Restoring Rainforest – the Capacities of Three Different Reforestation Pathways to Re-Establish Ecosystem Properties

Restoring Rainforest – the Capacities of Three Different Reforestation Pathways to Re-establish Ecosystem Properties

Author Paul, Miriam

Published 2011

Thesis Type Thesis (PhD Doctorate)

School Griffith School of Environment

DOI https://doi.org/10.25904/1912/1989

Downloaded from http://hdl.handle.net/10072/366066

Griffith Research Online https://research-repository.griffith.edu.au

Restoring Rainforest – the Capacities of Three Different Reforestation Pathways to Re-establish

Ecosystem Properties

Miriam Paul

Diplombiologin

(Universität Karlsruhe, Germany)

Griffith School of Environment

Science, Environment, Engineering and Technology

Griffith University

Brisbane, Australia

A dissertation submitted in fulfilment of the requirements of the degree of

Doctor of Philosophy

March 2010

Synopsis

The broad scale destruction of tropical and subtropical rainforests has been one of the largest land-cover conversions taking place on earth, with a wide range of deleterious consequences at local, landscape, and global scales. While the resulting loss of biodiversity and habitat for rainforest-dependent fauna and flora has been well-studied as a major effect, clearing of rainforests also significantly influences soil processes such as biochemical cycles and microbial functions.

More recently, there has been a growing public interest in reforestation activities. There are a range of different pathways by which rainforest cover can be restored to cleared land, including autogenic, or „natural‟, regrowth, management of this autogenic regrowth, and tree planting for ecological restoration. However, little is known about the recovery processes of ecosystem properties under different reforestation pathways in the same landscape.

The broad objective of this thesis was to assess both the effects of deforestation on a range of ecosystem processes and the potential of different reforestation pathways to restore these processes. The study was conducted in the Big Scrub area in subtropical eastern Australia, a basaltic plateau that once supported the continent‟s largest continuous stand of lowland subtropical rainforest, which was mostly cleared for pasture in the mid to late 19th century. In this landscape, the properties of five site-types were compared, with five replicate sites in each. The site-types consisted of two reference conditions, pasture and intact rainforest, and three different reforestation pathways. These pathways were: autogenic regrowth dominated by the non-native tree species camphor laurel (Cinnamomum camphora); similar regrowth managed in order to remove the camphor laurel and release the growth of recruited rainforest seedlings; and ecological restoration plantations. Camphor laurel is a dominant species in the Big Scrub region, where it readily colonises abandoned pastures and is known to facilitate the recruitment of later successional rainforest tree species. In ecological restoration plantings, a high diversity of native rainforest tree seedlings is planted to restore biodiversity.

The main ecosystem properties studied within the sites were as follows:

1. size and composition of viable soil seed banks; and

2. soil physical properties and nutrient cycles.

This study also assessed the consequences of altered soil properties and nutrient dynamics for the early growth of rainforest pioneer seedlings.

To assess the effects of deforestation and reforestation on soil seed banks, germination experiments were conducted using soil from all five site-types (three reforestation pathways, pasture, and rainforest sites). Germination trays were positioned in a shade-house and seedling emergence was monitored over a period of six months. Germinated seedlings were classified into functional groups according to their life-form, origin, successional stage, and dispersal mode. Additionally, floristic data from a subset of the study sites was used to examine how the seed bank was related to the standing vegetation. Despite a much larger total abundance of seeds in seed banks from pasture sites, these sites contained very few native woody plants and were dominated by grasses and herbaceous species. Further, seed banks of reforested and rainforest sites were mainly composed of pioneer and early secondary species, whereas late secondary and mature phase species appeared almost solely in the standing vegetation. The abundance and diversity of most of the functional groups that were impacted by deforestation showed values similar to rainforest in at least one of the three reforestation pathways; whilst the three pathways differed only slightly in their capacities to restore soil seed banks. In the initial phases of reforestation, however, seed banks play no vital role after long-term pasture establishment.

The effects of deforestation and reforestation on the physical and biochemical properties of soil were tested by measuring a range of properties in soil samples from all five site-types. The main emphasis was placed on carbon- and nitrogen-related soil properties, as they are major nutrients in terrestrial ecosystems. Before these data were collected, a study was performed in a subset of the sites to identify the variation of soil properties at different spatial scales (subplot, site, and site-type level) and to develop a spatial design for the collection of soil samples within sites. For each of the five site-types, two replicate sites were measured, with 16 subplots in each site. Subplots were seven cm in diameter and regularly aligned with a spacing of 10 m. The seven soil properties measured in this soil variability study were: gravimetric water content; soil organic matter; pH; total organic carbon; microbial biomass carbon; nitrate-nitrogen, and nitrification rate. Across all sites, water content, soil organic matter, and pH showed a consistently low variability at all three spatial scales. In contrast, soil properties related to microbial processes exhibited higher degrees of spatial variability at the site and the subplot level. However, even in soil properties with a high tendency for spatial variability, the physical mixing of subsamples from

ii subplots within a site, in contrast to analysing subsamples individually, could be validated as a useful technique to reduce analytical effort and cost.

In the main study of soil properties under deforestation and reforestation, 19 properties were measured at each of five sites in each of the five site-types (the same 25 sites used for the seedling germination experiment). These properties consisted of: eight nitrogen-related variables (total nitrogen (N), ammonium-N, nitrate-N, total inorganic N, plant-available ammonium-N, plant-available nitrate-N, nitrification rates, and denitrification rates); six carbon-related properties (total carbon (C), total organic carbon, soil organic matter, 13C value, microbial biomass carbon, and soil microbial activity); and five general soil properties (gravimetric water content, pH, bulk density, fine root biomass, and plant-available phosphate). Of the 19 soil properties, nine differed significantly between rainforest and pasture. Nitrate-N levels, plant-available nitrate-N levels, nitrification rates, and fine root biomass were significantly greater in rainforest than in pasture sites, while plant-available ammonium-N levels, 13C values, pH, bulk density, and plant-available phosphate concentrations showed greater levels in pasture sites. Apart from fine root biomass, all of these soil properties were re-established to a level similar to that in rainforest in at least one of the three reforestation pathways. However, the capacity to re-establish soil properties varied among the three reforestation pathways. For example, autogenic regrowth dominated by camphor laurel showed a good recovery of nitrification, ammonium, and phosphate levels, but did not significantly facilitate the re-establishment of nitrate-N and bulk density.

The impacts of soil properties – and hence deforestation and reforestation – on early seedling development were tested by measuring the growth of rainforest pioneer seedlings in soils collected from the three different reforestation pathways, as well as from pasture and rainforest soils. Three species, Alphitonia excelsa, Guioa semiglauca, and Omalanthus nutans, all fast-growing pioneer species that are common in the Big Scrub region, were chosen for this study. The seedlings were kept in a shade-house over a period of about seven months, and height and diameter were measured at regular time intervals. Although the three species varied significantly in height and diameter growth, they responded similarly to the five site-types, with generally lower growth rates in untreated autogenic regrowth and higher rates in soils from all other site-types, including pasture. However, there was little evidence that seedling performance was directly influenced by soil properties. Across all three species and all 25 sites, seedling growth rates were significantly correlated with only two out of 15 measured soil properties: pH and plant-available nitrate-N. Hence, reforestation could have the ability to affect seedling growth through its impact on nitrogen availability.

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This study showed that deforestation has a significant impact on most of the selected ecosystem processes, and also demonstrated that processes altered by deforestation can be re-established by reforestation. This was particularly the case for soil seed bank dynamics, carbon and nitrogen cycling, and some general soil properties (including pH, bulk density, phosphate-levels). While the three reforestation pathways showed similar abilities to re-establish viable soil seed banks, they varied in their capacities to restore soil properties back to rainforest levels. Nitrogen-related soil properties such as nitrate and nitrification rates could be re-established within 10-15 years of reforestation by ecological restoration planting and 25-50 years by autogenic regrowth with subsequent treatment of camphor laurel, whereas pH and bulk density were only fully restored by ecological restoration planting. However, autogenic regrowth (treated and untreated) showed a higher ability to restore ammonium availability than ecological restoration planting.

The three pathways varied significantly in their costs and outcomes. Untreated autogenic regrowth, as a result of pasture abandonment and therefore without any costs, showed a moderate ability to recover ecosystem processes, and would be useful to restore large areas. Ecological restoration planting showed the best ecosystem outcomes of the three pathways within a relatively short time (10-15 years), but is also associated with higher costs of tens of thousands of dollars per hectare and therefore suitable for only small areas where fast ecological outcomes are desired. Managed regrowth is associated with intermediate costs and a high capacity to restore ecosystem processes only slightly below that of ecological restoration planting. Therefore, it would be a useful pathway in larger areas where advanced regrowth already exists, but where an improvement of ecological outcomes is desired. Given these different outcomes and costs, the most important management issue does not lie with a choice of one particular reforestation pathway, but rather on a decision about how the different pathways can be combined within a landscape to achieve the best results.

Statement of Originality

I certify that this thesis is my original work and has not previously been submitted for a degree or diploma in any university. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made in the thesis itself.

______Miriam Paul

Table of Contents

Synopsis ______i Statement of Originality ______v Table of Contents ______vi List of Tables ______viii List of Figures ______ix List of Appendices ______x Colour plates ______xi Preface ______xv Acknowledgements ______xvi 1. General introduction ______1 1.1. Rainforest clearing and the impacts on ecosystem processes ______1 1.2 Restoration of cleared rainforest landscapes ______3 1.3 Can ecosystem processes be restored through reforestation? ______6 1.4 Aims and structure of this thesis ______10 2. Study area and experimental design ______13 2.1 Study area ______13 2.2 Experimental design and site-types ______14 3. Recovery of the soil seed bank ______17 3.1 Introduction ______17 3.2 Methods ______19 3.3 Results ______22 3.4 Discussion ______32 4. Variability of abiotic and biotic soil properties at different spatial scales ______37 4.1 Introduction ______37 4.2 Methods ______39 4.3 Results ______43 4.4 Discussion ______50 5. Recovery of soil properties and functions ______55 5.1 Introduction ______55 5.2 Methods ______56 5.3 Results ______62 5.4 Discussion ______67 6. Seedling growth ______73 6.1 Introduction ______73 6.2 Methods ______74

6.3 Results ______77 6.4 Discussion ______82 7. General discussion ______85 7.1 Summary and key findings of the data chapters (Chapters 3–6) ______85 7.2 Ecosystem processes and reforestation ______88 7.3 Advantages and disadvantages of different reforestation pathways ______90 References ______93 Appendix 1 ______117 Appendix 2 ______121 Appendix 3 ______127 Appendix 4 ______129 Appendix 5 ______131 Appendix 6 ______133 Appendices 7 & 8 ______135 Appendix 9 ______137 Appendix 10 ______139 Appendix 11 ______141 Appendix 12 ______143

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List of Tables

Table 3-1 Overall abundance and species richness of seedlings germinated from the top five cm of soil from 25 sites in rainforest, pasture and three types of reforestation ...... 22 Table 3-2 Most abundant woody plant species in the soil seed banks, seedling banks and as mature trees of rainforest, pasture and three types of reforestation...... 24 Table 3-3 Mean abundance of woody species (life-stages: germinants, recruits, adults), mean percentage of individuals that are late secondary and mean percentage of species that are late secondary in camphor-dominated, treated camphor and rainforest sites ...... 26 Table 3-4 Comparison of abundance (per m2) and species richness of seedlings germinated from the soil seed banks of rainforest, pasture and various forms of reforestation...... 27 Table 3-5 Results of pairwise ANOSIM conducted on the composition of soil seed banks among rainforest, pasture, and three types of reforestation ...... 29 Table 4-1 Mean values, coefficients of variation and total variation for each of the soil properties. .... 43 Table 4-2 Spearman correlation coefficients among seven soil properties ...... 47 Table 4-3 Results of linear regression between values of 16 combined subsamples and means of 16 individually analysed subsamples for each soil property ...... 48 Table 4-4 Paired t-tests comparing the values of 16 combined subsamples and means of 16 individually analysed subsamples for each soil property ...... 48 Table 5-1 Significant pairwise Spearman correlation coefficients among 19 soil properties across sites in rainforest, pasture, and three reforestation pathways ...... 62 Table 5-2 ANOVA results from comparison of soil properties among rainforest, pasture and three forms of reforestation ...... 63 Table 5-3 Patterns of similarity between rainforest, pasture and three types of reforestation for soil properties which differed significantly between rainforest and pasture...... 65 Table 5-4 Coefficients in the linear combinations of variables making up principal components of eight nitrogen-related soil properties...... 65 Table 6-1 The effect of species and site-type on exponential growth coefficients of seedlings using ANCOVA with initial size as a covariate...... 77 Table 6-2 The relationship between mean site-specific seedling growth rates and soil properties.. ... 80 Table 6-3 The effects of species and site-type on exponential growth coefficients of seedlings, using ANCOVA with the initial size and soil pH as covariates...... 81 Table 6-4 The effects of species and site-type on exponential growth coefficients of seedlings, using ANCOVA with the initial size and plant-available nitrate-N as covariates...... 81

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List of Figures

Figure 2-1 Map of the study area and the sampling sites...... 16 Figure 3-1 Pattern of variation in reforested sites for functional groups (abundance and species richness of seedlings germinated from the soil seed banks) that differed significantly between pasture and rainforest ...... 28 Figure 3-2 Two-dimensional ordination of 25 sites, five replicates in each of five site-types, based on the abundance of life-forms (A), the composition of woody species (B), and the composition of woody species other than the exotic tree species Cinnamomum camphora (C)...... 31 Figure 4-1 Diagram illustrating design and orientation of subplots for collection of soil samples within a site...... 40 Figure 4-2 Relationship between values of 16 physically combined samples, the means of 16 individually analysed samples, and the values for each of the 16 individually analysed samples for soil properties across five site-types ...... 45 Figure 4-3 Bubble plots showing the spatial pattern of variation of seven soil properties at 16 subplots within each of 10 sites ...... 46 Figure 4-4 Linear relationships between values of 16 physically combined subsamples and the means of 16 individually analysed subsamples for each soil property across pasture, rainforest and three types of reforestation ...... 49 Figure 5-1 Simplified flowchart of N-transformation processes ...... 58 Figure 5-2 Pattern of variation in reforested sites for soil properties which differed significantly between rainforest and pasture ...... 64 Figure 5-3 Ordination of rainforest, pasture and reforested sites in terms of eight nitrogen-related soil properties ...... 66 Figure 5-4 Values of PC1 scores based on eight nitrogen-related soil properties for pasture, rainforest and three types of reforestation ...... 66 Figure 5-5 Values of gravimetric water content and soil organic matter for pasture, rainforest and three types of reforestation ...... 67 Figure 6-1 Exponential growth coefficients of three variables describing seedling growth for three plant species (Alphitonia excelsa Ae, Guioa semiglauca Gs, and Omalanthus nutans On)...... 78 Figure 6-2 Exponential growth coefficients of three variables describing seedling growth in soils from pasture, rainforest, and three forms of reforestation ...... 78 Figure 6-3 Modelled growth curves for stem height of seedlings...... 79 Figure 6-4 Relationship between seedling growth and soil pH and plant-available nitrate-N...... 80

List of Appendices Appendix 1 Characteristics of the study sites ...... 118 Appendix 2 Non-grass species present in the soil seed bank, as recruits, or as mature trees of pasture, rainforest and three types of reforestation...... 122 Appendix 3 Overall seedling numbers (per m2 in the top five cm of soil) and species numbers of native woody plant seedlings according to their successional stage, germinated from the soil seed banks of all five site-types ...... 127 Appendix 4 Percentage contribution of the top five functional groups to the Bray-Curtis dissimilarity (SIMPER analysis) between rainforest, pasture, and three types of reforestation ...... 129 Appendix 5 Percentage contribution by the top five woody species to the Bray-Curtis dissimilarity (SIMPER analysis) between rainforest, pasture, and three types of reforestation ...... 131 Appendix 6 Percentage contribution by the top five woody species other than Cinnamomum camphora to the Bray-Curtis dissimilarity (SIMPER analysis) between rainforest, pasture, and three types of reforestation ...... 133 Appendix 7 Seedling numbers (per m2 in the top five cm of soil) and numbers of non-grass plant species germinated from the soil seed banks of all five site-types...... 135 Appendix 8 Seedling numbers (per m2 in the top five cm of soil) of most abundant non-grass plant species germinated from the soil seed banks of rainforest, pasture and various forms of reforestation...... 135 Appendix 9 Pairwise Spearman correlation coefficients among 19 soil properties across sites in rainforest, pasture, and three reforestation pathways ...... 138 Appendix 10 Raw data for nitrogen related soil properties across sites in rainforest, pasture, and three reforestation pathways ...... 140 Appendix 11 Raw data for carbon related and other soil properties across sites in rainforest, pasture, and three reforestation pathways ...... 142 Appendix 12 Mean starting height and diameter for seedlings in soils from different site-types...... 143

Colour plates

Plate 1 Rainforest (complex notophyll vine forest) in the Big Scrub region of Northern New South Wales.

Plate 2 Camphor laurel (Cinnamomum camphora) colonising an abandoned pasture in Northern New South Wales and fruits of camphor laurel (Photo: Terry Reis).

Plate 3 Autogenic regrowth dominated by the non-native tree species camphor laurel (Cinnamomum camphora). Native rainforest plants have been dispersed into the site by birds and bats and are growing beneath the camphor canopy.

Plate 4 Impressions from a camphor treated regrowth site in the study region. Camphor laurel trees have been poisoned with glyphosate about six years ago to promote the growth of native rainforest plant species.

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Plate 5 12 Year old ecological restoration planting site. The understorey is dominated by grasses and ferns and shows the occasional native tree seedling.

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A B C

Plate 6 Seedlings used in the seedling growth experiment. A: Alphitonia excelsa, B: Guioa semiglauca, C: Omalanthus nutans. All three species are early secondary rainforest species.

Plate 7 Allocation of seedlings in the bush house.

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Preface

During this project, I designed the study in collaboration with my supervisors Carla Catterall, John Kanowski, and Peter Pollard. I undertook the collection of all data with the assistance of my supervisors and volunteers for field experiments (see acknowledgements). Chapters 3, 5, and 6 of this thesis were prepared in the form of manuscripts for publication in peer-reviewed journals. These chapters have been slightly modified for inclusion within this thesis. I was responsible for all research conducted in these papers unless otherwise stated; the co-authors were my doctoral program supervisors.

Publications resulting from work reported in this thesis

Chapter 5: Paul, M., Catterall, C. P., Pollard, P. C., Kanowski, J. (2010) Recovery of soil properties and functions in different reforestation pathways. Forest Ecology and Management 259, 2983-2092.

Chapter 6: Paul, M., Catterall, C. P., Pollard, P.C., Kanowski, J. (2010) Does soil variation between rainforest, pasture and different reforestation pathways affect the early growth of rainforest pioneer species? Forest Ecology and Management, 260, 370-377.

Chapter 3 (seed bank study) has also been written in the form of a paper for submission.

Acknowledgements

Five years ago, I came to this beautiful country in search of adventure. And what an adventure it turned out to be! Having been involved in such an exciting and wonderful project over the last four years was truly one of the best things that has ever happened to me. I am deeply grateful for having been given such a unique opportunity, and my first and foremost thank goes to my principal supervisor Carla Catterall for her guidance, support and encouragement. Carla always showed an amazing clear-sightedness when I was struggling to find the pearls among the dross, and made sure I never lost the perspective of my work. Thanks for the freedom I had and also for your boundless patience; without you, this work would never have been possible.

I also thank my associate supervisor John Kanowski for his contribution to my work. From the first time you took me to my study sites, I admired your knowledge about „all things in the bush‟ and your deep love and respect for nature. Thanks for the many philosophical conversations we had and for always making yourself available when I needed some help.

Peter Pollard, an excellent and inspiring microbial ecologist, was my second associate supervisor, and as such he greatly contributed to my understanding of „all the little things in the soil‟. Peter always found the time to discuss a problem, to read a draft or to motivate me in difficult times. He also invited me to his weekly meeting with his lab group, where I had the opportunity to discuss all kinds of issues and be a part of a wonderful and supporting group. Special thanks to Carolyn Polson, Jeff Argo, Lisa Galbright, and Millicent O‟Ketch.

The Wildlife Discussion Group, guided by Carla, provided interesting discussions on all kinds of ecological issues and also helpful comments on early drafts of parts of this thesis. I especially thank Aki Nakamura, Billie Roberts, Cath Moran, Chris Burwell, Claire Hourigan, David Bouchard, Jarrad Cousin, Gayle Johnson, and Tang Yong. Thanks also to Andrezza Bellotto and Rodrigo Nobre for inspiring discussions about forest restoration and the amazing rainforests of Brazil. I am looking forward to seeing the beautiful forests of the Amazon in the near future!

This project would have been unimaginable without all the wonderful landholders, restoration practitioners, and caretakers in the Big Scrub region who generously allowed access to their land. Many thanks to Alan Jervis, Barry Ferrier, Charlie & Jenny Handley, John Foster, Mike Delany (and his friendly dog „Tigger‟ who always accompanied me to a swim in the creek), Ralph Woodford, Rosie Tongmar, Steve and Cherie Nash, Tom & Diane

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Wallbank, Tony & Rowena Parkes, and Will & Christine Jephcott. It was great to meet such inspiring people. A special thanks to Mark Dunphy and his team from the Firewheel Nursery for providing me with the seedlings for the plant growth experiment and for valuable information about their ecology. Sampling in National parks was undertaken under the sampling permit “Biodiversity Values in Reforestation”, No. 11037, issued by the New South Wales Parks and Wildlife Service.

A special thanks to Chengrong Chen and Rene & Noreen Diocares for conducting stable isotope and organic carbon analyses and to Boris Paul, Rachel Biggs, Peter Edwards, Michael Newham and Shahla Hosseini-Bai for kindly assisting me in laboratory work. Christy Fellows provided valuable advice on the measurement of denitrification rates, and Jane Gifkins kindly provided workspace in the microbiology lab. I am indebted to Janet Chaseling who greatly contributed to the development of statistical analysis for the seedling growth chapter. Jarrad Cousin, Kees Hulsman and Anand Tularam kindly offered their time to improve my understanding of statistical analyses, and I thank Rosie Steele and Jacinta Zalucki for generously creating space in the bush house for my seedling and seed bank experiments. Tang Yong shared his knowledge about setting up seed bank experiments with me and proved to be a great help in identifying seedlings, together with Paula Ibell and Bob Coutts. John Kanowski, Stephen McKenna and Wendy Neilan kindly allowed me to use their floristic data for my study. I also want to thank Zhihong Xu and Chengrong Chen for initially taking me on board as one of their students and for assisting me in the application for a scholarship.

Funding for this study was provided by a research grant from Griffith University; I am also grateful for having received a Griffith University Postgraduate Research Scholarship, a Griffith University Tuition Fee Scholarship, and a Griffith School of Environment RHD Completion Assistance Postgraduate Research Scholarship. The Australian Rivers Institute, Peter Pollard and Carla Catterall generously provided a tuition fee scholarship for the last semester of my studies.

During my time here at Griffith University, I was very fortunate to experience the joy of real friendship and collegiality. Many wonderful people here not only made my life so much easier, but also gave me a great sense of „feeling at home‟. Among them, I want to thank Paula Ibell, Siti Amri (thanks also for designing the map of the study area), Peter Edwards, Peter Daniels, Eddo Coiacetto, Leila & Bob, Zsuzsa Banhalmi-Zakar, Naema Shibani, Heidi Millington, Rawwa Abdul Jabbar, Kees Hulsman, and Anand Tularam. You always turned lunch at the common room into a „family gathering‟.

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Outside uni, many people have contributed to this work in their very own ways. I thank Rob McGilvray for his friendship and kindness, for his company on field trips and for making me laugh so many times. Terry Reis generously shared his immense knowledge about the fascinating Australian fauna with me and was a helpful companion in the field. Thanks, Terry, for all those deep conversations, and also for significantly improving my musical taste. I am grateful for having met Barbara Waha, who has become a close and very inspiring friend. Lee Swindell is a wonderful person to have as a friend, and I thank him for his kindness, support, and understanding. Thanks also to Beverley Walsh who kindly accommodated me in her house while being on a conference in Sydney.

I deeply thank Chris Walsh, my special friend and „brother‟, for generously sacrificing his time to accompany me on a field trip to collect “all that dirt” and for being by my side (which was not always easy, I know). Thanks for the laughter, the silence, and the tears, and for always encouraging me to go one step further. You have taught me a lot about myself, and I will continue to learn.

This list would not be complete without all my wonderful friends back home in the „old world‟ who always provided me with a real home on my visits back to Germany. Many thanks to Amarante Barambio, Ralf Becker, Harald Teufel, Iris Becker, Juergen Braeuer, Juergen Hense, Jochen Leve, Johannes Stober, Wolfgang Tischler, and Iris and Alexander Will. Thanks also to my lovely family back in Karlsruhe and Rastatt for all their kindness and unconditional love, my parents Renate and Karl-Heinz Spaeth, and my sisters Diana and Manuela Spaeth. And that little ray of sunshine which is also known as my niece Jessica.

Finally, and most of all, I wish to thank Boris Paul for having been a very special person in my life throughout many years. For believing in me, understanding me, and encouraging me in many ways. Your loving kindness, compassion and generosity are beyond limitation.

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"For me, trees have always been the most penetrating preachers. I revere them when they live in tribes and families, in forests and groves. And even more I revere them when they stand alone. They are like lonely persons. Not like hermits who have stolen away out of some weakness, but like great, solitary men, like Beethoven and Nietzsche. In their highest boughs the world rustles, their roots rest in infinity; but they do not lose themselves there, they struggle with all the force of their lives for one thing only: to fulfil themselves according to their own laws, to build up their own form, to represent themselves. Nothing is holier, nothing is more exemplary than a beautiful, strong tree. When a tree is cut down and reveals its naked death-wound to the sun, one can read its whole history in the luminous, inscribed disk of its trunk: in the rings of its years, its scars, all the struggle, all the suffering, all the sickness, all the happiness and prosperity stand truly written, the narrow years and the luxurious years, the attacks withstood, the storms endured. And every young farm boy knows that the hardest and noblest wood has the narrowest rings, that high on the mountains and in continuing danger the most indestructible, the strongest, the ideal trees grow.

Trees are sanctuaries. Whoever knows how to speak to them, whoever knows how to listen to them, can learn the truth. They do not preach learning and precepts, they preach, undeterred by particulars, the ancient law of life.”

-Hermann Hesse

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Chapter 1 General introduction

1. General introduction

1.1. Rainforest clearing and the impacts on ecosystem processes

At the time of this study, the clearing and disturbance of tropical forests was regarded as one of the largest land-cover conversions taking place on earth (Achard et al., 2002; Hansen and DeFries, 2004; Wright, 2005; Asner et al., 2006). Although deforestation has always been a part of human history, it is only during the past few centuries that humans have developed the technology which allows them to clear forests on a large scale (Williams, 2003). As a consequence, the extent of forests has been declining rapidly. Worldwide, as much as 800 to 1200 million hectares were cleared by 1999 (Wright and Muller-Landau, 2006), and in 2008 it was estimated that tropical forests disappeared at a rate of 13 million hectares per year (Laurance, 2008).

Over the past three decades, the rates of rainforest clearing have been increasing in many areas of the neotropics. In tropical Asia, deforestation rates increased from 1.8 million hectares per year during the 1980s to 2.6 million hectares per year in the 1990s (Hansen and DeFries, 2004). Similar numbers are available for the Brazilian Amazon, where annual deforestation rates increased from 1.3 million hectares in 1997 to 2.6 million hectares in 2002, with a rise of 40% from the year 2001 to 2002 (Fearnside and Barbosa, 2004).

In his publication “Reflections on the tropical deforestation crisis”, Laurance (1999) stated that there was “no simple consensus among biologists, economists and policymakers about the ultimate and proximate factors that promote tropical deforestation”. Although the underlying factors comprise a complex of social, political, economic, cultural and biophysical factors (Lambin et al., 2003), the most crucial causes of deforestation have been identified as the establishment of pasture for cattle grazing and farm land for agricultural production (Buschbacher, 1986; Aide et al., 1995; Myers, 2002; Kupfer and Franklin, 2009).

The clearing and fragmentation of rainforests on a large scale produces a wide range of deleterious consequences. The most evident consequences of deforestation are related to above-ground factors such as a loss of biological diversity and habitat for rainforest- dependent flora and fauna (Brooks et al., 2002; Dirzo and Raven, 2003; Lamb et al., 2005; Wright and Muller-Landau, 2006) and a decrease in timber and non-timber forest products (Lamb et al., 2005). On an ecosystem level, deforestation has also been linked to an increased susceptibility of soils to erosion (Laurance, 1999), increased risk and severity of

Chapter 1 General introduction extreme floods (Clark, 1987; Bruijnzeel, 2004; Bradshaw et al., 2007), and changes of hydrological cycles (D' Almeida et al., 2007). On a global level, large scale deforestation is a major source of atmospheric greenhouse gas emissions (Ramankutty et al., 2007; van der Werf et al., 2009) and accounts for about 20% of global anthropogenic carbon dioxide emissions (Gibbs and Herold, 2007; IPCC, 2007).

Less apparent are the impacts that deforestation has on below-ground ecosystem processes. As above-ground vegetation is closely linked to below-ground processes, deforestation can also lead to significant changes in a range of soil physical and chemical properties and biochemical cycles (Lamb, 1980; Robertson, 1984; Vitousek et al., 1989; Piccolo et al., 1994; Erickson et al., 2001; Greenwood and McKenzie, 2001; Powers, 2004; Lemenih et al., 2005; Zheng et al., 2005; Shrestha et al., 2006; Sharrow, 2007; Maloney et al., 2008).

Although substantial losses in soil carbon with conversion from rainforest to pasture have been reported (Mann, 1986; Lugo and Brown, 1993; Murty et al., 2002), such losses do not necessarily occur (Post and Kwon, 2000), and in the long term, managed pastures may show an equivalent or even higher potential to store soil carbon as forests (Franzluebbers et al., 2000; Guo and Gifford, 2002). Carbon plays an important role in nutrient cycling and primary productivity; nevertheless, the impacts of land-cover change on soil carbon dynamics are poorly understood (Guild et al., 1998; Kauffman et al., 2003; Marin-Spiotta et al., 2008). The difficulties in the characterisation of soil carbon pools are mainly based on climatic and structural variation in the different parts of the tropics, the complex processes and interactions between biota and physical characteristics, different soil types, and natural disturbances (Kauffman et al., 2009). All of these factors greatly limit our understanding of the role of tropical forests as carbon sources and sinks.

Due to the high connection between nitrogen cycling and vegetation productivity (Powers, 2004), soil nitrogen pools can also be severely affected by deforestation (Montagnini and Buschbacher, 1989; Piccolo et al., 1994; Reiners et al., 1994; Neill et al., 1997b; Rasiah et al., 2004), with generally lower total nitrogen and nitrate pools in pastures (Piccolo et al., 1994; Reiners et al., 1994; Neill et al., 1997b; Rasiah et al., 2004). Nitrogen is the primary limiting nutrient in terrestrial ecosystems (Paul and Clark, 1997) and therefore an important nutrient for carbon dioxide uptake by tropical plants (Reich et al., 2006; Thornton et al., 2007). Although changes in soil nitrogen may indirectly affect soil carbon storage, the impacts of rainforest conversion to pasture are substantially uncertain (Neill et al., 2005). However, an improved understanding of nitrogen dynamics with land-cover changes is essential to include nitrogen feedbacks in carbon cycling models (Thornton et al., 2007).

Chapter 1 General introduction

Apart from altered nutrient availabilities, a removal of the forest cover can also result in changed microbial community compositions and functions (Hunt et al., 1988; Reiners et al., 1994; Lavelle et al., 1995; Nuesslein and Tiedje, 1999; Waldrop et al., 2000; Carney et al., 2004; Bossio et al., 2005; Zheng et al., 2005; Ayres et al., 2006; Chaer et al., 2009), as well as soil physical properties such as bulk density (Keller et al., 1993; Murty et al., 2002; Rasiah et al., 2004; Zheng et al., 2005; Numata et al., 2007), water availability (Rasiah et al., 2004), and acidity (Rasiah et al., 2004; Zheng et al., 2005). Furthermore, elevated light levels and soil temperature after clearing often increase the germination of dormant tree seeds during the early stages of exposure, a process that leads to depletion of forest plant species in the soil seed bank (Webb, 1972; Young et al., 1987; Olsen and Lamb, 1988).

1.2 Restoration of cleared rainforest landscapes

Richards (1996) argued that the generally low nutrient levels of tropical soils would restrict cattle grazing and agricultural production on deforested sites to only a few years. However, agricultural practises in some former rainforest areas have, in many cases, continued for years or even decades on the same sites (Uhl et al., 1988; Reiners et al., 1994; Aide et al., 1996; Cavalier et al., 1998) before economic shifts, reduced productivity, or socio-political changes led to eventual abandonment of these sites (Thomlinson et al., 1996). As large areas of rainforest have and are being disturbed by humans, secondary forests are becoming the dominant forest cover types across the tropics (Brown and Lugo, 1990b; Houghton, 1994; Wright and Muller-Landau, 2006).

In the latter part of the 20th century, humans have begun to understand the environmental damage entailed by rainforest clearing, and increased public awareness about the importance of rainforests resulted in efforts to restore rainforest cover on previously cleared and now abandoned land (Catterall et al., 2004; Miyawaki and Abe, 2004; Erskine et al., 2007; Catterall et al., 2008; Streck et al., 2008). Large-scale restoration of cleared areas in former rainforest landscapes could be an important means to reverse land degradation and the impacts it has on ecological processes and ecosystem services.

The Society for Ecological Restoration (SER, 2004) defined the process of restoration as an “intentional activity that initiates or accelerates the recovery of an ecosystem with respect to its health, integrity and sustainability”. A more general definition of restoration is “a bringing back to a former position or condition” (Woolf, 1977). Although there is still considerable disagreement about the evaluation of restoration success, commonly

Chapter 1 General introduction advocated ecosystem attributes in restored landscapes are diversity, vegetation structure, and ecosystem processes (Elmqvist et al., 2003; Dorren et al., 2004).

There are a number of different pathways by which forest cover can be restored in former rainforest landscapes. Once a cleared site is abandoned, it may revert to some kind of regrowth or „secondary forest‟ (Buschbacher, 1986; Uhl et al., 1988; Brown and Lugo, 1990b), ranging from scrub dominated by exotic species through to regrowth that closely resembles rainforest in structure and composition (Holl, 2007). Such an autogenic, or „self- directed‟, succession occurs when fast growing, shade intolerant species establish after cessation of agricultural practices. These species may still be present in the soil seed bank or are dispersed into sites by frugivorous animals, wind, water or other vectors (Catterall and Harrison, 2006; Erskine et al., 2007). Often referred to as pioneer species, they may then facilitate the growth of a variety of other species characterising later successional phases (Rodrigues et al., 2004; Erskine et al., 2007; Gomez-Aparicio, 2009).

However, the capacity of a site to recover can vary greatly between sites and is influenced by various factors (Aide et al., 2000; Lamb et al., 2005; Crk et al., 2009). Sites which have been cleared only recently, where soil seed banks have remained nearly intact and where original forest remnants are present in close proximity, can show a rapid successional development (Lamb et al., 2005). By contrast, when a site has been used for many years, shows extreme soil degradation, has seed banks which are depleted of native species or occurs in a highly fragmented landscape, recovery of the original forest can be delayed or inhibited for many decades (Richards, 1996; Aide et al., 2000).

In some cases, non-native tree species may act as pioneer species and are the earliest colonisers on abandoned pastures (D'Antonio and Meyerson, 2002; Lugo and Helmer, 2004; Neilan et al., 2006; Erskine et al., 2007; Catterall et al., 2008), establishing regrowth forests which contain a novel mixture of indigenous and introduced tree species (Lugo and Helmer, 2004). Lugo and Helmer (2004) have referred to such forests as „new forests‟, as they show different species composition, forest structure and ecological features than the original forests. Nonetheless, „new forests‟ often support a high number of rainforest fauna and perform various ecosystem services (Kanowski et al., 2008b). Although non- native species could dominate regrowth at the expense of native species, for example by competing for nutrients with native species or by suppressing the growth of rainforest seedlings (Fine, 2002), there is growing evidence that non-native tree species can play an important, yet still often overlooked, role in the restoration of forest cover on cleared land (Lugo, 2002; Neilan et al., 2006; Kanowski et al., 2008b). However, there is a very limited

Chapter 1 General introduction understanding about ecological processes in „new forests‟, which impedes predictions of reforestation outcomes.

Natural recruitment of native rainforest species into cleared sites can be a very slow process (Aide et al., 1995; Chapman and Chapman, 1996), dependent on a range of factors such as previous land-use pattern (Uhl et al., 1988; Aide and Cavalier, 1994) and seed availability (Hopkins and Graham, 1984a, b; Nepstad et al., 1991; Bakker et al., 1996; Wijdeven and Kuzee, 2000). Even if native rainforest tree seedlings recruit into abandoned sites, their growth may be severely limited by competition from pasture grasses and herbs (Brown and Lugo, 1994; Erskine et al., 2007). To overcome such barriers and to accelerate natural regrowth, tree-planting for biodiversity purposes (subsequently referred to as „ecological restoration planting‟) provides an alternative reforestation pathway. Such plantings often incorporate a proportion of early successional species to create a closed canopy in a relatively short time as part of a high diversity of densely-planted native rainforest tree seedlings (Erskine, 2003; Lamb et al., 2005; Catterall et al., 2008). Hereby, the canopy fulfils two roles: it produces shade that stops light-demanding grasses and herbs from growing, and it has the potential to attract frugivorous birds which disperse the seeds of native rainforest trees from nearby remnants (Lamb et al., 2005; Catterall et al., 2008). However, the high costs of restoration plantings restrict this pathway to only small areas of cleared rainforest land (Brown and Lugo, 1994; Lamb et al., 2005; Erskine et al., 2007; Catterall et al., 2008).

Even though secondary and replanted forests show significant differences from the original forests in terms of species composition and forest structure (Chazdon, 2003; Lugo and Helmer, 2004; Erskine et al., 2007), they can provide important ecosystem goods and services, such as protection from erosion, climate stabilisation, and the supply of timber and non-timber products (Brown and Lugo, 1990b; Guariguata and Ostertag, 2001; Naughton- Treves and Chapman, 2002). However, information about the recovery of ecosystem processes is, at best, sparse and often controversial (Silver et al., 2000; Erickson et al., 2001; Guo and Gifford, 2002; Paul et al., 2002; Rasiah et al., 2004; Silver et al., 2004; Silver et al., 2005; Zheng et al., 2005), and most studies have only investigated one particular process. For example, the high potential of secondary forests to sequester atmospheric carbon has led to an increased interest in patterns of carbon dynamics during reforestation (Neill et al., 1997a; Post and Kwon, 2000; Rhoades et al., 2000; Guo and Gifford, 2002; Paul et al., 2002; Marin-Spiotta et al., 2008; Paul et al., 2008; Marin-Spiotta et al., 2009). Moreover, studies assessing ecosystem processes in reforested landscapes have usually taken one particular pathway into consideration (de Koning et al., 2003; Feldspauch et al.,

Chapter 1 General introduction

2004; Rasiah et al., 2004; Bautista-Cruz and del Castillo, 2005; Lemenih et al., 2005; Lemenih and Teketay, 2006; Macedo et al., 2008; Maloney et al., 2008; Marin-Spiotta et al., 2008; Gonzalez-Rivas et al., 2009; Marin-Spiotta et al., 2009; Wang et al., 2009), disregarding the possibilities that different reforestation pathways possible within one region may offer.

1.3 Can ecosystem processes be restored through reforestation?

1.3.1 Reforestation and soil carbon and nitrogen dynamics

Reforestation of abandoned pastures has been proposed as a means to increase rates of above-ground and below-ground carbon sequestration (Silver et al., 1996; Post and Kwon, 2000; Silver et al., 2004; Marin-Spiotta et al., 2008; Fahey et al., 2009; Marin-Spiotta et al., 2009; Kanowski and Catterall, 2010). While above-ground biomass, as the most dynamic carbon pool, can be accurately measured (Jenkins et al., 2003; Kloeppel et al., 2007) and is closely correlated with root biomass (Cairns et al., 1997), soil carbon pools are less dynamic and more difficult to quantify (Fahey et al., 2009). However, rates of carbon accumulation are typically much slower for soils than for above-ground biomass (Post and Kwon, 2000). Tropical forests, with their rapid growth rates, are particularly considered to have a high potential to sequester and conserve carbon, hence helping to mitigate CO2 emissions from land-use change and the burning of fossil fuels (Montagnini and Porras, 1998). Soils play a significant role in carbon uptake: it is estimated that more than twice as much carbon is stored in soils than in above-ground biomass (Post et al., 1982; Batjes and Sombroek, 1997), and forest soils may store about 40% of all belowground carbon in terrestrial ecosystems (Dixon et al., 1994; Huntington, 1995). However, in many forests, significant quantities of carbon accumulate in the form of coarse woody debris, but the size of this pool is not closely related to aboveground biomass (Keeton et al., 2007; Woodall et al., 2008).

Soil organic matter (SOM), a combination of plant, animal and microbial residues (Koegel-Knabner, 1993), is a significant, yet often overlooked, component of the soil carbon cycle (Post et al., 1982) and can act as a sink or source of atmospheric CO2 (Lugo and Brown, 1993). Soil organic matter forms the largest carbon pool in many forests, but changes in SOM are difficult to detect due to its high spatial variability (Fahey et al., 2009). Carbon is stored in the soil when inputs into SOM exceed carbon losses, or when stabilising mechanisms increase the residence-time of soil carbon, resulting in a longer lag-time

Chapter 1 General introduction

between carbon inputs and CO2-release (Marin-Spiotta et al., 2009). SOM has a strong impact on soil chemical and physical properties and plays a crucial role in nutrient dynamics (McColl and Gressel, 1995). Secondary forests generally show a rapid accumulation of above-ground biomass, combined with a high litter production (Brown and Lugo, 1990b). The recovery of SOM under secondary forest may therefore be relatively fast (Guggenberger and Zech, 1999).

