Defining and Mapping Rare Vegetation Communities: Improving Techniques to Assist Land-Use Planning and Conservation

Defining and mapping rare vegetation communities: improving techniques to assist land-use planning and conservation

Stephen A. J. Bell BSc. (Hons)

A thesis submitted for the degree of Doctor of Philosophy School of Environmental and Life Sciences The University of Newcastle

February 2013

Declaration

Statement of Originality This thesis contains no material which has been accepted for the award of any other degree or diploma in any university or other tertiary institution and, to the best of my knowledge and belief, contains no material previously published or written by another person, except where due reference has been made in the text. I give consent to this copy of my thesis, when deposited in the University Library, being made available for loan and photocopying subject to the provisions of the Copyright Act 1968.

Statement of Collaboration I hereby certify that the concepts embodied in Chapter 2 of this thesis have been done in collaboration with a fellow researcher at this university. I have included as part of the thesis in Chapter 2 a statement clearly outlining the extent of collaboration with whom and under what auspices.

Stephen A. J. Bell

Preface

“The vegetated landscape …. on first appearance presents a bewildering display of living matter, a higgledy-piggledy mass of trunks, leaves, branches, shrubs and grasses seemingly without form. The more observant may notice that the higgledy-piggledy mass varies from one place to another, that in some places there are trees as tall as large buildings while in other places there are no trees at all …. By the application of a systematic approach to viewing vegetation the bewildering display of plant life can take on new meaning thus altering one’s perception of what is being seen .… Suddenly the jumble of plant life reveals structures and beauties probably hitherto unseen”.

Ian G. Read. The Bush: A Guide to the Vegetated Landscapes of Australia. Reed Books Pty Ltd, French's Forest, 1987.

In the early 1990’s, Nic Gellie introduced me to the perils and challenges of vegetation classification when we worked together on the production of a classification and map of Yengo National Park for the New South Wales National Parks and Wildlife Service. At the time, it seemed to be such a daunting task to encapsulate all the variation I could see in the forests, woodlands, heaths and rainforests (the ‘higgledy-piggledy mass’) and transform them into a single classification of 15 communities, particularly as at the same time I was also mastering my plant identification skills. But we succeeded. Fifteen years later, when revising that classification with Daniel Connolly, it was remarkable how much community diversity there was that Nic and I had overlooked. Unraveling the intricacies of numerical classification in those early days with Nic was, however, when the classification ‘bug’ struck me, and I have not looked back ever since.

I remember that, in the mid 1990’s when I ventured into the consultancy field following the Yengo project, the thought struck me that I should start to collect systematic sample data for all projects I worked on, irrespective of it being a requirement of a project brief or not. The idea of collecting and storing data for use ‘sometime in the future’ was of little consequence at the time, but in hindsight it was one of the best professional decisions I have made. When the opportunity arose to produce the first ever classification and map of Wollemi National Park in the late 1990’s, I grabbed it with some trepidation but also with considerable gusto. At nearly half a million hectares, and the second largest national park in New South Wales, the challenge of sampling and mapping such a vast area was both a challenge and, it must be said, a bit frightening. But again, I managed to complete that project to some level of satisfaction, knowing however that a dataset of ~340 samples was not nearly enough to highlight the diversity of such an amazing wilderness. Once again, fifteen years hence it is with Daniel Connolly and his crew that the original Wollemi classification is being revised, and again we are uncovering further community diversity. What has changed in the intervening period? Apart from considerably larger

i budgets and more resources, with experience comes a better understanding of how to structure a sampling regime to capture more community diversity.

It was during these early years in my classification forays that I began to develop some of the pangs of uncertainty that have now morphed into the central themes of this thesis. When I began, the mouthful that was environmental stratified random sampling was standard practice in project design, and in the years that followed I never attempted to classify an area without implementing some form of environmental stratification from which to select my samples. But it eventually dawned on me that the stratification process was not detecting the full range of community diversity (the ‘structures and beauties hitherto unseen…’), and that there must be a more thorough way of sampling and classifying a patch of land. Plant species were not distributed randomly across a landscape, so why were we assuming that randomly locating sample plots within the landscape, albeit within broad environmental strata, would capture all variation? I started to target my sampling to those areas that were clearly different from their surrounds, a process I now know as preferential sampling. In my reading of the literature, I discovered that such a sampling method was actually at the core of the Zurich-Montpellier approach to classification, founded in Europe in the early 20th Century, and that knowing your study area intimately was the basis behind a good classification. Why, then, were we in many parts of Australia so intent on selecting our samples remotely within a computer generated environmental stratification, and actually spending less time in the field? I endeavored to find out.

When the Threatened Species Conservation Act was proclaimed in New South Wales in 1995, the concept of endangered communities came into the thinking of consultant ecologists and others charged with natural resource management. For a community to be endangered, it had to be definable and, often, rare or restricted in its geographical distribution. By the early 2000’s it became clear to me that vegetation communities defined through an environmentally stratified random sampling approach were often incapable of detecting many communities that may qualify as endangered; if they were, such communities were included in much broader units and their true significance was potentially lost. Some listed endangered communities were also difficult to rationalize with on-ground observations (bread & butter tasks for a working ecologist in the development industry), which in some cases could be traced back to the methods in which they were initially defined. A change was needed. Hopefully, the information presented in this thesis may facilitate a re-focusing of the way that classification is undertaken in Australia, so that truly rare and restricted communities can be protected accordingly. Or, in the prose of Ian Read, it is hoped that through this work ‘hitherto unseen structures and beauties can be extracted systematically from the higgledy-piggledy mass’.

“Since we cannot change reality, let us change the eyes which see reality”

Nikos Kazantzakis (1883-1957)

Acknowledgements

Undertaking an investigation of the scale at which this thesis has been done could not have occurred without data, and lots of it. I have been fortunate enough to earn a living collecting systematic vegetation data, and have done so for nearly 20 years. Those who know me will attest to the fact that I am, indeed, a data-junkie. But with data comes possibilities, and as I have found over the years those possibilities are endless. Working as a consultant botanist has allowed me access to numerous locations within (mainly) the Sydney Basin of New South Wales, where collecting systematic sample data has become second nature to me. However, I am indebted to all those landowners and government authorities (too numerous to mention all by name) that have allowed me access to their properties for data collection purposes, which may or may not have been used directly in this thesis. In particular, I wish to thank the NSW Office of Environment & Heritage (and all of its constituent parts and former incarnations), the Commonwealth Department of Defence, and the local government Councils of Lake Macquarie, Cessnock, Gosford and Wyong. For allowing access to private lands, I wish to thank Eve and Alan Lowson, Helen and Peter Horn, Rixs Creek Coal, Mt Owen Coal, Wallarah Coal, Bloomfield Coal, Wyong Shire Council, Lake Macquarie City Council, and the Roche Group. Lake Macquarie City Council and the Roche Group are particularly thanked for permission to use data from a previous study in northern Lake Macquarie as a case study for Chapter 2.

Within the NSW Office of Environment & Heritage, I am grateful to Daniel Connolly, Liz Magarey, Renee Woodford, Brian Flannery, Tricia Hogbin, Phil Craven, Sean Thompson, Karen Thumm and Lucas Grenadier for field assistance, logistics and/or general discussions about classification and its implications. Several of these people project managed or assisted with surveys over the years, making the job of data collection and analysis so much easier. NSW OEH also allocated funding towards a revision of Lower Hunter Spotted Gum-Ironbark Forest, detailed in Chapter 4, and this is greatly appreciated. To those staff involved, and particularly Andrew McIntyre, many thanks for your patience, particularly during those times when, for various reasons, the revision work was progressing slowly.

At the University of Newcastle, I extend particular thanks to Mike Cole, Tina Offler and John Patrick. Completing a research thesis on a part-time basis was always going to be difficult, but these three were exceedingly flexible in their supervision, allowing me the freedom and time to explore exactly what and where I wanted to go with this thesis, even when at times I was unsure myself. But, when it was time to bite the bullet they reined me in sufficiently enough to progress. I am also thankful to a number of colleagues and fellow ecologists for their often unspoken encouragement during the years I have been undertaking this research. Some I know very well, others I have only ever communicated via email. These include, in no particular order, Dee Murdoch, Martin Fallding, Max Elliott, Robbie Economos, Karen Thumm, Alison Hunt, Doug Beckers, Rachel Lonie, Travis Peake, Renee Woodward, Tricia Hogbin, Tim Curran, iii

Lachlan Copeland, and Miquel de Cáceres. For the review of specific chapters, I also thank Tim Curran and Martin Fallding; both provided new angles and helped to clarify my thinking on certain issues.

Over the years, there have been innumerable conversations discussing the problems and intricacies of vegetation classification, often shared around the campfire or while travelling to and from a study area, with Daniel Connolly and Colin Driscoll. These discussions have inevitably shaped the way in which I now approach classification, and I thank both chaps equally. Daniel and Colin have never met, but with me as the ‘meat in the sandwich’, my ideas and problems have been suitably massaged by these two, as each approach an issue from different angles. Colin, too, was the culprit who initially convinced me to enroll for a PhD, so it is he who I can blame for the stress (!), challenges and fun of the last few years.

To my parents and siblings, and nieces and nephews, I also extend thanks. Although none of you really understand what it is I actually do when I “go bush”, your support and lack of questioning and interrogation is, strangely, much appreciated. By all means, read this thesis and if you are still perplexed, feel free to ask questions.

Most of all, though, I wish to thank my family: Cathy, Emily, Andrew and Daniel. Having a husband and father who worked and studied at home, and who rarely seemed to go out to work (“like other Dads”) must have been difficult to comprehend over the last few years, so I thank them immensely for their understanding and support. They knew I had something worthwhile to say (even though they didn’t understand it), and encouraged me constantly and (on occasion) noisily. Once we domesticated the ‘stampeding wildebeest’ upstairs, all was well in the world…

Stephen Bell

February 2013

Cover image: Scribbly Gum-dominated dune forest at Cockle Creek (see Chapter 3).

A note on the use of grey literature: In addition to the published literature, work presented in this thesis draws upon a considerable amount of unpublished (grey) classification and mapping studies. While this is not ideal, it is important to acknowledge that many land-use planning and conservation decisions are actually based on such grey literature (from local to national scales), and discussion and use of these is critical in advancing our understanding of vegetation science. Use of this material in this thesis, from numerous sources, is gratefully acknowledged.

Table of Contents

Preface ...... i Acknowledgements ...... iii Table of Contents ...... v List of Figures ...... ix List of Tables ...... xiii Abbreviations ...... xv Abstract ...... xvi

Chapter 1: Introduction & Research Plan ...... 1 Overview ...... 2 1.1 Classification of Vegetation ...... 3 1.1.1 The Zurich-Montpellier School ...... 4 1.1.1.1 The quadrat ...... 5 1.1.1.2 The association ...... 5 1.1.1.3 Species fidelity ...... 7 1.1.1.4 Hierarchical classification ...... 8 1.1.1.5 Summary of the Zurich-Montpellier school ...... 9 1.1.2 Criticisms of the Zurich-Montpellier School ...... 10 1.1.3 Static and Dynamic Classification ...... 12 1.1.4 Defining rare communities ...... 15 1.1.4.1 What is a rare community? ...... 15 1.1.4.2 Why are rare communities important? ...... 17 1.1.4.3 Rarity & legislation ...... 18 1.2 Mapping Vegetation Communities ...... 22 1.2.1 Predictive Modelling vs Traditional Mapping ...... 22 1.2.2 Summary of Classification & Mapping in SE Australia ...... 25 1.2.2.1 Brief history in Australia & NSW ...... 25 1.2.2.2 Lower Hunter & Central Coast REMS ...... 27 1.2.2.3 Sampling design in Australia ...... 28 1.2.2.4 Assessing map and classification accuracy ...... 30 1.2.3 Recent NSW Government Initiatives ...... 32 1.2.3.1 The NSW Native Vegetation Interim Type Standard ...... 32 1.2.3.2 BioBanking ...... 35 1.2.3.3 The NSW Vegetation Information System ...... 36 v

1.3 Data Sampling ...... 37 1.3.1 Random vs Non-random Sampling ...... 37 1.3.2 Stratified Random Sampling ...... 38 1.3.3 Sampling Adequacy ...... 40 1.4 Issues of Classification Scale ...... 42 1.5 Key Questions to be Addressed ...... 44

Chapter 2: Improving Sampling Design: Data-informed Sampling and Mapping...... 47 Overview ...... 48 2.1 Introduction ...... 49 2.2 Methods ...... 51 2.2.1 Study Areas ...... 51 2.2.1.1 Edgeworth LEP ...... 52 2.2.1.2 Columbey National Park...... 53 2.2.1.3 Cessnock-Kurri ...... 53 2.2.2 Mapping Pathway ...... 54 2.2.3 Comparisons to Previous Mapping ...... 62 2.2.3.1 Previous Mapping Products ...... 62 2.2.3.2 Accuracy Assessment ...... 62 2.2.4 Time Resources ...... 66 2.3 Results ...... 66 2.3.1 Edgeworth LEP ...... 66 2.3.1.1 Mapping process ...... 66 2.3.1.2 Comparison to existing mapping ...... 68 2.3.1.3 Time resources ...... 71 2.3.2 Columbey National Park ...... 71 2.3.2.1 Mapping process ...... 71 2.3.2.2 Comparison to existing mapping ...... 73 2.3.2.3 Time resources ...... 78 2.3.3 Cessnock-Kurri Region ...... 79 2.3.3.1 Mapping process ...... 79 2.3.3.2 Comparison to existing mapping ...... 81 2.3.3.3 Time resources ...... 88 2.4 Discussion ...... 89 2.4.1 The D-iSM Process ...... 89 2.4.2 Sample Selection ...... 91 2.4.3 Map Accuracy ...... 94

2.4.4 Detecting Rare Communities ...... 97 2.4.5 Cost-benefit Analysis ...... 98 2.4.6 Application to Varying Projects ...... 103 2.4.7 Sources of Potential Errors ...... 106

Chapter 3: Defining new communities: Scribbly Gum Forest on the Central Coast ...... 109 Overview ...... 110 3.1 Introduction ...... 111 3.2 Methods ...... 115 3.2.1 Study Region ...... 115 3.2.2 Study Design ...... 119 3.3 Results ...... 122 3.3.1 Analysis 1: Scribbly Gum Analysis ...... 122 3.3.2 Analysis 2: Regional Central Coast Analysis ...... 127 3.3.3 Environmental Relationships ...... 130 3.4 Discussion ...... 131 3.4.1 Survey Design ...... 132 3.4.2 The Importance of Resolution ...... 134 3.4.3 Conservation Implications ...... 136 3.5 Conclusions: Key Principle in Community Definition ...... 139

Chapter 4: Refining existing communities: Lower Hunter Spotted Gum-Ironbark Forest ..... 141 Overview ...... 142 4.1 Introduction ...... 143 4.2 Methods ...... 149 4.2.1 Study Region ...... 149 4.2.2 Study Design ...... 151 4.3 Results ...... 157 4.3.1 Classification ...... 157 Analysis 1: Regional Spotted Gum-Ironbark Vegetation ...... 157 Analysis 2: Eucalyptus fibrosa – Corymbia maculata...... 161 Analysis 3: Eucalyptus fibrosa ...... 165 Analysis 4: Hunter Valley Floor ...... 169 4.3.2 Profiling Candidate-Lower Hunter Spotted Gum-Ironbark Forest ...... 173 4.3.3 Mapping ...... 180 4.4 Discussion ...... 180 4.4.1 Refining Existing Communities ...... 181 4.4.2 Current Understanding of Lower Hunter Spotted Gum-Ironbark Forest ...... 184 vii

4.4.3 Lower Hunter Spotted Gum-Ironbark Forest as a TEC ...... 193 4.4.4 Distribution & Reservation: Lower Hunter Spotted Gum-Ironbark Forest.... 199 4.4.5 A Revised Lower Hunter Spotted Gum-Ironbark Forest Determination? ..... 200 4.5 Conclusions: Key Principles in Community Refinement...... 201

Chapter 5: General Discussion ...... 204 Overview ...... 205 5.1 Identifying and Describing Rare Communities...... 206 5.2 Survey Design for Vegetation Classification ...... 208 5.3 Data-informed Sampling and Mapping ...... 211 5.4 Standards for Sampling Design ...... 217 5.4.1 International Standards ...... 217 5.4.2 Australian Standards ...... 218 5.5 Re-setting the Focus of Classification in Australia ...... 222 5.6 Conclusion ...... 230

Chapter 6: References ...... 233

Chapter 7: Appendices ...... 267 Appendix 1 – Edgeworth LES Confusion Matrix Examples ...... 268 Appendix 2 – Columbey NP Confusion Matrix Examples ...... 269 Appendix 3 – Cessnock-Kurri Confusion Matrix Examples ...... 272 Appendix 4 – Werakata NP Confusion Matrix Examples ...... 274 Appendix 5 – Lower Hunter Spotted Gum-Ironbark Forest EEC Final Determination ..... 275 Appendix 6 – Chronological history of LHSGIF Revision ...... 281 Appendix 7 – Spotted Gum-Ironbark Vegetation (NSWNPWS 2000b)...... 283 Appendix 8 – Vegetation superficially similar to LHSGIF ...... 285 Appendix 9 – LHSGIF 3-d nMDS ordination diagrams ...... 288

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List of Figures

Fig. 1.1. Conceptual representation of a dynamic vegetation classification, showing the process of initial and subsequent vegetation survey results (de Cáceres 2011)...... 13 Fig. 1.2 Types of community rarity. R1 – R7 refer to the seven forms of rarity (from Izco 1998)...... 15 Fig. 1.3. Cumulative number of Threatened Ecological Communities (TECs) listed for the Hunter-Central Rivers Catchment Management Area since inception of the NSW Threatened Species Conservation Act in 1995...... 21 Fig. 1.4. The Lower Hunter and Central Coast of New South Wales, showing member councils...... 28

Fig. 2.1 Location of the three study areas within the lower Hunter Valley, shown over Landsat TM imagery to illustrate differences in context and fragmentation...... 52 Fig. 2.2 Flowchart showing the D-iSM mapping pathway...... 55 Fig. 2.3. Potential relationships between floristic strata units, floristic communities and final vegetation map, after sampling and classification of full floristic sample quadrats...... 61 Fig. 2.4 Schematic representation of Steps 1-8 used to create a vegetation map of the Edgeworth LEP...... 67 Fig. 2.5 Vegetation maps of the Edgeworth LES study area thematically portrayed using the same regional (NSWNPWS 2000b) community nomenclature...... 69 Fig. 2.6 Mean user accuracy (+/- SE) of vegetation communities defined by NSWNPWS (2000b) for Edgeworth LEP, determined from a confusion matrix of 180 Rapid Data Points (10 random batches of 18 Rapid Data Points)...... 70 Fig. 2.7 Allocation of 180 Rapid Data Points across vegetation communities defined by NSWNPWS (2000b) for Edgeworth LEP...... 70 Fig. 2.8 Percentage task allocation for creating a vegetation map for Edgeworth LES using the D-iSM method...... 71 Fig. 2.9 Schematic representation of Steps 1-8 used to create a vegetation map of Columbey National Park ...... 72 Fig. 2.10 Vegetation maps of the Columbey National Park study area thematically portrayed using the same RN17 regional community nomenclature (FCNSW 1989) ...... 74 Fig. 2.11 Vegetation maps of the Columbey National Park study area thematically portrayed using the same CRA regional community nomenclature (NSWNPWS 1999a)...... 75

Fig. 2.12 Mean user accuracy (+/- SE) of forest types defined by FCNSW (1989) for Columbey National Park, determined from a confusion matrix of 540 Rapid Data Points (10 random batches of 54 Rapid Data Points)...... 76 Fig. 2.13 Allocation of 540 Rapid Data Points across forest types defined by FCNSW (1989) for Columbey National Park...... 76 Fig. 2.14 Mean user accuracy (+/- SE) of forest ecosystem units defined by NSWNPWS (1999a) for Columbey National Park, determined from a confusion matrix of 540 Rapid Data Points (10 random batches of 54 Rapid Data Points) ...... 77 Fig. 2.15 Allocation of 540 Rapid Data Points across forest ecosystems defined by NSWNPWS (1999a) for Columbey National Park...... 78 Fig. 2.16 Percentage task allocation for creating a vegetation map for Columbey National Park using the D-iSM method...... 79 Fig. 2.17 Schematic representation of Steps 1-8 used to create a vegetation map of the Cessnock-Kurri region...... 80 Fig. 2.18 Pre-1750 vegetation map of the Cessnock-Kurri study area produced by NSWNPWS (2000b) thematically portrayed using the regional community nomenclature (NSWNPWS 2000b)...... 82 Fig. 2.19 Pre-1750 vegetation map of the Cessnock-Kurri study area using the D-iSM method thematically portrayed using the regional community nomenclature (NSWNPWS 2000b)...... 82 Fig. 2.20 Mean user accuracy (+/- SE) of vegetation units defined by NSWNPWS (2000b) for Cessnock-Kurri, determined from a confusion matrix of 16,800 Rapid Data Points (10 random batches of 1,680 Rapid Data Points)...... 83 Fig. 2.21 Allocation of 16,800 Rapid Data Points across vegetation units defined by NSWNPWS (2000b) for Cessnock-Kurri...... 84 Fig. 2.22 Vegetation mapping for Werakata National Park, from (a-c) NSWNPWS (2000b), Bell (2004a) & the current study...... 86 Fig. 2.23 Mean user accuracy (+/- SE) of vegetation units defined by Bell (2004a) for Werakata National Park, determined from a confusion matrix of 1,400 Rapid Data Points (10 random batches of 140 Rapid Data Points)...... 87 Fig. 2.24 Allocation of 1,400 Rapid Data Points across vegetation units defined by Bell (2004a) for Werakata National Park...... 87 Fig. 2.25 Percentage task allocation for creating a vegetation map for Cessnock-Kurri using the D-iSM method...... 88 Fig. 2.26 Linear regression of person-hours and number of hectares required for Step 4 (map data acquisition) of D-iSM, 0-100 ha category (one-way ANOVA; F=115.9, p<0.0001)...... 100 Fig. 2.27 Linear regression of person-hours and number of hectares required for Step 4 (map data acquisition) of D-iSM, 100-1000 ha category (one-way ANOVA; F=48.5, p<0.0005)...... 100 Fig. 2.28 Linear regression of person-hours and number of hectares required for Step 4 (map data acquisition) of D-iSM, 1000-10,000 ha category (one-way ANOVA; F=20.1, p<0.005)...... 101

Fig. 2.29 The Lower Hunter and Central Coast region, showing distribution of Rapid Data Points collected by two ecologists over the course of six years (2006- 2011) ...... 105

Fig. 3.1. The NSW Central Coast region (shaded), with study locations (●)...... 116 Fig. 3.2. The NSW Central Coast region, showing annual average rainfall bands ...... 116 Fig. 3.3. Broad geology types of the Central Coast region, with study locations (●)...... 118 Fig. 3.4. Dendrogram of sample quadrats, showing the relationship between all samples (Bray-Curtis similarity measure, 42% similarity)...... 123 Fig. 3.5. Non-metric Multi Dimensional Scaling ordination, showing the relationship between sample quadrats, overlain with cluster analysis groups (Bray-Curtis similarity measure, 42% similarity)...... 124 Fig. 3.6. Contribution of canopy, mid and ground layer species to community diversity at each study location...... 127 Fig. 3.7. Partially collapsed sample dendrogram of 1771 quadrat from the regional analysis...... 128 Fig. 3.8. Distribution of the nine Scribbly gum communities defined in this study...... 137

Fig. 4.1. Data extract from survey database of NSW Office of Environment & Heritage showing quadrat samples where Red Ironbark (Eucalyptus fibrosa) and Spotted Gum (Corymbia maculata, C. variegata, C. henryii) co-occur relative to New South Wales Bioregions (Thackway & Cresswell 1995)...... 147 Fig. 4.2. Distribution of Permian and Triassic Narrabeen sediments within the Sydney Basin (Source: Geological Survey of NSW)...... 150 Fig. 4.3. Regional dataset of full floristic quadrats available for analysis...... 153 Fig. 4.4. Wyong focus area for mapping of Candidate-LHSGIF within Wyong local government area...... 158 Fig. 4.5. Non-metric Multi Dimensional Scaling (2-dimensional) ordination of Analysis 1, showing the relationship between samples (Bray-Curtis similarity measure). Candidate-LHSGIF samples are broadly defined within the larger matrix. Kruskal’s stress = 0.21...... 159 Fig. 4.6. Distribution of 999 random permutations of the ANOSIM test statistic R for Analysis 1, and the true value of R (0.19, vertical dotted line at right) (p<0.001)...... 159 Fig. 4.7. Geographical distribution of Candidate-LHSGIF and all other Spotted Gum- Ironbark forest sample quadrats in the Sydney Basin from Analysis 2...... 160 Fig. 4.8. Non-metric Multi Dimensional Scaling (2-dimensional) ordination of Analysis 2, showing the relationship between samples (Bray-Curtis similarity measure). Kruskal’s stress = 0.24...... 161 Fig. 4.9. Non-metric Multi Dimensional Scaling (2-dimensional) ordination of Cessnock Spotted Gum Ironbark group from Analysis 2, showing the

relationship between three defined sub-groups at 37% similarity (Bray- Curtis similarity measure). Kruskal’s stress = 0.18...... 162 Fig. 4.10. Distribution of 999 random permutations of the ANOSIM test statistic R for Analysis 2, and the true value of R (0.65, vertical dotted line at right) (p<0.001)...... 164 Fig. 4.11. Geographical distribution of seven floristic groups defined from Analysis 2...... 166 Fig. 4.12. Non-metric Multi Dimensional Scaling (2-dimensional) ordination of Analysis 3, showing the relationship between samples (Bray-Curtis similarity measure) and major groups defined at 19% similarity. Candidate-LHSGIF includes Cessnock Ironbark Forest, Hunter Floor Ironbark Forest and Hinterland Ironbark Forest. Kruskal’s stress = 0.21...... 167 Fig. 4.13. Distribution of 999 random permutations of the ANOSIM test statistic R for Analysis 3, and the true value of R (0.62, vertical dotted line at right) (p<0.001)...... 168 Fig. 4.14. Geographical distribution of seven floristic groups defined from Analysis 3 (Eucalyptus fibrosa dominated)...... 170 Fig. 4.15. Non-metric Multi Dimensional Scaling (2-dimensional) ordination of Analysis 4, showing the relationship between samples of Candidate-LHSGIF (Eucalyptus fibrosa-Corymbia maculata) and non Candidate-LHSGIF (Eucalyptus crebra-Corymbia maculata) from the Hunter Valley floor (Bray- Curtis similarity measure). Kruskal’s stress = 0.19...... 171 Fig. 4.16. Distribution of 999 random permutations of the ANOSIM test statistic R for Analysis 4, and the true value of R (0.35, vertical dotted line at right) (p<0.001)...... 172 Fig. 4.17. Distribution of 999 random permutations of the ANOSIM test statistic R for Analysis 4 (non Candidate-LHSGIF v Hunter Spotted Gum Ironbark Forest), and the true value of R (0.22, vertical dotted line at right) (p<0.001)...... 173 Fig. 4.18. Geographical distribution of samples representing Candidate-LHSGIF (Cessnock Spotted Gum Ironbark, Broken Back Spotted Gum Ironbark, Hunter Spotted Gum Ironbark) and non Candidate-LHSGIF from the Hunter Valley floor, as defined in Analysis 4...... 174 Fig. 4.19. Pre-1750 distribution of Candidate-LHSGIF within Wyong local government area, showing data collected during the map data acquisition phase of D- iSM...... 181 Fig. 4.20. Comparison of Candidate-LHSGIF groups against species lists contained in Paragraphs 1 (‘Define) and 2 (‘Characterise’) of the Lower Hunter Spotted Gum–Ironbark Forest EEC determination (NSW Scientific Committee 2010b)...... 189 Fig. 4.21. Extract of sample dendrogram of NSWNPWS (2000b) used to delineate LHSGIF...... 198

Fig. 5.1 Number of papers and preferred sample selection techniques published in the journals Pacific Conservation Biology, Austral Ecology, Australian Journal of Botany and Cunninghamia, from 2000-2011...... 224

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Fig. 5.2. Schematic representation of a Top-Down, Bottom-Up approach to sampling design for vegetation classification...... 231

List of Tables

Table 1.1 The five degrees of fidelity (Szafer & Pawlowski 1927, in Kent & Coker 2001)...... 7 Table 1.2 Threatened Ecological Communities listed on the TSC Act 1995, with their basis of definition in sampling designs incorporating stratified random sampling...... 20

Table 2.1 Study areas used to illustrate mapping method...... 52 Table 2.2 Symbolic representation of progressive Rapid Data Point collection and inferred floristic strata boundary...... 57 Table 2.3 Pre-existing studies used for comparative purposes...... 63 Table 2.4 Vegetation communities defined for Werakata National Park over three studies...... 85 Table 2.5 Summary of overall accuracies calculated from confusion matrices for existing and current vegetation maps of the Edgeworth LES, Columbey National Park and Cessnock-Kurri study areas...... 96 Table 2.6 Use of D-iSM across differing scales...... 99 Table 2.7 Progression and cost of major classification and mapping products for the Central Coast of New South Wales...... 102

Table 3.1 Summary of Scribbly gum (Eucalyptus haemastoma, E. racemosa) populations under study, showing localities and main topographical features...... 117 Table 3.2 Summary of floristic groups. Top 80% of diversity for each group and percentage contributions shown...... 125 Table 3.3 ANOSIM results (Global R values) for pair-wise comparisons of location- based groups...... 127 Table 3.4 Summary of inferred environmental relationships for floristic groups, showing geological age, geological group/ subgroup, and rainfall band...... 130 Table 3.5 Regional equivalent communities...... 138

Table 4.1 Summary of key features of the four data analyses...... 154

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Table 4.2 Percentage contributions to group species diversity of the five dominant ground and shrub species for the three Cessnock sub-groups, calculated using a SIMPER analysis in Primer (Clarke & Gorley 2006)...... 162 Table 4.3 ANOSIM results (Global R values) for pair-wise comparisons of location- based groups for Analysis 2 (p < 0.001)...... 163 Table 4.4 ANOSIM results (Global R values) for pair-wise comparisons of location- based groups for Analysis 3 (p < 0.001)...... 167 Table 4.5 ANOSIM results (Global R values) for pair-wise comparisons of location- based groups for Analysis 4 (p < 0.001)...... 173 Table 4.6 Summary of Candidate-LHSGIF from the Sydney Basin...... 175 Table 4.7 Summary of floristic composition for eleven defined units from Analysis 2 & Analysis 3. Top 90% of diversity shown for each unit and percentage contributions to overall diversity for that unit...... 176 Table 4.8 Comparison of Candidate-LHSGIF groups against the key characteristic species as listed in Paragraph 1 of the Lower Hunter Spotted Gum–Ironbark Forest EEC determination (NSW Scientific Committee 2010b)...... 186 Table 4.9 Comparison of Candidate-LHSGIF groups against the full species list as contained in Paragraph 2 of the Lower Hunter Spotted Gum–Ironbark Forest EEC determination (NSW Scientific Committee 2010b)...... 188 Table 4.10 Dichotomous key for field recognition of Candidate-LHSGIF groups defined in this study for the Sydney Basin...... 194 Table 4.11 Summary of reservation status of Candidate-LHSGIF communities...... 200

Table 5.1 Recommended sample selection techniques detailed for State and Territory guidelines in Australia...... 219

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Abbreviations

API Aerial Photographic Interpretation D-iSM Data-informed Sampling and Mapping ESUs Environmental Sampling Units EEC Endangered Ecological Community, a legal entity EPBC Act Environment Protection & Biodiversity Conservation Act 1999 (Australia) FCNSW Forestry Commission of New South Wales GIS Geographical Information System GPS Global Positioning System ha hectares km kilometres LEP Local Environment Plan LGA Local Government Area LHCCREMS Lower Hunter & Central Coast Regional Environmental Management Strategy NP National Park NSW New South Wales NSWNPWS NSW National Parks and Wildlife Service NSWDECC NSW Department of Environment and Climate Change NSWOEH NSW Office of Environment and Heritage NVITS NSW Native Vegetation Interim Type Standard pre-1750 pre-European settlement of Australia RDP Rapid Data Point TEC Threatened Ecological Community, a legal entity TSC Act Threatened Species Conservation Act 1995 (NSW) VDA Vehicular Data Acquisition WDA Walked Data Acquisition 4WD Four Wheel Drive vehicle

Abstract

Although steeped in history, the identification and classification of vegetation communities is rarely capable of addressing the full diversity of vegetation in an area, specifically those communities that are rare or of restricted distribution. This has proven particularly problematic for land managers who are charged with the responsibility of balancing human progress with meaningful conservation. However, in order to enable improvements in the detection of restricted or rare vegetation communities, it is necessary to revisit where the classification of vegetation began, and to determine how current day classification and mapping procedures are undertaken.

As detailed in Chapter 1 (General Introduction), the Zurich-Montpellier school of vegetation classification, established in the early 20th Century in Europe, outlined the basic building blocks of the current-day discipline of vegetation science. Within this school, the two central themes of sampling unit (‘quadrat’) and vegetation community (‘association’) are pivotal, and provide a mechanism for establishing order in a seemingly chaotic environment. Other workers argued for individualistic approaches and questioned the validity of a vegetation community. In recent decades, particularly in Australia, there has been a shift away from the ideals of the Zurich-Montpellier school, specifically that involving sampling design. Detailed, ground-based sampling, classification and mapping has transitioned into one where environmental surrogates, remotely accessed data (aerial photography, satellite imagery), and computer modelling takes precedence. At the same time, there has been an increasing demand for accurate, local-scale map products to inform land-use and conservation planning, and to satisfy legislative requirements for the protection of biodiversity. This juxtaposition of broad, regional-scale classification and mapping products onto local- scale landscapes and land conflicts is an unproductive dichotomy that requires resolution.

A new paradigm for the classification and mapping of vegetation at local and regional scales is outlined in this study, incorporating new methods based on old principles to xvi facilitate inclusion of rare and restricted communities in land-use planning. Data- informed Sampling and Mapping (D-iSM), outlined in Chapter 2, is illustrated through three common scenarios in natural resource management: assessment of vegetation for the development industry, defining and classifying vegetation within conservation reserves, and identifying significant vegetation within a sub-regional context. All seven steps in the new paradigm are detailed for each scenario, and the results are compared to previous classification and mapping for the three study areas, highlighting considerably improved accuracies. Central to the D-iSM method is the old adage, from the Zurich-Montpellier school, know your study area well, combined with preferential (non-random) sampling to ensure a thorough and representative dataset. For larger regional, State or National contexts, there is provision within D-iSM to incorporate ‘cutting-edge’ 3-dimensional interpretation of high resolution aerial images to overcome perceived shortfalls (financial, time, access constraints) in using the technique across extensive or rugged regions. Benefits of the D-iSM method include more efficient and more representative sampling, more realistic and repeatable classifications, considerably higher user accuracy in vegetation mapping, increased ability to detect and map rare vegetation communities and ready application to a range of classification and mapping projects.

In Chapter 3 (Defining New Communities), D-iSM has been applied to vegetation dominated by Scribbly Gum eucalypts (Eucalyptus haemastoma, E. racemosa: Myrtaceae) from Triassic Narrabeen and Permian sediments on the Central Coast of New South Wales. A preferential sampling strategy, combined with numerical classification analysis, has been used to compare the floristic composition of nine field- observed stands of native vegetation where Scribbly Gums are characteristic. Five of the nine vegetation types defined in this study were not sampled or classified during previous regional classifications using environmentally stratified random sampling techniques. Significantly, the five newly identified communities possibly represent the most threatened communities in the region: three are already listed as Threatened Ecological Communities in New South Wales (Kurri Sands Swamp Woodland, Quorrobolong Scribbly Gum Woodland, Kincumber Scribbly Gum Forest), and at least two further communities may be equally threatened. These outcomes highlight short- xvii comings of the environmentally stratified random sampling approach, relative to preferential sampling. They also demonstrate that classification projects incorporating a stratified random sampling methodology based on environmental variables should acknowledge the potentially undetected presence of significant vegetation communities due to deficiencies in survey design and issues of scale (resolution). Implementing a preferential sampling strategy, either in place of, or in combination with, a broader stratification is more likely to uncover such communities. Such an approach would require a thorough knowledge of a study area prior to or acquired during a project, and the flexibility to incorporate additional samples when necessary.

A similar yet different approach has been implemented in Chapter 4 (Refining Existing Communities), to illustrate how use of the D-iSM method can significantly improve the delineation and definition of vegetation communities originally defined by other methods. It also reiterates that vegetation classification is a dynamic process, and that with increased data collection and understanding of an area or vegetation type, a classification should evolve to better reflect current knowledge. In New South Wales, the Lower Hunter Spotted Gum – Ironbark Forest (LHSGIF), originally defined in 2000, was listed as a Threatened Ecological Community within the Sydney Basin Bioregion under the Threatened Species Conservation Act 1995 in 2005. Uncertainties in the on- ground recognition of this community and difficulties in distinguishing it from closely related communities in the decade since its inception have been common place. In part, these problems were caused by a sampling regime based on environmental stratification, which failed to adequately sample all variations within the region. With additional preferential sampling, this chapter uses numerical classification to review the floristic composition of Lower Hunter Spotted Gum-Ironbark Forest, and resolves many of the uncertainties established over ten years of its application in the Hunter region. Using the results of four different analysis datasets, eleven groups of candidate-LHSGIF have been defined, including one group occurring 250 km to the south of the Hunter Valley, on the South Coast of New South Wales near Nowra. As a separate case study, it also presents the results of pre-1750 mapping of candidate- LHSGIF, using the D-iSM method, over the Wyong local government area where traditionally it has not been previously identified. The extent of variation shown within xviii

LHSGIF throughout the Sydney Basin highlights the need to be more prescriptive with descriptions of communities listed as threatened under legislation, and more importantly, to ensure that classifications supporting such communities have confidently addressed all known variations.

In Chapter 5 (General Discussion), it is reiterated that implementing a more ground- based approach to classification and mapping, particularly for local- and regional-scale classification products, will significantly improve the detection and recognition of rare and restricted vegetation communities. The successful detection and mapping of rare communities is conditional upon a number of key themes in vegetation science. One of the most influential of these is that of sample selection: how sample locations are chosen within the wider environment. As has been shown in this thesis, adoption of a simple change in the way that sampling is undertaken (preferential rather than random) can dramatically improve the detection and definition of rare communities. The D-iSM approach to classification and mapping encapsulates the core principles of direct sampling of observed variations, rather than relying on chance that such variations will be captured by an environmental stratification. The collection of ground-data points prior to establishing a sampling framework enables more efficient and representative sampling and results in a more reliable vegetation map with lasting relevance.

Existing standards and guidelines for vegetation classification vary throughout Australia and the world. In Australia, it is suggested that a re-setting of the focus is required so that classification in general, and detection of rare communities in particular, can be more reliably documented to bring them onto an equal footing with knowledge on rare and threatened plant taxa. This shift in focus, away from environmentally stratified random sampling and towards preferential sampling, can be facilitated through the D-iSM process. Seven critical steps in thinking are outlined for this shift to occur: (1) raising the perception and value of rare vegetation communities; (2) improved use of public money in regional classifications; (3) improved reporting in the development industry; (4) improved assessment of conservation reserves; (5) review of existing threatened communities; (6) review of listing structure in threatened

xix species legislation; and (7) continued establishment of a hierarchical classification of Australian vegetation.

In conclusion, the simple conceptual framework of Top-Down, Bottom-Up information processing has been used to illustrate why this re-focusing is necessary, and to facilitate the change in thinking required for vegetation scientists in Australia. Classifications established on Bottom-Up theory will provide more reliable and accurate information than Top-Down classifications. Further research on adequacy of sampling, definition of community boundaries, transient and keystone communities are highlighted as the logical next steps in improving local-scale classification for land- use planning and conservation. However, perhaps even of more interest than any of these is the issue of whether or not biodiversity management should shift from a community approach to a species approach. As community resolutions improve, such questions will invariably be posed.

Chapter 1: Introduction & Research Plan

The Zurich-Montpellier Approach Rare vegetation communities Mapping and modelling Data sampling Scale issues

Key Questions

Introduction & Research Plan

Overview

Mankind has always strived to categorise and compartmentalise the natural world. Linnaeus developed his system of the classification of plants and animals on this idea that there was some semblance of order within the seemingly chaotic array of natural life-forms (Schuh 2003). Plants and the habitats in which they are found may be considered to be particularly open to a perception of organised chaos (Kűchler 1951; Read 1987); however such is almost always not the case.

Although steeped in history, the identification and classification of vegetation communities has rarely been capable of addressing the full diversity of vegetation in an area, particularly those of restricted distribution. This has proven particularly problematic for land managers who are charged with the responsibility of balancing human progress with sensible conservation. However, in order to enable improvements in the detection and portrayal of restricted or rare vegetation communities, it is necessary to revisit where the classification of vegetation began, and to determine how current day classification and mapping procedures are undertaken. To assist in this, two important concepts need to be examined:

the principles behind vegetation classification, as originally developed by the Zurich-Montpellier school (Braun-Blanquet 1928, 1932), including issues of sampling design, and whether current-day classifications remain true to the original intents; methods of mapping vegetation communities across spatial landscapes, how they relate to vegetation classifications, how their accuracy can be assessed, and if methods can be improved to better portray rare communities in mapping.

The work presented in this thesis examines why current day classification and mapping practices in Australia, and elsewhere in the world, often fail to detect and portray rare vegetation communities, many of which ultimately find themselves formally protected by threatened species legislation. Through a return to the ideals of the Zurich- Montpellier school of classification, together with some novel new ideas in sample

Introduction & Research Plan design and mapping, the broad goal of this body of work is to develop a new paradigm in vegetation classification and mapping which will instil a greater confidence in classification and mapping products, particularly for land management agencies and determining authorities.

1.1 Classification of Vegetation

In its infancy, the classification of vegetation was seen as a way in which the highly variable nature of plant distribution could be categorised into meaningful and manageable units. Flahault and Schröter (1910) initially proposed an association as the basic classification unit, describing it as a plant community type of definite floristic composition, uniform habitat conditions, and uniform physiognomy (cited in Jennings et al. 2003). However, as with many scientific disciplines, there were differing opinions in the years that followed on how such a classification unit could be identified and maintained (see McIntosh 1967; Whittaker 1973; Mucina & van der Maarel 1989).

Kent and Coker (2001) have also dealt with this in detail, and they state that the theory and practice of phytosociology (which includes vegetation classification) implies that ecologists must concur to a large extent with the ideas of Clements (1916). Clements (1916) argued that distinct assemblages of plant species, which repeat themselves over space, can be singled out and identified, and called plant communities. In contrast, the individualistic view of Gleason (1926; 1939) did not accept the concept of identifiable communities. Instead, plant species were seen as being distributed as a series of continua, their locations in space responding to different environmental gradients: “… we are treading upon rather dangerous ground when we define an association as an area of uniform vegetation, or, in fact, when we attempt any definition of it.” (Gleason 1926, p. 13). And again: “The individualistic concept … denies that all vegetation is thus segregated into communities” (Gleason 1939, p. 108).

Supporters of the Gleasonian view must therefore reject the whole basis of vegetation classification, since there cannot be order within a particular habitat while differing gradients of environmental variability exist. McIntosh (1967) argued for the continuum

Introduction & Research Plan concept, soliciting a range of responses from leading ecologists at the time in support of the Clements ideals (Dansereau et al. 1968).

The concepts developed by Clements are more widely accepted than those of Gleason, although not always in the strict sense (indeed Ewald 2003 presents a critique in support of phytosociology as a science in itself). The Gleasonian individualistic view prevailed until the 1980’s, when it was suggested that community classifications within specified regions may be useful for planning (Austin & Smith 1989; Scott 1995; Austin 2002). Austin (1985) suggested that the continuum concept is an accepted part of ecology, but that its limitations are well recognised. Later, Austin and Smith (1989) presented a reformulation of the continuum concept, concluding that a community is a spatial concept dependent on landscape pattern, while the continuum is abstract and reliant on environmental gradients. There has been, and there will continue to be, much debate over how a plant community should be defined, described and classified. Part of the answer will depend on the purpose for which a classification is to be developed, and how an end-user will be applying the product (Pressey & Bedward 1991; Sun et al. 1997; de Cáceres & Wiser 2012). Scale of classification and end-use is of particular relevance (the scale mismatches of Guerrero et al. 2013), since much environmental legislation in New South Wales (e.g. the Threatened Species Conservation Act 1995) and Australia (e.g. the Environment Protection & Biodiversity Act 1999), and indeed most of the world, relies on accurate, local-scale information.

1.1.1 The Zurich-Montpellier School

During the early part of the 20th Century, Professor Braun-Blanquet founded what is now known as the Zurich-Montpellier school of vegetation classification (Braun- Blanquet 1928, 1932). This system of classifying vegetation has been used throughout the world in numerous ecological studies, and with the availability of computer programs the principles involved can be undertaken in a very short time. There are two important themes within the Zurich-Montpellier method that enable some form of order to be attainable from a seemingly chaotic environment; the quadrat and the association, or vegetation community (Kent & Coker 2001). The related themes of

Introduction & Research Plan species fidelity (character species) and hierarchical classification complete the general thrust of the Zurich-Montpellier method.

1.1.1.1 The quadrat

Fundamental to the Zurich-Montpellier (or Braun-Blanquet) school is the use of replicated, standardised units of measure called quadrats (or reléve in the original description, a French term currently still in use in the Northern Hemisphere, but not in Australia), coupled with an extensive knowledge of the area under study. A critical and important point in relation to the quadrat is that its location in the landscape is entirely non-random: the site for vegetation description is deliberately selected to be representative of an area of interest, a process often referred to as preferential sampling. This of course assumes that the field ecologist knows his or her study area well, and may indeed already have an idea of the major vegetation types. It relies on the ecologist’s personal experience, anticipation, intuition and affinity to particular vegetation types (Hédl 2007). All quadrats are selected to be representative of those types, and it is important that the sample site is uniform and homogeneous, and that highly localised micro-environmental and micro-habitat variations are excluded or ignored. The quadrat must also be large enough and of the correct orientation for a representative sample of uniform vegetation to be recorded, but not too large that little new information is obtained per unit measure. Calculation of species-area curves are one such method of determining the minimal area for sampling (see reviews by Tjørve 2003 and Dengler 2009).

1.1.1.2 The association

An association is the basic unit of the classificatory system, corresponding to the hierarchical level of the plant community within the wider complex of vegetation (see also Section 1.1.1.4). In its simplest form, an association is a type of plant community, which can be constructed by grouping together various sample quadrats that have a number of species in common. Importantly, an association defined under the school of Braun-Blanquet is an abstract one. An abstract association is one that has not been mapped but has been classified and uses example quadrats to illustrate its

Introduction & Research Plan composition. Converse to this, a concrete association is one that has been mapped geographically, and can be precisely depicted in an area (Kent & Coker 2001).

To arrive at an association, tabular sorting and rearrangement of quadrat samples occur which essentially consolidate together quadrats supporting similar groups of species. Originally, such a process of sorting was undertaken by hand over many, many hours. However, the advent of computer programs over the latter half of the 20th Century has enabled much larger datasets to be sorted and rearranged in a fraction of the time than would have been spent by Braun-Blanquet and his colleagues.

The sorting of quadrats involves the following stages (Kent & Coker 2001), which using modern computer programs can be done in milliseconds:

Compilation of the raw data table – data will usually constitute cover abundance estimates of each species within the quadrat (using a standardised scale such as those developed by Braun-Blanquet or Domin 1923), or presence- absence data may also be used.

Calculation of the constancy of each species – the number of quadrats within which each species occurs. A table is created on the basis of constancy (frequency) of species across quadrats, and allows the identification of differential species (see later point).

Creating partial or extract tables – tables showing groups of quadrats characterised by sets of differential species.

Secondary sorting of quadrats – this occurs within the groups of the partial table, so that the most similar quadrats are placed next to each other. Only companion species (those that are not differential species) are used in this process, and the resulting table is known as the differential table. Occasionally a quadrat may not fit in any of the established groupings, in which case it can be discarded from further analysis.

Introduction & Research Plan

Finding good differential species – those species of medium-to-low constancy (frequency) which tend to occur together in a series of quadrats and can be used to characterise groups, or associations.

Community definition – each of these groupings would then be characterised as an association or plant community.

1.1.1.3 Species fidelity

The Braun-Blanquet system also adopts the fundamental concept of fidelity of component species. Fidelity refers to those species that can be strongly tied to a particular association, and which may be used to characterize an association. Fidelity is entirely dependent on the size of the region under study, and the comprehensiveness of sampling. Lists of high-fidelity species can be constructed for each association to guide end-users in the recognition of specific associations.

Szafer and Pawlowski (1927) described in detail the five degrees of fidelity (Table 1.1):

Table 1.1 The five degrees of fidelity (Szafer & Pawlowski 1927, in Kent & Coker 2001).

Fidelity 1 accidental species those which are rare and accidental intruders from another community or relics of a preceding community. Fidelity 2 indifferent species without a definite affinity for any particular community Fidelity 3 preferential species present in several communities more or less abundantly but predominantly in one certain community and there with a greater deal of vigour. Fidelity 4 selective species found most frequently in a certain community but also rarely in other communities. Fidelity 5 exclusive species completely or almost completely confined to one community.

Species within Fidelity Groups 4 and 5 in particular tend to characterize particular associations more than the others, and often represent those species intuitively recognisable as typifying the association. Cole (1949) and Goodall (1953) further extended the concept of fidelity to deriving an index based on the difference in frequency (constancy or presence) of a species within two communities expressed as a proportion of the lesser value. Later, Mueller-Domois and Ellenberg (1974) presented guidelines on allocating shared species to differing communities.

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In a critical evaluation of fidelity, Barkman (1989) emphasized the need for a new, simpler measure of fidelity, and defined such using the differing average cover values of specific taxa between communities. Bruelheide (2000) also introduced a new measure of fidelity (u), derived from the approximation of the binomial or the hypergeometric distribution by the normal distribution, and illustrated how groups of species showing the highest value of u could be used to define a community. A more recent review of fidelity measures undertaken by Chytrý et al. (2002) found that Bruelheide’s u value was an asymmetric measure that tended to assign comparatively high fidelity values to rare species. To correct this, a hypergeometric form of u was presented and tested across ~16,000 sample quadrats from the Czech Republic.

Clearly, the concept of species fidelity, as originally proposed by Braun-Blanquet, has undergone a considerable level of refinement over the last century or so. Despite this, it is a concept that remains central to the Zurich-Montpellier school and one that greatly assists in the dissemination of classifications based on sample data to land management agencies. Modern day examples of species fidelity calculations in computer programs include those in Primer (Clarke & Gorley 2006), Twinspan (Hill 1979), JUICE (Tichý 2002) and Fidel (M.Bedward NSWNPWS, unpublished).

1.1.1.4 Hierarchical classification

The Zurich-Montpellier school also allows for a hierarchical classificatory naming system for plant communities, much like that used in classical plant taxonomy. In this system, the level of association is fundamental and represents the basic unit of vegetation description, equivalent to the plant community. Under this system, higher and lower orders can be recognised within an overall floristic classification system. The classification units include:

Class Order Alliance Association Sub-association Variant Facies

Introduction & Research Plan

The whole hierarchy of vegetation units in a region can be described and their relationships to each other displayed. In Europe, an international code of botanical nomenclature has been established (indeed, it is a tool used throughout the world), and governs the naming of communities and hierarchies under the Braun-Blanquet system (Mucina et al. 1993; Rodwell et al. 1995). Efforts are now being made towards further unification of neighbouring European classifications (Bruelheide & Chytrý 2000), and elsewhere in the world (Mucina et al. 2000a; de Cáceres & Wiser 2012). Similar hierarchical systems have been presented for Australia (see Section 1.2.2), but there is still considerable progress to be made.

1.1.1.5 Summary of the Zurich-Montpellier school

In summary, the Zurich-Montpellier approach requires adherence to the following key points:

i. use of replicated, standard measures to sample the vegetation (quadrats); ii. an extensive knowledge of the area under study; iii. quadrats located non-randomly throughout the study area, but within representative, uniform and homogenous areas; iv. quadrats to be large enough to enable representative data to be recorded, but not too large that little new information is obtained per unit measure v. an association is the basic unit within a classification: following the terminology of Kent and Coker (2001), a concrete association is one that has been defined and mapped, an abstract association is one that has been defined only; vi. sorting and rearrangement of quadrat samples is undertaken to consolidate quadrats supporting similar groups of species: today, this is referred to as numerical classification; vii. fidelity is the term used to describe those species which can be strongly tied to a particular association; viii. hierarchical classification is the framework used for plant communities: the association is the base unit of description, and higher and lower orders can be recognised.

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1.1.2 Criticisms of the Zurich-Montpellier School

There have been a range of criticisms of the Braun-Blanquet method since its creation in the early half of the 20th Century. Egler (1954) claimed that the method was over- simplified and represented forcing of a weak methodology onto a much more complex real world, an opinion brought about by his Gleasonian view of the plant world. Kent and Coker (2001) summarise the main criticisms of Egler (1954):

Subjectivity of the methodology, particularly the methods of field sampling. The selection of typical or representative quadrats is often said to be highly biased and assumes a substantial knowledge of the vegetation prior to any attempt at description. Also, non-homogenous and transitional or ecotonal areas between typical and representative samples are not recorded under this method, yet are still clearly plant assemblages. Questions may then be asked, such as when does a transitional community become a community in its own right? Poore (1955, 1956) partly answered this problem with his concept of the nodum. Within any region there are a set of major plant communities that are distinctive and characteristic and can be recognised consistently in the field. However, between these major types there are zones of transition and ecotones. Each of the major plant communities can therefore be described as a nodum and every region has a set of noda. However, certain transitional types do exist between the noda and should be recognised as such. The concept of abstract communities, confusing for students and inexperienced ecologists. The process of tabular rearrangement, the exact methodology varies between ecologists, although the general principles are the same. Development of computerised methods has greatly helped with the practical side of quadrat sorting. Discarding quadrats which do not fit any of the associations. The reason for doing so lies under Point 1 above, in that such a quadrat must have been badly chosen at the start. However, this inevitably raises the question of circular reasoning along the lines that the communities must have already been defined

Introduction & Research Plan

in the field ecologists mind before data collection started and data which do not fit the preconceived ideas of community structure may be discarded. In reality, they may represent an association not previously recognised and not sampled. The answer to this point lies in the necessity of the researcher having a thorough knowledge of the vegetation before commencing. Terminology, strange and difficult terminology used throughout (an issue, however, becoming irrelevant over time). Poorly documented, the whole methodology is not described in the literature, has many variations and an air of mystique about it.

Poore (1955) also points out that the degree of fidelity of a particular species can only be fully assessed when all the vegetation of a region has been described – it is thus very much a geographically defined concept. While this may be true, this does not negate the application of the method to particular areas or purposes of study. For example, Goodall (1961) presents data for the Victorian mallee and other areas which fully support the idea of vegetational continua (Section 1.1), casting doubt on the concept of homogeneous areas of vegetation and vegetation communities. However, he also states that even with such a continuum, it can be convenient and completely satisfactory to delineate and describe particular vegetation types, while bearing in mind that not all vegetation will be accommodated comfortably.

According to Westfall et al. (1997), the main criticisms of the Braun-Blanquet approach relate to the minimum sampling area or quadrat size, and observer bias (both in sampling and classification). They have attempted to improve the objectivity of the approach through the development of a computer program (PHYTOTAB-PC) which reduces observer bias in the classification process, and which has been used in several South Africa studies (e.g. Myburgh & Bredenkamp 2004; Malan et al. 2007; Cronje, et al. 2008). However, use of PHYTOTAB PC outside of that country has been limited.

More recently, Podani (2006) has questioned the validity of the quadrat sampling method as one that is burdened by much bias, subjectivity, inconsistency, arbitrariness, circular argument and large sampling error. In particular, he considers that the ordinal nature of the raw cover abundance data collected within each quadrat 11

Introduction & Research Plan is incompatible with conventional dissimilarity functions, clustering and ordination algorithms. To counter this, Podani (2006) believes that such data should be examined only through ordinal methods of data analysis, such as non-metric multidimensional scaling using an ordinal dissimilarity measure (e.g. the Goodman & Kruskal 1954 γ coefficient). Arguments stimulated by Podani (2006) have continued (Ricotta & Avena 2006; Podani 2007), and are unlikely to be resolved easily. If little else, recognition of potential statistical invalidity when collecting and processing field data has been highlighted for practicing vegetation scientists.

1.1.3 Static and Dynamic Classification

The classification of vegetation is a dynamic process: sampling and analysis of data at any given time can produce a meaningful classification, but with new or additional data changes to that classification may ultimately be required (Jennings et al. 2003; de Cáceres & Wiser 2012). Indeed, de Cáceres (2011) notes that any vegetation classification is provisional, in the sense that it may require modification to suit altered datasets. Exploration of new areas or a review of an area already embellished with a classification will require testing of the original classification, which may find previously unexplained heterogeneity or create more robust and effective classifications (e.g. Diekmann et al. 1999; Rodwell et al. 2000; Simpson 2000; Kendall & Snelson 2009; Wiser & Cáceres 2013). A re-analysis of data will be required to accommodate new vegetation patterns, which may in turn lead to changes in the description of individual vegetation units (Fig. 1.1).

