A Study of the Carbon Stocks of Melaleuca Forests in the Coastal Regions of Southern Vietnam and South East Queensland Australia

A study of the carbon stocks of Melaleuca forests in the coastal regions of Southern Vietnam and South East Queensland Australia

Da Binh Tran

(BSc, MSc)

A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in January 2015 School of Geography, Planning and Environmental Management

ABSTRACT

The genus Melaleuca consists of around 260 species. The genus dominates forests that cover more than six million hectares of land globally. Most of that forest occurs in Australia, but there are also smaller areas in countries in South-East Asia and the Caribbean region, and the southern United States of America. Melaleuca forests predominantly occur in wetland and coastal ecosystems and as a result are substantially exposed to the influences of climate change and human development, particularly in regards to impacts on hydrological and soil systems. Like other wetland forest ecosystems, a large amount of carbon is stored in the biomass and soil components of Melaleuca forests. However, up until now very little research has been published on the extent and nature of those carbon stocks. This thesis addresses this gap in the research literature and presents the results of a study that involved collecting data on the carbon stocks of various Melaleuca forests in Australia and Vietnam.

A variety of widely recognised data collection methods were used to collect data on the carbon stocks of Melaleuca forests at field sites in Southern Vietnam and South East Queensland, Australia. Data was collected from five ‘typical’ Melaleuca forest stand-types in Vietnam including: primary Melaleuca forests on sandy soil (denoted VS1); regenerating Melaleuca forests on sandy soil (VS2); degraded secondary Melaleuca forests on clay soil with peat (VS3); regenerating Melaleuca forests on clay soil with peat (VS4); and regenerating Melaleuca forests on clay soil without peat (VS5). Data was also collected from four ‘typical’ Melaleuca forest stand-types in Australia including: primary Melaleuca forests under continuous water inundation (A1); primary Melaleuca forests not inundated by water (A2); degraded Melaleuca forests under continuous water inundation (A3); and regenerating Melaleuca forests under continuous water inundation (A4).

Carbon stock densities of VS1, VS2, VS3, VS4, and VS5 were found to be 275.98 tC/ha, 159.36 tC/ha, 784.68 tC/ha, 544.28 tC/ha, and 246.96 tC/ha, respectively. Carbon stock densities of A1, A2, A3, and A4 were found to be 381.59 tC/ha, 278.40 tC/ha, 210.36 tC/ha, and 241.72 tC/ha, respectively. In Australia, carbon accumulation in forest litter in wet conditions (A1) was likely to be 6.5 times higher than those in drained and drier conditions (A2). The total carbon stock densities in Melaleuca forests in the wildfire conditions were found to be lost around 45 % of those in primary forests under wetter conditions. Furthermore, the exchangeable sodium percentage of Melaleuca forests in Vietnam on sandy soil (VS1 and VS2) showed high sodicity, while those on clay soils (VS3, VS4, and VS5) varied from low to moderate sodicity.

The results of this thesis also show that: (1) the carbon stock densities of Melaleuca forests in Australia are much greater than previously thought and substantially higher than current estimates used in Australia’s national carbon accounting systems; (2) the carbon stock densities of Melaleuca forests on peat lands in Vietnam are large compared to many other forest types around the world; (3) and that Melaleuca forests on sandy soils in Vietnam are tolerant of highly sodic conditions, an important feature considering how climate change and human development in the region is impacting on local hydrological and soil systems. In these regards, the results of this thesis provide important information for the future sustainable management of Melaleuca forests in both Australia and Vietnam, particularly in regards to emerging forest carbon conservation and management initiatives.

Declaration by author

This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my research higher degree candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis.

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Publications during candidature

Peer-reviewed papers

In English

1. Tran, D.B., Dargusch, P., Moss, P., Hoang, T.V., 2013a. An assessment of potential responses of Melaleuca genus to global climate change. Mitigation and Adaptation Strategies for Global Change 18, 851-867.

2. Tran, D.B., Dargusch, P., Herbohn, J., Moss, P., 2013b. Interventions to better manage the carbon stocks in Australian Melaleuca forests. Land Use Policy 35, 417-420.

In Vietnamese

3. Tran Binh Da, Le Quoc Doanh, Le Thi Ngoc Ha, 2011. Study carbon stock of Tea production system to suggest approach of clean development mechanism. Science and Technology Journal of Agriculture and Rural Development, Vietnam 169, 13-19.

4. Hoang Vu Tho, Tran Binh Da, 2011. Preliminary evaluation adaptability and growth ability of Melaleuca hybrid varieties experimentally planted on waste-coaled soil in Quang Ninh Province. Science and Technology Journal of Agriculture and Rural Development, Vietnam. Special issue on Plant and animal Breeding 6(1), 191-195.

5. Hoang Vu Tho, Tran Binh Da, 2014. Influence of some factors to the possibility natural regeneration of Stereospermum colais under the afforestation canopy. Forestry Science and Technology Journal, Vietnam 9, 36-46.

Publications included in this thesis

This thesis includes four jointly authored published articles, as follows: two published papers and two jointly authored works that have been submitted for peer review. These papers have been reproduced in full as chapters of this thesis. I conducted the majority of the work contained within these articles, including: original idea, design, data collection, data analysis, interpretation, synthesis, drafting and writing. I also play a role as corresponding author on the behalf of others. Author contributions are indicated with the below relevant citations.

Chapter two

Tran, D.B., Dargusch, P., Moss, P., Hoang, T.V., 2013a. An assessment of potential responses of Melaleuca genus to global climate change. Mitigation and Adaptation Strategies for Global Change 18, 851-867.DOI 10.1007/s11027-012-9394-2.Received: 5 January 2012 / Accepted: 14 May 2012 / Published online: 31 May 2012

First author contributed 85 % of work, and co-authors jointly contributed to the editing [Moss (5 %), Dargusch (5 %), and Hoang (5 %)].

Chapter four

Tran, D.B., Dargusch, P., Herbohn, J., Moss, P., 2013b. Interventions to better manage the carbon stocks in Australian Melaleuca forests. Land Use Policy 35, 417-420.DOI: 10.1016/j.landusepol.2013.04.018. Received: 23 September 2012 / Received in revised: 15 April 2013 / Accepted: 28 April 2013

First author contributed 80 % of work, and co-authors jointly contributed to the design and editing [Dargusch (12.5 %), Moss (2.5 %), and Herbohn (5 %)].

Chapter six

This chapter was submitted to the journal ‘Mitigation and Adaptation Strategies for Global Change’ as an article manuscript: Tran, D.B., Dargusch, P., Moss, P., Hoang, T.V., 2015, under review. An assessment of the carbon stocks and sodicity tolerance of disturbed Melaleuca forests in Southern Vietnam.

First author contributed 85 % of work, and co-authors jointly contributed to the editing [Moss (5 %), Dargusch (5 %)], and data collection [Hoang (5 %)].

Chapter seven

This chapter was submitted to the journal ‘Climatic Change’ as an article manuscript: Tran, D.B., Dargusch, P., 2015, under review. Carbon stocks of Melaleuca forest ecosystems in South East Queensland Australia. Climatic Change.

First author contributed 85 % of work, and co-authors jointly contributed to the editing [Dargusch (15 %)].

Contributions by others to the thesis

Peter Stores contributed to the proofreading of Chapters 2 and 4. John Schiller contributed to the proofreading of Chapters 3 and 5. Paul Dargusch, Patrick Moss, and John Herbohn advised and commented on drafts of written materials for whole thesis.

Chapter 2: The manuscripts have been reviewed for Mitigation and Adaptation Strategies for Global Change by three anonymous reviewers and the Editor, Robert Dixon. Comments and corrections from reviewers and the Editor were incorporated into revised papers.

Chapter 4: The manuscript has been reviewed for Land Use Policy by two anonymous reviewers and the Editor, Guy M. Robinson. Comments and corrections from reviewers and the Editor were incorporated into published paper.

Statement of parts of the thesis submitted to qualify for the award of another degree

None

Acknowledgement

This thesis, involving published papers, is the culmination of my four years in the PhD program. I am indebted to many people, without their help my PhD would not have come to fruition. I owe a special debt of gratitude to my advisory team at University of Queensland: Dr Paul Dargusch; Associate Professor Patrick Moss; and Professor John Herbohn for their support and advice throughout the PhD program. In particular, I am very appreciative of the consideration shown me by Dr Dargusch - my principal advisor - from the very beginning of my studies. I would like to thank the staff members of the School of Geography, Planning and Environmental Management, and the School of Agriculture and Food Sciences for their assistance. I specially thank Dr John Schiller at the University of Queensland, and Mr. Peter Store for their assistance of thesis proofreading. I could not have continued my studies without financial support. I am very grateful to the Ministry of Education and Training of Vietnam (MOET) and the University of Queensland for their scholarships. I would also like to thank the International Foundation for Science (IFS) for the funding grant which enabled me to conduct a research in Vietnam. I would like to thank the staff of Phu Quoc National Park; U Minh Thuong National Park; the Vietnam Forestry University; and the National Institute of Agricultural Planning and Projection for their association during the fieldwork and laboratory work. I particularly thank my colleagues in the Practice and Experiment Centre, and Agroforestry Department - Silviculture Faculty at the Vietnam Forestry University, for their assistance and facility support. I also thank the Queensland Department of Environment and Heritage Protection for the permit to conduct field research at various sites in South East Queensland. Sincere thanks must go to Dr Hoang Vu Tho at the Vietnam Forestry University; Mr. Nguyen Huu Thang at the North-Western College of Vietnam; and my Vietnamese friends: Dr Tran Duy Hung; Mr. Nguyen Van Thuan; Mr. Trinh Nghia; and Ms Hong for their special assistance during my collection of data at sites in both Vietnam and Australia. I wish to acknowledge the important role played by my family in Vietnam, my colleagues and leaders at Vietnam Forestry University, thank them all for their practical help, moral support and encouragement. Last, but far from least, deeply heartfelt thanks to my wonderful wife and daughters for their love, patience and understanding, and most of all for their unfailing belief in me which sustained me and them together during the tough times in Australia. I believe that this fruition indicates not only my success, but also in part success of my family, my advisors, my universities, and my country as well.

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Keywords

Adaptation, Carbon, Climate Change, Ecosystem, Melaleuca, REED+, Sequestration, Sodicity, Wetland, Wildfire

Australian and New Zealand standard research classifications (ANZSRC)

ANZSRC code: 050209 Natural Resource Management, 50 %

ANZSRC code: 060204 Freshwater Ecology, 30 %

ANZSRC code: 050101 Ecological Impacts of Climate Change, 20 %

Fields of research (for) classification

FoR code: 0502 Environmental Science and Management, 50 %

FoR code: 0501 Ecological applications, 20 %

FoR code: 0602 Ecology, 30 %

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TABLE OF CONTENTS

ABSTRACT ...... i Declaration by author ...... iii Publications during candidature ...... iv Publications included in this thesis ...... v Contributions by others to the thesis ...... vi Statement of parts of the thesis submitted to qualify for the award of another degree ...... vi Acknowledgement ...... vii Keywords ...... viii Australian and New Zealand standard research classifications (ANZSRC) ...... viii Fields of research (for) classification ...... viii Chapter 1 - GENERAL INTRODUCTION ...... 1 1.1 Background ...... 1 1.2 Rationale ...... 2 1.3 Research problem and questions ...... 4 1.4 Thesis outline ...... 4 Chapter 2 - AN ASSESSMENT OF POTENTIAL RESPONSES OF MELALEUCA GENUS TO GLOBAL CLIMATE CHANGE ...... 7 2.1 Introduction ...... 8 2.2 Vulnerability of Melaleuca forests to global climate change ...... 10 2.3 Nature of the Melaleuca genus and it’s potential responses to climate change ...... 11 2.3.1 Wide natural distribution of Melaleuca forests ...... 11 2.3.2 The botanical characteristics of Melaleuca and potential responses to climate change 13 2.3.3 The resistance of Melaleuca species to harsh conditions and their capacity to respond to climate change ...... 15 2.3.4 Historical evidence of Melaleuca evolution and genotype and potential responses to climate change ...... 18 2.4 Discussion on adaptation of Melaleuca population and management for carbon storage 22 2.5 Conclusion ...... 23 Chapter 3 - A REVIEW OF PREVIOUS RESEARCH ON THE CARBON STOCKS IN MELALEUCA DOMINATED ECOSYSTEMS ...... 24 3.1 Introduction ...... 24 3.2 Carbon stocks of Melaleuca forests ...... 26 3.2.1 Potential of the carbon biomass of Melaleuca forests ...... 26 3.2.2 Low methane production rate of the forested wetlands ...... 33

3.3 Discussion of the potential of carbon storage in Melaleuca forest ecosystems for offsetting greenhouse gases ...... 34 3.4 Conclusions ...... 35 Chapter 4 - INTERVENTIONS TO BETTER MANAGE THE CARBON STOCKS OF AUSTRALIAN MELALEUCA FORESTS ...... 36 4.1 Rationale ...... 37 4.2 Deriving an estimate of carbon stocks ...... 40 4.3 Policy interventions and future research ...... 42 Chapter 5 - METHODOLOGY ...... 45 5.1 Introduction ...... 45 5.2 Methods to estimate carbon stocks in Melaleuca forests ...... 46 5.2.1 Carbon stocks in stand trees ...... 46 5.2.2 Carbon stocks in soil ...... 49 5.2.3 Carbon stocks in understory, litter and deadwood ...... 50 5.2.4 Carbon stocks in gaseous states of wetland soil and water ...... 52 5.3 Methods implicating for research on carbon stocks of Melaleuca forests ...... 54 Chapter 6 - AN ASSESSMENT OF THE CARBON STOCKS AND SODICITY TOLERANCE OF DISTURBED MELALEUCA FORESTS IN SOUTHERN VIETNAM ...... 56 6.1 Introduction ...... 58 6.2 Materials and Methods ...... 59 6.2.1 Study sites and disturbance context ...... 59 6.2.2 Field sampling and data collection ...... 60 6.2.3 Sample analysis ...... 61 6.2.4 Biomass allometric computation ...... 62 6.2.5 Statistical analysis ...... 63 6.3 Results and Discussion ...... 64 6.3.1 Characteristics of the typical Melaleuca forests in the study areas ...... 64 6.3.2 Carbon stocks of Melaleuca forests ...... 68 6.3.3 Variability of carbon stocks in different types of Melaleuca forests ...... 70 6.3.4 Sodicity tolerance of Melaleuca cajuputi forests toward the adaptation to global climate change ...... 73 6.4 Conclusion ...... 76 Chapter 7 - CARBON STOCKS OF MELALEUCA FOREST ECOSYSTEMS IN SOUTH- EAST QUEENSLAND AUSTRALIA ...... 77 7.1 Introduction ...... 78 7.2 Materials and Methods ...... 79 7.2.1 Study sites ...... 79

7.2.2 Field sampling and data collection ...... 80 7.2.3 Sample analysis ...... 80 7.2.4 Biomass allometric computation ...... 81 7.2.5 Statistical analysis ...... 83 7.3 Study results ...... 83 7.3.1 Characteristics of the typical Melaleuca forests in the study areas ...... 83 7.3.2 Carbon stocks of the Melaleuca forests ...... 86 7.4 Discussion ...... 87 7.4.1 Variability of carbon stock categories of the Melaleuca forests ...... 87 7.4.2 Disturbances of carbon stock in the Melaleuca forests ...... 90 7.4.3 Against the Australian Government Office’s under-estimation of the carbon storage in Melaleuca ecosystems ...... 91 7.4.4 Is it time to take a fresh look at Melaleuca ecosystems? ...... 92 7.5 Conclusion ...... 93 Chapter 8 - CONCLUSION AND RECOMMENDATIONS ...... 95 8.1 Summary of the study findings ...... 95 8.2 Research limitation ...... 97 8.3 Recommendations ...... 98 REFERENCES ...... 100 APPENDICES ...... 123 Appendix A: Statistic analyses of Melaleuca data collected from Vietnam’s sites ...... 123 Appendix B: Statistic analyses of Melaleuca data collected from Australia’s sites ...... 141

LIST OF FIGURES

Figure 1.1 Schematic overview of the thesis structure ...... 5

Figure 2.1 Six regions of Australia possible affected by climate change on forest (R1, R2, R3, R4, R5, and R6 are six regions conducted as possibly high effects by climate change). Source: Australian Government (2011c p1) ...... 11

Figure 2.2 The most significant locations of current Melaleuca populations around the globe (showing both native and introduced locations where these species have become naturalised via seed regeneration). Source: the world based map from Google map (free version)...... 12

Figure 2.3 Schematic of the estimated states of rainforest conifers, rainforest flowering plants, and sclerophyll vegetation over last 130,000 years, along with significant characteristics of the climate and geography in Australia [Source: adapt from ECOS (1980 p. 7)]...... 21

Figure 3.1 Dynamics of leaf litter mass remaining on the floor of Floridian Melaleuca forests . 29

Figure 4.1 Distribution and summary status of Melaleuca forest in Australia ...... 38

Figure 4.2 Typical habit of swamp Melaleuca forests in Bribie Island, Queensland, Australia . 39

Figure 5.1 Systematized diagram of SOC assessment approach. Source: (adapted from Chatterjee et al., 2009) ...... 50

Figure 5.2 Schematic diagram of four methods to conduct gas emission fluxes form the wetlands. Whereas, (1) (2) (3) (4) are four direction methods; GC with MS is gas chromatograph (GC) equipped with mass spectrometer detector (MS); NDIR is Non-Dispersive Infrared instrument; FTIR is Fourier Transform Infrared instrument (adapted from Chanton, 2005)...... 53

Figure 5.3 Systematic approach of the research on carbon stocks in Melaleuca swamp forests . 55

Figure 6.1 The study locations in Southern Vietnam: Phu Quoc National Park and U Minh Thuong National Park. Sources: Map from Department of Information Technology, Vietnam. Image Landsat from Google Earth (free version)...... 60

Figure 6.2 Stand densities, basal areas, diameter at bread height, and total height of five Melaleuca forest types in the study areas. Vertical bars are standard errors of the mean for 2-4 plots per category...... 66

Figure 6.3 Carbon densities of five typical Melaleuca forests in Southern Vietnam. Vertical bars are standard errors of the mean for 2-4 plots per carbon stock category...... 69

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Figure 6.4 Carbon densities of carbon stock categories of five Melaleuca forests types in the study areas. Vertical bars are standard errors of the mean for 2-4 plots per carbon categories. .. 72

Figure 7.1 The study locations in South East Queensland Australia: Buckley’s Hole Conservation Park and Hays Inlet Conservation Park. Source: Image Landsat from Google earth for free version...... 79

Figure 7.3 Carbon densities of four typical Melaleuca forests in South-East Queensland, Australia. Vertical bars are standard errors of the mean for 3-6 plots per carbon stock category...... 86

Figure 7.4 Carbon densities of the categories of four types of Melaleuca forests in South-East Queensland, Australia. Vertical bars are standard errors of the mean for 3-6 plots per carbon category...... 89

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LIST OF TABLES

Table 2.1 Summary of flood resistance of certain Melaleuca species ...... 16

Table 2.2 Summary of salinity resistance of certain Melaleuca species ...... 17

Table 2.3 Summary of the aluminiumresistance of certain Melaleuca species ...... 17

Table 3.1 Summary of the above-ground dry biomass of Melaleuca forests and other forested wetlands ...... 27

Table 3.2 Summary of the litter fall biomass of Melaleuca forests and other forested wetlands 28

Table 3.3 Mean average total C and N concentrations of the 30 cm surface soil layer of swamp vegetated soils in Queensland, Australia ...... 31

Table 3.4 Mean average concentrations of chemical elements in the surface 30 cm of swamp vegetated soils in Queensland, Australia ...... 33

Table 5.1 A summary of relevant allometric equations for estimating biomass in Melaleuca forests ...... 48

Table 6.1 List of allometric equations applied to examine stand biomass of the Melaleuca forests in the study sites of Vietnam ...... 63

Table 6.2 Major characteristics of five typical Melaleuca forests in the study areas ...... 67

Table 6.3 Potential carbon stocks in Melaleuca forests in U Minh Thuong National Park ...... 73

Table 6.4 Chemical element concentration and sodicity levels of the Melaleuca forest soils in Southern Vietnam ...... 75

Table 7.1 List of allometric equations applied to examine stand biomass of the Melaleuca forests in the study sites of Australia ...... 82

Table 7.2 Major characteristics of four typical Melaleuca forests in the study areas ...... 85

Table 7.3 Estimation of carbon stocks of Melaleuca forests and woodlands in Australia ...... 92

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LIST OF ABBREVIATIONS USED IN THE THESIS

BP Before Present

DBH Diameter at Breast Height

FTIR Fourier Transform Infrared Instrument

GC Gas Chromatograph

IPCC Intergovernmental Panel on Climate Change

LIBS Laser - induced breakdown spectroscopy

LULUCF Land Use, Land-Use Change and Forestry

MS Mass Spectrometer Detector

M tC Million tons carbon

NDIR Non-Dispersive Infrared Instrument

REDD+ Reducing Emissions from Deforestation and Forest Degradation plus

SOC Soil organic carbon

SOM Soil Organic Matter

tC Ton carbon

tC/ha Ton carbon per hectare

t/ha Tons per hectare

UNFCCC United Nations Framework Convention on Climate Change

USA United States of America

Chapter 1 - GENERAL INTRODUCTION

1.1 Background

Naturally occurring Melaleuca forests are found in Australia, New Zealand, Papua New Guinea, Solomon Islands, Indonesia, Malaysia, Myanmar, Cambodia, Thailand, and Vietnam (Tran et al., 2013b). Most Melaleuca forests occur in the coastal regions as wetlands and types of peatlands.

There are over 6,302,000 ha of (naturally occurring) Melaleuca forests and woodlands in Australia (MIG, 2008); equivalent to 5 % of the total forest area in Australia; about 2 times greater than the area of mangrove forests; and over 7 times greater than the area of rainforests in the country. Melaleuca forests in Australia occur mostly as sub-tropical and tropical woodland habits with around 84 % of the forest type occurring for privately-owned and publicly-owned land. Most of the publicly-owned land covered by Melaleuca forests is leasehold land and currently used for cattle grazing (MIG, 2008, 2013). Only around 4,500 ha of cultivated Melaleuca alternifolia exist in Australia and these plantations are used primarily for ‘Tea Tree Oil’ production (RIRDC, 2006). Of the total area of Melaleuca forest in Australia, over 5,698,000 ha are located in the state of Queensland of which 2,104,000 ha are located in protected areas such as National Parks, Conservation Areas, and Heritage Sites. Melaleuca forests in Australia occur predominantly as wetland forests, and occur in lacustrine and palustrine land systems. There are 24 more common species of the 260 species in the genus that occur in Australia including: the broad-leaved paperbark (Melaleuca viridiflora); weeping paperbark (Melaleuca leucadendra); silver paperbark (Melaleuca argentea); blue paperbark (Melaleuca dealbata); yellow-barked paperbark (Melaleuca nervosa) which dominates forests in northern Australia; Melaleuca citrolens; Melaleuca cajuputi; Melaleuca stenostachya; Melaleuca minutifolia; Melaleuca acacioides; Melaleuca tamariscina; Melaleuca bracteata; Melaleuca stenostachya; Melaleuca saligna; Melaleuca arcana; Melaleuca clarksonii; Melaleuca citrolens; Melaleuca foliolosa; Melaleuca fluviatilisin the Northern Territory and Queensland; Melaleuca quinquenervia; Melaleuca decora; Melaleuca sieberi; Melaleuca nodosa; and Melaleuca linariifolia, which is widespread in the coastal parts of Southern and South-Eastern Australia(Bureau of Rural Sciences, n.d.).These species occur over a very broad geographic range from 12oN - 18oS latitude and 95oE - 158oE longitude (Blake, 1968; Craven, 1999; Brown et al., 2001), although most Melaleuca forest (97 %) occurs in the sub-tropical and tropical zones (Bureau of Rural Sciences, n.d.).

1 In some South East Asian countries, only one Melaleuca species naturally occurs - Melaleuca cajuputi. Furthermore, large areas of Melaleuca forests are located in the peatlands and wetlands of Indonesia, Malaysia, Thailand and Vietnam [e.g. about 195,000 ha of peatland in the Mekong River Delta region of Vietnam was at one point dominated by Melaleuca forests (Torell and Salamanca, 2003)], with Melaleuca cajuputi forming the lower canopy in the primary swamp forests. The species also dominates the secondary vegetation the occurs after repeated burning of peat swamp areas in Indonesia and Malaysia (Whitmore, 1984). In Thailand, about 64,000 ha of peat swamp forests are also dominated by Melaleuca cajuputi (Hankaew, 2003).

Melaleuca forest ecosystems contribute important ecological and social-economic values, such as protecting soil, peat, and water; preserving biodiversity; providing habitat for mammals, birds, reptiles, amphibians and fish; and supporting food chains (EPA, 2005). Melaleuca ecosystems can also provide timber, fuel wood, charcoal materials, tea-tree oil, honey, and aesthetic values.

Melaleuca dominated forests are also located in Southern United States of America (USA), and the Caribbean. There are an estimated 202,000 ha of exotic invasive Melaleuca quinquenervia forests in the wetlands of Florida, and numerous isolated Melaleuca trees planted around the tropical and subtropical zones of South America, as well as in Hawaii. In some regions Melaleuca species, particularly Melaleuca quinquernervia, are considered high risk invasive species (Dray et al., 2006).

1.2 Rationale

The Melaleuca genus has a wide geographic range in Australasia and South East Asia, with substantial areas in remote locations that are difficult to access. There is also considerable variability in the wetland features of Melaleuca forests in different locations with varying influences from flooding and related litter and organic matter accretion patterns. There seems to be a traditional perception amongst land managers and policy makers in Australia that Melaleuca forests offered little commercial value, other than in some locales for mining and recent urban development. In my view, these types of factors have made the job of estimating carbon stocks in Melaleuca forests in Australia complex and costly and a low priority for land managers.

Melaleuca forests in Australia have high conservation value and occur in areas under pressure from mining and urban development. Management that conserves the carbon stocks of Melaleuca forests should also work to conserve the ecological values of those forests. However, development and planning controls in many jurisdictions in Australia do not give the carbon stocks in the soil components of Melaleuca forests due consideration; they mostly focus on vegetative components and place minimal or no restrictions on the development and drainage of areas of Melaleuca forests

2 that may have been ‘cleared’, even though those sites probably contain very large quantities of soil carbon. Much of the urban development that has taken place in South East Queensland over the last 20 years in the well-known regions of the Gold Coast and Sunshine Coast has taken place on land previously covered by Melaleuca forests. Most carbon stored on these sites would have been converted to atmospheric greenhouse gas emissions through deforestation, forest degradation and wetland drainage that took place as part of the urban development. The impacts of these emissions on climate change, and the potential financial value of the carbon stores in the forests systems, were not considered as key issues by planners and developers. Similarly, land-use changes, logging and burning in Indonesia (Anderson and Bowen, 2000), Malaysia (Wetlands International – Malaysia, 2010), and Vietnam (IUCN, 2010)] have also adversely impacted the health of Melaleuca ecosystems.

In addition, large areas of Melaleuca forests in Vietnam are disturbed ecosystems that experience extreme conditions, and are associated with different types of soils such as sandy soil, acid sulphate soil, and peat with mineral soil. They mostly occur in the lower Mekong Basin, which has been severely impacted by climate change (Le et al., 2007; Erwin, 2009; Renaud and Kuenzer, 2012; Bastakoti et al., 2014). However, Melaleuca cajuputi forests on peatland soils in Vietnam are likely to have a high potential for carbon sequestration that need to be clarified. Furthermore, that is also a significant gap of the worldwide literature.

In this thesis, in addition to estimating the volume of carbon stored in Melaleuca forests as ‘stocks’, I also give consideration to the processes that influence the dynamics of those stocks. The dynamics of carbon stocks in Melaleuca forests are a complex interaction of different formative factors such as flooding, sediment accretion, litter deposition and decomposition, salinity and sodicity, acidity, pollution and drainage. One of the key opportunities to enhance conservation outcomes for Melaleuca forests is to establish carbon market mechanisms through which the avoidance of emissions and rehabilitation of carbon stocks in those forests is financially rewarded (perhaps similarly to REDD+ models espoused for tropical forest ecosystems) but fundamental to the successful design of such mechanisms is a sound understanding of the processes that influence the dynamics of carbon stocks and an ability to reliably estimate the volume of emissions avoided and the yield of carbon sequestered through conservation activities. Recent developments in climate policy in Australia [notably the Carbon Credits Act on Carbon Farming Initiative (Australian Government, 2011a)] that encourage the development of innovative carbon offset project designs, might be possible in Melaleuca forests, and make a study of these issues particularly topical and timely .

3 Furthermore, Melaleuca forests are primarily located in the wetlands and coastal regions, and are therefore likely to be affected by climate change (Erwin, 2009; BMT-WBM, 2011), particularly sea level rise. It follows that the natural mitigation and adaptation responses of the Melaleuca genus to climate change needs to be better understood.

1.3 Research problem and questions

Given the rationale outlined above, and particularly the paucity of published research on the extent and nature of carbon stocks in Melaleuca forest ecosystems, the research problem of this thesis is: How much carbon is stored in various forms of Melaleuca forests in Vietnam and Australia? The specific questions of this thesis are therefore:

(i) What is the extent and nature of Melaleuca forest ecosystems globally, and what features of the forest type are particularly relevant to climate change adaptation and mitigation issues?

(ii) What are the typical carbon stocks in various forms of Melaleuca forests in Southern Vietnam and what are some of the factors that are likely to influence the dynamics of those carbon stocks?

(iii) What are the typical carbon stocks in various forms of Melaleuca forests in South East Queensland Australia and what are some of the factors that are likely to influence the dynamics of those carbon stocks?

1.4 Thesis outline

The thesis contains eight chapters (Figure 1.1). Chapter 1 (General Introduction) presents the rationale and background to the thesis, introduces the research problem and questions and provides a short outline of the whole thesis.

Chapters 2 to 5 of this thesis contain a critical review of relevant published literature. Two published journal papers constitute Chapters 2 and 4. Chapters 3 and 5 are also written and formatted as journal articles but as of the date of thesis submission, have not been submitted for publication.

Chapter 2 - An assessment of potential responses of Melaleuca genus to global climate change - provides information of the nature and extent as well as the potential responses of Melaleuca genus under global climate change. The article for this chapter was published in the Journal ‘Mitigation and Adaptation Strategies for Global Change’. This chapter addresses research question 1 of this thesis.

Chapter 1 - General Introduction

Chapter 2 - An assessment of potential responses of Melaleuca genus to global climate change

Chapter 3 - A review of previous research on the carbon stocks in Melaleuca dominated ecosystems Literature Critical Review Chapter 4 - Interventions to better manage the carbon stocks of Australian Melaleuca forests

Chapter 5 - Methodology

Chapter 6 - An assessment of the carbon stocks and sodicity tolerance of disturbed Melaleuca forests in Southern Vietnam Field Data Chapter 7 - Carbon stocks of Melaleuca forest ecosystems in South East Queensland Australia

Chapter 8 - Conclusion and Recommendations

Figure 1.1 Schematic overview of the thesis structure

Chapter 3 - A review of previous research on the carbon stocks in Melaleuca dominated ecosystems - is a review of previous published research on the carbon stocks of Melaleuca ecosystems at the global level. This chapter also provide information that is used on the publication presented in Chapter 4.

5 Chapter 4 - Interventions to better manage the carbon stocks of Australian Melaleuca forests - is a review focused on the potential of carbon stocks of Australian Melaleuca ecosystems. The article that forms this chapter was published in the Journal ‘Land Use Policy’. The results shown in this chapter further provide a more detailed explanation of the rationale for this thesis. This chapter also addresses research questions 1.

Chapter 5 - Methodology - is an overview of the methodology of the thesis and a detailed review of the inventory methods available to estimate the carbon stocks of Melaleuca forest ecosystems. This chapter outlines the methods used to collect and analyse field data that, the results of which are presented in Chapter 6 and Chapter 7.

Chapters 6 and 7 present the primary results and discussion of the research and addresses research questions 2 and 3 of this thesis. The results of data collection and analysis from field sites in Southern Vietnam and South East Queensland, Australia are presented and discussed. These chapters are formatted as journal articles.

Chapter 6 - An assessment of the carbon stocks and sodicity tolerance of disturbed Melaleuca forests in Southern Vietnam - focuses on the primary research in carbon stocks of the Melaleuca ecosystems in the coastal and wet-peatlands areas. The article for this chapter has been submitted and under review in the Journal ‘Mitigation and Adaptation Strategies for Global Change’.

Chapter 7 - The carbon stocks in Melaleuca forest ecosystems and their disturbances in South East Queensland - is also primary research chapter focused on the carbon stocks of the Melaleuca ecosystems in the coastal and wetlands areas in the South East Queensland, Australia. The article for this chapter has been submitted and under review in the Journal ‘Climatic Change’.

The thesis ends with Chapter 8 - Conclusion and Recommendation-which summarizes the thesis. This chapter also states the limitations of the research, and makes some recommendation for future research.

6 Chapter 2 - AN ASSESSMENT OF POTENTIAL RESPONSES OF MELALEUCA GENUS TO GLOBAL CLIMATE CHANGE

Summary

The genus Melaleuca consists of around 260 species covering over eight million hectares (including native and introduced species) and distributed mostly in Australia, but also occurring in South-East Asia, the Southern United States and the Caribbean. Melaleuca populations predominantly occur in wetland or/and coastal ecosystems where they have been significantly affected by climate change. This paper assesses the potential responses of the Melaleuca genus to climate change, based on the synthesis of worldwide published data. The main findings include: (i) that the Melaleuca genus has a rich species diversity, and significant phenotypic diversity in a variety of ecosystems; (ii) they demonstrate significant local adaptation to harsh conditions; and (iii) the fossil records and taxon biology indicate the evolution of the Melaleuca genus began around 38 million years ago and they have survived several significant climatic alterations, particularly a shift towards cooler and drier climates that has occurred over this period. These findings show that the Melaleuca genus is highly resilient and adaptable and based on this, this paper argues that Melaleuca can adapt to climate change through Wright’s ‘migrational adaptation’, and can be managed to achieve sustainable benefits.

This chapter was published as an article paper: Tran, D.B., Dargusch, P., Moss, P., Hoang, T., 2013. An assessment of potential responses of Melaleuca genus to global climate change. Mitigation and Adaptation Strategies for Global Change 18 (6), 851-867. DOI: 10.1007/s11027-012-9394-2. Received: 5 January 2012 / Accepted: 14 May 2012 / Published online: 31 May 2012.

7 2.1 Introduction

Humans and other organisms must find ways to adapt to the effects of global climate change. Vegetation is not only affected by climate change, but also plays an important role in its mitigation by the uptake of carbon dioxide [CO2 makes up the largest volume of greenhouse gases (IPCC, 2003)]. Plant communities, and/or populations that can respond quickly to climate change are highly valuable. Healthy vegetation ecosystems that can adapt to climate alterations associated with human activity would play a significant role in mitigating its effects. There are over eight million hectares of wetland or/and coastal plant assemblages dominated by the Melaleuca genus on Earth (personal record), so it is important to determine how the Melaleuca genus responds to climate change, from both ecological and humanistic viewpoints.

Firstly, Melaleuca forests are very important ecological systems, especially in the wetlands and/or peatlands of tropical and subtropical regions. In association with other plant species, Melaleuca dominated ecosystems can provide an important habitat for fauna, as well as protecting soil, peat and water and providing a significant carbon store. Melaleuca forests are very diverse and the species composition depends on the prevailing conditions. In the Australian swamp forests, several Melaleuca species are associated with other trees (e.g. Eucalyptus robusta, Lophostemon suaveolens), sedges (e.g. Baumea rubiginosa, Lepironia articulate, Schoenos breviofolius, Dapsilanthus ramosus, Gahnia sieberiana, Phragmites australis, and Ischaemum spp.), swamp rice grass (Leersia hexandra), blady grass (Imperata cylindrical), saltwater couch (Sporobolus virginicus), and ferns (e.g. Stenochlaena palustris and Blechnum indicum) (EPA, 2005).

In the Mekong Delta of Vietnam (Buckton et al., 1999; Wetlands of South East Asia), Melaleuca cajuputi is associated with 77 other plant species. In Tulang Bawang of Indonesia, Melaleuca cajuputi is found in secondary swamp forests with species such as Barringtonia acutangula, Lagerstroemia speciosa, Licuala paludosa, Sapium indicum, andferns (Zieren et al. 1999). In southern Thailand, Melaleuca trees are found alongside Melastoma malabathricum, ferns (e.g. Blechnum indicum, Stenochlaena palustris, and Lygodium microphyllum) and sedges (e.g. Lepironia articulata and Scleria sumatrana) (Tomita et al., 2000).

Melaleuca swamp forests are important sites for preserving biodiversity because they hold a remarkable diversity of fauna. About 23 species of mammals, 386 species and subspecies of birds, 35 species of reptiles, six species of amphibians, 260 species of fishes, and about 92 species of waterfowl have been reported in the Mekong Delta of Vietnam. These include large populations of cormorants, herons, egrets, storks and ibises, which nest in huge colonies in the mangroves and Melaleuca forests of this region (Buckton et al., 1999; Wetlands of South East Asia). A wide

8 variety of plants and animals also occur within the Melaleuca (Tea-tree/Paperbark) swamp forest of Australia, e.g. with Melaleuca trees providing shelter and nesting sites for a range of bird species, fallen timber providing shelter for reptiles and other terrestrial animals, and temporary ponds provide breeding habitat for frogs and other aquatic creatures. Koalas, echidnas, and wallabies also occur in these forests (EPA, 2005).

Secondly, from a human socio-economic perspective, Melaleuca forests can provide timber for building and furniture. Other traditional uses, such as for fuel wood, charcoal, tea-tree oil, and honey are still employed today [e.g. in Indonesia, Malaysia (Saberioon, 2009), Thailand (Nuyim, 1998), Cambodia (Hiramatsu et al., 2007), and Vietnam (Duong et al., 2005)]. In Australia, Melaleuca forests are used for tea-tree oil and honey and their landscape and aesthetic values are recognised as being extremely valuable (DAFF, 2008). Many Melaleuca species have been selected and incorporated into agroforestry systems (Turnbull, 1986). Some Melaleuca forests and woodlands in Australia have become cultural and heritage sites [e.g. there are more than 400 Indigenous Cultural Heritage sites in the coastal Melaleuca swamp wetlands in Queensland, most dating from the mid Holocene (last 4000 years) (EPA, 2005)]. Melaleuca forests can also store a large volume of soil organic carbon, especially in the peatlands (e.g. in Indonesia, Malaysia, Thailand, and Vietnam). The capacity of carbon stores in Melaleuca forests is particularly high in Australia, with a total area of over 7.556 million hectares (MIG, 2008).