However, the temporal patterns of soil carbon accumulation during reforestation are difficult to predict (Paul et al., 2002). A range of studies have estimated soil carbon stocks in reforestation, but only a few could actually show a considerable soil carbon accumulation in secondary forests (Brown and Lugo, 1992; Silver et al., 2000; Cerri et al., 2003; Silver et al., 2004; Lopez-Ulloa et al., 2005; Lemma et al., 2006; Marin-Spiotta et al., 2009). The majority of studies found no net change of soil carbon or even losses of carbon with reforestation (Bashkin and Binkley, 1998; Hughes et al., 1999; Rhoades et al., 2000; Guo and Gifford, 2002; Paul et al., 2002; de Koning et al., 2003; Feldspauch et al., 2004; Bautista-Cruz and del Castillo, 2005; Lopez-Ulloa et al., 2005). Guo and Gifford (2002) reviewed 74 studies about the influence of land-use changes on soil carbon stocks and reported that, in most cases, both natural forest regrowth on pasture as well as tree planting resulted in a reduction of soil carbon. However, soil carbon losses were particularly high in sites where timber plantations were established. Such losses could amount up to 10% of the total carbon pool and may have been caused by higher decomposition rates of soil organic matter due to soil disturbance during site preparation (Guo and Gifford, 2002). Some authors suggested that loss of soil carbon following reforestation could reflect the high amount and rapid turnover of fine grass roots in pasture sites, which increases the accumulation of organic matter and therefore soil carbon (Brown and Lugo, 1990a; Yakimenko, 1998). If correct, this could mean that woody plants are less effective in storing soil carbon than pasture grasses (Post and Kwon, 2000), as much of the tree root system has very slow turnover rates (Guo and Gifford, 2002). Detecting changes in the large soil carbon stock during reforestation is difficult, and to improve our understanding of soil carbon dynamics, more studies, particularly in a variety of reforestation pathways, are needed.

Nitrogen (N) is one of the main elements of fundamental importance to metabolic processes in all living organisms and an important nutrient for plant development (Paul and Clark, 1997), which makes it a major determinant of the primary productivity of terrestrial ecosystems. It is therefore not surprising that the effect of reforestation on nitrogen dynamics has been one of the most common aspects investigated, and most studies reported increases in nitrogen availability during forest succession in regrowth areas (Lamb, 1980;

Chapter 1 General introduction

Robertson, 1984; Vitousek et al., 1989; Erickson et al., 2001; Scowcroft et al., 2004; Macedo et al., 2008; Maloney et al., 2008). As soil nitrogen pools and transformation rates are highly connected with vegetation productivity and organic matter levels (Powers, 2004), the re- establishment of trees in deforested landscapes has the potential to reverse the changes in nitrogen dynamics induced by land-use change. Several authors have reported increased N- mineralisation and nitrification rates, combined with higher nitrate levels, in reforested sites (Lamb, 1980; Robertson and Vitousek, 1981; Robertson, 1984; Vitousek et al., 1989; Erickson et al., 2001; Scowcroft et al., 2004).

Because the fertility and productivity of secondary rainforests depend to a large amount on N cycling transformation processes, it is important to understand patterns of these processes in regenerating rainforest successions and the factors that regulate these patterns. Although there are some generalisations in the literature about the fate of nitrogen during reforestation, there is little information about the change in nitrogen dynamics under different reforestation pathways.

1.3.2 Reforestation and soil seed bank dynamics

Since the presence of viable seeds in the soil was first described for a secondary forest in Malaysia (Symington, 1933), numerous studies have looked at the composition and size of seed banks in rainforest soils. Soil seed banks may contain information about vegetation dynamics not evident in the standing vegetation (Hopkins et al., 1990) and have therefore been described as a „memory‟ of the original plant community (Bakker et al., 1996). However, soil seed banks of pastures and fields are almost entirely composed of seeds of grasses and herbaceous species (including many non-native plants) and rarely contain any tree seeds (Guevara and Gomez-Pompa, 1972; Gomez-Pompa and Vazquez-Yanes, 1981; Uhl et al., 1981; Uhl, 1982; Hopkins and Graham, 1984b; Uhl et al., 1988; Gomez-Pompa et al., 1991; Quintana-Ascencio et al., 1996). Such a shortage of viable seeds is a major barrier to forest regeneration on abandoned agricultural land (Holl, 1998; Holl, 1999; Wijdeven and Kuzee, 2000; Cubina and Aide, 2001).

Rainforest soil seed banks are typically composed of mainly long-lived seeds from pioneer species that can remain dormant in the soil until favourable conditions for germination and growth occur (Hopkins and Graham, 1983; Garwood, 1989; Graham and Hopkins, 1990; Holl, 1999). Although pioneer species contribute less than 3% of the canopy trees in original rainforest (Whitmore, 1989; Laurance et al., 2006), they are a major component of plant regeneration in regrowth forests (Garwood, 1983; Young et al., 1987;

Chapter 1 General introduction

Cummings and Reid, 2008; Vieira et al., 2009). The composition of seed banks can be severely depleted by the duration, intensity, and frequency of agricultural practises (Uhl et al., 1981; Garwood, 1989). Especially after prolonged agricultural production or grazing, soil seed banks can contain few native woody species, in which case the restoration of forest cover becomes highly dependent on the dispersal of seeds from forest remnants (Turner and Corlett, 1996; Aide et al., 2000; White et al., 2004; Lamb et al., 2005). However, the dispersal of seeds to open pasture sites may be restricted by a lack of suitable perching sites for frugivorous birds and bats, and/ or a lack of food sources to attract seed-dispersing animals (Charles-Dominique, 1986; Willson and Crome, 1989; Wunderle, 1997). Some studies have indicated that isolated trees, especially fleshy-fruited species, may attract seed dispersers to cleared land (Guevara et al., 1986; Toh et al., 1999), thereby gradually increasing the amount of tree seeds in the soil seed bank and facilitating the regeneration of natural forest (Turner and Corlett, 1996; Aide et al., 2000; White et al., 2004).

Although a number of studies have found that the abundance of woody species in the soil seed bank increases during forest regeneration (Aide et al., 1995; Baider et al., 2001; Cubina and Aide, 2001; Wang et al., 2009), there is little understanding of the processes that determine species composition as forest cover regenerates after large-scale disturbance (Swaine and Hall, 1983; Garwood, 1989; Janzen and Vasquez-Yanes, 1991). Knowledge of the processes which affect composition and dynamics of soil seed banks, especially in intermediate phases of forest succession, is crucial to the understanding and therefore to the facilitation of reforestation processes. Such information could help restoration practitioners to decide which pathway, or method of reforestation, would be most useful in order to guide and accelerate the recovery of secondary forests in particular circumstances (Gonzalez- Rivas et al., 2009). Although seed bank dynamics have been studied in a number of secondary forests (Hopkins and Graham, 1984b; Wijdeven and Kuzee, 2000; Baider et al., 2001) there is little information on the composition of soil seed banks under different types of reforestation within one former rainforest landscape.

1.3.3 Reforestation and seedling growth conditions

The early growth of rainforest seedlings is a critical phase in the reforestation of cleared landscapes and a yardstick for successful reforestation (Lawrence, 2003). Light availability is a key factor limiting the growth of rainforest seedlings (Portsmuth and Niinements, 2006; Kunstler et al., 2009), although light conditions during regrowth may be initially less limiting. The growth of tree seedlings, especially in their early stages, can also be strongly affected by soil properties (Nussbaum et al., 1995; Burslem, 1996; Gunatilleke et

Chapter 1 General introduction al., 1996; Woodward, 1996; Cubina and Aide, 2001). Growth in seedlings of pioneer tree species, which play an important role in reforestation dynamics (Connell and Slatyer, 1977; Brown and Lugo, 1990a; Aide et al., 1995; Erskine et al., 2007), can be limited by low nutrient availability (Vitousek and Matson, 1984; Ewel, 1986; Tilman, 1986). Pioneer species often have small, nutrient-poor seeds and rapid growth rates, making them relatively sensitive to variation in nutrient availability (Chapin et al., 1986). Studies on seedlings of tropical pioneer species in soils with different nutrient levels have shown negative correlations between growth rates and nutrient availability (Nussbaum et al., 1995; Lawrence, 2003).

In tropical forests, the capacity of a seedling to use soil nutrients effectively is largely determined by the availability of light (Dalling and Tanner, 1995; Huante et al., 1995; Lee et al., 1997; Barberis and Tanner, 2005; Ashton et al., 2006; Yavitt and Wright, 2008). Under relatively high light conditions, seedlings have a greater demand for soil resources and can respond positively to higher nutrient levels (Dalling and Tanner, 1995; Huante et al., 1995; Yavitt and Wright, 2008). Under the high light conditions of pasture sites and early stages of reforestation, tree communities may be especially affected by a lack of soil nutrients. Vitousek et al. (1993) and Tilman (1986) identified nitrogen as the most important nutrient limiting plant growth in early successional sites; however, a meta-analysis of 15 studies that addressed the effects of nutrient addition on tropical tree species (Lawrence, 2003) suggested that phosphorus may be more broadly limiting than nitrogen.

To understand and predict seedling establishment and growth is a key challenge in restoration ecology (Turner, 1990; Swaine, 1996). Although there is plenty of evidence that soil resources play an important role in the establishment of rainforest seedlings after disturbance, there has been little research which directly links soil conditions to seedling performance under different forms of rainforest restoration.

1.4 Aims and structure of this thesis

The overarching aim of this study is to provide information about the re-establishment of ecological processes during reforestation. In particular, this study compares the ability of different reforestation pathways to restore ecological processes, and considers the advantages and disadvantages of these different pathways.

The study was carried out in a network of sites distributed across a former rainforest landscape in subtropical eastern Australia. The study area once supported the „Big Scrub‟,

Chapter 1 General introduction the largest continuous stand of lowland subtropical rainforest in Australia (Floyd, 1990). This rainforest was cleared to less than 1% of its original size following European settlement in the mid-19th century, mainly for the establishment of pasture and agricultural land (Floyd, 1990). Remnants of the original rainforest were reduced to small, highly fragmented patches throughout the area (Floyd, 1990). In recent decades, much of the cleared land has become marginal for agricultural production, and forest cover has increased in the region (Kanowski et al., 2003; Neilan et al., 2005; Kanowski et al., 2008b). Reforestation in the Big Scrub region has mainly taken place through three different pathways: first, regrowth dominated by the exotic tree species Cinnamomum camphora (camphor laurel) has colonised extensive areas of abandoned agricultural land (Neilan et al., 2006; Kanowski et al., 2008b). Second, some stands of this regrowth have been „treated‟ by restoration practitioners to accelerate the development of secondary rainforest (Kanowski and Catterall, 2007; Kanowski et al., 2008b). Third, there are many small tree-planting projects in the region, including projects established with a diverse range of rainforest trees for the purpose of ecological restoration (Kanowski et al., 2003; Catterall et al., 2004). The occurrence of these three different types of reforestation in close proximity and the simultaneous existence of undisturbed rainforest stands within a uniform geological and climatic area make this region ideal to study the impacts of different reforestation pathways on ecological processes.

This thesis consists of seven chapters.

Chapter 2 introduces the study area, its geographic location and climatic conditions. It also gives on overview of the reforestation pathways and the experimental design used in this study.

Chapter 3 describes the impacts of land-use change on soil seed bank dynamics and examines how the seed bank is related to the standing vegetation in reforested sites and rainforest. The main questions addressed are as follows: (1) What are the impacts of pasture establishment on the size and composition of viable soil seed banks? (2) Do the composition and size of soil seed banks differ among different pathways of reforestation? and (3) How is the composition of above-ground vegetation related to the composition of the soil seed bank?

Spatial heterogeneity of soil properties is an important factor in the understanding of population, community and ecosystem processes, and has to be taken into account when developing an experimental design. High spatial variability of soil properties is usually addressed by sampling a number of subplots within a site and either averaging the data of the individually analysed subplots or physically combining the subsamples before analysis.

Chapter 1 General introduction

However, combining of subsamples may alter the soil microbial community and the microclimate and therefore lead to errors in the measurement of some microbial-related soil properties. Chapter 4 examines the spatial variability of a range of biotic and abiotic soil properties. Specifically, this chapter aims to answer two main questions: (1) Do soil properties differ more at any scale than at others? and (2) Does physically combining subsamples affect the measurement of soil properties?

In any terrestrial ecosystem, changes in above-ground vegetation are strongly linked to belowground processes and functions, as described in sections 1.1 and 1.3 of this chapter. Chapter 5 assesses the impacts of deforestation and reforestation on soil properties, with a strong emphasis on soil carbon and nitrogen availability. The approach is to compare values of soil properties in reforested sites with values in both pasture and rainforest sites. This chapter addresses the following three questions: (1) What is the impact of land-use change from primary subtropical rainforest to pasture on soil properties and functions? (2) Does reforestation have the ability to restore soil properties? and (3) How do the different reforestation pathways differ in their abilities to restore soil properties?

Seedling development can be strongly influenced by soil conditions, as described in section 1.3.3 of this chapter. In Chapter 6, this relationship is explored by directly linking the soil properties of the different land-use types to the early growth of rainforest pioneer species. The main questions addressed are: (1) How do biotic and abiotic soil properties affect the early growth of rainforest pioneer species? and (2) To which extent is seedling growth affected by the reforestation pathway?

In Chapter 7, the main findings of the study are summarised and discussed, particularly with regard to the advantages and disadvantages of the three different reforestation pathways.

Chapter 2 Study area

2. Study area and experimental design

2.1 Study area

The study area for this project was the Big Scrub region in Northern New South Wales, Australia, 28°40'-29 S; 153°10'-153°30' E (Fig. 2-1). The region was once extensively covered by lowland subtropical rainforest over an area of 75,000 hectares (Floyd, 1990). Following European settlement in the mid to late 19th century, the rainforest cover was largely cleared for the development of pasture and agricultural land (Floyd, 1990). Remnants of the original rainforest cover occur in small, highly fragmented patches throughout the region (Floyd, 1990), comprising only about 300 ha (less than 1%) of the area. The rainforest type in these remnants has been classified as complex notophyll vine forest; the canopy reaches a height of 35 m in most areas (Floyd, 1990).

At the time of this study, the major land-use types in the region included active and abandoned pastures, macadamia (Macadamia integrifolia x tetraphylla) plantations, revegetation sites and spontaneous regrowth of woody species, often dominated by non- native trees (Neilan et al., 2006). Recently, coffee (Coffee arabica) plantations have been established in the region.

Geologically, the region is defined by a basaltic plateau on the southern slopes of the Mt Warning shield volcano, 100-150 m above sea level, formed from basaltic flows and associated sediments of the Lismore basalt (Lott and Duggin, 1993). The major soil type is Krasnozem, a relatively fertile, typically red, well-structured, acid and porous soil with a high clay content (Lymburner et al., 2006). The climate in the region is subtropical, with mean daily temperatures ranging from 13°C to 26°C (Bureau of Meteorology, 2009). Mean annual rainfall varies spatially, in the range of around 1343 mm (Lismore) to 2327 mm (Whian Whian in Rummery Park), with wetter months generally between November and May (Bureau of Meteorology, 2009).

In the last few decades of the 20th century, much of the marginal agricultural land in the region was abandoned, or at least managed less intensively, due to changing economic circumstances (Neilan et al., 2006). These shifts in land management led to the development of regrowth vegetation on abandoned pastures (Neilan et al., 2006; Catterall et al., 2008; Kanowski et al., 2008b).

Chapter 2 Study area

Reforestation of former rainforest areas in the Big Scrub region has mainly occurred through three different pathways. First, the exotic tree species camphor laurel (Cinnamomum camphora) dominates regrowth on abandoned land. It can form dense groves which presently cover around 25% of the Big Scrub region (Neilan et al., 2006). Within this regrowth, the seedlings of a diverse array of native rainforest trees also occur, growing mainly from seeds carried in by fruit-eating birds (Neilan et al., 2006; Kanowski et al., 2008c). Second, some stands of camphor laurel have been treated to accelerate the development of secondary rainforest by strategically poisoning mature camphor trees and seedlings with herbicide, liberating a dense growth of rainforest seedlings (Kanowski and Catterall, 2007; Kanowski et al., 2008b). Third, ecological restoration plantings typically consist of the planting of a high density and diversity of native rainforest tree seedlings (Erskine et al., 2007). Prior to planting, the pasture is sprayed with herbicides and mulched (Erskine et al., 2007). Within 3-5 years, such plantings can form a closed canopy which shades out grasses and weeds and attracts seed-dispersing frugivores (Catterall et al., 2008).

2.2 Experimental design and site-types

The experimental design for this study consisted of five site-types (pasture, camphor- dominated, treated camphor, ecological restoration planting, and rainforest) with five replicate sites in each site-type, interspersed across a large part of the Big Scrub region (Fig. 2-1). The minimum distance between replicate sites was 5 km, and the sites spanned a total area of around 750 km2. All sites were located on soils developed from basalt, at elevations between 100 and 150 metres. Detailed characteristics of the five site-types are as follows.

Pasture sites (PA) were characterised by a dense cover of short grass and were actively grazed by cattle or horses. Discussions with landholders revealed that fertilisation with lime (CaCO3) and superphosphate (Ca(H2PO4)2) has been a common practise of pasture management.

Camphor-dominated sites (CD) contained regrowth 40-50 years old, in which camphor laurel was the dominant canopy tree (70-90% of the canopy) and the canopy was closed (above 70%). The mid- and understorey consisted of early secondary species and some mature phase species; the sites showed typical rainforest attributes such as palm trees, epiphytes and hemi-epiphytes and occasional vines. For descriptions of such sites see Kanowski et al. (2008b) and Neilan et al. (2006).

Chapter 2 Study area

Treated camphor sites (TC) had previously consisted of 30-40 year old camphor regrowth. At the time of sampling, these sites were in their third to sixth year following treatment by glyphosate poisoning of the mature and seedling camphor trees, and had a closed canopy around 10-15 metres high, dominated by rainforest species. For descriptions of such sites see Kanowski and Catterall (2007) and Kanowski et al. (2008b).

Ecological restoration plantings (ER) were 10-15 years old, and mixed-species plantings. The canopy cover varied from 60-85%, and the trees comprised a diverse range of native rainforest species, including cabinet timber tree species in some sites. Palm trees, a diverse range of rainforest pioneer species and occasional vines represented the midstorey. In some cases, the understorey was dominated by grasses and ferns. For further descriptions see Erskine et al. (2007), Catterall et al. (2004) and Catterall and Harrison (2006).

Rainforest sites (RF) were remnants of subtropical rainforest with a canopy cover of 80-95% and a high diversity of native rainforest trees. Mature phase species dominated the sites; the mid- and understorey foliage layers were dense and these sites were also characterised by vines, epiphytes and seedlings of rainforest species. For further descriptions of such remnants see Floyd (1990).

Appendix 1 gives an overview over the locations, age, sampling times, and structural attributes of all sites.

Chapter 2 Study area

Figure 2-1 Map of the study area and the sampling sites. Base map and vegetation data from Geoscience Australia (Commonwealth of Australia (Geoscience Australia), 2009).

Chapter 3 Seed banks

3. Recovery of the soil seed bank

3.1 Introduction

Throughout the tropics and subtropics, large areas of rainforest have been cleared extensively over the last century, mainly for the establishment of pasture and agricultural land (Buschbacher, 1986; Aide et al., 1995). It has been estimated that 350 million hectares of tropical forests have been cleared, and 500 million hectares of secondary and primary tropical forests been degraded (ITTO, 2002). In many areas of the neotropics, the conversion of rainforests to agricultural land is still increasing (Holl, 2007), resulting in a variety of factors such as a loss of biological diversity, habitat loss and landscape fragmentation (McNeely et al., 1990; Sawyer, 1990; Whitmore, 1997).

However, in some areas agricultural land is being abandoned for a range of reasons, such as declining productivity, soil degradation, weed invasion or a shift in economic circumstances (Buschbacher et al., 1988; Brown and Lugo, 1990a; Aide et al., 1995). When agricultural production or grazing by cattle cease, the development of various forms of successional rainforest cover increases, ranging from scrub dominated by exotic species through to regrowth that closely resembles rainforest (Erskine et al., 2007; Holl, 2007). Hereby, the composition of these secondary rainforests is strongly influenced by the size, intensity and duration of the initial disturbance (Hopkins 1990).

Currently, there are several pathways by which forest might be restored on cleared land. Spontaneous forest regrowth generally occurs soon after abandonment and initially consists of herbaceous species and shrubs (Holl, 2007). After a relatively short time, a small number of fast-growing, short-lived pioneer species establish, either from seeds in the soil seed bank or dispersed by frugivorous animals (Kanowski et al., 2003; Catterall et al., 2004). These initial colonisers may be native or non-native (exotic) species which act as successional pioneers and may then facilitate the growth of a variety of later secondary and mature-forest species (Erskine et al., 2007). However, exotic species may alter the structure and function of an area (Vitousek et al., 1997) and compete with native rainforest seedlings for nutrients and water (Vieira et al., 1994; Nepstad et al., 1996). In some areas, exotic pioneer species can dominate the regrowth and inhibit the natural process of succession. If this is the case, a strategic removal of these species may be used as a management strategy for such sites to further accelerate the development of a native tree community, providing an alternative reforestation pathway (Scanlon, 2000; Neilan et al., 2006; Kanowski

Chapter 3 Seed banks et al., 2008c). Typically, only rainforests cleared for a short time are likely to develop towards the composition of the original rainforest, because clearing causes dramatic changes in the size and composition of soil seed banks (Hopkins and Graham, 1984b; Young et al., 1987; Lemenih and Teketay, 2006). Ecological restoration plantings directly aim to overcome factors like altered soil seed banks and competition from pasture grasses that usually limit rainforest succession (Erskine et al., 2007). In this reforestation pathway, a diverse range of native tree seedlings is densely planted with the objective of biodiversity conservation and land rehabilitation (Erskine et al., 2007).

The rate and direction of rainforest succession can be affected by a range of factors, including the soil seed bank (Holl, 2007). Viable, long-lived seeds that can remain dormant in the soil until favourable conditions for germination and growth occur are an important source of plant regeneration in forest regrowth (Garwood, 1983; Young et al., 1987). In all major reforestation pathways, seed availability is an important factor limiting rainforest recovery (Wijdeven and Kuzee, 2000). For example, studies from two tropical forests after shifting agriculture showed that the soil seed bank was responsible for the establishment of 85% of woody plants in early regeneration phases (Putz and Appanah, 1987; Lawton and Putz, 1988). However, after prolonged agricultural production or grazing periods, soil seed banks can become depleted of native woody species (Hopkins and Graham, 1984b; Wunderle, 1997), and the regeneration of rainforest cover on such sites can be severely limited. Therefore, an important part of reforestation is the recovery of viable soil seed banks in order to facilitate further plant recruitment.

In both early and later successional phases of recovery, dispersal of seeds by frugivorous animals is an important factor to build up soil seed banks (White et al., 2004). However, late secondary and mature phase rainforest species usually have larger, short- lived seeds and germinate directly into seedlings, therefore rather contributing to seedling bank diversity than to seed bank diversity (Graham and Hopkins, 1990; Stewart, 1995; Dalling et al., 1997; Dalling et al., 1998; Dalling and Hubbell, 2002; Gonzalez-Rivas et al., 2009). Although persistent seed banks have been studied in a number of secondary rainforests (Hopkins and Graham, 1984b; Wijdeven and Kuzee, 2000; Baider et al., 2001) and in rainforest types that differ floristically, functionally and structurally (Hopkins et al., 1990), there is no comparative study of soil seed banks under different types of reforestation possible within one landscape.

This chapter investigates possible impacts of pasture establishment and subsequent abandonment on the size and composition of soil seed banks and the recovery of soil seed banks with reforestation. The emphasis was to compare three reforestation pathways (tree-

Chapter 3 Seed banks planting for ecological restoration, spontaneous regrowth dominated by an exotic tree species, and similar regrowth treated to promote native regrowth) in their abilities to restore the viable soil seed bank. I also examined how the seed bank is related to the standing vegetation in reforested sites and rainforest, with the aim of drawing inferences about the utility of different forms of rainforest reforestation. I hypothesized differences in the seed bank density and composition between pasture and rainforest sites and a recovery of the soil seed banks in all three types of reforestation. Specifically, I expected reforestation to result in a shift from grass- and herb-dominated soil seed banks towards rainforest-like seed banks, containing a higher abundance and species richness of native woody plants. In regard to the composition of the soil seed bank, I expected the seed banks of reforested sites to be mainly composed of pioneer species and not to strongly reflect the species composition of the seedling bank and mature trees. Furthermore, I hypothesized that the recovery of soil seed banks is different in the three reforestation pathways. For example, soil seed banks of ecological restoration plantings were expected to show a greater diversity of woody plant seeds, in contrast to unmanaged spontaneous regrowth which was dominated by one species.

3.2 Methods

3.2.1 Study area and experimental design

The study area for this project was the Big Scrub region in Northern New South Wales, Australia, a region which was once extensively covered by lowland subtropical rainforest over an area of 75,000 hectares (Floyd, 1990). The rainforest cover was largely cleared for the development of pasture and agricultural land in the mid to late 19th century (Floyd, 1990), and remnants of the original rainforest occur in small, highly fragmented patches throughout the region (Floyd, 1990). At the time of this study, the major land-use types in the region included active and abandoned pastures, macadamia (Macadamia integrifolia x tetraphylla) plantations, revegetation sites and spontaneous regrowth of woody species, often dominated by the non-native tree species camphor laurel (Cinnamomum camphora) (Neilan et al., 2006). Reforestation of former rainforest areas in the Big Scrub region has mainly occurred through three different pathways: autogenic regrowth dominated by the non-native tree species camphor laurel (Cinnamomum camphora), managed regrowth to accelerate the development of secondary rainforest, and ecological restoration planting.

The experimental design for this study consisted of five site-types (pasture, camphor- dominated, treated camphor, ecological restoration planting, and rainforest) with five

Chapter 3 Seed banks replicate sites in each site-type, interspersed across a large part of the Big Scrub region (Fig. 2-1). The study area and the experimental design are described in more detail in Chapter 2.

3.2.2 Soil collection and seedling germination

Soil was collected from a 20 x 20 m survey plot at each site in December 2007. Within each plot, five subplots, four at the corner points and one in the centre of the plot, were sampled. At each subplot, the litter or grass layer was removed over an area of about 20 cm2 and four soil cores (7 cm diameter, 5 cm deep) were taken with a soil auger. The total of 20 subsamples taken from each site were homogenised in a plastic bag, transported in a cool box to the laboratory and stored at 4°C for a period of five days until germination was started.

Germination was carried out in a shade-house which allowed 30 percent penetration of sunlight. Prior to germination, I removed stones, leaves and coarse roots and spread the soils to a depth of about 1.5 cm in plastic germination trays (15 x 15 x 5 cm). All trays contained a layer of paper and vermiculite to prevent soil loss. In total, I prepared nine germination trays for each of the 25 sites. The 225 trays were placed on benches in the shade-house and watered twice daily for three minutes.

Seedling emergence was monitored once a week over a period of six months. Emerging seedlings were identified, counted, and removed from the trays. Care was taken to avoid the removal of soil along with the seedling roots. Specimens of each species were transplanted to obtain larger specimens for final identification. Seedlings that could not be immediately identified were also transplanted to individual pots, numbered, and identified after further growth.

Three germination trays containing sterilised soil were placed amongst the seed bank samples to check for possible seed contamination. Towards the end of the germination experiment, I found only fern seedlings (non-seed plants) in the control trays, indicating a shade-house contamination. Fern species were excluded from all analyses.

3.2.3 Floristic survey

Data on the floristic composition of a subset of study sites (camphor-dominated, treated camphor and rainforest) were obtained from various sources. For all sites, these data comprised information on the density of seedlings (= recruit trees >50 cm high, <2.5 cm dbh) and adult trees (> 2.5 cm dbh) for each species present. The survey intensity varied

Chapter 3 Seed banks between sites, comprising the counting and identification of species along a single 50 m x 4 m transect (total area 200 m2, one camphor-dominated site, five treated camphor sites, two rainforest sites) (Kanowski and Catterall, 2007); five 50 m x 2 m transects (total area 500 m2, four camphor-dominated sites) (Neilan et al., 2006), or eight 50 m x 2 m transects (total area 800 m2, three rainforest sites) (Stephen McKenna, personal communication). Camphor- dominated sites were sampled between 2004 and 2007, treated camphor sites in 2007, and rainforest sites in 2004 (see Appendix 1 for sampling times). Since seed bank and vegetation sampling took place within a period of three years, data from different sites will be comparable.

3.2.4 Data analysis

Germinated seedlings were classified into functional groups according to a range of factors including life-form (grass, herb, vine, shrub, and tree), origin (native or exotic), dispersal mode (birds/bats; wind; or other) (Kanowski et al., 2008a), and successional stage (pioneer species, early secondary species, and late secondary species) (Kooyman, 1996; Kanowski et al., 2008a).

In all analyses, I used sites as replicates, with data combined from the nine germination trays per site. Seed density was defined as the mean number of seeds per square metre of soil to a depth of 5 cm; species richness was defined as the cumulative number of species per site.

Analysis of variance (ANOVA) was used to test for differences in seed density and species richness of functional groups, using site-type as independent factor and functional group as dependent factor. Least significant difference (LSD) was used at 5% levels to separate treatment means when overall differences were significant. Analyses were carried out separately for all functional groups and combinations of functional groups.

The connection between soil seed banks and stand vegetation in terms of woody plants was explored for three site-types, camphor-dominated, treated camphor, and rainforest sites. To account for differences in survey efforts, I compared the proportion of species recorded in different functional groups rather than richness or counts. To compare seedling densities, I standardised counts of seedlings for each site to individuals per 100 m2. One-way ANOVA was carried out to test for differences among these site-types in their percentages of late successional species that are present either in the seed bank, in the seedling bank, or as mature trees. Least significant difference (LSD) was used to assess

Chapter 3 Seed banks pairwise differences among site-types (at 5% levels). ANOVA was carried out on square-root transformed data using the statistical package SPSS 14.0 (SPSS, 2005).

Multivariate analyses were carried out on three data sets: functional groups, woody species, and woody species excluding Cinnamomum camphora. To explore seed bank patterns among all site-types, I used non-metric multi-scaling ordination (MDS), based on Bray-Curtis similarity matrices calculated between site-types (Clarke and Ainsworth, 1993). MDS was based on the abundance of species in the seed bank. Additionally, one-way analysis of similarity (ANOSIM) was carried out to test for treatment differences. If site-types were significantly different by ANOSIM, I identified the functional groups or species that contributed most to the difference using SIMPER (Clarke and Ainsworth, 1993). All multivariate analyses were performed on square-root transformed data, using the software package PRIMER 6 (Clarke and Gorley, 2006).

3.3 Results

3.3.1 Overall abundance and diversity of germinated seedlings

Seedlings from a range of different life-forms germinated from the soil seed bank (Table 3-1). Grasses were the most abundant germinants, followed by herbs, woody plants, and vines. The number of germinants was significantly higher in pasture sites than in other site-types (ANOVA p <0.001). Grass species dominated the seed banks in pastures (70% of germinants) and were also common in restoration plantings (44% of germinants), but less abundant in other site-types (13% of germinants in treated camphor sites, 8% in camphor- dominated sites, and 0.6% in rainforest sites).

Table 3-1 Overall abundance and species richness of seedlings germinated from the top five cm of soil from 25 sites in rainforest, pasture and three types of reforestation in subtropical eastern Australia. Data for germinants are expressed as the average of germinants m-2 across all sites. Grasses were not identified to species levels. Abundance Species richness Functional group Native Exotic Total Native Exotic Total Grass - - 948 - - - Herb 212 361 573 17 27 44 Vine 125 12 137 12 2 14 Shrub 37 77 114 3 4 7 Tree 230 68* 298 33 1 34 Total - - 2070 - - - Woody plants (shrub + tree) 267 145 412 36 5 41 Total non- grass 871 662 1122 65 34 99 * the only exotic tree species recorded was Cinnamomum camphora

Chapter 3 Seed banks

Woody plants comprised 41 of the 99 species of non-grass germinants (Table 3-1). Native woody species accounted for 65% of woody plant individuals germinated in the experiment, of which two species, Ficus coronata and Trema tomentosa, were particularly abundant (accounting for 12% and 8% of native woody plants, respectively). Among the exotic woody plants, two species - Cinnamomum camphora and Solanum mauritianum – were dominant, comprising 16% and 14% of total woody plant germinants, respectively (Table 3-2).

Amongst native woody plants, pioneer species comprised 57% of individuals and 31% of species germinated in the experiment. In comparison, early secondary species made up 11% of individuals and 17% of species of native woody plants; while late secondary species made up 19% of individuals and 35% of species.

Across all site-types, a total of 621 germinants were identified as being dispersed by birds and/or bats, representing 45 species, 29 of which were woody plants (see Appendix 7 for dispersal modes, life-form and origin of germinated seedlings).

Chapter 3 Seed banks

Table 3-2 Most abundant woody plant species in the soil seed banks, seedling banks and as mature trees of rainforest, pasture and three types of reforestation in subtropical eastern Australia. For each site-type, the five most abundant species have been selected. PA, pasture; CD, camphor-dominated; TC, treated camphor; ER, ecological restoration planting; RF, rainforest. For pasture and functional restoration plantings, only seed bank data was available. N, native species, X, exotic species, P, pioneer species; E, early secondary species, L, late secondary species, B, dispersed by birds and/or bats, G, dispersed by gravity, W, dispersed by Wind, - , no records.

Species Functional Total PA CD TC ER RF type Germinants (per m2) Alphitonia excelsa N, P, B 70 0 2 36 4 28 *Cinnamomum camphora X, -, B 338 34 232 62 4 6 Ficus coronata N, P, B 240 0 38 54 24 124 Lenwebbia prominens N, L, B 58 10 16 10 4 18 Macaranga tanarius N, P, B 66 0 14 24 26 2 Melicope micrococca N, E, B 60 0 14 0 4 42 *Phyllanthus multiflorus X, -, B 78 2 0 26 32 18 Polyscias elegans N, P, B 50 8 10 6 18 8 *Solanum mauritianum X, -, B 286 12 80 56 50 88 Solanum stelligerum N, P, B 16 10 6 0 0 0 Trema tomentosa N, P, B 172 0 18 76 14 64 Premna lignum-vitae N, L, B 60 0 6 6 24 24 Recruits (per 100 m2) Actephila lindleyi N, L, B 11 - 0 3 - 8 Archontophoenix cunninghamiana N, L, B 11 - 2 8 - 1 Argyrodendron trifoliatum N, L, W 9 - 0 0 - 9 Castanospermum australe N, L, G 31 - 1 0 - 30 *Cinnamomum camphora X, -, B 13 - 11 2 - 0 Cryptocarya glaucescens N, L, B 13 - 0 13 - 0 Dysoxylum mollissimum N, L, B 13 - 10 2 - 1 Guioa semiglauca N, E, B 41 - 6 36 - 1 *Ligustrum lucidum X, -, B 38 - 36 2 - 0 Linospadix monostachya N, L, B 5 - 0 0 - 5 Polyscias elegans N, P, B 10 - 0 10 - 0 Toona ciliata N, E, W 24 - 0 24 - 0 Wilkiea huegeliana N, L, B 14 - 1 10 - 3 Adult trees (per 100 m2) Anthocarapa nitidula N, L, B 1 - 0 0 - 1 Archontophoenix cunninghamiana N, L, B 1 - 1 0 - 0 Argyrodendron trifoliatum N, L, W 8 - 0 0 - 8 Castanospermum australe N, L, G 2 - 0 0 - 2 *Cinnamomum camphora X, -, B 9 - 9 0 - 0 Duboisia myoporoides N, P, B 2 - 0 2 - 0 Flindersia schottiana N, E, W 1 - 0 1 - 0 Guioa lasioneura N, E, B 2 - 0 2 - 0 Guioa semiglauca N, E, B 10 - 2 8 - 0 *Ligustrum lucidum X, -, B 5 - 5 0 - 0 *Ligustrum sinense X, -, B 7 - 7 0 - 0 Mallotus philippensis N, E, B 3 - 1 1 - 1 Neolitsea australiensis N, L, B 1 - 0 0 - 1 Pittosporum undulatum N, E, B 6 - 1 5 - 0 * exotic species

A complete species list is given in Appendix 2.

Chapter 3 Seed banks

3.3.2 Comparison between seed banks and vegetation

A vegetation study identified 134 woody plant species, representing 45 families, in the vegetation of camphor-dominated, treated camphor, and rainforest sites. The majority of these species (126) was of native origin. Pioneer and early secondary species made up 25% of all native woody species; the majority of species was characterised as late secondary (73%).

The species composition of woody plants in seed banks of camphor-dominated, treated camphor, and rainforest sites was markedly different from the standing vegetation in those sites. Only one species, the exotic tree Cinnamomum camphora, was abundant amongst germinants, recruits, and adults, and then only in camphor-dominated sites (Table 3-2). Polyscias elegans, a native pioneer to early secondary tree species, was relatively abundant among germinants (all site-types) and recruits (treated camphor sites only). Across all site-types, seed banks of woody plants were largely comprised of exotic and native pioneer to early secondary species (Table 3-2). In contrast, the composition of the standing vegetation (both the seedling bank (recruits) and adult trees) differed between site-types. In camphor-dominated sites, exotic species (Cinnamomum camphora and Ligustrum lucidum) were the most common recruits and adult trees, although these sites had also recruited a small number of native species across the spectrum of successional classes. In treated camphor sites, native species were the most abundant recruits and adult trees, particularly early secondary species (Guioa semiglauca and Toona ciliata) (Table 3-2). In rainforest sites, the seedling bank and adult trees were composed almost entirely of late secondary species.

I found no significant difference between camphor-dominated, treated camphor and rainforest sites in the abundance of woody species, whether represented as germinants from the soil seed bank, as seedlings or as adult trees (Table 3-3). Amongst germinants, the proportion of species and individuals that were late secondary also did not differ significantly between camphor-dominated, treated camphor and rainforest sites. However, amongst recruits and adult woody plants, proportionally more species and individuals were characterised as late secondary in rainforest than camphor-dominated or treated camphor sites; amongst recruits, there were also proportionally more individuals characterised as late secondary in treated camphor than camphor-dominated sites (Table 3-3).

Chapter 3 Seed banks

Table 3-3 Mean abundance of woody species (life-stages: germinants, recruits, adults), mean percentage of individuals that are late secondary and mean percentage of species that are late secondary in camphor- dominated, treated camphor and rainforest sites. P-values are from ANOVA between all three site-types and show LSD results (means with the same letter do not differ at p > 0.05). Camphor- Treated ANOVA- Rainforest dominated camphor p Abundance of woody species Germinants (m-2) 496 ± 114 514 ± 175 626 ± 94 0.656 Recruits (100 m-2) 40 ± 22 128 ± 33 61 ± 2.5 0.071 Adults (100 m-2) 18 ± 5.9 21 ± 4.7 14 ± 4.9 0.623 %. late secondary individuals Germinants 20 ± 6.1 27 ± 4.8 18 ± 5.1 0.490 Recruits 23 ± 9.9 A 53 ± 3.1 B 92 ± 2.9 C <0.001 Adults 14 ± 10.8 A 21 ±3.8 A 81 ±3.4 B <0.001 % late secondary species Germinants 28 ± 6.6 37 ± 3.4 25 ± 2.3 0.170 Recruits 61 ± 4.8 A 54 ± 4.7 A 81 ± 1.9 B <0.001 Adults 46 ± 8.1 A 34 ± 5.5 A 73 ± 5.3 B 0.003

See Appendix 3 for overall seedling and species numbers of native wood plant species according to their successional stage. Appendix 8 shows seedling numbers of most abundant non-grass species.

3.3.3 Variation of abundance and species richness of seed banks within functional groups among site-types

Most functional groups I tested differed in their representation between pasture and rainforest (Table 3-4). The abundance of grasses, native herbs and exotic herbs was significantly higher amongst germinants from pasture sites than from rainforest (Table 3-4). Conversely, native vines, native woody species, and woody species other than Cinnamomum camphora were significantly more abundant in rainforest than in pasture (Table 3-4). Species richness of a range of functional groups differed significantly between site-types (Table 3-4).

Densities of germinants in all functional groups which differed significantly between rainforest and pasture showed similar values to rainforest in at least one of the three reforestation pathways examined (Table 3-4 and Fig. 3-1).

All three reforestation pathways had a significantly higher species richness of exotic woody plant germinants than rainforest and successfully restored the species richness of late secondary woody plants (Table 3-4 and Fig. 3-1).

Chapter 3 Seed banks

Table 3-4 Comparison of abundance (per m2) and species richness of seedlings germinated from the soil seed banks of rainforest, pasture and various forms of reforestation in subtropical eastern Australia. The table presents ANOVA results for all five site-types and mean values for pasture and rainforest for all functional groups. Least significant difference (LSD) was used to contrast pasture and rainforest sites; significance was set at 5% levels. PA, pasture; CD, camphor-dominated; TC, treated camphor; ER, ecological restoration planting; RF, rainforest. In cases where there was a significant difference between pasture and rainforest sites, the recovery of abundance and species richness in CD, TC and ER was compared to values to pasture and rainforest. Similarity pattern was based on the similarity of the values in pasture and rainforest. PA, similar to pasture; RF, similar to rainforest; I, intermediate between pasture and rainforest.

