Spatial and Temporal Variation of Sources of Water Across Multiple Tropical Rainforest Trees

Spatial and temporal variation of sources of water across multiple tropical rainforest trees

Md. Shawkat Islam Sohel BSc (Hons) and MSc in Forestry MSc in Ecohydrology

A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2018 School of Agriculture and Food Science Abstract Tropical forest water use is important for maintaining ecosystem function, tree productivity, growth, survival and nutrient cycling. However, explaining such use is complex in the field. Water stable isotope tracing of plant water use can shed light on such plant water sources but to date, species numbers tested at any given site across the globe have been minimal. Additionally, past tree water source studies were mostly based on methods using single isotopes of either (δD or δ18O) at a coarse spatiotemporal resolution. This thesis used a combination of a dual stable isotope approach together with sap flux to understand species specific water utilization strategies of co-occurring tropical rainforest species at a fine spatiotemporal scale.

A systematic review of global research on woody plant water sources was undertaken to evaluate the spatiotemporal dynamics of research on woody species water sources and to assess the research priorities in the study of woody species water sources (Chapter 2). Most studies were from the USA with various other countries having between one and four studies only and mostly focused on the Pinaceae family. The review indicates there is a clear variation in woody plant water sources in the forest due to season, climate, leaf phenology and method of measurement. Majority of the tree species obtained water from soil, followed by groundwater. Most of the research focus has been on identifying plant water sources using a single isotope. Much less focus was given to the nexus between water source and tree growth, drought, water use efficiency, agroforestry, groundwater interaction and many other topics.

A further systematic review was undertaken on research related to tree water use (Chapter 3). This review found a clear bias in research focus relating to geographic area and species group selection. Most of the studies (33.33%) were undertaken in Central America. Only Fabaceae, Myrtaceae, Dipterocarpaceae and Anacardiaceae families were given priority. Tree size and seed mass was positively correlated with water use. In contrast, wood density showed a negative relationship with tree water use. Season is highly significant in explaining the variation of tree water use as was leaf phenology. Tropical trees’ water use significantly increases in dry season. There is no significant difference in water use between native and exotic species.

A comparison of δD and δ18O isotopes between soil water and xylem water was used to investigate niche segregation among wet tropical rainforest tree species at a fine spatial scale (Chapter 4). Tropical forest water use is critical for tree productivity, growth, survival and nutrient ii cycling, but describing such uses is difficult in the field. Stable isotope tracing of plant water use can illuminate plant water sources but to date, the number of species tested at any given site has been minimal. Here, 46 tropical hardwood tree species (49 individual trees) in a 0.32 ha plot with uniform soils were sampled. Soil water was characterized at 6 depths at 0.2 m intervals down to 1 m and showed simple and predictable depth patterns, and simple and spatially uniform isotope composition at each depth. But tree xylem water δD and δ18O showed remarkable variation covering the full range of soil composition, suggesting strong sorting and niche segregation across the small plot. A multivariable model Principle Component Analysis (PCA) incorporating wood density, tree size and mean basal area increment (MBAI) could explain 54.8% of the variance of xylem water isotope composition. This work suggests that stable isotope tracers may aid a better understanding of hydrological niche segregation among co-occurring tropical species and in turn, help inform better mixed-species plantation designs and predictions about future shifts in the composition and structure of tropical rainforest species under climate change.

Water uptake depth variation of 46 rainforest tree species was investigated using a dual isotope approach and a Bayesian mixing model (BMM) (Chapter 5). The null hypothesis is that all the co- located tropical hardwood species show the same xylem water isotope composition and hence the same depths of soil water extraction. By grouping the soil layers into five depths, the BMM showed that sampled trees were either sourcing their water from very shallow or deep soil layers, with very little contribution from the middle portion of the soil layer. The majority (83%) of the observed species relied on shallow soil water (0.0-0.2 m). This layer contributed approximately 62% to xylem water which was significantly higher than the contributions from all other depths. The contribution from shallow soil was highest for high wood density, slow-growing, small-sized trees. However, this explanation was not statistically strong. Therefore, the study infers that soil water uptake patterns are species-specific rather than trait-specific, although all species were exposed to the same environmental conditions.

A combination of continuous sap flux measurements and hourly sampling of xylem water stable isotope composition (δD and δ18O) were used to observe water use strategies through a 24 h transpiration cycle for co-occurring tree species (Chapter 6). Here, the study quantifies the high frequency changes in water sources and sap ﬂux patterns of two commonly co-occurring tropical rainforest tree species: Dendrocnide photinophylla (Kunth; Chew) and Argyrodendron peralatum (F.M. Bailey; Edlin ex J.H. Boas). Sap flux ranged from 2.82-28.50 L d-1 and was 66.67% higher in A. iii peralatum compare to D. photinophylla. For both tree species, sap flux increased with tree size and diurnal sap flux increase resulted in more isotopically enriched xylem water. A Bayesian Mixing Model analysis using sampled soil water isotopic composition from five soil depths from of 0 to 1 m showed that D. photinophylla used very shallow or surface layer (0-20 cm) water, while A. peralatum sourced its water mostly from deeper in the soil profile (>20 cm). These contrasting patterns of water use and water sources of co-occurring tree species suggest that to make proper conclusion on species water consumption pattern, plant water storage capacity, quantitative wood anatomical features and xylem isotope composition should be considered together with sap flux measurement and future studies should consider species level water use strategies to build improved process understanding for ecohydrological modeling.

Overall, this research work suggests that stable isotope tracers may aid a better understanding of hydrological niche segregation, factors responsible for spatial variation of xylem water and contrasting water use strategies among co-occurring tropical species. The approaches presented in this thesis can be applied in other climatic condition to investigate water uptake pattern of diverse tree species at fine spatiotemporal scale.

Declaration by author

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

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

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

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis and have sought permission from co-authors for any jointly authored works included in the thesis.

Publications during candidature Peer-reviewed publications- lead author Sohel, M.S.I., Akhter, S., Ullah, H., Haque, E., Rana, P. 2016. Predicting impacts of climate change on forest tree species of Bangladesh: evidence from threatened Dysoxylum binectariferum (Roxb.) Hook.f. ex Bedd. (Meliaceae). iForest – doi: 10.3832/ifor1608-009.

Sohel, M.S.I., Alamgir, M., Akhter, S., Rahman, M. 2015 Carbon storage in a bamboo (Bambusa vulgaris) plantation in the degraded tropical forests: Implications for policy development. Land Use Policy 49, 142-151.

Sohel, M.S.I., Mukul, S.A., Burkhard, B. 2015. Landscape’s capacities to supply ecosystem services in Bangladesh: A mapping assessment for Lawachara National Park. Ecosystem Services 12, 128- 135.

Sohel, M.S.I., Mukul, S.A., Chicharo, L. 2015. A new ecohydrological approach for ecosystem service provisions and sustainable management of aquatic ecosystems in Bangladesh. Ecohydrology & Hydrobiology 1, 1-12.

Peer-reviewed publications- co-author Wills, J., Herbohn, J., Hu, J., Sohel, M.S.I., Firn, J. 2018. Tree leaf trade-offs are stronger for sub- canopy trees: leaf traits reveal little about growth rates in canopy trees. Ecological Applications DOI:10.1002/eap.1715.

Hu, J., Herbohn, J., Chazdon, R.L., Baynes, J., Wills, J., Meadows, J., Sohel, M.S.I. 2018. Recovery of species composition in a logged and silviculturally treated Australian tropical forest over 46 years. Forest Ecology and Management 409, 660–666.

Akhter, S., McDonald, M.A., van Breugel, P., Sohel, M.S.I., Kjær, E.D., Mariott, R. 2017. Habitat distribution modelling to identify areas of high conservation value under climate change for Mangifera sylvatica Roxb. Of Bangladesh. Land Use Policy 60, 223–232.

Mukul, S.A., Sohel, M.S.I., Herbohn, J., Inostroza, L., König, H. 2017. Integrating ecosystem services supply potential from future land-use in protected area management: A Bangladesh case study. Ecosystem Services 26, 355-364.

Conference Sohel, M.S.I, Herbohn, J. 2015. A Synthesis of Tree Water Use Research in the Tropics Reveals Water Relation of Plant Functional Traits, Research Biasness and the Way Forward. Paper presented in the IUFRO 3.08 International Conference on “Small-Scale and Community Forestry and the Changing Nature of Forest Landscapes" held during 11-15 October, 2015 in Sunshine Coast, Queensland, Australia.

Hu, J., Wills, J; Sohel, M.S.I., Baynes, J., Meadows, J., Herbohn, J. 2015. Effects of silvicultural treatments on growth and species composition of tropical forests over the last 45 years. Paper presented in the IUFRO 3.08 International Conference on “Small-Scale and Community Forestry and the Changing Nature of Forest Landscapes" held during 11-15 October, 2015 in Sunshine Coast, Queensland, Australia.

Book chapter Sohel, M.S.I. 2015. Ecohydrology: A New Approach to Old Problems for Sustainable Management of Aquatic Ecosystem of Bangladesh for Ecosystem Service Provision. In: Ecosystem Services and River Basin Ecohydrology. L. Chicharo et al. (eds.), DOI 10.1007/978-94-017-9846-4_15.

Publications included in this thesis No publications included

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Contributions by others to the thesis

Members of the Tropical Forests and People Research Centre assisted with the collection of many soil and plant. I am indebted to my colleagues for the assistance I received. Professor John Herbohn and Professor Jeffrey J. McDonnell, through their advisory role, contributed to the conception and design of the research as well as providing editing and revision of papers and this thesis. Associate Professor David Doley provided comments on earlier drafts of Chapter 6 of this thesis. Assistant Professor Jaivime Evaristo provided excellent help in understanding the Bayesian Mixing Model included in chapter 5 and 6. Professor David Lamb also provided comments on earlier drafts of the Chapter 6.

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

None

Research Involving Human or Animal Subjects

No animal or human subjects were involved in this research.

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Acknowledgements

I would like to express my gratitude to the almighty for giving me the opportunity to complete my thesis successfully. I am highly indebted to my parents for their constant inspiration for my better future. Salute to them. This doctoral research would not have been possible without the guidance and help of many people.

I am very much grateful to my principal advisor, Professor John Herbohn (Director, Tropical Forests and People Research Centre, University of the Sunshine Coast, Maroochydore DC, QLD 4558, Australia and Honorary Professor in Tropical Forestry, School of Agriculture and Food Sciences, The University of Queensland) and co-supervisor Professor Jeffrey J. McDonnell (President, AGU Hydrology Section; Associate Director, Global Institute for Water Security and Professor, School of Environment and Sustainability, University of Saskatchewan, Canada) for also for their guidance, valuable advice, regular supervision, constructive criticism, prompt help in my research during the research period and preparation of dissertation.

I would also like to thank all the people from Tropical forestry research group (UQ/USC) [https://www.usc.edu.au/research-and-innovation/forests-for-the-future/tropical-forests-and- people-research-centre/contact-the-tropical-forests-and-people-research-centre] for their excellent help during my fieldwork in North Queensland wet tropical rainforest. I would like to thank Kim Janzen, Lab manager and isotope technician at the Global Institute for Water Security, University of Saskatchewan, Canada for her heartiest cooperation for extracting water from the samples and analysis. Thanks are also due to Kurt von Kleist for his great help in the field throughout my PhD project. Thanks to Dr. Jaivime Evaristo, Md Hadayet Ullah and Prof. Jennifer Firn for their unconditional help with statistics. Last but not the least, respect to all my teachers I came in contact throughout tertiary education who helped in building strong theoretical and practical knowledge. This helps me a lot to reach my goal.

This research was supported by a scholarship from the Australian Government through an IPRS (International Postgraduate Research Scholarship, currently named Research Training Program, RTP) to Md. Shawkat Islam Sohel. The research was also partially funded by the Wet Tropics Management Authority, Australia.

Throughout my doctoral journey my wife (Dr Sayma Akhter) motivated me to reach my desired destination. You are a true inspiration. I am very lucky to have you as my life partner. I am also lucky to have a beautiful daughter (Samara Manha) who was born during my PhD period. I have received endless happiness during stressful moments when I play with my angel.

Financial support

‘This research was supported by an Australian Government Research Training Program Scholarship’. This research was also partially funded by Wet Tropics Management Authority, Australia.

Keywords

Xylem, soil, sap flux, tree water source, wood anatomy, plant functional trait, Bayesian mixing model, dual isotope, spatiotemporal variation

Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 070504, Forestry Management and Environment, 70% ANZSRC code: 050205, Environmental Management, 30%

Fields of Research (FoR) Classification FoR code: 0502, Environmental Science and Management, 30% FoR code: 0705 Forestry Sciences, 70%

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Dedication

I would like to dedicate this work to my beloved Sayma and Manha.

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Table of Contents

CHAPTER 1: INTRODUCTION...... 1

1.1 Background ...... 1

1.2 Specific aim and objectives...... 1

1.3 Justification for the research ...... 3

1.4 Methods and approach ...... 4

1.5 Limitations and challenges ...... 7

1.6 Structure of the Thesis ...... 7

1.7 References ...... 8

CHAPTER TWO: AN ASSESSMENT OF WOODY PLANT WATER SOURCE STUDIES FROM ACROSS THE GLOBE: WHAT DO WE KNOW AFTER 30 YEARS OF RESEARCH AND WHERE DO WE GO FROM HERE? ...... 11

2.1 Abstract ...... 11

2.2. Introduction ...... 12

2.3 Methods...... 13

2.3.1 Literature search and article inclusion criteria ...... 13

2.3.2 Data extraction, interpretation and analysis ...... 14

2.4. Results ...... 15

2.4.1 Publication trends and geographical distribution of research on woody plant water source ...... 15

2.4.2 Research focus on woody plant species ...... 18

2.4.3 Woody plant water sources and their variation due to season, climate, leaf phenology and method of measurement ...... 19

2.5. Discussion ...... 23

2.5.1 Geographic biases in woody species water source research...... 23

2.5.2 Environmental significance and factors influencing plant water sources study ...... 23

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2.5.3 Research gaps and new research priorities ...... 23

2.5.3.1 The need for high-frequency sampling and dual isotope approaches ...... 23

2.5.3.2 Do species with high mortality rates source their water mainly from surface layers?24

2.5.3.3 During droughts, do large and small sized tree source their water from the same or different soil depths? ...... 24

2.5.3.4 What is the relationship between growth and the depth at which species/individuals uptake water? ...... 25

2.5.3.5 Is the assumption that water-stable isotope fractionation does not occur during water uptake by roots and sap transfer within the tree universally true? ...... 25

2.6. Concluding remarks and recommendations ...... 26

2.7 Acknowledgements ...... 27

2.8 References ...... 27

2.9 Supplementary information ...... 32

CHAPTER THREE: WATER USE BY TREES ACROSS TROPICAL FOREST AREAS: WHAT DO WE KNOW AFTER 30 YEARS OF FOREST HYDROLOGY RESEARCH? ...... 50

3.1. Abstract ...... 50

3.2 Introduction ...... 51

3.3 Study Area and method ...... 52

3.3.1 Defining “tropical forest area” for the present study ...... 52

3.3.2 Methods ...... 52

3.3.2.1 Literature search ...... 52

3.3.2.2 Research article inclusion criteria ...... 53

3.3.2.3 Data extraction from the selected articles and databases ...... 53

3.3.3 Data analysis ...... 54

3.3.3.1 Spatiotemporal dynamics of the research ...... 54

3.3.3.2 Relationship between tree functional traits and water use ...... 54

3.3.3.3 Set new research priorities ...... 55 xv

3.4 Results ...... 55

3.4.1 Spatiotemporal dynamics of research on tree water use in tropical forest areas ...... 55

3.4.2 Relationship between tree functional traits and water use ...... 56

3.4.3. The previous research priorities ...... 61

3.5 Discussion ...... 64

3.5.1 Geographic biases in tree water source research focus ...... 64

3.5.2 Effects of tree functional traits on water use ...... 64

3.5.3 Research needs and future research priorities based on a forest hydrological perspective ...... 66

3.5.3.1 Do more productive forests use more water? ...... 67

3.5.3.2 How do forestry practices and changes to forest design affect water use in the face of climate change? ...... 67

3.5.3.3 How realistic is scaling plant water use data from whole trees to stands and catchments? ...... 68

3.6 Conclusion...... 69

3.7 Acknowledgement ...... 69

3.8 References ...... 69

3.9 Supplementary information ...... 74

CHAPTER FOUR: TROPICAL FOREST WATER USE: SIMPLICITY IN SOIL WATER ISOTOPE COMPOSITION, COMPLEXITY IN PLANT XYLEM WATER ISOTOPE SIGNATURES ...... 93

4.1 Methods...... 98

4.2 Acknowledgements ...... 100

4.3 References ...... 100

4.4 Supplemental Information ...... 102

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CHAPTER 5: A BAYESIAN MIXING MODEL ANALYSIS OF TREE WATER ISOTOPE PATTERNS: UPTAKE DIFFERENCES ACROSS 46 SPECIES ON A UNIFORM TROPICAL FOREST PLOT ...... 109

5.1 Abstract ...... 109

5.2 Introduction ...... 110

5.3 Materials and methods ...... 111

5.3.1. Experimental site and studied species ...... 111

5.3.2 Tree species characteristics ...... 113

5.3.3 Plant and soil sample collection ...... 116

5.3.4 Water extractions ...... 117

5.3.5 Bayesian Mixing Model to determine soil water proportions in xylem tissue ...... 117

5.4 Results ...... 118

5.4.1 Soil moisture, texture and isotopic signature ...... 118

5.4.2 Tree water uptake depth and source water contribution ...... 121

5.4.3 Functional traits and their relation to water uptake depth...... 124

5.5 Discussion ...... 125

5.5.1 Interspeciﬁc differences in water uptake depth ...... 125

5.5.2 Tree functional traits and water uptake depth ...... 125

5.5.3 On dual isotopes and the BMM approach ...... 126

5.6 Conclusion...... 127

5.7 Acknowledgement ...... 128

5.8 References ...... 128

CHAPTER SIX: SAP FLUX AND STABLE ISOTOPES OF WATER SHOW CONTRASTING TREE WATER UPTAKE STRATEGIES IN TWO CO-OCCURRING TROPICAL RAINFOREST TREE SPECIES ...... 133

6.1 Abstract ...... 133

6.2 Introduction ...... 134

6.3 Materials and Methods ...... 135

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6.3.1 Experimental site and studied species ...... 135

6.3.2 Plant and soil sample collection ...... 137

6.3.3 Species growth trends ...... 137

6.3.4 Soil moisture measurement ...... 138

6.3.5 Sap flux measurement ...... 138

6.3.6 Stem increment measurements and their relation to stored water...... 139

6.3.7 Investigation of tree water uptake depth through stable isotope analysis ...... 139

6.3.8 Bayesian Mixing Model to determine soil water proportions in xylem tissue ...... 140

6.3.9 Evaporation enrichment calculation ...... 140

6.4 Results……………………………………………………………………………………………………………………………. 141 6.4.1 Soil volumetric water content with depth…………………………………………………………………. 141 6.4.2 Tree water uptake patterns of the two co-existing tree species ...... 142

6.4.2.1 Measured tree characteristics ...... 142

6.4.2.2 Tree water sources ...... 144

6.4.2.3 Sap flux pattern and its effect on isotope composition ...... 146

6.5 Discussion ...... 151

6.5.1 Species differences in water use ...... 151

6.5.2 Water uptake depth ...... 153

6.5.3 Does evaporative enrichment of xylem water isotope relates to low sap flux rate? .... 154

6.6 Conclusions ...... 155

6.7 Acknowledgements ...... 155

6.8 References ...... 156

6.9 Supplementary information ...... 162

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CHAPTER SEVEN: SUMMARY AND FUTURE RESEARCH DIRECTION ...... 174

7.1 Summary ...... 174

7.1.1 There is no niche segregation among co-occurring tropical rainforest species in a homogenous environmental condition (Hypothesis 1, incorporated as Chapter 4) ...... 174

7.1.2 Diverse tropical hardwood species across a 20x160 m plot show the same xylem water isotope composition. Therefore uptake water from same soil depth (Hypothesis 2, incorporated as Chapter 5) ...... 174

7.1.3. “Two neighbouring tree species will uptake water from similar soil depths and their isotope composition will be unchanging throughout the daily transpiration cycle” (Hypothesis 3, incorporated as Chapter 6) ...... 175

7.2 Future research direction ...... 175

7.3 References ...... 177

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List of Figures Fig. 2.1 Overview of the article inclusion and screening process...... 14

Fig. 2.2 The research journals that the reviewed articles were published...... 16

Fig. 2.3 (a) The map and bar graph indicates the global distribution of publications on woody plant water source (b) The pie chart represents the proportion of publications on woody plant water source across different climatic regions (c) The publication trend (between 1970-2014) for research articles related to woody plant water source in forest areas in 17 developed and developing countries......

Fig. 2.4 The terms most frequently cited in the abstracts of reviewed articles. Note: Each term was cited at least 10 times; Colours are based on the years for which the terms were most frequently cited; Distance between two nodes indicates the degree of relatedness 18 between the terms......

Fig. 2.5 Right side bar graph represents the distribution of research focus among woody plant families (Note: each publication may have more than one family). Left side bar graph indicates the research focus on woody plant water source...... 19

Fig. 2.6 (a) Woody plant water source variation in relation to leaf phenology b) Climatic variation of plant water sources; (c) Seasonal variation in water uptake from different soil depths; (d) Proportion of water sources of woody species...... 21

Fig. 2.7 (a) Water source variation due to changes in isotopic approach (b) Method used for plant water source determination...... 22

Fig. 3.1 (a) Red dots indicate documented locations of tree water use studies. The deep green color of the background map shows the “tropical forest areas” extracted from MODIS‐based land cover map (Friedl et al., 2010). (b) The distribution of studies on tree water use in the “tropical forest areas”. The legend on the left-hand side represents the number of research studies. (c) Global climate types map (Beck et al., 2016), with tropical 56 climatic regions indicated by green color......

Fig. 3.2 (a) The relationship between water use (kg/day) and seed mass (g). Data presented shows species from 106 trees; (b) the relationship between water use (kg/day) and DBH (cm). Data presented shows species from 107 trees; (c) the relationship between water use (kg/day) and sapwood area (m2). Data presented shows species from 42 trees; (d) the relationship between water use (kg/day) and wood density (g cm-3). Data presented shows species from 185 58 trees......

Fig. 3.3 (a) Tropical tree water transport variation due to leaf phenology. Brevideciduous (n=11), Deciduous (n=32), Evergreen (n=126) and Semi-deciduous (n=19). Effect size, Cohen’s f = 0.86 and Cohen’s d = 1.72 (effect sizes from ANOVAs with multiple groups, based on group means), indicates a large effect of brevideciduous trees on water use compare to deciduous, evergreen and semi-deciduous trees in the tropics. (b) Water use of native vs exotic 59 species......

Fig. 3.4. Seasonal variation of water uptake by tropical forest trees. The dry season 60 represents a sample of 90 trees and the wet season represents a sample of 78 trees......

Fig. 3.5 Distribution of research outputs among tree species’ family...... 62

Fig. 3.6 The research focuses of the reviewed studies...... 63

Fig. 3.7 Scaling tree water use from whole tree to a catchment scale where ecohydrological 68 constraints need to be considered......

Fig. 4.1 Spatial variations of xylem and soil water across the 0.32 ha plot using the Inverse distance weighting (IDW) interpolation method...... 95

Fig. 4.2 Influence of tree traits on spatial patterns of isotopic composition...... 96

Fig. 4.3 Principle Components Analysis (PCA) of tree traits and xylem water isotope ratios.

PC1 indicates that tree size, growth form and wood density explain the majority of variation in isotope ratios...... 97

Fig. 5.1 (a) Location of the study area within Australia. Green color in the inset map indicates wet tropical rainforest. The red dot indicates the location of the studied permanent plot in the tropical rainforest area (b) The light green dot indicates location of the plot in high

xxi resolution (c) Aerial view about the plot location...... 112

Fig. 5.2 The spatial distribution of all the studied tree species throughout the 20x160 m plot. Different sizes of the green circles indicate DBH differences among the trees. The whole plot was then divided into a 10x10 m grid. The codes outside of the grid indicate the grid ID. The black circles in each grid indicate the bore-hole location for soil sample collection. The 116 numbers within the grid represent the tree species ID. Details for each tree can be found in Table 1......

Fig. 5.3 Soil texture of the 20x160 m plot. The top figure represents the upper transect of the plot and the lower figure represents the lower transect of the 118 plot......

Fig. 5.4 Average soil water content difference between the (a) left (S10-S25)-right (S26-S40) and (b) upper-lower transect of the studied plot...... 119

Fig. 5.5 (a) Dual isotope plot of the soil water isotope difference between the left (S10-S25)- right (S26-S40) proportion and (b) upper-lower transect of the experimental plot. Each plot has the Local Meteoric Water Line (LMWL) as a reference. The boxplots show the average (dashed line) of the isotope ratios, while data extremes are shown by the respective symbol. Statistical grouping is indicated by a continuous line to the left or below for δ18O and δ2H, respectively. A similar color line indicates no significant difference (p<0.05) between plot proportion of the same soil depth. Soil water isotope for the 1-4 m depth is not shown here as those samples were collected outside of the plot sub- 120 grids......

18 Fig. 5.6 The δD–δ O relationship for soil water and xylem water. The local meteoric water line was calculated from rain samples collected during field work...... 122

Fig. 5.7 The BMM results for source water partitioning per depth interval and per tree 123 species......

Fig. 6.1 (a) Location of the study area. The arrow indicates the permanent plot where the study was conducted. (b) Spatial distribution of Argyrodendron peralatum (green dots) and Dendrocnide photinophylla (red dots) throughout the 20x200 m plot. The red box indicates the location of the sampled 20 m x20 m subplot(c) Spatial distribution of the A. peralatum and D. photinophylla trees sampled for diurnal variation in tree water sources within a

20x20 m subplot. Black circles indicate the locations of bore holes for soil sample collection...... 136

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Fig. 6.2 Variation in soil volumetric water content with depth. The data were measured th continuously at 10 minute intervals. The isotope sampling date was 26 of March, 2016.The inset graph represents the average soil volumetric water content [Note: all the presented data were from nine representative wet season days (20thMarch, 2016-28th March, 2016)] in the plot...... 141

Fig. 6.3 δD-δ18O isotope space plot showing diurnal variation of isotope composition...... 145

Fig. 6.4 Source water partitioning for each species determined by the Bayesian Mixing 146 Model......

Fig. 6.5 Diurnal variation of oxygen isotope composition in relation to sap flux variation of individual trees. The area between the red lines with blue dots represents daytime isotopic variation while the black dots indicate night-time isotopic variation...... 148

Fig. 6.6 Diurnal variation of hydrogen isotope composition in relation to sap flux variation of individual trees. The area between the red lines with blue dots represents daytime isotopic variation while the black dots indicate night-time isotopic variation...... 149

Fig. 6.7 (a, b, c & d) stem increment variation and sap flux relation; (a-f) sap flux variation between species and trees of different sizes; (g) soil volumetric water content (VSW %). Data presented in this figure cover an extended period from 26/3/2016-10/4/2016. The negative readings of stem increment in the graph (Fig. c) indicate that stem contraction was greater than the actual growth during the study period...... 150

Fig. 6.8 Contrasting wood anatomical features of two wet tropical rainforest trees from North Queensland, Australia. (a) Mature phase species A. peralatum and (b) pioneer/early secondary species D. photinophylla. The scale bar in D. photinophylla equals 5 mm. The scale is also similar for A. peralatum. Source: Reproduced with permission of Jean-Claude Cerre, from Australian Rainforest Woods by Morris Lake. Published by CSIRO Publishing 2015 (Lake, 2015)...... 153

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List of Tables

Table 1.1. Summary of the research focus, methods and measurements taken from the study sites in the North Queensland wet tropical rainforest ...... 06 Table 3.1. Results from the PerMANOVA of the distance matrix constructed from tree water use across the tropics ...... 61 Table 3.2. Results from the PerMANOVA of the seasonal variation in water use when leaf phenology is fixed ...... 61 Table 3.3. Results from the PerMANOVA of the between leaf phenological groups due to changes in season ...... 61 Table 4.1. Results from simple bivariate correlations between xylem water isotope (δD and δ18O) and Mean Basal Area Increment (MBAI), tree size (Diameter) and wood density...... 97 Table 5.1. Traits of the sampled woody tree species ...... 114 Table 5.2. Results from simple bivariate correlations between soil water contribution to xylem (%) and MBAI, tree size (DBH) and wood density...... 124 Table 6.1. Characteristics of the tree species selected for study. Comparison of wood density, diameter, water content and sapwood width were given in the table...... 143

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List of Abbreviations DBH Diameter at Breast Height MBAI Mean Basal Area Increment BA Basal Area LMWL Local Meteoric Water Line MWL Meteoric Water Line IDW Inverse Distance Weighting GLM Generalised linear model AICc Akaike information criterion PCA Principle Components Analysis BMM Bayesian Mixing Model MCMC Markov Chain Monte Carlo lc-excess Line-conditioned excess

CHAPTER 1: INTRODUCTION 1.1 Background Water plays an important role in plant growth and functions (e.g. Gholz et al., 1990; Ehleringer et al., 1991; Sala et al., 1997; Breckle and Walter, 2002; Snyder and Williams, 2003; Huxman et al., 2004). The dynamics of water availability in soils and water use by plants are consequently critical to ecosystem functions, e.g. maintaining a high resistance to a changing climate (Gazis and Feng, 2004; Cui et al., 2015). Where plant productivity is limited by water availability, understanding the water use strategies of plants is important (Webb et al., 1983). Many tropical forests experience a prolonged dry season with little or no precipitation (Wright, 1996), which results in increased competition for limited water resources. It is believed that competition for limited resources such as water may be minimized, and therefore species diversity maximized, by partitioning of resource utilization (Tilman, 1982). The assessment of water stable isotopes is one approach that can beused to study tree water uptake patterns from different sources and complementary water use (Ehleringer and Dawson, 1992). For example, a comparison of plant water isotopeswith that of soil water from different soil depths can reveal the actual depth of the soil water source for any plant.

The stable isotope composition of water has been used to trace the water uptake patterns of many plant communities and ecosystems, such as mountain forest, riparian forest, maritime forest, desert plants and cropland, karst plants and dry tropical forest (Dawson and Ehleringer, 1991; Jolly and Walker, 1996; Slavich et al., 1999; Meinzer et al., 1999; Atsuko et al., 2002; Sekiya and Yano, 2002; Peñuelas and Filella, 2003; McCole and Stern, 2007; Querejeta et al., 2007). This stable isotope approach together with the sapflux measurement technique were used in this thesis to understand the water use strategies of wet tropical rainforest trees at a fine-resolution spatiotemporal scale.

1.2 Specific aim and objectives The aim of this study is to understand species specific water utilization strategies of co-occurring tropical rainforest species at a fine spatiotemporal scale.The study’s objectives are outlined in the following hypotheses and their associated research questions:

Hypothesis One:

“There is no niche segregation among co-occurring tropical rainforest species in homogenous environmental conditions”.

To test this hypothesis, the following research question was asked:

-Are there any differences in xylem water isotope composition at a fine spatial resolution?

Information on niche segregation or complementary resource use is critically important for better understanding of species co-existence. However, there is still scant empirical evidence of niche segregation in plants to explain species co-existence (Silvertown, 2004; Moreno-Gutie´rrez et al., 2012).This thesis examines niche segregation among diverse tropical rainforest tree species in homogeneous environmental conditions.

Hypothesis Two:

“Diverse tropical hardwood species across a 20x160 m plot show the same xylem water isotope composition and hence the same depths of soil water extraction”.

To test this hypothesis, the following research questions were asked:

-What are the water uptake depths across 46 species within a 0.32 ha forest plot? -How do species’ water uptake depths relate to measured soil water isotopic composition?

-How do tree functional traits relate to water uptake depth?

It is not well understood what factors drive the depth of tree water uptake in diverse tropical rainforests. Most studies to date have used a single isotope and simple mass balance approach to quantify water uptake depth. However, this approach creates uncertainty in depth determination. To address this issue, dual isotope and a Bayesian Mixing Model (BMM) analysis of soil water source apportionment for 49 individuals from 46 species in North Queensland, Australia was used. The BMM outputs were then combined with tree functional traits to understand the water uptake strategies of diverse rainforest tree species.

Hypothesis Three:

“Two co-occurring tree species will uptake water from similar soil depths and their isotope

composition will be unchanging throughout the daily transpiration cycle”.

To test this hypothesis, the following research questions were asked:

-Does tree species type and size influence tree water use over a diurnal transpiration cycle? -Do two co-occurring species from different ecological guides’ source water from the same depth within the soil profile? -Does xylem water isotope composition change with sap flux dynamics?

Most past studies have made conclusions about tree water sources based on coarse resolution spatial and temporal isotope measurements such as seasonal observations and large spatial distances. Plant water use strategies using fine-resolution isotope measurements over short timescales and spatial distances remain poorly understood. The general background context of the above-listed research questions is discussed in detail later in this thesis.

1.3 Justification of the research Using dual isotope data from soil and xylem samples across a small (0.32 ha) uniform plot, this research provides the first insights into spatiotemporal dynamics of diverse rainforest tree species water uptake depth pattern. More specifically, this study provides insights on how soil water isotopes vary spatially, both down through the soil profile and across the plot and how this variability relates to isotopes in xylem water contained within trees located across the plot. This leads to a better understanding of the spatial distribution of isotopes within the soil profile and associated trees. This thesis contributes to the literature in several key areas. Firstly, the thesis uses a dual isotope approach. The majority of past studies have been based on a single isotope approach and these studies found that plants obtain their water from both mobile water (i.e. groundwater, stream water) and tightly bound water (i.e. soil) sources (Dawson and Ehleringer, 1991; Jolly and Walker, 1996; Meinzer et al., 1999; Slavich et al., 1999; Atsuko et al., 2002; Sekiya and Yano, 2002; Peñuelas and Filella, 2003; McCole and Stern, 2007; Querejeta et al., 2007). Several recent investigations have used a dual water stable isotopes approach and have found that plants typically use “tightly bound” water (i.e. “plant available” water). The other water pool

(“mobile water”) contributing to stream flow is not accessed by the plants (Brooks et al., 2010; McDonnell, 2014; Good et al., 2015; Evaristo et al., 2015; Evaristo et al. 2016; Bowling et al., 2017). Secondly, very few previous studieshave reported depth of water uptake by tropical tree species and have considered only a single species or a few species at best (Goldsmith et al., 2012; Evaristo et al. 2016). Using a combination of dual isotope analysis and a Bayesian mixing model, this study substantially extends the data for tropical species by estimating water uptake depth for 46 co- occurring species. Thirdly, because the soil water showed no signs of evaporative enrichment, it was possible to investigate the potential impacts of evaporative enrichment of xylem water and hence this study provides important methodical insights into whether evaporative enrichment can be a source of error in plant water source studies (e.g. see McDonnell, 2014).Fourthly, the findings of most past studies on plant water uptake depth are based on high spatial heterogeneity and have used only a handful of species which was unable to answer how so many plants can co-exist at a single uniform site. However, stable isotope tracers at fine spatial scales can explain plant coexistence. This thesis addresses all the uncertainties related to past studies and provides better understanding of spatial distribution of soil and tree water isotope across a uniform plot.

This thesis also presents the first detailed study on water use strategies of co-occurring tree species using a fine spatiotemporal resolution measurement of xylem water isotope composition and sap flux. This study provides insights on how species traits (i.e. species type and size) influence tree water use over a diurnal transpiration cycle and how xylem water isotopes vary with sap flux dynamics. This has important implications in determining tree water consumption and water uptake depth. Past research has addressed these issues using only single isotope and didn’t consider short-term dynamics of isotope composition changes.