Numerous studies in the literature encompass this dynamic nature of vegetation classifications, which all form part of the progression of ecological understanding of the environment (e.g. Lindsay & Horwith 1999; van Kley & Schaminée 2003; Lawson et al. 2010). Assuming a classification to be static does not allow for new information to be incorporated into conservation management, and can in fact lead to poor management decisions being made on outdated data. Much like plant taxonomy, community circumscriptions should be regularly reviewed to utilise new data and formalise new understandings.

Introduction & Research Plan

Fig. 1.1. Conceptual representation of a dynamic vegetation classification, showing the process of initial and subsequent vegetation survey results (de Cáceres 2011).

As an example, a revised vegetation classification for south-eastern New South Wales, covering some 4 million hectares, has recently been published (Tozer et al. 2010). Prior to this, there had been an array of classification and mapping studies for this region, including the unpublished forerunners of Tindale et al. (2004) and Tozer et al. (2006). Other previous studies covering part of this area include that for the southern forests (Thomas et al. 2000; Gellie 2005), the Eden region (Keith & Sanders 1990; Keith & Bedward 1999), the Cumberland Plain (Tozer 2003), the Warragamba Special Area (NSWNPWS 2003), and many other local-scale studies. For those study areas with considerable overlap (such as Keith & Bedward 1999; Thomas et al. 2000; Tindale et al. 2004; Gellie 2005; Tozer et al. 2006; and Tozer et al. 2010), there was a need for continual update in classifications to reflect increased knowledge gained from more in- depth study. For the southern forests subregion (~2 million ha), sampling increased from 1366 quadrats in Gellie (2005) to 3002 quadrats in Tozer et al. (2010), with classification units also rising from 92 to 133 vegetation communities. Similar situations occur in other parts of Australia, including the Hunter region (see Chapter 4),

Introduction & Research Plan and all illustrate the dynamic process of vegetation community classification, and the revision necessary to maintain information currency.

In New South Wales, the Threatened Species Conservation Act 1995 (see Section 1.1.4.3) allows for the incorporation of new information and data into consideration of the presence or absence of a listed Threatened Ecological Community (TEC) (NSW Scientific Committee 2010a). In effect, this provision recognises that classifications are not static and may be better understood with additional information, typically supported by numerical analysis of that data: “…multi-variate analyses of species composition, where adequate data are available, may assist in resolving descriptions of ecological communities (Kendall & Snelson 2009). Given the emphasis on assemblages of species in the TSC Act, quantitative approaches that address overall species composition are clearly desirable (Preston & Adam 2004a)” (NSW Scientific Committee 2010a, p. 41). To date, there have been few cases in New South Wales where revision to an existing TEC definition has occurred based on numerical classification of floristic data. Some examples include the abolition of Sydney Coastal Estuary Swamp Forest and Sydney Coastal River-flat Forest into Swamp Sclerophyll Forest on Coastal Floodplains and River-flat Forest on Coastal Floodplains respectively, following numerical analysis undertaken by Keith and Scott (2005). Rehwinkel (2007) undertook numerical analysis of data to better define the nationally listed Natural Temperate Grassland of the Southern Tablelands TEC, and defined six component associations. By way of update, this information was made available (http://www.environment.gov.au/cgi- bin/sprat/public/publicshowcommunity.pl?id=14) to assist other field ecologists in identifying which form of grassland they encountered. Given the amount of community classifications undertaken within New South Wales in recent years (see, for example, Benson 2006 and subsequent papers in that series), and the number of listed ecological communities (104 as at December 2012), it is surprising that few TECs have been subjected to similar revision.

Introduction & Research Plan

1.1.4 Defining rare communities

1.1.4.1 What is a rare community?

Despite considerable research undertaken on rarity in plant species over several decades (eg. Rabinowitz et al. 1986; Kunin & Gaston 1993; Pilgrim et al. 2004; Crain & White 2011; Arponen 2012; Tucker et al. 2012), little comparative work has been done on vegetation communities. Rabinowitz et al. (1986) outlined the seven forms of rarity for plant species of the British Isles, which have been adopted worldwide, but application of this work to vegetation communities has been only loosely applied. Izco (1998) extended the seven forms of rarity developed by Rabinowitz et al. (1986), applying them to a number of European vegetation communities, and these categories are equally applicable elsewhere. His seven (in reality, eight) forms are contingent on the geographical range, frequency of occurrence within a range and size of individual stands (IA) of a community (Fig. 1.2):

Fig. 1.2 Types of community rarity. R1 – R7 refer to the seven forms of rarity (from Izco 1998, p 643). IA = individual stands.

Clearly, those communities which fall within rarity classes R6 to R7 represent those of highest conservation significance, and which are more likely to be overlooked in a classification. Drever et al. (2010) further adapted this application of the seven forms of rarity to forests in Ontario, and identified several rare forest types applicable to narrow distributions, restricted landform associations, or few large stands. Few other examples are evident in the literature: the simple system proposed by Paal (1998) (0 =

Introduction & Research Plan extinct or probably extinct; 1 = very rare; 2 = rare; 3 = fairly rare; 4 = approaching rare) for Estonia does not appear to have been widely utilised.

Within Australia, there has been little categorical assessment of rarity in vegetation communities in a similar vein. Specht et al. (1974) provided the first major assessment of the conservation status of vegetation communities within Australia, but it was subjective in nature and relied on a committee of experts for its determinations. A review of the national status of communities was not undertaken until 1995, when Specht et al. (1995) presented their objectively classified conservation atlas. This was a massive undertaking, involving numerical analysis of data from over 4,700 floristic lists over 30’ latitude x 30’ longitude grids, and resulted in the description of 921 plant communities. However, assessment of conservation status (not rarity) was applied to each community on the basis of known representation within reserves.

For New South Wales, Benson (1989) listed 430 communities with assigned reservation and threat codes, but with little other descriptive information. Hager and Benson (1994) updated the conservation status of NSW plant communities by referring to available vegetation mapping, but again did not establish a framework for rarity. More recently, Benson (2006) proposed a three-tiered system based on the estimated extent prior to European occupation: common (extent >10,000 ha in 1750), restricted (1,000 – 10,000 ha in 1750), rare (<1,000 ha in 1750). This framework relies on expert knowledge or modelling showing how extensive a particular community may have been in 1750, which is not always available. In the absence of viable alternatives, such a system is perfectly objective and rational, and is a key element of the NSW Vegetation Classification and Assessment database (Benson 2006).

Potential problems with the classification of ecological communities in Australia have been briefly outlined by Benson (2008a), with Keith (2009) detailing key issues in the interpretation, assessment and conservation of communities. Benson (2008a) lists a range of limitations in the use of quantitative classification methods, and suggests that a review of the quality and consistency of data be undertaken prior to defining vegetation units. Such reservations on the use of quantitative data can influence how rare communities are identified, particularly when datasets comprise data collected by

Introduction & Research Plan numerous ecologists of varying expertise. Issues of scale are discussed by Keith (2009), and these will have implications for local-scale assessments of vegetation and the recognition of rare communities. The analogy proposed by Preston and Adam (2004a), and repeated by Keith (2009), of vegetation communities and Russian Dolls of progressively smaller size is very telling, as it encapsulates the problem of scale well (see further discussion on scale in Section 1.4).

In a sense, the ability to identify and define rare vegetation communities has far more practical management implications than does the definition of those that are more widespread. While the Zurich-Montpellier system of plant classification, when used correctly, provides every chance that rare communities will be encountered (through employment of preferential sampling techniques and good knowledge of a study area), in reality it appears to have been seldom used in Australia. One example is to be found in the work of Gibson et al. (2000, 2005), who described the threatened claypan and ironstone plant communities of the South-West botanical province in Western Australia. Investigations in this study were quadrat-based, with quadrat locations being drawn from knowledge gained during a major regional survey program, which effectively located all extant claypan and ironstone vegetation. These authors did not stratify their quadrat sampling (see later in Section 1.3), but attempted to be as comprehensive as possible. This approach adopts the Zurich-Montpellier methodology, where expert knowledge has been incorporated into the sampling design to ensure that all known variations have been sampled.

1.1.4.2 Why are rare communities important?

While an ability to locate, define and categorise a rare vegetation community is plausible, the question remains as to why it is important that we are able to do so. Why are rare communities considered of higher value than more common ones? Rare entities (species or communities) can have an inherently higher risk of extinction than common ones due to their higher susceptibility to stochastic events, may have specialised requirements for reproduction and maintenance, or may occur only on specific substrates (Drever et al. 2010). Managing for these entities will benefit biodiversity. Indeed, global biodiversity hotspots, recognised as supporting exceptional

Introduction & Research Plan concentrations of endemic species, are often rare ecosystems, and identifying these is paramount to directing conservation efforts and scant resource dollars (Gillespie et al. 2012). As an example, Damschen et al. (2012) have recognised that plant communities found on patches of unusual bedrock material contribute disproportionately to global biodiversity, and, as part of strategies to manage these areas, ask whether or not species associated with such habitats will be especially at risk from global climate change.

In addition, ecosystems are commonly used as surrogates for biodiversity (Noss 1996), and there is some evidence that rare communities also support rare or threatened plants (e.g. Kruckeberg & Rabinowitz 1985; Hunter & Clarke 1998; Walsh 1998; Harrison et al. 2008; Nowak & Novis 2010). In some cases, species known from only single locations are restricted to such communities (e.g. Gibson et al. 1992; Leeson & Kirkpatrick 2004), so the recognition of and planning for rare communities is a beneficial pursuit.

1.1.4.3 Rarity & legislation

Within Australia, as in many other parts of the world, environmental legislation exists which strives to protect biodiversity from unnecessary human-induced disturbance. Nationally, the Commonwealth’s Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act 1999) is applicable, while within New South Wales it is the Threatened Species Conservation Act 1995 (TSC Act 1995). Both support structures which recognise Critically Endangered (CEEC), Endangered (EEC) and Vulnerable (VEC) Ecological Communities. Each Act is supported by committees of independent and experienced scientists who oversee and preside over nominations for listing of ecological entities (Hughes 2009). From a legal standpoint, Preston and Adam (2004a, 2004b) have outlined the procedure behind describing and listing of threatened ecological communities in New South Wales, referring back to the Zurich-Montpellier school of classification. In some cases, the intricacies of a vegetation community that may qualify it for legal protection may be its naturally restricted extent, or its demonstrable reduction of extent due to anthropogenic sources.

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The concept of threat status in ecological communities, while related to rarity, is not automatically transferable. A vegetation community may be naturally rare, but not under any demonstrable threat of extinction, as may be documented for the nomination of a community to the national EPBC Act 1999 (Threatened Species Scientific Committee 2010). Nicholson et al. (2009) reviewed existing protocols for assessing the threat status of communities in various parts of the world, and found inconsistent assessment methodologies and problems with scale. They, too, outlined a three-tier system for identifying threat in communities (distribution size, declines in distribution, changes to key ecological functions), and argued that criteria should be explicit and repeatable, applicable to a broad range of communities and address synergies between different threat types. Evidently, the distribution of a community (rare, common etc) is but one element in the assessment of threat status. For the NSW Vegetation Classification & Assessment, Benson (2006) outlined a number of threat criteria, but only one of these (small geographical area, with demonstrable decline) aligns with the concept of rarity. Nationally, the guidelines for assessing the eligibility for listing of ecological communities to the EPBC Act 1999 recognize ‘extremely rare’ or ‘unique’ communities (Threatened Species Scientific Committee 2010), but provide no guidelines as to how to define them as such. Similarly for New South Wales, the TSC Act 1995 does not require an indication of rarity in the listing of a Threatened Ecological Community (Adam 2004).

After nearly 10 years of operation, Auld and Tozer (2004) reviewed the progress of the TSC Act 1995 in New South Wales and investigated how the legislation has impacted upon the conservation of ecological communities, focusing on two such communities from the Sydney area. They found that the increase in awareness following legal protection of specific ecological communities had considerably benefited conservation, and identified avenues for continuing research. At the same time, however, they stated that continued loss and fragmentation of vegetation has undermined threat- abatement activities, threatening the long-term viability of bushland remnants.

In recent years, several studies have examined in more detail the integrity of some vegetation communities that have been listed as threatened within eastern Australia

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(e.g. French et al. 2000; Simpson 2000; Keith & Holman 2003; Prober & Theile 2004; Kendall & Snelson 2009; Moxham et al. 2009a; Payne et al. 2010; Bell & Stables 2012). In part, these studies question the description and validity of the legal definitions, with the aim of improving or extending our understanding of them, and in some cases redefining the communities on which they are based (e.g. Kendall & Snelson 2009; Moxham et al. 2009a). Legally, Larkin (2009) has outlined how the Land and Environment Court in New South Wales considers and responds to questions about the integrity of listed Threatened Ecological Communities, which is paramount to improving definitions of them as determined by vegetation scientists. In some cases, even the source information behind a legal determination for a community can be challenged and won on the basis of the specific wording in that determination (e.g. Ross 2006).

Within New South Wales, at least twenty Threatened Ecological Communities listed on the TSC Act 1995 have been defined from projects utilizing a stratified random sampling design based on environmental strata (Table 1.2). As discussed later in Section 1.3, stratified random sampling often presents inconsistencies in and omissions from a vegetation classification. As a consequence, it remains to be seen how variable some of these legally protected communities are, particularly when floristic variation is examined in more detail (see, for example, Kendall & Snelson 2009). One of these communities (Lower Hunter Spotted Gum-Ironbark Forest) is the subject of detailed review in Chapter 4 of this thesis.

For the Hunter Valley and Central Coast region of New South Wales (see Section 1.2.2.2), embodied in the Hunter-Central Rivers Catchment Management Area, the last decade has seen a dramatic rise in the number of listed vegetation communities protected under State legislation since the few listings evident in 2000 (Bell 2000; Peake 2000; Fig. 1.3). As with elsewhere in New South Wales (e.g. Auld & Tozer 2004), the effectiveness of these listings in conserving rare communities will depend on how well they are recognized and accepted in the general population, and how effective threat abatement strategies such as weed control and amelioration strategies are.

Introduction & Research Plan

Table 1.2 Threatened Ecological Communities listed on the TSC Act 1995, with their basis of definition in sampling designs incorporating stratified random sampling.

Main basis for community definition Threatened Ecological Community (TEC)

Keith & Bedward (1999); Bangalay Sand Forest Tindall et al. (2004) Bega Dry Grass Forest Brogo Wet Vine Forest Candelo Dry Grass Forest Dry Rainforest of the South-East Forests

NSWNPWS (2000a) Cooks River/ Castlereigh Ironbark Forest Moist Shale Woodlands Shale-Gravel Transition Forest

NSWNPWS (2000b) Hunter Lowland Redgum Forest Kurri Sands Swamp Woodland Lower Hunter Spotted Gum-Ironbark Forest

Tozer (2003) Agnes Banks Woodland

Keith & Scott (2005) River-flat Eucalypt Floodplain Forest Freshwater Wetlands Subtropical Coastal Floodplain Forest Swamp Oak Floodplain Forest Swamp Sclerophyll Forest

Peake (2006) Central Hunter Grey Box-Ironbark Woodland Central Hunter Ironbark-Spotted Gum-Grey Box Forest Hunter Valley Footslopes Slaty Gum Woodland

35 30 25

Cumulative 20 No. of TEC's 15 10 5

1999 2006 1995 1996 1997 1998 2000 2001 2002 2003 2004 2005 2007 2008 2009 2010 2011 Year

Fig. 1.3. Cumulative number of Threatened Ecological Communities (TECs) listed for the Hunter-Central Rivers Catchment Management Area since inception of the NSW Threatened Species Conservation Act in 1995.

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1.2 Mapping Vegetation Communities

Braun-Blanquet (1928, 1932) discriminated between plant associations that have been classified and described using sample quadrat data (an abstract association: Kent & Coker 2001) and those that have been classified, described and mapped geographically in an area (a concrete association). Classification and mapping are intrinsically linked, and indeed as Kűchler (1951, p 282) stated, “The relation between classifying and mapping vegetation is very intimate, and each one strongly affects the other….a vegetation map is the meeting ground of two poles: an author’s systematic classification and nature’s kaleidoscopic arrangement of plants”. Abstract associations, although supported by quadrat data, can be difficult to validate in the field by other ecologists. On the other hand, the production of a vegetation map, underpinned by quadrat data, showing geographically where a particular association is to be found has immense management applications.

1.2.1 Predictive Modelling vs Traditional Mapping

Predictive Modelling - Environmentally-stratified sampling designs (see Section 1.3.2) are often used in vegetation studies as an economical cost alternative for classifications over large areas. Such designs are commonly linked to the spatial distribution of environmental strata in a Geographical Information System (GIS) to model through prediction the distribution of vegetation associations (e.g. Ferrier & Smith 1990; Sun et al. 1997; Keith & Bedward 1999; Bobbe et al. 2001; Cairns 2001; Cawsey et al. 2002; Keith 2002; Tozer 2003). A variety of predictive models are available, and inevitably some perform better than others (Wintle 2003; Muñoz & Felicísimo 2004; Segurado & Araújo 2004; Elith et al. 2006; Wintle et al. 2005). The use of multiple predictive methods has been suggested as preferable to better understand the complexities of most landscapes (Cairns 2001), but no single model is likely to suit all investigations (Segurado & Araújo 2004). Likewise, there are benefits to conservation through modelling the environment at different scales (Pressey & Bedward 1991), depending on the ultimate goal of a project.

The majority of modelling studies generally involve the collection of floristic and structural data from replicated survey points, spread across an environmental

Introduction & Research Plan stratification, which itself is often based on modelled GIS layers (Chapman et al. 2005). Survey data are then subjected to numerical analysis, such as multivariate cluster analysis, to assist in defining a vegetation classification. A series of GIS modelling rules are then developed, such as those described in Keith and Bedward (1999), and the distribution of vegetation types modelled across the landscape. In broad terms, these techniques are generally more than adequate for defining and mapping vegetation at regional or greater scale (but see following paragraph), and have been utilised extensively, for example, during the New South Wales forest reform process for large areas of the state (e.g. NSWNPWS 1999a; Ferrier et al. 2002), and Keith (2002, 2004) has since amalgamated all major regional modelling projects to form a single composite map layer for New South Wales. However, environmental data (terrain, climate and substrate surfaces) are often used for modelling uncritically and without consideration of potential errors: “Too often, models are run after hours and hours of data cleaning of the [ecological] occurrence data, but with blind acceptance of the environmental layers used” (Chapman et al. 2005, p 37). Indeed, van Niel et al. (2004) have shown how errors in digital elevation models affect many derived values (e.g. topographic position, topographic wetness index), and ultimately predictive vegetation mapping. Using soil type, too, as a surrogate for vegetation community prediction is unreliable in some landscapes (e.g. Rankin et al. 2007), and should not be included in modelling approaches without critical review.

One major danger in regional predictive models, however, is that they are increasingly being used for local-scale land-use decisions, including biodiversity offsetting. In modern GIS applications, models created at a regional scale can be viewed at a local- scale for site specific projects, which is often well beyond the intended purpose of such modelling. The Lower Hunter and Central Coast Regional Environmental Management Strategy (see Section 1.2.2.2), for example, produced a predictive vegetation model that on review was considered to be regional in utility (Nicholls et al. 2002), but which has been repeatedly used for local-scale applications. Guerrero et al. (2013) have recently outlined the scale mismatches that can impinge on robust conservation planning, and nominate a number of key challenges. Among these, the limited availability of fine resolution data to meet the conservation problem is included. 23

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Traditional Mapping - Traditional mapping studies generally involve the use of aerial photographs or satellite imagery, often combined with some level of ground-validation (but see Section 1.2.2.4). There are numerous examples in the literature where interpretation of aerial photographs (e.g. West et al. 1985; Griffith et al. 2000; Freeman & Buck 2003) or satellite imagery (e.g. Brown de Colstoun et al. 2003; Cingolani et al. 2004; Gergel et al. 2007) have mapped vegetation patterns, the concrete associations of Braun-Blanquet. In many cases, numerical classification is used in support of mapped vegetation types (e.g. Lewis 1998). Despite this, a range of potential errors are possible with mapping using traditional methods (Green & Hartley 2000). These include positional and attribute errors, which deal with boundaries between different plant associations and correct labelling respectively, and uncertainties related to heterogeneity within a mapped area. No map of vegetation is ever considered perfect, and given the difficulties commonly experienced in defining the boundaries between adjacent communities (e.g. Fortin et al. 2000), this is perfectly understandable.

Recent years have seen the development and increased availability of 3-dimensional (3-D) digital imagery which shows great promise in extending the traditional mapping methods into the digital age (e.g. Allard et al. 2003; Heiskanen et al. 2008; Hastedt et al. 2009a, b; all cited in Maguire et al. 2012). Using airborne digital imagery known as ADS40 (see Black & Peasley 2008), this technology is now enabling the production of vegetation maps for parts of New South Wales to high resolution and accuracy. In this state, ADS40 is captured at 50cm resolution, and offers considerable advantages over traditional 2-dimensional interpretation. Employing the use of 3-D Planar monitors, digitising of polygon boundaries can be captured directly on-screen within a 3-D environment. Maguire et al. (2012) provide the first regional vegetation map produced using these methods in Australia, which has been independently validated to 87% accuracy. Ongoing studies in the Greater Blue Mountains World Heritage Area of the Sydney Basin have also begun 3-D interpretation of digital imagery (D. Connolly, NSWOEH, pers. comm.). Interpretation of 3-D aerial imagery such as this shows good potential to transform the quality of mapping products achievable through traditional aerial photographic interpretation. 24

Introduction & Research Plan

For local area planning and management, such as is undertaken by local government (Fallding et al. 2001; Breuste 2004), regional predictive modelling and 2-dimensional aerial photographic interpretation (API) are often inappropriate due to the range and scale of errors (although some of these can be addressed with the new 3-D digital imagery: Maguire et al. 2012). In many cases, field ecologists struggle with the rationalisation of region-scale mapping units when confronted in the field with distinct, often locally restricted vegetation types. This is particularly the case for survey and mapping work involved in the development industry, where environmental consultants are required to assess the vegetation of a particular site against regional models developed by major land management authorities. Clearly, improving the accuracy and reliability of vegetation mapping requires some changes to the way in which these maps are constructed.

1.2.2 Summary of Classification & Mapping in SE Australia

1.2.2.1 Brief history in Australia & NSW

Keith (2004) and Benson (2006) provide excellent summaries of the history of vegetation classification and mapping in Australia and New South Wales. In these two works the early Australia-wide works of Diels (1906), Beadle and Costin (1952), Williams (1955), Cochrane (1963), Specht et al. (1974), Carnahan (1976) and Beadle (1981) are outlined, together with the more recent studies of Specht et al. (1995), Sun et al. (1997) and National Land and Water Resources Audit (2001). Beadle and Costin (1952) presented a method which combined floristic classification with physiognomy, diverging slightly from the methods of the Zurich-Montpellier school, and this system now forms the basis to most classifications currently used in this country. Beard (2001) discusses the mapping of Diels in more detail, and considers it the very first vegetation map of Australia, broad as it is with only eight vegetation units. Bridgewater (1981) promoted the adoption of the Zurich-Montpellier system of plant classification in Australia, and suggested that its use could be applied to all conservation reserves in the country: a prediction which has only partly been realised to date.

Early seminal studies in classification within New South Wales include those of Brough et al. (1924), Petrie (1925), McLuckie and Petrie (1926, 1927), Fraser and Vickery

Introduction & Research Plan

(1937), Osborn and Robertson (1939), Davis (1941) and Pidgeon (1941). Mapping studies within the State began with those of Beadle (1945, 1948), Moore (1953), Costin (1954) and Biddiscombe (1963), followed by numerous vegetation maps of conservation reserves or targeting specific regions of the State. The Royal Botanic Gardens (Sydney) began mapping the State at a coarse scale in 1972, and published numerous maps covering large areas ranging from 1:100,000 to 1: 1,000,000 scale until 1996 (e.g. Pickard & Norris 1994; Fisher et al. 1995; Ryan et al. 1996). Keith (2004) provided the latest classification and map of the native vegetation of New South Wales, comprising ninety-nine classes across twelve formations. The map accompanying his work is comprised of numerous pre-existing regional maps (e.g. NSWNPWS 2000b; Metcalfe et al. 2003) aligned with the ninety-nine classes (see Keith & Simpson 2008 for details on the amalgamation process).

The Royal Botanic Gardens (Sydney) is currently progressing through a detailed classification of the NSW vegetation, beginning in the west of the State and progressing eastward (the NSW Vegetation Classification & Assessment, or NSW VCA; Benson 2006, 2008b). To date, these classifications have encompassed the Western Plains (Benson et al. 2006), South-western Slopes (Benson 2008c), and the Brigalow Belt, Nandewar and west New England (Benson et al. 2010) Bioregions. The NSW VCA is based on the compilation and critical evaluation of existing classifications, complemented by expert opinion and rapid field checking. The inconsistency of existing vegetation data across the State prevents the use of a comprehensive numerical classification to underpin the NSW VCA, however descriptions are being completed subjectively to the association level.

The NSW Office of Environment and Heritage (and its former departments), with various funding sources, is currently in the process of preparing a detailed numerical classification and map of the Sydney Basin Bioregion, which will ultimately inform the NSW VCA (NSWDECCW 2010a). This project has already produced a wealth of fine- scale mapping and vegetation description for various sub-regions of the Basin, including comprehensive studies of the Cumberland Plain (NSWNPWS 2000a), the Blue Mountains (NSWNPWS 2003; NSWDEC 2006), the Wollemi and Yengo National Parks

Introduction & Research Plan region (NSWDECC 2008a), the Illawarra sub-region (NSWNPWS 2002a, 2002b), and other areas (e.g. NSWDECC 2008b). Combined with other studies, such as in the Hunter Valley (Peake 2006; Somerville 2010), a more thorough understanding of the Sydney Basin vegetation is imminent.

1.2.2.2 Lower Hunter & Central Coast Regional Environmental Management Strategy

The Lower Hunter & Central Coast Regional Environmental Management Strategy (LHCCREMS) was an initiative of seven local government councils in the lower Hunter Valley and Central Coast of New South Wales (Fig. 1.4). It aimed to develop and improve existing responses to natural resource and environmental management issues at a regional scale, the initial step of which was to describe and map the native vegetation (Lower Hunter & Central Coast Environmental Management Strategy 2004). Under contract, this was completed by the NSW National Parks and Wildlife Service (NSWNPWS 2000b), with an independent review of the work concluding that the results were of regional rather than local utility (Nicholls et al. 2002). An expansion of the role of the LHCCREMS occurred in 2004 so that it now incorporates the entire Hunter Valley and Central Coast with fourteen member councils: a name change to the Hunter and Central Coast Regional Environmental Management Strategy (HCCREMS) accompanied this expansion. Revisions of the vegetation classification and mapping products have progressed over the last decade (Eco Logical Australia 2003; McCauley et al. 2006; Somerville 2010).

Using a stratified environmental random sampling design (see Section 1.3), NSWNPWS (2000b) described and mapped 53 vegetation communities through numerical analysis of quadrat-based sample data. Predictive modelling was used to map the distribution of these communities, using 41 environmental variables and a set of intuitive, predictive rules. From that classification, three defined communities were subsequently listed as threatened in New South Wales under the Threatened Species Conservation Act 1995:

Lower Hunter Spotted Gum – Ironbark Forest Hunter Lowlands Redgum Forest Kurri Sands Swamp Woodland 27

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Fig. 1.4. The Lower Hunter and Central Coast of New South Wales, showing member councils.

The Lower Hunter and Central Coast region is the prime focus area for research presented later in this thesis, where new vegetation communities (Chapter 3) and revision of an existing threatened community (Chapter 4) is undertaken.

1.2.2.3 Sampling design in Australia

The vast majority of vegetation classifications undertaken within New South Wales and Australia in the last few decades have generally incorporated a stratified random sampling methodology (see Section 1.3.2), employing environmental variables as a surrogate for biodiversity. In New South Wales, such techniques have been standard and commonly used for both regional studies (e.g. NSWNPWS 1999a, 1999b; Keith & Bedward 1999; NSWNPWS 2003; Tozer 2003) and those of individual conservation reserves (e.g. Hunter et al. 1999; Griffith et al. 2000; Bell 2004a; Hunter 2011). One of the problems with this procedure, however, is that if certain vegetation types remain unsampled, or sufficient replicates of that vegetation are not obtained prior to analysis, any vegetation type which may be judged by field ecologists to be unique is unlikely to be supported numerically. This also holds true for more common communities that remain undersampled. 28

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Picard and Franc (2003) discuss this issue at depth, and point out that one solution commonly adopted is a two-step procedure whereby groups of species (communities) are defined from a core set of data, and then the remaining species are assigned through discriminate analysis or expert review. However, in reality the use of expert review in this context is rarely employed when planning for conservation or development (Pitt et al. 2012). More commonly, unique vegetation types are absorbed into broader mapping units and perhaps described in accompanying text as a specific variant, with the suggestion that further survey data will better define them. A problem with this scenario is that a certain level of biodiversity may well disappear at the hand of development, before it is even recognised as distinct and significant.

Most vegetation sampling studies undertaken in New South Wales over recent decades have adopted the six-point Braun-Blanquet cover abundance scale for scoring individual plant species (for details on this scoring system, see Chapter 3, Section 3.2.2). Other studies incorporate presence-absence recording into their design, however such a practice may sacrifice a higher level of detail in ensuing analyses, and mask important and significant vegetation patterns (Benson 1994; Keith & Bedward 1999; Benson & Ashby 2000). Presence-absence analysis is still used in some classification projects, such as that done by Keith and Scott (2005), but generally only in cases where large datasets of existing data collected using variable cover abundance scales have been used. The study of Benson (1994) compared classification results using cover abundances or presence-absence data, and also examined the influence of weed species and classification technique (hierarchical divisive vs flexible agglomerative) on grassland communities. In this case, flexible agglomerative analysis of cover abundance data, with weeds removed proved the better technique.

Some studies have adopted a survey design which ignores all reference to environmental stratification (e.g. Steenbeeke 1990; Stafford & Eldridge 2000). Steenbeeke (1990) described the vegetation in the Kowmung Valley of New South Wales by sampling at 150 locations within his study area, their locations determined by the intersection of kilometre gridlines on topographical maps. In a sense, the sampling design for this study was totally independent and unbiased, as it was not stratified on

Introduction & Research Plan environmental variables nor was it random in its approach. Nevertheless, Steenbeeke defined seventeen vegetation communities in his study, and was able to equate his units with broader scale mapping completed by other ecologists.

1.2.2.4 Assessing map and classification accuracy

Relatively little post-production assessment is undertaken on vegetation maps and classifications produced in Australia. In some vegetation modelling cases, sample data have been withheld from classification analysis so that they can be used to validate a final map product on completion (e.g. NSWNPWS 2000b). More typically, validation of a vegetation map is borne out through implementation after the release of a classification or map, and may result in revisions and updates being prepared (such as the procession of products prepared for the Hunter region: Section 1.2.2.2). Benson (2008a) suggested that few vegetation maps achieved accuracy rates of >90% for polygon attribution, and that in most cases finely defined communities are not shown at all. In other parts of the world, the assessment of map and classification accuracy appears most advanced in the United States (e.g. Nusser & Klaas 2003; Lunetta & Lyon 2005; Langford et al. 2006; Faber-Langendoen 2007), where GIS techniques have been used to interrogate data layers. De Cáceres & Wiser (2012) have recently promoted the need for consistency in vegetation classifications (i.e. in definitions of vegetation types and assignment of new data to a classification), advocating the publication of classification rules for such a purpose. Errors in classifications, and maps produced from them, would lessen if such rules were in place.

The concept of homogeneity analysis, as adapted from soil mapping research, was presented in relation to vegetation classification and mapping by Bedward et al. (1992). It was proposed as a way in which the information content and utility of a vegetation map could be assessed. The homogeneity algorithm is based on the ratio of the average within-group association between sites to the average association of the whole dataset. With this measure, Bedward et al. (1992) showed how it could be used to determine appropriate map scale or level of classification, assess species prediction values using environmental strata, and to examine representativeness of canopy-only classifications to a full floristic analysis. Homogeneity analysis has been used most

Introduction & Research Plan often in the literature to assist in defining the number of floristic groups in a classification (e.g. Keith & Bedward 1999; Benson & Ashby 2000; NSWDEC 2006), which effectively assesses sampling adequacy across an entire dataset. However, it has not been used to test for sampling adequacy of individual vegetation communities.

There are also comparative examples in the literature of intuitive vs numerical classifications, showing that the latter can be used in support of classifications constructed by ecologists who have a good knowledge of their study area (a basic principle of the Zurich-Montpellier school). For example, using numerical classification techniques on 51 sample quadrats, Neldner and Howitt (1991) were able to replicate site and floristic groups to map units developed during an earlier intuitive mapping exercise. Similarly, Lawson and Wardell-Johnson (2006) tested polygon homogeneity of mapped vegetation from Fraser Island in south-eastern Queensland, and found that eight numerically-derived plant communities derived from 50 quadrat samples had strong congruence with eight a priori regional ecosystem types. They suggested that limited mapping resources should be directed towards those habitats where high polygon heterogeneity may be expected, rather than in areas (such as, in their case, sand islands) where floristic differences between communities are evident. Other examples from elsewhere in the world are present, although not common, in the literature (e.g. Hakes 1994; Grabherr et al. 2003; Kočí et al. 2003).

Map accuracy, too, has been the subject of debate in many cases where conflicting depictions of vegetation community diversity have been presented for the same patch of land. Pickard and Boyland (1981) compared three small scale vegetation maps produced by three different Australian States using different methods. They found that discrepancies in interpretation of aerial imagery, mapping scale, and inconsistency in classification units and definitions to be the key problems. Harvey and Gill (2001) provide a further example where interpretation of different imagery types was compared for freshwater swamps in the Northern Territory, with aerial photography clearly outranking satellite imagery for detailed vegetation mapping. Elsewhere, Hearn et al. (2011) reported on seven independently constructed maps in the United Kingdom, where field ecologists were asked to apply the National Vegetation

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Classification to a site of 43 hectares. Spatial correspondence between the seven maps was assessed, revealing 78% average area of agreement at the habitat level, 34% at the community level and only 19% at the sub-community level. Most differences, they concluded, were due to discrepancies in the interpretation of classificatory units, as reflected in mapping units. Maps produced by those ecologists relying primarily on their own field experience had higher levels of mean agreement with other maps.

1.2.3 Recent NSW Government Initiatives

In recent years, there have been three initiatives coordinated by the New South Wales government aimed at improving the management and conservation of native vegetation. All of these initiatives detail specific methodologies for the gathering and processing of vegetation data, and a brief review here is necessary with respect to the recognition of full community diversity.

1.2.3.1 The NSW Native Vegetation Interim Type Standard

The Native Vegetation Interim Type Standard (NVITS: Sivertson 2010) has been developed by the NSW Office of Environment and Heritage to apply to all relevant vegetation activities to which the NSW Government makes a financial or in-kind contribution, or to which the NSW Government is a signatory. The NVITS addresses the nature and quality of the scientific processes for native vegetation activities, including remote sensing, field survey, data manipulation, data management and mapping. It is one of six Standards relating to native vegetation that have been or are soon to be published by the NSW Government. In the case of the biodiversity certification scheme established under Part 7A of the Threatened Species Conservation Act 1995, the Biodiversity Certification Assessment Methodology adopts much of the NVITS survey design methods (NSWDECCW 2011).

The NVITS has been designed to be flexible in its use and its outcomes: it is equally applicable to a spatial map layer derived from remote sensing as it is to a set of vegetation community descriptions. It seeks to ensure that consistent, transparent and repeatable methods are used for the capture and presentation of vegetation data. There are ten key components to the NVITS, covering metadata, data management

Introduction & Research Plan and existing data; interpretation of remotely sensed imagery; survey design, stratification and survey effort; survey design and quadrat size; field sampling; data analysis; classification hierarchies; spatial interpolation; accuracy assessment; and reporting requirements and summary statistics.

In summary, the NVITS:

promotes the use of both traditional and computer-based interpretation of remotely sensed imagery for mapping;

adopts the stratified random sampling methodology sampling design, based on Environmental Sampling Units or gradient analysis of sampling gaps;

recognises representation, replication and randomization as guiding principles in survey effort, with sample adequacy measured by sample number relative to project scale and resolution;

adopts sample sizes of 0.04 hectare as the standard (nominally 20 x 20m quadrats);

requires field sampling to include the collection of primary (unclassified), quantitative data relating to type, extent and condition;

adopts numerical analysis of quantitative quadrat-based data to define vegetation types, due to its objective, systematic and transparent nature. However, the Standard must not constrain the research and development of new analytical techniques in vegetation classification;

supports a hierarchy of vegetation classes for NSW, with local-scale mapping products nested within broader (State-wide) classification units;

accepts that all spatial interpolations of vegetation types are models, regardless of how they are derived, and can be based on remote imagery, environmental data or a combination of both. Environmental variables chosen for interpolation must be transparent and repeatable, and should have demonstrable relationships with floristic composition;

Introduction & Research Plan

supports thematic accuracy assessments which are practical and scientifically valid, clearly explained and appropriate to the spatial and thematic scales being evaluated. Such assessments should employ an equal probability sampling design with a fixed sampling unit (e.g. 20 x 20 m quadrat), be randomly selected and visited, and the three dominant species in each stratum be recorded and assessed against applicable documentation (using a 4-point, ‘absolutely right’ to ‘absolutely wrong’ scale). Results should be summarized using a confusion matrix comparing classes with assessment units.

Certain aspects of the NVITS limit the suitability of the Standard to all vegetation projects, and some of these will be addressed in this thesis (see Section 1.5). In particular, the following aspects are seen as constraints to the overall intent of the NVITS, and which may be overcome with some changes in sampling design and effort:

1. Sampling design - The NVITS does not allow for direct sampling of field- observed variations in vegetation patterns, and is consequently at odds with the Zurich-Montpellier school of phytosociology. The NVITS endorses stratified random sampling (either ESU classification or gradient analysis) for the selection of sample sites, both of which are commonly computer-based methods of choosing sampling locations. As a general guide, such processes are important for sampling the majority of expected diversity in vegetation patterns, but they cannot detect all variations due to the scale of GIS environmental layers currently available.

2. Sampling effort - The NVITS considers the level of survey (sampling) effort to be adequate based on the number of quadrats, rather than on the outcomes of the classification. Such an approach assumes that all community diversity will be detected through the random stratification process, and hence a simple calculation of the number of quadrats per ESU will suffice, dependent on project scale and level of detail required. The NVITS also allows for randomly selected sample quadrats within the range of a targeted specific vegetation type. However, it does not appear to allow such an approach across a range of communities or larger study area (i.e. it is in conflict with the random stratified

Introduction & Research Plan

sampling method). Within each sample quadrat, the NVITS promotes the collection of cover abundance data on a 24-point scale, rather than the 6-point Braun-Blanquet scale (and its derivatives) that has been traditionally used in NSW for several decades. This potentially increases observer error, particularly when data from different observers is amalgamated for analysis purposes, and also reduces efficiency in data collection. No other country in the World uses such a 24-point scale (see Jennings et al. 2008).

3. Accuracy assessment - The NVITS rightly includes the concept of accuracy assessment for thematic maps. However, assessment of accuracy is undertaken after the completion of a thematic map, and requires additional data collection to populate an ‘independent’ dataset for this purpose. Such a process will incur additional time and expense.

1.2.3.2 BioBanking

Payment or credits for biodiversity protection and conservation is a concept gaining momentum throughout the World (e.g. Gibbons et al. 2011; Kumaraswamy & Udayakumar 2011). The New South Wales government established the BioBanking Scheme under Part 7A of the Threatened Species Conservation Act 1995 in July 2008. Under this scheme, it is hoped that biobanking sites can be established under agreements between the Minister for Climate Change and the Environment and the relevant landowners; that biodiversity credits (calculated using the biobanking methodology) for management action can be created to improve or maintain biodiversity values of a site; that the trading of biodiversity credits can occur; and that biodiversity credits may be used to offset development impacts (NSWDECC 2009a). The BioBanking Scheme was established following the similar Biometric Scheme (Gibbons et al. 2005), which is a regulatory component of the Native Vegetation Act 2003. One of the important differences between the two is that the former allows no net habitat loss while the latter did not.

A specific methodology has been established for the biobanking scheme, which covers a range of biodiversity elements including vegetation condition, fauna habitat, floristic

Introduction & Research Plan diversity etc, each of which are measured against set benchmarks for different vegetation types (NSWDECC 2009a). Vegetation communities defined within the NSW Vegetation Classification & Assessment (Benson 2006) will contribute directly to the BioBanking Scheme via the NSW Vegetation Information System (see Section 1.2.3.3).

A few key points summarise the assessment of vegetation under the scheme:

a particular site is stratified into vegetation zones, typically on the basis of aerial photographic interpretation;

a minimum number of sample quadrats are randomly placed within each vegetation zone relative to their areal extent, and according to a set sampling density;

the number of native plant species (not their identities) present within each quadrat are recorded;

vegetation communities are compared against the NSW Vegetation Types Database, and the type of best fit chosen.

1.2.3.3 The NSW Vegetation Information System

The NSW Vegetation Information System (VIS) aims to provide the NSW Government, its clients and the general community with a central authoritative repository for native vegetation data. The VIS will provide users consistent, reliable and integrated information about NSW vegetation by developing publicly accessible databases and standards, and by refining data acquisition, management and delivery procedures (NSW OEH web site).

As part of the VIS, Plant Community Types (PCTs) have been established as the NSW master community-level classification. The PCT classification was developed in 2011 to establish an unambiguous master community-level classification for use in vegetation mapping programs, BioMetric-based regulatory decisions, and as a standard typology for other planning and data gathering programs. It consolidates two existing community-level classifications: the NSW Vegetation Classification & Assessment (Benson 2006), and the Biometric Vegetation Types database. All changes to the PCT 36

Introduction & Research Plan classification are to be evidence based, and moderated by the Plant Community Type Change Control Panel chaired by the NSW Royal Botanic Gardens and Domain Trust. BioMetric vegetation types and BioMetric condition benchmarks are also maintained in the VIS, and exported periodically for upload to the regulatory tools. A Plant Community Type Identification Tool has been developed to support the field identification of PCTs and NSW Vegetation Formations.

1.3 Data Sampling

Kent and Coker (2001) noted that issues relating to the sampling of data and sampling design have been less well researched than other sub-disciplines of vegetation science, and detail several of the more common sampling approaches. Inductive research, such as that undertaken for vegetation classification, requires different sampling methods than do deductive approaches. Inductive research does not require the generation and testing of a hypothesis, as does deductive research (i.e. the scientific method). While the Braun-Blanquet method of classification promotes the sampling of vegetation to be undertaken using the quadrat, its requirement that quadrat placement be subjectively determined by the ecologist has lead to some criticisms (see Section 1.1.2). However, it is this principle of subjective (preferential) selection of quadrats that has been overlooked in many classification studies in the last few decades, and one which has potentially contributed to a lack of recognition of rare communities.

1.3.1 Random vs Non-random Sampling

As noted in Section 1.1.2, one central criticism of the Zurich-Montpellier school of classification is that sample quadrats are selected non-randomly in uniform and homogeneous locations within an area. Such an approach potentially violates one of the basic principles of statistics, whereby samples should be selected randomly and be wholly independent. Lájer (2007) argued that this has been the case for many phytosociological studies, resulting in Folia Geobotanica, a journal of vegetation science from the Czech Republic, devoting an entire issue to the subject. In response to Lájer (2007), several authors have illustrated that preferential (non-random) sampling more thoroughly samples variation in vegetation than do the more traditional

Introduction & Research Plan sampling methods (i.e. random, stratified random, systematic) which may better meet statistical assumptions (e.g. Diekmann et al. 2007; Roleček et al. 2007; Michalcová et al. 2011). When the aim of the research is to collect data first, without a prior hypothesis, and later derive explanations from these data, then preferential sampling is valid (Kent & Coker 2001; Dale 2002). Preferential sampling also increases the chances of sampling rare or restricted vegetation types (Jennings et al. 2008), and has been used for this purpose in some studies to augment other sampling methods (e.g. Walker et al. 1995).

1.3.2 Stratified Random Sampling

Towards the latter half of the 20th Century, the idea of deliberate and strategically placed quadrats in the classificatory process appears to have been abandoned in favour of stratified random sampling (also sometimes referred to as restricted randomisation). In the original sense, stratified sampling involved some initial reconnaissance of a study area, with or without examination of aerial photography, followed by its division into major units on the basis of growth form, physiognomy and vegetation structure, and on occasion into secondary divisions comprising variations of dominant species (Kent & Coker 2001). Samples were then selected within each of these strata, relative to their geographical area.

However, the advent of powerful personal computers and geographical information systems worldwide has meant that a study area of any size can effectively be stratified into smaller units, typically using environmental variables rather than observed differences in the field, with quadrats selected remotely and randomly within the resulting strata (e.g. Cawsey et al. 2002). This has been done on the presumption that the distribution of most vegetation communities correlates well with environmental factors such as soil type and soil moisture (as may be concluded from deductive research), and that major differences in vegetation composition can be determined prior to any formal survey being conducted (Smartt 1978). Broadly, this may be the case for small-scale mapping (e.g. Higgins et al. 2011), but it does not necessarily hold true at larger scales. Indeed, there are examples in the literature where a GIS-based stratification has not been a good surrogate for on-ground diversity (e.g. Griffith et al.

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2000; Rankin et al. 2007; Holt et al. 2008; Adams et al. 2011), and errors in stratification layers add to this (e.g. van Niel et al. 2004; Rankin et al. 2007). Common stratification determiners can include any number or combination of such variables as geology, soil type, rainfall, aspect, elevation, structural type, proximity to drainage lines etc (some of which are themselves often based on modelled data; Chapman et al. 2005). In most cases, this process is undertaken remotely from the study area, allowing the researcher to effectively select sample sites from the comfort of an office environment.

In one sense, stratified random sampling does indeed concur with the Zurich- Montpellier school of plant classification, in that an area to be studied can be stratified on observed variation in vegetation (the homogeneity of Braun-Blanquet) rather than using environmental factors, and quadrats selected accordingly. In his critique of the widespread use of preferential sampling, Lájer (2007) admits that stratified random sampling within vegetation units is possible, and would meet the required statistical rules. Whether or not quadrats are selected randomly or preferentially should not be of great concern if the investigated vegetation is uniform and homogeneous. However, as Goodall (1953) pointed out, many ecologists do not consider vegetation to ever be homogeneous (cf. Gleason 1939) hence the concept of stratification may not apply.

Smartt (1978) proposed an extension to stratified sampling with his flexible systematic model for vegetation sampling, in which a sampling framework is constructed across the study area through establishing primary sampling points. Consideration of the degree of floristic variation within and between these primary sampling points dictates where the greatest survey effort is required, potentially leading to greater efficiencies in available sampling resources. Such a sampling strategy has an advantage in that sampling intensity is allowed to vary with changes in the observed floristic diversity of the vegetation, rather than being distributed proportionally to the environmental strata in use. Caldas and White (1983) extended this idea with their more generalized flexible systematic model.

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1.3.3 Sampling Adequacy

The question of sampling adequacy in vegetation classification studies appears to be rarely discussed in plant ecology texts (e.g. Kent & Coker 2001; Dale 2002). However, it is an issue that can influence how a final classification is presented, particularly in the definition of vegetation communities and how a third-party land manager makes use of a final product. Cost efficiency (cf Margules & Austin 1991), while related to sampling adequacy, is driven by available financial resources and should be considered separately.

Neldner et al. (1995) examined five methods of assessing sampling adequacy for projects in progress; that is, for projects where there is scope to adjust and improve upon the planned survey design. The five methods were intuitive reliability assessment (application of a 6-point reliability rating by an API photo-interpreter), sampling intensity index (number of samples/area of map unit), non-stratified environmental parameter analysis (number of samples and environmental grid cell analysis), gradsect analysis using DOMAIN (calculates low sample areas based on climate modelling), and environmental domain analysis (assessment of climate, terrain & substrate against accumulation curves). They concluded that, individually, none of the five methods can satisfactorily test for sampling adequacy in floristic and structural variation, but that all methods could be constructively applied to projects at various stages of progress (planning, in progress, published). An objective method for determining sampling adequacy appears unattainable using GIS methods, due in part to the unavailability of fine-resolution GIS layers suitable for this purpose.

Zerger et al. (2005) examined sampling adequacy for the Dubbo region of central western New South Wales and suggested in their introduction that formal and transparent environmental stratifications (such as were discussed in Section 1.3.2 above) are often used to capture the inherent variation present in a landscape. To counter this, they used two methods to test for sampling effectiveness: species accumulation curves and sampling densities per area (relative to other mapping programs). Of most relevance are species-area relationships, however Zerger et al. (2005) demonstrated that subjective visual estimation of the resulting species

Introduction & Research Plan accumulation curves to identify the asymptote can often be misleading and unhelpful. They tested four curve-fitting models in an attempt to prioritise assessments, and concluded that log (cumulative area) was the most appropriate. All 34 defined vegetation communities supporting eight or more samples within their study area were then tested for sampling adequacy in this way, and showed adequacy indices ranging from 37 – 61%, differing widely from the global figure of 85% calculated for all 34 communities combined. Zerger et al. (2005) concluded that a minimum of 1 hectare of each vegetation type (i.e. 25 sample quadrats of 20 x 20m in size) should be sampled in order to meet their measure of adequacy.

Few other such studies on sampling adequacy are evident in the literature, which is surprising given the need for accountability of vegetation classifications, particularly in regard to the increasing incidence of legal challenges to regional-scale classifications (e.g. Larkin 2009). Based on the available publications discussing species-area relationships (e.g. Cain 1938; Kent & Coker 2001; Tjørve 2003; Scheiner 2004; Fridley et al. 2005; Dengler 2008, 2009), it is evident that they may be well suited to assessing sampling adequacy. Early plant ecologists observed that a greater number of species could be seen in progressively larger areas (e.g. Gleason 1922), and as time progressed the species-area relationship began to be used in a number of applications, including examination of species diversity over specific areas or habitat types (e.g. Lunt 1990; Tjørve 2002; Hunter 2005; Neldner & Butler 2008), assisting in the design of nature reserves (e.g. Gladstone 2002), and use in predictive landscape models (e.g. Manne et al. 2007; Triantis et al. 2008). Issues of scale dependency were also addressed by Dolnik & Breuer (2008), who showed that species richness calculations will vary across different scale ranges and vegetation types. Dengler (2009) considered that in their simplest form, species-area relationships should be distinguished from the related species-sampling relationships because the two categories have fundamentally different curve shapes. He proposed two forms of species-sampling relationships; species accumulation curves (species increase according to the original sequence of recording) and rarefaction curves (mean species increase based on repeated randomised re-sampling from the total sample of a species accumulation curve). For

Introduction & Research Plan assessing sampling adequacy, rarefaction curves potentially provide the best method to complement a classification.

1.4 Issues of Classification Scale

By default, all vegetation classification projects are undertaken at a specific scale (level), which may or may not be driven by the requirements of an end-user. These scales may be national (e.g. Specht et al. 1995; Lawesson 2000; Jennings et al. 2003), regional (e.g. Keith & Bedward 1999; NSW NPWS 2000b; Somerville 2010) or local (e.g. Griffith et al. 2000; Bell 2009a), and although all are valid for their specified purpose, the transfer of information from one to another is difficult (Guerrero et al. 2013). Defining the difference between a regional and local classification is also difficult, and will depend on the context in which it applies. What is needed is a hierarchical system of classification, such as that originally outlined by Braun-Blanquet (1928, 1932), incorporating local, regional and national units that can be used by management and regulatory authorities for on-ground decision making. Keith (2004) and Benson (2006) have established a framework for this to occur within New South Wales, and a similar approach is being undertaken for the Sydney Basin region (NSWDECCW 2010a). More locally, however, such a system is not yet in place.

Kuželová and Chytrý (2004) demonstrated how vegetation communities delineated at a local scale were difficult to transfer to a regional scale, and concluded that regional- scale classifications were superior as they allowed better communication between researchers in different areas. This may be true for broad-scale planning, but for conservation and planning management undertaken at a local scale, regional classifications are rarely applicable (but there are exceptions, such as the documented variants of Cumberland Plain Woodland in western Sydney, which are maintained at local, regional & State scales: Tozer 2010). In the United Kingdom, Hearn et al. (2011) report on the problems of applying the National Vegetation Classification to a local scale, implicated through the preparation of seven different vegetation maps of a 43 hectare site by seven different ecologists. They raise serious concerns about using a national classification for local-scale site description and monitoring, and urge further work on interpretation of a classification and maintaining consistency. 42

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An example of this from the Hunter Valley of New South Wales is the regional classification completed by NSWNPWS (2000b), which at the time brought a new- found understanding to vegetation community diversity in the region (see Section 1.2.2.2). Over the decade that followed, however, numerous studies grappled with application of these data at a local scale (e.g. Bell 2004a; HSO Ecology 2009a), resulting in new classification projects across broad areas (e.g. Hill 2003; NSWDECC 2008b; Bell 2009b). A revision of regional classification using additional data followed six years later (McCauley et al. 2006), and again in 2010 (Somerville 2010). The current regional classification framework for the Hunter Valley, although improved from the original 2000 classification, is still difficult to apply locally in some areas. While this procession of classification products demonstrates the dynamic nature of vegetation classification, it is expected that improvements in a classification be achieved with subsequent iterations, which has not consistently occurred.

Legally, many vegetation communities afford some level of protection through threatened species legislation (see Section 1.1.4.3). In listings of communities present within, for example, the NSW Threatened Species Conservation Act 1995, we see communities of widely-ranging scales. Broadly, remnants of threatened communities such as White Box – Yellow Box – Blakely’s Redgum Woodland extend across vast areas of the western slopes and tablelands of New South Wales (and indeed Victoria and other States), manifesting themselves in a wide range of floristic variations (e.g. Prober & Theile 2004; Prober 1996). Communities such as these clearly represent the regional scale in vegetation description, and ones which equate to the vegetation classes of Keith (2004). At the other extreme, threatened community listings such as Umina Coastal Sandplain Woodland, Kincumber Scribbly Gum Forest and Quorrobolong Scribbly Gum Forest (see http://www.environment.nsw.gov.au/committee/final determinations.htm) from the Central Coast of New South Wales occupy very restricted ranges and show low floristic variation across stands. These communities represent the local scale of classification (not evident in Keith 2004), and perhaps the scale at which land management decisions should best be made.

Introduction & Research Plan

Scale as a concept in classification and mapping products is of critical importance to those charged with conservation and planning decisions at a local scale (Pressey & Bedward 1991). Adapting regional-scale classifications to local areas is always going to be difficult, and will inevitably be dependent on sampling design and sampling adequacy. Guerrero et al. (2013) have outlined the many challenges that exist when conservation planning is impacted upon by scale mismatches, and suggest that consideration of social networks (organisations & individuals) within the general community may be the most effective way to improve conservation outcomes across multiple scales. Yet it is at the local scale that planning decisions are being made which have the greatest impact on vegetation biodiversity. Rare or restricted communities (and some poorly sampled more common communities), potentially of highest significance in any classification, are those most likely to be overlooked at a regional scale and will suffer the most. Such communities may contain arrays of species assemblages, and perhaps some processes, that are not found in the more common regionally defined communities. Clearly, a better and more stream-lined way of detecting, describing and mapping this level of community biodiversity is required.

1.5 Key Questions to be Addressed

A good depiction of the diversity of vegetation communities within a geographical area evidently relies upon a rigorous classification (abstract association), based on a sound and systematic data set, and a reliable map (concrete association) to show the geographical distribution of community diversity. These two entities are created through separate processes, yet are inextricably linked (Kűchler 1951): both are required at the highest possible standard to manage landscapes for the best outcome. Existing systems of vegetation classification and mapping have provided countless classificatory and mapping products, but often when put to the test locally these are seen to fail.

The current research aims to build upon existing methods of classification and mapping, focusing on employing the basic scientific principles of sample selection and knowledge of a study area (a return to the ideals of the Zurich-Montpellier school) for describing and mapping community diversity. In particular, using these principles to 44

Introduction & Research Plan improve our ability to detect, describe and map rare vegetation communities. Key questions to be addressed include:

How do we adequately identify and define community diversity, including naturally rare communities?

Can an accurate map of vegetation community diversity be produced in a cost- efficient way, without overly relying on remote sensing techniques?

Is it possible to identify naturally rare communities with a simple change to sampling design?

Can an existing threatened community defined through a prior classification be satisfactorily reviewed to accommodate observable floristic variation within it?

What are the implications for land-use planning and conservation if classification and mapping products can be produced with higher user accuracy and reliability?

Chapter 2 of this thesis adapts existing methods to describe a simple yet effective protocol for mapping the distribution of vegetation communities based on ground- collected data, with a strong basis in the Zurich-Montpellier school. Examples are provided on how the process has been used to map community diversity across a range of study areas on the Central Coast of New South Wales.

Chapter 3 presents a case study on the diversity present within Scribbly Gum – dominated vegetation from the Central Coast of New South Wales. Using preferential sampling of observed variations in the field, numerical classification is used to show how a number of definable vegetation communities can be documented, and contrasted with a regional classification which failed to detect them.

Chapter 4 employs the concepts presented in Chapter 2 to redefine the Lower Hunter Spotted Gum-Ironbark Forest Threatened Ecological Community (TEC). Using preferential sampling of observable variations, a number of forms of this previously

Introduction & Research Plan broadly-defined TEC have been detailed, and implications for the conservation of each form discussed.