Similarity pattern ANOVA- Mean Mean Higher in Functional group p pasture rainforest CD TC ER Abundance Grass <0.001 3536 6 PA RF I I Herb native <0.001 712 30 PA RF RF I Herb exotic 0.002 650 100 PA RF RF PA Vine native <0.001 8 192 RF I RF RF Vine exotic 0.43 18 0 - - - - Cinnamomum camphora 0.003 34 6 - - - - Woody plants native <0.001 30 514 RF I RF I exotic 0.014 48 112 - - - - excl. C. camphora <0.001 44 620 RF I RF I exotic excl. C. camphora 0.102 14 106 - - - - % native bird/bat dispersed 0.43 74 80 - - - - % native late secondary 0.004 0 5 RF RF RF RF Total non-grass 0.15 1466 948 - - - - Species richness Herb native <0.001 9.8 1.0 PA I I I Vine native <0.001 0.6 5.0 RF RF RF RF Woody plants native <0.001 2 14 RF I I RF exotic 0.007 1.8 0.6 PA PA PA PA excl. C. camphora <0.001 2.8 14.2 RF I RF RF exotic excl. C. camphora 0.010 0.8 0.2 - - - - % native bird/bat dispersed 0.055 75 76 - - - - % native late secondary 0.001 0 25 RF RF RF RF Total non-grass 0.24 30 25 - - - -

Chapter 3 Seed banks

Grass Herb native Herb native 6000 D 1000 C 15 p < 0.001 p < 0.001 C p < 0.001 800 4000 10 600 B 400 B B 2000 C A 5 B

AB B A 200 A A ofspecies No. A No. of individuals of No. No. ofindividuals No. 0 0 0

2000 Herb exotic 800 Vine native 10 Vine native p = 0.002 p < 0.001 p < 0.001 8 B 1500 BC 600 C B 6 B B 1000 C AB 400 BC AB C 4 A B 500 200 2 A

A ofspecies No. No.of individuals 0 individuals of No. 0 0

1400 Woody plants native 30 Woody plants native 6 Woody plants exotic p < 0.001 p < 0.001 p = 0.007 1200 25 1000 BC BC B C 20 C 4 800 B B B B 15 B B 600 A 10 2 400 B A

A species of No. 5

200 ofspecies No.

No. ofindividuals No. 0 0 0

1800 Woody plants excluding 35 Woody plants excluding 80 % of native woody plants C. camphora C. camphora late successional 1500 30 p < 0.001 25 p = 0.010 60 p = 0.004 1200 C BC BC 20 BC C B 900 B 40 B B B 15 B 600 B 10 A 20 300 species of No. A 5 (individuals) % A

No. ofindividuals No. 0 0 0 PA CD TC ER RF PA CD TC ER RF 100 % native woody plants 80 late successional p = 0.001 60 B B B B 40

% (species)% 20 A 0 PA CD TC ER RF

Figure 3-1 Pattern of variation in reforested sites for functional groups (abundance and species richness of seedlings germinated from the soil seed banks) that differed significantly between pasture and rainforest: pasture (PA), rainforest (RF) and three types of reforestation (camphor-dominated CD, treated camphor TC, ecological restoration planting ER). The order of the variables follows the order in table 3.4. Each site-type had five replicates. “o” displays the averaged data for each site, “–“ indicates the mean for each site-type. P-values are from ANOVA between all five site-types and show LSD results (means with the same letter do not differ at p > 0.05).

Chapter 3 Seed banks

3.3.4 Differences in seed bank community composition among site-types

The study found a clear separation between pasture, reforestation, and rainforest sites in terms of functional groups; with reforestation sites being intermediate between pasture and rainforest (Fig. 3-2 and Table 3-5). However, all three reforestation types differed significantly from each other in terms of functional groups (Table 3-5).

Table 3-5 Results of pairwise ANOSIM conducted on the composition of soil seed banks among rainforest, pasture, and three types of reforestation in subtropical eastern Australia. PA, pasture; CD, camphor-dominated; TC, treated camphor; ER, ecological restoration planting; RF, Rainforest.

Abundance of functional Composition of woody spp. Composition of woody spp. groups other than C. camphora Source R-statistic p R-statistic p R-statistic p PA vs CD 1.00 0.008 0.58 0.008 0.42 0.016 PA vs TC 1.00 0.008 0.97 0.008 0.53 0.008 PA vs ER 0.96 0.008 0.97 0.008 0.53 0.008 PA vs RF 1.00 0.008 0.97 0.008 0.54 0.008 CD vs TC 0.39 0.024 0.32 0.032 0.00 0.452 CD vs ER 0.95 0.008 0.58 0.008 0.18 0.127 CD vs RF 0.92 0.008 0.72 0.008 0.48 0.008 TC vs ER 0.58 0.008 0.16 0.127 0.16 0.159 TC vs RF 0.60 0.016 0.37 0.008 0.40 0.008 ER vs RF 0.98 0.008 0.38 0.008 0.40 0.008

SIMPER analysis showed that the separation between pasture and rainforest was mainly caused by grasses and herbs, with both groups more abundant in the pasture sites (Appendix 4). Grasses alone contributed 39% to the dissimilarity between pasture and rainforest. The dissimilarity between reforested sites and rainforest was largely caused by exotic tree species (contributing 23% to the dissimilarity between camphor-dominated and rainforest sites) and grasses (contributing 30% and 39% to the dissimilarity between treated camphor sites and rainforest and between restoration plantings and rainforest, respectively).

The results of an MDS, based on the composition of woody species only, clearly separated the sites into two main groups, pasture and rainforest sites (Fig. 3-2 and Table 3- 5). Amongst the reforestation sites, camphor-dominated sites showed the lowest similarity to rainforest (R = 0.724) (Table 3-5). Ficus coronata, a native early successional tree species, contributed 12% to the dissimilarity between pasture and rainforest. The high dissimilarity between camphor-dominated and rainforest sites was mainly caused by the exotic tree species Cinnamomum camphora (13%) (see SIMPER analysis in Appendix 5), which showed a high abundance in camphor-dominated sites. Species that most contributed to the dissimilarity between treated camphor and rainforest and between restoration plantings and

Chapter 3 Seed banks rainforest were the natives Ficus coronata, Melicope micrococca, and Trema tomentosa, which showed higher abundances in rainforest sites.

Repeating the same analyses with woody species excluding Cinnamomum camphora demonstrated again a separation between pasture and rainforest sites (Fig. 3-2 and Table 3- 5). Ficus coronata, more abundant in rainforests, contributed 8% to the dissimilarity between camphor dominated sites and rainforest sites and 9% to the dissimilarity between restoration plantings and rainforest. The dissimilarity between treated camphor and rainforest was caused by Melicope micrococca and Ficus coronata (both 6%), which were more abundant in rainforest sites. Appendix 6 shows the results of a SIMPER analysis based on woody species other than Cinnamomum camphora.

Chapter 3 Seed banks

A Functional groups 2D Stress = 0.10

B Woody species 2D Stress = 0.17

C 2D Stress = 0.16 Woody species other than C. camphora

PA CD TC ER

Figure 3-2 Two-dimensional ordination (MDS) of 25 sites, five replicates in each of five site-types, based on the abundance of life-forms (A), the composition of woody species (B), and the composition of woody species other than the exotic tree species Cinnamomum camphora (C). All seedlings were germinated from the soil seed banks of pasture PA, rainforest RF and three types of reforestation (camphor-dominated CD, treated camphor TC, ecological restoration planting ER) in subtropical eastern Australia.

Chapter 3 Seed banks

3.4 Discussion

3.4.1 Impacts of deforestation on seed bank size and composition

Soil seed banks of pasture and rainforest sites differed drastically in their size and composition, which confirmed my hypothesis that rainforest clearing has an impact on soil seed banks. The total seed density in pasture sites ranged from 2140 to 6860 seeds per m2 and was about five times higher than in rainforest. My results are consistent with other studies which reported 4130 viable seeds (Wijdeven and Kuzee, 2000) and 6310 seeds (Campos and de Souza, 2003) per m2 in a pasture soil. In contrast, rainforest soils have been reported to show an overall seed density in the range of 430 to 4760 viable seeds per m2 (Hopkins et al., 1990; Wijdeven and Kuzee, 2000; Campos and de Souza, 2003). These findings suggest that the duration of grazing by cattle has clearly exceeded the longevity of tree seeds that were once present in the soil, which means that native soil seed banks play no vital role in the initial restoration of rainforest cover.

The soil seed bank was sampled only once (in December), which means that sampling did not take seasonal fluctuations of the seed bank composition into account. However, the soil seed banks of all study sites were mainly composed of pioneer species; such species are generally characterised by an abundance of long-lived seeds (Putz, 1983; Garwood, 1989; Dalling et al., 1997). Therefore, the time of sampling would be expected to mainly affect the representation of later successional and mature phase species which were uncommon in the soil seed banks. Since the main flowering phase of most late secondary and mature phase species in the area is between September and February, with the development of mature seeds between November and May, the sampling time could have contributed to the under-representation of these species in the soil seed bank.

Large-scale disturbance is likely to favour ruderal plants which produce a large number of persistent seeds, therefore, soil seed banks of pasture sites tend to be dominated by wind dispersed grasses and herbaceous species, mainly weeds, and do not contain a high number of woody species (Demel, 1997). I found 20 to 130 seeds of woody plants per m2 in pasture soils; these results are similar to those reported by other studies, including a range of 0 to 130 tree species seeds (Garwood, 1989), means of 21 (Wijdeven and Kuzee, 2000), and 33 (Campos and de Souza, 2003) woody species per m2 of pasture soil. In contrast, the seed banks of rainforest sites were dominated by woody species. These results were expected and are consistent with reports from other regions that clearing of rainforest cover causes a depletion of woody species in the soil seed bank (Uhl et al., 1988; Garwood, 1989;

Chapter 3 Seed banks

Aide et al., 1995) and an increase of opportunistic herbaceous woody weeds (Demel, 1997). Poor seed dispersal in deforested sites may further contribute to exhausted seed pools and delayed forest recovery (Garwood, 1989).

The loss of woody species from the soil seed banks of pastures is not only evident in lower seed densities, but also in a decrease in species richness. Compared to rainforest sites, which contained an average of 15 woody plant species, I only found an average of four woody plant species in the soil seed banks of pastures. Moreover, exotic weed species (esp. Cinnamomum camphora and Solanum mauritianum) dominated pasture seed banks; this finding is consistent with previous studies in the region (Abdulhadi and Lamb, 1988).

3.4.2 Characteristics of the soil seed bank in reforested sites

This study shows that the density of viable seeds of grasses and herbs significantly decreases following reforestation. Herbaceous species in soil seed banks of tropical forests are mainly light-demanding, which explains their low numbers in rainforests (Hopkins and Graham, 1984a; Vazquez-Yanes and Orozco-Segovia, 1993). Baider et al. (2001) argued that, as forest regrowth is accompanied with a decrease in light availability in the understorey, light-demanding herbaceous species are not able to produce new seeds. However, early secondary forests, in contrast to old-growth forests, can contain a high number of herbaceous species (Gomez-Pompa and Vazquez-Yanes, 1981; Gomez-Pompa et al., 1991), and within my study area, this was actually the case in replanted sites.

In contrast to herbaceous species, seed density and species richness of woody species notably increased with forest regeneration. This finding is consistent with Baider et al. (2001). Across all reforestation sites, woody species in the soil seed banks were dominated by the exotic species Cinnamomum camphora and Solanum mauritianum and by a suite of pioneer and early secondary native species, the relative proportions varying between site-types. These general patterns were to be expected, as C. camphora was the dominant tree species in camphor-dominated sites. Furthermore, previous studies show that soil seed banks of rainforests are mainly composed of short-lived pioneer species which are characterised by the production of small, long-lived seeds that require light to germinate, characteristics which enable them to initiate forest succession after abandonment of cleared land, but impede their recruitment beneath the mature forest canopy (Putz, 1983; Garwood, 1989; Dalling et al., 1997).

In contrast, the seeds of mature-phase species are usually short-lived (Kooyman, 1996) and rarely occur in the seed banks (Vazquez-Yanes and Orozco-Segovia, 1993).

Chapter 3 Seed banks

Mature-phase rainforest species therefore rather depend on seedling banks persisting beneath the canopy for regeneration (Garwood, 1996; Whitmore, 1996), which explains the low correspondence between the composition of the soil seed bank and the seedling bank. To a great extent, the soil seed banks and the seedling banks were composed of different sets of woody species. This finding is typical for tropical forests (Hall and Swaine, 1980; Hopkins and Graham, 1984a; Sulei and Swaine, 1988; Demel and Granstrom, 1995). A high proportion (77%) of the woody plant species that appeared in the seedling banks but not in the seed banks was classified as late secondary species. In contrast, most of the common pioneer and early secondary species in the area, such as Alphitonia excelsa, Ficus coronata, Macaranga tanarius, or Trema tomentosa, were poorly represented in the seedling banks of regrowth sites and rainforest, but had persistent seeds in the soil seed banks. Under a dense canopy with limited radiation, their seeds are usually not able to germinate, or if they do, to persist in the shaded understorey. However, such species can be found frequently during the early stages of secondary succession (Olsen and Lamb, 1988), but are likely to be shaded out rapidly as the forest canopy closes.

Common native species in the seed banks of rainforest sites, such as Ficus coronata, Alphitonia excelsa, Macaranga tanarius, Trema tomentosa, or Melicope micrococca, were also abundant in reforestation sites, although they could not be detected in the seed banks of pasture sites. This result gives rise to two hypotheses. First, dormant seeds of native woody species from the original rainforest play little role in the restoration of rainforest cover after pasture abandonment in regions subject to many decades of agriculture, such as my study region in subtropical eastern Australia. Second, soil seed banks have the potential to recover following reforestation, whether that is initiated by autogenic processes (and comprised mainly of exotics) or by deliberate replanting. In both regrowth and replanted sites, recovery depends to a variable extent on the dispersal of seeds into the sites (White et al., 2004). Hereby, the abundance and species richness of native woody species dispersed into sites decreases with the distance from a remnant rainforest (White et al., 2004) and increases with the age of the revegetated site (McKenna 2001).

3.4.3 Differences between reforestation pathways and implications for the choice of rainforest restoration strategies

The most remarkable difference between the three reforestation pathways was the significantly higher abundance of grass and herb seeds in ecological restoration plantings, which was probably due to the relatively open canopy (about 60%) in most of these sites. Such a high density of grasses may inhibit the germination and growth of native woody

Chapter 3 Seed banks species and therefore impede natural rainforest succession; however, as the canopy develops with increasing age, I expect a decrease of grass and herb seeds in such sites. On the other hand, ecological restoration plantings showed a high similarity with remnant rainforest sites in terms of abundance and species richness of native vines and late secondary species in the soil seed bank. I also found similarities to rainforest in the composition of native woody species of the seed banks, which reflects the use of a high- diversity mix of early secondary species in ecological restoration plantings (Erskine et al., 2007). Although such plantings were generally very effective in recovering soil seed banks, their implementation is restricted by high costs (Erskine et al., 2007) and intense management requirements, especially in the early phases of the plantings. Consequently, this pathway is limited to the reforestation of small areas.

The issue of camphor laurel management is still subject of a debate amongst conservation managers, and many recommendations tend towards the control or even elimination of this exotic tree species in some areas (Gilmore, 1999; Scanlon, 2000). However, camphor laurel successfully colonises abandoned pastures and facilitates the recruitment of native rainforest species over large areas, which can then lead to rainforest succession (Gilmore, 1999; Neilan et al., 2006). In this study, seed banks in camphor- dominated sites showed a higher abundance and species richness of native vines and native woody plants than seed banks of pastures, but these properties were not comparable to those in rainforest. Following the removal of the canopy cover in treated camphor sites, I observed major changes in the soil seed bank. First, seeds of camphor laurel were significantly reduced in treated camphor sites. Second, abundances of native vines and native woody species increased considerably, which shows that the seed banks of these functional groups could be fully restored. In camphor-dominated and treated camphor sites, species richness of native woody plant germinants was intermediate between pasture and rainforest. These results suggest that camphor-dominated regrowth can be an important and economic reforestation pathway for the recovery of soil seed banks which may even further develop with the removal of mature camphor trees. I also found a proportionally higher number of recruits characterised as late secondary species in treated camphor than in camphor-dominated sites, which implies that the treatment of the camphor overstorey has been successful in „moving‟ the composition of regrowth towards a more rainforest-like condition. Similar results are reported in a previous study (Kanowski and Catterall, 2007).

This study provides evidence that rainforest regeneration after long-term pasture management is highly dependent on seeds from adjacent rainforest remnants. Further, the three reforestation pathways only slightly differed in their overall abilities to restore the

Chapter 3 Seed banks original soil seed banks. However, the recovery of the soil seed bank is not necessarily tantamount to the reforestation success in such sites. Rather, the germination and growth of seeds in regrowth sites may be subject to a range of factors, including water and nutrient availability, shading, or the depth of the litter layer.

Chapter 4 Spatial variability of soil properties

4. Variability of abiotic and biotic soil properties at different spatial scales

4.1 Introduction

Spatial variability is a dominant feature of soil properties (Robertson et al., 1988; Roever and Kaiser, 1998; Cain et al., 1999; Ettema et al., 2000; Ettema and Wardle, 2002; Schume et al., 2003; Schoening et al., 2006). Nutrient cycles in soils are regulated by species-rich communities of bacterial, fungal, and invertebrate species, which generally exhibit strong spatial structure (Bengtson et al., 2007; Cole et al., 2009; Kounda-Kiki et al., 2009; Peigné et al., 2009). These soil organisms often show a patchy distribution both vertically and horizontally, at scales ranging from hectares to square millimetres (He et al., 1994; Stoyan et al., 2000; Ettema and Wardle, 2002). Therefore, microbially-mediated processes such as soil respiration, denitrification, and nitrification are often found in „hotspots‟ across a few centimetres (Heilmann and Beese, 1992; Morris et al., 1993). Soil properties, such as pH, organic matter and mineral concentrations, can vary by an order of magnitude at spatial scales of 5 m or less (Stoyan et al., 2000). For example, nitrate and ammonium concentrations have been shown to vary by two to three orders of magnitude within a 12 by 10 m plot (Jackson and Caldwell, 1993). Similarly, in a study on successional plant communities, there was a significant variation in surface soil nitrate concentration over a distance of 55 cm in an abandoned field (Gross et al., 1995). A range of studies have explored spatial variation of soil properties, especially for carbon- and nitrogen-related properties (Cain et al., 1999; Conant et al., 2003; Frogbrook et al., 2009; Schulp and Verburg, 2009; Yamashita et al., 2010). However, studies of soil heterogeneity have generally been concentrated within one site only; and data about the variability of soil properties at different spatial scales are rare (Martin and Bolstad, 2009).

Spatial soil heterogeneity is increasingly recognised as an important factor in the understanding of population, community, and ecosystem processes involving soil organisms (Tilman and Kareiva, 1997). This heterogeneity provides diverse micro-habitats which may enable resource partitioning and the spatial separation of competitors, and thereby assist in the maintenance of soil biodiversity (Ettema and Wardle, 2002). Spatial variation of soil properties also allows for biochemically competing processes to continue side by side, such as aerobic and anaerobic microbial activities (Ettema and Wardle, 2002; Schume et al., 2003). Such heterogeneity may also affect the spatial structure of plant communities

Chapter 4 Spatial variability of soil properties

(Reynolds et al., 1997; Ettema and Wardle, 2002) and potentially allow the local coexistence of a high diversity of plant species (Giller, 1996).

However, spatial soil heterogeneity is also a major challenge for the measurement and monitoring of nutrient transformations and microbial processes in terrestrial ecosystems (Roever and Kaiser, 1998). When formulating an experimental design and sampling technique, it is crucial to recognize spatial patterns of physical, chemical and biochemical soil properties, to prevent small-scale variability affecting the quality and interpretation of analyses that are targeted at more general issues (Mueller, 1998; Post et al., 2001).

A common approach to incorporate or overcome the effects of spatial heterogeneity within a given site is the sampling and analysis of multiple subsamples (Dutilleul, 1993; Bellehumeur and Legendre, 1998; Conant et al., 2003; Stein and Ettema, 2003). This involves taking a number of samples at randomly or systematically chosen subplots within a site. Four components of such a sampling design are of major importance: 1) The number of sampling units (Dutilleul, 1993; Bruckner et al., 1999; Webster et al., 2002); 2) the size of the sampling unit (large sampling units are more likely to filter out spatial variation than smaller sampling units) (He et al., 1994; Bellehumeur et al., 1997); 3) the size of the total sampling area (He et al., 1994; Bellehumeur et al., 1997); and 4) the sampling interval, the average distance between sampling units (Levin, 1992). However, as different soil properties may show different spatial patterns of heterogeneity (Schume et al., 2003), the collection of subsamples at a particular scale may not adequately take into account the variability of every property at other scales. Moreover, for data which are difficult or expensive to collect, the sampling and analysis of a large number of individual subsamples may not be feasible.

Many soil scientists deal with the problem of spatial variability by combining multiple subsamples to get a spatially averaged measure of soil properties (Smalla and van Elsas, 2010). In this approach, multiple soil samples are taken at randomly or systematically chosen "subplots" within the site, then physically combined and treated as one sample. Combining the soil is generally achieved by sieving the material of various subplot samples through a 2 mm mesh size sieve, and a subsample from each composite sample is then used for analyses (Smalla and van Elsas, 2010). While combining of subsamples is a cost- and time-saving practice, it also has the potential to affect the outcomes of the study, particularly if it is measuring properties that are related to microbial activities, because microbial communities may be altered in combined samples with the result that microbial functions are either stimulated or inhibited. However, there is little information on the extent to which mixing of subsamples influences the measurement of microbial functions.

Chapter 4 Spatial variability of soil properties

This chapter aims to characterise spatial heterogeneity of a range of abiotic and biotic soil properties at different spatial scales: site-type (habitat), site, and subplot. Two main questions are addressed in this chapter (1) Do soil properties differ more at any scale than at others? (2) Does physically combining subsamples affect the measurement of soil properties? It was hypothesised that the combining of subsamples will have a considerable impact on the measurement of soil properties related to microbial activities, but not on other properties. The study was conducted with the overarching aim of determining a suitable sampling strategy to investigate between-habitat variation in soil properties.

4.2 Methods

4.2.1 Study region and experimental design

The study area for this project was the Big Scrub region in Northern New South Wales, Australia, a region which was once extensively covered by lowland subtropical rainforest over an area of 75,000 hectares (Floyd, 1990). The rainforest cover was largely cleared for the development of pasture and agricultural land in the mid to late 19th century (Floyd, 1990), and at the time of the present study remnants of the original rainforest cover occured in small, highly fragmented patches throughout the region (Floyd, 1990); the major land-use types included active and abandoned pastures, macadamia (Macadamia integrifolia x tetraphylla) plantations, and reforested areas. Reforestation has mainly occurred through three different pathways: autogenic regrowth dominated by the non-native tree species camphor laurel (Cinnamomum camphora), managed regrowth to accelerate the development of secondary rainforest, and ecological restoration planting (Catterall et al., 2004).

The experimental design consisted of five site-types. Pasture sites (PA) were cleared former rainforest sites, covered with short grass and actively grazed. Camphor-dominated sites (CD) were autogenic regrowth sites dominated by the non-native tree species camphor laurel (Cinnamomum camphora). Treated camphor sites (TC) were former camphor- dominated sites in which the camphor canopy had been removed in order to encourage the growth of native rainforest seedlings. Ecological restoration sites (ER) consisted of mixed- species plantings, comprising a variety of native rainforest species. Rainforest sites (RF) were remnants of undisturbed subtropical rainforest, containing a high diversity of native rainforest tree species. There were two replicate sites in each site-type, interspersed across a large part of the region. At each site, a 30 x 30 m survey plot was established, containing 16 subplots with a spacing of 10 m (Fig. 4.1).

Chapter 4 Spatial variability of soil properties

4.2.2 Soil sampling

Soil samples were collected from all sites in January 2007 in the warm and rainy season, when maximum microbial activity and nutrient transformation rates could be expected. At each of the 16 subplots within each of the 10 sites, the litter and any grass layer were removed and two samples of approximately 200 g of top soil were taken with a 7- cm-diameter soil auger to a depth of 10 cm. One soil sample from each subplot was placed into a ziplock plastic bag and sealed; the other sample was thoroughly mixed in a plastic bag with other subplot samples from the same site to obtain a composite site-level sample. All samples were transported in a cool box to the laboratory and stored at 4°C until analysis (up to five days for microbiological measurements, up to 14 days for physical and biochemical analyses). In the laboratory, individual subsamples and combined subsamples were sieved through a 2 x 2 mm mesh.

10 m

m 30 30

30 m

Figure 4-1 Diagram illustrating design and orientation of subplots for collection of soil samples within a site.

4.2.3 Measurement of soil properties

The following seven soil properties were analysed in all individual subsamples and also in the combined site-level samples: five abiotic soil properties (gravimetric water content, soil organic matter, pH, total organic carbon, and nitrate-nitrogen), and two biotic soil properties (microbial biomass carbon and nitrification rate).

Chapter 4 Spatial variability of soil properties

Gravimetric water content (GWC) was determined gravimetrically by drying sieved fresh samples for 24h at 105°C and expressing dry soil weight as a percentage of the fresh soil weight.

Soil organic matter (SOM) was determined by measuring weight loss following combustion at 550°C for 6 h in a muffle furnace and expressed as a percentage of the dry soil weight.

For analysis of soil pH, a suspension of five grams of sieved fresh soil mixed with 12.5 ml distilled water was measured using a pH-meter at 21 °C.

Total organic carbon (TOC) was measured by mixing five grams of field moist soil with 50 ml of 2 M KCL in Erlenmeyer flasks. The suspension was shaken for 1 h, centrifuged and filtered through a Whatman 42 filter. The extracts were frozen until analysis of organic carbon in a Shimadzu 5050A TOC analyser. TOC concentrations were converted to g C kg dry soil-1.

Nitrate-nitrogen (NO3-N) was determined in fresh soil sub-samples using KCl- extraction. Five grams of sieved field-moist soil were mixed with 40 ml of 2 M KCl-solution in a 50 ml falcon flask. The soil suspension was placed on an orbital shaker at 180 rpm for 1 h, allowed to settle and filtered through a Whatman No. 40 filter. The filtrate was frozen until analysed in a Discrete Chemistry Analyser (Smartchem 200, Westco Scientific Instruments,

Bookfield CT 06804) NOx-N [NO3-N + NO2-N]. Because NO2-N levels were negligible, [NO3-

N + NO2-N] are reported as NO3-N (nitrate-N).

Microbial biomass carbon (MBC) was measured using a fumigation-extraction method (Vance et al., 1987). Fresh sieved soil was weighed out into four 10 g subsamples; two were placed in 50 ml glass beakers and fumigated with ethanol-free chloroform for a period of 24 h, and two were kept in the dark at 4 C. After fumigation all subsamples were extracted with

40 ml of 0.5 M K2SO4 (soil:extractant ratio 1:4), shaken for 30 min on an oscillating shaker, filtered through a Whatman 42 filter, and frozen until further analysis. Soluble organic C in the fumigated and non-fumigated subsamples was determined using a Shimadzu 5050A

VCPH/CPN analyser. MBC was calculated as the difference between fumigated and non- fumigated samples, using the proportionality coefficient for C (K = 0.35), and expressed as mg C kg -1 dry soil.

Nitrification rates were assessed using the shaken soil slurry method by shaking a sieved soil sample in ammonium phosphate solution and measuring nitrate accumulation

Chapter 4 Spatial variability of soil properties over a period of 24 hours (Belser, 1979; Weaver et al., 1994). Nitrification rates were -1 -1 expressed as mg NO3-N kg dry soil day .

4.2.4 Data analysis

All statistical analyses were conducted using the package SPSS 14.0 (SPSS, 2005). Analyses were based on five site-types, two replicate sites per site-type, and 16 subplots per site.

The minimum, maximum, mean, coefficient of variation, and total variation were calculated for each soil property across all 10 sites. To assess the relative amount of variation explained by each spatial scale (site-type, site, and subplot), a two-factor nested analysis of variance (ANOVA) was conducted for each of the seven soil properties, with „site- type‟ as a fixed factor, „site‟ as a random factor nested within „site-type‟, and „subplots‟ as replicates within „sites‟. Individual estimated variance components were calculated from the ANOVA mean squares, using the following equations (Quinn and Keough, 2002):

site-type variance = MS site-type –(MS site (site-type)/nq);

site variance = MS site (site-type) – (MS residual/n);

subplot (residual) variance = MS residual; where MS = mean square, n = 16 individually measured subplots per site (residual), and q = 2 replicate sites per site-type.

Estimated variance components within each of the seven soil properties were also standardised by dividing the estimated variance values for each soil property by the squared overall mean value. Standardisation enabled direct comparison across soil properties, by removing the effect of their differing measurement scales.

To evaluate the relationships between different soil properties, Spearman rank correlations were calculated across n = 16 individually analysed subsamples for each soil property within each site.

The relationships between the values of the physically combined subsamples and the mean values of the 16 individually analysed subsamples within each site were tested with regression analyses. The 95% confidence intervals of the regression intercepts and slopes were used to assess whether the intercepts differed significantly from zero, and whether the slopes differed significantly from 1.0. Paired t-tests were also performed for each soil

Chapter 4 Spatial variability of soil properties

property, to test whether physically combined values differed in magnitude from mean values of the 16 individually analysed subsamples; with the ten sites as replicates.

4.3 Results

4.3.1 Spatial variability of soil properties

Partitioning of total variation in soil properties across the three spatial scales revealed that spatial variability was generally lower at the site-type level than at the site and subplot levels (Table 4-1, Fig. 4.2). Standardised variance components were highest at the site level for three soil properties (GWC, TOC, and nitrification) and relatively high at the subplot levels for three properties (MBC, nitrate-N, and nitrification). However, the high variation in nitrification between sites was mainly driven by the relatively large difference in nitrification rates between the two rainforest sites (Fig. 4-2). In contrast, standardised variance components were lowest at the site-type level for all soil properties except pH and nitrate-N (Table 4-1). Low nitrate-N levels in pasture and camphor-dominated sites compared to the three other site-types mainly contributed to its high variability at the site-type level (Fig. 4-2).

Table 4-1 Mean values, coefficients of variation (%) and total variation for each of the soil properties. The amount of variation explained by each spatial scale is expressed as the estimated and the standardised variance components for each scale (site-type, site, and subplot). GWC = gravimetric water content, SOM = soil organic matter, TOC = total organic carbon, MBC = microbial biomass carbon, NO3-N = nitrate-nitrogen. Estimated variance Standardised component variance component Overall CV Total Site- Sub Site- Sub Source mean Site3 Site3 (%) variation1 type2 plot4 type2 plot4 value1 GWC 30.84 13.60 9.52 6.00 6.63 8.68 0.006 0.010 0.009 SOM 17.03 16.53 7.92 -0.94 2.79 6.15 -0.000 0.009 0.021 pH 5.48 8.66 0.22 0.08 0.09 0.08 0.003 0.003 0.003 TOC 0.23 63.32 0.02 -0.01 0.02 0.01 -0.181 0.404 0.192 MBC 1.29 58.07 0.64 -0.12 0.23 0.53 -0.071 0.138 0.315

NO3-N 18.84 86.61 266.40 122.71 40.56 130.89 0.346 0.114 0.369 Nitrification 3.83 91.43 12.24 1.64 8.19 3.51 0.112 0.559 0.239

1n = 160, 2n = 5, 3n =2, 4n = 16.

Across all spatial scales, the seven soil properties differed strongly in their variability. Overall spatial variability was particularly high for two soil properties: nitrification, varying -1 -1 from 0 to 15.3 mg NO3-N kg dry soil day , with a relative variability (CV) of 91%, and -1 nitrate-N, ranging from 0.4 to 92 mg NO3-N kg dry soil , with a CV of 87% (Table 4-1, Fig. 4- 2). Spatial variability was also relatively high for TOC and MBC, with CVs in the range between 58% and 87%, and with TOC levels varying from 0.01 to 0.9 g C kg dry soil-1 (Table

Chapter 4 Spatial variability of soil properties

4-1, Fig. 4-2) and MBC levels varying from 0 to 4.9 mg C kg dry soil-1. GWC and SOM were characterised by CVs between 14% and 17%, indicating a moderate to low overall spatial variability. GWC varied from between 21% to 42% of fresh soil, and SOM varied from 7% to 29 % of dry soil (Table 4-1, Fig. 4-2). Soil pH was relatively constant, with a CV of 9%, and was the only soil property with a similar variability at all three spatial scales (Table 4-1, Fig. 4-2).

4.3.2 Spatial within-site correlations between soil properties

There was no trend towards strong spatial correlations among the different soil properties within individual sites. Out of 210 possible spatial correlations between the seven tested soil properties within sites, only 26 were statistically significant (Table 4-2). The strongest consistent correlations occurred between nitrate-N and nitrification, which were significantly positively correlated in six out of ten sites (Table 4-2, Fig. 4-3). TOC and MBC were significantly positively correlated in three sites and negatively correlated in two sites (Table 4-2, Fig. 4-3). Although GWC was positively correlated with SOM in all 10 sites, this correlation was only statistically significant in two sites (Table 4-2).

Chapter 4 Spatial variability of soil properties

45 GWC 30 SOM 40 25

35 20

30 15 % dry dry mass %

% fresh mass % 25 10

20 5

7 pH 1 TOC

1 -

6 pH 0.5

5 dry soilkg g

4 0

5 Microbial biomass C 100 Nitrate-N

- 1 4 - 80 3 60

2 40 mg kg dry soilkg dry mg 1 soilkg dry mg 20 0 0

1 Nitrification -

12 1

- 8

day

N kg dry soil N dry kg - 3 4

mg NO mg 0

Figure 4-2 Relationship between values of 16 physically combined samples (x), the means of 16 individually analysed samples (-), and the values for each of the 16 individually analysed samples (o) for soil properties across five site-types (PA = pasture, CD = camphor-dominated, TC = treated camphor, ER = ecological restoration planting, RF = rainforest; with two sites of each site-type).

Chapter 4 Spatial variability of soil properties

GWC SOM pH TOC MBC NO3-N Nitrif. PA 1

PA 2

CD 1

CD 2

TC 1

TC 2

ER 1

ER 2

RF 1

RF 2

Figure 4-3 Bubble plots showing the spatial pattern of variation of seven soil properties at 16 subplots within each of 10 sites. The bubbles of different sizes represent five equally-spaced classes between the minimum and maximum values of each property. GWC = gravimetric water content, SOM = soil organic matter, TOC = total organic carbon, MBC = microbial biomass carbon, NO3-N = nitrate-nitrogen, Nitrif. = nitrification. PA = pasture, CD = camphor-dominated, TC = treated camphor, ER = ecological restoration planting, RF = rainforest.

Chapter 4 Spatial variability of soil properties

Table 4-2 Spearman correlation coefficients among seven soil properties, based on 16 individually analysed subsamples within each of 10 sites. GWC = gravimetric water content, SOM = soil organic matter, TOC = total organic carbon, MBC = microbial biomass carbon, NO3-N = nitrate-nitrogen, Nitrif. = nitrification rate. * p < 0.05, ** p < 0.01. Soil properties PA 1 PA 2 CD 1 CD 2 TC 1 TC 2 ER 1 ER 2 RF 1 RF 2 compared GWC/SOM 0.04 0.42 0.51* 0.28 0.31 0.43 0.15 0.44 0.59* 0.14 GWC/pH 0.32 -0.53* 0.40 -0.34 0.28 0.06 0.03 0.36 0.29 0.13 GWC/TOC -0.28 -0.34 0.08 0.33 0.24 0.01 0.02 -0.15 -0.06 -0.19 GWC/MBC 0.04 -0.34 0.23 0.14 0.43 0.13 0.04 0.18 -0.02 0.15

GWC/NO3-N 0.03 -0.14 0.40 -0.16 -0.02 -0.15 -0.08 -0.17 0.16 -0.09 GWC/Nitrif. 0.31 0.09 0.57* -0.34 -0.37 -0.33 0.18 0.04 -0.57* 0.05 SOM/pH 0.08 -0.36 0.31 -0.03 -0.02 0.35 -0.35 -0.05 0.31 -0.60* SOM/TOC 0.25 -0.07 -0.05 0.08 0.25 -0.38 -0.40 -0.03 -0.07 -0.15 SOM/MBC -0.15 0.03 0.06 0.10 0.34 -0.31 -0.33 0.02 0.47 -0.50

SOM/NO3-N 0.67** 0.08 0.27 0.25 0.30 -0.02 0.57* -0.07 0.09 0.24 SOM/Nitrif. 0.47 0.10 0.27 0.40 -0.37 -0.24 0.70** -0.08 -0.08 0.31 pH/TOC -0.28 -0.20 -0.28 -0.06 -0.20 -0.57* 0.65** -0.52* -0.09 0.22 pH/MBC 0.20 0.17 0.43 0.24 0.26 0.03 0.06 0.22 -0.01 0.43

pH/NO3-N -0.35 -0.19 0.01 -0.30 0.29 0.52* -0.13 -0.65** -0.21 -0.09 pH/Nitrif. 0.17 -0.33 0.28 0.06 -0.28 0.04 0.12 -0.02 0.09 -0.19 TOC/MBC -0.56* 0.71** -0.58* 0.58* -0.41 0.59* 0.20 -0.43 0.06 0.14

TOC/NO3-N 0.17 0.14 0.25 0.11 -0.41 -0.26 -0.24 0.19 -0.09 -0.13 TOC/Nitrif. -0.27 0.16 0.33 0.04 -0.28 0.26 0.05 -0.17 -0.11 0.03

MBC/NO3-N -0.31 -0.05 -0.21 0.00 0.44 0.41 -0.47 -0.02 -0.07 0.13 MBC/Nitrif. 0.17 -0.06 -0.16 0.00 -0.26 0.64** -0.42 0.49 0.06 0.03

NO3-N/Nitrif. 0.52* 0.77** 0.61* 0.66** -0.05 0.59* 0.38 0.29 -0.52 0.74**

4.3.3 Effects of physically combining subsamples

For five out of seven measured soil properties (GWC, SOM, pH, MBC, and nitrate-N), values of combined subsamples were significantly correlated with the average values of the 16 subsamples analysed individually (Table 4-3), indicating that the combining of subsamples („bulked‟ samples) did not affect the relative measured values of soil properties at different sites. Regression analysis showed strongest correlations for GWC, SOM, pH, and nitrate-N (Table 4-3). For all five soil properties with a significant correlation between the two methods of measurement, the 95% confidence limits of the y-intercept (a) included zero, and those of the slope (b) included 1.0, indicating that values for composite samples did not greatly differ in magnitude from means of the individually analysed samples (Table 4-3). In contrast, the measurements of the bulked samples and the averaged samples were not significantly correlated for the properties TOC and nitrification, even though they were positively associated (Table 4-3, Fig. 4-4).

Paired t- tests across sites between values of physically combined and individually analysed subsamples revealed a significant difference between the two measurements for

Chapter 4 Spatial variability of soil properties

only one soil property, MBC (Table 4-4), indicating that physically combining subsamples may result in an overestimation of MBC.

Table 4-3 Results of linear regression between values of 16 combined subsamples and means of 16 individually analysed subsamples for each soil property. n = 10 sites. Significance of regression (p), coefficient of determination (r2), intercept (a), slope of the regression line (b), standard errors for a and b (SE), and 95% confidence intervals for a and b (CI). Regression parameters are only shown for statistically significant correlations. GWC = gravimetric water content, SOM = soil organic matter, TOC = total organic carbon, MBC = microbial biomass carbon, NO3-N = nitrate-nitrogen. Regression parameters Soil property p r2 a b SEa SEb CI (95%) a CI (95%) b GWC <0.001 0.91 -1.64 1.05 3.58 0.11 -9.90 – 6.62 0.79 – 1.31 SOM < 0.001 0.82 2.21 0.86 2.45 0.14 -3.44 – 7.86 0.53 – 1.19 pH < 0.001 0.86 -0.48 1.11 0.85 0.16 -2 44 – 1.48 0.74 – 1.48 TOC 0.091 0.32 MBC 0.033 0.45 0.67 0.63 0.33 0.24 -0.10 – 1.44 0.07 – 1.19

NO3-N < 0.001 0.84 4.08 0.85 2.93 0.13 -2.59 – 10.84 0.55 – 1.15 Nitrification 0.381 0.09

Table 4-4 Paired t-tests comparing the values of 16 combined subsamples and means of 16 individually analysed subsamples for each soil property. n = 10 sites. GWC = gravimetric water content, SOM = soil organic matter, TOC = total organic carbon, MBC = microbial biomass carbon, NO3-N = nitrate-nitrogen. Soil property Sig. (2-tailed) t GWC 0.59 -0.55 SOM 0.67 0.45 pH 0.64 -0.48 TOC 0.43 -0.83 MBC 0.03 2.56

NO3-N 0.47 0.75 Nitrification 0.96 -0.06

Chapter 4 Spatial variability of soil properties

40 GWC 20 SOM (percent fresh mass) (percent dry mass) 35 19 18 30 17 25 16 bulked sample bulked r = 0.95** r = 0.91** 20 15 20 25 30 35 40 15 16 17 18 19 20 6.5 pH 0.5 TOC -1 0.4 (g kg dry soil ) 0.3 5.5 0.2

0.1 bulked sample sample bulked r = 0.93** r = 0.57 4.5 0 4.5 5 5.5 6 6.5 0 0.1 0.2 0.3 0.4 0.5 Microbial biomass C Nitrate-N 2.5 40 (mg kg dry soil-1) (mg kg dry soil-1) 2 30 1.5 20 1 10

bulked sample sample bulked r = 0.67* r = 0.92** 0.5 0 0.5 1 1.5 2 2.5 0 10 20 30 40 Mean individual samples Nitrification 12 -1 -1 (mg NO3-N kg dry soil day ) 8

bulked sample sample bulked r = 0.30 0 0 4 8 12 Mean individual samples

Figure 4-4 Linear relationships between values of 16 physically combined subsamples and the means of 16 individually analysed subsamples for each soil property across pasture, rainforest and three types of reforestation. There were two sites in each site-type. Total n = 10 sites. r-values represent correlation coefficients, * p < 0.05, ** p < 0.01.