1.4 Methods and approach The research for this thesis was conducted in a long-term experimental plot located in wet tropical rainforest in Danbulla State Forest, Atherton, Australia. Xylem and soil samples along with sapflux data were collected from within the experimental plot. Table 1.1 presents a summary of the methodology for each of the research hypotheses and their associated sampling designs and data types. Further details of the methodology are described in the following chapters of this thesis.

Table 1.1. Summary of the research focus, methods and measurements taken from the study sites in the North Queensland wet tropical rainforest

Objectives Hypothesis Research question Data required Data collection and analysis Sample size and method design -To investigate -There is no niche -Are there any differences in xylem -Xylem sample -Xylem and soil water was extracted -30 soil bore holes to a niche segregation segregation among water isotope composition at a fine -Soil sample through the cryogenic vacuum depth of 1m among co- co-occurring tropical spatial resolution? -X,Y coordinates of distillation and direct vapour -49 tree species (at least occurring tropical rainforest tree trees and soil bore equilibration methods. one individual from every rainforest species species in holes within this 0.32 -For δ2H,a Delta V Advantage mass species). in homogenous homogenous ha plot spectrometer and an HDevice peripheral environmental environmental were used. conditions. conditions. -For δ18O, a Delta V Advantage mass spectrometer and a GasBench II peripheral were used. -The inverse distance weighting method using ArcGIS 10.3.1 was used to interpolate water isotope data. -To investigate -Diverse tropical -What are the water uptake depths -Xylem sample -Xylem water was extracted through the -30 soil bore holes to a water uptake hardwood species across 46 species within a 0.32 ha -Soil sample cryogenic vacuum distillation method. depth of 1m depth pattern of across a 20x160 m forest plot? -Soil moisture -Soil water was extracted using the -3 soil bore holes to a diverse tropical plot show the same -How do species’ water uptake -Soil texture direct vapour equilibration method. depth of 4m tree species on a xylem water isotope depths relate to measured soil water -Tree functional trait -Bayesian Mixing Model. -49 tree species (at least uniform tropical composition and isotopic composition? data -Tree trait data from literature one individual from every forest plot using hence the same -How do tree functional traits relate -Tree growth data were collected from species). Bayesian Mixing depths of soil water to water uptake depth? the long-term experimental plot Model extraction. established in 1948. -To investigate -Two neighbouring -Does tree species type and size -Xylem sample -Xylem and soil water was extracted -Two tree species (three water use tree species will influence tree water use over a -Soil sample through the cryogenic vacuum individuals from each strategies of co- uptake water from diurnal transpiration cycle? -Sap flux distillation method. species). occurring tropical similar soil depths -Do two co-occurring species from -Soil moisture -For δ2H, a Delta V Advantage mass -Five soil bore holes to a rainforest tree and their isotope different ecological guides’ source spectrometer and an HDevice peripheral depth of 1m species at a fine composition will be water from the same depth within were used. -Xylem samples were spatiotemporal unchanging the soil profile? -For δ18O, a Delta V Advantage mass collected 12 times scale. throughout the daily -Does xylem water isotope spectrometer and a GasBench II throughout the day. transpiration cycle. composition change with sap flux peripheral were used. -72 xylem samples. dynamics? -Sap flux data was collected using an ICT -30 soil samples. sap flow meter.

1.5 Limitations and challenges Collection of samples in the field was challenging and time consuming. This was particularly the case for soil samples, where each soil auger hole and associated samples took over one hour to complete. The intensive sampling campaign was made possible through the assistance of around twenty volunteers from the Tropical Forests and People Research Centre. The twenty-four hour sampling campaign for hypothesis three was also challenging given the remote location of the site and dense forest that had to be traversed at night.

1.6 Structure of the Thesis

The thesis contains six chapters. The content of each chapter is described below:

Chapter 1. This chapter provides a background to the importance of tree water source study and the need to understand species-specific water utilization patterns at short spatiotemporal scales. It also outlines the research questions and the dissertation structure.

Chapter 2. This chapter focuses on tree water sources, providing a review of existing research into the sources that trees obtain their water from in different geographical locations and under different environmental conditions.

Chapter 3. This chapter is a literature review of current knowledge of tree water use. More specifically, this chapter contains information about the convergence of tree architecture and tree water use. This analysis identifies research gaps on tree water use in the tropics.

Chapters 4, 5 and 6. There are three data chapters (manuscripts) in the thesis. Chapter 4 contains details of niche segregation among wet tropical rainforest tree species at a fine spatial scale. Chapter 5 focuses on diverse tropical forest tree species’ water uptake depth patterns using the dual isotope and Bayesian Mixing Model approaches. Chapter 6 provides insights into the contrasting water use strategies of two co-occurring tropical rainforest tree species at a fine spatiotemporal scale.

Chapter 7. This chapter provides the study’s general conclusions about variations in tree water sources at a fine spatiotemporal scale, and discusses implications of the results and the direction of future research based on the present findings.

1.7 References Atsuko, S., Nao, Y., Daisuke, N., Daisuke, N., Noboru, F., Trofim, C.M. 2002. Importance of permafrost as a source of water for plants in east Siberian taiga. Ecological Research 17, 493–503. Bowling, D.R., Schulze, E.S., Hall, S.J. 2017. Revisiting streamside trees that do not use stream water: can the two water worlds hypothesis and snowpack isotopic effects explain a missing water source? Ecohydrology 10, e1771. Breckle, S. W., Walter, H. 2002. Walter’s Vegetation of the Earth: The Ecological Systems of the Geo-Biosphere, 4th Edition, Springer Berlin Heidelberg. Brooks, J.R., Barnard, H.R., Coulombe, R., McDonnell, J.J. 2010. Ecohydrologic separation of water between trees and streams in a Mediterranean climate. Nature Geoscience 3, 100–104. Cui, Y.Q., Ma, J.Y., Sun, W., Sun, J.H., Duan, Z.H. 2015. A preliminary study of water use strategy of desert plants in Dunhuang, China. Journal of Arid Land 7, 73–81. Dawson, T.E., Ehleringer, J.R. 1991. Streamside trees that do not use stream water. Nature 350, 335–337. Ehleringer, J.R., Dawson, T.E. 1992. Water uptake by plants: perspectives from stable isotopes. Plant, Cell & Environment 15, 1073–1082. Ehleringer, J.R., Phillips, S.L., Schuster, W.F.S., Sandquist, D.R. 1991. Differential utilization of summer rains by desert plants. Oecologia 88, 430–434. Evaristo, J., Jasechko, S., McDonnell, J.J. 2015. Global separation of plant transpiration from groundwater and streamflow. Nature 525, 91–94. Evaristo, J., McDonnell, J.J., Scholl, M.A., Bruijnzeel, L.A., Chun, K.P. 2016. Insights into plant water uptake from xylem-water isotope measurements in two tropical catchments with contrasting moisture conditions. Hydrological Processess 30, 3210–3227. Gazis, C., Feng, X. 2004. A stable isotope study of soil water: evidence for mixing and preferential flow paths. Geoderma 119, 97–111. Gholz, H.L., Ewel, K.C., Teskey, R.O. 1990. Water and Forest Productivity. Forest Ecology and Management 30, 1-18. 8

Goldsmith, G.R., Muñoz-Villers, L.E., Holwerda, F., McDonnell, J.J., Asbjornsen, H., Dawson, T.E. 2012. Stable isotopes reveal linkages among ecohydrological processes in a seasonally drytropical montane cloud forest. Ecohydrology 5, 779–790. Good, S.P., Noone, D., Bowen, G. 2015. Hydrologic connectivity constrains partitioning of global terrestrial water fluxes. Science 349, 175–177. Huxman, T. E., Smith, M. D., Fay, P. A., Knapp, A. K., Shaw, M. R., Loik, M. E., Smith, S. D., Tissue, D. T., Zack, J. C.,Weltzin J. F., Pockman, W. T., Sala, O. E., Haddad, B. M., Harte, J., Koch, G. W., Schwinning, S., Small, E. E., Williams, D. G. 2004. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654. Jolly, I.D., Walker, G.R. 1996. Is the field water use of Eucalyptus largiflorens F. Muell. affected by short-term flooding. Australian Journal of Ecology 21, 173–183. McCole, A.A., Stern, L.A. 2007. Seasonal water use patterns of Juniperus ashei on the Edwards Plateau, Texas, based on stable isotopes in Water. Journal of Hydrology 342, 238–248. McDonnell, J.J., 2014. The two water worlds hypothesis: ecohydrological separation of water between streams and trees? WIREs Water 1, 323–329. Meinzer, F.C., Andrade, J.L., Goldstein, G., Holbrook, M.N., Cavelier, J., Wright, S.J. 1999. Partitioning of soil water among canopy trees in a seasonally dry tropical forest. Oecologia 121, 293–301. Moreno-Gutie´rrez, C., Dawson, T.E., Nicola,´ E., Querejeta, J.I. 2012. Isotopes reveal contrasting water use strategies among coexisting plant species in a Mediterranean ecosystem. New Phytologist 196, 489–496. Peñuelas, J, Filella, I. 2003. Deuterium labelling of roots provides evidence of deep water access and hydraulic lift by Pinus nigra in a Mediterranean forest of NE Spain. Environmental and Experimental Botany 49, 201–208. Querejeta, J.I., Estrada-Medina, H., Allen, M.F., Jim´enez-Osornio, J.J. 2007. Water source partitioning among trees growing on shallow karst soils in a seasonally dry tropical climate. Oecologia 152, 26–36. Sala, O.E., Lauenroth, W.E., Golluscio, R.A. 1997. Plant functional types in temperate semi-arid regions. In: Smith, T.M., Shugart, H.H., Woodward, F.I. (eds.), Plant Functional Types, Cambridge University Press, Cambridge, pp 217–233. Sekiya, N., Yano, K. 2002. Water acquisition from rainfall and groundwater by legume crops developing deep rooting systems determined with stable hydrogen isotope compositions of xylem waters. Field Crops Research 78, 133–139. 9

Silvertown, J. 2004. Plant coexistence and the niche. Trends in Ecology and Evolution 19, 605-611. Slavich, P.G., Smith, K.S., Tyerman, S.D., Walker, G.R. 1999. Water use of grazed salt bush plantations with saline water table. Agricultural Water Management 39, 169–185. Snyder, K.A., Williams, D.G. 2003. Defoliation alters water uptake by deep and shallow roots of Prosopis velutina (Velvet Mesquite). Functional Ecology 17, 363–374. Tilman, D. 1982. Resource competition and community structure. Princeton University Press, Princeton, NJ. Webb, W.L., Lauenroth, W.K., Szarek, S.R., Kinerson, R.S. 1983. Primary production and abiotic controls in forests, grasslands, and desert ecosystems of the United States. Ecology 64, 134– 151. Wright, S.J. 1996. Phenological responses to seasonality in tropical forest plants. In: Mulkey, S.S., Chazdon, R.L., Smith, A.P. (eds.), Tropical forest plant ecophysiology, Chapman & Hall, New York, pp 440–460.

CHAPTER TWO: AN ASSESSMENT OF WOODY PLANT WATER SOURCE STUDIES FROM ACROSS THE GLOBE: WHAT DO WE KNOW AFTER 30 YEARS OF RESEARCH AND WHERE DO WE GO FROM HERE?

2.1 Abstract A plant’s water use strategy plays a crucial role in its growth and survival. In the face of global climate change, water availability and its impact on forest productivity is becoming an increasingly important issue. It is therefore necessary to evaluate the advancement of research in this field and to set new research priorities. Here, a systematic literature review was performed to evaluate the spatiotemporal dynamics of global research on woody plant water sources and to determine a future research agenda. Literature search using ISI Web of Science resulted 99 relevant studies between 1980 and 2013. Most of the reviewed studies were from the USA (n=33), followed by China (n=20) and Australia (n=16), with various other countries having between one and four studies only. Most of the studies were focused on the Pinaceae family (n=26). The research indicates there is a clear variation in woody plant water sources in forests due to season, climate, leaf phenology and method of measurement. A total of 276 trees from different geographic locations were investigated in the research. Of these 276 trees, 54% obtained water from soil and 26% obtained water from groundwater. Much of the research focus has beenon identifying plant water sources using a single isotope approach. Much less focus has been given to the nexus between water source and tree size, tree growth, drought, water use efficiency, agroforestry systems, groundwater interactions and many other topics. Therefore, a new set of research priorities is proposed that will address these gaps under different vegetation and climate conditions. This new research requires an appropriate sampling method -a dual isotope approach and investigation of isotopic fractionation. Studies are required to compare different water extraction techniques for soil and plant water to understand the effect of water extraction methods on water isotope composition. Once these issues are resolved, the research can inform forest process studies in new ways.

Keywords: Dual isotope; forest management; soil water; stable isotopes; xylem water

2.2. Introduction

The dynamics of soil water availability and water use by plants are important form any ecosystem functions, including ecosystem resilience to a changing climate (Gazis and Feng, 2004; Cui et al., 2015). Water availability to plants in dry habitats is a matter of concern because plant productivity is often limited by soil moisture (Noy-Meir, 1973; Webb et al., 1983). How this plant-water interaction responds to climate change, such as warming and droughts, has been a recent source of debate (Saleska et al., 2007; Anderson et al., 2010). Observations suggest that global mean temperatures are set to rise by 0.3 to 4.8°C by the late-21st century (IPCC, 2013). Hence, there is a high probability of increasing water stress in some regions of the world. Forest productivity and vulnerability to climate change are critically dependent upon how plants cope with water stress in warmer and drier climates (Malhi et al., 2009). Species diversity, ecosystem structure and forest compositionare alsohighly influenced by water availability (Sternberg and Swart, 1987; James and Zedler, 2000).

To understand complex plant-water interactions, isotopic compositions (δ2H and δ18O) of different water pools (e.g. soil water, stream water, rain water and groundwater) are compared with plant water isotope compositions (Ehleringer and Dawson, 1992; Gat, 1996; Yakir and Sternberg, 2000; Dawson et al., 2002). This is because in general, there is no isotopic fractionation occurring during water uptake by plants (Ehleringer and Dawson, 1992), although mangrove plants are a notable exception (Lin et al, 1993). Hence, identification of the isotopic composition of stem water can determine potential plant water sources (Ehleringer and Dawson, 1992; Brunel et al., 1995). This technique has been widely used in determining water sources used by plants (Schwinning et al., 2003; Querejeta et al., 2006, 2007; Asbjornsen et al., 2007; Hasselquist et al., 2010; Nie et al., 2011).

In dry environments where water is limited, some plants have deep roots and uptake water from deep soil or groundwater sources (Jackson et al., 1999). By doing so, deep roots may also contribute to the distribution of water in the upper soil layer through hydraulic lift (Prieto et al., 2012). This water uptake strategy can support plants that are dependent on water in the upper soil layer. Some plants also use different water sources during different seasons (Ehleringer et al., 1991; Jackson et al., 1999), thereby reducing competition for water and increasing the survival rate of plants during periods of water shortage (Ehleringer et al., 1991).

Research into tree water sources is of a trans-disciplinary scope and importance, and is commonly undertaken by ecologists, geographers, agriculturalists, foresters and hydrologists. As scientific development and environmental pressures such as global climate change increase, it is increasingly necessary to evaluate recent research progress and to challenge current research priorities. An evaluation of the research into tree species’ water sources and use strategies, with special consideration given to the water resource scarcity, can provide valuable information for mitigating the potential future impacts of global climate change on efficient water useand associated forest productivity and ecosystem functioning. From this perspective, it is time to evaluate the current state of global research into these topics. To do this, a systematic review of the scientific literature was undertaken and provides an update on its current relevance. This detailed review will help to increase our understanding of current trends in woody plant water sources research and identify relevant research gaps.The specific objectives are to: (1) evaluate the spatiotemporal dynamics of global research on woody species water sources; and (2) set new research priorities in the study of woody species water sources.

2.3 Methods

2.3.1 Literature search and article inclusion criteria

To identify the water sources of woody plants, a systematic literature search was undertaken using the ISI Web of Science covering the period from 1970 to 2014. The following search command with relevant key words was used –

Literature search command = (forest* OR tree* OR plant* OR “riparian tree” OR “stream side tree” OR planted* OR reforest* OR afforest* OR “mixed plantation” OR “mixed forest” OR “native forest” OR agroforest* OR “agro forest”) AND (“water source” OR “water use”) AND (“soil water” OR “stream water” OR “rain water” OR “ground water” OR stream* OR “soil water partition” OR “ecohydrologic separation”) AND (isotope* OR “stable isotope” OR “dual isotope”)

ISI web of science Literature search command = (forest* OR tree* OR plant* OR “riparian tree” OR “stream side tree” OR planted* OR reforest* OR afforest* OR “mixed plantation” OR “mixed forest” OR “native forest” OR agroforest* OR “agro forest”) AND (“water source” OR “water use”) AND (“soil water” OR “stream water” OR “rain water” OR “ground water” OR stream* OR “soil water partition” OR “ecohydrologic separation”) AND (isotope* OR “stable isotope” OR “dual isotope”). Time period:1stJanuary 1970 to 30th December, 2014 Document type: Article/peer reviewed journals Language considered: English Total articles yielded: 613

Excluded article(Zoology, library science, urban studies, anthropology, astronomy,

fisheries, government law, imaging science, nuclear science, remote sensing, marine freshwater biology, infectious diseases, social science, history): 59

Total article yielded: 554

Not relevant to the selection criteria: 401

Duplicates removed: 54

Total articles considered in the systematic review: 99

Fig. 2.1. Overview of the article inclusion and screening process

The search initially yielded 613 articles. All 613 retrieved articles were then reviewed based on their title, abstract and main text to evaluate their suitability for inclusion in the final analysis. Studies that met the following criteria were included:

 The research was undertaken in forest areas;  The research focused on woody species (trees and shrubs) only;

 The water source was measured without any treatment such as measuring isotopes before and after pruning or harvesting;  The article was written in English; and  The article had a clear description of the investigated species.

These selection criteria resulted in 99 articles being included in the literature review (See Fig. 2.1 and Supplementary Table 2.1).

2.3.2 Data extraction, interpretation and analysis To understand the research publication trends among the included articles, the number of papers published per year between 1970 and 2014 was calculated. To identify the woody plant water sources, information on the possible water sources such as rain, stream, fog, rock, soil and groundwater were extracted. In the case of soil water, data on the depth the woody plants were sourcing the water from was also extracted. Since the data on soil water depth reported in the different publications followed different standards (e.g. some are reported in meters (m) while others are reported in centimeters (cm), therefore, converted the data into a single unit. To identify the variation in woody plant water sources due to climate and leaf phenology, information on the study area's climatic conditions and species’ leaf phenology (i.e. evergreen, deciduous, semi-evergreen and semi-deciduous) were extracted. The research focus was identified from the article title and abstract. For mapping the spatial distribution of the included studies, ArcGIS 10.3.1 was used. All the extracted information was graphically represented using Sigma Plot.

2.4. Results

2.4.1 Publication trends and geographical distribution of research on woody plant water source The systematic review considers 99 peer-reviewed articles from 43 journals (see Supplementary Table 2.1). More than half of the publications identified (73%) were published in two journals (i.e. Oecologia, n=21; Plant and Soil, n=10). Figure 2.2 indicates that Oecologia is the higest cited journal in tree water source research. With 1359 citations Oecologia’s central position in Figure 2.2 and its connection with all the other journals suggests it has substantial influence in tree water source research. The second most influential journal is Plant and Soil with 437 citations.

Fig. 2.2. The research journals that the reviewed articles were published in.

Note: The size of the nodes/circles indicates the number of articles from each journal; the lines indicate citation connections between journals; similar colours indicate a single cluster and journals that are highly related are positioned close to each other.

The reviewed articles included research from 18 countries. Most of the studies were from the USA (n=33), followed by China (n=20) and Australia (n=16), with various other countrieshaving between one and four studies only (Fig. 2.3a). When considering the climatic regionof focus, most studies were undertaken insemi-arid regions (32%), followed by tropical (16%), arid (15%) and Mediterranean (14%) regions (Fig. 2.3b). The annual proportion of the published articles increased significantly with the year for both developed and developing countries (Fig. 2.3c). It is important to note that based on the search criteria, no relevant studies were published before 1991.

Fig. 2.3. (a) The map and bar graph indicates theglobal distribution of publications on woody plant water source (b) The pie chart representstheproportion ofpublications on woody plant water source across differentclimatic regions (c) The publication trend (between 1970-2014) for research articles related to woody plant water source in forest areas in developed and developing countries (based on the United Nations World Economic Situations and Prospects classification).

2.4.2 Research focus on woody plant species Figure 2.4 displays the research focus of the reviewed articles by mapping the terms most frequently cited in the abstracts. The term ‘use’ (i.e. tree water use) was most frequently cited in the abstracts. Related terms such as ‘drought’, ‘summer’, ‘transpiration’, ‘soil water partitioning’ and ‘precipitation’ were also frequently cited. Over recent years, the terms ‘drought’, ‘rainfall’, ‘deep soil water’ and ‘growth’ have been increasingly cited in research article abstracts. The reason of using these terms relates to increasing impacts of climate change.

Fig. 2.4. The terms most frequently cited in the abstracts of reviewed articles. Note: Each term was cited at least 10 times; Colours are based on the years for which the terms were most frequently cited; Distance between two nodes indicates the degree of relatedness between the terms.

Application of stable hydrogen and oxygen isotopes in the woody plant water source studies was mostly focused on identifying sources/uses of water. Among the 276 woody plant individuals investigated (from 193 species), the research focus for 83 of them was on their water sources such as soil, groundwater and stream water. Other key topics of research focus were partitioning of soil water sources (n=55) and seasonal changes in water sources (n=48). Much less focus was given to the nexus between water source and tree size, growth, transpiration partitioning, drought, water use efficiency, agroforestry systems, groundwater interactions and many other topics (Fig. 2.5).The research focus was not evenly distributed among plant families. The studied woody plant species (n=193) belonged to 53 families (Fig. 2.5). Most of the studies were focused on the Pinaceae family (n=26), followed by Myrtaceae and Fagaceae (n=14), and Fabaceae (n=13). All other families were represented in much fewer studies. 18

Fig. 2.5. Right side bar graph represents the distribution of research focus among woody plant families (Note: each publication may have more than one family). Left side bar graph indicates the research focus on woody plant water source.

2.4.3 Woody plant water sources and their variation due to season, climate, leaf phenology and method of measurement To investigate woody plant water source variation, data were obtained from 109 deciduous, 155 evergreen, 1 semi-evergreen and 11 semi-deciduous woody plants. Leaf phenology has a great influence on plant water use strategies. The evergreen and deciduous woody plants mostly used soil water and groundwater (Fig. 2.6a). There was climatic variation observed in the plant’s water sources. Groundwater as a source of water was more common in dry climates, such as in arid and semi-arid regions. Most of the plants in tropical regions sourced water from the soil (Fig. 2.6b), and there was a clear seasonal variation observed in soil water uptake depth. The data shows that in the dry season, most of the woody plants uptake water from the <100 cm soil layer. Water uptake from the deep soil layer (>100 cm) is found to be greatest during the wet season (Fig. 2.6c).

The literature reveals that plant uptake of water is from diverse sources i.e. soil water, groundwater, rain water, stream water and other sources. Among the 276 trees (from 193

19 species) from different geographic locations, 54% obtained water from soil, followed by groundwater (26%), rain water (12%) and stream water (6.75%) (Fig. 2.6d). To identify plant water sources, the stable Hydrogen and Oxygen isotope analysis approach was widely used. The review shows that the single isotope approach was most commonly used to detect plant water source. With this approach, soil water and groundwater were found to be the major sources of water. Among the 276 woody plants studied 115 were investigated through the dual isotope approach and 161 were investigated through the single isotope approach (Fig. 2.7a). Plant water sources were identified through the cryogenic vacuum distillation method for 215 of the 276 studied woody plants (Fig. 2.7b).

Fig. 2.6 (a) Woody plant water source variation in relation to leaf phenology; (b) Climatic variation of plant water sources; (c) Seasonal variation in water uptake from different soil depths; (d)Proportion of water sources of woody species

Azeotropic distillation

Cellulose synthesis and isotope

Cryogenically distillation

Dendrocronological method and oxygen isotope cellulose analysis Direct equilibration

Liquid–vapour equilibration

Pressure chamber and suction sysimeter Vacuum distillation

Wood core with a sap-press

Fig. 2.7. (a)Water source variation due to changes in isotopic approach (b) Method used for plant water source determination

2.5. Discussion 2.5.1 Geographic biases in woody species water source research This systematic literature review shows that the research on plant water sources is geographically biased. Most research has been undertaken in the USA, followed by China and Australia. Research history, research capability, interests of individuals and organizations, and priorities of funding agencies and governments have influenced the type of research conducted and published (Fazey et al., 2005). The geographic distribution of the research has also been influenced by the research capacity and level of support for research within particular countries and regions.

2.5.2 Environmental significance and factors influencing plant water sources study Plant water sources vary greatly with geographic location, climate, season and thetype of plant (Fig’s. 2.2, 2.4 and 2.5). Understanding how plants partition water sources has wide application in managing ecosystems and this knowledge is increasingly applied by ecologists and hydrologists. Research has found that some trees (e.g. Quercus gambelii) do not use summer rainfall while some species (e.g. Juniperusos teosperma) do (Williams and Ehleringer, 2000). Studies have also found other plant water use strategies including that streamside trees do not always utilise stream water (Dawson and Ehleringer, 1991), smaller sized tropical trees uptake more deep soil water than large sized tropical trees (Meinzer et al., 1999), some trees use fog water (Dawson, 1998), and some species seasonally shift their water sources (Mensforth et al., 1994; Dawson and Pate, 1996). Such strategies provide plants with the ability to adapt to competitive and extreme environments, which in turn influences the distribution and diversity of species in an ecosystem (Flanagan et al., 1992; Ehleringer and Phillips, 1996). Opportunistic use of different plant water sources and strategies improves complementarity among different tree species. Knowledge of these complementary relationships can help guide the design and management of mixed-species plantations including multiple-use agroforestry systems (Burgess et al., 2000).

2.5.3 Research gaps and new research priorities 2.5.3.1 The need for high-frequency sampling and dual isotope approaches The water sources of only a small number of woody plant species have been investigated to date. High-frequency sampling of soil and xylem waters across diverse climate and vegetation types is therefore needed and will be useful to inform forest process studies in new ways and generate a

23 new set of research priorities and questions.Many studies in different ecoregions have used the single isotope approach (i.e., using δ2H or δ18O) to measure tree water sources (see Supplementary Table 2.1). Some recent examples include Goujun et al. (2016) and Engel et al. (2017). However, very few studies have used the dual isotope approach for quantifying plant water sources (see Supplementary Table 2.1). The power of using the dual isotope approach is that compared to the single isotope approach, there is a better chance of determining if water samples in the environment or in the plant were influenced by post-precipitation evaporation effects.

2.5.3.2 Do species with high mortality rates source their water mainly from surface layers? Vegetation in water-limited ecosystems depends largely on access to deep water sources to withstand dry periods (Barbeta et al., 2015). Understanding the patterns of, or differences in, the use of water sources underdry conditions is therefore necessary to properly observe the responses of forest trees inseasonally dry ecosystems. By identifying trees with high mortality rates and determiningwhether these species primarily source their water from the surface layers of the soil profile or whether they are capable of taking up water from deeper sources (e.g. deep soil water or groundwater) during dry conditionsmay help to understand one of the causes of tree mortality. A recent study by Barbeta et al. (2015) shows that in extreme dry conditions, the contribution of deep water sources declines which results in widespread tree mortality and crown defoliation. A review by Anderegg et al. (2013) also indicates that tree mortality is triggered by drought and temperature stress. Hence, adetailed knowledge of plant water sourcing strategies may help understand one potential mechanism behind tree mortality and niche differentiation, thus improving predictions of future forest decline or community shifts with changing patterns of water availability.

2.5.3.3 During droughts, do large and small sized tree source their water from the same or different soil depths? Many regions of the world face increasing drought conditionswhich canalter forest structure and function (Nepstad et al., 2007). A recent study by Bennett et al. (2015) concludes that during drought, large trees suffer most in forests across the globe. However, there are other studies showing that small sized trees are more prone to mortality than large sized trees (Lorimer et al.,

2001; van Mantgem et al., 2009; Peng et al., 2011; Zhang et al., 2015). In general, compared to small trees, larger trees have greater access to deep soil water during drought because of their deeper root systems (Horton and Hart, 1998). This means large trees are less susceptibleto decline or mortality during drought. However, there are some exceptionsto this rule because somelargesized trees lack a deep root system (Mueller et al., 2005; Anderson-Teixeira et al., 2015). Therefore, investigating the depths that large and small sized trees uptake water from during drought or dry conditions will improve our understanding of the water use strategies of different sized trees.

2.5.3.4 What is the relationship between growth and the depth at which species/individuals uptake water? The relationship between access to soil water and rainforest tree productivity is an interesting issue in the field of ecohydrology. In forests where access to light is not a major limitation to productivity, exploring underground resource competition such as competition for water could help to answer the question of why the growth rates ofsome trees are higher than other trees of the same age. However, research into this topic has produced mixed results. For example, Romero-Saltos et al. (2005) found that large-diameter trees uptake water from deep in the soil profile. In contrast, Meinzer et al. (1999) discovered that during dry periods, small-diameter trees withdraw more water from deep soil layers than large-diameter trees. Therefore, using long-term datasets to identify trees with high growth rates and then investigating the soil depths from which those trees are taking up water will improve our understanding of whether access to soil water influences rainforest tree productivity. This will also help in identifying complementary tree species to design mixed-species plantations that can make better use of water resources in the era of climate change.

2.5.3.5 Is the assumption that water-stable isotope fractionation does not occur during water uptake by roots and sap transfer within the tree universally true? In the context of hydrological changes, understanding the mechanisms of water uptake by trees, which play a critical role for entire ecosystems, may help to determine ecosystem responses tovariations inwater availability. One possible way to evaluate the spatiotemporal patterns of water uptake is to use natural tracerssuch as oxygen and hydrogen isotopes. Many studies over

25 the last two decades have used δ2H and δ18O to determine the proportions of different water sources used by forest tree species (Dawson and Ehleringer, 1991; Dawson and Pate, 1996; Asbjornsen et al., 2007; Brooks et al., 2010). This is based on the assumption that δ2H and δ18O fractionation does not occur during water uptake by roots and sap transfer within the tree. However, there is evidence that isotope fractionations occur in halophyte, xerophyte and some deciduous species (Ellsworth and Williams, 2007; Zhao et al., 2016), which then challenges the accuracy of tree water source identifications.

Apart from δ2H and δ18O fractionation, various factors can modify tree water uptake. Such factors include climate (eg. season, water availability) (Ehleringer and Dawson, 1992; Dawson and Pate, 1996; Dawson et al., 2002), spatial variation (Weltzin and McPherson, 1997; Stratton et al., 2000) and species functional traits (eg. tree size, Dawson and Ehleringer, 1991). Though there has been much research into the climate aspects of tree water source variation (e.g. Ehleringer and Dawson, 1992; Dawson and Pate, 1996; Dawson et al., 2002) and tree size (e.g. Dawson and Ehleringer, 1991), there has been no research into whether isotope fractionation of different species can be characterised by species functional traits such as wood density, mean basal area ncrement and successional status. Also, no study hasyet been conducted on isotope fractionation at fine spatiotemporal resolutions across diverse climatic zones and ecoregions.

2.6. Concluding remarks and recommendations From this systematic literature review it is clear there has been limited research into woody plant water sources across the globe. Increased study of woody plant water sources will help in identifying complementarities among tree species which can then inform the design and management of mixed-species plantations. Addressing the research gaps identified in this paper will provide insights into the potential impacts of changing water regimes associated with global climate change. From the new research priorities described above, three broad recommendations are made:

1. There is a critical need to develop a single standard method to study plant water sources. This will lead to greater comparability of results from studies conducted across vegetation types and regions;

2. Catchment-scale hydrological models should consider plant water sources for better predictions of stream flows; and

3. Further research is required to better understand how plant water uptake strategies differ across vegetation types and climatic conditions, and how this understanding can be applied to the design of reforestation systems and understanding the likely impacts of climate change.

2.7 Acknowledgements The first author would like to thank The University of Queensland, Australia for granting an IPRS (International Postgraduate Research Scholarship) and APA (Australian Postgraduate Award) scholarship for conducting this research. Thanks to Dr John Meadows for proofreading and editing.