Chapter 5 discusses how improved mapping and delineation of vegetation communities through simple changes to the current systems in place can result in more reliable descriptions and mapping of community diversity, with better outcomes for both conservation and development. Comparisons are drawn between community delineation and plant species taxonomy, where new plant taxa are described only after targeted sampling and analysis of observed variations. It is suggested that, in Australia, a shift in focus is required in some jurisdictions so that rare vegetation communities can be adequately recognised and managed. Bottom-up information processing is promoted as a way to facilitate this change in thinking.

Chapter 2: Improving Sampling Design: Data-informed Sampling and Mapping

Sample Selection Map Production Map Accuracy Detecting Rare Communities Cost-benefit Analysis Application

Data-informed Sampling & Mapping

Overview

A new method of sample selection and mapping vegetation communities, refined from traditional preferential sampling, is described, and three case studies are presented to illustrate application of the method to real-world mapping projects. The new method of Data-informed Sampling and Mapping (D-iSM) combines saturation coverage of vegetation data with a preferential sampling design to produce locally accurate vegetation classifications and maps. Many existing mapping techniques rely entirely or in part on predictive capabilities of computer software programs, or on assumptions that photo-patterns detected through traditional aerial photographic interpretation can be attributed readily to a vegetation type. With D-iSM, ground data are used to inform both classification and mapping phases of a project, rather than using surrogates such as environmental variables or photo-patterns in aerial imagery.

Benefits of the D-iSM method include more efficient and more representative sampling, more realistic and repeatable classifications, considerably higher user accuracy in vegetation mapping, increased ability to detect and map rare vegetation communities and ready application to a range of classification and mapping products.

Statement of Collaboration. The methods documented in this chapter have been developed in collaboration with fellow PhD candidate Mr Colin Driscoll over a number of years, and have been used by us independently and together for a number of contract projects. Although the concepts behind the mapping methods have been jointly conceived, incorporation of field data collection techniques into sample site selection and classification has been initialised and formalised by me in this thesis. The third case study discussed in this chapter (Cessnock-Kurri) was undertaken by both Colin and I; however all other field work, all data analysis and all of the following text is my own.

Data-informed Sampling & Mapping

2.1 Introduction

Mapping vegetation communities is fundamental for innumerable land management and land-use planning applications, yet map products are often of poor utility (Fallding et al. 2001; Congalton 2005; Wardell-Johnson et al. 2007). Low quality map products can be explained by a number of issues, including positional and attribute errors associated with map polygon identification, uncertainties in vegetation heterogeneity, and several predictive modelling errors (Green & Hartley 2000; van Neil et al. 2004). Reliable and accurate vegetation maps form the basis of planning for threatened species protection, re-vegetation of cleared lands, directing effort in weed control and spread, establishment and enhancement of wildlife corridors, bushfire management and ensuring representativeness in a conservation reserve system. Commonly, mapping of vegetation communities is undertaken to guide management in conservation reserves (e.g. Hunter et al. 1999; Koroleva 1999; Griffith et al. 2000; Cronje et al. 2008; Garzon-Machado et al. 2011), to mitigate and assess potentially significant impacts to biodiversity in proposed development areas (e.g. Breuste 2004; GHD 2007; Bell 2010a) and for regional-scale strategic planning (e.g. NSWNPWS 2000b; Peake 2006; NSWDECC 2008b). As discussed in Chapter 1, vegetation mapping and classification are inextricably linked, and while it is possible to have a good classification without good mapping (an abstract association), the opposite is more difficult to achieve.

In order to develop a comprehensive classification of vegetation types in an area from which to map, sampling of observable variations is critical. Indeed, a key concept in the Zurich-Montpellier school of phytosociology is that a researcher must know his or her study area well, and sample accordingly (Kent & Coker 2001). A thorough knowledge of an area under study is imperative so that placement of sample quadrats in representative and homogeneous locations can be undertaken with confidence. Of course, personal experience, anticipation, intuition and affinity to particular vegetation types will influence perception of such knowledge (Hédl 2007), and has been the subject of some criticism of the approach (e.g. Egler 1954; Chiarucci 2007). Implementation of a sampling design that recognizes and tests observable variations in vegetation will likely adhere better to the Zurich-Montpellier school than one that 49

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samples randomly or within a stratification centered on environmental variations such as lithology, climate etc (Diekmann et al. 2007; Michalcová et al. 2011).

There are several ways in which a researcher may come to ‘know’ his or her study area. Reconnaissance surveys of an area are often undertaken to gain a broad appreciation of landscape changes (e.g. Neldner et al. 2005; Rodwell 2006), but these are rarely built systematically into a sampling design. In retrospect, after preparing a vegetation classification, it may be claimed that an ecologist knows his or her study area well, but that knowledge has been molded by outcomes of that classification, rather than by the landscapes initially studied. Examination of aerial photography and satellite imagery presents a further avenue in which an ecologist may gain knowledge of his or her study area remotely (Fassnacht et al. 2006). However, in most cases only broad appraisals and predictive assessments can be made using this method, which limits its use in the detection of, for example, restricted vegetation types. None of these methods shows a demonstrable knowledge of a study area with which to structure and guide sampling effort efficiently.

With the use of Global Positioning System (GPS) units, it is now possible to record field data and positional locations to a high level of accuracy (generally less than +/- 10 m, often +/- 3 m). This relatively simple tool has been used for many applications in ecology, including recording vegetation community boundaries (e.g. Payne & Harty 1998; Kelleway et al. 2009). GPS is widely used for locating and tracking significant plant and animal species (e.g. Moen et al. 1996; Dominy & Duncan 2001; Schofield et al. 2007), mapping weed occurrences (e.g. Webster & Cardina 1997), geometric correction of and collection of training data for satellite imagery (e.g. Kardoulas et al. 1996) and recording locations of sample sites (e.g. Brook & Kenkel 2002). With careful thought, GPS technology also can be used to build a knowledge base for a specific area on any topic, and when combined with a Geographical Information System (GIS) can provide a powerful and demonstrable understanding of an area under study (Hebblewhite & Hayden 2010).

This chapter describes a method in which GPS technology is used to efficiently allocate quadrat samples across a landscape (demonstrating attainment of a thorough

Data-informed Sampling & Mapping

knowledge of an area), to construct a vegetation map that is superior to one incorporating several other local or regional scale mapping methods. It can be applied at a small, local scale or to a large regional area covering tens or hundreds of thousands of hectares. At its core is the requirement to provide accurate maps of vegetation description and distribution. These can then be used for a multitude of conservation and planning uses, such as threatened species management, corridor and land-use planning. This chapter provides a description of the method, and presents some general examples of its use across different landscape and scale scenarios, testing them against existing mapping products. In particular, scenarios are explored for ‘typical’ applications in urban development, for conservation reserve management and in sub-regional strategic planning fields. These scenarios demonstrate use of the method to create and map general classifications of an area: more specific use of the method as it relates to defining rare vegetation communities are presented in Chapters 3 and 4.

2.2 Methods

2.2.1 Study Areas

To illustrate the new mapping method, three different study areas ranging in size from ~100 ha to ~66,000 ha are examined. All three areas formed part of commissioned studies within the lower Hunter Valley of New South Wales, and mapping native vegetation accurately at a local scale was a prime objective for each. Mapping for the Edgeworth Local Environment Plan (LEP) remains unpublished (but see http://leptracking.planning.nsw.gov.au/PublicDetails.aspx?Id=642), while that for Columbey National Park (Bell 2009a) and the Cessnock-Kurri sub-region (NSWDECC 2008b; http://catalogue.nla.gov.au/Record/4504723) are both publically available. For the first two examples, survey effort as described in Section 2.3 was undertaken by a single field ecologist, while for the larger sub-regional study at Cessnock-Kurri, two ecologists were involved. Table 2.1 summarises the three study areas, while Fig. 2.1 shows their locations within the lower Hunter Region. Within the context of land-use planning, both Edgeworth LEP and Columbey National Park may be considered to

Data-informed Sampling & Mapping

represent the ‘site’ scale, while Cessnock-Kurri would be of ‘local government area’ or ‘regional’ scale (Fallding et al. 2001).

Table 2.1 Study areas used to illustrate mapping method.

Study area Size (ha) Application Source

Edgeworth LEP 96 urban development Bell 2010a Columbey NP 870 conservation reserve management Bell 2009a Cessnock-Kurri 65,690 sub-regional planning NSWDECC 2008b

Fig. 2.1 Location of the three study areas within the lower Hunter Valley, shown over Landsat TM imagery to illustrate differences in context and fragmentation (base image courtesy EarthSat NaturalVue2000).

2.2.1.1 Edgeworth LEP

The Edgeworth LEP (32.9 o S, 151.6 o E) study area lies approximately 17 km west of Newcastle, within the Lake Macquarie local government area. It occupies 96 ha of mostly forested land, but with three cleared powerline easements traversing the land

Data-informed Sampling & Mapping

in a NE-SW direction. Numerous trails and tracks run across the site, which are heavily used by local residents for trail bike riding and other recreational pursuits. Vegetation studies were initially commissioned to identify potential constraints to development on behalf of Lake Macquarie City Council, including assessment of threatened plant species and ecological communities. The Edgeworth LEP study can be considered typical of small-scale investigations for the development industry, as it lies on the fringe of existing suburban areas in outer Newcastle within a rapidly expanding region.

2.2.1.2 Columbey National Park

Columbey National Park (32.6 o S, 151.7 o E) conserves approximately 870 ha of well forested land adjacent to the rural township of Clarence Town, and lies 36 km north of Newcastle. It was proclaimed as a national park in July 2007 and occupies the majority of the former Uffington State Forest. Being former state forest, several tracks traverse the area ensuring ready access to most parts: the exception to this is the smaller more rugged western portion where access is limited. Investigations as part of a larger study reviewing the status and distribution of the endangered Lower Hunter Spotted Gum – Ironbark Forest (see Chapter 4) prompted a full floristic survey and classification to be undertaken within the reserve. In addition to establishing the extent and composition of this endangered community, a full mapping and classification study of all vegetation was undertaken to guide ongoing management of vegetation in the reserve. Such a process is recommended for all conservation reserves within New South Wales (Sivertsen 2010) and that completed for Columbey is typical.

2.2.1.3 Cessnock-Kurri

The Cessnock-Kurri sub-region (32.8 o S, 151.4 o E) lies approximately 37 km west of Newcastle, and occupies nearly 70,000 ha in the eastern portions of the Cessnock local government area. Around half of this area supports well forested lands, including national park (including Werakata National Park: see later), state forest and privately owned tenures. Extensive clearing has occurred throughout much of the Cessnock- Kurri area to provide urban settlements, but also to support rural and tourism industries such as cattle grazing, chicken production and vineyards. The NSW Department of Environment and Climate Change commissioned a detailed vegetation

Data-informed Sampling & Mapping

classification and mapping study of this region, to guide future strategic planning and conservation. More specifically, the composition and extent of several Threatened Ecological Communities within the region, including Kurri Sands Swamp Woodland (see Chapter 3) and Lower Hunter Spotted Gum-Ironbark Forest (see Chapter 4), required clarification. In addition, a pre-1750 (post-European disturbance: sensu Burgman et al. 2001) vegetation map of this sub-region was required to assist in the assessment of conservation significance of all vegetation communities. The Cessnock-Kurri sub-region provides a good example of a large-scale vegetation mapping project where, more traditionally, greater reliance would be placed on remotely assessed data (e.g. aerial photography or Landsat imagery) or computer modelling.

2.2.2 Mapping Pathway

An eight step process, referred to as Data-Informed Sampling and Mapping (D-iSM), has been developed to enable production of an accurate depiction of vegetation community diversity, and to present that depiction in map form. Not all steps in this process are novel, but all are intrinsically linked. Fig. 2.2 shows the relationship of the new steps in this process relative to the more traditional steps in vegetation mapping. Steps 1 (construct photo-patterns), 6 (full floristic sampling) and 7 (classification analysis) are not dealt with in detail in this chapter, but are included for completeness in describing the new method (see Chapters 3 & 4 for practical application of these steps).

Descriptions of each of the eight steps comprising the D-iSM method follow. All of the mapping products produced for the study areas outlined in Section 2.2.1 have followed this pathway.

Step 1: Construct photo-patterns

The initial step in the mapping pathway begins with creating photo-patterns as observed from remotely-sensed imagery, and is a form of stratification. In the traditional sense, this may involve interpreting aerial photographs (API), either with stereoscopic pairs viewed with a stereoscope, or directly on-screen using orthorectified digital images and a Geographical Information System (GIS). Other options may include automated interpretation of Landsat or other imagery through 54

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object-based image analysis or segmentation (e.g. eCognition software; www.definiens.com), or the 3-dimensional interpretation of digital aerial photographs (see discussion later). Any method depicting the location and distribution of target vegetation will suffice, as all have advantages and disadvantages peculiar to certain situations (see Chapter 1, Section 1.2.1; and reviews by Xie et al. 2008 and Morgan et al. 2010). For the current study, on-screen interpretation of orthorectified imagery was employed.

1. Construct 2. Map data 3. Map data photo-patterns acquisition classification

6. Full floristic 5. Quadrat 4. Floristic strata sampling selection generation

7. Classification 8. Map analysis production

Fig. 2.2 Flowchart showing the D-iSM mapping pathway. Steps 2, 3, 4, 5 & 8 contain elements representing key changes to traditional mapping; Steps 1, 6 & 7 are standard processes.

Using the preferred image type, observable photo-patterns are constructed within or transferred into a GIS. Photo-patterns represent potentially different vegetation communities or landscape variations, and are recognisable as alterations in colour, graininess, density etc. The base photo-pattern map layer is incorporated in Step 2 to assist in field data collection; specifically, although field data in Step 2 is collected in a ‘saturation’ fashion, reference is also made to the photo-patterns identified in Step 1 to ensure such data is collected within each photo-pattern type.

Data-informed Sampling & Mapping

Step 2: Map data acquisition

The map data acquisition phase involves collecting basic vegetation data at as many different points in the study area as possible (saturation sampling; in the sense of Bowen 2008). These points (Rapid Data Points: RDP’s) essentially are summaries of floristic and habitat information recorded at specific points (or ‘plotless’ quadrats) in the field, and are similar to the quaternary data samples outlined in Neldner et al. (2005) for Queensland vegetation mapping, and the occurrence plots included in Federal Geographic Data Committee (2008) for the United States. There are two forms of map data acquisition which depend on both the scale of the particular project being investigated, and on suitability to vehicular survey. In the first form (Vehicular Data Acquisition, VDA), existing road and trail networks are traversed in a 4WD vehicle. A live feed to a laptop computer, running Manifold © GIS and background aerial photography images, records location information in real-time. At specific and regular locations (dependent on project scale and heterogeneity of habitats), vegetation summaries are entered onto a spreadsheet linked to the GIS. Information recorded can include canopy layer dominant species, shrub layer dominant species, ground layer dominant species, canopy height, a field-recognised vegetation unit code, and any other miscellaneous notes. Species are entered in decreasing order of dominance, and need only include ~3-10 species in each structural layer. Once familiarity with vegetation patterns has been achieved, it is also desirable to note good, representative areas for full floristic sampling at a later stage (Step 5).

The second form of map data acquisition (Walked Data Acquisition, WDA) collects the same information as VDA, but utilises a hand-held GPS unit and notepad to record data. If data collected through VDA has not addressed all photo-patterns delimited in Step 1, then WDA can target those areas. While it is tempting in both VDA and WDA to apply a preconceived vegetation community code to each Rapid Data Point, it is important to consistently only record dominant species in each structural layer, as reference back to the raw data may be necessary in the final stages of mapping (or for future revisions or amendments to the final vegetation map).

Data-informed Sampling & Mapping

Step 3: Map data classification

On completing data collection, each Rapid Data Point within the dataset is subjectively provided with a draft floristic strata code (floristic strata are described in Step 4). This code is developed intuitively by the user (‘on the fly’) to represent a particular vegetation variation and can be updated at any time. Importantly, careful application of codes at this step ensures that minor variations in vegetation composition noted in the field can be highlighted for future investigation (such as in Steps 5 & 6). Decisions on how to classify the map data will depend on the familiarity of the study area achieved by the researcher, now that Step 2 has been completed. It is a relatively easy step to subjectively apply codes to each point as a broad appreciation of community diversity develops. Typically, the researcher is looking for commonalities in dominant canopy, understorey and ground layer components, and over the course of progressive data points, he or she intuitively implements an averaging process (or an appreciation of local scale variation, and how important they may or may not be in a classification) from point to point. For example, in a progression of subsequent Rapid Data Points, species entered in the canopy field may change from one where Species 1 and 4 forms the core, often associated with varying occurrences of Species 2 and 3, to one where Species 5 and 6 are the sole dominants. This pattern is symbolically represented in Table 2.2, where different symbols represent different species, and the heavy line between Rapid Data Points (RDP) 4 and 5 represents an inferred strata boundary.

Table 2.2 Symbolic representation of progressive Rapid Data Point collection and inferred floristic strata boundary. Symbols represent different plant species.

RDP 1 2 3 4 5 6 7 8 9 10

Floristic Stratum 1 Stratum 2 stratum

Data-informed Sampling & Mapping

Examination of similar patterns in other structural layers, in combination with canopy patterns, is used to subjectively determine where differing strata boundaries lie. Floristic stratum can be represented by a single Rapid Data Point, if the research considers that the characteristics of that data point are clearly distinguishable from surrounding data (and not perhaps due to anthropogenic or natural disturbances). In some instances, there will be anomalies in patterns of progressive dominants. In these cases, the researcher will intuitively decide the relative importance of these anomalies: are they indicative of an impending change in dominant species and hence community type, or do they represent vagrant or adventurist specimens (the accidental species of Szafer & Pawlowski 1927) undergoing floristic drift (the occasional occurrence of species outside of their expected or realised habitats)? Applying numerical classification techniques to Rapid Data Points to classify them, while desirable, are unlikely to be effective in any but the most simple of ecosystems. This is because the full diversity of species present in any floristic unit will often dramatically exceed the few dominant species recorded in each Rapid Data Point, and such analysis will only deliver broad groups.

Step 4: Floristic strata generation

Floristic strata, here defined in the same sense as the “Environmental Sampling Units” (ESUs) of Sivertsen (2010) or the “abiotic grids” of Jennings et al. (2003), are areas of vegetation (in the form of Rapid Data Points) where dominant plant species in major structural layers are homogeneous. Generating a floristic strata map can be achieved in two ways, and is dependent on the scale of the project being investigated. For large projects, use of a GIS can be employed to extrapolate Rapid Data Point identities across the landscape, irrespective of photo-patterns developed in Step 1. This is done by applying the Voronoi algorithm, and is similar to the Dynamic Spatial Approximation Method of Alani et al. (2001). The Voronoi algorithm creates polygons where the boundary of each polygon lies midway between all neighbouring points of differing identity, and has been used in a range of applications (Okabe et al. 2000; Alani et al. 2001). Where a strata map is required to guide subsequent full floristic sampling effort, this process is a very rapid and economical method of highlighting observable differences in vegetation. It is important to appreciate that, at this stage of the 58

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process, the aim is not to predict where a final vegetation type may occur (such as may be attainable through employment of predictive models which use environmental variables to determine the distribution of vegetation types: see Elith et al. 2006), but to blindly prepare a surface that only incorporates collected floristic data.

For smaller projects, extrapolation using the Voronoi algorithm is not necessary. Instead, strata polygons can be constructed in the GIS manually through use of standard polygon tools (creation, editing and attribution of polygons), but guided by the classified RDP data.

Step 5: Quadrat selection

A completed floristic strata map layer can now be used to guide selection of full floristic sampling quadrat locations. The goal here is to sample all observable variations in floristic combinations, irrespective of preconceived perceptions of what makes a ‘valid’ community. The Zurich-Montpellier school dictates that the researcher should know their study area well prior to selecting sampling sites: use of the floristic strata map achieves that goal through implementing preferential sampling. Depending on time and budgeting constraints, a sampling regime (Step 6) can be constructed on the GIS that maximises strata replication and sampling of all mapped floristic strata. In this case, such a regime allows economical positioning of full floristic samples and replicates within floristic strata, but still retaining the flexibility to locate them subjectively in the field. Notes made about representative areas of the various floristic strata during Step 2 streamline the process of quadrat selection, and allow such economical fieldwork planning for Step 6. A minimum replication effort may be desirable for certain strata (e.g. rare or uncommon floristic combinations).

Step 6: Full floristic sampling

Full floristic quadrat sampling is then undertaken, guided by the fieldwork plan developed in Step 5. For the examples presented in this chapter, sampling methods followed standard procedures for vegetation sampling in New South Wales (Sivertsen 2010), entailing recording all vascular plant species within 0.04 ha sampling quadrats, with each being visually attributed a cover abundance code. Cover abundance categories (differing to the 24-class categories included in Sivertsen 2010) were 1 (<5% 59

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cover and rare); 2 (<5% cover and common); 3 (5-25% cover); 4 (26-50% cover); 5 (51- 75% cover); and 6 (76-100% cover). Other sampling methods and sample quadrat sizes are equally applicable.

Step 7: Classification analysis

Numerical classification of full floristic quadrat data are then undertaken to examine relationships between samples across a study area. In the current study, agglomerative hierarchical cluster analysis (using the group averaging strategy, the Bray-Curtis similarity measure and a Beta value of – 0.1) and non-metric multidimensional scaling (in two dimensions with 25 random starts and a minimum stress of 0.01) were performed on the data using the Primer software package (Clarke & Gorley 2006), but other similar classification methods and products are equally applicable (e.g. Twinspan, Hill 1979; PATN, Belbin 1995; PAST, Hammer et al. 2001; JUICE, Tichý 2002). During the classification process, quadrats located within novel or unusual floristic strata identified in Step 4 can be tested against more widespread strata, and decisions made in relation to their legitimacy as stand-alone communities. Delineation of cluster groups will depend on individual project situations, and how fine a classification is required. There are guidelines on where splits in a cluster dendrogram should be taken (e.g. in PATN, Belbin 1995 suggests 0.8 dissimilarity), but these will vary with different datasets and no clear rules are applicable. Clarke & Gorley (2006) include a module called SIMPROF in their Primer package, which is a permutation tests that detects statistically significant evidence of genuine clusters in a dataset. This test is, however, problematic in large datasets.

Step 8: Map production

Map production begins with establishing a simple rule set governing the relationship between identified floristic strata (Step 4) and a final classification of full floristic quadrat data (Step 7). This is done by examining the cluster analysis or ordination, and determining which of the floristic strata units (represented by full floristic sampling quadrats) comprise the final floristic community. Effectively, this involves documenting a hierarchy of floristic units, from stratum to community. Global changes using this hierarchy are then made within the GIS to reflect these relationships, and through

Data-informed Sampling & Mapping

simple GIS applications the floristic strata map can then be translated to a vegetation map (Fig. 2.3).

Floristic strata Floristic unit 1 community 1 Floristic strata unit 4 Floristic strata unit 2

Vegetation Floristic community Floristic strata community 2 unit 6 map

Floristic strata Floristic unit 3 community 3 Floristic strata unit 5

Fig. 2.3. Potential relationships between floristic strata units, floristic communities and final vegetation map, after sampling and classification of full floristic sample quadrats. Read from right to left.

For example, floristic strata 1, 2 and 4 (from Step 4) may be shown through full floristic sampling (Step 6) and classification analysis (Step 7) to represent a single floristic community, rather than three. Using basic GIS editing tools, these three floristic strata are then merged within the GIS to form a single floristic community. A review of digital imagery also may be desirable during this stage, to refine community boundaries where field experience has clarified photo-patterns. If the project is a large regional one, such boundary refinement may not be necessary if the end product is to be used for regional scale applications, where local-scale boundary definition is of little importance (e.g. when prioritizing defined communities for conservation adequacy assessments).

Data-informed Sampling & Mapping

2.2.3 Comparisons to Previous Mapping

2.2.3.1 Previous Mapping Products

Vegetation mapping projects have been completed previously for the study areas examined in this chapter. Table 2.3 summarises sampling and mapping methods undertaken for each project so that comparisons with the new mapping methodology can be later examined in context.

Of the three study areas examined in this chapter, the NSWNPWS (2000b) study encompassed all of the Cessnock-Kurri and Edgeworth LEP study areas, but only a small portion of the Columbey National Park study area. The NSWNPWS (1999a) classification and mapping project included only Columbey National Park. Both NSWNPWS (1999a) and NSWNPWS (2000b) were large studies developing regional- scale classifications. Hence for purposes of comparative analysis, the respective community nomenclatures of each were assessed (but see implications later in Section 2.4). The reserve-specific, previously completed mapping of Werakata National Park (Bell 2004a) falls within the regional studies of both NSWNPWS (2000b) and NSWDECC (2008b), and provides an added, local-scale dimension for comparison.

2.2.3.2 Accuracy Assessment

To facilitate comparing new with pre-existing mapping, confusion matrices (see Congalton & Green 1993; Congalton 2005) were constructed to show proportions of correctly mapped vegetation units (attribute accuracy) as determined by the newly collected field reference data. Confusion matrices calculate three separate statistics to provide indications of the level of accuracy in the data: an overall accuracy, which is the proportion of all correctly classified data points within the whole matrix for all vegetation units examined; a producer accuracy, which is the probability that any data point within a unit has been correctly classified on a map; and a users accuracy, which is the probability that a classified data point within a unit actually represents that unit in the field.

Data-informed Sampling & Mapping

Table 2.3 Pre-existing studies used for comparative purposes.

Project Scale Size (ha) Methods Source

RN17 # State 80,000,000 intuitive classification based on aerial photographic interpretation, at 1:25,000 scale, supplemented FCNSW 1989 with knowledge from field situations; no sampling or numerical classification undertaken. Forest types defined as any group of tree-dominated stands possessing a general similarity in composition and character.

CRA * Regional 8,232,280 numerical classification of full floristic sample quadrats to refine the intuitive classification of FCNSW NSWNPWS 1999a (1989), and derive forest ecosystems; quadrats selected on an environmental and geographical stratification; mapping adopted from FCNSW (1989), but with revised nomenclature to reflect forest ecosystems.

LHCCREMS ** Regional 563,000 environmentally stratified random sampling survey design to allocate full floristic sample quadrats; NSWNPWS 2000b vegetation classification developed using numerical analysis of quadrat data; mapping of resulting communities undertaken through predictive modelling within a Geographical Information System, in combination with an interactive modelling software program to generate decision rules by statistical induction and expert review.

Werakata NP Local 2,145 using traditional techniques, interpretation of aerial photographs initially completed to provide a base Bell 2004a layer of broad vegetation patterns; full floristic sample quadrats were then selected from a soil landscape-broad vegetation stratification, but based largely on API patterns; classification of quadrat data provided descriptions of component communities, which adopted the NSWNPWS (2000b) community nomenclature; ground truthing of mapping involved pedestrian survey throughout the existing trail network, but did not incorporate any additional data collection.

# Research Note 17, forest typing of New South Wales * Comprehensive Regional Assessment ** Lower Hunter and Central Coast Regional Environmental Management Strategy, incorporating seven local government areas (Gosford, Wyong, Lake Macquarie, Newcastle Port Stephens, Maitland & Cessnock).

Data-informed Sampling & Mapping

Users accuracy is of most relevance for post-production application of a map, as it provides a level of certainty in the product for specific vegetation units. Overall accuracy is a general measure for all vegetation units combined, useful for comparing different map products. As part of the NSW Native Vegetation Type Standard, Sivertsen (2010) recommends the use of such confusion matrices to assess vegetation map accuracy of mapping products completed in New South Wales. In the Standard, Sivertsen (2010) advocates the four degrees of correctness of Gopal and Woodcock (1993), often referred to as a ‘fuzzy’ set: absolutely wrong, understandable but wrong, reasonable or acceptable, absolutely right. Such a fuzzy set of correctness has not been adopted in the current assessment, as it would unnecessarily complicate map comparisons. Instead, a simple right or wrong answer was sought for each Rapid Data Point based on a broad interpretation of the relevant classification for each study.

Field reference data (Rapid Data Point, collected from Step 2) were subjectively classified into equivalent forest type/forest ecosystem/vegetation community units through review of documentation supporting those mapping products (FCNSW 1989; NSWNPWS 1999a; NSWNPWS 2000b; Bell 2004a). Rapid Data Point classification applied a ‘best-fit’ interpretation, so that each Rapid Data Point could be coded appropriately, and the maximum amounts of test data were available for accuracy assessment. In some cases, this meant that a broad interpretation of a classification was required (potentially masking new communities), but was justified in the knowledge that field testing of mapping data by a third party would occur in the same way. For the Cessnock-Kurri study, accuracy assessments utilised pre-1750 vegetation mapping, since this was available for both the NSWNPWS (2000b) and current mapping products. Extant mapping, rather than pre-1750 mapping, was assessed in the Edgeworth LES and Columbey National Park studies.

For each case study examined, a random selection of Rapid Data Points, equating to 10% of the total collected for that study, were subjected to confusion analysis to determine mean user and overall accuracies. This process was repeated ten times (without replacement) for each study area, so that each map would be subjected to ten runs of testing using confusion matrices. Each random sample batch was

Data-informed Sampling & Mapping

intersected in the GIS with the appropriate map layer to produce the data matrix for testing. Standard errors for each analysis were calculated using the ten replicates to provide a basis for accuracy assessment. Results were graphed to illustrate user accuracies for each mapping product. The spread of Rapid Data Points across the relevant community classifications were also graphed to highlight those communities that were misclassified or overlooked in previous mapping projects.

In respect to assessing the accuracy of maps created through the D-iSM process, the use of RDP data to aid in the construction of a map, and then also to test for post- production map accuracy potentially represents ‘circular reasoning’ (or lack of independence). However, there a number of additional steps in the mapping process that limit this possibility. Rapid Data Points, after field collection (Step 2), are attributed a draft floristic strata code (Step 3) and used to stratify (Step 4) and sample the vegetation (Steps 5 & 6). Classification of the resulting full floristic dataset (Step 7) then follows prior to revision of Rapid Data Point coding and final mapping (Step 8). Field-collected Rapid Data Points have consequently gone through a process of testing and reallocation (where necessary) prior to being used to guide final mapping. In addition, to allow map comparisons between existing and new maps, Rapid Data Points have been re-classified to that particular classification being tested (e.g. NSWNPWS 1999a, NSWNPWS 2000b), so that individual raw data (Rapid Data Points) have been subjectively linked to an independent classification. Despite this process, the potential for circularity, although distant, is acknowledged here and discussed further in Section 2.4.3.

Further, the purpose of accuracy assessment in this chapter is to allow comparisons to be made between maps produced through D-iSM with those produced by other methods. It is not intended as a validation of the D-iSM process per se: this can best be achieved, in time, through third party use of created maps, such as for land-use planning and reservation. In the absence of a separate independent dataset, the simplest way to assess accuracy is to randomly select data from the available dataset, and pool results. Ultimately, a newly collected, totally independent dataset across all three study areas examined would be desirable, but is beyond the scope of this work.

Data-informed Sampling & Mapping

2.2.4 Time Resources

During all steps of the mapping process, a record of time for each task was kept (in person-hours) so that a cost-benefit assessment could be developed.

2.3 Results

Preliminary note: Results presented in the following sections are intended to illustrate how the 8-step D-iSM process operates, using real data from previous classification and mapping studies, rather than providing full accounts of vegetation diversity. Consequently, details with respect to vegetation communities defined, diagnostic species compositions etc have not been presented here. Schematic maps and analysis figures are provided in the context of the 8-step process only, for illustrative purposes. Further details on the full classifications can be found in Bell (2009a) for Columbey National Park and NSWDECC (2008b) for Cessnock-Kurri (available at http://catalogue.nla.gov.au/Record/4504723). Full results from the Edgeworth LES are not publically available, but elements of it can be found on the NSW Department of Planning website (http://leptracking.planning.nsw.gov.au/PublicDetails.aspx?Id=642).

2.3.1 Edgeworth LEP

2.3.1.1 Mapping process

Fig. 2.4 shows the results of the eight step mapping process undertaken for the Edgeworth LES. Aerial photographic interpretation (Step 1) distinguished cleared, extant and disturbed vegetation, but could not consistently delineate any floristic patterns across the area. In total, 187 Rapid Data Points (1 point per 0.5 ha of vegetated area) were collected during the map data acquisition phase (Step 2), together with eighteen full floristic sample quadrats (Step 6). Each Rapid Data Point was allocated to one of five observable floristic strata (Step 3), based on the composition of dominant species recorded at each point. Locations of the eighteen full floristic quadrats were preferentially selected within floristic strata units (Step 4), and then floristic patterns explored through numerical analysis (Step 7). The final vegetation map (Step 8 and see Fig 2.4) combined the results of numerical analysis with stratified Rapid Data Points and the base API layer.

Data-informed Sampling & Mapping

Step 1 Step 2 Step 3 1 Step 4

Step 8 Step 7 Steps 5 & 6

Fig. 2.4 Schematic representation of Steps 1-8 used to create a vegetation map of the Edgeworth LEP. Step 1 = construct photo-patterns; Step 2 = map data acquisition; Step 3 = map data classification; Step 4 = floristic strata generation; Step 5 & 6 = quadrat selection & sampling; Step 7 = classification; Step 8 = map production.

Data-informed Sampling & Mapping

2.3.1.2 Comparison to existing mapping

By way of comparison, and using a consistent regional community nomenclature, the vegetation map produced for the Edgeworth LEP by NSWNPWS (2000b) shows dramatically different results (Fig. 2.5a vs 2.5b). The NSWNPWS (2000b) map shows that almost the entire site supports Coastal Plains Smooth-barked Apple Woodland; the D-iSM map shows only limited areas of this community. In its place, the D-iSM map has delineated a substantial area of Lower Hunter Spotted Gum – Ironbark Forest, absent in previous mapping. This is a vegetation type listed as endangered in New South Wales under the NSW Threatened Species Conservation Act 1995. Further, the NSWNPWS (2000b) map includes small portions of Alluvial Tall Moist Forest and Coastal Sheltered Apple-Peppermint Forest. However, neither of these communities is present on the site (Fig. 2.5a). The D-iSM map also identifies a small amount of Riparian Melaleuca Swamp Woodland. More importantly, it delineates a large area of Red Ironbark-Paperbark Scrub-Forest in the north-west of the site (Fig. 2.5a) that had not been defined in the NSWNPWS (2000b) regional study (Fig. 2.5b).

The results of the confusion matrices of Rapid Data Point against the existing mapping of NSWNPWS (2000b) are depicted in Fig. 2.6, and an example matrix is included in Appendix 1a. An overall mean accuracy of 29.4 +/- 3.0% (n = 180) was calculated for the NSWNPWS (2000b) mapping, based on the number of classified point locations (Rapid Data Point) correctly intersecting with the appropriate map polygons, relative to all available Rapid Data Point. For Coastal Plains Smooth-barked Apple Forest, mapping is shown to be correct (user accuracy) for only 29.9 +/- 3.1% of Rapid Data Point collected. In addition, neither Alluvial Tall Moist Forest nor Coastal Sheltered Apple-Peppermint Forest is represented in any of the 180 Rapid Data Points or indeed anywhere on the site. For the current mapping, an overall mean accuracy of 100% was achieved (Fig. 2.6 & Appendix 1b).

The majority of misclassification in the NSWNPWS (2000b) mapping can be attributed to a large area of unmapped Lower Hunter Spotted Gum-Ironbark Forest (Fig. 2.7), which includes the closely related Red Ironbark-Paperbark Scrub-Forest (see Chapter 4 for further details on this community). Lower Hunter Spotted Gum-Ironbark Forest was

Data-informed Sampling & Mapping

Fig. 2.5 Vegetation maps of the Edgeworth LES study area thematically portrayed using the same regional (NSWNPWS 2000b) community nomenclature. Upper (a) shows the new vegetation map using the D-iSM method (including the previously undefined Red Ironbark-Paperbark Scrub-Forest); lower (b) shows an extract from the existing regional vegetation map from NSWNPWS (2000b). (Base image courtesy LPI NSW).

Data-informed Sampling & Mapping

100

20 Mean User accuracy (%) Useraccuracy Mean 0 MU5 MU11 MU17 MU30 MU42 Vegetation Communities

NPWS (2000) current study

Fig. 2.6 Mean user accuracy (+/- SE) of vegetation communities defined by NSWNPWS (2000b) for Edgeworth LEP, determined from a confusion matrix of 180 Rapid Data Points (10 random batches of 18 Rapid Data Points). MU5 = Alluvial Tall Moist Forest; MU11 = Coastal Sheltered Peppermint-Apple Forest; MU17 = Lower Hunter Spotted Gum-Ironbark Forest; MU30 = Coastal Plains Smooth-barked Apple Woodland; MU42 = Riparian Melaleuca Swamp Woodland. Note that MU5 and MU11 are actually not present on the site. originally defined by NSWNPWS (2000b), but was not mapped for the area by that project.

No. of Rapid Data Points 0 20 40 60 80 100 120 140

MU5

MU11

MU17

MU30 Vegetation Community Vegetation MU42

Fig. 2.7 Allocation of 180 Rapid Data Points across vegetation communities defined by NSWNPWS (2000b) for Edgeworth LEP. Solid bars represent Rapid Data Points of communities correctly mapped for the area by NSWNPWS (2000b); open bars represent unmapped communities. MU5 = Alluvial Tall Moist Forest; MU11 = Coastal Sheltered Peppermint-Apple Forest; MU17 = Lower Hunter Spotted Gum- Ironbark Forest; MU30 = Coastal Plains Smooth-barked Apple Woodland; MU42 = Riparian Melaleuca Swamp Woodland.

Data-informed Sampling & Mapping

2.3.1.3 Time resources

A summary of the total person-hours required to produce a vegetation map using the D-iSM method is shown in Fig. 2.8. Total field-time for the 96 ha study area was 31 person-hours, encompassing 7 person-hours for map data acquisition and 24 person- hours for full floristic sampling of 18 sample quadrats. A further 7 person-hours were used to construct photo-patterns, map data classification, floristic strata generation, quadrat selection, classification analysis and map production.

Edgeworth LES 7. Classification 8. Map production analysis

1. Construct photo-patterns

2. Map data acquisition

3. Map data 6. Full floristic classification sampling

4. Floristic strata 5. Quadrat generation selection

Fig. 2.8 Percentage task allocation for creating a vegetation map for Edgeworth LES using the D-iSM method.

2.3.2 Columbey National Park

2.3.2.1 Mapping process

Fig. 2.9 shows the mapping process for Columbey National Park. Aerial photographic interpretation (Step 1) distinguished cleared, extant and disturbed vegetation, as well as distinct riparian corridors. Past forestry practices within Columbey meant that API patterns were difficult to confidently detect, with differing ages of regrowth and a history of light stock grazing confounding interpretation. In total, 549 Rapid Data Points (1 point per 0.63 ha of vegetated area) were collected during the map data acquisition phase (Step 2), while 42 full floristic sample quadrats (Step 6) were surveyed.

Data-informed Sampling & Mapping

Step 1 Step 2 Step 3 Step 4

Step 8

Step 7 Steps 5 & 6

Fig. 2.9 Schematic representation of Steps 1-8 used to create a vegetation map of Columbey National Park. Step 1 = construct photo-patterns; Step 2 = map

data acquisition; Step 3 = map data classification; Step 4 = floristic strata generation; Step 5 & 6 = quadrat selection & sampling; Step 7 = classification; Step 8 = map production.

Data-informed Sampling & Mapping

Each Rapid Data Point collected in Columbey National Park was allocated to one of fifteen observable floristic strata (Step 3), and the forty-two full floristic quadrats were preferentially located within floristic strata units (Step 4). Numerical analysis (Step 7) explored floristic patterns in the data, culminating in the final vegetation map (Step 8).

2.3.2.2 Comparison to existing mapping

Columbey National Park was previously included in the regional mapping of two separate projects: the forest type mapping of FCNSW (1989), and the forest ecosystem mapping of NSWNPWS (1999a). The latter project adopted the earlier forest type mapping, but updated community nomenclature to reflect that used for the remainder of the NSW North Coast Bioregion. Fig. 2.10a and 2.10b compare this original forest type mapping with the D-iSM map. Excluding plantation and grassland areas, the 1989 mapping showed Columbey to support only three forest types across the reserve. However, with the higher level of map resolution attainable during the present study, eleven forest types are present (Fig. 2.10a). Likewise, Fig. 2.11a and 2.11b compare the forest ecosystem mapping of NSWNPWS (1999a) with the D-iSM map, showing an increase in the detected number of communities from three to eight. In addition to the increase in community diversity, the D-iSM map shows considerable differences in the distribution of the three common communities from both the 1989 and 1999 mapping.

Results of the confusion matrices for 540 Rapid Data Points against the existing mapping of FCNSW (1989) and NSWNPWS (1999a) respectively are shown in Figs. 2.12 and 2.14, with example matrices in Appendix 2. Based on numbers of classified point locations (Rapid Data Point) correctly intersecting with the appropriate map polygons, relative to all available Rapid Data Point, a mean overall accuracy of only 11.9 +/- 1.4% (n = 540) was calculated for the FCNSW (1999) mapping (Appendix 2a). For the five forest types mapped for the National Park (Fig. 2.12), only Forestry Plantations (Unit 218) exceeded 70% mean user accuracy for Rapid Data Points recorded within that unit (72.5 +/- 11.1%; n = 540). None of the three naturally occurring communities (Units 60, 84 & 92) achieve greater than 22% mean user accuracy. This poor result is mainly attributable to incorrect mapping units depicted for the National Park by FCNSW (1989), in particular by the misclassification of Unit 74: Spotted Gum- Ironbark/Grey Gum (Fig. 2.13). 73

Data-informed Sampling & Mapping

Fig. 2.10 Vegetation maps of the Columbey National Park study area thematically portrayed using the same RN17 regional community nomenclature (FCNSW 1989). Upper (a) shows the new vegetation map using the D-iSM method; lower (b) shows the same area with existing mapping from FCNSW (1989).

Data-informed Sampling & Mapping

Fig. 2.11 Vegetation maps of the Columbey National Park study area thematically portrayed using the same CRA regional community nomenclature (NSWNPWS 1999a). Upper (a) shows the new vegetation map using the D-iSM method; lower (b) shows the same area with existing mapping from NSWNPWS (1999a).

Data-informed Sampling & Mapping

100

90 80 70 60 50 40 30 20 Mean user accuracy (%) accuracyuser Mean 10 0 14 23 31 60 70 74 80 84 92 93 127 218 Forest Types

FCNSW (1989) current study

Fig. 2.12 Mean user accuracy (+/- SE) of forest types defined by FCNSW (1989) for Columbey National Park, determined from a confusion matrix of 540 Rapid Data Points (10 random batches of 54 Rapid Data Points). Unit 14 = Lilly Pilly; Unit 23 = Myrtle; Unit 31 = Paperbark; Unit 60 = Narrowleaved Mahogany-Red Mahogany- Grey Box-Grey Gum; Unit 70 = Spotted Gum; Unit 74 = Spotted Gum- Ironbark/Grey Gum; Unit 80 = Grey Ironbark-Grey Box; Unit 84 = Ironbark; Unit 92 = Forest Red Gum; Unit 93 = Eastern Red Gums; Unit 127 = Stringybark- Smoothbarked Apple; Unit 218 = Forestry Plantations.

No. of Rapid Data Points 0 50 100 150 200 250 300 60 84 92 218

230 14 23 31

Forest Types Forest 70 74 80 93 127

Fig. 2.13 Allocation of 540 Rapid Data Points across forest types defined by FCNSW (1989) for Columbey National Park. Solid bars represent Rapid Data Points of communities correctly mapped for the National Park by FCNSW (1989); open bars represent unmapped forest types. 60 = Narrowleaved Mahogany-Red Mahogany- Grey Box-Grey Gum; 84 = Ironbark; 92 = Forest Red Gum; 218 = Forestry Plantations; 230 = Cleared/ Partially cleared; 14 = Lilly Pilly; 23 = Myrtle; 31 = Paperbark; 70 = Spotted Gum; 74 = Spotted Gum – Ironbark/Grey Gum; 80 = Grey Ironbark – Grey Box; 93 = Eastern Red Gums; 127 = Stringybark – Smoothbarked Apple. 76

Data-informed Sampling & Mapping

For the forest ecosystem mapping of NSWNPWS (1999a), a mean overall accuracy of 22.4 +/- 1.9% (n = 540) was calculated (Appendix 2c). Native Hardwood plantation (P) was shown to support 72.5 +/- 11.1% mean user accuracy (n = 540), while Forest Red Gum (E11) achieved a mean user accuracy of 34.2 +/- 7.4% (n = 540) (Fig. 2.14).

100 90 80 70 60 50 40 30

20 Mean user accuracy (%) accuracyuser Mean 10 0 E7 E9 E10 E11 E14 Rf Pb FP Forest Ecosystems

NSWNPWS (1999) current study

Fig. 2.14 Mean user accuracy (+/- SE) of forest ecosystem units defined by NSWNPWS (1999a) for Columbey National Park, determined from a confusion matrix of 540 Rapid Data Points (10 random batches of 54 Rapid Data Points). E7 = Other Moist Coastal; E9 = Spotted Gum Dominant or Co-dominant; E10 = Grey Box- Ironbark; E11 = Forest Red Gum; E14 = Bloodwood – Smoothbarked Apple; Rf = Rainforest; Pb = Paperbark; FP = Forestry Plantation.

The two other units mapped for the National Park (E7 & E10) were correctly mapped at just 22.2 +/- 6.2% (n = 540) and 19.0 +/- 2.2% (n = 540) respectively. The unmapped Spotted Gum ecosystem (E9) explained the bulk of the misclassified data (Fig. 2.15).

Mean overall accuracies for the current mapping returned values of 97.8 +/- 0.8 (n = 540) for the FCNSW (1989) community classification, and 98.0 +/- 0.7 (n = 540) for that of NSWNPWS (1999a) (Fig. 2.12, 2.14; Appendix 2b, 2d).

Data-informed Sampling & Mapping

No. of Rapid Data Points 0 50 100 150 200 250 300 350

E10

E11

Forest Ecosystems Forest E14

Fig. 2.15 Allocation of 540 Rapid Data Points across forest ecosystems defined by NSWNPWS (1999a) for Columbey National Park. Solid bars represent Rapid Data Points of communities correctly mapped for the National Park by NSWNPWS (1999a); open bars represent unmapped ecosystems. E7 = Other Moist Coastal; E10 = Grey Box-Ironbark; E11 = Forest Red Gum; FP = Native Hardwood; E9 = Spotted Gum Dominant or Co-dominant; E14 = Bloodwood-Smoothbarked Apple; Pb = Paperbark; Rf = Rainforest.

2.3.2.3 Time resources

Fig. 2.16 details the total person-hours required to produce a vegetation map of Columbey National Park using the D-iSM method. Total field-time for the 870 ha study area was 86 person-hours, encompassing 26 person-hours for map data acquisition and 60 person-hours for full floristic sampling of 42 sample quadrats. A further 19 person-hours were required to construct photo-patterns, classify map data, generate floristic strata, select floristic quadrats, undertake classification analysis and produce a final map.

Data-informed Sampling & Mapping

Columbey NP 7. Classification 8. Map production analysis

1. Construct photo-patterns

2. Map data acquisition

6. Full floristic sampling 3. Map data classification

5. Quadrat 4. Floristic strata selection generation

Fig. 2.16 Percentage task allocation for creating a vegetation map for Columbey National Park using the D-iSM method.

2.3.3 Cessnock-Kurri Region

2.3.3.1 Mapping process

The mapping process undertaken for the Cessnock-Kurri region is shown in Fig. 2.17. Aerial photographic interpretation (Step 1) distinguished cleared, extant and disturbed vegetation, together with instances of rainforest, heath, woodland, forest and riparian vegetation. As with Columbey National Park, past forestry practices within parts of the Cessnock-Kurri region meant that API patterns were difficult to confidently detect. In total, 16,994 Rapid Data Points (1 point per 2 ha of vegetated area) were collected during the map data acquisition phase (Step 2). This is considerable given the extent (~84%) of privately owned land within the region. Ninety-four full floristic sample quadrats (Step 6) were surveyed, to complement the existing 190 sample quadrats.

Each Rapid Data Point was allocated to one of forty-five observable floristic strata (Step 3), and new full floristic quadrats were preferentially located within under- represented floristic strata units (Step 4). Numerical analysis (Step 7) explored floristic patterns in the data, culminating in the final vegetation map (Step 8 and see Fig. 2.17).

Data-informed Sampling & Mapping

Step 1 Step 2 Step 3 Step 4

Step 8

Step 7 Steps 5 & 6

Fig. 2.17 Schematic representation of Steps 1-8 used to create a vegetation map of the Cessnock-Kurri region. Step 1 = construct photo-patterns; Step 2 = map data acquisition; Step 3 = map data classification; Step 4 = floristic strata generation; Step 5 & 6 = quadrat selection & sampling; Step 7 = classification; Step 8 = map production.

Data-informed Sampling & Mapping

2.3.3.2 Comparison to existing mapping

Cessnock-Kurri region. The Cessnock-Kurri region was previously included in the regional modelling of NSWNPWS (2000b). Using the same regional community nomenclature, Figs. 2.18 and 2.19 compare pre-1750 mapping from the 2000 product to that produced using D-iSM. As can be seen, there are marked differences in the distribution of vegetation communities, particularly in the reduction of lands supporting Lower Hunter Spotted Gum-Ironbark Forest, Kurri Sands Swamp Woodland and Central Hunter Ironbark-Spotted Gum-Grey Box Forest (all Threatened Ecological Communities within New South Wales). Major changes are also evident in the distribution of Coastal Foothills Spotted Gum-Ironbark Forest, and several new communities are present which do not align with any in the NSWNPWS (2000b) classification (noted in Fig. 2.19 as Not defined).

Fig. 2.20 shows the results of the confusion matrices for Rapid Data Points against the existing mapping of NSWNPWS (2000b), and an example matrix is shown in Appendix 3a. A mean overall accuracy of 42.6 +/- 0.4% (n = 16,800) was calculated for this mapping, based on the number of classified Rapid Data Point correctly intersecting with the appropriate map polygons. By comparison, a similar calculation revealed an overall accuracy of 95.3 +/- 0.2% (n = 16,800) for the D-iSM map completed as part of this study (Appendix 3b). All but three of the fifteen communities exceeded 90% mean user accuracy, with ten communities scoring above 95% mean user accuracy.

Fig. 2.21 shows the allocation of Rapid Data Point across the study area, using the NSWNPWS (2000b) community nomenclature. All Rapid Data Points could be accommodated broadly within the NSWNPWS (2000b) classification, although not all had been previously mapped for the area by NSWNPWS (2000b). Several communities as mapped by NSWNPWS (2000b) are not represented at all within the RDP test dataset (i.e. MU1a, MU5, MU6, MU9, MU16, MU21, MU46). Importantly, six communities as defined in NSWDECC (2008b) bore little resemblance to the NSWNPWS (2000b) community classification, and for the purposes of confusion analysis were broadly included within other units.

Data-informed Sampling & Mapping

Fig. 2.18 Pre-1750 vegetation map of the Cessnock-Kurri study area Fig. 2.19 Pre-1750 vegetation map of the Cessnock-Kurri study area produced by NSWNPWS (2000b) thematically portrayed using using the D-iSM method thematically portrayed using the the regional community nomenclature (NSWNPWS 2000b). regional community nomenclature (NSWNPWS 2000b).

Data-informed Sampling & Mapping

100

90 80 70 60 50 40 30

20 Mean user accuracy (%) accuracyuser Mean 10 0 1 3 8 10 12 13 14 15 17 18 19 21 30 35 42 Vegetation Units

NSWNPWS (2000) current study

Fig. 2.20 Mean user accuracy (+/- SE) of vegetation units defined by NSWNPWS (2000b) for Cessnock-Kurri, determined from a confusion matrix of 16,800 Rapid Data Points (10 random batches of 1,680 Rapid Data Points). 1 = Coastal Wet Gully Forest; 3 = Hunter Valley Dry Rainforest; 8 = Sheltered Blue Gum Forest; 10 = Sandstone Grey Myrtle Sheltered Forest; 12 = Hunter Valley Moist Forest; 13 = Central Hunter Riparian Forest; 14 = Wollombi Redgum-River Oak Forest; 15 = Coastal Foothills Spotted Gum-Ironbark Forest; 17 = Lower Hunter Spotted Gum- Ironbark Forest; 18 = Central Hunter Ironbark-Spotted Gum-Grey Box Forest; 19 = Hunter Lowland Redgum Forest; 21 = Hunter Range Grey Gum Forest; 30 = Coastal Plains Smoothbarked Apple Woodland; 35 = Kurri Sands Swamp Woodland; 42 = Riparian Melaleuca Swamp Woodland.

Based on Rapid Data Points, the Cessnock-Kurri study area is comprised predominantly of Lower Hunter Spotted Gum-Ironbark Forest (MU17), Kurri Sands Swamp Woodland (MU35), Coastal Foothills Spotted Gum-Ironbark Forest (MU15), Central Hunter Riparian Forest (MU13) and Central Hunter Ironbark-Spotted Gum-Grey Box Forest (MU18). Of these, only MU13 and MU18 exceed 80% mean user accuracy in the NSWNPWS (2000b) mapping (see Fig. 2.20).

Werakata National Park. Occupying 2,145 ha within the Cessnock-Kurri region, Werakata National Park also has been classified and mapped using more traditional methods (Bell 2004a). Consequently, there is a progression of vegetation maps available for comparison in this area, extending from NSWNPWS (2000b), through Bell (2004a) to the current study (Table 2.4, Fig. 2.22a-c). For the two earlier studies, four vegetation communities were defined and mapped, following the regional NSWNPWS (2000b) community nomenclature. However, the distributions of each community varied considerably. With the implementation of the D-iSM mapping methods outlined 83

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No. of Rapid Data Points 0 1000 2000 3000 4000 5000 6000 7000 8000

MU1 MU1a MU3 MU5 MU6 MU8 MU9 MU10

MU12 MU13 MU14 MU15 MU16 Vegetation Units Vegetation MU17 MU18 MU19 MU21 MU35 MU27 MU30 MU42 MU46

Fig. 2.21 Allocation of 16,800 Rapid Data Points across vegetation units defined by NSWNPWS (2000b) for Cessnock-Kurri. Solid bars represent Rapid Data Points of communities correctly mapped by NSWNPWS (2000b); open bars represent unmapped communities. MU1 = Coastal Wet Gully Forest; MU1a = Coastal Warm Temperate-Subtropical Rainforest; MU3 = Hunter Valley Dry Rainforest; MU5 = Alluvial Tall Moist Forest; MU6 = Coastal Narrabeen Moist Forest; MU8 = Sheltered Blue Gum Forest; MU9 = Coastal Ranges Open Forest; MU10 = Sandstone Grey Myrtle Sheltered Forest; MU12 = Hunter Valley Moist Forest; MU13 = Central Hunter Riparian Forest; MU14 = Wollombi Redgum-River Oak Forest; MU15 = Coastal Foothills Spotted Gum-Ironbark Forest; MU16 = Seaham Spotted Gum-Ironbark Forest; MU17 = Lower Hunter Spotted Gum-Ironbark Forest; MU18 = Central Hunter Ironbark-Spotted Gum-Grey Box Forest; MU19 = Hunter Lowland Redgum Forest; MU21 = Hunter Range Grey Gum Forest; MU27 = Exposed Yellow Bloodwood Woodland; MU30 = Coastal Plains Smoothbarked Apple Woodland; MU35 = Kurri Sands Swamp Woodland; MU42 = Riparian Melaleuca Swamp Woodland; MU46 = Freshwater Wetland Complex. in this chapter, a further three vegetation communities were defined (Coastal Foothills Spotted Gum-Ironbark Forest, Central Hunter Grey Box-Spotted Gum-Ironbark Forest, Wollombi Redgum-River Oak Forest), and boundaries of the existing four were refined. 84

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Table 2.4 Vegetation communities defined for Werakata National Park over three studies.

NSWNPWS Bell Study area this study 2000b 2004a Lower Hunter Spotted Gum-Ironbark Forest √ √ √ Hunter Lowlands Redgum Forest √ √ √ Kurri Sand Swamp Woodland √ √ √ Central Hunter Riparian Forest √ √ √ Coastal Foothills Spotted Gum-Ironbark Forest - - √ Central Hunter Grey Box-Spotted Gum-Ironbark Forest - - √ Wollombi Redgum-River Oak Forest - - √

Additional communities to those detailed in Table 2.4 can be defined from the present study. These have been delineated only as a result of the mapping and preferential sampling methods described in this study. Even at a sub-regional scale, a finer resolution of community description and mapping has been achieved for the most current map of Werakata National Park then was evident in the reserve-scale study of Bell (2004a). For the Werakata National Park subset, Fig. 2.23 shows the results of the confusion matrices for Rapid Data Points against the existing mapping of Bell (2004a) and the current study, with example matrices included in Appendix 4a and 4b. The mapping of Bell (2004a) returned a mean overall accuracy of 81.1 +/- 0.9% (n = 1,400), while that obtained for the new mapping improved this value to 96.0 +/- 0.4% (n = 1,400). In terms of mean user accuracy, the Bell (2004a) mapping performed reasonably well for units 1 (Lower Hunter Spotted Gum-Ironbark Forest) and 4 (Kurri Sand Swamp Woodland), but poorly for all others. High standard errors for Hunter Lowlands Redgum Forest (unit 3) and Riparian Melaleuca Thicket (unit 6) can be attributed to their restricted distributions and the consequent low number of Rapid Data Points with which to test. Overall, a greater reliance on observable photo- patterns from API during the site selection process in Bell (2004a), rather than strict adherence to an environmental stratification, resulted in a relatively accurate vegetation map. However, implementing the D-iSM process did allow further improvements to the 2004 mapping.