Chapter 4 Spatial variability of soil properties

4.4 Discussion

4.4.1 The nature of spatial variability in soil properties

The seven soil properties studied here are all crucial for the characterisation of soil processes and for plant growth. The results of this study confirm the generally high small- scale spatial heterogeneity of nutrient availability in surface soils (Schoening et al., 2006; Cole et al., 2009; Kounda-Kiki et al., 2009; Peigné et al., 2009). This spatial variation in nutrient supply from the soil is likely to influence the growth of individual plants (Ettema and Wardle, 2002) and the spatial patterning of plants within a site (Giller, 1996; Reynolds et al., 1997; Ettema and Wardle, 2002). In successional sites, spatial variation in plant species composition has been associated with spatial variability in nutrient cycling and availability (Robertson and Vitousek, 1981). In this study, the most variable soil properties across all sites were nitrification rates and nitrate-N. Similarly high variability for nitrification was reported by Gross et al. (1995) who suggested that variability in nitrogen concentrations at the site level can influence the occurrence of individual plants and thereby community composition.

Feedbacks among above-ground vegetation and below-ground organisms are major ecological drivers in terrestrial ecosystems (Wardle, 2002), and the high spatial variability of nitrogen-related soil properties may thus be either a cause or a consequence of variation in plant cover or species composition (Robertson et al., 1988; Palmer, 1990; Lechowicz and Bell, 1991; Jackson and Caldwell, 1993). Plant species can differ greatly in their ability to exploit various soil resources (Grime et al., 1986; Jackson and Caldwell, 1989). Different tree species differ in their effects on soil microbial communities (Saetre and Baath, 2000) and may therefore indirectly influence microbial functions and nutrient availability. In the study region, nitrogen-fixing plant species were common in pasture and forested sites and could have contributed to small-scale patterns of nitrate-N and nitrification rates. Differences in soil organic matter quality, associated with different plant species, can also determine spatial patterning of soil microbial communities (Saetre, 1998), a factor which could contribute to spatial variability of soil properties such as N mineralisation, microbial respiration, or soil organic matter. For example, these properties were particularly high in the 0.25 m2 area around individual plants in a study by Jackson and Caldwell (1993).

Across all soil properties, the largest spatial variability was found at the site and the subplot levels. However, GWC, SOM and pH showed a relatively low variability at all three spatial scales (site-type, site, and subplot level). A low variability of GWC within a site has

Chapter 4 Spatial variability of soil properties also been reported in previous studies of forested landscapes (Frankland et al., 1963; Farley and Fitter, 1999). Roever et al. (1999) found a strong correlation between GWC and total pore space in the upper layer (0–15 cm) in an agricultural soil, and concluded that the spatial distribution in water content is highly influenced by the structure of the soil. The consistently low pH values measured in this study are uncommon and an inherent characteristic of the basalt soils in the study region (Lymburner et al., 2006). A low spatial variability in surface soil pH has also been reported elsewhere (Pennock et al., 1992; Chung et al., 1995; Roever and Kaiser, 1998). However, since pH is a logarithmic value of the proton activity in solution, the actual variation in proton activity in soil solution may therefore be considerably higher than the low coefficient of variation suggests (Boekhold and van der Zee, 1992).

Soil properties related to microbial processes (total organic carbon, microbial biomass carbon, nitrate-N, and nitrification) showed especially high spatial variability at the site and the subplot levels. A high spatial variability of total organic carbon is common in forest soils (Mollitor et al., 1980; Grigal et al., 1991; Mottonen et al., 1999) and has been attributed to a patchy distribution of plants and small openings within a site (Saetre, 1999). Under individual plants, TOC levels are usually higher due to a higher production of above- and belowground litter, leachates, and exudates from plants which contribute organic compounds to soil (Hook et al., 1991). Previous studies have found a high correlation between soil organic carbon and microbial biomass carbon (Anderson and Domsch, 1989; Angers et al., 1992). However, in this study, there was no consistent correlation between these two parameters. Soil microbial biomass is a relatively labile nutrient pool (Duxbury et al., 1989; Jenkinson and Parry, 1989) and can be influenced by soil texture and soil organic matter quality (Wardle, 1992; Ross and Tate, 1993; Hassink, 1994), changes in the chemical and physical environment (Wardle, 1992; Bauhus and Khanna, 1994; Beck et al., 1995), and plant species (Scheu and Parkinson, 1995; Bauhus et al., 1998). The high spatial variation found in this study for microbial biomass carbon at the subplot level is consistent with several previous studies (Rochette et al., 1991; Cambradella et al., 1994).

4.4.2 Does physically combining spatial subsamples affect soil measurements?

The physical combination of multiple subsamples to yield a single aggregate sample from one site is a common approach to obtain spatially averaged measurements of soil properties (Smalla and van Elsas, 2010). The results of this study validate this physical pooling as a useful method for reducing analytical effort without sacrificing the accuracy of the data, for many soil properties. However, this was not the case for two soil properties: nitrification and total organic carbon. For nitrification, this lack of correlation is probably a

Chapter 4 Spatial variability of soil properties consequence of high spatial variability at the site level combined with an effect of physical combination. Nitrification is a highly sensitive aerobic bacterial process, and the bulking of soil samples is likely to have altered both the soil microclimate and the competitive interactions among bacterial species. These changes could easily cause shifts in the species composition or level of activity of nitrifying bacteria, thereby affecting nitrification rates. These results show that combining of subsamples may not be advisable for the measurement of nitrification and total organic carbon, especially when absolute values of these properties are needed.

The paired t-tests between physically combined site measurements and averages of individually measured subsamples gave a statistically significant difference for just one soil parameter, microbial biomass carbon, which was greater in combined subsamples. This suggests that, although there was a significant correlation between values of combined subsamples and individually analysed subsamples, there is a potential bias introduced by combining subsamples for the measurement of microbial biomass carbon. It is possible that combining subsamples altered the microclimate of the sample, particularly by providing increased oxygen levels, which could have led to higher microbial activities and therefore an increase in microbial biomass carbon. Nevertheless, because there was a significant positive correlation between the two measurements, this difference is unlikely to affect relative measurements across different sites.

4.4.3 Implications of spatial variation for sampling designs

The number of observations needed to achieve an accurate observation of any soil property is difficult to determine, and depends on the variation of the property within the site, the sampling technique, and the questions being asked in the study (McBratney and Webster, 1983; Smalla and van Elsas, 2010). If a site is sampled at randomly chosen subplots, the necessary sampling effort would be relatively high, as some subplots inevitably are close and could therefore tend to have similar soil properties (McBratney and Webster, 1983). Consequently, a systematic sampling scheme, in which subplots are located on a grid and as far from one another as possible, may yield more useful results for a given effort (McBratney and Webster, 1983).

This study identified a high spatial variability among subplots within sites, for a range of microbial-related soil properties; nitrate-N, nitrification, and microbial biomass. For these soil properties, the precision of measurement will therefore be strongly influenced by the number of subsamples measured in a site. If the aim is to compare soil properties across

Chapter 4 Spatial variability of soil properties different site-types, spatial variation of soil properties could be taken into account either by taking replicate subsamples within a site or by increasing the number of replicate sites to obtain a good representation of each site-type. In field studies and experiments, it can be difficult to obtain large numbers of replicate sites within particular site-types. Therefore, the results of this study demonstrate the importance of collecting sufficient subsamples to encompass the high within-site variability in many soil properties.

Chapter 4 Spatial variability of soil properties

Chapter 5 Recovery of soil properties

5. Recovery of soil properties and functions

5.1 Introduction

There is increasing evidence that deforestation not only alters above-ground vegetation, but also leads to significant changes in the physical and chemical characteristics and biochemical cycles of soil ecosystems (Vitousek et al., 1989; Piccolo et al., 1994; Erickson et al., 2001; Maloney et al., 2008). The fate of nitrogen in deforested landscapes is relatively well known: lower total nitrogen pools (Piccolo et al., 1994; Rasiah et al., 2004), decreased nitrate levels (Piccolo et al., 1994; Reiners et al., 1994; Neill et al., 1997b; Rasiah et al., 2004), and an altered N-mineralisation and nitrification capacity (Matson et al., 1987; Montagnini and Buschbacher, 1989; Steudler et al., 1991; Reiners et al., 1994) have been reported. However, the impacts of deforestation on carbon-related soil properties and functions are poorly understood, and different studies have yielded contradictory results. Although deforestation has been shown to result in a decrease in soil carbon (Lugo and Brown, 1993; van Nordwijk et al., 1997), some studies have also found an increase in soil carbon after conversion from forest to pasture (Chone et al., 1991; Guo and Gifford, 2002). For some general soil properties such as bulk density and water content, studies of deforestation impacts have shown fairly consistent patterns, with generally higher values in pasture than in forest (Reiners et al., 1994; Neill et al., 1997a; Rasiah et al., 2004; Zheng et al., 2005).

More recently, increasingly large areas of agricultural land in many former rainforest landscapes have been abandoned and support various forms of secondary forest, incorporating varying proportions of non-native plant species (Lugo and Helmer, 2004; Holl, 2007). Restoration of tree cover in degraded rainforest landscapes may occur through a variety of different pathways, including: ecological restoration planting, in which a diverse range of native tree species are planted for biodiversity conservation, relatively simple plantings of selected native and exotic species for timber production and land rehabilitation purposes, and spontaneous regrowth of woody vegetation on abandoned or marginal agricultural land (Catterall et al., 2008; Kanowski et al., 2008c). Such regrowth is typically dominated by a small number of fast-growing, shade-intolerant species which act as pioneers (Kanowski et al., 2003; Catterall et al., 2004), and which may facilitate the recruitment of a variety of later-successional species (Erskine et al., 2007).

Chapter 5 Recovery of soil properties

Nitrogen cycling processes and general soil properties have previously been studied in some forms of rainforest restoration (Lamb, 1980; Robertson, 1984; Brown and Lugo, 1990a; Mo et al., 2002), and there is a general consent that reforestation can successfully restore soil nitrogen stocks (Lamb, 1980; Robertson, 1984; Vitousek et al., 1989). However, there is less information about the regeneration of soil carbon stocks. Although significant amounts of carbon can accumulate in vegetation and soils within the first 20 years of forest regrowth (Brown and Lugo, 1992), major losses of residual soil carbon during early secondary succession of tropical forests have also been reported (Bashkin and Binkley, 1998; Rhoades et al., 2000). Furthermore, studies assessing the impact of reforestation on soil properties have generally focused on one particular reforestation pathway, rather than providing information on the regeneration of soil properties under the variety of different reforestation pathways that may occur within a landscape.

This chapter aims to assess changes in soil properties following clearing of rainforest and its conversion to pasture, and to evaluate the ability of different types of reforestation to reverse these changes. The following three questions were addressed, using a network of sites distributed across a former rainforest landscape in subtropical eastern Australia: (1) How does land-use change from primary subtropical rainforest to pasture affect soil properties? (2) Does reforestation restore soil properties that characterise primary rainforest? and (3) How do three different reforestation pathways (tree-planting for ecological restoration, regrowth dominated by a non-native tree species, and similar regrowth treated to promote native regrowth) differ in their ability to restore soil properties? Restoration success was quantitatively assessed by comparing values of soil properties measured in reforested sites (replicated within each pathway) with the values recorded from replicated reference sites in both rainforest and pasture.

5.2 Methods

5.2.1 Study area and experimental design

Chapter 5 Recovery of soil properties tetraphylla) plantations, revegetation sites and spontaneous regrowth of woody species, often dominated by the non-native tree species camphor laurel (Cinnamomum camphora) (Neilan et al., 2006). Reforestation of former rainforest areas in the Big Scrub region has mainly occurred through three different pathways: autogenic regrowth dominated by the non- native tree species camphor laurel (Cinnamomum camphora), managed regrowth to accelerate the development of secondary rainforest, and ecological restoration planting.

5.2.2 Measurement of soil properties

Soil sampling was conducted during March 2007 at the end of the warm and rainy season, when maximum microbial activities and nutrient transformation rates could be expected.

At each site, a 20 x 20 m survey plot was established, containing five subplots, four at the corner points and one in the centre. At each subplot, the litter and/or grass layer was carefully removed and two samples of approximately 300 g of top soil were taken with a 7- cm-diameter soil auger to a depth of 10 cm. One soil sample from each subplot was placed into a plastic bag and analysed as an individual sample, while the other sample was homogenised in a plastic bag with other subplot samples on the site and analysed as a site sample. All samples were transported in a cool box to the laboratory and stored at 4°C until analysis (up to five days for microbiological measurements, up to 14 days for physical and biochemical analyses).

Prior to analysis, course debris material and stones were removed and the samples were well mixed and sieved through a 2 x 2 mm mesh. The samples were then separated into two sub-samples: (1) air-dried and (2) fresh. The air-dried samples were dried at 60°C for two weeks, finely ground (<150 µm) and stored at room temperature in an air tight container prior to the analysis of total soil carbon, nitrogen, and 13Carbon values. Fresh soil samples were stored at 4°C prior to other analyses.

Chapter 5 Recovery of soil properties

Atmospheric Nitrogen N2

Symbiotic rhizobia Runoff Denitrification N-Fixation Leaching Nitrate - Vegetation Fauna NO3 Root uptake

Nitrification Root uptake Decomposition

Ammonium + Organic matter NH4 N-Mineralisation

Abiotic immobilisation

Clay-bound ammonium

Figure 5-1 Simplified flowchart of N-transformation processes (after Paul and Clark, 1997). Variables measured in this study are highlighted.

Nineteen soil properties from three functional subgroups were measured. First, there were eight nitrogen-related variables: total nitrogen (N), ammonium-N (NH4-N), nitrate-N

(NO3-N), total inorganic N, plant-available ammonium-N, plant-available nitrate-N, nitrification rates, and denitrification rates (Fig. 5-1). Second, there were six variables related to carbon and biomass: total carbon (C), extractable total organic carbon (TOC), soil organic matter (SOM), δ13C, microbial biomass carbon, and soil microbial activity. Third, there were five general soil properties: gravimetric water content (GWC), pH, bulk density, fine root biomass, and plant-available phosphate-phosphorus (PO4-P). Nine of these variables (GWC, pH, SOM, NH4-N, NO3-N, denitrification rates, TOC, microbial biomass carbon, and soil microbial activity) were measured at each of the five subplots in all 25 sites, and averaged to give a single value for each site. Three variables (plant-available NH4-N, NO3-N, and PO4-P) were measured at each of five subplots in 23 sites (four pasture sites, four camphor- dominated sites, and five of all other site-types). Four variables (nitrification rates, total C,

Chapter 5 Recovery of soil properties total N, 13Carbon values) were measured once per site from the bulked soil samples. The variables were measured according to the following protocols.

Nitrogen-related analyses

Total nitrogen (N) was analysed from air-dried, ground samples using an isotope ratio mass spectrometer with a Eurovector Elemental Analyser, with acetanilide as the elemental standard (GV Isoprime, Manchester, UK; inlet: Eurovector EA 3000, Milan, Italy) (Weaver et al., 1994).

Ammonium- and nitrate-N were determined in fresh soil sub-samples using KCl- extraction. Five grams of sieved field-moist soil were mixed with 40 ml of 2 M KCl-solution in a 50 ml falcon flask. The soil suspension was placed on an orbital shaker at 180 rpm for 1 h, allowed to settle and filtered through a Whatman No. 40 filter. The filtrate was frozen until analysed in a Discrete Chemistry Analyser (Smartchem 200, Westco Scientific Instruments,

Bookfield CT 06804) for NH4-N (as ammonium-N) and NOx-N [NO3-N + NO2-N]. Because

NO2-N levels were negligible, [NO3-N + NO2-N] are reported as NO3-N (nitrate-N). The method detection limit was 0.031 mg/l for NH4-N and 0.003 mg/l for NOx-N.

Total inorganic N was calculated as the sum of ammonium- and nitrate-N.

Plant-available ammonium- and nitrate-N were assessed using the ion-exchange resin (IER) method (Binkley, 1984; Carlyle and Malcolm, 1986). Five grams (moist weight) of cation exchange resin (Amberlite IR-120, with cation exchange provided by a sulphonic group) and five grams (moist weight) of anion exchange resin (Amberlite IRA-400, with anion exchange provided by an amide group) were placed in bags prepared from nylon mesh stockings, heat-sealed, and rinsed with deionised water before placing in the field. At each subplot, one cation exchange resin and one anion exchange resin bag were buried at a depth of about 5-10 cm for six weeks, comparable to methods used in similar studies (Binkley et al., 1986; Knops and Nash, 1996). After retrieval, IER bags were rinsed with deionised water to remove soil particles and extracted with 100 ml of 1M KCl solution. After shaking for 1 h, the eluate was filtered and analysed in the Discrete Chemistry Analyser for

NO3-N (anion exchange resins) and NH4-N (cation exchange resins). Results were calculated as µg N g dry resin-1.

Nitrification rates were assessed using the shaken soil slurry method by shaking a sieved soil sample in ammonium phosphate solution and measuring nitrate accumulation over a period of 24 hours (Weaver et al., 1994). Nitrification rates were expressed as mg -1 -1 NO3-N kg dry soil day .

Chapter 5 Recovery of soil properties

Denitrification rates were assessed using the acetylene block method with modifications (Robertson et al., 1987). Fresh subsamples from each site were transferred into glass bottles and mixed with distilled water. The bottles were sealed with rubber stoppers, anaerobic conditions were obtained by flushing the bottles with N2 for two minutes, acetylene was injected into the bottle headspace and the samples were incubated for 6 h.

Headspace gas samples were taken two times during the incubation and analysed for N2O in a gas chromatograph. Potential denitrification rates were calculated as the rates of N2O accumulation in the headspace of the sample bottles and expressed as mg NO2-N kg dry soil-1 day-1.

Carbon-related analyses

Total carbon and carbon isotope composition were analysed from air-dried, ground samples using an isotope ratio mass spectrometer (as for total N). The ratios 13C:12C were expressed as δ13C relative to the isotopic composition of the PDB (International limestone standard) standards (Bernoux et al., 1998).

Extractable total organic carbon (TOC) was measured by mixing five grams of field moist soil with 50 ml of 2 M KCL in Erlenmeyer flasks. The suspension was shaken for 1 h, centrifuged and filtered through a Whatman 42 filter. The extracts were frozen until analysis of organic carbon in a Shimadzu 5050A TOC analyser. TOC concentrations were converted to mg C kg dry soil-1.

Soil organic matter was determined by measuring weight loss following combustion at 550°C for 6 h in a muffle furnace and expressed as a percentage of dry soil.

Microbial biomass C was measured using a fumigation-extraction method (Vance et al., 1987). Fresh sieved soil was weighed out into four 10 g subsamples; two were placed in 50 ml glass beakers and fumigated with ethanol-free chloroform for a period of 24 h, and two were kept in the dark at 4 C. After fumigation all subsamples were extracted with 40 ml of

0.5 M K2SO4 (soil:extractant ratio 1:4), shaken for 30 min on an oscillating shaker, filtered through a Whatman 42 filter, and frozen until further analysis. Soluble organic C in the fumigated and non-fumigated subsamples was determined using a Shimadzu 5050A

VCPH/CPN analyser. Microbial biomass C was calculated as the difference between fumigated and non-fumigated samples, using the proportionality coefficient for C (K = 0.45) (Wu et al., 1990), and expressed as mg C kg -1 dry soil-1.

Chapter 5 Recovery of soil properties

Soil microbial activity was evaluated by measuring fluorescein diacetate (FDA) hydrolysis (Schnuerer and Rosswall, 1982; Adam and Duncan, 2001). FDA hydrolysis values were expressed as µg fluorescein hydrolysed per gram of dry soil.

General soil properties

Gravimetric water content (GWC) was determined gravimetrically after drying sieved fresh sub-samples for 24h at 105°C and expressed as a percentage of the soil. For the analysis of soil pH, a suspension of five grams sieved fresh soil mixed with 12.5 ml distilled water was measured using a pH-meter at 21 °C.

Bulk density was measured using a 10 x 10 x 5 cm steel frame; soil was extracted by hammering the frame into the top soil layer. The soil core was oven-dried at 105°C for 24 h and soil bulk density was expressed as g cm-3. Plant-available phosphate-P was measured using the ion exchange method as described in the measurement of plant-available ammonium- and nitrate-N. Eluates from the anion exchange resins were analysed for PO4-P, results were calculated as µg P g dry resin-1.

For the measurement of fine root biomass, the soil samples were sieved through a 2 mm mesh and roots were removed. Roots with diameter <2 mm were picked out, roots>2 mm were excluded from this study. The roots were then oven-dried at 60°C to a constant mass and fine root biomass was expressed as a percentage of dry soil.

5.2.3 Data analysis

In all analyses, sites were used as replicates (n = 25 sites for all variables except for plant-available ammonium-N, nitrate-N and phosphate-P, which were measured in 23 sites). Where subsamples were collected from a site, the mean of subsamples was used. Spearman rank-order correlation was used to assess the correlation between soil properties across all sites. One-way analysis of variance (ANOVA) was used to test whether physical, chemical, and biological soil properties varied among site types. To improve normality, data for all variables except bulk density and fine root biomass were square-root transformed prior to analysis. Least significant difference (LSD) was used to assess pairwise differences among site-types (at 5% levels).

Patterns of variation among sites according to correlation in the eight N-related soil properties (total N, total inorganic N, ammonium-N, nitrate-N, plant-available ammonium-N, plant-available nitrate-N, nitrification rates, and denitrification rates) were explored by principal components analysis (PCA). Prior to analysis, data points were range-standardised

Chapter 5 Recovery of soil properties

(expressing a given site‟s difference from the minimum as a proportion of the range in values across all sites). All statistical analyses were conducted using the package SPSS 14.0 (SPSS, 2005).

5.3 Results

5.3.1 Correlations among soil properties

Out of 170 possible correlations, 23 were statistically significant, and only three exceeded a correlation coefficient of 0.80 (Table 5-1). Nitrogen-related soil properties were relatively strongly intercorrelated: the various measurements of nitrogen-N, nitrification, and denitrification tended to be positively intercorrelated, but also negatively correlated with ammonium-N measurements. There were also significant correlations between N-related properties and some other soil properties. For example, total N was highly positively correlated with total C, and nitrate-N showed negative correlations with bulk density and gravimetric water content (Table 5-1). A complete correlation matrix is given in Appendix 9.

Table 5-1 Significant pairwise Spearman correlation coefficients (r) among 19 soil properties across sites in rainforest, pasture, and three reforestation pathways. n = 23 sites for plant-available ammonium-N, nitrate-N, and phosphate-P; n = 25 sites for other variables. * P<0.05, ** P<0.01

Variable Variable r-value Total N Total C 0.95** Total inorganic N Nitrate-N 0.82** Total inorganic N Bulk density -0.50** Ammonium-N Nitrification -0.45* Nitrate-N Plant-available nitrate-N 0.56** Nitrate-N Nitrification 0.53** Nitrate-N δ13Carbon -0.46* Nitrate-N Bulk density -0.70** Plant-available ammonium-N δ13Carbon 0.47* Plant-available nitrate-N Soil organic matter 0.40* Plant-available nitrate-N Gravimetric water content -0.58** Plant-available nitrate-N Bulk density -0.85** Nitrification Denitrification 0.41* Nitrification δ13Carbon -0.58** Nitrification Bulk density -0.53** Denitrification Total C 0.43* Denitrification δ13Carbon -0.41* Soil organic matter Microbial biomass C 0.41* Soil organic matter Gravimetric water content -0.70** Soil microbial activity Gravimetric water content -0.42** Gravimetric water content pH 0.70** Gravimetric water content Bulk density 0.46** pH Bulk density 0.49**

Chapter 5 Recovery of soil properties

5.3.2 Tests of difference between rainforest and pasture

The first step in the analysis was to consider the differences in soil properties between rainforest and pasture. Of the 19 soil properties, 11 showed an overall difference between site-types and nine of these differed significantly between rainforest and pasture (Table 5-2). Among N-related variables, overall nitrate-N levels, plant-available nitrate-N levels, and nitrification rates were significantly greater in rainforest than in pasture sites, with the reverse pattern for plant-available ammonium-N levels (Table 5-2).

Only one of six C-related variables differed significantly between rainforest and pasture; δ13Carbon values were significantly higher in pasture than in rainforest. Four of the five other soil properties differed between rainforest and pasture: bulk density, pH, fine root biomass, and plant-available phosphate-P (Table 5-2).

Table 5-2 ANOVA results from comparison of soil properties among rainforest, pasture and three forms of reforestation. n = 23 sites for plant-available ammonium-N, nitrate-N, and phosphate-P; n = 25 sites for all other variables. Least significant difference (LSD) was used to contrast forest and pasture sites; NS if p > 0.05.

Contrast rainforest (RF) ANOVA- Mean Mean and pasture Variable p rainforest pasture (PA) Nitrogen related Total N (g kg dry soil -1) 0.76 7.01 6.28 NS Total inorganic N (mg kg dry soil-1) 0.15 14.70 10.22 NS Ammonium-N (mg kg dry soil-1) 0.22 2.00 4.79 NS Nitrate-N (mg kg dry soil-1) 0.001 12.69 5.42 F>P* -1 Plant-available ammonium-N (µg NH4-N g resin ) 0.030 36.8 79.4 FP* -1 -1 Nitrification (mg NO3-N kg dry soil day ) 0.003 13.79 2.01 F>P* -1 -1 Denitrification (mg NO2-N kg dry soil day ) 0.13 41.7 24.19 NS Biomass C related Total C (g kg dry soil -1) 0.72 83.0 75.3 NS Total organic C (g kg dry soil -1) 0.17 43.4 48.8 NS Soil organic matter (% dry soil) 0.002 12.62 15.72 NS δ13Carbon (‰) <0.001 -16.38 -27.12 FP* -1 Plant-available phosphate-P (µg PO4-P g resin ) 0.005 3.5 68.0 F

Chapter 5 Recovery of soil properties

5.3.3 Tests of difference in recovery in different reforestation pathways

Apart from fine root biomass, all nine soil properties which differed significantly between rainforest and pasture had reached levels typical of rainforest in at least one of the three reforestation pathways (Table 5-3 and Fig. 5-2), although the specific patterns varied. The number of properties whose levels did not differ significantly from rainforest differed between the three types of reforestation. Replanted rainforest sites had five out of nine soil properties similar to rainforest, three intermediate between rainforest and pasture, and one similar to pasture (Table 5-3). Treated camphor sites had six soil properties resembling rainforest, two with intermediate values, and one that was similar to pasture, while camphor- dominated sites had four soil properties similar to rainforest, and four similar to pasture (Table 5-3).

NO -N Plant-available NO -N Plant-available NH -N 20 3 1200 3 140 4

p = 0.001 D p < 0.001 p = 0.030

1 1

1 B

- - - 17.5 120 1000 B B 15 BC 100 AB A CD 800 A 12.5 A 80

600 B A µg gresin µg 10 AB gresin µg A 60 7.5 400 40

mg kg dryy soil dryy kg mg A 5 200 20 2.5 0 0

Nitrification Soil pH Bulk density

1 8 - 25 1.75 p = 0.003 B p < 0.001 p < 0.001

day 20 1.5 C 1

- 7 C

B B B 3 15 BC - 1.25

B pH 6 A A B 10 cm g 1 AB A 5 A 5 0.75 A

mg N kg dry soil dry kg N mg 0 4 0.5

Fine root biomass Plant-available PO -P δ13 Carbon 5 180 4 -10 p < 0.001 B p = 0.005 p < 0.001

B 1 4 - 150 B -15 120 3 A A 90 AB

‰ -20 2 A gresin µg A A 60 A 1 A -25 A A % dry soildry% 30 A A 0 0 -30 PA CD TC ER RF PA CD TC ER RF PA CD TC ER RF

Figure 5-2 Pattern of variation in reforested sites for soil properties which differed significantly between rainforest and pasture: pasture (PA), rainforest (RF) and three types of reforestation (camphor-dominated CD, treated camphor TC, ecological restoration planting ER). “o” display the averaged data for each site, “–“ indicate the mean for each site-type. P-values are from ANOVA between all five site-types and show LSD results (means with the same letter do not differ at p > 0.05).

Chapter 5 Recovery of soil properties

Table 5-3 Patterns of similarity between rainforest, pasture and three types of reforestation for soil properties which differed significantly between rainforest and pasture: PA = similar to pasture; RF = similar to rainforest; I = intermediate between pasture and rainforest; O = some other pattern. n = 23 for plant- available ammonium-N, nitrate-N, and phosphate-P; n = 25 for all other variables

Similarity to pasture or rainforest Ecological Camphor Treated restoration Soil property Greater in dominated camphor planting Nitrate Rainforest PA RF I Plant-available ammonium-N Pasture RF RF I Plant-available nitrate-N Rainforest PA RF RF Nitrification Rainforest RF RF RF δ13Carbon Pasture RF RF I pH Pasture O I RF Bulk density Pasture I I RF Fine root biomass Rainforest PA PA PA Plant-available phosphate-P Pasture RF RF RF

5.3.4 Patterns of difference according to N-related soil properties

The first two principal components in PCA of the eight N-related variables accounted for 53% of the total variation among sites (Table 5-4). PC1 (32% of variation) was positively correlated with six of the eight soil properties, and negatively with plant-available ammonium-N; PC2 (21% of variation) was highly correlated only with ammonium-N (Table 5- 4).

The PCA plot (Fig. 5-3) showed a clear separation between pasture and rainforest sites along PC 1. All three reforestation pathways were intermediate between pasture and rainforest, but were not clearly separated from each other in the ordination space.

Table 5-4 Coefficients in the linear combinations of variables making up principal components of eight nitrogen-related soil properties. Variable PC 1 PC 2 32.2% 20.5% Total N 0.32 -0.06 Total inorganic N 0.32 0.48 Ammonium-N -0.23 0.79 Nitrate-N 0.41 0.14 Plant-available ammonium-N -0.35 0.20 Plant-available nitrate-N 0.33 0.06 Nitrification 0.40 -0.16 Denitrification 0.43 0.23

Chapter 5 Recovery of soil properties

1 Pasture

Camphor dominated 0.5 Treated camphor

Ecological restoration 0 planting

Rainforest PC2 (20.5%) PC2

-0.5

-1 -1 -0.5 0 0.5 1 PC1 (32.2 %)

Figure 5-3 Ordination of rainforest, pasture and reforested sites in terms of eight nitrogen-related soil properties

One-way ANOVA of the PCA values based on the eight N-related soil properties showed a significant difference among site-types, with reforested sites being intermediate between pasture and rainforest (Fig. 5-4). There were no significant differences in PC2 among the site-types (p>0.05).

C 1 p < 0.001 B B 0.5 B

0 A PC1 score PC1

-0.5

-1 PA CD TC ER RF

Figure 5-4 Values of PC1 scores based on eight nitrogen-related soil properties for pasture (PA), rainforest (RF) and three types of reforestation (camphor dominated CD, treated camphor TC, ecological restoration planting ER). “o” display the averaged data for each site, “–“ indicate the mean for each site- type. P-values are from ANOVA between all five site-types and show LSD results (means with the same letter do not differ at p > 0.05).

Chapter 5 Recovery of soil properties

5.3.2 Other differences in reforested sites

Although GWC and SOM levels did not differ between pasture and rainforest (Table 5- 2), they were significantly different in some reforestation pathways. GWC levels were highest in camphor-dominated regrowth and lowest in ecological restoration plantings. SOM levels in ecological restoration plantings were significantly higher than in any other site-type (Fig. 5- 5).

55 p = 0.001 30 p = 0.001 B A 50 D 25 A 45 BC 20 BC C A A 40 15 A

35 soil) dry (% SOM 10 GWC (% fresh soil) (% fresh GWC 30 5 PA CD TC ER RF PA CD TC ER RF

Figure 5-5 Values of gravimetric water content and soil organic matter for pasture (PA), rainforest (RF) and three types of reforestation (camphor dominated CD, treated camphor TC, ecological restoration planting ER). “o” displays the averaged data for each site, “–“ indicates the mean for each site- type. P-values are from ANOVA between all five site-types and show LSD results (means with the same letter do not differ at p > 0.05).

Appendix 10 shows raw data for nitrogen and carbon related soil properties.

5.4 Discussion

5.4.1 Differences between rainforest and pasture

The observed differences between rainforest and pasture are consistent with previous work indicating that clear-cutting of rainforest results in major losses of nitrogen availability (Piccolo et al., 1994; Reiners et al., 1994; Neill et al., 1997b; Rasiah et al., 2004). Nitrogen, as the major mineral nutrient for plants (Paul and Clark, 1997), often limits primary production in terrestrial ecosystems (Vitousek et al., 1989), and the long-term fertility and productivity of soils rely heavily on the mineralisation of organic forms of nitrogen and the continued cycling of nitrogen. Research in other regions supports the observation that nitrogen transformation processes such as N-mineralisation and nitrification are sensitive to land-use changes and disturbance (Piccolo et al., 1994) and change as a result of rainforest clearing (Matson et al., 1987; Montagnini and Buschbacher, 1989; Steudler et al., 1991).

Chapter 5 Recovery of soil properties

Several other studies have observed a decrease of total nitrogen pools, nitrate-N, ammonium-N, N-mineralisation, and nitrification rates following conversion of rainforest to pasture (Piccolo et al., 1994; Neill et al., 1997b; Rasiah et al., 2004). This study‟s findings are also in agreement with other studies that have found lower nitrate-N pools in pastures than in secondary or primary rainforests (Lamb, 1980; Robertson, 1984; Vitousek et al., 1989; Piccolo et al., 1994; Erickson et al., 2001; Silver et al., 2005).

In contrast, this study found no difference in total N pools between pasture and rainforest. In pasture, total soil N levels may be maintained while nitrate-levels are relatively low, since N-mineralisation and nitrification are often significantly lower in pasture than in forest (Piccolo et al., 1994; Reiners et al., 1994; Neill et al., 1997b; Erickson et al., 2001). The uptake of soil N by above-ground vegetation in rainforest may be higher than in pasture, which would tend to reduce total soil N levels, but this could be offset by increased amounts of N available from mineralisation of litter input from N-fixing species. In the present study N- fixing plants were present in both pasture (species of Fabaceae) and rainforest (Fabaceae and Mimosaceae), although the relative rates of N-fixation in the two site-types is unknown. The soil C/N ratios were similar in pasture and rainforest (12.0 and 11.8, respectively) and slightly lower than the average C/N ratios for tropical soils (13.3 – 13.6) (Batjes, 1996), indicating that both site-types provided suitable conditions for plant growth.

Soil carbon pools, total organic carbon, soil organic matter, microbial biomass carbon, and microbial metabolic activity did not differ markedly between pasture and rainforest sites in the study region, indicating that conversion of rainforest to pasture would not significantly alter these soil properties. The lack of difference in soil microbial activity and microbial biomass carbon was unexpected, as considerably greater carbon inflows from above-ground vegetation to soil ecosystems would be expected in rainforest than in pasture. Soil carbon stocks respond to such differences in flux rates, especially in tropical and subtropical soils (Fearnside and Barbosa, 1998). However, microbes metabolise organic carbon rapidly and efficiently convert it to CO2, even in situations where nitrogen is a limiting factor (Pollard, 1997). As a consequence, microbial biomass may not increase, as carbon is lost from the ecosystem through microbial respiration (Pollard, 1997).

Several studies suggest that deforestation of tropical and subtropical areas and subsequent pasture establishment can result in a loss of soil carbon (Lugo and Brown, 1993; van Nordwijk et al., 1997; Zheng et al., 2005), although the factors that influence such losses are poorly understood (Neill and Davidson, 2000). In this study, there was no difference in soil carbon between pasture and rainforest sites. Some authors have suggested that such similarities may occur because the continuous production of large amounts of fine grass

Chapter 5 Recovery of soil properties roots by the dense short pasture vegetation increases the accumulation of organic matter and soil carbon (Brown and Lugo, 1990a; Yakimenko, 1998), thus in time compensating for initial carbon losses through deforestation. Indeed, some authors have reported an increase in soil carbon after conversion from forest to pasture (Chone et al., 1991; Guo and Gifford, 2002), or a higher carbon content in grassland than in rainforest soils in the same area (Schlesinger, 1984; Buschbacher et al., 1988). Soil carbon levels after pasture establishment are influenced by a variety of factors such as the intensity of past land-use (Silver et al., 2000), pasture management (Fearnside and Barbosa, 1998), and precipitation (Guo and Gifford, 2002). This overall inconsistency of results prevents any global generalisations about the impacts of deforestation on soil carbon dynamics (Reiners et al., 1994).

Pasture sites in this study showed significantly higher bulk densities (1.11 g cm-3) than revegetated and rainforest sites (0.83 - 0.59 g cm-3). These values are consistent with the findings of Rasiah et al. (2004) who measured 1.00 to 1.11 g cm-3 under long-term abandoned pasture and 0.93 g cm-3 under tropical rainforest in north-eastern Australia. An increase in soil bulk density as rainforest is converted to pasture has also been shown elsewhere (Keller et al., 1993; Reiners et al., 1994; Murty et al., 2002; Zheng et al., 2005), and is accompanied by a decrease in total porosity (Reiners et al., 1994; Rasiah et al., 2004). Rainforest sites in my study showed fine root biomass levels of 2.3% - 3.9%, compared with 0.8% - 1.8% in pasture sites, which can be attributed to the loss of most above-ground vegetation. The large increase in plant-available phosphate-P in all pasture sites was probably due to phosphate-fertilisation.

5.4.2 The potential for recovery of soil properties through reforestation

Amongst the soil properties associated with the nitrogen cycle, nitrate-N concentrations (overall and plant-available) and nitrification rates increased with reforestation and reached levels similar to rainforest sites in at least one reforestation pathway. Several previous studies have reported similar trends (Robertson and Vitousek, 1981; Robertson, 1984; Erickson et al., 2001; Scowcroft et al., 2004). Changes in patterns of nitrogen transformation (Fig 5-1) with deforestation and reforestation are complex, with many possible outcomes. For example, some authors (Rice and Pancholy, 1972) hypothesised that nitrification in mature successional stages may be inhibited by tannins and tannin derivates; however, I found that nitrification increased as forests became re-established. The resulting nitrate may be lost from forest systems through denitrification or leaching, however, these losses may be diminished by the extensive root systems that trap much of the mineralised nitrate (Lamb, 1980). Scowcraft et al. (2004) argued that in forested sites,

Chapter 5 Recovery of soil properties nitrifying bacteria are strong competitors with heterotrophs for ammonia and account for approximately 50% of the consumed ammonia, which may have contributed to lower levels of plant-available ammonium-N and higher nitrate-levels in this study‟s reforested sites. Other studies of forest succession have found a positive association between levels of ammonia and nitrification rates (Lamb, 1980; Robertson and Vitousek, 1981; Robertson, 1984). However, the reverse occurred in the present study, with forested sites having higher levels of nitrification combined with lower ammonia levels, suggesting that the pattern of nitrification is not simply dependent on the availability of ammonia.

Pasture sites were characterised by significantly higher δ13C values than both rainforest and all restoration pathways, reflecting the change in vegetation cover (Bernoux et al., 1998). In C3 plants (most trees), δ13C ranges from -35 to -20 ‰, in C4 plants (most grasses and sedges) it varies from -19 to -9 ‰ (Bernoux et al., 1998). Volkoff and Cerri, (1987) also reported higher δ13C values in subtropical pasture soils than in rainforest soils. Our results further suggest that after 30-50 years for camphor regrowth, pasture-derived C4 carbon in the top soil layer had completely been replaced by C3 carbon, and this was maintained following treatment for conversion to rainforest. Intermediate δ13C levels in 10–15 year old replanted sites reflected a higher grass cover within them.

The results from my study also show that soil bulk densities, pH, and phosphate-P levels could successfully be re-established with reforestation. Fine root biomass was the only soil property that could not be restored by any of the three reforestation pathways, in contrast to other studies which have suggested that early successional stages may be characterised by a rapid accumulation of fine root biomass (Guariguata and Ostertag, 2001), or that secondary forests may have similar or even higher fine root biomass than old-growth forests (Ewel, 1971). However, the recovery of fine root biomass may not occur at the same rate at all sites (Silver et al., 1996), and different methods of measuring fine root biomass may complicate a direct comparison between studies.

Although soil organic matter (SOM) levels were similar in pasture and rainforest sites, SOM levels were elevated in replanted sites. Plant species selection in these sites may have affected C dynamics, since species composition can influence the residence patterns of C in the ecosystem through effects on plant growth rates, C allocation patterns, and C quality (Lugo, 1992; Hobbie, 1996). Alternatively, the higher SOM levels could also be a result of mulching with a thick layer of woodchip or hay in the early stages of the plantings.

Chapter 5 Recovery of soil properties

5.4.3 Differences among reforestation pathways

Camphor-dominated regrowth after 30–50 years had restored soil properties to a slightly lesser degree than replanted rainforest after 10–15 years. However, soil properties more closely resembled rainforest once the camphor overstorey of 25–30 year old regrowth had been removed, with 3–6 years of subsequent rainforest regrowth. Although nitrification rates, plant-available ammonium-N and phosphate-P levels were re-established in camphor- dominated regrowth, there was little evidence that this reforestation pathway had promoted the recovery of nitrate-N (overall and plant-available) and fine root biomass. However, soil bulk densities in camphor-dominated sites were significantly lower than under pasture, suggesting that this pathway promoted the partial recovery of bulk density. Camphor laurel produces a range of essential oils (mainly aromatic and acrylic monoterpenes including camphor, linalool, cineole or carvacrol) that may be released into the soil (Stubbs et al., 1999) via root exudation and litter fall. Essential oils in root exudates can influence soil microbial activities and affect the composition of microbial populations (Grayston and Campbell, 1995; Landi and Valori, 2005), and monoterpenes in particular have the ability to inhibit nitrification in soils (Paavolainen et al., 1998). However, nitrification rates were similar in camphor-dominated, treated camphor, and replanted sites, and camphor laurel growth did not show an inhibiting effect on soil microbial activities. Therefore, I cannot conclude that root exudates from camphor trees have a strongly inhibiting effect on nitrifying bacterial communities.