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2.9 Supplementary information Supplementary Table 2.1. Species-specific information on plant functional traits and water sources

Water source water Leaf Life DBH LAI Height Isotope Species Family Season Climate extraction References phenology Form (cm) (m2/m2) (m) Soil type Ground method (depth) Rock Rain Stream Fog Water cm Eucalyptus largiflorens Myrtaceae E T 14 1 na 20- 60 GW D T AD D Peter et al., 1993

Eucalyptus largiflorens Myrtaceae E T 14 2 na 20-90 GW D T AD D Peter et al., 1993

Eucalyptus largiflorens Myrtaceae E T 14 3 na 30-70 GW D T AD D Peter et al., 1993

Juniperus oxycedrus Cupressaceae E T na na na GW D M CD S Valentini et al.,1995

Quercus Fagaceae D T na na na GW D M CD S Valentini et al.,1995 pubescens Quercus cerris Fagaceae D T na na na GW D M CD S Valentini et al.,1995

Quercus ilex Fagaceae E T na na na R D M CD S Valentini et al.,1995

Pistacia lentiscus Anacardiaceae E T na na na R D M CD S Valentini et al.,1995

Phillyrea angustifolia Oleaceae E S na na na R D M CD S Valentini et al.,1995

Pinus sylvestris Pinaceae E T na na na R D M CD S Valentini et al.,1995

Larix decidua Pinaceae D T na na na GW D M CD S Valentini et al.,1995

Larix sibirica Pinaceae D T na 2.7 20 40 D SA CD D Li et al., 2007

Larix sibirica Pinaceae D T na 2.7 20 8 D SA CD D Li et al., 2007

Larix sibirica Pinaceae D T na 2.7 20 21 D SA CD D Li et al., 2007

Juniperus ashei Cupressaceae E T na na na <10 W TM VD D McCole and Stern, 2007

Juniperus ashei Cupressaceae E T na na na S D TM VD D McCole and Stern, 2007

Rademachera sinica Bignoniaceae SD T na na na S D A CD S Nie et al., 2012

Rademachera sinica Bignoniaceae SD T na na na R D A CD S Nie et al., 2012

Rademachera sinica Bignoniaceae SD T na na na S D A CD S Nie et al., 2012

Rademachera sinica Bignoniaceae SD T na na na 0-30 D A CD S Nie et al., 2012

Alchornea trewioides Euphorbiaceae D S na na na R D A CD S Nie et al., 2012

Alchornea trewioides Euphorbiaceae D S na na na 0-30 D A CD S Nie et al., 2012

Azadirachta indica Meliaceae E T na na na GW D SA DE S Smith et al., 1997

Azadirachta indica Meliaceae E T na na na 200 D SA DE S Smith et al., 1997

Azadirachta indica Meliaceae E T na na na 100 W SA DE S Smith et al., 1997

Acacia erioloba Fabaceae D T na na na 150-400 GW W A CD D Schachtschneider and February, 2010

Acacia erioloba Fabaceae D T na na na 150-400 GW D A CD D Schachtschneider and February, 2010

Acacia erioloba Fabaceae D T na na na 0-100 GW D A CD D Schachtschneider and February, 2010

Tamarix usneoides Tamaricaceae E T na na na 150-400 GW D A CD D Schachtschneider and February, 2010

Faidherbia albida Fabaceae E T na na na 0-100 W A CD D Schachtschneider and February, 2010

Faidherbia albida Fabaceae E T na na na 150-400 GW D A CD D Schachtschneider and February, 2010

Salix gooddingii Salicaceae D T 0.5 na na GW D SA CD D Snyder and Williams, 2000

Populus fremontii Salicaceae D T 0.5 na na 0–50 GW D SA CD D Snyder and Williams, 2000

Prosopis velutina Fabaceae D T 0.5 na na 0–50 GW D SA CD D Snyder and Williams, 2000

Pterocarpus officinalis Fabaceae E T 25 na na <60 D T CD D Colón-Rivera et al., 2014

Pinus ponderosa Pinaceae E T 73.8 na 28.7 39-41 R D SA CD S Kerhoulas et al., 2013

Pinus ponderosa Pinaceae E T 15.9 na 9.1 0-21 R W SA CD S Kerhoulas et al., 2013

Quercus Fagaceae E T 0.57 na 12.1 GW D M CD S David et al., 2013 suber Pinus elliottii Pinaceae E T na na na GW D T CD D Ewe et al., 1999

Eucalyptus Myrtaceae E T na na na 20-100 R D M CD S Burgess et al., 2000 camaldulensis Eucalyptus saligna Myrtaceae E T na na na 20-100 R D M CD S Burgess et al., 2000

Eucalyptus leucoxylon Myrtaceae E T na na na 20-100 R D M CD S Burgess et al., 2000

Nitraria tangutorum Nitrariaceae D S na na na 30-90 R D A CD S Zhu et al., 2011

Nitraria tangutorum Nitrariaceae D S na na na 0-30 R D A CD S Zhu et al., 2011

Nitraria tangutorum Nitrariaceae D S na na na >120 R D A CD S Zhu et al., 2011

Artemisia arenaria Asteraceae D S na na na 30-90 R D A CD S Zhu et al., 2011

Artemisia arenaria Asteraceae D S na na na 0-30 R D A CD S Zhu et al., 2011

Artemisia arenaria Asteraceae D S na na na >120 R D A CD S Zhu et al., 2011

Abies faxoniana Pinaceae E T na na na GW W A CD S Xu et al., 2011

Betula utilis Betulaceae E T na na na 0–40 R W A CD S Xu et al., 2011

Larix gmelinii Pinaceae D T na na na >120 R D TM CD S Sugimoto et al., 2003

Ulmus crassifolia Ulmaceae D T na na na S D TM CEI D Muttiah et al., 2005

Acer grandidentatum Sapindaceae D T >50 na na nm D SA CD S Dawson and Ehleringer, 1991

Acer negundo Sapindaceae E T >50 na na nm D SA CD S Dawson and Ehleringer, 1991

Quercus gambelii Fagaceae D T >50 na na nm D SA CD S Dawson and Ehleringer, 1991

Acer grandidentatum Sapindaceae D T <50 na na S D SA CD S Dawson and Ehleringer, 1991

Acer negundo Sapindaceae E T <50 na na S D SA CD S Dawson and Ehleringer, 1991

Quercus gambelii Fagaceae D T <50 na na S D SA CD S Dawson and Ehleringer, 1991

Chrysothamnus Asteraceae D S na na na R D SA CD S Donovan and Ehleringer, 1994 viscidiflorus Artemisia tridentata Asteraceae E S na na na R D SA CD S Donovan and Ehleringer, 1994

Pistacia lentiscus Anacardiaceae E T na na na R GW D M CD S Valentini et al.,1992

Phillyrea angustifolia Oleaceae E S na na na R GW D M CD S Valentini et al.,1992

Quercus ilex Fagaceae E T na na na R GW D M CD S Valentini et al.,1992

Quercus pubescens Fagaceae D T na na na GW D M CD S Valentini et al., 1992

Quercus cerri Fagaceae D T na na na GW D M CD S Valentini et al.,1992

Pinus edulis Pinaceae E T na na na R W A CD D Williams and Ehleringer, 2000

Juniperus osteosperma Cupressaceae E T na na na R W A CD D Williams and Ehleringer, 2000

Quercus gambelii Fagaceae D T na na na 50 D A CD D Williams and Ehleringer, 2000

Sabina vulgaris Cupressaceae E T na 1.61 0.9 250-350 R GW D A CD D Ohte et al., 2003

Artemisia ordosica Asteraceae D T na 1.89 0.7 <150 R D A CD D Ohte et al., 2003

Salix matsudana Salicaceae D T na 1.61 4.3 250-350 R GW D A CD D Ohte et al., 2003

Pinus flexilis Pinaceae E T na na na nm R D SA CD S Roberts et al., 2004

Rhus aromatica Anacardiaceae E T na na na nm R D SA CD S Roberts et al., 2004

Eucalyptus 40- Myrtaceae E T 1.5 10-30 S D SA AD D Thorburn and Walker, 1994 camaldulensis 120 Jacaranda copaia Bignoniaceae E T na na na 30-100 D T CD S Jackson et al., 1995

Annona spraguei Annonaceae D T na na na 30-100 D T CD S Jackson et al., 1995

Sterculia apetala Malvaceae D T na na na 30-100 D T CD S Jackson et al., 1995

Tetragastris Burseraceae E T na na na 30-100 D T CD S Jackson et al., 1995 panamensis Spondias radlkoferi Anacardiaceae D T na na na 0-30 D T CD S Jackson et al., 1995

Sapium aucuparium Euphorbiaceae D T na na na 0-31 D T CD S Jackson et al., 1995

Zanthoxylum Rutaceae D T na na na 0-32 D T CD S Jackson et al., 1995 setulosum Pseudobombox Malvaceae D T na na na 0-33 D T CD S Jackson et al., 1995 septentatum Rhizophora mangle Rhizophoraceae E T na na na <30 W T CD D Ewe et al., 2007

Rhizophora mangle Rhizophoraceae E T na na na <30 GW D T CD D Ewe et al., 2007

Juniperus phoenicea Cupressaceae E S na na na 5-50 D SA CD D Armas et al., 2010

Pistacia lentiscu Anacardiaceae E S na na na GW D SA CD D Armas et al., 2010

Populus nigra Salicaceae D T na na na <30 SA SP S Lambs et al., 2003

Salix Salicaceae D T na na na <30 SA SP S Lambs et al., 2003 alba Populus nigra Salicaceae D T na na na GW D SA SP S Lambs et al., 2003

Salix alba Salicaceae D T na na na GW D SA SP S Lambs et al., 2003

Chamaecytisus Fabaceae E T na na na GW D M na S Lefroy et al., 2001 proliferus Eucalyptus Myrtaceae E T na na na GW D SA AD S Thorburn and Ehleringer, 1995 camaldulensis Eucalyptus largiflorens Myrtaceae E T na na na GW D SA AD S Thorburn and Ehleringer, 1995

Acer grandidentatum Sapindaceae D T na na na GW D A AD S Thorburn and Ehleringer, 1995

Acer negundo Sapindaceae E T na na na GW D A AD S Thorburn and Ehleringer, 1995

Atriplex canescens Amaranthaceae E S na na na 30-40 D A AD S Thorburn and Ehleringer, 1995

Chrysothamnus Asteraceae D S na na na 30-40 D A AD S Thorburn and Ehleringer, 1995 nauseosus Vanclevea stylosa Asteraceae D S na na na 30-40 D A AD S Thorburn and Ehleringer, 1995

Melaleuca Myrtaceae E T na na na GW D M AD S Mensforth and Walker, 1996 halmaturorum Melaleuca Myrtaceae E T na na na 0-10 W M AD S Mensforth and Walker, 1996 halmaturorum Schinus Anacardiaceae E T na na na GW D ST CD S Ewe and Sternberg, 2002 terebinthifolius Myrica cerifera Myricaceae E T na na na GW D ST CD S Ewe and Sternberg, 2002

Baccharis halimifolia Asteraceae E S na na na GW D ST CD S Ewe and Sternberg, 2002

Rapanea punctata Myrsinaceae E S na na na GW D ST CD S Ewe and Sternberg,2002

Randia aculeata Rubiaceae E S na na na GW D ST CD S Ewe and Sternberg, 2002

Schinus Anacardiaceae E T na na na GW D ST CD S Ewe and Sternberg, 2002 terebinthifolius Myrica cerifera Myricaceae E T na na na GW D ST CD S Ewe and Sternberg, 2002

Baccharis halimifolia Asteraceae E S na na na GW D ST CD S Ewe and Sternberg, 2002

Schinus Anacardiaceae E T na na na 1-3 W ST CD S Ewe and Sternberg, 2002 terebinthifolius Myrica cerifera Myricaceae E T na na na 1-3 W ST CD S Ewe and Sternberg, 2002

Baccharis halimifolia Asteraceae E S na na na 1-3 W ST CD S Ewe and Sternberg, 2002

Pinus jeffreyi Pinaceae E T na na na <200 W M CD D Rose et al., 2003

Arctostaphylos patula Ericaceae E S na na na <200 W M CD D Rose et al., 2003

Doryphora aromatica Atherospermataceae E T na na na 0-100 W T AD S Drake and Franks, 2003

Argyrodendron Atherospermataceae SD T na na na 0-100 W T AD S Drake and Franks, 2003 trifoliolatum 35

Castanospora Sapindaceae E T na na na 0-100 W T AD S Drake and Franks, 2003 alphandii Doryphora aromatica Atherospermataceae E T na na na S D T AD S Drake and Franks, 2003

Argyrodendron Atherospermataceae SD T na na na 0-30 D T AD S Drake and Franks, 2003 trifoliolatum Castanospora Sapindaceae E T na na na S D T AD S Drake and Franks, 2003 alphandii Corymbia clarksoniana Myrtaceae E T 13.2 na na GW D ST AD S Cook and O’Grady, 2006

Eucalpytus platyphylla Myrtaceae E T 42.3 na na GW D ST AD S Cook and O’Grady, 2006

Melaleuca Viridiflora Myrtaceae E T 14.9 na na GW D ST AD S Cook and O’Grady, 2006

Spondias purpurea Anacardiaceae SD T 39–70 na na 0-15 D T CD D Querejeta et al., 2007

Cordia dodecandra Boraginaceae D T 38–42 na na 0-15 D T CD D Querejeta et al., 2007

Enterolobium Leguminosae E T 71–95 na na 70-300 GW D T CD D Querejeta et al., 2007 cyclocarpum Brosimum alicastrum Moraceae E T 32–99 na na 70-300 GW D T CD D Querejeta et al., 2007

Talisia olivaeformis Sapindaceae E T 32–59 na na 70-300 D T CD D Querejeta et al., 2007

Ficus cotinifolia Moraceae E T 45–79 na na 0-15 D T CD D Querejeta et al., 2007

Amorpha canescens Fabaceae D S na na na >30 D HC CD S Nippert and Knapp, 2007

Ceanothus spp. Rhamnaceae E S na na na >30 D HC CD S Nippert and Knapp, 2007

Eucalyptus coolabah Myrtaceae E T 120 na 6.8 <200 GW D A AD S Costelloe et al., 2008

Quercus fusiformis Fagaceae E T na na na nm D HC CD D Schwinning, 2008

Juniperus ashei Cupressaceae E T na na na nm D HC CD D Schwinning, 2008

Coccoloba diversifolia Polygonaceae E T na na na 75-300 D T CD S Hasselquist et al., 2010

Esenbeckia Rutaceae E T na na na 75-300 D T CD S Hasselquist et al., 2010 pentaphylla Vitex gaumeri Verbenaceae E T na na na 75-300 D T CD S Hasselquist et al., 2010

Caesalpinia gaumeri Fabaceae D T na na na <75 D T CD S Hasselquist et al., 2010

Lonchocarpus castilloi Fabaceae D T na na na <75 D T CD S Hasselquist et al., 2010

Lysiloma latisiliquum Fabaceae D T na na na <75 D T CD S Hasselquist et al., 2010

Abies fraseri Pinaceae E T na na na F W ST CD D Berry et al., 2014

Picea rubens Pinaceae E T na na na F W ST CD D Berry et al., 2014

Larix sibirica Pinaceae D T na na na 0-30 W SA CD S Li et al., 2006

Larix sibirica Pinaceae D T na na na >30 D SA CD S Li et al., 2006

Pinus sylvestris Pinaceae E T na na na 20-60 W SA CD D Wei et al., 2013

Pinus sylvestris Pinaceae E T na na na GW D SA CD D Wei et al., 2013

Ulmus pumila Ulmaceae D T na na na 0-40 SA CD S Su et al., 2014

Ulmus pumila Ulmaceae D T na na na >40 SA CD S Su et al., 2014

Ulmus pumila Ulmaceae D T na na na GW SA CD S Su et al., 2014

Pinus sylvestris Pinaceae E T 10.6 0.01 4.8 20-60 D SA CD D Song et al., 2014

Pinus sylvestris Pinaceae E T 10.6 0.01 4.8 20-60 GW W SA CD D Song et al., 2014

Pine spp Pinaceae E T na na na 30-200 D M CD S Ferna´ndez et al., 2008

Populus euphratica Salicaceae D T na na na 160-240 GW D A CD S Si et al., 2014

Brosimum alicastrum Moraceae E T na na na 50-250 D T CD S Querejeta et al., 2006

Asteraceae E S na na na 0-120 D SA CD D Kulmatiski et al., 2006 Artemisia tridentata Rosaceae D S na na na 0-120 D SA CD D Kulmatiski et al., 2006 Purshia tridentata Caragana microphylla Fabaceae D S na na na 100 D TM CD S Yang et al., 2011

Eucalyptus grandis Myrtaceae E T na na na GW D A CD D Feikema et al., 2010

Eucalyptus Myrtaceae E T na na na GW D A CD D Feikema et al., 2010 camaldulensis Radermachera sinica Bignoniaceae E T na na na 100 D ST CD D Nie et al., 2011

Alchornea trewioides Euphorbiaceae D S na na na R W ST CD D Nie et al., 2011

Sterculia euosma Malvaceae SD T na na na 100 W ST CD D Nie et al., 2011

Schefflera octophylla Araliaceae SD T na na na 100 W ST CD D Nie et al., 2011

Ficus orthoneura Moraceae E T na na na 100 W ST CD D Nie et al., 2011

Cyclobalanopsis >5 cm epikarst Fagaceae E T 5-34 na na S D ST CD D Deng et al., 2012 glauca zone Engelhardtia >5 cm epikarst Juglandaceae E T 5-34 na na S D ST CD D Deng et al., 2012 roxburyhiana zone Decaspermum >5 cm epikarst Myrtaceae E T 5-34 na na S D ST CD D Deng et al., 2012 esquiorlii zone >5 cm epikarst Platycarya strobilacea Juglandaceae D T 11 na na S D ST CD D Deng et al., 2012 zone Cyclobalanopsis >5 cm epikarst Fagaceae E T 1-4 na na S D ST CD D Deng et al., 2012 glauca zone Quercus emoryi Fagaceae D T na na na >50 W SA CD S Weltzin and McPherson, 1997

Sequoia sempervirens Cupressaceae E T na na na F D M CD D Dawson, 1998

Trichilia tuberculata Meliaceae E T 24-25 na na 70-100 D T CD S Meinzer et al., 1999

Quararibea asterolepis Malvaceae E T 23-37 na na 70-100 D T CD S Meinzer et al., 1999 luehea seemannii Malvaceae D T 24-41 na na >100 D T CD S Meinzer et al., 1999

Platypodium elegans Fabaceae D T 25-82 na na <100 D T CD S Meinzer et al., 1999

Platymiscium Fabaceae D T 33-68 na na <100 D T CD S Meinzer et al., 1999 pinnatum T 41-48 na na 80 D T CD S Meinzer et al., 1999 Tabebuia rosea Bignoniaceae D 37

Alseis T 22-34 na na 80 D T CD S Meinzer et al., 1999 blackiana Rubiaceae D Pinus taeda Pinaceae E T na na na 0-40 D ST CD S Retzlaff et al., 2001

Pinus taeda Pinaceae E T na na na 120 W ST CD S Retzlaff et al., 2001

Caltis pallida Cannabaceae SE S na na na >150 D ST AD D Midwood et al., 1998

Prosopis glandulosa Fabaceae D S na na na >150 D ST AD D Midwood et al., 1998

Acacia greggii Fabaceae D T na na na >150 D ST AD D Midwood et al., 1998

Azadirachta indica Meliaceae E T na na na <100 W A na S Smith et al., 1998

Sarcobatus D na na na W A S vermiculatus Chenopodiaceae S 50 CD Chimner and Cooper, 2004 Chrysothamnus E na na na W A S nauseosus Asteraceae S 50 CD Chimner and Cooper, 2004 Chrysothamnus E na na na A S greenei Asteraceae S >200 W CD Chimner and Cooper, 2004 Hyeronima Euphorbiaceae E T na na na <100 W T S Gutiérrez-Soto and Ewel, 2008 alchorneoides CD Cedrela odorata Meliaceae D T na na na <100 W T S Gutiérrez-Soto and Ewel, 2008 CD Eucalyptus largiflorens Myrtaceae E T na na na >200 R S D T Holland et al., 2006 CD D Banksia prionotes Proteaceae D T na na na GW D M CD D Pate et al., 1998

Fraxinus excelsior Oleaceae D T na na na nm D SA OI S Singer et al., 2013

Pinus nigra Pinaceae E T na na na GW D SA OI S Singer et al., 2013

Populus euphratica Salicaceae D T na na na 0-120 GW D A OI S Hao et al., 2013

Alhagi sparsifolia Leguminosae D T na na na 0-120 GW D A OI S Hao et al., 2013

Glycyrrhiza inflata Fabaceae D T na na na 0-120 GW D A OI S Hao et al., 2013

Apocynum venetum Apocynaceae D S na na na 0-120 GW D A OI S Hao et al., 2013

Retama monosperma Fabaceae D S na na na R GW D M S Esquivias et al., 2014 CD Thymus carnosus Lamiaceae D S na na na 0-60 GW D M S Esquivias et al., 2014 CD Hevea brasiliensis Euphorbiaceae E T 22 2.4 18 0-30 W ST Liu et al., 2014 CD S Hevea brasiliensis Euphorbiaceae E T 23 2.4 18 >70 D ST Liu et al., 2014 CD S Artemisia tridentata Asteraceae E S na na na 0-50 W SA Prieto et al., 2014 CD S Artemisia tridentata Asteraceae E S na na na 50-100 D SA Prieto et al., 2014 CD S Populas spp Salicaceae D T na na na <50 D SA Anderegg et al., 2013 CD D Pinus sylvestris Pinaceae E T na na na 20-80 D SA Yafen et al., 2012 CD D Juniperus osteosperma Cupressaceae E T na na na 20-40 D SA AD S Leffler and Caldwell, 2005

Artemisia tridentata Asteraceae E S na na na 20-40 D SA AD S Leffler and Caldwell, 2005

Terminalia sericea Combretaceae D T 4 na na 5-120 D ST Kulmatiski et al., 2010 CD S 38

Sclerocarya birrea Anacardiaceae D T 5 na na 5-120 D ST Kulmatiski et al., 2010 CD S Caragana intermedia Fabaceae D S na na na 0-60 D SA Jia et al., 2012 CD D Helianthemum Cistaceae D S na na na crystallization water of gypsum rocks D SA Palacio et al., 2014 squamatum CD D Acer negundo Sapindaceae D T na na na S GW D A Kolb et al., 1997 CD S Vochysia elliptica Vochysiaceae E T 6 na na <200 D T Jackson et al., 1999 CD S Roupala montana Proteaceae E T 6 na na <200 D T Jackson et al., 1999 CD S Miconia ferruginata Melastomataceae E T 6 na na <200 D T Jackson et al., 1999 CD S Sclerolobium Caesalpinoidae E T 5 na na <200 D T Jackson et al., 1999 paniculatum CD S Didymopanax Araliaceae E T 6 na na <200 D T Jackson et al., 1999 macrocarpum CD S Dalbergia Caesalpinoidae D T 7 na na <200 D T Jackson et al., 1999 myscolobium CD S Pterodon pubescens Faboideae D T 18 na na <200 D T Jackson et al., 1999 CD S Kielmeyera coriacea Guttiferae D T 6 na na <200 D T Jackson et al., 1999 CD S Qualea grandiflora Vochysiaceae D T 9 na na <200 D T Jackson et al. 1999 CD S Qualea parviflora Vochysiaceae D T 5 na na <200 D SA AD D Eggemeyer et al., 2008

Juniperus virginiana Cupressaceae E T na na na 5–50 D SA AD D Eggemeyer et al., 2008

Pinus ponderosa Pinaceae E T na na na 5–50 D SA AD D Eggemeyer et al., 2008

Juniperus virginiana Cupressaceae E T na na na <90 D SA AD D Eggemeyer et al., 2008

Pinus ponderosa Pinaceae E T na na na <90 D SA AD D Eggemeyer et al., 2008

Eucalyptus 38- Myrtaceae E T na na GW D SA CD S Drake et al., 2011 gomphocephala 170 Pinus contorta Pinaceae E T 17.6 na na 0-15 D TM CD S Andrews et al., 2012

Pseudotsuga menziesii Pinaceae E T 23.9 na na GW D TM CD S Andrews et al., 2012

Pseudotsuga menziesii Pinaceae E T 23.9 na na 0-15 W TM CD S Andrews et al., 2012

Pinus sylvestris Pinaceae E T 510 na na 20–60 W SA CD D Wei et al., 2013

Pinus sylvestris Pinaceae E T 510 na na GW D SA CD D Wei et al., 2013

Acer negund Sapindaceae D T na na na GW D HC na S Komor and Magner, 1996

Larix desidua Pinaceae D T na na na GW D HC CD S Valentini et al., 1994

Pinus sylvestris Pinaceae E T na na na R D HC CD S Valentini et al., 1994

Populus Salicaceae D T na na na GW D A CD S Smith et al., 1998 trichocarpa Poputus fremontii Salicaceae D T na na na GW D A CD S Smith et al., 1998

Safix gooddingii Salicaceae D T na na na GW D A CD S Smith et al., 1998

Tarnarix ramosissima Tamaricaceae D S na na na GW D A CD S Smith et al., 1998

Widdringtonia Cupressaceae E T na na na R W M CD D February et al., 2007 cedarbergensis Fagaceae E T na na na 40-100 D M CD D Kurz-Besson et al., 2006 Quercus suber Eucalyptus diversifolia Myrtaceae E T na na na nm GW D SA AD D Swaffer et al., 2014

Allocasuarina Casuarinaceae E T na na na GW D SA AD D Swaffer et al., 2014 verticillata Sabina vulgaris Cupressaceae E T na 1.61 0.9 GW D SA CD D Ohte et al., 2003

Artemisia ordosica Asteraceae D T na 1.89 0.7 0-50 D SA CD D Ohte et al., 2003

Salix matsudana Salicaceae D T na 1.28 4.6 >250 GW D SA CD D Ohte et al., 2003

Pinus Pinaceae E T na na na 20-100 D ST CD D Sun et al., 2008 massoniana Quercus aliena Fagaceae D T na na na 20-100 D ST CD D Sun et al., 2008

Coriaria sinica Coriariaceae. D T na na na 20-100 D ST CD D Sun et al., 2008

Melaleuca Myrtaceae E T na na na S GW D T CD D quinquenervia Wei et al., 2013 Casuarinaceae E T na na na S GW D T CD D Casuarina glauca Wei et al., 2013 Melaleuca Myrtaceae E T na na na R W T CD D quinquenervia Wei et al., 2013 Casuarinaceae E T na na na R W T CD D Casuarina glauca Wei et al., 2013 Alnus Betulaceae E T na na 10 20-80 D TM LVE D Bertrand et al., 2014 incana Pinus Pinaceae E T na na 7 20-80 GW D TM LVE D Bertrand et al., 2014 sylvestris Quercus aquifolioides Fagaceae E S na na na 0-30 W TM CD D Liu et al., 2011

Fagus spp Fagaceae D T na na na nm GW D TM PCSL D Penna et al., 2013 Fagus spp Fagaceae D T na na na nm W TM PCSL D Penna et al., 2013 Sapindaceae D T na na na nm D SA CD D Phillips and Ehleringer, 1995 Acer grandidentatum Quercus gambeli Fagaceae D T na na na nm D SA CD D Phillips and Ehleringer, 1995

Ulmaceae D T na na >3 R D SA CD D Ulmus crassifolia Kukowski et al., 2013 Quercus Fagaceae E T na na >3 R D SA CD D fusiformis Kukowski et al., 2013 Juniperus ashei Cupressaceae E T na na >3 R D SA CD D Kukowski et al., 2013 Eucalyptus Myrtaceae E T na na na GW D SA AD D camaldulensis Mensforth et al., 1994 Eucalyptus Myrtaceae E T na na na 5- 15 GW W SA AD D camaldulensis Mensforth et al., 1994 Pinaceae E T na na na 0-30 W T AD D Hartsough et al., 2008 Pinus hartwegii Philenoptera violacea Fabaceae SD T na na na GW W ST CD D February et al., 2007

Colophospermum Fabaceae D T na na na GW W ST CD D February et al., 2007 mopane Cedrela odorata Meliaceae SD T 11·8 na 12 0-30 D T CD D Schwendenmann et al., 2015

Tabebuia rosea Bignoniaceae D T 11·5 na 7.4 >30 D T CD D Schwendenmann et al., 2015

Hura crepitans Euphorbiaceae E T 18·1 na 5.6 >30 D T CD D Schwendenmann et al., 2015

Anacardium excelsum Anacardiaceae E T 9·9 na 6·4 >30 D T CD D Schwendenmann et al., 2015

Luehea seemannii Tiliaceae D T 11·1 na 8·3 >30 D T CD D Schwendenmann et al.,2015

Pseudotsuga menziesii Pinaceae E T na na na 0-100 D M CD D Brooks et al., 2010

N.B. Evergreen=E, Deciduous=D, Semi deciduous=SD, Semi Evergreen=SE, Tree=T, Shrub=S, Stream=S, Groundwater=GW, Fog=F, Rain=R, Dry=D, Wet=W, Tropical=T, Arid=A, Semiarid=SA, Temperate=TM, Mediterranean=M, Subtropical=ST, Humid continental=HC, cryogenical distillation=CD, azeotropic distillation=AD, liquid–vapour equilibration=LVE, dendrocronological method and oxygen isotope cellulose analysis=OI,cellulose synthesis and isotope=CEI, pressure chamber and suction lysimeter=PCSL, Dual isotope=D, Single isotope=S, na=not available, nm=soil depth not mentioned

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CHAPTER THREE: WATER USE BY TREES ACROSS TROPICAL FOREST AREAS: WHAT DO WE KNOW AFTER 30 YEARS OF FOREST HYDROLOGY RESEARCH?

3.1. Abstract Hydrological and vegetation models, with the help of various micro-meteorological methods, are widely used to calculate water fluxes. Correctly estimating these fluxes requires accurate transpiration measurements. Tree water use is known to be influenced by many factors but there is scant literature on the factors influencing tree water use in tropical forest areas. This study presents the first systematic review of research into tree water use in the tropics. The aim of the study was to understand the research trends and the influence of tree functional trait on water use. The study found a clear bias in research focus relating to geographic area and species group selection. Most of the studies (33.33%) were undertaken in Central America and focused on the Fabaceae, Myrtaceae, Dipterocarpaceae and Anacardiaceae families. The study found a clear trend between tree architecture and water use. The results indicate that with increased tree size (diameter at breast height and sapwood area), water use increases in tropical tree species. Seed mass is also a good predictor of tree water use. Seed mass showed a positive correlation with water use. On the other hand, wood density showed a negative relationship with tree water use. Season is highly significant in explaining variations in tree water use, as was leaf phenology. Tropical trees’ water use significantly increases during the dry season. There is no significant difference in water use between native and exotic species. Brevideciduous trees uptake a significantly larger amount of water compared to evergreen and deciduous species during the dry season. These tree traits and their relationship with water use can provide a tool for better understanding ecohydrological processes, which can support improved plantation management and conservation of water resources. This systematic review identified research gaps which, if addressed, could inform the development of hydrological and vegetation models for the efficient management of water resources.

Keywords: Forest management; sap flow; water resource management; water stress

3.2 Introduction

The interactions between plants and water are of fundamental interest to ecohydrologists because plants are an important element in the hydrologic cycle (Bosch and Hewlett, 1982; Asbjornsen et al., 2011). Whole-tree estimates of water use are becoming increasingly important in forest science and water resource management both at the catchment scale. Tree species re- introductions, or reforestation, is proving a successful technique for the restoration of depleted species populations, degraded habitats and ecosystems, and other ecosystem services (Ren et al., 2009; Zai et al., 2009; Rodríguez-Salinas et al., 2010; Polak and Saltz, 2011). As a consequence, the area of new plantations grew by 42% between 1990 and 2005, which is about 3.5% of total global forest cover (FAO, 2005). However, it is now established that large-scale reforestation development can significantly alter hydrologic regimes and thus affect water allocations. The most noticeable hydrologic response following large-scale reforestation will be a reduction in mean annual stream flow (Zhang et al., 2001) and groundwater recharge (Bruijnzeel, 2004). Reforestation can also affect the seasonal distribution of runoff or flow regimes, but predicting the seasonal impact has proven to be a difficult task (Best et al., 2003). To tackle this issue, scientists rely on hydrological models, vegetation models and direct measurements to calculate water fluxes (Bonan, 2008; Dierick and Holscher, 2009; Medlyn et al., 2011; Kunert et al., 2012). Accurate estimation of tree water use is therefore important for better understanding forest production, ecosystem functions and climate change (Pieruschka et al., 2010; Asbjornsen et al., 2011), and better managing catchments which supply water for public use (Chiew et al., 2009). Such information can help to resolve issues of water resource management and inform the development of rational water use policies (Cienciala and Lindroth, 1995; Lindroth et al., 1995; Schiller and Cohen, 1995; Loustau et al., 1996; Barrett et al., 1996).

Research on tree water use has a trans-disciplinary scope and importance, and is commonly undertaken by ecologists, geographers, agriculturalists, foresters and hydrologists. Tropical forest ecosystems serve as reference laboratories for the investigation of global climate change because of the high spatiotemporal variability of their environmental conditions, a rich and unique biodiversity, and a wide range of socio-economic conditions. These varying conditions can also influence tropical tree species’ water use. As scientific development and environmental pressures increase, it is increasingly necessary to evaluate recent progress in research and to challenge past priorities in the face of global climate change. An evaluation of the major research gaps in tree

51 species’ water use in the tropics, with special consideration given to water resource scarcity, has the potential to provide valuable information on how to advance handling future impacts of global climate change on efficient water use, forest production and functioning. In this context, it is time to evaluate the current state of tree water use knowledge and to propose a new set of research priorities for the coming decades. To do this, a systematic review of the scientific literature was undertaken. The specific objectives are to:

(1) evaluate the spatiotemporal dynamics of research on tree species’ water use in the tropics; (2) assess the nexus between tree functional traits and water use across tropical tree species; and (3) set new research priorities in tropical forest tree species’ water use.

3.3 Study Area and method

3.3.1 Defining “tropical forest area” for the present study To investigate tropical tree species’ water use research through a systematic review of the scientific literature, it was necessary to define “tropical forest areas”. To do so, tropical climates identified from the Köppen‐Geiger climate classification system (Beck et al., 2016) were combined with MODIS‐based land cover classifications (MOD12Q1) available from the NASA data centre (http://reverb.echo.nasa.gov/; Friedl et al., 2010). From the global land-cover data, the tropical climate region was extracted. Nine land-cover classes that included woody savannahs, savannahs, open shrubland, mixed forest, evergreen needle-leaved forest, evergreen broadleaf forest, vegetation mosaic, closed shrubland and decedious broadleaf forest were treated as “tropical forest areas”. Later, GPS locations of the reviewed research studies were extracted and plotted in the map to confirm that the study was conducted within the defined tropical forest areas (Fig. 3.1).

3.3.2 Methods 3.3.2.1 Literature search Literature search was conducted using the ISI Web of Science (covering 1980–2013) in May 2014 using the keywords ‘‘sap flow’’, ‘‘water use’’, ‘‘plantation’’ and “forest” and their associated expanded terms (see Supplementary Table 3.1). Keyword combinations were also used (see Supplementary Table 3.2). The ISI database includes documents stored in the web of science core 52 collection, CABI, BIOSIS preview, current contents connect, MEDLINE and SciELO Citation Index. The literature search covered journal articles, conference papers, reports and book chapters. From the initial search, 1,210 publications were found.

3.3.2.2 Research article inclusion criteria The 1,210 publications uncovered in the initial search were subject to further relevance screening. Only studies that met the following criteria were included in the systematic review: a) The study was conducted in a tropical country; b) The investigation was undertaken in forest areas; c) Only trees were the focus of the article, d) Tree water use was measured with thermal techniques e) The article was not related to methodological modifications; f) Tree water use was measured without any silvicultural treatment; g) The article was written in English; g) The article was not a tree water use review; h) And the article had a clear description of the investigated species. Considering all these criteria, from the initial 1,210 publications 52 articles were retained.

3.3.2.3 Data extraction from the selected articles and databases The systematic review involved extracting data from the 52 retained articles to assess the relationship between tree functional traits and water use. The extracted data included tree diameter at breast height (DBH), sapwood area, wood density, seed mass, leaf phenology,leaf area index (LAI), endemicity and daily water use. In most of the articles, wood density and seed size for the respective species were not mentioned. In these cases, wood density data were collected from the World Agroforestry Centre’s Wood Density Database (http://db.worldagroforestry.org/wd) and the Global Wood Density Database developed by Zanne et al. 2009 (http://hdl.handle.net/10255/dryad.235). Seed mass data were collected from the Kew Royal Botanic Gardens’ Seed Information Database (http://data.kew.org/sid/). To confirm the species’ family name, habitat distribution and leaf phenology, various databases were used including the Species 2000 Database (http://www.catalogueoflife.org/annual-checklist/2014/), the Useful Tropical Plants Database (http://tropical.theferns.info/), the Global Species Database (http://www.globalspecies.org/ntaxa/1311618), and the Atlas of Living Australia Database (http://www.ala.org.au/). To establish the seasonal variation of tree water use, seasonal information was extracted from the research article. Details of the extracted data are provided in Supplementray Table 3.3.

3.3.3 Data analysis 3.3.3.1 Spatiotemporal dynamics of the research The study sought to understand the spatiotemporal trends of research on tree water use in tropical areas’. The number of papers published per year was calculated and compared the number of research studies in different geographic regions. Also, a distribution map of research on tree water use in different tropical countries was prepared using ArcGIS 10.3.1.

3.3.3.2 Relationship between tree functional traits and water use A multiple linear regression was used to explore the relationship between trees DBH, sapwood area and water use. Since the data on tree water use reported in the different publications followed different standards, the data were converted into a single unit. A one-way ANOVA was conducted to investigate how leaf phenological variation (evergreen, deciduous, semi-deciduous and brevideciduous) influences water use. A two sample t-test was conducted to explain the mean difference in water use between native and exotic tree species. The same test was also applied to identify variations in seasonal water uptake. As the statistical significance test for mean difference does not explain the magnitude of the effect of different parameters, Cohen's dand f tests were applied (Cohen, 1988). Multiple linear regression, one-way ANOVA and t-test were applied using Minitab-16. However, the one-way ANOVA and t-tests did not explain the source of variation observed in water use by trees considering the combined effects of leaf phenology, endemicity and season. Therefore, Permutational multivariate analysis of variance (PerMANOVA) was used to test for significant differences in water use in relation to changes in leaf phenology, endemicity and season, both together and independently. PerMANOVA first calculates a distance matrix using a user-defined distance matrix. The test statistic (F-ratio) is calculated directly from the distance matrix. Permutations are then used to generate p-values to determine significance. For this analysis, water use of woody plant species was used to calculate the distance matrix, while leaf phenology, endemicity and season were treated as fixed factors. Euclideandistance matrices were used to calculate the distance matrix. A total of 9,999 permutations were used for this analysis. PerMANOVA was performed using PRIMER V6 (Plymouth Routines Multivariate Ecological Research) software.

3.3.3.3 Set new research priorities

The number of tree species within a family was recorded to determine if there was any bias towards specific family groups. To investigate the previous research priorities on tree water use in the tropical forest areas, the research focus of each study was identified from the article title. Simple counts of research focus and tree species’ family were then used to determine the previous research priorities on tree water use in the tropical forest areas. This helps to identify research gaps in tree water use research.