Data-informed Sampling & Mapping

a b c

Fig. 2.22 Vegetation mapping for Werakata National Park, from (a-c) NSWNPWS (2000b), Bell (2004a) & the current study. Community nomenclature follows NSWNPWS (2000b).

Data-informed Sampling & Mapping

100

20 Mean user accuracy (%) accuracyuser Mean 0 1 2 3 4 5 6 Vegetation Units

Bell (2004a) current study

Fig. 2.23 Mean user accuracy (+/- SE) of vegetation units defined by Bell (2004a) for Werakata National Park, determined from a confusion matrix of 1,400 Rapid Data Points (10 random batches of 140 Rapid Data Points). 1 = Lower Hunter Spotted Gum-Ironbark Forest; 2 = Central Hunter Riparian Forest; 3 = Hunter Lowlands Redgum Forest; 4 = Kurri Sand Swamp Woodland; 5 = Kurri Sand Melaleuca Scrub-Forest; 6 = Riparian Melaleuca Thicket.

The allocation of Rapid Data Points across Werakata National Park is shown in Fig. 2.24, using the Bell (2004a) community nomenclature. The vast majority of test Rapid Data Points fell within unit 1 (Lower Hunter Spotted Gum-Ironbark Forest), which contributed to the 90% mean user accuracy for this community.

No. of Rapid Data Points 0 200 400 600 800 1000 1200

Vegetation Units Vegetation 5

Fig. 2.24 Allocation of 1,400 Rapid Data Points across vegetation units defined by Bell (2004a) for Werakata National Park. Solid bars represent Rapid Data Points of communities correctly mapped by Bell (2004a). 1 = Lower Hunter Spotted Gum- Ironbark Forest; 2 = Central Hunter Riparian Forest; 3 = Hunter Lowlands Redgum Forest; 4 = Kurri Sand Swamp Woodland; 5 = Kurri Sand Melaleuca Scrub-Forest; 6 = Riparian Melaleuca Thicket. 87

Data-informed Sampling & Mapping

2.3.3.3 Time resources

The total person-hours required to produce a vegetation map of the Cessnock-Kurri region using the D-iSM method is summarised in Fig. 2.25. Total field-time for the ~66,000 ha study area was 939 person-hours (568 for map data acquisition, 371 for full floristic sampling), with map data acquisition requiring the greatest effort entailing two ecologists and ~40 days in 4WD vehicle. Unlike the Edgeworth LEP and Columbey National Park studies, time required for map data acquisition (Step 2) exceeded that required for full floristic quadrat sampling (Step 6) due to the need to use two ecologists over such a large study area. Note also that full floristic sampling allowed for the collection of data from ninety-four sample quadrats, in addition to the 190 already available in the area. Adjustments have been made in Fig. 2.25 to include time for the pre-existing floristic data for cost-benefit analysis (increasing the number of person- hours from 124 to 371, @ 1.3 hrs/quadrat). A further 310 person-hours were required to construct photo-patterns, classify map data, generate floristic strata, select floristic quadrats, undertake classification analysis and produce a final map.

Cessnock-Kurri 3. Map data 4. Floristic strata classification generation

2. Map data 5. Quadrat acquisition selection

6. Full floristic sampling

7. Classification 1. Construct analysis photo-patterns 8. Map production

Fig. 2.25 Percentage task allocation for creating a vegetation map for Cessnock-Kurri using the D-iSM method.

Data-informed Sampling & Mapping

2.4 Discussion

2.4.1 The D-iSM Process

Mapping of vegetation communities provides a tangible construct of an abstract classification: it is the concrete association in the terminology of the Zurich-Montpellier school (Kent & Coker 2001). While simple in theory, the compilation of a vegetation map is a complex procedure, and one that can be arrived at through a variety of different means. In recent decades, mapping of vegetation communities and habitat types largely has been reliant on remotely accessed imagery (aerial photographs or satellite imagery), often incorporating a significant degree of predictive interpolation based on limited ground data (see Fassnacht et al. 2006; Xie et al. 2008; Morgan et al. 2010), or through use of automated image analysis (e.g. eCognition, www.definiens.com). Most of these methods follow a common yet simple path: definition of communities through a classification process, then extrapolation of classification units onto a mapped area (Kent & Coker 2001; Keith 2004).

While some past studies have used GPS units to validate map products (e.g. Bennett & Kunzmann 1998; Welch et al. 1999), few have incorporated GPS into sampling design and map construction procedures. Gooding et al. (1997) presented a method of mapping plant communities in featureless terrain in Great Britain, whereby electronic surveying equipment was used to locate and record vegetation types across an area, then a vegetation map was created using the Voronoi algorithm. This procedure closely mirrors Steps 2 – 4 in the D-iSM method described in this chapter, except for two important points. Firstly, field ecologists at each data point ascribed the applicable vegetation community from the British National Vegetation Classification (BNVC) to each point, rather than recording observed plant species, and developing a classification later. Secondly, data points at which floristic composition varied from the BNVC, although noted by observers, were not used in a sampling design or classification of the area. As has been demonstrated earlier in this chapter, preferential sampling in areas of observed variation is the key to developing an accurate classification. Perhaps with hitherto unrecognised foresight, Gooding et al. (1997) stated that their technique “could be used worldwide to map phytosociological

Data-informed Sampling & Mapping

communities using the appropriate classification systems”, to which the current study, with some modifications, lends considerable support.

There are some other similarities to the D-iSM method in the literature, although none of these are applied to the same end or combined to form one holistic procedure. Quaternary samples (Neldner et al. 2005) and occurrence plots (Federal Geographic Data Committee 2008) gather information on dominant plant species, much the same way in which Rapid Data Points do in Step 2 of D-iSM. The visual ranking system of Scott (1989) for rapid sampling of vegetation data, where the relative order of plant species abundances is scored at sampling locations, is similar to the way in which map data acquisition occurs in Step 2 of D-iSM. Voronoi algorithms were used by Alani et al. (2001) to reconstruct administrative boundaries based on place co-ordinates, and indicated that the quality of resulting mapping is dependent on the amount and extent of ground data available. This process is similar to that described for floristic strata generation (Step 4) in D-iSM, and show how voronoi algorithms can be used in a range of applications.

In recent years, the NSW Department of Environment and Conservation has used a similar method to assist their mapping of remote and rugged parts of the Sydney Basin (the 650,000 ha Wollemi and Yengo National Parks wilderness), in part as a response to earlier discussions about the D-iSM procedures as outlined in this chapter. In their process, voice data recorders with in-built GPS capabilities are used to record dominant species and other habitat information at specific locations in the field, which are then stored as sound bites within the GIS. Activation of the sound bite allows a GIS viewer to obtain a snapshot of the vegetation at a particular point. This slight adaptation to their existing methods of mapping has proven an invaluable addition to the mapping of large areas of remote bushland within the Sydney Basin. It provides a higher level of confidence to an API worker when applying attributes to a polygon. At present, this technique is also used informally by the Department in sample site selection procedures.

Implementing the D-iSM methods for the three areas examined in this chapter resulted in the production of locally accurate classifications and map products

Data-informed Sampling & Mapping

unattainable in previous projects. For the Edgeworth LEP study, existing mapping (NSWNPWS 2000b) employing an environmentally stratified sampling strategy combined with predictive modelling of communities, over-simplified the site. Substantial areas of an Endangered Ecological Community (Lower Hunter Spotted Gum-Ironbark Forest) had been completely overlooked, and a novel new community (Red Ironbark-Paperbark Scrub-Forest) also remained undefined and unmapped. This undefined community, closely related to Lower Hunter Spotted Gum-Ironbark Forest, is in itself significant and worthy of protection (see Chapter 4). At Columbey National Park, previous classification projects (FCNSW 1989; NSWNPWS 1999a) based heavily on aerial photographic interpretation failed to differentiate several vegetation communities, and showed instead large areas of a vegetation community that in reality was quite restricted (see Figs. 2.10 & 2.11). For the considerably larger Cessnock-Kurri regional study, existing predictive modelling (NSWNPWS 2000b) showing eighteen vegetation communities considerably over-simplified community diversity. Within Werakata National Park, a progression in mapping quality and resolution was observed over three mapping products (NSWNPWS 2000b; Bell 2004a; current study), and the sub-regional study detailed in this chapter was capable of producing superior results to an earlier reserve-scale only study with a basis in environmental stratification.

The three case studies used to illustrate the D-iSM method range in scale from 96 to 65,690 hectares. In all three examples, however, local-scale classification and mapping has been achievable. With increasing study area size, there is the potential for a considerable amount of floristic strata units (Step 4) to be generated, and these units would require careful management for the process to operate effectively (an alternative hybrid approach to overcome this is discussed in Section 2.4.6). However, because raw data on species dominants is available for each Rapid Data Point across a study area, revision and reallocation of map data groups becomes an iterative process as a greater understanding of a study area is gained.

2.4.2 Sample Selection

Smartt (1978) proposed a flexible systematic model for vegetation sampling, in which a sampling framework (grid) is constructed across a study area through the

Data-informed Sampling & Mapping

establishment of primary sampling points. The degree of floristic variation within and between these primary sampling points would then dictate where the greatest survey effort is required, leading to greater efficiencies in sampling resources. In a general sense, this technique is similar to the D-iSM method described in this chapter. However, it requires an additional analysis stage to assess diversity changes between adjacent samples so that secondary sampling can then be implemented. Muttlak and Khan (2002), and other papers discussed therein, promote a similar two-stage sampling method, but it is one relying on random sampling in the initial stages, rather than the more representative preferential sampling recommended for the D-iSM method.

One of the key components of the D-iSM method is the ability to implement preferential full floristic sampling based entirely on observed variations in the field: that is, it promotes Data-informed Sampling. This is a central theme in the Zurich- Montpellier school, and ensures that a sampling regime can produce a robust and repeatable classification. Creation of floristic strata units in Step 4 of the D-iSM process directs available sampling resources to both widespread and common stratum, as well as those that are rare or highly restricted. In previous studies, sample stratification on environmental variables meant that rare communities were often completely overlooked (e.g. Red Ironbark-Paperbark Scrub-Forest, overlooked by NSWNPWS 2000b, but recognised following preferential sampling in the Edgeworth LEP and Cessnock-Kurri studies). As a possible alternative, simply increasing sampling density within a random sampling regime may improve the detection of rare communities. However, in those cases their sampling would still depend on chance, and indeed a classification that ensues may also be complicated by the equivalent likelihood of sampling ecotonal areas. Ensuring sampling can assess observable floristic variations dramatically increases the potential recognition of rare communities (Section 2.4.4).

There will, on occasion, be instances where observable floristic variations cannot be legitimately proposed as a distinct community (e.g. one that can be statistically supported, such as through the SIMPROF procedure in Primer: Clarke & Gorley 2006), despite sampling being directed there following map data classification. In these

Data-informed Sampling & Mapping

situations, it may then be a case of revising the position of that observed variation within the community hierarchy (describing it perhaps as an unusual or localised form, rather than a community or sub-community). Such a situation occurs, for example, in some grassy woodland environments in the Hunter Valley of New South Wales. In this environment, decades of cattle grazing have produced landscapes supporting consistent shrub and ground layer composition, but variable canopy diversity (any of Eucalyptus crebra, E. fibrosa, E. blakelyi, E. moluccana or Angophora floribunda). Indeed, current-day composition is often a reflection of paddock tree retention following past clearing (unpubl. data; and see Ramos et al. 2008, Burns et al. 2011).

Under the NSW Native Vegetation Type Standard (Sivertsen 2010), it is recommended that sampling be undertaken within an environmentally stratified survey design, such that quadrats are allocated within identified Environmental Sampling Units (ESUs). Rather than sampling within ESUs based on an environmental stratification (a surrogate for community diversity), the D-iSM method employs ESUs based entirely on observed floristic variation (the floristic strata generation in Step 4). Not only does this process meet the requirements of the Standard, it also significantly improves the chances of detecting rare and restricted communities that may otherwise not be represented in environmentally-based ESUs.

For the BioBanking scheme in New South Wales (NSWDECC 2009a), sampling is required to be undertaken within observable vegetation zones, as may be obtainable through the interpretation of aerial photographs. Identification of vegetation zones using such a method will rely heavily on the experience of the interpreter viewing the photographs, and may in fact overlook certain communities. Implementation of D-iSM within this process, with its map data acquisition phase, will improve the resolution of vegetation zones markedly. Relative to the NSW Native Vegetation Type Standard, the BioBanking methods for selecting sample locations are more likely to sample all community variation present. However, there is provision within the Standard of Sivertsen (2010) for ESUs to be represented solely by vegetation zones or floristic strata, and this is more desirable than those based on environmental stratification alone. More specifically, it makes more sense to use vegetation strata to assist in

Data-informed Sampling & Mapping

classifying vegetation communities than it does to use, for example, geology layers: if the aim of the investigation was to examine the relationship between certain communities and certain rock types, then perhaps an environmental stratification based on geology would be more appropriate.

2.4.3 Map Accuracy

Maps vary widely in accuracy, and to a certain extent the perception of accuracy is dependent on the methods employed in their production and in their desired end-use (Kumi-Boateng & Yakubu 2010). Assessing accuracy is typically undertaken post- production through interrogation of GIS layers, often with an independent or newly collected dataset (e.g. Welch et al. 1999; Nusser & Klaas 2003; Lunetta & Lyon 2005; Langford et al. 2006; Faber-Langendoen et al. 2007). Maps based on the interpretation of satellite imagery generally involve assessing how well spectral reflectance patterns may surrogate for vegetation communities. Generally, communities defined at a local- scale via traditional phytosociological methods cannot be consistently mapped adequately through the use of satellite imagery (Spjelkavik 1995; Nilsen et al. 1999; Bradter et al. 2011), although more recent techniques involving object-based segmentation analysis (e.g. Horn 2010; Roff et al. 2010) and 3-dimensional aerial photographic interpretation of high resolution aerial imagery (e.g. Maguire et al. 2012) offer feasible alternatives. Brook and Kenkel (2002) concluded from their study that detailed and extensive field data were essential in the interpretation and analysis of satellite imagery, and that image classification only should occur after a field data- based classification of vegetation has been completed. Further, they stated that mapping of vegetation communities must begin with a classification of on-ground vegetation, not with a classification of satellite spectral reflectance data.

In most situations, the D-iSM method described in this chapter circumvents a need for post-production accuracy assessments of vegetation maps, such as those noted above, as it supports a Data-informed Mapping protocol. Unlike other mapping techniques, map polygons are attributed based on classifying numerous ground control points, rather than extrapolation from relative few such points. In effect, this means that the end resolution of a map is entirely reliant on densities of ground-control data points,

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as it is these very points that inform boundary locations and identities of specific map polygons. As stated by Benson (2008a), “the accuracy of any vegetation map improves with the effort put into field verification”. A useful analogy of the D-iSM technique can be found in forensic anthropology, where facial features are reconstructed on a human skull based on known tissue depths for specific locations, then interpolated across surfaces in between (e.g. Snow et al. 1970, and see de Greef & Willems 2005; Cattaneo 2007). The greater the number of reliable reference points for tissue depth, the better the likeness of a reconstructed face.

Benson (2008a) also observed that few vegetation maps correctly depict vegetation types within mapped polygons at accuracy rates of >90%, and that most maps do not show all finely classified units derived from a floristic classification. Indeed, recent assessments of accuracy of maps produced by two differing methods in south-west New South Wales revealed accuracies of 63% for automated object-recognition interpretation from satellite imagery, and 86% for non-automated on-screen interpretation of aerial photographs (Eco Logical Australia 2010). Assessments of pre- existing maps in the current study revealed overall accuracy rates as low as 11.6% for Columbey National Park (n = 540), 31% for the Edgeworth LES (n = 180) and 46% for Cessnock-Kurri (n = 16,800) (Table 2.5). However, through implementation of the D- iSM method in the current study, accuracy rates of >95% were achieved for mapping of finely classified units, across study areas ranging from 96 ha at Edgeworth to 65,690 ha at Cessnock-Kurri (Table 2.5). For small study areas, 100% attribution accuracy is perfectly achievable, with miscoding in map data classification or map production phases (Steps 3 & 8) the likely contributor to lower accuracies. Maintaining the intrinsic link between classification and mapping (Küchler 1951) within the D-iSM process is the key to producing highly accurate maps, as greater control can be had of adequately sampling floristic variation and extent of extrapolation required to create a map.

Assessing the accuracy of maps produced through D-iSM, as outlined in this chapter, potentially involves circular reasoning. Testing a map’s accuracy through use of the same data that has contributed to its formation is flawed, and this possibility is fully

Data-informed Sampling & Mapping

Table 2.5 Summary of overall accuracies calculated from confusion matrices for existing and current vegetation maps of the Edgeworth LES, Columbey National Park and Cessnock-Kurri study areas.

Community Overall accuracy Study area Source Map n Nomenclature (mean % +/- se)

Edgeworth LES NSWNPWS 2000b NSWNPWS 2000b 29.4 +/- 3.0 180 current study NSWNPWS 2000b 100.0 +/- 0.0 180

Columbey NP FCNSW 1989 FCNSW 1989 11.9 +/- 1.4 540 current study FCNSW 1989 97.8 +/- 0.8 540

NSWNPWS 1999a NSWNPWS 1999a 22.4 +/- 1.9 540 current study NSWNPWS 1999a 98.0 +/- 0.7 540

Cessnock-Kurri NSWNPWS 2000b NSWNPWS 2000b 42.6 +/- 0.4 16,800 current study NSWNPWS 2000b 95.3 +/- 0.2 16,800

Bell 2004a Bell 2004a 81.1 +/- 0.9 1400 current study Bell 2004a 96.0 +/- 0.4 1400

acknowledged here.However, accuracy testing in this study has used the available datasets (those collected through Step 2 of D-iSM) to compare different maps, not to assess the new mapping method per se. One alternative would be to collect a new set of data to test for accuracy, and consequently avoid the potential for circular reasoning. Given the density of Rapid Data Points collected during the process (i.e. one RDP per 0.5 ha for Edgeworth; one per 0.6 ha for Columbey; one per 2 ha for Cessnock- Kurri), this was considered unproductive as new data would effectively duplicate the existing dataset. Instead, randomly selecting data from the existing pool of Rapid Data Points, when it has been collected at such densities, was seen as an alternative way of validly testing and comparing mapping. This process is equivalent to randomly selecting n locations within the map area, and then physically inspecting those sites to assess polygon attribution (as advocated by Sivertsen 2010; see below). Ultimately, however, independent data collected by a third party would be the most desirable accuracy test of D-iSM produced maps, and this process generally occurs over time as maps are put into practical use.

The NSW Native Vegetation Type Standard (Sivertsen 2010) supports map accuracy assessments which are practical and scientifically valid, clearly explained and

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appropriate to the spatial and thematic scales being evaluated. It is suggested that assessments employ fixed sampling units (e.g. 20 x 20 m quadrat), of random allocation, and that the three dominant species in each stratum be recorded and assessed using a 4-point, ‘absolutely right’ to ‘absolutely wrong’ scale. Results should be summarized using a confusion matrix comparing classes with assessment units. As with the mapping method described by Gooding et al. (1997), this process attempts to categorise test data into an existing classification, rather than using such data to create a classification. The D-iSM mapping method allows map data to inform a classification. It also effectively negates use of accuracy assessments, as the mapping process incorporates saturation coverage of dominant species data points into its design. Provided all steps have been carried out correctly (and recognising limitations: see Section 2.4.7), any assessment of accuracy undertaken in keeping with the Type Standard would be expected to return a high accuracy rate.

2.4.4 Detecting Rare Communities

Most vegetation classification and mapping products are wholly or partly used to guide management of natural resources (Pressey & Bedward 1991), and direct land-use planning (Fallding et al. 2001). For biodiversity conservation, local-scale mapping is more likely to detect rare and restricted communities than regional-scale or smaller. From a conservation perspective, rare or uncommon communities are potentially more threatened with extinction than their more widespread counterparts, given their higher susceptibility to stochastic environmental or anthropogenic disasters. In this context, Chapter 3 shows how some previously unidentified vegetation communities dominated by Scribbly Gum (Eucalyptus spp.) from the Central Coast of New South Wales are threatened both through habitat loss and lack of formal recognition.

Traditional and current sampling and mapping techniques, including recent advances in remote image processing and analysis (see papers in Australasian Remote Sensing & Photogrammetry Conference 2010), are rarely capable of detecting rare or restricted vegetation communities. For example, working in the Rocky Mountains of Colorado, Stohlgren et al. (1997) suggested that some rare vegetation communities may still be underestimated at minimum mapping scales of 0.02 ha, and that as management

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strategies move away from a species-by-species approach, resources managers will require better information on unique, locally rare habitats that are only apparent when small minimum mapping units are used. As a step towards countering overlooked rare communities, they suggested that at least some portion of a vegetation map should be validated to a higher level of resolution. More recently, Roff et al. (2010) found that object-based segmentation analysis of remote imagery in the Murray River catchment of Victoria was incapable of mapping rare or under-sampled communities.

By design, the D-iSM method will detect potentially rare or restricted vegetation communities, provided adequate field coverage of a study area can be achieved. The process of recording the observed floristic composition of the vegetation at numerous data points (Step 2), and the preparation of a floristic strata map (Step 4), ensures that full floristic sampling and classification (Steps 6 & 7) can be undertaken. Should an observed variation be sampled and shown to form a recognisable entity in a classification, then mapping that entity from the original ground data can occur. Rarity can be determined via an assessment of geographical spread throughout an area of interest, and if the technique is applied widely, regional significance can also be applied.

Several potentially rare vegetation communities have been defined through the current study. In all three study areas, communities were identified and mapped which did not equate with any of the units in the existing regional classification and mapping nomenclatures. In the Cessnock-Kurri study, for example, six new communities were mapped through implementation of the D-iSM method, one of which (Quorrobolong Scribbly Gum Forest) is now a listed Threatened Ecological Community in New South Wales, and others potentially qualify for such status.

2.4.5 Cost-benefit Analysis

Without data on resources required to complete mapping using other methods, it is difficult to undertake a full cost-benefit analysis of the D-iSM method in the traditional sense (cf. Layard & Glaister 2003). However, it is possible to provide estimates of required time resources for the map data acquisition phase (Step 2, possibly the most 98

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influential advance on current methods) based on several unpublished studies completed to date.

Table 2.6 summarises previous studies completed by the author using the D-iSM method, across a range of scales, terrains and purposes. Fig. 2.26 to Fig. 2.28 relate required time resources-to-land area to complete Step 2 (map data acquisition) for each study shown in Table 2.6, within the nominal size categories <100 ha, 100-1,000 ha and 1,000-10,000 ha respectively. The linear regression lines fitted to each scatter plot show progressively weaker trends (R2 values) as study area size increases, reflecting the interplay of factors such as terrain and accessibility. However, one-way ANOVA tests calculated for each determined them to be significant. Few opportunities have arisen for studies within the two larger size categories (>10,000 ha), hence relationships are uninformative and not presented. All of the studies completed have followed the same methods as outlined in this chapter, and can be used as a guide to budget planning.

Table 2.6 Use of D-iSM across differing scales.

Size category (ha) No. studies Terrain & Purpose < 100 11 gentle undulating to hilly; proposed developments, management plans, L&E court investigations 100 – 1,000 8 gently undulating to rugged; proposed developments, management plans, reserve management 1,000 – 10,000 8 gently undulating to rugged; LGA mapping, management plans, reserve management 10,000 – 50,000 4 gently undulating to rugged; LGA mapping, management plans, reserve management > 50,000 2 gently undulating to rugged; LGA mapping

Data-informed Sampling & Mapping

100 90 80

60 50

40 No. hectares No. 30 20 R² = 0.9279 10 0 0 1 2 3 4 5 6 7 8 9 Person-hours

Fig. 2.26 Linear regression of person-hours and number of hectares required for Step 4 (map data acquisition) of D-iSM, 0-100 ha category (one-way ANOVA; F=115.9, p<0.0001).

1000 900 800

700 600 500

400 No. hectares No. 300 200 R² = 0.8898 100 0 5 10 15 20 25 30 35 Person-hours

Fig. 2.27 Linear regression of person-hours and number of hectares required for Step 4 (map data acquisition) of D-iSM, 100-1000 ha category (one-way ANOVA; F=48.5, p<0.0005).

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10000 9000 8000

7000

6000 5000

4000 No. hectares No. 3000 2000 R² = 0.7697 1000 0 20 30 40 50 60 70 80 90 100 110 120 Person-hours

Fig. 2.28 Linear regression of person-hours and number of hectares required for Step 4 (map data acquisition) of D-iSM, 1000-10,000 ha category (one-way ANOVA; F=20.1, p<0.005).

The Central Coast of New South Wales (local government areas of Gosford, Wyong, Lake Macquarie, totalling 232,800 ha of land) has been subjected to several classification and mapping projects over the last decade, which collectively amount to several million dollars of expenditure. NSWNPWS (1999a, 2000b) provided the first regional classifications for the three Central Coast councils, but shortly afterwards new classifications and mapping products were requested by individual Councils and regional offices of the NSWNPWS to address shortcomings in accuracy and scale of the NSWNPWS (2000b) product. Table 2.7 summarises the progression of major classification and mapping products, and their estimated budgets, for the Central Coast over the past 12 years. Clearly, for a relatively small land area (<235 km2) sizeable sums of public monies have been spent on delivering a sound classification and mapping product, with new projects continually re-mapping the same lands. A more reliable classification and mapping program (such as D-iSM) would potentially enable more economical use of public monies, and deliver reliable mapping products. Initial outlays in field commitments to collect the necessary map data would be more than compensated for in the longevity and accuracy of the final product.

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Table 2.7 Progression and cost of major classification and mapping products for the Central Coast of New South Wales.

Project Year Area (ha) Cost ($) Source

NSWNPWS CRA (part) 1999 232,800 120,000 NSWNPWS 1999a LHCCREMS (part) 2000 232,800 120,000 NSWNPWS 2000b Wyong LGA 2002 74,800 66,000 Bell 2002a Gosford LGA 2004 93,700 63,000 Bell 2004b Central Coast 2006 232,800 730,000 McCauley et al. 2006 * Wyong LGA (west) 2006 48,500 30,000 Bell & Driscoll 2006a * Watagans NP/ Jilliby SCA 2006 20,000 20,000 Bell & Driscoll 2006b * Lake Macquarie LGA (part) current 33,600 # 80,000 Bell & Driscoll in prog. HCRCMA (part) current 232,800 # 1,300,000 NSWOEH in prog.

Total > $2,500,000

* incorporating the D-iSM method # incomplete

Prior to 2011, 6-8 million dollars has been spent on vegetation survey and mapping for the Hunter-Central Rivers Catchment Management Area (D. Connolly, NSWOEH, pers. comm.), an area encompassing 3.7 million hectares and which includes the Central Coast. Of this, 3.5 million dollars has been invested in vegetation sampling. A large proportion of this sampling expenditure was based on environmentally stratified random sampling, which potentially has overlooked considerable community diversity. Elsewhere in Australia, general principles of cost constraints and obtaining value for money in vegetation surveys have been reported (e.g. Burbidge 1991; Pressey & Bedward 1991), with mapping at relatively fine scales suggested despite low cost- effectiveness. Internationally, there is relatively little discussion in the literature on budgeting for vegetation classification and mapping projects, particularly in cases where the same landscapes are revised in quick succession. Instead, examination of cost-effective survey techniques for specific tasks is commonly reported. For example, Panagos & Zacharias (1995) discussed two cost-effective vegetation sampling techniques to guide future survey budgeting, comparing the two techniques for

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efficiency of overall species detection rather than classification and mapping of vegetation communities.

2.4.6 Application to Varying Projects

D-iSM is applicable to many mapping projects of widely ranging scales. As noted above, the NSWDECC has adapted parts of the D-iSM technique to assist in mapping vegetation in remote parts of the Sydney Basin, for study areas exceeding 250,000 ha. At the other extreme, the D-iSM method has been used to accurately define and map floristic variation in sites as small as one or two hectares (unpubl. data), and as a consequence has allowed more rational sampling and assessment of vegetation. Pre- disturbance (“pre-1750”) mapping has also been undertaken for large areas of Cessnock (this study, and see NSWDECC 2008b), Gosford (Bell 2009b) and Pittwater (de Lacey et al. 2011) local government areas, combining the features of map data acquisition (Step 2) and floristic strata generation (Step 4) with landscape features, terrain analysis and historical photography. In an additional application, map data acquisition and classification have been used on several projects in a review capacity, where boundaries of Threatened Ecological Communities and other vegetation types were assessed independently on behalf of government agencies (e.g. Bell 2008a, 2012).

One by-product of the map data acquisition phase (Step 2) is the potential for collation of new records of threatened plant species, or indeed to establish the local or regional distribution of specific plant species. For example, records of dominant eucalypt species collected by the author and colleagues during various mapping projects have contributed to the preparation of distribution maps for an identification manual of eucalypts of the Sydney region (Klaphake 2010). With respect to threatened species, map data collated for two separate mapping projects in the lower Hunter Valley subsequently enabled accurate distribution maps of the vulnerable Eucalyptus parramattensis subsp. decadens to be prepared as part of recovery planning (Bell 2006).

The most relevant application for D-iSM is, however, considered to be in small (<100 ha) to medium (100-10,000 ha) scale vegetation mapping projects, such as is required 103

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for the development industry and for management of conservation reserves or other vegetated lands. Through implementation of map data acquisition, classification and floristic strata generation (Steps 2-4), a far greater knowledge of a study area can be achieved, which is then reflected in full floristic quadrat selection, sampling and analysis (Steps 5-7), and ultimately in the final vegetation map (Step 8). Following such a process will considerably reduce the possibility and consequences of incorrect mapping, such as has occurred previously for Edgeworth LES, Columbey National Park and the Cessnock-Kurri region.

For large regional projects (>100,000 ha), there is little reason why the D-iSM method cannot be employed economically to produce accurate classifications and maps (but see discussion following for a potential hybrid approach). Because the collection of Rapid Data Points essentially is a way of harnessing expert knowledge, several teams of expert botanists working simultaneously across an area will quickly accumulate large datasets of position-accurate summaries of vegetation patterns. While this phase of a project may be time-consuming, particularly in areas of rugged terrain or with poor access, the benefits of obtaining a robust classification through preferential sampling of all observed variation, together with a vegetation map with very high accuracy and longevity that can serve many management purposes, need to be highlighted. In contrast to existing GIS-based methods of vegetation mapping, there is relatively little post-production processing of map layers required. Had the knowledge and method been available a decade ago, there is little doubt that the array of classification and mapping products undertaken within the Central Coast of New South Wales (i.e. those outlines in Table 2.7 above) may not have occurred, and the existing regional classification (NSWNPWS 2000b) may look significantly different. By extension, the composition of Threatened Ecological Communities, listed under the NSW TSC Act 1995, for the Central Coast may also differ to that present now.

By way of illustration, Fig. 2.29 shows the extent of Rapid Data Points collected under Step 2 of D-iSM for numerous projects within the Lower Hunter and Central Coast of NSW (~600,000 ha). This dataset has been collated by two field ecologists (myself and Mr Colin Driscoll) over the course of six years, and covers the same geographical area

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as the mapping completed by NSWNPWS (2000b). The coverage of data across this sub-region, effectively collected part-time by only two ecologists, illustrates the potential for application of D-iSM to a regional context. With appropriate training and support, the technique could be readily adopted for large-scale mapping projects, employing several field teams during the data acquisition phase.

Fig. 2.29 The Lower Hunter and Central Coast region, showing distribution of Rapid Data Points collected by two ecologists over the course of six years (2006-2011) (base image courtesy EarthSat NaturalVue2000).

There is, however, a potential alternative to Steps 2-4 of the D-iSM process, which may be applicable to such large study areas where remote terrain or tight deadlines require the production of accurate vegetation maps. Recent advances in digital imagery capture and processing have enabled viewing and interpretation in 3-dimensions (3-D),

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using high resolution imagery and 3-D Planar computer monitors (e.g. Allard et al. 2003; Heiskanen et al. 2008; Hastedt et al. 2009a, b; all cited in Maguire et al. 2012). On-screen capture of photo-patterns within a 3-D environment, using specialised software and ADS40 imagery (see Black & Peasley 2008), has enabled the capture of 100 vegetation communities from an existing classification for parts of the South Western Slopes of New South Wales to a user accuracy of 87% (n = 1,041) over 2 million hectares (Maguire et al. 2012). Ongoing studies in the rugged Greater Blue Mountains World Heritage Area of the Sydney Basin have also begun 3-D interpretation of digital imagery (D. Connolly, NSWOEH, pers. comm.) to assist in locating sampling locations and developing a community classification. While this new 3-D technology has not been reviewed in detail in the current work, there is reason to believe that 3-D interpretation of high resolution digital imagery may compliment the D-iSM process for large study areas, particularly in the development of a floristic strata layer (Step 4 in D-iSM) to guide sampling effort. This hybrid approach may allow relaxation of the requirement to incorporate saturation coverage of Rapid Data Points (Step 2) to identify variations in floristic compositions, but will be dependent on the skills and experience of the 3-D interpreter. Further testing of fine-scale mapping produced through this process is warranted.

2.4.7 Sources of Potential Errors

Like all methods of investigation and analysis, there are some potential limitations and sources of error within the D-iSM process. Acknowledgement of these prior to implementation of the method on a project will go some way towards reducing their impact. Good prior knowledge of a study area is not a pre-requisite for the method to work effectively. However, the ability to observe closely and record accurately is imperative.

There are six key sources of potential error in the D-iSM process:

i. quality of aerial imagery used for API – creation of photo-patterns using aerial imagery (Step 1) assists in ensuring adequate allocation of Rapid Data Points to all observable photo-patterns, and will affect sampling design (Step 5). Depending on the quality of available imagery, this process may not detect all 106

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potential communities. However, wherever possible, saturation collection of Rapid Data Points, irrespective of photo-patterns, will reduce the likelihood of such errors. Also, the use of the best available aerial imagery for Step 1 should always be the preference. ii. inadequate Rapid Data Point collection – the overall power of the D-iSM method lies in the quality and quantity of map acquisition data (Step 2), both for classification analysis (Step 7) and final mapping (Step 8). A key point to remember is that the more Rapid Data Points collected the better, and the more observant the researcher, the higher resolution of floristic strata (Step 4). Importantly, the researcher should record what is seen at each point location, rather than record data to complement a preconceived concept of a community. iii. misidentification of species – observer error or inconsistency in the identification of dominant plant species for Step 2 may lead to later problems in vegetation classification and mapping. This is particularly the case if multiple teams of recorders work on the one project, and the potential for misidentification should be managed. iv. misclassification of Rapid Data Points – concentrated effort is required during Step 3 that each Rapid Data Point has been classified correctly, so that the floristic strata map (Step 4) correctly portrays observed variation. A ‘first-cut’ of Rapid Data Point classification may reveal too fine or too broad a division of data and refinement may be necessary. However, it is better to retain a finer classification and strata map so that variations can be tested with full floristic analysis (Step 7) than to disregard potentially legitimate communities. Step 3 does involve the subjective allocation of Rapid Data Points to a classification (which is also reflected in Step 4), but in this sense such an allocation will be tested during full data analysis (Step 7), and can be revised if necessary. The classification is also based on ‘raw’ floristic data, and not a pre-conceived community classification, so that repetition of the strata definition process by a different researcher should, but not always, result in similar strata units.

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v. selection of sample quadrat locations – based on the floristic strata map (Step 4), care should be taken that sufficient replicates are selected from all strata, so that all variations have the potential to reveal their relative differences in the classification analysis (Step 7).

vi. use of dominant species to derive floristic strata units – floristic strata map generation (Step 4) relies on the classification of numerous Rapid Data Points (Step 3), where only dominant species have been recorded, to guide sampling effort (Step 5). Ultimately, this will lead to a classification where dominant species appear to have been influential. However, for any classification (Step 7) that is based on analysis of cover abundance scores, this should not be an issue because dominant species will logistically be well represented in a dataset. Care is required, however, in classifications (Step 7) relying on presence-absence data only, as floristic strata units may not be fully reflected in final classification units.

One of the important aspects of the D-iSM method is that, provided map data has been collected comprehensively and accurately, there is the potential for it to be easily re-classified should a later review be necessary to equate it, for example, to a broader regional classification. In effect, this would mean that collected data could be reviewed and categorised for incorporation into, for example, the New South Wales (Benson 2006) and Australian (ESCAVI 2003) hierarchical vegetation classifications. And, importantly for budgetary considerations, this can be accomplished without needing to revisit the area and incur additional field expenses.

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Chapter 3: Defining new communities: Scribbly Gum Forest on the Central Coast

Scribbly gum diversity Local v Regional scale Preferential sampling Rare communities Influence of scale

Defining new communities

Overview

Issues of sampling design and scale in vegetation classification have been examined in relation to a sub-set of sample data from the Central Coast of New South Wales. A preferential sampling strategy, combined with numerical classification analysis, has been used to compare the floristic composition of nine field-observed stands of native vegetation where Scribbly gums (Eucalyptus haemastoma, E. racemosa: Myrtaceae) are dominant and characteristic within Triassic Narrabeen or Permian sediment landscapes of the Central Coast of NSW. Five of the nine vegetation types defined in this study were not sampled or classified during previous regional classifications using environmentally stratified random sampling techniques; others were sampled but not distinguished. Significantly the five newly identified communities possibly represent the most threatened communities in the region: three are already listed as Threatened Ecological Communities in NSW (Kurri Sands Swamp Woodland, Quorrobolong Scribbly Gum Woodland, Kincumber Scribbly Gum Forest), and at least two further communities may be equally threatened. These outcomes highlight short-comings of the environmentally stratified random sampling strategy to vegetation classification, in contrast to preferential sampling which aims to sample all observable variation (the Zurich-Montpellier school).

This study demonstrates that classification projects incorporating a stratified random sampling methodology based on environmental variables should acknowledge the potentially undetected presence of significant vegetation communities due to deficiencies in survey design and issues of scale (resolution). Implementing a preferential sampling strategy, either in place of, or in combination with, a broader stratification is more likely to uncover such communities. Such an approach will require a thorough knowledge of a study area prior to or acquired during a project, and the flexibility to incorporate additional quadrats when required.

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3.1 Introduction

One of the central elements of the Zurich-Montpellier school of vegetation classification is that a researcher should know his or her study area well, so that a sampling design can be implemented with the greatest chance of success (see Chapter 1, Section 1.1.1). Such success can be measured in the perceived accuracy of a classification, particularly in the use of the classification by third parties. Both abstract and concrete associations benefit from a well designed sampling strategy, as both are relied upon to portray the diversity of vegetation communities in an area. Consequently, presenting a classification that is as accurate as possible is imperative.

The scale at which a classification and mapping program is undertaken should be related to how it will be applied (Guerrero et al. 2013). Issues of classification scale have been discussed in Chapter 1 (Section 1.4), where it was noted that local and regional scale products can be difficult to define, and vegetation units are best encased within a hierarchical classification (local  regional  national). For land use decisions, at least in heavily populated areas, the application of a classification product is usually of local scale (i.e. community diversity reflective of observable on-ground variation, in the form of sensible management units). For the lower Hunter Valley and Central Coast region, the classification and mapping produced by NSWNPWS (2000b) was created at a regional scale and was considered not to be of local utility (Nicholls et al. 2002). However, since that time this product has been used repeatedly within the region at a local scale, including forming the basis of several Threatened Ecological Communities as listed on the Threatened Species Conservation Act 1995. Further regional scale classifications (McCauley et al. 2005; Somerville 2010) for this same region failed to fully address local-scale issues.

Vegetation dominated by Scribbly gum eucalypts (Myrtaceae) is widespread along the eastern seaboard of Australia, with up to five described taxa in New South Wales and Queensland (Bean 1997; Hill 2002; Pfeil & Henwood 2004). Commonly, one or more of these taxa dominate forests and open woodlands, although some species also occur as scattered emergents in scrubs and heaths. Some Scribbly gums, such as Eucalyptus signata, reportedly show a clear preference for deep aeolian sand deposits along the 111

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coastal fringe, extending north from Newcastle along the New South Wales coast well into southern Queensland. Other species, such as E. haemastoma and E. sclerophylla, are more restricted in range and occur on shallower sandstone-derived soils (Hill 2002). Collectively, Scribbly gums are characterised by insect ‘scribblies’ commonly occurring on their otherwise smooth bark, the raised disc of the mostly hemispherical fruit and their small seed (Ladiges et al. 1992).

Scribbly gum dominated forests and woodlands are distinctive in the landscape due to the striking white trunks of these species and their association with diverse understorey species including many Proteaceous, Fabaceous and Rutaceous taxa. Surprisingly, some of these communities are often overlooked in classification and mapping studies. Within the Central Coast of NSW (local government areas of Gosford, Wyong, Lake Macquarie, Cessnock, Maitland, Newcastle, Port Stephens), three Scribbly gum taxa are present (E. haemastoma, E. racemosa, E. signata) occupying a range of habitats. Previous classifications of the vegetation within the NSW Central Coast have provided broad overviews of the diversity and distribution of native vegetation communities (NSWNPWS 2000b; McCauley et al. 2006; Somerville 2010). However, these classifications have failed to adequately distinguish a number of floristically distinct vegetation assemblages, including several where Scribbly gums are dominant. The regional classification of vegetation undertaken by NSWNPWS (2000b) defined only three communities where Scribbly gums (E. haemastoma, E. signata) characterise the vegetation, and a further two where such species may occur locally. These are:

Exposed Hawkesbury Woodland (map unit 26), a woodland, low open forest or scrub occurring on Hawkesbury Sandstone geology, supporting the canopy dominants Corymbia gummifera, Eucalyptus haemastoma and Angophora costata;

Scribbly Gum – Dwarf Apple Woodland (map unit 28), a low open woodland occurring on Hawkesbury Sandstone geology dominated by Angophora hispida but also with Eucalyptus haemastoma, Angophora bakeri and Corymbia eximia in the canopy;

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Coastal Plains Scribbly Gum Woodland (map unit 31), a woodland to low open woodland occurring on Narrabeen and Permian aged sediments, and clearly dominated in the canopy by Eucalyptus haemastoma and Corymbia gummifera;

Kurri Sand Swamp Woodland (map unit 35), a woodland occurring on poorly drained sand deposits of Permian or Tertiary age, and dominated by Eucalyptus parramattensis subsp. decadens and Angophora bakeri, but also with Eucalyptus signata or Eucalyptus sparsifolia;

Tomago Sand Swamp Woodland (map unit 36), a low open forest to wet heath on Quaternary sand deposits, dominated by Eucalyptus parramattensis subsp. decadens but with localised abundance of Eucalyptus signata.

Within the NSW Central Coast, two of these communities (map units 26 & 28) occur on the higher elevation, quartz-rich Hawkesbury Sandstone plateaux of Gosford and Wyong LGAs, two occur on the lower and older sediments of Narrabeen or Permian age, and the fifth occurs on the relatively recent deposits of Quaternary coastal sands.

McCauley et al. (2006) presented a revised classification over part of this region (local government areas of Gosford, Wyong & Lake Macquarie only), and described two communities both dominated by E. haemastoma, and both equating to map units 26 and 31 of NSWNPWS (2000b). More recently, the broader study of Somerville (2010) (encompassing the entire Hunter-Central Rivers Coastal Management Area) delineated five communities where E. haemastoma was characteristic, but none with E. racemosa or E. signata. All three studies incorporated a stratified random sampling strategy (Kent & Coker 2001) to select and sample vegetation, partitioning the study area into a number of environmental strata combinations (such as topography, geology, rainfall) and apportioning sampling effort accordingly. Using an environmental stratification to select sample sites diverges from the original Zurich-Montpellier intent of this sampling design. In particular, it is fraught with assumptions concerning correlations between vegetation distribution and specific environmental factors (see Chapter 1, Section 1.3.2).

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In contrast, a number of smaller scale studies within the NSW Central Coast over the past decade have utilised preferential sampling (as per the Zurich-Montpellier school: see Chapter 1, Section 1.1.1) to assist in classifying native vegetation. Many of these studies are unpublished, yet all have concentrated sampling effort on field-observable floristic variations in the landscape, rather than selecting sample sites remotely through GIS procedures. In this sense, preferential sampling has been used to test how one area of vegetation differs from another, and is a form of inductive research (Kent & Coker 2001). In Australia, such sampling is rarely used in classifying vegetation communities due to time and funding constraints, and because of political agendas which require rapidly produced classifications and maps (e.g. the New South Wales Comprehensive Regional Assessments undertaken by government in the 1990’s: NSWNPWS 1999a, b).

Using a subset of a regional vegetation dataset, this chapter aims to show how a preferential sampling design, across a floristically well-studied area, can produce differing local scale results to a regional classification based on an environmentally stratified random sampling technique. Preferential sampling is used to examine relationships and floristic diversity of vegetation communities within the NSW Central Coast where various Scribbly gum taxa are characteristic, and to discuss implications for conservation planning realised through their prior lack of recognition. This study is limited to vegetation occurring on Triassic Narrabeen and Permian-aged sediments within the NSW Central Coast, as it is these landscapes within the region that are under the greatest pressure for development. Scribbly gum vegetation occurring on coastal aeolian sand masses have not been examined, although it appears from field investigations that such ecosystems also have been over-simplified in existing regional classifications (e.g. Bell & Driscoll 2006c). Likewise, vegetation on Triassic Hawkesbury sandstone geology within the Sydney Basin includes a diversity of vegetation where Scribbly gums are prominent, but these too have been omitted from the present study.

The following questions are posed in regard to communities dominated by Scribbly gums on Triassic Narrabeen and Permian-aged sediments within the NSW Central

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Coast, to highlight how biodiversity can be overlooked using environmentally stratified random sampling as a basis for a classification:

i. Are observed vegetation communities dominated by Scribbly gums sufficiently distinct to represent hitherto undescribed communities?

ii. Does preferential sampling verify field-observed differences in vegetation communities?

iii. Can a preferential sampling approach assist and improve upon current regional vegetation classification approaches to conservation?

3.2 Methods

3.2.1 Study Region

The NSW Central Coast region lies between the major regional city of Newcastle (32.9 o S, 151.7 o E) and the northern part of the Sydney metropolis (33.8 o S, 151.2 o E) on the Central Coast of New South Wales (Fig. 3.1). The Central Coast falls within a warm temperate climatic zone, with a maritime influence near the coast, and experiences warm wet summers and cool dry winters. Rainfall generally peaks in late Summer and early Autumn, although local variations due to topography are evident. Annual average rainfall ranges from 700-800 mm in the west, to 1200-1300 mm in the extreme south- east of the NSW Central Coast (Fig. 3.2). Temperatures range from a daily average low of 4o C in July, to a high of 27 o C in January and February (Bureau of Meteorology 2012).

Within this wider region, previous investigations (Bell unpubl. data), both regional and local in context, have identified vegetation dominated by Scribbly gums (E. haemastoma, E. racemosa) at a number of disjunct locations (Table 3.1). It is these areas that are the focus of this study, and their locations are indicated on Fig. 3.1. Geologically, the NSW Central Coast falls within the Hornsby Plateau and Hunter Valley Dome Belt subdivisions of the Sydney Basin, and is comprised of consolidated sediments of the Triassic Hawkesbury and Narrabeen series in the south (Bembrick et al. 1980), and marine and non-marine sediments from the Permian period in the north (Diessel 1980; McClung 1980; and see Fig. 3.3).

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Fig. 3.1. The NSW Central Coast region (shaded), with study locations (●).

Fig. 3.2. The NSW Central Coast region, showing annual average rainfall bands (C. Driscoll unpubl., modelled from Bureau of Meteorology data).

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Table 3.1 Summary of Scribbly gum (Eucalyptus haemastoma, E. racemosa) populations under study, showing localities and main topographical features.

Population Locality Scribbly gum Topographical features Kurri Cessnock district E. racemosa Broad, sandy flats on gentle rises and slopes Doyalson Quorrobolong Valley E. racemosa Broad, sandy flats on gentle rises and slopes Quorrobolong * Central Coast lowlands E. racemosa Broad, sandstone ridges and slopes Lake Macquarie Lake Macquarie E. haemastoma Lower slopes and ridges around Lake Macquarie Kincumber * Gosford urban area E. racemosa Sandy flats on lower gentle slopes Cockle Creek * northern Lake E. racemosa Longitudinal sand dunes adjacent Macquarie to current creek Wyong * northern Wyong E. racemosa Broad flats adjacent to Porters Wetland, occasionally flooded Morisset * south-west Lake E. haemastoma Small, low elevation longitudinal Macquarie sandy dunes Beresfield * East Maitland E. racemosa Gentle sand flats

* Occur as small, distinct and disjunct units surrounded by different vegetation

Within the Narrabeen Group of sediments outcropping in the study area, the Clifton Sub-Group (Patonga Claystone, Tuggerah Formation, Munmorah Conglomerate and Dooralong Shale) have been delineated from Lake Macquarie to Tuggerah Lake (Uren 1980), while the Gosford (or Terrigal) Sub-Group, outcrops from Terrigal to Gosford (McDonnell 1980).

Permian aged sediments, specifically the Newcastle and Tomago Coal Measures, occupy the lower Hunter Valley from Newcastle to the Lochinvar anticline. Other adjacent Permian sediments in the lower Hunter Valley include the Maitland Group, Greta Coal Measures and the Dalwood Group (McClung 1980; Diessel 1980). Extensive areas of unconsolidated alluvial soils also occur along major valleys and streams, and large deposits of Quaternary marine and aeolian sands occur along the coastline.

Despite references to sand deposits of Tertiary age for the Cessnock area, where a listed Threatened Ecological Community occurs (e.g. NSWNPWS 2000b; NSW Scientific Committee 2001), there appears to be no documentation of this in the geological 117

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literature. It is likely that the sandy plains that characterise the area originate from in situ weathering of the Permian-aged Maitland Group and Greta Coal Measures, or possibly the younger Narrabeen series.

Fig. 3.3. Broad geology types of the Central Coast region, with study locations (●).

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Within the Maitland Group, the Branxton Formation is an upward-fining, quartz-lithic sandstone to siltstone unit, with a massive structure and strongly developed bioturbation (van Heeswijck 2001). In places, the Cessnock Sandstone Member occurs. This is coarser and exhibits planar cross-bedding, the lower boundary of which is defined by a conglomerate lag deposit. At the base of the Greta Coal Measures, the Neath Sandstone is fine-grained, well-sorted sandstone with minor conglomerate and shale lenses (van Heeswijck 2001). This unit is also massive and displays low angle planar cross-bedding towards the top.

For the Quorrobolong locality there is also limited geological or soil information published. Van de Graaff (1963) briefly describes the Quarrybylong (sic) family of red podzolic soils, supporting a rare medium to moderately fine-textured surface soil over grey coloured subsoils. Within the red earths, Van de Graaff (1963) also defined an equally rare Mulbring family, a soil of dark yellowish brown, yellowish brown, or strong brown coarse to moderately-coarse texture, found throughout the profile or in the upper metre of the profile. Below depths of one metre, sand, clay or bedrock may occur. Soils described as the Quarrybylong or Mulbring families may represent the sandy substrate at Quorrobolong, but there is no detailed mapping in support of van de Graaffs (1963) work.

Soil properties of the remaining study area localities have not been detailed within the literature, apart from broader descriptions and map units contained in Murphy (1993), Murphy and Tille (1993) and Matthei (1995). It is possible that the sands at Kincumber on the Terrigal Formation represent coarser overbank deposits described in part by McClung (1980), but this has not been confirmed.

3.2.2 Study Design

A preferential sampling strategy was used in this study to address key research questions outlined in Chapter 1, and supplemented a large existing regional dataset. This dataset (>1700 samples) comprised systematic sample quadrats within the NSW Central Coast, recording cover abundance data and full floristic information from numerous earlier and largely unpublished investigations. All sample quadrats were

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0.04 ha (20 m x 20 m) and randomly located within homogeneous stands of vegetation, with new quadrats subjectively located within target locations. Following the New South Wales vegetation survey standard (Sivertsen 2010), Braun-Blanquet cover abundance scores (Braun Blanquet 1932) were visually assessed and applied to all vascular plant species recorded within each quadrat. Cover abundance categories (differing to the 24-class categories included in Sivertsen 2010) were 1 (<5% cover and rare); 2 (<5% cover and common); 3 (5-25% cover); 4 (26-50% cover); 5 (51-75% cover); and 6 (76-100% cover).

Two complimentary data analysis approaches were used to examine variation in Scribbly gum vegetation, which permit evaluation of sampling design and influence of scale on a classification. Analysis 1 tests sampling observable variation in a specified sample group only, without the confounding influence of a large regional dataset (manifested in possible redundancy of common vegetation types, obscuring local scale patterns). Analysis 2 tests robustness of the resulting groups in a considerably larger regional dataset. These analyses are described further as follows:

Analysis 1 - From the regional dataset, Analysis 1 includes all quadrats dominated or co-dominated by either E. haemastoma or E. racemosa (syn. E. signata) as a dominant or characteristic component of the canopy, where they occurred within the NSW Central Coast region, and on Permian- or Triassic Narrabeen-aged sediments (see Figs. 3.1 & 3.3). Field knowledge of the NSW Central Coast was used to direct additional preferential sampling effort to ensure that all known occurrences of vegetation dominated by Scribbly gums were sampled, particularly in under sampled vegetation types. A maximum replicate size of six quadrats per target community (see Table 3.1) was selected. Larger sample sizes were impractical as several targeted locations are geographically restricted and/or constrained by private property ownership. For those target communities where >6 quadrats already existed (2 groups), six samples were selected randomly from each.

Analysis 2 - A larger data analysis on the full NSW Central Coast dataset (>1700 samples) was undertaken using the same methods, to examine how the derived 120

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Scribbly gum groups related to all native vegetation in the region. This analysis was reliant on availability and representativeness of sample data, and aimed to place delineated communities into a regional context. Regional data collection is an ongoing exercise, with many local-scale communities not yet fully sampled. Consequently, it was anticipated that trends evident in a broad regional analysis would be informative rather than conclusive, and issues of scale (local to regional) may still be apparent.

Plant nomenclature followed Harden (1990-1993) and revisions accepted by the National Herbarium of New South Wales. Five Scribbly gum taxa have been traditionally recognised in New South Wales (viz. E. haemastoma, E. racemosa, E. signata, E. sclerophylla and E. rossii: Hill 2002). However, recent taxonomic revisions variously recognize three taxa in eastern New South Wales, differing only in the taxonomic rank of two of these: E. haemastoma, E. racemosa and E. rossii (Bean 1997), or E. haemastoma, E. racemosa subsp. racemosa and E. racemosa subsp. rossii (Pfeil & Henwood 2004). Both revisions concluded that E. signata should be subsumed into E. racemosa as previously documented differences (geographical location and leaf glossiness) were considered too subjective, and were not supported by multivariate analysis. For the purposes of the current study, two taxa are recognized (E. haemastoma, E. racemosa), despite reference to E. signata in the Cessnock and Lake Macquarie areas in some previous studies (e.g. NSWNPWS 2000b).

All data analyses were undertaken using Primer v6 (Clarke & Gorley 2006). As the aim was to examine influence of sample site selection and scale on a classification, standard techniques used in New South Wales (Sivertsen 2010) for analysis of quadrat data were undertaken to negate any compounding effects of differing analysis techniques. Consequently, agglomerative hierarchical cluster analysis and non-metric multidimensional scaling (nMDS) were performed on the dataset using the group averaging strategy, the Bray-Curtis similarity measure and a Beta value of – 0.1. Ordinations were performed in two dimensions with 25 random starts and a minimum stress of 0.01. The SIMPER routine generated diagnostic species lists for each defined floristic group. This routine, which compares ‘similarity percentages’, decomposes

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average Bray-Curtis similarities among samples within a group into percentage contributions from each species, listing the species in decreasing order of contribution. Analysis of similarity within and between pre-defined location groups was undertaken with the ANOSIM routine. The R test statistic (Global R) examines the difference in mean ranks between and within groups, and places a value between -1 and +1; zero represents a random grouping of samples, unity (+1) shows that within group similarity is greater than between group similarity (Clarke 1993). Potential influence of geology, lithology, and rainfall also were examined by reference to available mapped information, and postulating potential relationships with floristic groups defined. Geology and lithology map data from NSW Department of Mineral Resources (1999) and rainfall data sourced from the Bureau of Meteorology (2012) were utilised. No new geology, soil or rainfall data were collected for this purpose.

3.3 Results

3.3.1 Analysis 1: Scribbly Gum Analysis

Multivariate cluster analysis of 44 sample quadrats and 281 vascular plant taxa defined nine distinct floristic groups at 42% similarity (Fig. 3.4). This level of similarity was chosen subjectively by examining the membership of progressively similar cluster groups, until such point that finer resolution did not produce sensible splits. Other methods of determining group membership (e.g. homogeneity analysis of Bedward et al. 1992; determining groups blindly at 20% similarity, as in Belbin 1995) were considered unconducive to the aims of the study, specifically in defining local scale community diversity. A more expert-driven, albeit subjective, approach was instead adopted, with ordination techniques used in support.

In Analysis 1, one sample appears misclassified: a sample from the Wyong area grouped with data from Cockle Creek, and may be attributable to the prominence of heath species in vegetation present at the Wyong site, atypical of the surrounding area (i.e. potentially a poorly located sample). The Wyong site also was located on a raised area of possible human origin, with relatively even-aged canopy species. Non-metric multidimensional scaling was in agreement with groupings evident in the cluster

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analysis, with a stress level of 0.18 and strong congruence with cluster analysis groups (Fig. 3.5). All nine groups are well defined in 2-dimensional space.