The reason why nitrate-N levels were not restored in camphor-dominated sites, but were significantly increased in treated camphor sites, is unclear. One possible explanation is that mature camphor tress have a high nitrate uptake, therefore, the removal of the camphor overstorey facilitates an increase in nitrate concentration. Alternatively, recruitment and development of N-fixing rainforest species after removal of camphor trees may have further contributed to increased nitrate-N levels. The restoration of forest-like nitrate-N levels in treated camphor sites indicates that the removal of the camphor overstorey could favour plant growth and recruitment processes in such sites, especially as young regrowth in the tropics and subtropics mainly uses nitrate-N as opposed to ammonium-N (Stewart et al., 1988; Bazzaz, 1991). Treated camphor was the only reforestation pathway in which all nitrogen-related soil properties were restored to a level similar to rainforest.

The relative effectiveness of different reforestation pathways as tools for managing rainforest cover depends not only on their abilities to restore ecosystem properties but also on their relative costs per unit land area. Therefore different pathways are likely to suit different situations. In the study region, camphor regrowth has occurred spontaneously

Chapter 5 Recovery of soil properties without cost over large areas and had restored many soil properties after 30-50 years. Camphor regrowth also provides habitat for some rainforest fauna and catalyses the recruitment of rainforest plants (Catterall et al., 2004; Neilan et al., 2006; Kanowski et al., 2008b); it is the cheapest means of restoring a more limited range of ecosystem properties over a wide area. Ecological restoration planting rapidly (within 10 years) recovers a substantial proportion of rainforest plant and animal species (Catterall et al., 2004; Erskine et al., 2007), and was also a rapid pathway for restoring soil properties, most of which resembled rainforest levels after 10-15 years in this study. However, the high costs of restoration plantings (up to AUD $50,000 per hectare) (Erskine, 2003; Catterall and Harrison, 2006) mean that in practice this technique is likely to be restricted to small areas. The camphor regrowth plus subsequent treatment pathway, after around 35-45 years in total at the sites studied here, had also restored most soil properties. Treatment of camphor regrowth additionally promotes increased recruitment and growth of rainforest plants, at a cost of around AUD $5-30,000 per hectare (Kanowski and Catterall, 2007). For restoring a wide range of ecosystem properties to large areas of cleared land, initial retention of camphor laurel regrowth for sufficient time to achieve canopy closure and a seedling bank of rainforest species (about two decades) followed by intervention to strategically remove the camphor overstorey, should be a cost-effective intermediate-effort management technique.

Chapter 6 Seedling growth

6. Seedling growth

6.1 Introduction

In all reforestation pathways, pioneer tree species play an important role in the dynamics and development of forest cover (Connell and Slatyer, 1977; Brown and Lugo, 1990a; Aide et al., 1995; Erskine et al., 2007). Rainforest pioneer species typically produce many small seeds that are widely dispersed by frugivores or by wind (Hopkins and Graham, 1984a; Garwood, 1989; Vazquez-Yanes and Orozco-Segovia, 1993; Dalling et al., 1997) and are fast-growing, which enables them to compete with grasses and weeds, thereby creating conditions which may favour the recruitment of later secondary rainforest species (Erskine et al., 2007). The growth of tree seedlings, especially in their early stages, can be strongly affected by soil properties (Nussbaum et al., 1995; Burslem, 1996; Gunatilleke et al., 1996; Woodward, 1996). This is particularly the case for pioneer species as a consequence of their small seeds and rapid growth habits (Chapin et al., 1986). In particular, changes in nitrogen (Tilman, 1986) and phosphorus availability (Vitousek and Matson, 1984; Ewel, 1986) are known to influence the growth of pioneer species.

Deforestation can significantly alter soil physical and chemical properties and biochemical cycles in rainforest landscapes (Lamb, 1980; Robertson, 1984; Vitousek et al., 1989; Piccolo et al., 1994; Erickson et al., 2001; Zheng et al., 2005; Maloney et al., 2008), however, there has been a debate about the extent to which reforestation re-establishes all soil properties (Lamb, 1980; Robertson and Vitousek, 1981; Robertson, 1984; Vitousek et al., 1989; Bashkin and Binkley, 1998; Rhoades et al., 2000; Silver et al., 2005). Nevertheless, there is a general consent that reforestation can successfully restore many aspects of the nitrogen cycle, including nitrogen (N) stocks (Lamb, 1980; Robertson, 1984; Vitousek et al., 1989), nitrogen-mineralisation, nitrate concentrations (Lamb, 1980; Robertson, 1984; Vitousek et al., 1989; Erickson et al., 2001; Rasiah et al., 2004), and nitrification rates (Robertson and Vitousek, 1981; Robertson, 1984; Erickson et al., 2001; Scowcroft et al., 2004). Such changes could be expected to influence the growth of rainforest plants and hence influence the rate of succession. However, there has been little research which directly links soil conditions to seedling performance under different forms of rainforest restoration.

This chapter examines how deforestation and reforestation affect soil properties, and whether these changes limit or enhance the growth of early secondary tree species.

Chapter 6 Seedling growth

Specifically, I compared the early growth of three rainforest pioneer species in soils collected from replicate sites in pasture, rainforest, and three reforestation pathways in subtropical Australia: tree-planting for ecological restoration, regrowth dominated by an exotic tree species, and similar regrowth treated to promote the growth of native tree seedlings. I tested the effects of site-type on seedling growth rates, and investigated the association between seedling growth and the chemical and biochemical properties of these soils. Furthermore, I addressed the question to which extent early seedling growth is affected by the reforestation pathway.

I hypothesized differences in seedling growth rates between the different site-types, based on differences in soil properties. Specifically, pastures and camphor-dominated regrowth exhibited significantly lower plant-available nitrate-N levels than the three other site-types (Paul et al., 2010). Furthermore, plant-available phosphate-P, a key nutrient for plant growth and most likely to have a limiting effect on seedling performance (Holl, 1999), was highest in pasture sites and significantly lower in the four other site-types (Paul et al., 2010). Based on these findings, lower seedling performance in camphor-dominated and pasture soils could be expected.

6.2 Methods

6.2.1 Study area and experimental design

The study area for this project was the Big Scrub region in Northern New South Wales, Australia, a region which was once extensively covered by lowland subtropical rainforest over an area of 75,000 hectares (Floyd, 1990). The rainforest cover was largely cleared for the development of pasture and agricultural land in the mid to late 19th century (Floyd, 1990), and remnants of the original rainforest cover occur in small, highly fragmented patches throughout the region (Floyd, 1990). At the time of this study, the major land-use types in the region included active and abandoned pastures, macadamia (Macadamia integrifolia x tetraphylla) plantations, revegetation sites and spontaneous regrowth of woody species, often dominated by the non-native tree species camphor laurel (Cinnamomum camphora) (Neilan et al., 2006). Reforestation of former rainforest areas in the Big Scrub region has mainly occurred through three different pathways: autogenic regrowth dominated by the non- native tree species camphor laurel (Cinnamomum camphora), managed regrowth to accelerate the development of secondary rainforest, and ecological restoration planting.

Chapter 6 Seedling growth

6.2.2 Seedling growth experiment

Soil was collected from each site in December 2007. A 20 x 20 m survey plot was established at each site, containing five subplots, four at the corner points and one in the centre. At each subplot, the litter or grass layer was removed over about 20 cm2 and approximately 4 kg of soil was removed at a depth of 0 – 5 cm. The five subsamples were homogenised in a plastic bag. The soil was transported in a cool box to the laboratory and stored at 4°C for up to five days until seedlings were planted in a shade-house.

Seedlings of three tree species were grown in these soils: Alphitonia excelsa (Rhamnaceae), Guioa semiglauca (Sapindaceae), and Omalanthus nutans (Euphorbiaceae); all are common fast-growing rainforest pioneer species that occur naturally in the Big Scrub region, and all are frequently used in ecological restoration plantings. Seedlings were purchased at a local nursery and were approximately three months old at the time of planting.

Prior to planting, collected soils were mixed with perlite in the ratio of 1:1 to improve their structure and water-holding capacity. Three replicate seedlings of each species were planted in soils from each of the 25 sites (total 225 seedlings). Seedlings were planted individually into 4L planting bags containing 3.5L of soil/perlite mixture. Seedlings were allocated randomly in a shade-house with 30% sunlight penetration, rotated every four weeks to eliminate possible local site effects, and watered twice daily for three minutes by an automatic sprinkler system. Seedling height and diameter were measured seven times over a period of 30 weeks, immediately after planting (12 December 2007), then again on 26 December 2007, 10 January, 24 January, 21 February, 21 April, and 4 July 2008. Stem height from ground level to the base of the highest leaf in the crown was measured with a tape measure. Basal diameter 2 cm above ground was measured with digital calipers. An index of overall size (height x diameter) was also calculated for each plant; this parameter is directly proportional to the stem surface area and is termed “size index”. The initial heights of the seedlings (mean ± SE) were: A. excelsa 13.41 ± 0.25 cm, G. semiglauca 10.03 ± 0.34cm, O. nutans 32.32 ± 0.56 cm. Initial basal diameters were A. excelsa 2.09 ± 0.03 mm, G. semiglauca 2.49 ± 0.05 mm, O. nutans 5.41 ± 0.09 mm.

Chapter 6 Seedling growth

6.2.3 Data analysis

Two-way analysis of variance (ANOVA) was performed to test if there was a significant difference in starting height and diameter between the seedlings planted in soils from the five different site-types. Least significant difference (LSD) was used for pairwise comparisons at p<0.05.

Experimental growth coefficients were calculated for each of the 225 seedlings using exponential regressions with time as the independent variable and the three different growth measurements (stem height, basal diameter or size index) as the dependent variables. The fitted model for each seedling was

bt Ht(or Dt or St) = ae ,

where Ht is the stem height at time t, Dt the basal diameter of seedlings at time t, St the size index, a the intercept (stem height, basal diameter or size index of seedlings at t = 0), and b the seedling‟s exponential growth coefficient.

Effects of site-type and species on growth rates (exponential growth coefficients) were analysed using 2-way analysis of covariance (ANCOVA), in which initial size was the covariate. Fixed factors were „site-type‟ (5) and „species‟ (3), and the dependent variable was the measure of growth rate (stem height, basal diameter of size index). Replicates were the average exponential coefficients of the three seedlings grown in soil from each site (n = five sites of each site-type, giving 75 species by site combinations in total). Least significant difference (LSD) was used for pairwise comparisons at p<0.05.

Corrected growth values (estimated marginal means) from the ANCOVA output were used to model the increase in stem height and basal diameter after a growth period of 180 days in each site-type. Two models were developed; model 1 used an initial stem height of 10 cm and a basal diameter of 3 mm. Model 2 used an initial stem height of 20 cm and a basal diameter of 5 mm. These values were selected to represent the typical range of variation in initial size during this experiment.

Corrected growth values (as above, across all three species) for stem height, basal diameter and size index were compared with the mean values of the 15 soil properties for each site using Spearman rank correlations (n = 23 sites for plant-available ammonium-N, nitrate-N and phosphate-P, n = 25 sites for all other soil properties). For soil properties which showed significant correlations with growth values, I performed further ANCOVAs similar to

Chapter 6 Seedling growth those presented above but also using these variables as additional covariates. All analyses were carried out using the statistical package SPSS 14.0 (SPSS, 2005).

6.3 Results

6.3.1 Seedling growth

At the beginning of the experiment, the species G. semiglauca and O. nutans showed a significant difference in seedling height between the five site-types (Appendix 12). In G. semiglauca, initial seedling height was lowest in soils from pasture and rainforest sites and highest in soils from camphor-dominated sites and ecological restoration plantings. In O. nutans, initial seedling height was lowest in pasture and treated camphor sites and higher in all other site-types (Appendix 12).

Across all site-types, species varied significantly in all three measurements of growth (Table 6-1). Stem height growth coefficients were highest in G. semiglauca and lowest in O. nutans. Growth coefficients of basal diameter were highest in A. excelsa, intermediate in O. nutans and lowest in G. semiglauca (Fig. 6-1). Growth coefficients of size index (stem height x basal diameter) were similarly high in A. excelsa and G. semiglauca and lowest in O. nutans.

Table 6-1 The effect of species and site-type on exponential growth coefficients of seedlings using ANCOVA with initial size as a covariate. Seedlings were grown in soils from pasture, rainforest, and three forms of reforestation with five replicate sites for each site-type. There were three plant species and three seedling size variables. Size variable Stem height Basal diameter Size index Source df F p F p F p Full model 16 35.4 <.001 44.3 <.001 40.5 <.001 Intercept 1 46.2 <.001 65.9 <.001 159.3 <.001 Covariate 1 3.8 .06 9.8 .003 1.9 .18 Species (n = 3) 2 7.8 .001 62.8 <.001 5.2 .03 Site-type (n = 5) 4 3.4 .01 1.4 .235 2.9 .008 Species x site-type 8 1.6 .14 1.9 .075 1.4 .22 Error 58

Chapter 6 Seedling growth

Height Diameter Size index 0.01 0.006 B 0.013 B C B B 0.008 B AB 0.004 A 0.006 A 0.009 A 0.004 0.002 0.002

0 0 0.005 Growth coefficient Growth Ae Gs On Ae Gs On Ae Gs On

Figure 6-1 Exponential growth coefficients (estimated marginal means from ANCOVA corrected for initial size; n = 25 sites) of three variables describing seedling growth for three plant species (Alphitonia excelsa Ae, Guioa semiglauca Gs, and Omalanthus nutans On). Error bars represent standard error. Superscripts show LSD results (means with the same letter do not differ at p > 0.05).

There was no significant interaction between species and site-type for any of the three variables describing seedling growth (Table 6-1). For all three species, height and diameter growth coefficients were positively correlated (r-values were: A. excelsa 0.60, G. semiglauca 0.48, O. nutans 0.45).

Site-type had a significant effect on growth in stem height and the size index (Table 6- 1), but no effect on growth in diameter. Across all three species, growth coefficients of stem height and size index were significantly lower in soil from camphor-dominated sites and higher in soils from all other site-types (Fig. 6-2). Growth coefficients of size index were lowest in soils from camphor-dominated sites, intermediate in soils from treated camphor sites and similarly higher in soils from the three other site-types (Fig. 6.2).

0.007 Height 0.005 Diameter 0.013 Size index B B B B B B 0.011 B AB 0.004 A 0.006 0.009 A 0.003 0.007

Growth coefficient Growth 0.005 0.002 0.005 PA CD TC ER RF PA CD TC ER RF PA CD TC ER RF

Figure 6-2 Exponential growth coefficients (estimated marginal means from ANCOVA corrected for initial size; n = 15 site-species combinations) of three variables describing seedling growth in soils from pasture, rainforest, and three forms of reforestation. PA = pasture, CD = camphor-dominated, TC = treated camphor, ER = ecological restoration planting, RF = rainforest. Error bars represent standard error. Superscripts show LSD results (means with the same letter do not differ at p > 0.05).

Chapter 6 Seedling growth

80 Model 1 80 Model 2 PA 70 70 CD 60 60 TC 50 50 40 40 ER 30 30 RF

20 20 Stem height (cm) height Stem 10 10 0 0 0 30 60 90 120 150 180 0 30 60 90 120 150 180 Time (days) Time (days)

Figure 6-3 Modelled growth curves for stem height of seedlings (based on pooled data for three species) after a growing period of six months. Model 1 initial stem height 10 cm, Model 2 initial stem height 20 cm. PA = pasture, CD = camphor dominated, TC = treated camphor, ER = ecological restoration planting, RF = rainforest.

The modelled stem height for seedlings of two fixed initial sizes after a growing period of six months did not vary greatly between site-types. In both models (model 1: initial height 10 cm, model 2: initial height 20 cm), stem height after six months was lowest in soils from camphor-dominated sites and higher in soils from pasture, treated camphor, ecological restoration plantings, and rainforest sites (Fig. 6-3). For seedlings with an initial stem height of 10 cm, the modelled height increase after six months was 16 cm in soils from camphor- dominated sites, 17 cm in soils from pasture sites, 20 cm in soils from treated camphor sites, and 22 cm in soils from ecological restoration plantings and rainforest sites. In this model, after a period of 180 days, heights of seedlings grown in soils from camphor-dominated sites were 5% less than those of seedlings grown in soils from pasture sites. Compared to seedlings grown in treated camphor sites and ecological restoration plantings/rainforest sites, the height difference was 20% and 27%, respectively. For seedlings with an initial stem height of 20 cm, the modelled height increase after six months was 33 cm in soils from camphor-dominated sites, 36 cm in soils from pasture sites, 18 cm in soils from treated camphor sites, and 23 cm in soils from ecological restoration plantings and rainforest sites. That is, after six months, the modelled heights of seedlings grown in soils from camphor laurel sites were 8% less than those of seedlings grown in soils from pasture sites and 18% less than those of seedlings from treated camphor sites. Compared to seedlings from ecological restoration plantings and rainforest sites, the height difference was 23%.

6.3.2 Seedling responses to soil chemical and biochemical properties

Across all three species and five site-types, exponential growth coefficients of height and size index showed a statistically significant relationship with only three out of 15 soil

Chapter 6 Seedling growth properties: plant-available nitrate-N (for p < 0.1), soil organic matter (for p < 0.1), and pH (for p < 0.01) (Table 6-2, Fig. 6-4). Growth coefficients were positively correlated with plant- available nitrate-N and soil organic matter and showed a negative correlation with soil pH. (Table 6-2, Fig. 6-4).

Table 6-2 The relationship between mean site-specific seedling growth rates and soil properties. Growth is defined as the exponential growth constant corrected for initial size. PC1 Nitrogen is the first axis from a principal components analysis of N-related soil properties. n = 23 sites for plant-available ammonium-N, nitrate-N and phosphate-P; n = 25 sites for all other variables. Values reflect Spearman‟s correlation coefficients; + p < 0.10, * p < 0.05, ** p < 0.01.

Size variable Soil property Stem height Basal diameter Size index Total N -.14 .17 -.19 Total inorganic N .17 -.04 .26 Nitrate-N .25 .08 .27 Ammonium-N -.12 -.21 .001 Plant-available nitrate-N .38+ .16 .50* Plant-available ammonium-N -.18 -.13 .01 Denitrification -.34 -.24 -.24 Nitrification .20 .12 .16 Total C -.17 .10 -.24 Total organic C .02 -.13 .08 Soil organic matter .37+ .09 .35+ Microbial biomass C .006 -.26 .-04 Total microbial activity .13 .20 .23 pH -.52** -.002 -.67** Plant-available phosphate-P -.07 -.21 .08 PC1 Nitrogen .13 .06 .09

0.012 0.012 PA

0.011 0.011 CD TC 0.010 0.010 ER

0.009 0.009 RF

Growth in size in Growth 0.008 0.008

0.007 0.007 4 5 6 7 0 250 500 750 1000 pH Plant-available nitrate-N

Figure 6-4 Relationship between seedling growth (size index) and soil pH and plant-available nitrate-N. Growth is the exponential constant corrected for initial size. n = 25 sites for soil pH and 23 sites for plant- available nitrate-N.

Chapter 6 Seedling growth

When soil pH was included as an additional covariate in the ANCOVA models of growth rates, the effect of site-type on growth was no longer significant for any growth variable (Table 6-3). Including plant-available nitrate-N as an additional covariate also reduced the significance of site-type effects on all three variables describing seedling growth (Table 6-4), with resulting p-values for stem height and size index becoming marginally significant. Pairwise comparison of estimated marginal means revealed a significantly lower stem height growth rate in camphor-dominated sites (mean 0.0053) than in any other site- type (0.0060 for treated camphor sites, 0.0063 for pasture, replanted and rainforest sites).

Table 6-3 The effects of species and site-type on exponential growth coefficients of seedlings, using ANCOVA with the initial size and soil pH as covariates. Seedlings were grown on soils from pasture, rainforest, and three forms of reforestation, with five replicate sites for each site-type. There were three plant species and three seedling size variables.

Growth measurement Stem height Basal diameter Size index Source df F p F p F p Full model 16 34.8 <.001 42.3 <.001 39.6 <.001 Intercept 1 32.8 <.001 40.2 <.001 39.7 <.001 Covariate1 (initial size) 1 3.3 .07 8.6 .005 1.3 .27 Covariate 2 (soil pH) 1 3.3 .07 1.9 .17 3.3 .08 Site-type (n = 5) 4 0.3 .85 0.9 .49 0.5 .72 Species (n = 3) 2 8.3 .001 64.0 <.001 6.1 .004 Species x site-type 8 1.7 .13 1.9 .08 1.4 .21 Error 58

Table 6-4 The effects of species and site-type on exponential growth coefficients of seedlings, using ANCOVA with the initial size and plant-available nitrate-N as covariates. Seedlings were grown on soils from pasture, rainforest, and three forms of reforestation, with five replicate sites for each site-type. There were three plants species and three seedling size variables.

Growth measurement Stem height Basal diameter Size index Source df F p F p F p Corrected model 16 32.8 <.001 41.6 <.001 37.5 <.001 Intercept 1 44.8 <.001 64.8 <.001 148.6 <.001 Covariate 1 (initial size) 1 3.9 .054 10.3 .002 2.0 .17 Covariate 2 (Plant av. nitrate-N) 1 0.16 .693 1.06 <.001 0.24 .063 Site-type (n = 5) 4 2.5 .055 0.4 .83 2.3 .07 Species (n = 3) 2 7.6 .001 62.8 <.001 4.9 .11 Species x site-type 8 1.6 .69 1.1 0.07 0.2 .23 Error 58

Chapter 6 Seedling growth

6.4 Discussion

6.4.1 Relationships between forest cover, plant growth, and soil properties

The present study was designed to focus on the relationships between seedling growth and the soil characteristics of each site-type. Therefore, I excluded a number of factors that influence seedling growth in the field, such as soil moisture conditions, light conditions, root competition, and herbivore damage.

The three species used in this study are all considered pioneers or early colonists in the study region (Stewart, 1995; Erskine et al., 2007). However, their specific growth patterns differed. Over a period of 180 days, A. excelsa grew intermediate in height and highest in diameter, while G. semiglauca showed highest growth rates in height, but grew less in diameter. O. nutans grew less in height and intermediate in diameter. In spite of these differences, which presumably reflect underlying ontogenetic or physiological variation, all species showed similar patterns of growth variation among site-types. Seedlings grew fastest in soils from pasture, replanted and rainforest sites and slowest in soils collected from camphor-dominated regrowth sites.

The seedling stage is often considered the most critical phase in a plant‟s life cycle (Harper, 1977; Fenner, 1987). In forest regrowth on abandoned pasture, rapid growth rates can enable a seedling to grow above grasses and herbs and thereby compete more successfully for height; poor seedling growth can result in the reversion of a site to grassland (Kanowski et al., 2008c). Hence, reforestation pathways which most rapidly establish soil properties that enable rapid seedling growth are likely to be most successful for rainforest regeneration.

Competition for nitrogen is an important factor for species establishment during early succession (Vitousek et al., 1993). Rainforest trees are reported to mainly use nitrate-N as opposed to ammonium-N in the early stages of their growth (Stewart et al., 1988; Bazzaz, 1991). I found a positive relationship between plant-available nitrate-N and seedling growth rates, consistent with previous suggestions that the growth of early successional species is commonly limited by soil nitrogen levels (Tilman, 1986).

Based on the low levels of plant-available nitrate-N in pasture and camphor-dominated soils, I expected to find the lowest plant growth rates in these site-types. However, this hypothesis was only correct for seedlings in soils from camphor-dominated sites. Seedling growth in soils from pasture sites was similar to growth in forested sites, although seedlings

Chapter 6 Seedling growth grown in pasture soils had lowest initial stem heights. Pasture sites showed a significantly higher amount of plant-available P than all other site-types (see Chapter 5), and since all soils were derived from the same parent material (basalt), it is reasonable to assume that the high P amounts in pasture sites are a product of phosphate fertilisation. Soil P is a major plant nutrient that regulates growth and development in forest ecosystems (Zech and Drechsel, 1992; Paul and Clark, 1997; Chen et al., 2000), and some authors have suggested that native forest productivity is often P limited (Cuevas and Medina, 1988; Gehring et al., 1999), especially in lowland tropical ecosystems (Asner et al., 1999; Townsend et al., 2002). Studies on the reforestation of pasture sites usually show a higher P availability under pasture than under forested sites (Davis and Lang, 1991; Chondron et al., 1996; Chen et al., 2000), and such a high P availability could have partly compensated for the low N availability in pasture sites, resulting in higher seedling growth rates than in camphor- dominated sites.

Although two out of the three species tested (G. semiglauca and O. nutans) showed highest initial seedling heights in camphor-dominated sites, seedling growth was significantly lower in soils from these sites. The lower seedling growth in soils from camphor-dominated sites suggests that camphor laurel may have directly or indirectly inhibited seedling growth. Camphor laurel is known to produce a range of essential oils (mainly aromatic and acrylic monoterpenes such as camphor, linalool, cineole or carvacrol) that may be released into the soil (Stubbs et al., 1999) via root exudation and litter fall. Several authors have suggested that volatile monoterpenes in soils can inhibit the growth of mycorrhizal fungi, thereby greatly decreasing root infection rates (Melin and Krupa, 1971; Ludley et al., 2008, 2009). Phosphate concentrations in camphor-dominated regrowth sites in my study were similar to those in treated camphor, replanted, and rainforest sites, but seedling growth was significantly lower in soils from camphor sites. It is possible that this may have occurred because non-mycorrhizal seedlings grown in soils from camphor-dominated regrowth were also restricted in their ability to take up phosphate. Another possibility is that camphor laurel may have inhibited seedling growth by increasing soil pH, as seedling growth was negatively correlated with soil pH in the present study. However, this relationship is contrary to the results reported by Gunatilleke et al. (1996), in which growth of seedlings of eight rainforest tree species was higher in soils with higher pH (Gunatilleke et al., 1996). The results of my study cannot discriminate the specific mechanism which led to the slight but significant reduction in seedling growth rates in soils from camphor laurel regrowth, elucidation of which would require further controlled experimentation.

Most of the measured soil properties were not correlated with seedling growth. Under field conditions, light rather than nutrient availability may be the primary factor limiting

Chapter 6 Seedling growth seedling growth (Nussbaum et al., 1995; Portsmuth and Niinements, 2006). Seedling growth may have been more strongly limited by nutrients under higher light conditions. In my experiment, seedlings were grown under 30% light penetration and may have had limited potential to adequately respond to changes in nutrient availability, although such semi- shaded conditions are typical in the understorey of secondary and replanted rainforests (Kanowski et al., 2003).

6.4.2 Implications for reforestation processes

Rapid early seedling growth is likely to be advantageous for seedling survival in the context of replanted rainforest and secondary succession in old-field habitats, enabling tree seedlings to over-top grasses and pasture herbs.

The growth of all three species used in this study was slower (around 75% the height increment) in soils from camphor-dominated regrowth than from other sites, suggesting that camphor laurel has the ability to suppress seedling growth. However, seedling growth rates in soils from treated camphor sites three to six years after camphor removal resembled those in rainforest soils. This indicates that an initial tolerance of camphor laurel regrowth, followed by a removal of the camphor overstorey, may be a successful pathway for regenerating rainforest on cleared land (Kanowski et al., 2008b).

Chapter 7 General discussion

7. General discussion

7.1 Summary and key findings of the data chapters

The overarching aim of this study was to examine if ecological processes can be re- established during reforestation. The main findings relating to this aim are in the Chapter 3- 6; the specific findings of these chapters can be synthesized into four key overall findings. Each of these findings will be discussed in this chapter.

Soil seed banks play no vital role in initial rainforest recovery after long-term pasture establishment

In Chapter 3, I investigated three main questions: (1) What are the impacts of pasture establishment on the size and composition of viable soil seed banks? (2) Are the composition and size of soil seed banks different in different pathways of reforestation? and (3) How is the composition of above-ground vegetation related to the composition of the soil seed bank? The impact of deforestation on soil seed banks was explored by comparing seed banks of pasture with reference rainforest. The study found a range of differences between the two site-types; indicating that the most significant changes following deforestation are an increase in total seed density and a decrease in species richness and seed densities of rainforest species. The seed banks of pastures were dominated by seeds of wind dispersed grasses and herbaceous species and contained a high number of non-native species. Native woody species – which accounted for a large part of the seed bank in rainforest – were extremely rare in pasture seed banks. These results were expected and are consistent with the findings of several other studies (Uhl et al., 1988; Hopkins et al., 1990; Aide et al., 1995; Demel, 1997; Wijdeven and Kuzee, 2000; Campos and de Souza, 2003), suggesting that the duration of cattle grazing on the pasture sites had clearly exceeded the longevity of dormant tree seeds that would have once been present in the soil. As a consequence, the restoration of rainforest cover would be dependent on seeds from adjacent rainforest remnants.

Soil seed banks of all reforested site-types were characterised by lower numbers of grasses and herbaceous species and increased numbers of native woody species, both in regards to species richness and abundance. A comparison of the three reforestation pathways found no major difference in their overall abilities to restore the original soil seed bank. In all forested site-types, including original rainforest, pioneer and early secondary

Chapter 7 General discussion species were the main constituent of the soil seed bank. Given the relative scarcity of tree species in pasture soils, these pioneer seeds must have been brought into the reforested sites by mobile frugivores, as discussed by Neilan et al. (2006). However, in all forested site- types, seedling banks did not reflect the composition of the seed bank, supporting the idea that while pioneer species accumulate as seeds in the seed bank, later secondary species primarily depend on the seedling bank for regeneration and show low seed dormancy (Sulei and Swaine, 1988; Vazquez-Yanes and Orozco-Segovia, 1993; Garwood, 1996; Whitmore, 1996).

Microbial-related soil properties show higher spatial variability than physical soil properties

Spatial variability is a common characteristic of soil properties (Robertson et al., 1988; Roever and Kaiser, 1998; Cain et al., 1999; Ettema et al., 2000; Ettema and Wardle, 2002; Schume et al., 2003; Schoening et al., 2006) and increasingly recognised as an important factor in the understanding of population, community, and ecosystem processes (Tilman and Kareiva, 1997). Chapter 4 aimed to characterise the heterogeneity of a range of soil properties at different spatial scales (site-type, site, and subplot level) and investigated the following research questions: (1) Do soil properties differ more at any scale than at others? and (2) Does physically combining subsamples affect the measurement of soil properties? Gravimetric water content, soil organic matter, and pH showed the lowest variability across all three spatial scales; whereas microbial related soil properties such as total organic carbon, microbial biomass carbon, nitrate-N, and nitrification had the highest levels of variability.

For most of the soil properties measured in this study, physically combining subsamples from regularly aligned subplots within a site was validated as a useful technique for reducing analytical effort without sacrificing the accuracy of the data. Although there was no significant correlation between the value of the physically combined subsamples and the mean value of the individually analysed subsamples for the soil properties total organic carbon and nitrification, there was still a positive relationship between these two measurements. However, paired t-tests between physically combined site measurements and averages of individually measured subsamples indicated a statistically significant difference for microbial biomass carbon, which means that physically combining subsamples may result in an over-estimation of the absolute value of microbial biomass carbon.

Chapter 7 General discussion

Soil properties altered by deforestation can largely be restored with reforestation

It is generally accepted that any changes in above-ground vegetation result in significant changes in the physical and chemical characteristics and biochemical cycles that connect vegetation and soil (Vitousek et al., 1989; Piccolo et al., 1994; Erickson et al., 2001; Maloney et al., 2008). In Chapter 5, I explored the impacts of land-use change from primary rainforest to pasture on a range of biotic and abiotic soil properties and the abilities of three different reforestation pathways to reverse these changes. Specifically, I addressed the following three questions: (1) How does land-use change from primary subtropical rainforest to pasture affect soil properties? (2) Does reforestation restore soil properties that characterise primary rainforest? and (3) How do three different reforestation pathways (tree- planting for ecological restoration, regrowth dominated by a non-native tree species, and similar regrowth treated to promote native regrowth) differ in their ability to restore soil properties? Unexpectedly, among soil properties related to the soil C cycle and soil microbial activity, there was little variation with deforestation or reforestation. However, although total soil nitrogen pools did not markedly differ among site-types, deforestation resulted in major losses of nitrogen availability, which supports the general finding of many other studies that nitrogen transformation processes are sensitive to land-use changes (Matson et al., 1987; Montagnini and Buschbacher, 1989; Steudler et al., 1991; Piccolo et al., 1994; Rasiah et al., 2004).

Across all measured soil properties, all three reforestation pathways were able to restore concentrations similar to these of forest soil to varying degrees, although the rate of recovery was lowest in camphor-dominated regrowth. Although nitrification rates, plant- available ammonium-N and phosphate-P levels were re-established in camphor-dominated regrowth, this reforestation pathway had not significantly facilitated the recovery of nitrate-N (overall and plant-available) and fine root biomass. Nitrate-N levels had, however, been significantly increased in treated camphor sites. It is possible that either camphor trees have a relatively high nitrate uptake, or nitrogen fixation rates in camphor regrowth are low because of a scarcity of leguminous species that were present in all other four site-types.

Early seedling growth of pioneer species did not differ greatly among reforestation pathways

Pioneer tree species play an important role in the dynamics and development of forest cover (Connell and Slatyer, 1977; Brown and Lugo, 1990a; Aide et al., 1995; Erskine et al.,

Chapter 7 General discussion

2007). Their growth, especially in the early stages, can be significantly affected by changes in soil properties (Nussbaum et al., 1995; Burslem, 1996; Gunatilleke et al., 1996; Woodward, 1996), which could then influence the rate of ecological succession. Chapter 6 used soil data from Chapter 5 and tested whether variation in soil properties between site- types was related to variation in growth rates of seedlings of rainforest pioneer species, thereby directly linking soil condition to seedling performance. The main questions this chapter addressed were (1) How do soil properties affect the early growth of rainforest pioneer species? and (2) to what extent is seedling growth affected by the reforestation pathway? Seedling growth rates were correlated with only two out of 15 measured soil properties: soil pH levels and plant-available nitrate-N. This finding suggests that variation in the early growth of seedlings in the study region depends mainly on factors other than soil nutrients. Since the seedlings in this study‟s experiment were grown under 30% light penetration, they may have had limited potential to show large responses to changes in nutrient availability.

Surprisingly (given the differences in nutrient status established in Chapter 5), seedling growth rates did not differ between pasture and rainforest soils, but were significantly lower (around 25%) in soils from camphor-dominated regrowth sites than in soils from treated camphor and replanted sites. The reason for lower seedling growth in camphor- derived soils may be related to its release of aromatic substances (Stubbs et al., 1999) which may inhibit root infection with symbiotic mycorrhizae. However, other unknown factors associated with camphor soils may also play a role; further research would be needed to clarify this.

7.2 Ecosystem processes and reforestation

Restoration ecology deals with the re-establishment of degraded ecosystems and has been called an “acid test” for ecological theories (Bradshaw, 1987; Ewel, 1987). If ecosystem recovery is correctly understood, the outcome of restoration efforts could be successfully predicted, and research projects could be better designed. There is currently debate about which ecosystem attributes should be considered when evaluating the success of restoration (Ruiz-Jaen and Mitchell Aide, 2005). Suggestions have ranged from vegetation characteristics (Walters, 2000) over species diversity or similarity to old-growth forests (Passell, 2000; McCoy and Mushinsky, 2002; Catterall et al., 2004; Gardner et al., 2007) to ecosystem processes (Rhoades et al., 1998). The measurement of ecosystem processes can provide important information about the recovery of an ecosystem, as it enables an

Chapter 7 General discussion estimation of the resilience of the ecosystem, which is the system‟s capacity to recover to its former state after disturbance (Constanza and Folke, 1996).

This study combined the measurement of soil seed bank dynamics, nutrient cycling, and seedling growth to evaluate the recovery of subtropical rainforest after pasture establishment. As one of the main goals of ecological restoration is the long-term re- establishment of biodiversity, the composition of the viable soil seed bank deserves particular attention in reforestation, as it may contain information relevant to future tree diversity. Once forest cover has established on such sites, either through autogenic processes (and, in some cases, comprising mainly non-native species) or tree-planting, soil seed banks have shown the potential to recover; with recovery rates depending on the dispersal of seeds into the sites (White et al., 2004). In this study, which looked at early successional stages of reforestation, soil seed banks had recovered almost fully after a period of only 10-50 years.

The extent to which soil properties, particularly nutrient pools, recover under reforestation, is presently not fully understood, and available information is often contradictory. For example, Rasiah et al. (2004) found no substantial improvement in soil properties after 40 years of regrowth on an abandoned pasture, suggesting a poor ability of tropical soils to recover from deforestation. In contrast, this study found a significant recovery of nitrogen-related soil properties, particularly nitrate and nitrification rates, within 10-15 years of reforestation by ecological restoration planting and 30-40 years by autogenic regrowth with subsequent treatment of non-native species. The results of this study are consistent with a range of previous studies (Robertson and Vitousek, 1981; Robertson, 1984; Erickson et al., 2001; Scowcroft et al., 2004). The patterns of nitrogen dynamics during reforestation are, without doubt, complex, and the factors that control these patterns range from soil organic matter content and pH value over microbial activities through to vegetation structure. For example, soil organic matter, a short-term sink of nitrogen with turnover rates of days to years (Zak et al., 1990; Davidson et al., 1992; Schimel and Bennett, 2004), can influence nitrogen retention and fluxes in grassland and forest soils (Barrett and Burke, 2000). Further, microbially mediated nitrogen immobilisation accounts for the largest proportion of stabilised nitrogen in soils (Vitousek and Matson, 1984; Schimel et al., 1989); hence, nitrogen fluxes are significantly influenced by microbial activities. Since substrate availability can limit N immobilisation in a range of ecosystems and soil types (Janssen, 1996; Hedin et al., 1998), soils with higher organic matter content may immobilise more nitrogen than soils with less organic matter. The complex interactions between carbon and nitrogen cycles in terrestrial ecosystems make it difficult to predict the re-establishment of

Chapter 7 General discussion soil properties during reforestation, as rates of recovery of nutrient dynamics may greatly vary in different stages of reforestation.

7.3 Advantages and disadvantages of different reforestation pathways

This study looked at three different pathways by which forest cover can be restored to former rainforest landscapes: autogenic regrowth dominated by a non-native tree species, management of this regrowth to promote the development of rainforest species, and ecological restoration planting. All pathways were capable of restoring many ecological attributes of rainforest. The decision concerning the usefulness of each reforestation pathway requires careful consideration of the restoration goals, the ecological outcomes, and the available resources.

The major advantage of untreated autogenic regrowth is that it is not associated with any costs and therefore suited to restore ecosystem properties over a large area. Studies have shown that such regrowth, although dominated by the non-native species Cinnamomum camphora, was capable of restoring a vegetation cover that showed remarkable similarities to rainforest in terms of structural attributes (Kanowski et al., 2003; Neilan et al., 2006; Kanowski and Catterall, 2007). Moreover, stands of C. camphora can support a variety of native rainforest fauna (Neilan et al., 2005), although in untreated autogenic rainforest regrowth, the full range of rainforest-dependent fauna may still be missing after several decades (Kitching et al., 2000; Crome et al., 2004; Moran et al., 2004a; Moran et al., 2004b).

In this study, the seed banks of camphor-dominated autogenic regrowth stands (30-50 years old) showed a higher abundance and species richness of native vines and native woody plants than seed banks of pastures, but these properties were not comparable to those in rainforest (see Chapter 3). Further, although nitrification rates, plant-available ammonium and phosphate levels in such sites were comparable to rainforest, there was little proof that this reforestation pathway had promoted the recovery of nitrate-N (overall and plant-available) and fine root biomass (see Chapter 5). Soil bulk densities in autogenic regrowth stands were significantly lower than in pasture sites, but not comparable to values under rainforest. This suggests that this pathway could only partially restore soil bulk density. Tree seedling growth rates in soils from camphor-dominated regrowth were lower than in soils from other site-types (see Chapter 6).

Chapter 7 General discussion

To accelerate succession on such sites, and to promote the development of a more native tree community, stands of camphor-dominated regrowth may be manipulated by restoration practitioners. In this approach, mature camphor trees and understorey weeds are treated with herbicide, often with the result that rainforest pioneer species and weeds recruit from the soil seed bank (Kanowski and Catterall, 2007), requiring weed control until the formation of a closed canopy. The higher maintenance requirement of such sites, compared to untreated regrowth, means that the use of this pathway incurs costs of around AUD $5- 30,000 per hectare (Kanowski and Catterall, 2007). However, in terms of seed bank and soil properties restoration, this approach showed major advantages over untreated regrowth: with the removal of the camphor laurel canopy, abundance and species richness of native vines and native woody species increased significantly (see Chapter 3). Further, seeds of camphor laurel were significantly reduced. Treatment of the camphor overstorey also led to increased levels of nitrate availability (Chapter 5), and seedling growth rates in soils from such sites closely resembled those in rainforest soils (Chapter 6).

Techniques of ecological restoration planting have been developed in order to rapidly overcome depleted soil seed banks and competition from pasture grasses; factors which may severely limit rainforest recovery (Erskine et al., 2007). Such sites have shown a species composition similar to rainforest sites after about ten years, although rainforest- dependent species were under-represented (Catterall et al., 2008). In this study, restoration planting was the only pathway in which species richness of native woody plants in the seed bank could be restored to rainforest levels. Moreover, it proved to be a rapid pathway for restoring soil properties, most of which were similar to rainforest levels after 10-15 years. Similar to treated camphor sites, seedling growth rates in such soils were comparable with rates in rainforest soils. However, the benefits of ecological restoration planting come at a high price: it has been estimated that restoration plantings can cost up to AUD $50,000 per hectare) (Erskine, 2003; Catterall and Harrison, 2006), an aspect which restricts this method to the reforestation of only small areas.

The costs and outcomes of the three reforestation pathways vary significantly. Autogenic regrowth dominated by camphor laurel, as a result of pasture abandonment and therefore not associated with any costs, is a relatively slow pathway and showed a moderate ability to recover ecosystem processes. Managed regrowth is associated with much higher costs, and although the capacities of this pathway to re-establish ecosystem processes are better, it is still a comparatively slow method. Ecological restoration planting showed the best outcomes of all pathways within a relatively short time, however, it is also the most expensive method. Such considerations may have various implications for the choice of a

Chapter 7 General discussion suitable pathway at a landscape scale. Therefore, the most important management issue is likely to be a decision about how the different pathways can be applied in combination across a landscape to achieve optimal results, rather than a choice of one particular pathway. Ecological restoration planting is most suitable for small areas where the emphasis is on fast outcomes. Managed regrowth is a useful pathway over larger areas where advanced regrowth already exists, but where ecological outcomes need to be improved. Untreated autogenic regrowth can be tolerated over larger areas where some ecological outcomes are needed, and also over areas where future treatment to encourage the development of rainforest tree species may be desired.