3.4 Results

3.4.1 Spatiotemporal dynamics of research on tree water use in tropical forest areas The distribution of research outputs by geographic region was assessed. The research outputs were uneven across continents. Most of the studies (33.33%) were undertaken in Central America followed by Asia (24.07%), South America (16.66%), Africa and Australia/Oceania (11.11%), and North America (3.70%). A total of 19 countries across the tropics were represented in the publications. Panama had the most studies (n=16), followed by Australia (n=6), Brazil (n=5) and other countries (Fig. 3.1b and supplementary Fig. 3.1).

Fig. 3.1. (a) Red dots indicate documented locations oftree water use studies. The deep green color of the background map shows the “tropical forest areas” extracted from MODIS‐based land cover map (Friedl et al., 2010) (b) The distribution of studies on tree water use in the “tropical forest areas”. The legend on the left-hand side representsthe number of research studies (c) Global climate types map (Beck et al., 2016), with tropical climatic regions indicated by green color.

3.4.2 Relationship between tree functional traits and water use The data on water flux by tropical forest trees are presented in Supplementray Table 3.3. The relationship between water use (kg/day) and seed mass (g), generated from Supplementray Table 3.3, is shown in Fig. 3.2a. A large data scattering was observed but a trend of increasing water use with seed mass was observed, althoughthe relationship is poorly correlated (r = 0.42). The R2 (adj) of the model is 16.92%, which means only 16.92% of the variation in water use can be accounted for by the regression model. This is another indication of poor correlation. Figure 3.2b shows the relationship between tree water use (kg/day) and DBH (cm). A large data scattering was not observed, and a very general trend of increasing water use with an increase in tree DBH (cm) was observed. The relationship between water use and DBH was strong (r= 0.86). The R2 (adj) of the model is 72.97%, meaning 72.97% of the variation in water use can be accounted for by the regression model.

A similar trend was found for tree water use (kg/day) and sapwood area (m2). Fig 3.2c shows an increase in tree water use with an increase in tree sapwood area. The relationship shows a high positive correlation (r = 0.97). The R2 (adj) of the model is 93.59%, which means 93.59% of the variation in water use can be explained by the regression model. The relationship between water use (kg/day) and wood density (g cm-3) is shown in Fig. 3.2d. Between these two parameters, a poor negative correlation (r =-0.21) was observed where water use decreases when wood density increases. This is also indicated where only 5.14% of the variation in water use can be explained by the regression model. Multiple linear regression were also conductedfor 73 trees taking into consideration DBH, seed mass, wood density and water use. A probability level of 0.1 was assumed given the exploratory nature of the study. The multiple linear regression suggests that wood density (p = 0.06) is negatively related and seed mass (p = 0.08) is positively related to water use when the impact of DBH is also considered. Due to a data shortage, sapwood area was not considered for multiple linear regression analysis.

Fig. 3.2. (a) The relationship between water use (kg/day) and seed mass (g). Data presented shows species from 106 trees; (b) the relationship between water use (kg/day) and DBH (cm). Data presented shows species from 107 trees; (c) the relationship between water use (kg/day) and sapwood area (m2). Data presented shows species from 42 trees; (d) the relationship between water use (kg/day) and wood density (g cm-3). Data presented shows species from 185 trees.

Leaf phenology has an influence on water use. Mean daily water use is higher in brevideciduous trees (350.91 kg/day) followed by evergreen (29.12 kg/day), deciduous (23.89 kg/day) and semi- deciduous trees (15.70 kg/day). No significant difference was found in mean water use among evergreen, deciduous and semi-deciduous trees. However, there is a significant difference (p=0.01 at p<0.5) between brevideciduous trees and the other three leaf phenological groups (Fig. 3.3a). The brevideciduous trees have a large effect (Cohen’s f = 0.86 and Cohen’s d = 1.72) on water use over deciduous, semi-deciduous and evergreen trees. In this era of global climate change where the supply of freshwater is expected to decline in many regions, it is often assumed that exotic tree species will use more water. This study shows that native trees used more water (average 51

58 kg/day) than exotic trees (24.90 kg/day) (Fig. 3.3b). However, no significant difference (p=0.173 at p<0.05) was found in water use between native and exotic tree species.

Fig. 3.3. (a) Tropical tree water transport variation due to leaf phenology. Brevideciduous (n=11), Deciduous (n=32), Evergreen (n=126) and Semi-deciduous (n=19). Effect size,Cohen’s f = 0.86 and Cohen’s d = 1.72 (effect sizes from ANOVAs with multiple groups, based on group means), indicates a large effect of brevideciduous trees on water use compare to deciduous, evergreen and semi-deciduous trees in the tropics. (b) Water use of native vs exotic species. Native trees are represented by 149 sample trees while exotic trees are represented by 49 sample trees.

The mean water use by trees in the dry season (74 kg/day) is higher than the wet season (21.9 kg/day). In the case of seasonal variation of water uptake by the trees, there is a significant difference (p=0.007 at p<0.05) between the dry and wet seasons (Fig. 3.4). The dry season has an adverse effect (Cohen's d = -0.416) on tree water use which means tropical trees tend to use more water in the dry season than the wet season.

Fig. 3.4. Seasonal variation of water uptake by tropical forest trees. The dry season represents a sample of 90 trees and the wet season represents a sample of 78 trees.

PerMANOVA results indicated that the season is highly significant in explaining the variation in tree water use (p=0.001), as was leaf phenology (p=0.001). In contrast, endemicity is not significant in explaining variation in tree water use (p=0.817). The interaction of season and leaf phenology (p=0.001) was considered significant in explaining the variation seen between samples (Table 3.1). While only within-leaf phenology was considered to examine if this explains seasonal variation, there was no significant variation in water uses observed due to changes in season at the within-leaf phenology level (Table 3.2). Whenthe season is fixed such as with the wet season, no significant variation was observed in water use between the leaf phenology group except between evergreen and brevideciduous. However, in the dry season, a highly significant variation was observed between brevideciduous and the other leaf phenological groups (Table 3.3).

Table 3.1. Results from the PerMANOVA of the distance matrix constructed from tree water use across the tropics

Source of variation df Mean square F P <0.05 Endemicity 1 2.02E-02 5.47E-02 0.817 Season 1 10.778 29.23 0.001 Leaf phenology 3 12.896 34.98 0.001 Season: Leaf phenology 3 6.1116 16.57 0.001

Table 3.2. Results from the PerMANOVA of the seasonal variation in water use when leaf phenology is fixed

Source of variation t P <0.05 Evergreen species’ water use in the dry and wet season 1.22 0.230 Brevi deciduous species’ water use in the dry and wet season 2.18 0.109 Deciduous species’ water use in the dry and wet season 0.88 0.428 Semi-deciduous species’ water use in the dry and wet season 1.31 0.202

Table 3.3. Results from the PerMANOVA of the between leaf phenological groups due to changes in season

Source of variation t P <0.05 Dry season Evergreen: Brevideciduous 9.62 0.001 Evergreen: Deciduous 0.50 0.645 Evergreen: Semi-deciduous 0.94 0.299 Brevideciduous: Deciduous 5.91 0.001 Brevideciduous: Semi-deciduous 2.64 0.040 Deciduous: Semi-deciduous 0.91 0.308 Wet season Evergreen: Brevideciduous 2.63 0.073 Evergreen: Deciduous 0.90 0.389 Evergreen: Semi-deciduous 0.66 0.574 Brevideciduous: Deciduous 2.00 0.115 Brevideciduous: Semi-deciduous 1.55 0.154 Deciduous: Semi-deciduous 1.97E-02 0.987

3.4.3. The previous research priorities The reviewed studies report on whole tree water use by 220 species from 73 families (Fig. 3.5). Most of the species are examined in only one study although three or more reports of water use are available for some species (e.g. Eucalyptus spp, Ficusinsipida, Huracrepitans, Cedrelaodorata, Lueheaseemannii) (Supplemetary Table 3.1). The research outputs were not distributed evenly 61 among families. Most of the studies focused on species from the family Fabaceae (9%), followed by Myrtaceae and Dipterocarpaceae (7%), Anacardiaceae (6.64%), Euphorbiaceae (3.08%), Malvaceae (6.16%) and Moraceae (3.08%). The studies compiled in Supplemetary Table 3.1 show the quantitative rate of tree water use ranges from 0.0000002 kg/day to 785 kg/day. Most of the published research work focused on measuring only the transpiration or water use of different trees (14.25%) (Fig. 3.6).

Vochysiaceae Verbenaceae Urticaceae Ulmaceae Tiliaceae Styracaceae Sterculiaceae Stemonuraceae Simaroubaceae Sapotaceae Sapindaceae Salicaceae Rutaceae Rubiaceae rhizosporaceae Rhamnaceae Proteaceae Polygonaceae Podocarpaceae Poaceae Piperaceae Pinaceae Olacaceae Ochnaceae Nyctaginaceae Myrtaceae Myrsinaceae Myristicaceae Myricaceae Moraceae Menispermaceae Meliaceae Melastomataceae Malvaceae Malpighiaceae Leguminosae Lecythidaceae Lauraceae Family Lamiaceae Icacinaceae Flacourtiaceae Fagaceae Fabaceae Euphorbiaceae Erythroxylaceae Ebenaceae Dipterocarpaceae Dilleniaceae Dillenaceae Cupressaceae Compositae Combretaceae Clusiaceae Cerambycidae Celastraceae Cecropiaceae Caryocaraceae Calycanthaceae Calophyllaceae Caesalpiniaceae caesalpinaceae Burseraceae Boraginaceae Bombacaceae Bignoniaceae Balanopaceae Asteraceae Arecaceae Araucariaceae Araliaceae Annonaceae Anacardiaceae Alzateaceae

0 2 4 6 8 10 Percentage

Fig. 3.5. Distribution of research outputs among tree species’ family 62

Fig. 3.6. The research focus of the reviewed studies

3.5 Discussion

3.5.1 Geographic biases in tree water source research focus A trend in the research on tree water use is an increasing focus on managing plant ecosystem and water resources more efficiently. The systematic review found that some tropical countries are strongly represented in the research on tree water use while other countries are critically under- investigated.It is difficult to determine any root cause of this bias in research outputs. Research history and the interests of individuals and organizations, along with priorities of funding agencies and governments all likely play a role in influencing the type of research conducted and published (Fazey et al., 2005). In addition, country-specific capacities for science and research support also have an influence on research outputs.

3.5.2 Effects of tree functional traits on water use Tree water use patterns depend on many factors. Species, forest types, climate and location, soil, and forest management an all have a large influence on tree water use. Despite variability in geographical distribution, trees show similar trends in their water use (Kallarackal et al., 2013). A strong linear relationship between tree size and daily water use has been observed by several researchers (Meinzer et al., 2001; Wullschleger et al., 2001; Motzer et al., 2005; McJannet et al., 2007). Present analysis of data from tropical regions shows a similar trend. With an increase in tree DBH and sapwood area, the water use by trees also increases. Unlike other studies, O’Grady et al. (1999) have reported a negative relationship between wood density, seed mass and tree water use. This previous study was the first to assess whether seed mass influences tree water use and it showed an interesting pattern of tree water use with an increase in seed mass.From the literature, it was found that large seeds have agreater probability of survival under extreme conditions (Khan, 2004), with higher germination rates and higher seedling growth rates (Blade´ and Vallejo, 2008). In addition, larger seeds generate larger seedlings and root systems (Milberg and Lamont, 1997; Green and Juniper, 2004; Castro et al., 2008). Therefore, using their larger root systems, large-seeded trees can reach deeper soil layers and extract more water resources (Milberg and Lamont, 1997). However, these findings raise the question of whether eucalypts or other species belonging to the Myrtaceae family use more water than others pecies given their small seed sizes. From the scatter plot of Fig. 3.2a, it was observed that the number of large- seeded trees was less and the correlation was poor (r = 0.43). Hence, more data is required to

64 address the question of whether or not large-seeded trees do use more water than smaller- seeded trees.

In this era of global climate change, when freshwater supply is expected to decline in many regions of the world, exotic tree species are often considered to be "high water users" (Cavaleri et al., 2014). However, there has been little research to support this statement. The assumption that exotic trees species use more water is based on the belief that they have fast growth rates and therefore use more water.But evidence suggests that the growth of exotic trees can be slower than co-occuring natives and that the growth rate is largely influenced by site conditions (Daehler, 2003). Therefore, the assumption that exotic trees grow faster and because of this they uptake more water is not always true. Recent studies have shown that in Hawaiian lowland tropical forest, the native species Metrosideros polymorpha had higher sap flow rates per tree than the invasive trees Macaranga mappa, Melastoma septemnervium and Cecropia obtusifolia, and that this was because of the larger DBH of the native trees (Cavaleri et al., 2014). Other studies have shown that the exotic species saltcedar (Tamarix ramosissima) used similar amounts of water to native cottonwoods and willows (Cleverly et al., 2002; Nagleret al., 2003). In Australia, research shows thatnative Eucalyptus camaldulensis and exotic willows (Salix spp.) have similar rates of water use when growing on river banks (Doody and Benyon, 2011; Doody et al., 2011). The systematic review supports these findings and shows that there is no significant difference in water use by native and exotic tree species in the tropics (Fig. 3.3b).

In contrast to endemicity, there are assumptions that evergreen species havelower water-use efficiency relative to deciduous species because they are subject to water stress during the dry season, which deciduous species avoid. Recent studies conducted on three continents support this assumption showing that evergreen species utilise water less efficiently than deciduous species possibly to protect their photosynthetic machinery against high temperatures and light during the dry season (Tomlinson et al., 2013). In this systematic review, no significant difference in water use was observed between evergreen and deciduous species. Brevideciduous species were found to use significantly more water than evergreen and deciduous species during the dry season (Fig. 3.3a and Table 3.3). This finding contradicts with the findings of other studies. The reason for this can be explained using the concept of hydraulic conductivity. Recent studies on the nexus of

65 hydraulic conductivity and phenological group (i.e. deciduous, brevideciduous and evergreen species) of tropical dry forest show that only evergreen species’hydraulic conductivity was consistent throughout the dry and wet seasons. In contrast, hydraulic conductivity in deciduous and brevideciduous tree species varies greatly, ranging from 6% in the dry season to 56% in the wet season, indicating thata significant portion of the xylem remains functional during the dry season (Brodribb et al., 2002). However, to make a definitive conclusion about whether brevideciduous species use more water than evergreen species requires further research.

3.5.3 Research needs and future research priorities based on a forest hydrological perspective This study has identified a number of research gaps. This synthesis also demonstrated that scientific research output on tree water use in the tropics is biased towards certain species. One example is tree species from the Myrtaceae family where Eucalyptus is the major species. There is a widespread myth that eucalypts consume more water than any other tree species or agricultural crop. This may be a reason for the large body of research on Eucalyptus species. Eucalyptus plantations are the subject of criticism because of their high water use and other negative environmental impacts in different regions of the world (Morris et al., 2004). Examination of the evidence for these claims has usually concluded that well-managed plantations are beneficial rather than detrimental to the environment (White et al., 1995; Casson, 1997). Though the tropical region is blessed with high tree species diversity, only the Myrtaceae species group has been rigorously investigated. Therefore, there is lack of research on the water use of trees from other species groups.

This systematic review also provides an insight into the focus of research intotree water use over the last 30 years. Most of the research has focused on estimating the water use and transpiration rates of different species. Little research has focused on the relationships between water use and endemicity (native and exotic), hydraulic traits, soil water availability, groundwater use and tree size (Fig. 3.6). Hence, there remain many questions abouthow tree water use interacts with ecosystem functioning. This review has identified the following priorities for future research into tree water use in the tropics.

3.5.3.1 Do more productive forests use more water? From this systematic review, it was found that there is scant literature on tropical forest trees water use. Moreover, when reviewing the existing literatureon water use rates under plantation conditions, information is only available for a very limited set of species and climatic conditions. The review also found there is controversy regarding plantation design and productivity. Some authors have reported that mixed-species plantations are more productive than monocultures (Forrester et al., 2010a). However, other authors disagree with these findings and report the opposite. For example, Nguyena et al. (2012) found that monocultures are more productive than mixtures, while Kanowski et al. (2005) found that plantations of exotic pines may also be acceptable from a biodiversity conservation perspective in well-forested landscapes.In addition, there is growing concern that mixed-species forests could use more water when they are more productive (Kunert et al., 2012). Greater water use could reduce soil moisture availability within the forest. To tackle this issue, there is a need for improved understanding of biophysical aspects of tree species and their complex interactions with mixed-species forest productivity and water use. This is particularly crucial under present changing climate conditions. Currently, little is known about how productivity may relate to stand water use and efficiency. Until recently, there have been few studies conducted on this topic (Forrester et al., 2010b; Forrester, 2015). Taking these issues into consideration, future study should focus on the questions surrounding whether mixtures that are more productive than monocultures also use more water, or whether they are more water-use efficient. Such questions can be addressed by testing the following 3 hypotheses: 1) On sites where mixtures grow faster than monocultures, trees also use more water, 2) Trees in mixtures are more water-use efficient and increases in water use are outweighed by greater increases in growth, and 3) Increases in water-use efficiency are restricted to sites where mixing also increases growth rates of that species. Research to address these hypotheses will help inform appropriate species selections and other design aspects of plantations. The resulting improved plantation designs can then support more sustainable water resource use at catchment scales.

3.5.3.2 How do forestry practices and changes to forest design affect water use in the face of climate change? Forest management and design can have a noticeable impact on the water use of a plantation stand. Further research is needed to better understand how partial or selective thinning and clear felling influence tree water use at a catchment scale.The species mix, tree ages and architecture 67 also influence tree water use. In addition, climate change is putting increasing pressure on limited water resources. Therefore, it is crucial to determine how forestry practices and changes to forest design can affect water use in the face of climate change? Currently, there is a lack of research into these questions in the tropics.

3.5.3.3 How realistic is scaling plant water use data from whole trees to stands and catchments? Extrapolating whole tree water use to stand-level and catchment-scale water use is anissueof much debate in the field of ecohydrology (Mackay et al., 2010). However, it is difficult to measure sapflux from a large number of plants due the high costs, and therefore, scaling approaches must consider tree architecture, species type and diversity, soil water availability and soil properties from single tree to stand and catchment scale (Fig. 3.7).

Fig. 3.7. Scaling tree water use from whole tree to a catchment scale where ecohydrological constraints need to be considered

3.6 Conclusion The role of forests in water resource management is likely to become more important in this era of global climate change as both the demand for freshwater and the duration of dry summers increase across many region of the world. Species-specific water use data are essential for the further development of hydrological models for better predictions of streamflows and the better design of plantations. From this systematic review it is clear that research on tree water use in the tropics is still scare. If the recommended research priorities can be addressed, many important forest-water relations issues could be better understood and more effectively managed.

3.7 Acknowledgement The first author would like to thank The University of Queensland, Australia for granting IPRS (International Postgraduate Research Scholarship) and APA (Australian Postgraduate Award) scholarships for conducting this research. Thanks also to Dr Ian Nuberg, The University of Adelaide for his insightful comment on an earlier version of this paper Thanks are due to Mr. Hadayet Ullah, The University of Adelaide for hishelp with PRIMER V6 statistical software. Thanks to Tim R. McVicar (Editor, Journal of Hydrology) and Anthony P. O’Grady (Associate Editor, Journal of Hydrology) for their insightful comments that helped to improve the paper. Thanks also to Dr. Jack Baynes and Dr John Medows for proofreading and English language editing.

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3.9 Supplementary information Supplementary Table 3.1. Keywords used for the literature search

ID Key term Expanded term 1 Sap flow 2 Water use Water use* OR “Water use efficiency” 3 Plantations Planted* OR reforest* OR afforest OR “Two species mixture” OR “multi species mixture” OR “mixed stand” OR “mixed plantation” 4 Forest Forest* OR agroforest* OR “agro forest”

Supplementary Table 3.2. Total number of publications found using the keyword combinations

ID Combination of keywords for literature WoS search (Web of science) all database 1 1 AND 2 AND 3 176 2 1 AND 2 AND 4 976 3 1 AND 2 AND 3 AND 4 58 Total 1210

Supplementary Table 3.3. Species-specific functional traits and water use of tropical forest trees

Wood Water DBH Sapwood Seed LAI leaf Species Family Age N/E density Season Flux Country Reference (cm) (m2) mass (g) (m2/m2) phenology (g/cm3) (kg/day) Abuta racemosa Menispermaceae n/a n/a 12 n/a n/a 579 n/a n/a n/a n/a Panama Meinzer et al., 2005

Acacia gaumeri Fabaceae n/a n/a 5.16 n/a 0.72 29.75 n/a D n/a 4.22 Mexico Reyes-Garcı´a et al., 2012

Acacia mangium Fabaceae 9.5 E 33.7 n/a 0.49 5.4 n/a D E 183 Malaysia Ciencialaa et al., 2000

Acacia mangium Fabaceae n/a E 32.3 n/a 0.49 5.4 n/a D E 27.5 Panama Kunert et al., 2010

Acacia mangium Fabaceae n/a E 32.3 n/a 0.49 5.4 n/a W E 17.8 Panama Kunert et al., 2010

Acronychia crassipetala Rutaceae n/a N n/a n/a n/a 4 4.5 W n/a 0.0017 Australia McJannet et al., 2007

Adansonia Sp. Malvaceae n/a N n/a n/a n/a 532 n/a W D n/a Madagascar Chapotin et al., 2006

Adansonia rubrostipa Malvaceae n/a N n/a n/a n/a 766 n/a W D n/a Madagascar Chapotin et al., 2006

Agathis borneensis Araucariaceae n/a N 63.4 n/a 0.4 n/a n/a W,D E n/a Brunei Becker et al., 1996

Allophylus psilospermus Sapindaceae n/a N n/a n/a n/a n/a W SD n/a Panama Phillips et al., 1999

Alseis blackiana Rubiaceae n/a N 28 n/a 0.536 0.1 n/a D SD n/a Panama Meinzer et al., 1999

Alseis blackiana Rubiaceae n/a n/a 22 n/a 0.536 0.1 n/a D SD n/a Panama Meinzer et al., 2001

Alvaradoa amorphoides Simaroubaceae n/a E 7.93 n/a 0.54 7.6 n/a W n/a 4.71 Mexico Reyes-Garcı´a et al., 2012

Alvaradoa amorphoides Simaroubaceae n/a E 7.93 n/a 0.54 7.6 n/a D n/a 7.07 Mexico Reyes-Garcı´a et al., 2012

Alzatea verticillata Alzateaceae n/a N 45 0.13526 n/a n/a 6.4 D E 28.7 Ecuador Motzer et al., 2005

Alzatea verticillata Alzateaceae n/a N 20.2 0.03139 n/a n/a 6.4 D E 5.6 Ecuador Motzer et al., 2005

Anacardium excelsum Anacardiaceae n/a N 10.1 n/a 0.41 2873 n/a D BD 13.7 Panama Kunert et al., 2010

Anacardium excelsum Anacardiaceae n/a N 10.1 n/a 0.41 2873 n/a W BD 10.1 Panama Kunert et al., 2010

Anacardium excelsum Anacardiaceae n/a N 98 0.66 0.41 2873 n/a D BD 750 Panama James et al., 2003

Anacardium excelsum Anacardiaceae n/a N 101.8 0.51 0.41 2873 n/a W BD 379 Panama Andrade et al., 1998

Anacardium excelsum Anacardiaceae n/a N 10.1 n/a 0.41 2873 n/a W BD 10.5 Panama Dierick et al., 2010

Anacardium excelsum Anacardiaceae n/a N 10.1 n/a 0.41 2873 2.44 W,D BD 10.9 panama Kunert et al., 2012

Anacardium excelsum Anacardiaceae n/a N 12.9 n/a 0.41 2873 2.44 W,D BD 21.8 Panama Kunert et al., 2012

Anacardium excelsum Anacardiaceae n/a N n/a 0·51 0.41 2873 n/a D BD 379·0 Panama Goldstein et al., 1998.

Anacardium excelsum Anacardiaceae n/a N 98 0.66 0.41 2873 n/a D BD 750 Panama Meinzer et al., 2003

Anacardium excelsum Anacardiaceae n/a N 98 n/a 0.41 2873 n/a D BD 750 Panama James et al., 2002

Anacardium excelsum Anacardiaceae n/a N 98 n/a 0.41 2873 n/a D BD 785 Panama Meinzer et al., 2004

Anacardium excelsum Anacardiaceae n/a N 98 n/a 0.39 2873 n/a D BD n/a Panama McCulloh et al., 2011

Anacardium excelsum Anacardiaceae n/a N 32 n/a 0.41 2873 n/a D BD n/a Panama Meinzer et al., 1999

Anacardium excelsum Anacardiaceae n/a N n/a n/a 0.48 2873 n/a W BD n/a Panama Phillips et al., 1999

Anacardium excelsum Anacardiaceae n/a N 74 n/a 0.41 0.48 n/a n/a BD n/a Panama Meinzer et al., 2005

Anacardium excelsum Anacardiaceae n/a N 32 n/a 0.41 2873 n/a D BD n/a Panama Meinzer et al., 2001

Anacardium occidentale Anacardiaceae 4 E 14.55 0.01521 0.41 4087 n/a D E 14 Ghana Oguntunde, 2007

Anacardium occidentale Anacardiaceae n/a E 15.53 0.012988 0.45 4087 4.5 W E n/a Ghana Oguntunde and Giesen, 2005

Aniba muca Lauraceae n/a N 12.7 0.01269 0.95 n/a 6.4 D n/a 7.5 Ecuador Motzer et al., 2005

Aniba sp. Lauraceae n/a N 15.9 0.01327 0.95 505 6.4 D n/a 9.7 Ecuador Motzer et al., 2005

Annona spraguei Annonaceae n/a N n/a n/a 0.559 37 n/a W n/a n/a Panama Phillips et al., 1999

Antirrhoea trichantha n/a N n/a n/a n/a n/a n/a W D n/a Panama Phillips et al., 1999

Apeiba membranacea Tiliaceae n/a n/a 41 n/a n/a 21 n/a D D n/a Panama Meinzer et al., 2001

Apoplanesia paniculata Fabaceae n/a n/a 9.39 n/a 0.7 3 n/a W n/a 6.75 Mexico Reyes-Garcı´a et al., 2012

Astronium graveolens Anacardiaceae n/a N n/a n/a 0.82 29 n/a W E n/a Panama Phillips et al., 1999

Balanops australiana Balanopaceae n/a N n/a n/a 0.705 6.1 3.3 W n/a 0.001 Australia McJannet et al., 2007

Balizia elegans Fabaceae n/a N n/a n/a 0.495 n/a n/a W E n/a Costa Rica O'brien et al., 2004

Beilschmiedia tooram Lauraceae n/a N n/a n/a 0.732 5800 4.5 W E 0.0017 Australia McJannet et al., 2007

Blepharocalyx salicifolius Myrtaceae n/a N 15.2 n/a 0.76 38 n/a W,D BD n/a Brazil Bucci et al., 2008

Bursera simaruba Burseraceae n/a N 15.38 n/a 0.43 117 n/a W D 15.33 Mexico Reyes-Garcı´a et al., 2012

Byrsonima crassa Malpighiaceae n/a N 7.3 n/a n/a 2.94 n/a W,D BD n/a Brazil Bucci et al., 2008

Byrsonima laxiflora Malpighiaceae n/a N n/a n/a n/a 2.02 n/a D E n/a Brazil Gotsch et al., 2010

Byrsonima pachyphylla Malpighiaceae n/a N n/a n/a n/a 2.02 n/a D E n/a Brazil Gotsch et al., 2010

Caesalpinia gaumeri Fabaceae n/a N 14.63 n/a 0.74 205.14 n/a W E 12.17 Mexico Reyes-Garcı´a et al., 2012

Caesalpinia gaumeri Fabaceae n/a N 16.24 n/a 0.74 205.14 n/a D E 14.9 Mexico Reyes-Garcı´a et al., 2012

Calophyllum costatum Clusiaceae n/a N n/a n/a 0.621 5600 4.5 W E 0.0017 Australia McJannet et al., 2007

Caryocar brasiliense Caryocaraceae n/a E 1110 n/a 0.65 n/a n/a D SD n/a Brazil Bucci et al., 2004

Caryocar brasiliense Caryocaraceae n/a N 8.6 n/a 0.65 n/a n/a W,D BD n/a Brazil Bucci et al., 2008 French Cassipourea guianensis rhizosporaceae n/a N 54 0.0186 0.82 n/a 8.6 D E n/a Granier et al., 1996 Guiana

Castanopsis acuminatissima Fagaceae n/a N 71.4 0.12115 0.59 n/a n/a W,D E 132 Indonesia Horna et al., 2011

Castanopsis foxworthyi Fagaceae n/a N 20.3 n/a n/a n/a W,D E n/a Brunei Becker et al., 1996

Cecropia insignis Urticaceae n/a N n/a n/a 0.309 0.6 0.7 D E n/a Panama Meinzer et al., 1995

Cecropia insignis Urticaceae n/a N 16 n/a 0.27 0.309 n/a D E n/a Panama McCulloh et al., 2011

Cecropia insignis Cecropiaceae n/a N n/a n/a 0.326 0.6 n/a W E n/a Costa Rica O'brien et al., 2004

Cecropia insignis Cecropiaceae n/a N 19 n/a 0.326 0.6 n/a D E n/a Panama Meinzer et al., 2001

Cecropia longipes Moraceae n/a N 19.7 0.02 0.475 n/a 5.54 W SD 46.5 Panama Andrade et al., 1998

Cecropia longipes Cecropiaceae n/a N n/a 0·02 0.29 n/a n/a D SD 46·5 Panama Goldstein et al., 1998.

Cecropia longipes Urticaceae n/a N 15 n/a 0.35 0.7 n/a D SD n/a Panama McCulloh et al., 2011

Cecropia longipes Cecropiaceae n/a N 13 n/a n/a 0.7 n/a D SD n/a Panama Meinzer et al., 2001

Cecropia montana Cecropiaceae n/a N 28.3 0.06304 0.29 n/a 6.4 D n/a 126.5 Ecuador Motzer et al., 2005

Cecropia obtusifolia Urticaceae n/a N n/a n/a 0.232 0.7 0.7 D E n/a Panama Meinzer et al., 1995

Cecropia obtusifolia Cecropiaceae n/a N n/a n/a 0.26 0.7 n/a W E n/a Costa Rica O'brien et al., 2004

Cecropia peltata Cecropiaceae n/a n/a 24 n/a 0.31 0.6 n/a D E n/a Panama Meinzer et al., 2001

Cedrela odorata Meliaceae n/a N 12 n/a 0.45 17 n/a D D 3.2 Panma Kunert et al., 2010

Cedrela odorata Meliaceae n/a N 12 n/a 0.45 17 n/a W D 7.9 Panama Kunert et al., 2010

Cedrela odorata Meliaceae n/a N 12 n/a 0.45 17 n/a W D 9.9 Panama Dierick et al., 2010

Cedrela odorata Meliaceae n/a N 12 n/a 0.45 17 0.8 W,D D 5.8 Panama Kunert et al., 2012

Cedrela odorata Meliaceae n/a N 18.1 n/a 0.45 17 0.8 W,D D 16.2 Panama Kunert et al., 2012

Ceiba schottii Malvaceae n/a E 14.89 n/a 0.48 54.92 n/a W D 21.19 Mexico Reyes-Garcı´a et al., 2012

Chloroleucon mangense Fabaceae n/a E 10.68 n/a 0.61 45 n/a W D 4.37 Mexico Reyes-Garcı´a et al., 2012

Cinnamomum porrectum Lauraceae n/a E 51.9 n/a 0.496 33 4.5 D E n/a Thailand Kume et al., 2007

Cinnamomum propinquum Lauraceae n/a N n/a n/a n/a 106 3.3 W E 0.001 Australia McJannet et al., 2007

Coccoloba manzanillensis Polygonaceae n/a N n/a n/a n/a 27.14 0.7 D E n/a Panama Meinzer et al., 1995

Coffea arabica Rubiaceae 4 E n/a n/a 0.62 188.3 n/a W,D E 0.000002 Costa Rica Kanten and Vaast, 2006

Coffea arabica Rubiaceae n/a E n/a n/a 0.62 188.3 n/a n/a E n/a Brazil Righi et al., 2008

Cordia alliodora Boraginaceae n/a N 34 0.07 0.52 33 n/a D D 46 Panama James et al., 2003

Cordia alliodora Boraginaceae n/a N 34 0.07 0.52 33 n/a D D 46 Panama Meinzer et al., 2003

Cordia alliodora Boraginaceae n/a N n/a n/a 0.52 33 n/a D D 37 Panama James et al., 2002

Cordia alliodora Boraginaceae n/a N 34 n/a 0.52 33 n/a D D 42 Panama Meinzer et al., 2004

Cordia alliodora Boraginaceae n/a N 36 n/a 0.56 33 n/a D D n/a Panama McCulloh et al., 2011

Cordia alliodora Boraginaceae n/a N 44 n/a 0.49 33 n/a D D n/a Panama Meinzer et al., 1999

Cordia alliodora Boraginaceae n/a N 34 n/a 0.49 33 n/a n/a D n/a Panama Meinzer et al., 2005

Cordia alliodora Boraginaceae n/a N 40 n/a 0.49 0.55 n/a D D n/a Panama Meinzer et al., 2001

Corymbia bella Myrtaceae n/a N 40 n/a n/a 8.64 n/a W,D E n/a Australia O'grady et al., 2006b

Corymbia clarksoniana Myrtaceae n/a N 29.8 n/a 0.827 n/a n/a D E 16.25 Australia O’Grady et al., 2006a

Corymbia tessellaris Myrtaceae n/a N 8.5 n/a 0.895 8.64 n/a D E 6.02 Australia O’Grady et al., 2006

Cotylelobium burckii Dipterocarpaceae n/a N 32.2 n/a 0.81 340 n/a W,D n/a n/a Brunei Becker et al., 1996

Croton macrostachys Euphorbiaceae n/a N n/a n/a 0.523 61 0.7 D D 8 Ethiopia Fetene and Beck, 2004

Cryptocarya laevigata Lauraceae n/a E 21.3 0.01127 0.687 n/a n/a W,D E 1 Indonesia Horna et al., 2011

Cryptocarya murrayi Lauraceae n/a N n/a n/a 0.676 2100 4.2 W E 0.0017 Australia McJannet et al., 2007

Cupressus lusitanica Cupressaceae n/a E 20 n/a 0.39 5.5 n/a W E 17.66 Ethiopia Fritzsche et al., 2006

Cupressus lusitanica Cupressaceae n/a E 20 n/a 0.39 5.5 n/a D E 9 Ethiopia Fritzsche et al., 2006

Curatella americana Dilleniaceae n/a N n/a n/a 0.65 18.89 1.14 W,D SD n/a Venezuela Herrera et al., 2012

Dalbergia miscolobium Leguminosae n/a N 7.9 n/a n/a 101.9 n/a W,D BD n/a Brazil Bucci et al., 2008

Davilla elliptica Dillenaceae n/a N 7.3 n/a n/a 24 n/a W,D BD n/a Brazil Bucci et al., 2008

Dialium guianense Caesalpiniaceae n/a N n/a 1.674 0.87 n/a 4.24 W,D E n/a Venezuela Rollenbeck and Anhuf ,2007

Diospyros cuneata Ebenaceae n/a n/a 4.62 n/a 0.66 305.34 n/a W E 1.62 Mexico Reyes-Garcı´a et al., 2012

Diospyros cuneata Ebenaceae n/a n/a 12.66 n/a 0.66 305.34 n/a D E 15.88 Mexico Reyes-Garcı´a et al., 2012

Dipterocarpus globosus Dipterocarpaceae n/a N 53.4 n/a 0.7 n/a n/a W,D E n/a Brunei Becker et al., 1996

Dipterocarpus pachyphyllus Dipterocarpaceae n/a N 113.1 n/a n/a 3199.7 6.2 W,D n/a n/a Malaysia Kume et al., 2008

Dipterocarpus pachyphyllus Dipterocarpaceae n/a N n/a n/a n/a 3199.7 6.2 W,D n/a n/a Malaysia Kume et al., 2011

Dipteryx panamensis Fabaceae n/a N n/a n/a n/a 7542 n/a W E n/a Costa Rica O'brien et al., 2004

Dipteryx panamensis Fabaceae n/a N 41 n/a n/a 7542 n/a D E n/a Panama Meinzer et al., 2001

Dobalanops aromatica Dipterocarpaceae n/a N 142.7 n/a 0.68 3750 6.2 W,D E n/a Malaysia Kume et al., 2008

Dobalanops aromatica Dipterocarpaceae n/a N n/a n/a 0.68 3750 6.2 W,D E n/a Malaysia Kume et al., 2011

Dobalanops aromatica Dipterocarpaceae n/a N 75 n/a 0.68 1270 n/a W,D E n/a Brunei Becker et al., 1996

Durio zibethinus Bombacaceae n/a N 19.8 n/a 0.57 n/a 5.2 W E 44.6 Philippines Dierick and Ho¨ lscher., 2009

Durio zibethinus Bombacaceae n/a N 19.8 n/a 0.57 n/a n/a W E 32.9 Philippines Dierick et al., 2010

Entada monostachya Fabaceae n/a n/a 25 n/a n/a n/a n/a n/a E n/a Panama Meinzer et al., 2005 French Eperua falcata caesalpinaceae n/a N 183 0.0658 0.69 7070 8.6 D E n/a Granier et al., 1996 Guiana French Eperua grandifolia caesalpinaceae n/a E 76 0.0339 n/a 16405 8.6 D E n/a Granier et al., 1996 Guiana Eremanthus glomerulatum Compositae n/a N 5.2 n/a n/a n/a n/a W,D E n/a Brazil Bucci et al., 2008

Eriotheca pubescens Bombacaceae n/a N 9.9 n/a n/a n/a n/a W,D BD n/a Brazil Bucci et al.,2008

Erythrina poeppigiana Fabaceae 4 E n/a n/a 0.2 0.33 1.24 W,D SD 0.0000005 Costa Rica Kanten and Vaast., 2006

Erythroxylum suberosum Erythroxylaceae n/a E 750 n/a n/a n/a n/a D E n/a Brazil Bucci et al., 2004

Eucalyptus deglupta Myrtaceae 4 E n/a n/a 0.594 0.16 1.77 W,D E 0.0000007 Costa Rica Kanten and Vaast., 2006

Eucalyptus globulus Myrtaceae n/a E n/a n/a 0.785 4.95 1 D E 55 Ethiopia Fetene and Beck., 2004

Eucalyptus globulus Myrtaceae n/a E 20 n/a 0.785 4.95 n/a W E 8 Ethiopia Fritzsche et al., 2006

Eucalyptus globulus Myrtaceae n/a E 20 n/a 0.785 4.95 n/a D E 38.33 Ethiopia Fritzsche et al., 2006

Eucalyptus miniata Myrtaceae n/a N n/a n/a 0.921 34.6 1 W,D E n/a Australia O'grady et al., 1999

Eucalyptus miniata Myrtaceae n/a N n/a n/a 0.921 34.6 0.57 W,D E n/a Australia Eamus et al., 2000

Eucalyptus platyphylla Myrtaceae n/a N 16.3 n/a 0.895 n/a n/a D E 14.91 Australia O’Grady et al., 2006a

Eucalyptus terminalis Myrtaceae n/a N n/a n/a n/a 15.4 0.57 W,D E n/a Australia Eamus et al., 2000

Eucalyptus tetrodonta Myrtaceae n/a N n/a n/a 0.878 22.07 1 W,D E n/a Australia O'grady et al., 1999

Eucalyptus tetrodonta Myrtaceae n/a N n/a n/a 0.878 22.07 0.57 W,D E n/a Australia Eamus et al., 2000

Eucalyptus urophylla Myrtaceae n/a E 9.1 8.17 0.55 41.1 2.34 W E 27.000000 China Morris et al., 2004

Eucalyptus urophylla Myrtaceae n/a E n/a n/a 0.55 41.1 2.77 W,D E n/a Venezuela Herrera et al., 2012

Eugenia coloradensis Myrtaceae n/a n/a 31 n/a n/a 525 n/a D n/a n/a Panama Meinzer et al., 2001

Eugenia muelleri Myrtaceae n/a N 25.7 n/a 0.87 525 n/a W,D E n/a Brunei Becker et al., 1996

Ficus crassiramea Moraceae n/a N 17.8 n/a 0.46 0.45 6.2 W,D E n/a Malaysia Kume et al., 2008

Ficus crassiramea Moraceae n/a N n/a n/a 0.46 0.45 6.2 W,D E n/a Malaysia Kume et al., 2011

Ficus insipida Moraceae n/a N 65 0.31 0.339 1 n/a D E 331 Panama James et al., 2003

Ficus insipida Moraceae n/a N 56.7 0.21 0.339 1 5.38 W E 164 Panama Andrade et al., 1998

Ficus insipida Moraceae n/a N n/a 0·21 0.339 1 n/a D E 164·0 Panama Goldstein et al., 1998.

