Mangrove Forest Ecosystem Services: Biodiversity Drivers, Rehabilitation and Resilience to Climate Change

Mangrove forest ecosystem services: Biodiversity drivers, rehabilitation and resilience to climate change

Clare Duncan

A dissertation submitted for the degree of Doctor of Philosophy UCL

UCL Department of Geography University College London

April 22nd, 2017

DECLARATION

I, Clare Anne Duncan, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated appropriately in the thesis.

Variations on Chapters 2 and 7 of this thesis have been published in peer-reviewed journals during the course of this PhD study. Citations for these respective publications are:

1. Duncan, C., Thompson, J.R., Pettorelli, N. (2015). The quest for a mechanistic understanding of biodiversity-ecosystem services relationships. Proceedings of the Royal Society B – Biological Sciences, 282, 20151348. 2. Duncan, C., Primavera, J.H., Pettorelli, N., Thompson, J.R., Loma, R.J.A., Koldewey, H.J. (2016). Rehabilitating mangrove ecosystem services: A case study on the relative benefits of abandoned pond reversion from Panay Island, Philippines. Marine Pollution Bulletin, 109, 772-782.

J.R. Thompson and N. Pettorelli contributed to the writing and structure of publication 1. R.J.A. Loma assisted in Dumangas Municipality abandoned fishpond identification and mapping from Google Earth imagery conducted in, and J.H. Primavera, N. Pettorelli, J.R. Thompson and H.J. Koldewey contributed to the writing and structure of, publication 2. Both publications are adapted versions of Chapters 2 and 7 of this thesis.

Clare Duncan, 22nd April 2017

ABSTRACT

Mangrove forests provide a significant contribution to human well-being; particularly through climate change mitigation and adaptation (CCMA) due to disproportionately high carbon sequestration and coastal protection from tropical storms. However, mangrove community structure drivers of these ecosystem services (ES), rehabilitation potential for high CCMA ES delivery, and their resilience to climate change impacts remain poorly understood and monitored. This thesis uses field- and satellite remote sensing-based methods and a dual focus at a Philippines-specific and West Africa to South Asian-scale to quantitatively assess mangrove CCMA ES delivery.

The first three chapters provide a background, and literature review on ES delivery, ecological restoration and resilience to perturbations, mangrove ES, their anthropogenic and climate change threats, and current management. Chapters 4 and 5 detail the case study selection and methodologies employed. Chapter 6 focuses on the flora community structure drivers of mangrove ES delivery, and shows that divergent controls can drive trade-offs in the delivery of key CCMA benefits. Chapter 7 focuses on the potential of mangrove rehabilitation for high CCMA ES delivery, and shows that mangrove rehabilitation in abandoned aquaculture ponds can provide high relative CCMA benefits, revealing large areas of abandoned aquaculture with favourable tenure status for greenbelt rehabilitation. Chapter 8 focuses on remote monitoring of mangrove resilience to sea level rise, and the potential anthropogenic and abiotic factors influencing these, establishing a methodology for continued remote monitoring and revealing variability in resilience and resistance across forests.

Overall, it is demonstrated that current mangrove management in the Philippines and globally may be insufficient to secure high CCMA ES delivery, due to (1) non- consideration of flora community structure, site-specific and areal requirements, (2) complexity in governance systems for reclamation of mangrove lands, and (3) a lack of spatial planning and zoning to accommodate mangrove resilience to climate- induced perturbations.

ACKNOWLEDGEMENTS

I would first like to thank my PhD supervisors for their excellent support, advice and expertise, and for enabling me to undertake this PhD in the first place. Nathalie Pettorelli for her tireless advice and mentoring, for all the laughter, and for putting up with me in the office every day! Julian Thompson for putting his faith in me and this project after a phone call out of the blue in 2013, and for providing brilliant guidance and support ever since. Heather Koldewey for sharing her extensive expertise in marine ecology and mangrove conservation, for always being on hand to help (often at very short notice!), and for the occasional bit of San Miguel and karaoke along the way. And Jurgenne Primavera, whose second-to-none passion for mangroves and their conservation continues to inspire me, for sharing her encyclopedic knowledge and experience, and for unwavering support from the other side of the world.

I am immensely grateful for the assistance I have received from the ZSL Marine and Freshwater Conservation Programmes teams, both in the Philippines and the UK. Special thanks go to Venus Sadio Calanda, my dear friend, literally without whom the research in Chapters 6 and 7 of this thesis would not have been possible, for all the fun, music (“Avicennia marina, marina, marina!”) and gruelling work in the mangroves. And to Pong Pong and Alex for missing her so enormously for very long periods in the field. This project would also not have been possible without the amazing advice and support of Jo Savaris and Glenn Labrado, nor the constant assistance and laughs from the ZSL-Philippines field and admin teams: huge thanks to Apol Loma, Jofel Coching, Christian Montilijao, Myrtle Arias, Dax Dequito, Pelsy Barber, Rodney Golbeque, Rosalie Villareal Joven, Precil Crispo, Smith Bajon, Lindy Señadoza, Scarlette Coopera, Jude Bagio, Mary Grace Opeña, Ana Mae Mendoza, Marjorie Cañada, Glen Galvez, Emily Sevilla and Gene Fernandez. I would also like to thank Surshti Patel, Louise Baldwin, Becca Short and Dave Curnick for their varied help (and pints) over the last three years.

And, of course, to “Barangay Tibay” for the friendship, second Iloilo home during typhoon Hagupit, and always far too many Pilsens – Dax, Rizza, Cindy and Bridget.

I am extremely grateful to Mayor Jaen of Leganes, Mayor Distura of Dumangas, Mayor Alvarez of Ajuy, Mayor Bermejo of Panay, Mayor Yap of Ivisan, Mayor Miraflores and Vice-Mayor Solidum of Ibajay, and Mayor Lachica and former-Mayor and -Congressman Quimpo of Kalibo municipalities, as well as all LGU staff (in

particular, Sir Wilson Batislaon of Leganes), for access to their wonderful mangrove areas, and for support and assistance provided to this project. In addition, I would like to extend enormous thanks to all of the communities and people’s organisations in Barangays Nabitasan, Ermita, Nanding Lopez, Pedada, Buntod, Balaring, Buswang and Bugtong-Bato for field assistance and logistical support, and for being incredible mangrove stewards. Special thanks go to Ildefonso Denila, Capitan Melquiades Ordena Jr., Elmer Barbiera, Freddie Barbiera, Capitan Felipo Base, Rodolfo Albay and family, Rex Balatayo and family, Capitan Jer Argoncillo, Capitan Teodorico Inguilo, Michelle Fernandez and Elizabeth Ramos. Huge thanks also go to Joey Ardales and Hansel Sacapaño and their familes, for giving up so much of their time to help me in the field, and for all the hilarity and ridiculous situations we got ourselves into.

I would also like to thank those who have given methodological assistance along the way. In particular, Esther Caligacion of the Bureau of Soils and Water Management, Cebu, who sacrificed a great deal of time to processing a lot more sediment samples than were originally anticipated. In addition, massive thanks to Lola Fatoyinbo for data access, Will Cornforth for sharing his radar remote sensing expertise, Martin Wegmann, Harry Owen, Jean-Francois Rysman and Judy Reise for analytical advice and support, Xav Harrison for statistical assistance (and, again, pints), Ellie Dyer and Dave Redding for INLA advice (and in Ellie’s case, for fielding frantic texts about thesis formatting in the middle of the night!), and Ben Thompson, Rebecca O’Brien, Dan Friess, Joanna Ellison and Stefano Cannicci, and my examiners Mark Huxham and Emily Lines, for discussion and advice.

This project would also not have been possible without the generous support of funders: the Rufford Foundation and the Darwin Initiative.

I would like to thank my friends and colleagues at the IoZ for making this process all the more bearable on a day-to-day basis; in particular, Kirsten McMillan, Lola Brooks, Monni Böhm, Jon Bielby and Sam Turvey for friendship, support, mentoring, and putting up with more than their fair share of moaning.

Lastly, my biggest thanks go to my friends and family for the relentless support and encouragement they have given me over the last three years, and for witnessing me in my most stressed-out moments. And, because she insists I must mention her by name here, to my best friend, flatmate and life partner, Izzy Thompson, for being truly horrid throughout.

TABLE OF CONTENTS

LIST OF TABLES ...... 1 LIST OF FIGURES ...... 3 LIST OF ACRONYMS ...... 6 CHAPTER 1: INTRODUCTION, STUDY AIMS AND OBJECTIVES ...... 9

1.1 INTRODUCTION ...... 9 1.2 MANGROVE FORESTS: A BACKGROUND ...... 9 1.3 STATEMENT OF THE PROBLEM...... 10 1.4 RESEARCH AIM AND OBJECTIVES ...... 13 1.5 CASE STUDY SITE SELECTION ...... 13 1.6 SCOPE AND METHODOLOGY ...... 15 1.7 THESIS STRUCTURE ...... 15 CHAPTER 2: ECOSYSTEM SERVICES: DRIVERS, RESTORATION AND RESILIENCE ...... 19

2.1 INTRODUCTION ...... 19 2.2 ECOSYSTEM SERVICES ...... 19 2.2.1 What are ecosystem services? ...... 20 2.2.2 Synergies and trade-offs in ecosystem services delivery ...... 21 2.3 CONTROLS ON THE DELIVERY OF ECOSYSTEM SERVICES ...... 23 2.3.1 Biotic controls: biodiversity and ecosystem services ...... 23 2.4 ECOLOGICAL RESTORATION ...... 27 2.5 ECOSYSTEM RESILIENCE AND RESISTANCE ...... 28 2.6 SUMMARY ...... 29 CHAPTER 3: MANGROVE FORESTS: ECOSYSTEM SERVICES, THREATS AND MANAGEMENT ...... 31

3.1 INTRODUCTION ...... 31 3.2 MANGROVE ECOSYSTEMS AND DIVERSITY ...... 31 3.2.1 Mangrove adaptations and traits ...... 34 3.2.2 General classification of mangrove forests ...... 36 3.2.3 Local-scale diversity, zonation and succession ...... 37 3.3 MANGROVE ECOSYSTEM FUNCTIONING AND SERVICES DELIVERY ...... 38 3.4 THREATS TO MANGROVE FORESTS AND THEIR ECOSYSTEM SERVICES ...... 40 3.4.1 Anthropogenic threats: coastal development and land use change ...... 43 3.4.2 Climate change and sea level rise ...... 44 3.5 MANGROVE CONSERVATION, REHABILITATION AND MANAGEMENT ...... 47 3.6 QUESTIONS AND HYPOTHESES ADDRESSED IN THIS THESIS ...... 50 3.6.1 Mangrove biodiversity drivers of ecosystem services delivery ...... 51 3.6.2 Rehabilitation for climate change mitigation and adaptation ecosystem services ...... 53 3.6.3 Mangrove ecosystem services resilience and resistance to SLR ...... 54 CHAPTER 4: STUDY SITES ...... 57

4.1 INTRODUCTION ...... 57 4.2 STUDY SITES: PHILIPPINES ...... 57 4.2.1 Background ...... 57 4.2.2 List of sites ...... 62 4.2.2.1 Natural sites ...... 64 4.2.2.2 Rehabilitated/replanted sites ...... 71 4.3 STUDY SITES: GLOBAL ...... 73 4.3.1 Background ...... 73 4.3.2 List of sites ...... 73 CHAPTER 5: METHODOLOGY ...... 87

5.1 INTRODUCTION ...... 87 5.2 FIELD DATA COLLECTION: PHILIPPINES ...... 87 5.3 LABORATORY ANALYSES: ECOSYSTEM SERVICES QUANTIFICATION ...... 90

5.3.1 Vegetation carbon stocks ...... 90 5.3.2 Sediment carbon stocks ...... 94 5.3.3 Harvestable timber/charcoal biomass ...... 95 5.3.4 Harvestable kindling ...... 95 5.3.5 Storm surge attenuation potential ...... 95 5.3.6 Coastal protection potential (regular wind waves) ...... 103 5.4 SATELLITE DATA AND REMOTE SENSING ...... 105 5.4.1 Satellite data & products ...... 105 5.4.1.1 Landsat multispectral data ...... 106 5.4.1.2 ALOS/PALSAR Synthetic Aperture Radar (SAR) data ...... 107 5.4.1.3 Shuttle Radar Topography Mission Digital Elevation Model data ...... 112 5.4.1.4 ENVISAT-MERIS Total Suspended Matter (TSM) data ...... 113 5.4.2 Satellite data pre-processing ...... 114 5.4.2.1 Landsat multispectral data pre-processing & indices calculation ...... 114 5.4.2.2 ALOS/PALSAR data pre-processing ...... 117 5.4.2.3 Satellite data filtering: wavelet transformation ...... 120 5.4.2.4 Resolution resampling & cross-product image co-registration ...... 122 5.4.2.5 Image masking: water and high elevation elimination ...... 123 CHAPTER 6: MANGROVE SPECIES AND FUNCTIONAL TRAIT DRIVERS OF ECOSYSTEM SERVICES DELIVERY ...... 127

6.1 INTRODUCTION ...... 127 6.2 HYPOTHESES...... 129 6.3 METHODOLOGY AND STATISTICAL ANALYSES ...... 131 6.3.1 Study sites used ...... 131 6.3.2 Ecosystem services data used ...... 132 6.3.3 Mangrove diversity indices ...... 133 6.3.3.1 Functional trait data ...... 133 6.3.3.2 Functional trait indices ...... 135 6.3.4 Statistical analyses ...... 140 6.4 RESULTS ...... 146 6.4.1 Harvestable kindling abundance ...... 147 6.4.2 Harvestable timber/charcoal production ...... 149 6.4.3 Vegetation carbon stocks ...... 152 6.4.4 Sediment carbon stocks ...... 155 6.4.5 Total plot carbon stocks ...... 158 6.4.6 Storm surge attenuation potential ...... 160 6.5 DISCUSSION ...... 165 CHAPTER 7: ECOSYSTEM SERVICES DELIVERY BY REHABILITATED AND NATURAL MANGROVE FORESTS ...... 173

7.1 INTRODUCTION ...... 173 7.2 HYPOTHESES...... 174 7.3 METHODOLOGY AND STATISTICAL ANALYSES ...... 176 7.3.1 Study sites used ...... 176 7.3.2 Climate change mitigation and adaptation ecosystem services data used ...... 176 7.3.3 Site-specific climate change mitigation and adaptation ecosystem services delivery .... 177 7.3.3.1 Site-specific carbon stocks ...... 177 7.3.3.2 Site-specific coastal protection potential ...... 178 7.3.4 Targeted abandoned fishpond reversion? Dumangas municipality ...... 179 7.4 RESULTS ...... 180 7.4.1 Vegetation structure...... 180 7.4.2 SRTM-based vegetation biomass modelling ...... 180 7.4.3 Carbon stocks ...... 181 7.4.4 Site-specific carbon stocks ...... 185 7.4.5 Site-specific coastal protection potential ...... 186 7.4.6 Targeted abandoned fishpond reversion? Dumangas municipality ...... 187 7.4 DISCUSSION ...... 189

CHAPTER 8: RESILIENCE AND RESISTANCE TO SEA LEVEL RISE IN GLOBAL MANGROVE FORESTS ...... 195

8.1 INTRODUCTION ...... 195 8.2 HYPOTHESES ...... 197 8.3 METHODOLOGY AND STATISTICAL ANALYSES ...... 198 8.3.1 Study sites used ...... 198 8.3.2 Satellite remote sensing data used ...... 199 8.3.3 Landcover classification in 2007 and 2015 ...... 199 8.3.4 Mangrove landward distribution change 2007-2015 (resilience) ...... 206 8.3.5 Mangrove seaward boundary change 2007-2010 (resilience and resistance) ...... 206 8.3.6 Mangrove biomass change 2007-2010 (resistance)...... 208 8.3.7 Drivers of mangrove resistance & resilience ...... 210 8.3.7.1 Topographic controls on resilience...... 210 8.3.7.2 Sediment input load controls on resistance ...... 213 8.3.7.3 Anthropogenic controls on resilience ...... 214 8.4 RESULTS ...... 215 8.4.1 Site-specific exposure: trend in mean sea level anomaly ...... 215 8.4.2 Site-specific landscape features (elevation, slope and sediment inputs) ...... 215 8.4.3 Unsupervised landcover classification ...... 217 8.4.4 Mangrove landward distribution change 2007-2015 (resilience) ...... 218 8.4.5 Mangrove seaward boundary change 2007-2010 (resilience and resistance) ...... 220 8.4.6 Mangrove biomass change 2007-2010 (resistance)...... 221 8.4.7 Topographic controls on landward migration (resilience) ...... 221 8.4.8 Sediment input load controls on seaward biomass change (resistance) ...... 224 8.4.9 Anthropogenic barriers to landward migration (resilience) ...... 224 8.5 DISCUSSION ...... 227 CHAPTER 9: DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS ...... 237

9.1 IN SUMMARY ...... 237 9.2 WHAT KINDS OF MANGROVE DO WE NEED TO PROTECT AND REHABILITATE FOR HIGH CCMA ES DELIVERY? 241 9.3 WHAT ARE THE INTERTIDAL LOCATION-SPECIFIC CONSTRAINTS TO REHABILITATION OF CCMA ES? ...... 246 9.4 WHAT IS THE CAPACITY FOR GLOBAL MANGROVE FOREST RESILIENCE AND RESISTANCE TO SLR? ...... 250 9.5 MANAGEMENT RECOMMENDATIONS ...... 252 9.5.1 Mangrove greenbelt rehabilitation for coastal protection ...... 253 9.5.2 Blue carbon PES...... 253 9.5.3 Coastal zone management and spatial planning ...... 254 9.5.4 Protected Area designation and zoning ...... 255 9.5.4 Governance and policy ...... 255 9.6 IN CONCLUSION ...... 256 REFERENCES ...... 259 APPENDIX 1. RESULTS OF ANALYSES OF FUNCTIONAL TRAIT AND SPECIES DIVERSITY DRIVERS OF ECOSYSTEM SERVICES AT THE KII SITE (CHAPTER 6)...... 291

A1 A PRIORI CANDIDATE LINEAR MIXED EFFECTS MODELS FOR THE KII SITE ...... 291 A2 HARVESTABLE KINDLING PRODUCTION AT THE KII SITE ...... 292 A3 HARVESTABLE TIMBER/CHARCOAL PRODUCTION AT THE KII SITE ...... 293 A4 VEGETATION CARBON STOCKS AT THE KII SITE ...... 294 A5 SEDIMENT CARBON STOCKS AT THE KII SITE ...... 295 A6 TOTAL PLOT CARBON STOCKS AT THE KII SITE ...... 296 APPENDIX 2. SLR IN MANGROVE FORESTS FROM WEST AFRICA TO SOUTH ASIA (CHAPTER 8). ... 297 APPENDIX 3. RESULTS OF BOUNDARY AND BIOMASS CHANGE DETECTION ANALYSES IN WEST AFRICAN TO SOUTH ASIAN MANGROVE FORESTS: FIGURES (CHAPTER 8)...... 302

LIST OF TABLES

3.1 Estimated mean valuation of coastal wetland (mangroves and tidal marshes) ecosystem services. 4.1 List of mangrove species (and hybrids) found on Panay Island, Philippines. 4.2 Summary of all natural and rehabilitated mangrove field sites surveyed in this thesis: Panay Island, Philippines. 4.3 Summary of the seven mangrove sites assessed for resilience and resistance to sea level rise (West Africa to South Asia). 5.1 Landsat multispectral scenes used within this thesis for the years 2007 and 2015. 5.2 ALOS/PALSAR scenes used within this thesis for the years 2007 and 2010. 5.3 Vegetation, water and soil indices considered in this thesis. 6.1 Description of functional trait data for mangrove species. 6.2 Description of the species and functional trait indices. 6.3 A priori candidate model list of linear mixed effects models for each ecosystem service. 6.4 Summary data of ecosystem services delivery and mangrove diversity at each site. 6.5 List of all plausible linear mixed effects models for harvestable kindling abundance. 6.6 Model-averaged regression coefficients for all fixed effects within plausible linear mixed effects models for harvestable kindling abundance. 6.7 List of all plausible linear mixed effects models for harvestable timber/charcoal production. 6.8 Model-averaged regression coefficients for all fixed effects within plausible linear mixed effects models for harvestable timber/charcoal production. 6.9 List of all plausible linear mixed effects models for vegetation carbon stocks. 6.10 Model-averaged regression coefficients for all fixed effects within plausible linear mixed effects models for vegetation carbon stocks. 6.11 List of all plausible linear mixed effects models for sediment carbon stocks. 6.12 Model-averaged regression coefficients for all fixed effects within plausible linear mixed effects models for sediment carbon stocks. 6.13 List of all plausible linear mixed effects models for total plot carbon storage. 6.14 Model-averaged regression coefficients for all fixed effects within plausible linear mixed effects models for total plot carbon stocks. 6.15 List of all plausible linear mixed effects models for storm surge attenuation potential. 6.16 Model-averaged regression coefficients for all fixed effects within plausible linear mixed effects models for storm surge attenuation potential. 7.1 Mean field-derived structural parameters across plots in all study sites. 7.2 Data summary from sediment analysis, organised by site and sediment depth interval. 7.3 Results of site-level carbon stock analysis. 7.4 Results of coastal greenbelt protection analysis. 8.1 Tide gauge record time-series data for each study site. 8.2 Summary of SRTM DEM-derived topographic data and ENVISAT MERIS-derived total suspended matter data across all pixels at each study site. 8.3 Accuracy assessment of unsupervised landcover classifications at each site for the years 2007 and 2015. 8.4 Results of mangrove distribution change assessment at each site over 2007-2015. 8.5 Results of mangrove landward migration assessment over 2007-2015.

8.6 Results of the ALOS/PALSAR HV backscatter amplitude change detection within the seaward boundary over 2007-2010. 8.7 Results of the ALOS/PALSAR HV backscatter amplitude change detection within the identified constant mangrove distribution for each site over 2007-2010. 8.8 INLA SPDE models of the effect of within-site SRTM DEM-derived topographic slope on the probability of landward migration in 2015 within each study site. 8.9 Best-fitting INLA SPDE models of the effecs of within-site total suspended matter (TSM) and Kendall’s tau in TSM (2006-2010) on percentage change in seaward boundary biomass (2007-2010) for each study site. 8.10 Results of the landward anthropogenic development (2007-2015) analyses. 8.11 Ranking of resilience and resistance of the mangrove forests assessed to inform adaptive management.

LIST OF FIGURES

1.1 Schematic outlining the research areas of the three data chapters of this thesis. 2.1 Millennium Ecosystem Assessment depiction of ecosystem services and their linkages to human well-being. 2.2 Schematic of the complex linkages between abiotic, biotic and societal controls on ecosystem services delivery. 2.3 Representation of observed static relationships between species richness and ecosystem functioning (B-EF relationships). 3.1 Global distribution of mangrove forests in 2000. 3.2 Map of global mangrove species richness. 3.3 Common aerial rooting adaptations in mangrove flora species. 3.4 An example of typical intertidal stratification of mangrove species in various estuarine positions. 3.5 Estimated change in global mangrove area from 1980-2005. 3.6 Global distribution of the proportion of threatened mangrove species. 3.7 Global sea level rise 1993-2008 from sea surface height. 4.1 Distribution of mangrove forests in the Philippines, showing a close-up of Panay Island. 4.2 Spatial distribution of mangrove loss across Southeast Asia 2000-2012. 4.3 Location of study areas across Panay Island, Philippines used in Chapters 6 and 7. 4.4 Site imagery and typical vegetation pictures from the natural area at the Ermita study site. 4.5 Site imagery and typical vegetation pictures from the Pedada study site. 4.6 Site imagery and typical vegetation pictures from the Buntod study site. 4.7 Site imagery and typical vegetation pictures from the Balaring study site. 4.8 Site imagery and typical vegetation pictures from the natural area of the Bakhawan ecopark study site. 4.9 Site imagery and typical vegetation pictures from the different species-dominated mangrove zones at the KII ecopark study site. 4.10 Site imagery and typical vegetation pictures from the 2007 rehabilitated area of the Ermita study site. 4.11 Site imagery and typical vegetation pictures from the 2006 rehabilitated area of the Bakhawan ecopark study site. 4.12 Site imagery and typical vegetation pictures from the Nabitasan abandoned fishpond study site. 4.13 Site imagery and typical vegetation pictures from the Dumangas abandoned fishpond study site. 4.14 Location of study areas through the tropics (West Africa to South Asia) used in Chapter 8. 4.15 Google Earth (2016) imagery over the Saloum Delta, Senegal study site. 4.16 Google Earth (2016) imagery over the Sherbro Bay, Sierra Leone study site. 4.17 Google Earth (2016) imagery over the Save River Delta, Mozambique study site. 4.18 Google Earth (2016) imagery over the Ruvuma Estuary, Tanzania study site. 4.19 Google Earth (2016) imagery over the Rufiji Delta, Tanzania study site. 4.20 Google Earth (2016) imagery over the Mahajamba, Madagascar study site. 4.21 Google Earth (2016) imagery over the Sundarbans, India & Bangladesh study site. 5.1 Stratified grid sampling and plot locations at the KII ecopark study site. 5.2 Overstimation of Sonneratia alba individual biomass from extrapolation beyond general allometric equation limits.