Melaleuca forests are mostly located in wetlands and coastal regions (Blake, 1968; DAFF, 2008), which makes them vulnerable to climate change as these regions are likely to be the most significantly affected by rising sea levels (Gilman et al., 2008) and/or increased climatic variability, particularly drought (IPCC, 2003, 2006). Scientists believe that global climate change threatens species survival and the health of natural systems. In the wetlands, both the quantity and quality of the water supply are vulnerable and wetland habitat responses will be different depending on regional conditions, and research is required to determine the effect of climate change on different habitats (e.g. floodplains, mangroves, seagrasses, salt marshes, Arctic wetlands, peatlands, freshwater marshes, and forests) (Erwin, 2009).

Several studies have been carried out worldwide on the vulnerability of plant species to climate change, and their mitigation and adaptation responses. These include: 66 species studied in Thailand (Trisurat et al., 2011); the impact on the distribution of plant forest species, such as tropical pines in South East Asia (van Zonneveld et al., 2009) and future potential distribution of Melaleuca quinquenervia (Watt et al., 2009); the response of a variety of tree species at global scale (Hansen et al., 2001); the adaptation of tropical and subtropical pine plantation forestry (Leibing et

9 al., 2009); the adaptation of species in tropical managed forests (Nkem et al., 2008). Several studies have investigated the responses of Melaleuca species to local climatic variability, but no studies have looked at the potential mitigation and adaptation responses of Melaleuca forests to climate change. Hence, this paper uses published data from around the world to review the natural mitigation and adaptation responses of the Melaleuca genus to climate change (past, contemporary and future), as well as discussing sustainable management practices of Melaleuca forest under future global climate change scenarios.

2.2 Vulnerability of Melaleuca forests to global climate change

As discussed above, Melaleuca forests mostly occur in coastal regions and wetland areas (e.g. in the coastal regions of the Eastern andNorthern Australia, Papua New Guinea, and New Caledonia, and in the peatlands and lowlands of Indonesia, Malaysia, Thailand, Cambodia, and Vietnam, and as introduced plants in the USA). Besides the threats of human activities to the forests (e.g. landuse change, logging and burning), particularly in Indonesia (Anderson and Bowen, 2000), and Malaysia (Wetlands International – Malaysia, 2010), Melaleuca swamp forests are also threatened by the early consequences of climate change [e.g. sea level rise is increasing flooding in the Mekong River Delta of Vietnam (Erwin, 2009)], which was predicted as one of three highest vulnerable coastal regions in the world by IPCC (Nicholls et al., 2007).

Coastal and lowland regions will be the areas most affected by global climate (Nicholls et al., 2007). If the sea level rises, large areas of the Melaleuca swamp forests will be affected by salinity, to which they are critically vulnerable. If the IPCC current scenario of future climate eventuates, the CLIMEXTM program suggests that the distribution of Melaleuca quinquenervia will change radically, with this species migrating to higher latitudes (Watt et al., 2009). This is one of the widespread species within the genus Melaleuca, though many other species may be just as or even more vulnerable to future climate change.

In Australia, the vulnerability index is a comparative measure of vulnerability for local areas of interest within the forestry regions. The government reported that all six regions (Figure 2.1) have been assessed as high and very high by this index (Australian Government, 2011c). They are all coastal regions that are at significantly high risk from climate change, particularly rising sea levels (Australian Government, 2009). Of those, R2, R3 and R4 occupy more than 90 % of the total Australian Melaleuca forest area. A notable example is Kakadu National Park, which is dominated by Melaleuca forests and has been also assessed to be particularly vulnerable to climate change (BMT-WBM, 2011).

2.3 Nature of the Melaleuca genus and it’s potential responses to climate change

2.3.1 Wide natural distribution of Melaleuca forests

In Australia, Melaleuca is the third largest plant genus of sclerophyll vegetation, after Acacia and Eucalyptus, consisting of about 260 species (DAFF, 2008). Most of them are endemic species (only occurring within Australia). There are seven species that occur naturally outside Australia, including Melaleuca cajuputi, Melaleuca dealbata, Melaleuca leucadendra, Melaleuca nervosa, Melaleuca quinquenervia, Melaleuca stenostachya, and Melaleuca viridiflora (Craven, 1999). The original natural distribution of Melaleuca species is generally considered as including Australasia, Oceania and South East Asia (Blake, 1968; Craven, 1999; Brown et al., 2001). The range of their distribution is generally considered to be within latitude 12oN-18oS and longitude 95oE-158oE, but this information may be out of date. Later studies show that, in Vietnam, Melaleuca cajuputi is naturally distributed as scattered shrub populations along the sandy coastal regions in the middle Provinces and up to the Northern low fertility hilly regions of Thai Nguyen and Vinh Phuc Provinces (Cuong et al., 2004) and suggests a more northerly natural occurrence of Melaleuca (i.e. to 21oN).

Other studies have reported that Melaleuca cajuputi occurs in Cambodia as natural populations in the swamp forests at the rear of Mangrove forests in the coastal regions (UP-MSI et al., 2002; WRM, 2006), and is found in Kampong Thom about 300 km inland (Hiramatsu et al., 2007). Melaleuca cajuputi also has been recorded to occur naturally in Burma, Myanmar (Weiss, 1997),

11 but detailed information is lacking. Along the coastal areas of New Zealand, Melaleuca howeana is the dominant species in the coastal scrub vegetation (Mueller-Dombois and Fosberg, 1998).

Beyond their natural distribution, the Melaleuca genus has invaded other parts of the globe. As shown in Figure 2.2, Melaleuca occurs as exotic plants that have invaded a wide area in the southern United States and South America. In Southern Florida, Melaleuca quinquenervia was imported and planted 100 years ago, and has become the worst invasive plant in the wetlands of this region, covering about 202,000 ha (Turner et al., 1998). Melaleuca plants also occur in others states of the USA [e.g. Hawaii, California, and Texas (Dray et al., 2006)], and Caribbean countries [e.g. Bahamas, Brazil, Colombia, Costa Rica, Cuba, Dominican Republic, Guyana, Honduras, Mexico, Nicaragua, Puerto Rico, and Venezuela (Ferriter, 2007)]. After a hundred years, Melaleuca quinquenervia has become a naturalised species in these regions through seed regeneration, as they do in their native regions.

Result from the CLIMEXTM model has demonstrated that the climate requirements of Melaleuca quinquenervia are best met in South-East Asia, the Caribbean, Central and South America, and the Gulf coast in the southern USA (Watt et al., 2009).

Introduced

Natural

Two species, Melaleuca quinquenervia and Melaleuca cajuputi, have the widest distribution, and they dominate the current Melaleuca populations around the globe. Most of the Melaleuca genus occurs in wetland and coastal regions, which are some of the most vulnerable locations in the world to future climate change. However, the current distribution also indicates that they have the ability to adapt to a wide range of climates. Under the climate change scenario of IPCC, the CLIMEXTM

12 model indicates there will be a marked contraction of suitable habitat in most regions, but they may be able to expand to south-east China, southern Europe, and northern New Zealand (Watt et al., 2009).

2.3.2 The botanical characteristics of Melaleuca and potential responses to climate change

The phenotypic characteristics of plants represent the interaction between the plants and the environment. Some characteristics (e.g. tree form, leaf, flower, seed and bark) can adjust to mitigate, and/or adapt to, changes in the environment, or even react to harsh stresses.

The wide variation in botanical characteristics demonstrates the ability of the Melaleuca genus to adapt to the different conditions in a variety of locations. There are 260 species of Melaleuca trees; some are very tall with large trunks (e.g. Melaleuca argentea, Melaleuca leucadendra, and Melaleuca quinquenervia), some are shrubs (e.g. Melaleuca arcana, Melaleuca bracteata, and Melaleuca symphyocarpa), some are small trees (e.g. Melaleuca dealbata, Melaleuca ericifolia, Melaleuca nervosa, and Melaleuca viridiflora), and some have different forms in different conditions (e.g. Melaleuca cajuputi). In Vietnam, Melaleuca cajuputi occurs naturally in different forms: as shrubs in the sandy and/or unfertile lands in the middle and north of this country, and as a small to big tree in the lowlands of south Vietnam (Cuong et al., 2004). In the Northern Territory, Melaleuca cajuputi occurs as very large and tall trees (up to 40 m in height, and 1.2 m diameter) (Blake, 1968; Doran and Turnbull, 1997).

Melaleuca leaves are also diverse, but are generally smaller than many other tropical plant species. Some are thick (e.g. Melaleuca bracteata and Melaleuca viridiflora), some are hairy (e.g. Melaleuca cajuputi and Melaleuca dealbata), and some are needle-like (e.g. Melaleuca ericifolia) (Blake, 1968; Turnbull, 1986; Doran and Turnbull, 1997; Australian Tropical Rainforest Plants; Victorian Resources Online; AgroForestry Tree Database; DSWHA), which shows the potential adaptations of Melaleuca foliage to various environmental conditions.

All species of the Melaleuca genus possess thick and layered bark. Many of them have papery layered bark (e.g. Melaleuca arcana, Melaleuca cajuputi, Melaleuca dealbata, Melaleuca leucadendra, Melaleuca nervosa, Melaleuca quinquenervia, and Melaleuca viridiflora), and some have hard and/or peeling bark (e.g. Melaleuca bracteata, Melaleuca ericifolia, and Melaleuca symphyocarpa) (Blake, 1968; Turnbull, 1986; Doran and Turnbull, 1997; Australian Tropical Rainforest Plants; Victorian Resources Online; AgroForestry Tree Database; DSWHA). These types of bark confer a remarkable ability to adapt to fire. As global climate progresses, fire may be one of the increasing hazards. Many Melaleuca species can resist fire and details are presented in a later section.

13 Many species of Melaleuca genus have a suckering habit (e.g. Melaleuca bracteata, Melaleuca cajuputi, Melaleuca leucadendra, and Melaleuca quinquenervia) (Blake, 1968; Turnbull, 1986; Doran and Turnbull, 1997; Australian Tropical Rainforest Plants; Victorian Resources Online; AgroForestry Tree Database; DSWHA), which gives them the ability to resist water logging in their communities. Wetlands have been considered as one of the habitats that will be most affected by climate change (IPCC, 2003, 2006), but Melaleuca species are well adapted to flooding conditions.

Flowering seasons are predicted to be affected by climate change because many plants require particular conditions to begin blooming. However, many Melaleuca species demonstrate flexibility in their flowering time. In Australian conditions, the flowering seasons of Melaleuca species vary: some flower from spring to summer (e.g. Melaleuca arcana, Melaleuca bracteata, and Melaleuca ericifolia), others from autumn to winter (e.g. Melaleuca argentea, Melaleuca nervosa, and Melaleuca leucadendra), and some flower throughout the year (e.g. Melaleuca cajuputi, Melaleuca dealbata, Melaleuca nervosa, Melaleuca quinquenervia, and Melaleuca viridiflora) (Blake, 1968; Turnbull, 1986; Doran and Turnbull, 1997; Australian Tropical Rainforest Plants; Victorian Resources Online; AgroForestry Tree Database; DSWHA). In addition, some species can reproduce very early [e.g. Melaleuca quinquenervia flowers in 3-4 years in Australian conditions (Doran and Turnbull, 1997) and 1-2 years in Florida (Franks et al., 2008); Melaleuca leucadendra, Melaleuca quinquenervia and Melaleuca viridiflora planted in Vietnam also flower throughout the year, with peaks occurring twice a year (Hoang, 2010)]. Thus, it is possible to predict that these species have the ability to adapt their time of flowering in response to climate change.

Fruits and seeds of Melaleuca species have particular characteristics to keep the species alive for future generations. Many species produce large numbers of fruits and huge numbers of tiny seeds [e.g. a single mature Melaleuca quinquenervia tree that is 10 m in height in Florida may hold 20 million seeds (Hofstetter, 1991)]. Generally, the seeds of all species comprise very small capsules. In Australia, the species with largest seed (Melaleuca argentea) comprises 788,000 capsules per kg, and the smallest (Melaleuca bracteata) comprises 10,500,000 capsules per kg (Blake, 1968; Turnbull, 1986; Doran and Turnbull, 1997; Australian Tropical Rainforest Plants; Victorian Resources Online; AgroForestry Tree Database; DSWHA). In Florida, the seed of Melaleuca quinquenervia is much smaller than in Australia, and can comprise 34,000,000 capsules per kg and its seeds appear to be well adapted to wet/dry conditions (Turner et al., 1998). Seed of Melaleuca cajuputi in Vietnam has 36,185,611 capsules per kg; planted Melaleuca leucadendra ranges from 22,251,544 to 20,874,523 capsules per kg; and planted Melaleuca viridiflora has 20,323,300 capsules per kg (Hoang, 2010). The huge number of seeds produced by these Melaleuca species gives them a remarkable potential to regenerate. Besides seeds, some species can regenerate by

14 shoot or clone-regeneration [e.g. Melaleuca ericifolia in Australia (Robinson, 2007; Salter et al., 2010a), Melaleuca cajuputi in the barren sandy regions of southern Thailand (Tanaka et al., 2001) and Melaleuca cajuputi communities in the unfertile regions of Northern Vietnam (personal record)].

Additionally, seedlings of Melaleuca quinquenervia that regenerated from seeds were very dense, with 500-2,250 individuals per m2 (Franks et al., 2006), and the density of mature stands in Florida was between 8,000-132,000 trees per hectare (Rayachhetry et al., 2001).

All the above aspects demonstrate the strong ability for Melaleuca species to be highly resilient in the face of climate change.

2.3.3 The resistance of Melaleuca species to harsh conditions and their capacity to respond to climate change

Research has shown that Melaleuca species can tolerate various types of extreme conditions (e.g. flood, drought, slight saline conditions, high aluminium concentrations, fire, and other conditions). These harsh conditions currently occur in many locations around the world, and are predicted to increase in future scenarios of global climate change and many Melaleuca species can respond to increases in these extreme circumstances.

2.3.3.1 Resistance to flood and drought conditions

Resistance to floods and drought is key characteristic of many species in the Melaleuca genus. Most Melaleuca communities occur naturally in wetlands and/or in dry lands. Some are very resilient in wet conditions (e.g. Melaleuca acacioides, Melaleuca alternifolia, Melaleuca argentea, Melaleuca ericifolia, Melaleuca leucadendra, Melaleuca quinquenervia, Melaleuca saligna, and Melaleuca viridiflora) and can tolerate deep floodwaters that are up to 3 m deep from several months to a year (Blake, 1968; Doran and Gunn, 1994). Some species survive very well in both deep floodwater and dry conditions [e.g. Melaleuca cajuputi communities in Thailand (Suzuki, 1999; Tanaka et al., 2001) and Vietnam (Cuong et al., 2004)]. Some species thrive in dry land situations [e.g. Melaleuca lanceolata (moonah), Melaleuca halmaturorum ssp. halmaturorum, Melaleuca brevifolia, and Melaleuca lanceolata ssp. Lanceolata (Blake, 1968; Barlow and Cowley, 1988; Cowley et al., 1990; Bureau of Rural Sciences)].

It is important to determine the survival of seedlings in such conditions. Table 2.1 summarizes the major studies on flood resistance in Melaleuca trees. Testing in pots using Melaleuca alternifolia, Melaleuca cajuputi, Melaleuca ericifolia seedlings revealed various results. Generally, older seedlings can survive much better than the younger ones, and seedlings can survive shorter floods

15 better than longer ones [e.g. 17 % of 2-3 month old Melaleuca cajuputi seedlings can still be alive after two months (Tanaka et al., 2011); 100 % of 12 month old Melaleuca alternifolia seedlings can be alive 100 % after 6 months (Jing et al., 2009)]. On the other hand, in natural conditions in Thailand, Melaleuca cajuputi seedlings can remain alive, and still grow, during floods 30 to 50 cm in depth lasting nine months (Yamanoshita et al., 2001). This suggests that in nature plants can adapt to unstable conditions than in experimental conditions and this is a key characteristic of some Melaleuca species to mitigate and adapt to climate change.

Table 2.1 Summary of flood resistance of certain Melaleuca species

Species Seedlings Study sites Literature Age Flooded Depth Survival (months) (months) (cm) (%) Melaleuca 12 6 20 100 South China (Jing et al., 2009) alternifolia Cheel. (in pots) Melaleuca Natural 9 30-50 GrowingNarathiwat, Thailand (Yamanoshita et cajuputi Powell. (at fields) al., 2001) Melaleuca 2-3 2 Sub- 17 Narathiwat, Thailand (Tanaka et al., cajuputi Powell. mergence (in pots) 2011) Melaleuca 6 9 Up to 44 2 Victoria, Australia (Raulings et al., ericifolia Smith. (in pots) 2007)

2.3.3.2 Resistance to salinity

Rising sea levels caused by global climate change (IPCC, 2003, 2006) will affect large areas of lowlands in coastal regions, thus threatening resident Melaleuca populations. Saline-tolerant species of plants, such as Mangroves, are less vulnerable. Unfortunately, many Melaleuca species are adapted to freshwater habitats, so they will be significantly threatened. However, some studies have shown that several species can tolerate slight increases in salinity (Table 2.2). Some Melaleuca species occur in slightly saline soil or water conditions [e.g. Melaleuca acacioides, Melaleuca bracteata, Melaleuca ericifolia, and Melaleuca hamaturomum (Blake, 1968; Ladiges and Foord, 1981; Doran and Gunn, 1994; Mensforth and Walker, 1996; Salter et al., 2006)], but these are mature communities. Some studies have been undertaken to investigate the ability of Melaleuca seedlings to tolerate saline conditions. Most of these experiments were undertaken in pots (in a greenhouse), and results show that two month old Melaleuca cajuputi seedlings can survive 3 months in 50 mM of salinity, with a tolerance level calculated from 40.3 to 57.5 % (Nguyen et al., 2009). Furthermore, 90 % of five month old Melaleuca ericifolia seedlings can survive in 60 dS m-1 salinity (Salter et al., 2006). Even more successful than the above two species was 21 day old Melaleuca hamaturomum seedlings, which can remain alive for 15 months in 64 dS m-1 salinity (Mensforth and Walker, 1996). Another field study in the Dowd Morass State Game Reserve, in

16 south-eastern Australia has shown that 42-100 % of wild Melaleuca ericifolia seedlings can survive salinities ranging from 2.5 to 5.4 dS m-1, and in these conditions plants can grow from 4.6 m to 5.5 m over 3 years (Salter et al., 2010b).

Table 2.2 Summary of salinity resistance of certain Melaleuca species Species Seedlings Study sites Literature Age Salinity Survival Melaleuca cajuputi 2 months 50 mM 3 months In pots (Nguyen et al., Powell. 2009) Melaleuca From seeds At 14 ‰ Growth inhibited In pots (Ladiges and ericifolia Smith. From seeds to 21 ‰ 48 days In pots Foord, 1981) Melaleuca 5 months 2 dS m-1 100% In pots - 10weeks (Salter et al., ericifolia Smith. 5 months 49 dS m-1 100% In pots - 10weeks 2006) 5 months 60 dS m-1 90% In pots - 10weeks Melaleuca Natural 2.5-5.4 42-100 % Dowd Morass State (Salter et al., ericifolia Smith. dS m-1 Game Reserve, South 2010b) East Australia Melaleuca 21 days 64 dS m-1 15 months South Australia (in (Mensforth and hamaturomum Miq. pots and fields) Walker, 1996)

2.3.3.3 Resistance to aluminium

In natural settings, several Melaleuca species occur in conditions with very high acidity, and aluminum concentrations [e.g. in Mekong Delta of Vietnam (Nguyen et al., 2009)]. Table 2.3 presents several results showing the remarkable resistance of some Melaleuca species to high aluminum concentrations. Melaleuca bracteata has been assessed as having high tolerance toaluminum in soils, but Melaleuca cajuputi is even more tolerant (Tahara et al., 2008a; Tahara et al., 2008b). From 63.6 to 76.4 % of two month old seedlings of Melaleuca cajuputi tolerated conditions of 10 mM AlCl3 (Nguyen et al., 2009). However, if exposed to both high aluminum and salinity concentrations, this species demonstrates much lower tolerance (Nguyen et al., 2009).

Table 2.3 Summary of the aluminiumresistance of certain Melaleuca species Species Seedlings Study sites Literature Age Aluminium Root tip uptake Tolerance Melaleuca 2-4 1mM Less Al tightly High Narathiwat, (Tahara et al., bracteata F. months AlCl3 bound to root Thailand 2008a) Muell. tips (in pots) Melaleuca 2-4 1mM Less Al tightly Higher Narathiwat, (Tahara et al., cajuputi Powell. months AlCl3 bound to root than M. Thailand 2008a; Tahara tips bracteata (in pots) et al., 2008b) Melaleuca 2 10 mM - From 63.6 In pots within (Nguyen et al., cajuputi Powell. months AlCl3 to 76.4 % 3 months 2009)

17 2.3.3.4 Resistance to fire and other factors

Besides the ability to tolerate the harsh conditions described above, Melaleuca species can also tolerate fire and post mining conditions.

Species that can tolerate fire include Melaleuca cajuputi (Tomita et al., 2000), Melaleuca quinquenervia (Turner et al., 1998; Martin et al., 2010), and Melaleuca viridiflora (Crowley et al., 2009). In southern Thailand, Melaleuca cajuputi seedlings regenerate strongly after fire, producing up to 73.8 individuals per m2, up to 179.8 cm high, and covering 1-4 % of the land area after three years (Tomita et al., 2000).

In Northern Australia, five major species (Melaleuca argentea, Melaleuca cajuputi, Melaleuca dealbata, Melaleuca viridiflora and Melaleuca leucadendra) share areas where the forests are disturbed by fire and/or floodwater that would otherwise be suitable for rainforest growth (Franklin et al., 2007). In Cape York Peninsula, after impacts from storms and burning over a three year period, Melaleuca viridiflora recolonised woodland areas within 20 years (Crowley et al., 2009).

In peatlands environments, where there is high potential for fire, Melaleuca cajuputi is a main pioneer species. The species germinates, survives and grows well under flood conditions, and its seeds do not lose their germination capacity even after heating to 100oC for one hour, which enables Melaleuca cajuputi to grow and develop in fire-ravaged peat swamps (Government of Indonesia, 2009).

Additionally, Melaleuca species can also be resistant to rust caused by Puccinia psidii [e.g. Melaleuca ericifolia has 100 % resistance (Zauza et al., 2010)].

In Quang Ninh Province - an area of post mining coal in Vietnam, the Melaleuca hybrid variety named L19L4 had high survival rate (95.3 %) after one year, which is better than Pinus merkusii, Pinus massoniana, natural hybrids of Acacia and Melaleuca leucadendra (with survival rates of 2.33 %, 15.3 %, 11.3 % and 7.3 %, respectively). Under such post mining conditions, the Melaleuca hybrid retained its high survival rate (70-95 %) at 20 months of age, with growth ranging from 1.59 to 2.04 m (Hoang and Tran, 2011). Thus, several Melaleuca species may be suitable to revegetation projects in post mining areas.

2.3.4 Historical evidence of Melaleuca evolution and genotype and potential responses to climate change

Fossil evidence found in Melaleuca Inlet, south-western Tasmania (Jordan et al., 1991), Coal Head, western Tasmania (Rowell et al., 2001), and Cape Van Diemen on Melville Island, Northern

18 Territory (Pole and Bowman, 1996) has confirmed that members of Melaleuca genus have been present in Australia for at least the last 38 million years. In Tasmania, fossil evidence has shown that the current vegetation in the lowlands of western and northern Tasmania is similar to the fossil vegetation (Rowell et al., 2001). In addition, Melaleuca communities still occur naturally around the fossil site at Melville Island (Pole and Bowman, 1996). This evidence shows the remarkable ability of the Melaleuca genus to adapt to climate change.

As illustrated in Figure 2.3, sclerophyll vegetation, including Proteaceae (Grevillea, Banksia and Hakea), Melaleuca, Acacia, Boronias, and the eucalypts, that there has been a significant increase in these sclerophyll taxa over the last 130,000 years (ECOS, 1980; Kershaw et al., 2003). Over time, this vegetation developed to share the Australian continent with rainforest conifers from 79,000 to 38,000 years BP due to the increasing rainfall, and sclerophyll became the dominant vegetation from 38,000 to 10,000 years BP. Today, sclerophyll vegetation still occurs, but the dominant position has been replaced by other plants (ECOS, 1980). Present conditions are roughly comparable with those 86 000-79 000 years ago, and with those more than 116 000 years ago (ECOS, 1980, p7). Furthermore, latter study on pollen fossil record in north-eastern Australia and South-East Asia has suggested that sclerophyll vegetation relevantly occurred in the last 250,000 years. The result also demonstrated the environmental conditions over variety of periods (Kershaw et al., 2003; Moss and Kershaw, 2007).

Over the past 250,000 years (Kershaw et al., 2003; Moss and Kershaw, 2007), due to the changes of climate and geography in Australia, the Melaleuca genus has undergone a remarkable evolution to comprise 260 species at present. These species are currently adapted to the varied conditions of Australia and other regions, even surviving in extreme situations. Not only are there morphological differences, but molecular studies have shown that there are the complex relations and evolution of Melaleuca species [e.g. molecular phylogeny and biography of Melaleuca species (Brown et al., 2001), genetic congruence with Melaleuca uncinata complex (Broadhurst et al., 2004), reticulate evolution of Melaleuca quinquenervia (Cook et al., 2008), and cpDNA sequences of Melaleuca leucadendra complex (Edwards et al., 2010)].

Published studies show that Melaleuca species have evolved rapidly since around 30,000 years BP (Before Present). Starting at one species at around 40,000 years BP, today there are about 27 species in the Melaleuca leucadendra complex. These details how the Melaleuca genus has evolved very rapidly over a relative short time period and is clearly presented in the ‘Chronogram showing relationships and estimated divergence times in the Melaleuca leucadendra complex, the rest of Melaleuca and Myrtaceae, in part’ (Cook et al., 2008 p513). The literature also shows that species

19 of the Melaleuca genus have a very close genetic relationship, but they are not monophyletic, which is demonstrated by Brown et al. (2001) and Edwards et al. (2010). We believe that the unique changing conditions of the Australian landscape over the last 130,000 years have provided an environment, which accelerates Melaleuca evolution. There is strong evidence to infer that the wide variation of phenotypes in the Melaleuca genus helps it to adapt to significant environmental differences and dramatic alterations in climate. Therefore, it is assumed that species of the Melaleuca genus will have the ability to adapt quickly to current and future climate alterations, as well as making this genus highly adaptable to new environments (i.e. introduction in Southern United States and the Caribbean).

The long history of the Melaleuca genus, along with its high genetic adaptability, means that this genera has survived a number of significant environmental alterations and catastrophes (Pole and Bowman, 1996; Rowell et al., 2001), particularly over the last 250,000 years (Kershaw et al., 2003; Moss and Kershaw, 2007), when there were significant changes in sea-levels (+2 to -120 m) and a dramatic increase in burning. So, we strongly agree with the argument put forward by Kuparinen et al. (2010) that increasing mortality can promote evolutionary adaptation of forest trees to climate change.

Estimated time line Characteristics of the climate and geography in Australia Dominant (years ago) vegetation Similar present Similar present The Holocene, at about 9,000 BP, the temperatures were higher than at the present in Australia, and there was increased rainfall. The highest sea levels reached in the Holocene was from about 7,500 to 6,000 BP. Since then, the sea levels have been approximately stable [(Monroe, 2011) cited from (White, 2000)]. Sclerophyll

10 000

Following this wet period, aridity spread towards the margins of the continent, the spread of aridity peaking at the last glacial maximum. 26 000 Melaleuca fossils age 26,000-38,000 years old were discovered in Tasmania and Melville islands, NT (Pole and Bowman, 1996; 38 000 Rowell et al., 2001), and in north-eastern Australia and South-

East Asia (a major stepwise change) (Kershaw et al., 2003). conifers and rainforest Sclerophyll

Between about 55,000 and 35,000 BP, a wet phase, which was less widespread, has been associated with high sea levels and activity of Palaeo channels in south-eastern Australia [(Monroe, 50 000 2011) cited from (White, 2000)].

In the Eyre Basin, dunes began to form about 95,000 BP during 63 000 the last interglacial. Dunes began to form on the areas bordering streams and lakes of the Riverine Plain; that is about half way between the center and the coast, from about 70,000 to 50,000 BP.

79 000 Dunes began forming in the Lake George Basin, close to the coast, and the Shoalhaven Basin, on the coast, during the last glacial maximum about 20,000 BP. Rainforest floweringplants 86 000 At this time, the peak of the last glacial, it has been estimated that Australia received half the present rainfall and the winds were double their present strength, up to 80% of the continent being covered by wind-blown sand.

The dune fields reached their present state. Also at this time much of the Murray Basin was a salt-sand desert (White, 2000)

116 000 White (2000) described that about 110,000 years ago fluvial activity was the peak, world temperature and sea level maxima by the state of 5,000-10,000 years BP. Around 130,000 years BP, one of two major stepwise changes occurred that increased in sclerophyll vegetation, especially Eucalyptus and Melaleuca (Kershaw et al., 2003). Rainforest flowering plants Rainforest conifers Sclerophyll vegetation

21 2.4 Discussion on adaptation of Melaleuca population and management for carbon storage

A detailed critical review (Breed et al., 2011) that discusses the climate change adaptation responses of trees in the landscape is very relevant to this study. It considers three factors: (i) ‘migrational adaptation’, which describes the process by which standing genetic variation is redistributed by gene flow and selection among populations (Breed et al., 2011 - acknowledged from Wright, 1932); (ii) ‘novel-variant adaptation’, which describes the process of increasing frequency, and possibly fixation of, new, beneficial genetic variants that are generated by mutation, recombination or other genetic processes (Breed et al., 2011 - acknowledged from Wright, 1932); and (iii) ‘plasticity adaptation’, which refers to adaptive plastic responses of organisms to environmental stresses. Additionally, landscape management actions were also considered to contribute to the process of evolutionary adaptation; that is, as human-mediated adaptation (Breed et al., 2011, p638).

Some Melaleuca species may adapt to increased inundation, while others might be killed. Therefore, further research into flexible Melaleuca species and populations is needed to develop greater understanding of their adaptive capacity so that the swamp forests can be managed to obtain sustainable benefits.

Enhancing the protection of Melaleuca forests is important, particularly in coastal regions, to generate food, medicines, fuel wood, and other products for local human communities and to integrate the introduction of new technologies with traditional uses. Studying and creating agroforestry systems based on Melaleuca forests (e.g. rice with Melaleuca forest; honey bee feeding with Melaleuca forest management; aquaculture combined with Melaleuca forests; and mixed planting oil palm and Melaleuca by bands or plots) that can adapt to more extensive and longer duration flood conditions, and provide livelihoods in such an environment, will be highly beneficial. Additionally, it is vital to enhance fire management and prohibit human activities that cause fire or degrade the land (e.g. fire used to exploit natural honey, or adding salt water into the canals to control fire in Mekong Delta of Vietnam).

Over 2.1 million hectares of Melaleuca forests in Australia are in the conservation areas (e.g. National Parks, Cultural and Heritage zones) (MIG, 2008). These conservation areas are valuable not only as ecological systems, but also derive economic value from carbon offsets. A personal estimate is that the financial value of preserving these forests is in excess of AU$15 billion (given the Australian Commonwealth Government’s price on carbon of AU$23 per tCO2e to be introduced from 1st July 2012). The climate policy in Australia can encourage the development of carbon offset project that might involve Melaleuca forests. Additionally, UNFCCC policy is also appropriate to

22 Melaleuca forests in South East Asian countries (e.g. Indonesia, Malaysia, and Vietnam) where REDD+ is legally allocated. However, the carbon sequestration of over 8 million hectares of Melaleuca plants should be assessed accurately to determine the available carbon offsets.

Although the introduced species (Melaleuca quinquenervia) rapidly invades other native plant communities in the wetlands of Florida and has become an exotic weed, it is considered as an important species for honey bee production (Dray et al., 2006). Although there is a need to prevent the further invasion of this species, the current extent of Melaleuca forest in Florida should be managed to produce carbon offsets from the current area of 202,000 hectares.

2.5 Conclusion

The Melaleuca genus can respond quickly to climate change for several major reasons, including: (i) having a rich species diversity (260 species), and a dramatic phenotypic diversity in a variety of ecosystems; (ii) the ability to resist various harsh environmental conditions, such as flooding, drought salinity, high aluminium concentration, and burning; (iii) the fossil records and taxon biology indicate the rapid evolution of the Melaleuca genus over the past 38 million years. This evidence emphasizes how well these species and populations can potentially adapt to global climate change, which provides the potential for the development of a sound management strategy for Melaleuca species and their populations that can significantly contribute to climate change mitigation strategies through the storage of a large volume of carbon.

23 Chapter 3 - A REVIEW OF PREVIOUS RESEARCH ON THE CARBON STOCKS IN MELALEUCA DOMINATED ECOSYSTEMS

Summary

Melaleuca dominated forest ecosystems occur mostly in freshwater wetlands and coastal regions. Like other forms of wetlands, Melaleuca ecosystems commonly contain large stores of carbon. Whilst measuring carbon in dry forests or in specific components of forest structure (e.g. forest litter) is comparatively straight forward, developing estimates of wetland forests like Melaleuca forests is likely to be more complex and requires an integrative approach. Forested wetlands involving swamp Melaleuca forests are structurally complex and involve multiple factors influencing carbon stock fluxes, such as the sites hydrological and soil properties. A more systematic multi-component comprehensive approach is likely required to estimate the total carbon stocks (i.e. carbon stored in biomass, soil, deadwood and litter) of Melaleuca forest ecosystems. Based on the collation of published secondary data and available literature, this chapter evaluates the potential of carbon stocks for offsetting greenhouse gases in worldwide Melaleuca forests. In summary: (i) the probable carbon density in Melaleuca forests globally ranges from 157.8 tC/ha to 363.0 tC/ha; (ii) it can take over 10 years for accumulated litter on the floor of Melaleuca forests to decompose; and (iii) methane emission rates are likely to be relatively low in Melaleuca forests, most likely between 0.11 tC/ha/year to 46.3 tC/ha/year.

3.1 Introduction

Wetland ecosystems such as mangroves, salt marshes, and tropical peatland forests, have been found to have large stores of carbon (De La Cruz, 1986; Neue et al., 1997; Danone Fund for Nature, 2010; Mitsch et al., 2010b). Forested wetlands are complex and are characterized by three major components; ground surface water (seasonal or permanent), hydrophytic plants (dominated by woody trees), and hydric soils (Welsch et al., 1995). Tran et al. (2013b) reportedthat forested wetlands dominated by Melaleuca species are significant freshwater ecosystems which are naturally occurring and widespread in Australia and South East Asian countries, while in Florida USA, they were introduced. As with many forested wetland systems, Melaleuca ecosystems provide many benefits for people; they are rich in biodiversity, provide protection to wildlife and human communities from climate extremes such as floods and storms, and helping conserve soil and in some places water quality. They also store carbon and have the potential to reduce the potential

24 impacts of climate change (Welsch et al., 1995; Mitra et al., 2005; Hajenko, 2010; Gosselink and Mitsch, 2011).

Developing a better understanding of the carbon stock dynamics in forested wetlands is important for improving land-use management and climate policy, at both national and international scales. This is particularly true for Melaleuca forests in palustrine and lacustrine wetland systems. The carbon content of the forests in these systems is likely to be large and comparable to that of freshwater wetlands, which are well known to be significant carbon sinks. For example, the estimated carbon stocks of the Cyperus papyrus tropical wetlands in Uganda is 4.8 tC/ha/yr (Saunders et al., 2007); the Quercus palustris forested wetlands in the USA’s temperate region is estimated at 4.73 tC/ha/yr (Bernal and Mitsch, 2012); the humid tropical wetlands in Costa Rica is 2.55 tC/ha/year (Mitsch et al., 2010b)]; while the carbon sink for mangrove forests has been estimated at 1,023 tC/ha (Donato et al., 2011). Large areas of Melaleuca forests are located in the peatlands of Indonesia, Malaysia, Thailand and Vietnam [e.g. about 195,000 ha of peatland in the Mekong River Delta region of Vietnam is largely based on Melaleuca forests (Torell and Salamanca, 2003)], with Melaleuca cajuputi forming the lower canopy in the primary swamp forests. The species also dominates the secondary vegetation after repeated burning of peat swamp areas in Indonesia and Malaysia (Whitmore, 1984). InThailand, about 64,000 ha of peat swamp forests are also dominated by Melaleuca cajuputi(Hankaew, 2003). In Australia there are from 6.3 to 7.6 million ha of Melaleuca forests and woodlands (MIG, 2008; DAFF, 2010a; MIG, 2013), while in Florida USA, there was about 202,000 ha of exotic invasive Melaleuca forests (Dray et al., 2006).

Melaleuca forests are primarily located in areas that are likely to be adversely affected by climate change [such as in the Mekong Delta region of Vietnam (Erwin, 2009), and in the Kakadu National Park of Australia (BMT-WBM, 2011)]. In addition, Melaleuca forests occur in areas under pressure from human activities, including sugarcane cropping, animal grazing, mining and new housing developments in Australia (DAFF, 2008), as well asland-use changes, logging and burning, particularly in Indonesia (Anderson and Bowen, 2000), Malaysia (Wetlands International – Malaysia, 2010), and Vietnam (IUCN, 2010)]. That noted, the genus Melaleuca is inherently ecological resilient and in relative terms, considerably adaptable to changes in ecological conditions, like those posed by climate change (Tran et al., 2013b). It seems rationale to assume that the sustainable management of the carbon stocks in Melaleuca forests - through either forest conservation or using the genus in reforestation and land rehabilitation projects - is likely to be an important part of efforts to mitigate and adapt to climate change.

25 While the dynamics of carbon stocks in other wetland systems is relatively well known, there is little published information regarding the carbon stocks in Melaleuca forests. This lack of information is reflected in national carbon accounts for Melaleuca forests that are likely to be incorrect. For example, for the total area of Australian Melaleuca forests and woodlands, the total carbon stocks were estimated in 2004 to be 210 million tC, equivalent to about 27.8 tC/ha (MIG, 2008, p.117). The following chapters of this thesis will argue why this is likely to be a substantial underestimate. This thesis provides important new information that should assist in the development of better carbon stock estimates and in-turn, better climate and land policy as far as it relates to Melaleuca forests.