Ficus insipida Moraceae n/a N 65 0.31 0.339 1 n/a D E 331 Panama Meinzer et al., 2003

Ficus insipida Moraceae n/a N 65 n/a 0.37 1 n/a D E n/a Panama McCulloh et al., 2011

Ficus insipida Moraceae n/a N 122 n/a 0.339 1 n/a D E n/a Panama Meinzer et al., 1999

Ficus insipida Moraceae n/a N 65 n/a 0.339 1 n/a n/a E n/a Panama Meinzer et al., 2005

Ficus insipida Moraceae n/a N 122 n/a 0.339 1 n/a D E n/a Panama Meinzer et al., 2001

Gliricidia sepium Fabaceae n/a E n/a n/a 0.74 0.14 1.3 D D 13.3 Indonesia Köhler et al., 2010

Gliricidia sepium Fabaceae n/a E 15 0.00955 0.74 0.14 1.3 W,D D 14 Indonesia K¨ohler et al., 2009

Gliricidia sepium Fabaceae n/a E 15 n/a 0.74 0.14 n/a W D 13.9 Indonesia Dierick et al., 2010

Gmelina arborea Verbenaceae n/a E 21.9 n/a 0.56 n/a 5.2 W SD 27.6 Philippines Dierick and Ho¨ lscher., 2009

Gmelina arborea Verbenaceae n/a E 17.8 n/a 0.56 n/a n/a D SD 8.6 Panama Kunert et al., 2010

Gmelina arborea Verbenaceae n/a E 17.8 n/a 0.56 n/a n/a W SD 4.1 Panama Kunert et al., 2010

Gmelina arborea Verbenaceae n/a E 21.9 n/a 0.56 n/a n/a W SD 19.8 Philippines Dierick et al., 2010

Goupia glabra Celastraceae n/a N n/a 6.462 0.7 n/a 4.24 W,D SD n/a Venezuela Rollenbeck and Anhuf, 2007

Graffenrieda emarginata Melastomataceae n/a N 17.8 0.02369 0.54 n/a 6.4 D E 7.9 Ecuador Motzer et al., 2005

Guapira areolata Nyctaginaceae n/a N n/a n/a n/a 7.08 n/a D E n/a Brazil Gotsch et al., 2010

Guapira noxia Nyctaginaceae n/a N n/a n/a n/a 7.08 n/a D BD n/a Brazil Gotsch et al., 2010

Guapira noxia Nyctaginaceae n/a N 7.6 n/a n/a 23.68 n/a W,D BD n/a Brazil Bucci et al., 2008

Guapira standleyanum Nyctaginaceae n/a N 57.5 n/a n/a 103.1 n/a D E n/a Panama Meinzer et al., 1999

Guapira standleyanum Nyctaginaceae n/a N 69 n/a n/a 103.1 n/a D E n/a Panama Meinzer et al., 2001

Guarea sp Meliaceae n/a E 42 0.12289 0.57 200 6.4 D E 101 Ecuador Motzer et al., 2005

Guatteria dumetorum Annonaceae n/a n/a 30 n/a 0.466 150 n/a n/a E n/a Panama Meinzer et al., 2005

Guatteria dumetorum Annonaceae n/a n/a 46 n/a 0.466 n/a n/a D E n/a Panama Meinzer et al., 2001

Guettarda elliptica Rubiaceae n/a E 6.24 n/a 0.58 86.3 n/a W E 2.62 Mexico Reyes-Garcı´a et al., 2012

Guettarda elliptica Rubiaceae n/a E 6.26 n/a 0.58 86.3 n/a D E 2.31 Mexico Reyes-Garcı´a et al., 2012

Gymnopodium floribundum Polygonaceae n/a N 7.84 n/a 0.6 76.32 n/a W D 4.26 Mexico Reyes-Garcı´a et al., 2012

Gymnopodium floribundum Polygonaceae n/a N 5.88 n/a 0.6 76.32 n/a D D 2.09 Mexico Reyes-Garcı´a et al., 2012

Heritiera albiflora Sterculiaceae n/a N 27.7 n/a n/a n/a n/a W,D n/a n/a Brunei Becker et al., 1996

Hevea spp Euphorbiaceae n/a E n/a n/a 0.487 2300 n/a n/a E n/a Brazil Righi et al., 2008

Hopea beccariana Dipterocarpaceae n/a N 31.8 n/a 0.59 n/a n/a W,D n/a n/a Brunei Becker et al., 1996

Hopea malibato Dipterocarpaceae n/a N 11.6 n/a 0.89 9.62 5.2 W E 9.1 Philippines Dierick and Ho¨ lscher, 2009

Hopea malibato Dipterocarpaceae n/a N 11.6 n/a 0.89 9.62 n/a W E 9.1 Philippines Dierick et al., 2010

Hopea plagata Dipterocarpaceae n/a N 6.6 n/a 0.89 1.5 5.2 W E 4 Philippines Dierick and Ho¨ lscher, 2009

Hopea plagata Dipterocarpaceae n/a N 6.6 n/a 0.89 1.5 n/a W E 4 Philippines Dierick et al., 2010

Hura crepitans Euphorbiaceae n/a N 18 n/a 0.34 868 n/a D SD 7.2 Panama Kunert et al., 2010

Hura crepitans Euphorbiaceae n/a N 18 n/a 0.34 868 n/a W SD 14.2 Panama Kunert et al., 2010

Hura crepitans Euphorbiaceae n/a N 18 n/a 0.34 868 n/a W SD 14.6 Panama Dierick et al., 2010

Hura crepitans Euphorbiaceae n/a N 18 n/a 0.34 868 1.79 W,D SD 12.4 Panama Kunert et al., 2012

Hura crepitans Euphorbiaceae n/a N 19.3 n/a 0.34 868 1.79 W,D SD 24.1 Panama Kunert et al., 2012

Hura crepitans Euphorbiaceae n/a N 60 n/a 0.34 868 n/a n/a SD n/a Panama Meinzer et al., 2005

Hura crepitans Euphorbiaceae n/a N 60 n/a 0.37 868 n/a D SD n/a Panama Meinzer et al., 2001

Hyeronima alchorneoides Euphorbiaceae n/a N n/a n/a n/a 7 n/a W E n/a Costa Rica O'brien et al., 2004

Hyeronima sp. Euphorbiaceae n/a N 18 0.02539 0.6 n/a 6.4 D E 11.7 Ecuador Motzer et al., 2005

Hymenolobium Fabaceae n/a N n/a n/a n/a n/a n/a W D n/a Costa Rica O'brien et al., 2004 mesoamericanum Idiospermum australiense Calycanthaceae n/a N n/a n/a 0.65 82000 4.2 W E 0.0017 Australia McJannet et al., 2007

Iryanthera elliptica Myristicaceae n/a N n/a 1.155 n/a n/a 4.24 W,D n/a n/a Venezuela Rollenbeck and Anhuf, 2007

Isonandra lanceolata Sapotaceae n/a n/a 42.5 n/a 0.93 n/a n/a W,D E n/a Brunei Becker et al., 1996

Jacaranda copaia Bignoniaceae n/a N 79.5 n/a n/a 5 n/a D BD n/a Panama Meinzer et al., 1999

Jacaranda copaia Bignoniaceae n/a N 73 n/a 0.35 5 n/a D BD n/a Panama Meinzer et al., 2001

Karwinskia humboldtiana Rhamnaceae n/a N 10.03 n/a 0.7 76.32 n/a W E 3.07 Mexico Reyes-Garcı´a et al., 2012

Karwinskia humboldtiana Rhamnaceae n/a N 10.03 n/a 0.7 76.32 n/a D E 8.19 Mexico Reyes-Garcı´a et al., 2012

Kielmeyera coriacea Calophyllaceae n/a E 4 n/a n/a 131.2 n/a n/a E n/a Brazil Meinzer et al., 2005

Kielmeyera coriacea Calophyllaceae n/a E 7 m n/a n/a n/a n/a D E n/a Brazil Bucci et al., 2004

Lecythis ampla Lecythidaceae n/a N n/a n/a 0.68 1245 n/a W D n/a Costa Rica O'brien et al., 2004

French Lecythis idatimon lecythidaceae n/a E 123 0.0515 0.85 1970 8.6 D E n/a Granier et al., 1996 Guiana Luehea seemannii Malvaceae n/a n/a 43 n/a 0.67 n/a n/a D E n/a Panama McCulloh et al., 2011

Lithocarpus elegans Fagaceae n/a n/a 28.5 n/a 0.799 8100 4.5 D E n/a Thailand Kume et al., 2007

Lonchocarpus latifolius Leguminosae n/a N 34 n/a n/a 81 n/a D BD n/a Panama Meinzer et al., 1999

Lonchocarpus latifolius Fabaceae n/a N 25 n/a n/a 81 n/a D BD n/a Panama Meinzer et al., 2001

Lophopetalum pachyphyllum Celastraceae n/a n/a 47.6 n/a 0.38 178 n/a W,D E n/a Brunei Becker et al., 1996

Lophopetalum subovatum Celastraceae n/a n/a 101 n/a n/a 178 n/a W,D E n/a Brunei Becker et al., 1996

Lophostemon suaveolens Myrtaceae n/a N 15.1 n/a 0.758 0.46 n/a D E 7.92 Australia O’Grady et al., 2006a

Luehea seemannii Malvaceae n/a N 11.8 n/a 0.417 n/a n/a D E 12.3 Panama Kunert et al., 2010

Luehea seemannii Malvaceae n/a N 11.8 n/a 0.417 n/a n/a W E 13.3 Panama Kunert et al., 2010

Luehea seemannii Malvaceae n/a N 38.2 0.1 0.417 n/a 3.74 W E 129 Panama Andrade et al., 1998

Luehea seemannii Malvaceae n/a N 11.8 n/a 0.417 n/a n/a W E 13.1 Panama Dierick et al., 2010

Luehea seemannii Malvaceae n/a N 11.8 n/a 0.417 2 3.22 W,D E 12.5 Panama Kunert et al., 2012

Luehea seemannii Malvaceae n/a N 13.3 n/a 0.417 2 3.22 W,D E 20.9 Panama Kunert et al., 2012

Luehea seemannii Malvaceae n/a N n/a 0·10 0.417 2 n/a D E 129·0 Panama Goldstein et al., 1998.

Luehea seemannii Malvaceae n/a N 32.5 n/a 0.417 2 n/a D E n/a Panama Meinzer et al., 1999

Luehea seemannii Malvaceae n/a N n/a n/a 0.417 2 n/a W E n/a Panama Phillips et al., 1999

Luehea seemannii Malvaceae n/a N 40 n/a 0.417 2 n/a n/a E n/a Panama Meinzer et al., 2005

Luehea seemannii Malvaceae n/a N 38 n/a 0.417 2 n/a D E n/a Panama Meinzer et al., 2001

Lysiloma latisiliquum Fabaceae n/a N 13.52 n/a 0.48 27.5 n/a W D 8.21 Mexico Reyes-Garcı´a et al., 2012

Lysiloma latisiliquum Fabaceae n/a N 13.52 n/a 0.48 27.5 n/a D D 10.26 Mexico Reyes-Garcı´a et al., 2012

Machaerium acutifolium Fabaceae n/a N n/a n/a 1.12 83 n/a D D n/a Brazil Gotsch et al., 2010

Machaerium opacum Fabaceae n/a N n/a n/a n/a 83 n/a D D n/a Brazil Gotsch et al., 2010

Melaleuca argentea Myrtaceae n/a N 40 n/a 0.87 0.11 n/a W,D E n/a Australia O'grady et al., 2006b

Melaleuca leucadendra Myrtaceae n/a N 88.8 n/a 0.633 0.41 n/a D E 206.1 Australia O’Grady et al., 2006a

Melaleuca quinquenervia Myrtaceae n/a N n/a 9.1 0.57 0.22 n/a W,D E 0.0000005 Australia McJannet, 2008

Melaleuca viridiflora Myrtaceae n/a N 7.2 n/a 0.861 0.26 n/a D E 3.65 Australia O’Grady et al, 2006

Melicope jonesii Rutaceae n/a N n/a n/a n/a n/a 4.5 W n/a 0.0017 Australia McJannet et al., 2007

Miconia argentea Melastomataceae n/a N n/a n/a 0.589 0.08 0.7 D n/a n/a Panama Meinzer et al., 1995

Miconia cuspidata Melastomataceae n/a N n/a n/a n/a 0.1 n/a D E n/a Brazil Gotsch et al., 2010

Miconia ferruginata Melastomataceae n/a n/a 4 n/a n/a 0.54 n/a n/a E n/a Brazil Meinzer et al., 2005

Miconia pohliana Melastomataceae n/a N n/a n/a n/a 0.1 n/a D E n/a Brazil Gotsch et al., 2010

Minquartia guianensis Olacaceae n/a N n/a n/a 0.77 n/a n/a W E n/a Costa Rica O'brien et al., 2004

Myrcia rostrata Myrtaceae n/a N n/a n/a 0.87 173 n/a D E n/a Brazil Gotsch et al., 2010

Myrcia tomentosa Myrtaceae n/a N n/a n/a 0.82 173 n/a D D n/a Brazil Gotsch et al., 2010

Myrica javanica Myricaceae n/a N 22.1 n/a 0.72 n/a 5.2 W E 61.7 Philippines Dierick and Ho¨ lscher, 2009

Myrica javanica Myricaceae n/a N 22.1 n/a 0.72 n/a n/a W E 43.2 Philippines Dierick et al., 2010

Myrsine ferruginea Myrsinaceae n/a N n/a n/a n/a 13.75 n/a D E n/a Brazil Gotsch et al., 2010

Myrsine guianensis Myrsinaceae n/a N n/a n/a n/a 13.75 n/a D E n/a Brazil Gotsch et al., 2010

Nephelium lappaceum Sapindaceae n/a N 18.8 n/a 0.73 1920 n/a W,D E n/a Brunei Becker et al., 1996

Ochroma pyramidale Malvaceae n/a N 51 n/a 0.14 8 n/a D E n/a Panama Meinzer et al., 2001

Ochroma pyramidale Malvaceae n/a N n/a n/a n/a 8 n/a W,D E n/a Panama Machado and Tyree, 1994

Ocotea cf. amazonica Lauraceae n/a N n/a 2.237 n/a n/a 4.24 W,D E n/a Venezuela Rollenbeck and Anhuf, 2007

Ocotea cf. Cinera Lauraceae n/a N n/a 0.735 n/a n/a 4.24 W,D E n/a Venezuela Rollenbeck and Anhuf, 2007

Ocotea sp Lauraceae n/a N 12.7 0.01062 0.48 n/a 6.4 D E 5 Ecuador Motzer et al., 2005

Oenocarpus bacaba Arecaceae n/a N n/a 0.268 0.65 n/a 4.24 W,D E n/a Venezuela Rollenbeck and Anhuf, 2007

Ouratea castaneaefolia Ochnaceae n/a N n/a n/a n/a 150 n/a D E n/a Brazil Gotsch et al., 2010

Ouratea hexasperma Ochnaceae n/a N n/a n/a n/a 150 n/a D E n/a Brazil Gotsch et al., 2010

Ouratea hexasperma Ochnaceae n/a E 950 n/a n/a 150 n/a D E n/a Brazil Bucci et al., 2004

Palaquium luzoniense Sapotaceae n/a E 52.7 0.43949 0.55 1200 n/a W,D E n/a Indonesia Horna et al., 2011

Palicourea guianensis Rubiaceae n/a N n/a n/a 0.52 14 0.7 D E n/a Panama Meinzer et al., 1995

Parashorea malaanonan Dipterocarpaceae n/a N 12 n/a 0.44 1860 5.2 W E 10.6 Philippines Dierick and Ho¨ lscher, 2009

Parashorea malaanonan Dipterocarpaceae n/a N 12 n/a 0.44 1860 n/a W E 10.6 Philippines Dierick et al., 2010

Parkia biglobosa Fabaceae n/a N 90 n/a 0.53 426.9 n/a D D 130.96 Burkina Faso Bayala et al., 2008

Pentace adenophora Dipterocarpaceae n/a N 56 n/a n/a n/a n/a W,D n/a n/a Brunei Becker et al., 1996

Pentaclethra macroloba Fabaceae n/a N n/a n/a 0.65 5840 n/a W E n/a Costa Rica O'brien et al., 2004

Persea cerulea Lauraceae n/a E 12.5 0.01193 0.44 n/a 6.4 D E 7.7 Ecuador Motzer et al., 2005

Phoebe mexicana Cerambycidae n/a N n/a n/a n/a 597.46 n/a W E n/a Panama Phillips et al., 1999

Phyllostylon brasiliense Ulmaceae n/a E 12.82 n/a 0.72 n/a n/a W n/a 19.13 Mexico Reyes-Garcı´a et al., 2012

Pinus patula Pinaceae n/a N 19.1 n/a 0.45 94 n/a W,D E n/a Mexico Alvarado-Barrientos et al., 2013

Piper obtusifolium Piperaceae n/a N 11.1 0.00964 0.457 n/a 6.4 D n/a 4.5 Ecuador Motzer et al., 2005

Piscidia piscipula Fabaceae n/a N 13.03 n/a 0.59 16.61 n/a W D 7.45 Mexico Reyes-Garcı´a et al., 2012

Piscidia piscipula Fabaceae n/a N 13.28 n/a 0.59 16.61 n/a D D 6.59 Mexico Reyes-Garcı´a et al., 2012

Pithecellobium dulce Fabaceae n/a N 9.65 n/a 0.71 100.8 n/a D D 6.62 Mexico Reyes-Garcı´a et al., 2012

Platea excelsa Stemonuraceae n/a N 28.7 0.02978 0.36 n/a n/a W,D n/a n/a Indonesia Horna et al., 2011

Platymiscium pinnatum Fabaceae n/a n/a 33 n/a 0.78 297 n/a n/a D n/a Panama Meinzer et al., 2005

Platymiscium pinnatum Fabaceae n/a n/a 39 n/a 0.78 297 n/a D D n/a Panama Meinzer et al., 2001

Podocarpus falcatus Podocarpaceae n/a N 20 n/a 0.469 425 n/a W E 10.26 Ethiopia Fritzsche et al., 2006

Podocarpus falcatus Podocarpaceae n/a N 20 n/a 0.469 425 n/a D E 1.66 Ethiopia Fritzsche et al., 2006

Pouteria firma Sapotaceae n/a E 21.3 0.07452 0.58 n/a n/a W,D E n/a Indonesia Horna et al., 2011

Prioria copaifera Fabaceae n/a N 68 n/a 0.405 53153 n/a D E n/a Panama Meinzer et al., 2001

Pseudobombax septenatum Bombacaceae n/a N 130 n/a 0.14 72 n/a n/a D n/a Panama Meinzer et al., 2005

Pseudobombax septenatum Malvaceae n/a N n/a n/a 0.14 72 n/a W,D D n/a Panama Machado and tyree, 1994

Pseudobombax septenatum Bombacaceae n/a n/a 98 n/a 0.14 72 n/a D D n/a Panama Meinzer et al., 2001

Psychotria tinctoria Rubiaceae n/a N 6.1 0.00207 0.52 n/a 6.4 D n/a 1.2 Ecuador Motzer et al., 2005

Qualea dichotoma Vochysiaceae n/a N n/a n/a n/a 44 n/a D BD n/a Brazil Gotsch et al., 2010

Qualea parviflora Vochysiaceae n/a N n/a n/a n/a 44 n/a D D n/a Brazil Gotsch et al., 2010

Qualea parviflora Vochysiaceae n/a N 5.9 n/a n/a n/a n/a W,D D n/a Brazil Bucci et al., 2008

Quararibea asterolepis Bombacaceae n/a N 30 n/a 0.45 249.5 n/a D E n/a Panama Meinzer et al., 1999

Quararibea asterolepis Bombacaceae n/a N 37 n/a 0.45 249.5 n/a n/a E n/a Panama Meinzer et al., 2005

Quararibea asterolepis Bombacaceae n/a N 28 n/a 0.45 249.5 n/a D E n/a Panama Meinzer et al., 2001

Randia obcordata Rubiaceae n/a n/a 5.54 n/a 0.7 14.23 n/a W E 1.91 Mexico Reyes-Garcı´a et al., 2012

Roupala montana Proteaceae n/a N 5.8 n/a 0.69 n/a n/a W,D E n/a Brazil Bucci et al., 2008

Ruagea pubescens Meliaceae n/a N 22 0.03796 0.47 n/a 6.4 D E 21.7 Ecuador Motzer et al., 2005

Ruagea pubescens Meliaceae n/a N 38.2 0.10367 0.47 n/a 6.4 D E 81.6 Ecuador Motzer et al., 2005

Ruizterania trichanthera Vochysiaceae n/a N n/a 4.817 n/a 660 4.24 W,D n/a n/a Venezuela Rollenbeck and Anhuf, 2007

Sandoricum koetjape Meliaceae n/a N 16.3 n/a 0.39 1220 5.2 W E 32.8 Philippines Dierick and Ho¨ lscher, 2009

Sandoricum koetjape Meliaceae n/a N 16.3 n/a 0.39 1220 n/a W E 23.4 Philippines Dierick et al., 2010

Santiria apiculata Burseraceae n/a N 36.4 0.05893 0.53 77 n/a W,D n/a n/a Indonesia Horna et al., 2011

Schefflera macrocarpa Araliaceae n/a N n/a n/a n/a 140 n/a D E n/a Brazil Gotsch et al., 2010

Schefflera macrocarpa Araliaceae n/a N 880 n/a n/a n/a n/a D E n/a Brazil Bucci et al., 2004

Schefflera macrocarpa Araliaceae n/a N 8.7 n/a n/a n/a n/a W,D E n/a Brazil Bucci et al., 2008

Schefflera macrocarpum Araliaceae n/a n/a 5 n/a n/a n/a n/a n/a E n/a Brazil Meinzer et al., 2005

Schefflera morototoni Araliaceae n/a N 47 0.15 0.47 140 n/a D E 108 Panama James et al., 2003

Schefflera morototoni Araliaceae n/a N 47 0.15 0.47 140 n/a D E 108 Panama Meinzer et al., 2003

Schefflera morototoni Araliaceae n/a N 13.1 0.01324 0.47 140 6.4 D E 2.4 Ecuador Motzer et al., 2005

Schefflera morototoni Araliaceae n/a N n/a n/a 0.47 140 n/a W E n/a Panama Phillips et al., 1999

Schefflera morototoni Araliaceae n/a N 47 n/a 0.47 140 n/a n/a E n/a Panama Meinzer et al., 2005

Schefflera morototonii Araliaceae n/a N n/a n/a n/a 140 n/a D E n/a Brazil Gotsch et al., 2010

Sclerolobium paniculatum Leguminosae n/a N 14.9 n/a n/a n/a n/a W,D E n/a Brazil Bucci et al., 2008

Senna racemosa Fabaceae n/a n/a 13.47 n/a 0.62 12.84 n/a W E 6.81 Mexico Reyes-Garcı´a et al., 2012

Shorea beccariana Dipterocarpaceae n/a N 97.5 n/a 0.53 6370 6.2 W,D n/a n/a Malaysia Kume et al., 2008

Shorea beccariana Dipterocarpaceae n/a N n/a n/a 0.53 6370 6.2 W,D n/a n/a Malaysia Kume et al., 2011

Shorea contorta Dipterocarpaceae n/a N 18.2 n/a 0.47 7.14 5.2 W E 18.4 Philippines Dierick and Ho¨ lscher, 2009

Shorea contorta Dipterocarpaceae n/a N 18.2 n/a 0.47 7.14 n/a W E 18.4 Philippines Dierick et al., 2010

Shorea faguetiana Dipterocarpaceae n/a N 90.7 n/a 0.46 690 n/a W,D n/a n/a Brunei Becker et al., 1996

Shorea mecistoperyx Dipterocarpaceae n/a n/a 101 n/a n/a n/a n/a W,D n/a n/a Brunei Becker et al., 1996

Shorea ovalis Dipterocarpaceae n/a N 64.5 n/a 0.45 1270 n/a W,D n/a n/a Brunei Becker et al., 1996

Shorea quadrinervis Dipterocarpaceae n/a N 37.5 n/a 0.41 n/a n/a W,D n/a n/a Brunei Becker et al., 1996

Shorea scaberrima Dipterocarpaceae n/a N 34.9 n/a 0.47 n/a n/a W,D n/a n/a Brunei Becker et al., 1996

Sideroxylon celastrinum Sapotaceae n/a E 6.89 n/a 0.62 67.72 n/a W E 4.88 Mexico Reyes-Garcı´a et al., 2012

Sideroxylon celastrinum Sapotaceae n/a E 7.32 n/a 0.62 67.72 n/a D E 6.98 Mexico Reyes-Garcı´a et al., 2012

Simarouba amara Simaroubaceae n/a N n/a n/a 0.35 219 n/a W E n/a Costa Rica O'brien et al., 2004

Simarouba amara Simaroubaceae n/a n/a 27 n/a 0.35 219 n/a D E n/a Panama Meinzer et al., 2001

Spondias mombin Anacardiaceae n/a N 33.1 0.06 0.39 2047 4.24 W D 80 Panama Andrade et al., 1998

Spondias mombin Anacardiaceae n/a N n/a 0·06 0.39 2047 n/a D D 80·0 Panama Goldstein et al., 1998.

Spondias radlkoferi Anacardiaceae n/a n/a 60 n/a 0.56 1086 n/a D n/a n/a Panama Meinzer et al., 2001

Stemonurus umbellatus Icacinaceae n/a N 23.8 n/a 0.61 2900 n/a W,D n/a n/a Brunei Becker et al., 1996

Sterculia apetala Malvaceae n/a N 30 n/a 0.345 1899 n/a D D n/a Panama Meinzer et al., 2001

Stryphnodendron adstrinens Leguminosae n/a N 7.1 n/a n/a n/a n/a W,D BD n/a Brazil Bucci et al., 2008

Styrax camporum Styracaceae n/a N n/a n/a n/a 56.3 n/a D E n/a Brazil Gotsch et al., 2010

Styrax ferrugineus Styracaceae n/a N n/a n/a n/a 56.3 n/a D E n/a Brazil Gotsch et al., 2010

Styrax ferrugineus Styracaceae n/a E 1130 n/a n/a n/a n/a D E n/a Brazil Bucci et al., 2004

Styrax ferrugineus Styracaceae n/a N 8.9 n/a n/a n/a n/a W,D E n/a Brazil Bucci et al., 2008

Swietenia macrophylla Meliaceae n/a E 14.6 n/a 0.49 566 5.2 W E 25.5 Philippines Dierick and Ho¨ lscher, 2009

Swietenia macrophylla Meliaceae n/a E 14.6 n/a 0.49 566 n/a W E 25.5 Philippines Dierick et al., 2010

Tabebuia rosea Bignoniaceae n/a N 11.5 n/a 0.48 35 n/a D SD 7.7 Panama Kunert et al., 2010

Tabebuia rosea Bignoniaceae n/a N 11.5 n/a 0.48 35 n/a W SD 8.5 Panama Kunert et al., 2010

Tabebuia rosea Bignoniaceae n/a N 11.5 n/a 0.48 35 n/a W SD 7.9 Panama Dierick et al., 2010

Tabebuia rosea Bignoniaceae n/a N 11.5 n/a 0.48 35 1.28 W,D SD 7.5 Panama Kunert et al., 2012

Tabebuia rosea Bignoniaceae n/a N 12.2 n/a 0.48 35 1.28 W,D SD 19.1 Panama Kunert et al., 2012

Tachigalia versicolor Fabaceae n/a N 4 n/a 0.57 26 n/a D E n/a Panama McCulloh et al., 2011

Tapirira guianensis Anacardiaceae n/a N 7 n/a 0.54 26 n/a D E n/a Panama McCulloh et al., 2011

Tectona grandis Verbenaceae n/a E 15.5 n/a 0.583 60 n/a D SD 9 Panama Kunert et al., 2010

Tectona grandis Verbenaceae n/a E 15.5 n/a 0.583 60 n/a W SD 13 Panama Kunert et al., 2010

Tectona grandis Lamiaceae n/a N 23.1 n/a n/a 645.3 0.3 W,D SD n/a Thailand Yoshifuji et al., 2011

Terminalia amazonia Combretaceae n/a N 21.4 n/a 0.68 3 n/a D E 20.7 Panama Kunert et al., 2010

Terminalia amazonia Combretaceae n/a N 21.4 n/a 0.68 3 n/a W E 19.9 Panama Kunert et al., 2010

Terminalia ivorensis Combretaceae 4 E n/a n/a 0.442 133 2.21 W,D D 0.000001 Costa Rica Kanten and Vaast., 2006

Theobroma cacao Malvaceae n/a E n/a n/a 0.43 1893 3.8 D E 10.6 Indonesia Köhler et al., 2010

Theobroma cacao Malvaceae n/a E 10.1 0.00681 0.43 1893 3.8 W,D E 10 Indonesia K¨ohler et al., 2009

Theobroma cacao Malvaceae n/a E 10.1 n/a 0.43 1893 n/a W E 10 Indonesia Dierick et al., 2010

Thouinia paucidentata Sapindaceae n/a N 16.53 n/a 0.67 13.7 n/a W n/a 13.74 Mexico Reyes-Garcı´a et al., 2012

Thouinia paucidentata Sapindaceae n/a N 12.36 n/a 0.67 13.7 n/a D n/a 5.53 Mexico Reyes-Garcı´a et al., 2012

Trachypogon vestitus Poaceae n/a N n/a n/a n/a n/a n/a W,D n/a n/a Venezuela Herrera et al., 2012

Trichilia guianensis Meliaceae n/a E 40.4 0.11663 0.688 n/a 6.4 D n/a 120.9 Ecuador Motzer et al., 2005

Trichilia tuberculata Meliaceae n/a N 24.5 n/a 0.605 151 n/a D E n/a Panama Meinzer et al., 1999

Trichilia tuberculata Meliaceae n/a N 23 n/a 0.605 151 n/a n/a E n/a Panama Meinzer et al., 2005

Trichilia tuberculata Meliaceae n/a N 23 n/a 0.605 151 n/a D E n/a Panama Meinzer et al., 2001

Vatica mangachapoi Dipterocarpaceae n/a N 22.9 n/a 0.75 59 n/a W,D E n/a Brunei Becker et al., 1996

Vernonia arborea Asteraceae n/a N 31.55 0.02569 0.3 n/a n/a W,D E n/a Indonesia Horna et al., 2011

Virola surinamensis Myristicaceae n/a n/a 51 n/a 0.395 1838 n/a n/a E n/a Panama Meinzer et al., 2005

Virola surinamensis Myristicaceae n/a n/a 28 n/a 0.395 1838 n/a D E n/a Panama Meinzer et al., 2001

Vitellaria paradoxa Sapotaceae n/a N 64 n/a 1.28 4264 n/a D D 43.8 Burkina Faso Bayala et al., 2008

Vitex parviflora Lamiaceae n/a N 20.4 n/a 0.99 n/a 5.2 W D 30.7 Philippines Dierick and Ho¨ lscher, 2009

Vitex parviflora Lamiaceae n/a N 20.4 n/a 0.94 n/a n/a W D 20.7 Philippines Dierick et al., 2010

Vochysia elliptica Vochysiaceae n/a N 6.8 n/a 0.487 n/a n/a W,D E n/a Brazil Bucci et al., 2008

Vochysia ferruginea Vochysiaceae n/a N 46 n/a 0.4 32 n/a D E n/a Panama McCulloh et al., 2011

Vochysia thyrsoidea Vochysiaceae n/a N n/a n/a n/a 40 n/a D E n/a Brazil Gotsch et al., 2010

Vochysia thyrsoideae Vochysiaceae n/a N 7.2 n/a 0.487 n/a n/a W,D E n/a Brazil Bucci et al., 2008

Vochysia tucanorum Vochysiaceae n/a N n/a n/a n/a 40 n/a D E n/a Brazil Gotsch et al., 2010

Xylopia frutescens Annonaceae n/a N n/a n/a 0.64 740 n/a W E n/a Panama Phillips et al., 1999

Zanthoxylum belizense Rutaceae n/a n/a 55 n/a 0.48 16 n/a D n/a n/a Panama Meinzer et al., 2001

Zuelania guidonia Flacourtiaceae n/a N 13.44 n/a 0.58 14.4 n/a W D 16.6 Mexico Reyes-Garcı´a et al., 2012

Zuelania guidonia Salicaceae n/a N n/a n/a 0.563 14.4 n/a W D n/a Panama Phillips et al., 1999 N.B. Dry=D, Wet=W, Evergreen=E, Deciduous=D, Semideciduous=SD, Brevideciduous=BD, Native=N, Exotic=E, n/a=not available or not mentioned.

