasture and natural temperate grassland seen at this site compared to the others (ACT Government 2004) affecting insect species presence or behavior and in turn dragon diet.

Diet and Reproduction

The major prey types found in scats collected from across the three sites studied were Coleoptera and Hymenoptera, but also included two or three other orders and small amounts of stones and vegetation (3.4). Coleoptera had the highest electivity in the diet of T. pinguicolla at all sites in 2007 . This is not an uncommon prey group for insectivorous lizards, particularly agamids (Znari et al. 1997; Vitt et al. 2003; Daly et al. 2008). However, we found Coleoptera were avoided in the 1999 sample. Such avoidance could be related to foraging strategy. In particular, sitandwait foraging has been observed in T. pinguicolla (WD pers. obs) and is a common foraging method in agamids (Pianka 1971; Greer 1989) who typically wait for prey to move within range and then move to intercept them. This sitandwait approach may have affected the selectivity for beetles during the times of low rainfall (Ummenhofer et al. 2009), low biomass and high kangaroo grazing experienced during the sampling period (ACT Government 2010). Specifically, these harsh conditions deplete the grass cover in the study sites and may have affected the movement and abundance patterns of invertebrate prey. For example, Tenebrionid beetle movements are strongly influenced by vegetation structure, moving further on bare ground and in areas with low grass cover (Crist et al. 1992); while carabids will clump in areas of cover (Nitterus et al. 2008). Changes in movement patterns of Coleoptera over time may have made them easier to capture and a more efficient resource for T. pinguicolla .

The occurrence of large numbers of ants in scats and a high percentage of scats that contained ants has led to the suggestion that T. pinguicolla favoured ants (Hymenoptera) (Benson 1999). However, Hymenoptera were avoided or only taken in close proportion to their abundance. The fact that Coleoptera represent a higher caloric value than Hymenoptera, which were more abundant in the field, may have made them more selected for in this study. Ants (Hymenoptera) may make easy prey, especially for juveniles (Znari et al. 1997). However, due to their relatively small size and low caloric value, large quantities have to be consumed daily (e.g. up to 2000 ants per day by M. horridus ) (Meyers et al. 2005). Foraging with a preference for higher caloric value food, however does not preclude the taking of less valuable or rarer prey items, should they be encountered (Paulissen 1987), allowing for the taking of ants or other orders by this species.

Diet and Reproduction

Lepidoptera at the Jerrabomberra site were most selected for in the 1999 sample as opposed to Coleoptera in 2007. This could be due to the difference in season of sampling between the two years (Feb – June in 1999 and Oct – Dec in 2007). Many lizards show a switch in prey preference across seasons due to availabilities or the ease of capture of prey changing across seasons (Gadsden et al. 1997) and weather conditions (Gadsden et al. 2001). In addition, juveniles are not present in the population from October – December, changes in diet between seasons may reflect changing needs of smaller, growing lizards over those that have reached maximum size (Pough 1973), or simply that smaller lizards are unable to eat larger prey (Verwaijen et al. 2002).

Faecal analysis has been found to be relatively reliable for determining the diet of generalist insectivores that eat hardbodied prey; however, there are limitations to the application of scat analysis in assessing the animals’ diet (Dickman et al. 1988). For example, material can already be digested before it reaches the hindgut much less the scat (Schoener 1989) and there can be difficulties with the identification of some for the smaller items (Dickman et al. 1988). A small proportion of the Lepidoptera and Coleoptera orders were discovered as larvae in scats indicating T. pinguicolla eat softbodied prey, but these are unlikely to be detectable in scats. Other orders such as Diptera (flies) and Araneae (spiders) may therefore be underrepresented in our study.

We assumed that pitfall traps and sticky traps (Benson 1999) adequately assessed prey availability and all of the invertebrates discovered in the scats at all sites were captured by these traps in this study. However, biases could have occurred in the determination of food availability for example in both years traps were not closed at night, which means that there was potential confounding by nocturnally active invertebrates to which the diurnal lizards would not have access. Pitfall and sticky traps measure the numbers of active invertebrate groups more accurately than they do inactive groups (Green 1989). Although studies of diet yield insight into what animals eat, they do not necessarily indicate preferred prey. This is because factors such as competition and predation can induce animals to forage in inferior habitats where only less preferred prey occur (Daly et al. 2008). There were clear differences in electivity across time (at Jerrabomberra West). We would recommend further sampling across seasons and years, including habitat condition and weather at the time of observations, to better explain the differences in electivities between the years (seasons) and sites in our study.

Diet and Reproduction

T. pinguicolla select for only a small number of orders of invertebrates, although they are clearly not a specialist on any particular order or species. In addition, they have low reproduction which may be reduced if environmental conditions are not suitable. This high level of selection in food and low recruitment rate are both characteristics of species that are highly vulnerable to extinction (Pimm et al. 1988; McKinney 1997). An acknowledged threat to endangered species is a lack of adequate data upon which conservation guidelines should be based (Mace 1995; Hoffmann et al. 2008). We have worked towards mitigating this lack of knowledge by describing some of the basic biology of T. pinguicolla in the ACT. Even amongst the bestknown species, gaps in knowledge remain (Hoffmann et al. 2008) and our data has firmly established gaps in the biological knowledge of this species that we recommend are the future points for further study in this species.

Population Viability Analysis

Diagnosing the decline of an endangered lizard ( Tympanocryptis pinguicolla ) using population viability analysis

ABSTRACT

Reptile declines are becoming a major concern for ecologists, policy makers and conservation agencies. A major limitation in reptile conservation is that little is known of the ecology of most reptile species making it difficult to identify threatening processes and to prioritise the scarce funding available for conservation. In many cases, an understanding of the ecology and dynamics of threatened species sufficient to predict the fate of remaining populations with confidence is not available. This can lead to poor conservation decision making that is based on little more than gut feeling. Here we develop a Population Viability Analysis (PVA) for the Australian agamid lizard Tympanocryptis pinguicolla which has experienced a recent and dramatic decline in distribution and abundance. We show that PVA can provide the direction for hypotheses for the causes of decline that can be tested with adaptive management. We constructed a model for the largest remaining population in the Australian Capital Territory on the basis of data collected from 2006 to 2008 and used it to test for demographic parameters that had the biggest effect on the probability of extinction. We show that this population has a very high probability of extinction in a short period (100% in 20 years) and that the parameters most likely to be driving this decline are juvenile survival and fecundity. We recommend, given the prevailing drought conditions, that management focus on the dragon’s grassland habitat to reduce overgrazing and thereby reduce desiccation experienced by eggs and juveniles. Our study demonstrates how research with few data can be used to inform management action and be built into an adaptive management framework that enables management action and research to advance together.

Population Viability Analysis

INTRODUCTION

Rates of species extinction have accelerated in recent times, particularly among mammals birds and amphibians (Mace 1994; Stuart et al. 2004; Avise et al. 2008; Brooke et al. 2008). These extinctions have been linked to the ‘evil quartet’ of habitat loss, overexploitation, introduced species and chains of extinctions (Diamond 1984; Caughley 1994; Purvis et al. 2000a; May 2010) as well as environmental pollution, disease, parasitism and global climate change (Gibbon et al. 2000; Huey et al. 2008; Mitchell et al. 2008) and the inherent vulnerability of small populations. Although reptiles are thought to be relatively resistant to these extinction processes (Wilcox 1980), declines in their populations have become a major concern for ecologists, policy makers and conservation agencies more recently, in particular with respect to global climate change (Gibbon et al. 2000; Whitfield et al. 2007; Stork 2010). In Australia, reptiles are declining across 30 percent of the country (Sattler et al. 2002) with fifty four species and subspecies of reptiles listed as vulnerable, endangered or critically endangered (Department of Environment and Heritage 2009). A major limitation in undertaking the conservation of reptile populations and species is that generally, little is known of the ecology of most species (Cogger et al. 1993; Gibbon et al. 2000; Brown et al. 2008). This lack of knowledge is particularly acute in Australia exacerbated as it is by high reptile diversity (>900 species, Uetz et al. 2010) attended by a relatively small number of professional herpetologists (<150, James Cook University 2005)

Effective conservation action requires a sound ecological knowledge of the species of concern. Of special significance are the size and dynamics of the remaining populations: particularly when their distribution is fragmented (Brown et al. 2008) and the analysis of those dynamics for the identification of life history features and extinction drivers of significance. Knowledge of these characteristics will help predict the fate of populations by identifying parameters that are likely to contribute most to the probability of extinction. Population viability analysis (PVA) has become a useful tool for marshalling the quantitative data available for species of concern (Bakker et al. 2002; Hauser et al. 2006; Dimond et al. 2007) by providing a framework for testing the impact on populations of changes in parameters and/or events. PVA is an analytical approach that simulates population dynamics through application of a wide array of models from simple, deterministic matrix models for estimating population change, to complex, spatially explicit individualbased models of landscape and population dynamics (Beissinger et al. 1998; Ralls et al. 2002). In most 64

Population Viability Analysis cases, PVAs are used to calculate the risk of extinction or a closely related measure of population viability (Gilpin et al. 1986; Ralls et al. 2002) and have been employed successfully to provide direction for, or assessment of, management actions (SlottaBachmayr et al. 2004).