Fig. 3.4. Dendrogram of sample quadrats, showing the relationship between all samples (Bray-Curtis similarity measure, 42% similarity).

Table 3.2 summarises defined floristic groups, together with their diagnostic species generated from SIMPER analysis (upper 80% of diversity shown for each group, representing those species contributing most to each group). No groups are dominated solely by Scribbly gum species, with most co-dominated by two or more other Eucalyptus species. All groups possess well developed mid- and ground-layer vegetation, although some (Cockle Creek, Lake Macquarie, Quorrobolong) support higher numbers of grasses and herbs (Fig. 3.6). Xanthorrhoea species are common in several groups, but are particularly prominent at Cockle Creek (Xanthorrhoea glauca, 9.18% contribution), Lake Macquarie (Xanthorrhoea latifolia, 7.13%) and Morisset (Xanthorrhoea latifolia, 6.11%).

Analysis of similarity of species composition between location-based groups of sample quadrats revealed significant differences in Global R values (Table 3.3). All group pairs returned R-values at or close to unity, indicating within group similarity to be greater

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than between group similarity (i.e. plots within a locality were more similar to each other than plots at different localities). Data from the Wyong group were the least significant, particularly when paired with Morisset, Lake Macquarie and Beresfield. Strongest groups were Beresfield, Kurri, Morisset, Quorrobolong, Kincumber and Doyalson, suggesting strong differentiation from each other.

Fig. 3.5. Non-metric Multi Dimensional Scaling ordination, showing the relationship between sample quadrats, overlain with cluster analysis groups (Bray-Curtis similarity measure, 42% similarity). Study sites: B, Beresfield; C, Cockle Creek; D, Doyalson; Ki, Kincumber; Ku, Kurri; L, Lake Macquarie; M, Morisset; Q, Quorrobolong; W, Wyong.

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Table 3.2 Summary of floristic groups. Top 80% of diversity for each group and percentage contributions shown. For Beresfield, dominant species shown for the only sample surveyed.

Group Canopy % Mid % Ground % Beresfield Eucalyptus racemosa - Leptospermum - Pteridium esculentum - Eucalyptus pilularis - polygalifolium - Microlaena stipoides - Angophora costata - Acacia ulicifolia - Mitrasacme - Corymbia gummifera - Acacia suaveolens - polymorpha - Dillwynia retorta - Poranthera microphylla - Dianella caerulea - Entolasia stricta - Pomax umbellata - Panicum simile - Paspalidium distans - Lomandra longifolia - Hibbertia linearis -

Cockle Ck Eucalyptus racemosa 6.28 Lambertia formosa 5.41 Xanthorrhoea glauca 9.18 Corymbia gummifera 6.02 Banksia serrata 4.03 Pteridium esculentum 4.39 Acacia ulicifolia 3.27 Entolasia stricta 3.92 Allocasuarina littoralis 3.08 Imperata cylindrica 3.92 Polyscias sambuccifolia 2.76 Themeda australis 3.92 Leptospermum trinervium 2.09 Pratia purpurascens 3.29 Hibbertia linearis 3.27 Microlaena stipoides 2.78 Glycine clandestina 2.74 Billardieria scandens 2.73 Lindsaea linearis 2.55 Lomandra obliqua 2.55 Lomandra cylindrica 2.10

Doyalson Eucalyptus haemastoma 5.13 Pultenaea tuberculata 5.03 Hibbertia obtusifolia 5.84 Corymbia gummifera 4.17 Epacris pulchella 4.92 Ptilothrix deusta 4.13 Angophora inopina 1.94 Banksia oblongifolia 4.73 Entolasia stricta 3.91 Lambertia formosa 4.28 Xanthorrhoea latifolia 3.45 Hakea laevipes 3.54 Patersonia sericea 3.24 Leptospermum trinervium 3.52 Anisopogon avenaceus 2.99 Petrophile pulchella 3.13 Lomandra oblique 2.93 Isopogon anemonifolius 3.09 Patersonia glabrata 2.38 Platysace linearifolia 3.09 Lepidsperma neesii 1.89 Persoonia levis 1.91 Mirbelia rubifolia 1.80

Kincumber Angophora costata 6.54 Syncarpia glomulifera 5.66 Entolasia stricta 5.20 Corymbia gummifera 4.98 Dodonaea triquetra 4.68 Billardieira scandens 4.36 Eucalyptus racemosa 3.23 Platylobium formosum 4.07 Lepidosperma laterale 4.36 Eucalyptus piperita 1.55 Allocasuarina littoralis 3.80 Pteridium esculentum 4.36 Polyscias sambuccifolia 3.51 Dianella caerulea 3.51 Persoonia levis 2.86 Tetrarrhena juncea 2.75 Banksia spinulosa 2.53 Cassytha glabella 2.65 Breynia oblongifolia 2.18 Smilax glyciphylla 1.96 Glochidion ferdinandi 1.98 Pratia purpurascens 1.94 Epacris pulchella 1.49

Kurri Eucalyptus racemosa 8.93 Lambertia formosa 8.47 Pteridium esculentum 5.43 Angophora bakeri 6.31 Bossiaea rhombifolia 7.13 Entolasia stricta 4.33 Eucalyptus 1.97 Leptospermum trinervium 4.59 Lomandra cylindrica 3.40 parramattensis Monotoca scoparia 4.35 Xanthorrhoea glauca 3.14 Persoonia linearis 3.16 Anisopogon avenaceus 2.92 Astrotricha obovata 2.70 Dianella revolute 2.69 Leucopogon virgatus 1.99 Aristida vagans 2.63 Lomandra longifolia 2.31 Themeda australis 2.20 Hibbertia obtusifolia 2.07

Lake Angophora costata 7.85 Allocasuarina littoralis 6.50 Xanthorrhoea latifolia 7.13 Macquarie Corymbia gummifera 7.38 Pittosporum undulatum 1.60 Entolasia stricta 5.07 125

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Group Canopy % Mid % Ground % Eucalyptus racemosa 5.08 Gonocarpus tetragynus 1.47 Themeda australis 5.05 Phyllanthus hirtellus 4.92 Billardieria scandens 3.28 Dianella caerulea 2.93 Lomandra cylindrica 2.62 Imperata cylindrica 2.58 Lepidosperma laterale 2.55 Cassytha glabella 2.13 Aristida vagans 2.07 Glycine clandestina 1.80 Lomandra obliqua 1.54 Damperia stricta 1.47 Anisopogon avenaceus 1.44 Microlaena stipoides 1.42 Goodenia heterophylla 1.42 Pratia purpurascens 1.42

Morisset Corymbia gummifera 6.11 Ricinocarpus pinifolius 6.77 Xanthorrhoea latifolia 6.11 Angophora costata 2.71 Dillwynia retorta 6.11 Entolasia stricta 4.07 Eucalyptus resinifera 2.70 Leptospermum 5.40 Pteridium esculentum 4.07 Eucalyptus haemastoma 2.66 polygalifolium 4.74 Dianella caerulea 2.73 Platysace linearifolia 4.07 Lindsaea linearis 2.70 Acacia suaveolens 4.07 Acacia ulicifolia 4.07 Epacris pulchella 2.71 Bossieae heterophylla 2.70 Banksia oblongifolia 2.70 Gompholobium latifolium 2.04 Allocasuarina littoralis 2.04 Monotoca scoparia 2.04

Quorrobolong Angophora costata 4.35 Acacia parvipinnula 4.08 Microlaena stipoides 5.87 Eucalyptus punctata 3.36 Leptospermum trinervium 3.87 Aristida vagans 5.86 Eucalyptus resinifera 3.08 Persoonia linearis 3.55 Panicum simile 5.08 Eucalyptus piperita 2.99 Leucopogon juniperinus 1.55 Pratia purpurascens 5.08 Eucalyptus racemosa 2.37 Themeda australis 3.78 Lomandra multiflora 3.29 Phyllanthus hirtellus 3.10 Entolasia stricta 3.05 Lomandra cylindrica 2.99 Billardieria scandens 2.54 Glycine clandestine 2.54 Platysace ericoides 2.31 Imperata cylindrica 2.29 Pteridium esculentum 2.29 Pomax umbellata 2.26

Wyong Eucalyptus racemosa 8.92 Leptospermum trinervium 6.12 Entolasia stricta 7.07 Angophora costata 7.99 Banksia spinulosa 4.65 Anisopogon avenaceus 4.24 Corymbia gummifera 2.17 Epacris pulchella 4.08 Themeda australis 3.89 Eucalyptus resinifera 1.43 Pultenaea paleacea 3.80 Ptilothrix deusta 3.68 Melaleuca sieberi 3.29 Cassytha glabella 3.09 Banksia oblongifolia 2.50 Panicum simile 2.34 Melaleuca nodosa 2.28 Cyathochaeta diandra 2.04 Bossiaea obcordata 1.62 Xanthorrhoea latifolia 1.72 Burchardia umbellata 1.69 Aristida warburgii 1.27 Imperata cylindrica 1.23

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Wyong Quorrobolong Morisset Lake Macquarie Kurri Kincumber Doyalson Cockle Creek Beresfield #

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Canopy Mid Ground

Fig. 3.6. Contribution of canopy, mid and ground layer species to community diversity at each study location. Values are proportion of 80% contribution to SIMPER analysis, excepting Beresfield (#), which is an estimate based on dominant species.

Table 3.3 ANOSIM results (Global R values) for pair-wise comparisons of location-based groups.

Kurri Beresfield Quorrob. Morisset Doyalson Wyong Cockle Ck Kincumber Lake Macq Kurri Beresfield 1 Quorrobolong 1 1 Morisset 1 1 1 Doyalson 1 1 1 1 Wyong 0.94 0.84 0.98 0.75 0.88 Cockle Ck 0.97 1 0.99 1 1 0.89 Kincumber 1 1 1 1 1 0.91 0.99 Lake Macquarie 1 1 0.93 1 1 0.82 0.97 0.94

3.3.2 Analysis 2: Regional Central Coast Analysis

When examined within a larger regional dataset of 1771 samples, six of nine defined groups maintained their associated membership while others were divided amongst other non-Scribbly gum vegetation (Fig. 3.7). As in Analysis 1, a similarity level of ~40% was chosen subjectively for regional cluster analysis by examining the membership of progressively similar cluster groups, until such point that finer resolution failed to produce logical splits. Analysis showed that five of the six Kurri quadrats formed a subgroup of the Kurri Sands Swamp Woodland community of NSWNPWS (2000b) at ~44.5% similarity, with all six comprising part of that community with other non-

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1a 2 3a 1b 4 5 3b 6 7 8 9

Fig. 3.7. Partially collapsed sample dendrogram of 1771 quadrat from the regional analysis. Highlighted are Wyong (Boxes 1a & 1b), Beresfield (Box 2), Quorrobolong (Boxes 3a & 3b), Cockle Creek (Box 4), Lake Macquarie (Box 5), Kincumber (Box 6), Morisset (Box 7), Doyalson (Box 8) and Kurri (Box 9) scribbly gum data. Similarity shown at 40% (horizontal dotted line).

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Scribbly gum samples at ~36% similarity (Box 9 in Fig. 3.7). Up to six sub-communities of the Kurri Sands Swamp Woodland can be defined at this similarity level. All six Doyalson quadrats grouped within a large clade of Coastal Plains Scribbly Gum Woodland (NSWNPWS 2000b) at ~40% similarity (Box 8 in Fig. 3.7), while all six Cockle Creek quadrats maintained their integrity at ~37.5% similarity (Box 4 in Fig. 3.7). For Wyong data, one of six quadrats grouped strongly with Cockle Creek data, three grouped together within a large clade of Coastal Plains Smooth-barked Apple Woodland (NSWNPWS 2000b) at ~36% similarity, and the remaining two quadrats grouped within Riparian Melaleuca Swamp Woodland (NSWNPWS 2000b) at ~35% similarity (Boxes 1a & 1b in Fig. 3.7).

The single quadrat from Beresfield (Box 2 in Fig. 3.7) paired with a non-Scribbly gum quadrat on sandy alluvial soils from the Cessnock region and both split off from other samples at ~31% similarity. At ~26% similarity, this same pair of sites formed part of a clade comprising four of five Quorrobolong quadrats and several samples representing a currently unrecognized regional community on deep alluvial sands (Box 3a in Fig. 3.7). The fifth Quorrobolong quadrat grouped with other samples representing Coastal Plains Smooth-barked Apple Woodland (NSWNPWS 2000b) (Box 3b in Fig. 3.7). All five Kincumber quadrats (Box 6 in Fig. 3.7) maintained cohesion at ~40% similarity, and equate to Kincumber Scribbly Gum Forest (NSW Scientific Committee 2008). At ~38% similarity, the three quadrats comprising the Morisset group (Box 7 in Fig. 3.7) split off from a single quadrat of Coastal Sand Apple – Blackbutt Forest (NSWNPWS 2000b). All six Lake Macquarie quadrats grouped with similar vegetation (broadly, the Coastal Plains Scribbly Gum Woodland of NSWNPWS 2000b) in a large clade at ~35% similarity, which also included the six Cockle Creek quadrats as a distinct sub-group at 37.5% similarity (Box 5 in Fig.3.7).

For all nine groups defined in this study, most were discernible at or below ~40% similarity when examined within the regional dataset. Clear definitions were seen in Kurri, Doyalson, Cockle Creek, Kincumber, Lake Macquarie and Morisset samples, while those for Wyong, Beresfield, and Quorrobolong were less obvious. The Wyong group was the least coherent of the nine defined groups. At this level of resolution

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(40% similarity), there are potentially many more local-scale communities recognisable within the regional dataset, some of which already have been highlighted in sub- regional studies (e.g. NSWDECC 2008b).

3.3.3 Environmental Relationships

Broad geology type and annual average rainfall appear indicative for each defined vegetation group (Table 3.4). Three broad geological strata represented in the data explained much of the variation observed in floristic communities. The bulk of data fell within Narrabeen sediment geology, outcropping predominantly in the southern sections of the region encompassing the Doyalson, Lake Macquarie, Wyong and Kincumber groups. Older Permian sediments support the Kurri, Quorrobolong, and Beresfield groups. Younger Quaternary sands support the Cockle Creek and Morisset groups. Within the large Narrabeen group, further subdivision is possible between the Gosford Subgroup (Kincumber), Clifton Subgroup Tuggerah Formation (Wyong) and Clifton Subgroup Munmorah Conglomerate (Doyalson & Lake Macquarie). Within Permian Sediments, the Kurri group occurs on the Maitland Group Branxton Formation, the Quorrobolong group occurs within the Maitland Group Muree Sandstone, and the Beresfield group occurs on the Tomago Coal Measures. There is one exception to this relationship, for a single quadrat occurring on Branxton Formation but showing floristic similarities to the Quorrobolong group.

Table 3.4 Summary of inferred environmental relationships for floristic groups, showing geological age, geological group/ subgroup, and rainfall band.

Group Age Geological group/ subgroup Rainfall Cockle Creek Quaternary - 1000-1100 Morisset Quaternary - 1100-1200

Kurri Permian Maitland Group, Branxton Formation 800-900 Quorrobolong Permian Maitland Group, Muree Sandstone 900-1000 Beresfield Permian Singleton Supergroup, Tomago Coal Measures 900-1000

Wyong Narrabeen Clifton subgroup, Tuggerah Formation 1100-1200 Doyalson Narrabeen Clifton subgroup, Munmorah Conglomerate 1100-1200 Lake Macquarie Narrabeen Clifton subgroup, Munmorah Conglomerate 1100-1200 Kincumber Narrabeen Gosford subgroup 1200-1300

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The nine defined floristic groups also follow a rainfall gradient irrespective of geological substrate (Table 3.4). The Kurri group occurs in the driest habitat of 800-900 mm/year, followed by the Quorrobolong and Beresfield groups (900-1000 mm/year), the Cockle Creek group (1000-1100 mm/year), and the Kincumber group (1200-1300 mm/year). The remaining groups (Doyalson, Lake Macquarie, Morisset and Wyong) all fall within the 1100-1200 mm/year isohyets.

3.4 Discussion

Three regional classifications within the Central Coast (NSWNPWS 2000b; McCauley et al. 2006; Somerville 2010), driven by an environmentally stratified random sampling methodology, have been shown to be inadequate in defining and classifying vegetation characterised by Scribbly gum eucalypts. Five of the nine vegetation types defined in the current study were not sampled or classified adequately during those studies, yet these five possibly represent some of the most threatened communities in the region: three are already listed as Threatened Ecological Communities in NSW (two as a consequence of previous preferential sampling), and two may be equally threatened. This study has illustrated the short comings of a regional classification for one facet of the NSW Central Coast vegetation, and it is likely that the processes described here can be repeated elsewhere in other regions around Australia with similar results.

Key issues of sampling design and scale of investigation can result in markedly different vegetation classifications, which at a local scale can have important conservation implications. By confining sampling to a preferential design, it has been shown in this study that observable differences in local-scale vegetation patterns can be supported and represented in a regional classification. Conversely, relying on use of an environmentally stratified random sampling design to detect restricted vegetation types depends on the scale of available environmental strata, and on the chance that a specific area will be sampled. Such a scenario is of limited use in local-scale management of native vegetation, where accurate depictions of the landscape are required.

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Scribbly gum vegetation in the Central Coast region of New South Wales is considerably more diverse than has been documented previously through regional classification projects (NSWNPWS 2000b; McCauley et al. 2006; Somerville 2010). Nine communities were defined (see Fig. 3.2) from Triassic Narrabeen or Permian sediments of the NSW Central Coast, and all can be readily recognised in the field by particular combinations of canopy and understorey species, lithology and rainfall (Tables 3.3 & 3.4). Previous regional classifications noted above failed to fully document the diversity of Scribbly gum vegetation present, largely a result of inappropriate survey design and classification scale (resolution). Within a broader context, seven of the defined communities comprise the Sydney Sand Flats Dry Sclerophyll Forests of Keith (2004), which are unique to the Sydney - Newcastle region, and occur on low nutrient, old alluvial deposits.

In summary, the implications of the results of this study are borne out in the key issues of survey design and resolution, and the consequences the interplay of these two factors can have on conservation and planning. Each of these points is discussed in the following sections.

3.4.1 Survey Design

Many classification projects by necessity employ an environmentally stratified random sampling technique for data collection, particular those undertaken within Australia (e.g. Ferrier & Smith 1990; Sun et al. 1997; Keith & Bedward 1999; Bobbe et al. 2001; Cairns 2001; Cawsey et al. 2002; Keith 2002; Tozer 2003). However, the assumption that plant species and vegetation communities are distributed randomly across landscapes, and that natural variability will be detected from an environmentally stratified sampling regime is misrepresentative (Rankin et al. 2007). Time and financial pressures dictate that large areas can best be examined and classified using environmentally stratified random sampling (Cocks & Baird 1991). Indeed such a system is recommended by government departments in New South Wales (Sivertsen 2010) and other parts of Australia (Neldner et al. 2005; Brocklehurst et al. 2007). Interestingly, despite attempts to incorporate more Zurich-Montpellier techniques into

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Australian classifications (Bridgewater 1981), only partial adoption of the methods has occurred.

Employing a stratified random sampling methodology in a classification study violates the concept of non-randomness of plant distribution. Certain plant species may be expected to be present at any given location, but variability in co-occurring species, both internally and between neighbouring communities, will result in uncertainties of group composition (Begon et al. 2006). In contrast, preferential sampling, when based on sound knowledge of a study area (see Chapter 2), allows the development of a classification which more accurately reflects the observable variations in a landscape, while at the same time increases the chances of detecting rare communities. In the case of Scribbly gum vegetation from the NSW Central Coast, eight of nine definable communities identified through preferential sampling had not been described in earlier studies, primarily because those studies relied on environmental layers for stratification that were too coarse to show local scale environmental variation. Of these communities, seven occupy very restricted distributions and are consequently more prone to extinction through anthropogenic or stochastic events.

Preferential sampling allows a researcher to sample and test specific areas in a landscape, in order to attribute an area to a vegetation class. Such inductive research is designed so that explanations may be derived from collected data without a prior hypothesis (Kent & Coker 2001; Dale 2002). When the aim of a research project is to identify and categorise the observable variation in vegetation community heterogeneity, and to subsequently manage that heterogeneity, then it is imperative that an accurate classification be developed. Vegetation community heterogeneity on the NSW Central Coast has not yet been adequately described in existing work, as evidenced through the results of this study. Through examination of a subset of the regional vegetation, it has been shown that preferential sampling can be used to detect hitherto undefined vegetation types and test whether these were recognised by previous regional sampling.

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3.4.2 The Importance of Resolution

Resolution of a classification is an important consideration in any vegetation community study, and one that is intrinsically linked to survey design. Vegetation communities can be defined at any level of resolution (Begon et al. 2006), provided that the rules behind demarcation of that community are clearly stated. When examined within a broader regional context, some of the vegetation groups defined in the current study area represent indistinct communities, and are subsumed into broader, more encompassing ‘groups’ of communities. Such is the case for the Wyong, Beresfield and Quorrobolong groups, although others (Kurri, Doyalson, Cockle Creek, Kincumber, Lake Macquarie and Morisset) maintained integrity at or below 40% similarity in the regional dataset. This process of upward grouping is also likely to have occurred during prior regional classifications of the NSW Central Coast (NSWNPWS 2000b; McCauley et al. 2006; Somerville 2010), and is a major concern when reliance of data collected and generated at a regional scale is used to inform local-scale conservation planning.

This dichotomy of resolution in vegetation classifications and the management of landscapes is not uncommon elsewhere, and was recognised over thirty years ago by Beard (1981). Lawson and Wardell-Johnson (2006) and Wardell-Johnson et al. (2007) documented similar concerns for the vegetation of Fraser Island and Brisbane respectively, where they found little homogeneity within mapped regional ecosystems relative to floristic composition. In north-eastern New South Wales, Kendall and Snelson (2009) re-examined vegetation dominated by Eucalyptus fastigata using quantitative floristic survey data to show that two assemblages could replace the former three subjectively determined communities. This process then allowed scarce conservation resources to be targeted at the assemblage where it was needed most. On the Cumberland Plain in Sydney, French et al. (2000) illustrated that potentially different subunits of Cumberland Plain Woodland TEC may simply represent the natural variability of that community, when placed within the context of its formal description. They found that the high variation in species composition between 51 sample sites of Cumberland Plain Woodland could not be consistently sorted into

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discrete floristic subunits, but that such variation within the community needed to be defined to assist management, conservation and rehabilitation of this TEC. And there are other examples from other areas where regional-scale classifications do not adequately deal with observed community diversity (e.g. Picone & McCaffrey 2004). In the United Kingdom, Hearn et al. (2011) raise concerns about the difficulties of imposing a national vegetation classification onto local-scale sites, and suggest that a review of classification consistency is necessary. Further, de Cáceres & Wiser (2012) advocate publication of classification rules to ensure consistency of classification between projects.

What is common between all of these studies, including the current investigation of Scribbly gum vegetation from the NSW Central Coast, is that developing a vegetation classification at a regional scale for land management does not always achieve the desired outcomes. Most regional-scale investigations aim to sample and describe floristic variation across large tracts of land, with a view to guiding the development of regional infrastructure and development, and to identify significant vegetation and habitat. However, such investigations should not always be blindly incorporated into local-scale management: this is an example of the scale mismatching of Guerrero et al. (2013). On the NSW Central Coast, the Lower Hunter and Central Coast Regional Environment Management Strategy was instigated to, in part, identify the conservation status and significance of all land in the Central Coast, and to provide a detailed map of the distribution of plant communities (NSWNPWS 2000b). At a local scale, such objectives could not be met through use of an environmentally stratified random sampling strategy, and indeed the end product was considered appropriate at regional rather than local-scale (Nicholls et al. 2002). The NSWNPWS (2000b) project, together with subsequent studies designed to improve the resolution and accuracy of vegetation knowledge in this region (McCauley et al. 2006; Somerville 2010), were unable to detect and define the variation in (among others) Scribbly gum vegetation, yet decisions guiding strategic development in the region are still based on these works. Indeed, several Threatened Ecological Communities as listed in NSW and Australian threatened species legislation are based on these regional studies, but their definitions are difficult to apply locally. Chapter 4 addresses one such community 135

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through a revision of its definition, using preferential sampling to segregate and describe observable differences in the field.

3.4.3 Conservation Implications

In combination, survey design and classification resolution, if not acknowledged as a potential failing of a classification project, can have important implications on conservation planning. Three of the nine vegetation communities defined in this study (as mapped in Fig. 3.8) are currently listed as, or form components of, Threatened Ecological Communities (TECs) in New South Wales (Kurri Sands Swamp Woodland, Quorrobolong Scribbly Gum Forest, Kincumber Scribbly Gum Forest). Importantly, only one of these (Kurri Sands Swamp Woodland) was defined during the initial regional classification process that used environmentally stratified random sampling (NSWNPWS 2000b): both the Quorrobolong Scribbly Gum Forest and Kincumber Scribbly Gum Forest TEC’s originated from classifications that employed preferential sampling to examine and compare their floristic compositions (Bell & Murray 2001; Bell 2004b).

The final determination for the Kurri Sands Swamp Woodland, arising from the NSWNPWS (2000b) study, includes Eucalyptus signata (syn. E. racemosa) as a diagnostic species for that TEC, but that species was later found to be present in very few locations of this community (NSWDECC 2008b), and illustrates how survey design (i.e. environmentally stratified random sampling) can have far-reaching affects beyond the initial definition of a community. Of the remaining four floristic groups, the Doyalson group is the most widespread, and occurs within secure conservation lands and large parcels of Crown lands. The Lake Macquarie group, although relatively widespread, occurs in and around highly urbanised environments still subject to development, and may be potentially under threat. The Wyong group is presently known from only one location near an industrial park; however the bulk of the lands have been included within council conservation zones. The Cockle Creek group occupies a distinct but restricted linear dune system adjacent to existing streams, and supports a significant number of old growth Xanthorrhoea specimens that distinguishes it from all other types, an uncommon feature in the local region with 136

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added conservation benefits. The Morisset group is perhaps the most limited in extent, currently known from only two locations, one in secure reserve but the other within a highly urbanised area.

Fig. 3.8. Distribution of the nine Scribbly gum communities defined in this study.

Vegetation communities that occur within or near to highly urbanised communities will always be subject to degradation, fragmentation and clearing (e.g. Kirkpatrick 2004; Crosti et al. 2007; Benson & Picone 2009; Smith & Smith 2010). However, rare communities are particularly prone to such processes due to their naturally restricted distributions. Two of the communities described in this study (Doyalson & Lake Macquarie) may be considered less threatened by urbanisation, however the remainder all occupy restricted habitats which may be easily rendered extinct by poor

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planning decisions or specific threatening processes. Under the national criteria for assessing threat status of Australian communities (Threatened Species Scientific Committee 2010), Beresfield, Cockle Creek, Kincumber, Kurri, Morisset, Quorrobolong and Wyong would all be considered critically endangered due to their restricted natural distributions (<10 km2) and potentially imminent extinction risk. Applying the rare community categories of Izco (1998), all seven would fall into R7, the highest form of rarity.

Several of the nine communities identified during this study have not previously been recognized in the NSW Central Coast region in major regional or sub-regional studies (Table 3.5). This has implications on planning for conservation and development for this region, and parallels can be drawn to many other habitats and regions elsewhere. Planning for development and conservation in the NSW Central Coast region is geared around existing classifications of vegetation (NSWNPWS 2000b; Somerville 2010), to ensure that those areas considered most under threat are considered accordingly. However, if some vegetation communities have not been recognized in regional classifications, then their protection and management is uncertain. In some cases, lack of recognition of these communities during regional studies can be traced back to a lack of quadrat data placed within them, which in part results from an environmentally stratified random sampling methodology. In other situations, (e.g. Quorrobolong Scribbly Gum Forest and Kincumber Scribbly Gum Forest TECs) equivalent communities are not recognised in the regional classification, despite data from these areas being included in regional analyses.

Table 3.5 Regional equivalent communities.

Group Regional Equivalent Source Kurri Kurri Sands Swamp Woodland NSWNPWS 2000b Doyalson Coastal Plains Scribbly Gum Woodland NSWNPWS 2000b Quorrobolong Quorrobolong Scribbly Gum Forest Bell & Murray 2001; NSW Scientific Committee 2002 Kincumber Kincumber Scribbly Gum Forest Bell 2004b; NSW Scientific Committee 2008 Lake Macquarie - - Cockle Creek - - Wyong - - Morisset - - Beresfield - -

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As a by-product of communities remaining undetected by regional-scale classifications, there is also the potential for unexpected impacts on threatened plant species (‘ecological surprises’: Lindenmayer et al. 2010). The vulnerable Tetratheca juncea (Elaeocarpaceae), for example, is abundant in at least two of the floristic groups (Cockle Creek, Morisset) delineated in this study of Scribbly gums, and these populations would have been otherwise overlooked if conservation planning relied upon regional classification and mapping. As noted above, neither vegetation types at the Cockle Creek nor Morisset sites are accommodated within the NSWNPWS (2000b), McCauley et al. (2006) or Somerville (2010) classifications. Existing regional mapping (NSWNPWS 2000b) portrays the Cockle Creek area as Swamp Oak Rushland Forest, a habitat typified by monospecific stands of Casuarina glauca, while lands supporting the Morisset group have been mapped as Riparian Melaleuca Swamp Woodland. Both communities provide inappropriate habitat for Tetratheca juncea.

3.5 Conclusions: Key Principle in Community Definition

Field-observed vegetation communities dominated or co-dominated by Scribbly gums within the NSW Central Coast have been verified through numerical classification, and illustrate how a preferential sampling approach can assist and improve upon a regional classification. Applied more widely, implications borne out of this study are that any classification project incorporating an environmentally stratified random sampling methodology should acknowledge the potential presence of significant vegetation communities that may remain undetected due to deficiencies in survey design and issues of scale (resolution).

The key principle of preferential sampling in a sampling design is instrumental in defining rare communities. Implementing a preferential sampling approach, either in place of, or in combination with, a broader environmental stratification is more likely to uncover such communities. Such an approach will require a thorough knowledge of a study area prior to or acquired during a project, and the flexibility to incorporate additional quadrats when required. The D-iSM method of identifying and mapping vegetation patterns discussed in Chapter 2 demonstrated how such knowledge of a study area can be achieved, and that process should be instigated in tandem with 139

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preferential sampling to improve vegetation classifications. Using Scribbly gum communities from the Central Coast of New South Wales as an example, the current study has demonstrated that six of nine defined communities maintained their local- scale resolution within a broader regional dataset. Previous classifications (NSWNPWS 2000b; McCauley et al. 2006; Somerville 2010) that adopted an environmentally stratified random sampling design could not delineate this diversity of Scribbly gum vegetation adequately. This implies that sampling design is pivotal in detecting all diversity.

For local-scale management, local-scale definition of communities gained from preferentially sampling is the best option. Given this, it may be appropriate to recommend that such sampling be mandatory for surveys and classifications relevant to the development industry, so that rare communities are not inadvertently overlooked and unwillingly become threatened with extinction.

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Chapter 4: Refining existing communities: Lower Hunter Spotted Gum-Ironbark Forest

Dynamic classification Rare vegetation communities Threatened Ecological Communities

Refining existing communities

Overview

Vegetation classification is a dynamic process. The collection and analysis of sample data to create a classification of vegetation communities should not be seen as a one- off procedure. In contrast, with the accumulation of more sample data and an increase in understanding of an area or vegetation type, that classification should evolve to better reflect current knowledge. For vegetation communities that are backed by legislative protection, acknowledgement of such a dynamic classification is particularly pressing.

In the Lower Hunter and Central Coast region of New South Wales, identification of the Lower Hunter Spotted Gum-Ironbark Forest in 2000, and its subsequent listing within the Sydney Basin Bioregion on the NSW Threatened Species Conservation Act 1995, has lead to innumerable uncertainties and disputes in response to land-use conflicts. In the main, these uncertainties centre on the on-ground recognition of the community and the difficulties of distinguishing it from closely related communities. In part, these problems were caused by a sampling regime based on environmental stratification, which failed to adequately sample all variations of the community within the region.

With additional preferential sampling following the D-iSM method (see Chapter 2), this chapter reviews the floristic classification of Lower Hunter Spotted Gum-Ironbark Forest, and resolves many of the uncertainties established over ten years of its application in the Hunter region. As a case study, it also presents the results of pre- 1750 mapping of the community, again using the D-iSM method, over the Wyong local government area. This chapter presents new information on the floristic similarities shown by Lower Hunter Spotted Gum-Ironbark Forest in the Hunter Valley to other forest types of the Sydney Basin Bioregion, highlighting the need to be more prescriptive with descriptions of communities listed as threatened under legislation. In conclusion, key principles in the refinement of existing communities are presented.

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4.1 Introduction

Vegetation classification is a dynamic process, and with new or additional data changes to a classification may be required (Jennings et al. 2003; de Cáceres & Wiser 2012). Chapter 1 (Section 1.1.3) discusses the dynamic nature of classification, and the need to embrace new understandings of vegetation description and distribution. It also outlines the importance of sampling design in a classification (Chapter 1, Section 1.3), highlighting the potential pitfalls of relying on environmentally stratified random sampling as the basis for a classification. A refinement of any vegetation community ultimately aims to improve understanding of that community, and this becomes increasingly important for those communities that have legislative support (Wiser & de Cáceres 2012).

The Threatened Species Conservation Act 1995 (TSC Act 1995) in New South Wales allows for revision and better resolution of listed Threatened Ecological Communities (TECs) through numerical classification techniques (NSW Scientific Committee 2010a). Over the past fifteen years, several studies have refined existing TECs in this way, including French et al. (2000), Prober and Theile (2004), Keith and Scott (2005), Keith and Holman (2003), Kendal and Snelson (2009), Payne et al. (2010) and Bell and Stables (2012). All of these studies have advanced understanding on the composition and variability of listed vegetation communities, improving their field recognition and guiding land-use planning. All demonstrate well the concept of dynamic classification in action.

Lower Hunter Spotted Gum-Ironbark Forest - As part of the Lower Hunter and Central Coast Regional Environmental Management Strategy, NSWNPWS (2000b) surveyed and classified 53 vegetation communities (see Chapter 1, Section 1.2.2.2). In that study, NSWNPWS (2000b) delineated four vegetation communities dominated by Spotted Gum (Corymbia maculata) and various species of Ironbark (mostly Eucalyptus fibrosa, E. crebra, E. paniculata subsp. paniculata, E. siderophloia). The distribution of these communities was modeled, and descriptive profiles were prepared which aimed to sufficiently describe them to allow clear separation in the field. One of these

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In February 2005, Lower Hunter Spotted Gum-Ironbark Forest in the Sydney Basin Bioregion was listed as an Endangered Ecological Community under the NSW Threatened Species Conservation Act 1995, with a minor amendment updating administrative boundaries added in 2010 (NSW Scientific Committee 2010b; see Appendix 5). As with all listings under this Act, a number of descriptive paragraphs (see Preston & Adam 2004a, 2004b) within the final determination allow a user to identify whether or not a particular site supports this EEC (see paragraphs 1, 2 and 4 in Appendix 5). For LHSGIF, the key descriptive determiners within the final determination which are not open to compromise are that it must:

lie on Permian or Triassic Narrabeen group geology;

support a canopy dominated by Corymbia maculata and Eucalyptus fibrosa, and;

occur principally within the Cessnock – Beresfield area of the Central and lower Hunter Valley, but potentially anywhere within the Sydney Basin Bioregion.

With this in mind, the co-dominance by Corymbia maculata and Eucalyptus fibrosa has been generally adopted by regional botanists as the key floristic determining feature of this community, and is the underlying feature behind its review. This rationale is supported by the range of environments within the NSW Central Coast and Hunter Valley where this combination of eucalypts dominates the vegetation, from the upper Hunter Valley in Manobalai Nature Reserve, to the coastal hinterland at Newcastle University, as far south as Wyong, and north to the Karuah and Booral areas. This geographical range encompasses dramatic changes in topography (elevational range of 15-350m ASL) geology and soil (Permian, Triassic and Carboniferous based), and with equally dramatic variations in rainfall (<700 mm/yr in the upper Hunter Valley up to 1200 mm/yr at Wyong: Bureau of Meteorology 2012). Disturbance history is also variable, and is discussed further below.

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Problems in Identification - Although few listings under the TSC Act 1995 are free from diagnostic problems, it is evident that LHSGIF is particularly problematic (e.g. Bell 2003; Peake 2006; Wells 2007; HSO Ecology 2009a). In part, this may be attributable to the methods in which it was originally defined (see Chapter 1, Section 1.2.2.2), but is also a consequence of the wide geographical distribution over which the community occurs. The original description of the community stipulated a geographical distribution centered on Cessnock, but extending from North Rothbury to Maitland and Beresfield across an area of 65 by 35 km (NSWNPWS 2000b). It was based on data from 95 full floristic quadrat samples collected by numerous observers. As a consequence, the description of this community is potentially prone to the influence of observer variability (Kirby et al. 1986; Scott & Hallam 2002; Kelly et al. 2011), making it necessary to interpret the list of supplied diagnostic species with caution. Peake (2006) found observer variability to be present in his data analysis of the Hunter Valley floor, which incorporated much of this same data.

Problems in applying the description of the LHSGIF began when it was recognised as a community of regional significance (Eco Logical Australia 2002), shortly prior to its 2005 listing as an EEC. Several studies have attempted to redefine this community to assist land managers in its conservation (see Appendix 6), incorporating specialist analyses of data from throughout the region. Typifying many attempts, HSO Ecology (2009a, b) described and mapped LHSGIF for northern sections of Lake Macquarie local government area, using NSW Scientific Committee (2005) as a reference, but consulting NSWNPWS (2000b) for more technical details on distinguishing LHSGIF from other similar communities. Unfortunately, identifying the four regional Spotted Gum – Ironbark communities delineated by NSWNPWS (2000b) (viz, Coastal Foothills Spotted Gum-Ironbark Forest; Seaham Spotted Gum-Ironbark Forest; Lower Hunter Spotted Gum-Ironbark Forest; and Central Hunter Spotted Gum-Grey Box Forest) is not resolved through recourse to the original classification (see Appendix 7).

Further complicating the identification of LHSGIF is the impact of logging. Australia has had a long association with managing forests for timber production, ensuring often colourful debates between the forestry and conservation industries (Dargavel 1995).

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Many forests within the Wyong-Cessnock region of the lower Hunter Valley and Central Coast, especially those supporting Corymbia maculata, have historically been managed for the production of mining timber (Grant 1989), meaning that current day forests cannot be considered free from European impacts. Consequently, depending on the extent of logging operations undertaken in specific forests, variations in floristic composition and structure can be expected, particularly in the canopy. Phenology and growth of important canopy species are also impacted upon by logging practices (e.g. Neave & Florrence 1994; Law & Chidel 2008, 2009), which may lead to long-term changes in both canopy and understorey composition (e.g. Kariuki & Kooyman 2005; Penman et al. 2008).

However, problems in the identification of LHSGIF are not restricted to the Lower Hunter and Central Coast of New South Wales. Vegetation communities supporting a canopy composition dominated by Corymbia maculata and Eucalyptus fibrosa occur in several locations along the eastern seaboard. A review of the literature shows that these species co-occur across a range of environments within New South Wales, and communities characterised by them have been reported in a number of reports and publications (Appendix 8). Indeed, an extract of floristic quadrat samples contained within the vegetation survey database of the NSW Office of Environment & Heritage reveals at least two further strongholds for this combination elsewhere, one in the South East Corner Bioregion of Thackway and Cresswell (1995) and another in and around the South East Queensland Bioregion (Fig. 4.1).

Elsewhere in the Sydney Basin Bioregion, for which the LHSGIF EEC has been specifically prescribed (NSW Scientific Committee 2010b), vegetation dominated by Eucalyptus fibrosa and Corymbia maculata occurs on Wianamatta shales in western Sydney (Tozer 2003), and on Permian sediments in and around Morton National Park and State Conservation Area west of Nowra (Gellie 2005). In the South East Corner Bioregion, it also occurs on Permian and Ordivician sediments in Murramarang National Park near Batemans Bay, Eurobodalla National Park near Narooma and on private lands between these two locations (Gellie 2005). In the South East Queensland Bioregion, a range of locations support vegetation dominated by these two species,

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Refining existing communities together with the closely related Corymbia henryi and Corymbia variegata. All of these locations support vegetation which is superficially similar to LHSGIF, but only one has been tested comparatively with Hunter Valley vegetation: Tozer (2010), in assessing the uniqueness of the Cumberland Plain Woodland (CPW) from western Sydney, used numerical analysis on an existing dataset to show that CPW differed from similar vegetation in other major coastal valleys in New South Wales, including the Hunter Valley.

A further complicating factor with regard to vegetation dominated by Corymbia maculata and Eucalyptus fibrosa arises with the classification of vegetation from the central-to-upper Hunter Valley. In that classification, Peake (2006) defined six Spotted Gum-Ironbark communities for the region extending from Branxton in the east, to

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Denman in the west, and north to Wingen. Communities supporting Corymbia maculata and Eucalyptus fibrosa in the canopy, with other associated species, included his Hunter Lowlands Redgum Forest, Lower Hunter Spotted Gum-Ironbark Forest and Central Hunter Ironbark-Spotted Gum-Grey Box Forest. All three of these are now listed threatened communities in New South Wales, with Central Hunter Ironbark- Spotted Gum-Grey Box Forest in the Sydney Basin Bioregion becoming formally protected in February 2010. In the final determination for this EEC, which is based on Peake (2006), the NSW Scientific Committee (2010c) states in Paragraph 4 that this community:

“…typically forms an open forest to woodland dominated by Eucalyptus crebra (Narrow-leaved Ironbark), Corymbia maculata (Spotted Gum) and Eucalyptus moluccana (Grey Box). Other tree species may be present and occasionally dominate or co-dominate, and include Eucalyptus fibrosa (Broad-leaved Ironbark) and Eucalyptus tereticornis (Forest Red Gum).”

What is not fully explained by NSW Scientific Committee (2010c) in relation to this EEC is how those areas that are dominated or co-dominated by Eucalyptus fibrosa differ from LHSGIF. This poses problems for field ecologists working in the region where “lower Hunter” and “central Hunter” localities adjoin (generally in the Singleton- Maitland district), together with flow-on implications for conservation offsets and regional planning (e.g. NSWDECCW 2009b).

Research Questions – The question now posed is which of these previously defined communities equates to LHSGIF EEC? Given the principle descriptive determiners for LHSGIF of vegetation dominated by Eucalyptus fibrosa and Corymbia maculata, occurring on Permian or Triassic Narrabeen sediments within the Sydney Basin Bioregion (NSW Scientific Committee 2010b), outside of the Hunter/ Central Coast only vegetation west of Nowra meets these characteristics (Appendix 8). Recognising that the original classification of vegetation within the Hunter region employed an environmentally stratified sampling strategy (NSWNPWS 2000b), it is prudent to now determine how well it stands up to a revision with additional, preferentially located data. More specifically, what will a re-examination of data broadly equating to Lower

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Hunter Spotted Gum-Ironbark Forest reveal about this community and the associated EEC? In this study, such vegetation is referred to as ‘Candidate-LHSGIF’ to distinguish it from the currently accepted view of LHSGIF EEC.

Using the principles of D-iSM (Chapter 2), this chapter aims to answer the following research questions:

Is their distinguishable local-level variation within a community (LHSGIF) that has been previously defined regionally?

How does vegetation currently considered to form part of an existing EEC (LHSGIF) compare to superficially similar vegetation (Candidate-LHSGIF) elsewhere in the Bioregion?

Can a revised classification of LHSGIF be mapped at a local-scale to assist conservation planning, using the Wyong local government area as an example?

The working hypothesis for this research was that if the regional classification of vegetation undertaken in 2000 was a robust one, then the outcomes would be repeatable and that additional data should strengthen the delineation of communities originally defined. If not, new communities or sub-communities could be erected which would reflect any justifiable changes.

4.2 Methods

4.2.1 Study Region

A revision of LHSGIF can be framed by the three main parameters implied in the final determination for this EEC, viz that it may occur anywhere within the Sydney Basin Bioregion, on Permian or Triassic Narrabeen geology, and where Eucalyptus fibrosa and Corymbia maculata dominate the canopy (Section 4.1). Consequently, review of relevant geological mapping (Geological Survey of NSW) within the Sydney Basin Bioregion (Thackway & Cresswell 1995) highlighted applicable physical locations, extending from the Hunter Valley to the South Coast (Fig. 4.2). A review of major floristic classification and mapping projects within these areas then isolated localities where Eucalyptus fibrosa and Corymbia maculata have been previously described as occurring within, and often dominating, a community (see Appendix 8).

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Fig. 4.2. Distribution of Permian and Triassic Narrabeen sediments within the Sydney Basin (Source: Geological Survey of NSW).

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4.2.2 Study Design

In order to refine and review LHSGIF, a reclassification of existing and new quadrat data are necessary, incorporating similar methods to those originally used in its description (NSWNPWS 2000b). Consequently, numerical analysis of full floristic data collected from replicated quadrats of 0.04 ha in size was undertaken. For a review of LHSGIF to be meaningful, it was important that a subset of the sampling occurred in the same stands of vegetation as were originally sampled by NSWNPWS (2000b), or in related communities revealed in literature reviews (e.g. Gellie 2005; Peake 2006) which adhered to the research parameters. Consequently, newly collected data were located within or near to remnants of vegetation that had been previously sampled by other ecologists. In reality, this was not always possible due to constraints imposed by private property ownership. In addition, new samples were also collected in previously un-sampled stands that met the research parameters.

Candidate-LHSGIF potentially covers large areas of the Sydney Basin, and it was impractical under the current study to accurately map its entire distribution. However, using the principles of D-iSM (Chapter 2), substantial portions of its distribution have been completed under previous projects (for example, see Chapter 2 for details on vegetation mapping in Columbey National Park and the Cessnock-Kurri subregion). One region where targeted and accurate mapping of Candidate-LHSGIF vegetation has not been completed is the Wyong local government area (LGA), on the Central Coast of New South Wales. Previous classification and mapping studies were either too broad, used environmentally stratified random sampling or modelled vegetation distribution (Payne & Duncan 1999; NSWNPWS 2000b; Bell 2002a; McCauley et al. 2006; Somerville 2010), or were indicative only and did not encompass the entire LGA (Bell & Driscoll 2006a).

For illustrative purposes, the current work re-maps Candidate-LHSGIF vegetation within the Wyong local government area, in an area where there has been much conjecture as to the presence of LHSGIF EEC. The principles of D-iSM, used in the completion of previous site-specific studies of LHSGIF within Wyong (Bell 2009c, 2010b), have been expanded to prepare a new map of Candidate-LHSGIF for Wyong

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LGA. As a practical application of D-iSM, pre-1750 mapping (sensu Burgman et al. 2001) has been implemented over a highly fragmented landscape to highlight threats posed to Candidate-LHSGIF by continued clearing for development.

Data Collection

An existing database of nearly 3,500 quadrats collected by the author for previous studies throughout many parts of the Sydney Basin over the last 19 years was available for analysis (Fig. 4.3). While these data are comprehensive, only some of it targeted vegetation dominated by Eucalyptus fibrosa and Corymbia maculata, hence could not be expected to encompass the full geographical range of all floristic variation. All existing data sampled vegetation over 0.04 ha areas, and subjectively applied a modified Braun-Blanquet cover abundance score to all vascular plant species present (1 = <5% cover and rare; 2 = <5% cover and common; 3 = 5-25% cover; 4 = 26-50% cover; 5 = 51-75% cover; and 6 = 76-100% cover). As an initial step, existing quadrats completed within the Sydney Basin, on Permian or Narrabeen sediments, and with a canopy dominated by Eucalyptus fibrosa and Corymbia maculata were extracted to form the core analysis dataset. New data were then collected for those areas identified as fitting the research parameters, but where existing data were lacking or limited. Sampling locations were guided by descriptive data and mapping available in existing classifications, and in general followed the preferential sampling methods outlined in D-iSM (Chapter 2).

Classification

As in Chapter 3, complimentary numerical data analyses were used to examine Candidate-LHSGIF throughout the Sydney Basin (Table 4.1). In all cases, the null hypothesis to be tested was that there would be no discernible differences (groups) within each dataset. Numerical analysis was used to answer the first two research questions, viz;

Is their distinguishable local-level variation within a community (LHSGIF) that has been previously defined regionally?

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Fig. 4.3. Regional dataset of full floristic quadrats available for analysis.

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How does vegetation currently considered to form part of an existing EEC (LHSGIF) compare to superficially similar vegetation (Candidate-LHSGIF) elsewhere in the Bioregion?

Table 4.1 Summary of key features of the four data analyses.

Feature Analysis 1 Analysis 2 Analysis 3 Analysis 4 Sydney Basin √ √ √ √ Permian sediments √ √ √ √ Narrabeen sediments √ √ √ E.fibrosa dominant or co-dominant √ √ √ √ C.maculata dominant or co-dominant √ √ √ other ironbarks √ √ Hunter Valley only √

Analysis 1 examines all regional vegetation that may be colloquially referred to as “Spotted Gum – Ironbark forest” (e.g. Spotted Gum/ Ironbark/ Grey Gum forest type of Forestry Commission of NSW 1989 or Hunter-Macleay Dry Sclerophyll Forests of Keith 2004), where Corymbia maculata co-dominates the canopy with any combination of Eucalyptus fibrosa, E. crebra, E. beyeriana, E. siderophloia, E. paniculata, E. placita or E. fergusonii.

Analysis 2 focused entirely on the subset of samples where Eucalyptus fibrosa and Corymbia maculata co-dominated the canopy, generally scoring cover abundance values of 3 (5-25% estimated cover) or 4 (26-50% cover). While other canopy species also may be present at these sites, they were sub-dominant to Eucalyptus fibrosa and Corymbia maculata, and generally scored cover abundance values of 1 or 2 as a maximum (<5% cover).

In Analysis 3, a further examination was made of vegetation dominated almost exclusively by Eucalyptus fibrosa (such as has been described in NSWDECC 2008b), to investigate how these forests relate to Candidate-LHSGIF and other E. fibrosa forests elsewhere in the Sydney Basin. In this dataset, quadrats comprised all available data where Eucalyptus fibrosa was dominant, but where Corymbia maculata was either absent or scored a cover abundance value of 1 (< 5% cover). Note that extensive

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For Analysis 4, data were filtered from the main dataset to examine the interplay between samples on Permian sediments supporting Corymbia maculata and either of Eucalyptus fibrosa or Eucalyptus crebra from within the central and upper Hunter Valley floor (as defined in Peake 2006). This analysis aimed to explore whether or not vegetation currently attributable to Central Hunter Ironbark-Spotted Gum-Grey Box Forest in Peake (2006) can be further divided to distinguish areas where Corymbia maculata and Eucalyptus fibrosa co-dominate the canopy.

Plant nomenclature for all sampling followed Harden (1990-1993), and revisions accepted by the National Herbarium of New South Wales. Eucalyptus nubila, formerly a subspecies of E. fibrosa but raised to species by Johnson (1962), has been inconclusively identified for parts of the southern Hunter Valley during past surveys. Klaphake (2010) suggested that this taxon is largely absent from the Hunter, with confirmed records only for lands north-west of Denman. He also describes Eucalyptus sp. Pokolbin for parts of the Broken Back Range near Cessnock, which superficially resembles E. nubila, and which has been referred to this species in previous studies (e.g. Thomas 1998). To avoid any influence misidentification of this taxon may have had on data analysis, all records of E. nubila within the analysis dataset were changed to E. fibrosa.

All data analyses were undertaken using Primer v6 (Clarke & Gorley 2006). Agglomerative hierarchical cluster analysis and non-metric multidimensional scaling (nMDS) were performed on the dataset using the group averaging strategy, the Bray- Curtis similarity measure and a Beta value of – 0.1. Ordinations were performed in two and three dimensions with 25 random starts and a minimum stress of 0.01. While both analysis methods were utilised, nMDS ordinations have been presented rather than cluster diagrams, as the former have been shown to be more robust and to provide more informative summaries in ecological studies (e.g. Kim et al. 2000; Quinn & Keough 2002; Cheng 2004). In some cases, results from cluster analysis have been

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Primer v6 by default employs Kruskal’s stress in nMDS ordinations for depicting the effort required to display the original resemblance data into a low-dimensional Euclidean space (2 or 3 dimensions) for ease of viewing. Quinn and Keough (2002) suggest that Kruskal stress values should ideally be <0.15 for configurations of objects to be reliable, although Clarke (1993) claims values <0.2 are generally acceptable; >0.35 and samples are effectively considered to be randomly placed. Clarke (1993) also notes that greater stress values are obtained with larger datasets, and in some cases ordinations of higher dimension may be required. Three dimensional ordinations generally return lower stress values, but are difficult to present in report format. In this study, results from 2-dimensional ordinations have been presented within the text for illustrative purposes, but Kruskal stress values from 3-dimensional ordinations have also been noted (Appendix 9 presents full 3-D ordinations).

Analysis of similarity within and between pre-defined location-based groups was undertaken with the ANOSIM routine. In this test, the R statistic (Global R) examines the difference in mean ranks between and within groups, and places a value between - 1 and +1; zero represents a random grouping of samples, unity signifies that within group similarity is greater than between group similarity (Clarke 1993).

Profiling

Profiling of Candidate-LHSGIF was undertaken using the results of the four data analyses as a guide. The SIMPER routine in Primer was used to generate diagnostic species lists for each defined floristic group, identifying the top 90% of floristic variation present in each group. This routine, which compares ‘similarity percentages’, decomposes average Bray-Curtis similarities among samples within a group into percentage contributions from each species, listing the species in decreasing order of contribution (Clarke & Gorley 2006). Broad environmental attributes (geology, rainfall) for each group were also assessed using geological mapping (NSW Department of Mineral Resources 1999) and annual rainfall figures from Bureau of Meteorology (2012). 156

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Mapping

The techniques of D-iSM (Chapter 2) have been implemented to prepare a map of Candidate-LHSGIF within Wyong local government area (LGA), and to answer the third research question:

Can a revised classification of LHSGIF be mapped at a local-scale to assist conservation planning, using the Wyong local government area as an example?

Previous knowledge of vegetation patterns within the LGA (Bell 2002a; Bell & Driscoll 2006a; Bell & Driscoll 2006b) allowed a mapping focus to be targeted where Candidate-LHSGIF was likely to occur (Fig. 4.4). Map data acquisition and classification (Steps 2 & 3 of D-iSM) were then implemented in remnant stands of vegetation within the target area, and also across urban and rural areas where remnant trees were recorded. Steps 4-7 in D-iSM were completed within the larger classification process (above), and are not detailed further here. For the process of pre-1750 mapping, remnant trees were linked with landscape features (terrain, elevation, drainage patterns) within a GIS to approximate the original distribution of Candidate-LHSGIF within Wyong LGA.

4.3 Results

4.3.1 Classification

Analysis 1: Regional Spotted Gum-Ironbark Vegetation

Analysis of 554 sample quadrats broadly referable to Spotted Gum-Ironbark forest showed a general trend of those representing Candidate-LHSGIF (dominated by Corymbia maculata and Eucalyptus fibrosa in the canopy), against all others (Fig. 4.5). With such a large dataset encompassing a broad geographical area (Hunter Valley to Nowra, separated by ~250 km), the spread of Candidate-LHSGIF samples within this data matrix will inevitably be difficult to resolve succinctly. From Fig. 4.5, the Candidate-LHSGIF group is not strongly differentiated from all other Spotted Gum- Ironbark forest types. In part, this may be due to subtle variations in floristic composition occurring between different Candidate-LHSGIF in different regions, and

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Fig. 4.4. Wyong focus area for mapping of Candidate-LHSGIF within Wyong local government area (base image courtesy EarthSat NaturalVue2000).

Analysis of similarity (ANOSIM) between these two broad groups (Candidate-LHSGIF & Other) returned a low Global R value of 0.19, yet was still significant at p<0.001 (Fig. 4.6). While this may indicate that the null hypothesis cannot be rejected (i.e. no real difference between the groups), this case warrants further consideration. In cases with many replicates in each group, Clarke and Warwick (2001) state that it is perfectly possible for R to be inconsequentially small yet significant. However, a more focused analysis on the apriori Candidate-LHSGIF group (see Analysis 2), excluding all other samples, will likely allow a clearer resolution of the data. Within the larger Spotted Gum-Ironbark dataset, Candidate-LHSGIF plots occupy a clear subset of the compositional space, but those plots are not clearly distinguished from all other plots.

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Fig. 4.5. Non-metric Multi Dimensional Scaling (2-dimensional) ordination of Analysis 1, showing the relationship between samples (Bray-Curtis similarity measure). Candidate-LHSGIF samples are broadly defined within the larger matrix. Kruskal’s stress = 0.21. (3-dimensional ordination, Kruskal’s stress = 0.16: see Appendix 9a).

Fig. 4.6. Distribution of 999 random permutations of the ANOSIM test statistic R for Analysis 1, and the true value of R (0.19, vertical dotted line at right) (p<0.001).

The geographical distribution of all 553 sample quadrats comprising the Candidate- LHSGIF and other Spotted Gum-Ironbark forest data are shown in Fig. 4.7. In most areas, Candidate-LHSGIF occurs within a wider mosaic of other Spotted Gum-Ironbark vegetation.

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Fig. 4.7. Geographical distribution of Candidate-LHSGIF and all other Spotted Gum- Ironbark forest sample quadrats in the Sydney Basin from Analysis 2.