Supplementray Fig. 3.1. The distribution of studies on tree water use in the “tropical forest areas”

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CHAPTER FOUR: TROPICAL FOREST WATER USE: SIMPLICITY IN SOIL WATER ISOTOPE COMPOSITION, COMPLEXITY IN PLANT XYLEM WATER ISOTOPE SIGNATURES

Sohel, M.S.I., Herbohn, J., McDonnell, J.J. Tropical forest water use: Simplicity in soil water isotope composition; complexity in plant xylem water isotope signatures. This chapter will be submitted to ‘Nature Communication’ and is presented in the format of that journal.

Tropical forest water use is critical for tree productivity, growth, survival and nutrient cycling, but describing such uses is difficult in the field. Stable isotope tracing of plant water use can illuminate plant water sources but to date, the number of species tested at any given site has been minimal. Here, 46 tropical hardwood tree species (49 individual trees) in a 0.32 ha plot with uniform soils were sampled. Soil water was characterized at 6 depths at 0.2 m intervals down to 1 m. This includes 0 (surface layer), 20, 40, 60, 80 and 100cm and showed simple and predictable depth patterns, and simple and spatially uniform isotope composition at each depth. But tree xylem water δ2H and δ18O showed remarkable variation covering the full range of soil composition, suggesting strong sorting and niche segregation across the small plot. A multivariable model Principle Component Analysis (PCA) incorporating wood density, tree size and mean basal area increment could explain 54.8% of the variance of xylem water isotope composition. This work suggests that stable isotope tracers may aid a better understanding of hydrological niche segregation among co-occurring tropical species and in turn, help inform better mixed-species plantation designs and predictions about future shifts in the composition and structure of tropical rainforest species under climate change.

The ‘complementarity hypothesis’ has been proposed as a key mechanism to explain the positive effects of increasing plant species richness on ecosystems (Haggar and Ewel, 1997; Tilman et al., 1997; Yachi and Loreau, 2007; Hooper et al., 2005; Bachmann et al., 2015). This hypothesis posits that because of niche differences among species, individuals in a mixture experience less niche overlap for resource use than in comparable monocultures (Tilman et al., 1997; Bachmann et al., 2015). But many ecologists, ecohydrologists and plant physiologists still question how so many plants can coexist at the same site. There is still scant empirical evidence of niche separation in plants to explain plant coexistence (Moreno-Gutie´rrez et al., 2012; Silvertown, 2004).

Stable isotope tracing of plant water δ2H and δ18O signals have become invaluable tools for defining where plants obtain water from within the soil profile (Dawson and Ehleringer, 1991; Brooks et al., 2010; Evaristo et al., 2015; 2016). Increasing numbers of studies have shown how dual isotopes of water can identify the extraction depth (Dawson and Ehleringer, 1991; Brooks et al., 2010; Evaristo et al., 2015; 2016). Past work addressing this issue (e.g. Brooks et al., 2010; Moreno-Gutie´rrez et al., 2012; McCutcheon et al., 2017; Bowling et al., 2017), has mainly focused on non-tropical areas and included only a handful of species. Very few studies from the tropics have assessed plant water sources using the dual isotope approach (Evaristo et al., 2016).

Here, data were presented from a 0.32 ha tropical forest plot (Supplementary Fig. 4.1) that includes 46 species (49 individual trees) of different mean basal area increment, successional status and other species-specific traits (see Supplementary Table 4.1). All the tree species were sampled in a 20x160 m plot. The plot was then divided into 10x10 m sub-grid. Within these 32 sub-grids, soil samples were collected at 0.2 m increments from 0-1 m depths. Xylem samples were also collected from at least one tree species within each of the 32 sub-grids. Xylem samples were collected from 49 trees and soil samples were collected from 30 bore holes from across the plot (see Supplementary Fig. 4.2). Water isotope ratios were used to generate interpolated δ2H and δ18O isoscapes using the Inverse Distance Weighting (IDW) method of the Geostatistical package of ArcMap 10.3.1 (see Method section).

Theresults show that the soil water displayed simple and predictable patterns in time and space across the plot (Fig. 4.1). Soils from each layer of the studied plot were homogenous in terms of soil water isotope composition, and all of these measures had a predictable pattern with depth. All the sampled soil water plotted along the Local Meteoric Water Line (LMWL), suggesting low soil water evaporation in the high humidity tropical forest (see Supplementary Fig’s. 4.3 and 4.4). Soil water isotopic signatures and interpolated isoscapes also show homogeneity of each soil layer across the plot (Fig. 4.1 and Supplementary Fig. 4.3). But surprisingly and despite spatial homogeneity and predictable linear changes with depth in soil isotope characteristics, tree xylem data showed striking variability across the plot (Fig. 4.1 and Fig. 4.2). Isotope depletion of xylem water was directly proportional to tree size. Species with higher growth rates and trees with lower wood density trees water were also more depleted. While most trees appeared to access water from <40 cm, these larger trees with lower wood densities appeared to extract soil water from deeper soil layers. Simple bivariate correlations show that tree size, wood density and Mean Basal 94

Area Increment (MBAI) for all trees were minimally to moderately related with xylem water isotope (Table 4.1). Tree size was significantly negatively correlated with both isotopes (δD and δ18O). However, wood density was significantly positively correlated with δD and δ18O isotopes. MBAI was significantly negatively correlated with δ18O isotope. However, the relation with δD and MBAI was not significant (Table 4.1). Generalised linear model (GLM) analysis further shows that tree size (AICc=340.53, Weight=0.37 and R2=0.12), and a combination of tree size and wood density (AICc=340.59, Weight=0.35 and R2=0.16) best explained the variation in dual isotope of xylem water (δD-δ18O) (Supplementary Table 4.2).

Fig. 4.1. Spatial variation of xylem and soil water across the 0.32 ha plot using the Inverse distance weighting (IDW) interpolation method

Fig. 4.2. Influence of tree traits on spatial patterns of isotopic composition

Table 4.1. Results from simple bivariate correlations between xylem water isotope (δD and δ18O) and Mean Basal Area Increment (MBAI), tree size (Diameter) and wood density. P value in bold indicates a significant relationship. 49 individual trees were considered to investigate bivariate correlations between water isotope with tree size and wood density traits, while 40 individual trees were considered for MBAI.

Functional trait δD δ18O r P r P MBAI -0.24 0.12 -0.40 0.01 Tree size -0.32 0.02 -0.30 0.03 Wood density 0.27 0.05 0.34 0.01

Fig. 4.3. Principle Components Analysis (PCA) of tree traits and xylem water isotope ratios. PC1 indicates that tree size, mean basal area increment and wood density explain the majority of variation in isotope ratios.

Using two axes from a Principal Component Analysis (PCA) and correlation coefficients, the wood density, tree size and MBAI significantly explained 54.8% of the variation of xylem water isotope (δD and δ18O) composition for each tree (Fig. 4.3). Overall, the study is supportive of niche differentiation of tree water use based on the stable isotope composition of xylem water. This niche segregation appears largely driven by tree traits such as tree size, wood density and mean basal area increment on the flat, homogeneous plots. This work supports the ‘complementarity

97 hypothesis’ in relation to tree water use, where in a homogenous environmental condition niche differentiation is largely driven by species functional traits. A better understanding of hydrological niche segregation among co-occurring tropical tree species will support improved mixed-species plantation designs and lead to better predictions about future species shifts in response to climate change in tropical forests.

4.1 Methods A graphical representation of the study’s sampling design is shown in Supplementary Fig. 4.2. All the sampled tree species were spread throughout the 20x160 m plot. The whole plot was then divided into a 10x10 m grid which resulted in 32 sub-grids. In each of these sub-grids, soil samples were collected by digging a soil bore hole. Xylem samples were collected from trees using a battery-powered drill at about 10 cm above or below the DBH mark. Xylem samples were taken on the side of the stem facing towards the soil bore hole. This is to ensure that the tree is souring water from the appropriate sources. All the xylem samples were collected between 9 am and 3 pm over a single day in 22nd July 2016. Before collecting the samples, all the bark tissue was removed prior to the collection of the sapwood sample. Sufficient xylem tissue was placed immediately in a 24 ml glass capped vial. The vial was then immediately sealed and wrapped in parafilm to prevent evaporation. Samples were then placed into a refrigerator until laboratory analysis. Soil samples were collected from the centre of each of the grids using a 100 mm soil auger, with samples being collected at 20 cm intervals down the soil profile (i.e. at 0, 20, 40, 60, 80 and 100 cm). All the soil samples were collected over a two-day period (22nd and 23rd July 2016). From each sampling location of 0-100 cm soil depth, 400 g of soil was placed into a Ziploc® bag, placed into a refrigerator, and stored until laboratory analysis. Gravemetric soil moisture content (%) at six depths (0, 0-20, 20-40, 40-60, 60-80 and 80-100 cm) were measured at each soil sample location (following Klute, 1986).

Water was extracted from the xylem samples using cryogenic vacuum distillation (Orlowski et al., 2013). The extracted water was then analyzed for δD and δ18O isotopes. In the case of δD, an established method on a Delta V Advantage mass spectrometer and a HDevice peripheral were used. For δ18O, samples were run using an established method on a Delta V Advantage mass spectrometer, and a GasBench II peripheral. Soil water was extracted using the direct vapour equilibration method described in Orlowski et al. (2016). 400 g of soil was added to a Ziploc® bag,

98 with subsequent amounts of reference water (i.e. known water isotope). The Ziploc®bags were evacuated, sealed and massaged to homogenize, placed inside a second Ziploc® bag, and then stored to equilibrate prior to analysis. The direct vapour equilibration method was chosen over cryogenic extraction because of its speed and lower cost (Orlowski et al. 2016), thus allowing for many more samples to be analysed. Plant water on the other hand, was extracted exclusively through cryo-vacuum extraction. This is because of the high amounts of co-extracted volatile substances (ethanol and methanol) that are not possible to measure properly on the vapour spectrometer (that is used with vapour equilibration). Therefore, different methods of extraction for tree water and soil water were purposeful. All δD and δ18O values were expressed relative to 2 18 Vienna Standard Mean Ocean Water (VSMOW): δ H or δ O = (Rsample/Rstandard -1)1000

18 16 Where the Rsample is the abundance ratio of the isotopes either the D/H or O/ O of the 18 16 sample, and the Rstandard either the D/H or O/ O ratio of standard mean ocean water. The precision of δD and δ18O analyses (including sampling, extraction and analytical errors) were estimated to be +/- 1 permil and +/- 0.2 permil, respectively.

A widely-used interpolation method known as the Inverse Distance Weighting (IDW) method was applied in ArcGIS10.3.1 for the spatial variation mapping of soil and xylem water isotopes. The IDW interpolation assumes that points that are close to one another are more alike than the points that are farther apart. To predict a value for any unmeasured location, IDW will use the measured values surrounding the prediction location. Those measured values closest to the prediction location will have more influence on the predicted value than those farther away. To see the impact of tree traits on isotope spatial variation, tree trait data were overlaid on xylem isoscapes using ArcMap. To analyse the relationship between xylem water isotope and tree functional traits simple bivariate spearman correlations were used. Generalized Linear Models (GLMs) were then used to explain the source of variation observed in xylem water isotope in trees due to changes in tree functional traits. These relationships were estimated using maximum likelihood. Models were fit using the glmulti package and model selection was undertaken using the MuMIN package. Within the GLM models, the dual isotope (δD and δ18O) of xylem water were considered as response variables. The traits tree size, wood density, successional status and MBAI were considered explanatory variables. A PCA was also conducted to observe the variation in xylem isotope explained by tree traits.

4.2 Acknowledgements Thanksto Kim Janzen for water extraction and isotope analysis, and the members of the Tropical Forests & People Research Centre at the University of Sunshine Coast for their help with the field. This work was supported by an Australian International Postgraduate Research Scholarship granted to the first author. Thanks also to Dr John Meadows for proofreading and editing.

4.3 References Bachmann, D., Gockele, A., Ravenek, J.M., Roscher, C., Strecker, T., Weigelt, A., Buchmann,Nina.2015. No Evidence of Complementary Water Use Along a Plant Species Richness Gradient in Temperate Experimental Grasslands. PLoS ONE 10, e0116367. Bowling, D.R., Schulze, E.S., Hall, S.J. 2017. Revisiting streamside trees that do not use stream water: Can the two water worlds hypothesis and snowpack isotopic effects explain a missing water source? Ecohydrology 10, e1771. Brooks, J. R., Barnard, H. R., Coulombe, R., McDonnell, J. J. 2010. Ecohydrologic separation of water between trees and streams in a Mediterranean climate. Nature Geoscience 3, 100– 104. Dawson, T.E., Ehleringer, J.R. 1991. Streamside trees that do not use stream water. Nature 350, 335–337. Evaristo, J., Jasechko, S., McDonnell, J.J. 2015. Global separation of plant transpiration from groundwater and streamflow. Nature 525, 91–94. Evaristo, J., McDonnell, J. J., Scholl, M. A., Bruijnzeel, L. A., Chun, K. P. 2016. Insights into plant water uptake from xylem-water isotope measurements in two tropical catchments with contrasting moisture conditions. Hydrological Processes 30, 3210–3227. Haggar, J.P., Ewel, J.J. 1997. Primary productivity and resource partitioning in model tropical ecosystems. Ecology 78, 1211–1221. Hooper, D.U., Chapin, F.S., Ewel, J.J., Hector, A., Inchausti, P., Lavorel, S., Lawton, J.H., Lodge, D.M., Loreau, M., Naeem, S., Schmid, B., Setälä, H., Symstad, A.J., Vandermeer, J., Wardle, D.A. 2005. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecological Monographs 75, 3–35. Klute, A. 1986. Water retention: Laboratory methods. In: Klute, A. (ed.), Methods of Soil Analysis, Part 1, Physical and Mineralogical Methods, ASA and SSSA, Madison, 635-662

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McCutcheon, R.J., McNamara, J.P., Kohn, M.J., Evans, S.L. 2017. An evaluation of the ecohydrological separation hypothesis in a semiarid catchment. Hydrological Processes 31, 783–799. Moreno-Gutie´rrez, C., Dawson, T.E., Nicola´, E., Querejeta, J.I. 2012. Isotopes reveal contrasting water use strategies among coexisting plant species in a Mediterranean ecosystem. New Phytologist 196, 489–496. Orlowski, N., Frede, H.G., Brüggemann, N., Breuer, L. 2013. Validation and application of a cryogenic vacuum extraction system for soil and plant water extraction for isotope analysis. Journal of Sensors and Sensor Systems 2, 179–193. Orlowski, N., Pratt, D.L., McDonnell, J.J. 2016. Inter-comparison of soil pore water extraction methods for stable isotope analysis. Hydrological Processes 30, 3434–3449. Silvertown, J. 2004. Plant coexistence and the niche. Trends in Ecology & Evolution 19, 605-611. Tilman, D., Lehman, C., Thompson, K. 1997. Plant diversity and ecosystem productivity: theoretical considerations. Proceedings of National Academy of Science of the United States of America 94, 1857–1861. Yachi, S., Loreau, M. 2007. Does complementary resource use enhance ecosystem functioning? A model of light competition in plant communities. Ecology Letters 10, 54–62

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4.4 Supplemental Information 4.4.1 Study site and plot information

The study site was located in a wet tropical rainforest located in the Danbulla State Forest, Atherton Tableland, North eastern Australia (Supplementary Fig. 4.1). The site was at an elevation of 760 m above sea level (Drake and Franks, 2003). Trees were located within a long term experimental plot (referred to as “Experiment 78, Plot 2”) established by the Queensland Department of Forestry in 1948, with regular measures of growth, mortality and recruitment of trees greater than 10 cm Diameter at Breast Height (DBH). Soils on the 20x200 m experimental plot were sandy clay loam to clay loam. Soils have developed basalt- from Pliocene to Holocene lave flows developed from volcanic eruption (Laffan, 1988). Annual average rainfall for the site is 1,680 mm, with over 1,000 mm falling between December and February (Drake and Franks, 2003). The area can be prone to seasonal droughts resulting from infrequent rainfall during the drier months (Drake and Franks, 2003). Most of the plant species in the study site are highly moisture dependent (Tracey, 1982; Drake and Franks, 2003).

Supplementary Fig. 4.1. (a) Location of the study site in Australia. Green color in the inset map indicates wet tropical rainforest. The red dot indicates the location of the studied experimental plot (b) The light green dot indicates the plot location at a higher spatial resolution (c) Aerial view about the plot location.

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Supplementary Fig. 4.2. The spatial distribution of all the studied tree species throughout the 20x160 m plot. Different sizes of the green circles indicate diameter differences among the trees. The whole plot was then divided into a 10x10 m grid. The codes listed outside each sub-grid indicate the grid ID. The black circles in each sub-grid indicate the bore-hole location for the soil sample collection. The numbers within each sub-grid represent the tree species ID in the respective Experiment 78, Plot 2. Details can be found in Supplementary Table 4.1.

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Supplementary Fig. 4.3. Dual isotope plot of soil water isotope differences between the upper- lower transect and left sub-grids (S10-S25)-right sub-grids (S26-S40) proportion of the experimental plot. Each plot has the Local Meteoric Water Line (LMWL) as references. Boxplots show averages (dashed line) of the isotope ratios, while data extremes are shown by the respective symbol. Statistical grouping is indicated by a continuous line to the left or below for δ18O and δ2H, respectively. Similar colored lines indicate no significant difference (p<0.05) between plot proportions at the same soil depth.

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7 350

6 300

5 250

4 200

3 150

Rain (mm) 2 100

Evaportaion (mm) 1 50

0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Supplementary Fig. 4.4. Average monthly evaporation and rainfall patterns from 1900-2015

4.4.2 Tree species characteristics Forty-nine individual trees were sampled (one tree from each species) from the studied plot (Supplementary Table 4.1). Tree functional trait information was gathered from the literature on ten factors: phylogenetic group, life form, leaf phenology, photosynthetic pathway,woodiness, leaf compoundness, tree size (tree diameter), wood density, successional status and growth form. Species successional status information source was collected from Goosem and Tucker (2013). Where species level information about successional status was not available, genus-level information was used. Family, leaf phenology, leaf type, photosynthetic pathway, woodiness, life form, phylogenetic group information were collected from the TRY Database - a global database of plant traits (Kattge et al., 2011). Wood density data were collected from the Global Wood Density Database (Zanne et al., 2009). If the species wood density was not available in this global database, Queensland government data were used (https://www.daf.qld.gov.au/forestry/using- wood-and-its-benefits/wood-properties-of-timber-trees/). Tree species growth form data were obtained from records associated with Experiment 78 Plot 2. Basal area increment (BAI) was used as a proxy for the temporal trend of tree growth. Mean annual tree growth rate was calculated as

= [BAcensus2- BAcensus1]/[time2-time1] where time2 and time1are the respective census period. Mean basal area increment was than calculated for each sampled species.

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Supplementary Table 4.1. Traits of the sampled woody tree species

tatus

Leaf

form

MBAI

S Group

Family

Tree Tree ID

Growth

Pathway

Leaf type Leaf

2016 DBH 2016

Successional

Phylogenetic ompoundness

Speciesname

Wood density Wood

Photosynthetic Photosynthetic

Leaf Phenology C

0 Agathisatropurpurea 113 Araucariaceae E F ES G B C3 S 0.413 N/A 1 Agathisrobusta 326 Araucariaceae E M LS G B C3 S 0.401 0.0012 2 Alangiumvillosum 119 Cornaceae E M LS AE B C3 S 0.607 0.0001 3 Aleuritesmoluccana 441 Euphorbiaceae E F ES AE B C3 S 0.400 0.0019 4 Aleuritesmoluccana 474 Euphorbiaceae E F ES AE B C3 S 0.400 0.0019 5 Alloxylonwickhamii 428 Proteaceae E M LS AE B C3 S 0.456 0.0016 6 Anthocarapanitidula 156 Meliaceae E M LS AE B C3 C 0.689 0.0002 7 Aphananthephilippinensis 182 Ulmaceae E M LS AE B C3 S 0.620 0.0008 8 Argyrodendronperalatum 304 Malvaceae E S M AE B C3 C 0.810 0.0010 9 Argyrodendrontrifoliolatum 205 Sterculiaceae E S M AE B C3 C 0.800 0.0005 10 Argyrodendrontrifoliolatum 163 Sterculiaceae E S M AE B C3 C 0.800 0.0001 11 Aryteradivaricata 137 Sapindaceae E M LS AE B C3 C 0.633 0.0001 12 Castanospermumaustrale 145 Fabaceae E M LS AE B C3 C 0.650 0.0013 13 Castanosporaalphandii 122 Sapindaceae E M LS AE B C3 C 0.607 0.0001 14 Celtispaniculata 605 Ulmaceae D M LS AE B C3 S 0.607 0.0041 15 Cryptocaryatriplinervis 189 Lauraceae E S M AM B C3 S 0.650 0.0004 16 Daphnandrarepandula 174 Atherospermataceae E S M AM B C3 S 0.581 0.0001 17 Dendrocnidephotinophylla 399 Urticaceae E VF P AE B C3 S 0.207 0.0025 18 Diospyroshebecarpa 220 Ebenaceae E S M N/A B C3 C 0.758 N/A 19 Diploglottisdiphyllostegia 137 Sapindaceae E M LS AE B C3 C 0.754 N/A 20 Doryphoraaromatica 103 Atherospermataceae E F ES AM B C3 S 0.482 0.0003 21 Dysoxylumoppositifolium 198 Meliaceae E M LS AE B C3 C 0.758 0.0001 22 Dysoxylumschiffneri 136 Meliaceae E M LS AE B C3 C 0.633 0.0001 23 Elaeocarpusgrandis 355 Elaeocarpaceae E M LS AE B C3 S 0.495 0.0018 24 Endiandralongipedicellata 457 Lauraceae E S M AM B C3 S 0.839 0.0004 25 Ficus spp 138 Moraceae E M LS AE B C3 S N/A 0.0005 26 Ficushispida 177 Moraceae E F ES AE B C3 S 0.413 N/A 27 Ficusleptoclada 185 Moraceae E F ES AE B C3 S 0.482 0.0003 28 Flindersiaschottiana 221 Rutaceae E M LS AE B C3 C 0.581 0.0005

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29 Glochidionferdinandi 126 Phyllanthaceae E F ES AE B C3 S 0.593 0.0001 30 Gmelinafasciculiflora 573 Lamiaceae E S M AE B C3 S 0.470 0.0008 31 Homaliumcircumpinnatum 207 Flacourtiaceae E M LS AE B C3 S 0.788 0.0006 32 Mallotusphilippensis 114 Euphorbiaceae E VF P AE B C3 S 0.650 0.0009 33 Mallotuspolyadenos 140 Euphorbiaceae E VF P AE B C3 S 0.650 0.0002 34 Memecylonpauciflorum 116 Memecylaceae E F ES AE B C3 S 0.808 0.0001 35 Mischocarpus pyriformis 167 Sapindaceae E M LS AE B C3 C 0.805 0.0004 36 Myristicainsipida 160 Myristicaceae E S M AM B C3 S 0.482 0.0003 37 Myristicainsipida 104 Myristicaceae E S M AM B C3 S 0.482 0.0003 38 Phaleriaclerodendron 115 Thymelaeaceae E M LS AE B C3 S N/A 0.0001 39 Polysciaselegans 423 Araliaceae E F ES AE B C3 C 0.410 0.0023 40 Pouteriaobovoidea 494 Sapotaceae E M LS AE B C3 S 0.630 N/A 41 Pouteriaxerocarpa 141 Sapotaceae E M LS AE B C3 S 0.607 0.0006 42 Pseudoweinmannialachnocarpa 183 Cunoniaceae E M LS AE B C3 C 0.758 N/A 43 Stenocarpussinuatus 160 Proteaceae E M LS AE B C3 S 0.629 0.0003 44 Streblusbrunonianus 212 Moraceae E M LS AE B C3 S 0.702 0.0001 45 Syzygiumclaviflorum 111 Myrtaceae E S M AE B C3 S 0.607 N/A 46 Terminalia sericocarpa 339 Combretaceae E M LS AE B C3 S 0.640 0.0016 47 Toonaciliata 318 Meliaceae D M LS AE B C3 C 0.383 0.0014 48 Zanthoxylum ovalifolium 120 Rutaceae E M LS AE B C3 C 0.610 N/A

N.B. Leaf phenology: E- Evergreen, D- deciduous; Growth form: VF-Very fast, M-Moderate, F-Fast, S-Slow; Successional status: LS-Late secondary, ES-Early secondary, M-Mature, P- Pioneer; Phylogenetic Group: AM-Angiosperm Magnoliid, AE-Angiosperm Eudicotyl, N/A-Not available, G-Gymnosperm; Leaf Type: B-Broadleaved; Leaf compundness: C- Compound leaf, S-Simple leaf

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Supplementary Table 4.2. Models for explaining the variation in dual water isotope of xylem (δD- δ18O). Models included the following fixed effects: 1= Tree size (DBH), 2 = MBAI, 3 = Successional status, 4 = Wood density. Lower AICc and higher weight value indicates better model performance.

Models df logLik AICc delta weight

1 3 -166.99 340.53 0.00 0.37 1,4 4 -165.83 340.59 0.06 0.35 4 3 -167.72 341.99 1.46 0.18 1,3 6 -166.55 347.15 6.63 0.01 1,2 6 -166.55 347.15 6.63 0.01 1,2,3 6 -166.55 347.15 6.63 0.01 1,2,4 7 -165.21 347.21 6.68 0.01 1,2,3,4 7 -165.21 347.21 6.68 0.01 3,4 6 -166.88 347.81 7.29 0.01 2,4 6 -166.88 347.81 7.29 0.01 2,3,4 6 -166.88 347.81 7.29 0.01 3 5 -169.37 350.16 9.63 0.00 2 5 -169.37 350.16 9.63 0.00

References used in Supplementray material

Drake, P.L., Franks, P.J. 2003. Water resource partitioning, stem xylem hydraulic properties, and plant water use strategies in a seasonally dry riparian tropical rainforest. Oecologia 137, 321–329. Goosem, S., Tucker, N.I.J. 2013. Repairing the Rainforest (second edition). Wet Tropics Management Authority and Biotropica Australia Pty. Ltd. Cairns. Kattge, J. et al. 2011. TRY - a global database of plant traits. Global Change Biology 17, 2905-2935. Laffan, M.D. 1988. Soils and Land Use on the Atherton Tableland, North Queensland. Report No 61. CSIRO Division of Soils, Australia, pp 55–57. Tracey, J.G. 1982. Vegetation of the humid tropical region of North Queensland. CSIRO, Melbourne Zanne, A.E., Lopez-Gonzalez, G., Coomes, D.A., Ilic, J., Jansen, S., Lewis, S.L., Miller, R.B., Swenson, N.G., Wiemann, M.C., Chave, J. 2009. Global wood density database. Dryad. Identifier: http://hdl.handle.net/10255/dryad.235.

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CHAPTER 5: A BAYESIAN MIXING MODEL ANALYSIS OF TREE WATER ISOTOPE PATTERNS: UPTAKE DIFFERENCES ACROSS 46 SPECIES ON A UNIFORM TROPICAL FOREST PLOT

Sohel, M.S.I., Herbohn, J., McDonnell, J.J. A Bayesian mixing model analysis of tree water isotope patterns: Uptake differences across 46 species on a uniform tropical forest plot. This chapter will be submitted to ‘New Phytologist’and is presented in the format of that journal.

5.1 Abstract Little is known about factors controlling the differential uptake of soil water across species-rich tropical rainforests. Here, this study present a Bayesian Mixing Model (BMM) analysis of soil water source apportionment for 49 individual trees from 46 species in an experimental plot located in wet tropical rainforest in North Queensland, Australia. The null hypothesis is that tropical hardwood species show the same xylem water isotope composition and hence the same depths of soil water extraction. The dual isotopes of water (δD and δ18O) were used to trace soil water uptakedepth. The plot was divided into thirty 100 m2 grids. Soil samples were collected from each grid down the soil profile at 0.2 m intervals from 0-1 m. By grouping the soil layers into five depths, the BMM showed that sampled trees were either sourcing their water from very shallow or deep soil layers, with very little contribution from the middle portion of the soil layer. The majority of the observed species relied on shallow soil water (0.0-0.2 m). This layer contributed approximately 62% to xylem water which was significantly higher than the contributions from all other depths. The contribution from shallow soil was highest for high wood density, slow-growing, small-sized trees. However, this explanation was not statistically strong. Therefore, the study infers that soil water uptake patterns are species-specific rather than trait-specific, although all species were exposed to the same environmental conditions.

Keywords: Bayesian Mixing Model, dual isotope, tree water uptake depth, soil water partitioning, rainforest tree species

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5.2 Introduction Water uptake patterns across diverse tropical rainforest tree species are poorly known (Goldsmith et al., 2012). This is because such patterns are difficult to measure with traditional sap flux methodologies and tree root distributions are extremely difficult to quantify (Kleidon and Heimann, 1998). But in the past decade, studies have begun to sample the stable isotope composition (δD and δ18O) of soil water and plant xylem water to quantify tree water uptake depths. Most site-based work in the tropics has employed a single isotope approach (i.e. using either δD or δ18O) to quantify the depth in the soil profile where trees source their soil water or groundwater (Dawson and Ehleringer, 1991; Jolly and Walker, 1996; Meinzer et al., 1999; Slavich et al., 1999; Sekiya and Yano, 2002; Atsuko et al., 2002; Peñuelas and Filella, 2003; McCole and Stern, 2007). Recent investigations using dual water stable isotopes have shown that soil source water can often plot below the meteoric water line (MWL)1. As such, δD and δ18O can give two different source depths (Brooks et al., 2010; McDonnell, 2014; Evaristo et al., 2015; Evaristo et al., 2016). Aside from Goldsmith et al. (2012), few studies in the tropics have used the dual isotope approach to quantify plant water uptake depth (Querejeta et al., 2007; Schwendenmann et al., 2015).

Here, this study examine the differences in water uptake depth for 49 individual trees from 46 species in a 20x160 m experimental plot located in wet tropical rainforest in North Queensland, Australia. The null hypothesis is that diverse tropical hardwood species show the same xylem water isotope composition and hence the same depths of soil water extraction. A dual isotope approach to trace soil water uptake and a BMM (following Evaristo et al., 2017) was used to quantify water uptake depth for each tree. Past work using mixing models and dual isotope data in the tropics has shown differing water sources for a single species during wet and dry seasons (e.g. Evaristo et al., 2016). But that work, and most other studies (Querejeta et al., 2007; Schwendenmann et al., 2015) to date, have considered only a single species or a few species at best. This limits complete understanding of tropical plant species’ water uptake strategies. Here, the dual isotope and BMM approach were applied to 46 tree species—the largest in a single plot

1The Meteoric Water Line (MWL), is a simple regression line which is derived from precipitation water isotope data (δD and δ18O) from single or multiple sites or from across the globe. If the data were collected and analysed from across the globe, then it is known as the“Global Meteoric Water Line (GMWL)”. However, if the data were collected from single site or set of "local" sites then it is known as the“Local Meteoric Water Line (LMWL)". LMWL can be significantly different from the GMWL. This ‘MWL’ helps to identify evaporation effect on water samples. The isotopic ratios and fractionations of the two elements are usually discussed together using MWL considering this line has is no evaporation effect.

110 studied to date, to advance our understanding of tropical rainforest tree water uptake depth patterns. Specifically, this study addresses the following research questions:

1. What are the water uptake depths across 46 species within a 0.32 ha forest plot? 2. How do species uptake depths relate to measured soil water isotopic composition? 3. How do tree functional traits relate to water uptake depth?

5.3 Materials and methods

5.3.1. Experimental site and studied species The study site was located in a wet tropical rainforest in the Danbulla State Forest on the Atherton Tableland in North eastern Australia (Fig. 5.1). The site has an elevation of 760 m above sea level (Drake and Franks, 2003). Trees were located within a long-term experimental plot (referred to as “Experiment 78, Plot 2”) established by the Queensland Department of Forestry in 1948, with regular measures of the growth, mortality and recruitment of trees greater than 10cmDiameter at Breast Height (DBH). Soils onthe 200x20 m experimental plot were sandy clay loam to clay loam. The soils have developed from basalt laid down during Pliocene to Holocene lave flows from volcanic eruptions (Laffan, 1988). The annual average rainfall for the site is 1,680 mm, with over 1,000 mm falling between December and February (Drake and Franks, 2003). The area can be prone to seasonal droughts resulting from infrequent rainfall during the drier months (Drake and Franks, 2003). Most of the plant species in the study site are highly moisture dependent (Tracey, 1982; Drake and Franks, 2003).

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Fig. 5.1. (a) Location of the study area within Australia. Green color in the inset map indicates wet tropical rainforest. The red dot indicates the location of the studied permanent plot in the tropical rainforest area (b) The light green dot indicates location of the plot in high resolution (c) Aerial view about the plot location.

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5.3.2 Tree species characteristics Forty-nine individual trees were sampled from the studied plot (Table 5.1). The 49 trees included 46 different species. Information on the following ten tree functional traits was gathered from the literature: phylogenetic group, life form, leaf phenology, photosynthetic pathway, woodiness, leaf compoundness, tree size (tree diameter), wood density, successional status and growth form. Information on the species’ successional status was collected from Goosem and Tucker (2013). Where species-level information about successional status was not available, genus-level information was used. Information on the species’ family, leaf phenology, leaf compoundness, photosynthetic pathway, woodiness, life form and phylogenetic group was collected from the TRY global database of plant traits (Kattge et al., 2011). Wood density data were collected from the Global Wood Density Database (Zanne et al., 2009). If a species’ wood density was not available from this global database, Queensland government data were used (https://www.daf.qld.gov.au/forestry/using-wood-and-its-benefits/wood-properties-of-timber- trees). Data on the species’ mean basal area increment were obtained from records associated with Experiment 78, Plot 2. Basal area increment (BAI) was used as a proxy for the temporal trend of tree growth. The mean annual tree growth rate was calculated as = [BAcensus2- BAcensus1]/[time2- time1]. Mean BAI (MBAI) was then calculated for each sampled species.