Ideally, PVAs are most accurate when based on robust, longterm data. However, the population sizes of endangered species are frequently small, and the data available, sparse and collected over short periods of time (Armstrong et al. 2007b; Sabo 2008). Beissinger et al. (1998) pointed out that poor data cause difficulties in parameter estimation leading to unreliable estimates of extinction risk. There are additional problems too when it comes to model validation, especially if all available data have been used to estimate parameters (Ellner et al. 2002). A number of approaches have been developed to improve the effectiveness of PVAs when working with small datasets. For example, Zambrano et al. (2007) incorporated random variation in their matrix model of axolotl ( Ambystoma mexicanum ) populations as opposed to using deterministic values as is common when only sparse data is available. Adding some form of stochasticity is more realistic than employing models that are deterministic only, and randomness in vital rates, where random catastrophic events can extinguish a few individuals, will have a disproportionately large effect on small populations (Morris et al. 2002; Zambrano et al. 2007). Zambrano et al. (2007) predicted, even with random variation in survival and fecundity, high probabilities of survival for a small population of axolotls. Importantly, when working with limited data, focus should be on the sensitivity of individual parameters in the model (NaujokaitisLewis et al. 2009), simple models should be used rather than complicated ones (Beissinger et al. 1998), and projections of extinction probability should be made over short periods of time only (Beissinger et al. 1998; White 2000).

PVAs have rarely been employed in studies of reptiles (Blomberg et al. 2001; Henle et al. 2004b; ZunigaVega et al. 2007). However, those few that have, have provided direction for research (OrtegaLeon et al. 2007), identified sensitive life history stages (Blomberg et al. 2001), made recommendations for listing (ZunigaVega et al. 2007), incorporated sensitivity analyses (Wiegand et al. 2002) and used presence/absence field data to test the veracity of a PVA study of declining populations (Wiegand et al. 2001). Therefore PVAs based on small data sets can provide a rigorous framework for identifying potential demographic parameters for management

Population Viability Analysis and further research and knowledge gaps in the ecology of rare species with small distribution ranges and multiple threats (Zambrano et al. 2007).

The Australian lizard, Tympanocryptis pinguicolla an earless dragon that occurs in the Southern Tablelands of New South Wales (NSW) and the Australian Capital Territory (ACT), is an example of a rare and endangered species confined to small, remnant populations (Robertson et al. 2000). T. pinguicolla is a habitat specialist, and found only in endangered Natural Temperate Grassland communities where they shelter in arthropod burrows or the bases of tussocks (Stevens et al. 2010). Southeastern Australia had extensive areas of native grasslands before European settlement commenced in the late 1700’s. However, over the past 200 years, those grasslands have been reduced in area to 0.5% of their original extent with reduction attributed to agriculture and urbanisation (Benson 1997). Currently these grasslands are also undergoing severe drought conditions (Murphy et al. 2008). Little is known of the population size and dynamics of this species with periodic censuses over a range of grassland sites providing some information (Chapter 2). T. pinguicolla are a largely annual species (Robertson et al. 2000), which lay one clutch of five to seven eggs from November to December (spring and early summer) and hatch between December and March (Chapter 3). Juveniles can mature by the start of the next breeding season although it is possible that individuals that hatch later in the season will not reach maturity until the next season (Nelson 2004). The species provides a flagship for the conservation of Natural Temperate Grassland and other endangered species within it, and as such it is important to elucidate the extinction risk of the dragon as well as the best approach to its conservation.

Here, we determine the probability of persistence into the near future of the largest remaining population of T. pinguicolla in ACT. Given the sparse data available, we focus on determining which population parameters are the most likely to be critical to population persistence and develop a strategy in which management could test the propositions posed by the PVA while undertaking remedial conservation action. Specifically we (i) use estimates of fecundity and survival rates for this population based on mark recapture data from 2006 – 2008 (Chapters 2 and 3) to establish a matrixbased PVA model and (ii) perform a sensitivity analysis to identify the life stages most critical to persistence and which can be used as the focus for future research and management action.

Population Viability Analysis

Our model provides the first quantitative assessment of the influence of key demographic parameters on the decline of T. pinguicolla, provides the starting point for an adaptive management scheme for this species, and highlights the role PVAs can play in establishing recommendations for experimental management actions for little known species.

METHODS

Study site

Jerrabomberra Grassland Reserve West (Jerrabomberra) (35º 22' 19'' S and 149º 10' 00'' E) on the eastern edge of Canberra (ACT), was held under a pastoral lease and used to graze merino sheep (Ovis aries ) for about 150 years, until it was removed for listing as a reserve in early 2005. Grazing pressure by eastern grey kangaroos ( Macropus giganteus ) has generally been low and the site has not been ploughed or pasture improved resulting in treeless native grassland dominated by Austrodanthonia and Austrostipa tussocks. The reserve is 116.9 ha of which 115.2 ha is estimated to consist of natural temperate grassland (ACT Government 2004).

In the ACT T. pinguicolla has been discovered at ten sites over the last 17 years although re surveys conducted from 20062009 confirmed their presence at only five of seven sites with one of these capturing only one individual in 2007 and none in 2008 (Chapter 2). All sites in the ACT where T. pinguicolla have been trapped are in natural temperate or native grassland (ACT Government 2004). Jerrabomberra Valley Grassland Reserve West is contiguous with one other site (Callum Brae) where T. pinguicolla had been collected and separated by a highway and stream from two other contiguous sites (Jerrabomberra Valley Grassland Reserve East and Bonshaw).

Monitoring of Tympanocryptis pinguicolla

We conducted population surveys in the Jerrabomberra Grassland Reserve West from 2006 to 2008 to obtain data on survival and population size (Chapter 2). Dragons were caught using artificial shelter tubes (tubes) constructed of a capped piece of PVC drain pipe (31mm diameter x 142mm deep) lined with brown paint and sand, surrounded by an outer sleeve and shaded by a 67

Population Viability Analysis

200 x 200 mm roof (Nelson et al. 1996; Fletcher et al. Unpublished data). Artificial shelters were buried vertically with the opening flush with the ground in four grids of seven by eight burrows at 10 m intervals. Grids were located at least 100 m apart to maintain their independence within the site (Evans et al. 2002) and were situated authoritatively, on well drained slopes, within the patchy matrix of Natural Temperate Grassland (Benson 1999; ACT Government 2004; Stevens 2006).

The dragons were marked by a combination of methods to ensure long and shortterm recognition. For all new captures a small section (≥ 1cm) of tail tip was removed for genetic analyses. The tail tip does not grow back and so individuals that have not previously been caught can also readily be identified as recaptures by this marking. In addition, all captured dragons were marked with a nonpermanent, unique number written with a nontoxic sharpie pen on their ventral side and photographed dorsally for individual identification. Grassland earless dragons have dark crossbars located between three pale longitudinal stripes which run the length of the dorsal surface (Wilson et al. 2003). This pattern of bars and stripes is unique to each individual and does not change over time allowing us to identify individuals between trapping periods and years (Chapter 2).

At each capture, T. pinguicolla were sexed and measured for snout vent length (SVL) and then classified into one of three stages on the basis of size as follows. We defined adults by the smallest gravid female identified over the study (>48.3mm) and juveniles by the largest individual captured during the breeding season (October – November) (<38.3mm). All individuals between these two size classes were categorised as sub adults (>38.3mm, <48.3mm) and considered unable to breed. We included the sub adult stage as it has been suggested that individuals that hatch later in summer do not reach breeding size in time for the following breeding season and therefore do not contribute to breeding until their second year (L. Nelson pers. comm.). Similar effects of early hatching and rapid growth influencing age at sexual maturity were found in another agamid, the jacky dragon ( Amphibolorus muricatus ) (Warner et al. 2007). In the first trapping period, sex was determined visually by the width of the tail base alone while later trapping periods included an external inspection of the vent for hemipenal swellings and openings. We were able to determine the presence or absence of hemipenes in adults only; juveniles and sub adults were considered of unknown sex for the purpose of analysis. 68

Population Viability Analysis

PVA model

Based on our knowledge of the ecology of the species (Fig. 4.1) we built a model that mimics the yearly population dynamics over 20 years. We programmed the model in R (R Development Core Team 2008) such that during each time step (one year), the model cycles through four time periods (summer, winter, spring and new year, Fig. 4.1). The model performs three steps in each time period, to calculate (1) survival of individuals; (2) adult reproduction (this is zero for all periods except new year); and (3) the stage individuals transition to within that period. The PVA then reruns this model 1000 times and counts the number of runs, where the population survived. The population dynamic model is a densityindependent, twosex, stage and periodic matrix model, representing survival and transitions within and between each survey period (Fig. 4.1). Parameter values for survival and transition probabilities and their standard errors were taken from estimates using mark recapture analysis in chapter 2.

In total the model employed eight period type matrices, (four for survival and four for transitions probabilities between stages). We chose an individual based approach where the destiny of each individual is determined by random numbers drawn from a normal distribution according to the survival and transition probabilities in the matrices and thus our model includes demographic stochasticity , which is appropriate when dealing with low numbers (Zambrano et al. 2007). The model assumes that all survival values and transition values vary around their mean with one standard error. As there is no change in the mean, no environmental stochasticity was incorporated. Given the short period of monitoring available to us, we were unable to estimate reasonable values for environmental variation and therefore we considered chance of extinction predictions over a short time period (up to 20 years) only (White 2000).