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Analysis 2: Eucalyptus fibrosa – Corymbia maculata

Seven floristic groups were defined in this study from 215 quadrats where Eucalyptus fibrosa and Corymbia maculata dominated the canopy. These included five groups from the Hunter Valley (Cessnock Spotted Gum Ironbark, Hunter Spotted Gum Ironbark, Seaham Spotted Gum Ironbark, Broken Back Spotted Gum Ironbark, Sandstone Spotted Gum Ironbark), and one each from the NSW Central Coast (Hinterland Spotted Gum Ironbark) and South Coast (Morton Spotted Gum Ironbark). Fig. 4.8 presents the non-metric multidimensional scaling ordination for this analysis, showing the relationships between all seven groups. Within this ordination, the Cessnock Spotted Gum Ironbark group forms the core about which all other groups emanate. Stronger and better-defined groups include the Hinterland Spotted Gum Ironbark and Hunter Spotted Gum Ironbark. The Cessnock Spotted Gum Ironbark group is relatively diffuse and includes a higher level of heterogeneity difficult to define in a large data set (but see below). Interestingly, the Morton Spotted Gum Ironbark group from the NSW South Coast is floristically more closely aligned with the Cessnock and Hinterland Spotted Gum Ironbark groups than some other groups from the Hunter Valley.

Fig. 4.8. Non-metric Multi Dimensional Scaling (2-dimensional) ordination of Analysis 2, showing the relationship between samples (Bray-Curtis similarity measure). Kruskal’s stress = 0.24. (3-dimensional ordination, Kruskal’s stress = 0.17: see Appendix 9b).

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On further analysis, the Cessnock Spotted Gum Ironbark group of 66 quadrats can be resolved to a finer level by examining it in isolation (termed ‘progressive fragmentation’: Peet 1980). Using the same numerical techniques, a re-analysis of the Cessnock-only data showed that at 37% similarity, three distinct sub-groups are distinguishable primarily by the composition of the shrub layer (Fig. 4.9, Table 4.2). The presence and abundance of paperbarks (Melaleuca nodosa, M. decora) in the shrub layer is the most marked difference between the three sub-groups.

Fig. 4.9. Non-metric Multi Dimensional Scaling (2-dimensional) ordination of Cessnock Spotted Gum Ironbark group from Analysis 2, showing the relationship between three defined sub-groups at 37% similarity (Bray-Curtis similarity measure). Kruskal’s stress = 0.18. (3-dimensional ordination, Kruskal’s stress = 0.13: see Appendix 9c).

Analysis of similarity (ANOSIM) of species composition between groups of quadrats revealed a range of significant R values (Table 4.3; Fig. 4.10), with an overall Global R of 0.65 (p < 0.001). Thirteen of the thirty-six pair-wise comparisons returned values <0.80, indicating relatively poor resolution for some groups. In essence this suggests that some of the communities defined in this study comprise a continuum of Candidate-LHSGIF forms, rather than a collection of distinct vegetation types. Strongest paired comparisons within the ANOSIM were Sandstone Spotted Gum Ironbark (all Rs >0.82) and Cessnock Spotted Gum Ironbark (transitional) (all Rs >0.75).

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Sub-group No. % contributions of five % contributions of five dominant ground species dominant shrub species Cessnock paperbark* Entolasia stricta (9.25) Melaleuca nodosa (15.19) Aristida vagans (5.42) Bursaria spinosa (4.27) Lepidosperma laterale (4.53) Maytenus silvestris (3.90) Microlaena stipoides (3.52) Bursaria longisepala (1.89) Lomandra multiflora (2.67) Melaleuca decora (1.61) Cessnock non-paperbark* Entolasia stricta (6.82) Davieisia ulicifolia (4.55) Aristida vagans (5.75) Bursaria spinosa (3.17) Cheilanthes sieberi (3.93) Lissanthe strigosa (1.72) Lepidosperma laterale (3.66) Persoonia linearis (1.53) Lomandra filiformis (3.29) Acacia falcata (0.80) Cessnock transitional Entolasia stricta (7.06) Leptospermum parvifolium (5.89) Imperata cylindrica (6.82) Melaleuca nodosa (3.68) Pomax umbellata (5.36) Persoonia linearis (3.30) Joycea pallida (4.79) Grevillea parviflora (2.56) Lepidosperma laterale (4.67) Daviesia ulicifolia (1.99)

* ‘paperbark’ refers to shrubs or small trees within the genus Melaleuca (Myrtaceae).

Table 4.3 ANOSIM results (Global R values) for pair-wise comparisons of location-based groups for Analysis 2 (p < 0.001). R values closer to 1 represent stronger dissimilarity between group pairs.

Broken Hunter Morton Seaham Hinter Cess Cess Cess (t) Sandstone Back (npb) (pb) Broken Back Hunter 0.57 Morton 0.99 0.94 Seaham 0.94 0.81 0.95 Hinterland 0.72 0.80 0.62 0.62 Cessnock (npb) 0.51 0.58 0.61 0.53 0.32 Cessnock (pb) 0.88 0.84 0.92 0.73 0.63 0.53 Cessnock (t) 0.88 0.95 0.91 0.98 0.87 0.75 0.82 Sandstone 0.82 0.97 0.95 0.98 0.97 0.93 0.92 0.95

NB: npb = non-paperbark; pb = paperbark; t = transitional

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Fig. 4.10. Distribution of 999 random permutations of the ANOSIM test statistic R for Analysis 2, and the true value of R (0.65, vertical dotted line at right) (p<0.001).

Hunter Spotted Gum Ironbark returned R values >0.80 for all but two comparisons, which returned 0.57 (Broken Back Spotted Gum Ironbark) and 0.58 (Cessnock Spotted Gum Ironbark (non-paperbark)). These two communities merge with Hunter Spotted Gum Ironbark in the Belford-Pokolbin area of the Hunter Valley. Seaham Spotted Gum Ironbark returned R values of >0.81 for all but three comparisons, with Cessnock Spotted Gum Ironbark (non-paperbark and paperbark forms) and Hinterland Spotted Gum Ironbark returning Rs of 0.53 to 0.73. Once again, geographically these four communities merge in the Beresfield-Maitland-Clarencetown area of the Hunter, so similar floristic compositions may be expected.

Morton Spotted Gum Ironbark returned R values >0.91 for all pairs except Cessnock Spotted Gum Ironbark (non-paperbark) and Hinterland Spotted Gum Ironbark, with R values of 0.61 and 0.62 respectively. This confirms that the Morton Spotted Gum Ironbark group is more closely related to these two groups than it is to all other groups from the Hunter Valley. Poorest pairings included Cessnock Spotted Gum Ironbark (non-paperbark) (all but two Rs <0.61), and Hinterland Spotted Gum Ironbark. The latter returned a low R value particularly for Cessnock Spotted Gum Ironbark (non- paperbark) (0.32), but also for Seaham Spotted Gum Ironbark (0.62), Morton Spotted Gum Ironbark (0.62) and Cessnock Spotted Gum Ironbark (paperbark) (0.63). With the exception of Morton Spotted Gum Ironbark, these four communities merge in the

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Beresfield-Clarencetown area of the Hunter: Morton Spotted Gum Ironbark occurs 250 km away on the southern edge of the Sydney Basin.

In the absence of comprehensive mapping of these Candidate-LHSGIF groups, Fig. 4.11 shows the geographical distribution of quadrats comprising each of the nine types (the three Cessnock forms are shown collectively).

Analysis 3: Eucalyptus fibrosa

In total, 70 sample quadrats where Eucalyptus fibrosa dominated the canopy in near- monospecific stands of vegetation within the Sydney Basin were compared (Fig. 4.12). Seven floristic groups can be defined from this analysis, comprising samples extending from the Hunter Valley to the Burragorang Valley, south-west of Sydney. Four broad groups are distinguishable at 19% similarity. The first of these separates quadrats from moister forests in the Burragorang Valley from all others (Burragorang Moist Ironbark Forest). These forests commonly support the co-occurring canopy species Eucalyptus agglomerata, Allocasuarina torulosa, Eucalyptus hypostomatica and Melaleuca styphelioides. The second separation encompasses quadrats representing dry (<700 mm/yr) grassy vegetation from the central Hunter Valley (Hunter Floor Ironbark Forest), a Candidate-LHSGIF.

The third group of quadrats includes a major sub-group of samples from Permian sediments of the lower Hunter Valley (Cessnock Ironbark Forest) and the Central Coast hinterland (Hinterland Ironbark Forest), both also representing Candidate-LHSGIF. A second sub-group of quadrats from Permian sediments in the Burragorang Valley (Burragorang Dry Ironbark Forest) and Triassic Narrabeen sediments from the Yengo National Park area (Yengo Ironbark Forest) is also present here. The final group of quadrats collectively represents dry (<700 mm/yr) shrubby forests from Triassic Narrabeen sediments of the upper Hunter Valley (Goulburn Valley Ironbark Forest). This group of samples shows considerable floristic variation, possibly related to fire history, but as it is not considered a Candidate-LHSGIF it is not defined further in this study.

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Fig. 4.11. Geographical distribution of seven floristic groups defined from Analysis 2. Note that the three forms within Cessnock Spotted Gum Ironbark are shown as one.

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Fig. 4.12. Non-metric Multi Dimensional Scaling (2-dimensional) ordination of Analysis 3, showing the relationship between samples (Bray-Curtis similarity measure) and major groups defined at 19% similarity. Candidate-LHSGIF includes Cessnock Ironbark Forest, Hunter Floor Ironbark Forest and Hinterland Ironbark Forest. Kruskal’s stress = 0.21. (3-dimensional ordination, Kruskal’s stress = 0.14: see Appendix 9d).

The analysis of similarity (ANOSIM) between the seven defined groups returned a range of significant results (Table 4.4; Fig. 4.13), with an overall Global R value of 0.62 (p < 0.001).

Table 4.4 ANOSIM results (Global R values) for pair-wise comparisons of location-based groups for Analysis 3 (p < 0.001). R values closer to unit represent stronger dissimilarity between group pairs.

Burragorang Burragorang Goulburn Cessnock Hinterland Yengo Hunter Dry IF Moist IF Valley IF IF IF IF Floor IF Burragorang Dry IF

Burragorang Moist IF 0.79

Goulburn Valley IF 0.56 0.92

Cessnock IF 0.72 0.98 0.58

Hinterland IF 0.94 0.98 0.93 0.56

Yengo IF 0.41 0.90 0.46 0.51 0.67

Hunter Floor IF 1.00 0.95 0.82 0.87 0.98 0.89

NB: IF = ironbark forest

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Fig. 4.13. Distribution of 999 random permutations of the ANOSIM test statistic R for Analysis 3, and the true value of R (0.62, vertical dotted line at right) (p<0.001).

Of the seven groups, Burragorang Moist Ironbark Forest proved the most robust, with R values >0.90 for all comparisons except Burragorang Dry Ironbark Forest (0.79). Hunter Floor Ironbark Forest, a Candidate-LHSGIF, returned Rs >0.82 against all other groups, however the other two Candidate-LHSGIF groups (Cessnock Ironbark Forest, Hinterland Ironbark Forest) did not perform as strongly. Although Hinterland Ironbark Forest significantly differed from most other groups (Rs >0.93), it compared poorly against Cessnock Ironbark Forest (0.56) and Yengo Ironbark Forest (0.67). Cessnock Ironbark Forest returned R values >0.72 for Hunter Floor Ironbark Forest, Burragorang Dry and Burragorang Moist Ironbark Forests, but values <0.58 for other Hunter Valley groups. Wide ranging R values for Burragorang Dry Ironbark Forest (0.41 to 1.00), Goulburn Valley Ironbark Forest (0.46 to 0.93) and Yengo Ironbark Forest (0.41 to 0.90) reflected broad similarities between these and other groups, perhaps suggesting that additional data collection is required to better understand internal floristic variation.

The ANOSIM result from this analysis suggests that not all groups are equally defined. The strength of each defined group, based on pair-wise comparisons and in decreasing order, is Burragorang Moist Ironbark Forest > Hunter Floor Ironbark Forest > Hinterland Ironbark Forest > Burragorang Dry Ironbark Forest > Goulburn Valley Ironbark Forest > Cessnock Ironbark Forest > Yengo Ironbark Forest. Of the Candidate- LHSGIF groups, Hunter Floor Ironbark Forest and Hinterland Ironbark Forest can be

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All seven defined groups in this analysis can be categorised into two geological units. On Permian-aged geology, three groups occur in the Hunter Valley (Hinterland Ironbark Forest, Cessnock Ironbark Forest, Hunter Floor Ironbark Forest) and two in the Burragorang Valley (Burragorang Dry Ironbark Forest, Burragorang Moist Ironbark Forest). The remaining two groups (Goulburn Valley Ironbark Forest, Yengo Ironbark Forest) occur on Triassic Narrabeen geology, the former in dryer areas (<700 mm/yr) and the latter in slightly wetter areas (700-900 mm/yr). Fig. 4.14 shows the geographical distribution of these seven floristic groups.

This ANOSIM analysis supports the notion that the three Permian-based Hunter Valley vegetation types (Hinterland Ironbark Forest, Cessnock Ironbark Forest, Hunter Floor Ironbark Forest) are distinct, floristically and geographically, from similar vegetation elsewhere in the Sydney Basin. While the Cessnock Ironbark Forest performed only moderately in the pair-wise ANOSIM analysis, it differed sufficiently from groups in the Burragorang Valley and on the Hunter Valley Floor to be retained as distinct. Similarities of this group with others on Triassic Narrabeen (Goulburn Valley Ironbark Forest, Yengo Ironbark Forest) and Permian (Hinterland Ironbark Forest) geology, although reasonably strong, do not fully concur with the results of the numerical analysis (i.e. groups in the ordination are quite distinct, but this is not reflected in the ANOSIM analysis).

Section 4.3.2 profiles more fully the Candidate-LHSGIF groups from this analysis.

Analysis 4: Hunter Valley Floor

Eighty-nine quadrats from the main database met the criteria of a canopy dominated by Corymbia maculata and Eucalyptus fibrosa or Eucalyptus crebra within the central and upper Hunter Valley floor. As this area comprises the zone of confluence of three previously defined Candidate-LHSGIF groups (see Analysis 2), it was expected that these three groups also would be identifiable in this analysis.

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Fig. 4.14. Geographical distribution of seven floristic groups defined from Analysis 3 (Eucalyptus fibrosa dominated).

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Fig. 4.15 shows the nMDS ordination of the Hunter Valley floor data, highlighting a dichotomy between quadrats co-dominated by Eucalyptus fibrosa (Candidate-LHSGIF; Cessnock Spotted Gum Ironbark, Broken Back Spotted Gum Ironbark, Hunter Spotted Gum Ironbark) and those co-dominated by Eucalyptus crebra (non Candidate-LHSGIF; not defined further in this study). Evidently, with the addition of more data the Central Hunter Ironbark-Spotted Gum-Grey Box Forest of Peake (2006) can be sub-divided into one group where Eucalyptus crebra co-dominates with Corymbia maculata (the non Candidate-LHSGIF in Fig. 4.15), and a second group where Eucalyptus fibrosa co- dominates with Corymbia maculata (Hunter Spotted Gum Ironbark in Fig. 4.15).

Fig. 4.15. Non-metric Multi Dimensional Scaling (2-dimensional) ordination of Analysis 4, showing the relationship between samples of Candidate-LHSGIF (Eucalyptus fibrosa-Corymbia maculata) and non Candidate-LHSGIF (Eucalyptus crebra- Corymbia maculata) from the Hunter Valley floor (Bray-Curtis similarity measure). Kruskal’s stress = 0.19. (3-dimensional ordination, Kruskal’s stress = 0.13: see Appendix 9e).

The analysis of similarity (ANOSIM) between these groupings returned a Global R value of 0.35 (p<0.001). This low R value, although still significant (Fig. 4.16), is likely to be a reflection of the poor resolution of the large non Candidate-LHSGIF group. This group of data potentially includes several discernible sub-groups; however these have not been determined in this study (but see Peake 2006 for an overview of likely communities). In the pair-wise comparisons (Table 4.5), Cessnock Spotted Gum Ironbark Forest returned the strongest R values (0.77 to 0.91). Both Broken Back

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Spotted Gum Ironbark Forest (0.47) and Hunter Spotted Gum Ironbark Forest (0.22) returned low R values against the non Candidate-LHSGIF group. This result is likely due to the poor resolution of this latter group in this analysis (not an aim of this study), but also potentially may be a result of the high levels of disturbance evident in quadrats comprising it (e.g. most samples collected from current or past grazing lands, where weeds and regrowth from past clearing affect species composition and abundance: Clarke 2003; Prober et al. 2011). To counter the low result for Hunter Spotted Gum Ironbark Forest, an ANOSIM was re-run specifically for the low scoring Hunter Spotted Gum Ironbark Forest/non Candidate-LHSGIF pair (Fig. 4.17). This revealed that the true value of R (0.22) remained greater than any of the 999 random permutations, confirming that the null hypothesis (that there is no difference between these two groups) could be rejected.

The geographical distribution of component sample quadrats comprising the four defined groups in Analysis 4 is shown in Fig. 4.18.

Fig. 4.16. Distribution of 999 random permutations of the ANOSIM test statistic R for Analysis 4, and the true value of R (0.35, vertical dotted line at right) (p<0.001).

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Table 4.5 ANOSIM results (Global R values) for pair-wise comparisons of location-based groups for Analysis 4 (p < 0.001). R values closer to 1 represent stronger dissimilarity between group pairs.

Broken Back SG-I Non Candidate- Hunter SG-I Cessnock SG-I Forest LHSGIF Forest Forest Broken Back SG-I Forest

Non Candidate-LHSGIF 0.47

Hunter SG-I Forest 0.66 0.22

Cessnock SG-I Forest 0.77 0.88 0.91

NB: SG-I = spotted gum-ironbark; LHSGIF = Lower Hunter Spotted Gum-Ironbark Forest

Fig. 4.17. Distribution of 999 random permutations of the ANOSIM test statistic R for Analysis 4 (non Candidate-LHSGIF v Hunter Spotted Gum Ironbark Forest), and the true value of R (0.22, vertical dotted line at right) (p<0.001).

4.3.2 Profiling Candidate-Lower Hunter Spotted Gum-Ironbark Forest

The preceding data analyses have established that Candidate-LHSGIF, as a whole, occupies a clear subset of all Spotted Gum-Ironbark vegetation in the region, but individual plots are not clearly distinguished from those representing this broad regional unit (Analysis 1). When examined more closely Candidate-LHSGIF can be seen to comprise nine classificatory units (Analysis 2). Related vegetation in the Hunter region dominated by Eucalyptus fibrosa, with little or no Corymbia maculata, is distinct from other E. fibrosa-dominated vegetation elsewhere in the Sydney Basin (Analysis 3), and Candidate-LHSGIF from the Hunter Valley floor is distinct from Eucalyptus crebra- dominated vegetation from the same area (Analysis 4). It is appropriate now to draw 173

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Fig. 4.18. Geographical distribution of samples representing Candidate-LHSGIF (Cessnock Spotted Gum Ironbark, Broken Back Spotted Gum Ironbark, Hunter Spotted Gum Ironbark) and non Candidate-LHSGIF from the Hunter Valley floor, as defined in Analysis 4. together the results of these four analyses, to profile the various forms of Candidate- LHSGIF.

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For the purposes of profiling, the nine floristic groups defined in Analysis 2 have been combined with the three from Analysis 3. Due to low sample numbers and perceived impacts from past clearing and selective logging, Hunter Floor Ironbark Forest from Analysis 3 has been subsumed into Hunter Spotted Gum Ironbark Forest from Analysis 2. Prior to European settlement of the Hunter Valley in the 1800s, it is likely that Hunter Ironbark Forest supported more Corymbia maculata, but that the latter has been selectively logged out of some remnant stands. Floristic groups defined in Analysis 4 form part of those defined in Analysis 2. In summary, Table 4.6 outlines the diversity in Candidate-LHSGIF delineated in this study, and their major environmental parameters. For geographical distributions, refer to Fig. 4.11 (Groups 1-9) and Fig. 4.14 (Groups 10-11).

Table 4.6 Summary of Candidate-LHSGIF from the Sydney Basin.

Group Name Geology Rainfall (mm/yr) Distribution

1 Hinterland Spotted Gum Ironbark Forest Triassic/Permian 900-1200 Fig. 4.11 2 Seaham Spotted Gum Ironbark Forest Permian 900-1100 Fig. 4.11 3 Cessnock Spotted Gum Ironbark Forest (npb) Permian 800-1100 Fig. 4.11 4 Cessnock Spotted Gum Ironbark Forest (pb) Permian 700-1000 Fig. 4.11 5 Cessnock Spotted Gum Ironbark Forest (t) Permian 700-900 Fig. 4.11 6 Broken Back Spotted Gum Ironbark Forest Permian 700-800 Fig. 4.11 7 Hunter Spotted Gum Ironbark Forest Permian 700-800 Fig. 4.11 8 Sandstone Spotted Gum Ironbark Forest Triassic <700 Fig. 4.11 9 Morton Spotted Gum Ironbark Forest Permian 700-800 Fig. 4.11 10 Hinterland Ironbark Forest Permian 1000-1100 Fig. 4.14 11 Cessnock Ironbark Forest Permian 700-1000 Fig. 4.14

From Analysis 2 and Analysis 3, Table 4.7 presents the results of the SIMPER analysis, detailing the characteristic species defining each of the nine floristic groups where Eucalyptus fibrosa and Corymbia maculata co-dominate the canopy (Groups 1-9 in Table 4.6), together with comparable data from Analysis 3 for the two groups dominated by Eucalyptus fibrosa alone (Groups 10-11 in Table 4.6).

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Table 4.7 Summary of floristic composition for eleven defined units from Analysis 2 & Analysis 3. Top 90% of diversity shown for each unit and percentage

contributions to overall diversity for that unit.

npb)

(pb) (t)

SGIF

SGIF( SGIF SGIF

SGIF

Hunter Morton Seaham Hinterland Cessnock Cessnock Cessnock Sandstone IF Hinterland CessnockIF Growth Form Back Broken Tree

Angophora costata 1.89

Corymbia gummifera 1.26

Corymbia maculata 7.61 6.95 8.38 8.41 8.98 9.74 8.92 6.14 12.36 Eucalyptus acmenoides 0.62

Eucalyptus crebra 1.12

Eucalyptus fibrosa 9.90 9.85 8.16 6.18 9.68 9.77 13.87 6.35 12.16 8.88 16.28 Eucalyptus globoidea 1.54 1.29

Eucalyptus moluccana 0.96

Eucalyptus punctata 0.52

Eucalyptus sp (Kurri Kurri) 1.70

Eucalyptus umbra 2.58

Shrub

Acacia amblygona 4.51

Acacia elongata 1.78

Acacia falcata 0.78 0.80

Acacia implexa 0.61

Acacia parvipinnula 0.89 0.92

Acacia ulicifolia 1.92 0.94 0.66 0.85 0.68 1.22

Astrotricha obovata 0.73 0.96

Breynia oblongifolia 1.01

Bursaria longisepala 1.89

Bursaria spinosa subsp spinosa 0.94 1.49 0.76 3.17 4.27 2.62 2.12 2.31

Callistemon linearis 0.94 1.32

Choretrum sp. A 1.23 2.13

Daviesia ulicifolia subsp. ulicifolia 1.03 1.88 4.20 1.01 1.56 4.55 1.99

Dillwynia retorta 1.79 1.29

Dillwynia sieberi 1.92

Dodonaea viscosa subsp. cuneata 1.77 4.13

Exocarpos strictus 0.71

Grevillea montana 1.88 1.21

Grevillea parviflora subsp. parviflora 2.56

Hakea sericea 1.12 1.20

Indigofera australis 0.65

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npb)

(pb) (t)

SGIF

SGIF( SGIF SGIF

SGIF

Hunter Morton Seaham Hinterland Cessnock Cessnock Cessnock Sandstone IF Hinterland CessnockIF Growth Form Back Broken Jacksonia scoparia 0.85

Leptospermum parvifolium 5.89

Leucopogon juniperinus 0.75 0.50 1.48

Leucopogon muticus 6.14

Lissathe strigosa subsp. subulata 4.96 1.11 0.80 1.72 1.53 3.21

Maytenus silvestris 3.90

Melaleuca decora 1.61 5.65 2.23

Melaleuca nodosa 0.73 15.19 3.68 6.35 16.28

Melichrus urceolatus 2.43

Notelaea longifolia 0.46

Persoonia linearis 4.62 0.48 1.53 3.30 3.13 0.93

Persoonia mollis subsp. leptophylla 0.63

Pultenaea spinosa 1.89 0.59 2.46 2.24

Pultenaea paleacea 1.17

Pultenaea villosa 0.69 0.62 0.67

Styphelia triflora 3.00

Subshrub

Brachyscome multifida var. multifida 1.05

Eremophile debilis 1.83

Gonocarpus tetragynus 2.12 2.11 5.82

Hibbertia diffusa 0.92

Hibbertia peduncularis 0.86

Platysace ericoides 1.30

Pomax umbellata 3.78 1.01 5.37 2.49 0.83 5.36 5.11 2.25

Solanum prinophyllum 1.00 0.42

Forb

Arthropodium milleflorum 1.99

Brunoniella australis 1.77 0.73 1.80 0.83 1.72

Calotis lappulacea 0.74

Desmodium gunnii 0.75

Dichondra repens 1.09 2.50

Glossocardia bidens 0.65

Goodenia hederacea subsp. hederacea 2.03

Goodenia heterophylla subsp. heterophylla 2.44

Goodenia rotundifolia 3.77 3.41 0.98 0.91 3.48

Lagenophora stipitata 1.42 1.41

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npb)

(pb) (t)

SGIF

SGIF( SGIF SGIF

SGIF

Hunter Morton Seaham Hinterland Cessnock Cessnock Cessnock Sandstone IF Hinterland CessnockIF Growth Form Back Broken Laxmannia gracilis 0.95

Opercularia diphylla 0.85 1.64 1.38 0.57 0.58 2.28

Oxalis perennans 0.99 0.82

Phyllanthus hirtellus 0.80 2.19 0.74 4.57 5.15 1.99

Pratia purpurascens 1.71 4.57 2.17 1.68 1.61 2.42

Pseuderanthemum variabile 3.51

Stackhousia muricata 0.73

Tricoryne simplex 0.81

Vernonia cinerea var. cinerea 0.95 3.25 3.57 0.79 1.44

Grass

Anisopogon avenaceus 3.98

Aristida ramosa 1.81 4.75 0.82 1.59

Aristida vagans 2.65 6.07 1.84 5.69 3.44 5.75 5.42 1.77 2.83 6.73

Cleistochloa rigida 4.58

Cymbopogon refractus 0.83 4.18 1.17 0.92

Dichelachne micrantha 0.70

Digitaria ramularis 0.55 3.42

Echinopogon caespitosus var. caespitosus 0.85

Echinopogon ovatus 1.01 2.00

Entolasia marginata 0.53

Entolasia stricta 3.28 0.73 5.40 6.12 7.60 6.82 9.25 7.06 6.85 11.15

Eragrostis brownii 0.85 0.70 1.23 2.26 2.88 1.14 1.01

Eragrostis leptostachya 0.70

Imperata cylindrica 1.56 0.85 0.86 1.08

Joycea pallida 2.87 4.45 0.75 4.79 2.10

Microlaena stipoides var. stipoides 1.49 4.17 3.39 5.01 4.56 1.97 3.52 5.07

Panicum effusum 1.95

Panicum simile 3.48 3.93 3.84 2.84 1.26 4.98 4.05

Paspalidium distans 1.39 3.05 1.72 1.20 0.69 4.11

Themeda australis 3.18 6.10 3.07 4.1

Graminoid

Dianella caerulea var. assera 0.99 1.69

Dianella caerulea var. caerulea 0.73

Dianella revoluta var. revoluta 4.93 3.73 4.45 1.74 2.43 1.08 1.25 2.55

Dianella tasmanica 1.15

Lepidosperma gunnii 4.53

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npb)

(pb) (t)

SGIF

SGIF( SGIF SGIF

SGIF

Hunter Morton Seaham Hinterland Cessnock Cessnock Cessnock Sandstone IF Hinterland CessnockIF Growth Form Back Broken Lepidosperma laterale 5.70 0.50 2.70 3.66 4.53 4.67 2.16 1.15 6.03

Lomandra confertifolia subsp. pallida 3.37 2.37 0.83 1.65 0.51

Lomandra confertifolia subsp. rubiginosa 3.11 5.38

Lomandra cylindrica 6.82

Lomandra filiformis subsp. coriacea 3.77 2.27 1.59 3.29 0.64 1.45 1.12

Lomandra filiformis subsp. filiformis 2.92 0.96 2.47

Lomandra glauca 1.90

Lomandra longifolia 0.76

Lomandra multiflora subsp. multiflora 2.96 4.33 3.80 4.74 3.31 2.40 2.67 1.98 2.99 2.54 1.43 Lomandra obliqua 1.61 1.01 1.15

Patersonia sericea 1.11

Fern

Cheilanthes sieberi subsp. sieberi 3.36 3.94 1.42 2.39 3.93 2.35 1.78 3.42 3.13

Twiner

Billardiera scandens 0.64 0.58

Cassytha glabella forma glabella 2.06

Desmodium rhytidophyllum 0.47

Eustrephus latifolius 0.70

Glycine clandestina 1.36 0.71 2.70 1.70 1.82 3.65

Glycine tabacina 2.28

Hardenbergia violacea 0.87 1.17 1.69

Parsonsia straminea 0.82

Sedge

Cyperus gracilis 0.62

Ptilothrix deusta 1.06 1.42 4.00

Cycad

Macrozamia communis 2.40

Macrozamia reducta 3.41

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4.3.3 Mapping

Map data acquisition across the Wyong focus area revealed observable patterns in the distribution of the key canopy species Eucalyptus fibrosa and Corymbia maculata, particularly over fragmented lands. Using these data, in combination with topographical and drainage features, the original distribution of Candidate-LHSGIF within Wyong can be recreated with a level of confidence not previously available in mapping studies of Wyong LGA. Ongoing data collection in privately owned lands as permission is granted will improve the resolution of this mapping.

Fig. 4.19 shows the pre-1750 distribution of Candidate-LHSGIF within Wyong LGA, created using elements of the D-iSM process. Unsuitable habitat, in the form of coastal wetlands protected under State Environmental Planning Policy No. 14 (Coastal Wetlands) is also shown. In total, 997 ha have been mapped across a highly fragmented landscape, although sizeable remnants still remain. Full floristic sampling (see Section 4.3.1) classified these areas into the Hinterland SGIF group of Candidate- LHSGIF.

4.4 Discussion

Revising an existing vegetation community with new data, particularly an endangered one that has legislative support, can either clarify previous understandings of that community, or create management problems through increased complexity. Discussion here begins with a brief review of studies elsewhere that have revised existing vegetation communities, followed by a summary of current understanding of Lower Hunter Spotted Gum-Ironbark Forest as a consequence of the research now reported. Questions are then raised in regard to reservation and the threatened status of this community, and some key principles in community refinement for wider application are presented.

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Fig. 4.19. Pre-1750 distribution of Candidate-LHSGIF within Wyong local government area, showing data collected during the map data acquisition phase of D-iSM. SEPP14 Wetlands represent lands protected under NSW State Environmental Planning Policy No. 14 Coastal Wetlands, and unsuitable habitat for Candidate-LHSGIF (base image courtesy LPI NSW).

4.4.1 Refining Existing Communities

Vegetation classification is dynamic, and vegetation communities are abstract entities created to facilitate communication about them (de Cáceres & Wiser 2012). For a vegetation classification to be applicable to management and conservation, it is

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Refining existing communities essential that it be undertaken at a scale which reflects the floristic variation and ecological complexity of its component units. If communities are defined too broadly, important regional variants may be overlooked; if too narrow, there is a risk that conservation measures will be lost in community complexity (Tozer 2010). Threatened Ecological Communities are intrinsically linked to the scale of their description, and are equally prone to broad and narrow circumscription depending on perceived management and conservation requirements. When threatened communities become legal entities, such as through listing in threatened species legislation, the importance of scale in their description is particularly paramount, and reviews may be required to clarify descriptions.

Implementing a preferential sampling strategy is often the only way in which further refinement of a community or group of communities can be undertaken. There are many examples from throughout the world where a particular class or type of vegetation has been examined in closer detail through implementation of preferential sampling (e.g. Avis & Lubke 1996; Shaltout & Mady 1996; Peinado et al. 1998; Koroleva 1999; Tsuyuzaki 2002; Vandvik & Birks 2002; Durigan et al. 2003; Prober & Theile 2004; Curran et al. 2008; Skull & Congdon 2008; Matthews et al. 2011; Miehe et al. 2011; Wesche & von Wehrden 2011). All of these studies aimed to elucidate the floristic relationships within a particular study group to facilitate management and conservation. Some studies combine existing sample data with newly collected data to examine a common theme. Diekmann et al. (1999), for example, investigated forests dominated by Beech (Fagus sylvatica) in Denmark, Sweden and Norway. As in the present study, multivariate analysis was carried out on a dataset comprising existing and new sample data, with existing data having satisfied stringent criteria including dominance by Fagus sylvatica. Diekmann et al. (1999) identified fifteen Beech types for their study area across a large area of Scandinanvia. In many European countries, large existing datasets of samples are available from which to draw data for specific research questions such as these (see Dengler et al. 2011), and there are currently moves to streamline the classification of vegetation throughout the world (Wiser et al. 2011; de Cáceres & Wiser 2012).

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Several studies have been undertaken in Australia where Threatened Ecological Communities (TECs; vulnerable, endangered or critically endangered) have been reviewed with additional data collection and analysis. For example, French et al. (2000) documented some of the problems associated with classifying vegetation from the critically endangered Cumberland Plain Woodland in western Sydney. As with the current study, Cumberland Plain Woodland (CPW) supports associations of Spotted Gum, Grey Box (Eucalyptus moluccana) and Ironbarks. French et al. (2000) found that the high variation in species composition between sites of CPW could not be consistently sorted into discrete floristic subunits, and that the high spatial variability ideally should be recognised as part of the diversity of that community. They suggested that the variations evident across the landscape may not warrant a separate classification, but instead were a measure of the natural variation within the one community. Tozer (2010) further examined the significance of CPW within a State-wide context, and concluded that it occupies a unique ecological niche governed by rainfall and temperature, and that it supports assemblages of species not replicated in coastal valley landscapes elsewhere in New South Wales.

Similar difficulty with rationalising an existing vegetation classification with new data has been reported by Simpson (2000) for the lower Blue Mountains, west of Sydney, including the nationally endangered Shale-Sandstone Transition Forest. In his study, Simpson (2000) found that the addition of 141 new sample quadrats to an existing dataset resulted in the creation of five new communities, as the original classification could not accommodate the variation present within the additional sites. Elsewhere, Keith and Holman (2002), in preparation for a NSW Land and Environment Court hearing, used existing data to compare the vegetation present within four disjunct bodies of sand-based vegetation within the Sydney Basin to determine their extent of similarity. Using similar methods to that in the current study, they found that there was a significant difference in the vegetation occurring in all four bodies, such that distinct communities could be delineated. One of these communities, Kurri Sands Swamp Woodland, is endangered in New South Wales (and a component of this was highlighted in Chapter 3 of this thesis).

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Further examples examining Threatened Ecological Communities in New South Wales are many. Prober (1996) and Prober and Theile (2004) presented patterns in floristic composition within the wide-ranging White Box-Yellow Box-Blakely’s Redgum Woodland, while Payne et al. (2010) suggested a new (broader) definition of Umina Coastal Sandplain Woodland following regional-scale numerical analysis. D.Bell et al. (2008) examined variability in upland wetlands of the New England tablelands, and Bell and Stables (2012) argue for an expansion of range of Pittwater Spotted Gum Forest following additional sampling and analysis in the region. Other studies have examined regional datasets with the aim of identifying those types most at risk (e.g. Whinam & Chilcott 2002; Whinam et al. 2003; Keith & Scott 2005; Kendall & Snelson 2009).

Establishment of a classification of vegetation communities for a particular area should not cease at the end of a project. Rather, recognition of the dynamic nature of a classification (Jennings et al. 2008; de Cáceres & Wiser 2012) should be acknowledged. Refinement and improvement with new or additional data should always remain an option to improve resolution and understanding. In the present study, a decade of new data and greater field reconnaissance has resulted in a finer resolution of the LHSGIF complex.

4.4.2 Current Understanding of Lower Hunter Spotted Gum-Ironbark Forest

In the current study, Candidate-LHSGIF from the Sydney Basin has been examined through numerical classification of new and existing quadrat data, refining the description of this and related communities. Listing of LHSGIF as an EEC in February 2005 followed its classification and mapping by NSWNPWS (2000b) in the lower Hunter and Central Coast. However, it was not long before problems in the field application of this community were evident (e.g. Bell 2003; Peake 2006; Wells 2007; HSO Ecology 2009a). Had it been recognized sooner by relevant authorities (e.g. NSW Scientific Committee), vegetation described by Gellie (2005) for the South Coast of New South Wales may have had important conservation implications in the practical application of this EEC, not the least of which may have necessitated a name change for LHSGIF. In that study, Gellie (2005) defined a Northern Coastal Hinterland Shrub/Grass Dry Forest from Nowra that effectively equated to LHSGIF. Interestingly,

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Refining existing communities subsequent broad-scale classification subsumed that community into a broader unit covering over 20,000 hectares (Tozer et al. 2010). Broadening of existing vegetation units in this way curtails the cause of conservation, particularly in regard to rare or geographically restricted vegetation communities.

Under the three constraining determiners of region (Sydney Basin), geology (Permian or Triassic Narrabeen) and canopy dominants (Corymbia maculata, Eucalyptus fibrosa) (extracted from NSW Scientific Committee 2010b), analysis of a key dataset of over 200 sample quadrats collected by a single observer has shown there to be eleven distinct vegetation communities within the Lower Hunter Spotted Gum-Ironbark Forest complex (see Tables 4.6 & 4.7 in Section 4.3.2). But which of these should qualify for legal protection under the current definition of LHSGIF EEC? The NSW TSC Act 1995 dictates that a final determination is to include a number of descriptive paragraphs to allow a user to identify whether or not a particular site supports a TEC, but it does not stipulate the definitive number or identity of species that need to be present (Preston & Adam 2004a, 2004b; Larkin 2009).

Of the fourteen paragraphs outlining LHSGIF EEC shown in NSW Scientific Committee (2010b) (see Appendix 5), two of these (Paragraphs 1 & 2) provide the key descriptors to assist a user. Paragraph 1 lists twenty-four plant species which occur within (i.e. define) the community, based on information available at the time of listing. When the eleven Candidate-LHSGIF groups (Section 4.3.2) are compared against this list of 24 typical species, eight groups score greater than 80%, or >19 of the listed species (Table 4.8). In this context, these eight groups may be considered the ‘core’ LHSGIF in the region, although the remaining three groups also arguably may be considered so. A few points of particular interest are worthy of mention here:

Hunter Spotted Gum Ironbark Forest, previously considered a component of the Central Hunter Ironbark-Spotted Gum-Grey Box Forest (NSWNPWS 2000b; Peake 2006), returned a score of 96% of species on the list (Table 4.8).

Hinterland Spotted Gum Ironbark Forest returned a score of 92% in Table 4.8. This group had been previously overlooked in regional classification studies, yet

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extends north from the Wyong area on the Central Coast to at least Clarencetown on the North Coast.

Table 4.8 Comparison of Candidate-LHSGIF groups against the key characteristic species as listed in Paragraph 1 of the Lower Hunter Spotted Gum–Ironbark Forest EEC determination (NSW Scientific Committee 2010b). Those groups scoring >80% of

the 24 characteristic species are shaded, and may be considered ‘core’ LHSGIF.

Species

CessnockSGIF (npb) SGIF Hunter SGIF Hinterland SGIF Seaham Back SGIF Broken CessnockSGIF (pb) SGIF Morton CessnockIF CessnockSGIF (t) IF Hinterland SGIF Sandstone n = 39 24 81 13 10 16 13 11 11 12 8

Acacia parvipinnula √ √ √ √ √ √ √ Breynia oblongifolia √ √ √ √ √ √ Bursaria spinosa √ √ √ √ √ √ √ √ √ √ Cheilanthes sieberi √ √ √ √ √ √ √ √ √ √ √ Corymbia maculata √ √ √ √ √ √ √ √ √ √ √ Cymbopogon refractus √ √ √ √ √ √ √ √ √ Daviesia ulicifolia √ √ √ √ √ √ √ √ √ √ Dianella revoluta √ √ √ √ √ √ √ √ √ √ √ Entolasia stricta √ √ √ √ √ √ √ √ √ √ √ Eucalyptus crebra √ √ √ √ Eucalyptus fibrosa √ √ √ √ √ √ √ √ √ √ √ Eucalyptus punctata √ √ √ √ √ √ Glycine clandestina √ √ √ √ √ √ √ √ √ Lepidosperma laterale √ √ √ √ √ √ √ √ √ √ √ Lissanthe strigosa √ √ √ √ √ √ √ √ Lomandra multiflora √ √ √ √ √ √ √ √ √ √ √ Maytenus silvestris √ √ √ √ √ √ √ √ Melaleuca nodosa √ √ √ √ √ √ √ √ Microlaena stipoides √ √ √ √ √ √ √ √ √ √ Persoonia linearis √ √ √ √ √ √ √ √ √ √ √ Phyllanthus hirtellus √ √ √ √ √ √ √ √ √ √ √ Pomax umbellata √ √ √ √ √ √ √ √ √ √ Pratia purpurascens √ √ √ √ √ √ √ √ Themeda australis √ √ √ √ √ √ √ √ Total spp (from 24) 23 23 22 22 21 21 21 20 17 16 14 % present 96 96 92 92 88 88 88 83 71 67 58

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Seaham Spotted Gum Ironbark Forest, formerly a component of a broader unit of the same name (NSWNPWS 2000b), returned a score of 92% (Table 4.8). For both this group and the Hunter Spotted Gum Ironbark Forest, this study has shown that portions of these former broad units are very closely related to LHSGIF.

Morton Spotted Gum Ironbark Forest, not considered to be related to LHSGIF prior to this study, returned a score of 88% (Table 4.8). This is of particular interest, given its occurrence ~250 km to the south of the Hunter Valley, west of Nowra, but still within the Sydney Basin and on Permian sediments.

Paragraph 2 of NSW Scientific Committee (2010b) includes a list of 55 species which are said to ‘characterize’ the EEC. This list includes 19 eucalypts, which represents a disproportionate number of canopy species for a forest of this type (i.e. of fifty-one TECs listed for the Sydney Basin Bioregion, forty-eight (94%) support fewer than 10 eucalypts in their descriptions). Despite this, a similar comparison of the eleven Candidate-LHSGIF groups shows a broadly similar pattern (Table 4.9), with percentage congruence ranging from 33 to 78% (note that 5 of the 55 species were not present in the analysis dataset, and the presence of these as characteristic species within the EEC is now questioned as a result of this study). If Cessnock SGIF (npb), at 78%, is considered the benchmark (since this area is considered the core of distribution in the current determination: NSW Scientific Committee 2010b), then it may be reasonable to assume that those groups scoring perhaps >60% congruence equate to LHSGIF (Note: this is an arbitrary figure, and ultimately the NSW Scientific Committee will need to determine an appropriate cut-point). Fig. 4.20 illustrates the results of both comparisons combined, and highlights those Candidate-LHSGIF groups most similar to the current determination for LHSGIF.

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Table 4.9 Comparison of Candidate-LHSGIF groups against the full species list as contained in Paragraph 2 of the Lower Hunter Spotted Gum–Ironbark Forest EEC determination (NSW Scientific Committee 2010b). Those groups scoring >60% of

the 24 characteristic species are shaded, and may be considered ‘core’ LHSGIF.

Species

CessnockSGIF (npb) SGIF Hinterland SGIF Seaham SGIF Morton SGIF Hunter CessnockSGIF (pb) Back SGIF Broken IF Hinterland CessnockIF CessnockSGIF (t) SGIF Sandstone n = 39 81 13 13 24 16 10 12 11 11 8

Acacia parvipinnula √ √ √ √ √ √ √ Angophora costata √ √ √ Aristida vagans √ √ √ √ √ √ √ √ √ √ √ Billardiera scandens √ √ √ √ √ √ √ Breynia oblongifolia √ √ √ √ √ √ Bursaria spinosa √ √ √ √ √ √ √ √ √ √ Cheilanthes sieberi √ √ √ √ √ √ √ √ √ √ √ Corymbia eximia √ √ Corymbia gummifera √ √ √ Corymbia maculata √ √ √ √ √ √ √ √ √ √ √ Cymbopogon refractus √ √ √ √ √ √ √ √ √ Daviesia leptophylla Daviesia ulicifolia √ √ √ √ √ √ √ √ √ √ Dianella caerulea √ √ √ √ √ √ Dianella revoluta √ √ √ √ √ √ √ √ √ √ √ Digitaria parviflora √ Entolasia stricta √ √ √ √ √ √ √ √ √ √ √ Eucalyptus acmenioides √ √ √ Eucalyptus agglomerata Eucalyptus caniculata Eucalyptus crebra √ √ √ √ Eucalyptus fergusonii √ Eucalyptus fibrosa √ √ √ √ √ √ √ √ √ √ √ Eucalyptus globoidea √ √ √ Eucalyptus moluccana √ √ Eucalyptus nubila Eucalyptus paniculata √ √ Eucalyptus punctata √ √ √ √ √ √ Eucalyptus siderophloia √ √ √ Eucalyptus sparsifolia √ √ Eucalyptus tereticornis Eucalyptus umbra √ √ √ √ Glycine clandestina √ √ √ √ √ √ √ √ √ Goodenia hederacea √ √ √ Grevillea montana √ √ √ √ √ √ Hardenbergia violacea √ √ √ √ √ √ √ √ √ √ √ Laxmannia gracilis √ √ √ √ √ Lepidosperma laterale √ √ √ √ √ √ √ √ √ √ √ Lissanthe strigosa √ √ √ √ √ √ √ √ Lomandra filiformis √ √ √ √ √ √ √ √ √ √ √ Lomandra longifolia √ √ √ √ √ √ 188

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Species

CessnockSGIF (npb) SGIF Hinterland SGIF Seaham SGIF Morton SGIF Hunter CessnockSGIF (pb) Back SGIF Broken IF Hinterland CessnockIF CessnockSGIF (t) SGIF Sandstone Lomandra multiflora √ √ √ √ √ √ √ √ √ √ √ Macrozamia flexuosa √ √ √ √ √ Maytenus silvestris √ √ √ √ √ √ √ √ Melaleuca nodosa √ √ √ √ √ √ √ √ Microlaena stipoides √ √ √ √ √ √ √ √ √ √ Ozothamnus diosmifolius √ √ √ √ Panicum simile √ √ √ √ √ √ √ √ √ √ Persoonia linearis √ √ √ √ √ √ √ √ √ √ √ Phyllanthus hirtellus √ √ √ √ √ √ √ √ √ √ √ Pomax umbellata √ √ √ √ √ √ √ √ √ √ Pratia purpurascens √ √ √ √ √ √ √ √ Syncarpia glomulifera √ √ √ Themeda australis √ √ √ √ √ √ √ √ Veronica cinerea √ √ √ √ √ √ √ Total spp (from 55) 43 43 34 34 32 32 29 28 27 24 18 % present 78 78 62 62 58 58 53 51 49 44 33

Sandstone SGIF

Hinterland IF

Cessnock SGIF (t)

Cessnock IF

Broken Back SGIF

Cessnock SGIF (pb) Characterise Morton SGIF Define Seaham SGIF

Hinterland SGIF

Hunter SGIF

Cessnock SGIF (npb)

0 20 40 60 80 100 No. of Species

Fig. 4.20. Comparison of Candidate-LHSGIF groups against species lists contained in Paragraphs 1 (‘Define) and 2 (‘Characterise’) of the Lower Hunter Spotted Gum– Ironbark Forest EEC determination (NSW Scientific Committee 2010b).

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Floristic profiles of the eleven Candidate-LHSGIF groups have been provided in Section 4.3.2 (Table 4.7), but some additional points can be made for each of these. As presented in the results section of this chapter, all groups have been given a geographical tag for ease of reference, which is a reflection of the floristic analyses performed earlier. In the following text, ‘SGIF’ refers to ‘Spotted Gum-Ironbark Forest’.

Cessnock SGIF Group – The Cessnock area represents the core of the Candidate- LHSGIF, from where most other groups emanate. Three forms of LHSGIF have been defined in this group, one [Cessnock SGIF (pb)] where paperbarks (in particular, Melaleuca nodosa) dominate the understorey, one [Cessnock SGIF (npb)] where paperbarks are absent but the prickly-leaved shrubs Daviesia ulicifolia and Bursaria spinosa dominate, and one [Cessnock SGIF (t)] occurring on transitional sandstone soils with species such as Leptospermum parvifolium, Persoonia linearis and Grevillea parviflora subsp. parviflora. A fourth group (Cessnock Ironbark Forest) is characterised by very little or no Corymbia maculata, and a mid-storey of Melaleuca decora and Melaleuca nodosa.

Hinterland SGIF Group - Within the Hinterland Group of communities, there are many areas where the influence of adjacent vegetation communities is strongly evident, considerably more so than in equivalent areas in the Cessnock district. In particular, a range of other canopy species is likely to be present within Hinterland SGIF where it adjoins Coastal Plains Smooth-barked Apple Woodland (unit 30 of NSWNPWS 2000b). In these situations, it is not uncommon for species such as Angophora costata, Corymbia gummifera, Eucalyptus punctata, Eucalyptus umbra, Eucalyptus capitellata, Eucalyptus globoidea, and in some restricted locations Eucalyptus propinqua, to also occur sporadically with the Corymbia maculata-Eucalyptus fibrosa dominanted canopy. This feature is potentially an important diagnostic feature in delineating Hinterland SGIF from other groups within the region, and may be attributable to the complex geology present within the Permian aged sediments. A second form within this group, Hinterland Ironbark Forest, is dominated by Eucalyptus fibrosa in the canopy, with a mid-storey of Melaleuca decora and Melaleuca nodosa.

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Hunter SGIF Group – Formerly considered a component of the Central Hunter Ironbark-Spotted Gum-Grey Box Forest (NSWNPWS 2000b; Peake 2006), Hunter SGIF is characterised by a sparse shrub layer, but with a well developed ground layer dominated by the grasses Aristida ramosa, Aristida vagans, Cymbopogon refractus and Microlaena stipoides var. stipoides. Within the central-upper Hunter Valley, this group is restricted in distribution, and is replaced by similar forests dominated by Eucalyptus crebra, Corymbia maculata and/or Eucalyptus moluccana. Under current legislation, this group is included within the Central Hunter Ironbark–Spotted Gum–Grey Box Forest EEC (NSW Scientific Committee 2010c), rather than the LHSGIF EEC. In the original study of Peake (2006) for the Hunter Valley floor, only ten of over 300 quadrats supported Eucalyptus fibrosa in the canopy, and vegetation where Eucalyptus fibrosa co-occurs with Corymbia maculata was restricted to the Branxton-Ravensworth area.

Seaham SGIF Group – NSWNPWS (2000b) originally described Seaham Spotted Gum- Ironbark Forest as occurring on Carboniferous sediments north of the Hunter River, and including a range of canopy species. As defined in the current study, Seaham SGIF is dominated by Corymbia maculata and Eucalyptus fibrosa, with a ground layer characterised by the grasses Aristida vagans, Entolasia stricta, Microlaena stipoides, Pratia purpurascens, Pseuderanthemum varibile and Veronica cinerea var. cinerea. Seaham SGIF as here defined is a component of the NSWNPWS-defined Seaham SGIF.

Morton SGIF Group – The disjunct stands of Candidate-LHSGIF along the Shoalhaven River near Nowra on the South Coast add a further level of complexity to the issue of LHSGIF EEC. Despite previous sampling and profiling of vegetation in this area (Gellie 2005), the current study is the first to highlight the similarities of this vegetation to that occurring in the lower Hunter Valley. In the Nowra area, Candidate-LHSGIF occurs on Permian sediments, which outcrop as mid and lower slopes along the southern side of the Shoalhaven River near Burrier. Typically, these forests occur on dryer northerly or westerly aspects, with associated species including occasional individuals of Corymbia gummifera, Corymbia eximia, Eucalyptus punctata, Eucalyptus beyeriana and Eucalyptus sparsifolia. Similar forests dominated by Corymbia maculata and Eucalyptus

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Two forms of LHSGIF are evident in Morton SGIF. The first supports many of the species typical of LHSGIF in the Cessnock area of the Hunter Valley, where Daviesia ulicifolia, Acacia ulicifolia, Lissanthe strigosa, Dillwynia sieberi and Macrozamia communis are characteristic in the understorey, with Entolasia stricta, Lomandra confertifolia subsp. rubiginosa and Lepidosperma laterale on the ground. The second form is a transitional type, where species more typically associated with sandstone substrates occur prominently. Examples here would include Persoonia mollis subsp. leptophylla, Banksia spinulosa var. spinulosa, Grevillea arenaria, Leptospermum trinervium and Hakea laevipes var. laevipes. As in the Cessnock group, transitional forms occur where geographical proximity (e.g. upslope) or depth to sandstone substrate are indicative.

The Yalwal Shale-Sandstone Transition Forest of Tozer et al. (2010), which includes the Morton SGIF, covers some 21,100 ha of the region, yet forests dominated by Eucalyptus fibrosa and Corymbia maculata appear to comprise only a small proportion of this area (~200-300 ha). At a finer scale, and based on limited reconnaissance in the Nowra area, the Yalwal Shale-Sandstone Transition Forest includes at least three forms: a Corymbia maculata–Eucalyptus fibrosa alliance (the Morton SGIF here defined); a Corymbia maculata–Eucalyptus beyeriana alliance; and a Eucalyptus punctata–Eucalyptus sparsifolia–Eucalyptus beyeriana alliance.

Broken Back SGIF Group – Along the foothills of the Broken Back Range near Cessnock, Candidate-LHSGIF vegetation occurs that is characterised by the shrubs Acacia amblygona, Lissanthe strigosa, Pultenaea spinosa and Acacia elongata. A strong graminoid presence (Lepidosperma gunni, Dianella revoluta var. revoluta, Lomandra spp) is also typical. On current knowledge, this form of LHSGIF occurs only on the Broken Back Range, immediate west of Cessnock SGIF and south of Hunter SGIF.

Sandstone SGIF Group – Restricted to Triassic Narrabeen sediments in the upper Hunter Valley, in the vicinity of Manobalai Nature Reserve north of Sandy Hollow, 192

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Sandstone SGIF is characterised by the shrubs Leucopogon muticus and Dodonaea viscosa subsp cuneata, and the grass Cleistochloa rigida. Of all defined groups, Sandstone SGIF is the most dissimilar to the current (NSW Scientific Committee 2010b) definition of LHSGIF.

As an aid to the identification of the various groups of Candidate-LHSGIF defined in this study, a dichotomous key has been prepared to assist users to differentiate between the eleven forms (Table 4.10). Keys such as this have been used relatively rarely in the literature for vegetation communities (but see Webb 1978; Adam 1981; Pickard 1983; Aaseng et al. 1993; NSWNPWS 2002; Harris & Kitchener 2005; Moxham et al. 2009; Struckhoff et al. 2010), yet represent a powerful method to aid field recognition.

4.4.3 Lower Hunter Spotted Gum-Ironbark Forest as a TEC

Given the variability and distribution of Candidate-LHSGIF shown in the current study, should LHSGIF be considered threatened in New South Wales? This is a complex question which requires a careful answer. Although not exclusively the domain of the term endangered, EECs are often rare or highly restricted in distribution (see Chapter 1, Section 1.1.4). Logically, rare communities can become endangered through land- use change far easier than widespread communities. This study has shown that LHSGIF, in a broad sense, comprises eleven different definable communities (Tables 4.6 & 4.7), so it follows that some of these communities may be more threatened than others. For example, those forms of Candidate-LHSGIF occurring in the rapidly developing coastal zone (e.g. Hinterland SGIF) or lower Hunter Valley (e.g. Cessnock SGIF) can be considered under greater threat (see NSWDECC 2009b) than those occurring elsewhere (e.g. Broken Back SGIF, Morton SGIF). Prioritising the level of threat to each of the Candidate-LHSGIF communities is perhaps the next step towards sensible conservation, but awaits completion of detailed range-wide mapping (see Section 4.4.4).

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Table 4.10. Dichotomous key for field recognition of Candidate-LHSGIF groups defined in this study for the Sydney Basin.