5.3 Above- and belowground biomass estimates for study individuals with DBH greater than the general mangrove allometric equation limit. 5.4 Theoretical mangrove sampling area and volume for effective vegetation length calculation. 5.5 Sketch of individual mangrove tree simplification for storm surge attenuation potential calculation. 5.6 Plots exploring the potential impact of the choice of canopy volume filling threshold on plot-specific storm surge attenuation potential. 5.7 Example sketch of Rhizophora spp. prop root simplification and volume and surface area calculation. 5.8 Example of image classification results for canopy closure estimation at the rehabilitated area of the Bakhawan ecopark study site. 5.9 Landsat 8 OLI/TIRS Red-Green-Blue composite imagery over Ruvuma Estuary, Tanzania. 5.10 ALOS/PALSAR L-Band HV backscatter amplitude scene over Ruvuma Estuary, Tanzania. 5.11 Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data over Ruvuma Estuary, Tanzania. 5.12 ENVISAT-MERIS Total Suspended Matter (TSM): mean over 2006-2010. 5.13 Schematic of the satellite pre-processing methodology conducted in this thesis. 5.14 Dark-Object-Subtraction (DOS) atmospheric correction on Landsat imagery. 5.15 Landsat imagery mosaicking for Saloum Delta, Senegal. 5.16 Landsat bands and vegetation, soil and water indices considered in this thesis. 5.17 ALOS/PALSAR scene misalignment: image co-registration and mosaicking. 5.18 Raw and wavelet-transformed Landsat 8 OLI/TIRS Band 2 (Blue) and ALOS/PALSAR L-Band HV backscatter amplitude imagery over Ruvuma Estuary, Tanzania. 5.19 Landsat imagery resolution resampling. 5.20 ALOS/PALSAR L-Band HV backscatter amplitude comparison with Landsat 8 OLI/TIRS calculated Soil-Adjusted Vegetation Index (SAVI). 5.21 Masking Landsat imagery with Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) and Automated Water Extraction Index (AWEIsh) data. 6.1 Location of mature natural mangrove sites considered in Chapter 6, and the location of Panay Island in Western Visayas (Region VI), Philippines, 6.2 Plots of relationships between observed allometrically-estimated total plot biomass and plot-specific height and diameter at breast height (DBH) measures. 6.3 Plots of relationships between plot-specific diameter at breast height (DBH) and height measures. 6.4 Plots of relationships between plot-specific basal area-calculated community weighted mean maximum height (퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡) and observed height and diameter at breast height (DBH) measures. 6.5 Correlation plot of all diversity and dominance indices across all mangrove plots considered in Chapter 6. 6.6 Mangrove species specific leaf area (SLA) data according to intertidal position. 6.7 Plots of relationships between harvestable kindling abundance and all fixed effects included within all plausible linear mixed effects models. 6.8 Plots of relationships between harvestable timber/charcoal production and all fixed effects included within all plausible linear mixed effects models. 6.9 Plots of relationships between vegetation carbon stocks and all fixed effects included within all plausible linear mixed effects models. 6.10 Plots of relationships between sediment carbon stocks and all fixed effects included within all plausible linear mixed effects models.

6.11 Plots of relationships between total plot carbon stocks and all fixed effects included within all plausible linear mixed effects models. 6.12 Plots of relationships between storm surge attenuation potential and all fixed effects included within all plausible linear mixed effects models. 6.13 Relationship between plot-specific stem density and storm surge attenuation potential and all identified functional trait indices predictors of storm surge attenuation potential across all plots. 7.1 Location of rehabilitated and natural mangrove sites considered in Chapter 7, and the location of Panay Island in Western Visayas (Region VI), Philippines. 7.2 Regression of observed plot-level mean mangrove height against SRTM DEM- derived elevation. 7.3 Best-fitting regressions of above- and belowground biomass against observed mean mangrove height. 7.4 Predicted above-ground mangrove biomass across the two SRTM DEM tiles on Panay Island. 7.5 Mean mangrove vegetation and sediment carbon stocks across all study sites. 7.6 Identified distribution of abandoned fishponds across Dumangas municipality from Google Earth imagery (Google Earth 2016). 7.7 Map of fishpond tenure status across Dumangas municipality, Iloilo, Western Visayas, Philippines. 7.8 Distribution of identified sea-facing abandoned fishponds across Dumangas municipality containing coastline with a potential greenbelt width greater than that required for effective coastal protection potential from regular waves after eight years of rehabilitation. 8.1 Mangrove forests considered in Chapter 8, and their location from West Africa to South Asia. 8.2 Schematic of the analyses conducted to assess resilience and resistance of mangroves at all study sites. 8.3 Principal Components Analysis plots for Landsat bands and calculated indices at Ruvuma Estuary, Tanzania. 8.4 Unsupervised kmeans landcover classification for the Ruvuma Estuary, Tanzania study site in 2007. 8.5 Classified mangrove extent at the Ruvuma Estuary, Tanzania study site: close-up of removal of small classified patches (≤3,600 m2). 8.6 Landcover classification validation from high resolution multispectral imagery (Google Earth 2016). 8.7 Spatial overlay of mangrove distribution change over 2007-2015 at the Ruvuma Estuary, Tanzania study site. 8.8 Combined 2007-2010 coastline extraction from ALOS/PALSAR L-Band HV backscatter amplitude at Ruvuma Estuary, Tanzania. 8.9 Raw pixel-specific change in ALOS/PALSAR L-Band HV backscatter amplitude between 2007 and 2010 imagery at the Ruvuma Estuary, Tanzania study site. 8.10 Example of the extracted 2007 mangrove distribution adjacent perimeter at Ruvuma Estuary, Tanzania. 8.11 SRTM DEM elevation and slope data used to assess topographic controls on mangrove resilience.

LIST OF ACRONYMS

AGEDI Abu Dhabi Global Environmental Data Initiative AIC Akaike Information Criterion ALOS/PALSAR Advanced Land Observing Satellite Phased Array L-Band Synthetic Aperture Radar ASF Alaska Satellite Facility (USA) ASF DAAC (ASF) NASA Distributed Active Archive Centre (USA) AUU abandoned, underutilised and undeveloped aquaculture ponds (fishponds; Philippines)

AWEIsh Automated Water Extraction Index B-EF biodiversity-ecosystem functioning B-ES biodiversity-ecosystem services Brgy. barangay (village; Philippines) CBD Convention on Biological Diversity CCMA climate change mitigation and adaptation CMRP (ZSL) Community-Based Mangrove Rehabilitation Project (Philippines) CI confidence interval CWM community-weighted mean (functional trait value) DA-BFAR Bureau of Fisheries and Aquatic Resources of the Department of Agriculture (Philippines) DBH diameter at breast height DEM digital elevation model DENR Department of Environment and Natural Resources (Philippines) DENR-BMB (DENR) Biodiversity Management Bureau (Philippines) DOS Dark Object Subtraction ES ecosystem services ESA European Space Agency EVI Enhanced Vegetation Index FAO Food and Agriculture Organisation of the United Nations FDis multi-trait functional dispersion FDvar single trait functional divergence FLA Fishpond Lease Agreement (Philippines) glm generalised linear (regression) model GSFC (NASA) Goddard Space Flight Centre (USA) IPCC Intergovernmental Panel on Climate Change IUCN International Union for Conservation of Nature JAXA/METI Japanese Aerospace Exploration Agency and Ministry of the Economy, Trade and Industry JICA Japan International Cooperation Agency KASAMA Kalibo Save the Mangroves Association (Philippines) KII Katunggan-It-Ibajay ecopark (Philippines)

LGU local government unit (Philippines) LiDAR Light Imaging, Detection, And Ranging Lm linear (regression) model Lmer linear mixed effects regression model MEA Millennium Ecosystem Assessment Mg megagram (tonne) MNDWI Modified Normalised Difference Water Index MNRT Ministry of Natural Resources and Tourism (Tanzania) MPA marine protected area NASA National Aeronautics and Space Administration (USA) NDVI Normalised Difference Vegetation Index NDWI Normalised Difference Water Index NGO non-governmental organisation NGP National Greening Programme (Philippines) NIR near-infrared NMDI Normalised Multi-Band Drought Index NPP net primary productivity OC organic carbon PCA Principal Components Analysis PES Payments for Ecosystem Services PO people’s organisation (Philippines) R-SET Rod Surface Elevation Table Marker Horizon RGB Red-Green-Blue SAR Synthetic Aperture Radar SAVI Soil-Adjusted Vegetation Index s.d. standard deviation s.e. standard error SER Society for Ecological Restoration SLA specific leaf area SLR sea level rise SRTM Shuttle Radar Topography Mission SWIR short-wave infrared T-o-A Top-of-Atmosphere reflectance TSM total suspended matter UNEP-WCMC United Nations Environment Program World Conservation Monitoring Centre UNESCO United Nations Educational, Scientific and Cultural Organization USGS United States Geological Survey ZSL Zoological Society of London

CHAPTER 1: Introduction, study aims and objectives

1.1 Introduction Mangrove forests uniquely both afford ecosystem services (ES) that could aid in climate change mitigation (carbon stocks and coastal storm attenuation; Dahdouh- Guebas et al. 2005; Alongi 2008; Barbier et al. 2008; 2011; Donato et al. 2011; Lee et al. 2014; Barbier 2015) and are simultaneously highly susceptible to climate change impacts (e.g. sea level rise [SLR], modifications to freshwater inflows, increases in the intensity of tropical typhoons: Duke et al. 2007; Alongi 2008; Gilman et al. 2008; Kirwan & Megonigal 2013; Krauss et al. 2014; Lovelock et al. 2015). This thesis investigates the drivers and rehabilitation of mangrove climate change mitigation and adaptation (CCMA) ES, and the resilience and resistance of mangroves and their ES to climate change processes.

1.2 Mangrove forests: A background Mangroves are tropical, salt-tolerant forests inhabiting the intertidal zone at the interface between the land and sea. Mangrove forests are located between 30˚N– 30˚S latitude (Giri et al. 2011), and distributed throughout 123 countries (Spalding et al. 2010). Their distribution is best explained as being limited by the 20°C winter seawater isotherm (Alongi 2002). There are acknowledged to be 70 true mangrove species, covering a global area of ~152,400 km2 (Spalding et al. 2010). Mangroves differ structurally from other tropical forests due to their low floral diversity and lack of a distinct understory (in most cases made up solely of mangrove seedlings). Many mangrove forests are comprised of low flora species diversity, with most containing one or few mangrove species (Field et al. 1998). The most diverse mangrove forests are found in the Indo-West Pacific biogeographic region, where diversity can be as high as 30-40 mangrove species (Duke 1992; Duke et al. 1998; Field et al. 1998). Mangrove species are uniquely adapted to existence in the intertidal zone, with specialised root, leaf, wood and reproductive morphology and physiology to cope with and exclude salt, and for stabilisation in unstable sediments under tidal action (Tomlinson 1986; Primavera et al. 2004; Hogarth 2007; Alongi 2008). Mangrove species are phylogenetically diverse (Tomlinson 1986; Hogarth 2007; Spalding et al. 2010), and despite inhabiting a narrow, stressed niche space, species exhibit greater functional diversity in these traits than may be expected (Balun 2011).

1. Introduction, study aims and objectives

Mangrove forests are among the world’s most productive ecosystems (Alongi 2008; 2009; 2014; Donato et al. 2011), with a standing biomass greater than that of any other aquatic system (Alongi 2002) and rivalling that of terrestrial tropical forests (Komiyama et al. 2008). These highly productive intertidal forests are of great importance as habitat for both marine and terrestrial animal species (high bird species diversity and abundance, reptiles, mammals, fish, crustaceans; Gopal & Chauhan 2006; Spalding et al. 2010), in forming the basis of coastal food webs (Alongi 1998; Duke et al. 2007; Brown et al. 2010), and underpinning the productivity of seagrass and coral reef systems (Mumby 2006; McMahon et al. 2012; Honda et al. 2013). The nursery function of mangroves for reef and subsistence- and commercially-important fish species makes them enormously important for the maintenance of local and global fisheries (Dittmar et al. 2006; Barbier et al. 2011). Where levels of extraction are sustainable, mangroves can also be an important source of terrestrial bush meat, medicinal products and timber for millions of people living within and at their boundaries (Gopal & Chauhan 2006; Feka 2015). Due to trapping of heavy sediment loads from freshwater river inputs, mangroves are of great importance in stabilising tropical coastlines from erosion and often in land-building (McKee et al. 2007a; Barbier et al. 2008; 2011; Lee et al. 2014; Lovelock et al. 2015). Crucially, mangroves are of particular importance in the context of global climate change; being among the most productive and carbon-rich forests per unit area in the tropics, by storing vast amounts of organic carbon (OC) in their dense vegetation and deep, anoxic sediments (Donato et al. 2011), and providing protection for coastal communities and infrastructure from frequent tropical storms, typhoons and tsunamis (Dahdouh-Guebas et al. 2005; Alongi 2008; Koch et al. 2009; Lee et al. 2014).

1.3 Statement of the problem Despite their importance to human populations at local to global scales, mangrove forests are now among the world’s most threatened biomes (Field et al. 1998). Mangroves are in alarming global decline: estimates of between 30-50% of mangrove cover has been lost over the last half a century (Alongi 2002; Duke et al. 2007; FAO 2007; Polidoro et al. 2010; Giri et al. 2011), and losses continue at a rate of 0.16-0.39% per annum (Hamilton & Casey 2016; Richards & Friess 2016). It is estimated that if these rates of decline continue, 100% of mangrove forests may be lost by 2100 (Duke et al. 2007).

1. Introduction, study aims and objectives

Mangrove forest losses are a result of multi-faceted concurrent anthropogenic threats: land clearing for development of urban and agricultural land and aquaculture, over-exploitation of timber resources, pollution and alterations to freshwater and sediment input regimes (Valiela et al. 2001; Primavera 2005; Duke et al. 2007; Fatoyinbo et al. 2008; Feka 2015; Richards & Friess 2016). Simultaneous to these anthropogenic impacts to their extent and health, mangrove forests are also highly susceptible to climate change processes. Mangroves in parts of the world have been degraded due to increased salinity as a result of recent desertification and associated declines in freshwater inflows (Atlantic West Africa: Saenger & Bellan 1995; Ndour et al. 2011). But perhaps the greatest climate change challenge to the future of mangrove forests is rising sea levels (Duke et al. 2007; Alongi 2008; Gilman et al. 2008; Kirwan & Megonigal 2013; Krauss et al. 2014; Lovelock et al. 2015). Mangroves may keep pace with SLR via sediment accretion (parts of the Indo-West Pacific; Lovelock et al. 2015) and root biomass production (Caribbean; McKee et al. 2007a); however, in other areas, inland human development and diversion of freshwater inputs puts many mangroves at high risk through reduced sediment inputs (Gilman et al. 2008).

In addition to direct effects, the interaction of anthropogenic pressures and climate change threats may result in degradation, loss of productivity and altered species composition despite maintained areal coverage (‘cryptic degradation’ or ‘cryptic loss of functionality’; Dahdouh-Guebas et al. 2005; Lee et al. 2014). Indeed, such processes have dramatically reduced species diversity within mangrove forests (e.g. Walters 2005), and one out of six mangrove species is listed as threatened with global extinction under the International Union for Conservation of Nature (IUCN) Red List of Threatened Species (Polidoro et al. 2010). Continued global loss of mangrove cover and cryptic degradation are estimated to release 84-159 million tonnes of carbon dioxide emissions annually (Siikamaki et al. 2012). Mangrove loss will also place large human populations in tropical coastal areas at high risk from projected increases in the frequency of intense tropical storm and typhoon events into the future (IPCC 2013).

In response to widespread mangrove areal and species losses, semi-protected areas and ecoparks have been created in parts of the tropics. These aim to promote multi- use mangrove management and education and tourism (e.g. India: Untawale 1993; Singapore: Bird et al. 2004; Bangladesh: Biswas & Choudhury 2007; Alam et al.

1. Introduction, study aims and objectives

2010; Hong Kong: Tao & Fung 2011; Philippines: Cadaweng & Aguirre 2005; Primavera et al. 2012b). Such areas can in some cases serve to protect small to large pockets of diverse, intact mangroves (Primavera et al. 2004; Walton et al. 2006). However, multi-layered policy and governance systems, low capacity and heavy reliance on coastal resources means mangrove protection is lacking and can be difficult to implement (Feka & Ajonina 2011; Friess et al. 2016). Accordingly, much global mangrove conservation and management has focused on the restoration and rehabilitation of mangrove forests (Ellison 2000; Kairo et al. 2001; Lewis 2005; Bosire et al. 2008; Primavera & Esteban 2008; Primavera et al. 2012b). However, ignoring ecological requirements for successful rehabilitation and poor management have often resulted in failure of rehabilitation attempts (Primavera & Esteban 2008). Furthermore, rehabilitation efforts in many parts of the tropics have focused on planting for monoculture mangrove stands, despite acknowledgement of the potential importance of species diversity in enhancing mangrove functionality (Field et al. 1998; Dahdouh-Guebas et al. 2005; Kirui et al. 2008; Huxham et al. 2010; Lang’at et al. 2012; Lee et al. 2014).

This begs the question: what types of mangrove forest do we need to rehabilitate and protect for high ES delivery? The feasibility of Payments for Ecosystem Services (PES) schemes for mangrove forest conservation is currently being widely explored (see Section 3.5; Alongi 2011; Locatelli et al. 2014; Thompson et al. 2014; Friess et al. 2016; Wylie et al. 2016). In order to inform the potential success of such schemes, it is necessary to derive adequate information on (1) the mangrove community structure and diversity drivers of multiple mangrove ES delivery (in particular carbon stocks and coastal protection ES, given their significant role in mitigating the impacts of climate change), and (2) intertidal location-specific constraints on rehabilitation of these essential ES (Primavera et al. 2014). In addition, the potential implementation of PES schemes for mangrove forests will depend heavily on their long-term outlook under climate change. Thus an understanding of the landscape- level drivers of mangrove resilience and resistance (the ability to persist or maintain functionality following disturbance: Holling 1973; Walker et al. 2004) to climate change is necessary in order to inform management and rehabilitation into the future, and to identify those sites in which potential PES schemes are most likely to be successful.

1. Introduction, study aims and objectives

1.4 Research aim and objectives In light of current widespread mangrove and coastal ES loss (Alongi 2002; Duke et al. 2007; FAO 2007; Polidoro et al. 2010; Giri et al. 2011), low success rates of historical rehabilitation efforts (Bayraktarov et al. 2016; Primavera & Esteban 2008), and high future susceptibility to the impacts of climate change (Gilman et al. 2008; Lovelock et al. 2015), this thesis aims to fill current gaps in improving understanding of the factors promoting multiple ES delivery in mangroves, climate mitigation-related ES rehabilitation, and resilience to climate change. The overall aims of this thesis are to provide evidence to inform mangrove management for CCMA ES delivery.

Specific objectives are: (1) to quantify the type and diversity of mangrove communities required for high ES delivery (in particular CCMA ES: carbon stocks and storm surge attenuation potential); (2) to investigate the relative ability of rehabilitated mangroves to replicate CCMA ES delivery of natural mangrove stands, quantify the impact of intertidal location on the rehabilitation of CCMA ES, and assess the potential for land tenure constraints to rehabilitation for CCMA ES, and; (3) to quantify the capacity for resilience and resistance of mangroves and their CCMA ES to SLR, and the landscape-level factors influencing these. The third objective relates to informing landscape-level coastal resource management for mangrove ES into the future under climate change.

The following Sections address the case study sites selected to address these objectives, the scope and methodology employed in this research, and the structure of the remainder of this thesis. Specific hypotheses addressed relating to the three main objectives are outlined at the end of Chapter 3, following a review of the relevant literature.

1.5 Case study site selection Two groups of case study sites were used in this thesis, which are described in detail in Chapter 4.

The first group of case study sites were chosen to investigate objectives 1 and 2, and are located in Panay Island, Western Visayas, Philippines. These sites were appropriate for multiple reasons. First, the Philippines lies at the heart of global mangrove diversity, containing among the world’s most species-rich mangroves

1. Introduction, study aims and objectives

(Primavera et al. 2004; Spalding et al. 2010). Due to historical land clearing across Panay, mangroves are now limited to relatively small patches of varied species richness (see Section 4.3; Primavera et al. 2004). The presence of multiple mangrove sites with similar levels of human exploitation but varied flora community structure means that Panay Island affords a natural experiment in which to explore mangrove community structure and diversity drivers of climate mitigation-related ES delivery. Second, non-governmental organisation (NGO)- (Zoological Society of London [ZSL]) and people’s organisation (PO)-led rehabilitation efforts on Panay Island are extensive and well-managed. The existence of multiple rehabilitated mangrove areas of similar ages in varying intertidal zone locations (mid- to upper-intertidal abandoned fishpond rehabilitation vs. low-intertidal seafront rehabilitation) and proximity to adjacent reference natural mangrove stands enables exploration of constraints to the rehabilitation of CCMA ES. Finally, a huge proportion of people on Panay Island inhabit coastal areas and are reliant on mangroves for subsistence, livelihoods and coastal protection (Walton et al. 2006). Findings from study of these sites will be valuable in providing evidence to underpin the success of protection and rehabilitation efforts for Panay Island mangroves.

The second set of case study sites is a series of larger mangrove forests located throughout the tropics (West Africa to South Asia), employed to address objective 3. The sites were selected based on numerous specific criteria outlined in Section 4.3. These sites provide an opportunity to investigate the resilience and resistance of global mangrove forests to SLR, as (1) they have experienced substantial SLR (5- 15cm over 1993-2008, according to sea surface height data: Beckley et al. 2010; GSFC 2013), (2) they are all large forests (all >5,000ha in size), (3) they represent a range of estuarine and deltaic mangrove forms, (4) they cover a wide geographic range, (5) despite human use, all sites currently have good functioning, (6) they represent variation in topographic and human-modified landscapes at their inner margins, (7) Synthetic Aperture Radar (SAR) and non-cloudy multispectral imagery are available for analysis over the time period of investigation (2007-2015), and (8) similarly, reasonable historical high resolution multispectral imagery are available in Google Earth (Google Earth 2016) for use in validation of remote sensing analyses.

1. Introduction, study aims and objectives

1.6 Scope and methodology This thesis uses multi-disciplinary surveying approaches to investigate the objectives outlined above. Mangrove forests can be notoriously difficult to survey on the ground, and satellite remote sensing approaches to their monitoring have been long advocated, with high levels of accuracy (Fatoyinbo et al. 2008; Giri et al. 2011; Kuenzer et al. 2011). Field sites in Panay Island, Philippines (Section 4.2) were surveyed for mangrove community structure and ES delivery using standardised mangrove surveying methods (Kauffman & Donato 2012; Thompson et al. 2014), allometric equations (Komiyama et al. 2005) and methods for assessing coastal protection (see Sections 5.2 and 5.3; Mazda et al. 1997; Bao 2011). In Chapter 7, field data are combined with satellite remote sensing information (SAR- based digital elevation model (DEM) and backscatter imagery) to estimate site to landscape scale delivery of climate mitigation-related ES using existing robust methods in the literature (Fatoyinbo et al. 2008). In Chapter 8, a remote sensing only-based approach (multispectral image classification and SAR change detection) is taken to monitor changes in seaward and landward mangrove margins under SLR, as well as within-forest biomass changes. The approaches considered are widely applied to mangrove forests and have good accuracy (Fatoyinbo et al. 2008; Giri et al. 2011; Cornfort4h et al. 2013). These broad approaches moreover have the merits of not requiring field access to a high number of widely distributed mangrove sites, and reliance on repeat path, freely-available satellite data (see Section 5.4).

1.7 Thesis structure The remainder of the thesis is structured into a further eight chapters. Chapters 2 and 3 make up the literature review sections of the thesis, providing the background and rationale for the research undertaken. Chapter 2 constitutes a literature review into ES approaches to biodiversity conservation, current knowledge on community structure and biodiversity drivers of ES delivery, ecological restoration for ES delivery, and theory and knowledge on ecosystem resilience to natural and human- induced perturbations. Chapter 3 provides a literature review of mangrove forests, their ES, current threats and management options. Specific gaps in current understanding and evidence for management options for CCMA ES and their resilience and resistance to climate change processes (specifically SLR) are outlined. Chapter 3 ends by outlining the main questions and specific hypotheses addressed

1. Introduction, study aims and objectives in the remainder of the thesis, relating to the three main objectives outlined in Section 1.4.

Chapter 4 provides specific information on the Philippines and tropics-wide (West Africa to South Asia) mangrove forests considered in this thesis. Chapter 5 outlines the field-based, laboratory-based and satellite remote sensing pre-processing methodologies employed in various parts of the thesis.

Chapters 6-8 comprise the data chapters addressing the three main research objectives outlined in Section 1.4. A Schematic of the research undertaken in these three data chapters is outlined in Figure 1.1.

Figure 1.1. Schematic outlining the research areas of the three data chapters of this thesis.

Chapter 6 uses results from the field survey programme to investigate potential mangrove flora species and functional trait diversity and dominance drivers of multiple ES delivery in mature mangrove forests across Panay Island, Philippines (Figure 1.1). Chapter 7 also uses results from the field survey programme, combined with satellite remote sensing and GIS methods, to investigate: CCMA ES delivery of rehabilitated mangroves relative to adjacent reference natural mangrove areas; the impact of intertidal zone location of mangrove rehabilitation on CCMA ES delivery (mid- to upper-intertidal abandoned fishpond rehabilitation vs. low-intertidal seafront rehabilitation), and; the potential CCMA benefits of a strategy of abandoned fishpond mangrove rehabilitation in Panay Island, Philippines (Figure 1.1). Chapter

1. Introduction, study aims and objectives

8 employs multiple types of satellite remote sensing imagery to investigate the potential for resilience and resistance of global mangrove forests to SLR, and potential drivers influencing these processes (Figure 1.1).

Chapter 9 focuses on the implications of the research findings for mangrove management in the Philippines and globally. It has specific focus on the implications of the research for protection and rehabilitation of mangroves for CCMA ES delivery, and for coastal resource management to enable mangrove resilience and resistance to SLR. Recommendations for both future research and for incorporating results of the research presented in this thesis for mangrove management and conservation are also provided.

CHAPTER 2: Ecosystem services: drivers, restoration and resilience

2.1 Introduction This thesis investigates ES delivery and resilience of mangrove forests, with an aim to inform management initiatives (protection and rehabilitation) for climate mitigation-related ES delivery into the future. The overarching aims of this thesis are therefore to explore: (1) the potential effects of community structure and flora biodiversity on mangrove multiple ES delivery; (2) the location-specific ability of rehabilitation efforts to effectively replace and enhance former mangrove ES; and (3) the capacity for, and potential constraints to, mangrove forest ES resilience and resistance to climate change (sea level rise [SLR]). This chapter provides a general overview of current knowledge on ES and their drivers (Section 2.2), of ecological restoration activities (Section 2.3), and of current theory and knowledge of ecosystem resilience and resistance (Section 2.4).

2.2 Ecosystem services In recent decades, conservation science has seen a gradual shift of focus away from traditional ‘fortress conservation’ toward balancing the requirements of both biodiversity and humans (Doak et al. 2014; Mace 2014). The means through which humans derive benefit from the world’s ecosystems are through the services that their healthy functioning provides. Following the publication of the Millennium Ecosystem Assessment in 2005 (MEA 2005) and The Economics of Ecosystems and Biodiversity study in 2010 (TEEB 2010), ES approaches to biodiversity conservation are now high on the ecological research and policy agendas (Mace 2014). This is, for example, demonstrated by the recent creation of the International Platform for Biodiversity and Ecosystem Services (IPBES; Díaz et al. 2015). However, significant debate remains over the relevance of ES approaches to biodiversity conservation (McShane et al. 2011; Schröter et al. 2014; Silvertown 2015; Spash 2015). This is due to (1) criticism of the philosophy of anthropocentric commodification of nature (Silvertown 2015; Spash 2015), (2) sometimes low spatial congruence between areas of high biodiversity and ES delivery (Naidoo et al. 2008; Anderson et al. 2009; Raudsepp-Hearne et al. 2010; Phalan et al. 2011), and (3) the fact that understanding of the complex drivers of ES delivery remains far from complete (Cardinale et al. 2012; Balvanera et al. 2014).