The starting population for the PVA was drawn from the population density (individuals/ha) estimated for the summer of 2006 (Chapter 2) and multiplied by the total area of Natural Temperate Grassland available for T. pinguicolla (115.2 Ha; Table 4.1) at that site (ACT Government 2004). Tympanocryptis pinguicolla are found in natural temperate and native grassland (pasture dominated by native grasses but lacking the floristic richness of natural temperate grassland) where it borders natural temperate grassland (Stevens et al. 2010) so our

Population Viability Analysis , , SAny , survival , survival of population population Awin , transition probability probability , transition SAJ , survival of sub adults in spring; spring; S in adults sub of survival ,

, survival of adults in summer; S summer; in of adults , survival SAspr A sum A , survival of juveniles in winter; winter; T in juveniles ,survival of ; F, fecundity F,; J win J lity each survivaland and of stage transition for , survival of adults in new year; S new year; inadults survivalof , Any Parameters used in population model: model: S population in Parameters used a . , survival of juveniles in summer; S summer; in juveniles of , survival J sum J , transition probability of juveniles to sub adults sub to juveniles of probability ,transition JSA , survival of adults in spring; spring; S in adults survivalof , Asp Populationused to model schematic estimate probabi Tympanocryptis pinguicollaTympanocryptis adults in winter; S adults in Figure 4.1: Figure viabilityin of sub adults to juveniles T juveniles to ofadults sub S innew year; subadults survival of

Population Viability Analysis

estimate, based on the area of natural temperate grassland alone, is conservative. We assumed a uniform distribution of individuals across the grassland, a reasonable assumption to make when lacking spatial information on a species (Berec 2002).

Table 4.1: Parameter values used during the model simulation of Tympanocryptis pinguicolla at Jerrabomberra Grassland Reserve West. Parameter Description Sex ratio (proportion female) 0.50 Maturity All individuals when they reach adult stage Fecundity rate Beta distribution (2) with mean 5.5 and range 4 to 7 Survival and initial population structure: Initial number Survival Probability Standard deviation Adults in summer (S A sum ) 498 0.631 0.124 Adults in winter (S A win) 0.332 0.093 Adults in spring (S A spr ) 0.279 0.066 Adults in new year (S A ny ) 0.413 0.101 SubAdults in spring (S SA spr ) 0.279 0.066 SubAdults in new year (S SA ny ) 0.413 0.101 Juveniles in summer (S J sum ) 1789 0.334 0.058 Juveniles in winter (S J win ) 0.443 0.074 Transition rates Transition Probability Standard deviation Juveniles to Sub Adults (T JSA ) 0.194 0.071 Juveniles to Adults (T JA) 0.806 0.071 Model iterations All scenarios repeated 1000 times, over 20 years

Juveniles were usually observed in greatest numbers during February and March, therefore reproduction was assumed to follow a birthflow process over the summer time period (Keyfitz et al. 2005). The reproduction rate in the first three years was modelled based on that calculated from the monitoring data. The reproduction rate (number of eggs/gravid female) incorporating individual variation was estimated to be a mean of 5.5 with a range 47 and was based on clutch sizes (57 eggs) observed in the small number of T. pinguicolla nests that have been identified (Chapter 2). This clutch size is akin to that of similar sized agamids as reported in the literature (Henle 1991; Greer 2006). Environmental variation in reproduction rate was not included in the model.

Population Viability Analysis

Sensitivity analysis

Sensitivity can be measured in a number of ways depending on the type of model and data outputs (Pulliam et al. 1992; Cross et al. 2001; Wiegand et al. 2001). We used the standard method of varying selected parameters by ±10% from the mean (Pulliam et al. 1992; Cross et al. 2001). First, we ran the model 1000 times using the parameter values in Table 4.1 to establish a baseline value for the rate of extinction. We then repeated this process but varied each parameter by plus and minus 10% from the baseline values. We varied one parameter at a time so each parameter’s sensitivity was tested independently of all other parameters. We then calculated a sensitivity index (S x) based on the change of the parameter ( X ) from the baseline and the change in the average time to extinction ( TE ) from that estimated from the baseline measure:

X S = x TE This index results in a value that, when greater than one, indicates a greater percentage of change in the probability of extinction is experienced than expected from the change in the parameter i.e. a sensitive parameter. A value of one or lower indicates a change in extinction probability equal to or less than that made in the parameter i.e. low sensitivity.

RESULTS

The highest numbers of individuals were captured during the first trapping period (Summer 2006, Table 4.2). The number of individuals captured declined over the length of the study showing only a slight increase in the final sample (Summer 2008, Table 4.2).

Population Viability Analysis

Table 4.2: Captures of Tympanocryptis pinguicolla at Jerrabomberra Grassland Reserve West, over all four grids at each site. Period Summer 2006 Spring 2006 Summer 2007 Spring 2007 Summer 2008 (18 sessions) (20 sessions) (20 sessions) (20 sessions) (20 sessions) Total captures 433 169 138 26 104 Total population 208 75 28 11 30 Adults Males 24 24 12 6 3 Females 18 26 3 3 0 Unknown 1 0 0 0 1 Sub adults 0 25 0 1 0 Juveniles 165 0 13 0 26 Surviving residents 35 12 7 2 New individuals 208 40 16 4 28

Population model and sensitivity

To determine whether the population model accurately reflects the situation in the field, we compared the predictions generated by the model to our field population estimates (Fig. 4.2). The model appeared to estimate population sizes for each stage and year closely, with substantial deviations seen only at the subadult stage in the 2006 sample (Fig. 4.2c). Such deviation may be expected given the low recapture rates found in the survival analysis for this stage (see above).

The probability of extinction for this population over the 20 years from 2006 is 100% (Fig. 4.3). The modelling predicts a 0% probability of extinction until 2010 after which it rises to a 65% (95% CI ± 3%) probability of extinction by 2015 and a 96% (95% CI ± 1%) probability by 2019. The average time to extinction is most sensitive to changes in juvenile survival parameters and increases in fecundity (Fig. 4.4). We investigated these parameters by varying them across their range and rerunning the PVA 1000 times for each new value (Fig. 4.5). The mean time to extinction is far more sensitive to changes in juvenile survival in winter and to fecundity than to juvenile survival in summer (Fig. 4.5). In fact, juvenile survival in winter has a thresholdlike value where the mean time to extinction increases dramatically with survival greater than 0.4 (Fig. 4.5a).

Population Viability Analysis

900 (a) 800 700 600 500 400 300 200 Population size (N) 100 0 sum 06 spr 06 sum 07 spr 07 sum 08

2500 (b) 2000

1500

1000

500 Population size (N)

0 sum 06 spr 06 sum 07 spr 07 sum 08

400

250

200

150

100

Population size (N) 50

0 sum 06 spr 06 sum 07 spr 07 sum 08

Season Figure 4.2: Population size in 115 ha estimated from mark recapture (dotted line) of Tympanocryptis pinguicolla at Jerrabomberra Grassland Reserve West, compared to simulated mean (± 1 SE) (solid line) population trajectory predicted by the population model (a) Adult stage (b) Juvenile stage (c) Subadult stage .

Population Viability Analysis

1.8

1.6

1.4

1.2

0.8

0.6 Sensitivity Index (Sx) Sensitivity

0.4

0.2

SAS Asum sum SA SA win win SA SA spr spr SASA ny SSA SSA spr spr SSA SSA nyny SJ SJ sum sum SJ SJ winwin TTJ-J-SASAF F Parameters

Population Viability Analysis

(a)

4 Mean Mean time to extinction (Years)

0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Juvenile Survival

(b) 20

4 Mean Mean time to extinction (Years)

0 0 1 2 3 4 5 6 7

Fecundity

Population Viability Analysis

DISCUSSION

Tympanocryptis pinguicolla has experienced a dramatic decline in both population size at Jerrabomberra Valley Grassland Reserve West and in distribution (Chapter 2). Trapping rates at Jerrabomberra Valley Grassland Reserve West declined at a rate that would be sufficient to consider a IUCN listing of critically endangered (Hoffmann et al. 2008) and, although not as significant, a similar decline was indicated at a nearby population (Majura Training Area) (Chapter 2 and Table 4.1). In Chapter 2 we found that two of seven populations that had been detected in the 1990’s could not be detected between 2006 and 2009. Diagnosing a declining population is just one step in conservation biology, identifying the factors that are responsible for that decline is essential for designing a management strategy to recover an endangered species (Peery et al. 2004).

Our population viability analysis shows that if this decline continues, extinction will occur within the next decade. The high probability of extinction, combined with low survival and decline in numbers captured (Chapter 2) over the three years of our study show an alarming trend towards total collapse in this population. The close agreement between the model and real data gave us confidence in the model’s predictions and serves to reinforce our concern of the high likelihood of extinction presented by this model. However, our focus remains primarily on the sensitivity analysis as this leads us towards identifying parameters that are depressed and require management (Caughley 1994). Most importantly, our sensitivity analyses show that the probability of extinction of this species is highly sensitive to juvenile survival and fecundity more than any of the adult or subadult life phases. Unfortunately, these are also the life history phases of this species about which the least is known.

Our sensitivity analysis shows that juvenile survival has a greater effect on the decline than adult survival in T. pinguicolla , with an increase in the juvenile survival probability in winter to 0.6 alone increasing the mean time to extinction beyond 20 years. This winter period would be the period during which most individuals have entered torpor before reemerging in spring to either a breeding, adult stage or a nonbreeding subadult stage. Low survival during this period would reduce the number of potential breeders able to contribute to the population that year. Intense

Population Viability Analysis drought periods, such as those experienced over the course of the present study, are likely to impact severely on factors critical to over winter survival and over winter survival has been shown to be a key contributor to population dynamics in lizard populations. For example, Civantos et al (1999) found that winter survival in the lacertid Psammodromus algirus was increased by larger body size, earlier hatching date and greater vegetation cover while in populations of the sideblotched lizard ( Uta stansburiana ), overwintering survival has been shown to be depressed by warmer winter environments (Zani 2008).