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Table 5.1. Traits of the sampled woody tree species

Leaf

DBH

form

MBAI

status

Group

Family

Tree Tree ID

Growth

Pathway

Leaf type Leaf

Successional

Phylogenetic

Speciesname

Wood density Wood

compoundness

Photosynthetic Photosynthetic Leaf Phenology

0 Agathis atropurpurea 113 Araucariaceae E F ES G B C3 S 0.413 N/A 1 Agathis robusta 326 Araucariaceae E M LS G B C3 S 0.401 0.0012 2 Alangium villosum 119 Cornaceae E M LS AE B C3 S 0.607 0.0001 3 Aleurites moluccana 441 Euphorbiaceae E F ES AE B C3 S 0.400 0.0019 4 Aleurites moluccana 474 Euphorbiaceae E F ES AE B C3 S 0.400 0.0019 5 Alloxylon wickhamii 428 Proteaceae E M LS AE B C3 S 0.456 0.0016 6 Anthocarapa nitidula 156 Meliaceae E M LS AE B C3 C 0.689 0.0002 7 Aphananthe philippinensis 182 Ulmaceae E M LS AE B C3 S 0.620 0.0008 8 Argyrodendron peralatum 304 Malvaceae E S M AE B C3 C 0.810 0.0010 9 Argyrodendron trifoliolatum 205 Sterculiaceae E S M AE B C3 C 0.800 0.0005 10 Argyrodendron trifoliolatum 163 Sterculiaceae E S M AE B C3 C 0.800 0.0001 11 Arytera divaricata 137 Sapindaceae E M LS AE B C3 C 0.633 0.0001 12 Castanospermum australe 145 Fabaceae E M LS AE B C3 C 0.650 0.0013 13 Castanospora alphandii 122 Sapindaceae E M LS AE B C3 C 0.607 0.0001 14 Celtis paniculata 605 Ulmaceae D M LS AE B C3 S 0.607 0.0041 15 Cryptocarya triplinervis 189 Lauraceae E S M AM B C3 S 0.650 0.0004 16 Daphnandra repandula 174 Atherospermataceae E S M AM B C3 S 0.581 0.0001 17 Dendrocnide photinophylla 399 Urticaceae E VF P AE B C3 S 0.207 0.0025 18 Diospyros hebecarpa 220 Ebenaceae E S M N/A B C3 C 0.758 N/A 19 Diploglottis diphyllostegia 137 Sapindaceae E M LS AE B C3 C 0.754 N/A 20 Doryphora aromatica 103 Atherospermataceae E F ES AM B C3 S 0.482 0.0003 21 Dysoxylum oppositifolium 198 Meliaceae E M LS AE B C3 C 0.758 0.0001 22 Dysoxylum schiffneri 136 Meliaceae E M LS AE B C3 C 0.633 0.0001 23 Elaeocarpus grandis 355 Elaeocarpaceae E M LS AE B C3 S 0.495 0.0018 24 Endiandra longipedicellata 457 Lauraceae E S M AM B C3 S 0.839 0.0004 25 Ficus spp. 138 Moraceae E M LS AE B C3 S 0.390 0.0005 26 Ficus hispida 177 Moraceae E F ES AE B C3 S 0.413 N/A 27 Ficus leptoclada 185 Moraceae E F ES AE B C3 S 0.482 0.0003 28 Flindersia schottiana 221 Rutaceae E M LS AE B C3 C 0.581 0.0005

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29 Glochidion ferdinandi 126 Phyllanthaceae E F ES AE B C3 S 0.593 0.0001 30 Gmelina fasciculiflora 573 Lamiaceae E S M AE B C3 S 0.470 0.0008 31 Homalium circumpinnatum 207 Flacourtiaceae E M LS AE B C3 S 0.788 0.0006 32 Mallotus philippensis 114 Euphorbiaceae E VF P AE B C3 S 0.650 0.0009 33 Mallotus polyadenos 140 Euphorbiaceae E VF P AE B C3 S 0.650 0.0002 34 Memecylon pauciflorum 116 Memecylaceae E F ES AE B C3 S 0.808 0.0001 35 Mischocarpus pyriformis 167 Sapindaceae E M LS AE B C3 C 0.805 0.0004 36 Myristica insipida 160 Myristicaceae E S M AM B C3 S 0.482 0.0003 37 Myristica insipida 104 Myristicaceae E S M AM B C3 S 0.482 0.0003 38 Phaleria clerodendron 115 Thymelaeaceae E M LS AE B C3 S N/A 0.0001 39 Polyscias elegans 423 Araliaceae E F ES AE B C3 C 0.410 0.0023 40 Pouteria obovoidea 494 Sapotaceae E M LS AE B C3 S 0.630 N/A 41 Pouteria xerocarpa 141 Sapotaceae E M LS AE B C3 S 0.607 0.0006 42 Pseudoweinmannia lachnocarpa 183 Cunoniaceae E M LS AE B C3 C 0.758 N/A 43 Stenocarpus sinuatus 160 Proteaceae E M LS AE B C3 S 0.629 0.0003 44 Streblus brunonianus 212 Moraceae E M LS AE B C3 S 0.702 0.0001 45 Syzygium claviflorum 111 Myrtaceae E S M AE B C3 S 0.607 N/A 46 Terminalia sericocarpa 339 Combretaceae E M LS AE B C3 S 0.640 0.0016 47 Toona ciliata 318 Meliaceae D M LS AE B C3 C 0.383 0.0014 48 Zanthoxylum ovalifolium 120 Rutaceae E M LS AE B C3 C 0.610 N/A

N.B. Leaf phenology: E- Evergreen, D- Deciduous, Growth form: VF-Very fast, M-Moderate, F-Fast, S-Slow; Successional status: LS-Late secondary, ES-Early secondary, M-Mature, P-Pioneer; Phylogenetic Group: AM-Angiosperm Magnoliid, AE-Angiosperm Eudicotyl, N/A-Not available, G-Gymnosperm; Leaf Type: B-Broadleaved; Leaf compoundness: C- Compound leaf, S-Simple leaf.

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5.3.3 Plant and soil sample collection The sample design is shown in Fig. 5.2. All the sampled tree species were located within the 20x160 m plot. The plot was further divided into 10x10 m grids. Soil samples were collected from the centre of each of the 10x10 m grids using a 100 mm soil auger, with samples being collected at 20 cm intervals down the soil profile (i.e. at 0, 20, 40, 60, 80 and 100 cm). All the soil samples were collected over a two-day period (22nd and 23rdJuly 2016). Additional soil samples were collected from 1.0 to 4.0 m (150, 200, 250, 300, 350 and 400 cm) down the soil profile using a truck- mounted soil auger from a location adjacent to a small access track about 60 m south of the plots on 15thJuly 2016. From each of the 0-100 cm soil depth sampling locations, 400 g of soil wasplaced into a Ziploc® bag, placed into a refrigerator, and stored until laboratory analysis. For the additional soil depth sampling locations (100-400 cm), samples were placed in capped vials, wrapped with parafilm, placed into a refrigerator, and stored until laboratory analysis. Gravemetric soil moisture content (%) at six depths (0, 0-20, 20-40, 40-60, 60-80 and 80-100 cm) were measured at each soil sample location (following Klute, 1986). However, soil moisture was not measured for the 100-400 cm depth soil samples.

Xylem samples were collected from trees using a battery-powered drill at approximately 10 cm above or below the DBH mark. Xylem samples were taken on the side of the stem facing towards the soil bore hole. This was to ensure that the tree was souring water from the appropriate sources. All the xylem samples were collected between 9 am and 3 pm on 22nd July 2016. Before collecting the samples, all the bark tissue was removed. Sufficient xylem tissue was then collected and placed immediately in a 24 ml glass capped vial. The vial was then immediately sealed and wrapped in parafilm to prevent evaporation. The xylem samples were then placed into a refrigerator until laboratory analysis.

Fig. 5.2. The spatial distribution of all the studied tree species throughout the 20x160 m plot. Different sizes of the green circles indicate DBH differences among the trees. The whole plot was then divided into a 10x10 m grid. The codes outside of the grid indicate the grid ID. The black

116 circles in each grid indicate the bore-hole location for soil sample collection. The numbers within the grid represent the tree species ID. Details for each tree can be found in Table 1.

5.3.4 Water extractions Water was extracted from the xylem samples using cryogenic vacuum distillation (Orlowski et al., 2013). The extracted water was then analyzed for δD and δ18O on a Delta V Advantage mass spectrometer following Nelson (2000). Soil water was extracted using the direct vapour equilibration method described in Orlowski et al. (2016) by placing 400 g of soil into a Ziploc® bag, with subsequent amounts of reference water (i.e. known water isotope). The Ziploc®bags were evacuated, sealed and massaged to homogenize, placed inside a second Ziploc® bag, and stored to equilibrate prior to analysis. The direct vapour equilibration method was chosen over cryogenic extraction because of its speed and lower cost (Orlowski et al. 2016), thus allowing for many more samples to be analysed. Plant water on the other hand, was extracted exclusively through cryo- vacuum extraction. This is because of the high amounts of co-extracted volatile substances (ethanol and methanol) that are not possible to measure properly on the vapour spectrometer (that is used with vapour equilibration). Therefore, different methods of extraction for tree water and soil water were purposeful.

All δD and δ18O values were expressed relative to Vienna Standard Mean Ocean Water 2 18 (VSMOW):δ H or δ O = (Rsample/Rstandard -1)1000

18 16 Where the Rsample is the abundance ratio of those isotopes either the D/H or O/ O of the sample, 18 16 and Rstandard either the D/H or O/ O ratio of standard mean ocean water. The precision levels of δD and δ18O (including sampling, extraction and analytical errors) were estimated to be +/- 1 permil and +/- 0.2 permil, respectively.

5.3.5 Bayesian Mixing Model to determine soil water proportions in xylem tissue The MixSIAR (stable-isotope analysis in R) BMM statistical package (Moore and Semmens, 2008; Parnell et al., 2013; Stock and Semmens, 2016) was used to partition source water contributions to xylem tissue. MixSIAR is widely used in food web and animal foraging studies, and was used in this study to determine the relative importance of various sources of water that may contribute to xylem water using Markov Chain Monte Carlo (MCMC) methods. Five potential source classeswere used for xylem water when running the BMM: (1) soil water at 0-0.2 m; 2) soil water at 0.2-1.0 m; 117

3) soil water at 1.0-2.0 m; 4) soil water at 2.0 m-3.0 m; and 5) soil water at 3.0-4.0 m. The source classifications are used here only to designate the soil water end-members that can be resolved by MixSIAR. The MCMC model run was set to ‘normal’ (100,000 iterations) and the source water’s most likely contribution (i.e. the mean of the posterior distribution of the MCMC simulation) to xylem water was obtained for all of the sampled trees.

5.4 Results

5.4.1 Soil moisture, texture and isotopic signature Soils across the small plot ranged from sandy loam to clay loam (Fig. 5.3). The upper soil layer comprised mostly sand and gravel together with organic matter. Roots were highly dense in the shallower parts of the soil profile. Sandy loam dominates the middle portion of the soil layer where root density was less compared with the shallow soil layer. The deep soil layer was mostly hard clay. Few roots were observed in the deep soil layers throughout the small plot.

Fig. 5.3. Soil texture of the 20x160 m plot. The top figure represents the upper transect of the plot and the lower figure represents the lower transect of the plot.

Figure 5.4 shows that water content increased with soil depth. Water content differed significantly (ANOVA-Tukey Method, p<0.5) among the sampled soil depths. However, moisture content is

118 homogeneous across the plot in each layer. Water content in the top 0.5 m was 5-15% during the sampling period and soil moisture content increased with depth to a maximum of 27% (Fig. 5.4).

Fig. 5.4. Average soil water content difference between the (a) left (S10-S25)-right (S26-S40) and (b) upper-lower transect of the studied plot.

Figure 5.5 shows a dual isotope plot of water extracted from each soil depth. There was a clear and predictable variation with depth, with lighter signatures in the surface soil and more negative signatures with depth. Each depth clustered in a spatially homogeneous region on the dual isotope plot, such that soil water isotopic signatures were relatively homogeneous for each soil layer. A simple One-way ANOVA with the Tukey method showed there is no significant difference in soil water isotope (p>0.05) between the different sections for each soil layer (Fig. 5.5).

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(a)

(b)

Fig. 5.5. (a) Dual isotope plot of the soil water isotope difference between the left (S10-S25)-right (S26-S40) proportion and (b) upper-lower transectof the experimental plot. Each plot has the Local Meteoric Water Line (LMWL) as a reference. The boxplots show the average (dashed line) of the isotope ratios, while data extremes are shown by the respective symbol. Statistical grouping is indicated by a continuous line to the left or below for δ18O and δ2H, respectively. A similar color line indicates no significant difference (p<0.05) between plot proportion of the same soil depth. Soil water isotope for the 1-4 m depth is not shown here as those samples were collected outside of the plot sub-grids.

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5.4.2 Tree water uptake depth and source water contribution

Figure 5.6 shows all xylem and soil samples in the dual isotope (δ18O‐δ2H) space. Soil water showed a consistent trend of increasing isotopic depletion with depth (Fig. 5.6). All the sampled soil water plotted along the Local Meteoric Water Line (LMWL), suggesting low evaporation effects in this humid rainforest setting (Fig. 5.6). Qualitative assessment of 41 of the 49 xylem samples suggests that these trees used mostly shallow soil water since they overlap isotopically with soil water from the 0-0.4 m depth (Fig. 5.6). Clear outliers to this trend included the following 8 trees species: Alloxylon wickhamii, Alangium villosum, Glochidion ferdinandi, Argyrodendron peralatum, Ficus hispida, Pouteria obovoidea, Syzygium claviflorumand Toona ciliata. These outliers had stable isotope values consistent with soil water deeper than 0.4 m (Fig. 5.6).

To quantify the water uptake depths beyond the simple visual analysis, Figure 5.7 shows the BMM results for the contributions of different soil water depths to the 49 individual xylem water samples. Soil water contributions to xylem water from the 0.0-0.2 m depth in all the sampled trees averaged 62.78±0.20% (mean ±1SD). This was significantly higher than the contributions from all other depths. Source contributions to xylem water from the 2.0-3.0 m depth and the 1.0-2.0 m depth were 17.89±0.11% and 11.59±0.06%, respectively. Contributions from the deepest layer (3.0-4.0 m) were 5.28±0.02%, but were relatively similar across all species.

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Fig. 5.6. The δD–δ18O relationship for soil water and xylem water. The local meteoric water line was calculated from rain samples collected during field work.

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Fig. 5.7. The BMM results for source water partitioning per depth interval and per tree species.

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5.4.3 Functional traits and their relation to water uptake depth Four of the ten tree functional traits (i.e. tree size, tree growth, successional status and wood density) were used to test their relationship with soil water uptake depth. A simple bivariate analysis showed that tree size, wood density and MBAI for all trees were somewhat related to soil water depth contribution (Table 5.2), but these relationships were weak overall. Tree size was significantly negatively correlated (r=-0.34) with very shallow soil water (0-0.2 m), meaning that the contribution from shallow soil was high for small-sized trees (Table 5.2). However, in grouping all trees into large-sized (>17.4-60.5 cm DBH) and small-sized (<17.4 cm DBH) categories, the trees showed no significant (T-Value = 1.39, p<0.171, df = 47) differences in source depths from the 0.0- 0.2 m depth. Other soil layer contributions to xylem water were not significantly different (p>0.05) or related to tree size difference. Apart from tree size, mean basal area increment (slow-growing and fast-growing species) shows no significant (p>0.05) relation to soil water depth contribution to xylem water.

Wood density was significantly positively correlated (r=0.28) with very shallow (<0.2 m) soil water contribution, indicating that with an increase in wood density, the contribution of very shallow soil (<0.2 m) would increase. However, for other soil depths, this relationship become significantly negatively correlated, suggesting that higher wood density trees sourced their water from deeper (>0.2 m) soil layers (Table 5.2). Grouping all trees into high (0.61-0.83 g/cm3) and low (0.20-0.61 g/cm3) wood density categories showed significant (T-Value = -2.68, p<0.01, df = 46) differences in sourcing soil water from the 0.0-0.2 m depth. Other soil layer contributions to xylem water were also significantly different (p<0.05) with wood density differences, which justifies the correlation between wood density and soil depths.

Table 5.2. Results from simple bivariate correlations between soil water contribution to xylem (%) and MBAI, tree size (DBH) and wood density. The P value in bold indicates a significant relationship. 49 individual trees were considered to investigate bivariate correlations between soil water contribution to xylem (%) and tree size and wood density traits, while 40 individual trees were considered for MBAI.

Tree trait 0.0-0.2m 0.2-1.0m 1.0-2.0m 2.0-3.0m 3.0-4.0m

R p r p r p r p r P

Tree size -0.34 0.01 0.36 0.01 0.35 0.01 0.31 0.02 0.34 0.01 MBAI -0.30 0.05 0.39 0.01 0.30 0.05 0.27 0.07 0.32 0.03 Wood density 0.28 0.05 -0.27 0.06 -0.28 0.05 -0.27 0.05 -0.27 0.05

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5.5 Discussion

5.5.1 Interspeciﬁc differences in water uptake depth

The study found that most of the 46 species of rainforest trees sampled relied on shallow water from the top 20 cm of the soil profile. It was also striking that none of the 46 species sourced any significant amount of water from within the 0.2 to 1.0 m depth of the soil profile. These patterns are in stark contrast to past research on tree water uptake depth in tropical regions using a single isotope approach which have reported that most of the studied tree species generally source soil water from depths of 0.2-1.0 m (Jackson et al., 1995; Meinzer et al., 1999; Oliveira et al., 2005; Hasselquist et al., 2010; Liu et al., 2010). However, deep soil water use (>1 m) has also been reported in tropical rainforests (Nepstad et al., 1994; Moreira et al., 2000). Recent studies on tree water sources in the tropics using a dual isotope approach show trees were using >0.2 m deep soil water (Querejeta et al., 2007; Schwendenmann et al., 2015). Overall, all previous studies using only a few species suggest that different species use soil water from different depths. The study shows thatmore than 83% of the observed wet tropical rainforest tree species rely on water from the very shallow soil layer (0.0-0.2 m). Results of the BMM further showed that the contribution of very shallow (0.0-0.2 m) soil water to xylem water was approximately 62%, which is higher compared to all deeper (0.2-4.0 m) soil layers. This finding further indicates that the majority of the trees across this small 0.32ha plot have shallow root systems. Work near the study site by Graham (2006) found that tree roots were most dense above a depth of 0.6 m in all the observed soil profiles. The study found some species such as Alloxylon wickhamii, Alangium villosum, Glochidion ferdinandi, Argyrodendron peralatum, Ficus hispida, Pouteria obovoidea, Syzygium claviflorum and Toona ciliata relied on deep soil water. Soil water uptake patterns were therefore species-specific, although all species were exposed to the same environmental conditions. It is also interesting to note that all 46 species sourced at least part of their water from deep within the soil profile, though this was only a small proportion for most species.

5.5.2 Tree functional traits and water uptake depth It is generally assumed that large-sized trees use more deep soil water than small-sized trees. This is assumed because large-sized trees generally have deeper root systems than small-sized trees (Dawson and Ehleringer, 1991; Horton and Hart, 1998; Romero-Saltos et al., 2005; Goldsmith et al., 2012). However, the present studyshowsthat contributions from soil water to xylem were not

125 significantly different between small and large-sized trees. Nevertheless, some small-sized trees do tap into deep soil water. In contrast, some large-sized trees relied on shallow soil water. Others too have found ambiguous water uptake depths with respect to tree size.Stahl et al. (2013) showed that tree dimensions did not influence water uptake depth in tropical tree species. Thorburn and Ehleringer (1995) as well as Evaristo et al. (2016) found that the roots of a tree in a specific soil layer did not always use water from that layer. There is evidence available which shows that large-sized trees uptake water from shallow soil layers (Mueller et al. 2005). Meinzer et al. (1999) showed that smaller tropical trees relied more on deep water than did larger, co- located trees. Past work in Puerto Rico (Evaristo et al., 2016) using mixing models and dual isotope data has shown that during the dry season, large, shallow-rooted Mahogany trees (Swietenia spp.) located on ridge tops relied mostly on deep soil (>20 cm) water, while smaller, deep-rooted Mahogany trees used mostly groundwater.

When wood density was considered, the results showed that there was a significant difference between low and high wood density trees in terms of water isotope composition. Some of the trees with low wood density showed a depleted isotope composition, indicating the use of deep soil water. But other functional traits such as different growth rates (based on MBAI) did not show significant difference between low and high growth rate trees water isotope composition.

Although all the observed species were exposed to the same environmental conditions and the study assessed a range of functional traits, it was not possible find any clear patterns regarding tree water uptake depth. Future studies should examine tree root architecture which is likely an important factor in tree water uptake patterns. Some studies show that belowground plant functional traits such as root distribution and water uptake depth will be similar among species, as these traits are closely related and governed by soil water availability (Meinzer et al., 2001; Ogle et al., 2004).

5.5.3 On dual isotopes and the BMM approach Previous studies have shown that using only a single isotope (δD or δ18O) approach to assess plant water sources can be problematic since each isotope on its own may provide different information on the potential sources of plant water uptake (McDonnell, 2014). The dual isotope plot shows that all the soil water isotopes plotted along the LMWL, indicating low evaporation from the soil

126 samples. When xylem water isotope samples were plotted with the soil samples in the dual isotope plot, few individual xylem isotopes were deviated from the soil water isotopes, indicating possible fractionation associated with transpiration. Interestingly, this fractionation effect cannot be incorporated into the traditional mass balance method. However, most recent study by Evaristo et al. (2017) shows that the BMM is less sensitive to fractionation effects than simple mass balance methods because a BMM can incorporate specific error terms for the discrimination factors (Tarroux et al., 2010). Another reason for BMM being less sensitive to fractionation is because it assumes that the variability and uncertainty associated with the sources data are normally distributed (Evaristo et al., 2017).

TheBMM was used in this study because of its higher capacity to deal with multiple water sources and mixing (‘xylem’) compared to the traditional and simpler mass balance method. Early work showed that a simple mass balance approach might be suitable for two to three sources (e.g., Thorburn and Walker, 1994). However, more recent work has shown that when sources are greater than the number of isotope tracers, the simple mass balance method cannot adequately deal with this (Moore and Semmens, 2008; Stock and Semmens, 2016). The dataset used in this study included some outliers of xylem water isotopes, indicating possible fractionation effects. The BMM approach with multiple sources and mixer (‘xylem’), provided a range of feasible solutions, where prior ecological knowledge can be applied to determine the correct tree water source (Evaristo et al., 2017). By applying BMMthe study found, 83% of the sampled trees relied mainly on water from a soil depth of 0-0.2 m. This is supported by previous work in tropical rainforest settings showing that trees have most of their root biomass in the upper soil layers to optimize their acquisition of resources such as nutrients (Stahl et al., 2013).The BMM can be further applied to the same study site to determine the efficiency of detecting seasonal variations in tree water sources.

5.6 Conclusion The study performed a Bayesian mixing analysis of tree water isotope patterns to better understand the water uptake depth patterns of 46 tropical rainforest tree species. There was very little variation in soil water uptake depth among the observed species regardless of tree size, wood density and growth rate, suggesting species-specific behaviours. The dual isotope approach together with the BMM shows that the very shallow soil water pool (0-0.2 m) was heavily used by

127 the majority of the sampled tropical rainforest tree species. This finding is consistent with the general assumption that tropical rainforest trees have most of their root biomass in the upper soil layers to optimize their acquisition of resources such as nutrients and water. The study provides a good example of how a combined dual isotope and BMM approach can be applied to better understand the water uptake strategies of tropical forest tree species. The knowledge acquired from this study has great implications for the design of mixed-species plantations and future predictions of shifts in tropical forest species composition in response to global climate change.

5.7 Acknowledgement Thanks to Kim Janzen for extracting water from the samples and analysis, and the Tropical Forestry Group at the University of the Sunshine Coast for their help during the field work. This work was supported by a scholarship from the Australian Government through an IPRS (International Postgraduate Research Scholarship, currently named Research Training Program, RTP) awarded to Md. Shawkat Islam Sohel. Thanks also to Dr John Meadows for proofreading and English language editing.

5.8 References

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CHAPTER SIX: SAP FLUX AND STABLE ISOTOPES OF WATER SHOW CONTRASTING TREE WATER UPTAKE STRATEGIES IN TWO CO-OCCURRING TROPICAL RAINFOREST TREE SPECIES

Sohel, M.S.I., Herbohn, J., von Kleist K., Doley, D., Evaristo, J., McDonnell, J.J. Sap flux and stable isotopes of water show contrasting tree water uptake strategies in two co-occurring tropical rainforest species. This chapter will be submitted to ‘Forest Ecology and Management’ and is presented in the format of that journal.

6.1 Abstract Several studies have now examined tropical tree water use strategies and their seasonal variations. However, little is known about the short‐term dynamics of tree water use strategies particularly for neighbouring co-occurring species. Here, the study quantifies the high frequency changes in water sources and sap ﬂux patterns of two commonly co-occurring tropical rainforest tree species: Dendrocnide photinophylla (Kunth; Chew) and Argyrodendron peralatum (F.M. Bailey; Edlin ex J.H. Boas). A combination of continuous sap flux measurements and hourly sampling of xylem water stable isotope composition (δD and δ18O) were used to observe water use strategies through a 24 h transpiration cycle. Sap flux ranged from 2.82-28.50 L d-1 and was 66.67% higher in A. peralatum compare to D.photinophylla. For both tree species, sap flux increased with tree size and diurnal sap flux increase resulted in more isotopically enriched xylem water. A Bayesian Mixing Model analysis using sampled soil water isotopic composition from five soil depths from of 0 to 1 m showed that D. photinophylla used very shallow or surface layer (0-20 cm) water, while A. peralatum sourced its water mostly from deeper in the soil profile (>20 cm). These contrasting patterns of water use and water sources of co-occurring tree species suggest that to make proper conclusion on species water consumption pattern, plant water storage capacity, quantitative wood anatomical features and xylem isotope composition should be considered together with sap flux measurement and future studies should consider species level water use strategies to build improved process understanding for ecohydrological modeling.

Keywords: Tree water consumption, dual isotope, xylem water isotope, wood anatomy, tree water storage

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6.2 Introduction Water plays an important role in plant growth and function (Ehleringer et al., 1991; Snyder and Williams, 2003). The dynamics of water availability in soils and water use by plants are critical to ecosystem function and resilience (Gazis and Feng, 2004; Cui et al., 2015). Thus, understanding plant water sources and water use strategies are a major research concern (Pausas and Austin, 2001; Cheng et al., 2006). Most studies to date have shown that plant water sources and water use strategies vary in different ecosystems. Plant water use strategies also vary with differences in species and plant functional traits such as rooting depth, phenology, plant form and growth stages (Schwinning and Ehleringer, 2001;Xu and Li, 2005;Sun et al., 2006; Duan and Ou, 2007; Peñuelas et al., 2011). But little is known about water use strategies of tropical tree species, even though tropical forest ecosystems comprise about 52% of the earth’s forest cover (FAO, 2016). The high plant diversity of these ecosystems and the complex interactions among their component species suggests that species within these systems may have a variety of water use and water uptake strategies. Studies of the water use of tropical forest plant species and analyses of their responses and adaptive strategies to certain environmental conditions are of great significance to the formulation of effective ecosystem conservation and restoration strategies.

Stable isotope composition of water (δD and δ18O) has been used to trace water uptake patterns for many plant communities and environments (Dawson and Ehleringer, 1991; Jolly and Walker, 1996; Meinzer et al., 1999; Slavich et al., 1999; Atsuko et al., 2002; Sekiya and Yano, 2002; Pe˜nuelas and Filella, 2003; McCole and Stern, 2007; Querejeta et al., 2007). Past research using isotopes to quantify tree water sources in the tropics has been based on low-resolution spatial and temporal isotope measurements (Atsuko et al., 2002; Peñuelas and Filella, 2003; Querejeta et al., 2007; McCole and Stern, 2007). Although Goldsmith et al. (2012) used fine temporal resolution measurements; these authors did not combine stable isotopes with sap flux. Consequently, plant water use strategies over short time-scales and spatial distances remain poorly understood. Recent commentary by Berry et al. (2017) has emphasized the need for fine-resolution water isotope sampling. To date, only Volkmann et al. (2016) have investigated tree water use strategies over short time-scales in a temperate setting.

Here sap flux measurements and stable isotope tracing of xylem water were combined to explore through a day the plant water sources and water use strategies in two neighbouring tropical hardwood tree species in Australia.A dual isotope approach was used and relates the plant water

134 with the meteoric water line (MWL)2 as a key reference point for evaporative enrichment and water pool differentiation (McDonnell, 2014). No studies yet conducted on fine spatiotemporal resolution measurements to understand water isotope composition in the tropics. Understanding the resulting dynamic interplay between patterns of water availability and species-specific utilization is needed to model the tropical forest water balance and associated ecosystem structure and function (Breshears and Barnes, 1999; Caylor et al., 2009). This study combines the use of stable isotopes with sap flux measurements in a tropical rainforest in North Queensland, Australia, to determine the dynamics of water use strategies. Specifically, the following questions were asked: -Does tree species type and size influence tree water use over a diurnal transpiration cycle? -Do two co-occurring species from different ecological guides’ source water from the same depth within the soil profile? -Does xylem water isotope composition change with sap flux dynamics?

6.3 Materials and Methods

6.3.1 Experimental site and studied species The study site was located in a wet tropical rainforestin the Danbulla State Forest on the Atherton Tableland in northeastern Australia (Fig. 6.1a). The site has an elevation of 760 m above sea level (Drake and Franks, 2003). Trees were located within a long-term experimental plot established by the Queensland Department of Forestry in 1948, referred to as Experiment 78 (Plot 1). The plot measured 200x20 m (4,000 m2or 0.4 ha) (Fig. 6.1b). Regular measurements of the growth, mortality and recruitment of trees within the plot that are greater than 10 cm diameter at breast height (DBH) have been undertaken since the plot’s establishment.Soils within the plot are sandy clay loams to clay loams.The soils have developed from basalt lava flows of Pliocene to Holocene age (Laffan, 1988). Annual average rainfall for the site is 1,680 mm, with over 1,000 mm falling between December and February (Drake and Franks, 2003). The area is prone to seasonal droughts resulting from infrequent rainfall during the typically drier months of April to October

2The Meteoric Water Line (MWL), is a simple regression line which is derived from precipitation water isotope data (δD and δ18O) from single or multiple sites or from across the globe. If the data were collected and analysed from across the globe,then it is known as the“Global Meteoric Water Line (GMWL)”. However, if the data were collected from single site or set of "local" sites then it is known as the“Local Meteoric Water Line (LMWL)". LMWL can be significantly different from the GMWL. This ‘MWL’ helps to identify evaporation effect on water samples. The isotopic ratios and fractionations of the two elements are usually discussed together using MWL considering this line has is no evaporation effect.

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(Drake and Franks, 2003). Most of the plant species in the study site are highly moisture dependent (Tracey, 1982; Drake and Franks, 2003).

To investigate the diurnal variation in water use strategies of co-existing rainforest tree species from different successional groups, one pioneer/early secondary species (i.e. Dendrocnide photinophylla) and one mature phase species (i.e. Argyrodendron peralatum) were selected. The rationale for this selection was that pioneer species are generally fast growing in their early growth stages while mature phase species are slow growing (Goosem and Tucker, 2013). The location of all individuals of the two species within the plot is shown in Figure 6.1(b) and location of the six individuals relative to each other is shown at a finer resolution in Figure 6.1(c). These species were chosen on the basis of: (1) their co-existence at a fine spatial scale (the reason for selecting co-existing tree species is to avoid geomorphologic variation), (2) their taxonomic and morphological differences, and (3) because they were represented by different size class pairs (i.e. small, medium and large sized trees).

Fig. 6.1. (a) Location of the study area. The arrow indicates the permanent plot where the study was conducted. (b) Spatial distribution of Argyrodendron peralatum (green dots) and Dendrocnide photinophylla (red dots) throughout the 20x200 m plot. The red box indicates the location of the sampled 20x20 m subplot(c) Spatial distribution of the A. peralatum and D. photinophylla trees

136 sampled for diurnal variation in tree water sources within a 20x20 m subplot. Black circles indicate the locations of bore holes for soil sample collection.

6.3.2 Plant and soil sample collection Three trees for each species were selected based on their spatial proximity to each other and paired according to their DBH (Fig. 6.1b and c). These selection criteria sought to avoid possible tree size and location effects on water use. Xylem samples were collected from these six trees on twelve occasions during a 24 hour sampling period starting from pre-dawn at 0300 and then at 0700, 0900, 1000, 1100, 1200, 1300, 1400, 1500, 1700, 2100 and 2300 hr. The xylem core samples were collected from the northern, southern, western and eastern sides of the trees using a battery-operated drill and 16 mm Forstner drill bit. Before collecting the samples, all bark tissue was removed. For both species, there was a clear delineation between the outer bark and the sapwood, and between the sapwood and heartwood. Three xylem samples were collected from each of the four sides of each tree. For the small and medium diameter trees, these samples were collected from approximately 1.1 to 1.4 m from the base of the tree. Both large diameter trees had substantial buttresses and for these individuals, samples were collected between 3.0 to 3.4 m from the base of the tree, which was where above the buttresses merged with the round tree bole. Samples were collected avoiding the section of the bole on which the sap flux sensor was located. Sufficient xylem tissue was collected and immediately placed into a 24 ml glass vial so as to completely fill the vial. The vial was then immediately sealed and wrapped in parafilm to prevent evaporation. Samples were then placed into a refrigerator until laboratory analysis. On the sampling day, soil samples were collected from five bore holes at 6 different depths (0, 20, 40, 60, 80 and 100 cm). The locations of the bore holes in relation to the trees are shown in Figure 6.1. All soil samples were immediately placed into capped vials, wrapped with parafilm, placed into a refrigerator, and stored until laboratory analysis.

6.3.3 Species growth trends Growth rates of the sampled trees were determined from the long term experimental plot inventory data in which DBH measurements were collected on 18 occasions from 1948 to 2016. Using this data, basal area (BA) was calculated using the formula BA=0.00007854 x DBH (cm2).

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6.3.4 Soil moisture measurement Volumetric soil moisture content (here after VSM%) was measured continuously at five depths (0- 20, 20-40, 40-60, 60-80 and 80-100 cm) were measured continuously using a SMM Soil Moisture Meter (ICT Australia, 2017) standing wave sensors (MP406) and a data logger. Soil moisture data were collected at five minute intervals throughout the day. The MP406 sensors were calibrated as per the ICT manual (http://www.ictinternational.com/content/uploads/2014/03/SMM- Manual3.pdf).

6.3.5 Sap flux measurement The six sampled trees were equipped with sap flux sensors (ICT International PTY LTD, Armidale, NSW, Australia). Each tree’s sap flux was measured and logged every 10 minutes using the heat ratio method (HRM) as described by Burgess et al. (2001). For the small and medium sized trees sap flux probes were installed between 1.1 and 1.2 m. For both large diameter trees which had substantial buttresses, the sap flux probes were inserted above where the buttresses merged with the bole, which was around 3m. The sap flux sensors were inserted into the xylem tissue. Each sap flux sensor consisted of three probes, 0.13 cm in diameter and 3.5 cm in length, and spaced 0.5 cm apart axially on the tree trunk. A drill guide (ICT International PTY LTD, Armidale, NSW, Australia) was used to minimise errors in spacing and probe alignment. The centre probe contained a heater wire, and the upper and lower probes each contained two thermocouples. Heat pulse velocity was converted to sap velocity (cm h-1) following the calculations provided by Burgess et al. (2001) and as described in Ambrose et al. (2010) after accounting for variations in sapwood properties and correcting for errors associated with probe spacing and wound responses. Sapwood volume, water content (fresh and dry water content) and sapwood depth were determined from cores collected near each probe set using a 5.15 mm diameter increment borer. Stem diameter was measured with a diameter tape at the point where the sensors were installed. The 5.15 mm increment borer was also used to extract a sample to determine bark thickness, with the transition of sapwood and bark easily identified by changes in colour and texture for both species. From the extracted core, bark thickness was measured with a ruler. Bark thickness was used to guide the location of the sap flux sensors in the sapwood. To calculate sap flux, the cross-sectional area of live sapwood was determined by calculating the cross-sectional area from the under-bark radius (Burgess and Downey, 2014). The total sapwood area of each sampled tree was calculated using the formula, 4πrb-2πb2, where r is the tree radius under bark

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[Under bark DBH=Over bark DBH-(bark thickness*2)] and b sapwood depth (after deducting bark thickness). Sap velocities for each tree were then determined using the Sap Flow Tool software package (ICT International PTY LTD, Armidale, NSW, Australia-Phyto-IT, Mariakerke, Belgium).