References

Abdulhadi, R., Lamb, D., 1988. Soil seed stores in a rainforest succession. Proceedings of the Ecological Society of Australia 15, 81-87.

Achard, F., Eva, H.D., Stribig, H.-J., Mayaux, P., Gallego, J., Richards, T., Malingreau, J.-P., 2002. Determination of deforestation rates of the world's humid tropical forests. Science 297, 999-1002.

Adam, G., Duncan, H., 2001. Development of a sensitive and rapid method for the measurement of total microbial activity using fluorescein diacetate (FDA) in a range of soils. Soil Biology and Biochemistry 33, 943-951.

Aide, T.M., Cavalier, J., 1994. Barriers to lowland tropical forest restoration in the Sierra Nevada de Santa Marta, Colombia. Restoration Ecology 2, 219-229.

Aide, T.M., Zimmermann, J.K., Herrera, L., Rosario, M., Serrana, M., 1995. Forest recovery in abandoned tropical pastures in Puerto Rico. Forest Ecology and Management 77, 77-86.

Aide, T.M., Zimmermann, J.K., Pascarella, J.B., Rivera, L., Marcano-Vega, H., 2000. Forest regeneration in a chronosequence of tropical abandoned pastures: implications for restoration ecology. Restoration Ecology 8, 328-338.

Aide, T.M., Zimmermann, J.K., Rosario, M., Marcano, H., 1996. Forest recovery in abandoned cattle pastures along an elevational gradient in northeastern Puerto Rico. Biotropica 28, 537-548.

Anderson, T.-H., Domsch, K.H., 1989. Ratio of microbial biomass to total organic carbon in arable soils. Soil Biology and Biochemistry 21, 471-479.

Angers, D.A., Pesant, A., Vigeux, J., 1992. Early cropping-induced changes in soil aggregation, organic matter, and microbial biomass. Soil Science Society of America Journal 56, 115-119.

Ashton, M.S., Singhakumara, B.M.P., Gamage, H.K., 2006. Interaction between light and drought affect performance of Asian tropical tree species that have differing topographic affinities. Forest Ecology and Management 221, 42-51.

Asner, G.P., Broadbent, E., Oliveira, P., Keller, M., Knapp, D., Silva, J., 2006. Condition and fate of logged forests in the Brazilian Amazon. Proceedings of the National Academy of Sciences 103, 12947-12050.

Asner, G.P., Townsend, A.R., Bustamante, M.C., 1999. Spectrometry of pasture condition and biogeochemistry in the Central Amazon. Geophysical Research Letters 26, 2769-2772.

Ayres, E., Dromph, K., Bardgett, R.D., 2006. Do plant species encourage soil biota that specialise in the rapid decomposition of their litter? Soil Biology and Biochemistry 38, 183- 186.

Baider, C., Tabarelli, M., Mantovani, W., 2001. The soil seed bank during atlantic forest regeneration in southeast Brazil. Review of Brazilian Biology 61, 35-44.

Bakker, J.P., Poschlod, P., Strykstra, R.J., Bekker, R.M., Thompson, K., 1996. Seed banks and seed dispersal: important topics in restoration ecology. Acta Botanica Neerlandica 45, 461-490.

Barberis, I.M., Tanner, E.V.J., 2005. Gaps and root trenching increase tree seedling growth in Panamanian semi-evergreen forest. Ecology 86, 667-674.

Barrett, J.E., Burke, I.C., 2000. Potential nitrogen immobilization in grassland soils across a soil organic matter gradient. Soil Biology and Biochemistry 32, 1707-1716.

Bashkin, M.A., Binkley, D., 1998. Changes in soil carbon following afforestation in Hawaii. Ecology 79, 828-833.

Batjes, N.H., 1996. Total carbon and nitrogen in the soils of the world. European Journal of Soil Science 47, 151-163.

Batjes, N.H., Sombroek, W.G., 1997. Possibilities for carbon sequestration in tropical and subtropical soils. Global Change Biology 3, 161-173.

Bauhus, J., Khanna, P.K., 1994. Carbon and nitrogen turnover in two acid forest soils of southeast Australia as affected by phosphorus addition and drying and rewetting. Biology and Fertility of Soils 17, 212-218.

Bauhus, J., Pare, D., Cote, L., 1998. Effects of tree species, stand age and soil type on microbial biomass and its activity in a southern boreal forest. Soil Biology and Biochemistry 30, 1077-1089.

Bautista-Cruz, A., del Castillo, R.F., 2005. Soil changes during secondary succession in a tropical montane cloud forest area. Soil Science Society of America Journal 69, 906-914.

Bazzaz, F.A., 1991. Regeneration of tropical forests: Physiological responses of pioneer and secondary species. In: Gomez-Pompa, A., Whitmore, T.C., Hadley, M. (Eds.), Rain Forest Regeneration and Management. UNESCO and The Parthenon Publishing Group, Paris.

Beck, T., Capriel, P., Borchert, H., Brandhuber, R., 1995. Die mikrobielle Biomasse in landwirtschaftlich genutzten Boeden. 2. Mitteilung: Beziehungen der Biomasse zu chemischen und physikalischen Bodeneigenschaften. Agrobiological Research 48, 74-82.

Bellehumeur, C., Legendre, P., 1998. Multiscale sources of variation in ecological variables: modelling spatial dispersion, elaborating sampling designs. Landscape Ecology 13, 15-25.

Bellehumeur, C., Legendre, P., Marcotte, D., 1997. Variance and spatial scales in a tropical rain forest: changing the size of sampling units. Plant Ecology 130, 89-98.

Belser, L.W., 1979. Population Ecology of Nitrifying Bacteria. Annual Review of Microbiology 33, 309-333.

Bengtson, P., Basiliko, N., Prescott, C.E., Grayston, S.J., 2007. Spatial dependency of soil nutrient availability and microbial properties in a mixed forest of Tsuga heterophylla and Pseudotsuga menziesii, in coastal British Columbia, Canada. Soil Biology and Biochemistry 39, 2429-2435.

Bernoux, M., Cerri, C., Neill, C., de Moares, J., 1998. The use of stable carbon isotopes for estimating soil organic matter turnover rates. Geoderma 82, 43-58.

Binkley, D., 1984. Ion exchange resin bags: factors affecting estimates of nitrogen availability. Soil Science Society of America Journal 48, 1181-1184.

Binkley, D., Aber, J., Pastor, J., Nadelhoffer, K., 1986. Nitrogen availability in some Wisconsin forests: comparisons of resin bags and on-site incubations. Biology and Fertility of Soils 2, 77-82.

Boekhold, A.E., van der Zee, S.E.A.T.M., 1992. Significance of soil heterogeneity for spatial behaviour of cadmium in field soils. Soil Science Society of America Journal 56, 747-754.

Bossio, D.A., Girvan, M.S., Verchot, L., Bullimore, J., Borelli, T., Albrecht, A., Scow, K.M., Ball, A.S., Pretty, J.N., Osborn, A.M., 2005. Soil microbial community response to land use change in an agricultural landscape of Western Kenya. Microbial Ecology 49, 50-62.

Bradshaw, A.D., 1987. Restoration: an acid test for ecology. In: W., J., Gilpin, M., Aber, J. (Eds.), Restoration Ecology: A Synthetic Approach to Ecological Research. Cambridge University Press, Cambridge, pp. 23-30.

Bradshaw, C.J.A., Sodhi, N.S., Peh, K.S.-H., Brook, B.W., 2007. Global evidence that deforestation amplifies floodrisk and severity in the developing world. Global Change Biology 13, 2379-2395.

Brooks, T.M., Mittermeier, R.A., Mittermeier, C.G., da Fonseca, G.A.B., Rylands, A.B., Konstant, W.R., Flick, P., Pilgrim, J., Oldfield, S., Magin, G., Tilton-Taylor, C., 2002. Habitat loss and extinction in the hotspots of biodiversity. Conservation Biology 16, 909-923.

Brown, A., Lugo, A.E., 1994. Rehabilitation of tropical lands: a key to sustaining development. Restoration Ecology 2, 97-111.

Brown, S., Lugo, A., 1992. Tropical forests as sinks of atmospheric carbon. Forest Ecology and Management 54, 239-255.

Brown, S., Lugo, A.E., 1990a. Effects of forest clearing and succession on the carbon and nitrogen content of soils in Puerto Rico and US Virgin Islands. Plant and Soil 124, 53-64.

Brown, S., Lugo, A.E., 1990b. Tropical secondary forests. Journal of Tropical Ecology 6, 1- 32.

Bruckner, A., Kandeler, E., Kampichler, C., 1999. Plot-scale spatial patterns of soil water content, pH, substrate-induced respiration and N-mineralization in a temperate coniferous forest. Geoderma 93, 207-223.

Bruijnzeel, L.A., 2004. Hydrological functions of tropical forests: not seeing the soil for the trees? Agriculture, Ecosystems and Environment 104, 185-228.

Bureau of Meteorology, 2009. Climate information: climate averages and extremes. Accessed 19 November 2009.

Burslem, D.F.R.P., 1996. Differential responses to nutrients, shade and drought among tree seedlings of lowland tropical forest in Singapore. In: Swaine, M.D. (Ed.), The Ecology of Tropical Forest Tree Seedlings. UNESCO, Paris.

Buschbacher, R., 1986. Tropical deforestation and pasture development. BioScience 36, 23- 27.

Buschbacher, R., Uhl, C., Serrao, E.A.S., 1988. Abandoned pastures in eastern Amazonia. 2. Nutrient stocks in the soil and vegetation. Journal of Ecology 76, 682-699.

Cain, M.L., Subler, S., Evans, J.P., Fortin, M., 1999. Sampling spatial and temporal variation in soil nitrogen availability. Oecologia 118, 397-404.

Cairns, M.A., Brown, S., Helmer, E.H., Baumgardner, G.A., 1997. Root biomass allocation in the world's upland forests. Oecologia 111, 1-11.

Cambradella, C.A., Moormann, T.B., Novak, J.M., Parkin, T.B., Karlen, D.L., Turco, R.F., Konopka, A.E., 1994. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal 58, 1501-1511.

Campos, J.B., de Souza, M.C., 2003. Potencial for natural forest regeneration from seed bank in an upper Parana river floodplain, Brazil. Brazilian Archives of Biology and Technology 46, 625-639.

Carlyle, J.C., Malcolm, D.C., 1986. The use of ion exchange resin bags to assess N availability beneath pure spruce and larch + spruce stands growing on a deep peat soil. Plant and Soil 93, 123-127.

Carney, K.M., Matson, P.A., Bohannan, B.J.M., 2004. Diversity and composition of tropical soil nitrifiers across a plant diversity gradient and among land-use types. Ecology Letters 7, 684-694.

Catterall, C.P., Harrison, D.A., 2006. Rainforest Reforestation Activities in Australia's Tropics and Subtropics. Cooperative Research Centre for Tropical Rainforest Ecology and Management. Rainforest CRC, Cairns, Australia (94 pp).

Catterall, C.P., Kanowski, J., Wardell-Johnson, G., Proctor, H., Reis, T., Harrison, D., Tucker, N.I.J., 2004. Quantifying the biodiversity values in reforestation: perspectives, design issues and outcomes in Australian rainforest landscapes. In: Lunney, D. (Ed.), Conservation of Australia's Forest Fauna. Royal Zoological Society of New South Wales, Mosman, New South Wales, Australia, pp. 359-393.

Catterall, C.P., Kanowski, J., Wardell-Johnson, G.W., 2008. Biodiversity and new forests: Interacting processes, prospects and pitfalls of rainforest restoration. In: Stork, N.E., Turton, S.M. (Eds.), Living in a dynamic tropical forest landscape. Wiley-Blackwell, Oxford, pp. 510- 525.

Cavalier, J., Aide, T.M., Santos, C., Eusse, A.M., Dupuy, J.M., 1998. The savannization of moist forests in the Sierra Nevada de Santa Marta, Colombia. Journal of Biogeography 25, 901-912.

Cerri, C.E.P., Coleman, K., Jenkinson, D.S., Bernoux, M., Victoria, R., Cerri, C.C., 2003. Modelling soil carbon from forest and pasture ecosystems of Amazon, Brazil. Soil Science Society of America Journal 67, 1879-1887.

Chaer, G., Fernandes, M., Myrold, D., Bottomley, P., 2009. Comparative resistance and resilience of soil microbial communities and enzyme activities in adjacent native forest and agricultural soils. Microbial Ecology 58, 414-424.

Chapin, F.S., Vitousek, P.M., van Cleve, K., 1986. The nature of nutrient limitation in plant communities. American Naturalist 134, 88-119.

Chapman, C.A., Chapman, L.J., 1996. Exotic tree plantations and the regeneration of natural forest in Kibale National Park, Uganda. Biological Conservation 76, 253-257.

Charles-Dominique, P., 1986. Interactions between frugivorous vertebrates and pioneer plants: Ceropia, birds and bats in French Guyana. In: Estrada, A., Fleming, T.H. (Eds.), Frugivores and seed dispersal. Dr. W. Junk, The Hague, The Netherlands, pp. 119-135.

Chazdon, R.L., 2003. Tropical forest recovery: Legacies of human impact and natural disturbances. Perspectives in Plant Ecology, Evolution and Systematics 6, 51-71.

Chen, C.R., Chondron, L.M., Davis, M.R., Sherlock, R.R., 2000. Effects of afforestation on phosphorus dynamics and biological properties in a New Zealand grassland soil. Plant and Soil 220, 151-163.

Chondron, L.M., Davis, M.R., Newman, R.H., Cornforth, I.S., 1996. Influence of conifers on the forms of phosphorus in selected New Zealand grassland soils. Biology and Fertility of Soils 21, 37-42.

Chone, T., Andreux, F., Correa, J.C., Volkoff, B., Cerri, C.C., 1991. Changes in organic matter in an oxisol from the Central Amazonian forest during eight years of pasture, determined by 13C isotopic composition. In: Berthelin, J. (Ed.), Diversity of environmental biogeochemistry. Elsevier, Amsterdam, The Netherlands, pp. 307-417.

Chung, C.K., Chong, S.-K., Varsa, E.C., 1995. Sampling strategies for fertility on a stoy silt loam soil. Communication in Soil Science and Plant Analysis 26, 741-763.

Clark, C., 1987. Deforestation and floods. Environmental Conservation 14, 67-69.

Clarke, K.R., Ainsworth, M., 1993. A method of linking multivariate community structure to environmental variables. Marine Ecology Progress Series 92, 205-219.

Clarke, K.R., Gorley, R.N., 2006. PRIMER v6: User Manual/Tutorial, PRIMER-E, Plymouth.

Cole, L.J., Pollock, M.L., Robertson, D., Holland, J.P., McCracken, D.I., Harrison, W., 2009. The influence of fine-scale habitat heterogeneity on invertebrate assemblage structure in upland semi-natural grassland. Agriculture, Ecosystems & Environment 136, 69-80.

Commonwealth of Australia (Geoscience Australia), 2009.

Conant, R.T., Smith, G.R., Paustian, K., 2003. Spatial variability of soil carbon in forested and cultivated sites: implications for change detection. Environmental Quality 32, 278-286.

Connell, J.H., Slatyer, R.O., 1977. Mechanisms of succession in natural communities and their role in community stability and organisation. The American Naturalist 111, 1119-1144.

Constanza, R., Folke, C., 1996. The structure and function of ecological systems in relation to property-rights regimes. In: Hanna, S.S., Folke, C., Maeler, K.-G. (Eds.), Rights to Nature: Ecological, Economic, Cultural, and Political Principles of Institutions for the Environment. Island Press, Washington, DC.

Crk, T., Uriarte, M., Corsi, F., Flynn, D., 2009. Forest recovery in a tropical landscape: what is the relative importance of biophysical, socioeconomic, and landscape variables? Landscape Ecology 24, 629-642.

Crome, F., Issacs, J., Moore, L., 2004. The utility to birds and mammals of remnant riparian vegetation and associated windbreaks in the tropical Queensland uplands. Pacific Conservation Biology 1, 328-343.

Cubina, A., Aide, T.M., 2001. The effect of distance from forest edge on seed rain and soil seed bank in a tropical pasture. Biotropica 33, 260-267.

Cuevas, E., Medina, E., 1988. Nutrient dynamics within Amazonian forests. II. Fine root growth, nutrient availability and leaf litter decomposition. Oecologia 76, 222-235.

Cummings, J., Reid, N., 2008. Stand-level management of plantations to improve biodiversity values. Biodiversity and Conservation 17, 1187-1211.

D' Almeida, C., Voeroesmarty, C.J., Hurtt, G.C., Marengo, J.A., Dingman, S.L., Keim, B.D., 2007. The effects of deforestation on the hydrological cycle in Amazonia: a review on scale and resolution. International Journal of Climatology 27, 633-647.

D'Antonio, C., Meyerson, L.A., 2002. Exotic plant species as problems and solutions in ecological restoration: A synthesis. Restoration Ecology 10, 703-713.

Dalling, J.W., Hubbell, S.P., 2002. Seed size, growth rate and gap microsite conditions as determinants of recruitment success for pioneer species. Journal of Ecology 90, 557-568.

Dalling, J.W., Hubbell, S.P., Silvera, K., 1998. Seed dispersal, seedling establishment and gap partitioning among tropical pioneer trees. Journal of Ecology 86, 674-689.

Dalling, J.W., Swaine, M.D., Garwood, N.C., 1997. Soil seed bank community dynamics in seasonally moist lowland tropical forest, Panama. Journal of Tropical Ecology 13, 659-680.

Dalling, J.W., Tanner, E.V.J., 1995. An experimental study of regeneration on landslides in montane forest in Jamaica. Journal of Ecology 83, 55-64.

Davidson, E.A., Hart, S.C., Firestone, M.K., 1992. Internal cycling of nitrate in soils of a mature coniferous forest. Ecology 73, 1148-1156.

Davis, M.R., Lang, M.H., 1991. Increased nutrient availability in topsoils under conifers in the South Island high country. New Zealand Journal of Forestry Science 21, 165-179. de Koning, D.H.J., Veldkamp, E., Lopez-Ulloa, M., 2003. Quantification of carbon sequestration in soils following pasture to forest conversion in northwestern Ecuador. Global Biogeochemical Cycles 17, 1098.

Demel, T., 1997. The impact of clearing and conversion of dry Afromontane forests into arable land on the composition and density of soil seed banks. Acta Oecologica 18, 557-573.

Demel, T., Granstrom, A., 1995. Soil seed banks in dry Afromontane forests of Ethiopia. Journal of Vegetation Science 6, 777-786.

Dirzo, R., Raven, P.H., 2003. Global state of biodiversity and loss. Annual Review of Environment and Resources 28, 137-167.

Dixon, R.K., Brown, S., Houghton, R.A., Solomon, A.M., Trexler, M.C., Wisniewski, J., 1994. Carbon pools and flux of global forest ecosystems. Science 263, 185-190.

Dorren, L.K.A., Berger, F., Imeson, A.C., Maier, B., Rey, F., 2004. Integrity, stability and management of protection forests in the European Alps. Forest Ecology and Management 195, 165-176.

Dutilleul, P., 1993. Spatial heterogeneity and the design of ecological field experiments. Ecology 74, 1646-1658.

Duxbury, J.M., Smith, M.S., Doran, J.W., 1989. Soil organic matter as a source and sink of plant nutrients. In: Coleman, C.D., Oades, J.M., Uehara, G. (Eds.), Dynamics of Soil Organic Matter in Tropical Ecosystems. University of Hawaii Press, Honolulu, pp. 33-67.

Elmqvist, T., Folke, C., Nystrom, M., Peterson, G., Bengtsson, J., Walker, B., Norberg, J., 2003. Response diversity, ecosystem change, and resilience. Frontiers in Ecology and the Environment 1, 488-494.

Erickson, H., Keller, M., Davidson, E.A., 2001. Nitrogen oxide fluxes and nitrogen cycling during postagricultural succession and forest fertilization in the humid tropics. Ecosystems 4, 67-84.

Erskine, P., 2003. Diversity and Production in Tropical Reforestation. In: Erskine, P., Catterall, C.P. (Eds.), Production versus Rainforest Biodiversity: Trade-offs or Synergies in Farm Forestry Systems?

Erskine, P., Catterall, C.P., Lamb, D., Kanowski, J., 2007. Patterns and processes of old field reforestation in Australian rainforest landscapes. In: Cramer, V.A., Hobbs, R.J. (Eds.), Old Fields: Dynamics and Restoration of Abandoned Farmlands. Island Press, Washington D.C., pp. 119-144.

Ettema, C., Rathbun, S., Coleman, D., 2000. Spatiotemporal patchiness and the coexistence of five species of Chronogaster (Nematoda: Chronogasteridae) in a riparian wetland. Oecologia 125, 444-452.

Ettema, C.H., Wardle, D.A., 2002. Spatial Soil Ecology. Trends in Ecology and Evolution 17, 177-183.

Ewel, J.J., 1971. Biomass changes in early tropical succession. Turrialba 21, 10-12.

Ewel, J.J., 1986. Designing agricultural ecosystems for the humid tropics. Annual Review of Ecology and Systematics 17, 245-271.

Ewel, J.J., 1987. Restoration is the ultimate test of ecological theory. In: Jordan, W., Gilpin, M., Aber, J. (Eds.), Restoration Ecology: A Synthetic Approach to Ecological Research. Cambridge University Press, Cambridge, pp. 31-34.

Fahey, T.J., Woodybury, P.B., Battles, J.J., Goodale, C.L., Hamburg, S., Ollinger, S., Woodall, C.W., 2009. Forest carbon storage: ecology, management, and policy. Frontiers in Ecology and the Environment 7.

Farley, R.A., Fitter, A.H., 1999. Temporal and spatial variation in soil resources in a deciduous woodland. Journal of Ecology 87, 688-696.

Fearnside, P., Barbosa, R., 1998. Soil carbon changes from conversion of forest to pasture in Brazilian Amazonia. Forest Ecology and Management 108, 147-166.

Fearnside, P.M., Barbosa, R.I., 2004. Accelerating deforestation in Brazilian Amazonia: towards answering open questions. Environmental Conservation 31, 7-10.

Feldspauch, T.R., Rondon, M.A., Fernandes, E.C.M., Riha, S.J., Wandelli, E., 2004. Carbon and nutrient accumulation in secondary forests regenerating on pastures in Central Amazonia. Ecological Applications 14, 164-176.

Fenner, M., 1987. Seedlings. New Phytologist 106, 35-47.

Fine, P.V.A., 2002. The invasibility of tropical forests by exotic plants. Journal of Tropical Ecology 18, 687-705.

Floyd, A.G., 1990. Australian rainforests in New South Wales: Volume 1. Chipping Norton, N.S.W., Surrey Beatty and Sons Pty Ltd in assoc with National Parks and Wildlife Service of New South Wales.

Frankland, J.C., Ovington, J.D., Macrae, C., 1963. Spatial and seasonal variations in soil, litter and ground vegetation in some Lake District woodlands. Journal of Ecology 51, 97-112.

Franzluebbers, A.J., Stuedemann, J.A., Schomberg, H.H., Wilkinson, S.R., 2000. Soil organic C and N pools under long-term pasture management in the Southern Piemont USA. Soil Biology and Biochemistry 32, 469-478.

Frogbrook, Z.N., Bell, J., Bradley, R.I., Evans, C., Lark, R.M., Reynolds, B., Smith, P., Towers, W., 2009. Quantifying terrestrial carbon stocks: examining the spatial variation in two upland areas in the UK and a comparison to mapped estimates of soil carbon. Soil Use and Management 25, 320-332.

Gardner, T.A., Ribeiro-Junior, M.A., Barlow, J., Avila-Pires, T.C.S., Hoogmoid, M.S., Peres, C.A., 2007. The value of primary, secondary, and plantation forests for a neotropical herpetofauna. Conservation Biology 21, 775-787.

Garwood, N.C., 1983. Seed germination in a seasonal tropical forest in Panama: a community study. Ecological Monographs 53, 159-181.

Garwood, N.C., 1989. Tropical soil seed banks: a review. In: Leck, M.A. (Ed.), Ecology of soil seed banks. Academic Press, New York, USA, pp. 149-209.

Garwood, N.C., 1996. Functional morphology of tropical tree seedlings. In: Whitmore, T.C., Swaine, M.D. (Eds.), Ecology of Tropical Forest Tree Seedlings. UNESCO and Parthenon Publishing Paris, pp. 59-117.

Gehring, C., Denich, M., Kanashiro, M., Vlek, P.L.G., 1999. Response of secondary vegetation in eastern Amazonia to relaxed nutrient availability constraints. Biogeochemistry 45, 223-241.

Gibbs, H.K., Herold, M., 2007. Tropical deforestation and greenhouse gas emissions. Environmental Research Letters 2, 045021.

Giller, P.S., 1996. The diversity of soil communities, the "poor man's tropical rainforest". Biodiversity and Conservation 5, 135-168.

Gilmore, S., 1999. Fauna and rainforest fragmentation - developing improved conservation planning. In: Horton, S. (Ed.), Rainforest Remnants: a Decade of Growth. NSW National Parks and Wildlife Service, Sydney.

100

Gomez-Aparicio, L., 2009. The role of plant interactions in the restoration of degraded ecosystems: a meta-analysis across life-forms and ecosystems. Journal of Ecology 97, 1202-1214.

Gomez-Pompa, A., Vazquez-Yanes, C., 1981. Successional studies of a rain forest in Mexico. In: West, D.C., Schugart, H.H., Botkin, D.B. (Eds.), Forest Concepts and Application. Springer-Verlag, New York, pp. 247-266.

Gomez-Pompa, A., Whitmore, T.C., Hadley, M., 1991. Tropical Rain Forest: Regeneration and Management. Blackwell, New York.

Gonzalez-Rivas, B., Tigabu, M., Castro-Marin, G., Oden, P.C., 2009. Soil seed bank assembly following secondary succession on abandoned agricultural fields in Nicaragua. Journal of Forestry Research 20, 349-354.

Graham, A.W., Hopkins, M.S., 1990. Soil seed banks of adjacent unlogged rain-forest types in North-Queensland. Australian Journal of Botany 38, 261-268.

Grayston, S.J., Campbell, C.D., 1995. Functional biodiversity of microbial communities in the rhizospheres of hybrid larch and sitka spruce. Tree Physiology 16, 1031-1038.

Greenwood, K.L., McKenzie, B.M., 2001. Grazing effects on soil physical properties and the consequences for pastures: a review. Australian Journal of Experimental Agriculture 41, 1231-1250.

Grigal, D.F., McRoberts, R.E., Ohmann, L.F., 1991. Spatial variation in chemical properties of forest floor and surface mineral soil in the North Central United States. Soil Science 151, 282-290.

Grime, J.P., Crick, J.C., Rincon, J.A., 1986. The ecological significance of plasticity. In: Jennings, D.H., Trewavas, A.J. (Eds.), Plasticity in Plants. Cambridge University Press, Cambridge.

Gross, K.L., Pregitzer, K.S., Burton, A.J., 1995. Spatial variation in nitrogen availability in three successional plant communities. Journal of Ecology 83, 357-368.

Guariguata, M.R., Ostertag, R., 2001. Neotropical secondary forest succession: changes in structural and functional characteristics. Forest Ecology and Management 148, 185-206.

Guevara, S., Gomez-Pompa, A., 1972. Seeds from surface soils in a tropical region of Veracruz, Mexico. Journal of the Arnold Arboretum 53, 312-315.

Guevara, S., Purata, S.E., Maarel, E., 1986. The role of remnant forest trees in tropical secondary succession. Plant Ecology 66, 77-84.

Guggenberger, G., Zech, W., 1999. Soil organic matter composition under primary forest, pasture, and secondary forest succession, Region Huetar Norte, Costa Rica. Forest Ecology and Management 124, 93-104.

Guild, L.S., Kauffman, J.B., Ellingson, L.J., Cummings, D.L., de Castro, E.A., Babbitt, R.E., Ward, D.E., 1998. Dynamics associated with total aboveground biomass, C, nutrient pools and biomass burning of primary forest and pasture in Rondonia during SCAR-B. Journal of Geophysical Research 103, 32091-32100.

101

Gunatilleke, C.V.S., Perera, G.A.D., Ashton, P.M.S., Ashton, P.S., Gunatilleke, I.A.U.N., 1996. Seedling Growth of Shorea Section Doona (Dipterocarpaceae) in Soils from topographically different Sites of Sinharaja Rain Forest in Sri Lanka. In: Swaine, M.D. (Ed.), The Ecology of Tropical Forest Tree Seedlings (Man & the Biosphere Series; Vol. 18). United Nations Educational, Scientific and Cultural Organization (UNESCO), Paris, France.

Guo, L., Gifford, R., 2002. Soil carbon stocks and land use change: a meta analysis. Global Change Biology 8, 345-360.

Hall, J.B., Swaine, M.D., 1980. Seed stocks in Ghanaian forest soils. Biotropica 12, 256-263.

Hansen, M.C., DeFries, R.S., 2004. Detecting long-term global forest change using continuous fields of tree-cover maps from 8-km advanced very high resolution radiometer (AVHRR) data for the years 1982-99. Ecosystems 7, 695-716.

Harper, J.L., 1977. Population biology of plants. Academic Press, London.

Hassink, J., 1994. Effects of soil texture on the size of the microbial biomass and on the amount of C mineralized per unit of microbial biomass in Dutch grassland soil. Soil Biology and Biochemistry 26, 1573-1581.

He, F., Legendre, P., Bellehumeur, C., LaFrankie, J.V., 1994. Diversity pattern and spatial scale: a study of a tropical rain forest of Malaysia. Environmental and Ecological Statistics 1, 265-286.

Hedin, L.O., Von Fisher, J.C., Ostrom, N.E., Kennedy, B.P., Brown, M.G., Robertson, G.P., 1998. Thermodynamic constraints on nitrogen transformations and other biogeochemical processes at soil-stream interfaces. Ecology 79, 784-703.

Heilmann, B., Beese, F., 1992. Miniaturized method to measure carbon dioxide production and biomass of soil microorganisms. Soil Science Society of America Journal 56, 596-598.

Hobbie, S., 1996. Temperature and plant species control over litter decomposition in Alaskan tundra. Ecological Monographs 66, 503-522.

Holl, K.D., 1998. Do bird perching structures elevate seed rain and seedling establishment in abandoned tropical pasture? Restoration Ecology 6, 253-261.

Holl, K.D., 1999. Factors limiting tropical rain forest regeneration in abandoned pasture: seed rain, seed germination, microclimate, and soil. Biotropica 31, 229.

Holl, K.D., 2007. Old field vegetation succession in the neotropics. In: Cramer, V.A., Hobbs, R.J. (Eds.), Old Fields: Dynamics and Restoration of Abandoned Farmland. Island Press, Washington D.C., pp. 93-113.

Hook, P.B., Burke, I.C., Lauenroth, W.K., 1991. Heterogeneity of soil and plant N and C associated with individual plants and openings in North American shortgrass steppe. Plant and Soil 138, 246-257.

Hopkins, M.S., Graham, A.W., 1983. The species composition of soil seed banks beneath lowland tropical rainforests in North Queensland, Australia. Biotropica 15, 90-99.

Hopkins, M.S., Graham, A.W., 1984a. The role of soil seed banks in regeneration in canopy gaps in Australian tropical lowland rainforest - preliminary field experiments. Malaysian Forester 47, 146-158.

102

Hopkins, M.S., Graham, A.W., 1984b. Viable soil seed banks in disturbed lowland tropical rainforest sites. Australian Journal of Ecology 9, 71-79.

Hopkins, M.S., Tracey, J.G., Graham, A.W., 1990. The size and composition of soil seed- banks in remnant patches of three structural rainforest types in North Queensland. Australian Journal of Ecology 15, 43-50.

Houghton, R.A., 1994. The worldwide extent of land-use change. BioScience 44, 305-313.

Huante, P., Rincon, E., Chapin, F.S., 1995. Responses to phosphorus of contrasting successional tree-seedling species from the tropical deciduous forest of Mexico. Functional Ecology 9, 760-766.

Hughes, R.F., Kauffman, J.B., Jaramillo, V.J., 1999. Biomass, carbon and nutrient dynamics of secondary forests in a humid tropical region of Mexico. Ecology 80, 1892-1907.

Hunt, H.W., Ingham, E.R., Coleman, D.C., Elliot, E.T., Reid, C.P.P., 1988. Nitrogen limitation of production and decomposition in prairie, mountain meadow and pine forest. Ecology 69, 1009-1016.

Huntington, T.G., 1995. Carbon sequestration in an aggrading forest ecosystem in the southeastern USA. Soil Science Society of America Journal 59, 1459-1467.

IPCC, 2007. Mitigation of Climate Change. Cambridge University Press, Cambridge, UK, and New York.

ITTO, 2002. Guidelines for the Restoration, Management and Rehabilitation of Degraded and Secondary Tropical Forests. International Tropical Timber Organisation, Yokohama, Japan.

Jackson, R.B., Caldwell, M.M., 1989. The timing and degree of root proliferation in fertile-soil microsites for three cold-desert perennials. Oecologia 81, 149-153.

Jackson, R.B., Caldwell, M.M., 1993. The scale of nutrient heterogeneity around individual plants and its quantification with geostatistics. Ecology 74, 612-614.

Janssen, B.H., 1996. Nitrogen mineralization in relation to C-N ratio and decomposability or organic materials. Plant and Soil 181, 39-45.

Janzen, D.H., Vasquez-Yanes, C., 1991. Aspects of tropical seed ecology of relevance to management of tropical forested wildlands. In: Gomez-Pompa, A., Whitmore, T.C., Hadley, M. (Eds.), Rain forest regeneration and management. UNESCO and The Parthenon Publishing Group Inc., Paris, pp. 137-157.

Jenkins, J.C., Chojnacky, D.C., Heath, L.S., Birdsey, R.A., 2003. National-scale biomass estimators for United States tree species. Forest Science 49, 12-35.

Jenkinson, D.S., Parry, L.C., 1989. The nitrogen cycle in the broadbalk wheat experiment: A model for the turnover of nitrogen through the soil microbial biomass. Soil Biology and Biochemistry 21, 535-541.

Kanowski, J., Catterall, C.P., 2007. Converting stands of camphor laurel to rainforest: What are the costs and outcomes of different control methods? Griffith University, Nathan 4111 Australia (16 pp.).

103

Kanowski, J., Catterall, C.P., 2010. Carbon stocks in above-ground biomass of monoculture plantations, mixed species plantations and environmental restoration plantings in north-east Australia. Ecological Management and Restoration 11, 119-126.

Kanowski, J., Catterall, C.P., Freebody, K., Harrison, D.A., 2008a. Monitoring Revegetation Projects in Rainforest Landscapes. Toolkit Version 2. Report to the Marine and Tropical Sciences Research Facility. Reef and Rainforest Research Centre Limited, Cairns (76 pp.).

Kanowski, J., Catterall, C.P., Neilan, W., 2008b. Potential value of weedy regrowth for rainforest restoration. Ecological Management and Restoration 9, 88-99.

Kanowski, J., Catterall, C.P., Wardell-Johnson, G., Proctor, H., Reis, T., 2003. Development of forest structure on cleared rainforest land in eastern Australia under different styles of reforestation. Forest Ecology and Management 183, 265-280.

Kanowski, J., Kooyman, R.M., Catterall, C.P., 2008c. Dynamics and restoration of Australian tropical and subtropical rainforests. In: Hobbs, R.J., Suding, K. (Eds.), New Models for Ecosystem Dynamics and Restoration. Island Press, Washington DC, pp. 206-220.

Kauffman, J.B., Hughes, R.F., Heider, C., 2009. Carbon pool and biomass dynamics associated with deforestation, land use, and agricultural abandonment in the neotropics. Ecological Applications 19, 1211-1222.

Kauffman, J.B., Steele, M., Cummings, D.L., Jaramillo, V.J., 2003. Biomass dynamics associated with deforestation, fire, and conversion to cattle pasture in a Mexican tropical dry forest. Forest Ecology and Management 176, 1-12.

Keeton, W.S., Kraft, C.E., Warren, D.R., 2007. Mature and old-growth riparian forests: structure, dynamics and effects on Adirondack stream habitats. Ecological Applications 17, 852-868.

Keller, M., Veldkamp, E., Weitz, A.M., Reiners, W.A., 1993. Pasture age effects on soil- atmosphere trace gas exchange in a deforested area of Costa Rica. Nature 365, 244-246.

Kitching, R.L., Orr, A.G., Thalib, L., Mitchell, L., Hopkins, M.S., Graham, A.W., 2000. Moth assemblages as indicators of environmental quality in remnants of upland Australian rain forest. Journal of Applied Ecology 37, 284-297.

Kloeppel, B.D., Harmon, M.E., Fahey, T.J., 2007. Estimating above-ground net primary productivity in forest-dominated ecosystems. In: Fahey, T.J., Knapp, A.K. (Eds.), Principles and Standards for Measuring Primary Production. Oxford University Press, New York.

Knops, J.M.H., Nash, T.H., 1996. The influence of epiphytic lichens on the nutrient cycling of an oak woodland. Ecological Monographs 66, 159-179.

Koegel-Knabner, I., 1993. Biodegradation and humification processes in forest soils. In: Bollag, J.M., Stotzky, G. (Eds.), Soil Biochemistry, pp. 101-135.

Kooyman, R.M., 1996. Growing Rainforest. Rainforest Restoration and Regeneration – Recommendations for the Humid Subtropical Region of Northern NSW and South-east Qld. Greening Australia, Brisbane.

Kounda-Kiki, C., Celini, L., Ponge, J.F., Mora, P., Sarthou, C., 2009. Nested variation of soil arthropod communities in isolated patches of vegetation on a rocky outcrop. Soil Biology and Biochemistry 41, 323-329.

104

Kunstler, G., Coomes, D.A., Canham, C.D., 2009. Size-dependence of growth and mortality influence the shade tolerance of trees in a lowland temperate rain forest. Journal of Ecology 97, 685-695.

Kupfer, J.A., Franklin, S.B., 2009. Linking spatial pattern and ecological responses in human-modified landscapes: the effects of deforestation and forest fragmentation on biodiversity. Geography Compass 3, 1331-1355.

Lamb, D., 1980. Soil nitrogen mineralisation in a secondary rainforest succession. Oecologia 47, 257-263.

Lamb, D., Erskine, P., Parrotta, J.A., 2005. Restoration of degraded tropical forest landscapes. Science 310, 1628-1632.

Lambin, E.F., Geist, H.J., Lepers, E., 2003. Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources 28, 205-241.

Landi, L., Valori, F., 2005. Root exudate effects on the bacterial communities, CO2 evolution, nitrogen transformations and ATP content of rhizosphere and bulk soils. Soil Biology and Biochemistry 38, 509-516.

Laurance, W.F., 1999. Reflections on the tropical deforestation crisis. Biological Conservation 91, 109-117.

Laurance, W.F., 2008. Can carbon trading save vanishing forests? BioScience 58, 286-287.

Laurance, W.F., Nascimento, H.E.M., Laurance, S.G., Andrade, A.C., Fearnside, P.M., Ribeiro, J.E.L., Capretz, R.L., 2006. Rain forest fragmentation and the proliferation of successional trees. Ecology 87, 469-482.

Lavelle, P., Lattaud, C., Trigo, D., Barois, I., 1995. Mutualism and biodiversity in soils. Plant and Soil 170, 23-33.

Lawrence, D., 2003. The response of tropical tree seedlings to nutrient supply: meta- analysis for understanding a changing tropical landscape. Journal of Tropical Ecology 19, 239-250.

Lawton, R.O., Putz, F.E., 1988. Natural disturbance and gap-phase regeneration in a wind- exposed tropical cloud forest. Ecology 69, 764-777.

Lechowicz, M.J., Bell, G., 1991. The ecology and genetics of fitness in forest plants. II. Microspatial heterogeneity of the endaphic environment. Journal of Ecology 79, 687-696.

Lee, D., Oberbauer, S.F., Krishnapilay, B., Mansor, M., Mohamad, H., Yap, S.K., 1997. Effects of irradiance and spectral quality on seedling development of two Southeast Asian Hopea species. Oecologia 110, 1-9.

Lemenih, M., Karltun, E., Olsson, M., 2005. Soil organic matter dynamics after deforestation along a farmfield chronosequence in southern highlands of Ethiopia. Agriculture, Ecosystems and Environment 109, 9-19.

Lemenih, M., Teketay, D., 2006. Changes in soil seed bank composition and density following deforestation and subsequent cultivation of a tropical dry Afromontane forest in Ethiopia. Tropical Ecology 47, 1-12.

105

Lemma, B., Kleja, D.B., Nilsson, I., Olsson, M., 2006. Soil carbon sequestration under different exotic tree species in the south-western highland of Ethiopia. Geoderma 136, 886- 898.

Levin, S.A., 1992. The problem of pattern and scales in ecology. Ecology 73, 1943-1967.

Lopez-Ulloa, M., Veldkamp, E., de Koning, H.G.J., 2005. Soil carbon stabilization in converted tropical pastures and forests depends on soil type. Soil Science Society of America Journal 69, 1110-1117.

Lott, R.H., Duggin, J.A., 1993. Conservation significance and long term viability of subtropical rainforest remnants of the Big Scrub, north-eastern New South Wales. Australian Heritage Commission and NWS Department of Planning.

Ludley, K.E., Robinson, C.H., Jickells, S., Chamberlain, P.M., Whitaker, J., 2008. Differential response of ectomycorrhizal and saprotrophic fungal mycelium from coniferous forest soils to selected monoterpenes. Soil Biology and Biochemistry 40, 669-678.