Fecundity is the second critical, but largely unknown parameter, identified in the PVA as important for this population. Changes in fecundity in T. pinguicolla showed significant effects on the probability of extinction for this species with a mean fecundity increase from four to seven juveniles produced, more than doubling the mean time to extinction. The sensitivity of the model to this parameter suggests that a decrease in fecundity could be key to the decline in population size and density observed in T. pinguicolla . The fecundity parameter in our PVA model, incorporates a number of variables that all contribute to the effect of fecundity on population survival including clutch size, percentage of females that become gravid and eventually lay, and clutch survival (how many actually hatch). Which of these components has the biggest contribution to the fecundity cannot be answered with the existing data. Identifying nesting sites will help provide information on these variables and lead to questions about optimal conditions (e.g. temperature and moisture) for laying or hatching and whether this species might be temperature sex dependent or not.

The driving cause of the dramatic decline of this species across its range over the past 200 or more years of European settlement has been attributed to be the loss of native grasslands through conversion to improved pasture dominated by exotic species, changes to more intensive agriculture, and urbanisation (Robertson et al. 2000). In the present study, the population for which the PVA was developed is not faced with habitat loss through conversion as it is now contained within a nature reserve, but rather the loss of grassland cover owing to extreme drought conditions being experienced in southeastern Australia (Bond et al. 2008; Murphy et al. 2008). The overriding feature of the years 20062009 being the drought and associated kangaroo grazing which reduced foliage biomass (ACT Government 2010). In addition, spatial patterns of

Population Viability Analysis trends in annual averages from 1979 to 2006 have shown that east and southeastern Australia have experienced a decrease in soil moisture content (between 0.5 and 1 percent per year) (Liu et al. 2009). These in turn are likely to have been exacerbated by unusually high levels of herbivore grazing caused by other anthropogenic changes such as increased surface watering points and release from toppredator control. As a direct result of the data reported here and elsewhere (Chapter 2), this site and others containing populations of T. pinguicolla have been fenced to reduce grazing by kangaroos. Continued monitoring of fenced and unfenced populations will be carried out to test the effect of this management on survival and fecundity.

Analysis of the results generated by the PVA, in the context of the recent severe drought, leads to two possible explanations for the decline of this species observed in the Jerrabomberra West population and more generally across its range.

The first possible explanation is that low production of and high mortality in eggs through low soil moisture, increased exposure and dry conditions is driving this decline. Reduced soil moisture as a result of the drought can cause desiccation which lowers egg survivorship (Watling et al. 2005; Marco et al. 2008). In addition it is likely that drought conditions will cause adults to produce fewer eggs or fewer adults to produce clutches at all because of an unfavourable environment for creating nests (Stamps 1976) or a lack of resources (such as food) which in turn reduces the energy available for reproduction (Niejalke 2006). For example, the fecundity of females in a population of tree lizards ( Urosaurus ornatus ) (Ballinger 1977) was reduced during years with resources limited by drought, chuckwallas ( Sauromalus obesus ) can fail to reproduce during drought (Nagy 1973) and reproductive activity was low in six of seven species of scincid lizards ( Ctenotus ) during dry years (James 1991). Tympanocryptis centralis kept in captivity will refuse to lay, become egg bound and die if substrate moisture and structure is not to their liking (J. McAuliffe pers. comm.). Our data show T. pinguicolla to be short lived, seldom surviving longer than one year. Consequently, annual productivity must be high for the population to persist (Tinkle 1969). Prolonged drought conditions that affect productivity in successive years can therefore be expected to have major impacts on population size.

Population Viability Analysis

The second possible explanation is that there is a high predation risk of juveniles through increased exposure. Reduced levels of plant growth caused by drought conditions, combined with increased grazing pressure from kangaroos, or other herbivores will reduce ground cover within the grasslands and consequently increase the exposure of lizards to predation from aerial and terrestrial predators (Barrows 2006; Daly et al. 2008). T. pinguicolla use both burrows and grass tussock bases as refuges (Stevens et al. 2010), with an increased reliance on tussocks during summer (Nelson 2004). A lack of tussock cover requires them to thermoregulate in more open areas where they are more exposed to predation. This premise has been tested by Daly et al (2008) on two agamid lizards ( Ctenophorus isolepis and C. nuchalis ) they used plasticine models out in the open and under spinifex cover, models of both species were attacked more often in the open. The most likely predators at Jerrabomberra Valley Grassland Reserve West are aerial predators like Australian kestrels ( Falco cenchroides ), ground foraging predatory birds like magpies ( Gymnorhina tibicen ) and foxes ( Vulpes vulpes ) and cats ( Felis catus ). In the Canberra region Magpies have been shown to eat lizards and have been observed taking small bearded dragons Amphibolurus barbatus (Vestjens et al. 1974) and foxes have been recorded with at least four species of reptile in their stomach contents (McIntosh 1963). In other parts of Australia cats have been recorded eating large numbers of reptiles including T. lineata and T. intima (Read et al. 2001).

The situation for this species is critical and requires a rapid management response. Ideally, that response would incorporate adaptive management (the incorporation of experimentation and knowledge feedback loops in management action), based on the best hypotheses available on the causes of decline derived from field measurement and simple but robust modelling (Holling 1978) and the use of PVAs in this framework is highly instructive (Armstrong et al. 2007a; Dimond et al. 2007). In some situations with limited resources it has been suggested that effort should be expended on investigating general interactions between environmental variables and population dynamics, rather than gathering data to estimate population parameters for a PVA (Reed et al. 1998; Fieberg et al. 2000). For many endangered species for which these recommendations are made, there is a lack of baseline data from which to assess changes in these interactions and/or to identify interactions that will give the maximum amount of management outcome for limited resources. Baseline data can prove critically important to the study of

Population Viability Analysis environmental impacts (Edgar et al. 2004) and to pointing towards priorities for management concerns (e.g. Fujita et al. 1991). Population viability analysis can assist with these problems by directing the collection of baseline data, identifying gaps in the knowledge of factors affecting species persistence and providing a basis for adaptive management. Our data demonstrate that by being aware of the drawbacks of creating PVAs from limited data and focusing on sensitivity analyses and not prediction of extinction (Beissinger et al. 1998), even short term sparse data can be useful when used with a PVA to direct management hypotheses. Our findings, however, require validation through good and careful experimentation. The recent establishment of herbivore grazing exclusion fences around a number of native temperate grassland reserves provides the opportunity to test some of the hypotheses developed as part of our PVA analysis. For example, testing the effects of changing grass cover and consequently exposure, on survival and capture rates of T. pinguicolla using an experimental framework is now possible through reduced grazing and other manipulations.

T. pinguicolla represents a classic case of an endangered species of extremely limited distribution threatened by drought and habitat degradation. As such, it presents a considerable challenge for any confluence between rigorous (but often theoretical) conservation science and the reality of changes in climatic conditions. Three years of markrecapture estimation provided the foundation for us to model and to test the sensitivity of the probability of extinction to key demographic parameters for this remnant population. As a consequence, we are now able to suggest the areas of life history that should become the focus for research and management resources. Further study is still needed to fully assess the ecological relationships and functions of T. pinguicolla within natural temperate grasslands. Population monitoring programs are needed for conservation and it is imperative to conduct studies that extend our knowledge through basic research in a directed, adaptive manner that makes the best use of limited resources. Efforts that investigate ecological interactions show promise for informing our conservation and management efforts for this species.

Population Viability Analysis

Detection 5 Detecting the endangered grassland earless dragon in remnant grasslands

ABSTRACT

The ability to discriminate between a failure to locate a rare species when it is present and a true absence is critically important because declaring a species absent, when it is actually present could have catastrophic consequences for its management. In particular, the use of survey to determine the presence or absence of a species when habitat modifying development may be the outcome of reporting on an absence has the potential to seriously undermine conservation effort. Detectability may vary with place and within and between years, and it is important that those variations be identified if sound conservation decisions are to be made. Here, we draw on extensive ecological data, collected over four years to assess the power of grids comprised of artificial shelter burrows to detect Tympanocryptis pinguicolla , an endangered dragon lizard in ACT natural temperate grasslands. We combined the use of zero inflated models (MacKenzie et al. 2002b) with Driscoll’s (2010) approach to recording occurrence: the proportion of traps occupied at a location. We found the threevisit detection probabilities for T. pinguicolla to be low at the beginning of a six week sampling period (February – March) (0.120.22) but reached levels (up to 0.5) comparable with the few other lizards for which detection probability has been estimated. Timing of the sampling period was shown to be the most important factor when trying to increase detection with a worst case scenario suggesting 167 artificial burrows checked for six weeks (18 checks) from February into March is needed to have 99% confidence of detecting the species if it is present. The approach described here provides the tools necessary to determine the current distribution and abundance of T. pinguicolla in unsurveyed areas.