1. Canopy strongly dominated by Eucalyptus fibrosa; with Melaleuca decora & M. nodosa as a mid-layer ...... 2 2. Low grass diversity, characterised by Entolasia stricta, Aristida vagans and Panicum simile ...... Cessnock Ironbark Forest 2* Higher grass diversity, characterised by above 3 spp and Microlaena stipoides, Paspalidium distans, Themeda australis, Joycea pallida...... Hinterland Ironbark Forest 1* Canopy dominated by Eucalyptus fibrosa with Corymbia maculata ...... 3* 3. Mid-storey characterised by paperbarks, particularly Melaleuca nodosa, with Bursaria spinosa ...... Cessnock SGIF (pb) 3* Mid-storey with paperbarks (Melaleuca spp.) sparse or completely absent ...... 4 4. Shrub layer with ‘sandstone’ species common, though not dominant, such as Persoonia linearis, Dillwynia sieberi, Hakea sericea ...... 5

5. Shrub layer including Grevillea arenaria, Persoonia mollis subsp leptophylla and Macrozamia communis ...... Morton SGIF 5* Shrub layer including Grevillea parviflora subsp parviflora, Grevillea montana and Dillwynia retorta ...... Cessnock SGIF (t) 4* Shrub layer with ‘sandstone’ species uncommon ...... 6 6. Shrubs dominated by Acacia amblygona, Dodonaea viscosa var. cuneata, Leucopogon muticus or Lissanthe strigosa ...... 7 7. Ground layer dominated by Lepidosperma gunnii, Lomandra spp., Dianella revoluta ...... Broken Back SGIF 7* Ground layer dominated by Cleistochloa rigida...... Sandstone SGIF 6* Shrubs dominated by prickly-leaved shrubs, including Daviesia ulicifolia, Bursaria spinosa, Podolobium ilicifolium, P. aciculiferum ...... 8 8. Ground layer dominated by the grasses Themeda australis, Joycea pallida & Entolasia stricta ...... Hinterland SGIF 8* Ground layer with the grasses Themeda australis and Joycea pallida not dominant, often sparse or absent ...... 9 9. Ground layer dominated by the grasses Aristida vagans, Aristida ramosa & Cymbopogon refractus ...... Hunter SGIF 9* Ground layer dominated by the grasses Entolasia stricta, Aristida vagans, Panicum simile, Themeda australis...... 10 10. Ground layer with common herbs & forbs Pratia purpurascens, Vernonia cinerea, Dichondra repens, Pseuderanthemum variabile ...... Seaham SGIF 10*. Ground layer with common herbs & forbs Phyllanthus hirtellus, Pomax umbellata, Goodenia rotundifolia ...... Cessnock SGIF (npb)

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In recent years, there has been some debate within land-use authorities over the inclusion of some components of LHSGIF as detailed in this chapter within the existing determination for LHSGIF in the Sydney Basin Bioregion. In particular, vegetation within the Hinterland group of communities has been scrutinised more heavily than others due to development pressures (e.g. Bell 2009c, 2010b, 2012). Some developments in the Wyong local government area have been dogged by the contrasting views of ecological consultants, who must rely on legal definitions to determine concurrence with the EEC. Recent cases in the NSW Land and Environment Court have shown that the absence of one species, even an identified canopy species used in the name of a TEC, is not considered conclusive evidence of the presence of a TEC [e.g. Motorplex (Australia) Pty Limited v Port Stephens Council (2007) NSWLEC74 & Providence Projects Pty Ltd v Gosford City Council (2006) NSWLEC52: see Larkin 2009].

The 2010 amendment to the final determination to list Lower Hunter Spotted Gum– Ironbark Forest in the Sydney Basin Bioregion (NSW Scientific Committee 2010b) as endangered chose to excise Dungog local government area from the list of known local council areas supporting the community. This was undertaken because Dungog LGA does not occur within the Sydney Basin Bioregion (as defined in Thackway & Cresswell 1995), and the two descriptive determiners were consequently in conflict (see Appendix 5). However, prior to this amendment, a published account of the occurrence of LHSGIF within Columbey National Park (Bell 2009a), predominantly in Dungog LGAs, established that 475 ha of this EEC were conserved within that reserve, and therefore within the NSW North Coast Bioregion. The formal name of the community, viz. Lower Hunter Spotted Gum-Ironbark Forest in the Sydney Basin Bioregion, may have been better changed to “… in the Sydney Basin and NSW North Coast Bioregions” to avoid the conflict in descriptive determiners, rather than assuming (wrongly) that the community could not occur there. The present study has confirmed that LHSGIF does indeed extend into the Dungog LGA and the NSW North Coast Bioregion.

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In respect to the original definition of LHSGIF (NSWNPWS 2000b), there are two key methodological features that are likely to have influenced its description:

Sample selection – The selection of sample quadrats within the NSWNPWS (2000b) project was undertaken using an environmentally stratified random sampling approach (see Chapter 1, Section 1.3.2). As was shown in Chapter 2, such a sampling strategy fails to consistently sample observable floristic variations in a landscape, leading to low confidence classifications and maps. Similarly, in the Wyong local government area, classification and mapping also was undertaken using an environmentally stratified random sampling approach (Bell 2002a), which inevitably overlooked some rare communities, and included others (i.e. LHSGIF) within broader units.

Observer variability - One of the problems associated with the original description of LHSGIF was that it was defined from 95 sample quadrats collected by six different observers. For practical reasons, employing multiple teams of botanists to survey across large areas of land is often the most economical sampling strategy, and one which is hard to avoid. However, with multiple observers comes increased chance of errors associated with seasonality and misidentification of plant species (Kirby et al. 1986; Scott & Hallam 2002; Kelly et al. 2011), and in the application of cover abundance codes (Hatton et al. 1986; Cheal 2008; Bergstedt et al. 2009). Such errors are rarely acknowledged in classification projects; however there are exceptions (eg. Diekmann et al. 1999; Peake 2006; Tozer et al. 2010). Tozer et al. (2010) represents one of the few studies where supplementary data analysis was undertaken to assess potential differences among observers in the estimation of cover abundance scores. The level of experience of individual botanists can have far-reaching implications if, for example, a dominant species of shrub remains unidentified because it was not flowering at the time of survey, when a more competent botanist with strong regional experience will recognize it immediately. Similarly, if the recording of cover abundance codes is consistently lower by one botanist than in all others, an artificial group in the

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data analysis may result, potentially leading to poor classification and conservation outcomes. In relation to seasonality, samples collected from the same area in different seasons or years can also impact on a final classification (Hall & Okali 1978; Kirby et al. 1986; Scott & Hallam 2002), and should be acknowledged.

With six different observers partaking in the survey that originally identified the LHSGIF, it was plausible that a wider diversity of plant taxa would be recorded than may otherwise be present. Fig. 4.21 shows an extract from the NSWNPWS (2000b) classification, specifically the 95 component quadrats which defined the LHSGIF. As can be seen, there is evidence of potential observer variability with little mixing within the 95 quadrats, particularly with Observer 6, but also with Observers 1 and 3. An alternative explanation for this display of data may be that each observer was sampling in different geographical areas or different plant assemblages, but a review of the data (at least against 1:25,000 topographical map sheets) shows this not to be the case. Data from all 95 sample quadrats were used to formulate a description of LHSGIF, which was then adopted for listing as an EEC. Hearn et al. (2011) highlight similar problems between different observers in the United Kingdom, although their study compared vegetation maps prepared by different observers from the one classification.

Although the revision of Candidate-LHSGIF as detailed in the current study extends both the geographical distribution and diversity of this community, certain components of it would still meet requirements for threatened status. Mapping for the entire distribution of Candidate-LHSGIF is not complete or consistent across its range; however sufficient information is currently available to determine threat status (Peake 2006; NSWDECC 2008b; Bell 2009c). Most forms (Hinterland SGIF, Hinterland IF, Cessnock SGIF (pb, npb, t), Cessnock IF, Hunter SGIF) occur within highly urbanised or rural landscapes, and only four (Morton SGIF, Seaham SGIF, Sandstone SGIF, Broken Back SGIF) incur a low threat status due to current conservation-based land tenures or low development pressures.

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● ( 62)______● ( 326)______|_|_____ | | | | ● ( 1667)______| ○ ( 346)______| | | | | ● ( 1668)______|____ | ○ ( 347)______|__|_|__ | | | ● ( 1670)______| | ■ ( 319)______| | | | ● ( 1671)______|_____|__|__ ■ ( 744)______|___ | | | | ● ( 68)______| ■ ( 320)______| | | | | ● ( 79)______|_ | ■ ( 738)______||_ | | | | ● ( 72)______|_____|_ ■ ( 737)______|__ | | | | ● ( 182)______| ■ ( 736)______|______|_| | | ● ( 211)______|__ | ■ ( 186)______| || | ● ( 195)______| | ■ ( 188)______|_ | || | ● ( 196)______|____| | ■ ( 187)______| | || | ● ( 2126)______||___ | ■ ( 189)______|___|______| || | ● ( 336)______| | ■ ( 312)______| | || | ● ( 739)______|___ | | ■ ( 1674)______|__ | | || | ○ ( 344)______|____ | | ■ ( 1673)______|__ | | || | ○ ( 345)______|_| | □ ( 760)______| | | || | ● ( 1457)______|| | □ ( 772)______|_____|_ | | || | ● ( 1458)______| || | ■ ( 335)______|_| | || | ● ( 1459)______|__|_|| | ■ ( 1675)______||_ | || | ■ ( 317)______| | ■ ( 310)______| | || | ● ( 574)______| | | ■ ( 318)______| | | || | ● ( 575)______|___ | | | ■ ( 331)______|__|_|__|| | | ● ( 733)______|_|_ | | ■ ( 334)______|||_ | | ● ( 337)______| | | □ ( 750)______| | | ● ( 732)______| | | | □ ( 757)______| | | | ● ( 1652)______|__| | | | □ ( 758)______|_|____ | | | ● ( 1656)______|_|__|_|____ □ ( 751)______| | | | ● ( 74)______| □ ( 752)______|____ | | | | ● ( 167)______|_ | □ ( 754)______|_|__ | | | ○ ( 145)______|___ | □ ( 759)______| | | | ○ ( 343)______| | □ ( 766)______| | | | | ○ ( 2042)______| | | □ ( 767)_____|______|__ | | | | ○ ( 349)______| | | □ ( 765)______|_____|_ | | | ○ ( 1685)______|___|___ | | □ ( 753)______| | | | ● ( 1654)______| | | □ ( 768)______| | | | | ● ( 1655)______|____ | | | □ ( 769)______|______| | | | | ● ( 2125)______|_| | | □ ( 773)______| | | | | | ● ( 1669)______|___| | □ ( 770)______|____|_ | | | | ■ ( 2128)______||__|_ □ ( 771)______|_|____ | | | ● ( 181)______| □ ( 761)______| | | | ● ( 1612)______|__ | □ ( 762)______|_ | | | | ▲ ( 1442)______| | □ ( 764)______|____ | | | | ◙ ( 1462)______|______|__ | □ ( 763)______| | | | | ■ ( 313)______| | □ ( 775)______|_|______|___|______| | ■ ( 745)______| | | |

■ ( 746)______|_____|_____|_ | | ● ( 309)______| | | ● ( 325)______| | | | ● ( 327)______|____ | | | |

Fig. 4.21. Extract of sample dendrogram of NSWNPWS (2000b) used to delineate LHSGIF. Observer 1 = ●; Observer 2 = ○; Observer 3 = ■; Observer 4 = ▲; Observer 5 = ◙; Observer 6 = □.

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4.4.4 Distribution & Reservation of Lower Hunter Spotted Gum-Ironbark Forest

Mapping the full geographical distribution of Candidate-LHSGIF has not yet been accomplished in a detailed fashion. NSWNPWS (2000b), Eco Logical Australia (2003), McCauley et al. (2006) and NSWOEH (in prog.) have modeled its distribution, but these products are of limited accuracy (see Chapter 1, Section 1.2). Some areas have, however, been mapped to a higher level of detail using the D-iSM method, including the Cessnock subregion (NSWDECC 2008b), some conservation reserves (Bell 2009a; Bell & Driscoll 2009), Lake Macquarie local government area (Bell 2009d) and Wyong local government area (current study). Other detailed mapping in the region is continuing.

Within the Wyong focus area of the current study, it was possible to reconstruct the probable pre-1750 distribution of Candidate-LHSGIF, with just under 1000 ha mapped. Prior regional and LGA-wide mapping projects (Payne & Duncan 1999; NSWNPWS 2000b; Bell 2002a; Eco Logical Australia 2003; McCauley et al. 2006) failed to identify this vegetation within the local government area, or included aspects of it within broader communities. For Wyong LGA, the recording of dominant canopy, shrub and ground species in remnant vegetation has, together with a good working knowledge of local vegetation patterns, allowed a reconstructed map to be created for this rare vegetation community. Similar studies elsewhere in Australia have used remnant tree species, among other methods, to inform pre-disturbance landscapes (e.g. Fensham & Fairfax 1997; Bickford & Mackey 2004; Moxham et al. 2009; Sinclair & Atchison 2012) with similar degrees of success.

Recognising that detailed mapping of Candidate-LHSGIF throughout its regional distribution is still incomplete, it is possible to review the current reservation status of this EEC based on information available to date (Table 4.11). In total, over 4000 ha of Candidate-LHSGIF are known from dedicated conservation reserve in the Lower Hunter region (National Park, Nature Reserve, State Conservation Area or Regional Park), from an overall modeled distribution of 26,518 ha (Eco Logical Australia 2003). Largely excluded from these figures are the proposed conservation lands as detailed in the National Parks Estate (Lower Hunter Region Reservations) Act 2006. Even with the

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Table 4.11. Summary of reservation status of Candidate-LHSGIF communities.

Extent Group LGA Reserve Source (ha) Hinterland Lake Macquarie Sugarloaf SCA 81 Bell & Driscoll 2009 Cessnock Sugarloaf SCA 166 Bell & Driscoll 2009 Newcastle Bluegum Hills RP ~10 Unpubl. data Great Lakes/ Port Karuah NP ~30 Bell 2002b; unpubl. Stephens data

Cessnock Cessnock Werakata NP 1818 NSWDECC 2008b Cessnock Werakata SCA 1161 NSWDECC 2008b Port Stephens Columbey NP 12 Bell 2009a Dungog Columbey NP 463 Bell 2009a Dungog/ Port Stephens Wallaroo NP ~300 Bell 2002b; unpubl. data

Hunter Singleton Belford NP ~200 Unpubl. data

Morton Shoalhaven Morton SCA ~120 Unpubl. data Morton NP ~10 Unpubl. data

Sandstone Muswellbrook Manobalai NR ~300 Unpubl. data

TOTAL 11 reserves ~4671

NB: NP = National Park; NR = Nature Reserve; SCA = State Conservation Area; RP = Regional Park; LGA = Local Government Area

4.4.5 A Revised Lower Hunter Spotted Gum-Ironbark Forest Determination?

Within the Sydney Basin, it is evident that vegetation meeting the criteria of the Lower Hunter Spotted Gum–Ironbark Forest EEC (NSW Scientific Committee 2010b) is more varied, and more widely distributed than originally thought (see Table 4.6, Section 4.3.2). Following the parameters outlined in the final determination for this EEC, this study has used preferential sampling and the principles of D-iSM to define eleven distinct vegetation units with strong affinities to LHSGIF. But what of similar stands of vegetation that occur outside of the Sydney Basin, and on geological substrates other than Permian- or Triassic-aged? Vegetation within Columbey National Park (Dungog local government area) and in the Nowra district clearly show strong affinities to

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LHSGIF (this study, and see Bell 2009a), but how does Castlereigh Ironbark Forest in western Sydney (Tozer 2003), forests on Ordivician sediments near Batemans Bay and Narooma (Gellie 2005), or more broadly, components of the regional ecosystems in Queensland (Sattler & Williams 1999) compare?

The New South Wales Threatened Species Conservation Act (1995) recognizes that with additional numerical data analysis and study, improved understanding and clarification of listed TECs is achievable (NSW Scientific Committee 2010a). Following the data analysis detailed in this chapter, the current determination for listing Lower Hunter Spotted Gum–Ironbark Forest as endangered in New South Wales (NSW Scientific Committee 2010b) requires some revision. In particular, further detail on the various floristic forms of this and related communities should be considered, together with a rethinking of the locality descriptor in the name under which this vegetation is currently known.

4.5 Conclusions: Key Principles in Community Refinement

Lower Hunter Spotted Gum-Ironbark Forest (in a broad sense) has been used to illustrate that a shift towards preferential sampling should be incorporated more fully into the identification and definition of rare or threatened vegetation communities. In doing so, descriptions can be utilized more confidently by third party users to assist management and conservation.

As a result of this study, some key principles can be outlined which will assist in the refinement of vegetation communities through revision by numerical classification:

1. Community nomenclature – existing communities which include a geographical location or key component species in their name can be misleading (unless it is certain that it occurs no-where else). At the time, such a community may well encapsulate current knowledge, but revision should acknowledge the dynamic nature of classification (Jennings et al. 2003; de Cáceres & Wiser 2012). As in plant taxonomy, naming a new species after the location in which it is known does not predetermine that species to only occur there: Pomaderris bodalla, for example, was formerly named by Walsh and Coates (1997) for a site near

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Bodalla on the South Coast of New South Wales, but soon after was discovered 500 km to the north in the Hunter Valley (Bell 2001).

2. Sample selection – given the same sampling intensity, locating sample quadrats preferentially, rather than through an environmentally stratified random stratification is more likely to detect unusual or rare variations within a community. By its nature, preferential sampling can also target under-sampled variations or forms of an existing community. If existing community definitions are known to be difficult to apply practically, then it is likely that sampling has not encompassed all variation.

3. Diagnostic species – for third party use of a classification, lists of diagnostic plant species are critical, for it is these that differentiate between one community and another. Once enshrined in legislation as part of a TEC, such lists become particularly powerful. Lists of diagnostic plant species can be generated from a dataset with ease, but it is crucial that such a list is based on a sound classification. Preferential sampling to address all observable variation, such as has been done for LHSGIF in this chapter, is the key to a sound classification and reliable diagnostic species lists.

4. Observer variability – awareness of how an original classification was developed, including sample methodology, can be important. If multiple observers were involved, does analysis reveal artificial groupings which are difficult to rationalise? As in Tozer et al. (2010), a supplementary data analysis may clarify such groupings. In LHSGIF, for example, the original classification of NSWNPWS (2000b) comprised data from six observers, some of which showed limited mixing in data analysis (Fig. 4.21), but all data contributed to the LHSGIF EEC determination.

5. Perceived distribution – an existing community will often have an implied geographical distribution within which it is expected to occur. Often these are mapped or modelled to form a concrete association (Braun-Blanquet 1928, 1932), but these are not always definitive. Candidate-LHSGIF has been shown in

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Refining existing communities the current study to occur 250 km to the south of the Hunter Valley, where 88% of 24 species that typify LHSGIF EEC are present (see Table 4.8). Mapping of vegetation variability, specifically the floristic strata units in Step 4 of D-iSM (Chapter 2, Section 2.2.2), can be instrumental in documenting the distribution of a community.

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Chapter 5: General Discussion

Rare communities Survey design Sampling design standards Re-setting the focus Conclusion & Further work

General Discussion

Overview

The successful detection and mapping of rare vegetation communities is conditional upon a number of key themes in vegetation science. One of the most influential of these is that of sample selection: how sample locations are chosen within the wider environment. As has been shown in this thesis, adoption of a simple change in the way that sampling is undertaken (preferential rather than random stratification; or for large study areas, preferential complimenting random stratification) can dramatically improve the detection and definition of rare communities. Chapter 2 outlines a simple procedure to facilitate this change in methodology, strongly based on the Zurich- Montpellier school of vegetation science. The collection of ground-data points prior to establishing a sampling framework enables more efficient and representative sampling and results in a more reliable and long-lived vegetation classification and map. Chapters 3 and 4 use this new method to investigate community diversity in parts of the Central Coast of New South Wales to define new diversity (Chapter 3) and to refine an existing endangered community (Chapter 4). Existing standards and guidelines for vegetation classification vary throughout Australia and the world. In Australia, it is suggested that a re-setting of the focus is required so that classification in general, and detection of rare communities in particular, can be more reliably documented to bring them onto an equal footing with knowledge on rare and threatened plant taxa. Seven critical steps are outlined for this shift in focus to occur.

In conclusion, the simple conceptual framework of Top-Down, Bottom-Up information processing has been used to illustrate why this re-focusing is required, and to facilitate a change in thinking for vegetation scientists. Classifications established on Bottom-Up theory will provide more reliable and accurate information than Top-Down ones. Further research on sampling adequacy, definition of community boundaries, and transient and keystone communities are highlighted as the logical next steps in improving local-scale classification for land-use planning and conservation. Perhaps even of more interest than these, however, is the issue of when biodiversity management should shift from an ecosystem approach to a species approach - as the resolution of community classifications improve, such questions will invariably be posed. 205

General Discussion

Research presented in this thesis has addressed some of the problems recognised in vegetation classification in Australia, and elsewhere in the world, particularly as they relate to the recognition and protection of rare communities. It stems from practical implementation of a range of classifications in eastern Australia over many years, and the difficulties they present for land managers in respect to land use. In essence, four key themes in vegetation classification underlie or facilitate the body of work examined:

the identification and description of rare vegetation communities sampling design for vegetation classification sample selection and mapping based on ground-data existing standards and guidelines for sampling design

Using refinements to traditional preferential sampling and mapping methods (Chapter 2), the central theme of sampling design has been shown to be critical in the identification of new communities (Chapter 3), and in the revision of existing communities (Chapter 4). In Australia, and potentially other parts of the World where vegetation science is in its infancy, a re-assessment of sampling design is required to ensure that rare communities are not overlooked in conservation planning.

5.1 Identifying and Describing Rare Communities

Like rare plant species, rare vegetation communities can be problematic to locate, and their presence can create conflicts in land-use management and planning (Chapter 1, Section 1.1.4). However, unlike rare plant species, relatively little research has been expended on rare vegetation communities (Kontula & Raunio 2009). Frameworks for the categorisation of rare communities have been proposed for some parts of Europe (Izco 1998; Paal 1998; Kontula & Raunio 2009) and America (Drever et al. 2010), typically based on the seminal work of Rabinowitz et al. (1986) for plant species, but there has been no worldwide application of these. In New Zealand, rare terrestrial ecosystems have been listed from literature reviews and expert knowledge (Williams et al. 2007) and their status assessed (Holdaway et al. 2012), but a lack of comprehensive vegetation data there necessitated a diagnosis based on physical

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General Discussion environmental parameters (soil, particle size, drainage etc) rather than vegetation attributes. The scheme for assessing community extinction risk proposed by Rodríguez et al. (2007) shows promise, and is analogous to the International Union for the Conservation of Nature (IUCN) species assessments. It directly addresses reduction of land cover and continuing threat, rate of land cover change, fragmentation and (importantly) highly restricted geographical distribution. Along these lines, a recent expansion of the coverage of the IUCN ‘Red List’ for ecosystems was proposed at the 4th World Congress (Resolution 4.020 - Quantitative thresholds for categories and criteria for threatened ecosystems: IUCN 2009), and subsequent revisions of the IUCN ‘Red List’ will address current deficiencies. In Australia, several studies have examined the conservation status of vegetation communities (Specht et al. 1974, Benson 1989; Hager & Benson 1994; Specht et al. 1995; Woinarski et al. 1996), but none have established frameworks for rarity. With the IUCN proposal, more formalised recognition of rare communities seems likely, and will facilitate much needed change in Australia and other countries.

For vegetation communities to be recognised as rare, a certain level of contextual information is required, against which geographical distribution and floristic composition can be compared. This presents an intriguing situation in that a vegetation community may be considered rare in one jurisdiction, but with increased contextual data it may be seen to be quite common (as is also the case for rare plant species: Crain & White 2011). This illustrates the dynamic nature of vegetation classification (Jennings et al. 2003; de Cáceres & Wiser 2012) and, in this sense, issues of scale are critical (Preston & Adam 2004a; Keith 2009; Wiser et al. 2010). In many parts of the world, including Australia, sufficient contextual information is often unavailable at appropriate scales to enable comprehensive assessments of rarity to be made, meaning that the application of the term ‘rarity’ to a community may be transient and quickly loose currency. For conservation planning based on legislation, this presents further problems as perceived rare communities become legally protected, often defined at inappropriate scales or prior to a full understanding of their distributions and compositions. Again, acceptance of the dynamic nature of

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General Discussion vegetation classifications is critical, together with a willingness to embrace updating and revision (Fallding et al. 2001).

Although still adhered to in many parts of the world (Kent & Coker 2001; Dale 2002), some regions have diverged away from the ideals of the Zurich-Montpellier school of vegetation classification. In some countries, random sampling is still practised widely, as evidenced in the recent literature (e.g. Clarkson et al. 2011; Garzón-Machado et al. 2011). An inclination towards environmentally stratified random sampling in place of preferential sampling, perhaps to economise on limited research funds or to maximise effort over large study areas, has resulted in often misleading or inaccurate classifications with limited life spans (e.g. Wardell-Johnson et al. 2007). In these instances, rare or restricted vegetation communities are particularly prone to neglect (Jennings et al. 2003), as they rely on chance for their detection. Increasingly, field ecologists are forced to adapt local-scale classification of community diversity, based on preferential sampling, onto classifications established under environmentally stratified random sampling.

5.2 Survey Design for Vegetation Classification

In the Northern Hemisphere, where vegetation classification has a considerably longer history than in Australia, concepts of the Zurich-Montpellier school have been more widely followed (Kent & Coker 2001; Dale 2002). Despite criticisms of preferential sampling (e.g. Lájer 2007), a concept central to the Zurich-Montpellier technique, there are numerous published examples where the method has been adopted for classification studies (see Chapter 1, Section 1.3), and indeed is the recommended standard in many jurisdictions (see later in Section 5.4). Preferential sampling powerfully informs a vegetation classification, as it allows observable differences in floristic composition to be targeted and tested. It also lends support to data balance issues, which have been shown to be important in classification studies (e.g. Cooper et al. 2006). Knowing where to place sample quadrats relies on a good knowledge of a particular study area (another key concept of the Zurich-Montpellier school), so that representative and homogeneous stands of vegetation can be selected and examined. There have been some published attempts to formally reproduce, via computer 208

General Discussion programs, the objectivity of the traditional expert-based Zurich-Montpellier system (e.g. Westfall et al. 1997; Kočí et al. 2003). However, to date these methods are not commonly represented in the worldwide literature.

Issues of scale can be pivotal in vegetation classification. Keith (2009) has synthesised interpretation, assessment and conservation of ecological communities in Australia, and suggested that communities be viewed within a framework that distinguishes thematic, spatial and temporal scales. Thematic scale (resolution) describes the level of resemblance at which a given community is distinguished from others, and is often expressed in a hierarchical manner (i.e. broad to narrow). Such an approach can allow for intergradations of different classifications undertaken at different resolutions, such as is currently being undertaken in New South Wales (Benson 2006). However, there is no guarantee that finer resolution communities will be afforded an appropriate level of recognition. This presents problems for region-based planning where resources are insufficient for production of fine resolution classifications, and reliance is placed on broader classifications to surrogate for community biodiversity (such has been happening in the Hunter Valley of New South Wales: see Chapters 3 & 4). An inherent assumption in this scenario is that region-scale community diversity, in the absence of a local-scale alternative, overlooks the possibility that significant communities of high resolution may be present in an area.

This problem is not restricted to Australia: Kuželová and Chytrý (2004), for example, examined local and regional scale classifications in the Czech Republic and found a range of responses in coherency of vegetation units across the two resolutions. They concluded that successful transferral of region-scale classifications to local-scale communities occur best in more diverse landscapes, or in those landscapes that are centrally located along a regional biodiversity gradient. In New Zealand, Wiser et al. (2010) concluded from their study on the vegetation of gravel beaches that a greater understanding of rare ecosystems can be achieved through recognition of landscape context and investigating at multiple scales. In reality, there is often a tendency for regional-scale classifications to be forced upon local-scale diversity (and despite scale mismatch: Guerrero et al. 2013), irrespective of the ‘goodness of fit’, to assist and

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General Discussion expedite land-use decisions (e.g. NSW BioBanking Scheme: NSWDECC 2009a). Most of the nine Scribbly Gum communities defined in Chapter 3 of this thesis, for example, are currently attributable to two regional communities. The importance of resolution in a classification and its implications for conservation has been discussed at length in Chapter 3 (Sections 3.4.2 & 3.4.3).

Under Australian conditions, Benson (2008a) outlined potential problems resulting from numerical classifications of sample data, including (1) poor stratification or low density of quadrats; (2) variations in survey times and seasons; (3) analysing combined datasets collected using different methods, in different seasons or by different observers; (4) subjectivity in selecting floristic groups from cluster analyses; and (5) selection of species to include in analyses. He suggested that careful assessment of quality and consistency of quantitative data be made prior to extrapolating results to mapping. While all of these issues are true and valid, a sixth problem, sample selection, is worth adding to this list. Given the variability in standards and guidelines for vegetation classification (see later in Section 5.4), conservation implications of poor sample selection techniques have not been truly realised. Preferential sampling based on a thorough knowledge of on-ground diversity was central to the identification of rare vegetation communities dominated by Scribbly Gum on the Central Coast of New South Wales (Chapter 3). It was also critical to the revision of Lower Hunter Spotted Gum-Ironbark Forest EEC in this same region (Chapter 4). In both circumstances, the existing regional-scale classification was not able to fully accommodate the observable local-scale community diversity, despite attempts to do so. A major part of this problem is likely to be that the regional classification was based on a sampling regime guided by environmentally stratified random sampling. Data balance (Cooper et al. 2006) may also have contributed to this problem, and it has not yet been tested whether or not simply increasing sampling density within environmental strata would have improved that classification.

Selection of floristic groups from numerical analysis still involves a certain level of expert judgement and intuition (i.e. problem 4 identified in Benson 2008a), which impacts directly on the resolution at which a classification is constructed. In Australia,

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General Discussion such expert input has not yet been widely replaced by applying objective methods of partitioning datasets, which does have a long history of development, particularly in the Northern Hemisphere (e.g. Hill 1980; Popma et al. 1983; Xu et al. 1993; Casado et al. 1997; Steinhaueser & Chawla 2010). Such methods call upon, for example, mathematical algorithms and parameters of entropy from Information Theory (Shannon & Weaver 1963) to search for stopping rules in a sample dendrogram. Other non-mathematical approaches include the OptimClass of Tichý et al. (2010), which uses ‘faithful’ species within cluster groups to define the optimum number of groups, and the method of Wiser and de Cáceres (2013), who defined invalid groups as those having a small number of members but high cluster dispersion.

However, few of these methods have shown widespread use in vegetation classification studies in Australia. As the single Australian exception, homogeneity analysis (Bedward et al. 1992) has been used to assist in defining numbers of floristic groups in a classification, but generally in combination with expert review (e.g. Tozer 2003; Gellie 2005; Tozer et al. 2010). More commonly, expert knowledge has been harnessed during the data gathering and interpretation phases of a project, where its application can be more immediate. As noted by Pitt et al. (2012), local expert knowledge is “an important addition to the conservation planning toolbox, and certainly its use throughout the world deserves more research”. Indeed, Martin et al. (2012) have recently published a review of the use of expert knowledge in conservation science, and provide a stepped process to show how best it can be employed. Ultimately, however, application of expert knowledge during the sample selection phase of a classification project can only create superior classification and mapping products.

5.3 Data-informed Sampling and Mapping

Chapter 2 has described with case studies a method for sampling and mapping which produces vegetation classifications that are more reflective of on-ground community diversity than some other procedures. This method, Data-informed Sampling and Mapping (D-iSM), incorporates two core principles from the Zurich-Montpellier school of classification, preferential sampling and a good knowledge of a study area, and 211

General Discussion combines them with more traditional elements of classification and mapping to increase detection of rare vegetation communities. It also relies on GPS technology to facilitate saturation coverage of ground data points (similar to the occurrence plots of FGDC 2008), upon which sample selection and mapping are based. Few similar methods are evident in the literature. Scott (1989) described a ‘visual ranking’ method of rapid sampling, whereby the relative order of plant species abundances were scored at sampling locations, rather like the way people would describe the vegetation of an area in general conservation (i.e. ‘this site is dominated by X, Y & Z’). Gooding et al. (1997) used surveying equipment to record vegetation types across areas of featureless terrain in Great Britain, and produced a vegetation map using the Voronoi algorithm. The key difference to D-iSM, however, is that in the Gooding et al. (1997) method, surveyors were asked to attribute each ground data point to an existing National vegetation type, rather than recoding raw plant species data at each point. Later, Alani et al. (2001) used Voronoi algorithms to construct geographical locations based on place co-ordinates. They coined their strategy the Dynamic Spatial Approximation Method, and showed that with sufficient placename data they could closely replicate administrative boundaries of the same areas with the method. As in Alani et al. (2001), the quality of resulting Voronoi diagrams using D-iSM is dependent on the amount and extent of ground data available, and can be readily updated as new data becomes available.

For larger study areas, where terrain, access, time or financial constraints may impinge on the ability of D-iSM to be undertaken successfully, there is opportunity for a hybrid approach to be implemented (see Section 2.4.6). In Step 2 of D-iSM, saturation coverage of Rapid Data Points is a key element in the design of subsequent floristic strata (Step 4) and numerical analysis, and which may otherwise involve extensive time and financial commitments across large areas. One of the key aims of Steps 2-4 in D- iSM is to identify variation in floristic and structural elements of the vegetation, principally through the gaining of this on-ground knowledge via Rapid Data Points. To counter this, an alternative across large areas may be to replace these three steps with the interpretation of newly available high resolution 3-dimensional aerial imagery (e.g. Allard et al. 2003; Heiskanen et al. 2008; Hastedt et al. 2009; Maguire et al. 2012), 212

General Discussion which may allow recognition of local scale floristic strata in the landscape. The use of this technology as a surrogate for Steps 2-4 in D-iSM shows promise, and this hybrid approach should be investigated further.

D-iSM (as defined in Chapter 2, or the potential hybrid approach described above) represents an important advance in the recognition and mapping of rare vegetation communities, as evidenced in the studies on Scribbly Gum vegetation (Chapter 3) and the endangered Lower Hunter Spotted Gum-Ironbark Forest (Chapter 4). Existing methods of classification and mapping, particularly those governing sample selection and map production, unintentionally but actively exclude rarity in vegetation communities. If a community of naturally restricted distribution is not represented in the overall dataset by a sufficient number of sample quadrats (if at all) then by default it is absorbed into other more wide-ranging communities (Jennings et al. 2003; Pitt et al. 2012). Rare communities occupying specific and localised environmental niches, termed here cryptic ecosystems following Pitt et al. (2012), are particularly prone to such absorption. Unfortunately, the relatively recent transition in some countries, Australia included, from the ideals of the Zurich-Montpellier school (specifically, preferential sampling and knowing a study area well) to the process of describing and mapping vegetation remotely via computers is a major contributor to this process.

A second major problem with the lack of recognition of cryptic ecosystems is the conservation implications (Pitt et al. 2012), and particularly the procedure through which the development approval process operates (such as governs the protection of Lower Hunter Spotted Gum-Ironbark Forest: Chapter 4). In many countries, legislation has been enacted to ensure the conservation of significant aspects of the vegetation (Chapter 1, Section 1.1.4.3). Under New South Wales legislation (Threatened Species Conservation Act 1995), a test of significance is required for any proposed development where a perceived impact on Threatened Ecological Communities occurs (the “seven-part test”). If, for example, a particular listed community occupies 20 000 hectares in a landscape (based on regional modelling), a development proposing the removal of 5 hectares will generally not be seen as a significant impact. However, if those 5 hectares destined for removal actually supports the only known stand of a

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General Discussion cryptic ecosystem, formerly overlooked in classification and mapping projects, extinction is a real threat. As a case in point, eleven potential forms of the endangered Lower Hunter Spotted Gum-Ironbark Forest have been delineated in Chapter 4, with geographical distributions differing markedly (including one form ~250 km south of the Lower Hunter Valley). Under current legislation, all of these recognisable forms may be treated collectively by determining authorities, but some are markedly more threatened than others. A similar situation applies to the five new communities dominated by Scribbly Gum eucalypts described in Chapter 3. Elsewhere in the literature there have been few studies discussing the implications of broad vegetation classifications on conservation planning, although some have documented previously undescribed community heterogeneity for particular areas (e.g. Picone & McCaffrey 2004; Wardell-Johnson et al. 2007; Pitt et al. 2012). In an interview based study, Bottrill et al. (2012) found there to be a clear divergence between expectations and outcomes of conservation actions arising from ecoregional assessments in the United States and Asia-Pacific region, suggesting a broader perspective on the use of regional studies was needed.

In some cases, cryptic ecosystems support taxa of conservation significance, with particular habitat types known for their high proportion of threatened or rare species (e.g. Kruckeberg & Rabinowitz 1985; Hunter & Clarke 1998; Walsh 1998; Harrison et al. 2008), or rare species may drive differences in geographically disjunct ecosystems (e.g. Richardson et al. 2012). Such habitats support endemic species occupying small ranges and restricted habitats, one of the seven classes of rarity described in Rabinowitz et al. (1986). Some taxa occupy highly restricted distributions related to these habitats, such as Stypandra jamesii (Hopper 1999), Cassinia scabrida (Orchard 2004) and Boronia hapalophylla (Duretto et al. 2004). Elsewhere, the mountainous endemics inhabiting rocky screes and grasslands in Tajikistan (Nowak & Novis 2010) provide a particularly significant example. On the Central Coast of New South Wales, the vulnerable Tetratheca juncea was abundant in two of the communities described in Chapter 3. In one sense, there is ‘collateral benefit’ for conservation when threatened plant species occur within cryptic ecosystems, much like those threatened plants that are inadvertently protected through particular disturbance regimes (Kirkpatrick 2007). 214

General Discussion

Cryptic ecosystems may also harbour plant species known from only single populations, which may otherwise be overlooked through existing mapping techniques. Leeson and Kirkpatrick (2004), for example, discuss the population dynamics and habitat of Ozothamnus reflexifolius, a Tasmanian shrub restricted to a single population north of Hobart. They concluded that the single known occurrence is due to the variety of aspects of its large rock plate habitat, which provides protection from fire (a common feature of other rock plate habitats: Clarke 2002). Other potential habitat types in the region would be impacted upon by fire events, which would kill individual plants and populations of Ozothamnus. Other examples of single-population taxa in Australia include Tetratheca gunnii and Epacris virgata from igneous geology in Tasmania (Gibson et al. 1992), Eucalyptus purpurata from Western Australia (Nicolle 2002), and Eucalyptus castrensis from the Hunter Valley of New South Wales (Hill & Stanberg 2002). Elsewhere in the world, single-population endemics are many, and include Lavoixia macrocarpa and Pritchardiopsis jeanneneyi from New Caledonia (Jaffre et al. 1998), Agrostis masafuerana from the Robinson Crusoe Islands (Baeza et al. 2002), Asplenium tutwilerae from the United States (Keener & Davenport 2007), and several species from high elevation mountains in Tajikistan (Nowak & Nobis 2010).

There are some interesting parallels to be drawn between cryptic ecosystems as defined here, and threatened and rare plant species. In general, the determination of what constitutes a threatened plant species is often far simpler than a threatened community (Adam 2002), although changing criteria relating to threatened status may be important (Possingham et al. 2002). Typically, a threatened plant will be highly restricted in distribution, and/or prone to demonstrable threats (Rabinowitz et al. 1986; Crain & White 2011). Unlike vegetation communities, the majority of plant species are clearly defined taxonomically, and hence their inclusion in threatened species lists is reasonably straight forward. But there are exceptions to this rule. Under Australian legislation (the Environment Protection and Biodiversity Conservation Act 1999), there are currently 12 critically endangered, 21 endangered, and 10 vulnerable taxa listed under phrase names and awaiting formal description. In New South Wales, several undescribed plant taxa are protected under the Threatened Species Conservation Act 1995. Eucalyptus sp. Cattai (NSW 318983), for example, is listed as an 215

General Discussion endangered species, and is thought to be a phantom hybrid between Eucalyptus notabilis and Eucalyptus resinifera (Douglas 1996). Eucalyptus sp. Howes Swamp Creek (M. Doherty 26) is known only from a single location in Wollemi National Park and is also listed as endangered in New South Wales and Australia (Bell 2008b). Both of these taxa are scientifically undescribed, highly restricted in distribution and legally protected – how do their parameters differ from highly restricted vegetation communities which have not formally been defined in regional-scale vegetation classifications (‘undescribed’), but have been recognised locally (with a ‘phrase name’)?

In relation to spatial mapping of vegetation communities, the importance of ground truthing and local expert knowledge cannot be overemphasised (Martin et al. 2012; Pitt et al. 2012). Any map of vegetation distribution can only be as good as the amount of field validation that has been undertaken to support it (see case studies in Chapter 2). In general terms, it involves ensuring that what is portrayed on a map actually represents what occurs on the ground, within reasonable bounds. For maps developed through GIS modelling, this is particularly important (see Chapter 1, Section 1.2.2.4). In many cases, the validation of a vegetation map will vastly improve all subsequent planning decisions that rely on its accuracy, and can ultimately affect a wide range of conservation-related decisions. Ironically, it is during the ground truthing phase of a vegetation mapping project that many cryptic ecosystems are first encountered, and if these are not recognised and dealt with appropriately at this point, then quite possibly the validation exercise will have been in vain. It is also at this point that expert input is the most critical, as it is important for the ecologists undertaking the validation to be familiar with the entire classification such that any deviations can be suitably documented. The collection of new detailed data can also be undertaken here to assist further description. Pitt et al. (2012), for example, document how local expert knowledge was imperative in locating diverse yet isolated wetlands in South Carolina, where remote sensing was inadequate to do so.

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5.4 Standards for Sampling Design

5.4.1 International Standards

In a synthesis of the status of vegetation classification, Mucina (1997) considered that sampling design was one of the weaker elements of vegetation science. Since that time, attempts have been made in many countries to streamline the way in which vegetation data are collected, and trends towards worldwide consistency are continuing (Solomeshch & Mirkin 1999; Bruelheide & Chytrý 2000; Mucina et al. 2000b; Wiser et al. 2001; Schultz et al. 2009; de Cáceres& Wiser 2011; Dengler et al. 2011). Recent developments are also pursuing the establishment of an international data exchange standard (“Veg-X”) for quadrat based vegetation data, to facilitate the exchange of sample data in collaborative research programs (Wiser et al. 2011). Such a standard aims to allow scientists to examine vegetation change and community dynamics at local and global scales.

Several countries and jurisdictions in the world have developed standards for vegetation survey and classification, in the hope that more consistent application of adopted techniques will better inform conservation planning initiatives. Recently, de Cáceres & Wiser (2012) stress that clarity in published classification standards is required, distinguishing between how communities are defined and how they are validated. Particularly pertinent to issues raised in this thesis is the concept of sample selection. The importance of preferential sampling for detecting rare communities has been stressed on many occasions throughout this thesis, and it is of interest to review where adherence to this basic trait of the Zurich-Montpellier school continues. In European countries, which have the longest history of vegetation classification (Chytrý et al. 2011), preferential sampling is still practiced widely (e.g. Koroleva 1999; Vandvik & Birks 2002). Although there is no formal standard for Europe, Dengler et al. (2008) clearly indicate that preferential sampling is considered superior to random or systematic sampling, although stratified-random strategies are gaining in popularity.

In the United States, the National Vegetation Classification Standard does not specify how sample quadrats (termed ‘classification plots’) are to be located, but acknowledges that a variety of methods and criteria can be implemented (FGDC 2008).

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Each classification plot should represent a homogeneous stand of vegetation, with all methods fully documented. Earlier, the Ecological Society of America, through its Vegetation Classification Panel, had recommended either stratified or preferential sampling, or a combination of both techniques (Jennings et al. 2003). They also highlighted the fact that stratified sampling (termed ‘representative sampling’) may overlook rare vegetation types, and should be supplemented by preferential sampling.

Rodwell (2006) provides information on the National Vegetation Classification operating in the United Kingdom, and describes the methodology used for sampling and description now widely adopted as standard (Rodwell et al. 2000). Samples are subjectively chosen within homogeneous areas of vegetation following reconnaissance of an area, looking for consistencies and repetitions in colour, texture and structure. Indeed, Rodwell (2006) states that it is “vital to walk over a substantial part of a site first, gradually gaining a picture of the pattern and scale of variation, rather than to spend a lot of time in the first recognisable stand..”. Systematic, stratified or random sampling are less economical than preferential sampling, as they tend to yield data dominated by common communities and overlook rarer ones (Rodwell 2006).

Few other countries appear to have established national guidelines, or are unclear in their preferred methods. Canada, for example, does not specify which method of sample location is preferable (BC Ministry of Forests and Range & BC Ministry of Environment 2010).

5.4.2 Australian Standards

In Australia, the National Vegetation Information System (NVIS) considers as the highest quality data that which has been sampled with reference to environmental attributes and aerial imagery (ESCAVI 2003). Standards for vegetation survey detailed in Hnatiuk et al. (2008) promote stratified random sampling (environmental or through use of aerial imagery) but ensuring that samples are undertaken within homogeneous locations within such strata. At the same time, they explicitly state that sampling should be “as free from observer bias as possible”, which potentially limits selection within such homogeneous stands. This process effectively excludes the use of preferential sampling, unless strata units are based on observed floristic variation 218

General Discussion

(such as the floristic strata outlined for the D-iSM process, based on the original intent of stratified sampling in the Zurich-Montpellier approach: Kent & Coker 2001).

Despite establishment of the NVIS, the various State and Territories within Australia remain inconsistent in their own guidelines for vegetation sampling (Table 5.1). Two States (South Australia & Queensland) and the Northern Territory advocate preferential sampling within a stratification based on remotely sensed data and environmental parameters. New South Wales and Western Australia opt for random sampling within a stratification based on environmental parameters (NSW) or remote sensing (WA). Tasmania does not specify a preferred method, and none could be located for Victoria (recent work by Cheal et al. 2011, however, suggests that they are using stratified-random).

Table 5.1 Recommended sample selection techniques detailed for State and Territory guidelines in Australia. NT = Northern Territory; SA = South Australia; QLD = Queensland; NSW = New South Wales; WA = Western Australia; TAS = Tasmania; VIC = Victoria.

State Primary Method Secondary Method Authors

NT stratified-preferential: random, stratified-random, grid Brocklehurst et al. 2007 remote sensing SA stratified-preferential: - Heard & Channon 1997 remote sensing QLD stratified-preferential: - Neldner et al. 2005 environmental & remote sensing

NSW stratified-random: stratified-random: remote sensing Sivertsen 2010 environmental WA stratified-random: - Clarke 2009 remote sensing

TAS not specified - TAS DPIPWE 2009

VIC none found - -

In New South Wales (within which the vegetation examined in Chapters 3 & 4 occurs), the Native Vegetation Interim Type Standard (NVITS: Sivertson 2010) seeks to ensure that consistent, transparent and repeatable methods are used for the capture and presentation of vegetation data. In its present form, much of the NVITS meets international standards and accepted practice for vegetation classification and

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General Discussion mapping. However, certain components limit its capabilities in producing reliability, accuracy and longevity in mapping products:

The NVITS endorses computer-based random stratified sampling methods for choosing sampling locations, rather than selecting sample quadrats based on observable variation. It does this through use of Environmental Sampling Units (ESUs) based on environmental or other remotely-sensed data, or gap analysis of existing samples. The NVITS does not allow for the direct sampling of field- observed variations in vegetation patterns, conflicting with the ideals of the Zurich-Montpellier school, increasing the risk of overlooking rare communities.

For specifically targeted vegetation types, the NVITS allows for randomly selected sample quadrats within the range of that vegetation type. Direct sampling of observed variation within a target vegetation type is not permissible under the standard, and again is in conflict with the Zurich- Montpellier school. This prevents further refinement of vegetation types that may be regional in their utility.

The NVITS assesses sampling adequacy by examining representativeness, replication and randomness of quadrats within ESUs, rather than on the outcomes of a classification. Such an approach assumes that all community diversity will be detected through the random stratification process, which is directly related to the resolution and quality of the ESUs.

Within the NVITS, assessment of accuracy is undertaken after the construction of a vegetation map, and requires additional data collection to populate an independent dataset for this purpose. Such a process will incur additional time and expense.

Similarly, the BioBanking Scheme (NSWDECC 2009a) recently established in New South Wales under Part 7A of the Threatened Species Conservation Act 1995 stipulates that sample quadrats be stratified into vegetation zones (such as through aerial photographic interpretation) and be randomly placed within these zones. Vegetation communities are to be allocated to one of the ~1600 contained within the NSW

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Vegetation Types Database. Difficulties in this methodology include, once again, use of random stratification of quadrats, and the requirement to attribute a particular site to a pre-existing classification. There is little scope for the correct assessment of rare vegetation communities recognised at a local scale, meaning that local-scale diversity becomes absorbed into a regional-scale classification.

Limitations within the New South Wales NVITS and the BioBanking Scheme can be overcome using the D-iSM method of classification and mapping (Chapter 2). Through use of preferential sampling and mapping based on sound ground knowledge, sample locations can specifically target and test observed variations to create a classification, and map this classification based on numerous ground data points. If completed correctly, incurring additional time and expense in creating and using an independent data set for accuracy assessment post-production will not be necessary, as mapping will be based on ground data points linked directly to a classification.

To differing degrees, none of the guidelines for Australia and its States and Territories truly adhere to the Zurich-Montpellier approach for data sampling (Chapter 1, Section 1.1.1). All require a preliminary stratification of a study area before selecting sample locations, without reference to observed on-ground variation in the vegetation. Assessing photo-patterns on remotely sensed imagery (particularly the new 3- dimensional imagery now becoming available) is preferable to intersecting various environmental layers, but it too may not consistently surrogate well for floristic or structural diversity (e.g. Fensham & Fairfax 2007). Similarly, randomly placing quadrats within a stratification unit does not automatically sample all diversity because plant distribution is non-random (Chapter 1, Section 1.3.1).

Floristic strata generation (Step 4) within the D-iSM process (Chapter 2, Section 2.2.2) can effectively replace the need to stratify a sampling design on environmental parameters or remotely sensed imagery. As has been demonstrated for three study areas in the Hunter Valley of New South Wales (Chapter 2), and also during the process of defining new (Chapter 3) and refining existing (Chapter 4) communities from this same region, it allows sampling to target observed on-ground variation through preferential sampling. This very process adheres to the Zurich-Montpellier school, and

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General Discussion consequently negates surrogacy assumptions implied in current stratification techniques. For very large study areas, a hybrid approach involving the use of 3- dimensional interpretation of specialized digital imagery could be used in the place of Step 4 of D-iSM, particularly in remote or rugged regions where time and access constraints are real (see Section 5.3). Recent examples of the use of 3-dimensional interpretation are promising and improve greatly on traditional 2-dimensional aerial photographic interpretation (e.g. Maguire et al. 2012).

5.5 Re-setting the Focus of Classification in Australia

Chapters 3 and 4 of this thesis have demonstrated that use of key elements of the D- iSM technique for sampling and mapping results in more reliable and comprehensive classification products. By implementing the core principles of the Zurich-Montpellier school (preferential sampling & good knowledge of a study area), rare communities can be recognised and existing communities revised with a view to identifying those most at risk. While these principles are long-standing, their use has not been universally adopted worldwide (Section 5.4). However, it is likely that many field ecologists have, by following their own instincts, adhered to some or all of the steps formalised here in the D-iSM process.

Despite a wealth of publications describing classification and mapping products (e.g. Benson 1993, 2006; Sun et al. 1997; Keith 2004), Australia is well behind other parts of the world in understanding the diversity of vegetation present across this continent. With only 200 years of European occupation, and a land area of some 7.7 million square kilometres, this is understandable. Vegetation science has advanced considerable in this country over the past 60 years, but there still remains little visible presence within vegetation science internationally. For example, within the journals of the International Association of Vegetation Science (IAVS), only seven papers from Australia have been published in Applied Vegetation Science since 1998, and sixty papers in the Journal of Vegetation Science since 1990. Contributions to the advancement of vegetation science from Australia have clearly been limited. To facilitate greater contribution from countries such as Australia, Chytrý et al. (2011) have outlined a new ‘vegetation survey’ section of Applied Vegetation Science, where 222

General Discussion the IAVS hope to solicit papers on new methods and results from across the world showcasing best practice in advancing the science of vegetation study.

Within Australia, it is evident that preferred techniques for sampling and classifying vegetation are varied (Keith 2004; and see Section 5.4.2). The National Vegetation Information System aims to streamline classification in Australia through standardised methods and allocation of existing vegetation units into hierarchical classes (ESCAVI 2003), but this will take some time for complete adoption. In relation to sample selection techniques, a review of publications where numerical classification has been used to describe floristic patterns in the journal Cunninghamia, which publishes papers on plant ecology and distribution in eastern Australia, shows an interesting trend in sampling methods. Of forty-four papers describing vegetation classifications since 2000, five (11%) have utilised preferential sampling in their study design (Fig. 5.1). Fifteen papers (34%) have relied on an environmental stratification to guide sampling effort. A further fifteen (34%) papers have selected samples within an initial stratification based on interpretation of photo-patterns on aerial photographs (API), and eight (18%) papers either did not specify or were unclear on how samples were selected. One paper (2%) opted to sample the vegetation by first establishing a 30m grid across their study area, and sampling at each grid corner. In sum, 68% of papers opted for sampling techniques that rely on assumptions between vegetation distribution and environmental parameters or observable photo-patterns.

Three other leading Australian plant science journals, Australian Journal of Botany, Austral Ecology and Pacific Conservation Biology show similar patterns (also shown in Fig. 5.1). Since 2000, of the twelve papers incorporating numerical classification of vegetation patterns published in Australian Journal of Botany, only two (17%) opted for preferential sampling while seven (58%) used stratification. For Austral Ecology, which publishes papers on both flora and fauna ecology, none of the five papers since 2000 incorporated preferential sampling, but four (80%) stratified samples on environmental or broad vegetation patterns. Seven applicable papers appeared in Pacific Conservation Biology since 2000, with two (29%) of these incorporating preferential sampling and two (29%) using environmental stratification.

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Pacific Conservation Biology Austral Ecology Australian Journal of Botany Cunninghamia

Preferential

Random stratification (broad vegetation)

Random stratification (environmental)

Grid-based

Not specified

0 2 4 6 8 10 12 14 16 Number of papers

Using this brief census as a guide to the level of classification being completed in Australia, there is a clear trend showing around one third of all published classification projects undertaken in recent years relying on various combinations of environmental parameters (e.g. geology, soil type, elevation) to surrogate for floristic variation. However, environmental variables such as these do not consistently predict vegetation types (see Chapter 2; and Griffith et al. 2000; Rankin et al. 2007; Holt et al. 2008; Adams et al. 2011). Assumptions that photo-patterns visible on aerial photographs directly reflect floristic variation comprise a further third of projects. In some cases (e.g. Meurk et al. 1994; Walker et al. 1995; NSWDEC 2004; Griffith & Wilson 2008), additional opportunistic (preferential) sampling has augmented that stratified on photo-patterns or within environmental strata, but rarely is this noted. While improvements in the detection of floristic variations can be achieved with API-based stratification over environmental stratification (particularly the potential shown by 3- dimensional imagery), elements of doubt still remain with regard to the detection of rare communities using remote imagery (e.g. Pitt et al. 2012). Experience and quality of the API interpreter can be critical at this stage, and depending on photograph scale, localised photo-patterns can be easily overlooked. Some field ecologists (e.g. Griffith et al. 2003) ensure that, irrespective of budget limitations, even the smallest photo-

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General Discussion patterns discernible through API are sampled, raising the chances of detecting rare communities.

The grey literature in eastern Australia over the last decade or so is similarly endowed with sampling designs incorporating environmentally stratified sampling rather than those using preferential sampling. Inevitably, much of the land-use and conservation planning decisions made in this region are based on such unpublished literature. For the Central Coast of New South Wales, this includes the regional classifications of NSWNPWS (2000b), Bell (2002a), Bell (2004b), McCauley et al. (2006), Peake (2006) and Somerville (2010). Further afield in other regions, examples include Sivertsen and Cameron (1999), VICDNRE (2001), Seddon et al. (2002), NSWDEC (2004), Cheal et al. (2011) and Hunter (2011). Others have stratified sampling effort on existing vegetation maps or on photo-patterns observable on aerial imagery (e.g. NSWDEC 2006; NSWDECCW 2010b; Maquire et al. 2012). Most, if not all, of these classifications have been commissioned or designed to delineate vegetation community diversity and conservation significance to guide planning and management, but few have confidently delineated rare communities.

Neither sample selection based on an environmental stratification, nor that stratified on photo-patterns observed during API can replace that where samples are based on observed variation in situ (interpretation of recent 3-dimensional digital imagery may be the exception here, but is yet to be fully tested in relation to detection of understorey diversity). Rare communities in particular are more likely to be detected if preferential sampling based on extensive ground knowledge has been implemented. Five of nine communities dominated by Scribbly Gum eucalypts on the Central Coast of New South Wales, defined through preferential sampling, potentially represent some of the most threatened communities in the region (see Chapter 3). Similarly, preferential sampling of the endangered Lower Hunter Spotted Gum-Ironbark Forest revealed eleven definable units attributable to this community, with varying levels of perceived threat for each (see Chapter 4). Few of the units delineated in Chapter 3 or 4 were recognisable during previous classification projects using an environmentally stratified sampling regime.

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A Shift in Focus…

To improve the identification, recognition and protection of rare vegetation communities, particularly in Australia, a shift in focus is required away from environmentally stratified random sampling and towards preferential sampling. The D- iSM method (Chapter 2) facilitates such a change, and ensures that effort expended in ground data collection is repaid with accurate classification and mapping products, with longevity negating the need for regular revisions (such as has been done in the Central Coast of NSW in recent years: see Chapter 2, Section 2.4.5). Importantly, quality vegetation mapping takes time to prepare, and it is crucial that those involved in conservation and land-use planning recognise this. The rapid production of vegetation maps incorporating satellite imagery and image-based segmentation, for example, are unlikely to deliver satisfactory results for local-scale planning (Fassnacht et al. 2006).