2. Ecosystem services: drivers, restoration and resilience

2.2.1 What are ecosystem services? ES are defined as “the activities or functions of an ecosystem that provide benefit (or occasionally disbenefit) to humans” (Mace et al. 2012; p. 19). ES as outlined by the Millennium Ecosystem Assessment (MEA 2005) are organised into four categories: provisioning (e.g. food, fuel, fibre), regulating (e.g. climate regulation, storm and flood attenuation), cultural (e.g. education, recreation), and supporting ES (e.g. nutrient cycling, primary productivity; Figure 2.1).

Figure 2.1. Millennium Ecosystem Assessment depiction of ecosystem services (ES) and their linkages to human well-being, with examples of provisioning, regulating and cultural ES, as well as supporting ES (ecosystem functions; Duncan et al. 2015). Source: MEA (2005). The distinction must be clearly drawn between ES and their underlying ecosystem functions (Díaz et al. 2007; Cardinale et al. 2012; Balvanera et al. 2014). Rigorous re-characterisation of the ES concept to include a “cascade” from ecosystem functioning through to “final ES” (those ES directly underpinning a good with benefit and/or value to humans; Fisher & Turner 2008; Mace et al. 2012) means that supporting ES cannot be considered as final ES in most situations (with the exception of, for example, agricultural pollination; Boyd & Banzhaf 2007; Wallace 2007; Fisher & Turner 2008; Mace et al. 2012; see also Duncan et al. 2015). Supporting ES have been termed “intermediate service” (those “services” underpinning specific ES and the transference of benefits to humans; Figure 2.2;

2. Ecosystem services: drivers, restoration and resilience

Fisher & Turner 2008; Haines-Young & Potschin 2010). However, the terms supporting or intermediate services in reality refer to underpinning ecosystem functions, and should not be considered or measured as final ES underpinning benefits to humans (see Duncan et al. 2015). This distinction is of critical importance for the accounting and ecology of ES delivery (Díaz et al. 2007; Fisher & Turner 2008; Mace et al. 2012; Duncan et al. 2015).

Figure 2.2. Schematic diagram of the complex linkages (“cascade”; Haines-Young & Potschin 2010) between abiotic, biotic (biodiversity) and societal controls on ES delivery. Examples of ES, underlying ecosystem functions (also termed “intermediate services”; Fisher & Turner 2008), abiotic and societal factors represent a non-exhaustive selection.

2.2.2 Synergies and trade-offs in ecosystem services delivery There is now a wealth of evidence demonstrating the existence of synergies and trade-offs between the delivery of different ES in space (Rodríguez et al. 2006; Bennett et al. 2009; Raudsepp-Hearne et al. 2010; Maes et al. 2012). In particular, strong trade-offs exist between delivery of provisioning ES (e.g. agricultural production) and multiple regulating and cultural ES (e.g. carbon storage, water quality, coastal protection, recreation: Barbier et al. 2008; Maes et al. 2012; Maskell et al. 2013). Indeed, multifunctionality (the simultaneous high functioning of multiple ecosystem functions) is enhanced in natural ecosystems over human- modified areas (such as agricultural lands or urban areas; Raudsepp-Hearne et al. 2010). This has led to a long-recognized need to better understand how the distribution and functioning of the world’s ecosystems results in synergies and trade-offs between specific ES at a landscape level (Bennett et al. 2009; Raudsepp- Hearne et al. 2010; Mace et al. 2012; Maskell et al. 2013). However, we are still a

2. Ecosystem services: drivers, restoration and resilience long way from teasing out the complex linkages between abiotic, biotic and anthropogenic factors both driving and threatening ecosystem functioning and multiple ES delivery (Cardinale et al. 2012; Mace et al. 2012; Duncan et al. 2015).

A major factor constraining our current understanding of synergies and trade-offs between ES at a landscape level is a lack of investigation into how and which ecosystem functions scale-up and interact to result in final ES delivery (Díaz et al. 2007; Cardinale et al. 2012; Balvanera et al. 2014; Duncan et al. 2015). ES research has been slow to adopt the re-characterisation of the “cascade” of final ES delivery (Fisher & Turner 2008; Haines-Young & Potschin 2010; Mace et al. 2012; Figure 2.2). This has resulted in use of misinformed proxies of ES delivery (Eigenbrod et al. 2010; Ayanu et al. 2012; Balvanera et al. 2014; Stephens et al. 2015). Underlying ecosystem functions are routinely measured (e.g. soil retention, net primary productivity; NPP), under the assumption that such proxies will hold to the full delivery of single (Egoh et al. 2009; Maskell et al. 2013) or multiple final ES (Costanza et al. 2007) (see Balvanera et al. 2014). A recent study has revealed that this latter assumption is not always plausible, with some natural ES (pest control and pollination) diminished in highly productive cropland areas (Werling et al. 2014).

Research into ES ‘bundles’ (groups of ES commonly positively associated in space; Raudsepp-Hearne et al. 2010; Maes et al. 2012; van der Beist et al. 2014) has revealed synergies between groups of ES that can qualitatively be largely attributable to positive connections with underlying ecosystem functions. For example, the ‘forest services’ (carbon storage [vegetation], timber, air cleansing, erosion control, recreation) and ‘soil and water services’ (water provision, soil carbon, infiltration) bundles identified across Europe (Maes et al. 2012) are positively underpinned by many similar ecosystem functions (e.g. high NPP, plant aboveground biomass, plant belowground biomass, sediment stabilisation, and high soil infiltration, nutrient cycling, bioturbation, herbivory, organic soil inputs, respectively; see Duncan et al. 2015). However, we still lack quantitative understanding of interlinkages between such multiple underlying ecosystem functions (Lavorel & Grigulis 2012). Furthermore, the ecosystem functions underpinning specific final ES are not always positively correlated (Naeem et al. 2009; Potvin et al. 2011), different ecosystem functions may contribute both positively and negatively to specific final ES (Lavorel & Grigulis 2012; Balvanera et

2. Ecosystem services: drivers, restoration and resilience al. 2014), and importantly some final ES are the sum of contributions from multiple different ecosystem compartments (Balvanera et al. 2014). For example, ecosystem carbon sequestration and storage ES rely upon plant biomass production, nutrient cycling, soil turnover and water retention ecosystem functions, which may have complex interconnections (e.g. Potvin et al. 2011; Maskell et al. 2013). Focusing on singular proxies or measures of such specific final ES delivered by multiple ecosystem compartments (e.g. soil or vegetation carbon as a proxy for total ecosystem carbon) may thus limit our understanding of their drivers and responses to environmental or anthropogenic changes (Cardinale et al. 2012; Hooper et al. 2012).

2.3 Controls on the delivery of ecosystem services The distribution of ecosystems, their healthy functioning and final ES delivery is a result of complex interactions between several abiotic factors, human and societal influences, and the distribution and abundance of organisms and their ecological interactions across multiple scales (Hooper et al. 2005; Loreau 2010; Raudsepp- Hearne et al. 2010; Maes et al. 2012; Lavorel et al. 2013; Maskell et al. 2013; Figure 2.2). At a landscape scale, ES delivery is strongly controlled by abiotic and anthropogenic impacts on the distribution of organisms and biodiversity, and the distribution, fragmentation and functioning of ecosystems (Mace et al. 2012; Raudsepp-Hearne et al. 2010; Maes et al. 2012; Maskell et al. 2013). Within ecosystem types, both the type and diversity of organisms present, and human societal influences on these, can strongly influence the capacity of areas to deliver ES (Díaz et al. 2007; Mokany et al. 2008; Cardinale et al. 2011; 2012; Mouillot et al. 2011; 2013; Maestre et al. 2012; Lavorel et al. 2011; Lavorel & Grigulis 2012; Lavorel 2013). In this section, current theory and evidence on the biodiversity drivers of ES are outlined.

2.3.1 Biotic controls: biodiversity and ecosystem services Ecosystem functions that result in final ES delivery are carried out or underpinned by organisms (Hooper et al. 2005; Mace et al. 2012), and evidence is mounting that, in many cases and for many ES, biodiversity is key to driving delivery of specific ES (Cardinale et al. 2011; 2012; Balvanera et al. 2014; Lefcheck et al. 2015). However, a positive linear association between biodiversity and delivery of individual ES (B- ES relationships) is not always manifest. B-ES relationships have been found to (1) take varying forms and shapes (e.g. non-linear relationships; Kremen 2005; Maes et

2. Ecosystem services: drivers, restoration and resilience al. 2012; Gamfeldt et al. 2013), (2) display mixed relationships across studies (Cardinale et al. 2012), or (3) be altogether non-existent (Cardinale et al. 2012; Harrison et al. 2014). The existence of mixed B-ES relationships for individual ES highlights the great variability in the influence of biodiversity on a given ES in a given context (Cardinale et al. 2012; Harrison et al. 2014). Variation in individual B- ES relationships can ultimately be a cause of trade-offs, as well as synergies, between multiple ES (Rodríguez et al. 2006; Raudsepp-Hearne et al. 2010; Cardinale et al. 2012; Maes et al. 2012; Lavorel & Grigulis 2012; Gamfeldt et al. 2013). Improved understanding of biodiversity drivers of ES trade-offs is thus essential for managing collective biodiversity conservation and ES delivery aims; yet, study of such biodiversity-mediated trade-offs between ecosystem functions and ES is still in its infancy (Bennett et al. 2009; Lavorel et al. 2011; Lavorel & Grigulis 2012).

Decades of B-EF research has explored two main hypotheses for how organisms promote ecosystem functions. The first is the diversity hypothesis, with potential mechanisms including the “sampling effect” (increased likelihood of the presence of species with traits promoting ecosystem functions with increasing biodiversity; Loreau 1998), and niche complementarity (greater diversity in resource-use strategies resulting in filling of niche space and more complete resource use by communities) and insurance (compensatory dynamics through space and time; Figure 2.3). Complementarity may arise from either non-overlapping niches or facilitative interactions between species (Fox 2005). The opposing hypothesis is the mass ratio hypothesis, which predicts that the identity and traits of the dominant, most abundant species cheifly promote ecosystem functions, with the influence of high numbers of less abundant species being comparatively minimal (Grime 1998; Loreau 2000; Fox 2005; Hooper et al. 2005; de Bello et al. 2010). In the latter scenario, the maintenance of high biodiversity may be of little importance, and merely the presence of a specific ecosystem (e.g. vegetation type) with a dominance (or monoculture) of species with key functional traits may result in high (static) ES delivery.

Experimental and observational B-EF research focusing on species richness has provided broad support for the diversity hypothesis (Grime 1998; Loreau 2000; Isbell et al. 2011; Cardinale et al. 2011; 2012; Reich et al. 2012; Lefcheck et al. 2015). However, relationships between species richness and individual ecosystem functions are often found to be positively saturating, with the presence of only a few

2. Ecosystem services: drivers, restoration and resilience species resulting in high levels of ecosystem functioning in a given situation (see Figure 2.3). Such saturating effects of species richness on ecosystem functions may reflect the static effects of species identity and abundance, and thus support the mass ratio hypothesis.

Figure 2.3. Representation of observed static relationships between species richness and ecosystem functioning (B-EF relationships). B-EF relationships can vary from linear to rapidly saturating, where high levels of ecosystem functioning occur in the presence of few species (Cardinale et al. 2011; Gamfeldt et al. 2015). Commonly-observed saturating B-EF relationships show complementarity between species at low species richness (complementarity in niche partitioning resulting in increased overall resource use) driving increased functionality, while at higher levels of species richness many species may exhibit redundancy (Loreau 2010; Cardinale et al. 2011; Isbell et al. 2011; Gamfeldt et al. 2015). Note that static saturating curves do not imply actual functional redundancy in some species; temporal heterogeneity increases the insurance value of biodiversity through time (Loreau 2010; Isbell et al. 2011; Reich et al. 2012). In order to disentangle the potential complementarity versus dominance effects of species on ecosystem functioning, the B-EF field has now turned toward the use of functional trait measures (phenotypic attributes that direct niche exploitation; Díaz et al. 2007). Functional trait-based research has shown that many ecosystem functions are driven predominantly by mass ratio (e.g. NPP, decomposition, nitrification, carbon content; Mokany et al. 2008; Grigulis et al. 2013; Lavorel 2013), and in some cases high functional diversity has been found to be negatively associated with ecosystem functioning (e.g. Mokany et al. 2008). However, high functional diversity may additionally promote some ecosystem functions alongside

2. Ecosystem services: drivers, restoration and resilience dominant functional traits (Díaz et al. 2007; Ruiz-Jaen & Potvin 2010; Laughlin 2011; Conti & Díaz 2013; Valencia et al. 2015); especially so by providing stability (Isbell et al. 2011; Reich et al. 2012). Furthermore, greater levels of biodiversity may be required to support multiple ecosystem functions simultaneously (multifunctionality: Hector & Bagchi 2007; Gamfeldt et al. 2008; Zavaleta et al. 2010; Isbell et al. 2011; Maestre et al. 2012; Lefcheck et al. 2015), as the functional traits and importance of complementarity may vary for different ecosystem functions (Mokany et al. 2008; but see de Bello et al. 2010). Given the high functional distinctiveness of rare species, biodiversity may thus remain paramount to maintaining multifunctionality in space and time (Mouillot et al. 2013).

Studies into relationships between biodiversity and ecosystem functioning have recently focused on ecosystem ‘multifunctionality’, revealing the greater role of biodiversity in supporting multiple over single ecosystem functions (Hector & Bagchi 2007; Gamfeldt et al. 2008; Zavaleta et al. 2010; Isbell et al. 2011; Maestre et al. 2012; Lefcheck et al. 2015). Frameworks for quantifying ecosystem multifunctionality are fast-developing (Byrnes et al. 2014), enabling thorough investigation of the relative merits of biodiversity in controlling multiple functions. However, these studies rarely examine separately the direction and shapes of relationships between biodiversity and specific individual functions that may be important for final ES delivery (but see e.g. Mokany et al. 2008). This may present a hinderance to the mechanistic understanding of B-ES relationships for multiple ES within given ecosystems, as linkages between biodiversity and multifunctionality indices have recently been shown to not always reflect the strength, direction and mechanisms of all individual component relationships (Bradford et al. 2014). Indeed, there may be key trade-offs in the functional traits (Lavorel & Grigulis 2012) and mechanisms (diversity versus dominance; Diaz et al. 2007; Mokany et al. 2008) controlling the influence of organisms on specific ES (Duncan et al. 2015). Furthermore, most studies examining the influence of functional traits on ecosystem functions or ES delivery have focused on experimental systems (see Lavorel 2013; Conti & Díaz 2013). In such simplified systems, often containing very few species and lacking real-world environmental perturbation, the importance of functional trait diversity in driving ecosystem functioning may be weakened due to a reduction in complementary and facilitative species interactions (Mora et al. 2014).

2. Ecosystem services: drivers, restoration and resilience

In the search for simplification, the complexities that are key to ecosystem management on the ground may be lost, and studies in real systems examining multiple traits and ES are required if ecosystems are to be protected, restored and managed for functional trait distributions (Pichancourt et al. 2014). For this, we need to understand via which functional traits and by which mechanisms organisms drive the delivery of multiple ES (Balvanera et al. 2014; Pichancourt et al. 2014; Duncan et al. 2015). A limited mechanistic understanding of how ES are delivered by given ecosystems furthermore limits the applicability of ecological production functions and thus the ability for ES arguments to inform decision-making (Daily & Matson 2008).

2.4 Ecological restoration With more than a third of the world’s ecosystems converted for anthropogenic purposes, and much of the remainder heavily degraded and fragmented (MEA 2005), ecological restoration – a comparatively young arm of conservation biology – is becoming of ever-increasing importance (Jordan et al. 1987; Rey Benayas et al. 2009; Bullock et al. 2011). Ecological restoration is defined as re-establishing (by initiating or accelerating) the known or assumed species diversity, community structure and functionality of an ecosystem that has been degraded, damaged or destroyed to its original state (SER 2004). This definition differs from ecological rehabilitation, for which the end goals are not as comprehensive. Rehabilitation focuses on returning some, not all, key functions and species of a degraded or destroyed ecosystem in order to meet specific biodiversity- or ES-based goals (SER 2004). Rehabilitation can be considered as a special case of restoration, in which return of full functionality is not the goal or where restoration to the former ecosystem state is still in progress (Bradshaw 1984; 1996; Field 1998; Primavera et al. 2012b).

It was predicted at the end of last century that ecological restoration “…is a means to end the great extinction spasm. The next century will, I believe, be the era of restoration in ecology” (Wilson 1992; p. 340). Indeed, ecosystem restoration is now supported at a policy level as a key means to mitigate global biodiversity and ES loss (CBD 2000). Restoration and rehabilitation projects for biodiversity and ecosystem functioning enhancement are now widespread (Rey Benayas et al. 2009; Nelleman & Corcoran 2010). A meta-analysis of global restoration and rehabilitation projects has shown a reasonable level of success in achieving biodiversity and ES delivery

2. Ecosystem services: drivers, restoration and resilience goals (Rey Benayas et al. 2009). Furthermore, where restoration and rehabilitation efforts have been focused on biodiversity conservation goals, ES delivery has been a simultaneous positive outcome (Rey Benayas et al. 2009). Restoration and rehabilitation efforts are most extensively implemented for wetland and forested ecosystems (Zedler 2000; Primavera & Esteban 2008; Nelleman & Corcoran 2010; Suding 2011; Clilverd et al. 2013; 2016; Pichancourt et al. 2014). However, there is comparatively little published evidence of restored and rehabilitated ecosystem functionality for tropical aquatic ecosystems, and thus a lack of a basis to support the success of such activities at a global scale (Rey Benayas et al. 2009; see also Lee et al. 2014). Furthermore, few restoration efforts have been quantitatively monitored for delivery of multiple ES (Rey Benayas et al. 2009), hindering our ability to assess the benefits of restoration and rehabilitation for restoring multiple ES. Because the benefit to cost ratio associated with restoration activities for some ecosystems can be small and unappealing to decision-makers (de Groot et al. 2013), it is essential to gain better understanding of the effectiveness of interventions for multiple ES, for which cumulative ES benefits and valuation is increased.

2.5 Ecosystem resilience and resistance Ecological resilience is the ability of a species or ecosystem to persist or maintain functionality following disturbance (Holling 1973; Walker et al. 2004). Disturbance processes to ecosystems can be natural (e.g. fire, flood, drought, lightning) or anthropogenically-induced (pressures pushing ecosystems from equilibrium: degradation, fragmentation, pollution, human-induced fires; Odum & Barrett 2004). An ambiguous definition means that resilience can be considered and quantified in multiple ways (Hodgson et al. 2015); the time taken for an ecosystem to return to equilibrium following a disturbance, or “the capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity and feedbacks” (Walker et al. 2004 p. 2; Hodgson et al. 2015). Recent rethinking on the theory of resilience has better split resilience into two distinct categories: “resistance” – the impact of a disturbance on a system in terms of the state of functionality – and “recovery” – the processes and capability of an ecosystem to return to equilibrium (Morecroft et al. 2012; Hodgson et al. 2015). Others still have used the terms “resistance” to mean the passive maintenance of functionality in the face of disturbance, as above, and “resilience” to mean the active capacity of a system to maintain overall functioning via maintaining areal cover through dispersal (Gilman et al. 2008). Resilience theory is essential to

2. Ecosystem services: drivers, restoration and resilience the conservation of ecosystems into the future under continued and interacting pressures, and has informed the design and planning of conservation and management initiatives for several decades (e.g. Hughes et al. 2005; Steneck et al. 2011; see Bernhardt & Leslie 2013).

The ecology of resilience is complex and often context-dependent, and becomes of yet greater concern in the context of climate change, which is predicted to be the major driver of ecological changes over the 21st Century (Côté & Darling 2010). Climate change threatens species and ecosystems both through mean shifts in temperature and precipitation, but also due to increased intensity and frequency of extremes (disturbances; IPCC 2013). The resilience of species and ecosystems to climate change disturbance is related to three groups of factors: exposure, sensitivity and adaptive capacity (potential for adaptation: phenotypic plasticity, dispersal ability, evolutionary potential) (Williams et al. 2008; Ameca y Juárez et al. 2012). Sensitivity to climate change is comprised of multiple factors: diversity (response diversity, functional redundancy, complementarity), and connectivity (strength and number of species interactions, ecosystem connectivity; Williams et al. 2008; Bernhardt & Leslie 2013). For some ecosystems, their resilience to climate change processes also depends on landscape-scale abiotic features that may greater influence resilience than their potential for adaptation, diversity and connectivity (McLoed & Salm 2006; Alongi 2008; Gilman et al. 2008; Krauss et al. 2014; Lovelock et al. 2015). Furthermore, ecosystems degraded by anthropogenic impacts, and possibly more species-poor systems, may be more susceptible to climate change disturbances than intact systems due to, for example, a loss of response diversity and functional redundancy, or limited opportunity for dispersal (adaptive capacity: Gilman et al. 2008; Côté & Darling 2010; Standish et al. 2011; Isbell et al. 2015).

2.6 Summary This chapter has provided an overview of current knowledge on ES, their drivers, rehabilitation and resilience to climate change processes. The key issues that have emerged are: the potential existence of functional trait and mechanism-driven trade-offs between delivery of multiple ES within ecosystems, a lack of evidence for the ability of rehabilitated systems (and in particular tropical aquatic systems; Rey Benayas et al. 2009) to deliver multiple ES simultaneously, and a lack of consideration of abiotic constraints to the resilience of ecosystems to climate change processes. Having outlined key concepts and issues relevant to the research

2. Ecosystem services: drivers, restoration and resilience aim and objectives of this thesis, the following chapter provides an overview of the current state of knowledge of mangroves and the concepts and issues outlined in the current chapter as applied to mangrove ecosystems.

CHAPTER 3: Mangrove forests: ecosystem services, threats and management

3.1 Introduction This thesis investigates some key gaps in understanding of mangrove B-ES relationships, rehabilitation for multiple ES delivery, and ecosystem and ES resilience to climate change processes. The work focuses on mangrove forests, as key coastal ecosystems for multiple climate mitigation-related ES and with high susceptibility to climate change. This chapter provides an overview of knowledge on mangrove forests, their biogeography, diversity and ecosystem structure (Section 3.2), ecosystem functioning and ES delivery (Section 3.3). The anthropogenic and climate change threats to mangroves are discussed (Section 3.4), as are mangrove protection, management and rehabilitation strategies (Section 3.5). Finally, the chapter outlines the specific questions and hypotheses addresses within the remainder of the thesis in relation to the three main objectives outlined in Section 1.4 (Section 3.6).

3.2 Mangrove ecosystems and diversity Mangrove ecosystems are tropical and sub-tropical wetland forests (latitudes 30˚N– 30˚S; see Figure 3.1), comprising saltwater-adapted tree, shrub, palm and fern mangrove species and associates inhabiting the intertidal zone between the land and ocean. Mangroves grow at or above mean sea level and range from near- constant to infrequent periodic inundation (Duke 1992; Primavera et al. 2004; Polidoro et al. 2010; Spalding et al. 2010; Giri et al. 2011). The current global extent of mangrove ecosystems was estimated by Spalding et al. (2010) to be ~152,400 km2, distributed throughout 123 countries (Spalding et al. 2010), and accounting for 30-35% of global tropical wetland forest area (FAO 2007; Giri et al. 2011). Mangroves do not represent a single taxonomic group, but rather a highly phylogenetically diverse range of plant species with specific adaptations enabling them to inhabit the harsh intertidal zone (Duke 1992; Hogarth 2007; Polidoro et al. 2010). It has been estimated that the mangrove “habit” (characteristic adaptations to intertidal survival) may have arisen from convergent evolution in at least 16 independent events (Hogarth 2007). Dependent on the definition considered, there are between 50 and 73 true mangrove species and hybrids from 20 genera (Tomlinson 1986; Duke 1992; Polidoro et al. 2010; Spalding et al. 2010; Primavera et al. 2012b). Relatively high diversity of associate species brings

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3. Mangrove forests: ecosystem services, threats and management estimates of mangrove floral diversity to between 80 and 114 species (Saenger et al. 1983; Tomlinson 1986; Duke 1992; Ellison et al. 1999). Mangrove species occur within two major floral realms: the low diversity Atlantic-East Pacific and the more highly diverse Indo-West Pacific realm (Duke 1992), with the Southeast Asian region (~90–150˚E) of the Indo-West Pacific being a major global hotspot of mangrove species diversity (Tomlinson 1986; Duke 1992; Polidoro et al. 2010; Spalding et al. 2010; Figure 3.2).

3.2.1 Mangrove adaptations and traits Mangrove plant species are uniquely adapted to the often waterlogged, saline and disturbance-prone intertidal zone conditions via both morphological and physiological adaptations: (1) extensive aerial rooting systems; (2) salt exclusion, tolerance or secretion mechanisms; and (3) conservative resource capture and growth strategies; as well as investment in buoyant, viviparous propagules in several species (Tomlinson 1986). Investment in extensive rooting systems and high root:shoot ratios affords the benefit of supporting aboveground biomass in waterlogged, unstable sediments and under tidal action (Tomlinson 1986; Alongi 2008). Tall lateral prop (or stilt) roots in Rhizophora spp. (and occasionally in Avicennia spp. [e.g. officinalis]), shallow but far-reaching aerial roots producing surface-penetrating pneumatophores in Avicennia, Laguncularia, Lumnitzera, Sonneratia and Xylocarpus spp., surface-penetrating knee roots in Bruguiera, Ceriops and Xylocarpus spp., plank roots in Camptostemon and Xylocarpus spp., and buttress-forming stems in Heritiera and Kandelia spp. assist in stabilising mangrove stems (Figure 3.3; Tomlinson 1986; Primavera et al. 2004; Hogarth 2007; Spalding et al. 2010). High root:shoot ratios enable greater water uptake in the saline intertidal zone (Tomlinson 1986), and resistance to strong intertidal disturbance processes (Alongi 2008). Surface-penetrating aerial roots further enable root aeration in anaerobic, water-logged sediments through the presence of specialised lenticels enabling gas exchange when exposed (Tomlinson 1986; Hogarth 2007). Roots of some mangrove species (e.g. Aegialitis, Aegiceras, Avicennia, Bruguiera, Ceriops, Excoecaria, Osbornia, Rhizophora and Xylocarpus spp.) are further able to exclude salt from their tissues via ultrafiltration; other species actively secrete salt from tissues (e.g. Acanthus, Aegialitis, Aegiceras, Avicennia, Laguncularia and Sonneratia spp.) or in senescent leaves (e.g. Excoecaria and Xylocarpus spp.) (Tomlinson 1986; Hogarth 2007). Mangrove species invest heavily in leaf

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3. Mangrove forests: ecosystem services, threats and management production, often forming long-lived, succulent leaves with thick epidermal layers and specialised salt excretion glands, which minimise transpiration losses at the cost of reduced leaf area and photosynthesis (Primavera et al. 2004; Hogarth 2007; Balun 2011).