3.2 Carbon stocks of Melaleuca forests

3.2.1 Potential of the carbon biomass of Melaleuca forests

3.2.1.1 Potential of above-ground biomass

Above-ground biomass is an important component of the Intergovernmental Panel on Climate Change’s (IPCC’s) of carbon in a forest ecosystem. Based on the collation of data from published literature, this section summarizes the data on the dry biomass of standing Melaleuca forests and other forested wetlands (Table 3.1). Generally, the aboveground biomass of Melaleuca forests can be considered to have a moderate level of carbon storage. In natural conditions, the above-ground biomass of Melaleuca forests in Northern Australia has been estimated at 132 t/ha (Finlayson et al., 1993), but had a considerable range from 54 t/ha to 184 t/ha (converted from data of Franklin et al., 2007). This indicates that the earlier estimate of the Australian Government [about 16 tC/ha in the above ground biomass (MIG, 2008)]is likely to have been a gross underestimation. The volume of biomass is much higher in the introduced Melaleuca forest conditions of the Florida wetlands, accounting for 181 t/ha in young forests (Van et al., 2000b) and 331 t/ha in mature forests (Rayamajhi et al., 2008). When compared with other wetland forests, the above-ground biomass of Melaleuca forests is higher than that of the mangrove forests in Sarawak, Malaysia [estimated at 116.79 t/ha (Chandra et al., 2011)], and in Pernambuco, Brazil [105.00 t/ha (Medeiros and Sampaio, 2008)], but lower than the mangrove and riverine peat swamp forests in Central Kalimantan, Indonesia [greater than 400 t/ha (Murdiyarso et al., 2009)] (Table 3.1).

26 Table 3.1 Summary of the above-ground dry biomass of Melaleuca forests and other forested wetlands Forest type Dry biomass Carbon Site Source (t/ha) content (t/ha) Melaleuca spp. *132.00 66.00 Northern Australia Finlayson et al. (1993) Melaleuca argentea **168.00 84.00 Northern Territory Franklin et al. Australia (2007) Melaleuca cajuputi **81.00 40.05 Northern Territory, Franklin et al. Australia (2007) Melaleuca dealbata **54.00 27.00 Northern Territory, Franklin et al. Australia (2007) Melaleuca leucadendra **96.00 48.00 Northern Territory, Franklin et al. Australia (2007) Melaleuca viridiflora **80.00 40.00 Northern Territory, Franklin et al. Australia (2007) Melaleuca quinquenervia 181.00 90.50 Florida, USA Van et al. (2000b) (regenerated forests) Melaleuca quinquenervia 331.16 165.58 Florida, USA Rayamajhi et al. (mature forests) (2008) Mangrove 246.00 123.00 Hinchinbrook, Clough (1998) (Rhizophora stylosa) Australia Mangrove 116.79 58.40 Sarawak, Malaysia Chandra et al. (Rhizophora apiculata) (2011) Mangrove 105.00 52.50 Pernambuco, Brazil Medeiros and (Rhizophora mangle) Sampaio (2008) Mangrove 78.00 39.00 Pernambuco, Brazil Medeiros and (Laguncularia racemosa) Sampaio (2008) Mangrove 19.00 9.50 Pernambuco, Brazil Medeiros and (Avicennia schaueriana) Sampaio (2008) Mangroves (dominantly 400.00 200.00 Central Murdiyarso et al. Rhizophora sp., Avicennia Kalimantan, (2009) sp., and Sonneratia sp.) Indonesia Riverine peat swamp 400.00 200.00 Central Murdiyarso et al. forests Kalimantan, (2009) Indonesia

* converted from above-ground fresh weight biomass of 263 t/ha, according to Van et al. (2000b).

** values are converted and calculated using the equation of Finlayson et al. (1993) [log(FW) = 2.266log(D) - 0.502, where FW = fresh above-ground biomass, kg/tree; D = DBH (diameter (cm) at breast height)], and dry rate biomass is 0.5 from Van et al. (2000b).

27 3.2.1.2 High biomass and slow decomposition of Melaleuca’s litter

The amount of Melaleuca forest litter fall have been found to be quite high; from 7.00 to 7.67 t/ha/year in Australia (Finlayson et al., 1993; Greenway, 1994); and from 8.30 to 8.91 t/ha/year in Florida, USA (Van et al., 2002; Rayamajhi et al., 2006) (Table 3.2). Compared with other USA wetland forests, the mass of Melaleuca litter fall was much higher than in Cypress swamps in Illinois and Western Kentucky [0.79 t/ha/year (Mitsch et al., 1991), and 2.35 t/ha/year (Middleton, 1994), respectively]; hardwood wetlands in Western Kentucky [5.04 t/ha/year (Mitsch et al., 1991)]; and in the coastal floodplain forests in South Carolina [3.71 - 5.48 t/ha/year (Busbee et al., 2003)]. However, the mass of Melaleuca forest litter fall was much lower than that of tropical forested wetlands in the coastal floodplains of the Gulf of Mexico [9.00 - 15.00 t/ha/year (Infante-Mata et al., 2012)].

Table 3.2 Summary of the litter fall biomass of Melaleuca forests and other forested wetlands Forest type Total litter fall Site Source (t/ha/year) Melaleuca spp. 7.00 Floodplains, Northern Finlayson et al. (1993) Australia Melaleuca spp. 7.67 Floodplains, South East Greenway (1994) Queensland, Australia Melaleuca spp. 7.45 Riparian, South East Greenway (1994) Queensland, Australia Melaleuca spp. 8.30 Wetlands, Florida, Van et al. (2002) USA Melaleuca spp. 8.91 Wetlands, Florida, Rayamajhi et al. (2006) USA Casuarina glauca 8.48 Coastal wetland forests, Clarke and Allaway (1996) NSW, Australia Cypress swamp 2.35 Lower Cache, Illinois, Middleton (1994) (Taxodium spp.) USA Cypress swamp 0.79 Western Kentucky, Mitsch et al. (1991) (Taxodium spp.) USA Hardwood wetlands 5.04 Western Kentucky, Mitsch et al. (1991) USA Forested wetlands 3.71 - 5.48 Coastal floodplains, Busbee et al.(2003) South Carolina, USA Tropical forested 9.00 - 15.00 Coastal floodplains, Infante Mata et al.(2012) wetlands Gulf of Mexico

Litter decomposition is also an important factor to consider when developing estimates of carbon stocks in Melaleuca forest ecosystems. It is assumed that the slower the decomposition rate or the longer litter remain the greater carbon storage. Under wetland conditions, a long term study recorded that 14 % of the leaf litter of Melaleuca forests in Florida remained after 322 weeks (Rayamajhi et al., 2010), while the litter of Cypress-gum forests in South East USA was completely

28 decomposed within 53 weeks (Battle and Golladay, 2001), and 50 % the litter of River Red Gum forests in the Lower Darling River area of Australia had decomposed within 26 weeks (Francis and Sheldon, 2002). Decomposition of Melaleuca litter is much slower than for the litter of other forests types [e.g. the tropical forest litter in Southwest China has been reported to become totally decomposed within 183 weeks (Tang et al., 2010); for the litter of Alphitonia petriei, Eucalyptus grandis, and mixed primary rain forests in North Queensland Australia, 39 %, 29 %, and 28 %, respectively, were found to be still remaining after 73 weeks (Parsons and Congdon, 2008)].

In terms of carbon stock dynamics, litter accumulation is more influential than litter fall. Based on the published values for Floridian Melaleuca leaf litter, reported by Rayamajhi et al. (2010), the mass litter breakdown rate and litter accumulation, calculated using a progressive calculation method, showed long-term maintenance and high mass accumulation of swamp Melaleuca forest litter. Figure 3.1 illustrates the dynamics of the leaf litter remaining in the Floridian Melaleuca forests, for which complete decomposition takes a period of 10 years. The slow decomposition of litter is regarded as having positive effects. The litter accumulation data relating to Floridian Melaleuca forests was 12.27 t/ha, 21.38 t/ha, and 25.63 t/ha, for small, medium, and large trees, respectively (Rayamajhi et al., 2010). In Australia, the accumulated litter mass on forest floors has also been found to be high, ranging from 23.1 to 34.57 t/ha in riparian and floodplain sites, respectively (Greenway, 1994).

g/m2 % 600 100 Xt 90 500 % 80 400 70 60 300 50 40 200 30 100 20 10 ‐ ‐ 01234567891011121314151617181920 Time (years)

Figure 3.1 Dynamics of leaf litter mass remaining on the floor of Floridian Melaleuca forests

Note: Data is based on the conversion of published information by Rayamajhi et al. (2010) in which leaf litter dynamics were studied over a period of 322 weeks (6 years). At the end of the experiment, the leaf litter still remaining accounted for 14% of that at the beginning of the study (74.48 g/m2 of 564 g/m2). Based on the use of this data, the decomposition rate constant, k, was calculated from the decay curve using the equation of Olson (1963): ln (X0/Xt) = k* t ; where X0 = mass of litter at time 0, Xt = mass of litter at time t, t = time of incubation (year), k = decomposition rate constant. The decomposition rate constant was calculated as k = 0.33; the litter mass remaining in the Melaleuca forest

29 after 6 years was extrapolated using the conversion equation of Olson: Xt = X0/(k * t) ^ 2.7182818; where X0 = 564 g/m2, k = 0.33, and t is from 7 to 20 years.

It is assumed that other litter products in the Melaleuca forests, particularly the stems and fruit, may remain on the forest floor longer than leaf material, due to their stronger structure. The total mass of stem litter is second to that of leaf litter, and had been recorded at about 17.8 % of the total litter in Melaleuca forests in Queensland (Greenway, 1994), and between 15 % to 34.33 % in Florida (Rayamajhi et al., 2010). Wetland conditions and oily litter, which may protect the latter from microbial activity, can help Melaleuca litter to remain on the forest floor for long periods.

3.2.1.3 Potential of soil organic carbon (SOC) of Melaleuca forests

Generally, wetland soils have a great capacity for carbon storage, due to the slow decomposition rate of soil organic matter (SOM) and/or soil organic carbon (SOC) which forms under the anaerobic conditions of water saturation or poor drainage (Hobbie et al., 2000). The forested wetlands are usually associated with a significant amount of annual litter fall, which is one of the input sources for producing SOM. The SOC capacity is high in swamp peatlands and rich-organic soils; the rich-organic soils of the tropics contain from between 49 % and 98 % of carbon storage at depths ranging from 0.5 m to 3 m (Donato et al., 2011)].

The following sections of this thesis focus on the assessment of SOC capacity, using secondary data of the Queensland Wetlands Programme to provide a case study for the assessment of the potential of carbon stocks in soils in the Melaleuca forest wetlands. There are 20 wetland sites distributed throughout the state of Queensland, with 11 of these sites being associated with Melaleuca swamp vegetation. The state of Queensland contains 85 % of the Australian Melaleuca forest area (DAFF, 2008). Two significant findings relating to the potential of SOC relating to the high SOC content and slow rate of SOC decomposition are evaluated.

Example - Queensland Wetland Program: SOC concentration in swamp Melaleuca forest soils

The SOC content in most sites of the Queensland Wetlands Program was high, excepting site 34 (SOS accounting for 1.93 %) and site 67 (1.41 %). Significant sites are 22, 126, 127 and 128, with SOS contents of 28.0 %, 31.25 %, 36.05 %, and 30.20 %, respectively (Table 3.3). Separate sites with Melaleuca trees (both isolated and dominant) are also potential of carbon concentration sites (sites 15, 16, 17, 23, 28, 125 and 126 account for 18.26 %, 11.30 %, 16.50 %, 5.0 %, 7.35 %, 18.3 % and 31.25 %, respectively).

30 In addition, the C:N ratio is very important in assessing soil carbon storage that represent the two processes in the soil involving SOC composition and mineralization. In tropical soils, the two processes are in balance if the C:N equals 10. This indicator was used to assess the soil carbon stock by Miyajima et al. (1997) and Todorova et al. (2005). In this study, the C:N ratio of all wetlands sites was found to be higher than 10, ranging from 13.2 to 45.6. This indicates that the SOC composition process is stronger than mineralization, thereby allowing the SOC process to retain and accumulate carbon. Sites with significant C:N ratios with very high SOS accumulation are the sites in 18-Mile Swamp on North Stradbroke Island (accounting for 25.2, 27.7, 45.6, and 30.8 in sites 125, 126, 127 and 128, respectively) (Table 3.3). In addition, the sites covered by Melaleuca communities also have significantly high C:N ratios (the ratios being17.6, 13.5, 19.2, 22.2, 17.9, 16.4, and 17.7 in sites 15, 16, 17, 23, 26, 28, and 30, respectively) (Table 3.3).

Table 3.3 Mean average total C and N concentrations of the 30 cm surface soil layer of swamp vegetated soils in Queensland, Australia Site Vegetation Site No C (%) N (%) C:N Bribie Island Melaleuca community Melaleuca spp. 15 18.26 1.04 17.6 Lower Swamp Melaleuca community Melaleuca spp. 16 11.30 0.84 13.5 Crossing Melaleuca community Melaleuca spp. 17 16.50 0.86 19.2 Bribie Island Swamp mahogany and Eucalyptus robusta 21 9.70 0.43 22.8 Middle Swamp fern and Dicranopteris spp. Crossing Sedge community Carex spp. 22 28.00 1.18 23.8 Melaleuca and Banksia Melaleuca spp. and 23 5.00 0.23 22.2 Banksia spp. Goorganga Plain Isolated mangrove Aegiceras spp. 25 5.95 0.36 16.5 Lagoon Melaleuca community Melaleuca spp. 26 3.14 0.18 17.9 Melaleuca community Melaleuca spp. 28 7.56 0.46 16.4 Goorganga Plain Sedge community Carex spp. 29 3.61 0.23 15.7 Swamp Melaleuca community Melaleuca spp. 30 3.19 0.18 17.7 Isolated Melaleuca Melaleuca spp. 31 3.38 0.24 14.4 trees Goorganga Plain Mangrove community Aegiceras spp. 33 7.97 0.44 18.1 Tidal Flat Saltwater couch grass Sporobolus virginicus 34 1.93 0.12 16.0 Sedge community Carex spp. 35 2.96 0.23 13.2 Archer River Melaleuca community Melaleuca spp. 67 1.41 0.07 20.1 Swamp 18 Mile Swamp, Isolated Melaleuca Melaleuca spp. 125 18.30 0.73 25.2 North Stradbroke trees Island Isolated Melaleuca Melaleuca spp. 126 31.25 1.13 27.7 trees Sedge community Carex spp. 127 36.05 0.79 45.6 Sedge community Carex spp. 128 30.20 0.98 30.8

Sources: Queensland Wetlands Programme (2009a, b, c, d, e, f, g).

31 Example - Queensland Wetland Program: Slow decomposition of soil organic carbon

The main factors that affect the decomposition of soil organic matter in wetland conditions are the level of soil saturation and soil temperature. However, their effect is dependent on soil type and climate. Field study results had shown that the dissolved organic matter concentrations in forested wetlands is controlled by biotic/abiotic retention mechanisms (D'Amore et al., 2010). It is assumed that these biotic and/or abiotic retention mechanisms only relate to specific environments.

The most important element determining soil chemical concentrations is pH; soil pH plays a major role in creating and controlling the soil environment which strongly influences all processes. The 2+ 2+ + 2- 2- + soil pH is affected by alkaline elements (Ca , Mg , K ) and by acidic elements (SO4 , NO3 , H , Fe3+, Mn2+). In the Queensland wetlands studied, when the pH increases from 5.5 to 7.4 in response to soil Mn- and Fe- reduction processes, more than 60 % of total soil organic carbon was released as dissolved organic matter, indicating that the pH rise is the key factor driving organic matter solubilisation in the wetland soils under reducing conditions (Grybos et al., 2009). Similarly, an acidic environment (pH = 3.7) inhibited the dissolution rate of peat organic matter in the swamp forests of Sarawak, Malaysia (Satrio et al., 2009). In addition, Fe reduction contributed to organic matter decomposition in the wetlands of Central New York state (Todorova et al., 2005).

In the Queensland study, the soil pH of the wetlands studied was generally low, ranging from 2.1 to 6.7, indicating that the soils ranged from being very acidic to acidic (Table 3.4). In addition, values of acidic elements such as Mn and Fe were very high, while the alkaline elements such as Ca, Mg, P, and K were generally low, thereby allowing the low soil pH to be maintained. These results are contrary to those presented by Grybos et al. (2009). It is therefore presumed that the dissolution of the soil organic matter of the wetlands in Queensland was weak. This is significant in relation to the soil carbon stock potential of the wetlands. In relation to the Melaleuca vegetation sites (both isolated and dominant), they had very low pH values, ranging from 4.1 to 5.1 (Table 3.4), lower than the pH in the experiment of Grybos et al. (2009) which ranged from 5.5 to 7.4. The SOC can therefore be thought to be stored permanently in the soils.

The literature also indicates that there are various factors influencing SOC decomposition in the wetlands. Some of these factors include litter quality, moisture deficiency, low soil temperature, high acidity, and oxygen deficiency in at depth. These factors were likely to interact to constrain organic matter dissolution of peat (Laiho, 2006). Disturbance was also a factor affecting organic matter decomposition in the coastal plain of North Carolina (Neher et al., 2003). Enzyme activity and microbial biomass were also related to the decomposition of wetland macrophytes in the lowlands of Northern Belize, Central America (Rejmánková and Sirová, 2007). Humic acids were

32 the electron acceptors in wetland decomposition (Keller et al., 2009); electron donors and acceptors 2- (Fe(III) and SO4 ) influence the mineralization of anaerobic soil organic matter in the tidal freshwater marshes of the Maryland, USA (Sutton-Grier and Megonigal, 2011). It is likely that there is a similar occurrence in the Queensland wetlands, due to the high concentrations of S and Fe 2- (Table 3.4), which are materials that create Fe(III) and SO4 .

Table 3.4 Mean average concentrations of chemical elements in the surface 30 cm of swamp vegetated soils in Queensland, Australia Site pH P S Ca Mg K Cu Zn Mn Fe No mg/kg mg/kg meq/100g meq/100g meq/100g mg/kg mg/kg mg/kg mg/kg 15 4.8 47 224 2.745 3.690 0.813 0.20 0.15 0.15 45.0 16 4.6 23 51 0.922 2.770 0.744 0.20 0.30 1.50 57.7 17 4.4 23 70 3.460 3.930 0.465 0.10 0.40 0.80 237.0 21 4.1 16 90 0.700 2.835 0.541 0.25 0.35 0.10 87.9 22 2.1 54 219 0.400 1.605 0.043 - - - - 23 5.0 6 57 0.368 1.439 0.236 0.10 0.20 0.10 148.0 25 5.6 79 816 4.745 8.250 0.740 1.90 4.95 4.85 269.5 26 5.0 17 243 5.595 10.375 0.377 1.15 1.70 18.25 236.0 28 5.1 19 162 3.215 4.350 0.381 0.40 0.60 9.80 93.5 29 4.6 7 346 6.030 9.580 0.470 0.90 1.70 13.60 238.5 30 4.1 18 983 3.585 4.855 0.279 0.20 0.35 17.50 146.6 31 4.7 15 155 2.305 2.250 0.323 0.20 0.35 6.65 86.0 33 6.7 29 302 ------34 6.3 30 816 1.895 7.600 0.900 0.40 0.45 2.15 40.1 35 5.7 19 724 2.920 7.550 1.300 0.55 1.30 4.75 88.5

Note: ‘-’not available values for sites 67, 125, 126, 127 and 128 unavailable. Source: Queensland Wetlands Programme (2009a, b, c, d, e, f, g)

3.2.2 Low methane production rate of the forested wetlands

In the wetland soils, CO2 and CH4 are the major products released during anaerobic decomposition of organic matter as a result of interdependent microbial processes (Megonigal et al., 2003). Incubation experiments conducted in the freshwater wetlands in South East USA had demonstrated that microbial Fe(III) oxide reduction can suppress sulphate reduction and methanogensis in the sediments (Roden and Wetzel, 1996). Due to the absence of oxygen in inundated soils, it is believed that microbes reduce the types of alternative terminal electron acceptors for respiration. In addition, - 2- the primary inorganic terminal electron acceptors, including NO3 , Fe(III), Mn(III, IV), and SO4 , occur in wetland soils. The reduction of these electrons was coupled to the oxidation of organic matter to produce CO2 and CH4 which suppress competition (Keller et al., 2009). Sutton-Grier and

Megonigal (2011) also reported that Fe(III) inhibits methanogensis using a similar mechanism, with

33 a decrease in total respiration being caused by a reduction in CH4 respiration and no CO2 respiration.

Based on the above data and information, the concentrations of Fe, Mn, and S in the soil are likely 2- to be high (Table 3.4), and are considered likely to react to create Fe(III), Mn(III, IV), and SO4 . There is evidence to suppose that the CH4 producing processes of the Melaleuca forested wetlands is likely to be at the low level compared to other wetlands worldwide. The level of CH4 emissions in freshwater Australian wetlands is low [e.g. the CH4 flux of the wetlands in River Murray range from 0.05 to 12.85 µg CH4/m2/h (Boon and Mitchell, 1995)]. This is the equivalent to between 0.11 to 27.02 tC/ha/year, while in the oxbow lake sediments it was equal to between 12 and 22 gC/m2/year(Sorrell and Boon, 1992), which was the equivalent of between 25.23 to 46.25 tC/ha/year. In a long-term, it is indicated that methane emissions become unimportant (within 300 years) when compared to carbon storages in the temperate and tropical wetlands. Therefore, freshwater wetlands typically stock an average of 118 gC/m2/year (1.18 tC/ha/year) in net carbon retention (Mitsch et al., 2012).

3.3 Discussion of the potential of carbon storage in Melaleuca forest ecosystems for offsetting greenhouse gases

There is a lack of direct studies on carbon storage in Melaleuca forests. Based on estimates using secondary biomass data, it is estimated that there can be considerable carbon stocks in Melaleuca forests, ranging from 27 to 165 tC/ha above-ground, 4.8 - 14.0 tC/ha in the form of accumulated litter, and 126 - 184 tC/ha of SOC. In summary, worldwide carbon stocks in Melaleuca forests have been estimated to range from 157.8 to 363.0 tC/ha. In addition, it is likely to be between 156 tC/ha and 288 tC/ha stored in Australian Melaleuca forests (Tran et al., 2013a). Carbon storage, particularly in Melaleuca swamp forests, can be regarded as considerable and of global significance, not only in the form of the carbon volume but also on account of the long life of accumulated litter and slow decomposition rates of soil organic carbon.

The 2.1 million ha of protected Melaleuca forests in Australia that include National Parks, Conservation Reserves, as well as Cultural and Heritage Areas, are likely to have a carbon storage potential of between 328 million tC and 605 million tC (equivalent to 1,203 million to 2,218 million tons of CO2e) (Tran et al., 2013a). In addition, over 5 million hectares of other Melaleuca forests and woodlands also exist in Australia for the volume of carbon stored in these forests is globally significant, and potentially commercially very valuable under current Australian climate change policy. The Australian ‘Carbon Farming Initiative’ potentially provides opportunities for Australian land managers, forest growers and farmers, to receive an income from reducing emissions or/and

34 storing carbon in the landscape. In addition, hundreds of thousand hectares of Melaleuca forests in other developing countries, including Indonesia, Malaysia, Thailand and Vietnam, are likely to be eligible under emerging REDD+ policy.

It is significant that Melaleuca species are not considered for their commercial wood and timber values in Australia. These forests are mostly located in the wetlands of protected areas, and/or on private land used for grazing, where scattered Melaleuca trees (and/or patches of Melaleuca trees) play a role in providing shade or shelter for animals. In other countries, Melaleuca forests are also located in peat swamp areas that can be used to protect carbon sinks. A strategy of carbon offset management might be a very useful way of funding sustainable use and protection of Melaleuca tree species. The high volume of carbon stocks in Melaleuca forests is likely to play a significant role in mitigating the impact of climate change, while also providing economic value through carbon budgets for sustainable management.

3.4 Conclusions

By collating published data and research literature, this chapter has revealed that:

(i) The potential of carbon volume in Melaleuca forests ranges from 157.8 to 363.0 tC/ha.

(ii) Litter accumulated in Melaleuca forests decomposes slowly, typically taking over 10 years to be completely decomposed on the forest floor.

(iii) Methane emissions in Melaleuca forests are relatively low, with a wide range reported (between 0.11 tC/ha/year to 46.3 tC/ha/year).

35 Chapter 4 - INTERVENTIONS TO BETTER MANAGE THE CARBON STOCKS OF AUSTRALIAN MELALEUCA FORESTS

Summary

Forests and woodlands dominated by tree species of the genus Melaleuca cover around 7,556,000 hectares in Australia and predominantly occur as wetland ecosystems. In this Viewpoint, we use published secondary data to estimate that there is likely to be between 158 tC/ha and 286 tC/ha stored in Melaleuca forests. There are 2.1 million ha of protected Melaleuca forest which likely stock between 328 M tC to 601 M tC; equivalent to between 2.7 % to 5.0 % of total carbon storage of all Australian native forests. These estimates are significant because it appears that carbon stocks in Melaleuca forests are currently dramatically under-estimated in Australia’s national greenhouse gas emissions inventory reported under the United Nations Framework Convention on Climate Change (UNFCCC). Whilst the precision of the estimates is limited by the availability of rigorous primary data, we also argue that the estimates are indicative and meaningful, and this synopsis highlights the fact that this forest type should be considered a significant carbon store nationally and globally.

This chapter was published as an article paper: Tran, D.B., Dargusch, P., Herbohn, J., Moss, P., 2013. Interventions to better manage the carbon stocks in Australian Melaleuca forests. Land Use Policy 35, 417-420. doi:10.1016/j.landusepol.2013.04.018. Received: 23 September 2012 / Received in revised: 15 April 2013 / Accepted: 28 April 2013.

36 4.1 Rationale

Melaleuca forests occur over a very broad area of Australasian and South East Asian countries (Blake, 1968; Craven, 1999; Brown et al., 2001). Within Australia, there are 7,556,000 ha of Melaleuca forests and woodlands (naturally occurring), equivalent to 5 % of the total forest area (MIG, 2008; DAFF, 2010a) (a distribution map of the Genus and example photographs of Melaleuca forests are provided in the Figure 4.1 and Figure 4.2). They are 7.7 and 2.3 times greater than the area of mangrove forests and rainforests in the country, respectively. Melaleuca forests in Australia mostly occur in a sub-tropical and tropical woodland habit (~97 %) with around 84 % of the forest type located on publicly owned land and the remainder on privately owned land (MIG, 2008; DAFF, 2010a). Most of the publicly owned land covered by Melaleuca forests is leasehold land which is currently used for cattle grazing. Only about 4,500 ha of plantations of Melaleuca have been established for the production of tea tree oil (RIRDC, 2006). Of the total area of this forest type in Australia, over 5,698,000 ha are located in the state of Queensland, of which 2,104,000 ha are located in protected areas such as national parks (MIG, 2008; DAFF, 2010a). Melaleuca forests in Australia are predominantly wetland forests, and occur in lacustrine and palustrine land systems. Common species of the genus Melaleuca in Australia include Melaleuca viridiflora, Melaleuca leucadendra, Melaleuca argentea, Melaleuca quinquenervia, Melaleuca nervosa, Melaleuca preissiana, and Melaleuca rhaphiophylla.

This paper collates secondary data on the carbon stocks of Melaleuca forests in Australia, and estimates the current financial value of the carbon contained in these forests. Importantly, there is literally only a handful of papers and reports in the public domain that deal with the carbon stocks of Melaleuca forests. A discussion on carbon stocks in Melaleuca forests in Australia is therefore both timely and important for several reasons. Firstly, this forest type is likely to contain carbon stocks of global significance, because many freshwater wetlands are known to store high amounts of carbon [e.g. the rate of carbon accumulation estimated in the Quercus palustris forested wetland community in temperate region of USA was 4.73 tC/ha/yr (Bernal and Mitsch, 2012), and in the Cyperus papyrus tropical wetland in Uganda was 4.8 tC/ha/yr (Saunders et al., 2007)]. However, the carbon stocks of Melaleuca forests, including any change or disturbance to those stocks, appears to have been inadequately reported in Australia’s national greenhouse gas emissions accounts, which is required under the Kyoto Protocol to the United Nations Framework Convention on Climate Change (UNFCCC). For example, the Australian Government estimated in 2004 that there was 210 million tC in Australian Melaleuca forests, equivalent to around 27.8 tC/ha (MIG, 2008, p.117). This paper shows why this is likely to be a dramatic underestimate of the amount of carbon stocks in Melaleuca forests. Secondly, Melaleuca forests in Australia also have high conservation

37 values [e.g. biodiversity, and as indigenous cultural and heritage sites (EPA, 2005)], high vulnerability but aggressive responses of adaptation to climate change (Tran et al., 2013b), and occur in localities under pressure from mining and urban development. Management that aims to conserve the carbon stocks of Melaleuca forests should also work to conserve the ecological value of those forests. However, the development and planning controls in many jurisdictions in Australia do not give the carbon stocks in the soil components of Melaleuca forests adequate consideration, despite this pool containing substantial amounts of carbon in some Melaleuca forest types. Focus is primarily on the vegetative aspects of Melaleuca ecosystem, and little consideration is given to the development and drainage of areas of these forest types that might be cleared, even though such sites probably contain very large quantities of soil carbon.

Melaleuca forest status in Australia

1. Total area: 7.556 million hectares 2. Total species of Melaleuca genus: 260 species 3. Major species of swamp Melaleuca forests: broad- leaved paperbark (Melaleuca viridiflora), weeping paperbark (Melaleuca leucadendra), silver paperbark (Melaleuca argentea), blue paperbark (Melaleuca dealbata) and yellow-barked paperbark (Melaleuca nervosa). 4. Natural distribution: 75 % in Queensland and 23 % in North Territory 5. Widely appearance as swamp Sources: MIG (2008), and DAFF (2008) forests in the palustrine and lacustrine areas

Typology States and territory Wood Open Closed IUCN National land areas* Estate NSW NT Qld SA Tas Vic WA areas** Areas 48 1690 5698 14 19 24 62 6654 878 26 832 1272 (000’ha) Figure 4.1 Distribution and summary status of Melaleuca forest in Australia

(*Forest areas are protected under IUCN; ** forest areas within the National Estate that contain historical, indigenous and natural values).

i ii

iii

iv v

vi Photos: Da B. Tran

Figure 4.2 Typical habit of swamp Melaleuca forests in Bribie Island, Queensland, Australia i - a natural Melaleuca tree ii - natural Melaleuca forest iii - swamp condition in forest iv - understorey v - deadwood vi - degraded forest (secondary forest)

39 4.2 Deriving an estimate of carbon stocks

Here we use published data and International Panel of Climate Change (IPCC) carbon stock estimation methods to derive an estimate of the carbon stocks of Melaleuca forests in Australia. As noted above, there is a stark paucity of published information on the carbon stocks of Melaleuca forests in Australia and this makes deriving a reliable estimate using existing data problematic. The estimate presented here needs to be considered in this context, and that whilst it is limited by lack of existing data, our estimate highlights the likely significance of Melaleuca forests as a carbon store and we use the estimate to call for more research and better policy intervention on this topic.

As per IPCC guidelines (IPCC, 2003), the carbon fraction of dry matter in above-ground biomass and litter can be assumed to be 50 % and 45 %, respectively. There is no published Australian data related to the biomass in the understorey and deadwood components of Melaleuca forests in Australia, so these two IPCC categories were not included in the estimates. The amount of carbon in Melaleuca forests was estimated as follows:

C density (tC/ha) = C standing biomass + C litter biomass + C soil organic.

Soil organic carbon (SOC) content was estimated using the equation:

SOC = Depth (m) * Bulk density (g cm-3) * C (%) * 100, where the SOC formular derives from IPCC, and 100 is the default unit conversion factor.

Only one publication has to date presented an estimate of the amount of tree biomass in Melaleuca forest ecosystems in Australia based on forest inventory data collected in the field. Finlayson et al. (1993) estimated that the average above ground dry biomass in Melaleuca forests is approximately 132 t/ha [the value was converted from the original fresh biomass value by Van et al. (2000b), and did not include root mass]. Additionally, based on the values of diameter at breast height and tree densities from Franklin et al. (2007), biomass of five species including Melaleuca argentea, Melaleuca cajuputi Melaleuca dealbata, Melaleuca leucadendra, andMelaleuca viridiflora were converted and estimated accounting for approximately 168 t/ha, 81 t/ha, 54 t/ha, 96 t/ha, and 80 t/ha, respectively. These compare with introduced Melaleuca forests in Florida USA, for which the estimates are 181 t/ha (Van et al., 2000b), and 331 t/ha (Rayamajhi et al., 2008). Assuming that the estimates from values of Franklin et al. (2007) (with the lowest and highest records 54 t/ha and 168 t/ha, respectively) are representative of the above ground biomass in Melaleuca forest ecosystems in Australia, then the typical carbon stocks in tree biomass in Melaleuca forests is likely to be between 27 tC/ha and 84 tC/ha.

40 Four studies have examined the dynamics of litter fall (leaves, bark, fruit and branches) and accumulation in Melaleuca forests. Congdon (1979), Finlayson et al. (1993) and Greenway (1994) examined litter dynamics in Melaleuca forests in Australia, while Rayamajhi et al. (2006) examined the litter dynamics in Melaleuca forests in Florida, USA. The rate of litter fall in Melaleuca forests in Australia has been estimated to be between 7.00 and 7.67 t/ha/yr in the floodplains of Northern Queensland (Finlayson et al., 1993) and South East Queensland (Greenway, 1994), respectively. Litter accumulation was 23.20 and 34.57 t/ha/yr on the floodplain and the forest floor, respectively, based on data from eight 0.0625 m2 quadrat samples at each site in Native Dog Creek/Logan River floodplain, South East Queensland (Greenway, 1994). Greenway (1994) also reported various indicators of high carbon content in the forest litter components of Melaleuca forests (notably C:N = 60:1, C:P = 1400:1 for litter with decay size ≥ 5 mm, and C:N = 25:1 and C:P = 300:1 for litter decay size < 5 mm). This probably reflects slow rates of litter decomposition. For instance, Rayamajhi et al. (2006) recorded that after 322 weeks, 14 % of litter fall had not decomposed in the Floridian Melaleuca forests.

If we assume that the estimates of accumulated forest litter range from 7.00 t/ha/yr to 34.57 t/ha/yr in Australian sites [as per the studies of Finlayson et al. (1993) and Greenway (1994)] are representative of the amount of accumulated forest litter in Melaleuca forests, then it is reasonable to estimate that the typical carbon content in the litter of Melaleuca forests is between 3.15 tC/ha and 15.56 tC/ha.

The following estimates of soil carbon stocks are based on the results of soil analyses reported by the Queensland Wetlands Programme using samples taken in Melaleuca forest sites located in different parts of Queensland, Australia. Studies on soil properties conducted around Australian wetlands show that soil bulk density ranges from 1.5 g/cm3 at 0.1 m depth in the coastal lowlands of South East Queensland (Costantini, 1995), to 1.6 g/cm3 at 1 m depth in woodland-open forest landscape within the Brigalow Belt South bioregion of Queensland (Roxburgh et al., 2006), to 0.55 g/cm3 at 0.25 m depth in hydrosols in Darwin Harbour (Hill and Edmeades, 2008); the latter being consistent with other reported values from North Queensland (McKenzie et al., 2000), and the Hunter River estuary in South East Australia (Howe et al., 2009). Therefore, assuming that Melaleuca forest soils have a bulk density of ranging from 0.55 g/cm3 (Hill and Edmeades, 2008) to 0.8 g/cm3 (personal record) with mean average carbon concentration of 7.75 %, and these data are representative of Melaleuca forest soils, the typical soil carbon content in at 0-30 cm depth of Melaleuca forests in Australia is likely to be range between 128 tC/ha and 186 tC/ha. These estimates are consistent with those reported by Page and Dalal (2011) for soil carbon stocks in wetlands of other vegetation types in Queensland (from 12 to 557 tC/ha).

41 In summary, the typical carbon stocks in Australian Melaleuca forests range from 158 tC/ha to 286 tC/ha. This estimate does not include understorey, deadwood, root and other under-ground biomass components and therefore is likely to be an underestimate of actual levels of carbon stocks. The estimates also appear reasonable when compared to data from other forest types, particularly natural Eucalyptus forests in Australia and while there is clearly a lack of precision in our estimate of carbon stocks due to the paucity of data available, we are confident that they provide an indicative estimate. In Tasmanian forest landscapes, the above-ground carbon density of dry forests, wet forests (eucalypt and non-eucalypt), wet eucalypt forests, mature eucalypt forests, mature dry eucalypt forests, and mature wet eucalypt forests was 118 tC/ha, 185 tC/ha, 222 tC/ha, 179 tC/ha, 121 tC/ha, and 232 tC/ha, respectively (Moroni et al., 2010). Additionally, mean soil C stocks in Tasmanian native forests was 97 and 193 tC/ha at 0.3 m and 1.0 m depth, respectively (Cotching, 2012). The total carbon stocks of the Eucalyptus forests of South-eastern Australia has been estimated at 640 tC/ha, of which soil organic carbon accounts for 280 tC/ha, with the forest biomass accounting for 360 tC/ha (Mackey et al., 2008). Moreover, Keith et al. (2009) estimated that there is in excess of 1800 tC/ha in the world’s tallest hardwood forests dominated by Eucalyptus regnans.

It also appears that previously published estimates of Melaleuca carbon stocks [i.e. about 27.8 tC/ha (MIG, 2008, p.117)] are likely to be a gross underestimate of actual stocks. That estimate is seven times lower than carbon stocks of the tropical savannah in northern Australia [204 tC/ha, of which soil organic carbon content (depth 0.1 m) accounts for 151 tC/ha and above-ground accounts for 53 tC/ha (Chen et al., 2003)].

4.3 Policy interventions and future research

The Melaleuca genus has a wide geographic range within Australia, with substantial areas in remote locations that are difficult to access. It is also appears that there has been a perception amongst land managers and policy makers in Australia to date that Melaleuca forests offered little commercial value other than for grazing purposes and honey production in some localities. Partly because of these reasons, the estimation of carbon stocks in Melaleuca forests in Australia has been attributed a low priority. We acknowledge that the carbon storage estimates for Melaleuca forests that have been made and reported in this paper are broad and do not rigorously reflect the variability and complexity across the total area of Australian Melaleuca forests. However, we also argue that the estimates are indicative and meaningful, and highlight the need for future research and policy action.