Ludley, K.E., Robinson, C.H., Jickells, S., Chamberlain, P.M., Whitaker, J., 2009. Potential for monoterpenes to affect ectomycorrhizal and saprotrophic fungal activity in coniferous forests is revealed by novel experimental system. Soil Biology and Biochemistry 41, 117- 124.

Lugo, A., 1992. Comparison of tropical tree plantations with secondary forests of similar age. Ecological Monographs 62, 2-41.

Lugo, A., Brown, S., 1993. Management of tropical soils as sinks or sources of atmoshperic carbon. Plant and Soil 149, 27-41.

Lugo, A.E., 2002. Can we manage tropical landscapes? - an answer from the Carribean. Journal for Landscape Ecology 17, 601-615.

Lugo, A.E., Helmer, E., 2004. Emerging forests on abandoned land: Puerto Rico‟s new forests. Forest Ecology and Management 190, 145-161.

Lymburner, S., Handley, C., Handley, N., 2006. Rainforest rehabilitation on a productive Macadamia property: The Brockley story. Ecological Management and Restoration 7, 184- 196.

Macedo, M.O., Resende, A.S., Garcia, P.C., Boddey, R.M., Jantalia, C.P., Urquiaga, S., Campello, E.F.C., Franco, A.A., 2008. Changes in soil C and N stocks and nutrient dynamics 13 years after recovery of degraded land using leguminous nitrogen-fixing trees. Forest Ecology and Management 255, 1516-1524.

Maloney, K.O., Garten, C.T., Ashwood, T.L., 2008. Changes in soil properties following 55 years of secondary forest succession at Fort Benning, Georgia, USA. Restoration Ecology 16, 503-510.

Mann, L., 1986. Changes in soil carbon storage after cultivation. Soil Science 142, 279-288.

Marin-Spiotta, E., Cusack, D.F., Ostertag, R., Silver, W.L., 2008. Trends in above and belowground carbon with forest regrowth after agricultural abandonment in the neotropics. In: Myster, R.W. (Ed.), Post-Agricultural Succession in the Neotropics. Springer-Verlag, New York, USA, pp. 22-72.

106

Marin-Spiotta, E., Silver, W.L., Swanston, C.W., Ostertag, R., 2009. Soil organic matter dynamics during 80 years of reforestation of tropical pastures. Global Change Biology 15, 1584-1597.

Martin, J.G., Bolstad, P.V., 2009. Variation of soil respiration at three spatial scales: Components within measurements, intra-site variation and patterns on the landscape. Soil Biology and Biochemistry 41, 530-543.

Matson, P.A., Vitousek, P.M., Ewel, J.J., Mazzarino, M.J., Robertson, G.P., 1987. Nitrogen transformation following tropical forest felling and burning on a volcanic soil. Ecology 68, 491-502.

McBratney, A.B., Webster, R., 1983. How many estimations are needed for regional estimation of soil properties? Soil Science 135, 177-183.

McColl, J.G., Gressel, N., 1995. Forest soil organic matter: characterization and modern methods of analysis. In: McFee, W.W., Kelly, J.M. (Eds.), Carbon Forms and Functions in Forest Soils. Soil Science Society of America, Madison, WI, pp. 13-32.

McCoy, E.D., Mushinsky, H.R., 2002. Measuring the success of wildlife community restoration. Ecological Applications 12, 1861-1871.

McNeely, J.A., Miller, K.R., Reid, W.V., Mittemeier, R.A., Werner, T.B., 1990. Conserving the world's biological diversity, Washington, DC.

Melin, E., Krupa, S., 1971. Studies on Ectomycorrhizae of Pine. 2. Growth inhibition of mycorrhizal fungi by volatile organic constituents of Pinus silvestris (Scots Pine) roots. Physiologia Plantarum 25, 337-340.

Miyawaki, A., Abe, S., 2004. Public awareness generation for the reforestation in Amazon tropical lowland region. Tropical Ecology 45.

Mo, J., Brown, S., Peng, S., Kong, G., 2002. Nitrogen availability in disturbed, rehabilitated and mature forests of tropical China. Forest Ecology and Management 175, 573-583.

Mollitor, A.V., Leaf, A.L., Morris, L.A., 1980. Forest soil variability on Northeastern flood plains. Soil Science Society of America Journal 44, 617-620.

Montagnini, F., Buschbacher, R., 1989. Nitrification rates in two undisturbed tropical rain forests and three slash-and burn sites of the Venezuelan Amazon. Biotropica 21, 9-14.

Montagnini, F., Porras, C., 1998. Evaluating the role of plantations as carbon sinks: An example of an integrative approach from the humid tropics. Environmental Management 22, 459-470.

Moran, C., Catterall, C.P., Green, R.J., Olsen, M., 2004a. Fates of feathered fruit eaters in fragmented forests. In: Lunney, D. (Ed.), Conservation of Australia's Forest Fauna. Royal Zoological Society of New South Wales, Sydney, pp. 699-712.

Moran, C., Catterall, C.P., Green, R.J., Olsen, M.F., 2004b. Functional variation among frugivorous birds: implications for rainforest seed dispersal in a fragmented subtropical landscape. Oecologica 141, 584-595.

107

Morris, L.A., Moss, S.A., Garbett, W.S., 1993. Competitive interference between selected herbaceous and woody plants and Pinus taeda L. during two growing seasons following planting. Forest Science 39, 166-187.

Mottonen, M., Jarvinen, E., Hokkanen, T.J., Kuuluvaninen, T., Ohtonen, R., 1999. Spatial distribution of soil erosterol in the organic layer of a mature Scots Pine (Pinus sylvestris L.) forest. Soil Biology and Biochemistry 31, 503-516.

Mueller, W.G., 1998. Collecting Spatial Data - Optimum Design of Experiments for Random Fields. Physical-Verlag, Heidelberg.

Murty, D., Kirschbaum, M., Mcmurtrie, R., Mcgilvray, H., 2002. Does conversion of forest to agricultural land change soil carbon and nitrogen? a review of the literature. Global Change Biology 8, 105-123.

Myers, N., 2002. Biodiversity and biodepletion: The need for a paradigm shift. In: O'Riordan, T., Stoll-Kleeman, S. (Eds.), Biodiversity, sustainability and human communities. Cambridge University Press, Cambridge, England, pp. 46-60.

Naughton-Treves, L., Chapman, C.A., 2002. Fuelwood resources and forest regeneration on fallow land in Uganda. Journal of Sustainable Forestry 14, 19-32.

Neilan, W., Catterall, C.P., Kanowski, J., 2005. A new role for weeds in rainforest restoration? Rainforest CRC Issues in Tropical Forest Landscapes Series No.4.

Neilan, W., Catterall, C.P., Kanowski, J., McKenna, S., 2006. Do frugivorous birds assist rainforest succession in weed dominated oldfield regrowth of subtropical Australia? Biological Conservation 129, 393-407.

Neill, C., Davidson, E.A., 2000. Soil carbon accumulation or loss following deforestation for pasture in the Brazilian Amazon. In: Lal, R., Kimble, J.M., Steward, B.A. (Eds.), Global Climate Change and Tropical Ecosystems. CRC Press, Boca Raton, Florida.

Neill, C., Melillo, J.M., Steudler, P.A., Cerri, C.C., Moraes, J.F.L., Piccolo, M.C., Brito, M., 1997a. Soil carbon and nitrogen stocks following forest clearing for pasture in the southwestern Brazilian Amazon. Ecological Applications 7, 1216-1225.

Neill, C., Piccolo, M.C., Cerri, C.C., Steudler, P.A., Melillo, J.M., Brito, M., 1997b. Net nitrogen mineralization and net nitrification rates in soils following deforestation for pasture across the southwestern Brazilian Amazon Basin landscape. Oecologia 110, 243-252.

Neill, C., Steudler, P.A., Garcia-Montiel, D.C., Melillo, J.M., Feigl, B.J., Piccolo, M.C., Cerri, C.C., 2005. Rates and controls of nitrous oxide and nitric oxide emissions following conversion of forest to pasture in Rondonia. Nutrient Cycling in Agroecosystems 71, 1-15.

Nepstad, D.C., Uhl, C., Pereira, C.A., da Silva, J.M.C., 1996. A comparative study of tree establishment in abandoned pasture and mature forest in eastern Amazonia. Oikos 76, 25- 39.

Nepstad, D.C., Uhl, C., Serrao, E.A.S., 1991. Recuperation of degraded Amazonian landscape: forest recovery and agricultural restoration. Ambio 20, 248-255.

Nuesslein, K., Tiedje, J.M., 1999. Soil bacterial community shift correlated with change from forest to pasture vegetation in a tropical soil. Applied and Environmental Microbiology 65, 3622-3626.

108

Numata, I., Chadwick, O.A., Roberts, D.A., Schimel, J.P., Sampaio, F.F., Leonidas, F.C., Soares, J.V., 2007. Temporal nutrient variation in soil and vegetation of post-forest pastures as a function of soil order, pasture age, and management, Rondônia, Brazil. Agriculture, Ecosystems & Environment 118, 159-172.

Nussbaum, R., Anderson, J., Spencer, T., 1995. Factors limiting the growth of indigenous tree seedlings planted on degraded rainforest soils in Sabah, Malaysia. Forest Ecology and Management 74, 149-159.

Olsen, M., Lamb, D., 1988. Recovery of subtropical rainforest following storm damage. Proceedings of the Ecological Society of Australia 15, 297-301.

Paavolainen, L., Kitunen, V., Smolander, A., 1998. Inhibition of nitrification in forest soil by monoterpenes. Plant and Soil 2, 147-154.

Palmer, M.W., 1990. Spatial scale and patterns of species-environment relationships in hardwood forests of the North Carolina Piedmont. Coenoses 5, 79-87.

Passell, H.D., 2000. Recovery of bird species in minimally restored Indonesian tin strip mines. Restoration Ecology 8, 112-118.

Paul, E.A., Clark, F.E., 1997. Soil Microbiology and Biochemistry, third edition. Academic Press, San Diego, USA.

Paul, K.I., Polglase, P.J., Richards, G.P., 2002. Predicted change in soil carbon following afforestation or reforestation, and analysis of controlling factors by linking a C accounting model (CAMFor) to models of forest growth (3PG), litter decomposition (GENDEC) and soil C turnover (RothC). Forest Ecology and Management 177, 485-501.

Paul, M., Catterall, C.P., Kanowski, J., Pollard, P.C., 2010. Recovery of soil properties and functions in different rainforest restoration pathways. Forest Ecology and Management 259, 2083-2092.

Paul, S., Veldkamp, E., Flessa, H., 2008. Soil organic carbon in density fractions of tropical soils under forest - pasture - secondary forest land use changes. European Journal of Soil Science 59, 359-371.

Peigné, J., Vian, J.-F., Cannavacciuolo, M., Bottollier, B., Chaussod, R., 2009. Soil sampling based on field spatial variability of soil microbial indicators. European Journal of Soil Biology 45, 488-495.

Pennock, D.J., van Kessel, C., Farell, R.E., Sutherland, R.A., 1992. Landscape-scale variations in denitrification. Soil Science Society of America Journal 56, 770-776.

Piccolo, M.C., Neill, C., Cerri, C.C., 1994. Net nitrogen mineralization and net nitrification along a tropical forest-to-pasture chronosequence. Plant and Soil 162, 61-70.

Pollard, P.C., 1997. Bacterial growth dynamics in activated sludge batch assays. Water Research 32, 587-596.

Portsmuth, A., Niinements, U., 2006. Interacting controls by light availability and nutrient supply on biomass allocation and growth of Betula pendula and B. pubescens seedlings. Forest Ecology and Management 227, 122-134.

109

Post, W.M., Emanuel, W.R., Zinke, P.J., Stangenberger, A.G., 1982. Soil carbon pools and world life zones. Nature 298, 156-159.

Post, W.M., Izaurralde, R.C., Mann, L.K., Bliss, N., 2001. Monitoring and verifying changes of organic carbon in soils. Climatic Change 51, 73-99.

Post, W.M., Kwon, K.C., 2000. Soil carbon sequestration and land-use change: processes and potential. Global Change Biology 6, 317-327.

Powers, J.S., 2004. Changes in soil carbon and nitrogen after contrasting land-use transitions in Northeastern Costa Rica. Ecosystems 7, 134-146.

Putz, F.E., 1983. Treefall pits and mounds, buried seeds, and the importance of soil disturbance to pioneer trees on Barro Colorado Island, Panama. Ecology 64, 1069-1074.

Putz, F.E., Appanah, B., 1987. Buried seeds, newly dispersed seeds, and the dynamics of a lowland forest in Malaysia. Biotropica 19, 326-339.

Quinn, G.P., Keough, M.J., 2002. Experimental Design and Data Analysis for Biologists. In. Blackwell Synergy.

Quintana-Ascencio, P.F., Gonzalez-Espinosa, N., Ramirez-Marcial, G., Dominguez- Vasquez, Martinez-Ico, M., 1996. Soil seed banks and regeneration of tropical rain forest from Milpa fields at the Selva Lacandona, Chiapas, Mexico. Biotropica 28, 192-209.

Ramankutty, N., Gibbs, H.D., Achard, F., 2007. Challenges to estimating carbon emissions from tropical deforestation. Global Change Biology 13, 51-66.

Rasiah, V., Florentine, S.K., Williams, B.L., Westbrooke, M.E., 2004. The impact of deforestation and pasture abandonment on soil properties in the wet tropics of Australia. Geoderma 120, 35-45.

Reich, P.B., Hobbie, S.E., Lee, T., Ellsworth, D.S., West, J.B., Tilman, D., Knops, J.M.H., Naeem, S., Trost, J., 2006. Nitrogen limitation constrains sustainability of ecosystem response to CO2. Nature 440, 922-925.

Reiners, W.A., Bouwman, A.F., Parsons, W.F.J., Keller, M., 1994. Tropical rain forest conversion to pasture: changes in vegetation and soil properties. Ecological Applications 4, 363-377.

Reynolds, H.L., Hungate, B.A., Chapin III, F.S., D'Antonio, C.M., 1997. Soil heterogeneity and plant competition in an annual grassland. Ecology 78, 2076.

Rhoades, C., Eckert, G., Coleman, D., 2000. Soil carbon differences among forest, agriculture, and secondary vegetation in lower montane Ecuador. Ecological Applications 10, 497-505.

Rhoades, C.C., Eckert, G.E., Coleman, D.C., 1998. Effect of pasture trees on soil nitrogen and organic matter: implications for tropical montane forest restoration. Restoration Ecology 6, 262-270.

Rice, E., Pancholy, S., 1972. Inhibition of nitrification by climax ecosystems. American Journal of Botany 59, 1033-1040.

110

Richards, P.W., 1996. The Tropical Rainforest: an Ecological Study. Cambridge University Press, Cambridge.

Robertson, G., Vitousek, P.M., 1981. Nitrification potentials in primary and secondary succession. Ecology 62, 376-386.

Robertson, G.P., 1984. Nitrification and nitrogen mineralization in a lowland rainforest succession in Costa Rica, Central America. Oecologia 61, 99-104.

Robertson, G.P., Huston, M.A., Evans, F.C., Tiedje, J.M., 1988. Spatial variability in a successional plant community: patterns of nitrogen availability. Ecology 69, 1517-1524.

Robertson, G.P., Vitousek, P.M., Matson, P.A., Tiedje, J.M., 1987. Denitrification in a clearcut Loblolly pine (Pinus taeda L.) plantation in the southeastern U.S. Plant and Soil 97, 119-129.

Rochette, P., Desjardins, R.L., Pattey, E., 1991. Spatial and temporal variability in agricultural fields. Canadian Journal of Soil Science 71, 189-196.

Rodrigues, P.R., Martins, S.V., de Barros, L.C., 2004. Tropical rain forest regeneration in an area degraded by mining in Mato Grosso State, Brazil. Forest Ecology and Management 190, 323-333.

Roever, M., Kaiser, E.-A., 1998. Spatial heterogeneity within the plough layer: low and moderate variability of soil properties. Soil Biology and Biochemistry 31, 175-187.

Ross, D.J., Tate, K.R., 1993. Microbial C and N, and respiratory activity, in litter and soil of a Southern beech (Nothofagus) forest: Distribution and properties. Soil Biology and Biochemistry 25, 477-483.

Ruiz-Jaen, M.C., Mitchell Aide, T., 2005. Restoration Success: How Is It Being Measured? Restoration Ecology 13, 569-577.

Saetre, P., 1998. Decomposition, microbial community structure and earthworm effects along a birch-spruce soil gradient. Ecology 79, 834-846.

Saetre, P., 1999. Spatial patterns of ground vegetation, soil microbial biomass and activity in a mixed spruce-birch stand. Ecography 22, 183-192.

Saetre, P., Baath, E., 2000. Spatial variation and patterns of soil microbial community structure in a mixed spruce-birch stand. Soil Biology and Biochemistry 32, 909-917.

Sawyer, D., 1990. The future of deforestation in Amazonia: a socioeconomic and political analysis. In: Anderson, A.B. (Ed.), Alternatives to Deforestation: Steps Toward Sustainable Use of the Amazon Rain Forest. Columbia University Press, New York.

Scanlon, T., 2000. Camphor Laurel Kit: Everything You Need to Know about Camphor and its Control. North Coast Weed Advisory Committee. NSW North Coast Camphor Laurel Taskforce.

Scheu, S., Parkinson, D., 1995. Successional changes in microbial biomass, respiration and nutrient status during litter decomposition in an aspen and pine forest. Biology and Fertility of Soils 19, 327-332.

111

Schimel, J.P., Bennett, J., 2004. Nitrogen mineralization: challenges of a changing paradigm. Ecology 85, 501-602.

Schimel, J.P., Jackson, L.E., Firestone, M.K., 1989. Spatial and temporal effects on plant- microbial competition for inorganic nitrogen in a California annual grassland. Soil Biology and Biochemistry 21, 1056-1066.

Schlesinger, W.H., 1984. Soil organic matter: a source of atmospheric CO2. In: Woodwell, G.M. (Ed.), The Role of Terrestrial Vegetation in the Global Carbon Cycle: Measurement by Remote Sensing. John Wiley and Sons, Chichester, England.

Schnuerer, J., Rosswall, T., 1982. Fluorescein diacetate hydrolysis as a measure of total microbial activity in soil and litter. Applied Environmental Microbiology 43, 1256-1261.

Schoening, I., Totsche, K.U., Koegel-Knabner, I., 2006. Small scale spatial variability of organic carbon stocks in litter and solum of a forested Luvisol. Geoderma 136, 631-642.

Schulp, C.J.E., Verburg, P.H., 2009. Effect of land use history and site factors on spatial variation of soil organic carbon across a physiographic region. Agriculture, Ecosystems and Environment 133, 86-97.

Schume, G., Jost, K., Katzensteiner, K., 2003. Spatio-temporal analysis of the soil water content in a mixed Norway Spruce (Picea albies (L ) Karst.) - European beech (Fagus sylvatica L.) stand. Geoderma 12, 273-287.

Scowcroft, P.B., Haraguchi, J.E., Hue, N.V., 2004. Reforestation and topography affect montane soil properties, nitrogen pools, and nitrogen transformations in Hawaii. Soil Science Society of America Journal 68, 959-968.

SER, 2004. International Science & Policy Working Group 2004. The SER International Primer of Ecological Restoration. Society for Ecological Restoration International, Tuscan, Arizona.

Sharrow, S.H., 2007. Soil compaction by grazing livestock in silvopastures as evidenced by changes in soil physical properties. Agroforestry Systems 71, 215-223.

Shrestha, R.K., Ladha, A.E., Gami, J.K., 2006. Total and organic soil carbon in cropping systems of Nepal. Nutrient Cycling in Agroecosystems 75, 257-269.

Silver, W., Ostertag, R., Lugo, A., 2000. The potential for carbon sequestration through reforestation of abandoned tropical agricultural and pasture lands. Restoration Ecology 8, 394-407.

Silver, W.L., Brown, S., Lugo, A.E., 1996. Biodiversity and Biogeochemical Cycles. In: Orians, G.H., Dirzo, R., Cushmann, J.H. (Eds.), Biodiversity and Ecosystem Processes in Tropical Forests. Springer-Verlag Berlin Heidelberg, Heidelberg.

Silver, W.L., Kueppers, L.M., Lugo, A.E., Ostertag, R., Matzek, V., 2004. Carbon sequestration and plant community dynamics following reforestatian of tropical pasture. Ecological Applications 14, 1115-1127.

Silver, W.L., Thompson, A.W., Reich, A., Ewel, J.J., Firestone, M.K., 2005. Nitrogen cycling in tropical plantation forests: potential controls on nitrogen retention. Ecological Applications 15, 1604-1614.

112

Smalla, K., van Elsas, J.D., 2010. The soil environment. In: Liu, W.-T., Jansson, J.K. (Eds.), Environmental Molecular Biology. Caister Academic Press, Norfolk, UK.

SPSS, 2005. SPSS for Windows Release 14.0, Chicago, IL, USA.

Stein, A., Ettema, C., 2003. An overview of spatial sampling procedures and experimental design of spatial studies for ecosystem comparisons. Agriculture, Ecosystems and Environment 94, 31-47.

Steudler, P.A., Melillo, J.M., Bowden, R., Castro, M., 1991. The effects of natural and human disturbances on soil nitrogen dynamics and trace gas fluxes in a Puerto Rican wet forest. Biotropica 23, 356-363.

Stewart, B.C., 1995. Seed and Seedling Ecology in a Secondary Subtropical Rainforest. PhD thesis, Southern Cross University, Lismore.

Stewart, G.R., Hegarty, E.E., Specht, R.L., 1988. Inorganic nitrogen assimilation in plants of Australian rainforest communities. Physiologia Plantarum 15145/88, 1-8.

Stoyan, H., De-Polli, H., Boehm, S., Robertson, G.P., Paul, E.A., 2000. Spatial heterogeneity of soil respiration and related properties at the plant scale. Plant and Soil 222, 203-214.

Streck, C., O'Sullivan, R., Janson-Smith, T., Tarasofski, G., 2008. Climate Change and Forests: Emerging Policy and Market Opportunities. Brookings Institution Press, London.

Stubbs, B.J., Cameron, D.M., O'Neill, M., O'Neill, R.D., 1999. From pest to profit: prospects for the commercial utilisation of camphor laurel (Cinnamomum camphora) in the Northern Rivers Region of NSW. Centre for Coastal Marine Management, Southern Cross University, Lismore.

Sulei, S.M., Swaine, M.D., 1988. Rain forest seed dynamics during succession at Gogol, Papua New Guinea. Journal of Ecology 76, 1133-1152.

Swaine, M.D., 1996. The ecology of tropical forest seedlings. The Parthenon Publishing Group, Carnforth.

Swaine, M.D., Hall, J.B., 1983. Early succession on cleared forest land in Ghana. Journal of Ecology 71, 601-627.

Symington, C.F., 1933. The study of secondary growth on rain forest sites in Malaysia. Malaysian Forestry 2, 107-117.

Thomlinson, J.R., Serrano, M.I., Lopez, T.M., Aide, T.M., Zimmermann, J.K., 1996. Land-use dynamics in a post-agricultural Puerto Rican landscape (1936-1988). Biotropica 28, 525-536.

Thornton, P.E., Lamerque, J.F., Rosenbloom, N.A., Mahowald, N.M., 2007. Influence of carbon-nitrogen coupling on land model response to CO2 fertilization and climate variability. Global Biogeochemical Cycles 21.

Tilman, D., 1986. Nitrogen-limited growth in plants from different successional stages. Ecology 67, 1360-1367.

Tilman, D., Kareiva, P., 1997. Spatial Ecology: The Role of Space in Population Dynamics and Interspecific Interactions. Princeton University Press, New Jersey.

113

Toh, I., Gillespie, M., Lamb, D., 1999. The role of isolated trees in facilitating tree seedling recruitment at a degraded sub-tropical rainforest site. Restoration Ecology 7, 288-297.

Townsend, A.R., Asner, G.P., Cleveland, C.C., Lefer, M.E., Bustamante, M.C., 2002. Unexpected changes in soil phosphorus dynamics following tropical deforestation to cattle pasture. Journal of Geophysical Research 107, 8067.

Turner, I.M., 1990. The seedling survivorship and growth of three Shorea species in a Malaysian tropical rain-forest. Journal of Tropical Ecology 6, 469-478.

Turner, L., Corlett, R., 1996. The conservation value of small, isolated fragments of lowland tropical rain forest. Trends in Ecology and Evolution 11, 330-333.

Uhl, C., 1982. Recovery following disturbances of different intensities in the Amazon rain forest of Venezuela. Interciencia 7, 19-24.

Uhl, C., Buschbacher, R., Serrao, E.A.S., 1988. Abandoned pastures in eastern Amazonia. I. Patterns of plant succession. Journal of Ecology 76, 663-681.

Uhl, C., Clark, K., Clark, H., Murphy, P., 1981. Early plant succession after cutting and burning in the upper Rio Negro region of the Amazon basin. Journal of Ecology 68, 631-649. van der Werf, G.R., Morton, D.C., DeFries, R.S., Olivier, J.G.J., Kasibhatla, P.S., Jackson, R.B., Collatz, G.J., Randerson, J.T., 2009. CO2 emissions from forest loss. Nature Geoscience 2, 737-738. van Nordwijk, M., Cerri, C.C., Woomer, P.L., Kusumo, N., Bernoux, M., 1997. Soil carbon dynamics in the humid tropical forest zone. Geoderma 79, 187-225.

Vance, E.D., Brookes, P.C., Jenkinson, D.S., 1987. An extraction method for measuring soil microbial biomass C. Soil Biology and Biochemistry 19, 703-707.

Vazquez-Yanes, C., Orozco-Segovia, A., 1993. Patterns of seed longevity and germination in the tropical rain forest. Annual Review of Ecology and Systematics 24, 69-87.

Vieira, D.L.M., Holl, K.D., Peneireiro, F.M., 2009. Agro-successional restoration as a strategy to facilitate tropical forest recovery. Restoration Ecology 17, 451-459.

Vieira, I.C.G., Uhl, C., Nepstad, D.C., 1994. The role of the shrub Cordia multispicata Cham. as a succession facilitator in an abandoned pasture, Patagonia, Amazonia. Vegetation 115, 91-99.

Vitousek, P.M., Antonio, C.M.D., Loope, L.L., Rejmanek, M., 1997. Introduced species: A significant component of human caused global change. New Zealand Journal of Ecology 21, 1-16.

Vitousek, P.M., Matson, P.A., 1984. Mechanisms of nitrogen retention in forest ecosystems: a field experiment. Science 225, 51-52.

Vitousek, P.M., Matson, P.A., Cleve, K., 1989. Nitrogen availability and nitrification during succession: Primary, secondary, and old-field seres. Plant and Soil 115, 229-239.

Vitousek, P.M., Walker, L.R., Whitaker, L.D., Matson, P.A., 1993. Nutrient limitations to plant growth during primary succession in Hawaii Volcanoes National Park. Biochemistry 23, 197- 215.

114

Waldrop, M.P., Basler, T.C., Firestone, M.K., 2000. Linking microbial community composition to function in a tropical soil. Soil Biology and Biochemistry 32, 1837-1846.

Walters, B.B., 2000. Local mangrove planting in the Philippines: are fisherfolk and fishpond owners effective restorationists? Restoration Ecology 8, 237-246.

Wang, J., Ren, H., Yang, L., Li, D., Guo, Q., 2009. Soil seed banks in four 22-year-old plantations in South China: Implications for restoration Forest Ecology and Management 258, 2000-2006.

Wardle, D.A., 1992. A comparative assessment of factors which influence microbial biomass carbon and nitrogen levels in soil. Biological Reviews 67, 321-356.

Wardle, D.A., 2002. Communities and Ecosystems: Linking the Aboveground and Belowground Components. Princeton University Press.

Weaver, R.W., Angle, S., Bottomley, P., Bezdicek, D., Smith, S., Tabatabai, A., Wollum, A., 1994. Methods of Soil Analysis Part 2: Microbiological and Biochemical Properties. Soil Science Society of America, Wisconsin USA.

Webb, L.J., 1972. Regeneration and pattern in the subtropical rainforest. Journal of Ecology 60, 675-695.

Webster, G., Embley, T., Prosser, J.I., 2002. Grassland management regimes reduce small- scale heterogeneity and species diversity of beta-proteobacterial ammonia oxidizer populations. Applied Environmental Microbiology 68, 20-30.

White, E., Tucker, N., Meyers, N., Wilson, J., 2004. Seed dispersal to revegetated isolated rainforest patches in North Queensland. Forest Ecology and Management 192, 409-426.

Whitmore, T.C., 1989. Canopy gaps and the two major groups of forest trees. Ecology 70, 536-538.

Whitmore, T.C., 1996. A review of some aspects of tropical rain forest seedling ecology with suggestions for further enquiry. UNESCO and Parthenon Publishing Group, Paris.

Whitmore, T.C., 1997. Tropical forest disturbance, disappearance, and species loss. In: Laurance, W.F., Bierregaard, R.O. (Eds.), Tropical Forest Remnants: Ecology, Conservation and Management of Fragmented Communities. University of Chicago Press, Chicago, USA.

Wijdeven, S.M.J., Kuzee, M.E., 2000. Seed availability as a limiting factor in forest recovery processes in Costa Rica. Society for Ecological Restoration 8, 414-424.

Williams, M., 2003. Deforesting the earth: from prehistory to global crisis. The University of Chicago Press, Chicago.

Willson, M., Crome, F., 1989. Patterns of seed rain at the edge of a tropical Queensland rain forest. Journal of Tropical Ecology 5, 301-308.

Woodall, C.W., Heath, L.S., Smith, J.E., 2008. National inventories of death and downed forest carbon stocks in the United States: opportunities and challenges. Forest Ecology and Management 256, 221-228.

Woodward, C.L., 1996. Soil compaction and topsoil removal effects on soil properties and seedling growth in Amazonian Ecuador. Forest Ecology and Management 82, 197-209.

115

Woolf, H.B., 1977. Webster's New Collegiate Dictionary (7th edition), G. & C. Merrian Co, Springfield, MA.

Wright, J.S., Muller-Landau, H.C., 2006. The future of tropical forest species. Biotropica 38, 287-301.

Wright, S.J., 2005. Tropical forests in a changing environment. Trends in Ecology and Evolution 20, 553-560.

Wu, J., Joergensen, R.G., Pommerening, B., Chaussod, R., Brookes, P.C., 1990. Measurement of soil microbial biomass by fumigation-extraction - an automated procedure. Soil Biology and Biochemistry 20, 1167-1169.

Wunderle, J.M., 1997. The role of animal seed dispersal in accelerating native forest regeneration on degraded tropical lands. Forest Ecology and Management 99, 223-235.

Yakimenko, E.Y., 1998. Soil comparative evolution under grasslands and woodlands in the forest zone of Russia. In: Lal, R., Kimble, J.M., Follett, R.F., Steward, B.A. (Eds.), Management of Carbon Sequestration in Soil. CRC Press, Boca Raton, pp. 391-404.

Yamashita, N., Ohta, S., Sase, H., Luangjame, J., Visaratana, T., Kievuttinon, B., Garivait, H., Kanzaki, M., 2010. Seasonal and spatial variation of nitrogen dynamics in the litter and surface soil layers on a tropical dry evergreen forest slope. Forest Ecology and Management 259, 1502-1512.

Yavitt, J.B., Wright, S.J., 2008. Seedling growth responses to water and nutrient augmentation in the understorey of a lowland moist forest, Panama. Journal of Tropical Ecology 24, 19-26.

Young, K.R., Ewel, J.J., Brown, B.J., 1987. Seed dynamics during forest succession in Costa Rica. Plant Ecology 71, 157-173.

Zak, D.R., Groffman, P.M., Pregitzer, K.S., Christiansen, S., Tiedje, J.M., 1990. The Vernal Dam: plant-microbe competition for nitrogen in northern hardwood forests. Ecology 71, 651- 656.

Zech, W., Drechsel, P., 1992. Multiple mineral deficiencies in forest plantations of Liberia. Forest Ecology and Management 48, 121-143.

Zheng, H., Ouyang, Z.Y., Wang, X.K., Miao, H., Zhao, T.Q., Peng, T.B., 2005. How different reforestation approaches affect red soil properties in southern China. Land Degradation and Development 16, 387-396.

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Appendix 1

Characteristics of the study sites

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Appendix 1 Characteristics of the study sites

Sampling time for

Seed bank Site Site- experiment Soil Floristic Location Age (Years)b Structure and general description name typea and seedling properties data growth 27 November 28 March 56 537753 E BR P PA N/A N/A Surrounded by mature camphor laurel trees, one small camphor tree on the site. 2007 2007 68 12001 N 27 November 28 March 56 548469 E JE P PA N/A N/A Actively grazed pasture (cattle), no trees. 2007 2007 68 19438 N 15 December 29 March 56 548065 E NA P PA N/A N/A Actively grazed pasture (horses), no trees. 2007 2007 68 16076 N 27 November 29 March 56 548858 E TA P PA N/A N/A Actively grazed pasture (cattle). 2007 2007 68 22381 N 25 November 18 March 56 533009 E Actively grazed pasture (cattle), some ferns, two trees (Flindersia australis, Alphitonia petrei), RCD P PA N/A N/A 2007 2007 68 31857 N situated approximately 50 m from a rainforest remnant boundary. Canopy height 12-15 m, canopy closure 80-85%. No palm trees, vines and epiphytes. Dense 27 November 28 March 56 537511 E BR CD CD 2007 30-40 understorey lianas, native rainforest species and some ferns. Forest floor covered by a dense 2007 2007 68 12008 N leaf litter layer. 15 December 30 March 56 547981 E Canopy height 15-20 m, canopy closure 80-90%. Canopy consists to 90% of camphor. Dense WA CD CD 2004 35-40 2007 2007 68 15399 N understorey dominated by camphor seedlings and native rainforest species. Canopy height 15–20 m, canopy closure 80-90%. Canopy consists to 90 % of camphor (large, old trees). Mature phase species and palm trees, epiphytes, hemi epiphytes, and vines in 25 November 18 March 56 533972 E RCD CD CD 2004 45 under- and midstorey. Open understorey with numerous camphor seedlings, soil with a strong 2007 2007 68 31286 N camphor odour. Forest floor covered by a dense leaf litter layer. Situated on an East, North- East facing slope (10-20°). Canopy height 10-12m, canopy closure 70-80%. Canopy consists to 90 % of camphor laurel 16 December 29 March 56 551686 E FE CD CD 2004 40-50 trees, some Guioa spec. and Pittosporum spec. Understorey dominated by Ligustrum sinense 2007 2007 68 28230 N and some ferns. Forest floor covered by a dense leaf litter layer, some rocky patches. Canopy height 12-15 m, canopy closure 80-90%. Canopy consists to over 90% of camphor 16 December 30 March 56 537494 E laurel trees, understorey dominated by Ligustrum sinense and Cordyline spec.; occasional DE CD CD 2004 30-40 2007 2007 68 04296 N ferns. No palm trees and epiphytes. Ground very rocky and covered by a dense leaf litter layer. Site is situated on a steep slope towards a creek. Canopy height 10-12 m, canopy closure 70-90%. Dense understorey of vines, epiphytes (mainly on dead camphor), occasional palm trees, rainforest pioneer species and mature RCD1 26 November 18 March 56 533889 E TC 2004 38/8c phase species. Dead decomposing camphor trees on the site. Forest floor covered by a dense TC 2007 2007 68 32130 N leaf litter layer and decaying wood. Edges treated with herbicides. Common native species: Cinnamomum oliveri, Polyscias elegans and Diploglottis australis.

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Sampling time for

Seed bank Site Site- experiment Soil Floristic Location Age (Years)b Structure and general description name typea and seedling properties data growth Canopy height 12-15 m, canopy closure 70-85 %. Stand is dominated by decaying camphor RCD2 15 December 29 March 56 533389 E TC 2004 33/12c laurel trees; some vines and epiphytes, occasional palm trees. Forest floor covered by a dense TC 2007 2007 68 32889 N leaf litter layer. Canopy height 10-15 m, canopy closure 40-60%. Camphor trees treated 6 years ago, understorey treated 8 years ago. Old dead camphor trees, rainforest pioneer species, 15 December 29 March 56 547939 E NA TC TC 2004 30-35/4c occasional vines, no epiphytes. Common species: Castanospermum australe, Pittosporum 2007 2007 68 16044 N undulatum, Mallotus philippensis, and Mallotus discolor. Forest floor covered by a dense leaf litter layer, rocky underground. Canopy height 10-12 m, canopy closure 90%. Old dead camphor trees, rainforest pioneer 27 November 28 March 56 537756 E BR TC TC 2004 30-35/6c species, occasional vines, no palm trees and epiphytes. Dense understorey of lianas and 2007 2007 68 12000 N native rainforest saplings. Forest floor covered by a dense leaf litter layer. Canopy height 10-12 m, canopy closure 60-80%. Old dead camphor trees, mid- and 16 Dezember 19 March 56 547254E understorey dominated by early successional rainforest species, some ginger and ferns, vines. TO TC TC 2004 30-40/5c 2007 2007 68 18446 N Forest floor covered by a dense leaf litter layer and very rocky. Site is situated on a steep slope towards a creek. Canopy height 10-12 m, canopy closure 80-90%. Mixed-species planting, no palm trees and 27 November 28 March 56 548468 E JE ER ER N/A 15d epiphytes. Understorey of native rainforest saplings, Forest floor covered with a dense leaf 2007 2007 68 19433 N litter layer. Stand is situated on a slope facing North-East. Canopy height 10-12 m, canopy closure varies 70-85%. Occasional gaps in the canopy. Palm trees, diverse range of rainforest pioneer species, some ferns, occasional vines, no epiphytes. 27 November 29 March 56 549006 E TA ER ER N/A 10d Common species: Grevillea robusta, Commersonia bartramia, Accacia spec., Araucaria 2007 2007 68 22816 N cunninghamii. Forest floor covered by a dense leaf litter layer. Site is situated on a South- facing slope (5-10°). Canopy height 12-15 m, canopy closure varies 60-80%. Mixed-species planting with cabinet timber species, planted in rows with a tree spacing of 3x4 m. Occasional palm trees and vines, 26 November 19 March 56 533960 E RCD ER ER N/A 12d no epiphytes, no strangler figs. Understorey: dominated by grasses and ferns, patches of leaf 2007 2007 68 32013 N litter on the forest floor, native plant and camphor tree seedlings. Site is situated on the top of a hill, facing East, 10° slope. Canopy height 12-15 m, canopy closure 70-90%. Stand was not planted in rows; dense 28 November 29 March 56 541273 E JV ER ER N/A 12d midstorey of Ligustrum sinense and wattle trees, dense understorey with patches of Ageratina 2007 2007 68 28540 N riparia Forest floor covered with a dense leaf litter layer. Canopy height 10-15 m, canopy closure 60-80%. Mainly cabinet timber species planted in 25 November 19 March 56 538274 E rows, no vines and epiphytes. Mid-and understory dominated by camphor laurel seedlings, RB ER ER N/A 14d 2007 2007 68 28279 N Elaeocarpus grandis, Passiflora suberosa, Flindersia brayleyana. Forest floor covered with a dense leaf litter layer, some grassy patches.

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Sampling time for

Seed bank Site Site- experiment Soil Floristic Location Age (years)b Structure and general description name typea and seedling properties data growth Canopy height 20-30 m, canopy closure 90-95%. Mostly mature phase species and some 27 November 28 March 56 537757 E BR RF RF 2004 N/A rainforest pioneers. Dense understorey of epiphytes, vines, Linospadix monostachya, and 2007 2007 68 12002 N rainforest saplings, some ferns. Forest floor covered by a dense leaf litter layer. Canopy height 25-30 m with some higher fig trees, canopy closure 90-95%. Mostly mature 28 November 30 March 56 541466 E phase species like Castanospermum austral and fig trees. Mid-and understorey dominated by AJ RF RF 2004 N/A 2007 2007 68 25355 N palm trees, Linospadix monostachya, vines, and native tree saplings. Forest floor covered by a dense leaf litter layer, some rocky patches Canopy height 20-35 m, canopy closure 95%. Common canopy species: Ficus spp. and 28 November 30 March 56 539584 E DS RF RF 2004 N/A Castanospermum australe. Understorey plants: Linospadix monostachya, palm trees, and 2007 2007 68 06479 N vines. Forest floor covered by a dense leaf litter layer. Canopy height 20-30 m, canopy closure 80-95%. Mostly mature phase species 56 532809E RCD1 26 November 19 March (Castanospermum australe) and some rainforest pioneers. Vines, epiphytes, strangler figs, RF 2004 68 31708N N/A RF 2007 2007 hemi epiphytes, dense understorey with mainly ferns and rainforest species seedlings. Forest

floor covered with a dense leaf litter layer, some rocky patches. Canopy height 25-35 m, canopy closure 90-95%. Mostly mature phase species RCD2 26 November 19 March 56 532797 E (Castanospermum australe) and some rainforest pioneers. Vines, epiphytes, strangler figs, RF 2004 N/A RF 2007 2007 68 33264 N hemi epiphytes, dense understorey with mainly ferns and rainforest species seedlings. Forest floor with a dense leaf litter layer. a Site-type: PA, pasture; CD, camphor dominated; TC, treated camphor; ER, ecological restoration planting; RF, rainforest b Site age was estimated on discussions with landholders and resstoration practitioners c Age of regrowth before camphor treatment / years since camphor treatment d Years since planting has been undertaken

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Appendix 2

List of non-grass species present in the soil seed bank, as recruits or as mature trees of pasture, rainforest and three types of reforestation

121

Appendix 2 Non-grass species present in the soil seed bank, as recruits, or as mature trees of pasture, rainforest and three types of reforestation.