Detection

INTRODUCTION

One of the biggest problems faced in conservation is the ability to discriminate between the failure to locate a rare species when it is present and a true absence. This is especially important for rare or declining species where declaring a species absent, when it is actually present could have catastrophic consequences for its management; a situation of relevance to metapopulation research, biodiversity assessment, habitat modelling and wildlife monitoring (Driscoll 2010). While the presence of a target species can often be confirmed at a location, it is generally impossible to confirm its absence (MacKenzie 2005) . Presenceabsence information is difficult to interpret because animal detectability is not constant in time or space and assuming equal and complete detectability, results in a negative bias in the estimation of species' presence (Vojta 2005). Importantly, knowledge of the species present at a specific site can direct conservation efforts and land management initiatives (Tuberville et al. 2009). Moreover, estimates of species detectability are imperative in the design of field surveys, impact assessments, and monitoring strategies because they provide the basis for determining the survey effort required to be confident of detecting the species if it is present (Wintle et al. 2004).

The analysis of survey and census data to estimate detection in ecological studies has been hampered by high levels of nondetection or zeros where, the number of zeros is so large that the data do not readily fit standard distributions (e.g. normal, Poisson, binomial, negativebinomial and beta) and the data set is referred to as ‘zeroinflated’ (Martin et al. 2005). Recently, improvements in approaches to the analysis of zeroinflated data (Heilbron 1994; Hall 2000; Martin et al. 2005) have enabled practitioners to account for data overdispersion and made tractable more robust estimation of the true probability of detection for a given species and environment. The use of these zeroinflated models (Martin et al. 2005) has spawned a suite of occupancy analyses that use an estimator based on whether individuals are detected or not detected during repeated visits throughout a defined sampling area (MacKenzie et al. 2002b). These presence/absence surveys provide a relatively low intensity approach to monitoring when compared to mark recapture or abundance methods (Wintle et al. 2004) but can still incorporate data ranging from biodiversity inventory (Pellet 2008), to species abundance monitoring (MacKenzie et al. 2002a) and habitat studies (Wintle et al. 2004). Essential to the conduct of

Detection these surveys, is the identification of a detection probability before an application is made to the problems of occupancy or abundance. This is so that the probability that a species is present is estimated whether or not they are detected in a given survey (MacKenzie et al. 2002b; Tyre et al. 2008).

We surveyed for Tympanocryptis pinguicolla (grassland earless dragons) in the Australian Capital Territory, Australia to estimate population sizes and density (Chapter 2) as well as to create a population model to identify knowledge gaps and identify directions for management of the species. T. pinguicolla is a small (mean adult snout–vent length 54 mm), endangered lizard that is a habitat specialist, restricted in distribution to the natural temperate grassland habitats in southeastern Australia (Robertson et al. 2000; ACT Government 2004), It is thought to be dependent on arthropod burrows, which provide shelter sites within those grasslands (McCoy 1890; Stevens et al. 2010). The species has not been sighted in Victoria since 1990 (Melville et al. 2007) and, for about 30 years, was believed to have become extinct in the Canberra area until rediscovered in 1991 at three locations (Osborne et al. 1993a). Recent records suggest that T. pinguicolla persist in only two small regions of remnant grassland; one in the Australian Capital Territory (ACT) and adjacent New South Wales region between Canberra and Queanbeyan, and the other in the Southern Tablelands in the Monaro region between Cooma and Nimmitabel (Chapter 2) (Robertson et al. 2000).

Surveys for T. pinguicolla are being conducted to determine distribution and conservation status in New South Wales, to monitor populations in the ACT, and to detect any remaining populations in Victoria. In the past, such surveys were typically conducted by government conservation agencies. However, surveys are increasingly being conducted by consultants in the employment of private companies. Of interest in surveying for T. pinguicolla is the survey effort required to detect the species with some level of confidence. This is particularly important for sites where the species is not detected, so that an assessment can be made of the chance that the survey ‘missed’ the species when in fact it was present at the site. Knowing the detection probability is also useful for designing efficient surveys.

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In the ACT survey efforts have variously employed up to six different trap layouts and designs over the last 16 years (Chapter 2). From 2006 we have employed a standardised grid layout using artificial burrows (Chapters 2 and 3). Our major objectives in this larger study were to collect data across sites in a standard, comparable approach using a markrecapture framework to estimate characteristics at the individual level. The particular trapping design that we have been using, artificial burrows arranged in a grid pattern, has been adopted by wildlife consultants for use in site assessment in which the main objective is to determine the presence or absence of this species. This is being done without a clear understanding of the likelihood of detecting the species should it be present. Here, we use our extensive ecological data, collected over four years to assess the ability of this design to detect grassland earless dragons, estimate the trapping effort required for detection, and if appropriate, suggest improvements for people wishing to simply detect the species at a site.

METHODS

Monitoring protocol

We surveyed grassland earless dragons ( Tympanocryptis pinguicolla ) using a gridbased system. A detailed description of our approach is contained in chapter 4, but briefly, artificial burrows (that mimic the natural arthropod burrows that the lizards shelter in) were buried vertically flush with the ground in grids of seven by eight (56) burrows 10 m apart. The grids were placed authoritatively in areas considered most likely to capture dragons (locations on well drained slopes, within the patchy matrix of natural temperate grassland (Benson 1999; ACT Government 2004; Stevens et al. 2010)) and separated by at least 100 m in order to maintain their independence (Evans et al. 2002). The capture sessions were conducted for 67 weeks from early February to midMarch in each year when high activity levels and juvenile emergence provide the highest likelihood of encounters between dragons and artificial burrows. Each grid was checked for dragons three times a week (every other day) with the burrows checked in a systematic order randomised with respect to time of the day. Each dragon encountered was identified and its key attributes (weight, length) recorded.

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In all, sampling was conducted in 224 burrows (four grids) at each of two locations, Majura Training Area (Majura) and Jerrabomberra Valley Grassland West (Jerrabomberra West) from 2006 to 2008 and with an increased sampling effort of 336 burrows (six grids) at each location in 2009.

Statistical analysis

We applied a method similar to the markreleaserecapture approach of Mackenzie et al (2002b) to estimate detection probability ( p) and occurrence. That model is based on a species’ detection history, whether it was seen (1) or not (0) on each visit to a site. Following the site definition of MacKenzie et al (2002b) means that we would have two sites (Majura and Jerrabomberra West) where, the final estimate is the number of sites required to detect the species. We wished to ascertain sampling effort (the number of artificial burrows needed to achieve a high probability of detection within a location). Therefore in this study we employed Driscoll’s (2010) approach to recording occurrence, which is based on the proportion of traps occupied at a location, and contrasts in scale with the frequently used psi ( ψ), which is the proportion of sites occupied (sites are equivalent to our locations) (MacKenzie et al. 2002b). We pooled our detection histories across three checks such that 18 daily checks were pooled to 6 weekly occasions to reduce the difficulties in assessing model fit.

We constructed a set of models representing six hypotheses of detection probability and applied those models across all four years. The data contained few captures in later years and especially at Majura so we selected candidate models that minimised the number of parameters. Occurrence was modelled as a function of location in all years, (QAIC >3) (Burnham et al. 2002). Weather is a factor that is likely to affect detectability in reptiles. In particular, the exothermic nature of lizards makes it likely that temperature will affect detection by increasing or decreasing activity (Lettink et al. 2009) (Hoare et al. 2007) while rainfall has been shown to affect detection in common skinks (Oligosoma nigriplantare polychrome ) (Hoare et al. 2007) and bluetongue lizards ( Tiliqua scincoides ) (Koenig et al. 2001). Consequently, three of our models included the potential weather effects of rainfall and average maximum and minimum daily temperature. Weather data (minimum and maximum daily temperature and daily rainfall, Fig.s 5.15.2) were

Detection provided by the Bureau of Meteorology from the Canberra Airport weather station (70014), which is located between the two study sites at distances of 8 and 2.5 km from Jerrabomberra West and Majura respectively.

Figure 5.2: Average total weekly rainfall at the Canberra International Airport (February – March). Error bars are ± 1 standard error. (Data from Australian Bureau of Meteorology).

Detection

The models, framed as hypotheses, were tested as follows: Model 1, detection probability [denoted p(.)] is constant across the trapping season; Model 2, detection probability is a function time, modelled with a linear relationship between survey week and detection probability [( p(WEEK)]; Model 3, detection probability differs between locations [( p(LOCATION)]; Model 4, detection probability is maximised by the optimal total rainfall [ p(RAINFALL)] over the week of capture; Model 5 and 6, detection probability is affected in a linear fashion by average daily minimum temperature [ p(AV DAILY MIN TEMP)] and maximum temperature [ p(AV DAILY MAX TEMP)] over the week of capture.

We used an information theoretic approach to model selection (Burnham et al. 2002) in which the Akaike Information Criterion (AIC) values were used for model selection without the small sample correction AICc because the effective sample size could not be identified (MacKenzie et al. 2006). Models with the lowest AIC were considered most parsimonious, and pairs of models with an absolute difference in AIC of less than 2 were considered equally good (Burnham et al. 2002). As there was no overall global model we used 10 000 bootstrap samples to check goodness of fit for the model with the largest number of parameters in each year (MacKenzie et al. 2006). Where over dispersion was found, we accounted for it by adjusting ĉ. The AIC then becomes the Quasi Akaike Information Criterion (QAIC) and will adequately select parsimonious models for ĉ values ranging between one and three (Anderson et al. 1994).