In addition to developing better information related to estimating the mass of carbon stored in Melaleuca forests as ‘stocks’, future research should also be directed into the processes that

42 influence the dynamics of those stocks. The carbon dynamics in Australian Melaleuca forests are likely to be complex, involving a range of factors including flooding, sediment accretion, litter deposition and decomposition, salinity, acidity, pollution and drainage. There is a pressing need for further research to help better understand these dynamics.

One of the key opportunities for enhancing the conservation of Melaleuca forests in Australia is to develop carbon offset opportunities, through which the avoidance of emissions and rehabilitation of carbon stocks in those forests is financially rewarded. However, fundamental to the successful design of such mechanisms, is a sound understanding of the processes that influence the dynamics of carbon stocks, and an ability to reliably estimate the amount of emissions avoided and the yield of carbon sequestered, through conservation activities. Recent developments in climate policy in Australia [notably the Carbon Credits Act and Carbon Farming Initiative (Australian Government, 2011a)] that encourage the development of innovative carbon offset project designs, might also be possible in Melaleuca forests, and make discussion of these issues particularly topical and timely. Under the current rules of the Carbon Farming Initiative, it is difficult to have an offset methodology that focuses on avoiding emission losses from deforestation and degradation related activities approved. However, it is believed that such methodologies can be justified under certain circumstances, particularly where issues of leakage and permanence can be reasonably addressed. Given the features of Melaleuca forests in Australia in regards to their distribution, land tenure and disturbance pressures, a creative and rigorous methodology for Melaleuca forests is entirely plausible and worthy of consideration.

Emission from urban development activities is likely an omission or inadequacy in Australian national greenhouse inventory systems. Under the Australian National Carbon Accounting System, emission arisen from land use change is mainly from deforestation (DCCEE, 2012), and deforestation is from the conversion of land to cropland and grassland, but land use change from urbanisation is obviously excluded (AGEIS, 2012). Moreover, urban development activities are not in the List of Australian and New Zealand Standard Industrial Classification (NPI, 2006).

In addition, an understanding of the carbon stocks and dynamics also has important implications in relation to the approval of urban development and the environmental conditions placed on such developments. Ignoring the carbon stocks and dynamics in Melaleuca ecosystems may result in substantial carbon emissions, and these costs need to be taken into account in any future development activities. A current example is the Stockland Caloundra development, which proposes ‘to construct and operate a master planned community catering for up to 50,000 residents on Queensland’s Sunshine Coast’ (DSEWPaC, 2011). In a report of more than 500 pages relating to

43 environmental impact analysis and plans for this proposed development, the topic of carbon stocks in the soils of the site’s Melaleuca forest ecosystems is not mentioned. Much of the urban development that has taken place in South East Queensland over the last 20 years in the Gold Coast and Sunshine Coast regions, has taken place on land previously covered by Melaleuca forests. Most carbon stored on these sites would have been converted to atmospheric greenhouse gas emissions through deforestation, forest degradation and wetland drainage that took place in the early stages of urban development. The impacts of these emissions on climate change, and the potential financial value of the carbon stores in the forests systems, were not, and are still not, considered as important issues for planners and developers.

There is an urgent need for more research into the carbon stocks of Australian Melaleuca forests. Field studies are required to more rigorously assess the carbon stocks and dynamics of those stocks in the forest type. Furthermore, Melaleuca forests are at high risk from impacts of climate change like sea level rise (Australian Government, 2009; Bowman et al., 2010), and bush fires (Franklin et al., 2007; Crowley et al., 2009)]. Policy makers need to consider various interventions including carbon offset development initiatives that give more attention to this important carbon-rich forest type in conservation and land management decisions.

Note that the authors are currently undertaking a research project that seeks to verify some of the carbon stock estimates presented in this paper. Data on carbon stored in biomass, soil and litter is being collected from a series of different types of Melaleuca forests in South-East Queensland to not only assist in verifying the estimates presented in this paper but also help better understand the dynamics of carbon sequestration and carbon loss in forests with different water level profiles, soil types and degrees of disturbance. There has been growing recognition in recent years in the climate policy and natural resource policy discourse of the importance of various wetland types (particularly in regards to tropical peat-lands and mangroves) as sizeable and vulnerable carbon stores. The management of these land systems and their carbon stores have become a critical issue for sustainable land management and related policy intervention. The estimates we present in this paper highlight the importance of Melaleuca forests in Australia as another globally important wetland carbon store and reinforce the need to grow awareness amongst policy-makers around the world for more research into the dynamics of carbon sequestration and carbon loss in other poorly understood wetland types.

44 Chapter 5 - METHODOLOGY

Summary

This chapter presents the methodology of the thesis, including data collection and analysis methods. The chapter starts with a review of the inventory methods used to estimate the carbon stocks of various forest ecosystems and considers how these are relevant to estimating carbon stocks of Melaleuca forest ecosystems. The relevant methods reviewed included allometric equations to estimate above and below ground biomass, soil organic carbon (SOC), understorey, litter and deadwood, and gaseous states of wetland soil and water. There are 17 allometric equations for above- and below-ground biomass estimates; two ex situ and three in situ methods for SOC estimates; and four major gas flux methods

for CH4 measurement which are relevant to estimating carbon stocks of Melaleuca forest ecosystems. Based on the review, a research method is presented to develop a better understanding of the dynamics of carbon stocks of Melaleuca forests with minimum destructive approach. Note that the data collection and analysis methods used in this thesis are also outlined in further detail in each of the papers presenting the key results in chapters 6 and 7 of this thesis.

5.1 Introduction

As noted in earlier parts of this thesis, developing a better understanding of the dynamics of carbon stocks in wetland ecosystems is of considerable importance to land managers and climate policy- makers. This is particularly so for Melaleuca forests in palustrine, estuarine and lacustrine systems in Australia and South East Asian countries (e.g. Indonesia, Malaysia, Thailand, and Vietnam) for several reasons.

First, the total area of Melaleuca forests in the world is likely to be in excess of 8 million hectares. Given that there are about between 6.3 and 7.6 million ha of Melaleuca forests in Australia (MIG, 2008, 2013), the way in which Melaleuca forests are managed is likely to have a substantial impact on climate change issues at both regional and local scales.

Second, Melaleuca forests have high conservation values and occur in locales under pressure from deforestation and landuse changes [e.g. sugarcane, grazing, mining and new resident development in Australia (DAFF, 2008); oil palm plantation expansion on the peat land swamp forest area in Indonesia, and Malaysia (Rieley and Page, 2005; SarVision, 2011; Miettinen et al., 2012)].

45 Third, Melaleuca forests are under the impacts of global climate change [e.g. in Mekong delta of Vietnam (Erwin, 2009); the Kakadu National Park in Australia (BMT-WBM, 2011)]. However, Melaleuca species have evolved to be ecological resilient and are generally quite adaptable to changing ecological conditions (Tran et al., 2013b). Melaleuca forest ecosystems are of considerableimportance for carbon offset production and management and can provide benefits for the mitigation on climate change and for human communities living in these areas.

Last, whilst the dynamics of carbon stocks in other wetland systems is relatively well known, there has been very little published on the carbon stocks of Melaleuca forests. Moreover, developing a better understanding of carbon stocks in Melaleuca forests is particularly challenging given the complex interactive influence that different formative factors such as flooding, accretion, litter deposition and decomposition, salinity, acidity, pollution and drainage can have on carbon stocks in palustrine and lacustrine ecosystems. This chapter aims to help resolving this knowledge gap by: (1) reviewing relevant literature on how to estimate aboveground and below-ground carbon stocks in Melaleuca forests and similar forest systems; and (2) establishing a research method to collect and analyse data to better understand the dynamics of carbon stocks in Melaleuca forests.

5.2 Methods to estimate carbon stocks in Melaleuca forests

5.2.1 Carbon stocks in stand trees

In preparation to meet its reporting obligations under the Kyoto Protocol, the Australian Greenhouse Office undertook a review of scientific literature on allometric relationships used to estimate volumes of biomass in vegetation across multiple types of ecosystems of Australia (Keith et al., 2000; Eamus et al., 2002). The review collated the results from 129 studies conducted between 1969 and 1999. Most of the research involved establishing allometric equations for above ground biomass. A few of the reviewed papers established allometric equations for both above and belowground biomass [e.g. pine trees (Dargavel, 1970; Jackson and Chittenden, 1981), subtropical eucalypt forest (Westman and Rogers, 1977), Eucalyptus pilularis forests (Applegate, 1982), Casuarina plantation (Chen and Lu, 1988), mangroves species (1989), and woodlands of North East Australia (Burrows et al., 1998)]. The review concluded that a single relationship could not be applied appropriately across all Australian forest ecosystems (Keith et al., 2000; Eamus et al., 2002). Since 2000, numbers of additional studies have established allometric equations for both above and belowground biomass in Australian forest ecosystems, but most investigated various species of Eucalyptus [e.g. Eucalyptus woodlands in Queensland (Back et al., 2000; Burrows et al., 2000; Burrows et al., 2002), eucalypt plantations in Tasmania (Resh et al., 2003), native eucalypt forests of temperate Australia (Bi et al., 2004), young Eucalyptus globulus stands in Southern

46 Tasmania (Battaglia et al., 2006), Eucalyptus populnea woodland communities of Northeast Australia (Zerihun et al., 2006), Eucalyptus globulus trees (Tome et al., 2007), and Eucalyptus forest (Jacobsen et al., 2008)].

Importantly, forests dominated by Melaleuca species were inadequately studied in the report of the Australian Greenhouse Office (Australian Government, 2011b). To date, only one published study has examined the allometric relationship of biomass stocks in Melaleuca forests in Australia [i.e. Finlayson et al. (1993)] and two other papers have examined biomass in invasive Melaleuca forests in Florida [i.e. Van et al. (2000b), and Rayachhetry et al. (2001)].

Allometric equations developed for mixed tropical species are usually in the form of B = aDb or log(B) = log(a) + blog(D), where B is individual tree biomass; D is diameter at breast height; a and b are parameters (Ketterings et al., 2001). Some studies of tropical forests have included additional variables such as  (wood density, g cm-3) [i.e. Ketterings et al. (2001)], basal area (BS) [i.e. Chave et al. (2005)], and crown diameter (CD) [i.e. Henry et al. (2010)].

Table 5.1 lists a number of allometric equations that should be relevant to estimating carbon stocks in Melaleuca forests. Of these, equations 5 and 6 are perhaps the most relevant to estimating above ground biomass in Melaleuca forests in Australia. Equation 1 was developed using fresh biomass and did not include a dry matter ratio, so whilst it was developed for similar forest types, it cannot be applied to other circumstances. Equations 11 and 12 seem most relevant for estimating belowground biomass in Melaleuca forests.

47 Table 5.1 A summary of relevant allometric equations for estimating biomass in Melaleuca forests

2 N0 Allometric equations R Vegetation Sites References

(1) Log10(FW) = 2.266log10(D) - 0.502, whereFW = fresh 0.98 Melaleuca spp. North Finlayson et aboveground biomass, kg/tree; D = DBH (diameter at Territory al. (1993) breast height), cm. (2) y = exp[-2.134 + 2.53ln(D)], wherey = aboveground 0.97 Mixed species Tropical, IPCC (2003) biomass, kg/tree; D= diameter at breast height, cm. moist forest or Brown (1997) (3) ln(B) = 2.383ln(D) - 1.820, whereB = aboveground 0.97 Eucalypts North Eamus et al. biomass, kg/tree; D = DBH (diameter at breast Territory (2000) height), cm, with DBH ≥ 2.6cm, and QRFI (4) ln(y) = 2.4855ln(x) - 2.3267, wherey = aboveground 0.96 Native NSW, ACT, Keith et al. biomass, kg/tree; x = DBH (diameter at breast height), sclerophyll VIC, TAS, (2000) cm. forest and SA

(5) loge(W) = - 1.83 + 2.01Loge(DOB), whereW = 0.96 Natural Florida, USA Van et al. aboveground biomass, kg/tree; DOB diameter outside Melaleuca (2000b) bark, cm quinquenervia forest (6) ln(y) = 2.0409ln(D) - 2.0163, wherey = aboveground 0.96 Natural Florida, USA Rayachhetry biomass, kg/tree; D = diameter at breast height, cm Melaleuca et al. (2001) (all tree height ≥ 1.3m be counted and DBH recorded) quinquenervia forest (7) ln(AGB) = - 1,554 + 2.420ln(D) +ln(), whereAGB = 0.99 Tropical America, Chave et al. aboveground biomass, kg/tree; D= diameter at breast Forests Asian and (2005) height, cm; = wood density, g cm-3. Oceania

(8) ln(AGB) = - 2.667 + 2.507ln(D30cm), whereAGB = 0.95 Eucalyptus Northeast Zerihun et al. aboveground biomass, kg/tree; D30cm = diameter at 30 populnea Australia (2006) cm height from ground), cm; woodland (9) Wt = 0.0829D2.43, whereWt = aboveground biomass, 0.96 Tropical Sarawak, Kenzo et al. kg/tree; D = diameter at breast height, cm. secondary Malaysia (2009) forests ln(RBD) = - 1,085 + 0.926 ln(ABD), whereRBD = IPCC (2003) Upland (10) Root biomass density, Mg/ha; ABD = Aboveground 0.83 Worldwide or Cairn et al. Forests biomass density, Mg/ha. (1997) Wrb = 26.99 ln(Wab) - 57.024, whereWrb = Root (11) -2 -2 0.87 Open biomass, kg m ; Wab = aboveground biomass, kg m . North Eamus et al. Eucalyptus Wrb = 72.029 ln(DBH) - 158.05, whereWrb = Root Australia (2002) (12) 0.81 forest biomass, kg; DBH = diameter at breast height, cm. y = 0.27x, wherey = total root biomass, Mg ha-1; x = Natural Mokany et al. (13) 0.81 Worldwide total shoot biomass Mg ha-1 forests (2006) ln(CRB) = - 4.108 + 2.531ln(D ), whereCRB = Eucalyptus 30cm Northeast Zerihun et al. (14) Coarse root biomass, kg/tree; D = diameter at 30 0.95 populnea 30cm Australia (2006) cm height from ground, cm; woodland Tropical W = 0.0214 D2.33, whereW = Root biomass, kg/tree; Sarawak, Kenzo et al. (15) r r 0.94 secondary D = diameter at breast height, cm. Malaysia (2009) forests y = 1.0791 ln(A) -2.0763, wherey = total belowground Tropical Sarawak, Kenzo et al. (16) biomass, Mg ha-1; A = total aboveground biomass, Mg 0.99 secondary Malaysia (2010) ha-1. forests Tropical W = 0.023 D2.59, whereW = Coarse root biomass, Sarawak, Niiyama et al. (17) r r 0.97 secondary kg/tree; D = diameter at breast height, cm. Malaysia (2010) forests

48 5.2.2 Carbon stocks in soil

Soil carbon is an important carbon pool, particularly in wetland forests which typically have peat and mud that are often rich stores of carbon. There are two types of soil carbon - soil organic carbon (SOC) and soil inorganic carbon (Lal, 2005). This section focuses on soil organic carbon.

Recent studies have identified the need to develop a rapid, accurate and inexpensive method to quantify of soil carbon in terrestrial and marine ecosystems (Chatterjee et al., 2009). The most common current principle for estimating soil organic carbon uses ex situ methods (cited from Chatterjee et al., 2009). However, several new in situ methods have been developed that promise to reduce uncertainty, time and cost of the assessment processes. Details of both ex situ and in situ approaches to assess soil organic carbon pools were reviewed and compared critically in regards of sensitivity, predictability, and efficiency of time and cost (Chatterjee et al., 2009). These methods are summarized in Figure 5.1. There are typically three broad approaches to estimating soil organic carbon: (1) automated dry combustion method (ex situ); (2) laser induced breakdown spectroscopy (ex situ); and (3) inelastic neutron scattering (in situ). Of these, inelastic neutron scattering is the newest approach, with relatively few published studies on the application of the method in forest soil systems [i.e. Wielopolski et al. (2010)]. The other two ex situ methods provide a more reliable approach to estimating soil organic carbon in forest systems.

Both wet and dry combustion methods involve considerable processing and analysis and have relatively limited accuracy (Chatterjee et al., 2009). Some published research show that it is a great potential to apply portable LIBS systems with low resolution to determine soil organic carbon, particularly tropical soils (Silva et al., 2008). Details of LIBS technique systems have been stated and reviewed to addressed generally soil properties [i.e. Reisfeld and Panczer (2005), Cremers and Radziemski (2006), Pasquini et al. (2007), and Walker (2009)], and other studies undertaken to quantify SOC [i.e. Cremers et al. (2001), Ebinger et al. (2003), Edwards (2007), and Pasquini et al. (2007)]. They all discussed and confirmed the advantages of this technique (e.g. getting rapid and accurate results).

Excepting the INS method, other methods have also involved soil sampling across study sites. Soil sampling methods for soil organic carbon analysis refer to numbers of samples and sub-samples, placement of samples and sub-samples, and depth of samples. To calculate organic carbon stocking in soil unit, examination of total SOC and soil bulk density of numbers of soil samples are necessary.

49 A large number of studies on SOC have used the Intergovernmental Panel on Climate Change (IPCC) guidelines of soil samples and soil deep layers which specifically related to SOC in the region 0.3 m below the surface layer (IPCC, 2003). Several long term studies of SOC have examined different soil layers from 0.1 m to 1 m in depth [e.g. layers were conducted involving 0- 20, 20-50, and 50-100 cm (Sun et al., 2004)]. However, later published research on SOC has confirmed that soil samplings should be undertaken to at least of 0.3 m surface layer (VandenBygaart et al., 2011). Moreover, most studies use three or four soil samples to estimate the SOC of a particular site and apply the analysis methods reported by Cox (Cox, 1958; Cox and Mallows, 1959).

SOC assessment approach

Combustion train Wet combustion Van - Slyke - Neil apparatus Ex situ methods Walkley - Black

Weight loss on ignition Dry combustion Automated

Mid and near-infrared reflectance spectroscopy (MIRS/NIRS)

In situ Laser - induced breakdown spectroscopy (LIBS) methods

Inelastic neutron scattering (INS) Figure 5.1 Systematized diagram of SOC assessment approach. Source: (adapted from Chatterjee et al., 2009)

5.2.3 Carbon stocks in understory, litter and deadwood

The major method used to estimate the biomass of understorey and forest litters involves destructive sampling and measurements. Understorey has been defined as including all vegetation layers growing beneath the forest canopy. Biomass carbon of understorey can be determined from all trees with height of ≤ 1.3 m and minimum diameter of 2.5 cm [e.g. Van et al. (2000b), Rayachhetry et al. (2001), Kenzo et al. (2009), and Kenzo et al. (2010)]. Some studies defined understorey as all trees with ≤ 5 cm diameter at breast height [e.g. Brown (1997), Ketterings et al. (2001), Chave et al. (2005), Zewdie et al. (2009), Kim et al. (2010), Lugo et al. (2011)]. To date, no

50 published research has established a definition for understorey biomass in the Melaleuca forests. However, the definition for tropical forests presented by Hairiah et al. (2001) are arguably quite applicable to Melaleuca forests.

Deadwood biomass can be divided into two types; dead standing and dead fallen wood. The fallen 3 -1 2 2 dead wood can be calculated with the following formula (IPCC, 2003 p104): V (m ha ) = π (D1 + 2 2 D2 + ... + Dn )/8L, where D1, D2, ... Dn = diameter of each of n pieces intersecting the line (cm).

The round equivalent of an elliptically shaped log is computed as square root of (D minimum × D maximum) for that log; L = the length of the line (m). Standing dead trees can be estimated using the same criteria as live trees, the details of which are in the Good Practice Guide for LULUCF (IPCC, 2003). However, there are some different opinions in regard to the dimension of the dead fallen trees/stems. Generally, dead fallen wood with diameters equal to or greater than 10 cm is measured to estimate biomass (IPCC, 2003). But some have suggested that limit should be 5 cm diameter of deadwood because there is a big gap from deadwood (10 cm in diameter) to litter wood (≤ 2.5 cm in diameter) (Takahashi et al., 2010). Other literature has guided to sample the tree necromass with all dead trees having diameter ≥ 5 cm and a length of ≥ 0.5 m, and each fallen dead wood biomass is calculated with the formula: B = π.r2.L.ᵟ,whereB = biomass (kg), r = ½ diameter (cm), L = length (m), and ᵟ = wood density (g cm3) (Hairiah et al., 2001 p12).

In Melaleuca forests, litter fall has been conducted in several studies; e.g. Congdon (1979) used quadrat traps of 0.5 x 0.5 m with depth of 10 cm to collect litter fall, and later Greenway (1994) and Rayamajhi et al. (2006) used a similar approach. To adapt to flood seasons, Finlayson et al. (1993) used both trays and nets to collect litter fall seasonally. Litter was separated in different components (leaf, bark, fruit, branches, etc.) and the biomass of each component was estimated.

Some litter in Melaleuca forests might float away during flood seasons, particularly at or near main water flows. In the context of carbon stocks, only litter components which are kept or deepened under the water within the systems should be included as a store of carbon. Thus, it is very challenging to collect and measure litter in the flooding season because some components might float on the water surface, and other fractions might be deposited under water.

In other tropical forests, litter has been harvested without trays/traps/nets, but instead using quadrats on the forest floor in which litters are divided into coarse and fine litter fractions (Hairiah et al., 2001).

51 5.2.4 Carbon stocks in gaseous states of wetland soil and water

Greenhouse gas emissions, namely CO2, N2O, and CH4 are produced by activities of the Agriculture, Forestry and Landuse Change sector in wetland areas. In wetlands involving Melaleuca forests, CH4 is realised from marsh areas. Methane emissions from human activities in wetlands is one of the largest sources of anthropogenic greenhouse gas emissions (Hansen et al., 2000).

However, the amount of CH4 emitted from the wetland is still being debated, with estimates of up to 240 Tg yr-1(Keppler et al., 2006), and with lower estimates of between zero to 85 Tg yr-1 reported (Houweling et al., 2006; Dueck et al., 2007).

As per the description of Schutz et al. (1991), greenhouse gasses are naturally emitted from the wetlands via three mechanisms; transport by emergent aquatic plants, ebullition, and diffusion (re- graphic drawing by Chanton, 2005 p755). Various publications have reported on gases emitted by aquatic plants [e.g. Vann and Megonigal (2003), Garnet et al. (2005), Butenhoff and Khalil (2007), Picek et al. (2007), Gauci et al. (2010), Kao-Kniffin et al. (2010), Askaer et al. (2011)]. Chanton (2005) reviewed and generalized that aquatic plants transfer considerable quantities of methane in both natural and artificial wetlands, with plants in vegetated areastransferring10 times the amount of

CH4 than at the non-vegetated interface. In contrast, a study of alder trees - an aquatic mature plant - indicated that CH4 emitted in un-forested areas were higher than in forested areas (Gauci et al., 2010).

According to the conceptual model of the mechanism of methane emitted from seasonal flooded swamp forest (Happell et al., 1994), CH4 transported to the atmosphere from soil, sediments through floodwater by ebullition and diffusion with δA = δB = - 55 ‰, δC = - 45 ‰ δD = - 43 ‰ δE = - 45 ‰ (Chanton, 2005 p758).

Four methods with three types of flux analysis, calculated by measuring the dissolved gas concentration in the water, integrated concentration of gas above the water surface, and gas across the air-water interface (Lambert and Fréchette, 2005) are schematised in the Figure 5.2. The thin boundary layer is an indirect method to measure gas flux over water. Water samples can be analysed with in situ or ex situ laboratory analysis. Several studies have utilized this method, [e.g. Canuel et al. (1997), Duchemin et al. (1999), Zhang et al. (2007)]. Details of the floating chamber technique with in situ and ex situ laboratory analysis was presented by Canuel et al. (1997). This method has been used to conduct various studies [e.g. methane emission of the wetlands in Taiwan

(Chang and Yang, 2003); fluxes of CO2, CH4 and N2O from drained coniferous forests (Arnold et al., 2005); from constructed wetlands (Teiter and Mander, 2005); from Indonesian peatland

(Watanabe et al., 2009); comparison of manual and automated chambers to measure N2O, CH4,

52 CO2fluxes (Yao et al., 2009); CH4 emissions from tropical freshwater wetlands of Costa Rica (Nahlik and Mitsch, 2011), and from freshwater riverine wetlands (Sha et al., 2011)].

Gas flux methods

(1)

Thin boundary Floating chamber layer techniques

Water samples Air samples

(3) (2) (4)

Laboratory analysis Ex situ laboratory In situ laboratory Laboratory analysis analysis with analysis with

GC with MS NDIR or FTIR

Semi-empirical equation Calculation

Gas fluxes

Chanton (2005) reviewed details of mechanisms of CH4 transportation form wetlands in regards to stable isotope terminology, isotope fractionation by diffusion, fractionation by ebullition, and isotope fractionation associated with transport by aquatic plants. Lambert and Fréchette (2005). Detailed the automated chamber technique is where air is analysed with automated equipment involving Non-dispersive (NDIR) or Fourier Transform Infrared (FTIR). This method has been applied to analyse gas emission fluxes [e.g. estimating soil-atmosphere diffusion gas fluxes

(Nakano et al., 2004); CH4 emissions from lakes and floodplains in Brazil (Marani and Alvalá,

2007); evaluating CO2 exchange in cypress forest (Ohkubo et al., 2007); emission of CO2, CH4 and

N2O from a freshwater marsh in China (Song et al., 2008); estimating diffusive CH4 fluxes in peatland (Forbrich et al., 2010); and measurement of CO2 fluxes in free-air (Selsted et al., 2011)].

53 The later studies on gas emissions are tending to apply automated chamber techniques that are easier to install under wet conditions and quicker to obtain the analysed data. In addition, the stable isotope carbon as δ13C, δD and δ14C has been also used to analyse gas flux and its mechanism. Studies investigated the methane transport mechanisms and isotopic fractionation in macrophytes of the wetlands [e.g. wetland in Alaskan Tundra Lake (Chanton et al., 1992a); Peltandra virginica wetland (Chanton et al., 1992b); C3-plantdominated in tidal wetlands (Cheng et al., 2006); and 13 terrestrial plants (Dueck et al., 2007)]. Sources of C-depleted CH4 in a temperate bog (Lansdown et al., 1992), δ13C along a moisture gradient in Botswana (Lloyd et al., 2004), δ13C and δD identification in sediments (Chikaraishi and Naraoka, 2005) have also been investigated. In addition, stable carbon isotope was conducted in the wetland sediment that showed the relation and factors influencing gas emission and its ratio (Longstaffe et al., 2000; Beach et al., 2011; Lambert et al., 2011).

Various factors influence gas emission flux in natural ecosystems. Light influences emissions of

CH4 from wetlands. In daylight more CH4was oxidated so the gas flux was less at night (King,

1990). Coefficient transferring of CH4 related to organic matter of wetland soil/marsh or sediment layer (Happell et al., 1995). Water depth also affects CH4flux [e.g. the deeper water depth caused the transport of CH4 through diffusion and ebullition more slowly (Vann and Megonigal, 2003;

Chanton, 2005)]. In addition, SO4-S and temperature played a role of controlling CH4 emitted from peat land (Gauci et al., 2004).

To measure CO2 flux emitted from soil surface, three major chamber techniques have been widely applied including closed static chamber (non-steady state with non-through-flow chamber), closed dynamic chamber (non-steady state non with through-flow chamber), and opened dynamic chamber (steady state through-flow chamber) (Pumpanen et al., 2004). These techniques was classified by (Livingston and Hutchinson, 1995). Within these techniques, non-steady-state diffusive flux estimator was considered as a very accurate and robust technique to determine gas emission (Livingston et al., 2006).

5.3 Methods implicating for research on carbon stocks of Melaleuca forests

Given the complexity of factors influencing the dynamic of carbon stocks in Melaleuca forests discussed hereto, an integrative, systematic approach is required to estimate total carbon densities in any specific forest area. This approach (used in this thesis and discussed in detail in the following chapters) is shown in Figure 5.3.

54 Melaleuca forest Identifying ecosystem types of forests

Stand biomass

Understory biomass

Litter biomass

Dead wood biomass

Soil organic carbon Carbon density, volumes, and carbon dynamics

Produce carbon offsets of the Melaleuca forests through management

Figure 5.3 Systematic approach of the research on carbon stocks in Melaleuca swamp forests

Details of the inventory and analysis methods to conduct 5 categories (stand, understory, litter, deadwood, and soil) of carbon stocks in Melaleuca forests are presented in the sections ‘Material and Methods’ of Chapters 6 and 7 which are logical and appropriate for the study results.

55 Chapter 6 - AN ASSESSMENT OF THE CARBON STOCKS AND SODICITY TOLERANCE OF DISTURBED MELALEUCA FORESTS IN SOUTHERN VIETNAM

Summary

In the lower Mekong Basin and coastal zones of Southern Vietnam, forests dominated by the genus Melaleuca have two notable features: most have been substantially disturbed by human activity and can now be considered as degraded forests; and most are subject to acute pressures from climate change, particularly in regards to changes in the hydrological and sodicity properties of forest soil. This paper presents the results of an assessment of the carbon stocks and sodicity tolerance of natural Melaleuca cajuputi communities in Southern Vietnam, in order to gather better information to support the improved management of forests in this region. Data was collected and analysed from five typical Melaleuca stands including: (1) primary Melaleuca forests on sandy soil (VS1); (2) regenerating Melaleuca forests on sandy soil (VS2); (3) degraded secondary Melaleuca forests on clay soil with peat (VS3); (4) regenerating Melaleuca forests on clay soil with peat (VS4); and (5) regenerating Melaleuca forests on clay soil without peat (VS5). Carbon densities of VS1, VS2, VS3, VS4, and VS5 were found to be 275.98 tC/ha, 159.36 tC/ha, 784.68 tC/ha, 544.28 tC/ha, and 246.96 tC/ha, respectively. The exchangeable sodium percentage of Melaleuca forests on sandy soil (VS1 and VS2) showed high sodicity, while those on clay soil (VS3, VS4, and VS5) varied from low to moderate sodicity. These results indicate that Melaleuca forests on peatland in Vietnam hold relatively large stores of carbon and that Melaleuca forests on sandy soils in Vietnam are tolerant of highly sodic conditions. The results provide important information for the future sustainable management of Melaleuca forests in Vietnam, particularly in regards to forest carbon conservation initiatives and the potential of Melaleuca species for reforestation initiatives on degraded sites with highly sodic soils.

56 This chapter was submitted to the journal ‘Mitigation and Adaptation Strategies for Global Change’ as an article manuscript: Tran, D.B., Dargusch, P., Moss, P., Hoang, T.V., 2015, under review. An assessment of the carbon stocks and sodicity tolerance of disturbed Melaleuca forests in Southern Vietnam.

57 6.1 Introduction

Numerous studies have shown that tropical wetlands typically contain large carbon stocks (Bernal and Mitsch, 2008; Bernal, 2008; Mitsch et al., 2008; Bernal et al., 2010; Mitsch et al., 2010a; Bernal and Mitsch, 2012; Mitsch et al., 2012). Protecting and restoring tropical coastal wetlands is considered a critical part of how society adapts to and mitigates global climate change (Irving et al., 2011).

Large areas of Melaleuca forests in Vietnam are disturbed ecosystems that experience extreme conditions, and are associated with floods and/or sodic soils. They mostly occur in the lower Mekong Basin, which has been severely impacted by climate change (Le et al., 2007; Erwin, 2009; Renaud and Kuenzer, 2012; Bastakoti et al., 2014). Little is known about the carbon sequestration potential of disturbed Melaleuca forests in Australasia and South-East Asia where the genus occurs. Carbon stocks of Melaleuca forests are generally considered to be low [i.e. about 27.8 tC/ha estimated by Australian Government Office (MIG, 2008)]. However, Tran et al. (2013a) suggested that this has been grossly under-estimated and that Melaleuca cajuputi forests on peatland soils in Vietnam, Indonesia and Malaysia are likely to have a high potential for carbon sequestration.

Sea level rise has significant impacts on the coastal zone, where soils will become saline and/or highly sodic (Renaud et al., 2014). Sodic soils are distinguished by an excessively high concentration of Sodium (Na) in their cation exchange complex. High sodicity causes soil instability due to poor physical and chemical properties, which affects plant growth and can have a more significant impact than excessive salinity growth(Bernstein, 1975; Rengasamy and Olsson, 1991). Sodicity impacts plant growth in three ways, including: soil dispersion, specific ion effects, and nutritional imbalance in plants (Warrence et al., 2002; Mahmood, 2007). Excessive sodium concentrations cause clay dispersion which is the primary physical effect of the sodic soil. Sodium- induced dispersion can reduce water infiltration, decrease hydraulic conductivity, and increase soil surface crusting that strongly affect roots such as root penetration, root development, and blocking plant uptake of moisture and nutrients (Warrence et al., 2002).

Except for those containing mangroves and other halophytes, most ecosystems are severely affected by salinity and/or sodicity. A few studies have examined saline-sodic soils in shrimp farming areas in the coastal regions of Vietnam [i.e. ECe= 29.25 dS/m and exchangeable sodium percentage ranged from 9.63 % to 72.07 %, which had a big impact on plant cultivation systems (Tho et al., 2008)].

58 Several studies [such as Dunn et al. (1994), Niknam and McComb (2000), van der Moezel et al. (1988; 1991)] have examined the tolerance of woody species such as Acacia, Eucalyptus, Melaleuca, and Casuarina species to salinity and/or sodicity, but more research is required. This paper examines the carbon stocks of disturbed Melaleuca forests and the sodicity tolerance of Melaleuca cajuputi forests in Southern Vietnam.

6.2 Materials and Methods

6.2.1 Study sites and disturbance context

As presented in Chapter 2, Melaleuca cajuputi is naturally distributed as scattered shrub populations along the coastal regions in the middle Provinces and up to the Northern hilly regions, and as tall forests in the Mekong Delta of Vietnam (Cuong et al., 2004). Thus, the study focussed on the sites in Southern Vietnam (involving Mekong Delta). The study investigated two sites: the Phu Quoc National Park and U Minh Thuong National Park, which both contain extensive Melaleuca forests in coastal wetlands (Figure 6.1). A total of 14 plots were randomly selected for carbon storage assessment, covering five types of Melaleuca stands: ‘Primary Melaleuca forests on sandy soil’ (VS1); ‘Regenerating Melaleuca forests on sandy soil’ (VS2); ‘Degraded secondary Melaleuca forests on clay soil with peat’ (VS3); ‘Regenerating Melaleuca forests on clay soil with peat’ (VS4); and ‘Regenerating Melaleuca forests on clay soil without peat’ (VS5).

Phu Quoc National Park is located on the northern Phu Quoc Island of Vietnam (at N 10012’07”-N 10027’02”, E 103050’04”-E 104004’40”) (Figure 6.1). Melaleuca forest areas cover 1,667.50 ha out of the total area of 28,496.90 ha. These forests naturally occur on lowland regions of the island where they are seasonally inundated and/or permanent saturated, and also on permanent sand bars where no inundation occurs (Phu Quoc National Park, 2012). The rest area of the Park is hilly and mountainous tropical forests. Two Melaleuca forest types are found in the park: primary Melaleuca forest (VS1); and regenerating Melaleuca forest (VS2). Before the park was established in 2001, key disturbance included forest fires and human intrusion for crop cultivation. The regenerating Melaleuca forests were up to 10-12 years of age at the time this study was conducted.

U Minh Thuong National Park is located in the Kien Giang Province (at N 9o 31’-N 9o 39', E 105o 03'-E 105o 07') (Figure 6.1). Melaleuca forest on swamp peatland is an endemic ecosystem in the lower Mekong Basin of Vietnam. The core area of the park is 8,038 ha, which is surrounded by a buffer zone of 13,069 ha. Here, the key disturbance is fire, with the last major fire occurring in April 2002, which burnt the primary vegetation as well as the peat soil. The Vietnamese Environment Protection Agency (2003) reported that 3,212 hectares of Melaleuca forests was almost destroyed, so a canal system was expended and used a key management solution to increase

59 water inundation of the forest to prevent fires. Currently, there are three Melaleuca forest types in U Minh Thuong National Park: VS3, VS4, and VS5. At the time of this study, the VS4 and VS5 areas were up to 10 years old.

6.2.2 Field sampling and data collection

The major plots were set out as 500 m2 quadrats (20 m × 25 m), and all trees with a diameter at breast height (DBH) ≥ 10 cm were measured and recorded. Sub-plots also were set out as 100 m2 quadrats (20 m × 5 m) within the major plots to measure all trees with DBH < 10 cm and a total height of > 1.3 m [modified from Van et al. (2000b)]. Data on DBH, alive or dead, and height were recorded for all standing trees.

Deadwood (dead fallen trees) with a diameter ≥ 10 cm were measured within the major plots (500 m2), while deadwood with 5 cm ≤ diameter < 10 cm were measured within the sub-plots (100 m2). Diameters at both ends of the trunk (D1 and D2), length (if ≥ 50 cm length), and the decay classes [involved sound, intermediate, and rotten (IPCC, 2003, 2006)] were recorded for all deadwood.

60 Seventy random quadrats (1 m × 1 m) were located in the main plots to collect and record the ‘fresh weight’ of the understorey. Samples of all species from the understorey were collected in each major plot and taken back to the Vietnam Forestry University laboratory for drying.

Seventy random coarse litter samples and seventy random fine litter samples were collected in the major plots. The fresh weight of each litter sample was recorded. Each litter type (coarse litter and fine litter) collected in every major plot were well mixed and taken to the laboratory for drying.

Twenty-eight random soil cores (including peat) were collected from the major plots and taken back to the National Institute of Agricultural Planning and Projection laboratory for further analysis to investigate soil characteristics. The maximum depth of the peat layer was measured and recorded to quantify the carbon stock [modified from Murdiyarso et al. (2009) and Donato et al. (2011)].

6.2.3 Sample analysis

Each understorey and litter sample was divided into three sub-samples and dried in a drying oven at 60oC to measure the moisture content, based on the equation (6.1) below:

n Wfi Wdi i1 Wfi Rmoist  (6.1) n

where Rmoist = moist ratio [0:1], Wfi = fresh weight of sub-sample i, Wdi = dry weight of sub-sample i,n = number of sub-samples. The scales used to weight sub-samples were accurate to ± 0.01 g.