3.2.2 General classification of mangrove forests Geomorphological and hydrological conditions along coastlines delimit a number of settings in which mangrove forests establish, provided tidal energy is sufficiently low and suitable sediment present. Mangrove forests exist in riverine, deltaic, estuarine and open seafront areas. Within these coastal areas, three major categories of Old World mangrove forest have been established based on the dominant physical processes driving their structure and functionality (Woodroffe 1992; Ewel et al. 1998): tide-dominated (fringe), river-dominated (riverine), and interior (basin) mangroves. Fringe mangroves consist of mostly narrow strips of mangrove along steeply sloping coastal, estuarine or deltaic margins, and are subject to high salinities and tidal action (Woodroffe 1992; Ewel et al. 1998). Riverine mangroves exist along river channels; they are regularly flushed by freshwater and tidal flows, and thus have moderate salinity and high productivity (Twilley et al. 1986; Ewel et al. 1998). Basin mangroves inhabit large inland depressions landward of fringe and riverine mangroves and are comparatively unexposed to normal tide action (Ewel et al. 1998; Hogarth 2007; Spalding et al. 2010). High biomass production in mangroves depends on the presence of some freshwater input from either high precipitation or river flushing and surface run-off inputs (Tomlinson 1986; Hogarth 2007; Spalding et al. 2010). In arid, high salinity conditions where freshwater inputs are minimal, mangroves may form dwarf or scrub forests where growth-restricted mangroves may reach maximum heights of 3m (e.g. along the Red Sea coastline; Spalding et al. 2010; Curnick & Duncan 2013).

The coastal position and associated hydrological conditions of individual forests thus have strong controls on mangrove ecosystem functioning (Ewel et al. 1998; Field et al. 1998; Hogarth 2007). Local currents and tidal regimes, and salinity conditions are an important influence on the species diversity of mangrove forests, controlling propagule dispersal, establishment and survival and competitive interactions (Tomlinson 1986; Lugo & Snedaker 1974; Yakushima et al. 1994; Field et al. 1998; Kirui et al. 2008). Propagule trapping and variable topography in basin

3. Mangrove forests: ecosystem services, threats and management mangrove forests with low tidal action can result in comparatively high species richness in these forest types.

3.2.3 Local-scale diversity, zonation and succession Despite their high productivity (Malhi & Grace 2000; Donato et al. 2011), mangrove forests mostly occur at low species diversity. Many forests contain only one or few mangrove species, with the most diverse forests in the Southeast Asian region reaching up to 30-40 species (Duke et al. 1998; Field et al. 1998). Species richness varies with distance from diversity centres and dispersal ability (Woodroffe 1987; Field et al. 1998), and with tidal inundation and storm frequency (Lugo & Snedaker 1974; Tomlinson 1986). Diversity is reduced in areas of low precipitation and cold extremes, and is often constrained by forest area and width (Lugo & Snedaker 1974; Tomlinson 1986; Duke et al. 1998; 2007; Field et al. 1998; Mendelssohn & McKee 2000; Ellison 2002).

A common (but far from ubiquitous; Tomlinson 1986) feature of mangrove forests is their often regular zonation in bands forming parallel to the coastline (Mendelssohn & McKee 2000; Hogarth 2007). Zones exhibit marked differences in their mangrove species composition and the identity of dominant species (Figure 3.4). Several hypotheses have been raised to explain zonation in some mangrove forests, including landward succession via land-building (Davis 1940; Putz & Chan 1986), landward sedimentation and geomorphological change (Thom 1967; Blasco et al. 1996), propagule size sorting (Rabinowitz 1978; Clarke et al. 2001), and differential propagule predation (Smith 1987; McKee 1995). Perhaps most intuitive and supported is that zonation arises from differences in the competitive ability of species along a tidal inundation regime and salinity gradient from coastline to inland (Ball 1980; 1988). A combination of all of these processes likely contributes to overall mangrove zonation (Hogarth 2007).

Within forests, successional processes are in constant occurrence, and may break down regular zonation patterns (Tomlinson 1986; Hogarth 2007). Tree death from both natural (e.g. storm and lightning damage) and human-induced (e.g. timber extraction) processes opens canopy gaps, enabling establishment of other novel, shade-intolerant seedlings, and creating intermingling of different species and ages through forests, and; immigration of more shade-tolerant species increases mangrove forest species richness through time (Putz & Chan 1986; Hogarth 2007).

3. Mangrove forests: ecosystem services, threats and management

Figure 3.4. An example of typical intertidal stratification of mangrove species (x) in various estuarine positions (y) in Australia. Source: Duke (2006).

3.3 Mangrove ecosystem functioning and services delivery Mangrove forests are among the world’s most productive ecosystems (Alongi 2008; 2009; 2014; Donato et al. 2011). They are considered to be some of the most productive ecosystems in the coastal zone (Alongi 2014), contributing 10-11% of global particulate and dissolved organic carbon (OC) to coastal ecosystems (Dittmar et al. 2006; Alongi 2014). Mangroves provide a great plethora of ES from local to global scales, which have been globally valued at $14,000-194,000 per hectare per year (Barbier et al. 2011; de Groot et al. 2012; Costanza et al. 2014; Table 3.1). The unique adaptations of mangrove roots provide high structural complexity in the root zone, providing shelter and nursery habitat for subsistence and commercially- important fish and shellfish species (Hutchison et al. 2014), while simultaneously trapping and stabilising coastal sediments (Carlton 1974; Lovelock et al. 2015). Mangrove ecosystems also support water purification functions and afford high nutrient inputs to adjacent seagrass and coral reef systems, as well as nursery habitat for reef fishes (Dittmar et al. 2006; Barbier et al. 2011; McMahon et al. 2012). Both mangrove and associate flora have multiple medicinal uses (Primavera et al. 2004), and honey extraction supports livelihoods in some coastal communities (Udin et al. 2013). Mangroves moreover have strong cultural significance, and

3. Mangrove forests: ecosystem services, threats and management provide recreation and tourism ES (Thiagarajah et al. 2015). High above- and belowground biomass production provides raw materials for timber, charcoal and kindling harvest (Uddin et al. 2013; Feka 2015), and carbon sequestration and storage (UNEP-WCMC 2006; Donato et al. 2011; Giri et al. 2011; Thompson et al. 2014). Indeed, mangroves are of particular importance in the context of global climate change. A combination of high productivity and trapping of organic matter in anaerobic sediments puts mangroves among the most carbon-rich forests in the tropics (Alongi 2008; 2009; 2014; Donato et al. 2011). Mangroves offer further climate change impact mitigation through the coastal protection they afford from storm surges, typhoons and tsunamis in highly affected tropical areas (Barbier et al. 2008; McIvor et al. 2012; Zhang et al. 2012).

Table 3.1. Estimated mean valuation of coastal wetlands (mangroves and tidal marshes) ES (estimates unavailable for ornamental services, air quality regulation, water flow regulation, pollination, biological control, and aesthetic, spiritual and educational cultural services). Source: de Groot et al. (2012).

Service Value (US$/ha/yr) Service Value (US$/ha/yr)

Food 1,111 Waste treatment 162,125

Water 1,217 Erosion prevention 3,929

Raw materials 358 Nutrient cycling 45

Genetic resources 10 Nursery habitat 10,648

Medicinal resources 301 Genetic diversity 6,490

Climate regulation 65 Recreation 2,193

Disturbance moderation 5,351 TOTAL 193,845

Despite the great number, importance and value of mangrove ES, we have relatively little understanding of their drivers. There is high non-linearity in delivery of some mangrove ES, with, for example, coastal protection declining exponentially with losses of mangrove landward extent (Barbier et al. 2011). Mangrove functionality and ES delivery, in particular high carbon stocks and storm attenuation, require large and dense areas of mangrove forest (Barbier et al. 2008; 2011; Alongi 2011; Donato et al. 2011; Bao 2011). In the case of both carbon stocks (vegetation and sediment compartments) and storm attenuation, high above and belowground mangrove biomass is required to maximise CCMA ES delivery. In the case of storm attenuation potential, both the amount of aboveground biomass and the physical

3. Mangrove forests: ecosystem services, threats and management projection of vegetation surface area to produce drag force against incoming wind and storm surges (i.e. high stem density; Mazda et al. 1997; Bao 2011) are important to ES delivery. Both the distribution of aboveground biomass and density of mangroves may be heavily influenced by the flora species and community structure within (Ewel et al. 1998; Lee et al. 2014; Primavera et al. 2016; Villamayor et al. 2016).

However, understanding of the importance of mangrove floral diversity for ES delivery remains minimal (but see Kirui et al. 2008; Huxham et al. 2010; Lang’at et al. 2012). Indeed, despite acknowledgements of their potential importance, study of interactions between mangrove species is limited (Field et al. 1998; Ellison 2000; 2002; Alongi 2011; Record et al. 2013), yet positive interactions between species are predicted to be comparatively high in intertidal communities (Bertness & Leonard 1997). Most studies on mangrove functionality have been focused on low diversity forests at relatively high latitudes (Ball & Farquhar 1984; Twilley et al. 1992; Barr et al. 2009), and experimental studies have examined the effects of relatively species-poor mixtures on ecosystem functions and in all cases in juvenile stages (Kirui et al. 2008; Huxham et al. 2010; Lang’at et al. 2012; see Tomlinson 1986). To date, research has shown that particular species and in some cases higher species diversity in planting experiments can enhance biomass production and overall survival (Kirui et al. 2008; Huxham et al. 2010; Lang’at et al. 2012). Yet, little research has been conducted to explore species diversity and identity influences on mangrove ES that are perhaps less related to biomass production, in mature forests, and none considering the influence of functional trait differences between species. Understanding of potential complementarity and facilitative interactions in mature forests is essential in directing decisions on management initiatives and protection for enhanced functionality and multiple ES delivery, as well as informing on how mangroves should be rehabilitated.

3.4 Threats to mangrove forests and their ecosystem services Despite the clear importance of mangroves, they are in precipitous global decline. An estimated 30-50% of mangrove forests have been lost over the last 50 years (Valiela et al. 2001, Alongi 2002, Duke et al. 2007, FAO 2007, Polidoro et al. 2010, Donato et al. 2011), and the rate is greatest in the Asian region (see Figure 3.5). Global mangrove losses are continuing at a rate of 0.16-0.39% per annum (Hamilton & Casey 2016; Richards & Friess 2016). Mangroves are now considered to be among

3. Mangrove forests: ecosystem services, threats and management the world’s most threatened biomes (Field et al. 1998), and it is estimated that if current rates of decline continue, 100% of mangrove forests may be lost by 2100 (Duke et al. 2007). Mangroves have not only suffered declines in ecosystem extent, but also in the distribution ranges and global population sizes of individual mangrove flora species (Duke et al. 2007; Polidoro et al. 2010). Of 70 mangrove species assessed on the IUCN Red List of Threatened Species, one in six are now considered to be threatened with extinction (Polidoro et al. 2010; Figure 3.6). The loss of mangrove forests is a matter of great concern, particularly due to reduced storm protection for vulnerable coastal communities (Dahdouh-Guebas et al. 2005) in the face of a predicted increase in the intensity and frequency of tropical storms and typhoons into the future (IPCC 2013). Mangrove species population reductions and losses of species from given mangrove forests may further threaten important coastal ES due to cryptic losses of functionality (altered species composition and functionality with maintained areal coverage; Dahdouh-Guebas et al. 2005; Lee et al. 2014; Polidoro et al. 2014). Loss and degradation of mangrove ecosystems is a result of a multitude of interacting factors arising from anthropogenic and climate change impacts, which will be outlined in the following subsections.

Figure 3.5. Estimated change in global mangrove area from 1980-2005. The rate of mangrove loss in Asia has been notably rapid, due primarily to clearing for fishpond aquaculture (Primavera 2005). Source: FAO (2007).

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Critically Endangered categories). categories). Endangered Critically Figure

3. Mangrove forests: ecosystem services, threats and management

3.4.1 Anthropogenic threats: coastal development and land use change Due to their coastal, intertidal position, mangroves are at risk from a wide plethora of interacting anthropogenic threats. Two and a half billion people – 38% of the global population – inhabit low elevation coastal zones (Barbier 2015). In the tropics these large coastal populations are heavily dependent on mangroves and other coastal ecosystems for livelihoods and shelter. Mangrove wood is extensively harvested in many parts of the world for timber, boat building materials and other construction purposes, often resulting in losses of mangrove area and functionality (Kairo et al. 2001; Valiela et al. 2001; Fatoyinbo et al. 2008; Feka 2015), and altered species composition (Wagner et al. 2004; Walters et al. 2007; Mohamed et al. 2009; Aziz & Paul 2015). Selective harvesting of mangrove products for such purposes not only serves to reduce biomass, but can also heavily alter species composition (Walters 2005). In the rare cases where it is sustainably managed, wood extraction activities can have comparatively minimal impact on overall mangrove ecosystem functioning (e.g. Matang mangrove forest in Perak, Malaysia; Goessens et al. 2014). Other threats are pollution of upstream freshwater sources and the release of pollution directly into the marine environment (e.g. petroleum pollution; Spalding et al. 2010). An important impact on mangrove ecosystem functioning and potential resilience to disturbance is altered upstream hydrology (Spalding et al. 2010; Lovelock et al. 2015). Damming and channelling of rivers can result in heighted salinities within mangrove forests (Gopal & Chauhan 2006) and decreased sediment loads to facilitate accretion of mangrove sediments (Lovelock et al. 2015).

The greatest anthropogenic driver of historic and current mangrove decline is, however, land use change in the coastal zone. In many parts of the world, mangroves are cleared for conversion to agricultural land, e.g. for rice farming in West Africa (Saenger & Bellan 1995; Whitney et al. 2003; Anthony 2004; Spalding et al. 2010; Ndour et al. 2011), or under development of urban sprawls (Upadhyay et al. 2002). In much of Asia, where current rates of mangrove loss are among the highest (Figure 3.4; FAO 2007), vast areas of mangrove have been lost to fishpond aquaculture (Primavera 2005; Duke et al. 2007; Primavera et al. 2012a). Former mangrove areas in much of Southeast Asia (e.g. Indonesia, Malaysia, Burma, the Philippines, Thailand) are now replaced by thousands of hectares of sprawling fishponds (Primavera et al. 2012a). In parts of Southeast Asia, up to 85% (Borneo, Indonesia; Valiela et al. 2001) and 95% (Iloilo Province, Western Visayas, Philippines;

3. Mangrove forests: ecosystem services, threats and management

Primavera et al. 2012a) of coastal zone mangrove area has been converted for fishpond aquaculture.

Much global conversion of mangrove forests occurs at the landward edges, eliminating vast areas of mangrove in the mid- to upper-intertidal zone. This land clearing is taking its toll not just on areal coverage but on mangrove species diversity, with most species listed in threatened categories on the IUCN Red List being species that are typically located in the mid- to upper-intertidal zone (Polidoro et al. 2010). This is of great concern for the future of mangrove ES delivery, as coastal storm protection is exponentially related to mangrove width inland (Barbier et al. 2011), and vast amounts of stored mangrove carbon have been released. The mid-intertidal zone has the greatest potential for achieving high species and functional diversity in mangrove, due to increased potential for salinity tolerance overlap (Primavera et al. 2004; 2012; see Figure 3.4). It is now essential to improve understanding of how potential species and functional trait diversity may underpin multiple ES delivery (Kirui et al. 2008; Huxham et al. 2010; Lang’at et al. 2012) in order to better understand the potential impacts of the ongoing mid- to upper-intertidal zone mangrove losses. Furthermore, continued global loss of diverse mangrove cover and cryptic degradation may result in the release of vast amounts of stored carbon, and put the large tropical coastal population at high risk from the projected increase in the frequency and intensity of tropical storm and typhoon events into the future (IPCC 2013).

3.4.2 Climate change and sea level rise In addition to direct anthropogenic drivers of loss, mangroves are also highly susceptible to climate change processes. Temperature and precipitation are strong controls on the distribution, biogeography and functioning of mangrove forests (Tomlinson 1986; Duke et al. 1998; Hogarth 2007; Gilman et al. 2008). Because of their controls on mangrove productivity, freshwater inflows and salinity, sediment supply and erosion, alterations to temperature and precipitation regimes under climate change are predicted to have substantial and variable impacts on the distribution and functioning of mangrove forests (Gilman et al. 2008; McKee et al. 2012; Ellison 2015; Ward et al. 2016). Indeed, mangroves in parts of the world have been degraded due to increased salinity as a result of recent upstream desertification (Atlantic West Africa: Saenger & Bellan 1995; Ndour et al. 2011); and reductions in winter extreme cold events are now causing the poleward expansion

3. Mangrove forests: ecosystem services, threats and management of mangroves in other parts of their distribution (North and South America; China; south-eastern Australia: Cavanaugh et al. 2014; Saintilan et al. 2014).

Arising from elevated atmospheric CO2 and associated temperature increases, perhaps the greatest climate change challenge to the future of mangrove forests is rising sea levels (Duke et al. 2007; Alongi 2008; Gilman et al. 2008; McKee et al. 2012; Kirwan & Megonigal 2013; Krauss et al. 2014; Lovelock et al. 2015; Ellison 2015; Ward et al. 2016). Global sea levels are rising rapidly throughout much of the distribution range of mangrove forests, with the West Pacific being the most affected region (Figure 3.7; Nicholls & Cazenave 2010). Rising sea levels relative to mangrove sediment surface elevation can increase the inundation frequency and salinity of individual mangrove trees, and thus impact their health and productivity (Gilman et al. 2008). Such climate change impacts have been predicted to shift the global distribution of mangrove flora species (Record et al. 2013), which may produce important impacts on multiple ES delivery (Polidoro et al. 2010; Lee et al. 2014). Mangrove response to SLR has been characterised into two categories: resistance, the ability to keep pace with SLR without altered functionality and structure (via rapid rates of normal sediment accretion); and resilience, the ability of mangroves to maintain areal coverage by migrating landward (e.g. Saintilan et al. 2014) via propagule establishment under SLR and thus reorganise from disturbance effects and regain functioning (Gilman et al. 2008).

Figure 3.7. Global sea level rise 1993-2008 from sea surface height. Source: Beckley et al. (2010); GSFC (2013). The susceptibility of mangrove forests to SLR has long been recognized, but sufficient long-term field data have only recently emerged for scientists to begin to

3. Mangrove forests: ecosystem services, threats and management assess the ability of mangroves to keep pace (Lovelock et al. 2015). Via sediment accretion and land-building, mangroves can exhibit high resistance to SLR; provided sediment loads and forest density are great enough to accrete sediment at a sufficient rate (Lovelock et al. 2015). However, more than half of Indo-Pacific mangrove forests have already lost surface elevation relative to sea level, and mangroves in Java, Papua New Guinea and the Solomon Islands are estimated to be at the greatest risk from SLR (Lovelock et al. 2015). Other global mangrove regions have been semi-quantitatively predicted to be at high risk from SLR (e.g. areas of the coast of Cameroon, Tanzania, Fiji, Mozambique, much of the coastline of the wider African, Asian and Oceanian continents; Nicholls & Cazenave 2010; Ellison 2015). In these mangroves, potential resistance has not yet been quantified, and resilience via landward migration has not been investigated beyond a few isolated mangroves (e.g. American Samoa: Gilman et al. 2007; Bay of Douala, Cameroon: Ellison & Zouh 2012).

Remote sensing analyses of mangrove seaward and landward boundary change through time have revealed high levels of coastline retreat (Gilman et al. 2007; Ellison & Zouh 2012; Cornforth et al. 2013); however, there is little remotely-sensed evidence to date of landward migration in response to SLR (resilience). Anthropogenic – structures such as cities, roads and modified agricultural ecosystems – and abiotic landscape factors – topography (steep slopes at landward margins) – are predicted to produce strong controls on mangrove resilience to SLR (Gilman et al. 2008; Lee et al. 2014). Similarly to predictions from temperate salt marsh systems (e.g. Wolters et al. 2005; French & Burningham 2013), where surface accretion and elevation gain are minimal such barriers to expansion, and human activity in mangrove landward margins, result in the ‘squeezing’ of mangrove forests to reduced areal coverage, and thus the decline of multiple key ES (Alongi 2008; Gilman et al. 2008; Koch et al. 2009; Barbier et al. 2011; Lee et al. 2014).

The recent opening of key satellite imagery archives applicable for mangrove monitoring and at medium to high resolution (Advanced Land Observing Satellite Phased Array L-Band Synthetic Aperture Radar [ALOS/PALSAR]; Cornforth et al. 2013; Hamdan et al. 2014) for scientific applications (European Space Agency [ESA]) now affords a unique opportunity to explore these potential constraints to mangrove resilience in a repeatable manner across large scales.

3. Mangrove forests: ecosystem services, threats and management

3.5 Mangrove conservation, rehabilitation and management In many tropical countries, particularly in the Indo-West Pacific region, mangrove protection is hindered by multi-layered, overlapping or inadequate governance and policy structures, and the intertidal location of mangroves means they have historically slipped through the gaps of marine and terrestrial institutional bodies (Primavera 2000; Duke 2006; Walters et al. 2008; Lebel 2012; Polidoro et al. 2014; Friess et al. 2016). The governments of some tropical countries have responded to widespread mangrove degradation and areal losses with the creation of forest conservation initiatives involving legal protection and the creation of protected areas (e.g. Tanzania: Spalding et al. 2010; McNally et al. 2011; Sri Lanka: Vyawahare 2016; Indonesia: Miteva et al. 2015; Malaysia: Azahar & Nik 2003; Goessens et al. 2014; Australia: Duke 2006). However, information on the current effectiveness of strict traditional protected areas in conserving mangrove forests under current management regimes is limited. Excepting in some rare cases, the little quantitative information that exists suggests that the success of protected areas for mangrove preservation is minimal (Long et al. 2013; Miteva et al. 2015; but see McNally et al. 2011). National-level mangrove protection can be problematic where resource conflict exists between local communities and regional and national government interests, and where monitoring and implementation capacity is low (Primavera 2000; Sukardjo 2009; Beymer-Farris & Bassett 2012). As a result, overexploitation by multiple stakeholders can drive extensive degradation and deforestation within formally protected areas (Feka 2015; Friess et al. 2016), or may contribute to extractive spillover into adjacent unprotected areas (Murray 2009).

To address some of the key governance and policy issues surrounding mangrove protection, the mechanisms of mangrove inclusion in coastal marine protected area (MPA) designation and management, and community-based mangrove co- management are now advocated (see Friess et al. 2016). The coverage and enforcement of tropical MPA protection, as well as mangrove inclusion, has substantially increased in recent years (e.g. Maypa et al. 2012). In addition, several community-based co-managed mangrove areas and ecoparks, promoting multi-use mangrove management and education and tourism, have now been established, for example, in the Philippines (Primavera et al. 2012b). Such mangrove areas can serve to protect small to large pockets of diverse, intact mangroves, while supporting local livelihoods (Primavera et al. 2004; 2012a; Walton et al. 2006). However, realisation

3. Mangrove forests: ecosystem services, threats and management of the cultural, ecological and economic value of mangroves only in recent decades has meant that much of the 30-50% global mangrove loss occurred long before MPAs or community-based management networks could be established (Valiela et al. 2001; Primavera & Esteban 2008). In many cases, a focus on protection alone may now be insufficient to sustain high levels of mangrove ES where land clearance has occurred along extensive stretches of coastline (see e.g. Primavera et al. 2012a; Possingham et al. 2015).

Alongside the strategies above for mangrove protection and management, to rejuvenate former mangrove ES, and in response to intense recent natural disasters (e.g. the 2004 tsunami and 2013 typhoon Yolanda [Haiyan] in Southeast Asia), countries throughout the tropics have now turned to mangrove rehabilitation and restoration efforts (Ellison 2000; Kairo et al. 2001; Lewis 2005; Bosire et al. 2008; Primavera & Esteban 2008; Primavera et al. 2012b; 2012b). Mangroves are rehabilitated for multiple different biodiversity and ES goals (Rey Benayas et al. 2009), being mostly focused on timber production or coastal protection (Primavera & Esteban 2008; Primavera et al. 2012a; Goessens et al. 2014).

Some long-term rehabilitation and management projects have seen great successes (e.g. the Matang forest in Malaysia; Goessens et al. 2014; Hamdan et al. 2014; or the Mekong Delta in Vietnam; Arnaud-Haond et al. 2009; numerous community-based mangrove areas in the Philippines: Primavera et al. 2012b), and provide means to enhance mangrove ES delivery in formally protected or community co-managed areas. However, most are doomed to failure due to ignorance of or non-compliance to scientifically-based approaches rooted in the sound understanding of mangrove ecology (Primavera & Esteban 2008; Primavera et al. 2012b; 2012b; Bayraktarov et al. 2016). Throughout the Indo-West Pacific, many top-down government- or large NGO-led mangrove rehabilitation projects have resulted in ecologically misguided conservation efforts in three ways (Ellison 2000; Iftekhar 2008; Primavera & Esteban 2008; Primavera et al. 2012b; 2012b). First, low survival and project success rates have been borne out of mangrove planting in inappropriate ecological locations; e.g. in the high tidal action low-intertidal zone. Second, mangroves have been planted and established in inappropriate locations where they have replaced other coastal ecosystems; e.g. intertidal mudflats and seagrass beds. Lastly, large- scale mangrove rehabilitation project methodologies have resulted in low potential for CCMA ES delivery though planting in narrow low-intertidal coastal fringe

3. Mangrove forests: ecosystem services, threats and management locations, and through planting for monoculture stands (often of inappropriate species for the intertidal location: Ellison 2000; Iftekhar 2008; Primavera & Esteban 2008; Primavera et al. 2012b; 2012b; Bayraktarov et al. 2016).