Detection probability was calculated for each week at both locations by model averaging across all six models (Burnham et al. 2002). This procedure derives parameter estimates (detection probability p and occurrence) for each week and location from all candidate models on the basis of each model’s weight. Maximum likelihood optimization, model selection and model averaging were performed with the program PRESENCE 3.0 Beta (Hines 2006).

The objective of this study was to better define occupancy and detection probability within a location so that effective broadscale surveys for the species could be designed. After calculating the probability of detection with a single artificial burrow in one week (three checks), and the occurrence (the proportion of burrows that were within occupied areas within a location, and so

Detection were able to capture individuals at a rate equal to the probability of detection (Driscoll 2010)), we estimated the number of burrows required to detect the species:

n = log(1C) / log(1Pcatch )

Where C is the desired confidence, n is the number of burrows and pcatch is the probability of capture with a single sample effort (in our case, one burrow over three checks) given that the species occurs in that area (McArdle 1990; Driscoll 2010). The probability of capture ( pcatch ) is calculated as the product of the locationwide occurrence and the probability of detection for a single artificial burrow (Driscoll 2010). We estimated the number of artificial burrows required to detect the species given 1 to 6 weeks trapping (3 to 18 checks) for values of C from 0.1 to 0.99.

To be conservative we used the lowest probability of capture ( pcatch ) each week over all four years and across both locations.

RESULTS

Tympanocryptis pinguicolla were detected at both study sites. Captures were obtained on between 50 to 100% of weeks of trapping at Majura and for all weeks at Jerrabomberra (Table 5.1). Tympanocryptis pinguicolla were detected on all grids in 2006 but not in later years. At Jerrabomberra, they were found on all grids until 2009 when one grid did not detect them and at Majura detection across the girds declined to two of six in 2009 (Table 5.1).

Table 5.1: Trapping success at Majura training Area (MTA) and Jerrabomberra Valley Grassland West (Jerrabomberra West) of Tympanocryptis pinguicolla over four to six grids and six weeks. Year Location #Grids lizards identified on* Proportion of weeks trapping** Majura † 2006 4 0.83r Jerrabomberra 4 1 Majura † 2007 3 0.83r Jerrabomberra 4 1 Majura 2 0.5 2008 Jerrabomberra 4 1 Majura 2 1 2009 Jerrabomberra 5 1 * At both locations, four grids were in place from 2006 to 2008 and six in 2009 ** Proportion of weeks (out of six) in which T. pinguicolla were detected † r denotes a recurring value

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Overall in the analysis, model 2 [( p(WEEK)] was most commonly selected (2006, 2008 and 2009) with the effect strongest in 2006 (QAIC >15, weight = 100%) (Table 5.2). All other models in 2006 carried no weight and very little support (QAIC >> 3). For 2008 and 2009 the weight was spread fairly evenly between the top three models (0.490.11, Table 5.2) with model 2 selected best and second best respectively. However, model 1 [ p(.)] , which assumed a constant detection probability across time and locations was selected as best in 2007. Although it was ranked as well as models 35 (QAIC <2), it carried more than two times the weight of these other models. Across all years there was little support for differences in detection probability between Majura and Jerrabomberra West [( p(LOCATION)]. From 2007 – 2009 model selection was weaker and in these years models including weather variables (temperature and rainfall, models 46) showed no clear pattern, with their combined weights each year around 50%. Therefore, while it is not possible to come to any definite conclusions regarding the importance of the weather covariates in relation to detection probability, detectability seems to vary little in relation to variation in any of the environmental variables measured, especially when taking into consideration the results obtained in 2006 when more individuals were detected (Table 5.2).

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Table 5.2: Model selection and parameter estimation. (Q)AIC is the model (Quasi) Akaike Information Criterion, (Q)AIC is the absolute difference in (Q)AIC with the best model, w is the model weight, and K is the number of parameters included in the model. Year Model* (Q)AIC** (Q)AIC** w K 2006 2 p(Week) 1006.15 0.00 1.00 8 3 p(Location) 1024.03 17.88 0.00 4 5 p(Av Daily Min Temp) 1027.64 21.49 0.00 4 1 p(.) 1029.09 22.94 0.00 3 6 p(Av Daily Max Temp) 1030.42 24.27 0.00 4 4 p(Rainfall) 1030.99 24.84 0.00 4 2007 1 p (.) 509.02 0.00 0.39 3 5 p(Av Daily Min Temp) 510.96 1.94 0.15 4 4 p(Rainfall) 510.98 1.96 0.15 4 6 p(Av Daily Max Temp) 511.00 1.98 0.15 4 3 p(Location) 511.01 1.99 0.15 4 2 p(Week) 515.32 6.30 0.02 8 2008 6 p(Av Daily Max Temp) 568.27 0.00 0.49 4 2 p(Week) 569.16 0.89 0.31 8 4 p(Rainfall) 571.19 2.92 0.11 4 1 p(.) 573.06 4.79 0.04 3 3 p(Location) 574.44 6.17 0.02 4 5 p(Av Daily Min Temp) 575.02 6.75 0.02 4 2009 2 p(Week) 666.55 0.00 0.28 8 4 p(Rainfall) 666.80 0.25 0.25 4 1 p(.) 667.10 0.55 0.21 3 6 p(Av Daily Max Temp) 668.87 2.32 0.09 4 3 p(Location) 668.97 2.42 0.08 4 5 p(Av Daily Min Temp) 669.08 2.53 0.08 4 * In all years occurrence was modelled as a function of location. ** QAIC was used in 2006, 2007 and 2009, AIC was used in 2008. (See methods for full description).

Occurrence was best modelled across all years as a location model such that Majura and Jerrabomberra West had large differences in estimated occupancy (Fig. 5.3). There was approximately a tenfold difference in model averaged occurrence estimates between the sites (Fig. 5.3). Occurrence decreased at Jerrabomberra West (from 0.7 to 0.2) between 2006 and 2009 but stayed fairly constant at Majura with the lowest occupancy estimated in 2008 (0.03, Fig. 5.3).

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0.8 0.7 0.6 0.5 0.4 0.3 0.2

Probability Probability of Occurrence 0.1 0 2006 2007 2008 2009 Year

Model averaged estimates of detection probability were very similar between Majura (Fig. 5.4a) and Jerrabomberra West (Fig. 5.4b) from 2006 to 2008, reflecting the low weight of models based on location (Table 5.2). At both locations the detection probability was highest in 2006 (0.21 – 0.45) dropping down in the last three years (0.13 – 0.23, Fig. 5.4). In all four years at both sites the effect of week is noticeable with the highest probability in detection seen over weeks 5 and 6 (Fig. 5.4), which corresponds to the first two weeks of March.

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(a) 1

0.8

0.6

0.4

Probability Detection of 0.2

0 (b) 1 2 3 4 5 6 1

0.8

0.6

0.4

Probability of Detection Probability 0.2

0 1 2 3 4 5 6 Week

Figure 5.4: Probability of detection with a single burrow in one week (three checks) for Tympanocryptis pinguicolla at (a) Majura Training Area (b) Jerrabomberra Valley Grassland Reserve West over four years (2006 black, 2008 dashed black, 2007 light grey, 2009 lightest grey). Error bars are ± 1 standard error on model averaged estimates of detection.

Detection

The lowest probability of captures ( pcatch ) each week was at Majura in 2008, given our density of approx 133 tubes per hectare. If we assume this population to be at the lower limits of viability for this species and therefore a worst case scenario for an extant population of this species, then one week of trapping in February would require 1285 artificial burrows to have a 99% probability of detecting the species were it present. That number would fall to 167 artificial burrows (Table 5.3) were the sampling to run for six weeks (18 checks) from February into March.

Table 5.3: Number of artificial burrows required to detect Tympanocryptis pinguicolla at a location given the number of weeks of trapping (where one week of trapping entail three checks of the artificial burrows) and for values of detection confidence ( C) from 0 to 0.99. Trapping effort is based on the lowest probability of capture found in this study each week from 2006 to 2009. Weeks of Trapping C 1 2 3 4 5 6 0.1 30 14 10 7 6 4 0.15 46 22 15 11 8 6 0.2 63 30 20 15 11 9 0.25 81 38 26 20 14 11 0.3 100 47 32 24 18 13 0.35 121 57 38 29 21 16 0.4 143 67 45 34 25 19 0.45 167 78 53 40 29 22 0.5 194 91 61 46 34 26 0.55 223 104 70 53 39 29 0.6 256 120 80 61 44 34 0.65 293 137 92 70 51 38 0.7 336 157 105 80 58 44 0.75 387 181 121 92 67 51 0.8 449 210 141 107 77 59 0.85 530 248 166 126 91 69 0.9 643 300 201 153 111 84 0.95 836 391 261 199 144 109 0.96 898 420 281 214 154 117 0.97 979 457 306 233 168 127 0.98 1092 510 341 260 187 142 0.99 1285 600 402 306 221 167

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DISCUSSION

The determination of absences when a population is in fact present could have extremely negative consequences for the management of a species that is rare or declining. Therefore, being able to establish a desirable level of detection probability and to predetermine the effort and approach required to achieve that level becomes a critical tool for planning and decision making. We found the threevisit detection probabilities for T. pinguicolla to be low at the beginning of the trapping period (0.120.22) but reached levels (up to 0.5) comparable with the few other lizards for which detection probability has been estimated (Fig. 5.4) (Roughton et al. 2006; Kéry et al. 2008). We also observed substantial variation in occurrence between the two locations at the beginning of the study in 2006 (0.04 versus 0.72, Fig. 5.3) becoming more comparable between the locations as the population declined at Jerrabomberra West in 2009 (0.09 versus 0.19, Fig. 5.3). Together with other observations, which suggest that this species is in low population abundances across its range (Chapter 2), our findings suggest that substantial effort will be required to identify true absences of this species.