Total organic carbon (C %) was measured using an automated dry combustion method, which is commonly used to examine soil organic carbon via infrared detection of CO2 during dry combustion (Matejovic, 1993). Total nitrogen was measured using the Kjeldahl method, which is the standard way to determine the total organic nitrogen content of soil (Bradstreet, 1954). A standard bulk density test was used to analyse all soil bulk samples in a dry oven. Bulk density was calculated using equation (6.2):

Ms BD  (6.2) V where BD = the bulk density of the oven-dry soil sample (g/cm3), Ms = the oven dry-mass of the soil sample (gram), V = the volume of the ring sample (cm3).

Basal area (BA) was calculated with equation (6.3) [modified from Jonson and Freudenberger (2011)]:

61 n 0.00007854 DBHi BA  i1 10000 (6.3) Splot

2 where BA = basal area (m /ha), DBHi = diameter at bread height of tree i (cm), i = stand individual 2 (i =[1 :n]), n = number of trees of sample plot, Splot = area of the sample plot (m ), 0.00007854 = basal area factor.

6.2.4 Biomass allometric computation

Nine allometric equations, which are most common way to measure forest carbon stocks, were applied to calculate the above-ground and root biomass of the stands (Table 6.1). The selected allometric equations were tested for statistical significance using the R Statistic Program (Appendix A1). Using these equations, the average biomass was analysed for five typical Melaleuca stands (VS1, VS2, VS3, VS4, and VS5). To convert from fresh to dry biomass, a moisture rate of 0.5 was applied as suggested by Van et al. (2000b) for the allometric equation of Finlayson et al. (1993). According to the Global Wood Density Database, the density of Melaleuca cajuputi timber ranges from 0.6 g/cm3 to 0.87 g/cm3 (Chave et al., 2009), so 0.6 g/cm3 was applied for the above-ground biomass allometric equation of Chave et al. (2005).

The fallen deadwood biomass were calculated using equation (6.4) (Hairiah et al., 2001.p12):

B = π × r2 × L × δ (6.4) where B = biomass (kg), r = ½ diameter (cm), L = length (m), and δ = wood density (= 0.6 g/cm3).

Then, the biomass of the fallen deadwood was determined using the IPCC (2003, 2006) density reduction factors (sound = 1, intermediate = 0.6, and rotten = 0.45). The biomass of standing dead trees was measured using the same criteria as live trees, but a reduction factor of 0.975 is applied to dead trees that have lost leaves and twigs, and 0.8 for dead trees that have lost leaves, twigs, and small branches (diameter < 10 cm) (IPCC, 2003, p. 4.105).

To convert biomass to carbon mass for all categories (stands, roots, deadwood, understorey, and litter), a factor of 0.45 was applied.

Soil organic carbon (SOC) was calculated using equation (6.5) (IPCC, 2003, 2006):

SOC  Dep × BD × Csample × 100 (6.5)

3 where SOC = Soil organic carbon, Dep = depth of soil layer (m), BD = bulk density (g/cm ), Csample = organic carbon content of soil sample (%), and 100 is the default unit conversion factor.

62 6.2.5 Statistical analysis

One-way ANOVA tests were applied to compare stand densities, DBH, height classes, basal areas, and six categories of carbon stocks of the five Melaleuca forest types. LSD post-hoc tests were also used for all pairwise comparisons between group means. Statistical analysis was undertaken using Microsoft Excel 2010 and the R Statistic Program.

Table 6.1 List of allometric equations applied to examine stand biomass of the Melaleuca forests in the study sites of Vietnam Allometric equations R2 Vegetation Sites References log10(FW) = 2.266log10(D) - 0.502 0.98 Melaleuca spp. Northern Finlayson et where FW = fresh above-ground Territory al. (1993) biomass (kg/tree), D = diameter at breast height (cm). y = 0.124˟DBH 2.247 0.97 Melaleuca Vietnam Le (2005) where y = above-ground biomass cajuputi (kg/tree), DBH = diameter at breast height (cm). y = exp[-2.134 + 2.53ln(D)] 0.97 Mixed species Tropical, moist IPCC (2003) where y = above-ground biomass forest or Brown (kg/tree), D = diameter at breast height (1997) (cm). ln(y) = 2.4855ln(x) - 2.3267 0.96 Native NSW, ACT, Keith et al. where y = above-ground sclerophyll VIC, TAS, and (2000) biomass(kg/tree), x = diameter at breast forest SA height (cm). ln(AGB) = - 1,554 + 2.420ln(D) +ln() 0.99 Tropical America, Asian Chave et al. where AGB = above-ground biomass forests and Oceania (2005) (kg/tree), D= diameter at breast height (cm),  = wood density (g/cm3). ln(RBD) = - 1,085 + 0.926 ln(ABD) 0.83 Upland Worldwide IPCC (2003) where RBD = Root biomass density forests or Cairn et al. (t/ha), ABD = above-ground biomass (1997) density (t/ha). y = 0.27x 0.81 Natural Worldwide Mokany et al. where y = total root biomass (tons/ha), x forests (2006) = total shoot biomass (t/ha). Wr = 0.0214 ˟ D2.33 0.94 Tropical Sarawak, Kenzo et al. where Wr = Coarse root biomass secondary Malaysia (2009) (kg/tree), D = diameter at breast height forests (cm). 2.59 Wr = 0.023 ˟ D 0.97 Tropical Sarawak, Niiyama et al. where Wr = Coarse root biomass secondary Malaysia (2010) (kg/tree), D = diameter at breast height forests (cm). Note: NSW - New South Wales, ACT - Australian Capital Territory, VIC - Victoria, TAS - Tasmania, SA - South Australia

63 6.3 Results and Discussion

6.3.1 Characteristics of the typical Melaleuca forests in the study areas

The major characteristics of five Melaleuca forests types examined include standing trees, an understorey, and saturated conditions (Table 6.2). The variation in these characteristics not only distinguishes the different stands but also improves understanding of their carbon stocks.

The stand densities of the five typical Melaleuca forest types varied considerably: they were 2,330 trees/ha, 10,950 trees/ha, 980 trees/ha, 9,833 trees/ha, and 6,867 trees/ha for VS1, VS2, VS3, VS4, and VS5, respectively (Table 6.2). Within each study site, the tree densities of regenerating forests (VS2, VS4, and VS5) were significantly higher than primary forests (VS1) and secondary forests (VS2) (Figure 6.2a). The increased stand densities of types VS2, VS4, and VS5 were mostly comprised of trees with a DBH < 10 cm. In contrast, VS1 was dominated by trees with DBH < 20 cm (accounting for 84.3 %), with the balance of trees having a DBH ≥ 20 cm (including 4.2 % of trees with DBH ≥ 30), while VS3 was mostly dominated by trees with a 5 cm ≤ DBH < 20 cm (accounting for 96 %), with the balance having a 20 cm ≤ DBH < 40 cm (accounting for 4 %) (Table 6.2).

Average DBH of all stand classes were 16.71 cm, 5.36 cm, 12.93 cm, 5.88 cm, and 6.20 cm for VS1, VS2, VS3, VS4, and VS5, respectively (Figure 6.2b). There was a significant difference in DBH in the five Melaleuca forest types (χ2 = 446.86, p = 2.2e-16). However, post-hoc test shows that there is no significant difference in tree DBH between VS1 and VS3, and between VS2, VS4, and VS5 (Appendix A2b).

Average total height of all stand classes were 14.69 m, 7.11 m, 9.69 m, 5.68 m, and 7.50 m for VS1, VS2, VS3, VS4, and VS5, respectively (Figure 6.2c). There was a significant difference in the total height of the five Melaleuca forest types (χ2 = 11.616, p = 0.0088) (Appendix A2c). Furthermore, the tree density of the five forest types was generally very high, especially of VS2, VS4 and VS5 (over 2,000 individuals/ha), which can contribute to a large biomass. The basal areas shown in Figure 6.2b further confirm the potential high biomass of VS2, VS4 and VS5 (BA = 28.41 m2/ha, 30.14 m2/ha, and 23.14 m2/ha, respectively). Furthermore, the basal area of VS1 is significantly greater than VS3, accounting for 41.45 m2/ha and 10.29m2/ha, respectively (F = 3.341, p = 0.0423) (Appendix A2d).

Different species were found in the understorey of the various Melaleuca forest types. Key species for VS1 and VS2 include Leptocarpus sp., Lepironia sp., Hanguana sp., Eleocharis sp., Euriocaulon sp., Xyris sp., Stenochlaena sp., Melastoma sp., and Imperata cylindrica. For VS3,

64 VS4, VS5, the following species dominate the understorey: Stenochlaenapalustris sp., Phragmitesvallatoria sp., Melastomadodecandrum sp., Diplaziumesculentum sp., Lygodiumscandens sp., Aspleniumnidus sp., Scleriasumatrensis, Cassia tora, Paederiafoetida sp., Flagellariaindica sp.,andCayratiatrifolia sp. (Table 6.2).

d Figure 6.2 Stand densities, basal areas, diameter at bread height, and total height of five Melaleuca forest types in the study areas. Vertical bars are standard errors of the mean for 2-4 plots per category.

66 Table 6.2 Major characteristics of five typical Melaleuca forests in the study areas Forest types Tree classes Code Stand trees Understorey Saturation Density (trees/ha) DBH(cm) BA (m2/ha) Height (m) levels

Mean SE MeanSE Mean SE MeanSE Primary Melaleuca DBH < 5 cm VS1C0 800 248.3 3.87 0.11 na na 6.00 0.28 Including non- on sandy soil 5 cm ≤ DBH < 10 cm VS1C1 400 100.0 7.18 0.36 na na 9.81 0.68 Leptocarpus sp. inundated, Lepironia sp. seasonal, and 10 cm ≤ DBH < 20 cm VS1C2 750 273.8 14.63 0.22 na na 14.80 0.26 Hanguana sp. permanent 20 cm ≤ DBH < 30 cm VS1C3 285 34.0 24.33 0.49 na na 18.44 0.40 Eleocharis sp. inundation 30 cm ≤ DBH < 40 cm VS1C4 80 28.3 34.37 0.90 na na 20.17 0.97 Euriocaulon sp. DBH ≥ 40 cm VS1C5 20 10.0 48.73 3.75 na na 22.20 1.77 Xyris sp. Stenochlaena sp. All classes VS1 2,330 558.0 16.71 0.55 41.54 6.16 14.69 0.30 Melastoma sp. Regenerating DBH < 5 cm VS2C0 5,450 2,850.0 3.63 0.07 na na 6.13 0.16 Imperata sp. Including non- Melaleuca on sandy 5 cm ≤ DBH < 10 cm VS2C1 5,500 700.0 7.07 0.14 na na 8.08 0.15 inundated and soil seasonal DBH ≥ 10 cm na na na na na na na na na inundation All classes VS2 10,950 3,550.0 5.36 0.14 28.41 3.14 7.11 0.13 Degraded secondary DBH < 5 cm VS3C0 150 na 4.41 0.23 na na 5.00 0.29 Including Melaleuca on clay 5 cm ≤ DBH < 10 cm VS3C1 350 na 7.12 0.68 na na 4.57 0.38 Stenochlaena palustris seasonal and soil with peat Phragmites vallatoria permanent 10 cm ≤ DBH < 20 cm VS3C2 440 20.0 13.11 0.36 na na 10.44 0.41 Melastoma inundation 20 cm ≤ DBH < 30 cm VS3C3 30 na 25.00 1.20 na na 14.33 0.17 dodecandrum 30 cm ≤ DBH < 40 cm VS3C4 10 na 35.35 na na na 12.50 na Diplazium esculentum DBH ≥ 40 cm VS3C5 na na na na na na na na Lygodium scandens Asplenium nidus All classes VS3 980 560.0 12.93 0.71 10.29 4.74 9.69 0.45 Scleria sumatrensis Regenerating DBH < 5 cm VS4C0 3,867 2,258.6 3.84 0.06 na na 4.15 0.11 Cassia tora Including Melaleuca on clay 5 cm ≤ DBH < 10 cm VS4C1 5,967 176.4 7.20 0.12 na na 6.68 0.17 Paederia foetida seasonal and soil with peat permanent DBH ≥ 10 cm na na na na na na na na na Flagellaria indica Cayratia trifolia inundation All classes VS4 9,833 2,265.9 5.88 0.12 30.14 1.46 5.68 0.13 Regenerating DBH < 5 cm VS5C0 2,133 592.6 3.82 0.09 na na 4.95 0.17 Including Melaleuca on clay 5 cm ≤ DBH < 10 cm VS5C1 4,733 1,560.3 7.27 0.13 na na 8.65 0.31 seasonal and soil without peat permanent DBH ≥ 10 cm na na na na na na na na na inundation All classes VS5 6,867 1,970.1 6.20 0.14 23.02 8.53 7.50 0.25

67 6.3.2 Carbon stocks of Melaleuca forests

The carbon densities of five typical Melaleuca forests in Southern Vietnam were 275.98 tC/ha, 159.36 tC/ha, 784.68 tC/ha, 544.28 tC/ha, and 246.96 tC/ha, respectively, for primary Melaleuca forests on sandy soil (VS1), regenerating Melaleuca forests on sandy soil (VS2), degraded secondary Melaleuca forests on clay soil with peat (VS3), regenerating Melaleuca forests on clay soil with peat (VS4), and regenerating Melaleuca forests on clay soil without peat (VS5) (Figure 6.3a). There is significant difference in carbon densities between the forest types (χ2 = 10.419, p = 0.0339) (Appendix A2e). On sandy soils, the carbon density of VS1 was significantly greater (1.7 times) than VS2. The carbon density of Melaleuca forests on clay soil with peat was still high after disturbance (VS3 was 1.4 times higher than VS4). The carbon density of VS5 was lower than VS3 and VS4. This is likely to be a consequence of the VS5 site no having any peat layers, whereas the VS3 and VS4 sites do have peat layers. Peat layers are likely to have higher carbon densities, although further research would help establish the validity of this claim.

On sandy soil, the stands and soil layers were the highest contributors to carbon density of VS1 (accounting for 41.34 % and 29.11 %, respectively), while VS2 has a high contribution from the soil layer, then stands (soil and stand categories contribute for carbon density of 56.15 % and 28.53 %, respectively) (Figure 6.3b). However, in the peatland, the greatest contribution of carbon densities for VS3 and VS4 are the peat and soil categories (accounting for 61.41 %, 22.10 % of VS3, and 57.66 %, and 16.72 % of VS4, respectively). Separately, carbon density of VS5 is mostly linked to the soil, deadwood, and stand categories (accounting for 33.54 %, 32.16 %, and 14.66 %, respectively) (Figure 6.3b).

68 a

b Figure 6.3 Carbon densities of five typical Melaleuca forests in Southern Vietnam. Vertical bars are standard errors of the mean for 2-4 plots per carbon stock category.

69 6.3.3 Variability of carbon stocks in different types of Melaleuca forests

This study investigated the carbon stocks of six components: stands, understorey, deadwood, litter, root, and soil for five types of Melaleuca forests in Southern Vietnam (Figure 6.4).

The carbon densities of stands of the various forest types were 110.67 tC/ha, 44.27 tC/ha, 22.79 tC/ha, 48.25 tC/ha, and 37.20 tC/ha for VS1, VS2, VS3, VS4, and VS5, respectively (Figure 6.4a). There was a significant difference in stand carbon density between the forest types (χ2 = 48.3184, p = 8.1e-10) (Appendix A2f). The carbon density of the stand VS1 is the highest and is 2.5, 4.9, 2.3, and 3.0 times higher than VS2, VS3, VS4, and VS5.Surprisingly, there is no statistical difference in stand carbon densities between secondary forests (VS3) and regenerating forests (VS2), VS4 and VS5) (Appendix A2f). These carbon stocks were lower those from other studies of different forests (e.g. 144 tC/ha for Asian tropical forests (Brown et al., 1993); 200.23 tC/ha and 92.34 tC/ha of primary and secondary swamp forests in Indonesia, respectively [involving Melaleuca vegetation, (Rahayu and Harja, 2012)].

The carbon densities of the understorey in the Melaleuca forests of Vietnam were 2.45 tC/ha, 2.48 tC/ha, 6.23 tC/ha, 1.65 tC/ha, and 5.27 tC/ha for VS1, VS2, VS3, VS4, and VS5, respectively (Figure 6.4b). There was a statistically significant difference in understorey carbon density between the forest types (χ2 = 30.7189, p = 3.49e-6) (Appendix A2g). However, there was no significant difference in understorey carbon density between Melaleuca forest types on sandy soils (VS1 and VS2). On clay soils, the understorey carbon densities of VS3 and VS5 were significantly higher than VS4.

The carbon densities of deadwood in the forest types were 30.47 tC/ha, 0 tC/ha, 67.90 tC/ha, 45.06 tC/ha, and 74.59 tC/ha for VS1, VS2, VS3, VS4, and VS5, respectively (Figure 6.4c). There was a statistically significant difference in deadwood carbon density between the Melaleuca forest types (χ2 = 3.0978, p = 0.5416), but pairwise comparisons show no significant differences (Appendix A2h). Surprisingly, deadwood was not present in regenerating forests in the study sites on Phu Quoc Island. This is probably due to frequent forests fires and/or fuelwood collection by people associated crop cultivation.

Some of the carbon stock of Melaleuca forests is contributed by layers of coarse and fine litter. The carbon densities of the total litter layer of the forest types were 31.03 tC/ha, 14.45 tC/ha, 23.76 tC/ha, 57.35 tC/ha, and 39.23 tC/ha for VS1, VS2, VS3, VS4, and VS, respectively (Figure 6.4d). There was a statistically significant difference in overall litter carbon density between these

70 forest types (χ2 = 1.5619, p = 0.08156), but pairwise comparisons show no significant differences (Appendix A2i).

The carbon densities from peat of the Melaleuca forests were 479.62 tC/ha and 294.57 tC/ha for secondary forests (VS3) and regenerating forests (VS4), respectively (Figure 6.4e). The carbon density from peat of VS3 is significantly greater than that of VS4 (χ2 = 5.2359, p = 0.0221) (Appendix A2j). This is almost certainly due to peat being partly burned in the regenerating forest by the severe fire of 2002. In U Minh Thuong National Park, peat comprises the top soil layer, with a deep layer of clay below. The depth of the peat layer ranged from 15 cm to 62 cm in18 soil cores, and the peat bulk density ranged from 0.19 to 0.3. The depths of the peat layer in this study were much thinner than in other forests [i.e. primary peat layer in U Minh Thuong was over 90 cm depth (Vietnam Environment Protection Agency, 2003), and the thick peat layer in U Minh Ha was over 120 cm depth (Le, 2010)].

The carbon densities of roots in the Melaleuca forests were 22.75 tC/ha, 16.97 tC/ha, 11.97 tC/ha, 6.99 tC/ha, and 8.35 tC/ha for VS1, VS2, VS3, VS4, and VS5, respectively (Figure 4f). There was a statistically significant difference in root carbon density between the forest types (χ2 = 22.437, p = 0.00016). The carbon densities of roots in Melaleuca forests in sandy soil were higher than those in clay soil, in particular, the root carbon density of VS2 was significant higher than that of VS4 (Appendix A2k).

Organic soil carbon densities to a 30 cm depth in the study areas were 75.81 tC/ha, 89.22 tC/ha, 178.93 tC/ha, 93.94 tC/ha, and 83.58 tC/ha for VS1, VS2, VS3, VS4, and VS5, respectively (Figure 6.4g). There was a statistically significant difference in organic soil carbon density between the forest types (χ2 = 1.7333, p = 0.230), but pairwise comparisons showed no significant differences (Appendix A2k).These results are consistent with those of other studies of soil carbon stocks in wetlands [e.g. organic soil carbon stocks in swamp forests in Indonesia (with Melaleuca vegetation) were 106.00 tC/ha and 135.63 tC/ha in the top 30 cm of soil of primary and secondary forests, respectively (Rahayu and Harja, 2012)].

a b

c d

e f

Figure 6.4 Carbon densities of carbon stock categories of five Melaleuca forests types in the study areas. Vertical bars are standard errors of the mean for 2-4 plots per carbon categories. g

Overall, the carbon density of Melaleuca forests on sandy soil in Southern Vietnam ranged from 159.36 tC/ha for regenerating forests to 275.98 tC/ha for primary forests. The carbon densities of forests on clay soil ranged from 246.96 tC/ha for regenerating forests without peat to 784.68 tC/ha of secondary forests with peat. Compared with the carbon stocks of other forests on peatlands [e.g.

72 the carbon density of mangrove forests in the Indo-Pacific region was 1,030 tC/ha (Donato et al., 2011)], the carbon density of disturbed Melaleuca forests on the peatlands of Southern Vietnam are about one half, but the results are consistent with other studies on peat swamp forests [e.g. the carbon density of undisturbed swamp forests in South-East Asia ranged from 182 tC/ha to 306 tC/ha (Verwer and Meer, 2010)]. Despite this, Melaleuca forests in the peatlands of Vietnam still have high potential as carbon stores. The case of U Minh Thuong National Park is an example. The total carbon stock of 8,038 ha of Melaleuca forests in the park is about 2.69 M tC (Table 6.3), which is equivalent 9.43 M tCO2e. Furthermore, there were 8,576 ha of Melaleuca forested peatland in U Minh Ha National Park that have peat layers ranging from 40 cm to over 120 cm deep (Le, 2010), which provides an even higher potential carbon store.

Table 6.3 Potential carbon stocks in Melaleuca forests in U Minh Thuong National Park Land cover type Area Carbon density Carbon storage (ha) (tC/ha) tC Mature Melaleuca forests on clay soil without peat 1765 305.06 538,431 Mature Melaleuca forests on clay soil with peat 601 784.68 471,593 Regenerating Melaleuca on clay soil with peat 2106 544.28 1,146,254 Regenerating Melaleuca on clay soil without peat 1106 246.96 273,138 Others (open water, reeds and grasses) 2460 107.91 265,459 Total 8,038 2,694,874 Note: The areas of Melaleuca forests in U Minh Thuong National Park are adapted from a Vietnam Environment Protection Agency report (2003).

6.3.4 Sodicity tolerance of Melaleuca cajuputi forests toward the adaptation to global climate change

Sea-level rise is a consequence of global climate change that will severely affect coastal and wetland ecosystems. Melaleuca forests are largely located in coastal and wetland areas that may be affected by climate change (Tran et al., 2013b), so the risk of salinization of the region will increase. Salinity in soils can damage woody plant species by stunting buds, reducing leaf size and causing necroses in buds, roots, leaf margins and shoot tips (Larcher, 1980). Salinity can also inhibit seed germination, and can even kill non-halophytic species (Kozlowski, 1997). Both vegetative and reproductive growth of woody species are also reduced by high concentrations of sodium chloride in soil (Greenway and Munns, 1980; Kozlowski, 1997). The combination of flooding and salinity can create a more pronounced effect on growth and survival of plants than either stress alone(Kozlowski, 1997). High concentrations of sodium can affect the structure of sodic soils (Department of Primary Industries, 2008; Wong et al., 2008; Mavi et al., 2012). In contrast, low sodium concentration, soil structure is not affected by salinity in saline soil (Howat,

73 2000). Sodicity and salinity always occur together and have negative impacts on soil properties and plants (Nuttall et al., 2003; Department of Primary Industries, 2008), but sodic soils may be either non-saline or saline (Bernstein, 1975).

The lower Mekong Basin and coastal regions of southern Vietnam are highly vulnerable to global climate change impacts (Erwin, 2009; Toan, 2009; Tran et al., 2013b; Nicholls et al., 2007). Most of Vietnam’s Melaleuca forests occur in these areas and will be affected projected sea-level rise. Fortunately, this study has shown that Melaleuca cajuputi has the ability to tolerant increases in sodic soils.

About 28 soil samples collected from Melaleuca forests in Southern Vietnam were examined and all were shown to be sodic (Table 6.4). While the exchangeable sodium percentage (ESP) of soil layers of Melaleuca forests on clay soil (VS3, VS4, and VS5) ranges from low to moderate sodicity, those of Melaleuca forests on sandy soil (VS1 and VS2) were significantly higher, particularly VS1, which had an ESP of up to 39.78 % in soil taken from depths of 10-30 cm (Table 6.4). This indicates that both mature and young Melaleuca cajuputi forests have a high tolerance of sodic soils. Furthermore, Melaleuca cajuputi seeds can germinate and grow in highly sodic soil [e.g. Melaleuca cajuputi in forest type VS2 was able to grow in highly sodic soil with ESP up to 21.16 % in the top 0-10 cm (Table 6.4)].

With the exception of mangroves, few woody species can tolerate saline and/or sodic soils. Many woody species have been examined for their tolerance of salinity and/or sodicity. For example, Eucalyptus, Melaleuca, Acacia, Casuarina (van der Moezel et al., 1988; van der Moezel et al., 1991; Dunn et al., 1994; Niknam and McComb, 2000), Grevillea robusta, Lophostemon confertus and Pinus caribea (Sun and Dickinson, 1993), and Moringa oleifera (Seidahmed et al., 2013) have been examined and their tolerance to salinity assessed in the field and in glasshouses. In extremely saline soils in Australia, Niknam and McComb (2000) suggested that the land care benefit of establishing species such as Melaleuca or Casuarina is more important than their commercial value. As well as the land care value, this study has shown that Melaleuca cajuputi forests in Vietnam can adapt to climate change through their tolerance to sodicity, and other harsh conditions (Tran et al., 2013b), and can help to mitigate climate change through their carbon storage abilities.

74 Table 6.4 Chemical element concentration and sodicity levels of the Melaleuca forest soils in Southern Vietnam

2+ 2+ + + 3+ 3+ Forest types Soil pH(KCl) Ca Mg Na K Al Fe ESP Sodicity layers (meq/100g) (meq/100g) (meq/100g) (meq/100g) (meq/100g) (mg/100g) (%) (cm) Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE

Primary Melaleuca 0-10 3.97 0.15 1.413 0.75 1.783 1.58 1.790 1.56 0.600 0.47 0.910 0.29 3.303 1.20 32.05 4.28 High on sandy soil 10-30 4.12 0.17 1.065 0.41 1.138 1.02 1.708 1.58 0.383 0.33 0.660 0.24 7.310 2.23 39.78 7.90 High (VS1)

Regenerating 0-10 3.68 0.03 0.690 0.10 0.310 0.10 0.310 0.15 0.155 0.02 1.860 0.14 1.615 0.36 21.16 7.82 High Melaleuca on sandy soil 10-30 3.86 0.04 0.645 0.03 0.175 0.00 0.150 0.02 0.065 0.02 1.280 0.24 1.810 0.56 14.49 2.28 Moderate (VS2)

Degraded 0-10 4.12 0.25 7.585 1.82 6.320 1.81 1.705 0.81 0.455 0.19 0.100 0.10 37.155 17.63 10.61 2.37 Moderate secondary Melaleuca on clay 10-30 4.07 0.32 7.585 2.96 5.795 2.59 1.470 0.41 0.705 0.07 1.680 1.64 48.245 7.19 9.45 1.09 Low soil with peat (VS3)

Regenerating 0-10 4.67 0.19 8.845 0.55 6.685 0.58 1.760 0.51 0.585 0.27 0.00 0.00 47.550 16.06 9.85 3.05 Low Melaleuca on clay soil with peat 10-30 5.00 0.04 5.855 0.33 4.860 0.42 1.320 0.19 0.575 0.18 0.00 0.00 54.825 36.49 10.47 1.27 Moderate (VS4)

Regenerating 0-10 4.16 0.26 11.580 4.19 5.557 0.55 1.330 0.24 0.663 0.07 5.533 5.53 67.433 9.03 6.95 0.49 Low Melaleuca on clay soil without peat 10-30 3.91 0.40 8.603 1.65 5.170 0.08 1.507 0.24 0.717 0.09 8.000 7.02 78.440 10.37 9.42 0.37 Low (VS5)

Note: ESP = exchangeable sodium percentage. Sodicity classification of soils included non-sodic soil (ESP < 6), low sodic soil (ESP = 6-10), moderately sodic soil (ESP = 10-15), and highly sodic soil (ESP > 15) (Northcote and Skene, 1972; Rengasamy et al., 2010). ESP = (Na+ / Σ [Na+][K+][Mg2+][Ca2+]) ˟ 100 (Abrol et al., 1988; Warrence et al., 2002; Department of Primary Industries, 2008)

75 6.4 Conclusion

By undertaking original field research, this study examined the carbon sequestration potential of five types of Melaleuca forests including ‘Primary Melaleuca forests on sandy soil’ (VS1), ‘Regenerating Melaleuca forests on sandy soil’ (VS2), ‘Degraded secondary Melaleuca forests on clay soil with peat’ (VS3), ‘Regenerating Melaleuca forests on clay soil with peat’ (VS4), and ‘Regenerating Melaleuca forests on clay soil without peat’ (VS5). The study also assessed the sodicity tolerance of Melaleuca cajuputi forests in coastal and wetland regions of Vietnam.

The carbon densities of VS1, VS2, VS3, VS4, and VS5 were 275.98 (± 38.62) tC/ha, 159.36 (± 21.01) tC/ha, 784.68 (± 54.72) tC/ha, 544.28 (± 56.26) tC/ha, and 246.96 (± 27.56) tC/ha, respectively. Most carbon stocks were contributed from the soil (including peat) and stands.

The exchangeable sodium percentage (ESP) of soil from Melaleuca forests on clay soil (VS3, VS4, and VS5) ranged from low to moderate sodicity, but those from Melaleuca forests on sandy soil (VS1 and VS2) were highly sodic.

The results provide important information for the future sustainable management of Melaleuca forests in Vietnam, particularly in regards to forest carbon conservation initiatives and the potential of Melaleuca species for reforestation initiatives on degraded sites with highly sodic soils. In Vietnam, forest carbon conservation initiatives such as REDD+ have hereto, in our view, not placed appropriate priority or consideration on the protection of carbon stocks of Melaleuca forests. The results presented in this paper suggest that Melaleuca forests in Vietnam, particularly those on peatland areas, hold globally significant carbon stocks -arguably greater than those found in upland rainforest ecosystems, which have so far been given higher priority in REDD+ planning in Vietnam. Furthermore, the results presented in this paper suggest that some Melaleuca forest species in Vietnam, particularly those on sandy soils, exhibit a tolerance for highly sodic soils. This suggests that those species might be useful in reforestation initiatives on degraded sites with highly sodic soils. As degradation pressures including climate change continue to alter the hydrological features of soil systems in areas such as the Mekong Delta in Vietnam, and the sodicity of soils in some areas increases, Melaleuca species could offer a useful option for reforestation and rehabilitation initiatives.

76 Chapter 7 - CARBON STOCKS OF MELALEUCA FOREST ECOSYSTEMS IN SOUTH-EAST QUEENSLAND AUSTRALIA

Summary

In Australia, the genus Melaleuca dominates three forest types: Melaleuca woodlands, open Melaleuca forest, and closed Melaleuca forest, many of which have been substantially disturbed by human activity, thus altering the prevailing hydrological conditions. This paper presents the results of an assessment of the carbon stocks of natural Melaleuca forests and investigates their disturbances in order to provide better information for improving forest management. Data was collected and analysed from four typical Melaleuca stands including: (i) primary Melaleuca forests subject to continuous water inundation (A1); (ii) primary Melaleuca forests not inundated by water (A2); (iii) degraded Melaleuca forests subject to continuous water inundation; (A3), and (iv) regenerating Melaleuca forests subject to continuous water inundation (A4). The carbon densities of A1, A2, A3, and A4 were found to be 381.59 tC/ha, 278.40 tC/ha, 210.36 tC/ha, and 241.72 tC/ha, respectively. Carbon accumulation in litter was 6.5 times higher in forests subject to inundation (A1) than those in dry conditions (A2), and 45 % of the carbon stocks were lost by wildfire. The results provide important information for the future sustainable management of Melaleuca forests at both the national and regional scale, particularly in regards to forest carbon conservation and carbon farming initiatives.

77 7.1 Introduction

In Australia, more than 7.558 million ha of Melaleuca forests and woodlands were recorded in 2008 (MIG, 2008). This was reduced to about 6.3 million ha in 2013 (MIG, 2013) due to a re-classification amendment in the spatial analysis used by the relevant national reporting mechanism. In Australia, Melaleuca forest ecosystems mostly occur as wetland forests, predominantly in the coastal regions of Queensland and the Northern Territory. These Melaleuca forests provide society with multiple ecological and cultural benefits [e.g. biodiversity, habitat, natural Heritage, indigenous Estate (Mitra et al., 2005; DAFF, 2010b)]. They also serve as substantial stores of carbon and substantial sources of carbon emissions, and as such play an important role global climate change (Tran et al., 2013b), in a similar way to other types of wetland ecosystems (Bernal and Mitsch, 2008; Bernal, 2008; Mitsch et al., 2012), tropical wetlands (Mitsch et al., 2008; Mitsch et al., 2010a), and temperate freshwater wetlands (Bernal and Mitsch, 2012).

Data from the Australian Greenhouse Office showed that the total carbon store in 7.558 million ha of Melaleuca forests and woodlands was 210 M tC, which equates to about 27.8 tC/ha (MIG, 2008, p.117). However, Tran et al. (2013a) showed that Melaleuca forests have a much higher potential for carbon storage than these Australian Greenhouse Office estimates. More information is needed on the magnitude and nature of carbon stocks in Melaleuca forests, particularly in regards to how these stocks vary between sites exhibiting different levels of disturbance and different hydrological features. Comprehensive studies covering all forest types and associated site conditions are needed, but these require long time periods and considerable resources. To begin the process, this paper presents the findings of a detailed analysis of the carbon stocks of Melaleuca forest areas in South-East Queensland, Australia.

As well as Australia, Melaleuca ecosystems also naturally occur in Cambodia, Indonesia, Malaysia, Myanmar, Papua New Guinea, Thailand, and Vietnam, and have been introduced in South America and the Caribbean (Dray et al., 2006). Building on information from other studies, such as the carbon stocks of Melaleuca forest in Southern Vietnam (Tran et al., under review) and studies in biomass of introduced Melaleuca communities in Florida’s wetlands, this paper provides further information for future sustainable forest management about producing carbon offsets at the national and regional levels from both native and introduced Melaleuca ecosystems.

78 7.2 Materials and methods

7.2.1 Study sites

Two study sites were selected on the basis that they: (1) were generally representative of Melaleuca forests in South East Queensland; (2) contained Melaleuca forest areas exhibiting different levels of disturbance and different types of water inundation; and (3) were accessible within the logistical constraints of the study. The study investigated two sites in South-East Queensland, Australia: Buckley’s Hole Conservation Park and Hays Inlet Conservation Park (Figure 7.1). A total of 18 major plots were randomly located for carbon assessment covering the following types of Melaleuca stands: primary (undisturbed) Melaleuca forests subject to continuous water inundation (A1); primary (undisturbed) Melaleuca forests not inundated by water (A2); degraded Melaleuca forests subject to continuous water inundation (A3); and regenerating Melaleuca forests subject to continuous water inundation (A4).

Figure 7.1 The study locations in South East Queensland Australia: Buckley’s Hole Conservation Park and Hays Inlet Conservation Park. Source: Image Landsat from Google earth for free version.

79 7.2.2 Field sampling and data collection

The major plots were set out as 500 m2 quadrats (20 m × 25 m), and all trees with a diameter at breast height (DBH) ≥ 10 cm were measured and recorded. Sub-plots also were set out as 100 m2 quadrats (20 m × 5 m) within the major plots to measure all trees with DBH < 10 cm and a total height of > 1.3 m [modified from Van et al. (2000a)]. Data on DBH, alive or dead, and height were recorded for all standing trees.

Deadwood (dead fallen trees) with a diameter ≥ 10 cm were measured within the major plots (500 m2), while deadwood with 5 cm ≤ diameter < 10 cm were measured within the sub-plots (100 m2). Diameters at both ends of the trunk (D1 and D2), length (if ≥ 50 cm length), and the decay class [sound, intermediate, and rotten (IPCC, 2003, 2006)] were recorded for all deadwood.

Ninety random quadrats (1 m × 1 m) were located in the main plots to collect and record the ‘fresh weight’ of the understorey. Samples of all species from the understorey were collected in each major plot and taken back to the University of Queensland laboratory for drying.

Ninety random coarse litter samples and seventy random fine litter samples were collected in the major plots. The fresh weight of each litter sample was recorded. Each litter type (coarse litter and fine litter) collected in every major plot were well mixed and taken to the laboratory for drying.

Thirty-six random soil cores (from two layers: 0-10 cm depth and 10-30 cm depth) were collected from the major plots and taken back to the University of Queensland laboratory for further analysis to investigate soil characteristics.

7.2.3 Sample analysis

Each understorey and litter sample was divided into three sub-samples and dried in a drying oven at 60oC to measure the moisture content, based on the equation (7.1) below:

Wfi  Wdi n i1 Wfi Rmoist  (7.1) n

where Rmoist = moist ratio [0:1], Wfi = fresh weight of sub-sample i, Wdi = dry weight of sub- sample i,n = number of sub-samples. The scales used to weight sub-samples were accurate to ± 0.01 g.

80 Total organic carbon (C %) was measured using an automated dry combustion method, which is commonly used to examine soil organic carbon via infrared detection of CO2 during dry combustion (Matejovic, 1993). Total nitrogen was measured using the Kjeldahl method, which is the standard way to determine the total organic nitrogen content of soil (Bradstreet, 1954). A standard bulk density test was used to analyse all soil bulk samples in a dry oven. Bulk density was calculated using equation (7.2):

Ms BD  (7.2) V where BD = the bulk density of the oven-dry soil sample (g/cm3), Ms = the oven dry-mass of the soil sample (gram), V = the volume of the ring sample (cm3).

Basal area (BA) was calculated using equation (7.3) [modified from Jonson and Freudenberger (2011)]:

n 0.00007854 DBHi BA  i1 10000 (7.3) Splot

2 where BA = basal area (m /ha), DBHi = diameter at bread height of tree i (cm), i = stand 2 individual (i = [1 :n]), n = number of trees of sample plot, Splot = area of the sample plot (m ), 0.00007854 = basal area factor.