In addition to mangrove rehabilitation and conservation within MPA networks and community-managed lands, a promising emerging mechanism is involvement of the private sector in financing mangrove rehabilitation and protection efforts (Friess et al. 2016). Most particularly through the inclusion of mangrove forests within blue carbon-based PES projects: the management of mangrove areas for the creation of marketable carbon credits (Nelleman et al. 2009; Alongi 2011; Camacho et al. 2011; AGEDI 2014; Locatelli et al. 2014; Thompson et al. 2014; Huxham et al. 2015; Wylie et al. 2016). Potential successful avoided deforestation- or afforestation-focused mangrove PES projects provide a means through which to enhance and safeguard CCMA ES delivery and financially support community-based mangrove management at a local scale. However, only one accredited mangrove blue carbon- based PES project has successfully traded carbon credits globally to date (the Mikoko Pamoja project in Gazi Bay, Kenya: Huxham et al. 2012). High methodological and verification uncertainty in blue carbon-based PES projects, low voluntary carbon market prices (Locatelli et al. 2014; Wylie et al. 2016), and low benefit to cost ratios for successful coastal rehabilitation projects (de Groot et al. 2013) may make investment in, particularly afforestation-based, blue carbon PES schemes unappealing. Because as more mangrove ES are considered the benefits and value of mangrove ES substantially increase, recent studies on potential blue carbon PES schemes have concluded that projects would benefit from inclusion of “bundled services” to offset low voluntary carbon market prices (Locatelli et al. 2014; Thompson et al. 2014). In particular, inclusion of coastal protection ES into PES schemes is considered to be an important means to enhance benefit to cost ratios of mangrove avoided deforestation- and afforestation-based projects (Kairo et al. 2009; Locatelli et al. 2014).

Monitoring of the success and functionality of mangrove rehabilitation efforts is often minimal (Bosire et al. 2008; Primavera & Esteban 2008; Bayraktarov et al. 2016). Monitoring mostly focuses on vegetation structure and biomass production (Kairo et al. 2001; Bosire et al. 2008); sediment dynamics and carbon stocks are rarely considered (but see Ren et al. 2010; Salmo et al. 2013), and storm surge attenuation potential complex to assess (McIvor et al. 2012; Lee et al. 2014).

3. Mangrove forests: ecosystem services, threats and management

However, in order to inform the potential success of mangrove rehabilitation for multiple CCMA ES delivery, and afforestation-based PES schemes, going forward, assessment of multiple ES delivery by rehabilitated mangroves relative to reference natural stands, and investigation into the potential site-specific factors influencing delivery of these key ES, is essential. Furthermore, because many current projects focus on monoculture (or very few species) rehabilitation (Ellison 2000; Iftekhar 2008; Primavera & Esteban 2008; Primavera et al. 2012a; 2012b), study of the potential influence of mangrove floristic diversity on the delivery of multiple CCMA ES is also required. Finally, because global climate change, and in particular SLR, presents a key threat to mangrove ecosystems (see Section 3.4.2), the potential success of implemented mangrove protection and rehabilitation projects into the future will be in part dictated by factors shaping their levels of resilience and resistance. Investigation in to these three key areas, and their implications for local- to landscape-scale mangrove management for multiple CCMA ES delivery into the future, is the focus of the remainder of this thesis. The following section outlines specific questions and hypotheses that will be addressed through Chapters 4-9.

3.6 Questions and hypotheses addressed in this thesis This chapter has provided an overview of mangrove forests, and current knowledge on their ES, potential biodiversity drivers, threats and resilience, and protection and rehabilitation efforts globally. The chapter has built on the previous chapter (Chapter 2) on ES, ecological restoration and resilience and resistance, in order to draw out key gaps in these areas as relates to mangrove forests. The main emergent unknowns are: (1) How does mangrove flora identity, diversity and community structure drive multiple ES delivery, and are there key trait-mediated trade-offs between the delivery of multiple mangrove ES? (2) How well do rehabilitated mangroves rejuvenate multiple CCMA ES compared to natural reference systems, how may rehabilitation location impact multiple CCMA ES delivery, and what is the feasibility for rehabilitation in more ecologically-appropriate locations under current tenure complexities? And (3) what is the potential resilience and resistance of global mangrove forests to SLR, and how may abiotic landscape features and anthropogenic activity influence these processes? Having outlined these key knowledge gaps throughout the last two sections, the following section outlines the specific questions and hypotheses addressed in the data chapters of this thesis (Chapters 6-8), which follow after two chapters on the study sites considered

3. Mangrove forests: ecosystem services, threats and management

(Chapter 4) and field- and laboratory-based, and satellite remote sensing pre- processing methodologies employed (Chapter 5).

3.6.1 Mangrove biodiversity drivers of ecosystem services delivery Chapter 6 investigates potential mangrove flora species and functional trait diversity and dominance (e.g. functional identity of dominant species) drivers of multiple ES delivery in mature mangrove forests across Panay Island, Philippines. In particular, the chapter examines the influences of mangrove flora diversity on CCMA ES (carbon stocks and storm surge attenuation potential). The chapter addresses four main questions: (1) Are functional traits (diversity and dominance) stronger drivers of mangrove ES over species richness per se in these relatively species-poor, functionally convergent systems? (2) Is diversity or dominance of functional traits (e.g. community functional structure) the main mechanism key to driving multiple mangrove ES? (3) Do different functional traits or mechanisms (diversity or dominance) drive different mangrove ES, and if so may these result in functional trait or mechanism (diversity or dominance)-based trade-offs between multiple mangrove ES delivery? And (4) some final ES (e.g. carbon stocks) are delivered via ecosystem functions operating within different ecosystem compartments (e.g. sediment nutrient cycling and vegetation productivity). Do different mangrove functional traits, or different mechanisms (diversity or dominance) operating on the same functional traits, predominantly drive ecosystem functions from different ecosystem compartments, and result in complexity in final ES delivery (see Duncan et al. 2015)? Specific hypotheses regarding the influences of mangrove species richness and functional traits on mangrove ES are:

H6.1: Diversity hypothesis (Díaz et al. 2007; Paquette & Messier 2011; Gamfeldt et al. 2013; Lefcheck et al. 2015). Increasing mangrove species richness and functional trait diversity (flora resource use strategy [conservative vs. acquisitive] and structural traits [size, growth form and rooting structure]) is expected to lead to increased delivery of all ES related to biomass production and organic sediment inputs: harvestable timber/charcoal production, vegetation carbon stocks and storm surge attenuation potential; and harvestable kindling abundance and sediment carbon stocks, respectively. Through increased diversity in sediment profile and light resource use, increasing species and functional trait diversity (resource use strategy and functional traits) is anticipated to increase mangrove stem density and thus storm surge attenuation potential.

3. Mangrove forests: ecosystem services, threats and management

H6.1.2: Species richness vs. functional trait diversity. Because mangroves are naturally comparatively species-poor relative to tropical terrestrial forests, complementary and facilitative interactions between the few species present are expected to be high, and thus species richness per se is expected to increase delivery of all ES (Duncan et al. 2015; Huxham et al. 2010; Lang’at et al. 2012). Because complementary interactions between species are driven by differences in niche requirements and resource use (Díaz et al. 2004; 2007; Fox 2005; Cadotte et al. 2011; Lavorel 2013), it is expected, however, that functional trait diversity has a greater influence on the delivery of all ES than species richness per se.

H6.2: Mass ratio hypothesis (Grime 1998; Díaz et al. 2007; Mokany et al. 2008; Laughlin et al. 2011; Conti & Díaz 2013). Increasing dominance of mangrove flora species with resource conservative life history strategies and structural functional traits for large size is expected to increase delivery of ES related to aboveground biomass production (harvestable timber/charcoal production, vegetation carbon stocks) and organic sediment inputs (sediment carbon stocks). Increasing dominance of such species is expected to have less influence on ES that are related to both vegetation size and/or biomass and mangrove stem density (storm surge attenuation potential, harvestable kindling abundance).

H6.3: ES trade-offs (Lavorel & Grigulis 2012). Because of a greater influence of a dominance of mangrove flora species with resource conservative life histories and structural functional traits for large size on harvestable timber/charcoal production and vegetation carbon stocks expected under H6.2, it is anticipated that there may be trade-offs in the delivery of multiple ES under differing mangrove community compositions.

H6.4: Divergent controls on differing ecosystem compartments (Duncan et al. 2015). In the case of mangrove carbon stocks, total ES is delivered by mangrove community composition controls on ecosystem functioning in the vegetation and sediment compartments. Under H6.2, mangrove vegetation carbon stocks are anticipated to be strongly controlled by a dominance of mangrove flora species with resource conservative life history strategies and functional traits and structural traits for large size. It is also expected that a dominance of such species increases organic inputs into sediments and thus enhances sediment carbon stocks (H6.2). However, diversity in resource use strategies and structural traits is anticipated to

3. Mangrove forests: ecosystem services, threats and management increase stem density (H6.1), thus also contributing to increased organic inputs to sediments (sediment carbon stocks).

3.6.2 Rehabilitation for climate change mitigation and adaptation ecosystem services Chapter 7 examines the CCMA potential of rehabilitated mangroves across Panay Island, Philippines. It investigates CCMA ES delivery (carbon stocks and coastal protection potential from regular wind waves) of rehabilitated mangroves relative to adjacent reference natural mangrove areas. It also investigates the impact of intertidal zone location of mangrove rehabilitation on CCMA ES delivery (mid- to upper-intertidal [leased] abandoned fishpond rehabilitation vs. low-intertidal seafront rehabilitation). The potential CCMA benefits of a strategy of abandoned fishpond mangrove rehabilitation are then modelled in a municipality-specific case study. Tenure status of identified mid- to upper-intertidal zone abandoned fishponds across the municipality is analysed to assess the feasibility of a targeted strategy of abandoned fishpond reversion. The chapter addresses five main questions: (1) How well do rehabilitated mangroves replicate CCMA ES delivery (carbon stocks and coastal protection potential) of natural mangrove areas? (2) Is CCMA ES delivery from rehabilitated mangroves greater in the mid- to upper- intertidal zone where abandoned fishponds have been rehabilitated compared to low-intertidal seafront rehabilitated areas? (3) How much coastal greenbelt rehabilitation is required for effective coastal protection potential in Panay Island? (4) What are the CCMA benefits that could be derived from adopting a strategy of targeted abandoned fishpond reversion? and (5) is a strategy of targeted abandoned fishpond reversion for coastal greenbelt protection feasible under current land tenure in the Philippines? Specific hypotheses regarding CCMA delivery by rehabilitated low-intertidal seafront and abandoned fishpond mangroves are:

H7.1: Per unit area carbon stocks. Vegetation and sediment carbon stocks per unit area are lower in rehabilitated low-intertidal seafront areas than in abandoned fishponds (mid-intertidal location in former mangrove sediment; Iftekhar 2008; Iftekhar 2008; Primavera & Esteban 2008; Primavera et al. 2012b; 2012b; 2014) and natural mangrove stands (greater biomass and carbon burial with mangrove age; Bosire et al. 2008; Alongi 2011).

H7.2: Site-specific carbon stocks. Site-specific vegetation and sediment carbon stocks are greatest in natural mangrove areas (greater biomass and carbon burial

3. Mangrove forests: ecosystem services, threats and management with mangrove age; large areal coverage; Bosire et al. 2008; Alongi 2011), followed by rehabilitated abandoned fishpond areas (mid-intertidal location in former mangrove sediment; large areal coverage), and are lowest in rehabilitated low- intertidal seafront areas (low-intertidal location; small areal coverage; Iftekhar 2008; Iftekhar 2008; Primavera & Esteban 2008; Primavera et al. 2012b; 2012b; 2014).

H7.3: Site-specific coastal protection potential. Site-specific coastal protection potential is greatest in natural mangrove areas (greater biomass and structural complexity with age; Bosire et al. 2008; Alongi 2011; large greenbelt width; Ewel et al. 1998; Barbier et al. 2008; Koch et al. 2009), followed by rehabilitated abandoned fishpond areas (mid-intertidal location enabling greater biomass production; large greenbelt width), and is lowest in rehabilitated low-intertidal seafront areas (low- intertidal location restricting biomass production: Iftekhar 2008; Iftekhar 2008; narrow greenbelt width: Ewel et al. 1998; Barbier et al. 2008; Koch et al. 2009; Ellison 2000; Primavera & Esteban 2008; Primavera et al. 2012b; 2012b; 2014).

H7.4: Abandoned fishpond reversion for coastal protection. A high occurrence of seaward bank breaches in large sea-facing fishponds in the Philippines (Primavera et al. 2014), and high biomass production in mid-intertidal abandoned fishpond areas (H7.1 and H7.2), means a substantial amount of coastline could have effective coastal protection potential if a strategy of abandoned fishpond reversion to mangrove forest was prioritised.

H7.5: Abandoned fishpond tenure status. A high proportion of Fishpond Lease Agreement (FLA) leaseholds in the Philippines (see Section 4.2; Primavera et al. 2014) means tenure status of a large proportion of abandoned fishponds will be favourable for reversion to former mangrove forest.

3.6.3 Mangrove ecosystem services resilience and resistance to SLR Chapter 8 investigates the capacity for resilience and resistance of mangrove forests under varying stress from SLR, and the landscape-level factors influencing these proceses. It employs satellite remote sensing approaches at seven sites, encompassing a range of geographic locations from West Africa to South Asia. Resistance is assessed via SAR imagery change detection (biomass change) across forests over 2007-2010, and resilience is assessed via seaward and landward distribution change over 2007-2015 as indexed from multispectral imagery classification and SAR-derived coastline detection. Using topographic, sediment and

3. Mangrove forests: ecosystem services, threats and management anthropogenic landcover information, the relative influences of anthropogenic and landscape-level abiotic processes on these satellite-derived measures of resilience and resistance are then modelled. The main questions addressed in this chapter are: (1) What potential do mangroves forests have for resilience to SLR (maintained areal coverage through landward migration) under current use and management? (2) What potential do mangrove forests have for resistance to SLR (maintained biomass/productivity through time) under current use and management? (3) What are the landscape-level topographic constraints to the resilience of mangrove forests? (4) What are the sediment load constraints to mangrove resilience and resistance? and (5) To what extent does anthropogenic development constrain mangrove forest resilience and resistance? Specific hypotheses addressed relating to these main questions are:

H8.1: Topographic constraints to resilience (Gilman et al. 2008). Non-mangrove areas on shallower topographic slopes will facilitate higher mangrove resilience (landward migration and maintenance of areal coverage).

H8.2: Sediment load constraints to resilience and resistance (Alongi 2008; Gilman et al. 2008; Krauss et al. 2014; Lovelock et al. 2015). Mangroves with higher satellite-derived coastal total suspended matter (TSM; Blondeau-Patissier et al. 2014) will have greater resistance to SLR in their seaward boundaries. These mangroves will have a higher potential for sediment trapping in their root systems, thus having higher surface accretion and elevation gain, and will have lower susceptibility to erosion, which will enable them to keep pace with SLR.

H8.3: Anthropogenic constraints to resilience and resistance (Dahdouh-Guebas et al. 2005; Gilman et al. 2008; Lee et al. 2014). Mangroves with a greater rate of increase (2007-2010) in anthropogenic development at their landward margins will exhibit lower resistance (cryptic degradation, alterations to freshwater and sediment supply) and resilience (physical barriers to landward migration).

CHAPTER 4: Study Sites

4.1 Introduction This chapter provides an overview of mangrove forest study sites used in this thesis. Multiple study sites were utilised to explore the thesis aims and objectives. Chapters 6 and 7 focus on natural (Chapter 6) and rehabilitated (Chapter 7) mangrove forests in Panay Island, Philippines (Section 4.2), focusing at a regional scale to investigate mangrove identity and diversity drivers of ES delivery, and ES delivery by rehabilitated mangroves respectively. Chapter 8 investigates potential constraints to the resilience and resistance of mangrove forests and their ES to SLR throughout the tropics (West Africa to South Asia; Section 4.3).

4.2 Study sites: Philippines 4.2.1 Background The Philippine archipelago is an important global hotspot of endemic floral and faunal diversity (Myers et al. 2000). Located in the Southeast Asian region of the Indo-West Pacific mangrove distribution (Tomlinson 1986; Duke 1992; Polidoro et al. 2010; Spalding et al. 2010; Figure 3.1), the Philippines is a mangrove diversity hotspot, containing 35 true mangrove species (Primavera et al. 2004; Table 4.1). Of the ~450,000 ha of Philippine mangrove forests in the early 1900s (Brown & Fischer 1920; Primavera & Esteban 2008), mangrove distribution in the country was dramatically reduced to ~241,000 ha in 2010 (Long et al. 2013; see Figure 4.1), and declines are on-going (Richards & Friess 2016; Figure 4.2). Over 60% of the country’s human population inhabits low-lying coastal areas (DENR-BMB 2014), and causes of mangrove declines include overexploitation of mangrove resources and coastal development. However, the major cause of mangrove loss has been land conversion for fishpond aquaculture (Primavera 2000; 2005; Primavera & Esteban 2008; Richards & Friess 2016), predominantly for rearing milk fish (Chanos chanos) and historically for tiger prawn (Penaeus monodon) during the 1980’s shrimp boom (Primavera 1995). A national push toward aquaculture development in the first half of the 20th century, and continued through strong public and private sector backing in the 1980s, has resulted in a dramatic loss of mangrove areal cover: of 279,000 ha of mangroves lost from 1951-1988, half are estimated to have been converted for fishpond aquaculture

4. Study sites

Figure 4.1. Distribution of mangrove forests in the Philippines, showing a close-up of Panay Island. Adapted from: Long & Giri (2011) and Long et al. (2014).

Figure 4.2. Spatial distribution of mangrove loss across Southeast Asia 2000-2012. Source: Richards & Friess (2016).

4. Study sites

Table 4.1. List of mangrove species (and hybrids: Rhizophora x lamarckii) found on Panay Island, Philippines (35 species). Philippines-wide flora also includes Kandelia candel; absent from Panay. IUCN Red List criteria: LC = least concern; NT = near threatened; VU = vulnerable; EN = endangered; CR = critically endangered.

Red List Population Family Species Form status trend Acanthaceae Acanthus ebracteatus Shrub LC decreasing Acanthaceae Acanthus ilicifolis Shrub LC unknown Acanthaceae Acanthus volubilis Shrub LC decreasing Myrsinaceae Aegiceras corniculatum Shrub LC decreasing Myrsinaceae Aegiceras floridum Shrub NT decreasing Avicenniaceae Avicennia alba Tree LC decreasing Avicenniaceae Avicennia marina Tree/Shrub LC decreasing Avicenniaceae Avicennia officinalis Tree LC decreasing Avicenniaceae Avicennia rumphiana Tree VU decreasing Rhizophoraceae Bruguiera cylindrica Tree LC decreasing Rhizophoraceae Bruguiera gymnorrhiza Tree LC decreasing Rhizophoraceae Bruguiera parviflora Tree LC decreasing Rhizophoraceae Bruguiera sexangula Tree LC decreasing Bombacaceae Camptostemon philippinense Tree EN decreasing Rhizophoraceae Ceriops decandra Shrub LC decreasing Rhizophoraceae Ceriops tagal Tree LC decreasing Euphorbiaceae Excoecaria agallocha Tree LC decreasing Sterculiaceae Hetitiera littoralis Tree LC decreasing Combretaceae Lumnitzera littorea Tree LC decreasing Combretaceae Lumnitzera racemosa Shrub LC decreasing Palmae Nypa fruticans Palm LC unknown Myrtaceae Osbornia octodonta Shrub LC decreasing Lythraceae Pemphis adicula Shrub LC decreasing Rhizophoraceae Rhizophora apiculata Tree LC decreasing Rhizophoraceae Rhizophora x lamarckii Tree – – Rhizophoraceae Rhizophora mucronata Tree LC decreasing Rhizophoraceae Rhizophora stylosa Tree LC decreasing Sonneratiaceae Sonneratia alba Tree LC decreasing Sonneratiaceae Sonneratia caseolaris Tree LC decreasing Sonneratiaceae Sonneratia ovata Tree NT decreasing Rubiaceae Scyphiphora hydrophylacea Shrub LC decreasing Meliaceae Xylocarpus granatum Tree LC decreasing Meliaceae Xylocarpus moluccensis Tree LC decreasing

4. Study sites

(Primavera 2000; Primavera & Esteban 2008). The peak aquaculture conversion occurred during the 1950s-1970s, when the Philippines government promoted a national pro-aquaculture policy, and mangroves declined at a rate of ~5,000ha yr-1 (Primavera 1995; Primavera et al. 2014). Some of the highest fishpond densities occur in the Western Visayas Region; e.g. on Panay Island (Primavera & Esteban 2008). Development is largely unregulated, and despite laws mandating 50–100m of mangrove greenbelt (Primavera et al. 2012a), fishponds are often built to the shoreline. Abandonment is high (estimates in the thousands of hectares; see Samson & Rollon 2011; Primavera et al. 2012a), due primarily to bank breaches in sea-facing fishponds over low productivity (Primavera et al. 2014). Fishponds are tenured by among the wealthiest in society, and operated by the poorest. Reversion of abandoned fishponds to former mangroves for greenbelt resurrection could thus benefit coastal community livelihoods through associated fisheries enhancement (Walton et al. 2006).

Philippines’ public mangrove land is released by the Department of Environment and Natural Resources (DENR) for aquaculture under multiple tenure arrangements: from titled ownership, to temporary leaseholds under Fishpond Lease Agreements (FLAs) granted under the jurisdiction of the Bureau of Fisheries and Aquatic Resources of the Department of Agriculture (DA-BFAR). Under Philippine law, failure to adhere to FLA terms should preclude FLA cancellation by the Bureau of Fisheries and Aquatic Resources of the Department of Agriculture (DA-BFAR), and reversion of jurisdiction to the Forest Management Bureau of the Department of Environment and Natural Resources (DENR) for subsequent mangrove rehabilitation. This includes Abandoned (no operational activity, subleasing, or neglect of payments), Underutilised (no commercial production within three years), and Undeveloped (pond infrastructure absent) (AUU) FLA fishponds (see Primavera et al. 2014). Herein, the term ‘abandoned fishpond’ refers to all AUU fishponds. However, the lack of coordination between government departments (DA-BFAR and DENR), low institutional capacity, exclusion of local government units (LGUs) and coastal communities from decision-making, and a lack of political will means FLA monitoring is minimal, and cancellation and reversion rarely occurs. As a result, large areas of former mangrove lie fallow. Furthermore, cancelled abandoned FLAs are often absorbed and re-tenured under new FLA leases or operated illegally (Primavera et al. 2014).

Relatively large tracts of healthy, diverse mangrove forests still remain in some areas of the Philippines: notably in the islands of Palawan, Bohol, Eastern Samar, Sulu and Siargao (Long et al. 2013; see Figure 4.1), and in some parts of the Western Visayas region, central Philippines (e.g. Aklan Province; Primavera et al. 2004; Cadaweng & Aguirre 2005; Walton et al. 2007). Many of these mangrove forest occur within IUCN protected areas. However, areal declines within protected areas since 1990 (49%) have been comparable to national rates (52%; Long et al. 2013). Community-managed ecoparks have been created in areas of Western Visayas, with the aims of preserving mangrove area and diversity while allowing sustainable use and aquaculture by local communities (Primavera et al. 2004; Cadaweng & Aguirre 2005; Walton et al. 2007). These ecoparks have seen successes in terms of maintaining mangrove floral diversity and functionality under multi-use activities; however, delivery of multiple ES within these areas has mostly not yet been quantified (but see Walton et al. 2006; 2007; Thompson et al. 2014).

The Philippines is among the most typhoon-ravaged countries in the world (Peduzzi et al. 2012; UNU 2014), and high institutional and infrastructural vulnerability place the Philippines as the second most vulnerable country worldwide to natural hazards (UNU 2014). Small-scale mangrove planting and rehabilitation by local communities to recuperate lost ES (predominantly coastal protection from typhoons) has occurred in the Philippines since the early part of the 20th century (Primavera 2000; Walters 2000; 2003; 2004; Primavera & Esteban 2008). In recent decades, and particularly following the devastating impacts of super-typhoon Yolanda (international name Haiyan) in 2013 (Lagmay et al. 2015; Soria et al. 2015; Long et al. 2016; Primavera et al. 2016; Villamayor et al. 2016), the benefit and value of mangrove functionality and ES have become more widely appreciated both in the Philippines and globally (Barbier et al. 2008; 2011; de Groot et al. 2012; Primavera et al. 2012b; Costanza et al. 2014), large-scale replanting and rehabilitation activities are now occurring throughout the Philippines under nationally- and internationally-funded programmes.

Mangrove rehabilitation throughout the country is conducted through schemes implemented by government departments (i.e. DENR), local government units (LGUs), NGOs and local community organisations (Primavera & Esteban 2008; Primavera et al. 2012b; 2014). Due to the challenges of abandoned fishpond reversion, national greenbelt rehabilitation programmes (the National Greening

4. Study sites

Program [NGP]; DENR) continue to focus on low- and sub-intertidal planting seaward of coastal infrastructure and fishponds (‘seafront rehabilitation’). Surviving seafront rehabilitated mangroves are often small areas growing at the limits of their physiological tolerance ranges (Tomlinson 1986). High mortality in plantations of inappropriate species (10-20%; Primavera & Esteban 2008) in inappropriate (low- or sub-intertidal) locations wastes public and international funds, while threatening other intertidal systems (seagrasses, mudflats; Primavera & Esteban 2008; Samson & Rollon 2008; 2011). In contrast, rehabilitation of abandoned and disused fishpond areas (in large areas of the mid- to upper- intertidal zones) is now widely advocated by researchers and NGOs (Primavera et al. 2012b; 2014). ZSL Philippines-led projects, in partnership with specific LGUs, have begun to target rehabilitation of abandoned fishponds in the mid- to upper- intertidal zone where more natural hydrological conditions largely remain (Primavera et al. 2012a; 2014).

4.2.2 List of sites Philippines-based study sites used in this thesis are focused on multiple mangrove areas across Panay Island, Western Visayas. Study sites were selected for multiple purposes relating to objectives 1 (species and functional diversity drivers of ES delivery in mature natural mangroves; Chapter 6) and 2 (CCMA ES delivery in rehabilitated mid- to upper-intertidal abandoned fishpond and low-intertidal seafront areas, relative to adjacent mature natural stands; Chapter 7) outlined in Section 1.4. A total of eight study sites were included (see Figure 4.3). Some study sites comprised only minimally-degraded mature natural mangroves (Brgy. Pedada, Ajuy, Iloilo; Brgy. Buntod, Panay, Capiz; Brgy. Balaring, Ivisan, Capiz; Table 4.2; Figure 4.3; Chapter 6), some comprised both minimally degraded mature natural mangroves and low-intertidal seafront rehabilitated areas (“sites”) of comparable age (7-8 years) within their boundaries (Brgy. Ermita, Dumangas, Iloilo; Bakhawan ecopark, Brgy. Buswang, Kalibo, Aklan; Table 4.2; Figure 4.3; Chapters 6 and 7), and lastly, some study sites were rehabilitated mid- to upper-intertidal abandoned fishponds (5-8 years old: Brgy. Nabitasan abandoned fishpond and Dumangas abandoned fishpond; Table 4.2; Figure 4.3; Chapter 7).