6.3.6 Stem increment measurements and their relation to stored water To examine the moisture content, heartwood and sapwood water content was measured using the gravimetric method. This involved the collection of tree core (sapwood-heartwood) samples using an increment borer and direct measurement of the water content by weighing tissue samples before and after drying. Water content was calculated using the formula: fresh weight – dry weight / dry weight)* 100.

Each of the sampled trees was equipped with a Dendrometer band (DBL60–Logging Band Dendrometer, ICT International, Armidale, NSW, Australia). For the small and medium sized trees Dendrometer band were installed between 1.2 and 1.3 m. For both large diameter trees which had substantial buttresses, the Dendrometer band were installed above where the buttresses merged with the bole, which was around 3.2 m. This recorded stem diameter every 10 min, from which stem diameter variations were derived. Logistical constraints resulted in the dendrometer band being installed on the large A. peralatum after the bands on the other five trees. The changes in stem radius were used to calculate changes in stem volume. These volume changes were then used to calculate stem water storage.

6.3.7 Investigation of tree water uptake depth through stable isotope analysis Water was extracted from the xylem and soil samples using cryogenic vacuum distillation (Orlowski et al., 2013).The extracted water was then analysed for δD and δ18O. In the case of δD, an established method on a Delta V Advantage mass spectrometer and an HDevice peripheral were used. For δ18O, samples were run using an established method on a Delta V Advantage mass spectrometer and a GasBench II peripheral. For a description of these methods, see Nelson (2000). AllδD and δ18O values were expressed relative to Vienna Standard Mean Ocean Water (VSMOW) in % using the equation:

18 δD or δ O = (Rsample/Rstandard -1)1000

139 where R is the ratio of 18O/16O or 2H/1H in the sample or in Standard Mean Ocean Water(SMOW).The analyses’ precisions of δD and δ18O (including sampling, extraction and analytical errors) were estimated to be +/- 1 permil and +/- 0.2 permil, respectively.

6.3.8 Bayesian Mixing Model to determine soil water proportions in xylem tissue MixSIAR (stable-isotope analysis in R) Bayesian Mixing Model (BMM) statistical package (Moore and Semmens, 2008; Parnell et al., 2013; Stock and Semmens, 2016) was used to partition source water contributions to xylem tissue. One of the major advantages of using BMM is that the model is less sensitive to fractionation effects than simple mass balance methods (Evaristo et al. 2017). MixSIAR is widely used in food web and animal foraging studies, and was used in this study to determine the relative importance of various sources of water that may contribute to xylem water using Markov Chain Monte Carlo (MCMC) methods. Two potential sources of xylem water were used when running the BMM: (1) soil water at <0-20 cm (“shallow soil water”); (2) soil water at 20- 100 cm (“deep soil water”). Isotope fractionation was set to ‘0’ for D. photinophylla in the MixSIAR model. However, this was calculated for A. peralatum as xylem isotopes were deviated from the soil isotope compositional range. The MCMC model run was set to ‘long’ (300,000 iterations) and the source water’s most likely contribution (i.e. the mean of the posterior distribution of the MCMC simulation) to xylem water was obtained for all sampled trees.

6.3.9 Evaporation enrichment calculation To investigate whether increase or decrease in sap flux had an effect on evaporative enrichment within the xylem, Line-conditioned excess (lc-excess) was used (Landwehr and Coplen, 2006). lc- excess using dual isotope composition quantifies the degree of ‘offset’ between a meteoric water line and soil water, xylem water and stream water. lc-excess value ‘0’ indicates no evaporative enrichment (for soil water, xylem water and stream water). Negative value indicates the water sample is evaporated with greater evaporative enrichment with distance from “zero” (Landwehr and Coplen, 2006). lc-excess was calculated as δD-aδ18O-b]/S, where a and b are the slope and y- intercept, respectively, of the LMWL, calculated from δD and δ18O of local rainfall samples and S is one standard deviation measurement uncertainty for both δ18O and δD.

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6.4 Results 6.4.1 Soil volumetric water content with depth Volumetric water content of soil differed significantly among the sampled soil depths. Volumetric water content in the top 50 cm was 25-35% during the sampling period and soil moisture content increased with depth to a maximum of 39% (Fig. 6.2). There was little variation in volumetric water content for all the soil layers except the shallowest soil depth. Immediately prior to the sampling date, there was 10 mm of rainfall for two days, and so the volumetric water content in the upper soil layers was high.

Fig. 6.2. Variation in soil volumetric water content with depth. The data were measured continuously at 10 minute intervals. The isotope sampling date was 26th of March, 2016.The inset graph represents the average soil volumetric water content [Note: all the presented data were from nine representative wet season days (20thMarch, 2016-28th March, 2016)] in the plot.

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6.4.2 Tree water uptake patterns of the two co-existing tree species

6.4.2.1 Measured tree characteristics The two sampled tree species were from different successional groups: one is a pioneer species (Dendrocnide photinophylla) and other is a mature phase species (Argyrodendron peralatum). Sapwood width and water content varies among individuals of these two different species. D. photinophylla has a large sapwood width and water content compared to A. peralatum (Table 6.1). The long term growth records from the plot revealed that there were distinct differences in the growth patterns between trees of different diameters and species (Supplementary Fig. 6.1). Small and medium-sized trees of both species had negligible growth over the past 68 years, although the medium-sized A. peralatum did exhibit a modest increase in BA compared to the other three individuals. This suggests that the growth of the small and medium-sized trees has been suppressed through competition from larger adjacent trees. The two large individuals of both species initially had similar high rates of growth, but with the growth of the D. photinophylla stagnating around 1982 resulting in little increase in BA over the next 32years.

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Table 6.1. Characteristics of the tree species selected for study. Comparison of wood density, diameter, water content and sapwood width were given in the table.

Species Wood Tree diameter (cm) Water content of sapwood Water content of heartwood Sapwood width (cm) density (dry weight basis %) (dry weight basis %) (g/cm3) S M L S M L S M L S M L

A.peralatum 0.62 16.3 31.5 61.0 56.54 47.83 58.12 90.45 70.61 77.04± 3.58 5.93 8.93 ±4.13 ±1.11 ±2.05 ±26.69 ±41.23 13.16 ±1.31 ±0.85 ±2.51 D.photinophylla 0.21 20.3 33.5 46.2 542.39± 418.91 368.23 861.67 397.27 348.06 7.60± 9.75± 9.65± 38.45 ±71.12 ±11.11 ±700.06 ±151.22 ±50.06 0.69 0.53 1.46 N.B. S=Small, M=Medium and L=Large.Wood density data is based on llic et al. (2000)

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6.4.2.2 Tree water sources The δD and δ18O soil values ranged from -24.6 to -49.9‰ and from -3·9‰ to -5.3‰, respectively (Fig. 6.3). The dual isotope plot in Figure 3 shows that soil water isotope values plot along the Local Meteoric Water Line (LMWL), indicating negligible evaporative enrichment of soil water. The D. photinophylla xylem water isotope values also plot along the LMWL. This is similar to the soil water isotopes and indicates that this species was using very shallow soil water or surface soil layer water. In contrast, A. peralatum xylem water isotope values were highly enriched and plotted below the measured soil water isotope values (Fig. 6.3).

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Fig. 6.3. δD-δ18O isotope space plot showing diurnal variation of isotope composition.

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The contributions of soil water from different depths to xylem water were determined using a BMM. Figure 6.4 shows that “Deep” soil water comprised about 66% of the contribution to xylem water in all the sampled A. peralatum trees. In contrast, “Shallow” soil water contributed 99% of the soil water contribution to xylem water in all the sampled D. photinophylla trees. Overall differences in source water proportions were statistically significant (student t-test, PDeep<0.000 and Pshallow<0.000) for shallow vs deep soil layers for both species.

Fig. 6.4. Source water partitioning for each species determined by the Bayesian Mixing Model.

6.4.2.3 Sap flux pattern and its effect on isotope composition A. peralatum had greater sap flux rates than D. photinophylla across for all size classes, with the sap flux rate increasing with tree size (Fig’s 6.5, 6.6 and 6.7). The average daily water uptakes for D. photinophylla were 0.36 L d-1, 2.22 L d-1 and 7.44 L d-1 for the small, medium and large-sized trees, respectively. For A. peralatum, average uptake rates were 2.82 L d-1, 18.78 L d-1 and 28.50 L d-1 for the small, medium and large-sized trees, respectively. Dendrometer data showed that sap flux decrease with stem diameter increment for small and medium-sized A. peralatum trees. This is also true for small-sized D. photinophylla tree. However, the medium-sized D. photinophylla tree stem diameter increased during the active transpiration period from 9 am-3 pm (Fig. 6.7). For both species, water consumption increased with increasing sapwood area. There was a sharp contrast

146 between species in terms of sapwood area where A. peralatum had much less sapwood but substantially higher sap flux compared to D. photinophylla (Supplementary Fig. 2). A. peralatum BA was higher than D. photinophylla, and higher sap flux rates were recorded for the mature phase species (A. peralatum) than the pioneer species (D. photinophylla).

There was no significant difference in xylem water δD and δ18O isotope variation throughout the daily transpiration cycle for D. photinophylla (ANOVA-Tukey Method; p<0.158 and p<0.087 consecutively) (Supplementary Fig. 6.3). For A. peralatum, there was no significant difference for xylem water δD isotope composition (ANOVA-Tukey Method; p<0.708). In contrast, δ18O isotope shows significant difference throughout the day transpiration cycle (ANOVA-Tukey Method; p<0.000). When δ18O and δD were compared between species, significant differences were found (student t-test, P<0.014 for δ18O and P<0.000 for δD). This suggests that diurnal variation of isotope composition is species specific. There was no significant difference in diurnal variation of xylem δ18O isotope among small, medium and large size individuals of similar species (ANOVA- Tukey Method; For A. peralatum p<0.710 and for D. photinophylla p<0.475). There was no influence of tree size on xylem δD isotope for D. photinophylla (ANOVA-Tukey Method; D. photinophylla p<0.128). For A. peralatum, the medium trees’ xylem δD isotope was significantly different throughout the diurnal transpiration cycle compared to the small and large sized individuals (ANOVA-Tukey Method; For A. peralatum p<0.045) (Supplementary Fig. 6.4). Even though there was no significant difference in xylem water δD and δ18O isotope variation (Supplementary Fig. 6.3), when the sap flux rate was high, the isotope composition of xylem water was more depleted for both species (Fig. 6.5 and Fig. 6.6). lc-excess and sap flux data, however, showed with the increase of sap flux, lc-excess values became more negative indicating xylem isotope composition became more enriched for both species(Supplementary Fig. 6.5).

147

Fig. 6.5. Diurnal variation of oxygen isotope composition in relation to sap flux variation of individual trees. The area between the red lines with blue dots represents daytime isotopic variation while the black dots indicate night-time isotopic variation.

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Fig. 6.6. Diurnal variation of hydrogen isotope composition in relation to sap flux variation of individual trees. The area between the red lines with blue dots represents daytime isotopic variation while the black dots indicate night-time isotopic variation

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Fig. 6.7. (a,b,c& d) stem increment variation and sap flux relation; (a-f) sap flux variation between species and trees of different sizes; (g) soil volumetric water content (VSW%). Data presented in this figure cover an extended periodfrom 26/3/2016-10/4/2016. The negative readings of stem increment in the graph (Fig. c) indicate that stem contraction was greater than the actual growth during the study period.

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6.5 Discussion

6.5.1 Species differences in water use

The results showed that tree size played a dominant role in determining tree species water use. The sap flux data indicated that the amount of water consumption increased with tree size. However, strong differences were found in water consumption between pairs of D. photinophylla and A. peralatum individuals of similar size through a diurnal cycle. D. photinophylla consumed less water even though it had a larger sapwood area. This finding is unexpected since expected that fast growing pioneer/early secondary species with a low wood density such as D. photinophylla would use more water than the typically slower growing later secondary species such as A. peralatum. However, the findings is consistent with past research (e.g. Bucci et al., 2004; Choat et al., 2005) which suggests that daily transpiration should decrease with the increase in wood density. Very low diameter increments of all three D. photinophylla trees for the last 30 years were observed (Supplementary Fig. 6.1). The lower than expected water consumption by D. photinophylla is thus potentially related to the trees being “over mature”, with their growth stagnating prior to the trees dying. From core samples it was observed that those trees were hollow inside the trunk (Supplementary Table 6.1). Conversely, higher wood density species A. peralatum showed higher water use. It has been hypothesize that there could be large amounts of water stored in the xylem tissue of D. photinophylla compared to A. peralatum. This would be consistent with the measurements of water content (Supplementary Table 6.2). Lake (2015) has noted that D. photinophylla wood is ‘heavy’ when freshly cut because of its higher water content. Past research suggests that stored water is not typically a significant source of water for transpiration in most woody plants (Roberts, 1976; Tyree and Yang, 1990). However, recent research by Köcher et al. (2013) has shown that stored water can contribute 10–22% of daily transpiration for diffuse-porous species. D. photinophylla has very large vessels and it is possible that this species exhibits a similar water use pattern as the porous species reported in Kocher et al. (2013).

The high-resolution stem increment data shows that night-time water storage was largely absent for the medium-sized D. photinophylla (Fig. 6.7, Supplementary material 6.6, 6.7, 6.8 & 6.10; Supplementary Table 6.2). This suggests some sort of physiological stress (Edaphic Scientific, 2017). The small sized D. photinophylla trees’ stem increment variation was less compared to the

151 small-sized A. peralatum during night time. This suggests that recharge of the stem is small for D. photinophylla (Fig. 6.7, Supplementary Fig’s. 6.6, 6.7, 6.8 & 6.9, Supplementary Table 6.2).The large- sized D. photinophylla trees’ stem increment variation was only slightly higher at night-time compared to the medium and small-sized D. photinophylla tree. However, stem increment variation of the large-sized D. photinophylla was less compared to the large-sized A. Peralatum (supplementary Fig. 6.10). This suggests contributions of stored water to transpiration. Overall, water storage was higher in D. photinophylla than A. peralatum (Supplementary Table 6.2). Therefore, the study hypothesize further that D. photinophylla might use only negligible amount of stored water for transpiration, although, this varies with different tree size.

From a wood anatomical perspective, D. photinophylla is extremely soft, light, fibrous and has diffuse vessels (Bonsen and terWelle, 1984; Lake, 2015). This is in strong contrast to the wood anatomy of A. peralatum (Apgaua et al., 2015; Lake, 2015) (Fig. 6.8). This suggests that Dendrocnide could store water than it transports. The wood anatomy differences between the two species is also reﬂected in the measurements of wood density and percentage moisture content, which showed a lower density and higher moisture content for the sapwood and heartwood tissue of the D. photinophylla trees (Table 6.1 and Supplementary Table 6.1). The results are consistent with Choat et al. (2005) who showed that Australian rainforest tree species with lower wood density, wider and more fibrous xylem vessel had higher water storage capacity. Such species are likely more hydraulically efﬁcient, but more vulnerable to drought (Choat et al., 2005). Further work is needed to better understand the linkages between species water consumption pattern, plant water storage capacity, and quantitative wood anatomical features (such as vessel areas and densities)together with sap flux measurements.

152

Fig. 6.8. Contrasting wood anatomical features of two wet tropical rainforest trees from North Queensland, Australia. (a) Mature phase species A. peralatum and (b) pioneer/early secondary species D. photinophylla. The scale bar in D. photinophylla equals 5 mm. The scale is also similar for A. peralatum. Source: Reproduced with permission of Jean-Claude Cerre, from Australian Rainforest Woods by Morris Lake. Published by CSIRO Publishing 2015 (Lake, 2015).

6.5.2 Water uptake depth

The soil and xylem water isotope data showed that there are sharp differences in water uptake depth between the two co-occurring species. However, irrespective of tree size, individuals of the same species sourced their water from the same depth within the profile. D. photinophylla sources almost all of its water from within the top 20 cm of the soil profile which is in stark contrast to A. 153 peralatum which sources most of its water from deeper in the profile. These results are in contrast to other studies which show that larger trees have greater access to deep soil water than small trees because of their deep root systems (Horton and Hart, 1998; Romero-Saltos et al., 2005). However, there are some exceptions where large trees do not have deep root systems (Mueller et al., 2005). Meinzer et al. (1999) reported that smaller tropical trees relied more on deep water than did larger co-located trees. Thorburn and Ehleringer (1995) found that roots in a particular soil layer did not always match the water uptake depth. While rooting depth data was not available, the findings suggest that one of the investigated species, D. photinopylla has large surface roots and takes water from surface soil layers (Fig. 6.3 and 6.4).

The results showed that D. photinophylla xylem water and soil water plotted along the LMWL, indicating these trees use very shallow or upper-surface soil layers to obtain water. Previous work in the same region of North Queensland by Drake and Franks (2003) is consistent with the findings and showed that three co-dominant canopy species (i.e. Doryphora aromatica, Argyrodendron trifoliolatum and Castanospora alphandii) and two climbing palms (i.e. Calamus australis and Calamus caryotoide) used shallow soil water during the wet season.In this study, the xylem water isotope signatures of A. peralatum were highly enriched even though the soil water isotope composition plotted along the LMWL, indicating likely additional fractionating mechanisms associated with water uptake within these trees. This phenomenon has been observed in a few other species, including a semiarid forest/floodplain ecosystem in Switzerland (Bertrand et al., 2014), and a moist cold Scots Pine forest in Scotland (Geris et al., 2015).

6.5.3 Does evaporative enrichment of xylem water isotope relates to low sap flux rate?

This study found no strong diurnal variation in xylem water isotope for both species. However, during daylight hours when the sap flux rates were high, the isotope composition (both δ18O and δD) of the xylem water showed evidence of isotopic depletion, though statistically the differences between night-time and day-time isotopic composition were not significant (Supplementary Table 3). Xylem water isotope composition became enriched for both species during times of limited sap flux. This suggests that isotopically enriched xylem water might not reflect the soil water source used by plants if sampled during limited low sap flux periods. Some previous studies also found evaporative enrichment in matured, suberized stems during limited sap flux (Cernusak et al., 2005;

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Bertrand et al., 2014; Ellsworth and Sternberg, 2014; Martín‐Gómez et al., 2016; del Castillo et al. 2016). However, these studies used single isotope data. Lc-excess and sap flux data showed that with the increase of sap flux, xylem isotope signatures became more enriched. This finding is counter intuitive and opposite to some other studies that have showed that decreased in sap flux is responsible for xylem isotope enrichment via phloem diffusion (Bertrand et al., 2014; Martín‐Gómez et al., 2016). It has been hypothesizedthat depleted xylem water could be the mixture of xylem water with enriched water from the leaf (Brandes et al., 2007; Ellsworth and Williams, 2007). Back‐diffusion of enriched water from the leaf (Dawson and Ehleringer, 1993), or by means of water exchange between xylem and phloem tissues (Cernusak et al., 2005; Brandes et al., 2007) could be the reason for this phenomena. Additionally “soil processes” such as species- specific use of contrasting water sources retained at different water potential forces in soil (Gómez et al., 2014; Bowling et al., 2017) can cause isotopic enrichment of xylem water. More research on evaporative enrichment using dual isotope approach is needed for better understanding of isotope composition dynamics during transpiration. Solving this problem will aid better xylem sample collection time. These processes warrant additional study at the same site.

6.6 Conclusions The measured sap flux and stable isotopes of water in two co-occurring tropical rainforest species showed strongly contrasting tree water uptake strategies. Although these two species were located in close proximity to each other, A. peralatum was used much more water than D. photinophylla. While tree size had no effect on water uptake depth, the species’ growth patterns and size did have a significant influence on the amount of water being used. Water use increased with tree growth increment. D. photinophylla was used water from surface soil layers, while A. peralatum was used deeper soil water. The combination of sap flux and stable isotope analysis showed that xylem water isotope became enriched when sap flux was high. Though the findings are based on only two tropical species, the dual isotope approach combined with sap flux analysis highlights the dynamic nature of tree water use strategies. Future studies should consider exploring additional factors with additional species traits to build improved process understanding for ecohydrological modeling.

6.7 Acknowledgements Thanks to Kim Janzen and Cody Millar for performing the water extractions and isotope analysis. This work was supported by a scholarship from the Australian Government IPRS (International 155

Postgraduate Research Scholarship, currently named Research Training Program, RTP) to Md. Shawkat Islam Sohel. This work was also partially funded by the Wet Tropics Management Authority.

6.8 References

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6.9 Supplementary information

Supplementary Fig. 6.1. Growth pattern of the three size class trees of the sampled species

Supplementary Fig. 6.2. Sapwood area and tree water consumption relation

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Supplementary Fig. 6.3. The δ18O and δD diurnal variations in xylem water of the studied species

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Supplementary Fig. 6.4. δ18O and δD isotope variation of individual trees of different sizeswithin species. The inset graphs represent differences among individual trees. Similar colour dots in the inset graph indicate a non-significant (p >0.05) difference of isotope among individual trees by ANOVA, while different coloured dots indicate a significant (p<0.05) difference of isotope among individual trees.

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Supplementary Fig. 6.5. Relation between sap flux and evaporative enrichment of xylem water isotope

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Supplementary Fig. 6.6. Diurnal variation of sap flux and stem incrementfor medium sizedD. photinopylla. The negative readings of stem increment in the graphs indicate that stem contraction was greater than the actual growth during observe study period.

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Supplementary Fig. 6.7. Diurnal variation of sap flux and stem incrementfor medium sizedA. peralatum

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Supplementary Fig. 6.8. Diurnal variation of sap flux and stem incrementfor small sizedA. peralatum

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Supplementary Fig. 6.9. Diurnal variation of sap flux and stem incrementfor small sizedD. photinopylla.

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Supplementary Fig. 6.10. Stem increment variation and sap flux relation of large sizedD. photinopylla and A. peralatum. Data presented in this figure is for an extended period from 01/12/2017-17/12/2017

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Supplementary Table 6.1. Moisture content of extract wood core samples

S1 S1 S2 S3

- - -

(cm) (cm) (cm) (cm)

Bark Bark

(MC%) (MC%) (MC%) (MC%)

Remarks

Sapwood Sapwood Sapwood

Heartwood Heartwood

Core length Core length

Orientation

Species and and Species

Heartwood Heartwood Heartwood

Small-D. photinophylla-East 84.67 78.26 10.35 0.50 8.00 1.80 S1-1.8cm Small-D.photinophylla-West 83.33 61.25 62.58 10.15 0.10 8.00 2.00 S1, S2-1cm Small-D.photinophylla-South 85.19 55.78 54.56 10.15 0.50 6.80 2.80 S1,S2-1.4cm Medium-D.photinophylla-East 77.02 82.26 84.13 16.75 0.10 10.30 6.20 S1-2.8cm;S2-3cm; rotten near heart wood Medium-D.photinophylla-South 79.58 86.21 12.6 0.20 9.40 2.90 hollow inside Medium-D.photinophylla-West 81.94 58.62 81.82 13 0.20 10.10 2.70 hollow inside; S1,S2-1.35cm Medium-D.photinophylla-North 83.25 76.92 77.36 74.55 16.75 0.30 9.20 7.20 S1,S2,S3-2.4cm Large D.photinophylla-East 78.76 80.43 11 0.40 8.60 2.00 hollow inside; core should be 20.1 cm Large D.photinophylla-South 78.23 76.60 77.78 80.00 19.2 0.30 11.70 7.20 hollow inside; S1,S2,S3-2.4cm Large D.photinophylla-North 79.29 73.68 76.19 14 0.50 9.70 3.70 hollow inside; S1,S2-1.85cm Large D.photinophylla-West 78.26 9 0.40 8.60 0.00 hollow inside; core should be 20.1 cm Small A.peralatum-East 33.63 51.61 50.00 8.15 0.20 4.8 3.1 S1-1.6cm; S2-1.6cm Small A.peralatum-West 37.25 40.82 35.00 8.15 0.30 2 5.7 S1,S2-2.8 Small A.peralatum-South 36.17 48.39 57.14 8.15 0.50 4.5 3 S1,S2-1.5cm Small A.peralatum-North 37.29 38.46 51.11 8.15 0.10 3 5 S1,S2-2.5cm Large A.peralatum-West 36.62 47.56 48.78 47.44 22 0.10 12.2 9.5 core should be 30.5; S1,S2,S3-3.16cm Large A.peralatum-South 36.13 36.45 38.26 42.15 21.2 0.10 6.1 15 core should be 30.5; S1,S2,S3-5cm Large A.peralatum-North 37.93 40.00 42.70 45.56 20.5 0.10 8.5 11.9 core should be 30.5; S1-3.4; S2,S3-4cm Large A.peralatum-Eeast 36.32 38.64 47.92 43.30 21 0.10 8.9 12 core should be 30.5; S1-3.4;S2,S3-4cm Medium A.peralatum-North 33.09 37.18 38.89 43.66 15.75 0.10 5.2 10.4 core should be 15.75; S1-3.4; S2,S3-3.4cm Medium A.peralatum-West 32.26 40.00 36.71 66.25 15.75 0.10 5.3 10.2 core should be 15.75; S1-3.4; S2,S3-3.4cm Medium A.peralatum-East 32.03 35.19 35.94 43.33 15.3 0.20 7 8 core should be 15.75; S1-2.4; S2,S3-3cm Medium A.peralatum-South 32.03 32.50 25.86 35.94 15.5 0.20 6.2 9 core should be 15.75; S1-3.4; S2,S3-3cm

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Supplementary Table 6.2. Stem water storage calculation. Calculated the changes in stem (sapwood) area due to shrinkage over the 16 days (Fig. 8) and converted that to estimates of daily volume shrinkage

Stem water storage calculation SST_Small SST_Medium SST_Large RDT_Small RDT_Medium RDT_Large Stem circumference (cm) 63.74 105.19 145.07 51.18 98.91 191.54 Diameter (cm)-over bark 20.30 33.50 46.20 16.30 31.50 61.00 Diameter (cm)-under bark 19.00 32.00 45.92 15.10 30.30 60.20 Bark thickness (cm) 0.65 0.75 0.14 0.60 0.60 0.40 xylem radius (cm)-under bark 9.50 16.00 22.96 7.55 15.15 30.10 Sapwood width (cm)(b) 7.60 9.75 9.65 3.58 5.93 8.93 4πrb-2πb2(sapwood area) (cm2) 544.38 1363.06 2199.15 259.13 908.01 2876.70 Stem diameter increment (mm) 0.80 1.00 1.00 1.00 1.00 1.00 Changes in stem radius (cm) 0.04 0.05 0.05 0.05 0.05 0.05 Change in water volum (L) in 1m stem 54.44 136.31 219.91 25.91 90.80 287.67 Length of stem (m)/ girth 0.64 1.05 1.45 0.51 0.99 1.92 Total change in volume of water in stem (L) 34.70 143.38 319.03 13.26 89.81 551.00 Duration of study (days) 16.00 16.00 16.00 16.00 16.00 16.00 Change in total stem water volume (L/day) 2.17 8.96 19.94 0.83 5.61 34.44 Daily Sap flux (L/day) 0.36 2.22 7.44 2.82 18.78 28.50 Stem volume change/daily sap flux 6.02 4.04 2.68 0.29 0.30 1.21 Change in basal area due to stem shrinkage (cm2) 2.38 5.01 7.20 2.36 4.74 9.44 Change in stem volume due to shrinkage per day 0.015 0.031 0.045 0.015 0.030 0.059 (L per m stem per day) Estimated bole length (m) 10.00 20.00 20.00 10.00 20.00 20.00 Total change in stem water volume due to shrinkage 0.15 0.63 0.90 0.15 0.59 1.18 (L/day) Shrinkage water/sap flux (%) [water storage] 41.28 28.21 12.09 5.22 3.16 4.14

N.B. SST= D. photinophylla and RDT= A. peralatum

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Supplementary Table 6.3. Day and night-time xylem water isotope (δ18O and δD) differences of the observed co-occurring tree species. Simple t-test was applied to see the differences (p<0.05)

Species δD isotope significant δ18O isotope significant differences differences Small D. photinophylla p<0.120 p<0.230 Medium D. photinophylla p<0.461 p<0.723 Large D. photinophylla p<0.778 p<0.646 Small A. peralatum p<0.677 p<0.795 Medium A. peralatum p<0.656 p<0.065 Large A. peralatum p<0.698 p<0.327

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CHAPTER SEVEN: SUMMARY AND FUTURE RESEARCH DIRECTION

7.1 SUMMARY The aim of this study is to understand the factors responsible for variations in tree water sources in a tropical rainforest environment at a fine spatial and temporal scale. In addressing this aim, three high-level research questions (hypotheses) were proposed. In the following section, each of these research questions is addressed.

7.1.1 There is no niche segregation among co-occurring tropical rainforest species in a homogenous environmental condition (Hypothesis 1 incorporated as chapter 4) Tropical forest water use is critical for tree productivity, growth, survival and nutrient cycling, but describing such uses is difficult in the field. Stable isotope tracing of plant water use can illuminate plant water sources but to date, the number of species tested at any given site has been minimal. Chapter 4 assesses the water sources of 46 tropical hardwood tree species (49 individual trees) in a 0.32 ha plot with uniform soils. Soil water was characterized at 6 depths at 0.2 m intervals down to 1 m and showed simple and predictable depth patterns, and simple and spatially-uniform isotope composition at each depth. But tree xylem water δ2H and δ18O showed remarkable variation across the full range of soil water isotope composition, suggesting strong sorting and niche segregation across the small plot. A multivariate PCA model’ incorporating wood density, tree size and mean basal area increment could explain 54.8% of the variance of xylem water isotope composition. This work suggests that stable isotope tracers may aid a better understanding of hydrological niche segregation among co-occurring tropical species and in turn, help inform better mixed-species plantation designs and predictions about future shifts in the composition and structure of tropical rainforest species under climate change.

7.1.2 Diverse tropical hardwood species across a 20x160 m plot show the same xylem water isotope composition and therefore uptake water from the same soil depth (Hypothesis 2 incorporated as chapter 5) The aim of this hypothesis was to investigate the water uptake depth patterns of multiple rainforest tree species in a uniform plot using dual isotope analysis and a Bayesian Mixing Model (BMM). The findings show that diverse tropical hardwood species do not uptake soil water from the same depth at a fine spatiotemporal scale. This further indicates soil water uptake patterns 174 are species-specific rather than trait-specific. The majority of the observed species relied on shallow soil water (0.0-0.2 m). The BMM proved to be very useful in determining the water uptake patterns of tropical rainforest trees’. Information on species-speciﬁc soil water uptake patterns could improve forest landscape restoration through better plantation management during drought.

7.1.3 Two co-occurring tree species will uptake water from similar soil depths and their isotope composition will be unchanging throughout the daily transpiration cycle (Hypothesis 3 incorporated as chapter 6) This hypothesis provides insights into the contrasting water use strategies of co-occurring tropical trees. Sap flux rates differed markedly between the two species (A. peralatum and D. photinophylla) across three tree size classes. More specifically, large-sized trees consumed more water regardless of the species. This study also provides evidence that fast-growing pioneer/early secondary species do not always consume more water than slower-growing mature phase species’, although this is probably related to D. photinophylla being in a senescence phase as evidenced by long-term growth records. Wood structure/anatomy largely influences tree water consumption. Also, there is a sharp difference in the water uptake depth pattern of the two co- occurring tree species. With an increase in sap flux rates, the xylem isotope of water became enriched for both species. This contradicts with past studies. Therefore, this hypothesis calls for more studieson evaporative enrichment of xylem water isotopes across multiple species. This study also suggests that care needs to be taken in the timing of collection of xylem water samples. The findings from this study can inform improved predictions of forest water useand ecohydrological modeling at a landscape scale.

7.2 Future research direction This doctoral research indicates that species-specific traits have a significant influence on xylem water isotope variation as well as overall wateruse strategies at a fine spatiotemporal scale. The following are some suggestions for further research:

1. There is emerging evidence that isotope fractionation may occur within the xylem in some species during water uptake and as such, xylem water isotope composition may not be representative of soil water isotope composition (Lin et al., 1993; Adar et al., 1995; Ellsworth and Williams, 2007; Zhao et al., 2016). Research in my thesis (in chapter 6) also 175

suggests that fractionation may also occur during evapotranspiration. Many of my tree xylem water isotopes plotted on the MWL along with soil water isotope, suggesting no fractionation occurs during transpiration. However, some trees did plot below the MWL suggesting those trees do have evaporative fractionation during transpiration. As such, care needs to be taken in interpreting some of the results where fractionation may have occurred. The reason for this is unclear and further research is needed to identify the mechanisms behind this enrichment and to what extent this enrichment occurs across different species. Therefore, a working hypothesis could be ‘Diverse tropical hardwood species show the same xylem water isotope composition during active transpiration periods’.

2. Up until now, xylem isotope enrichment has been explained as being due to either diffusion through tree bark (Dawson and Ehleringer, 1993) or an exchange between phloem and xylem water (Cernusak et al., 2005). However, no study has yet been conducted to determine the effect of sample collection procedure on isotope fractionation. No study has determined the sapwood depth before collecting xylem samples for isotope analysis. There is a possibility that collecting heartwood portions might lead to misrepresentation of the water source’. Therefore, a working hypothesis could be ‘Isotopes from known sapwood depth xylem samples better represent the source water than unknown sapwood depth samples’.

3. It is generally assumed that plants temporally alter their water uptake depth based on soil water availability. There is limited evidence emerging from the literature that there is seasonal variation in the depth from which some species take up water. The data from the two different time periods from which data was collected from the D. photinophylla and A. peralatum trees suggest that, at least for the A. peralatum, this might be the case. This suggests that there might be some seasonal variation in the uptake of water from different soil depths. As such, a further area of research is to investigate the extent to which there is seasonal variation between rainforest species in respect to theiruptake of water from different depths within the soil profile and whether this is related to functional traits or species guilds. Therefore, a new working hypothesis could be ‘Diverse tropical rainforest trees’ water uptake depth patterns do not change due to seasonal variation’.

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7.3 References Adar, E.M., Gev, I., Lip, J., Yakir, D., Gat, J.R. 1995. Utilization of oxygen-18 and deuterium in stream flow for the identification of transpiration sources: soil water versus groundwater in sand dune terrain. In: Adar, E., Leibundgut, C. (ed.), Application of Tracers in Arid Zone Hydrology, International Association for Scientific Hydrology Publication 232, 329–38. Cernusak, L.A., Farquhar, G.D., Pate, J.S. 2005. Environmental and physiological controls over oxygenand carbon isotope composition of Tasmanian blue gum, Eucalyptus globulus. TreePhysiology 25, 129–146. Dawson, T.E., Ehleringer, J.R. 1993. Isotopic enrichment of water in the “woody” tissues of plants: implications for plant water source, water uptake, and other studies which use the stable isotopic composition of cellulose. Geochimica et Cosmochimica Acta57, 3487–3492. Ellsworth, P.,Williams, D. 2007.Hydrogen isotope fractionation during water uptake by woody xerophytes. Plant and Soil291, 93-107. Lin, G., Sternberg, L., Ehleringer, J., Hall, A., Farquhar, G. 1993. Hydrogen isotopic fractionation by plant roots during water uptake in coastal wetland plants. In: Stable isotopes and plant carbon-water relations, pp. 497–510. Academic Press Inc. Zhao, L., Wang, L., Cernusak, L.A., Liu, X., Xiao, H., Zhou, M., Zhang, S. 2016. Significant difference in hydrogen isotope composition between xylem and tissue water in Populuseuphratica. 39, 1848–1857.

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