7.2.4 Biomass allometric computation

Seven allometric equations, which are most common way to measure forest carbon stocks, were applied to calculate the above-ground and root biomass of the stands (Table 7.1). The selected allometric equations were tested for statistical significance using the R Statistic Program (Appendix B1). Using these equations, the average biomass was analysed for typical Melaleuca stands (A1, A2, A3, and A4). To convert from fresh to dry biomass, a moisture rate of 0.5 was applied as suggested by Van et al. (2000a) for the allometric equation of Finlayson et al. (1993). According to the Global Wood Density Database, the density of Melaleuca timber ranges from 0.6 g/cm3 to 0.87 g/cm3 (Chave et al., 2009), so 0.6 g/cm3 was applied for the above-ground biomass allometric equation of Chave et al. (2005).

The fallen deadwood biomass were calculated using equation (7.4) (Hairiah et al., 2001.p12):

B = π × r2 × L × δ (7.4)

81 where B = biomass (kg), r = ½ diameter (cm), L = length (m), and δ = wood density (= 0.6 g/cm3).

To convert biomass to carbon mass for all categories (stands, roots, deadwood, understorey, and litter), a factor of 0.45 was applied.

Table 7.1 List of allometric equations applied to examine stand biomass of the Melaleuca forests in the study sites of Australia Allometric equations R2 Vegetation Sites References log10(FW) = 2.266log10(D) – 0.502 0.98 Melaleuca Northern Finlayson et where FW = fresh above-ground biomass spp. Territory al. (1993) (kg/tree), D = diameter at breast height (cm). y = exp[–2.134 + 2.53ln(D)] 0.97 Mixed Tropical, moist IPCC (2003) where y = above-ground biomass (kg/tree), species forest or Brown D= diameter at breast height (cm). (1997) ln(y) = 2.4855ln(x) – 2.3267 0.96 Native NSW, ACT, Keith et al. where y = above-ground biomass(kg/tree), x sclerophyll VIC, TAS, and (2000) = diameter at breast height (cm). forest SA ln(AGB) = –1,554 + 2.420ln(D) +ln() 0.99 Tropical America, Asian Chave et al. where AGB = above-ground biomass forests and Oceania (2005) (kg/tree), D= diameter at breast height (cm),  = wood density (g/cm3). ln(RBD) = –1,085 + 0.926 ln(ABD) 0.83 Upland Worldwide IPCC (2003) where RBD = Root biomass density (t/ha), forests or Cairn et al. ABD = above-ground biomass density (t/ha). (1997) y = 0.27x 0.81 Natural Worldwide Mokany et al. where y = total root biomass (t/ha), x = total forests (2006) shoot biomass (t/ha). 2.59 Wr = 0.023 ˟ D 0.97 Tropical Sarawak, Niiyama et al. where Wr = Coarse root biomass (kg/tree), D secondary Malaysia (2010) = diameter at breast height (cm). forests Note: NSW - New South Wales, ACT - Australian Capital Territory, VIC - Victoria, TAS - Tasmania, SA - South Australia

Soil organic carbon (SOC) was calculated using equation (7.5) (IPCC, 2003, 2006):

SOC  Dep × BD × Csample × 100 (7.5)

82 where SOC = Soil organic carbon, Dep = depth of soil layer (m), BD = bulk density (g/cm3),

Csample = organic carbon content of soil sample (%), and 100 is the default unit conversion factor.

7.2.5 Statistical analysis

One-way ANOVA tests were applied to compare stand densities, DBH, height classes, basal areas, and six categories of carbon stocks of the four Melaleuca forest types. LSD post-hoc tests were also used for all pairwise comparisons between group means. Statistical analysis was undertaken using Microsoft Excel 2010 and the R Statistic Program.

7.3 Study results

7.3.1 Characteristics of the typical Melaleuca forests in the study areas

The characteristics of the four typical Melaleuca forest types examined are summarized in Table 7.2. Note that all forest types were growing on sandy and/or clay soils.

The stand densities of the four forest types were 2,253 trees/ha, 2,144 trees/ha, 1,700 trees/ha, and 11,625 trees/ha for the Melaleuca forest types A1, A2, A3, and A4, respectively (Table 2). The tree density of A4 was significantly higher than A1, A2, and A3 (χ2 = 9.231, p = 0.026) (Figure 7.2a). StandA4 was very dense and mostly dominated by trees with DBH < 10 cm (accounting for 91.4 %), and had no trees with DBH ≥ 30 cm because of the naturally uniform seed-regenerated trees. On the other hand, stands A1, A2 were similar, comprising trees with DBH from < 5 cm to > 40 cm, but mostly dominated by trees with 10 cm ≤ DBH < 30 cm (accounting for 68.2 % and 51.9 %, respectively). Stand A3 was dominated by trees with 5 cm ≤ DBH < 20 cm (accounting for 43.9 %), and DBH < 5 cm (accounting for 41.2 %) (Table 7.2).

Average DBH of all stand classes were 17.90 cm, 19.91 cm, 16.38 cm, and 8.31 cm for A1, A2, A3, and A4, respectively (Figure 7.2c). There was a significant difference in DBH in the four Melaleuca forest types (χ2 = 9.867, p = 0.019), but the post-hoc test shows that there was only significant difference in DBH of A2 and A4 (Appendix B2a).

Average total height of all stand classes were 15.61 m, 15.73 m, 9.26 m, and 9.35 m for A1, A2, A3, and A4, respectively (Figure 7.2d). There was a significant difference between total height of the four forest types (χ2 = 11.616, p = 0.0088) (Appendix B2b). Furthermore, the tree density of the four forest types was generally very high, especially for forest class A4 (6,000 individual stems/ha), which can contribute to a large biomass. The basal areas shown in Figure 2b further confirm the potential high biomass of the forest types, particularly A1,

83 A2, and A4 (the basal areas were 50.60 m2/ha, 48.29 m2/ha, and 40.57 m2/ha, respectively). There was a significant difference in basal areas in A1, A2, A3, and A4 (F = 6.192, p = 0.0067), particularly in A1 and A3 (p = 0.0056) (Appendix B2c).The basal area of A3 was only 22.27 m2/ha, which is much lower than A1, A2, and A4, but still a good potential biomass.

a b

c d

Figure 7.2 Stand densities, basal areas, diameter at bread height, and total height of four Melaleuca forest types in South-East Queensland, Australia. Vertical bars are standard errors of the mean for 3-6 plots per category. The number of understorey species varied between the four forest types. The frequencies of sedges (Cyperus spp., Schoenoplectus spp., Eleocharis spp., Lepironia spp., Lepidosperma spp., Carex spp.), reed (Phragmites australis), and swamp water fern (Blechnum indicum) were high in forest types A1 and A3, where the conditions are always wet. The number of understorey species in A1 indicates that it is more diverse than A3. In drier areas, satintail grass (Imperata sp.) and several other grasses were the main species contributing the understorey of A2 (Table 7.2). Notably, forest type A4 has no understorey at all because of very dense stand canopy and thick coarse litter layer. Forest type A3 was regularly subjected to wildfire that burned the biomass of the understorey, but many understory species quickly re-grow after fire, particularly ferns (personal record).

84 Table 7.2 Major characteristics of four typical Melaleuca forests in the study areas

Forest types Tree classes Code Standing trees Understorey Soils Saturation Density DBH Basal area Height levels

Mean se Mean se Mean se Mean se (trees/ha) (cm) (m2/ha) (m) Primary DBH < 5 cm A1C0 201 180.6 3.48 0.22 na na 5.26 0.26 Cyperus spp., Sandy, Seasonal Melaleuca Schoenoplectusspp., and and/or 5 cm ≤ DBH < 10 cm A1C1 467 212.2 6.92 0.24 na na 10.08 0.47 forests subject Eleocharis spp., clay permanent to continuous 10 cm ≤ DBH < 20 cm A1C2 887 145.7 14.46 0.18 na na 14.69 0.22 Lepironia spp., soils inundation water 20 cm ≤ DBH < 30 cm A1C3 650 85.6 23.74 0.19 na na 18.05 0.16 Lepidosperma spp., inundation 30 cm ≤ DBH < 40 cm A1C4 50 13.4 32.91 0.66 na na 19.29 0.64 Carex spp., (A1) Phragmitesaustralis, DBH ≥ 40 cm A1C5 na na na na na na na na Blechnumindicum All classes A1 2,253 277.8 17.90 0.97 50.60 3.96 15.61 0.74 Primary DBH < 5 cm A2C0 300 175.9 3.89 0.19 na na 5.40 0.27 Imperatasp. Sandy, Never Melaleuca and inundated 5 cm ≤ DBH < 10 cm A2C1 640 263.6 6.68 0.25 na na 8.16 0.43 forests not clay inundated by 10 cm ≤ DBH < 20 cm A2C2 576 161.7 14.89 0.24 na na 15.31 0.20 soils water 20 cm ≤ DBH < 30 cm A2C3 536 41.7 24.31 0.26 na na 17.66 0.14 (A2) 30 cm ≤ DBH < 40 cm A2C4 68 32.5 33.27 0.71 na na 18.46 0.21 DBH ≥ 40 cm A2C5 24 7.3 45.75 1.40 na na 19.42 0.63 All classes A2 2,144 501.8 19.91 2.27 48.29 3.50 15.73 0.83 Degraded DBH < 5 cm A3C0 700 556.6 3.15 0.11 na na 4.19 0.25 Cyperusspp., Sandy, Seasonal Melaleuca Blechnumindicum and inundation 5 cm ≤ DBH < 10 cm A3C1 367 233.1 6.95 0.40 na na 5.91 0.35 forests subject clay to continuous 10 cm ≤ DBH < 20 cm A3C2 380 87.2 14.62 0.41 na na 9.75 0.50 soils water 20 cm ≤ DBH < 30 cm A3C3 227 6.7 24.54 0.45 na na 12.09 0.67 inundation 30 cm ≤ DBH < 40 cm A3C4 27 13.0 36.19 2.35 na na 15.01 1.87 (A3) DBH ≥ 40 cm A3C5 na na na na na na na na All classes A3 1,700 663.0 16.38 2.26 22.27 1.98 9.26 0.08 Regenerating DBH < 5 cm A4C0 6,000 2,985.8 3.39 0.05 na na 6.47 0.08 No Sandy, Seasonal Melaleuca understorey and and/or 5 cm ≤ DBH < 10 cm A4C1 4,625 1,395.5 6.28 0.09 na na 8.76 0.09 forests subject present because of clay permanent to continuous 10 cm ≤ DBH < 20 cm A4C2 921 645.6 13.28 0.18 na na 11.76 0.13 dense stands, soils inundation water 20 cm ≤ DBH < 30 cm A4C3 81 67.1 23.01 0.62 na na 14.72 0.25 and thick inundation coarse 30 cm ≤ DBH < 40 cm A4C4 na na na na na na na na (A4) litter layers DBH ≥ 40 cm A4C5 na na na na na na na na All classes A4 11,625 3,751.0 8.31 2.33 40.57 7.17 9.35 1.35

85 7.3.2 Carbon stocks of the Melaleuca forests

The carbon densities of four Melaleuca forests types in South-East Queensland were 381.59 tC/ha, 278.40 tC/ha, 210.36 tC/ha, and 241.72 tC/ha, for A1, A2, A3, and A4, respectively (Figure 7.3a). There was a significant difference in carbon densities in the four forest types (χ2 = 8.3187, p = 0.0398) (Appendix B2d). The carbon density of A1 was significantly greater than that of A3 (82 % higher).

275 root soil litter deadwood understorey stand 225

175

125

Mean carbon density (tC/ha) density carbon Mean 75

125

175 A1 A2 A3 A4

a Types of Melaleuca forests

140 soil root litter dead-wood under-storey stand 120

100

Distribution of carbon density of by carbon density (%) Distribution categories - A1 A2 A3 A4 Types of Melaleuca forests b

Figure 7.3 Carbon densities of four typical Melaleuca forests in South-East Queensland, Australia. Vertical bars are standard errors of the mean for 3-6 plots per carbon stock category.

86 The highest contribution to carbon density of A1 and A2 was from the stand category (accounting for 35.6 % and 49.5 %, respectively), and the soil (accounting for 26.8 % and 25.3 %, respectively), followed by litter and roots (accounting for 14.9 % and 13.7 %, respectively). In contrast, the three highest contributors to carbon density of type A3 were soil, stand, and deadwood (accounting for 41.8 %, 28.4 %, and 18.3 %, respectively), while stand, litter, and soil were the major contributions for A4 (accounting for 30.1 %, 29.5 %, and 17.7 %, respectively). The understorey contributed very little towards total carbon density in all types of Melaleuca forests (Figure 7.3b).

7.4 Discussion

7.4.1 Variability of carbon stock categories of the Melaleuca forests

This study examined the carbon stocks of six forest components including stand, understorey, deadwood, litter, root, and soil for four typical Melaleuca forests types in South-East Queensland (Figure 7.4).

The carbon densities of stands of the various forest types were 133.27 tC/ha, 133.96 tC/ha, 58.52 tC/ha, and 68.19 tC/ha for A1, A2, A3, and A4, respectively (Figure 7.4a). There was a significant difference in stand carbon density in four Melaleuca forest types (χ2 = 40.582, p = 8.018e-9) (Appendix B2e). Stand carbon density of A1 and A2 is twice that of A3 and 1.95 times higher than A4. These carbon stocks were similar to those found by other studies [e.g. the above- ground carbon density of Asian tropical forests was 144 tC/ha (Brown et al., 1993); of primary and secondary swamp forests in Indonesia (involving Melaleuca vegetation) were 200.23 tC/ha and 92.34 tC/ha, respectively (Rahayu and Harja, 2012)].

The carbon densities of the understorey in the Melaleuca forests were 1.76 tC/ha, 1.06 tC/ha, 1.39 tC/ha, and 0 tC/ha for A1, A2, A3, and A4, respectively (Figure 7.4b). There was no significant difference in understorey carbon density in the four forest types (χ2 = 0.228, p = 0.988) (Appendix B2f). However, in Melaleuca forests regenerating in swamps, understorey plants cannot grow because of the high density of the stands (11,625 individuals/ha in A4), which exclude light, and the thick coarse litter layer (accounting for 9.99 tC/ha of coarse litter) covering the forest floor.

The carbon densities of deadwood in the Melaleuca forests were 44.70 tC/ha, 23.46 tC/ha, 41.32 tC/ha, and 30.13 tC/ha for A1, A2, A3, and A4 respectively (Figure 7.4c). There was a significant difference in deadwood carbon density in Melaleuca forest types (χ2 = 1.697, p = 0.6376), but pairwise comparisons show no significant difference (Appendix B2g).

The coarse and fine litter layers of the four Melaleuca forest types contributed carbon densities of 53.73 tC/ha, 8.33 tC/ha, 3.07 tC/ha, and 74.13 tC/ha for A1, A2, A3, and A4, respectively (Figure

87 7.4d). There was a significant difference in total litter carbon densities between the four forest types (χ2 = 36.137, p = 5.3.3e–10) (Appendix B2h). The litter carbon densities of A1 and A4 were not significantly different, but they were 6.5 times and 8.9 times greater than A2, and 17.5 times and 24 times higher than A3, respectively.

Coarse litter occurs in all Melaleuca forest types, but fine litter is mostly restricted to primary Melaleuca forests subject to inundation, and regenerating Melaleuca forests subject to continuous inundation. The carbon densities of coarse litter in four Melaleuca forest types were17.51 tC/ha, 8.33 tC/ha, 3.07 tC/ha, and 9.99 tC/ha, while the carbon densities of fine litter were 40.94 tC/ha, 0.00 tC/ha, 0.00 tC/ha, and 66.73 tC/ha for A1, A2, A3, and A4, respectively (Figure 7.4e, f). The carbon stocks of fine litter in Melaleuca forests subject to continuous inundation (A1 and A4) were far higher than those of woodlands and open forests in the Brigalow Belt South bioregion of Queensland [ranging from 1.0 to 7.0 tC/ha, with a mean of 2.6 tC/ha (Roxburgh et al., 2006)].

The carbon densities of roots in the Melaleuca forests were 36.48 tC/ha, 36.59 tC/ha, 20.69 tC/ha, and 22.40 tC/ha for A1, A2, A3, and A4, respectively (Figure 7.4g). There was a significant difference in root carbon density in four forest types (χ2 = 82.765, p = 2.2e–16). The carbon densities of roots in A1 and A2 are more than 1.5 times higher than A3 and A4 (Appendix B2i).

Organic soil carbon densities to 30 cm depth in the Melaleuca forests studied were110.23 tC/ha, 76.79 tC/ha, 86.87 tC/ha, and 41.68 tC/ha for A1, A2, A3, and A4 respectively (Figure 7.4h). There was a significant difference in organic soil carbon density in the four forest types (χ2 = 4.308, p = 0.230), but pairwise comparisons showed no significant differences (Appendix B2j). These results are similar to those of other studies of soil carbon stocks up to 30 cm depth for primary and secondary Melaleuca forests: 106.00 tC/ha in wetlands (Page and Dalal, 2011), and 135.63 tC/ha in swamp forests in Indonesia (Rahayu and Harja, 2012). The organic soil carbon densities of Melaleuca forests are higher than those of woodlands and open forests up to 30 cm depth [ranging from 10.7 to 61.8 tC/ha (Roxburgh et al., 2006)].

a b

c d

e f

g h

Figure 7.4 Carbon densities of the categories of four types of Melaleuca forests in South-East Queensland, Australia. Vertical bars are standard errors of the mean for 3-6 plots per carbon category.

89 7.4.2 Disturbances of carbon stock in the Melaleuca forests

Various types of disturbances to forests may affect to carbon stocks [e.g. wildfire, outbreaks, hurricanes, logging, thinning, pests and diseases (Chapin et al., 2011; Shuguang et al., 2011; Seidl et al., 2014), ice storms, landslides, floods, glacial advances, earthquakes and volcanic eruptions (Chapin et al., 2011)]. Two disturbances affecting Melaleuca forests in Australia were investigated in this study: inundation and wildfire. Flooding occurred in some of the study areas regularly, depending on the rainfall. Some of the areas were subject to seasonal and/or permanent inundation, while others remained dry. It is very easy to determine by observation at any time of year whether a site is subject to inundation; the bark at the base of stands is always stained dark when Melaleuca forests have been inundated at any time, but remains white if the area is always dry.

This study examined the effects of inundation, by comparing primary Melaleuca forests subject to continuous water inundation (A1) and primary Melaleuca forests not inundated by water (A2). The inundation disturbance does not affect the carbon stocks of the stand, understorey, deadwood, root or, soil, but has a strong effect on the litter carbon stock (Figure 7.4d, e, f). Under saturated conditions (forest type A1), both coarse and fine litter accumulated to significantly higher levels than in dry conditions (forest type A2). Importantly, there was no fine litter in A2, which suggests that the litter was mostly decomposed. These results are consistent with those of another in Melaleuca quinquenervia forests, which found that litter accumulation in a floodplain site was higher than in a riparian site (Greenway, 1994). Neiff et al. (2006) also reported that leaf litter decomposition in riverine forest was more rapid than that in oxbow lakes or palm swamp forest. It is therefore likely that longer inundation results in greater accumulation of fine litter in wetland forests. Conversely, drainage can deplete the litter carbon stocks of Melaleuca swamp ecosystems.

Wildfires occurred soon after we located the sample plots in Buckley’s Holes Conservation Park, and a few weeks before in Hays Inlet Conservation Park. Furthermore, wildfires occurred in two consecutive years (2012 and 2013, personal record) in the study areas. Kimmins (2004) reported that frequent fires can have a negative effect on forest stands, with little accumulation of decaying branches and logs, but an increase in standing dead trees. Frequent forest fires can also change the condition of mature Melaleuca cajuputi swamp forest in the wetlands of southern Sumatra, Indonesia (Chokkalingam et al., 2007), which probably impacts the carbon stocks of the forests.

The degraded Melaleuca forests subject to seasonal inundation (A3) gave us the opportunity to examine the effect of wildfire disturbance on carbon stocks of the forests. The results show that wildfires significantly depleted the carbon stocks of stands and litter of the Melaleuca ecosystems in South-East Queensland. The carbon density of the total litter of A3 was significantly lower than A1

90 (Figure 7.4d, e, f). It was likely that regular wildfires burned most of the coarse litter and reduced the sources of fine litter. Furthermore, wildfires may produce conditions that result in the rapid decomposition of the fine litter. Field data show that there was no fine litter at all in site A3. Consequently, the total carbon density of A3 was equivalent to 55 % of A1, which was not affected by any disturbances. It is likely the 45 % of the carbon stock was lost due by disturbances involving wildfires and others which made A3 being degraded forests.

Multiple interacting disturbances may present a challenge for sustaining carbon stocks of ecosystems (Bradford et al., 2012). The results of this study indicate that fire may be more detrimental to carbon storage in Australian sclerophyll ecosystems than in other forests [i.e. fires reduced C stocks by only 9 % in the Pacific Northwest national forests (Gray and Whittier, 2014)]. Our study results were likely consistent with other studies [e.g. natural disturbances can have a considerably impact on the carbon stocks of ecosystems (Bradford et al., 2013), with disturbances can reduce above-ground carbon stocks of disturbed forests by about 40 % (Brown, 2014)]. We suggest that longer continuous inundation in Melaleuca ecosystems lowers the risks of forest fires and increases the potential for carbon storage.

7.4.3 Against the Australian Government Office’s under-estimation of the carbon storage in Melaleuca ecosystems Overall, the carbon densities of Melaleuca forests in South-East Queensland ranged from 210.36 tC/ha for degraded forests subject to inundation to 381.59 tC/ha for primary forests subject to inundation. These results are very similar to the estimates of Melaleuca forests carbon stocks derived from secondary data by Tran et al. (2013a). The results contrast starkly with the current estimates of carbon storage in Melaleuca ecosystems published in Australia’s National Greenhouse Gas Emissions Inventory Report [210 M tC stored from 7.558 million ha of Melaleuca forests and woodlands, which equates to about 27.8 tC/ha (MIG, 2008, p.117)]. Compared with other Australian native forests [i.e. the world’s tallest hardwood forests was estimated to contain in excess of 1800 tC/ha (Keith et al., 2009)], the carbon density of Melaleuca forests was far lower. But our results can contribute to improving the data on carbon storage from Melaleuca forests and woodlands in Australia. Based on the latest data, Australia’s 6.302 million ha of Melaleuca forests and woodlands probably contain from 362.0 to 521.31 M tC (Table 7.3). These carbon stocks are much higher than the previous estimate by the Australian Forest Bureau (about 210 M tC). If using only the Melaleuca forest areas of ‘Register of the National Estate’ with 1.158 million ha (MIG, 2008), the carbon stocks could range from 244 M tC to 442 MtC [equal to from 2 % to 4 % of total carbon storage of all native forests and woodlands (accounted for 12,063 M tC) (MIG, 2008)].

91 This study did not conduct field research in Melaleuca woodlands, which are mostly situated on privately owned land in Australia. However, these areas could have potential for carbon farming initiatives, even based on the carbon density estimates produced by the Australian Government Office (27.8 tC/ha).

Table 7.3 Estimation of carbon stocks of Melaleuca forests and woodlands in Australia

Forest types Area in Carbon Area in Carbon densities Carbon stocks * * 2008 stocks 2013 (tC/ha) (M tC) (000’ ha) (M tC) (000’ ha) from to from to Melaleuca woodland 6,654 na 5,357 30.00§ 30.00§ 160.71 160.71 Open Melaleuca forest 878 na 907 210.36 381.59 190.80 346.10 Closed Melaleuca forest 26 na 38 278.40 381.59 10.58 14.50 Total 7,558 210# 6,302 – – 362.09 521.31

* Area of Melaleuca forests and woodlands reported by Montreal Process Implementation Group for Australia and National Forest Inventory Steering Committee (MIG, 2008, 2013) # Carbon stocks of Melaleuca forest and woodlands estimated by Montreal Process Implementation Group for Australia and National Forest Inventory Steering Committee (MIG, 2008) § Carbon density calculated from carbon stock estimation of Montreal Process Implementation Group for Australia and National Forest Inventory Steering Committee (MIG, 2008)

7.4.4 Is it time to take a fresh look at Melaleuca ecosystems?

The generally view is that Melaleuca ecosystems are economically less valuable than other Australian native forests and woodlands. However, Melaleuca ecosystems have been assessed as valuable ecological systems, especially in wetlands with conservation and indigenous cultural heritage values (EPA, 2005), but Melaleuca forests have not been commercially exploited [except for 4,500 ha of cultivated tea tree of Melaleuca alternifolia for ‘Tea Tree Oil’ production (RIRDC, 2006)]. Large areas of Melaleuca ecosystems occur in localities under pressure from mining and urban development [e.g. the ‘Stockland Caloundra Downs Pty Ltd Caloundra South Master Planned Community Queensland’ development, which proposes ‘to construct and operate a master planned community catering for up to 50,000 residents on Queensland’s Sunshine Coast’(DSEWPaC, 2011)].

On a global scale, Melaleuca ecosystems are widely distributed in Oceania and South-East Asia, and Melaleuca species have been introduced in about 50 geographical areas around the world, particularly in South America and the Caribbean. Melaleuca quinquenervia is considered as a high- risk invasive species in some introduced areas, especially in Florida’s wetlands and Hawaii, USA (Dray et al., 2006).

92 However, it’s time to change these negative views and look at the more positive aspects of Melaleuca ecosystems, particularly in an era of climate change. Just as in Australia, this type of forests is considered an important ecosystem in Vietnam, especially in the wetlands of the Mekong Delta. Under the Vietnamese classification, Melaleuca forest is a special use forest type that plays a significant role in wetland and/or peatland conservation, restoration, habitat, history, and heritage. They also have socio-economic importance, with millions of people living in Melaleuca forests. In addition, our results have shown that Melaleuca ecosystems have a high potential as a carbon store, both in Australia and in Vietnam. Carbon densities ranged from 159.36 tC/ha in regenerating Melaleuca forests on sandy soil to 784.68 tC/ha in secondary Melaleuca forests on clay soil with peat (Tran et al., under review). Melaleuca species are also highly resilient and adaptable to climate change and can be managed to achieve sustainable benefits (Tran et al., 2013b). Furthermore, Melaleuca cajuputi forests on sandy soils in Vietnam are tolerant of highly sodic conditions, which has important implications for future sustainable management and the potential of Melaleuca species for reforestation on degraded sites with highly sodic soils (Tran et al., under review). Lastly, it should not be concluded that Melaleuca species are generally invasive. Dataset in the CABI system shows that only Melaleuca quinquenervia is an invasive species in Bahamas, Belize, Cuba, Dominican Republic, French Guiana, Jamaica, Mexico, USA (Florida and Hawaii), but there are no records of it being invasive in other introduced areas(CABI, 2014). Moreover, Melaleuca quinquenervia is only one of 260 species of Melaleuca genus occurring in Australia and other countries. Therefore many Melaleuca species (e.g. Melaleuca cajuputi) may prove useful in dealing with global change.

7.5 Conclusion

By conducting original fieldwork, this study considered the carbon sequestration of four types of Melaleuca forests: primary Melaleuca forests subject to continuous water inundation (A1); primary Melaleuca forests not inundated by water (A2); degraded Melaleuca forests subject to continuous water inundation (A3); and regenerating Melaleuca forests subject to continuous water inundation (A4). The study also examined the disturbances including inundation and wildfires that affect the carbon stocks.

Carbon densities of A1, A2, A3, and A4 were 381.59 tC/ha, 278.40 tC/ha, 210.36 tC/ha, and 241.72 tC/ha, respectively. Most carbon stocks were contributed from the stands and soil, but deadwood and litter made an important contribution to the carbon stocks of A3 and A4.

93 Inundation had a significant impact on the carbon stocks of litter: longer inundation periods resulted in greater carbon accumulated in litter. The carbon stock contribution from litter of Melaleuca forests subject to continuous water inundation was 6.5 times higher than dry forests.

Forest fires significantly affected the carbon stocks of the stand and litter categories. About 45 % of carbon stocks in Melaleuca forests in the study sites were probably lost as a result of wildfires.

Forest managers and governments need to investigate whether conservation and farming initiatives based on Melaleuca forests and woodlands could produce valuable carbon offsets. An integrated approach to Melaleuca forest ecosystem management, involving conservation, biodiversity, carbon offsets, payment for environment services, REDD+, and invasive control, would be beneficial at both national and international scales.

94 Chapter 8 - CONCLUSION AND RECOMMENDATIONS

8.1 Summary of the study findings

(i) the Melaleuca genus has a rich species diversity, and significant phenotypic diversity in a variety of ecosystems;

(ii) the Melaleuca genus demonstrate significant local adaptation to harsh conditions; and

(iii) the fossil records and taxon biology indicate the evolution of the Melaleuca genus began around 38 million years ago and they have survived several significant climatic alterations, particularly a shift towards cooler and drier climates that has occurred over this period.

These findings show that the Melaleuca genus is highly resilient and adaptable. Based on this, Chapter 2 argued that Melaleuca species can adapt to climate change through Wright’s ‘migrational adaptation’, and can be managed to achieve sustainable benefits.

Chapters 3 and 4 highlighted that even though carbon stocks in Melaleuca forests are likely to be considerable, there is a paucity of published information on the extent and nature of carbon stocks in Melaleuca forests. The highlighted results include:

(iv) the potential carbon density in Melaleuca forests ranges from 157.8 tC/ha to 363.0 tC/ha;

(v) it can take over 10 years for the decomposition of accumulated litter on the floor of Melaleuca forests; and

(vi) methane emission rates are relatively low in the forested wetlands, estimated at a wide range between 0.11 tC/ha/year to 46.3 tC/ha/year.

Chapter 4 speculated that there is likely to be between 158 tC/ha and 286 tC/ha stored in Australian Melaleuca forests. These estimates are at least five times greater than the previous estimate made by

95 the Australian Government (about 27.8 tC/ha). There are 2.1 million hectares of protected Melaleuca forest which likely stock between 328 M tC to 601 M tC; equivalent to between 2.7 % to 5.0 % of total carbon storage of all Australian native forests. These estimates are significant because it appears that carbon stocks in Melaleuca forests are currently dramatically under-estimated in Australia’s national greenhouse gas emissions inventory reported under the United Nations Framework Convention on Climate Change (UNFCCC).

Chapter 5 reviewed methods for estimating carbon stocks in similar types of forests to Melaleuca forests and presented the thesis methodology, notably a summary of the data collection and analysis methods used to estimate carbon stocks in field sites in Vietnam and Australia.

(vii) carbon densities of primary Melaleuca forests on sandy soil (VS1); regenerating Melaleuca forests on sandy soil (VS2); degraded secondary Melaleuca forests on clay soil with peat (VS3); regenerating Melaleuca forests on clay soil with peat (VS4); and regenerating Melaleuca forests on clay soil without peat (VS5) were found to be 275.98 tC/ha, 159.36 tC/ha, 784.68 tC/ha, 544.28 tC/ha, and 246.96 tC/ha, respectively.

(viii) the exchangeable sodium percentage of Melaleuca forests on sandy soil (VS1 and VS2) showed high sodicity, while those on clay soil (VS3, VS4, and VS5) varied from low to moderate sodicity.

These results indicate that Melaleuca forests on peatlands in Vietnam hold relatively large stores of carbon and that Melaleuca forests on sandy soils in Vietnam are tolerant of highly sodic conditions. The results provide important information for the future sustainable management of Melaleuca forests in Vietnam, particularly in regards to forest carbon conservation initiatives and the potential of Melaleuca species for reforestation initiatives on degraded sites with highly sodic soils.

In Australia, forests dominated by the genus Melaleuca have three major types: Melaleuca woodlands, open Melaleuca forest, and closed Melaleuca forest that have been substantially

96 disturbed by human activity and the hydrological condition. Chapter 7 presents the results of an assessment of the carbon stocks and their disturbances of natural Melaleuca forests in order to contribute better information for improving forest management. Data was collected and analysed from four typical Melaleuca stands, and the major findings include:

(ix) carbon densities of primary Melaleuca forests under continuous water inundation (A1), primary Melaleuca forests not inundated by water (A2), degraded Melaleuca forests under continuous water inundation (A3), and regenerating Melaleuca forests under continuous water inundation (A4) were found to be 381.59 tC/ha, 278.40 tC/ha, 210.36 tC/ha, and 241.72 tC/ha, respectively.

(x) carbon accumulation from litters in wet condition (A1) was likely 6.5 times higher than those in dry condition (A2), and total carbon stocks of Melaleuca forests were properly lost about 45 % of the primary forests by wildfires.

Due to the differences of the forest structures and soil types, the carbon stocks of the Melaleuca forests are different between Australia and Vietnam. Generally, carbon stocks of the Melaleuca forests in Vietnam are likely higher than those in Australia because of high carbon stock from peat soil in the Mekong Delta.

The results in this thesis provide further scientific information to support better Melaleuca ecosystem management. The results should help policy makers make better decisions in an era of global change. The results have particular relevance for the application of REED+ in the Southeast Asia, and the Carbon Farming Initiative in Australia.

8.2 Research limitation

The research presented in this thesis was undertaken as part of a PhD program and as such, it was constrained by time and resources. This meant that field areas were restricted to a handful of sites in Southern Vietnam and South East Queensland Australia. Ideally, a larger extensive number of sites should have been used to draw more reliable estimates of national-level carbon stocks. The research results are still useful and provide an important step towards a better understanding of the nature of carbon stocks in Melaleuca forests and the development of more reliable national-level estimates.

Due to the limitation of time and budget, however, some aspects which could be examined within the research have not been addressed. These include:

97  Data on Melaleuca forest areas for Australia, Vietnam, and the United States were available, but were still lacking for other Southeast Asian countries. So, this research has not provided additional information on new areas of Melaleuca forests and woodlands at regional and global scales.

 The study focussed on the Melaleuca forests at National Parks and/or Conservation Areas that were able to be accessed. Therefore, this thesis has not examined carbon stocks of Melaleuca woodlands on privately-owned land in Australia.

8.3 Recommendations

Melaleuca ecosystems have a natural geographic range in Australasia and South East Asia, and introduced distribution in North America, South America and the Caribbean. So, future research should provide a more detailed examination of the relatively studied regions, particularly for South East Asia, which in turn will improve our understanding of Melaleuca distribution, and associated carbon stock, at a regional to global scale.

In Australia, Melaleuca ecosystems are divided into three major type including closed Melaleuca forests, open Melaleuca forests, and Melaleuca woodlands. This thesis examined closed and open Melaleuca forests, but Melaleuca woodlands were not included. Thus, future research should be extended to further understand the carbon stocks of Melaleuca woodlands in Australia.

In addition, further research should synthesise all results of carbon stocks of the Melaleuca ecosystems to build an application framework and tools for carbon sequestration aimed for the adoption of the Australian policy on Carbon Farming Initiative. Based on this, the role of carbon storage to the ecological values of Melaleuca ecosystems should be included.

There was a significant discrepancy (~1.256 million ha loss) in the area of Melaleuca forests and woodlands reported for 2008 and 2013. Thus, a critical issue that needs to be clarified is “How did such a large area of Melaleuca forest and woodland disappear within 5 years from 2008 to 2013?” Was it due to methodological differences or has there been a significant loss in Melaleuca ecosystems over this five year period (and is so what was the cause)?

The field study results suggest that Australian Government Office has under-estimated carbon storage in Melaleuca ecosystems. Furthermore, Melaleuca forest has special values for wetland and/or peatland conservation, restoration, habitat, history, and heritage. They also have important socio-economic values, with millions of people living in Melaleuca areas. Thus, it’s time to alter negative views associated with these ecosystems and focus on the more positive aspects of

98 Melaleuca ecosystems, particularly in an era of climate change. Further research is a key aspect for this, particularly updating information on carbon storage potential of the Australian Melaleuca forests and woodlands for the Australia’s national greenhouse gas emissions inventory reported under the United Nations Framework Convention on Climate Change (UNFCCC).