Study sites containing minimally degraded mature natural mangroves included four community-managed mangrove areas supported by ZSL Philippines projects (Brgy. Ermita; Brgy. Pedada; Brgy. Buntod; Brgy. Balaring), as well as two large ecopark

areas in Aklan province (Bakhawan ecopark; Katunggan-It-Ibajay [KII] ecopark). These study sites were selected to represent variation in mangrove species richness and diversity (low [6] to high [15] species richness) and species dominance (A. marina, S. alba, C. philippinense, C. decandra, A. rumphiana, A. officinalis; Table 4.2). This natural variation in diversity and dominance (and within-site variation in diversity and dominance at the KII site; see Section 4.2.2; Table 4.2) across relatively large minimally degraded mature natural mangrove areas (3.2-72.0 ha; Table 4.2) was required assess potential species and functional trait diversity and dominance drivers of mangrove ES delivery (Chapter 6).

Figure 4.3. Location of study sites across Panay Island, Philippines. Green circles donate natural mangrove sites study sites (Section 4.2.2), half-filled circles denote study sites containing natural and rehabilitated mangrove areas (Sections 4.2.2 and 4.2.3), and red circles denote rehabilitated (replanted or recolonised) abandoned fishpond study sites. Adapted from: Primavera et al. (2012).

4. Study sites

Rehabilitated sites were selected to contain comparable low-intertidal seafront and mid- to upper-intertidal abandoned fishpond rehabilitation areas. Rehabilitated sites were selected per the criteria that: (1) they had been in the course of rehabilitation for five to ten years (Table 4.2); and either (2) in the case of low- intertidal seafront rehabilitated areas, they were contained within a study site that also contained adjacent reference mature natural mangroves (Brgy. Ermita and Bakhawan ecopark), or (3) in the case of abandoned fishpond rehabilitated areas, they were sea-facing fishponds in the mid- to upper-intertidal zone, rehabilitated due to seaward fishpond bank breeches (Brgy. Nabitasan abandoned fishpond and Dumangas abandoned fishpond). The low-intertidal seafront rehabilitated sites included a community-managed rehabilitated mangrove area supported by ZSL- Philippines projects (Brgy. Ermita), as well as a community-managed rehabilitated mangrove area managed by the Kalibo Save the Mangroves Association (KASAMA; Bakhawan ecopark).

4.2.2.1 Natural sites 1. Brgy. Ermita, Dumangas, Iloilo Province (10.794 N, 122.686 E) (Primavera et al. 2012b; Figure 4.4). A seafront fringing mangrove of 3.2 ha with Avicennia marina dominant.

Figure 4.4. Site imagery and typical vegetation pictures from the natural area at the Ermita study site. 2. Brgy. Pedada, Ajuy, Iloilo Province (11.058 N, 122.950 E) (Primavera et al. 2012b; Thompson et al. 2014; Figure 4.5). A seafront fringing mangrove of 29.5 ha with Sonneratia alba dominant.

Figure 4.5. Site imagery and typical vegetation pictures from the Pedada study site.

3. Brgy. Buntod, Panay, Capiz Province (11.594 N, 122.651 E) (Primavera et al. 2012b; Figure 4.6). A seafront mangrove of 7.7 ha fringing an estuary with A. marina dominant.

Figure 4.6. Site imagery and typical vegetation pictures from the Buntod study site.

4. Brgy. Balaring, Ivisan, Capiz Province (11.557 N, 122.651 E) (Primavera et al. 2012b; Figure 4.7). A seafront fringing mangrove of 7.8 ha with A. marina dominant.

5. Bakhawan ecopark, Brgy. New Buswang, Kalibo, Aklan Province (11.719 N, 122.387 E; Figure 4.8). A fringing mangrove of 75.5 ha at the mouth of Aklan River, sheltered behind a long coastal sandbank. The site has been subject to on-going planting activities since the early 1990’s and now contains distinct areas of natural and younger rehabilitated mangrove vegetation (Primavera et al. 2004; Cadaweng & Aguirre 2005; Walton et al. 2007). The remaining natural mangrove at Bakhawan ecopark exists toward the landward, mid-intertidal portions of the site, covering ~38.6 ha with A. marina and S. alba the dominant species.

4. Study sites

Figure 4.7. Site imagery and typical vegetation pictures from the Balaring study site.

Figure 4.8. Site imagery and typical vegetation pictures from the natural areas of the Bakhawan ecopark study site. 6. Katunggan-It-Ibajay (KII) ecopark, Brgy.s Bugtong-Bato and Naisud, Ibajay, Aklan Province (11.806 N, 122.202 E). A hyper-diverse basin mangrove (27 species), spanning 72 ha from the mouth of Naisud River (Primavera et al. 2004; Walton et al. 2007; Thompson et al. 2014; Figure 4.9). The forest comprises six distinct vegetation zones, of which five were considered here; a zone dominated by Bruguiera sexangula was omitted from study due to it’s very small size (0.4 ha). The surveyed natural zones surveyed were: an A. marina dominated downstream zone; a C. philippinense dominated downstream-intermediate zone; a C. decandra dominated intermediate zone; a mixed community intermediate-upstream zone with A. rumphiana, C. decandra and C. philippinense in high abundance, and; an A. officinalis and A. rumphiana dominated upstream zone.

dominated mangrove zones at the KII ecopark study site study KII ecopark the at zones mangrove dominated

mixed species mixed

. Site imagery and typical vegetation pictures from the the from pictures typicalvegetation and imagery Site .

4.9

Figure

Table 4.2. Summary of all natural and rehabilitated mangrove field sites surveyed in this thesis: Panay Island, Philippines. FP = fishpond.

Mean Dominant Mean height Basal area Name Coordinates Type Form Area (ha) species Other species 푫푩푯 (cm) (m) (cm2/m2)

Excoecaria agallocha; Brgy. 10.794 N, Seafront / Avicennia Sonneratia alba; Rhizophora 9.3 (max 6.0 (max Natural 3.2 25.2 Ermita 122.686 E fringing marina apiculata; R. mucronata; R. = 110.0) = 16.9) stylosa

A. marina; A. rumphiana; Camptostemon philippinense; Brgy. 11.058 N, Seafront / 15.5 (max 5.5 (max Natural 29.5 S. alba Osbornia octodonta; Ceriops 33.8 Pedada 122.950 E fringing = 85.6) = 15.8) decandra; R. apiculata; R. mucronata; R. stylosa

Bruguiera gymnorrhiza; C. decandra; E. agallocha; Brgy. 11.594 N, 2.3 (max 3.3 (max Natural Seafront 7.7 A. marina Lumnitzera racemosa; R. 12.4 Buntod 122.651 E = 58.4) = 11.2) apiculata; R. mucronata; R. stylosa; S. alba

Avicennia alba; Nypa fruticans; Brgy. 11.557 N, Seafront / 4.2 (max 4.4 (max Natural 7.8 A. marina R. apiculata; R. mucronata; R. 7.5 Balaring 122.651 E fringing = 23.7) = 15.6) stylosa; S. alba

75.5 N. fruticans; R. apiculata; R. Bakhawan 11.719 N, S. alba; A. 4.9 (max 6.0 (max Natural Seafront Natural area: mucronata; R. stylosa; 20.2 ecopark 122.387 E marina = 27.8) = 19.6) 38.6 Sonneratia ovata

Table 4.2 (cont.). Summary of all natural and rehabilitated mangrove field sites surveyed in this thesis: Panay Island, Philippines.

Mean Dominant Mean height Basal area Name Coordinates Type Form Area (ha) species Other species 푫푩푯 (cm) (m) (cm2/m2)

Aegiceras corniculatum; A. alba; A. officinalis; A. rumphiana; Katunggan- Bruguiera cylindrica; C. 72.0 It-Ibajay 11.806 N, philippinense; C. decandra; C. 2.8 (max 3.4 (max Natural Basin A. marina zone: A. marina 27.9 ecopark 122.202 E tagal; E. agallocha; N. fruticans; = 85.1) = 16.7) 7.5 (KII) R. mucronata; S. alba; Xylocarpus granatum; X. moluccensis

A. corniculatum; A. marina; A. officinalis; A. rumphiana; C. C. philippinense C. decandra; C. tagal; E. agallocha; 3.0 (max 3.3 (max KII 23.4 zone: 2.6 philippinense N. fruticans; Scyphiphora = 60.2) = 19.7) hydrophyllacea; S. alba; X. granatum; X. moluccensis

A. corniculatum; A. marina; A. officinalis; A. rumphiana; C. C. decandra 1.7 (max 2.8 (max KII C. decandra philippinense; N. fruticans; S. 16.1 zone: 2.7 = 43.7) = 15.6) alba; X. granatum; X. moluccensis

A. rumphiana; A. corniculatum; A. marina; A. Mixed C. officinalis; B. cylindrica; E. 3.5 (max 3.8 (max KII community zone 33.8 philippinense; agallocha; N. fruticans; S. alba; = 69.3) = >20) 26.0 C. decandra X. granatum; X. moluccensis

Table 4.2 (cont.). Summary of all natural and rehabilitated mangrove field sites surveyed in this thesis: Panay Island, Philippines.

Mean Dominant Mean height Basal area Name Coordinates Type Form Area (ha) species Other species 푫푩푯 (cm) (m) (cm2/m2)

A. rumphiana / B. cylindrica; C. decandra; E. 11.806 N, A. officinalis; A. 8.1 (max 5.3 (max KII Natural Basin A. officinalis agallocha; N. fruticans; X. 52.6 122.202 E rumphiana = 83.4) = > 20) zone: 16.6 granatum; X. moluccensis

Brgy. 10.794 N, Rehabilitated Seafront / 5.8 (max 4.5 (max 0.5 S. alba A. marina 6.5 Ermita 122.686 E (replanted) fringing = 14.4) = 9.9)

75.5 Bakhawan 11.719 N, Rehabilitated R. apiculata; R. mucronata; R. 2.3 (max 3.8 (max Seafront 2006 replanting: A. marina 8.3 ecopark 122.387 E (replanted) stylosa; S. alba = 22.6) = 11.3) 7.7

Brgy. 10.781 N, Rehabilitated Abandoned A. officinalis; R. mucronata; S. 0.8 (max 1.9 (max Nabitasan 9.1 A. marina 0.2 122.625 E (replanted) fishpond alba = 4.6) = 4.4) FP

N/A – anonymity Dumangas Rehabilitated Abandoned 1.9 (max 2.8 (max requested 45.9 A. marina Rhizophora spp.; S. alba 2.9 FP (recolonised) fishpond = 8.0) = 7.6) by fishpond owner

4. Study sites

4.2.2.2 Rehabilitated/replanted sites 7. Brgy. Ermita, Dumangas, Iloilo Province (10.794 N, 122.686 E) (Primavera et al. 2012b; Figure 4.10). A replanted seafront fringing mangrove planted in 2007 (0.5 ha). The site was originally planted with seedlings of S. alba, A. marina and Rhizophora spp.; however, heavy algal blankets (A. marina) and barnacle infestation (Rhizophora spp.) led to survival of only S. alba, which avoided smothering and broken stems due to sturdy young stems and periodic bark shedding. The plantation contains some remaining matural (natural) A. marina trees.

Figure 4.10. Site imagery and typical vegetation pictures from the 2007 rehabilitated area of the Ermita study site.

8. Bakhawan ecopark, Brgy. New Buswang, Kalibo, Aklan Province (11.719 N, 122.387 E; Figure 4.11).

Figure 4.11. Site imagery and typical vegetation pictures from the 2006 rehabilitated area of the Bakhawan ecopark study site.

4. Study sites

The site contains two distinct replanted zones: (1) a Rhizophora spp. monoculture plantation in the mid-intertidal zone planted in the early 1990s, and (2) a younger replanted zone (7.7 ha) planted predominantly with Rhizophora spp. in 2006, but which has experienced high levels of natural colonisation from S. alba and A. marina. Sampling was focused in the latter replanted area for comparability with other rehabilitated sites. The dominant species in this area is A. marina.

9. Brgy. Nabitasan, Leganes, Iloilo Province (10.781 N, 122.625 E; Figure 4.12). A replanted abandoned fishpond of 9.1 ha located in the low- to mid-intertidal zone. Repeated replanting of predominantly A. marina, but also Rhizophora spp. and S. alba has occurred at the Nabitasan abandoned pond since 2009 under ZSL’s Community-Based Mangrove Rehabilitation Project (CMRP) (Primavera et al. 2012b). The dominant natural species is A. marina.

Figure 4.12. Site imagery and typical vegetation pictures from the Nabitasan abandoned fishpond study site. 10. Dumangas, Iloilo Province (Figure 4.13).

Figure 4.13. Site imagery and typical vegetation pictures from the Dumangas abandoned fishpond study site.

4. Study sites

An abandoned fishpond in the low- to mid-intertidal zone with sea-facing bank breach and subsequent recolonization of mangrove forest since ~2006 (J.H. Primavera & H.J. Koldewey pers. comm.). Natural hydrology is still returning to the Dumangas fishpond and subsequently many areas suffer extreme waterlogging, which has resulted in high mortality of replanted A. marina seedlings in these areas (J.H. Primavera pers. comm.). The dominant species is A. marina.

4.3 Study sites: Global 4.3.1 Background Estuarine and deltaic mangrove forests distributed throughout Africa and South Asia were selected to examine their potential resilience (maintenance of areal coverage) and resistance (maintained functionality; biomass; Gilman et al. 2008) to SLR. These sites were selected as those mangrove forests which (1) were large (all >5,000 ha) in size, (2) had experienced an increase in sea level of 5-15cm over 1993-2008 (from sea surface height data: Beckley et al. 2010; GSFC 2013), (3) had ALOS/PALSAR imagery available (JAXA/METI 2010; ASF DAAC 2016; ESA 2016a) for 2007 and 2010, (4) had non-cloudy Landsat 5 TM or Landsat 8 OLI/TIRS imagery available (USGS 2015) for 2007 and 2015, and (5) had reasonable high resolution historical multispectral imagery available in Google Earth (Google Earth 2016) for validation of satellite remote sensing analyses.

4.3.2 List of sites A total of seven mangrove study sites satisfied the above criteria (Figure 4.14; Table 4.3). Due to ALOS/PALSAR imagery availability, these sites were limited to a distribution from West Africa to South Asia, with most forests occurring on the East and West coasts of the African continent (Figure 4.14; Table 4.3). The seven sites are under varying levels of protection, from virtually no protection (e.g. Sherbro Bay, Sierra Leone; Save River Delta, Mozambique; Mahajamba, Madagascar), to multi-use protection in the form of protected areas, and UNESCO World Heritage and Ramsar Sites (e.g. Saloum Delta, Senegal; Ruvuma Estuary, Tanzania; Rufiji Delta, Tanzania; The Sundarbans, India & Bangladesh). Nevertheless, all sites sustain human use (e.g. timber extraction, land clearing; Diop et al. 2002; UNEP- WCMC 2003; 2007; Spalding et al. 2010), mostly occurring at their landward, upper- intertidal edges. All of the forests similarly have varying levels of development of human settlement and unconverted land present at their landward boundaries. All

4. Study sites sites sustain various further threats above extractive activities, as detailed in the following site descriptions.

Figure 4.14. Location of study sites across the tropics (West Africa to South Asia). Data displayed in white to greyscale is Shuttle Radar Topography Mission Digital Elevation Model data (SRTM DEM; 30m resolution; USGS 2014) for each study area region. (a) Saloum Delta, Senegal; (b) Sherbro Bay, Sierra Leone; (c) Save River Delta, Mozambique; (d) Ruvuma Estuary, Tanzania; (e) Rufiji Delta, Tanzania; (f) Mahajamba, Madagascar; (g) The Sundarbans, India & Bangladesh. 1. Saloum Delta, Senegal (13.663 N, -16.691 W; Figure 4.14a; Figure 4.15). Close to the northern limit of mangrove distribution (Mauritania), the Saloum River Delta in the south of Senegal is the most northerly large mangrove in West Africa, covering an extensive area of 64,286 ha (JICA 2005). The delta is low-lying, with average elevation of <5 m above sea level (JICA 2005). The mangrove flora is highly zoned, owing to low topographic variability and annual precipitation (mean annual rainfall of 686 mm; Table 4.3) and thus low variability in salinity (Spalding et al. 2010; Ndour et al. 2011), with Rhizophora spp. dominant along watercourses and Avicennia germinans increasing in dominance inland due to higher salinity tolerance. In addition to typical mangrove marine and intertidal fauna, the forest is host to a vast diversity of seabirds, cetaceans, marine turtles, and aquatic and terrestrial mammals (UNEP-WCMC 2007; Spalding et al. 2010; Ramsar 2015). The area’s mangroves are protected in the Saloum Delta National Park, a Biosphere Reserve and Ramsar Site (Saenger & Bellan 1995). However, the mangrove remains threatened by desertification and invasion from saltmarsh communities, clearing

4. Study sites for rice production and continued timber and shellfish extractive activities (Saenger & Bellan 1995; Spalding et al. 2010; Ndour et al. 2011; Ramsar 2015). Included in this thesis are the mangroves of the Saloum Delta and those deltaic mangroves extending southward through southern Senegal and Guinea-Bissau for ~600km (total area of ~550,000 ha; Giri et al. 2011; Table 4.3), collectively termed “Saloum Delta” herein.

Figure 4.15. Google Earth (2016) imagery over the Saloum Delta, Senegal study site.

2. Sherbro Bay, Sierra Leone (7.666 N, -12.625 W; Figure 4.14b; Figure 4.16). Among the most extensive mangroves in Sierra Leone, the Sherbro Bay estuary mangroves cover an area of 45,000 ha (Anthony 2004; Spalding et al. 2010). The area receives some of the highest rainfall of West African mangroves (mean annual rainfall of 2,526 mm; Table 4.3), resulting in high productivity (Anthony 2004). Multiple rivers empty into the bay, bringing high sediment loads, and the mangrove is dense and dominated by Rhizophora racemosa, R. mangle and A. germinans, though distinct zonation patterns are mostly absent (Anthony 2004). Extending landward, the mangrove forms shrubby zones dominated by A. germinans at higher salinities (Anthony 2004; Spalding et al. 2010). The mangroves of Sierra Leone are

4. Study sites under no formal protection (Spalding et al. 2010). However, unlike much of the West African mangrove area, they have been little affected by timber extraction and rice cultivation, due to comparatively lower population densities at the landward boundaries in Sierra Leone (Anthony 2004). Included in this thesis are the mangroves of Sherbro Bay, extending northward to the northern border of Sierra Leone (~213,000 ha; Giri et al. 2011; Table 4.3), collectively termed “Sherbro Bay” herein.

Figure 4.16. Google Earth (2016) imagery over the Sherbro Bay, Sierra Leone study site.

3. Save River Delta, Mozambique (-20.953 S, 35.735 E; Figure 4.14c; Figure 4.17). The mangroves of central Mozambique (Sofala and Zambezia provinces: ~190,000ha in 2002; Fatoyinbo et al. 2008) are among the most extensive mangrove areas in East Africa (UNEP-WCMC 2003; Spalding et al. 2010). The topography of the region is low and gently sloping, enabling vast areas of mangroves to flourish (Spalding et al. 2010), yet potentially enhancing the impact of SLR on these forests (Alongi 2008; Gilman et al. 2008; Massuanganhe et al. 2015). The Save River Delta contains dense mangroves and spills over to neighbouring fringing areas, creating a forest extending ~100 km (Massuanganhe et al. 2015). Owing to

4. Study sites satellite remote sensing data availability, only the central portions of the Save River Delta are considered within this thesis (Figure 4.17); covering an area of ~48,000 ha (Giri et al. 2011; Table 4.3). Sediments are organic-rich, and mangroves in most parts of the delta are sheltered from strong tidal action by the presence of coastal dunes and sand banks (Massuanganhe et al. 2015). Individual Sonneratia alba trees may reach heights of 20m and Rhizophora mucronata in more landward locations may reach 30m (Spalding et al. 2010). There is a comparatively low level of protection for Mozambique mangroves (Spalding et al. 2010), and combined impacts of land clearing, unsustainable fishing activities, timber extraction and upstream water diversion have resulted in a loss of 50,000-118,000 ha of mangrove area since 1972; however, it is acknowledged that this estimate of substantial loss may be due in part to remote sensing classification errors (Marzoli 2007; Fatoyinbo et al. 2008; Sitoe et al. 2014). The mangroves of the central region of Mozambique (e.g. the Save River Delta), however, experience relatively little direct impact from anthropogenic activities (UNEP-WCMC 2003), and anthropogenic development at landward margins is currently minimal.

Figure 4.17. Google Earth (2016) imagery over the Save River Delta, Mozambique study site.

Table 4.3. Summary of the seven mangrove sites assessed for resilience and resistance to sea level rise (West Africa to South Asia; see Figure 4.14).

Mean Mean Mean above- Mean Mean Spring Mean Mean basal stem ground monthly annual tidal Area Dominant DBH height area density biomass temp. rainfall amplitude Name Form (ha) species Other species (cm) (m) (cm2 m-2) (N ha-1) (Mg ha-1) (˚C) (mm) (m)

64,286 (main R. racemosa; R. harisonii; 2,720 5.0 5.1 25.3 Saloum delta)1 Rhizophora Laguncularia racemosa; (1,000– 95.0 15–28 686 0.3–1.6 D (2.9–14.1) (2.2–11.7) (2.5–89.6) Delta, SN 552,497 mangle 1,3 Avicennia germinans; 4,800) 6 7 8 7 1 1 1 (study Conocarpus erectus 1,3-5 1 area)2

45,000 (main R. racemosa; 4–10 500– Sherbro estuary)9 L. racemosa; R. harisonii; C. 112.0 28–32 2,526 0.4–3.0 E R. mangle; A. – (max 23) – 1,000 Bay, SL 213,281 erectus9,10 6 7 8 7 germinans 9,10 9,10 9 (study area)2

Avicennia marina; A. 4.8 Sonneratia officinalis; Bruguiera Save River 48,177 (± 3.3 s.e.; alba; gymnorrhiza; Ceriops tagal; 101.0 21–28 1,032 5.6 Delta, D (study – max – – Rhizophora Heritiera littoralis; 6 7 8 7 MZ area)2 20–30) mucronata10 Lumnitzera racemosa; 10-12 Xylocarpus granatum10

7,000 (within A. marina; B. gymnorrhiza; C. 24.4 Ruvuma MPA)13,14 136.0 23–28 1,071 3.2 E Mixed13 tagal; H. littoralis; S. alba; R. – – (8.0-40.5) – Estuary, TZ 12,660 6 7 8 7 mucronata; X. granatum13 13 (study area)2

Table 4.3 (cont.). Summary of the seven mangrove sites assessed for resilience and resistance to sea level rise (West Africa to South Asia; see Figure 4.14).

H. littoralis; R. A. aureum; Barringtonia 27.3 729 mucronata; A. Rufiji Delta, 44,218 racemosa; L. racemosa; S. (11.3– (±34 136.0 23–28 1,071 3.2 D marina; B. – – TZ 2 alba; X. granatum; X. 50.4) s.e.) 6 7 8 7 gymnorrhiza; moluccensis 15-17 15,16 15 C. tagal 15,16

27,718 (bay) 5.8 4,570 A. marina; R. B. gymnorrhiza; C. tagal; H. 10.9 Mahajamba, 64,747 (2.3–9.3; (1,089– 121.0 1,513 3.5 D mucronata; X. littoralis; L. racemosa; S. (4.0–18.2) – – MG (study max 20) 12,003) 6 8 7 granatum10,18 alba18 18 area) 10,18 18 2

Aegialitis rotundifolia; Aegiceras corniculatum; Heritiera Aglaia cuculata; Avicennia fomes; alba; Avicennia marina; Excoecaria Varied: 638,420 Avicennia officinalis; Varied: Varied: Varied: agallocha; 14,885 1,083– Sundarbans, (study Bruguiera cylindrica; B. 5.6 2.9 12.3 93.7 19–29 1.9–5.0 D Ceriops (4,723– 2,666 IN & BD area) gymnorrhiza; B. parviflora; B. (1.9–8.4) (1.1–4.3) (4.9–20.3) 21 7 7 decandra; 23,751) 8 2 19 19 19 Sonneratia sexangula; C. tagal; Kandelia 19,20 apetala10,19,20 candel; L. racemosa; N. fruticans; Phoenix paludosa; Rhizophora apiculata; R. mucronata;

Table 4.3 (cont.). Summary of the seven mangrove sites assessed for resilience and resistance to sea level rise (West Africa to South Asia; see Figure 4.14).

Sonneratia caseolaris; S. Sundarbans, griffithii; X. granatum; X. IN & BD moluccensis10,19 1 JICA (2005); 2 Giri et al. (2011); 3 Ndour et al. (2011); 4 Saenger & Bellan (1995); 5 UNEP-WCMC (2007); 6 Fatoyinbo & Simard (2013); 7 Spalding et al. (1997); 8 The World Bank (2015); 9 Anthony (2004); 10 Spalding et al. (2010); 11 UNEP-WCMC (2003); 12 Fatoyinbo et al. (2008); 13 Wagner et al. (2004); 14 MNRT (2005); 15 Erfemeijer & Hamerlynck (2005); 16 Semesi (1992); 17 Punwong et al. (2013); 18 Jones et al. (2015); 19 Joshi & Ghose (2014); 20 Barik & Chowdhury (2014); 21 Ray et al. (2011).

4. Study sites

4. Ruvuma Estuary, Tanzania (-10.457 S, 40.445 E; Figure 4.14d; Figure 4.18). Tanzania is host to extensive mangrove areas. Among the largest of these (~7,000 ha) is the Mnazi Bay and Ruvuma River Estuary at the southern border with Mozambique (UNEP-WCMC 2003; Wagner et al. 2004; Spalding et al. 2010). Mangrove species at the site form multiple small zones and communities in different areas of the estuary, with high species mixing in some areas (Wagner et al. 2004). Multiple communities are present within the inland areas, and extractive activities of fish and shellfish harvesting, timber extraction and land clearing occur within the site (Wagner et al. 2004).

Figure 4.18. Google Earth (2016) imagery over the Ruvuma Estuary, Tanzania study site. The mangroves of Tanzania are comparatively well protected (Spalding et al. 2010), and Ruvuma Estuary mangroves and connected coastal ecosystems are protected within the Mnazi Bay – Ruvuma Estuary Marine Park (MNRT 2005). A survey of the area in 2004 has demonstrated that extractive resource use within the area was occurring at sustainable levels; however, species composition is altered in some areas via harvesting of specific species such as Bruguiera gymnorrhiza (Wagner et al. 2004). The Ruvuma Estuary study site considered within this thesis includes the

4. Study sites main estuary and small pockets of mangrove to the north (~12,600 ha; Giri et al. 2011; Table 4.3; Figure 4.18).