For this shift of focus to occur, seven critical steps in thinking are required:

1. Raising the perception and value of rare vegetation communities. While rare and threatened plant species garner considerable research and management monies (e.g. Joseph et al. 2008; Szabo et al. 2009), the same cannot be said of rare vegetation communities. This is despite inclusion of both entities in most threatened species legislation. Several texts on strategies and techniques for sampling rare plant species are available (e.g. Cropper 1993; Thompson 2004), yet similar publications for rare communities are not evident. In the development industry, there is an expectation and requirement that specific surveys will be undertaken to target rare species likely in an area (e.g. NSWDECC 2007), but no such surveys are specified for rare (cryptic) communities (except where listed Threatened Ecological Communities may occur). In the past, difficulties in defining and mapping rare communities have contributed to this lower perceived value. However, implementation of the D-iSM technique described in Chapter 2 of this thesis provides a mechanism to raise the perception and importance of rare communities, ultimately to parity with rare species. Legislation that includes protection of Threatened Ecological Communities (e.g. Environment Protection and

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Biodiversity Conservation Act 1999) has gone some way towards increasing awareness of these entities; however, problems in the resolution of community definitions in the past have clouded values (see Chapter 4).

In parallel with this, there may also be scope for a review of current standards on sample plot sizes, as these will directly impact on the ability of a sampling design to detect those rare vegetation communities that occupy highly restricted environments (less than 0.04 hectares in size). Examples here include vegetated rock faces, scree slopes, water and mound springs, vernal pools etc, but which may remain ‘neglected orphans’ in conservation planning because they are not sampled. Chytrý & Otýpková (2003) have reviewed the issue of plot size in relation to structure for European classifications, and it may be prudent to learn from the European experience in this regard.

2. Improved use of public money in regional classification. There is a need for improved use of public monies to support more detailed regional vegetation classifications, given that there is legislative support in Australia (and other countries) for protecting rare and threatened communities. Following the examples provided in Chapter 2, a considerable amount of public money has been expended in the past on vegetation classification and mapping studies within the Hunter Valley region of New South Wales (Chapter 2, Section 2.4.5), including several projects effectively re-classifying the same vegetation. Such a scenario is likely to be repeated in many other regions of Australia and in other countries. Available funding needs to ensure longevity of mapping products, which can be achieved through the production of higher quality products using approaches such as the D- iSM method. While there may be greater outlays of effort required to gather ground data initially, superior returns will be seen in the quality and longevity of final mapping products. Quality vegetation mapping requires time to prepare.

3. Improved reporting in the development industry. Within the development and planning industry, there is a need for environmental consultants to sample and report on community diversity more accurately. Such concern has been previously highlighted throughout many parts of the world (e.g. Buckley 1995; Warnken &

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Buckley 1998; Cherrill & McClean 1999; Wegner et al. 2005), and overall quality of environmental impact statements continues to be questionable in many countries (e.g. Canelas et al. 2005; Androulidakis & Karakassis 2006; Petersen 2010). The Ecological Society of Australia maintains a position statement on the subject (http://www.ecolsoc.org.au/Position_papers/EIA.htm), indicating its concern with the quality of information presented to determining authorities. The step-by-step guide outlined in Chapter 2 for the D-iSM process shows how high quality classifications and maps for a development site can be achieved.

4. Improved assessment of conservation reserves. Classification and mapping of vegetation present within conservation reserves is a critical step in the assessment of threat to vegetation communities. Accurate mapping products are required for such areas, so that off-reserve impacts can be confidently assessed in contextual studies. Chapter 2 has shown how such a mapping product can be achieved (for Columbey National Park) using the D-iSM process. In addition, best practice methods for natural resource management plans and assessing potential impacts on biodiversity (Fallding 2000; Briggs 2011) should always be strived for.

5. Review of existing threatened communities. Within Australia, there are currently 53 federally listed critically endangered, endangered or vulnerable ecological communities (EPBC Act 1999), and 102 in New South Wales (TSC Act 1995). Many of these communities may benefit from a critical review to determine if some elements of them are more threatened than others. Some threatened communities in Australia have already been reviewed (see Chapter 1, Section 1.1.4.3). Chapter 4 of this thesis has shown how a review of Lower Hunter Spotted Gum-Ironbark Forest, endangered in New South Wales, was achieved using the principles of D- iSM, and identifying some forms potentially more threatened than others. Such reviews will ensure that public monies are directed to those areas under the most threat.

6. Review of listing structure in threatened species legislation. Many land-use decisions are made by local government agencies using regional-scale data. This is an unhealthy dichotomy which works towards the extinction of local-scale

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General Discussion vegetation communities (including cryptic ecosystems: Section 5.3). In New South Wales, for example, many listed threatened communities can be considered local in utility, yet have been created from regional-scale classifications (e.g. Lower Hunter Spotted Gum-Ironbark Forest, Chapter 4). To overcome this, a similar dichotomy of listings may be useful, separating local-scale communities from those of regional- scale. If such were the case already, disputes over the occurrence of some of the more broadly-defined threatened communities within local-scale sites may not have occurred. Where it exists, local-scale definition of vegetation communities should not be subsumed into regional-scale frameworks to satisfy legislative requirements.

7. Continued establishment of a hierarchical classification of Australian vegetation. Australia, like several other countries (e.g. Bruelheide & Chytrý 2000; Rodwell 2006; Jennings et al. 2008), is currently working towards a comprehensive classification of its vegetation (National Land and Water Resources Audit 2001; ESCAVI 2003). In doing this, consistency problems have arisen between classification and mapping products emanating from different States and Territories (e.g. Lewis et al. 2008). For individual States, regional-scale classifications commonly experience problems in equivalency: for example, accommodating the finer resolution classification of Benson (2006) within the New South Wales framework of Keith (2004). Similarly, local-scale classifications are difficult to accommodate within regional-scale classifications (e.g. NSWDECC 2008b). As a consequence, work towards the establishment of a hierarchical classification of Australian vegetation (ESCAVI 2003) should continue, so that local-scale classifications (such as those attainable through use of the D-iSM process: Chapter 2) can be housed within the Sub-association  Association  Sub-formation  Broad floristic formation  Structural formation  Class hierarchical system of ESCAVI (2003). Rare plant communities, defined at a local-scale, can then be systematically assessed accordingly.

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5.6 Conclusion

Küchler (1951) stated that the “relation between classifying and mapping vegetation is very intimate, and each one strongly affects the other….”. This basic relationship is never more important than when placed in the context of land-use and conservation planning. Without classification, mapping is misleading and may over-simplify: without mapping, classification is theoretical and difficult to apply practically. Rare vegetation communities, in particular, need to be accurately classified and mapped to ensure correct management. In one sense, rare vegetation communities can be considered ‘ecological surprises’ (Lindenmayer et al. 2010), as they often represent unexpected findings from the natural environment, and accurate mapping of such surprises is desirable.

Improving the way in which rare vegetation communities are classified and mapped was the principle aim of this thesis. In doing so, it was hoped that a new level of confidence could be instilled in those who use vegetation classifications and maps on a day-to-day basis; the land managers and determining authorities who balance human progress with nature conservation. By ensuring that sampling is preferentially selected to cover all observable variation, and that mapping is based on ground-data, classification and mapping products can be created with high levels of accuracy and longevity of use, while at the same time adhering to the principles of the Zurich- Montpellier school. As a consequence, the dissemination of public monies and conservation resources can be channelled where most needed.

A simple conceptual framework for the classification of communities in vegetation science is shown in Fig. 5.2, to facilitate a change in thinking particularly for Australian ecologists. In this graphic, elements of sampling design have been equated to ‘Top- Down, Bottom-Up’ information processing (Grimm 1999; Ellis 2012). Selecting sample quadrats for vegetation classification from existing environmental layers or via remote sensing imagery, classifying them and formulating a community classification can be seen as processing information in a Top-Down (deductive) fashion (Fig. 5.2a). In this instance, pre-determined and assumed relationships between environmental patterns

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Top-Down Bottom-Up

1. Stratification 4. Profiling & Mapping (environmental / remote sensing)

↓ ↑

2. Random selection of 3. Sampling & sample locations within Classification strata

↓ ↑

3. Sampling & 2. Preferential selection Classification of sample locations

↓ ↑

4. Profiling & Mapping 1. Collection of on- ground variability data

(a) (b)

Fig. 5.2. Schematic representation of a Top-Down, Bottom-Up approach to sampling design for vegetation classification. In (a), concepts and assumptions on community relations with environmental strata and/or remote sensing imagery are theorized, followed by stratified sampling and classification. In contrast, (b) begins with baseline data on observed variation, then sampling preferentially to develop a classification.

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General Discussion and photo-patterns or spectral signatures are examined in an effort to determine their component parts (communities). Conversely, collecting baseline data on these component parts, and sampling the observed variability in order to classify them can be seen as Bottom-Up (inductive) processing (Fig. 5.2b). Important here is the order in which these steps are undertaken in each approach. For the detection of rare vegetation communities, adopting a Bottom-Up approach is more reliable, and adheres more closely to the Zurich-Montpellier approach to vegetation classification.

A logical next step to further improve the identification of rare vegetation communities is to examine sampling adequacy. In particular, determining how many samples may be required within a specified community to ensure that internal variation has been adequately addressed. There is much literature on this topic (e.g. Neldner et al. 1995; DePatta Pillar 1998; Zerger et al. 2005; Garrad et al. 2008; Schultz et al. 2009; Whittaker & Triantis 2012); however, the establishment of general rules for guiding sampling adequacy in different vegetation types appears unresolved to date. Knowing when enough sampling has been completed within a particular community will assist end-users to confidently implement a classification. From a mapping perspective, exploration of more definitive methods to define boundaries between adjacent communities, such as those discussed in Brown (1998), Fortin et al. (2000) and Choesen and Boerner (2002), is warranted within local-scale applications. Transient or successional communities, created through natural or human-induced disturbances, may also be rare yet can form important components of an ecosystem: further research into their definition and mapping is warranted. The recent concept of ‘keystone communities’ (Mouquett et al. 2013) may too prove pivotal in conservation planning, and is worthy of further consideration.

However, perhaps even of more interest than any of these is the issue of when should biodiversity management shift from an ecosystem approach to a species approach? Ecosystems are commonly used as surrogates for biodiversity (Noss 1996), and the effectiveness of these surrogates in conserving biodiversity will depend on how finely a classification unit has been defined. As community resolutions improve, such questions will invariably be posed.

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Chapter 6: References

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266

Chapter 7: Appendices

Appendices 1-4: Confusion Matrices

Appendix 5: LHSGIF EEC Final Determination

Appendix 6: Chronological History of LHSGIF EEC Revision

Appendix 7: Spotted Gum-Ironbark Vegetation from NSWNPWS (2000)

Appendix 8: LHSGIF Superficially Similar Vegetation

Appendix 9: LHSGIF 3-d Ordination Diagrams

Appendices

Appendix 1 – Edgeworth LES Confusion Matrix Examples

1a) Run 1 (of 10 runs) from accuracy testing of the mapping of NSWNPWS (2000b). MU5 = Alluvial Tall Moist Forest; MU11 = Coastal Sheltered Peppermint-Apple Forest; MU17 = Lower Hunter Spotted Gum-Ironbark Forest; MU30 = Coastal Plains Smooth-barked Apple Woodland; MU42 = Riparian Melaleuca Swamp Woodland.

Reference Data (RDP) RUN 1

MU30 MU5 MU11 MU17 MU42 Total User Accuracy % MU30 5 0 0 13 0 18 28

MU5 0 0 0 0 0 0 0

MU11 0 0 0 0 0 0 0 MU17 0 0 0 0 0 0 0 MU42 0 0 0 0 0 0 0 Total 5 0 0 13 0 18

ClassificationData Producer Accuracy % 100 0 0 0 0

Overall Accuracy %

27.8

1b) Run 1 (of 10 runs) from accuracy testing of mapping from the current study with the NSWNPWS (2000b) classification. MU5 = Alluvial Tall Moist Forest; MU11 = Coastal Sheltered Peppermint-Apple Forest; MU17 = Lower Hunter Spotted Gum-Ironbark Forest; MU30 = Coastal Plains Smooth-barked Apple Woodland; MU42 = Riparian Melaleuca Swamp Woodland.

Reference Data (RDP) RUN 1

MU30 MU5 MU11 MU17 MU42 Total User Accuracy %

MU30 5 0 0 0 0 5 100 MU5 0 0 0 0 0 0 0 MU11 0 0 0 0 0 0 0 MU17 0 0 0 13 0 13 100

ClassificationData MU42 0 0 0 0 0 0 0

Total 5 0 0 13 0 18

Producer Accuracy % 100 0 0 100 0

Overall Accuracy %

100.0

268

Appendices

Appendix 2 – Columbey NP Confusion Matrix Examples

2a) Run 1 (of 10 runs) from accuracy testing of the mapping of FCNSW (1989). Unit 14 = Lilly Pilly; Unit 23 = Myrtle; Unit 31 = Paperbark; Unit 60 = Narrowleaved Mahogany-Red Mahogany-Grey Box-Grey Gum; Unit 70 = Spotted Gum; Unit 74 = Spotted Gum-Ironbark/Grey Gum; Unit 80 = Grey Ironbark-Grey Box; Unit 84 = Ironbark; Unit 92 = Forest Red Gum; Unit 93 = Eastern Red Gums; Unit 127 = Stringybark-Smoothbarked Apple; Unit 218 = Forestry Plantations.

Reference Data (RDP) RUN 1

RN17 60 84 92 218 230 23 70 74 80 93 127 14 31 Total User Accuracy (%) 60 2 0 0 0 0 0 2 3 1 0 0 0 0 8 25 84 0 2 1 0 0 0 3 31 3 0 0 1 0 41 5 92 0 0 0 0 0 0 0 0 2 1 0 0 1 4 0 218 0 0 0 1 0 0 0 0 0 0 0 0 0 1 100 230 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 70 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

74 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 93 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 127 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

ClassificationData 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Total 2 2 1 1 0 0 5 34 6 1 0 1 1 54

Producer Accuracy (%) 100 100 0 100 0 0 0 0 0 0 0 0 0

Overall Accuracy (%)

9.3

269

Appendices

2b) Run 1 (of 10 runs) from accuracy testing of mapping from the current study with the FCNSW (1989) classification. Unit 14 = Lilly Pilly; Unit 23 = Myrtle; Unit 31 = Paperbark; Unit 60 = Narrowleaved Mahogany-Red Mahogany-Grey Box-Grey Gum; Unit 70 = Spotted Gum; Unit 74 = Spotted Gum-Ironbark/Grey Gum; Unit 80 = Grey Ironbark-Grey Box; Unit 84 = Ironbark; Unit 92 = Forest Red Gum; Unit 93 = Eastern Red Gums; Unit 127 = Stringybark-Smoothbarked Apple; Unit 218 = Forestry Plantations.

Reference Data (RDP) RUN 1 RN17 60 84 92 218 230 23 70 74 80 93 127 14 31 Total User Accuracy (%) 60 2 0 0 0 0 0 0 0 0 0 0 0 0 2 100 84 0 2 0 0 0 0 0 0 0 0 0 0 0 2 100 92 0 0 1 0 0 0 0 0 0 0 0 0 0 1 100 218 0 0 0 1 0 0 0 0 0 0 0 0 0 1 100 230 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 70 0 0 0 0 0 0 5 0 0 0 0 0 0 5 100

74 0 0 0 0 0 0 0 34 0 0 0 1 0 35 97

80 0 0 0 0 0 0 0 0 6 0 0 0 0 6 100 93 0 0 0 0 0 0 0 0 0 1 0 0 0 1 100 127 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -

sificationData 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -

Clas 31 0 0 0 0 0 0 0 0 0 0 0 0 1 1 100 Total 2 2 1 1 0 0 5 34 6 1 0 1 1 54 Producer Accuracy (%) 100 100 100 100 - - 100 100 100 100 - 0 100 Overall Accuracy (%) 98.1

270

Appendices

2c) Run 1 (of 10 runs) from accuracy testing of the mapping of NSWNPWS (1999a). E7 = Other Moist Coastal; E9 = Spotted Gum Dominant or Co-dominant; E10 = Grey Box- Ironbark; E11 = Forest Red Gum; E14 = Bloodwood – Smoothbarked Apple; Rf = Rainforest; Pb = Paperbark; FP = Forestry Plantation.

Reference Data (RDP) RUN 1 CRAFTI E7 E10 E11 FP E9 Rf E14 Pb G Total User Accuracy % E7 0 1 0 0 3 1 0 0 0 5 0 E10 1 9 0 2 29 0 0 2 0 43 21 E11 0 2 2 0 1 0 0 0 0 5 40

FP 0 0 0 1 0 0 0 0 0 1 100

E9 0 0 0 0 0 0 0 0 0 0 - Rf 0 0 0 0 0 0 0 0 0 0 - E14 0 0 0 0 0 0 0 0 0 0 - Pb 0 0 0 0 0 0 0 0 0 0 -

ClassificationData G 0 0 0 0 0 0 0 0 0 0 - Total 1 12 2 3 33 1 0 2 0 54 Producer Accuracy % 0 75 100 33 0 0 - 0 - Overall Accuracy % 22.2

2d) Run 1 (of 10 runs) from accuracy testing from the current study with the NSWNPWS (1999a) classification. E7 = Other Moist Coastal; E9 = Spotted Gum Dominant or Co- dominant; E10 = Grey Box-Ironbark; E11 = Forest Red Gum; E14 = Bloodwood – Smoothbarked Apple; Rf = Rainforest; Pb = Paperbark; FP = Forestry Plantation.

Reference Data (RDP) RUN 1 CRAFTI E7 E10 E11 FP E9 Rf E14 Pb G Total User Accuracy % E7 2 0 0 0 0 0 0 0 0 2 100 E10 0 8 0 0 0 0 0 0 0 8 100 E11 0 0 6 0 1 0 0 0 0 7 86

FP 0 0 0 2 0 0 0 0 0 2 100

E9 0 0 0 1 32 0 0 0 0 33 97 Rf 0 0 0 0 0 1 0 0 0 1 100 E14 0 0 0 0 0 0 0 0 0 0 - Pb 0 0 0 0 0 0 0 1 0 1 100

ClassificationData G 0 0 0 0 0 0 0 0 0 0 - Total 2 8 6 3 33 1 0 1 0 54 Producer Accuracy % 100 100 100 67 97 100 - 100 - Overall Accuracy % 96.3

271

Appendices

Appendix 3 – Cessnock-Kurri Confusion Matrix Examples

3a) Run 1 (of 10 runs) from accuracy testing of the mapping of NSWNPWS (2000b). 1 = Coastal Wet Gully Forest; 3 = Hunter Valley Dry Rainforest; 8 = Sheltered Blue Gum Forest; 10 = Sandstone Grey Myrtle Sheltered Forest; 12 = Hunter Valley Moist Forest; 13 = Central Hunter Riparian Forest; 14 = Wollombi Redgum-River Oak Forest; 15 = Coastal Foothills Spotted Gum-Ironbark Forest; 17 = Lower Hunter Spotted Gum-Ironbark Forest; 18 = Central Hunter Ironbark-Spotted Gum-Grey Box Forest; 19 = Hunter Lowland Redgum Forest; 21 = Hunter Range Grey Gum Forest; 30 = Coastal Plains Smoothbarked Apple Woodland; 35 = Kurri Sands Swamp Woodland; 42 = Riparian Melaleuca Swamp Woodland. Reference Data (RDP) RUN 1 NPWS (2000b) 1 1a 3 5 6 8 9 10 12 13 14 15 16 17 18 19 21 27 30 35 42 46 Total User Accuracy % 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1a 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 5 0 0 0 0 0 0 0 0 0 5 0 0 0 1 0 0 0 0 0 0 0 0 6 0 6 0 0 0 0 0 0 0 0 1 0 0 2 0 1 0 0 0 0 0 0 0 0 4 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 9 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 12 0 0 1 0 0 0 0 0 1 2 0 7 0 0 0 0 0 0 0 0 0 0 11 9 13 0 0 0 0 0 1 0 0 0 18 0 0 0 1 3 0 0 0 0 0 0 0 23 78 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 15 0 0 0 0 0 0 0 0 2 12 0 19 0 6 1 0 0 3 2 0 0 0 45 42 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 17 0 0 3 0 0 0 0 0 0 69 1 166 0 560 124 5 0 48 1 140 0 0 1117 50

18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -

19 0 0 0 0 0 0 0 0 0 67 3 23 0 98 10 2 0 1 6 17 1 0 228 1 21 0 0 0 0 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 3 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 35 0 0 0 0 0 0 0 0 0 19 5 8 0 55 0 4 0 2 0 143 4 0 240 60

42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - ClassificationData 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - Total 0 0 5 0 0 1 0 0 4 192 9 229 0 723 138 11 0 54 9 300 5 0 1680 Producer Accuracy % - - 0 - - 0 - - 25 9 0 8 - 77 0 18 - 0 0 48 0 - Overall Accuracy % 44.2 272

Appendices

3b) Run 1 (of 10 runs) from accuracy testing from the current study with the NSWNPWS (2000b) classification. 1 = Coastal Wet Gully Forest; 3 = Hunter Valley Dry Rainforest; 8 = Sheltered Blue Gum Forest; 10 = Sandstone Grey Myrtle Sheltered Forest; 12 = Hunter Valley Moist Forest; 13 = Central Hunter Riparian Forest; 14 = Wollombi Redgum-River Oak Forest; 15 = Coastal Foothills Spotted Gum-Ironbark Forest; 17 = Lower Hunter Spotted Gum-Ironbark Forest; 18 = Central Hunter Ironbark-Spotted Gum-Grey Box Forest; 19 = Hunter Lowland Redgum Forest; 21 = Hunter Range Grey Gum Forest; 30 = Coastal Plains Smoothbarked Apple Woodland; 35 = Kurri Sands Swamp Woodland; 42 = Riparian Melaleuca Swamp Woodland.

Reference Data (RDP) RUN 1 current study 1 3 8 10 12 13 14 15 17 18 19 27 30 35 42 Total User Accuracy % 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 3 0 5 0 0 0 0 0 1 0 0 0 0 0 0 0 6 83 8 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 100 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 12 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 4 100 13 0 0 0 0 0 189 1 0 1 0 2 0 0 48 0 241 78 14 0 0 0 0 0 0 7 0 1 0 0 0 0 0 0 8 88

15 0 0 0 0 0 0 0 226 4 0 0 1 0 0 0 231 98

17 0 0 0 0 0 3 0 2 715 0 0 0 0 8 0 728 98 18 0 0 0 0 0 0 0 0 0 138 0 0 0 0 0 138 100 19 0 0 0 0 0 0 0 0 1 0 9 0 0 0 0 10 90 27 0 0 0 0 0 0 0 0 0 0 0 53 0 0 0 53 100 30 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 9 100 35 0 0 0 0 0 0 1 0 1 0 0 0 0 244 1 247 99

ClassificationData 42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 100 Total 0 5 1 0 4 192 9 229 723 138 11 54 9 300 5 1680 Producer Accuracy % - 100 100 - 100 98 78 99 99 100 82 98 100 81 80 Overall Accuracy % 95.5

273

Appendices

Appendix 4 – Werakata NP Confusion Matrix Examples

4a) Run 1 (of 10 runs) from accuracy testing of the mapping of Bell (2004a). 1 = Lower Hunter Spotted Gum-Ironbark Forest; 2 = Central Hunter Riparian Forest; 3 = Hunter Lowlands Redgum Forest; 4 = Kurri Sand Swamp Woodland; 5 = Kurri Sand Melaleuca Scrub-Forest; 6 = Riparian Melaleuca Thicket.

Reference Data (RDP) RUN 1 Bell 2004a 1 2 3 4 5 6 Total User Accuracy (%)

1 100 5 0 10 0 0 115 87

2 3 1 1 1 1 0 7 14 3 0 0 0 0 0 0 0 0 4 0 0 0 5 1 0 6 83 5 6 0 0 3 3 0 12 25

ClassificationData 6 0 0 0 0 0 0 0 0 Total 109 6 1 19 5 0 140 Producer Accuracy (%) 92 17 0 26 60 0 Overall Accuracy (%)

77.9

4b) Run 1 (of 10 runs) from accuracy testing from the current study with the Bell (2004a) classification. 1 = Lower Hunter Spotted Gum-Ironbark Forest; 2 = Central Hunter Riparian Forest; 3 = Hunter Lowlands Redgum Forest; 4 = Kurri Sand Swamp Woodland; 5 = Kurri Sand Melaleuca Scrub-Forest; 6 = Riparian Melaleuca Thicket.

Reference Data (RDP) RUN 1 Bell 2004a 1 2 3 4 5 6 Total User Accuracy (%)

1 109 0 0 0 0 0 109 100

2 0 6 0 0 0 0 6 100 3 0 0 0 0 0 0 0 - 4 0 0 1 19 4 0 24 79 5 0 0 0 0 1 0 1 100

ClassificationData 6 0 0 0 0 0 0 0 - Total 109 6 1 19 5 0 140 Producer Accuracy (%) 100 100 0 100 20 - Overall Accuracy (%)

96.4

274

Appendices

Appendix 5 – Lower Hunter Spotted Gum-Ironbark Forest EEC Final Determination

Lower Hunter Spotted Gum - Ironbark Forest in the Sydney Basin Bioregion - Determination to make minor amendment to Part 3 of Schedule 1 of the Threatened Species Conservation Act The Scientific Committee, established under the Threatened Species Conservation Act, has made a Determination to make a minor amendment to Part 3 of Schedule 1 (Endangered ecological communities) of the Act by inserting the Lower Hunter Spotted Gum – Ironbark Forest in the Sydney Basin Bioregion (as described in the determination of the Scientific Committee under Division 5 Part 2) and as a consequence to omit reference to the Lower Hunter Spotted Gum – Ironbark Forest in the Sydney Basin Bioregion (as described in the final determination to list the ecological community) which was published on pages 420 to 426 in the NSW Government Gazette No. 26 dated 18 February 2005. Minor amendments to the Schedules are provided for by Division 5 of Part 2 of the Act. The Scientific Committee is of the opinion that the amendment is necessary or desirable to correct a minor error in the determination by removing reference to the Dungog Local Government Area as this Area does not occur in the Sydney Basin Bioregion (Thackway & Cresswell, 1995) The Scientific Committee has found that: 1. Lower Hunter Spotted Gum – Ironbark Forest in the Sydney Basin Bioregion is the name given to the ecological community that occurs principally on Permian geology in the central to lower Hunter Valley. The Permian substrates most commonly supporting the community belong to the Dalwood Group, the Maitland Group and the Greta and Tomago Coal Measures, although smaller areas of the community may also occur on the Permian Singleton and Newcastle Coal Measures and the Triassic Narrabeen Group (NSW Department of Mines 1966, 1969). The community is strongly associated with, though not restricted to, the yellow podsolic and solodic soils of the Lower Hunter soil landscapes of Aberdare, Branxton and Neath (Kovac and Lawrie 1991). These substrates are said to produce ‘moderately fertile’ soils (Kovac and Lawrie 1991). Lower Hunter Spotted Gum – Ironbark Forest is dominated by Corymbia maculata, (Spotted Gum) and Eucalyptus fibrosa (Broad-leaved Ironbark), while E. punctata (Grey Gum) and E. crebra (Grey Ironbark) occur occasionally. A number of other eucalypt species occur at low frequency, but may be locally common in the community. One of these species, E. canaliculata, intergrades extensively in the area with E.punctata. The understorey is marked by the tall shrub, Acacia parvipinnula, and by the prickly shrubs, Daviesia ulicifolia, Bursaria spinosa, Melaleuca nodosa and Lissanthe strigosa. Other shrubs include Persoonia linearis, Maytenus silvestris and Breynia oblongifolia. The ground layer is diverse; frequent species include Cheilanthes sieberi, Cymbopogon refractus, Dianella revoluta, Entolasia stricta, Glycine clandestina, Lepidosperma laterale, Lomandra multiflora, Microlaena stipoides, Pomax umbellata, Pratia purpurascens, Themeda australis and Phyllanthus hirtellus (NPWS 2000, Hill 2003, Bell 2004). In an undisturbed condition the structure of the community is typically open forest. If thinning has occurred, it may take the form of woodland or a dense thicket of saplings, depending on post-disturbance regeneration. 2. Lower Hunter Spotted Gum – Ironbark Forest in the Sydney Basin Bioregion is characterised by the following assemblage of species: Acacia parvipinnula Angophora costata Aristida vagans Billardiera scandens Breynia oblongifolia Bursaria spinosa Cheilanthes sieberi Corymbia eximia Corymbia gummifera Corymbia maculata Cymbopogon refractus Daviesia leptophylla Daviesia ulicifolia Dianella revoluta Dianella caerulea Digitaria parviflora

275

Appendices

Entolasia stricta Eucalyptus acmenoides Eucalyptus agglomerata Eucalyptus canaliculata intergrades Eucalyptus crebra Eucalyptus fergusonii Eucalyptus fibrosa Eucalyptus globoidea Eucalyptus moluccana Eucalyptus nubila Eucalyptus paniculata Eucalyptus punctata Eucalyptus siderophloia Eucalyptus sparsifolia Eucalyptus tereticornis Eucalyptus umbra Glycine clandestina Goodenia hederacea subsp. hederacea Grevillea montana Hardenbergia violacea Laxmannia gracilis Lissanthe strigosa Lepidosperma laterale Lomandra filiformis Lomandra longifolia Lomandra multiflora Macrozamia flexuosa Maytenus silvestris Melaleuca nodosa Microlaena stipoides Persoonia linearis Ozothamnus diosmifolius Panicum simile Phyllanthus hirtellus Pomax umbellata Pratia purpurascens Syncarpia glomulifera Themeda australis Vernonia cinerea

3. The total species list of the community is considerably larger than that given above, with many species present in only one or two sites or in low abundance. The species composition of a site will be influenced by the size of the site, recent rainfall or drought condition and by its disturbance (including fire and logging) history. The number of species, and the above ground relative abundance of species will change with time since disturbance, and may also change in response to changes in fire regime (including changes in fire frequency). At any one time, above ground individuals of some species may be absent, but the species may be represented below ground in the soil seed banks or as dormant structures such as bulbs, corms, rhizomes, rootstocks or lignotubers. The list of species given above is of vascular plant species, the community also includes micro-organisms, fungi, cryptogamic plants and a diverse fauna, both vertebrate and invertebrate. Some of these components of the community are poorly documented. 4. Lower Hunter Spotted Gum – Ironbark Forest in the Sydney Basin Bioregion is restricted to a range of approximately 65 km by 35 km centred on the Cessnock – Beresfield area in the Central and Lower Hunter Valley (NPWS 2000). Within this range, the community was once widespread. A fragmented core of the community still occurs between Cessnock and Beresfield. Remnants occur within the Local Government Areas of Cessnock, Maitland, Singleton, Lake Macquarie, Newcastle, and Port Stephens but may also occur elsewhere within the bioregion. Outliers are also present on the eastern escarpment of Pokolbin and Corrabare State Forests on Narrabeen Sandstone. 5. Threatened species recorded within this community include Callistemon linearifolius, Grevillea parviflora subsp. parviflora, Persoonia pauciflora, Rutidosis heterogama, Swift Parrot Lathamus discolor (Saunders 2002), Turquoise Parrot Neophema pulchella, Glossy Black Cockatoo Calyptorhynchus lathami, Regent Honeyeater Xanthomyza phygria, Black-chinned Honeyeater Melithreptus gularis gularis, Brown Treecreeper Climacteris picumnus victoriae, Powerful Owl Ninox strenua, Koala Phascolarctos cinereus, Yellow-bellied Glider Petaurus australis, Squirrel Glider Petaurus norfolcensis (Smith and Murray 2003), Common Bentwing Bat Miniopterus schriebersii and Eastern Freetail Bat Mormopterus norfolkensis. 6. Lower Hunter Spotted Gum – Ironbark Forest in the Sydney Basin Bioregion belongs to a complex of ecological communities that were identified in an analysis of floristic data gathered in a vegetation 276

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survey of the Lower Hunter – Central Coast region (NPWS 2000). The methods of survey and analysis employed by NPWS (2000) were found to produce a reliable regional-scale overview of native vegetation in the Lower Hunter – Central Coast area, although limitations apply to fine-scale uses of the map (Nicholls et al. 2003). This analysis, and subsequent analyses based on additional floristic data from the Hunter valley floor (e.g. Hill 2003, Bell 2004, Peake unpubl. data), identified Lower Hunter Spotted Gum – Ironbark Forest as a distinct assemblage of species. Other assemblages that may include spotted gum as a dominant species, have geographically distinct distributions outside the core area where this community primarily occurs (Cessnock – Beresfield). These other assemblages include: Coastal Foothills Spotted Gum – Ironbark Forest, Seaham Spotted Gum – Ironbark Forest and Central Hunter Spotted Gum – Ironbark – Grey Box Forest (NPWS 2000). Analysis of additional data from north of the Hunter River and other parts of the Hunter valley indicates the existence of another distinct assemblage dominated by spotted gums and ironbarks on Carboniferous sediments of the footslopes of the Barrington plateau. Lower Hunter Spotted Gum – Ironbark Forest in the Sydney Basin Bioregion belongs to the Hunter - Macleay Dry Sclerophyll Forests vegetation class of Keith (2004). 7. Eucalyptus fibrosa, Acacia parvipinnula and prickly shrub species occur more frequently or in greater abundance in Lower Hunter Spotted Gum – Ironbark Forest than in any of the other communities mentioned above. Around the margins of its core distribution, Lower Hunter Spotted Gum – Ironbark Forest may intergrade with other communities (e.g Hill 2003). Toward the coast and south, Lower Hunter Spotted Gum – Ironbark Forest may be replaced by Coastal Foothills Spotted Gum – Ironbark Forest, in which Eucalyptus umbra, E. siderophloia, Syncarpia glomulifera and Angophora costata occur more frequently, as do Polyscias sambucifolia, Imperata cylindrica and Pseuderanthemum variabile. Toward the north-east, Lower Hunter Spotted Gum – Ironbark Forest may be replaced by Seaham Spotted Gum – Ironbark Forest, in which Eucalyptus crebra, E. punctata, E. acmenoides, E. moluccana and E. siderophloia, occur more frequently, along with Acacia falcata, A. implexa, Leucopogon juniperinus, Aristida vagans and Pseuderanthemum variabile. Seaham Spotted Gum – Ironbark Forest typically occurs on sediments of Carboniferous age, in contrast to the younger Permian sediments that support Lower Hunter Spotted Gum-Ironbark Forest, although the two communities intergrade where these substrates adjoin (NPWS 2000, Hill 2003). Toward the west and north-west, Lower Hunter Spotted Gum – Ironbark Forest may be replaced by Central Hunter Spotted Gum – Ironbark – Grey Box Forest, which has a higher frequency of Eucalyptus crebra and E. moluccana and a more open grassy understorey distinguished by herbs such as Desmodium varians, Glycine tabacina, Dichondra repens, Brunoniella australis and Calotis lappulacea. On open depressions and drainage flats within the Cessnock-Beresfield area, Lower Hunter Spotted Gum – Ironbark Forest may be replaced locally by Hunter Lowlands Redgum Forest, in which Eucalyptus tereticornis, E. punctata, E. crebra and Angophora floribunda, occur more frequently, as do Breynia oblongifolia, Leucopogon juniperinus, Jacksonia scoparia and Brunoniella australis (NPWS 2000). 8. Clearing and other disturbances have resulted in a high degree of fragmentation of the community. Four large patches of Lower Hunter Spotted-Gum – Ironbark Forest are estimated to have covered nearly 50000ha prior to European settlement, representing 75% of the total distribution. The community is currently mapped as occurring in more than 4 800 fragments, of which more than 4 500 are less than 10 ha in area (House 2003). The four largest patches now cover about 7 000 ha, representing less than one-quarter of the current distribution, or about 10% of the estimated pre- European distribution (House 2003). Clearing of native vegetation is listed as a Key Threatening Process under the Threatened Species Conservation Act (1995). 9. Using recently updated mapping of extant Lower Hunter Spotted Gum – Ironbark Forest based on fine-scale aerial photograph interpretation of extant woody native vegetation, House (2003) estimated that approximately 26500 ha of the community remains with its tree canopy cover in a ‘substantially unmodified’ condition, representing approximately 40% of its pre-European distribution. However, this estimate is based on the collective canopy cover of trees (i.e. where tree canopy cover was estimated to be greater than 20%, the canopy was assumed to be ‘unmodified’ and not substantially thinned), and does not consider the growth stages of trees that contribute to the cover. Growth stage mapping is available for approximately 6000 ha of Lower Hunter Spotted Gum – Ironbark Forest on public land (RACAC 1995), of which only 3% was assessed as containing a sub-dominance of ‘overmature’ and 277

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‘senescent’ tree crowns indicative of old growth forest. Seventy-five per cent of this area was assessed as ‘young forest’, indicating regeneration from past logging and wildfire. Some areas of Lower Hunter Spotted Gum – Ironbark Forest on private land also reflect a continuing history of degradation. In the Blackhill district, for example, much of the existing vegetation was cleared, and is now largely composed of dense stands of juvenile saplings. This regrowth has since been further affected by clearing and thinning, creation of electricity transmission easements, and ongoing grazing by goats and cattle. In addition, House (2003) estimated that there are a further 4650ha of Lower Hunter Spotted Gum – Ironbark Forest with a modified or substantially modified tree canopy cover. 10. The condition of the understorey has not been mapped systematically. There are no quantitative estimates of the area of the community that retains a substantially unmodified understorey. However, qualitative information suggests that there has been extensive disturbance to the understorey associated with logging, expansion of unplanned tracks and trails, rubbish dumping, off-road vehicle use, arson and weed invasion, even in stands that are currently within a conservation reserve (Bell 2004). These pressures are likely to intensify with the projected increases in the density of the human population within the region (Progress Economics 2004). 11. Much of the remaining Lower Hunter Spotted Gum – Ironbark Forest in the Sydney Basin Bioregion shows evidence of disturbance. Past logging practices and fire regimes have heavily modified some parts of the community, resulting in a simplified structure and floristics. Production areas of State Forests are actively logged at intensities specified by regulations. Frequent fires (<3 years) dramatically simplify understorey vegetation (Bell 2004). Grazing, uncontrolled human access and associated dumping of solid and garden waste, as well as weed invasion (notably by Lantana camara and Solanum mauritianum, wild tobacco) have degraded the more accessible remnants of the community, while transport corridors and power and communication easements have further fragmented them. As a likely consequence of continuing habitat loss and degradation, local bird observers have noted declines in species associated with spotted gum/ironbark forests, including the Swift Parrot, Regent Honeyeater, Brown Treecreeper, Black-chinned Honeyeater, Diamond Firetail, Turquoise Parrot, Fuscous Honeyeater, Eastern Shriketit and Spotted Quailthrush. 12. Clearing pressures from rural residential and residential subdivisions, industrial developments and new cropping enterprises (e.g. vineyards) continue to threaten the community particularly in Cessnock Local Government Area where the core of this community occurs. Over the past 10 years, demand for housing lots in the Lower Hunter area has nearly doubled from 1 726 in 1991-92 to 3 904 in 2003-04 (Progress Economics 2004). The ‘medium’ forecast for housing demand in Lower Hunter is 2500 lots/yr; the current supply of land zoned for housing is 12 000 lots and is projected to meet demand only for the next 5 years. Hence there are substantial pressures for rezoning land for housing within the next 10 years (Progress Economics 2004). A study of the Thornton-Killingworth sub-region projected the population to expand by 169000 people, requiring 2600 new dwellings annually over the next 25 years (Parsons Brinckerhoff 2003). Existing proposals to rezone land from rural to rural /residential around the villages of Millfield and Paxton and applications for clearing associated with rural residential and residential developments around Paxton, Bellbird, Ellalong and Mulbring will affect the ecological community. Loss of remnants of Lower Hunter Spotted Gum – Ironbark Forest will be associated with the Cessnock LEP Amendment No 60 - Hunter Economic Zone, Donaldson and Bloomfield coalmine sites at Thornton/Killingworth and F3 to Branxton National Highway link (Ecotone Ecological Consultants 1999, 2000; Connell Wagner 1997). In the Maitland Local Government Area, Hill (2003) assessed Lower Hunter Spotted Gum – Ironbark Forest as exposed to high levels of threat from development, tree dieback and grazing, and under moderate levels of threat from fragmentation, weeds, and fire. 13. Approximately 1600 hectares of Lower Hunter Spotted Gum – Ironbark Forest in the Sydney Basin Bioregion occurs within Werakata National Park (Bell 2004). This represents less than 2.5% of the community’s modelled pre-1750 distribution (House 2003), is distributed among several separate patches and is predominantly young regrowth forest (Bell 2004). Of an estimated 2800 ha of the community currently within State Forests, approximately 1770 ha is excluded from timber harvesting in Forest Management Zone reserves (State Forests of NSW, in litt.), although these areas may be subject to development of service easements, transport infrastructure and mineral exploration. Within the

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Hunter Employment Zone (HEZ), 460 ha of Lower Hunter Spotted Gum – Ironbark Forest is estimated to occur within zone 7(b) ‘Environmental Protection’. However, 7(b) zoning does not exclude development for rural properties (buildings, roads, fences, bushfire hazard reduction) and coal mining. 14. In view of the above the Scientific Committee is of the opinion that Lower Hunter Spotted Gum – Ironbark Forest in the Sydney Basin Bioregion is likely to become extinct in nature in New South Wales unless the circumstances and factors threatening its survival cease to operate, or it might already be extinct. Dr Richard Major Chairperson Scientific Committee Proposed Gazettal date: 05/11/10 Exhibition period: 05/11/10 - 21/01/11

References: Bell SAJ (2004) The vegetation of Werakata National Park, Hunter Valley New South Wales. Cunninghamia 8, 331-347. Connell Wagner (1997) ‘Fauna Impact Statement of proposed highway link F3 Freeway to Branxton.’ Roads and Traffic Authority of NSW, Sydney. Ecotone Ecological Consultants (1999) ‘Flora and fauna investigations and planning assessment for the Tomalpin Employment Zone within the Cessnock Local Government Area.’ Cessnock City Council, Cessnock. Ecotone Ecological Consultants (2000) ‘Additional flora and fauna investigations within the Tomalpin Employment Zone - supplementary report.’ Cessnock City Council, Cessnock. Hill L (2003) ‘The natural vegetation of the Maitland Local Government Area.’ Maitland City Council, Maitland. House S (2003) ‘Lower Hunter and Central Coast Regional Biodiversity Conservation Strategy, Technical Report, Digital Aerial Photo Interpretation and Updated Extant Vegetation Community Map, May 2003.’ Lower Hunter and Central Coast Regional Environmental Management Strategy, Callaghan. Keith DA (2004) ‘Ocean shores to desert dunes: the native vegetation of New South Wales and the ACT.’ (NSW Department of Environment and Conservation: Sydney.) Kovac M, Lawrie JW (1991) ‘Soil landscapes of the Singleton 1:250000 sheet.’ Soil Conservation Service of NSW, Sydney. Nicholls AO, Doherty M Newsome AE (2003) ‘Evaluation of Data, Modelling Techniques and Conservation Assessment Tools for the Lower Hunter Central Coast Regional Biodiversity Conservation Strategy.’ CSIRO Sustainable Ecosystems, Canberra. NSW Department of Mines (1966) ‘Newcastle. 1:250 000 geological sheet S1 56-2.’ NSW Government Printer, Sydney. NSW Department of Mines (1969) ‘Singleton. 1:250 000 geological sheet S1 56-1.’ NSW Government Printer, Sydney. National Parks and Wildlife Service (2000) ‘Vegetation Survey, Classification and Mapping: Lower Hunter and Central Coast Region.’ NSW National Parks and Wildlife Service, Sydney. Parsons Brinckerhoff (2003) Thornton-Killingworth sub-regional conservation and development strategy. Department of Infrastructure, Planning and Natural Resources, Sydney. Progress Economics (2004) An analysis of the projected demand and supply of land in the Lower Hunter from 2004-05 to 2013-14. A report prepared for the Urban Development Institute of Australia, Sydney.

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RACAC (1995) Draft Interim Forestry Assessment. Resource and Conservation Assessment Council, Sydney. Retrieved December 2004 from http://www.racac.nsw.gov.au/reports/iap/ . Saunders D (2002) ‘Assessment of Swift Parrot sites near Cessnock, Lower Hunter valley region, NSW.’ National Parks and Wildlife Service, Sydney. Smith A, Murray M (2003) Habitat requirements of the Squirrel Glider (Petaurus norfolcensis) and associated possums and gliders on the New South Wales Central Coast. Wildlife Research 30, 291-301. Thackway R, Cresswell ID (1995) An interim biogeographic regionalisation for Australia: a framework for setting priorities in the National Reserve System Cooperative Program. (Version 4.0. ANCA: Canberra)

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Appendix 6 – Chronological history of LHSGIF Revision

Date Project Notes Source April 2000 LHCCREMS classification Initial delineation and mapping of NSWNPWS 2000b LHSGIF, defined from 95 full floristic quadrats out of 1143 available. June 2003 Thornton North master plan Example of a small-scale study raising Bell 2003 the difficulties of identifying LHSGIF in the Thornton area (Maitland LGA), and problems separating it from Seaham Spotted Gum-Ironbark Forest. 2003 Maitland LGA vegetation Applied the existing definition to Hill 2003 mapping vegetation within the Maitland area. 2004 Preliminary Determination, Preliminary listing of the EEC NSW Scientific TSC Act 1995 Committee 2004 August 2004 Donaldson coal mine, Analysed 134 floristic quadrats from Driscoll & Bell 2004 Beresfield the region, identifying a core group of sites from the Cessnock area as most probably representing the LHSGIF. Also showed that with greater controls on data quality, the original broad circumscription of this community could be improved. February 2005 Final Determination, TSC Act Final listing of the EEC NSW Scientific 1995 Committee 2005 October 2005 Sweetwater development, Analysed 192 floristic quadrats from Bell & Driscoll 2005 North Rothbury the region, and showed that ground layer vegetation could be used to distinguish LHSGIF from CHSGIF. The 700-800mm/yr rainfall band postulated as marking the change from one to the other. December 2006 Hunter remnant vegetation Applied the existing definition to Peake 2006 project vegetation within the more easterly portions of the study area, but noting apparent differences to Final Determination. September 2007 Wallalong Survey and analysis of Spotted Gum – Bell 2007 Ironbark vegetation on private property at Wallalong (Port Stephens LGA), identifying high quality examples of LHSGIF February 2008 Cessnock – Kurri region A major re-classification of vegetation NSWDECC 2008b within the Cessnock-Kurri area, including a revision of the LHSGIF for that area. Builds further on Bell & Driscoll 2005, and includes a wider regional contextual analysis of all Spotted Gum – Ironbark vegetation (360 floristic quadrats). September 2008 Western Wyong LGA A revision of the Wyong LGA mapping, Bell & Driscoll identifying LHSGIF in and around 2006a Warnervale. January 2009 Coal & Allied Northern Lands Identification and mapping of LHSGIF HSO Ecology 2009a 281

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Date Project Notes Source on lands around Minmi and Seahampton. March 2009 Sugarloaf SCA Classification and mapping of LHSGIF Bell & Driscoll 2009 within Sugarloaf SCA, with regional analyses showing distinct sub-type within the wider LHSGIF group. May 2009 Columbey NP Survey and analysis of LHSGIF at Bell 2009a Columbey NP (Dungog LGA), showing strong similarities to vegetation at Cessnock. May 2009 Edgeworth (east) LES Identification and mapping of LHSGIF HSO Ecology 2009b on lands around Edgeworth and Glendale. September 2009 Lake Macquarie LHSGIF Detailed mapping supplemented with Bell 2009d mapping data analysis to show the distribution and composition of LHSGIF within Lake Macquarie. November 2009 North Wyong LGA Sampling and analysis of data to Bell 2009c determine if LHSGIF is present within a proposed development area. LHSGIF shown to be similar to that reported for Lake Macquarie LGA. May 2010 Dooralong Resort Analysis of sample data to assist in Bell 2010c determination that LHSGIF was not present at this site. August 2010 Edgeworth (west) LES Classification and mapping of LHSGIF Bell 2010a near Barnsley, raising the question as to how much LHSGIF is present within Lake Macquarie LGA. Identification of grass species raised as important in delineating this form of LHSGIF. September 2010 Warnervale (Wyong LGA) Sampling and analysis of data to show Bell 2010b that LHSGIF is present at Warnervale in a number of locations, and that it is consistent with the form present in Lake Macquarie to the north. November 2010 Amendment to TSC Act Amendment made to final NSW Scientific determination to exclude Dungog LGA Committee 2010b as it occurs outside of the Sydney Basin Bioregion. August 2011 Singleton Training Area Sampling and analysis of data Bell & Carty 2011 highlighting two forest types dominated by Spotted Gum and Red Ironbark, representing LHSGIF and an affinity from the Central Hunter. August 2011 Warnervale (Wyong LGA) Directive from the Director-General of NSWDECC pers NSWDECC indicating that LHSGIF does comm not occur within Wyong LGA.

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Appendix 7 – Spotted Gum-Ironbark Vegetation (NSWNPWS 2000b)

Species presence in each of the four Spotted Gum (Corymbia maculata) communities identified for the region by NSWNPWS (2000b). With the exception of Eucalyptus paniculata, species listed include only those noted as positively diagnostic by NSWNPWS (2000b) for at least one community. Note: as full species lists are not included in NSWNPWS (2000b), it is likely that many species listed below occur in most communities.

Note: Map Unit 15 = Coastal Foothills Spotted Gum – Ironbark Forest Map Unit 16 = Seaham Spotted Gum – Ironbark Forest Map Unit 17 = Lower Hunter Spotted Gum – Ironbark Forest Map Unit 18 = Central Hunter Spotted Gum – Ironbark Forest Canopy Species Map Unit 15 Map Unit 16 Map Unit 17 Map Unit 18

Corymbia maculata positive positive positive positive

Eucalyptus fibrosa uninformative positive positive uninformative Eucalyptus crebra positive uninformative positive Eucalyptus siderophloia positive uninformative uninformative Eucalyptus paniculata uninformative uninformative

Eucalyptus punctata uninformative positive positive Eucalyptus umbra positive uninformative Eucalyptus moluccana uninformative uninformative positive

Understorey Species Map Unit 15 Map Unit 16 Map Unit 17 Map Unit 18

Themeda australis positive positive positive positive Microlaena stipoides var. stipoides positive positive positive positive Pratia purpurascens positive positive positive positive

Entolasia stricta positive positive positive Cymbopogon refractus positive positive positive Cheilanthes sieberi ssp sieberi positive positive positive Vernonia cinerea var cinerea uninformative positive positive positive

Imperata cylindrica var. major positive positive Pseuderanthemum variabile positive positive Lomandra multiflora ssp multiflora positive positive Desmodium varians positive positive Panicum effusum positive positive Lepidosperma laterale positive positive Glycine clandestina uninformative positive positive Acacia parvipinnula positive positive Dianella revoluta var revoluta positive positive

Dianella caerulea uninformative positive Eustrephus latifolius uninformative positive Desmodium rhytidophyllum uninformative positive Lomandra longifolia uninformative positive Hardenbergia violaceae uninformative positive Daviesia ulicifolia uninformative positive uninformative Melaleuca nodosa uninformative positive

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Unique to MU15 Brachycome graminea positive Pterostylis baptistii positive Pterostylis furcillata positive

Unique to MU16 Acacia falcata positive Aristida vagans positive Arthropodium milleflorum positive Dianella tasmanica positive Dichelachne micrantha positive Dichondra repens positive Digitaria ramularis positive Echinopogon caespitosus positive Echinopogon ovatus positive Entolasia marginata positive Eragrostis brownii positive Gahnia aspera positive Galium gaudichaudii positive Laginifera stipitata positive Lomandra filiformis positive Notodanthonia longifolia positive Oplismenus imbecillis positive Oxalis perennans positive Pandorea pandorana positive Panicum simile positive Paspalidium distans positive Plectranthus parvifolius positive Sigesbeckia orientalis positive

Unique to MU17 Aristida lignosa positive Austrodanthonia induta positive Calotis cuneata var cuneata positive Daviesia leptophylla positive Oxylobium ellipticum positive Phyllanthus hirtellus positive Pomax umbellata positive Pterostylis ophioglossa positive Solanum papaverifolium positive Sporobolus caroli positive

Unique to MU18 Aristida ramosa positive Austrodanthonia bipartita positive Brunoniella australis positive Calotis lappulacea positive Cheilanthes distans positive Eleocharis cylindrostachys positive Eragrostis leptostachya positive Geijera latifolia positive Glossogyne tannensis positive Glycine tabacina positive Linum marginale positive Paspalidium aversum positive Senna aciphylla positive Vittadinia hispidula var setosa positive Wahlenbergia communis positive

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Appendix 8 – Vegetation superficially similar to LHSGIF

Vegetation superficially similar to Lower Hunter Spotted Gum-Ironbark Forest. Bold

entries meet all four criteria of NSW Scientific Committee (2010b) (see Appendix 5).

codominant

Existing classification

SydneyBasin Permian/Narrabeen C.maculata E.fibrosa

Lower Hunter & Central Coast REMS (NSWNPWS 2000b) 12: Hunter Valley Moist Forest √ √ √ - 15: Coastal Foothills Spotted Gum - Ironbark Forest √ √ √ √ 16: Seaham Spotted Gum - Ironbark Forest √ - √ √ 17: Lower Hunter Spotted Gum - Ironbark Forest √ √ √ √ 18: Central Hunter Ironbark - Spotted Gum - Grey Box Forest √ √ √ - 19: Hunter Lowlands Redgum Forest √ √ - - 43: Wyong Paperbark Swamp Forest √ √ √ -

Hunter Valley (Peake 2006) 24: Hunter Lowlands Redgum Forest √ √ - - 26: Lower Hunter Spotted Gum - Ironbark Forest √ √ √ √ 27: Central Hunter Ironbark - Spotted Gum - Grey Box Forest √ √ √ √

Hunter-Central Rivers CMA (Somerville 2010) 65: Spotted Gum – Broad-leaved Mahogany - Red Ironbark Moist √ √ √ √ Shrubby Open Forest 67: Spotted Gum - Red Ironbark - Large-fruited Grey Gum √ √ √ √ Shrub/Grass Open Forest 68: Red Ironbark – Paperbark Shrubby Open Forest √ √ √ √ 71: Spotted Gum – Red Ironbark – Narrow-leaf Ironbark Shrub/Grass √ √ √ √ Open Forest 72: Spotted Gum – Narrow-leaf Ironbark – Red Ironbark Shrub/Grass √ √ √ - Open Forest

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codominant

Existing classification

SydneyBasin Permian/Narrabeen C.maculata E.fibrosa Western Sydney (Tozer 2003) 1: Shale-Sandstone Transition Forest (low sandstone) √ - - √ 2: Shale-Sandstone Transition Forest (high sandstone) √ - - √ 9: Shale Hills Woodland √ - - - 10: Shale Plains Woodland √ - √ - 103: Shale-Gravel Transition Forest √ - - √

Sydney Metropolitan CMA (NSWDECCW 2010b) S_GW03: Cumberland Shale Plains Woodland √ - √ √ S_GW04: Cumberland Shale-Sandstone Ironbark Forest √ - √ √

Southern forests (Gellie 2005) 14: Northern Coastal Hinterland Shrub/Grass Dry Forest √ √ √ √

Warragamba Special Area (NSWNPWS 2003) MU48: Escarpment Slopes Dry Ironbark Woodland √ √ - √

Western Blue Mountains (NSWDEC 2006) 38: Capertee Grey Gum – Narrow-leaved Stringybark – Scribbly Gum – √ √ - √? Callitris – Ironbark Shrubby Forest 39: Capertee Hills Blue-leaved Ironbark – Callitris Shrubby Forest √ √ - √? 40: Capertee Slopes Red Ironbark – Red Stringybark – Narrow-leaved √ √ - √ Stringybark Shrubby Woodland 41: Capertee Slopes Slaty Gum – Grey Gum – Mugga – Callitris Open √ √ - √? Forest

South-eastern NSW (Tozer et al. 2010) DSF_e32A: Deua-Brogo Foothills Dry Shrub Forest - - - - DSF_p5: Burragorang Hillslope Forest √ √ - √

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codominant

Existing classification

SydneyBasin Permian/Narrabeen C.maculata E.fibrosa DSF_p89: Batemans Bay Foothills Forest - - - - DSF_p246: Yalwal Shale-Sandstone Transition Forest √ √ - - DSF_p502: Castlereagh Shale-Gravel Transition Forest √ - - √ GW_p2 Cumberland Shale-Sandstone Transition Forest √ - - √ GW_p28: Cumberland Shale Hills Woodland √ - - - GW_p29: Cumberland Shale Plains Woodland √ - - - RF_p40: Temperate Dry Rainforest √ √ - - WSF_p86: Murramarang-Bega Lowlands Forest √ √ √ - WSF_p87: Sydney Turpentine-Ironbark Forest √ - - - WSF_p90: Batemans Bay Cycad Forest - - √ - WSF_p104: Southern Lowland Wet Forest √ √ √ -

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Appendix 9 – LHSGIF 3-d nMDS ordination diagrams

9a) Analysis 1: All Spotted Gum-Ironbark data (3-dimensional ordination, Kruskal’s stress = 0.16)

9b) Analysis 2a: All E. fibrosa/ C. maculata data (3-dimensional ordination, Kruskal’s stress = 0.17)

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9c) Analysis 2b: Cessnock Spotted Gum-Ironbark data (3-dimensional ordination, Kruskal’s stress = 0.13)

9d) Analysis 3: E. fibrosa only data (3-dimensional ordination, Kruskal’s stress = 0.14)

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9e) Analysis 4: Hunter Valley floor data (3-dimensional ordination, Kruskal’s stress = 0.13)

290