We found very strong selection between detection models only in the first year (2006) of the study. In that year, meteorological covariates performed poorly (total weight of models 4, 5 and 6 being zero) indicating that detection was not affected by weather. This is an unusual finding for an ectothermic species where we might expect that temperature in particular would affect detection (Hoare et al. 2007). However, the nature of the ‘traps’ used to survey the lizards may affect the manner of detection in this species. The traps are a form of artificial refugia mimicking the arthropod burrows used by this species (Stevens et al. 2010). Burrows are well known as thermal refugia for lizards that make use of them (Pough 1970; Cooper Jr 2000). Generally lizards use burrows more when temperatures drop or rise substantially (Cooper Jr 2000) as they provide either sites for retreat or opportunities for thermoregulation in conditions of extreme heat or cold (Cooper Jr 2000; Cooper et al. 2007). Therefore, we may expect to see changes in detection with dramatic changes in temperature; however there was little variation in average daily temperatures across the trapping season (Fig. 5.1). Even rainfall in 2006 (Fig. 5.2) had low variation compared to later years, and with detection and occurrence probabilities dropping it became harder to select between models with no way of generalising about the importance of

Detection weather. We therefore suggest that temperature is unlikely to be an important factor in detection of this species, provided that surveys are conducted in February and March when temperatures are comparatively stable. Rainfall may play a bigger role in detection than temperature, especially in years when rainfall is low (e.g. 2009, Table 5.2, Fig. 5.2) however, higher capture rates may be needed to really tease out the importance of this parameter with relation to detection.

We show that detection probability for T. pinguicolla is timedependent and that this is common to at least two locations in the ACT. We found that sampling during the first two weeks of March is the most efficient way to detect the species. The fact that detection probabilities are strongly dependent on survey week emphasizes the importance of the timing of juvenile emergence in detection. Changes in detection with seasonal abundance has been documented in butterflies (Pellet 2008), and it is no surprise that it should also be present in other species where emergence from nests can occur throughout the season (Warner et al. 2007). We have shown previously (Chapter 3) that T. pinguicolla lay eggs from November to January and it is also known that juveniles are usually seen in the field from January to May (Nelson 2004) indicating that hatchling emergence can occur over a period of several months during the summer. These results, along with the likelihood that juveniles will keep emerging into May (Nelson 2004) means that it is possible that the optimal time for sampling may exist outside of the period examined in the present study. Surveys conducted from March through to May would be required to determine whether a higher probability of detection could be achieved by shifting the survey time. If this were the case, we may expect a quadratic relationship between survey week and detection where, as time proceeds, detection decreases again as the peak of juvenile emergence passes.

Occurrence varied considerably between the two locations (Majura and Jerrabomberra) and thus the required trapping effort will be dependent on the location that is being investigated. It is likely that occurrence will be related to habitat preferences (Bailey et al. 2004; Gorresen et al. 2009). At both sites artificial burrows were placed on well drained slopes, within the patchy matrix of natural temperate grassland (Benson 1999; ACT Government 2004; Stevens et al. 2010). Although these sites may be similar at the large scale level (slope, grass structure and patchiness at the level of hectares) there may be substantial differences at the microhabitat level among factors such as burrow availability, cover, and grass species which may affect suitability.

Detection

Both locations were under drought pressures over the course of this study (Murphy et al. 2008) although Majura suffered from a much greater grazing pressure than Jerrabomberra West owing to very high numbers of grey kangaroos (ACT Government 2010) and reduced grass and forb cover. In addition differences in ratios of forbs to grass tussocks have been noted between these sites (ACT Government 2004). Given that these habitat variables also change over time monitoring of detectability and occurrence as we have done would assist with adjustments to survey efforts over time.

Detection probabilities in this study were estimated from artificial burrows that were first placed in areas most likely to capture dragons and in a grid layout such that artificial burrows were close together (10 m spacing). These two factors can bias our detection probabilities so that the estimated trapping effort may also be biased. By concentrating on areas where it is believed dragons are more likely to be, we are not estimating occurrence at the location but occurrence within the areas at the location where we most likely expect T. pinguicolla to be (MacKenzie et al. 2006). Thus, the placement of artificial burrows across the entire study area, encompassing any areas of less suitable habitat, may produce lower estimates of detectability. If this is indeed the case, then the number of artificial burrows required would be greater than suggested by the present study. Therefore literal estimates of the survey effort suggested by the present study, would apply only if habitat preferences are also taken into consideration. Expert opinion may be necessary to place grids authoritatively as little has been published on habitat preferences for this species (but see Stevens et al. 2010).

Detection and occurrence probabilities may also change given the layout of the artificial burrows themselves (Efford et al. 2005; Wilson et al. 2007). The use of linear transects rather than the grid layout used here may allow for greater coverage of the site (Pearson et al. 2003) thereby increasing the probability of detection and occurrence estimation at a given site but potentially also underestimating the probability of presence if occurrence in the location is patchy (Driscoll 2010). The use of transects would allow for artificial burrows to cross a greater number of home ranges, as currently a grid is narrowly smaller (4200m 2) than the largest home range (4768m 2) estimated for this species (Stevens et al. 2010). T. pinguicolla have overlapping home ranges (Stevens et al. 2010) such that a grid is likely to intersect with the ranges of a number of

Detection individuals. However the high concentration of artificial burrows in our grid means that most individuals are likely to use several artificial burrows within their home range, whereas a transect with a lower concentration of artificial burrows would have a reduced probability of overlapping the home ranges of lizards spanned by the transect. Further work comparing transects with grids, or other layouts (such as clusters) (e.g. Parmenter et al. 2003; Pearson et al. 2003; Reese et al. 2005) would be required to provide guidance to people focussing on detecting this species and add to our knowledge of spatial patterns in the distribution and abundance of T .pinguicolla .

The need for repeated surveys creates a tradeoff between the number of sites to survey and the number of replicate surveys needed at each site (GuilleraArroita et al. 2010). We have estimated the number of artificial burrows required given the number of checks to be made based on the lowest trap capture probability ( pcatch ) estimated each week over all years and two locations. This therefore gives us a worsecase scenario for future surveys for this species. When the lowest pcatch obtained over the trapping period at both locations was used, we estimated that 1285 artificial burrows surveyed three times in one week would be needed to have 99% confidence that the species was truly absent. The number of artificial burrows multiplied by the number of checks means that once your confidence goes above 60% surveying over a longer time period results in the overall trap days required decreasing. For example surveying for the first week requires 3855 trap days to have 99% confidence of detecting the species if it is there. Spreading this across six weeks reduces the number of trap days required to 3006 trap. Ultimately, decisions as to how many artificial burrows should be employed and over what duration will be a function of the resources available, the number of locations to be surveyed in the season in question, and the probability of false absence that is acceptable. Most importantly, the objective of the survey program must first be clear – whether it is simply to find the species or in some way monitor changes in abundance or occurrence (Yoccoz et al. 2001; GuilleraArroita et al. 2010).

Compared to the lengthy (up to 142 m) estimated daily movements of T. pinguicolla (Stevens 2006), the distance between the artificial burrows used in this study is quite small (10 m); thus we must address the issue of nonindependence between artificial burrows (or sites in the terminology of MacKenzie et al (2002b)). Nonindependence can arise if individuals are detected at multiple sites simultaneously (MacKenzie et al. 2006). In our study that would be like catching

Detection the same individual at more than one artificial burrow in the same survey. We addressed this issue by not releasing individuals back to their sites until all artificial burrows in the grid had been checked. In addition this species has been shown to have overlapping home ranges such that they do not show territoriality over the time period during which we surveyed (Stevens et al. 2010). On a small number of occasions more than one individual has been found in the same artificial burrow suggesting that the chance of a lizard occupying an artificial burrow is not influenced by whether one nearby is occupied (MacKenzie et al. 2004).

Detection failures represent a serious problem for T. pinguicolla conservation because such a large proportion of the known sites are occupied by relatively few animals (Chapter 2). For cryptic or rare fauna, such as most endangered species, that are difficult to capture in large numbers, it may not be possible to obtain reliable population estimates, especially as these species decline further (Fisher et al. 2000). Researchers may therefore be constrained to examining presence absence and more generally spatial distributions. Efforts that require ascertaining presence or absence with any reliability will also increase as populations decline and some idea of the kind of effort required becomes critical to reliable monitoring of these species. In the ACT, survey efforts have been ongoing since the early 1990’s with up to 11 locations identified as having T. pinguicolla at some time since then (Chapter 2). Later surveys, however, have detected declines in populations making resurvey and identification of survey effort critical to efficient monitoring for this species. This is particularly important as this species is known to still occur in large areas of native grassland in the Monaro region of NSW, south of Cooma. There are similar grasslands at several locations between the ACT and Cooma that have not yet been subject to systematic survey. The approach described here provides the tools necessary to determine the current distribution and abundance of T. pinguicolla in these areas as required by the recovery plan for this endangered species (Robertson et al. 2000).