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122 APPENDICES

Appendix A: Statistic analyses of Melaleuca data collected from Vietnam’s sites

Appendix A1: ALLOWMETRIC EQUATION TESTS

Appendix A1a: Summary of pairwise comparisons of allometric equation tests for above-ground biomass

Comparisons AGB.Ray AGB.Fin AGB.Keith AGB.Le AGB.IPCC AGB.Chave F, F, F, T, F, T,T, T, T, F, F, T, F, F, T, T, F, F, F, T, F, T, T, T, F, F, F, T, F, T, T, T, F, F, F, T, F, T, T, AGB.Ray T, T, T, T, T, F T, T, T, T, T, F T, T, T, T, T, F T, T, T, T, T, T T, T, T, T, T, T, F F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, AGB.Fin F, F, F, F, F, F F, F, F, F, F, F T, F, F, F, F, F F, F, F, F, F, F, F F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, AGB.Keith F, F, F, F, F, F F, F, F, F, F, F F, F, F, F, F, F, F F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, AGB.Le T, F, F, F, F, F F, F, F, F, F, F, F F, F, F, F, F, F, F, AGB.IPCC F, F, F, F, F, F, F

AGB.Chave

Appendix A1b: Percentages of pairwise comparisons of allometric equation tests for above-ground biomass Stand biomass equations References Codes Count “T” % Count “F” % Rayachhetry et AGB.Ray 46 65.71 24 34.29 ln(y) = 2.0409ln(D) - 2.0163 al. (2001) Finlayson et al. AGB.Fin 10 14.29 60 85.71 Log (FW) = 2.266log (D) - 0.502 10 10 (1993) Keith et al. AGB.Keith 9 12.86 61 87.14 ln(y) = 2.4855ln(x) - 2.3267] (2000) y = 0.124 ˟ DBH 2.247 Le (2005) AGB.Le 10 14.29 60 85.71 IPCC (2003) or AGB.IPCC 12 17.14 58 82.86 y = exp[-2.134 + 2.53ln(D)] Brown (1997) Chave et al. AGB.Chave 9 12.86 61 87.14 ln(AGB) = - 1,554 + 2.420ln(D) +ln(r) (2005)

>testEquVN<- read.csv("K:/PhD PROGRAM/A-PhD PROPOSAL/FIELD DATA/DATA ANALYSIS/CSV FILES/VN/testEquVN.csv")

Plot PQ1 > kruskal.test(pq1~eq1) > qqnorm(pq1) Kruskal-Wallis rank sum test > qqline(pq1) data: pq1 by eq1 Kruskal-Wallis chi-squared = 81.5227, df = 6, p-value = 1.731e-15 > kruskalmc(pq1, eq1) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference AGB.Chave - AGB.Fin 11.095238 71.52007 FALSE AGB.Chave - AGB.IPCC 25.190476 71.52007 FALSE AGB.Chave - AGB.Keith 20.083333 71.52007 FALSE AGB.Chave - AGB.Le 4.988095 71.52007 FALSE AGB.Chave - AGB.Ray 51.619048 71.52007 FALSE AGB.Fin - AGB.IPCC 36.285714 71.52007 FALSE AGB.Fin - AGB.Keith 8.988095 71.52007 FALSE AGB.Fin - AGB.Le 6.107143 71.52007 FALSE AGB.Fin - AGB.Ray 62.714286 71.52007 FALSE AGB.IPCC - AGB.Keith 45.273810 71.52007 FALSE AGB.IPCC - AGB.Le 30.178571 71.52007 FALSE AGB.IPCC - AGB.Ray 26.428571 71.52007 FALSE AGB.Keith - AGB.Le 15.095238 71.52007 FALSE AGB.Keith - AGB.Ray 71.702381 71.52007 TRUE AGB.Le - AGB.Ray 56.607143 71.52007 FALSE

123 Plot PQ2 > kruskal.test(pq2~eq2) > qqnorm(pq2) Kruskal-Wallis rank sum test > qqline(pq2) data: pq2 by eq2 Kruskal-Wallis chi-squared = 513.2837, df = 6, p-value < 2.2e-16 > kruskalmc(pq2, eq2) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference AGB.Chave - AGB.Fin 12.318182 139.7514 FALSE AGB.Chave - AGB.IPCC 4.954545 139.7514 FALSE AGB.Chave - AGB.Keith 2.090909 139.7514 FALSE AGB.Chave - AGB.Le 8.454545 139.7514 FALSE AGB.Chave - AGB.Ray 14.090909 139.7514 FALSE AGB.Fin - AGB.IPCC 17.272727 139.7514 FALSE AGB.Fin - AGB.Keith 14.409091 139.7514 FALSE AGB.Fin - AGB.Le 3.863636 139.7514 FALSE AGB.Fin - AGB.Ray 1.772727 139.7514 FALSE AGB.IPCC - AGB.Keith 2.863636 139.7514 FALSE AGB.IPCC - AGB.Le 13.409091 139.7514 FALSE AGB.IPCC - AGB.Ray 19.045455 139.7514 FALSE AGB.Keith - AGB.Le 10.545455 139.7514 FALSE AGB.Keith - AGB.Ray 16.181818 139.7514 FALSE AGB.Le - AGB.Ray 5.636364 139.7514 FALSE >

Plot PQ3 > kruskal.test(pq3~eq3) > qqnorm(pq3) Kruskal-Wallis rank sum test > qqline(pq3) data: pq3 by eq3 Kruskal-Wallis chi-squared = 321.4749, df = 6, p-value < 2.2e-16 > kruskalmc(pq3, eq3) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference AGB.Chave - AGB.Fin 8.812500 81.93653 FALSE AGB.Chave - AGB.IPCC 22.984375 81.93653 FALSE AGB.Chave - AGB.Keith 9.953125 81.93653 FALSE AGB.Chave - AGB.Le 0.656250 81.93653 FALSE AGB.Chave - AGB.Ray 4.906250 81.93653 FALSE AGB.Fin - AGB.IPCC 14.171875 81.93653 FALSE AGB.Fin - AGB.Keith 1.140625 81.93653 FALSE AGB.Fin - AGB.Le 8.156250 81.93653 FALSE AGB.Fin - AGB.Ray 13.718750 81.93653 FALSE AGB.IPCC - AGB.Keith 13.031250 81.93653 FALSE AGB.IPCC - AGB.Le 22.328125 81.93653 FALSE AGB.IPCC - AGB.Ray 27.890625 81.93653 FALSE AGB.Keith - AGB.Le 9.296875 81.93653 FALSE AGB.Keith - AGB.Ray 14.859375 81.93653 FALSE AGB.Le - AGB.Ray 5.562500 81.93653 FALSE

Plot PQ4 > kruskal.test(pq4~eq4) > qqnorm(pq4) Kruskal-Wallis rank sum test > qqline(pq4) data: pq4 by eq4 Kruskal-Wallis chi-squared = 27.6855, df = 5, p-value = 4.193e-05 > kruskalmc(pq4, eq4) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference AGB.Chave - AGB.Fin 9.8310811 64.08825 FALSE AGB.Chave - AGB.IPCC 17.2364865 64.08825 FALSE AGB.Chave - AGB.Keith 20.9391892 64.08825 FALSE AGB.Chave - AGB.Le 20.0743243 64.08825 FALSE AGB.Chave - AGB.Ray 85.3378378 64.08825 TRUE AGB.Fin - AGB.IPCC 27.0675676 64.08825 FALSE AGB.Fin - AGB.Keith 11.1081081 64.08825 FALSE AGB.Fin - AGB.Le 10.2432432 64.08825 FALSE AGB.Fin - AGB.Ray 75.5067568 64.08825 TRUE AGB.IPCC - AGB.Keith 38.1756757 64.08825 FALSE AGB.IPCC - AGB.Le 37.3108108 64.08825 FALSE AGB.IPCC - AGB.Ray 102.5743243 64.08825 TRUE AGB.Keith - AGB.Le 0.8648649 64.08825 FALSE AGB.Keith - AGB.Ray 64.3986486 64.08825 TRUE AGB.Le - AGB.Ray 65.2635135 64.08825 TRUE

124 Plot PQ5 > kruskal.test(pq5~eq5) > qqnorm(pq5) Kruskal-Wallis rank sum test > qqline(pq5) data: pq5 by eq5 Kruskal-Wallis chi-squared = 364.3507, df = 6, p-value < 2.2e-16 > kruskalmc(pq5, eq5) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference AGB.Chave - AGB.Fin 5.169492 85.33782 FALSE AGB.Chave - AGB.IPCC 14.389831 85.33782 FALSE AGB.Chave - AGB.Keith 6.779661 85.33782 FALSE AGB.Chave - AGB.Le 2.966102 85.33782 FALSE AGB.Chave - AGB.Ray 34.203390 85.33782 FALSE AGB.Fin - AGB.IPCC 9.220339 85.33782 FALSE AGB.Fin - AGB.Keith 1.610169 85.33782 FALSE AGB.Fin - AGB.Le 2.203390 85.33782 FALSE AGB.Fin - AGB.Ray 29.033898 85.33782 FALSE AGB.IPCC - AGB.Keith 7.610169 85.33782 FALSE AGB.IPCC - AGB.Le 11.423729 85.33782 FALSE AGB.IPCC - AGB.Ray 19.813559 85.33782 FALSE AGB.Keith - AGB.Le 3.813559 85.33782 FALSE AGB.Keith - AGB.Ray 27.423729 85.33782 FALSE AGB.Le - AGB.Ray 31.237288 85.33782 FALSE

Plot PQ6 > kruskal.test(pq6~eq6) > qqnorm(pq6) Kruskal-Wallis rank sum test > qqline(pq6) data: pq6 by eq6 Kruskal-Wallis chi-squared = 28.3659, df = 5, p-value = 3.087e-05 > kruskalmc(pq6, eq6) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference AGB.Chave - AGB.Fin 2.806818 67.50959 FALSE AGB.Chave - AGB.IPCC 18.170455 67.50959 FALSE AGB.Chave - AGB.Keith 26.272727 67.50959 FALSE AGB.Chave - AGB.Le 18.681818 67.50959 FALSE AGB.Chave - AGB.Ray 92.863636 67.50959 TRUE AGB.Fin - AGB.IPCC 20.977273 67.50959 FALSE AGB.Fin - AGB.Keith 23.465909 67.50959 FALSE AGB.Fin - AGB.Le 15.875000 67.50959 FALSE AGB.Fin - AGB.Ray 90.056818 67.50959 TRUE AGB.IPCC - AGB.Keith 44.443182 67.50959 FALSE AGB.IPCC - AGB.Le 36.852273 67.50959 FALSE AGB.IPCC - AGB.Ray 111.034091 67.50959 TRUE AGB.Keith - AGB.Le 7.590909 67.50959 FALSE AGB.Keith - AGB.Ray 66.590909 67.50959 FALSE AGB.Le - AGB.Ray 74.181818 67.50959 TRUE

Plot UM1 > kruskal.test(um1~equm1) > qqnorm(um1) Kruskal-Wallis rank sum test > qqline(um1) data: um1 by equm1 Kruskal-Wallis chi-squared = 38.6443, df = 5, p-value = 2.8e-07 > kruskalmc(um1~equm1) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference AGB.Chave - AGB.Fin 3.414773 67.50959 FALSE AGB.Chave - AGB.IPCC 19.318182 67.50959 FALSE AGB.Chave - AGB.Keith 32.937500 67.50959 FALSE AGB.Chave - AGB.Le 24.136364 67.50959 FALSE AGB.Chave - AGB.Ray 109.272727 67.50959 TRUE AGB.Fin - AGB.IPCC 22.732955 67.50959 FALSE AGB.Fin - AGB.Keith 29.522727 67.50959 FALSE AGB.Fin - AGB.Le 20.721591 67.50959 FALSE AGB.Fin - AGB.Ray 105.857955 67.50959 TRUE AGB.IPCC - AGB.Keith 52.255682 67.50959 FALSE AGB.IPCC - AGB.Le 43.454545 67.50959 FALSE AGB.IPCC - AGB.Ray 128.590909 67.50959 TRUE AGB.Keith - AGB.Le 8.801136 67.50959 FALSE AGB.Keith - AGB.Ray 76.335227 67.50959 TRUE AGB.Le - AGB.Ray 85.136364 67.50959 TRUE

125 Plot UM2 > kruskal.test(um2~equm2) > qqnorm(um2) Kruskal-Wallis rank sum test > qqline(um2) data: um2 by equm2 Kruskal-Wallis chi-squared = 50.1692, df = 5, p-value = 1.28e-09 > kruskalmc(um1,equm1) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference AGB.Chave - AGB.Fin 3.414773 67.50959 FALSE AGB.Chave - AGB.IPCC 19.318182 67.50959 FALSE AGB.Chave - AGB.Keith 32.937500 67.50959 FALSE AGB.Chave - AGB.Le 24.136364 67.50959 FALSE AGB.Chave - AGB.Ray 109.272727 67.50959 TRUE AGB.Fin - AGB.IPCC 22.732955 67.50959 FALSE AGB.Fin - AGB.Keith 29.522727 67.50959 FALSE AGB.Fin - AGB.Le 20.721591 67.50959 FALSE AGB.Fin - AGB.Ray 105.857955 67.50959 TRUE AGB.IPCC - AGB.Keith 52.255682 67.50959 FALSE AGB.IPCC - AGB.Le 43.454545 67.50959 FALSE AGB.IPCC - AGB.Ray 128.590909 67.50959 TRUE AGB.Keith - AGB.Le 8.801136 67.50959 FALSE AGB.Keith - AGB.Ray 76.335227 67.50959 TRUE AGB.Le - AGB.Ray 85.136364 67.50959 TRUE

Plot UM3 > kruskal.test(um3~equm3) > qqnorm(um3) Kruskal-Wallis rank sum test > qqline(um3) data: um3 by equm3 Kruskal-Wallis chi-squared = 61.4215, df = 5, p-value = 6.178e-12 > kruskalmc(um3,equm3) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference AGB.Chave - AGB.Fin 17.857143 34.23736 FALSE AGB.Chave - AGB.IPCC 18.857143 34.23736 FALSE AGB.Chave - AGB.Keith 9.333333 34.23736 FALSE AGB.Chave - AGB.Le 19.714286 34.23736 FALSE AGB.Chave - AGB.Ray 64.666667 34.23736 TRUE AGB.Fin - AGB.IPCC 36.714286 34.23736 TRUE AGB.Fin - AGB.Keith 8.523810 34.23736 FALSE AGB.Fin - AGB.Le 1.857143 34.23736 FALSE AGB.Fin - AGB.Ray 46.809524 34.23736 TRUE AGB.IPCC - AGB.Keith 28.190476 34.23736 FALSE AGB.IPCC - AGB.Le 38.571429 34.23736 TRUE AGB.IPCC - AGB.Ray 83.523810 34.23736 TRUE AGB.Keith - AGB.Le 10.380952 34.23736 FALSE AGB.Keith - AGB.Ray 55.333333 34.23736 TRUE AGB.Le - AGB.Ray 44.952381 34.23736 TRUE

Plot UM4 > kruskal.test(um4~equm4) > qqnorm(um4) Kruskal-Wallis rank sum test > qqline(um4) data: um4 by equm4 Kruskal-Wallis chi-squared = 52.309, df = 5, p-value = 4.662e-10 > kruskalmc(um4, equm4) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference AGB.Chave - AGB.Fin 21.666667 60.5332 FALSE AGB.Chave - AGB.IPCC 24.356061 60.5332 FALSE AGB.Chave - AGB.Keith 23.575758 60.5332 FALSE AGB.Chave - AGB.Le 31.340909 60.5332 FALSE AGB.Chave - AGB.Ray 110.409091 60.5332 TRUE AGB.Fin - AGB.IPCC 46.022727 60.5332 FALSE AGB.Fin - AGB.Keith 1.909091 60.5332 FALSE AGB.Fin - AGB.Le 9.674242 60.5332 FALSE AGB.Fin - AGB.Ray 88.742424 60.5332 TRUE AGB.IPCC - AGB.Keith 47.931818 60.5332 FALSE AGB.IPCC - AGB.Le 55.696970 60.5332 FALSE AGB.IPCC - AGB.Ray 134.765152 60.5332 TRUE AGB.Keith - AGB.Le 7.765152 60.5332 FALSE AGB.Keith - AGB.Ray 86.833333 60.5332 TRUE AGB.Le - AGB.Ray 79.068182 60.5332 TRUE

126 Plot UM5 > kruskal.test(um5~equm5) > qqnorm(um5) Kruskal-Wallis rank sum test > qqline(um5) data: um5 by equm5 Kruskal-Wallis chi-squared = 34.4796, df = 5, p-value = 1.911e-06 > kruskalmc(um5, equm5) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference AGB.Chave - AGB.Fin 6.925532 51.10832 FALSE AGB.Chave - AGB.IPCC 15.127660 51.10832 FALSE AGB.Chave - AGB.Keith 18.648936 51.10832 FALSE AGB.Chave - AGB.Le 17.191489 51.10832 FALSE AGB.Chave - AGB.Ray 75.765957 51.10832 TRUE AGB.Fin - AGB.IPCC 22.053191 51.10832 FALSE AGB.Fin - AGB.Keith 11.723404 51.10832 FALSE AGB.Fin - AGB.Le 10.265957 51.10832 FALSE AGB.Fin - AGB.Ray 68.840426 51.10832 TRUE AGB.IPCC - AGB.Keith 33.776596 51.10832 FALSE AGB.IPCC - AGB.Le 32.319149 51.10832 FALSE AGB.IPCC - AGB.Ray 90.893617 51.10832 TRUE AGB.Keith - AGB.Le 1.457447 51.10832 FALSE AGB.Keith - AGB.Ray 57.117021 51.10832 TRUE AGB.Le - AGB.Ray 58.574468 51.10832 TRUE

Plot UM6 > kruskal.test(um6~equm6) > qqnorm(um6) Kruskal-Wallis rank sum test > qqline(um6) data: um6 by equm6 Kruskal-Wallis chi-squared = 35.1999, df = 5, p-value = 1.373e-06 > kruskalmc(um6, equm6) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference AGB.Chave - AGB.Fin 12.0370370 38.78754 FALSE AGB.Chave - AGB.IPCC 13.0370370 38.78754 FALSE AGB.Chave - AGB.Keith 6.4444444 38.78754 FALSE AGB.Chave - AGB.Le 12.9629630 38.78754 FALSE AGB.Chave - AGB.Ray 57.4814815 38.78754 TRUE AGB.Fin - AGB.IPCC 25.0740741 38.78754 FALSE AGB.Fin - AGB.Keith 5.5925926 38.78754 FALSE AGB.Fin - AGB.Le 0.9259259 38.78754 FALSE AGB.Fin - AGB.Ray 45.4444444 38.78754 TRUE AGB.IPCC - AGB.Keith 19.4814815 38.78754 FALSE AGB.IPCC - AGB.Le 26.0000000 38.78754 FALSE AGB.IPCC - AGB.Ray 70.5185185 38.78754 TRUE AGB.Keith - AGB.Le 6.5185185 38.78754 FALSE AGB.Keith - AGB.Ray 51.0370370 38.78754 TRUE AGB.Le - AGB.Ray 44.5185185 38.78754 TRUE

Plot UM7 > kruskal.test(um7~equm7) > qqnorm(um7) Kruskal-Wallis rank sum test > qqline(um7) data: um7 by equm7 Kruskal-Wallis chi-squared = 43.0752, df = 5, p-value = 3.568e-08 > kruskalmc(um7,equm7) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference AGB.Chave - AGB.Fin 13.0172414 69.47831 FALSE AGB.Chave - AGB.IPCC 22.7413793 69.47831 FALSE AGB.Chave - AGB.Keith 26.3390805 69.47831 FALSE AGB.Chave - AGB.Le 26.4885057 69.47831 FALSE AGB.Chave - AGB.Ray 115.6896552 69.47831 TRUE AGB.Fin - AGB.IPCC 35.7586207 69.47831 FALSE AGB.Fin - AGB.Keith 13.3218391 69.47831 FALSE AGB.Fin - AGB.Le 13.4712644 69.47831 FALSE AGB.Fin - AGB.Ray 102.6724138 69.47831 TRUE AGB.IPCC - AGB.Keith 49.0804598 69.47831 FALSE AGB.IPCC - AGB.Le 49.2298851 69.47831 FALSE AGB.IPCC - AGB.Ray 138.4310345 69.47831 TRUE AGB.Keith - AGB.Le 0.1494253 69.47831 FALSE AGB.Keith - AGB.Ray 89.3505747 69.47831 TRUE AGB.Le - AGB.Ray 89.2011494 69.47831 TRUE

127 Plot UM8 > kruskal.test(um8~equm8) > qqnorm(um8) Kruskal-Wallis rank sum test > qqline(um8) data: um8 by equm8 Kruskal-Wallis chi-squared = 14.3254, df = 5, p-value = 0.01367 > kruskalmc(um8, equm8) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference AGB.Chave - AGB.Fin 2.421569 53.23136 FALSE AGB.Chave - AGB.IPCC 9.264706 53.23136 FALSE AGB.Chave - AGB.Keith 13.823529 53.23136 FALSE AGB.Chave - AGB.Le 10.774510 53.23136 FALSE AGB.Chave - AGB.Ray 50.833333 53.23136 FALSE AGB.Fin - AGB.IPCC 11.686275 53.23136 FALSE AGB.Fin - AGB.Keith 11.401961 53.23136 FALSE AGB.Fin - AGB.Le 8.352941 53.23136 FALSE AGB.Fin - AGB.Ray 48.411765 53.23136 FALSE AGB.IPCC - AGB.Keith 23.088235 53.23136 FALSE AGB.IPCC - AGB.Le 20.039216 53.23136 FALSE AGB.IPCC - AGB.Ray 60.098039 53.23136 TRUE AGB.Keith - AGB.Le 3.049020 53.23136 FALSE AGB.Keith - AGB.Ray 37.009804 53.23136 FALSE AGB.Le - AGB.Ray 40.058824 53.23136 FALSE

Appendix A1c: Summary of pairwise comparisons of allometric equation tests for root biomass Kenzo et al. IPCC (Cairn et al. Mokany et al. Niiyama et al (2009) (1997)) (2006) (2010) Kenzo et al. (2009) F F F IPCC (Cairn et al. (1997)) F F Mokany et al. (2006) F

Niiyama et al (2010)

> TestEquR <- read.csv("K:/PhD PROGRAM/A-PhD PROPOSAL/FIELD DATA/DATA ANALYSIS/CSV FILES/VN/testEquVN/TestEquR.csv") > qqnorm(Rmass) > kruskal.test(Rmass~Requ) > qqline(Rmass) Kruskal-Wallis rank sum test

data: Rmass by Requ Kruskal-Wallis chi-squared = 7.9318, df = 3, p-value = 0.04744

> kruskalmc(Rmass, Requ) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference IPCC (Cairn et al. (1997))-Kenzo et al. (2009) 11.5714286 16.26331 FALSE IPCC (Cairn et al. (1997))-Mokany et al. (2006) 3.0714286 16.26331 FALSE IPCC (Cairn et al. (1997))-Niiyama et al. (2010) 3.6428571 16.26331 FALSE Kenzo et al. (2009)-Mokany et al. (2006) 14.6428571 16.26331 FALSE Kenzo et al. (2009)-Niiyama et al. (2010) 15.2142857 16.26331 FALSE Mokany et al. (2006)-Niiyama et al. (2010) 0.5714286 16.26331 FALSE

128 Appendix A2: DATA ANALYSIS

Appendix A2a: Tree densities of all classes >describe.by(sum, group=typeVS) group: VS1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 4 2330 1115.97 2110 2330 778.37 1230 3870 2640 0.41 -1.85 557.99 ------group: VS2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 2 10950 5020.46 10950 10950 5263.23 7400 14500 7100 0 -2.75 3550 ------group: VS3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 2 980 791.96 980 980 830.26 420 1540 1120 0 -2.75 560 ------group: VS4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 9833.33 3924.71 8700 9833.33 3113.46 6600 14200 7600 0.26 -2.33 2265.93 ------group: VS5 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 6866.67 3412.23 5100 6866.67 593.04 4700 10800 6100 0.38 -2.33 1970.05 qqnorm(sum) >kruskal.test(sum~typeVS) >qqline(sum) Kruskal-Wallis rank sum test > data: sum by typeVS Kruskal-Wallis chi-squared = 10.6714, df = 4, p-value = 0.03052 >kruskalmc(sum, typeVS) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference VS1-VS2 7.75 10.169446 FALSE VS1-VS3 2.25 10.169446 FALSE VS1-VS4 6.75 8.968608 FALSE VS1-VS5 4.75 8.968608 FALSE VS2-VS3 10.00 11.742665 FALSE VS2-VS4 1.00 10.719537 FALSE VS2-VS5 3.00 10.719537 FALSE VS3-VS4 9.00 10.719537 FALSE VS3-VS5 7.00 10.719537 FALSE VS4-VS5 2.00 9.587846 FALSE

129 Appendix A2b: DBH of all classes >describe.by(dbh, group=typeVS) group: VS1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 277 16.71 9.13 15.29 15.9 6.61 2.55 60.51 57.96 1.16 2.58 0.55 ------group: VS2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 219 5.36 2.07 5.1 5.19 2.36 2.55 9.9 7.35 0.59 -0.66 0.14 ------group: VS3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 58 12.93 5.41 12.25 12.43 2.75 3.98 35.35 31.37 1.52 4.18 0.71 ------group: VS4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 295 5.88 2.12 5.41 5.76 2.13 2.55 9.9 7.35 0.47 -0.88 0.12 ------group: VS5 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 206 6.2 2.07 6.13 6.15 2.48 1.27 9.9 8.63 0.13 -0.99 0.14

>qqnorm(dbh) >kruskal.test(dbh~typeVS) >qqline(dbh) Kruskal-Wallis rank sum test data: dbh by typeVS Kruskal-Wallis chi-squared = 446.8565, df = 4, p- value < 2.2e-16 >kruskalmc(dbh, typeVS) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference VS1 - VS2 475.21488 77.33821 TRUE VS1 - VS3 24.90415 123.50477 FALSE VS1 - VS4 419.05727 71.55868 TRUE VS1 - VS5 380.23960 78.68923 TRUE VS2 - VS3 450.31074 126.30456 TRUE VS2 - VS4 56.15761 76.28931 FALSE VS2 - VS5 94.97528 83.01451 TRUE VS3 - VS4 394.15313 122.85068 TRUE VS3 - VS5 355.33545 127.13630 TRUE VS4 - VS5 38.81767 77.65858 FALSE

130 Appendix A2c: Total height of all classes >describe.by(H, group=typeVS) group: VS1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 277 14.69 5 15 14.85 5.19 2.5 26 23.5 -0.28 -0.56 0.3 ------group: VS2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 219 7.11 1.88 7 7.15 1.48 2 11 9 -0.17 -0.49 0.13 ------group: VS3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 58 9.69 3.43 10.25 9.75 4.08 3.5 15.5 12 -0.19 -1.17 0.45 ------group: VS4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 295 5.68 2.31 5 5.39 1.48 2 12.5 10.5 1.22 1.4 0.13 ------group: VS5 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 206 7.5 3.62 6 7.23 2.59 2 14.5 12.5 0.64 -1.08 0.25

>qqnorm(H) >kruskal.test(H~typeVS) >qqline(H) Kruskal-Wallis rank sum test data: H by typeVS Kruskal-Wallis chi-squared = 464.9131, df = 4, p-value < 2.2e-16 >kruskalmc(H, typeVS) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference VS1 - VS2 360.56863 77.33821 TRUE VS1 - VS3 212.16330 123.50477 TRUE VS1 - VS4 526.81236 71.55868 TRUE VS1 - VS5 394.02997 78.68923 TRUE VS2 - VS3 148.40533 126.30456 TRUE VS2 - VS4 166.24374 76.28931 TRUE VS2 - VS5 33.46134 83.01451 FALSE VS3 - VS4 314.64906 122.85068 TRUE VS3 - VS5 181.86667 127.13630 TRUE VS4 - VS5 132.78239 77.65858 TRUE

131 Appendix A2d: Basal areas of all classes >describe.by(BA, group=typeVS) group: VS1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 4 41.54 12.33 41.44 41.54 12.6 26.73 56.57 29.84 0.02 -1.93 6.16 ------group: VS2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 2 28.41 4.45 28.41 28.41 4.66 25.26 31.55 6.29 0 -2.75 3.14 ------group: VS3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 2 10.29 6.7 10.29 10.29 7.03 5.55 15.03 9.48 0 -2.75 4.74 ------group: VS4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 30.14 2.53 31.52 30.14 0.24 27.22 31.68 4.46 -0.38 -2.33 1.46 ------group: VS5 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 23.02 14.78 15.49 23.02 2.91 13.53 40.05 26.52 0.38 -2.33 8.53 >qqnorm(BA) Df Sum Sq Mean Sq F value Pr(>F) >qqline(BA) typeVS 4 1440.5 360.1 3.341 0.0614 . > Residuals 9 970.1 107.8 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 >TukeyHSD(av) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = BA ~ typeVS) $typeVS diff lwr upr p adj VS2-VS1 -13.137500 -43.37103 17.09603 0.6083603 VS3-VS1 -31.252500 -61.48603 -1.01897 0.0423666 VS4-VS1 -11.402500 -38.06597 15.26097 0.6212885 VS5-VS1 -18.519167 -45.18263 8.14430 0.2181402 VS3-VS2 -18.115000 -53.02567 16.79567 0.4559533 VS4-VS2 1.735000 -30.13394 33.60394 0.9996959 VS5-VS2 -5.381667 -37.25061 26.48727 0.9766591 VS4-VS3 19.850000 -12.01894 51.71894 0.2998401 VS5-VS3 12.733333 -19.13561 44.60227 0.6739544 VS5-VS4 -7.116667 -35.62111 21.38778 0.9114682

132 Appendix A2e: Total carbon densities

>describe.by(totalC, group=typeVS) group: VS1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 4 275.98 77.23 267.26 275.98 56.85 190.9 378.47 187.57 0.25 -1.86 38.62 ------group: VS2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 2 159.36 29.72 159.36 159.36 31.16 138.34 180.37 42.03 0 -2.75 21.01 ------group: VS3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 2 784.68 77.39 784.68 784.68 81.13 729.96 839.4 109.44 0 -2.75 54.72 ------group: VS4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 544.28 270.66 512.42 544.28 328.32 290.97 829.46 538.49 0.12 -2.33 56.26 ------group: VS5 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 246.96 47.73 274.47 246.96 0.15 191.85 274.57 82.72 -0.38 -2.33 27.56 >qqnorm(totalC) >kruskal.test(totalC~typeVS) >qqline(totalC) Kruskal-Wallis rank sum test data: totalC by typeVS Kruskal-Wallis chi-squared = 10.419, df = 4, p- value = 0.03393 >kruskalmc(totalC, typeVS) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference VS1-VS2 4.5000000 10.169446 FALSE VS1-VS3 7.0000000 10.169446 FALSE VS1-VS4 5.0000000 8.968608 FALSE VS1-VS5 0.3333333 8.968608 FALSE VS2-VS3 11.5000000 11.742665 FALSE VS2-VS4 9.5000000 10.719537 FALSE

VS2-VS5 4.8333333 10.719537 FALSE VS3-VS4 2.0000000 10.719537 FALSE VS3-VS5 6.6666667 10.719537 FALSE VS4-VS5 4.6666667 9.587846 FALSE

133 Appendix A2f: Stand carbon densities >describe.by(agbC, group=typeVS) group: VS1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 20 110.67 43.21 101.03 105.55 35.56 57.65 234.55 176.9 1.17 1.01 9.66 ------group: VS2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 10 44.27 6.04 42.89 43.73 6.84 36.96 55.98 19.02 0.48 -1.05 1.91 ------group: VS3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 10 22.79 12.79 21.59 21.6 16.18 9.92 45.15 35.23 0.33 -1.58 4.04 ------group: VS4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 15 48.25 9.06 48 47.91 8.69 34.67 66.17 31.5 0.32 -0.76 2.34 ------group: VS5 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 15 37.2 21.58 25.49 35.31 7.69 19.15 79.79 60.64 0.74 -1.27 5.57 >qqnorm(agbC) >kruskal.test(agbC~typeVS) >qqline(agbC) Kruskal-Wallis rank sum test data: agbC by typeVS Kruskal-Wallis chi-squared = 48.3184, df = 4, p-value = 8.1e-10 >kruskalmc(agbC, typeVS) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference VS1 - VS2 29.350000 22.12488 TRUE VS1 - VS3 48.350000 22.12488 TRUE VS1 - VS4 25.083333 19.51231 TRUE VS1 - VS5 36.283333 19.51231 TRUE VS2 - VS3 19.000000 25.54761 FALSE VS2 - VS4 4.266667 23.32167 FALSE VS2 - VS5 6.933333 23.32167 FALSE VS3 - VS4 23.266667 23.32167 FALSE VS3 - VS5 12.066667 23.32167 FALSE VS4 - VS5 11.200000 20.85953 FALSE

134 Appendix A2g: Understorey carbon densities >describe.by(underC, group=typeVS) group: VS1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 15 2.45 1.2 2.36 2.39 0.85 0.87 4.85 3.98 0.72 -0.5 0.31 ------group: VS2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 5 2.48 0.56 2.25 2.48 0.64 1.82 3.25 1.43 0.2 -1.89 0.25 ------group: VS3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 10 6.23 2.52 6.26 6.18 3.8 3.27 9.58 6.31 0.05 -1.89 0.8 ------group: VS4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 15 1.65 0.99 1.15 1.54 0.71 0.54 4.12 3.58 0.95 0.02 0.26 ------group: VS5 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 10 5.27 3.48 4.73 5.12 4.36 1.54 10.16 8.62 0.13 -1.97 1.1

>qqnorm(underC) >kruskal.test(underC~typeVS) >qqline(underC) Kruskal-Wallis rank sum test > data: underC by typeVS Kruskal-Wallis chi-squared = 30.7189, df = 4, p- value = 3.493e-06 >kruskalmc(underC, typeVS) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference VS1 - VS2 6.775 22.12488 FALSE VS1 - VS3 28.825 22.12488 TRUE VS1 - VS4 7.350 18.06489 FALSE VS1 - VS5 19.425 22.12488 FALSE VS2 - VS3 35.600 25.54761 TRUE VS2 - VS4 0.575 22.12488 FALSE VS2 - VS5 26.200 25.54761 TRUE VS3 - VS4 36.175 22.12488 TRUE VS3 - VS5 9.400 25.54761 FALSE

VS4 - VS5 26.775 22.12488 TRUE

135 Appendix A2h: Deadwood carbon densities >describe.by(D.woodC, group=typeVS) group: VS1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 4 30.47 53.59 5.7 30.47 8.29 0 110.48 110.48 0.73 -1.7 26.8 ------group: VS2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 2 0 0.01 0 0 0.01 0 0.01 0.01 0 -2.75 0 ------group: VS3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 2 67.9 67.39 67.9 67.9 70.65 20.25 115.55 95.3 0 -2.75 47.65 ------group: VS4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 45.06 51.53 33.94 45.06 50.32 0 101.24 101.24 0.21 -2.33 29.75 ------group: VS5 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 74.59 66.64 95.48 74.59 48.63 0 128.28 128.28 -0.28 -2.33 38.48

>qqnorm(D.woodC) >kruskal.test(D.woodC~typeVS) >qqline(D.woodC) Kruskal-Wallis rank sum test > data: D.woodC by typeVS Kruskal-Wallis chi-squared = 3.0978, df = 4, p-value = 0.5416 >kruskalmc(D.woodC, typeVS) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference VS1 - VS2 3.125000 10.169446 FALSE VS1 - VS3 3.625000 10.169446 FALSE VS1 - VS4 0.625000 8.968608 FALSE VS1 - VS5 1.958333 8.968608 FALSE VS2 - VS3 6.750000 11.742665 FALSE VS2 - VS4 3.750000 10.719537 FALSE VS2 - VS5 5.083333 10.719537 FALSE VS3 - VS4 3.000000 10.719537 FALSE VS3 - VS5 1.666667 10.719537 FALSE VS4 - VS5 1.333333 9.587846 FALSE

136 Appendix A2i: Litter carbon densities >describe.by(litC, group=typeVS) group: VS1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 4 31.03 23.52 33.12 31.03 22.18 0.94 56.94 56 -0.18 -1.96 11.76 ------group: VS2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 2 14.45 19.28 14.45 14.45 20.21 0.82 28.08 27.26 0 -2.75 13.63 ------group: VS3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 2 23.75 30.98 23.75 23.75 32.48 1.85 45.66 43.81 0 -2.75 21.9 ------group: VS4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 39.54 35.95 42.31 39.54 47.01 2.29 74.02 71.73 -0.08 -2.33 20.75 ------group: VS5 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 39.23 61.96 4.31 39.23 2.54 2.6 110.77 108.17 0.38 -2.33 35.78

>qqnorm(litC) > kruskal.test(litC~typeVS) >qqline(litC) Kruskal-Wallis rank sum test > data: litC by typeVS Kruskal-Wallis chi-squared = 1.5619, df = 4, p-value = 0.8156 > kruskalmc(litC, typeVS) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference VS1 - VS2 3.0000000 10.169446 FALSE VS1 - VS3 0.5000000 10.169446 FALSE VS1 - VS4 1.5000000 8.968608 FALSE VS1 - VS5 0.8333333 8.968608 FALSE VS2 - VS3 2.5000000 11.742665 FALSE VS2 - VS4 4.5000000 10.719537 FALSE VS2 - VS5 3.8333333 10.719537 FALSE VS3 - VS4 2.0000000 10.719537 FALSE VS3 - VS5 1.3333333 10.719537 FALSE VS4 - VS5 0.6666667 9.587846 FALSE

137 Appendix A2j: Peat carbon densities >describe.by(Cp2, group=typeVS) group: VS3 vars n mean sd median trimmed mad min max range skew kurtosisse

1 1 10 479.62 160.68 506.47 488.16 144.75 183.06 707.83 524.77 -0.27 -1.0250.81 ------group: VS4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 9 294.57 152.27 208.69 294.57 126.05 123.67 479.21 355.54 0.18 -2.0650.76

>kruskal.test(Cp2~typeVS) Kruskal-Wallis rank sum test data: Cp2 by typeVS Kruskal-Wallis chi-squared = 5.2359, df = 1, p-value = 0.02213 >kruskalmc(Cp2, typeVS) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference VS3-VS4 5.911111 5.067629 TRUE

138 Appendix A2k: Root carbon densities >describe.by(Cr, group=typeVS) group: VS1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 16 22.75 13.92 20.71 21.55 14.31 6.02 56.21 50.19 0.88 -0.15 3.48 ------group: VS2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 8 16.97 8.44 12.88 16.97 5.5 7.02 30.95 23.93 0.48 -1.55 2.98 ------group: VS3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 8 11.97 4.36 10.13 11.97 3.71 5.83 17.76 11.93 0.2 -1.7 1.54 ------group: VS4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 12 6.99 4.62 6.31 6.76 4.55 1.6 14.63 13.03 0.61 -1.27 1.33 ------group: VS5 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 12 8.35 3.67 7.96 8.3 3.74 3.12 14.01 10.89 0.27 -1.47 1.06

> qqnorm(Cr) > kruskal.test(Cr~typeVS) > qqline(Cr) Kruskal-Wallis rank sum test data: Cr by typeVS Kruskal-Wallis chi-squared = 22.437, df = 4, p-value = 0.000164 > kruskalmc(Cr, typeVS) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference VS1-VS2 3.812500 19.82390 FALSE VS1-VS3 11.562500 19.82390 FALSE VS1-VS4 24.854167 17.48304 TRUE VS1-VS5 21.770833 17.48304 TRUE VS2-VS3 7.750000 22.89067 FALSE VS2-VS4 21.041667 20.89623 TRUE VS2-VS5 17.958333 20.89623 FALSE VS3-VS4 13.291667 20.89623 FALSE VS3-VS5 10.208333 20.89623 FALSE VS4-VS5 3.083333 18.69015 FALSE

139 Appendix A2k: Soil carbon densities group: VS1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 4 75.81 36.37 90.18 75.81 10.59 21.97 100.93 78.96 -0.69 -1.73 18.18 ------group: VS2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 2 89.22 13.87 89.22 89.22 14.54 79.41 99.02 19.61 0 -2.75 9.8 ------group: VS3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 2 178.93 128.61 178.93 178.93 134.83 87.99 269.87 181.88 0 -2.75 90.94 ------group: VS4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 93.94 64 62.69 93.94 16.49 51.57 167.56 115.99 0.37 -2.33 36.95 ------group: VS5 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 83.58 25.29 82.02 83.58 33.97 59.11 109.61 50.5 0.06 -2.33 14.6

>qqnorm(Csoil30) >kruskal.test(Csoil30~typeVS) >qqline(Csoil30) Kruskal-Wallis rank sum test data: Csoil30 by typeVS Kruskal-Wallis chi-squared = 1.7333, df = 4, p-value = 0.7847 >kruskalmc(Csoil30, typeVS) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference VS1-VS2 0.5000000 10.633770 FALSE VS1-VS3 4.0000000 10.633770 FALSE VS1-VS4 0.6666667 9.378104 FALSE VS1-VS5 0.0000000 9.378104 FALSE VS2-VS3 3.5000000 12.278820 FALSE VS2-VS4 1.1666667 11.208978 FALSE VS2-VS5 0.5000000 11.208978 FALSE VS3-VS4 4.6666667 11.208978 FALSE VS3-VS5 4.0000000 11.208978 FALSE VS4-VS5 0.6666667 10.025615 FALSE