5. Rufiji Delta, Tanzania (-7.809 S, 39.343 E; Figure 4.14e; Figure 4.19). The Rufiji Delta contains the largest area of mangroves in Tanzania (~44,000 ha; Giri et al. 2011; Table 4.3); 46% of the country’s mangrove, and the largest mangrove forest in East Africa (UNEP-WCMC 2003; Whitney et al. 2003; Wagner et al. 2004; Erftemeijer & Hamerlynck 2005; Spalding et al. 2010). Species diversity is high, and composition varies across areas of the delta, with a dominance of Heritiera littoralis, Rhizophora mucronata, Avicennia marina, B. gymnorrhiza and Ceriops tagal occurring in different zones (Erftemeijer & Hamerlynck 2005). Much of the delta is protected from strong tidal action by large sand banks, and high freshwater inputs and organic sediment loads enable high productivity (Whitney et al. 2003; Spalding et al. 2010; Lupembe 2013; Pundwong et al. 2013). The delta supports a highly productive crustacean and fishery industry; however, catch productivity was reported to have fallen to 50% in the decade following 1988 (Whitney et al. 2003; Spalding et al. 2010).

Figure 4.19. Google Earth (2016) imagery over the Rufiji Delta, Tanzania study site.

4. Study sites

While mangrove losses have occurred, owing largely to land conversion for rice cultivation and timber extraction, these have been comparatively lower than losses across the African continent (Whitney et al. 2003; Spalding et al. 2010; Pundwong et al. 2013). Much of the area is strictly protected as a forest reserve, and the delta is included within the Rufiji-Mafia-Kilwa Ramsar site (Whitney et al. 2003; Ramsar 2009; Spalding et al. 2010).

6. Mahajamba, Madagascar (-15.485 S, 46.653 E; Figure 4.14f; Figure 4.20). The mangroves in the Bays of Mahajamba and Bombetoka in the Northwest of Madagascar are some of the largest mangrove formations in the country (Mahajamba: ~28,000 ha; Bombetoka: ~ 12,000 ha; Giri et al. 2011; Spalding et al. 2010). As well as these two bays, the Madagascar study site (collectively termed “Mahajamba” in this thesis) also includes mangroves in smaller bays to the east of Bombetoka and fringing mangroves along the coastline (collectively ~65,000 ha; Giri et al. 2011; Table 4.3). R. mucronata, A. marina and Xylocarpus granatum are dominant, with B. gymnorrhiza, C. tagal, H. littoralis, L. racemosa and S. alba also present (Jones et al. 2015). High precipitation (mean annual rainfall of 1,513 mm; Table 4.3) and inflows from multiple freshwater river sources make these among the most dense and productive mangroves in Madagascar (Jones et al. 2015). However, with increased distance inland and decreased tidal flushing, the forests become stunted and dominated by A. marina (Spalding et al. 2010; Jones et al. 2015).

Figure 4.20. Google Earth (2016) imagery over the Mahajamba, Madagascar study site.

4. Study sites

Madagascar’s mangroves are state owned and regionally managed, with self- consumptive extractive activities permitted only by local coastal communities (Giri & Muhlhausen 2008; Jones et al. 2015). However, exploitation of mangrove timber and charcoal resources, and concessions for commercial aquaculture, are the major threats to mangroves in Madagascar; in places reducing areal coverage and degrading mangrove stands (Giri & Muhlhausen 2008; Spalding et al. 2010; Jones et al. 2015). These anthropogenic impacts are yet to produce substantial damage to the mangroves surrounding Mahajamba; however, increased activity has been observed in recent years (Jones et al. 2015).

7. The Sundarbans, India & Bangladesh (21.995 N, 89.065 E; Figure 4.14g; Figure 4.21). The Sundarbans in the Bay of Bengal is among the largest contiguous stretches of mangrove in the world, spanning ~640,000 ha (Giri et al. 2011). The delta is highly active, fed by the Ganges, Brahmaputra, Meghna and Padma rivers (Gopal & Chauhan 2006; Spalding et al. 2010). Topography is low, with average elevation at <3 m and maximum elevation at 10 m (Gopal & Chauhan 2006; UNEP-WCMC 2011), making the area highly susceptible to SLR impacts (Loucks et al. 2010; Rahman et al. 2011; Cornforth et al. 2013).

Figure 4.21. Google Earth (2016) imagery over the Sundarbans, India & Bangladesh study site. The dominant species is Heritiera fomes, but mangrove floral diversity is high at a total of 25 species (Table 4.3; Spalding et al. 2010; Barik & Chowdhury 2014). The area is a biodiversity hotspot for both marine and terrestrial invertebrates and

4. Study sites vertebrates (IUCN Bangladesh 2001; Spalding et al. 2010). The Sundarbans supports over four million people, being an essential source of livelihood for famers, fisher folk, wild game hunters, fuel and timber collectors, and honey collectors, and suffering extensive anthropogenic development (housing, agri- and aquaculture) at its current landward boundary (Gopal & Chauhan 2006; Spalding et al. 2010). Extraction and diversion of freshwater upstream have altered salinity ranges and sediment input regimes, and top-dying disease (affecting H. fomes throughout the forest) has resulted in on-going biomass loss (Gopal & Chauhan 2006; Giri et al. 2007). However, in spite of these manifold threats, areal coverage is reported to have not changed significantly since the 1970s (Giri et al. 2007).

CHAPTER 5: Methodology

5.1 Introduction This thesis uses an inter-disciplinary approach to address the research aim and objectives outlined in Section 1.4, and specific hypotheses outlined in Section 3.6. This chapter provides an overview of the general field- and lab-based methodologies utilised to index mangrove ES and community structure in Philippines-based sites (Section 4.2), and remote sensing data and pre-processing stages employed to assess change in mangrove forest extent and biomass in the global mangrove sites (Section 4.3).

5.2 Field data collection: Philippines Field data collection was conducted from September 2014 to January 2015. Temporary circular plots (radius = 7m) were established according to stratified sampling (Kauffman & Donato 2012) within a 15×15m grid at each site. Mangrove community composition and zonation varies with distance inland and tidal inundation (Tomlinson 1986; Hogarth 2007; Spalding et al. 2010). Heterogeneous sites with distinct vegetation zones (e.g. species composition: KII [basin mangrove]; see Section 4.2.1; Table 4.2) were first stratified into different vegetation zones (Kauffman & Donato 2012) using existing GIS shapefiles of community composition (Primavera et al. 2004; Thompson et al. 2014), to index topographic variation and non-linear zonation in these mangrove types. In order to index the strong heterogeneity in community composition, diversity and species dominance at the KII site, six sampling plots were selected within each of five large vegetation zones (N = 30 plots; Section 4.2.1; Table 4.2). In order to select plot locations, a 15×15m grid covering the extent of each distinct vegetation zone was created using the ‘Vector Grid’ tool in QGIS version 2.14.0 (QGIS Development Team 2016). The centre coordinates of each grid cell were then extracted using the ‘Polygon centroids’ tool, and six plot centres were then selected via random sampling from a uniform distribution of all plot centres in R version 3.2.5 (R Development Core Team 2016). Once the first plot centre was randomly drawn, the second plot centre drawn would not be selected unless it was >1 grid cell distance away from a the previously selected plot (e.g. no finally selected plot centre could be within a 30m distance of any other plot centre at a given study site). This enabled a randomised but representative coverage of mangrove community structure within each vegetation zone. See Figure 5.1 for a visualisation of the stratified sampling method.

5. Methodology

Figure 5.1. Stratified grid sampling and plot locations at the KII ecopark study site (see Section 4.2.1; Table 4.2). Stratified sampling was conducted across the entire site, excluding: A Ceriops tagal timber plantation; several Nypa fruticans plantations and invaded areas; aquaculture ponds; a small (<0.5ha) area in the eastern side of the site dominated by Bruguiera sexangula, and; an extension of the mixed community vegetation zone that extends to the south of the site (challenging access). The blue outlined area denotes the Avicennia marina-dominated vegetation zone, the yellow outlined area denotes the Camptostemon philippinense-dominated vegetation zone, the red outlined area denotes the Ceriops decandra-dominated vegetation zone, the orange outline area denotes the mixed community vegetation zone, and the green outlined area denotes the A. rumphiana and A. officinalis-dominated vegetation zone. The black grids donate the 15×15m sampling grid employed to select plot locations within each vegetation zone. White circles donate randomly selected plot locations within each of the five vegetation zones at the KII ecopark site (N = 6 per vegetation zone). See text for further details. Imagery: Google Earth (2016). For all other study sites, where distinct vegetation zones were absent (Section 4.2.1), sites were instead stratified into distance bands from the site shoreline. This was to index both small changes to species composition and relative dominance with tidal inundation, and potential salinity controls on mangrove biomass with distance inland (Tomlinson 1986; Hogarth 2007; Spalding et al. 2010). Distance bands from the site shoreline were created at 100m intervals (e.g. 0-100m, 100- 200m, 200-300m distance classes), and 15×15m sampling grids and centre coordinates created within these bands as above. For all of these other smaller (see Table 4.2) and less heterogeneous study sites, eight sampling plots were created per site. Here, the number of plots selected within each distance class was dependent

5. Methodology on the number of distance classes present within the study site; for example, four plots in each of the 0-100m and 100-200m distance classes if only these two were present. For sites with an odd number of distance classes (e.g. three), plot centres were randomised across all 15×15m sampling grids for all distance classes and a maximum number (e.g. three) of plot centres was permitted within each distance class.

For each plot, the tree species, health (presence or absence of leaves), 퐷퐵퐻 (diameter at breast height: 1.37m height, or above the highest prop root for

Rhizophora spp.; Kauffman & Donato 2012), diameter at the base of the trunk (퐷퐵), tree height (clinometer-derived), and maximum canopy width (two perpendicular measures [퐶1 and 퐶2]; Kauffman & Donato 2012) were measured. Small trees (taller than 1.37m and ≤1.5cm DBH) were measured within a sub-plot of a 3m radius from the plot centre, while all larger trees (>1.5cm DBH) were measured throughout the plot. The number and species of each small tree was also recorded for the remainder of the 7m radius plot. The definition of ‘small trees’ here is smaller than that traditionally considered in mangrove biomass studies (≤5cm DBH; Kauffman & Donato 2012). This was to avoid under-calculation of vegetation biomass in plots with a high abundance of such small trees (e.g. in younger rehabilitated sites; see Kauffman & Donato 2012).

Downed wood transects (N = 4; 10m length from the plot centre) were conducted at each plot under the non-destructive line intersect technique (van Wagner 1968) outlined in Kauffman & Donato (2012). At each plot, four equally spaced 10m line transects were set up from the plot centre to the plot ‘corners’. Along each transect, the point-of-intersection diameters of all pieces of downed mangrove wood (broken off and fallen mangrove wood: e.g. excluding non-mangrove driftwood, nipa palm or pneumatophores) intersecting the transect line were recorded (Kauffman & Donato 2012). Each piece of downed wood recorded was attributed a size class (<0.67cm, 0.67-2.54cm, 2.54-7.6cm, >7.6cm diameter) and decay status (sound [knife bounces off or slightly sinks in when struck] or rotten [machete sinks in deeply and wood is crumbly with significant loss when struck]; Kauffman & Donato 2012).

Sediment cores were taken in a triangular configuration within the 3m subplot (N = 3 per plot) with an Eijkelkamp gouge auger (30 mm core diameter, 50 cm sampling length). Within each core, six 5cm samples were taken at specified depths and aggregated to represent the 0-50cm (N = 3 depth subsamples * 3 cores = 9)

5. Methodology and 50-150cm (N = 3 depth subsamples * 3 cores = 9) sediment horizons (5-15cm, 15-30cm and 30-50cm, and 50-100cm, 100-150cm and 150cm respectively; see Kauffman & Donato 2012). Due to laboratory time constraints, and as the aim of analyses in Chapter 6 was to compare carbon stocks between plots and sites rather than to obtain total ecosystem carbon volume, sediments were sampled for organic carbon (OC) content to only 150cm depth (see also Thompson et al. 2014) rather than the entire sediment profile (see Donato et al. 2011; Kauffman & Donato 2012). Where total ecosystem (sediment profile) carbon stock estimates were required in Chapter 7, sediment profile depth estimates were also measured at each plot. Mean plot sediment profile depth was measured (N = 3 per plot) in a triangular configuration within 3.5m of the plot centre, avoiding areas close to large trees (aerial roots), by inserting an iron rod (diameter = 1.5cm) vertically into the sediment by hand until it could no longer be pushed (see also Gress et al. 2017). The 50-150cm depth sediment core samples were later used to extrapolate sediment carbon to the entire mean sediment profilie depth for each plot for analyses in Chapter 7 (see Section 5.3.2), as OC does not vary greatly below 30cm (Thompson et al. 2014).

Four horizontal position plot photographs were taken at each plot within each site, for use in canopy closure estimation (see Section 5.3.5). Photographs were taken from the plot centres facing outwards to the plot ‘corners’ in equally spaced positions at right angles from each other; i.e. shoreline-facing, landward-facing, and the remaining two plot ‘corners’.

5.3 Laboratory analyses: ecosystem services quantification Following field data collection, laboratory analyses were conducted to calculate ES delivery via standardised mangrove methodologies (Kauffman & Donato 2012).

5.3.1 Vegetation carbon stocks Mangrove above- and belowground vegetation biomass was calculated via mangrove-specific allometric equations (Komiyama et al. 2005; Kauffman & Donato 2012). For large trees (>1.5cm DBH), biomass and carbon calculations were performed for each tree in the 7m plot. For small trees (≤1.5cm DBH), biomass and carbon calculations were performed for all trees within the 3m radius sub-plot; these calculations were then averaged for all small trees in the sub-plot and average sub-plot small tree biomass and carbon estimates attributed to all small trees within the remainder of the 7m radius plot. For single-stemmed trees, biomass was

5. Methodology estimated via the general mangrove allometric equations derived by Komiyama et al. (2005) (Equations 1 & 2). Species-specific mangrove allometric equations exist for some species (see Kauffman & Donato 2012); however, in the absence of specific equations for all species in this study, the general equations were applied (Komiyama et al. 2005) in order to ensure continuity in biomass estimation across plots and sites. Individual tree above- (퐵퐴퐺; kg) and belowground biomass (퐵푇퐵; kg) are:

ퟐ.ퟒퟔ 1 푩푨푮 = ퟎ. ퟐퟓퟏ ∗ 풑 ∗ 푫

ퟎ.ퟖퟗퟗ ퟐ.ퟐퟐ 2 푩푻푩 = ퟎ. ퟏퟗퟗ ∗ 풑 ∗ 푫 where 푝 is species-specific wood density (g cm-3) and 퐷 is the tree 퐷퐵퐻 (cm; Komiyama et al. 2005). The observed 퐷퐵퐻 limit of these equations is 49cm (Komiyama et al. 2005), beyond which derived individual mangrove biomass is substantially overestimated due to the exponential nature of allometric equations (see Figure 5.2; Kauffman & Donato 2012; Chave et al. 2005).

Figure 5.2. Overestimation of Sonneratia alba individual biomass from extrapolation beyond general mangrove allometric equation limits (Komiyama et al. 2005; 2008; Chave et al. 2005), versus the species-specific, wide 퐷퐵퐻 range-derived allometric equation of Kauffman & Cole (2010). Source: Kauffman & Donato (2012). Some mangrove trees in this study (individuals at Brgy.s Ermita, Pedada, Buntod and KII ecopark) exceeded the observation limits of Komiyama et al. (2005)’s general allometric equation. In the absence of guidelines to accommodate such a situation (see Kauffman & Donato 2012; Thompson et al. 2014), these individuals were here treated as multiple trees. That is, their 퐷퐵퐻 was divided by 49 to create

5. Methodology multiple (maximum three as maximum observed 퐷퐵퐻 = 111.41cm) ‘individuals’ (e.g. 퐷퐵퐻1 = 49cm, 퐷퐵퐻2 = 12cm) and biomass was calculated for each of these. While this adopted method may still overestimate biomass for these large trees, the resulting overestimates are substantially smaller than those caused by calculation via their actual 퐷퐵퐻 (Figures 5.2 & Figure 5.3; Kauffman & Donato 2012). Estimates of 푝 were taken from the Global Wood Density Database (Chave et al. 2009; Zanne et al. 2009). Species-specific averages of 푝 were taken across all estimates in the database from the Southeast Asian region, in the absence of Philippines-specific estimates for all species in this study.

Figure 5.3. Above- (퐵퐴퐺; left) and belowground (퐵푇퐵; right) biomass estimates (kg) for study individuals with 퐷퐵퐻 (cm) greater than the general mangrove allometric equation limit (49cm; Komiyama et al. 2005). Individuals are of species Avicennia marina, A. officinalis, A. rumphiana, Camptostemon philippinense and Sonneratia alba from sites at Brgy.s Ermita, Pedada, Buntod and KII ecopark (N = 30). Grey circles depict individual tree biomass estimates from individuals’ true 퐷퐵퐻 (overestimates), and black circles depict estimates from the methodology employed within this thesis (see text for further details).

For multi-stemmed trees, aboveground biomass was estimated via the method of Fu

& Wu (2011), in which 퐵퐴퐺 (kg) is calculated as:

ퟐ 3 푩푨푮 = 푪푫 ∗ 푯 where 퐶퐷 is the average maximum canopy diameter (퐶1 and 퐶2; m) and 퐻 is tree height (m). This method has been found to be highly applicable to both multi- stemmed species (e.g. A. corniculatum) and those with variable growth form (e.g. A. marina; Fu & Wu 2011). Belowground biomass of multi-stemmed trees was calculated via the general equation of Komiyama et al. (2005) (Equation 2) as above, using an artificial 퐷퐵퐻 calculated from the 퐷퐵퐻 of the largest stem and the number of stems per individual. This method may have produced slight overestimates of

5. Methodology belowground biomass for multi-stemmed trees; however, multi-stemmed trees were fairly evenly distributed across plots.

No general allometric equation for biomass estimation of nipa palm (N. fruticans) currently exists; however, aboveground biomass can be estimated from the product of the mean biomass per leaf (frond) and the number of fronds (Kauffman & Donato 2012). The allometric equations for aboveground nipa palm biomass developed by Matsui et al. (2014) in Thailand based on frond 퐷퐵퐻 and length and the number of fronds for each individual (Equations 4-6) were used. First, frond length (퐿; cm) is calculated as:

푳 = ퟏퟏퟔ. ퟒퟏ ∗ 푫푩푯 + ퟑퟐ. ퟒퟐퟒ 4

Next, frond biomass (퐷푊; kg) is calculated based on this estimate of 퐿 as:

퐥퐨퐠 푫푾 = ퟎ. ퟎퟎퟏ ∗ (ퟎ. ퟖퟓ ∗ 퐥퐨퐠 푳 + ퟏ. ퟓퟒ) 5

Lastly, aboveground biomass of the individual nipa palm (퐵푁퐼푃퐴; kg) is calculated as:

6 푩푵푰푷푨 = 푫푾 ∗ 푵푭푹 where 푁퐹푅 is the total number of fronds on the individual nipa palm.

Carbon content of the above- and belowground vegetation compartments was calculated by multiplying the biomass of each compartment by its average carbon concentration: 0.464 (Donato et al. 2011), 0.39, 0.50 and 0.47 (Kauffman & Donato 2012) for live tree aboveground carbon concentration, belowground carbon concentration, dead tree above- and belowground carbon concentration, and nipa palm carbon concentration respectively. Abiotic controls (temperature, precipitation, salinity, sediment composition) on biomass production may have introduced error in nipa palm biomass and carbon stock estimates produced here from allometric equations developed in Thailand. However, nipa palm abundance was low across all sites, and relative carbon stocks of individual nipa palms contribute very little to overall carbon stocks (e.g. relative to mangrove trees; Kauffman & Donato 2012). Thus, allometric uncertainty in nipa palm carbon stock estimates is very unlikely to have significantly impacted on the carbon stock estimates derived in this study.

The carbon pool in the downed wood component was calculated from the four transects conducted from each plot centre via the method outlined in Kauffman &

5. Methodology

Donato (2012). Estimates of p for each downed wood size class (see Section 5.2) were taken from Kauffman & Cole (2010). The quadratic mean diameter for each size class was calculated for each plot and the volume of downed wood for each class calculated via the equations outlined in Kauffman & Donato (2012). Downed wood biomass (kg) was then calculated as the sum of the product of volume and size class- specific p for each downed wood size class (Kauffman & Donato 2012). Carbon content of the downed wood component for each plot was then calculated as the product of its downed wood biomass and an estimate of 0.50 carbon concentration for dead wood in tropical forests, as advised by Kauffman & Donato (2012).

5.3.2 Sediment carbon stocks Sediment samples were analysed at the soil laboratory of the Bureau of Soils, Department of Agriculture, Cebu. Bulk density (BD; g cm-3) of each sample was determined against the sample volume (318 cm3) as:

푫푴 7 푩푫 = 푺푽 where DM is the oven-dried mass of the sample (g) and SV is the sediment volume (cm3). Air-dried subsamples were homogenised and sieved through a 2mm mesh, with all material passing through representing the sediment matrix (Donato et al. 2011; Thompson et al. 2014). OC content (%) was determined gravimetrically for a subsample of each subsample (0.25g) via the dry combustion method (Schumacher 2002). Sediment profile carbon stocks were calculated by summing the sediment carbon stocks for sampled depth intervals (Kauffman & Donato 2012). Sediment carbon (SC; Mg ha-1) is calculated for each depth interval as:

8 푺푪풊 = 푩푫 ∗ 푫 ∗ 푶푪 where BD is the sample bulk density (g cm-3), D is the sediment depth interval (cm) and OC if the sample organic carbon content (%; expressed as a whole number; Kauffman & Donato 2012). For Chapter 6 analyses, sediment carbon stocks to 150cm depth were considered, in order to enable consistency between analysis of potential mangrove flora diversity drivers of sediment carbon stocks. Here, sediment profile stock was calculated by summing the sediment carbon stock for each of the two sampled depth intervals (0.-50cm and 50-150cm), calculated from their respective sample OC estimates. For Chapter 7 analyses, in order to account for total plot-level stocks, sediment profile carbon stocks were calculated to the entire sediment profile depth (see Section 5.2). Here, as OC does not vary greatly

5. Methodology below 30cm (Thompson et al. 2014), sediment profile carbon stock was calculated by summing the sediment carbon stock of two depth intervals: (1) 0-50cm, calculated from its respective sample OC estimate, and (2) 50cm-total sediment profile depth, calculated from the 50-150cm depth interval sample OC estimate.

5.3.3 Harvestable timber/charcoal biomass Harvestable timber/charcoal biomass was calculated for each plot as the standing aboveground biomass of trees (i.e. not including nipa palm vegetation), including the large prop roots of Rhizophora spp., minus the canopy biomass (excepting for standing dead trees). Canopy biomass was set at a constant of 2.5% of total aboveground biomass of each tree, as recommended by Kauffman & Donato (2012). The general allometric equation for aboveground tree biomass of Komiyama et al. (2005) (Equation 1) does not include the biomass of prop roots for Rhizophora spp. (this is included in the belowground biomass component; Equation 2). Biomass of

Rhizophora spp. prop roots (BPROP; kg) was calculated and added to total plot harvestable timber/charcoal biomass using the allometric equation developed by Clough & Scott (1989):

9 푩푷푹푶푷 = ퟑ. ퟏퟑퟓퟑ ∗ 푫푩푯 − ퟐ. ퟏퟔퟔퟑ where 퐷퐵퐻 is the diameter of the stem of the individual above the highest prop root (cm; Clough & Scott 1989; Kauffman & Donato 2012).

5.3.4 Harvestable kindling Harvestable kindling abundance was estimated from the four downed wood transects conducted at each plot. Downed mangrove wood regularly collected for use as kindling or firewood in the Philippines is that of sound condition (see Section 5.2), and diameter ≥1.5cm (J.H. Primavera pers. comm.). For each plot, the number of such downed wood pieces intersecting the four 10m transects (Section 5.2) was counted and summed to give an index of potential harvestable kindling/firewood abundance.

5.3.5 Storm surge attenuation potential A major control (in addition to surface topography, and wind and wave height and velocity) on hydrodynamics and storm surge reduction in coastal forests is the frictional drag force created by the vegetation surface (Shuto et al. 1987; Wolanski et al. 1992; Mazda et al. 1997; Zhang et al. 2012). In mangroves, vegetation structure and complexity vary in the vertical direction, with variable and complex aerial

5. Methodology rooting systems (e.g. prop roots, large pneumatophores and knee roots) contributing to drag force in the lower section, followed by stems and larger lateral branches within the canopy in the vertical direction (Mazda et al. 1997; Mazda 2009). Drag force from vegetation contributing to reducing larger storm surges (e.g. above mean high tide level) is the sum contribution of all submerged vegetation; i.e. the contribution of aboveground roots, stems and branches (Mazda 2009), while large, tall canopies contribute to reducing wind velocity (Wolanski 2006). The controlling parameter of frictional drag force, and thus wave and surge reduction, in mangrove forests has been found to be the effective vegetation length scale (퐿푒; m; Mazda et al. 1997), defined as:

푽 − 푽푴 10 푳 = 풆 푨 where 푉 is the total volume of a sampling area (i.e. the volume of a wave of a given

3 3 height in a sampling area; m ; Figure 5.4), and 푉푀 is the volume of aboveground (m ) and 퐴 is the surface area (m2) of submerged mangrove vegetation (roots, stems and branches) within the same given sampling area.

Figure 5.4. Theoretical mangrove sampling area and volume (푉) for effective vegetation length (퐿푒) calculation. Source: Mazda et al. (1997).

푉, 푉푀 and 퐴 vary for a given area with tidal elevation (i.e. wave height) and strongly according to mangrove species (due to differences in growth form, plant height [and thus 퐷퐵퐻 and biomass production] and aerial rooting systems; Mazda et al. 1997).

Values of 푉푀 and 퐴 can be calculated using traditional field-based tree structural measurements (Mazda et al. 1997) or via remotely-sensed field measures such as Light Imaging, Detection and Ranging (LiDAR)-based Terrestrial Laser Scanning

5. Methodology

(e.g. Feliciano et al. 2014). Here, field-derived structural measurements of trunk base diameter, tree height, canopy diameter (퐶1 and 퐶2; see Section 5.2), and prop root diameter for Rhizophora spp. were used to simplify tree structures for the calculation of 푉푀 and 퐴 at set wave heights for each plot. Smaller aboveground aerial root structures (e.g. pneumatophores, knee or plank roots) were not considered due to time constraints on field data collection. However, as the aim of these analyses was to quantify vegetation 퐿푒 under large storm surge waves, the contribution of such comparatively small structures is unlikely to have been substantial.