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Synopsis 6

Synopsis

When managing threatened or declining species and developing conservation strategies, managers and scientists often enlist the help of population viability analysis (PVA) (Possingham et al. 1993; Samson 2002; Efroymson et al. 2009) . Indeed, Beissinger (2002) claims PVA to be the cornerstone of conservation science by acting as a process of risk analysis, where hazards are identified, risks are considered and the model is developed in the final step. A major limitation in the application of PVA to conservation problems is the lack of detailed population data for taxa of concern (Shaffer et al. 2002). The process of gathering those data is the first step necessary to determining the causative agents in any species decline and subsequent recovery . Building a PVA provides the framework within which this can occur (Possingham et al. 1993; Pereira et al. 2004). I have applied this conceptual framework to the grassland earless dragon ( Tympancryptis pinguicolla ) to address three questions of critical concern: 1) Is T. pinguicolla facing population declines? 2) What are our knowledge gaps associated with the ecology of T. pinguicolla ? 3) Should management of T. pinguicolla be focusing on particular life history parameters? The net result of this approach has been information on key ecological parameters of the life history of T. pinguicolla, the identification of population declines, and the development of a PVA that provides the first quantitative assessment of the influence of key demographic parameters on the decline of T. pinguicolla. This work provides the starting point for an adaptive management scheme for this species, and highlights the role PVAs can play in establishing recommendations for experimental management actions for little known species.

Identifying the key ecological parameters of the life history of a species is the first step in building a PVA and requires all existing information on the species be collated (Possingham et al. 1993). Owing to the lack of published research on T. pinguicolla , this step was particularly important for effective status assessment and management of the species. I compiled a database of all T. pinguicolla monitoring in the Canberra region (Table S1) and drew on that to demonstrate

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Synopsis that long term declines in trap rates at all but two sites where T. pinguicolla have been found. Moreover, our recent surveys have indicated that from ten previously occupied sites the species is no longer detectable at three sites (30%) and is currently in extremely low numbers at the remaining seven sites (Chapter 2). In addition, my intensive monitoring of the site identified as having the largest trap rates (Jerrabomberra Grassland Reserve West) has revealed that significant declines have occurred since 2006 (Chapter 2). These trapping data are shared with ACT Parks, Conservation and Lands and provide the basis for consistent comparable survey techniques and by providing a list of surveyed sites allow identification of potential grassland sites still to be surveyed for T. pinguicolla in the region.

In addition, I collated the few reproductive data, essential for use in a PVA, and used this to highlight some important information gaps (Chapter 3). One of the most important of these is the lack of information on the oviposition sites used by T. pinguicolla . Consequently, and compounded by the lack of gravid females in museum collections, there is little known of the breeding biology of the species. Although over time, a few nests have been found leading to some information on clutch sizes (Chapter 3), we lack information on survival of eggs to hatching and whether all females that present as gravid actually lay their eggs. This is an important gap, as it is known that some lizards may retain eggs under unfavourable conditions (Shanbhag et al. 2003) and some Tympanocryptis may even become egg bound and die (J. McAuliffe pers. comm.). This information is critical for identifying causes of the declines as described in chapter 2 and underscores the need for further research into reproductive parameters for this species, especially in the light of the results obtained from the PVA (Chapter 4).

A PVA was developed to investigate the declines identified in chapter 2 in which the monitoring and reproductive data were integrated. The PVA was essential in identifying the driving parameters behind the decline and has led to new research into the effects of grassland condition and structure on population size. Firstly, the PVA showed that if this decline continues with no change in management or environment, extinction of the Jerrabomberra population (the largest currently known population in the ACT) will occur within the next decade. The high probability of extinction of this and other ACT populations (Chapter 4), combined with low survival and decline in numbers captured (Chapter 2) from 2006 to 2008 presents conservation managers with

102

Synopsis an alarming situation – the trend of a very significant population towards total collapse. The PVA demonstrated that population size and probability of extinction is most sensitive to juvenile survival and fecundity. In particular reduced juvenile survival in winter substantially decreases time to extinction (Chapter 4) and ultimately in the following season leads to a reduction in the number of potential breeders able to contribute to the population. Intense drought periods, such as those experienced over the course of the present study, are likely to impact severely on factors critical to over winter survival (Chapter 3).

The possibility of extending the current population monitoring to the investigation of more potential sites for this species (Chapter 2) led me to consider the survey effort that is required to identify the presence of T. Pinguicolla at a site if it is there, with a given confidence. I investigated the survey effort required to determine a true presence or absence by combining zero inflated models (MacKenzie et al. 2002b) with Driscoll’s (2010) approach to recording occurrence: the proportion of traps occupied at a location (Chapter 5). Through that analysis, I estimated that 1285 artificial burrows surveyed three times in one week would be needed to have 99% confidence that the species was truly absent using the lowest density scenario. The effort required can be reduced by spreading the surveying over several weeks. The sampling regime currently employed begins in February and ends in mid March, but detection probabilities for T. pinguicolla increase over this time period. The model showed that detection probability was strongly dependent on survey week, which emphasizes the importance of the timing of juvenile emergence in detection. These results, along with the likelihood that juveniles will keep emerging into May (Nelson 2004) means that it is possible that the optimal time for sampling may exist outside of the period examined in the present study. Surveys conducted from March through to May would be required to determine whether a higher probability of detection could be achieved by shifting the survey time.

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Synopsis

CONCLUSIONS

My data demonstrates that T. pinguicolla has experienced a dramatic decline in the ACT between 1993 and 2009 and analyses of trends in two of the most intensely studied populations show this situation to be severe. The extent of the decline is most evident in the largest known population (Jerrabomberra West) and T. pinguicolla are no longer detectable at three (30%) of the ten sites formerly known to contain populations of T. pinguicolla suggesting that at least three populations are close to, or have even gone to, extinction.

My research has shown that T. pinguicolla select for only a small number of orders of invertebrates and has low reproduction which may be reduced if environmental conditions are not suitable. This high level of selection in food and low recruitment rate are both characteristics of species that are highly vulnerable to extinction (Pimm et al. 1988; McKinney 1997).

The work I have presented here has shown how PVA is a powerful approach to identifying the key population parameters that are the most likely to be affected by environmental factors. In this case, PVA demonstrated a link between poor survival of juveniles and low fecundity with population declines, and, by predicting a very short time to extinction, highlighted a major conservation concern for these populations. In the ACT this may in part be occurring through drought conditions and overgrazing. Two hypotheses to explain the recent population declines have emerged from this research:

1. Low production of, and high mortality in, eggs through low soil moisture, increased exposure and dry conditions is driving the decline.

2. Reduced levels of plant growth caused by drought conditions, combined with increased grazing pressure from kangaroos, or other herbivores, has reduced ground cover within the grasslands and consequently increase the exposure of lizards – particularly hatchlings and juveniles to predation.

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Synopsis

CONSERVATION MANAGEMENT AND FUTURE RESEARCH

As a direct result of the findings reported here, three sites containing populations of T. pinguicolla have been enclosed by herbivore exclusion fencing to reduce grazing by kangaroos. In addition experimental research is underway with the aim to determine the impacts of changes in vegetation cover and structure on the reproduction and population demography of T. pinguicolla in the ACT

Further work is in progress to incorporate results from genetic data collected concurrently with this study (Hoehn et al. 2010) to develop spatially explicit matrix population models to evaluate the impact of development and other threats on longterm trends in population change. The model will be used to evaluate different management scenarios (e.g. translocation and harvesting to create a captive population management) and recommendations made to promote effective conservation of T. pinguicolla .

Declines to small numbers can be detrimental to the conservation of T. pinguicolla , because unlike in previous extreme droughts, the grasslands are small and isolated so the opportunities for populations to move out of refugia and recolonise where extinctions have occurred will be few. With such small populations T. pinguicolla are at the mercy of extreme climatic events such as the recent drought, which is widespread and likely to have an influence on all populations of this species. Added to this concern is the prediction that climate change will cause an increase in the frequency and severity of droughts (Karoly et al. 2003), increasing the chances of extinction for not only this species but other in the grassland specialists (e.g. the striped legless lizard, Delma impar ) that are facing similar conditions.

Regular population monitoring should be continued to allow for assessment of management actions and the detection of future changes in population trends. This work highlights the importance of consistent monitoring in detecting population trends and providing demographic data. Demographic studies of survival become more difficult as population numbers decrease; however the work required gathering this data is not wasted as it allows for collection of comparative parameters e.g. raw counts of capture rate, and the opportunity to monitor individuals’ health and collect genetic data. Given the large number of small, historic populations

105

Synopsis it is recommended a system for regular monitoring of more sites be developed for this species (Chapter 2). Extended sampling across more populations by using a staggered sampling method across years and sites could be facilitated by skipping some consecutive years in a monitoring program and putting the money saved into extending the time series or improving estimates for each year that data are collected (Chapter 2). In addition, a season of monitoring that extends past the current six week period is recommended to test if the current survey period is the most effective for detecting the species (Chapter 5). It is possible that surveying for a shorter period later in summer will provide adequate data for less effort.

The effects on survival and fecundity of the herbivore exclusion fencing should be tested by continued monitoring of both fenced and unfenced populations. An experimental approach to fencing has been discussed with ACT Parks, Conservation and Lands such that it has been proposed that monitoring at Jerrabomberra East be extended to include concurrent trapping inside and outside fenced areas within the reserve. These areas are continuous and, apart from the fence, have the same management regime. This would then be the first use of adaptive management in the recovery of this species.

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