140 Appendix B: Statistic analyses of Melaleuca data collected from Australia’s sites

Appendix B1: ALLOWMETRIC EQUATION TESTS

Appendix B1a: Summary of pairwise comparisons of allometric equation tests for above-ground biomass AGB.Ray AGB.Fin AGB.Keith AGB.IPCC AGB.Chave T F F, T, F, T, F, F, F, T, F, T, F, F, F, T, F, T, F, F, F, T, F, T, F, F, 40 32 T, T, F, T, F, T, T, T, F, T, F, T, T, T, F, T, F, T, T, T, F, T, F, T, AGB.Ray F, T, T, T, T, F F, T, T, T, T, F F, T, T, T, T, F F, T, T, T, T, F F, F, F, F, F, F, F, T, F, T, F, F, F, T, F, F, F, F, 8 46 F, F, F, F, F, F, T, T, F, F, F, T, F, F, F, F, F, F, AGB.Fin F, F, F, F, F, F F, F, F, T, T, F F, F, F, F, F, F F, T, F, F, F, F, F, F, F, F, F, F, 3 33 F, F, F, F, F, F, F, F, F, F, F, F, AGB.Keith F, T, T, F, F, F F, F, F, F, F, F F, T, F, F, F, F, 1 17 F, F, F, F, F, F, AGB.IPCC F, F, F, F, F, F

AGB.Chave T 10 10 20 12 F 8 26 34 60

Appendix B1b: Percentages of pairwise comparisons of allometric equation tests for above-ground biomass Allometric Equations for AGB References Code Count “T” % Count “F” % Rayachhetry et al. (2001) ln(y) = 2.0409ln(D) - 2.0163 AGB.Ray 40 55.56 32 44.44

Log10(FW) = 2.266log10(D) - 0.502 Finlayson et al. (1993) AGB.Fin 18 25.00 54 75.00 ln(y) = 2.4855ln(x) - 2.3267] Keith et al. (2000) AGB.Keith 13 18.06 59 81.94 IPCC (2003) or Brown y = exp[-2.134 + 2.53ln(D)] AGB.IPCC 21 29.17 51 70.83 (1997) ln(AGB) = - 1,554 + 2.420ln(D) Chave et al. (2005) AGB.Chave 12 16.67 60 83.33 +ln(r)

Plot 1 Plot 2 qqnorm(P1) qqnorm(P2) > qqline(P1) > qqline(P2)

> > > kruskal.test(P2~G2) > kruskal.test(P1~G1) Kruskal-Wallis rank sum test Kruskal-Wallis rank sum test data: P2 by G2 data: P1 by G1 Kruskal-Wallis chi-squared = 311.1142, df = 4, p-value < Kruskal-Wallis chi-squared = 968.6377, df = 5, p-value < 2.2e-16 2.2e-16 > kruskalmc(P2, G2) > kruskalmc(P1, G1) Multiple comparison test after Kruskal-Wallis Multiple comparison test after Kruskal-Wallis p.value: 0.05 p.value: 0.05 Comparisons Comparisons obs.dif critical.dif difference obs.dif critical.dif difference AGB.Fin - AGB.Ray 248.97842 70.68896 TRUE AGB.Fin - AGB.Ray 22.981132 164.6801 FALSE AGB.Fin -AGB.Chave 71.69065 70.68896 TRUE AGB.Fin -AGB.Chave 2.226415 164.6801 FALSE AGB.Fin -AGB.IPCC 147.96403 70.68896 TRUE AGB.Fin -AGB.IPCC 19.716981 164.6801 FALSE AGB.Fin -AGB.Keith 34.57554 70.68896 FALSE AGB.Fin -AGB.Keith 5.169811 164.6801 FALSE AGB.Ray -AGB.Chave 320.66906 70.68896 TRUE AGB.Ray -AGB.Chave 20.754717 164.6801 FALSE AGB.Ray -AGB.IPCC 396.94245 70.68896 TRUE AGB.Ray -AGB.IPCC 3.264151 164.6801 FALSE AGB.Ray -AGB.Keith 283.55396 70.68896 TRUE AGB.Ray -AGB.Keith 17.811321 164.6801 FALSE AGB.Chave-AGB.IPCC 76.27338 70.68896 TRUE AGB.Chave-AGB.IPCC 17.490566 164.6801 FALSE AGB.Chave-AGB.Keith 37.11511 70.68896 FALSE AGB.Chave-AGB.Keith 2.943396 164.6801 FALSE AGB.IPCC-AGB.Keith 113.38849 70.68896 TRUE AGB.IPCC-AGB.Keith 14.547170 164.6801 FALSE

141

Plot 3 Plot 4 qqnorm(P3) qqnorm(P4) > qqline(P3) > qqline(P4)

> > > kruskal.test(P3~G3) > kruskal.test(P4~G4)

Kruskal-Wallis rank sum test Kruskal-Wallis rank sum test data: P3 by G3 data: P4 by G4 Kruskal-Wallis chi-squared = 983.8829, df = 5, p-value < Kruskal-Wallis chi-squared = 98.3499, df = 4, p-value < 2.2e-16 2.2e-16 > kruskalmc(P3, G3) > kruskalmc(P4, G4) Multiple comparison test after Kruskal-Wallis Multiple comparison test after Kruskal-Wallis p.value: 0.05 p.value: 0.05 Comparisons Comparisons obs.dif critical.dif difference obs.dif critical.dif difference AGB.Fin - AGB.Ray 1.625 189.561 FALSE AGB.Fin - AGB.Ray 98.40278 50.90971 TRUE AGB.Fin -AGB.Chave 28.550 189.561 FALSE AGB.Fin -AGB.Chave 31.19444 50.90971 FALSE AGB.Fin -AGB.IPCC 41.525 189.561 FALSE AGB.Fin -AGB.IPCC 60.77778 50.90971 TRUE AGB.Fin -AGB.Keith 23.425 189.561 FALSE AGB.Fin -AGB.Keith 22.19444 50.90971 FALSE AGB.Ray -AGB.Chave 26.925 189.561 FALSE AGB.Ray -AGB.Chave 129.59722 50.90971 TRUE AGB.Ray -AGB.IPCC 39.900 189.561 FALSE AGB.Ray -AGB.IPCC 159.18056 50.90971 TRUE AGB.Ray -AGB.Keith 21.800 189.561 FALSE AGB.Ray -AGB.Keith 120.59722 50.90971 TRUE AGB.Chave-AGB.IPCC 12.975 189.561 FALSE AGB.Chave-AGB.IPCC 29.58333 50.90971 FALSE AGB.Chave-AGB.Keith 5.125 189.561 FALSE AGB.Chave-AGB.Keith 9.00000 50.90971 FALSE AGB.IPCC-AGB.Keith 18.100 189.561 FALSE AGB.IPCC-AGB.Keith 38.58333 50.90971 FALSE

Plot 5 Plot 6 qqnorm(P5) qqnorm(P6) > qqline(P5) > qqline(P6)

> > kruskal.test(P5~G5) > Kruskal-Wallis rank sum test > kruskal.test(P6~G6) data: P5 by G5 Kruskal-Wallis rank sum test Kruskal-Wallis chi-squared = 895.2766, df = 5, p-value < data: P6 by G6 2.2e-16 Kruskal-Wallis chi-squared = 992.6151, df = 5, p-value > kruskalmc(P5, G5) < 2.2e-16 Multiple comparison test after Kruskal-Wallis > kruskalmc(P5, G5) p.value: 0.05 Multiple comparison test after Kruskal-Wallis Comparisons p.value: 0.05 obs.dif critical.dif difference Comparisons AGB.Fin - AGB.Ray 48.460674 127.0820 FALSE obs.dif critical.dif difference AGB.Fin -AGB.Chave 36.831461 127.0820 FALSE AGB.Fin - AGB.Ray 48.460674 127.0820 FALSE AGB.Fin -AGB.IPCC 56.224719 127.0820 FALSE AGB.Fin -AGB.Chave 36.831461 127.0820 FALSE AGB.Fin -AGB.Keith 27.359551 127.0820 FALSE AGB.Fin -AGB.IPCC 56.224719 127.0820 FALSE AGB.Ray -AGB.Chave 11.629213 127.0820 FALSE AGB.Fin -AGB.Keith 27.359551 127.0820 FALSE AGB.Ray -AGB.IPCC 7.764045 127.0820 FALSE AGB.Ray -AGB.Chave 11.629213 127.0820 FALSE AGB.Ray -AGB.Keith 21.101124 127.0820 FALSE AGB.Ray -AGB.IPCC 7.764045 127.0820 FALSE AGB.Chave-AGB.IPCC 19.393258 127.0820 FALSE AGB.Ray -AGB.Keith 21.101124 127.0820 FALSE AGB.Chave-AGB.Keith 9.471910 127.0820 FALSE AGB.Chave-AGB.IPCC 19.393258 127.0820 FALSE AGB.IPCC-AGB.Keith 28.865169 127.0820 FALSE AGB.Chave-AGB.Keith 9.471910 127.0820 FALSE AGB.IPCC-AGB.Keith 28.865169 127.0820 FALSE

142

Plot 7 Plot 8 qqnorm(P7) qqnorm(P8) > qqline(P7) > qqline(P8)

> > > kruskal.test(P7~G7) > kruskal.test(P8~G8)

Kruskal-Wallis rank sum test Kruskal-Wallis rank sum test data: P7 by G7 data: P8 by G8 Kruskal-Wallis chi-squared = 53.1309, df = 4, p-value = Kruskal-Wallis chi-squared = 67.1746, df = 4, p-value = 8.001e-11 8.956e-14 > kruskalmc(P7, G7) > kruskalmc(P8, G8) Multiple comparison test after Kruskal-Wallis Multiple comparison test after Kruskal-Wallis p.value: 0.05 p.value: 0.05 Comparisons Comparisons obs.dif critical.dif difference obs.dif critical.dif difference AGB.Fin - AGB.Ray 55.94 42.4506 TRUE AGB.Fin - AGB.Ray 74.65574 46.87138 TRUE AGB.Fin -AGB.Chave 21.58 42.4506 FALSE AGB.Fin -AGB.Chave 25.40984 46.87138 FALSE AGB.Fin -AGB.IPCC 43.00 42.4506 TRUE AGB.Fin -AGB.IPCC 47.09836 46.87138 TRUE AGB.Fin -AGB.Keith 14.96 42.4506 FALSE AGB.Fin -AGB.Keith 11.98361 46.87138 FALSE AGB.Ray -AGB.Chave 77.52 42.4506 TRUE AGB.Ray -AGB.Chave 100.06557 46.87138 TRUE AGB.Ray -AGB.IPCC 98.94 42.4506 TRUE AGB.Ray -AGB.IPCC 121.75410 46.87138 TRUE AGB.Ray -AGB.Keith 70.90 42.4506 TRUE AGB.Ray -AGB.Keith 86.63934 46.87138 TRUE AGB.Chave-AGB.IPCC 21.42 42.4506 FALSE AGB.Chave-AGB.IPCC 21.68852 46.87138 FALSE AGB.Chave-AGB.Keith 6.62 42.4506 FALSE AGB.Chave-AGB.Keith 13.42623 46.87138 FALSE AGB.IPCC-AGB.Keith 28.04 42.4506 FALSE AGB.IPCC-AGB.Keith 35.11475 46.87138 FALSE

Plot 9 Plot 10 qqnorm(P9) qqnorm(P10) > qqline(P9) > qqline(P10)

> > kruskal.test(P9~G9) > kruskal.test(P10~G10) Kruskal-Wallis rank sum test Kruskal-Wallis rank sum test data: P9 by G9 data: P10 by G10 Kruskal-Wallis chi-squared = 988.491, df = 5, p-value < Kruskal-Wallis chi-squared = 42.7578, df = 4, p-value = 2.2e-16 1.162e-08 > kruskalmc(P9, G9) > kruskalmc(P10, G10) Multiple comparison test after Kruskal-Wallis Multiple comparison test after Kruskal-Wallis p.value: 0.05 p.value: 0.05 Comparisons Comparisons obs.dif critical.dif difference obs.dif critical.dif difference AGB.Fin - AGB.Ray 2.424242 208.6998 FALSE AGB.Fin - AGB.Ray 46.605263 37.03085 TRUE AGB.Fin -AGB.Chave 17.000000 208.6998 FALSE AGB.Fin -AGB.Chave 15.947368 37.03085 FALSE AGB.Fin -AGB.IPCC 9.363636 208.6998 FALSE AGB.Fin -AGB.IPCC 29.842105 37.03085 FALSE AGB.Fin -AGB.Keith 3.636364 208.6998 FALSE AGB.Fin -AGB.Keith 10.157895 37.03085 FALSE AGB.Ray -AGB.Chave 19.424242 208.6998 FALSE AGB.Ray -AGB.Chave 62.552632 37.03085 TRUE AGB.Ray -AGB.IPCC 11.787879 208.6998 FALSE AGB.Ray -AGB.IPCC 76.447368 37.03085 TRUE AGB.Ray -AGB.Keith 6.060606 208.6998 FALSE AGB.Ray -AGB.Keith 56.763158 37.03085 TRUE AGB.Chave-AGB.IPCC 7.636364 208.6998 FALSE AGB.Chave-AGB.IPCC 13.894737 37.03085 FALSE AGB.Chave-AGB.Keith 13.363636 208.6998 FALSE AGB.Chave-AGB.Keith 5.789474 37.03085 FALSE AGB.IPCC-AGB.Keith 5.727273 208.6998 FALSE AGB.IPCC-AGB.Keith 19.684211 37.03085 FALSE

143

Plot 11 Plot 12 qqnorm(P11) > qqnorm(P12) > qqline(P11) > qqline(P12) >

> > > kruskal.test(P12~G12) > kruskal.test(P11~G11) Kruskal-Wallis rank sum test Kruskal-Wallis rank sum test data: P12 by G12 data: P11 by G11 Kruskal-Wallis chi-squared = 132.2813, df = 4, p-value < Kruskal-Wallis chi-squared = 934.431, df = 5, p-value < 2.2e-16 2.2e-16 > kruskalmc(P12, G12) > kruskalmc(P11, G11) Multiple comparison test after Kruskal-Wallis Multiple comparison test after Kruskal-Wallis p.value: 0.05 p.value: 0.05 Comparisons Comparisons obs.dif critical.dif difference obs.dif critical.dif difference AGB.Fin - AGB.Ray 137.25253 59.67437 TRUE AGB.Fin - AGB.Ray 26.698630 140.3194 FALSE AGB.Fin -AGB.Chave 39.85859 59.67437 FALSE AGB.Fin -AGB.Chave 3.178082 140.3194 FALSE AGB.Fin -AGB.IPCC 79.62626 59.67437 TRUE AGB.Fin -AGB.IPCC 10.369863 140.3194 FALSE AGB.Fin -AGB.Keith 24.03030 59.67437 FALSE AGB.Fin -AGB.Keith 1.150685 140.3194 FALSE AGB.Ray -AGB.Chave 177.11111 59.67437 TRUE AGB.Ray -AGB.Chave 29.876712 140.3194 FALSE AGB.Ray -AGB.IPCC 216.87879 59.67437 TRUE AGB.Ray -AGB.IPCC 16.328767 140.3194 FALSE AGB.Ray -AGB.Keith 161.28283 59.67437 TRUE AGB.Ray -AGB.Keith 27.849315 140.3194 FALSE AGB.Chave-AGB.IPCC 39.76768 59.67437 FALSE AGB.Chave-AGB.IPCC 13.547945 140.3194 FALSE AGB.Chave-AGB.Keith 15.82828 59.67437 FALSE AGB.Chave-AGB.Keith 2.027397 140.3194 FALSE AGB.IPCC-AGB.Keith 55.59596 59.67437 FALSE AGB.IPCC-AGB.Keith 11.520548 140.3194 FALSE

Plot 13 Plot 14 qqnorm(P13) qqnorm(P14) > qqline(P13) > qqline(P14)

> > > kruskal.test(P13~G13) > kruskal.test(P14~G14)

Kruskal-Wallis rank sum test Kruskal-Wallis rank sum test data: P13 by G13 data: P14 by G14 Kruskal-Wallis chi-squared = 904.2138, df = 5, p-value < Kruskal-Wallis chi-squared = 48.284, df = 4, p-value = 2.2e-16 8.235e-10 > kruskalmc(P13, G13) > kruskalmc(P14, G14) Multiple comparison test after Kruskal-Wallis Multiple comparison test after Kruskal-Wallis p.value: 0.05 p.value: 0.05 Comparisons Comparisons obs.dif critical.dif difference obs.dif critical.dif difference AGB.Fin - AGB.Ray 49.011628 129.27954 FALSE AGB.Fin - AGB.Ray 140.04194 74.64107 TRUE AGB.Fin -AGB.Chave 21.720930 129.27954 FALSE AGB.Fin -AGB.Chave 16.53871 74.64107 FALSE AGB.Fin -AGB.IPCC 57.406977 129.27954 FALSE AGB.Fin -AGB.IPCC 15.31290 74.64107 FALSE AGB.Fin -AGB.Keith 23.255814 129.27954 FALSE AGB.Fin -AGB.Keith 59.53871 74.64107 FALSE AGB.Ray -AGB.Chave 27.290698 129.27954 FALSE AGB.Ray -AGB.Chave 123.50323 74.64107 TRUE AGB.Ray -AGB.IPCC 8.395349 129.27954 FALSE AGB.Ray -AGB.IPCC 155.35484 74.64107 TRUE AGB.Ray -AGB.Keith 25.755814 129.27954 FALSE AGB.Ray -AGB.Keith 80.50323 74.64107 TRUE AGB.Chave-AGB.IPCC 35.686047 129.27954 FALSE AGB.Chave-AGB.IPCC 31.85161 74.64107 FALSE AGB.Chave-AGB.Keith 1.534884 129.27954 FALSE AGB.Chave-AGB.Keith 43.00000 74.64107 FALSE AGB.IPCC-AGB.Keith 34.151163 129.27954 FALSE AGB.IPCC-AGB.Keith 74.85161 74.64107 TRUE

144 Plot 15 Plot 16 qqnorm(P15) qqnorm(P16) > qqline(P15) > qqline(P16)

> > > kruskal.test(P16~G16) > kruskal.test(P15~G15) Kruskal-Wallis rank sum test Kruskal-Wallis rank sum test data: P15 by G15 data: P16 by G16 Kruskal-Wallis chi-squared = 56.7749, df = 4, p-value = Kruskal-Wallis chi-squared = 86.658, df = 4, p-value < 1.379e-11 2.2e-16 > kruskalmc(P15, G15) > kruskalmc(P16, G16) Multiple comparison test after Kruskal-Wallis Multiple comparison test after Kruskal-Wallis p.value: 0.05 p.value: 0.05 Comparisons Comparisons obs.dif critical.dif difference obs.dif critical.dif difference AGB.Fin - AGB.Ray 164.430 81.07259 TRUE AGB.Fin - AGB.Ray 78.137255 42.87133 TRUE AGB.Fin -AGB.Chave 1.285 81.07259 FALSE AGB.Fin -AGB.Chave 23.921569 42.87133 FALSE AGB.Fin -AGB.IPCC 30.280 81.07259 FALSE AGB.Fin -AGB.IPCC 47.882353 42.87133 TRUE AGB.Fin -AGB.Keith 53.365 81.07259 FALSE AGB.Fin -AGB.Keith 17.215686 42.87133 FALSE AGB.Ray -AGB.Chave 163.145 81.07259 TRUE AGB.Ray -AGB.Chave 102.058824 42.87133 TRUE AGB.Ray -AGB.IPCC 194.710 81.07259 TRUE AGB.Ray -AGB.IPCC 126.019608 42.87133 TRUE AGB.Ray -AGB.Keith 111.065 81.07259 TRUE AGB.Ray -AGB.Keith 95.352941 42.87133 TRUE AGB.Chave-AGB.IPCC 31.565 81.07259 FALSE AGB.Chave-AGB.IPCC 23.960784 42.87133 FALSE AGB.Chave-AGB.Keith 52.080 81.07259 FALSE AGB.Chave-AGB.Keith 6.705882 42.87133 FALSE AGB.IPCC-AGB.Keith 83.645 81.07259 TRUE AGB.IPCC-AGB.Keith 30.666667 42.87133 FALSE

Plot 17 Plot 18 > qqnorm(P17) qqnorm(P18) > qqline(P17) > qqline(P18)

> kruskal.test(P17~G17) > kruskal.test(P18~G18) Kruskal-Wallis rank sum test Kruskal-Wallis rank sum test data: P17 by G17 data: P18 by G18 Kruskal-Wallis chi-squared = 104.8713, df = 4, p-value Kruskal-Wallis chi-squared = 907.1772, df = 5, p-value < < 2.2e-16 2.2e-16 > kruskalmc(P17, G17) > kruskalmc(P18, G18) Multiple comparison test after Kruskal-Wallis Multiple comparison test after Kruskal-Wallis p.value: 0.05 p.value: 0.05 Comparisons Comparisons obs.dif critical.dif difference obs.dif critical.dif difference AGB.Fin - AGB.Ray 107.5625 53.65611 TRUE AGB.Fin - AGB.Ray 6.4166667 130.80953 FALSE AGB.Fin -AGB.Chave 33.3500 53.65611 FALSE AGB.Fin -AGB.Chave 12.3928571 130.80953 FALSE AGB.Fin -AGB.IPCC 66.2500 53.65611 TRUE AGB.Fin -AGB.IPCC 12.1666667 130.80953 FALSE AGB.Fin -AGB.Keith 22.1500 53.65611 FALSE AGB.Fin -AGB.Keith 7.5952381 130.80953 FALSE AGB.Ray -AGB.Chave 140.9125 53.65611 TRUE AGB.Ray -AGB.Chave 5.9761905 130.80953 FALSE AGB.Ray -AGB.IPCC 173.8125 53.65611 TRUE AGB.Ray -AGB.IPCC 5.7500000 130.80953 FALSE AGB.Ray -AGB.Keith 129.7125 53.65611 TRUE AGB.Ray -AGB.Keith 1.1785714 130.80953 FALSE AGB.Chave-AGB.IPCC 32.9000 53.65611 FALSE AGB.Chave-AGB.IPCC 0.2261905 130.80953 FALSE AGB.Chave-AGB.Keith 11.2000 53.65611 FALSE AGB.Chave-AGB.Keith 4.7976190 130.80953 FALSE AGB.IPCC-AGB.Keith 44.1000 53.65611 FALSE AGB.IPCC-AGB.Keith 4.5714286 130.80953 FALSE

145

Appendix B1c: Summary of pairwise comparisons of allometric equation tests for root biomass Eamus et al., IPCC (Cairn et al. Mokany et al. Niiyama et al 2002 (1997)) (2006) (2010) Eamus et al., 2002 T T F

IPCC (Cairn et al. F F (1997)) Mokany et al. (2006) F

Niiyama et al (2010)

> describe.by(Rmass, group=equ) group: (Eamus et al., 2002) vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 72 87.49 13.57 89.95 88.31 14.11 53.02 111.51 58.49 -0.54 -0.41 1.6 ------group: IPCC (Cairn et al. (1997)) vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 72 53.01 22.14 52.34 52.03 24.24 14.74 109.64 94.9 0.32 -0.61 2.61 ------group: Mokany et al. (2006) vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 72 63.93 28.63 62.59 62.44 30.88 15.92 139.06 123.14 0.38 -0.54 3.37 ------group: Niiyama et al. vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 17 70.06 31.26 68.48 70.23 39.17 16.43 121.14 104.71 -0.02 -1.39 7.58 > kruskalmc(Rmass, equ) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference (Eamus et al., 2002) - IPCC (Cairn et al. (1997)) 90.28472 29.63884 TRUE (Eamus et al., 2002) - Mokany et al. (2006) 63.79861 29.63884 TRUE (Eamus et al., 2002) - Niiyama et al. 46.22141 47.95310 FALSE IPCC (Cairn et al. (1997)) - Mokany et al. (2006) 26.48611 29.63884 FALSE IPCC (Cairn et al. (1997)) - Niiyama et al. 44.06332 47.95310 FALSE Mokany et al. (2006) - Niiyama et al. 17.57721 47.95310 FALSE

146 Appendix B2: DATA ANALYSIS

Appendix B2a: DBH >describe.by(dbh, group=typeA) group: A1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 6 17.9 2.37 18.06 17.9 3.11 15.12 20.43 5.31 -0.05 -2.19 0.97 ------group: A2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 5 19.91 5.07 17.13 19.91 2.62 15.36 27.24 11.88 0.43 -1.91 2.27 ------group: A3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 16.38 3.92 15.27 16.38 3.17 13.13 20.73 7.6 0.26 -2.33 2.26 ------group: A4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 4 8.31 4.66 8.07 8.31 5.57 4.04 13.07 9.03 0.03 -2.39 2.33

>kruskal.test(dbh~typeA)

Kruskal-Wallis rank sum test data: dbh by typeA Kruskal-Wallis chi-squared = 9.8667, df = 3, p-value = 0.01973

>kruskalmc(dbh, typeA) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference A1-A2 2.033333 8.528552 FALSE A1-A3 1.833333 9.959203 FALSE A1-A4 8.666667 9.091467 FALSE A2-A3 3.866667 10.285820 FALSE A2-A4 10.700000 9.448129 TRUE A3-A4 6.833333 10.757168 FALSE

147 Appendix B2b: Total height >describe.by(h, group=typeA) group: A1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 6 15.61 1.82 16.37 15.61 0.97 12.51 17.05 4.54 -0.69 -1.411 0.74 ------group: A2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 5 15.73 1.85 15.15 15.73 2.49 13.47 18.09 4.62 0.11 -1.95 0.83 ------group: A3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 9.26 0.14 9.22 9.26 0.12 9.14 9.42 0.28 0.26 -2.33 0.08 ------group: A4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 4 9.35 2.71 9.05 9.35 2.86 6.76 12.54 5.78 0.13 -2.24 1.35 >qqnorm(h) >qqline(h) >kruskal.test(h~typeA)

Kruskal-Wallis rank sum test data: h by typeA Kruskal-Wallis chi-squared = 11.6158, df = 3, p-value = 0.008822

>kruskalmc(h, typeA) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference A1-A2 0.90 8.528552 FALSE A1-A3 8.50 9.959203 FALSE A1-A4 8.25 9.091467 FALSE A2-A3 9.40 10.285820 FALSE A2-A4 9.15 9.448129 FALSE A3-A4 0.25 10.757168 FALSE

148 Appendix B2c: Basal areas >describe.by(BA, group=typeA) group: A1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 6 50.6 9.7 49.8 50.6 7.65 35.39 63.88 28.49 -0.16 -1.36 3.96 ------group: A2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 5 48.29 7.83 49.5 48.29 4.14 35.53 56.33 20.8 -0.62 -1.35 3.5 ------group: A3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 22.27 3.44 23.72 22.27 1.53 18.35 24.75 6.4 -0.35 -2.33 1.98 ------group: A4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 4 40.57 14.33 42.42 40.57 13.21 22.06 55.39 33.33 -0.24 -2.01 7.17 >qqnorm(BA) >qqline(BA) >av=aov(BA~typeA) >summary(av) Df Sum Sq Mean Sq F value Pr(>F) typeA 3 1798 599.5 6.192 0.00672 ** Residuals 14 1356 96.8 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 >TukeyHSD(av) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = BA ~ typeA) $typeA diff lwr upr p adj A2-A1 -2.306667 -19.624521 15.011187 0.9794954 A3-A1 -28.323333 -48.546230 -8.100437 0.0055737 A4-A1 -10.024167 -28.485061 8.436728 0.4211666 A3-A2 -26.016667 -46.902784 -5.130549 0.0131053 A4-A2 -7.717500 -26.902624 11.467624 0.6549138 A4-A3 18.299167 -3.544058 40.142392 0.1156382

149 Appendix B2d: Total carbon densisty >describe.by(totalC, group=typeA) group: A1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 6 381.59 114.87 364.28 381.59 58.33 248.5 584.29 335.79 0.62 -1.06 46.9 ------group: A2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 5 278.4 75.71 251.33 278.4 18.24 233.24 412.88 179.64 1.04 -0.96 33.86 ------group: A3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 210.36 29.48 206.76 210.36 35.45 182.85 241.47 58.62 0.12 -2.33 17.02 ------group: A4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 4 241.72 48.71 257.64 241.72 24.24 171.52 280.09 108.57 -0.59 -1.8 24.35 >qqnorm(totalC) >qqline(totalC) >kruskal.test(totalC~typeA)

Kruskal-Wallis rank sum test

data: totalC by typeA Kruskal-Wallis chi-squared = 8.3187, df = 3, p-value = 0.03986

>kruskalmc(totalC, typeA) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference A1-A2 5.000000 8.528552 FALSE A1-A3 10.333333 9.959203 TRUE A1-A4 6.250000 9.091467 FALSE A2-A3 5.333333 10.285820 FALSE A2-A4 1.250000 9.448129 FALSE A3-A4 4.083333 10.757168 FALSE

150 Appendix B2e: Standcarbon density >describe.by(agbC, group=typeA) group: A1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 24 133.27 39.07 124.66 130.34 39.36 70.1 231.77 161.67 0.65 -0.131 7.97 ------group: A2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 20 133.96 36.95 133.47 132.77 31.04 70.03 204.53 134.5 0.27 -0.841 8.26 ------group: A3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 12 58.52 12.71 57.48 58.19 13.05 39.02 81.32 42.3 0.4 -1.04 3.67 ------group: A4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 16 68.19 32.12 62.88 66.3 43.75 26.54 136.29 109.75 0.45 -0.921 8.03 >qqnorm(agbC) >qqline(agbC kruskal.test(agbC~typeA)

Kruskal-Wallis rank sum test

data: agbC by typeA Kruskal-Wallis chi-squared = 40.5824, df = 3, p-value = 8.018e-09

>kruskalmc(agbC, typeA) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference A1-A2 0.3583333 16.71706 FALSE A1-A3 35.1250000 19.52132 TRUE A1-A4 29.4166667 17.82045 TRUE A2-A3 35.4833333 20.16153 TRUE A2-A4 29.7750000 18.51955 TRUE A3-A4 5.7083333 21.08544 FALSE

151 Appendix B2f: Under-storey carbon density >describe.by(underC, group=typeA) group: A1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 30 1.76 2.28 0.82 1.17 0.55 0.39 9.29 8.9 2.08 3.15 0.42 ------group: A2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 25 1.06 0.63 1.22 1.05 0.61 0 2.62 2.62 0.09 -0.27 0.13 ------group: A3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 10 1.39 1.21 1.1 1.21 0.96 0.3 3.85 3.55 0.84 -0.77 0.38 ------group: A4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 0 NaN NA NA NaN NA Inf -Inf -Inf NA NA NA >qqnorm(underC) >qqline(underC) >kruskal.test(underC~typeA)

Kruskal-Wallis rank sum test

data: underC by typeA Kruskal-Wallis chi-squared = 0.0228, df = 2, p-value = 0.9887

>kruskalmc(underC, typeA) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference A1-A2 0.7666667 13.50846 FALSE A1-A3 0.4666667 18.21481 FALSE A1-A4 NaN Inf NA A2-A3 0.3000000 18.66462 FALSE A2-A4 NaN Inf NA A3-A4 NaN Inf NA

152 Appendix B2g:Deadwood carbon density >describe.by(D.woodC, group=typeA) group: A1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 6 44.7 39.27 30.52 44.7 19.91 12.86 119.81 106.95 1.02 -0.67 16.03 ------group: A2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 5 23.46 10.25 19.18 23.46 0.53 17.9 41.76 23.86 1.06 -0.93 4.58 ------group: A3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 41.32 46.95 22.48 41.32 23.38 6.71 94.76 88.05 0.34 -2.33 27.11 ------group: A4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 4 30.13 14.37 30.93 30.13 14.93 12.45 46.21 33.76 -0.11 -2.03 7.19 >qqnorm(D.woodC) >qqline(D.woodC) >kruskal.test(D.woodC~typeA)

Kruskal-Wallis rank sum test

data: D.woodC by typeA Kruskal-Wallis chi-squared = 1.6971, df = 3, p-value = 0.6376

>kruskalmc(D.woodC, typeA) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference A1-A2 4.133333 8.528552 FALSE A1-A3 2.333333 9.959203 FALSE A1-A4 1.333333 9.091467 FALSE A2-A3 1.800000 10.285820 FALSE A2-A4 2.800000 9.448129 FALSE A3-A4 1.000000 10.757168 FALSE

153 Appendix B2h: Litters carbon density >describe.by(litC, group=typeA) group: A1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 31 53.73 46.48 27.03 49.69 33.28 2.76 155.24 152.48 0.58 -1.17 8.35 ------group: A2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 25 8.33 3.88 7.41 7.98 2.62 2.8 17.66 14.86 0.98 0.29 0.78 ------group: A3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 10 3.07 1.32 2.58 2.85 0.77 1.72 6.21 4.49 1.19 0.4 0.42 ------group: A4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 27 74.13 69.74 32 69.83 42.43 2.52 195.33 192.81 0.59 -1.33 13.42 >qqnorm(litC) >qqline(litC) >kruskal.test(litC~typeA) Kruskal-Wallis rank sum test data: litC by typeA Kruskal-Wallis chi-squared = 46.1372, df = 3, p-value = 5.303e-10 >kruskalmc(litC, typeA) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference A1-A2 29.276129 19.14146 TRUE A1-A3 50.116129 25.89662 TRUE A1-A4 4.280167 18.74490 FALSE A2-A3 20.840000 26.64379 FALSE A2-A4 33.556296 19.76431 TRUE A3-A4 54.396296 26.36033 TRUE

Coarse litter >describe.by(ClitC, group=typeA) group: A1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 25 17.51 11.51 13.11 15.77 6.52 6.32 49.41 43.09 1.45 1 2.3 ------group: A2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 25 8.33 3.88 7.41 7.98 2.62 2.8 17.66 14.86 0.98 0.29 0.78 ------group: A3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 10 3.07 1.32 2.58 2.85 0.77 1.72 6.21 4.49 1.19 0.4 0.42 ------group: A4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 20 9.99 2.72 9.34 10.01 2.97 4.89 14.12 9.23 0.04 -1.28 0.61 >qqnorm(ClitC) >qqline(ClitC) Kruskal-Wallis rank sum test

data: ClitC by typeA Kruskal-Wallis chi-squared = 39.5698, df = 3, p-value = 1.314e-08 >kruskalmc(ClitC, typeA) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference A1-A2 25.84 17.34040 TRUE A1-A3 51.92 22.93919 TRUE A1-A4 14.62 18.39227 FALSE A2-A3 26.08 22.93919 TRUE A2-A4 11.22 18.39227 FALSE A3-A4 37.30 23.74432 TRUE

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Fine litter >describe.by(FlitC, group=typeA) group: A1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 30 40.94 43.55 14.79 36.21 17.08 0 120.31 120.31 0.7 -1.26 7.95 ------group: A2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 0 NaN NA NA NaN NA Inf -Inf -Inf NA NA NA ------group: A3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 0 NaN NA NA NaN NA Inf -Inf -Inf NA NA NA ------group: A4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 27 66.73 68.12 19.76 61.98 24.92 2.52 185.92 183.4 0.59 -1.38 13.11 >qqnorm(FlitC) >qqline(FlitC) >kruskal.test(FlitC~typeA)

Kruskal-Wallis rank sum test data: FlitC by typeA Kruskal-Wallis chi-squared = 2.6575, df = 1, p-value = 0.1031 >kruskalmc(FlitC, typeA) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference A1-A2 NaN Inf NA A1-A3 NaN Inf NA A1-A4 7.177778 11.61643 FALSE A2-A3 NaN Inf NA A2-A4 NaN Inf NA A3-A4 NaN Inf NA

155 Appendix B2i: Root carbon density >describe.by(Cr, group=typeA) group: A1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 78 36.48 9.59 37.53 36.49 10.68 16.3 62.58 46.28 0.02 -0.52 1.09 ------group: A2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 64 36.59 9.25 38.34 36.87 10.1 16.29 55.22 38.93 -0.25 -0.86 1.16 ------group: A3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 39 20.69 8.97 16.7 20.17 6.49 9.48 37.46 27.98 0.62 -1.25 1.44 ------group: A4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 52 22.4 10.72 22.05 22.02 12.94 6.63 43.73 37.1 0.21 -1.17 1.49 >qqnorm(Cr) >qqline(Cr) >kruskal.test(Cr~typeA)

Kruskal-Wallis rank sum test data: Cr by typeA Kruskal-Wallis chi-squared = 82.765, df = 3, p-value < 2.2e-16 >kruskalmc(Cr, typeA) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference A1 - A2 1.055889 29.99296 FALSE A1 - A3 87.602564 34.87593 TRUE A1 - A4 77.086538 31.83722 TRUE A2 - A3 88.658454 36.12507 TRUE A2 - A4 78.142428 33.20089 TRUE A3 - A4 10.516026 37.67030 FALSE

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Appendix B2j: Soilcarbon density >describe.by(Csoil30, group=typeA) group: A1 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 6 110.23 105.24 88.34 110.23 43.94 11.24 312.76 301.52 1.02 -0.57 42.96 ------group: A2 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 5 76.79 66.97 50.05 76.79 44.61 19.96 187.82 167.86 0.74 -1.36 29.95 ------group: A3 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 3 86.87 22.77 75.13 86.87 4.11 72.36 113.11 40.75 0.38 -2.33 13.15 ------group: A4 vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 4 41.68 6.95 42.1 41.68 7.48 33.32 49.21 15.89 -0.1 -2.11 3.47 >qqnorm(Csoil30) >qqline(Csoil30) >kruskal.test(Csoil30~typeA)

Kruskal-Wallis rank sum test

data: Csoil30 by typeA Kruskal-Wallis chi-squared = 4.3082, df = 3, p-value = 0.2301

>kruskalmc(Csoil30, typeA) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference A1-A2 2.7000000 8.528552 FALSE A1-A3 0.8333333 9.959203 FALSE A1-A4 6.2500000 9.091467 FALSE A2-A3 3.5333333 10.285820 FALSE A2-A4 3.5500000 9.448129 FALSE A3-A4 7.0833333 10.757168 FALSE

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