PA CD TC ER RF Life Succ. c Species Family Life stage forma stageb S R M S R M S R M S R M S R M Acacia melanoxylon R.Br. Mimosaceae MT P  u u  - -     u u  - - Acmena hemilampra subsp. Hemilampra Myrtaceae MT LS - u u ------u u -   Acmena ingens (F.Muell. ex C.Moore) Guymer & B.Hyland Myrtaceae LT LS - u u ------u u -   Acmena smithii (Poir.) Merr. & L.M.Perry Myrtaceae ST LS - u u -   -   - u u - -  Acronychia baeuerlenii Rutaceae ST LS - u u ------u u -  - Acronychia oblongifolia (A.Cunn. ex Hook.) Endl. ex Heynh. Rutaceae MT LS - u u -  - -  - - u u - - - Acronychia pubescens (F.M.Bailey) C.T.White Rutaceae ST LS - u u  ------u u   - Actephila lindleyi (Steud.) Airy Shaw Euphorbiaceae ST LS - u u - - - -  - - u u -   *Ageratina adenophora Asteraceae H N/A - u u  - -  - -  u u - - - *Ageratina riparia Asteraceae H N/A  u u  - -  - -  u u - - - *Ageratum houstonianum Asteraceae H N/A  u u  - -  - -  u u - - - Akania bidwillii (Hogg) Mabb. Akaniaceae ST u - u u ------u u -   Alangium villosum subsp. Polyosmoides Alangiaceae ST LS - u u  - -  - -  u u   - Alectryon subcinereus (A.Gray) Radlk. Sapindaceae ST LS - u u ------u u -  - Alphitonia excelsa (A.Cunn. ex Fenzl) Reissek ex Benth. Rhamnaceae ST P - u u        u u  -  Alphitonia petriei C.T.White & Braid Rhamnaceae MT ES - u u - - - -   - u u - - - Alpinia caerulea Zingiberaceae H N/A - u u  - -  - -  u u  - - *Amaranthus viridis Amaranthaceae H N/A  u u  ------u u - - - Anthocarapa nitidula (Benth.) T.D.Penn. ex Mabb. Meliaceae MT LS - u u - - - -  - - u u -   Aphananthe philippinensis Planch. Ulmaceae MT ES - u u - - - -  - - u u  - - Apium prostratum Apiaceae H N/A  u u  - - - - -  u u  - - Araucaria cunninghamii Aiton ex D.Don Araucariaceae LT LS - u u -   - - - - u u - - - Archidendron muellerianum (Maiden & R.T.Baker) Mimosaceae ST LS - u u -  -  - - - u u    Archontophoenix cunninghamiana (H.Wendl.) Arecaceae LT LS - u u -   -   - u u -  - Argyrodendron trifoliolatum Sterculiaceae MT LS - u u ------u u -   Arytera distylis Sapindaceae SH LS - u u -  - -  - - u u -   Arytera divaricata F.Muell. Sapindaceae MT LS - u u ------u u -  - Atractocarpus chartaceus Rubiaceae SH LS - u u ------u u -   Austromyrtus acmenoides (F.Muell.) Burret Myrtaceae ST LS - u u ------u u -  - Baloghia inophylla (G.Forst.) P.S.Green Euphorbiaceae ST LS - u u ------u u -   Beilschmiedia elliptica Lauraceae MT LS - u u ------u u -   Beilschmiedia obtusifolia (F.Muell. ex Meisn.) F.Muell. Lauraceae MT LS - u u ------u u -   *Bidens bipinnata Asteraceae H N/A - u u  - -  - - - u u - - - *Bidens pilosa Asteraceae H N/A - u u  - -  - - - u u  - - *Brachychiton acerifolius (A.Cunn. ex G.Don) Macarthur Sterculiaceae MT LS - u u -  - - -  - u u -  - Breynia oblongifolia (Muell.Arg.) Muell.Arg. Euphorbiaceae SH P - u u - -  - -  - u u - -  Briedelia exaltata F.Muell. Phyllanthaceae MT LS - u u - - - -  - - u u - - - Canarium australasicum (F.M.Bailey) Leenh. Burseraceae MT LS - u u - - - -  - - u u -  - Canthium coprosmoides F.Muell. Rubiaceae ST U - u u ------u u -  - Capparis arborea (F.Muell.) Maiden Capparaceae ST LS - u u - -  - - - - u u -   Carissa ovata R.Br. Apocynaceae ST LS - u u - - - -  - - u u -  - Castanospermum australe A.Cunn. & Fraser ex Hook. Fabaceae MT LS - u u -  - - - - - u u -  

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PA CD TC ER RF Life Succ. c Species Family a b Life stage form stage S R M S R M S R M S R M S R M Castanospora alphandii (F.Muell.) Sapindaceae MT LS - u u -  - - - - - u u - - - Celtis paniculata Ulmaceae MT LS - u u ------u u -  - *Celtis sinensis Pers. Ulmaceae ST N/A - u u -   - - - - u u - - - Centella asiatica Mackinlayaceae H N/A  u u ------ u u - - - Cephalaralia cephalobotrys Araliaceae V N/A - u u  - -  - -  u u  - - *Cerastium glomeratum Caryophyllaceae H N/A  u u ------u u - - - *Cinnamomum camphora (L.) Nees & Eberm. Lauraceae MT N/A  u u        u u  - - Cinnamomum oliveri Lauraceae MT LS - u u - - -   - - u u -   Cinnamomum virens Lauraceae MT LS - u u  - -  - -  u u   - *Cirsium vulgare Asteraceae H N/A  u u  - - - - -  u u - - - Cissus antarctica Vitaceae V N/A - u u - - -  - -  u u  - - Cissus hypoglauca Vitaceae V N/A - u u ------ u u  - - Citrus australasica Rutaceae SH LS - u u ------u u -   *Citrus limon (L.) Burm.f. Rutaceae ST N/A - u u -  - - - - - u u - - - Claoxylon australe Baill. ex Muell.Arg. Euphorbiaceae ST LS - u u ------u u -   Cleistanthus cunninghamii Euphorbiaceae ST LS - u u ------u u -   Clematis glycinoides Ranunculaceae V N/A - u u  - -  - -  u u - - - Clerodendrum floribundum R.Br. Lamiaceae SH P - u u -  - - - - - u u -   Commelina cyanea Commelinaceae H N/A - u u - - -  - -  u u - - - Commersonia bartramia Sterculiaceae ST P - u u ------ u u  - - Commersonia fraseri Sterculiaceae ST P - u u - - -  - -  u u - - - *Conyza albida Asteraceae H N/A  u u - - -  - -  u u  - - *Conyza canadensis Asteraceae H N/A  u u  - -  - -  u u - - - Cordyline petiolaris (Domin) Pedley Agavaceae ST LS - u u - -  - -  - u u - - - Cordyline rubra Otto & A.Dietr. Agavaceae ST LS - u u -   - - - u u - -  *Crassocephalum crepidioides Asteraceae H N/A  u u  - -  - -  u u  - - Croton verreauxii Baill. Euphorbiaceae ST ES - u u - - - - -  - u u - - - Cryptocarya glaucescens R.Br. Lauraceae MT LS - u u - - - -   - u u - - - Cryptocarya laevigata Blume Lauraceae ST LS - u u - - - -  - - u u - - - Cryptocarya microneura Meisn. Lauraceae MT LS - u u -  - -  - - u u - - - Cryptocarya obovata Lauraceae MT LS - u u -  - -  - - u u -  - Cupaniopsis flagelliformis (F.M.Bailey) Radlk. Sapindaceae ST LS - u u ------u u -   Daphnandra sp. Monimiaceae MT LS - u u - -  - - - - u u - - - *Daucus glochidiatus Apiaceae H N/A  u u ------u u - - - Dendrocnide excelsa (Wedd.) Chew Urticaceae MT ES - u u - - -   - - u u -  - Dendrocnide photinophylla (Kunth) Chew Urticaceae ST ES - u u ------u u - -  *Denhamia celastroides (F.Muell.) Jessup Celastraceae ST ES - u u - - - -   - u u -   *Desmodium unicinatum Fabaceae V N/A  u u ------u u - - - Dichondra repens Convolvulaceae H N/A - u u ------ u u - - - Dioscorea transversa Dioscoraceae V N/A - u u  - -  - - - u u  - - Diospyros fasciculosa (F.Muell.) F.Muell. Ebenaceae ST LS - u u - - - -  - - u u - - - Diospyros pentamera Ebenaceae MT LS - u u - - - -  - - u u -  - Diploglottis australis (G.Don) Radlk. Sapindaceae MT ES - u u - - - -  - - u u -   Doryphora sassafras Endl. Monimiaceae MT LS - u u - - - - -  - u u - -  *Drymaria cordata Caryophyllaceae H N/A  u u  ------u u - - -

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PA CD TC ER RF Life Succ. c Species Family a b Life stage form stage S R M S R M S R M S R M S R M Duboisia myoporoides R.Br. Solanaceae ST P - u u - - - - -  - u u - - - Dysoxylum fraserianum (A.Juss.) Benth. Meliaceae MT LS - u u - - - -   - u u -  - Dysoxylum mollissimum subsp. molle (Miq.) Mabb. Meliaceae MT LS - u u -  - -  - - u u -  - Dysoxylum rufum Meliaceae ST LS - u u -  - - - - - u u -  - Elaeaocarpus obovatus Elaeocarpaceae MT LS - u u  - -   -  u u  - - Elaeocarpus eumundi F.M.Bailey Elaeocarpaceae MT LS - u u - - - -   - u u - - - Elaeocarpus kirtonii F.Muell. ex F.M.Bailey Elaeocarpaceae MT LS - u u - - - -  - - u u - - - Elattostachys nervosa Sapindaceae ST LS - u u -  - - - - - u u -   *Emilia sonchifolia Asteraceae H N/A  u u ------u u - - - Endiandra discolor Benth. Lauraceae MT LS - u u - - - -  - - u u - - - Endiandra muelleri subsp. Muelleri Lauraceae MT LS - u u ------u u -   Endiandra pubens Meisn. Lauraceae ST LS - u u -  - - - - - u u -   *Euphorbia heterophylla Euphorbiaceae H N/A  u u ------ u u - - - Eupomatia laurina R.Br. Eupomatiaceae SH LS - u u - - - -  - - u u - - - Ficus coronata Spin Moraceae ST P - u u   -   -  u u  - - Ficus fraseri Miq. Moraceae MT LS - u u -   -   - u u - -  Ficus obliqua Moraceae LT LS - u u - - -  - - - u u - - - Ficus virens Moraceae LT LS - u u ------ u u - - - Ficus watkinsiana F.M.Bailey Moraceae LT LS - u u - - -  -  - u u  -  Flindersia australis R.Br. Rutaceae MT LS - u u - - - - -  - u u - - - Flindersia bennettiana F.Muell. ex Benth. Rutaceae MT LS - u u - - - -   - u u - -  Flindersia schottiana F.Muell. Rutaceae LT ES - u u -   -   - u u -   Galium migrans Rubiaceae H N/A  u u ------u u - - - Geissois benthamii Cunoniaceae MT LS - u u ------u u - -  Geranium neglectum Geraniaceae H N/A - u u - - -  - - - u u - - - Geranium solanderi Geraniaceae H N/A  u u  - - - - -  u u - - - Glochidion ferdinandi (Muell.Arg.) F.M.Bailey Euphorbiaceae MT P - u u - - - -   - u u - - - Gmelina leichhardtii Lamiaceae MT LS - u u ------u u -  - Gnaphalium spec. Asteraceae H N/A  u u  - -  - - - u u - - - Grevillea robusta Proteaceae LT LS - u u ------ u u - - - Guilfoylia monostylis Surianaceae ST LS - u u ------u u -  - Guioa lasioneura Radlk. Sapindaceae ST ES - u u - - - -   - u u - - - Guioa semiglauca (F.Muell.) Radlk. Sapindaceae ST ES - u u -   -   - u u -  - Harpullia alata F.Muell. Sapindaceae ST LS - u u ------u u -  - Helicia glabriflora F.Muell. Proteaceae ST LS - u u - - - -   - u u -  - Hicksbeachia pinnatifolia F.Muell. Proteaceae ST LS - u u ------u u - -  Hodgkinsonia ovatiflora F.Muell. Rubiaceae SH LS - u u -  - - - - - u u - - - *Hydrocotyle bonariensis Apiaceae H N/A  u u ------u u - - - Hydrocotyle peduncularis Apiaceae H N/A  u u - - -  - -  u u - - - Hymenosporum flavum (Hook.) F.Muell. Pittosporaceae ST ES - u u - - - -   - u u - - - *Impatiens walleriana Balsamiaceae H N/A  u u  ------u u - - - *Ipomoea cairica Convolvulaceae H N/A  u u ------u u - - - Jagera pseudorhus (A.Rich.) Radlk. Sapindaceae ST ES - u u -   -   - u u -  - *Lantana camara Verbenaceae V N/A - u u  - -  - -  u u - - - Legnephora moorei Menispermaceae V N/A - u u  ------u u  - -

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PA CD TC ER RF Life Succ. c Species Family a b Life stage form stage S R M S R M S R M S R M S R M Lenwebbia prominens Myrtaceae ST u  u u  - -  - -  u u  - - *Lepidium virginicum Brassicaceae H N/A  u u ------u u - - - *Ligustrum lucidum Aiton Oleaceae ST N/A - u u -   -  - - u u -  - *Ligustrum sinense Lour. Oleaceae SH N/A - u u     - -  u u - - - Linospadix monostachya Arecaceae SH LS - u u - - - -  - - u u -  - Litsea australis B.Hyland Lauraceae MT LS - u u - - - -  - - u u - - - Litsea reticulata (Meisn.) F.Muell. Lauraceae MT LS - u u ------u u - -  Lophostemon confertus (R.Br.) Myrtaceae LT P - u u - -  - - - - u u  - - Macaranga tanarius (L.) Muell.Arg. Euphorbiaceae ST P - u u  - -     u u  - - Mallotus discolor F.Muell. ex Benth. Euphorbiaceae MT ES - u u  - -     u u  - - Mallotus philippensis Euphorbiaceae ST ES - u u -   -   - u u    Melia azedarach L. Meliaceae LT ES - u u - -  - - - - u u - - - Melicope elleryana (F.Muell.) T.G.Hartley Rutaceae MT ES - u u - - - -   - u u - - - Melicope micrococca Rutaceae LT ES - u u  - - -    u u  -  Melodinus australis Apocynaceae V N/A - u u  - -  - -  u u  - - Mischocarpus pyriformis (F.Muell.) Radlk. subsp. pyriformis Sapindaceae ST LS - u u - - - - -  - u u -   Myrsine subsessilis (F.Muell.) Mez Myrsinaceae SH u - u u ------u u -  - Neolitsea australiensis Kosterm. Lauraceae ST LS - u u -  - - -  - u u -   Neolitsea dealbata (R.Br.) Merr. Lauraceae ST LS - u u -   -   - u u -  - Notelaea johnsonii Oleaceae ST ES - u u ------u u -  - Notelaea longifolia Vent. Oleaceae ST LS - u u -  - -  - - u u - - - Nyssanthes diffusa Amaranthaceae H N/A  u u ------u u - - - Omalanthus nutans (G.Forst.) Guill. Euphorbiaceae ST P - u u  - -   -  u u  - - Oxalis corniculata Oxalidaceae H N/A  u u  - -  - -  u u - - - Pararchidendron pruinosum (Benth.) I.C.Nielsen Mimosaceae ST LS - u u -   - - - - u u -  - Passiflora herbertiana Passifloraceae V N/A - u u - - -  - - - u u  - - Pentaceras australe (F.Muell.) Benth. Rutaceae MT ES - u u -   -   - u u -  - *Phyllanthus multiflorus Euphorbiaceae SH N/A  u u - - -  - -  u u  - - *Phytolacca octandra Phytolaccaceae H N/A  u u  - -  - -  u u  - - Pilidiostigma glabrum Myrtaceae ST LS - u u - - - -   - u u -   Pipturus argenteus Urticaceae SH u - u u - - -  - - - u u - - - Pittosporum multiflorum Pittosporaceae SH u - u u -  - - - - - u u -   Pittosporum revolutum Pittosporaceae SH LS - u u ------u u -  - Pittosporum undulatum Vent. Pittosporaceae ST ES - u u    -   - u u - - - Polyosma cunninghamii Grossulariaceae ST LS - u u ------u u -   Polyscias elegans (C.Moore & F.Muell.) Harms Araliaceae MT P  u u  -      u u    Polyscias murrayi (F.Muell.) Harms Araliaceae LT P - u u -  - -    u u  - - *Potentilla indica Rosaceae H N/A - u u  - -  - - - u u - - - Pouteria australis (R.Br.) Baehni Sapotaceae LT LS - u u - - - - -  - u u -   Pseuderanthemum variabile Acanthaceae H N/A  u u ------u u - - - Pseudoweinmania lachnocarpa Cunoniaceae LT LS - u u - - - - -  u u  - - Quassia sp. (Mt Nardi B.L.Walker AQ330746) Simaroubaceae ST LS - u u - - - -  - - u u -  - Quintinia verdonii Grossulariaceae MT u - u u ------ u u  - - Rhodamnia rubescens (Benth.) Miq. Myrtaceae MT ES - u u ------u u - -  Rhodomyrtus psidioides (G.Don) Benth. Myrtaceae ST ES - u u - - - -   - u u - - -

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PA CD TC ER RF Life Succ. c Species Family Life stage forma stageb S R M S R M S R M S R M S R M Rhysotoechia bifoliolata ssp. bifoliolata Sapindaceae ST LS - u u ------u u -  - Rubus rosifolius Rosaceae V N/A  u u  - -  - -  u u  - - Sarcomelicope simplicifolia (Endl.) T.G.Hartley Rutaceae ST LS - u u - - -  - - u u - -  Sarcopteryx stipata (F.Muell.) Radlk. Sapindaceae MT LS - u u -  - -   - u u -   Schizomera ovata D.Don Cunoniaceae LT LS - u u - - - -   - u u - - - Scolopia braunii (Klotzsch) Sleumer Flacourtiaceae MT LS - u u -  - - - - - u u - -  Senecio bipinnatisectus Asteraceae H N/A  u u ------u u - - - Senecio linearifolius Asteraceae H N/A - u u ------ u u - - - *Senecio madagascariensis Asteraceae H N/A  u u  - -  - -  u u - - - *Senna septemtrionalis (Cav.) Irwin & Barneby Fabaceae SH N/A - u u    -  - - u u - - - Sicyos australis Cucurbitaceae V N/A - u u  - -  - - - u u - - - *Sida rhombifolia Malvaceae H N/A  u u ------ u u - - - Sloanea australis (Benth.) F.Muell. subsp. australis Elaeocarpaceae MT LS - u u - - - -  - - u u -  - Sloanea woollsii F.Muell. Elaeocarpaceae MT LS - u u - - - -  - - u u -  - *Solanum americanum Solanaceae H N/A  u u  - -  - -  u u - - - Solanum aviculare Solanaceae SH P - u u - - -  - - - u u - - - *Solanum mauritianum Scop. Solanaceae SH N/A  u u  - -   -  u u  - - *Solanum nigrum Solanaceae H N/A  u u  - -  - -  u u  - - *Solanum opacum Solanaceae H N/A - u u - - -  - - - u u - - - Solanum stelligerum Solanaceae ST P  u u  ------u u - - - *Sonchus asper Asteraceae H N/A  u u ------u u - - - Stenocarpus sinuatus (Loudon) Endl. Proteaceae MT LS - u u - - -    - u u   - Stephania japonica Menispermaceae V N/A - u u - - -  - - - u u  - - Symplocos thwaitesii F.Muell. Symplocaceae ST LS - u u - - - - -  - u u -  - Synoum glandulosum (Sm.) A.Juss. var. glandulosum Meliaceae ST LS - u u -  - -  - - u u - - - Syzygium corynanthum Myrtaceae MT LS - u u ------u u -  - Syzygium crebrinerve (C.T.White) L.A.S.Johnson Myrtaceae MT LS - u u ------u u -   Syzygium francisii (F.M.Bailey) L.A.S.Johnson Myrtaceae MT LS - u u - - - -  - - u u - - - Syzygium hodgkinsoniae Myrtaceae ST LS - u u ------u u -  - Syzygium oleosum (F.Muell.) B.Hyland Myrtaceae ST LS - u u - - - -  - - u u - - - Tabernaemontana pandacaqui Apocynaceae ST ES - u u -  - - - - - u u -  - Toechima dasyrrhache Sapindaceae ST LS - u u ------u u -   Toona ciliata M.Roem. Meliaceae MT ES - u u -  - -   - u u -   Trema tomentosa var. viridis (Planchon) Hewson Ulmaceae SH P - u u  - -     u u  - - *Trifolium repens Fabaceae H N/A  u u ------u u - - - Veronica plebeia Scrophulariaceae H N/A  u u ------u u - - - Premna lignum-vitae Lamiaceae LT LS - u u  - -  - -  u u  - - Wikstroemia indica (L.) C.A.Mey. Thymelaeceae SH P - u u - - - -  - - u u - - - Wilkiea austroqueenslandica Monimiaceae ST LS - u u ------u u -  - Wilkiea huegeliana Monimiaceae ST LS - u u -  - -  - - u u -  - Wilkiea macrophylla (A. Cunn.) A.DC. Monimiaceae ST LS - u u  - -   - - u u -  - a Life-form: H, herb; LT, large tree; MT, medium tree; SH, shrub; ST, small tree; V, vine b Successional stage: ES, early secondary; LS, late secondary; N/A, not applicable; P, pioneer; u, unknown c Life stage: M, mature plant; R, recruit; S, seed * non-native species

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Appendix 3

Overall seedling numbers and species numbers of native woody plant seedlings according to their successional stage, germinated from the soil seed banks of all five site- types

Appendix 3 Overall seedling numbers (per m2 in the top five cm of soil) and species numbers of native woody plant seedlings according to their successional stage, germinated from the soil seed banks of all five site-types. Successional stage Number of % of native Number of % of native individuals woody plants species woody plants Pasture Pioneer 10 33 2 50 Early secondary 0 0 0 0 Late secondary 0 0 0 0 Unknown 20 67 2 50 Camphor-dominated Pioneer 86 50 7 38 Early secondary phase 28 16 3 17 Late secondary phase 32 18 5 28 Unknown 28 16 3 17 Treated camphor Pioneer 240 66 8 35 Early secondary 12 3 2 9 Late secondary 84 23 9 39 Unknown 28 8 4 17 Restoration planting Pioneer 154 61 10 48 Early secondary 16 6 2 10 Late secondary 48 19 6 29 Unknown 36 14 3 13 Rainforest Pioneer 278 54 10 38 Early secondary 92 18 5 19 Late secondary 92 18 8 31 Unknown 52 10 3 12 Total Pioneer 768 57 11 31 Early secondary 148 11 6 17 Late secondary 256 19 13 35 Unknown 164 13 6 17

For each site-type, numbers are averaged across all five replicate sites.

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Appendix 4

SIMPER analysis between all five site-types based on functional groups

Appendix 4 Percentage contribution of the top five functional groups to the Bray-Curtis dissimilarity (SIMPER analysis) between rainforest, pasture, and three types of reforestation in subtropical eastern Australia. PA, pasture; CD, camphor dominated; TC, treated camphor; ER, ecological restoration planting; RF, rainforest. Contributions in bold type indicate higher average abundance at the first named site-type. Av. Group 1 Group 2 Group 3 Group 4 Group 5 Total Site types Diss. Contrib. (%) Contrib. (%) Contrib. (%) Contrib. (%) Contrib. (%) (%) (%) PA vs CD 58.2 Grass Herb native Herb exotic Tree exotic Tree native 82.3 41.2 18.2 9.4 7.2 6.3 PA vs TC 55.7 Grass Herb native Vine native Tree native Herb exotic 79.5 36.4 14.9 9.8 9.2 9.2 PA vs ER 35.6 Grass Herb native Tree native Vine native Herb exotic 80.3 32.6 15.9 12.3 11.0 8.5 PA vs RF 73.5 Grass Herb native Herb exotic Tree native Vine native 86.0 38.6 15.1 12.2 11.8 8.3 CD vs TC 29.8 Tree exotic Herb exotic Grass Vine native Herb native 67.6 25.2 12.5 11.1 9.5 9.3 CD vs ER 38.8 Grass Tree exotic Herb exotic Herb native Tree native 81.2 32.5 17.5 13.1 11.6 6.5 CD vs RF 36.8 Tree exotic Tree native Herb exotic Grass Shrub exotic 77.3 23.1 17.7 14.8 11.4 10.3 TC vs ER 30.3 Grass Herb exotic Shrub native Herb native Tree native 74.9 30.4 16.1 10.1 10.1 8.2 TC vs RF 32.6 Grass Herb exotic Tree native Shrub native Herb native 71.2 21.9 15.9 13.3 10.9 9.2 ER vs RF 43.5 Grass Herb exotic Herb native Tree native Shrub exotic 84.1 38.7 18.8 10.9 9.0 6.7

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Appendix 5

SIMPER analysis between all five site-types based on the five most abundant woody species

Appendix 5 Percentage contribution by the top five woody species to the Bray-Curtis dissimilarity (SIMPER analysis) between rainforest, pasture, and three types of reforestation in subtropical eastern Australia. PA, pasture; CD, camphor dominated; TC, treated camphor; ER, ecological restoration planting; RF, rainforest. Contributions in bold type indicate higher average abundance at the first named site-type.

Av. Site Total Diss. Species 1 Species 2 Species 3 Species 4 Species 5 types (%) (%) PA vs CD 70.79 *Cinnamomum *Solanum Ficus Trema Melicope 49.7 camphora mauritianum coronata tomentosa micrococca 15.0 12.0 9.4 7.8 5.6 PA vs TC 87.25 Trema Cinnamomum Ficus *Solanum Macaranga 39.4 tomentosa camphora coronata mauritianum tanarius 10.0 8.9 7.5 7.3 5.7 PA vs ER 84.73 Macaranga *Solanum *Cinnamomum Premna Commersonia 34.1 tanarius mauritianum camphora lignum-vitae fraseri 8.1 7.5 7.0 6.1 5.4 PA vs RF 87.45 Ficus *Solanum Melicope Trema *Cinnamomum 36.6 coronata mauritianum micrococca tomentosa camphora 12.3 7.2 6.6 6.0 4.6 CD vs TC 61.34 *Cinnamomum Ficus *Solanum Acacia Trema 34.7 camphora coronata mauritianum melanoxylon tomentosa 16.0 5.3 4.5 4.4 4.4 CD vs ER 63.94 *Cinnamomum Ficus *Solanum Commersonia Premna 35.2 camphora coronata mauritianum fraseri lignum-vitae 16.1 5.2 5.1 4.6 4.2 CD vs RF 64.53 *Cinnamomum Ficus Solanum Trema Alphitonia 32.4 camphora coronata mauritianum tomentosa excelsa 12.7 6.6 5.4 3.9 3.8 TC vs ER 61.86 Trema Ficus *Phyllanthus Commersonia *Cinnamomum 25.8 tomentosa coronata multiflorus fraseri camphora 6.8 5.4 5.1 4.2 4.2 TC vs RF 64.12 Ficus Melicope Trema Alphitonia *Solanum 25.6 coronata micrococca tomentosa excelsa mauritianum 6.0 5.9 5.1 4.5 4.1 ER vs RF 62.62 Ficus Trema *Solanum Melicope Macaranga 25.6 coronata tomentosa mauritianum micrococca tanarius 8.3 5.1 4.8 4.8 4.6 * non-native species

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Appendix 6

SIMPER analysis between all five site-types based on the five most abundant woody species other than Cinnamomum camphora

Appendix 6 Percentage contribution by the top five woody species other than Cinnamomum camphora to the Bray- Curtis dissimilarity (SIMPER analysis) between rainforest, pasture, and three types of reforestation in subtropical eastern Australia. PA, pasture; CD, camphor dominated; TC, treated camphor; ER, ecological restoration planting; RF, rainforest. Contributions in bold type indicate higher average abundance at the first named site-type. Av. Site Total Diss. Species 1 Species 2 Species 3 Species 4 Species 5 types (%) (%) PA vs CD 80.57 *Solanum Ficus Trema Lenwebbia Melicope 47.3 mauritianum coronata tomentosa prominens micrococca 13.9 10.9 9.1 6.7 6.4 PA vs TC 87.71 Trema tomentosa Ficus *Solanum Macaranga Acacia 38.7 11.1 coronata mauritianum tanarius melanoxylon 8.3 8.0 6.2 5.2 PA vs ER 86.59 Macaranga *Solanum Premna Commersonia Ficus 34.28 tanarius mauritianum lignum-vitae fraseri coronata 8.7 8.1 7.0 5.9 5.0 PA vs RF 89.38 Ficus *Solanum Melicope Trema Premna 37.9 coronata mauritianum micrococca tomentosa Lignum-vitae 12.9 7.5 6.9 6.5 4.4 CD vs TC 59 Ficus *Solanum Acacia Trema Melicope 27.1 coronata mauritianum melanoxylon tomentosa micrococca 6.4 5.4 5.3 5.3 4.8 CD vs ER 61.15 *Solanum Ficus Commersonia Premna Macaranga 27.6 mauritianum coronata fraseri lignum-vitae tanarius 6.2 6.2 5.5 5.0 4.8 CD vs RF 62.75 Ficus *Solanum Alphitonia Trema Lophostemon 26.7 coronata mauritianum excelsa tomentosa confertus 7.6 6.2 4.3 4.3 4.2 TC vs ER 61.03 Trema tomentosa Ficus *Phyllanthus Commersonia Omalanthus 26.8 7.2 coronata multiflorus fraseri nutans 5.7 5.5 4.4 4.2 TC vs RF 63.28 Melicope Ficus Trema Alphitonia *Solanum 26.6 micrococca coronata tomentosa excelsa mauritianum 6.2 6.2 5.4 4.7 4.2 ER vs RF 62.54 Ficus Trema Melicope *Solanum Macaranga 28.1 coronata tomentosa micrococca mauritianum tanarius 8.5 5,1 4.9 4.9 4.7 * non-native species

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Appendices 7 & 8

Supplementary data based on the results of the soil seed bank chapter (Chapter 3)

Appendix 7 Seedling numbers (per m2 in the top five cm of soil) and numbers of non-grass plant species germinated from the soil seed banks of all five site-types. Seedlings are listed according to their dispersal mode, life form, and origin. Data has been pooled across all 25 sites. Herb Herb Vine Vine Woody Woody Dispersal Total native exotic native exotic native exotic vector Ind Spec Ind Spec Ind Spec Ind Spec Ind Spec Ind Spec Ind Spec Birds/Bats 10 2 146 3 114 10 8 1 215 26 128 3 621 45 Wind 66 6 171 15 14 2 0 0 11 3 0 0 268 26 Other 62 1 12 2 0 0 4 1 5 2 1 1 84 7 Unknown 74 9 32 6 0 0 0 0 36 5 16 1 158 21 Total 212 18 361 26 128 12 12 2 267 36 145 5 1131 99

Appendix 8 Seedling numbers (per m2 in the top five cm of soil) of most abundant non-grass plant species germinated from the soil seed banks of rainforest, pasture and various forms of reforestation. Data has been pooled across all 25 sites. Species Family Life form Origin Seedlings % of total Solanum nigrum Solanaceae Herb Exotic 108 5.2 Cinnamomum camphora Lauraceae Tree Exotic 68 3.3 Oxalis corniculata Oxalidaceae Herb Native 62 3.0 Solanum mauritianum Solanaceae Tree Exotic 57 2.8 Ficus coronata Moraceae Tree Native 48 2.3 Rubus rosifolius Rosaceae Vine Native 46 2.2 Trema tomentosa Ulmaceae Tree Native 34 1.7 Phytolacca octandra Phytolaccaceae Herb Exotic 30 1.4 Senecio bipinnatisectus Asteraceae Herb Native 29 1.4 Ageratina riparia Asteraceae Herb Exotic 24 1.2 Hydrocotyle peduncularis Apiaceae Herb Native 22 1.1 Sonchus asper Asteraceae Herb Exotic 21 1.0 Cissus antarctica Vitaceae Vine Native 20 0.9

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Appendix 9

Correlation matrix for all measured soil properties across pasture, rainforest and three types of reforestation

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Appendix 9 Pairwise Spearman correlation coefficients (r) among 19 soil properties across sites in rainforest, pasture, and three reforestation pathways. n = 23 sites for plant-available NH4-N, NO3-N, and PO4-P; n = 25 sites for other soil properties.

available NH available NO available

- -

microbialactivity

TotalN TotalinorganicN NH NO Plant Plant Nitrification Denitrification TotalC C Totalorganic matterSoil organic Carbonratio 13 MicrobialC biomass Soil content Gravimetricwater pH Bulkdensity Fine biomass root Nitrogen-related soil properties Total inorganic N .087

NH4-N -.159 .166 NO3-N .130 .818 -.295 Plant-available NH4-N -.236 -.135 .156 -.207 Plant-available NO3-N .358 .362 -.223 .558 .013 Nitrification .002 .282 -.451 .528 -.135 .301 Denitrification .338 .345 .169 .278 -.198 .196 .408 Carbon-related soil properties Total C .952 .117 -.023 .120 -.188 .399 .029 .423 Total inorganic C -.178 .129 .090 .200 .043 .368 .084 .179 -.063 Soil organic matter .155 .109 .048 .112 .130 .403 .129 .063 .125 .243 Carbon 13 ratio -.164 -.325 .301 -.458 .466 -.178 -.584 -.411 -.249 -.103 .200 Microbial biomass C .147 .204 .114 .093 -.198 .310 -.138 .192 .110 .202 .405 .269 Microbial activity .350 .126 -.013 .183 .115 .381 -.249 -.160 .326 -.094 .047 .325 -.017 Other soil properties Gravimetric water content .015 -.165 .044 -.288 -.091 -.584 -.154 .113 .068 -.249 -.698 -.393 -.205 -.419 pH .319 -.212 .114 -.299 -.259 -.389 -.204 .305 .329 -.007 -.387 -.234 -.052 -.255 .693 Bulk density -.099 -.495 .277 -.699 .165 -.848 -.532 -.255 -.107 -.133 -.217 .358 -.127 -.266 .464 .491 Fine root biomass .122 .391 -.279 .319 -.374 .097 -.122 -.004 .028 -.105 -.237 -.213 .046 .097 .135 -.015 -.161

Plant-available PO4-P -.193 -.383 -.116 -.369 .409 -.226 -.356 -.193 -.223 .011 .124 .278 -.055 -.062 .145 .140 .378 -.007

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Appendix 10

Raw data for nitrogen related and general soil properties

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Appendix 10 Raw data for nitrogen related soil properties across sites in rainforest, pasture, and three reforestation

pathways. n = 23 sites for plant-available NH4-N and NO3-N; n = 25 sites for other soil properties.

)

day

)

) -

)

N kg dry soil dry kg N

) 2

Site 1

N kg dry soil dry kg N

N (µg g resin (µg N g

N (µg gresin (µg N

N (mg kg dry soil dry kg (mg N

N (mg kgsoil dry (mg N

available NH available NO available

- -

Totalsoil dry kg (g N Ammonium Nitrate soil kgTotal dry inorganic (mg N Plant Plant NitrificationNO (mg DenitrificationNO (mg Pasture BR P 6.15 7.79 4.06 11.85 76.86 81.30 1.01 14.58 JE P 7.57 2.33 9.36 11.69 - - 1.85 29.62 NA P 4.81 4.48 1.57 6.05 69.97 18.03 1.75 1.74 TA P 7.28 2.18 5.71 7.89 107.22 75.27 0.13 32.34 RCD P 5.58 7.19 6.43 13.62 63.37 43.08 5.34 42.67 Camphor-dominated BR CD 6.34 2.63 7.10 9.73 25.17 322.09 8.76 63.28 WA CD 9.47 2.01 5.67 7.68 - - 12.13 59.38 RCD CD 8.32 3.45 5.44 8.89 16.54 51.62 0.28 39.31 FE CD 7.21 5.48 5.70 11.18 42.75 136.67 7.30 63.32 DE CD 4.97 3.30 8.04 11.34 70.80 36.98 14.12 48.80 Treated camphor RCD1 TC 7.49 5.70 11.49 17.19 55.00 313.00 9.46 59.98 RCD2 TC 4.66 2.45 7.89 10.34 34.27 295.50 8.30 11.49 NA TC 7.25 1.92 9.33 11.25 16.86 452.58 4.53 26.02 BR TC 7.52 2.61 11.09 13.70 58.02 1071 9.02 31.01 TO TC 5.57 3.36 8.35 11.71 16.84 118.88 6.80 33.48 Ecological restoration planting JE ER 9.70 3.85 9.97 13.82 37.52 507.28 7.51 73.89 TA ER 5.99 1.80 8.47 10.27 86.90 592.20 13.80 35.15 RCD ER 5.81 8.47 13.17 21.64 62.07 579.30 8.54 43.19 JV ER 7.21 4.98 5.82 10.80 54.90 780.05 6.68 27.73 RB ER 7.24 2.09 6.51 8.60 81.91 431.52 8.81 6.38 Rainforest BR RF 8.44 2.43 17.82 20.25 32.41 443.57 11.42 66.87 AJ RF 7.86 1.84 10.50 12.34 13.54 437.01 18.43 40.16 DS RF 5.05 1.76 11.14 12.90 35.43 347.34 20.60 45.88 RCD1 RF 6.72 1.71 12.12 13.83 20.13 250.36 11.24 17.75 RCD2 RF 6.96 2.27 11.91 14.18 82.50 800.44 7.24 37.86

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Appendix 11

Raw data for carbon related and general soil properties

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Appendix 11 Raw data for carbon related and other soil properties across sites in rainforest, pasture, and three reforestation

pathways. n = 23 sites for plant-available PO4-P; n = 25 sites for other soil properties.

)

fresh soil fresh

)

dry soil dry

)

dry soil dry

Site 1

P (µg gresin (µg P

)

(µg fluorescein g soil dry (µg fluorescein

available PO available

Totalsoil dry kg (g C kg(g C Totalsoil dry organic matterSoil(% organic C13ratio (‰) drysoil Microbialkg C (mg C biomass Microbialactivity (% content Gravimetricwater pH cmBulkdensity(g Fine(% biomass root Plant Pasture BR P 78.52 39.23 14.02 -16.4 1.75 55.20 14.58 5.12 0.94 1.411 22.34 JE P 84.85 82.22 14.84 -15.7 2.62 64.59 29.62 6.14 1.35 1.079 - NA P 58.85 19.87 11.02 -16.0 1.32 50.34 1.74 4.93 1.29 0.799 77.09 TA P 85.92 46.55 14.14 -16.3 1.82 65.19 32.34 5.20 0.99 1.615 143.08 RCD P 68.61 35.46 24.56 -17.5 6.16 49.15 42.67 5.09 1.00 1.849 33.10 Camphor-dominated BR CD 79.83 42.97 12.14 -26.8 1.24 54.70 63.28 5.95 0.73 1.012 17.05 WA CD 120.3 25.58 15.58 -27.9 2.04 39.56 59.38 6.30 0.88 0.301 - RCD CD 97.96 37.45 5.36 -26.5 2.61 50.41 39.31 6.33 0.84 1.435 2.01 FE CD 93.27 116.05 9.64 -26.5 2.57 51.18 63.32 5.95 0.89 1.331 9.08 DE CD 60.88 96.08 13.80 -27.2 1.43 33.80 48.80 6.57 0.80 0.423 8.17 Treated camphor RCD1 TC 104.1 74.26 12.02 -27.6 1.60 51.07 59.98 5.82 0.67 1.115 2.40 RCD2 TC 60.46 63.20 12.00 26.0 2.16 67.93 11.49 4.86 0.63 0.622 4.25 NA TC 85.03 93.13 14.66 -27.0 2.31 53.83 26.02 5.40 0.667 2.098 50.52 BR TC 104.7 71.93 16.98 -26.9 1.80 62.25 31.01 5.03 0.78 0.810 7.09 TO TC 71.60 123.74 23.04 -27.3 2.46 36.07 33.48 5.65 0.79 1.993 23.10 Ecological restoration planting JE ER 108.1 68.97 25.30 -20.5 3.16 57.27 73.89 5.35 0.59 0.859 10.31 TA ER 76.76 97.31 24.72 -23.9 2.62 46.30 35.15 4.46 0.61 0.563 123.44 RCD ER 78.77 66.61 22.18 -22.7 5.44 54.70 43.19 4.51 0.59 0.507 1.20 JV ER 83.08 96.93 26.50 -20.5 2.31 55.17 27.73 4.81 0.67 1.014 1.84 RB ER 83.25 40.56 22.66 -20.5 1.37 59.74 6.38 4.88 0.66 1.011 5.42 Rainforest BR RF 104.0 51.05 17.72 -27.6 1.70 66.65 66.87 4.52 0.54 2.886 5.78 AJ RF 89.69 18.91 15.48 -26.7 3.06 55.57 40.16 5.20 0.60 2.432 2.97 DS RF 64.02 94.92 13.86 -26.8 2.08 49.62 45.88 4.66 0.60 3.029 2.72 RCD1 RF 77.37 34.12 9.86 -27.3 1.67 40.88 17.75 5.07 0.69 3.242 2.71 RCD2 RF 82.52 43.52 6.18 -27.2 1.88 61.05 37.86 5.21 0.55 3.913 18.83

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Appendix 12

Starting height and diameter for seedlings in soils from different site-types

Appendix 12 Mean starting height and diameter data for seedlings in soils from different site-types. PA, pasture; CD, camphor dominated; TC, treated camphor; ER, ecological restoration planting; RF, rainforest. Superscripts show LSD results (means with the same letter do not differ at p > 0.05). n = 9 replicate seedlings of each species per site-type. Site-type ANOVA- Species PA CD TC ER RF p A. excelsa 0.329 12.36 13.95 13.35 13.97 13.41 Height G. semiglauca < 0.001 7.69 A 11.29 CD 9.75 BC 12.15 D 9.25 AB (cm) O. nutans < 0.001 28.93 A 33.61 B 28.32 A 34.92 B 35.81 B A. excelsa 0.404 2.09 2.11 1.99 2.09 2.18 Diameter G. semiglauca 0.257 2.33 2.54 2.61 2.58 2.43 (mm) O. nutans 0.234 5.48 5.27 5.19 5.34 5.77

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