Figure 5.5. Sketch of individual mangrove tree simplification for storm surge attenuation potential (퐿푒; Mazda et al. 1997) calculation. 퐻 = field-measured mangrove height (cm); 퐷퐵 = field-measured trunk base diameter (cm); 퐷푇 = hypothetical frustrum top diameter (set at 1% of 퐷퐵; cm); 퐶1 = canopy width measure 1 (cm); 퐶2 = canopy width measure 2 (퐶 + 퐶 ) (cm); 퐶퐵 = canopy base height (퐻 − 1 2 ; cm); 퐷 = frustrum top diameter at 퐶퐵 (cm). 2 푇2 See text for further details. Stems were simplified and modelled here based on trunk base measurements (Figure 5.5). For all trees (both single- and multi-stemmed), trunks were simplified to a single conical frustum-shaped stem to the field-measured height (퐻; cm) of the given individual (Figure 5.5). Frustum base diameter was set to the field-measured trunk base diameter (퐷퐵; cm; thus taking into account variation in trunk diameter

5. Methodology for more buttress-like root-types [e.g. C. decandra, X. granatum]; see Figure 3.3) and the hypothetical top diameter (퐷푇; cm) at height 퐻 was set to 1% of 퐷퐵 (Figure 5.5).

All dead trees were considered only as the simplified frustum portion of Figure 5.5,

3 2 and their volume (푉푇푟푢푛푘; m ) and area (퐴푇푟푢푛푘; m ; lateral surface [e.g. not including the top and base area]) were calculated using Equations 17 and 18. Each live tree was considered in a simplified form in which lateral branching from the simulated stem frustum occurred from the beginning of its canopy.

Canopy shape was simulated for each tree in an ellipsoid form (Figure 5.5), and canopy base height (퐶퐵; cm) was determined as:

푪ퟏ + 푪ퟐ 11 푪푩 = 푯 − ퟐ

With 퐶퐵 established, frustum 퐷푇 was recalculated (퐷푇2; Figure 5.5) via the application of trigonometric equations. First, the length of the original 퐷퐵 to 퐷푇 frustum side (푠; cm) was calculated as:

ퟐ ퟐ 12 풔 = √(풓푩 − 풓푻) + 푯 where 퐻 is the field-measured mangrove tree height (cm), 푟퐵 is the field-measured radius at the trunk base (퐷퐵/2; cm) and 푟푇 is the simulated radius at the top of the frustum (퐷푇/2; cm).

Next, the acute angle (°) at the base frustum edge was calculated as:

13 −ퟏ 푯 푩풂풏품 = 퐭퐚퐧 풓푩 − 풓푻

Next, the length of the frustum side from 퐶퐵 to 퐷푇 (푠2; cm) was calculated, in order to later establish 퐷푇2, as:

푯 − 푪푩 14 풔ퟐ = 퐬퐢퐧 푩풂풏품

Next, 퐷푇2 (cm) was calculated as:

15 푫푻ퟐ = ퟐ ∗ ([퐜퐨퐬 푩풂풏품 ∗ 풔ퟐ] + 풓푻)

Finally, the length of the frustum side from 퐷퐵 to 퐶퐵 (푠푇2) was calculated as:

푪푩 16 풔푻ퟐ = ퟐ ∗ 퐬퐢퐧 푩풂풏품

5. Methodology

3 2 The volume (푉푇푟푢푛푘; m ) and area (퐴푇푟푢푛푘; m ; lateral surface [e.g. not including the top and base area]) of the frustum to 퐶퐵 were then calculated as:

ퟏ 푽 = ∗ (훑 ∗ 푪푩 ) ∗ 풓 ퟐ + 풓 ퟐ + (풓 ∗ 풓 ) 17 푻풓풖풏풌 ퟑ 푩 푻ퟐ 푩 푻ퟐ

18 푨푻풓풖풏풌 = 훑 ∗ (풓푩 ∗ 풓푻ퟐ) ∗ 풔푻ퟐ

푤here 퐶퐵 is the canopy base height (m), 푟퐵is the field-measured radius at the trunk base (퐷퐵/2; m) and 푟푇2 is the simulated radius at the top of the frustum at 퐶퐵 (퐷푇/2; m), and 푠푇2 is the length of the side from 퐷퐵 to 퐶퐵 (m).

Mangrove trees can take sprawling (and multi-stemmed) forms with maximum canopy area (퐶1 and 퐶2) being large and thus much of the simulated canopy ellipsoid representing an absence of mangrove canopy. Thus, here the assumption was made that 5% of the simulated ellipsoid canopy would be filled with tree biomass

3 (branches and leaves). Accordingly, ellipsoid canopy volume (푉퐶푎푛; m ) and surface 2 area (퐴퐶푎푛; m ) were first calculated for the simulated canopy based on the formulae for determining volume and surface area of an ellipsoid:

ퟒ 19 푽 = ∗ 훑 ∗ 풓 ∗ 풓 ∗ 풓 푪풂풏 ퟑ ퟏ ퟐ 풎풆풂풏 ퟏ 20 (풓 ∗ 풓 )ퟏ.ퟔ + (풓 ∗ 풓 )ퟏ.ퟔ + (풓 ∗ 풓 )ퟏ.ퟔ ퟏ.ퟔ 푨 = ퟒ ∗ 흅 ∗ ( ퟏ ퟐ ퟏ 풎풆풂풏 ퟐ 풎풆풂풏 ) 푪풂풏 ퟑ

푤here 푟1 is the field-measured radius at 퐶1 (퐶1/2; m), 푟2 is the field-measured radius at 퐶2 (퐶2/2; m), and 푟푚푒푎푛 is the simulated radius of the vertical canopy width

(푚푒푎푛(퐶1, 퐶2)/2; m). 푉퐶푎푛 was then reduced by 95%. A 5% threshold was chosen in order to create a conservative estimation of canopy 푉퐶푎푛. An investigation into the potential impact of this choice of proportional canopy volume filling with branches and leaves (푉퐶푎푛) was conducted, estimating the impact of a choice of 1%, 5%, 10%,

15% and 20% thresholds on plot-specific 퐿푒 (Mazda et al. 1997). The choice of threshold did not alter plot-specific 퐿푒 differentially for plots with varying median and maximum height and 퐷퐵퐻 (Figure 5.6). Thus, the choice of propotional canopy volume filling (푉퐶푎푛) threshold (here 5%) did not impact on the findings of this thesis, excepting for higher variation in 퐿푒 estimation in plots with a high proportion of canopies below simulated storm surge height (see below; Figure 5.6; variation in plot-specific 퐿푒).

5. Methodology

In reality, the surface area of mangrove vegetation within the simulated canopy ellipsoid is much greater than the entire ellipsoid surface area (퐴퐶푎푛). However, much of this surface area is comprised of leaves and small branches, which would not produce resistance and drag force reduction to fast-flowing storm surges.

Accordingly, 퐴퐶푎푛 was not increased to reflect the high surface area of this vegetation within the canopy, so as to provide a highly conservative estimate of large branch surface area.

Figure 5.6. Plots exploring the potential impact of the choice of canopy volume (푉퐶푎푛) filling (branches and leaves) threshold on plot-specific storm surge attenuation potential (퐿푒; Mazda et al. 1997). Calculated plot-specific 퐿푒 under assumptions of 1% (yellow circles), 5% (black circles), 10% (green circles), 15% (blue circles) and 20% (red circles) canopy volume filling according to variation in plot-specific median tree height (m; top left), maximum tree height (m; top right), median 퐷퐵퐻 (cm; bottom left), and maximum 퐷퐵퐻 (cm; bottom right) across all natural mangrove plots employed in analyses for Chapter 6. See text for further details. For Rhizophora spp., which possess large prop root structures contributing significantly to wave reduction (Mazda et al. 1997; 2006), prop roots were simulated for each tree based on simplification to cylindrical shapes (Mazda et al. 1997) and the assumption of forking from the main trunk at a 50˚ angle (Figure 5.7). For each plot containing Rhizophora spp. individuals, a representative individual

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5. Methodology was selected as the closest individual to the plot centre. The proportion of the height of the individual at which prop root structures were located was then determined

(i.e. 1/5, 1/4, 1/3 of total tree height) and highest prop root height (퐻푃푅), as well as the number of prop roots positioned at four intervals: at maximum prop root height, and within 75%, 50% and 25% of the 퐻푃푅. The diameter of each prop root (퐷푝푟표푝) was set to be 25% of the individual’s 퐷퐵퐻 (diameter above the highest prop root for Rhizophora spp.; Figure 5.7). The length of each prop root (퐿) was calculated based on trigonometric equations based on the angle at forking (50˚) and its height interval: 75% of the 퐻푃푅 to the 퐻푃푅 (푃푚푎푥); 50%-75% of the 퐻푃푅 (푃75); 25%-50% of the 퐻푃푅 (푃50), and; 0%-25% of the 퐻푃푅 (푃25) (Figure 5.7). All prop roots within each height interval were attributed the highest height within that interval: 퐻푃푅, and

75%, 50% and 25% 퐻푃푅respectively.

Figure 5.7. Example sketch of Rhizophora spp. prop root simplification and volume and surface area calculation. 퐷푝푟표푝 = diameter of all prop roots for a given Rhizophora spp. tree (set at 25% of the diameter at breast height [DBH; above the highest prop root for

Rhizophora spp.]); 퐿 = simulated prop root length; prop root height intervals: 푃푚푎푥 = 75% of the height of the highest prop root (퐻푃푅) to the 퐻푃푅; 푃75 = 50%-75% of the 퐻푃푅; 푃50 = 25%-50% of the 퐻푃푅, and; 푃25 = 0%-25% of the 퐻푃푅. See text for further details. Thus, the length of each prop root (퐿; cm) was calculated as:

(푯푷푹 ∗ 푷풊) 푳 = 21 풊 퐜퐨퐬 ퟓퟎ°

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5. Methodology where 퐻푃푅 is the height of the highest prop root (cm), 푃푖 is the proportion of the height interval of the prop root (푖: 푃푚푎푥 [1], 푃75 [0.75], 푃50 [0.50] or 푃25 [0.25]), and 3 the angle at forking is set at 50°. Prop root volume (푉푝푟표푝; m ) and lateral surface

2 area (퐴푝푟표푝; m ) were then calculated according to equations for a cylinder, as:

ퟐ 22 푽푷풓풐풑 = 흅 풓 푳

23 푨푷풓풐풑 = ퟐ 흅 풓 푳 where 푟 is the radius of all prop roots for a given Rhizophora spp. tree (25% of the tree’s DBH; m), and 퐿 = is the length of the given prop root (m), as calculated in Equation 21.

Potential storm surge height was simulated in this study to represent surges experienced by Philippine mangroves in tropical typhoons. A potential storm surge travelling through mangrove plots was used here based on the mean storm surge simulated for the provinces of Aklan, Capiz and Iloilo during typhoon Yolanda (international name Haiyan) in 2013 (Aklan River, Aklan: 2.5m; Libas, Capiz: 2.4m; Banate, Iloilo: 3.9m; Miagao, Iloilo: 2.0m; Lagmay et al. 2015). The potential storm surge height was thus set at the mean of these storm surge heights: 2.7m.

3 푉 for each plot was 415.63 m , 푉푀= 푉푇푟푢푛푘+ 푉퐶푎푛 + 푉푃푟표푝 for all vegetation below

2.7 m, and 퐴 = 퐴푇푟푢푛푘+ 퐴퐶푎푛 + 퐴푃푟표푝 for all vegetation below 2.7m. All prop roots across all study plots were <2.7m in height (퐻푃푅) and so no further calculations were required. For all trunks >2.7m in height (퐶퐵), frustrum parameters, 푉푇푟푢푛푘 and 퐴푇푟푢푛푘 were recalculated for 퐶퐵 = 2.7m using Equations 12-20. For all live trees with 퐶퐵 < 2.7m, 푉퐶푎푛 and 퐴퐶푎푛 were calculated via proportions. That is, the proportion of canopy height (퐶퐻 [푚푒푎푛(퐶1, 퐶2)]; m) was calculated and used to 3 2 recalculate 푉퐶푎푛 and 퐴퐶푎푛 (푉퐶푎푛2.7 [m ] and 퐴퐶푎푛2.7 [m ]). The proportion of the canopy height within the sampling area (to 2.7m; 퐶푝푟표푝) was calculated as:

ퟐ. ퟕ − 푪푩 푪 = 24 풑풓풐풑 푪푯

25 푽푪풂풏ퟐ.ퟕ = 푽푪풂풏 ∗ 푪풑풓풐풑

26 푨푪풂풏ퟐ.ퟕ = 푨푪풂풏 ∗ 푪풑풓풐풑 where 퐶퐵 is the canopy base height (m), 퐶퐻 is the simulated canopy height

3 (푚푒푎푛(퐶1, 퐶2); m), 푉퐶푎푛 is the simulated total canopy volume (m ), and 퐴퐶푎푛 is the simulated total canopy surface area (m2).

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5.3.6 Coastal protection potential (regular wind waves) For Chapter 7 analyses, the model of regular wind wave attenuation of Bao (2011) was employed to assess the coastal protection potential of each study site (Section 4.2). This model estimates coastal protection potential as the width of mangrove required to attenuate a regular wave of three metres height to a ‘safe height’ of 0.3m behind the forest (Bao 2011). A Forest Structure Index (FSI), calculated from field- derived mangrove structural parameters, is calculated and characterises different forests into protection level categories (I–V). For each site, mean plot-level stem density and mangrove tree height were calculated from the plot-specific field data.

In addition, calculation of FSI and greenbelt width requirements necessitates canopy closure estimates (Bao 2011). Low, shrubby, heterogeneous canopies in younger rehabilitated sites restricted traditional methods of rapid canopy closure estimation (e.g. spherical densitometer or vertical position digital photography; Korhonen et al. 2006). To avoid error from ocular estimates (Korhonen et al. 2006), plot-level canopy closure (%) was estimated from horizontal position digital photographs (N = 4 per plot; at the plot centre facing toward the plot ‘corners’; see Section 5.2). Plot photographs were separated into red, green and blue light wavelength channel images in GNU Image Manipulation Program v.2.8.16 (The GIMP Team 2016). Photograph-specific canopy closure was then estimated as the percentage of non-sky, water or sediment image pixels, classified from the ratio of green to red light (see Figure 5.5). This was a step-wise process, where first the sediment surface was first identified within each plot photograph by first image thresholding for all dark pixels (sediment or mangrove bark), as:

27 푺풆풅풃풂풓풌 = 푩푳푼푬 − 푹푬푫 ≥ −ퟐퟓ where 퐵퐿푈퐸 and 푅퐸퐷 are visible blue and red light captured in the plot photographs respectively. The sediment surface within the image was identified as the first row of pixels from the top row working downwards in which the percentage of 푆푒푑푏푎푟푘 pixels was >50%. For all photographs in plots for which the plot centre was not located in a large canopy gap, all rows of pixels from the identified sediment surface downwards were then removed from the image for further classification. Within each cropped image, mangrove leaves and bark were then identified. Mangrove bark (and remaining sediment) was identified in the same manner as above, as:

푩풂풓풌 = 푩푳푼푬 − 푹푬푫 ≥ −ퟐퟓ 28

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Mangrove leaves and small (green) stems were classified using a threshold level on the green and red reflectance in the photographs, as:

29 푳풆풂풇풔풕풆풎 = 푮푹푬푬푵 − 푹푬푫 ≤ ퟏퟎ where 퐺푅퐸퐸푁 and 푅퐸퐷 are the visible green and red light reflectance in the plot photographs respectively. At each step, image subsets were visually inspected and and verified to assess chosen thresholds. The percentage of pixels within each cropped image classified as either mangrove leaves and small stems or mangrove bark was then calculated to index canopy closure (Figure 5.8).

Figure 5.8. Example of image classification results for canopy closure (퐶퐶; %) estimation at the rehabilitated area of the Bakhawan ecopark study site (Section 4.2.2; Table 4.2). Green pixels = pixels classified as mangrove leaves and small stems; yellow pixels = pixels classified as mangrove bark (and remaining sediment); white pixels = pixels removed (sky pixels). Units are numbers of pixels. Mean plot-level 퐶퐶 for this plot was 90.52%.

For all photographs in plots for which the plot centre was located in a large canopy gap, canopy closure was estimated as:

푪푪 = ퟏퟎퟎ − 푺푷푨푪푬 30

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5. Methodology where 퐶퐶 is canopy closure (%), and 푆푃퐴퐶퐸 is the percentage of sky pixels (e.g. those pixels not classified as either mangrove bark or mangrove leaves and small stems in the upper portion of image) and pixels identified as being part of the sediment surface as identified above within the image (%). This method enabled indexing of both canopy penetration and large canopy gaps (where present) across highly heterogeneous plots and sites. For each plot, canopy closure estimates from each plot photograph (N = 4) were averaged to calculate plot-level canopy closure (%). These plot-level estimates were then averaged across all plots in each site (N = 8) to estimate site-specific average canopy closure (%).

Using all site-specific mean plot level data, FSI (Bao 2011) was then calculated as:

푭푺푰 = −ퟎ. ퟎퟒퟖ + ퟎ. ퟎퟎퟏퟔ 푯 + ퟎ. ퟎퟎퟏퟕퟖ 퐥퐨퐠(푺푫) 31 + ퟎ. ퟎퟎퟕퟕ 퐥퐨퐠(푪푪) where 퐻 is mean mangrove height (m), 푆퐷 is mean stem density (N ha-1), and 퐶퐶 is mean canopy closure (%). The required greenbelt width (퐵푊; m) required at each site was calculated as:

32 퐥퐨퐠(푯풔풂풇풆) − 퐥퐨퐠(ퟎ. ퟗퟖퟗퟗ 푯ퟎ + ퟎ. ퟑퟓퟐퟔ) 푩 = 푾 ퟎ. ퟎퟒퟖ − ퟎ. ퟎퟎퟏퟔ 푯 − ퟎ. ퟎퟎퟏퟕퟖ 퐥퐨퐠(푺푫) − 퐥퐨퐠(푪푪) where 퐻푠푎푓푒 is the ‘safe wave height’ (30cm), and 퐻0is initial wave height (300cm).

5.4 Satellite data and remote sensing In addition to field-derived data, this thesis employs satellite-derived imagery to assess mangrove forest distribution, biomass and primary productivity for use in Chapter 8. Some remote sensing data products are also employed for Chapter 7; however, these existing data products do not require pre-processing steps and thus the main methodology of their application is described within Chapter 7. The following subsections provide an overview of the satellite data (Section 5.4.1) and data pre-processing methods (Section 5.4.2) employed in data preparation for analyses conducted in Chapter 8 (see Section 8.3). Chapter 8 investigates mangrove resilience and resistance to SLR, and potential abiotic landscape (topography), and anthropogenic (land-use and sediment loads) affecting resilience and resistance.

5.4.1 Satellite data & products Multiple sources of multispectral, SAR, and SAR-derived DEM data were used to examine mangrove distribution, biomass and productivity changes through time for use in Chapter 8 (2007-2010 and 2007-2015).

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5.4.1.1 Landsat multispectral data Freely-available multispectral satellite data from the National Aeronautics and Space Administration (NASA) and United States Geological Survey (USGS)’s Landsat satellites (see Figure 5.9) have high applicability to capturing the dynamic processes of and changes to ecological systems (Kerr & Ostrovsky 2003; Kennedy et al. 2014). The medium to high spatial resolution (30m) and consistent temporal scale (up to 16-day intervals) allows repeated monitoring of ecosystems at an appropriate spatial resolution, which improves data availability in sub-tropical and tropical areas with high cloud cover (Kerr & Ostrovsky 2003; Kennedy et al. 2014). Due to the unique spectral characteristics of mangrove forests (Figure 5.9), Landsat imagery has been employed extensively in recent years to map and monitor mangrove distribution (e.g. Giri et al. 2011; Kirui et al. 2013; Long & Giri 2011), and health and threats (e.g. Fatoyinbo et al. 2008; Hamilton & Casey 2016; Richards & Friess 2016) (see Kuenzer et al. 2011).

Figure 5.9. Landsat 8 OLI/TIRS Red-Green-Blue (RGB; Band 4-Band 3-Band 2) composite imagery over Ruvuma Estuary, Tanzania (9th June 2015; Path: 165; Row: 67) (USGS 2015).

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Landsat imagery was employed here to identify change in mangrove landward boundary extent over 2007-2015 across all study sites (see Section 4.3). This was desired in order to (1) examine any potential landward migration (resilience; Gilman et al. 2008) of mangroves in response to the process of SLR across this relatively long time-period, and (2) delineate the constant area of stable mangrove distribution through this time-period in which to examine any potential change in ecosystem biomass/productivity (resistance; Gilman et al. 2008) over the years 2007-2010 (SAR imagery coverage; see section 5.4.1.2) at each study site.

Landsat 5 TM (2007) and Landsat 8 OLI/TIRS (2015) raw scenes were sourced and downloaded from the USGS’s online Earth Explorer platform (http://earth explorer.usgs.gov/; USGS 2015). The use of Landsat 7 ETM+ data was avoided due to the SLC-off sensor issue, which results in ~22% of image data loss (USGS 2010). Scene searches were filtered to contain only those with <15% cloud cover. Most study sites were covered by multiple Landsat scenes, and scenes were selected for acquisition dates as close as possible to neighbouring scenes (Table 5.1). Corresponding 2007 and 2015 scenes for the same site were similarly selected so that acquisition dates matched as closely as possible (at least within the same annual season; Table 5.1; Cornforth et al. 2013). Due to limitations in data availability at some sites (e.g. date, season and visual tide-matching), scene coverage occasionally diverged from the 2007-2015 time-period (e.g. 2006-2013 for Sherbro Bay, Sierra Leone; see Table 5.1).

5.4.1.2 ALOS/PALSAR Synthetic Aperture Radar (SAR) data SAR imagery provides superior data to assess the vegetation structure, biomass and health/degradation of mangrove forests over passive multispectral imagery, due to the ability of the active sensors to penetrate dense tropical and sub-tropical cloud cover and image the earth in the absence of sunlight (Lucas et al. 2007; 2014; Fatoyinbo et al. 2008; Kuenzer et al. 2011; Cornforth et al. 2013; Thomas et al. 2015). In particular, the recently freely-available medium to high resolution (12.5m) SAR imagery from the Japanese Aerospace Exploration Agency and Ministry of the Economy, Trade and Industry (JAXA/METI)’s ALOS/PALSAR sensor (see Figure 5.10), collected over 2007-2010, is highly applicable to mangrove monitoring (e.g. Lucas et al. 2007; 2014; Cornforth et al. 2013; Hamdan et al. 2014).

The HV polarisation of ALOS/PALSAR L-band imagery has been found to have particularly high utility for assessing distribution and change in mangrove biomass

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Table 5.1. Landsat multispectral scenes used within this thesis for the years (2006-)2007(-2008) and (2013-)2015.

2007 2015

Cloud UTM Cloud UTM cover Zone cover Zone Site Sensor Path/Row Acquisition Date (%) (WGS84) Sensor Path/Row Acquisition Date (%) (WGS84)

Saloum Delta, LS5 TM 204/51 28th Feb 2007 0.00 28N LS8 OLI/TIRS 204/51 18th Feb 2015 0.03 28N SN LS5 TM 204/52 28th Feb 2007 0.00 28N LS8 OLI/TIRS 204/52 18th Feb 2015 0.02 28N

LS5 TM 205/50 7th March 2007 0.00 28N LS8 OLI/TIRS 205/50 25th Feb 2015 0.01 28N

LS5 TM 205/51 7th March 2007 0.00 28N LS8 OLI/TIRS 205/51 25th Feb 2015 0.01 28N

Sherbro Bay, SL LS5 TM 201/55 6th Jan 2007 0.00 28N LS8 OLI/TIRS 201/55 24th Dec 2013 0.34 28N

LS5 TM 202/54 12th Dec 2006 0.00 28N LS8 OLI/TIRS 202/54 31st Dec 2013 0.01 28N

LS5 TM 202/55 12th Dec 2006 0.00 28N LS8 OLI/TIRS 202/55 31st Dec 2013 4.29 28N

Save River LS5 TM 166/75 28th July 2007 0.00 36N LS8 OLI/TIRS 166/75 2nd July 2015 0.71 36N Delta, MZ LS5 TM 167/74 20th Aug 2007 0.00 36N LS8 OLI/TIRS 167/74 9th July 2015 0.04 36N

Ruvuma LS5 TM 165/67 2nd May 2007 4.00 37N LS8 OLI/TIRS 165/67 9th June 2015 0.44 37N Estuary, TZ

Table 5.1 (cont.). Landsat multispectral scenes used within this thesis for the years (2006-)2007(-2008) and (2013-)2015.

2007 2015

Cloud UTM Cloud UTM cover Zone cover Zone Site Sensor Path/Row Acquisition Date (%) (WGS84) Sensor Path/Row Acquisition Date (%) (WGS84)

Rufiji Delta, TZ LS5 TM 166/65 12th June 2008 11.00 37N LS8 OLI/TIRS 166/65 3rd Aug 2015 8.86 37N

LS5 TM 166/66 14th July 2008 8.00 37N LS8 OLI/TIRS 166/66 3rd Aug 2015 0.06 37N

Mahajamba, LS5 TM 159/71 22nd April 2007 0.00 38N LS8 OLI/TIRS 159/71 12th April 2015 0.67 38N Madagascar LS5 TM 160/71 16th June 2007 0.00 38N LS8 OLI/TIRS 160/71 19th April 2015 0.00 38N

Sundarbans, LS5 TM 137/44 21st Dec 2006 0.00 46N LS8 OLI/TIRS 137/44 17th March 2015 1.33 46N India & Bangladesh LS5 TM 137/45 21st Dec 2006 0.00 46N LS8 OLI/TIRS 137/45 17th March 2015 0.10 46N

LS5 TM 138/44 12th Dec 2006 2.00 45N LS8 OLI/TIRS 138/44 8th March 2015 0.26 45N

LS5 TM 138/45 13th Jan 2007 0.00 45N LS8 OLI/TIRS 138/45 8th March 2015 0.12 45N

5. Methodology through time, due to its high sensitivity to variation in mangrove structure and biomass (Proisy et al. 2003), and can be employed to describe mangrove zonation, basal area and height (Fatoyinbo et al. 2008; Kuenzer et al. 2011; Lucas et al. 2014). ALOS/PALSAR L-band imagery was employed here above freely-available and high repeat ESA Copernicus Sentinel 1-A data (Copernicus 2016a) due to (1) the greater utility of L-band in describing mangrove forest biomass, which results from greater canopy penetration at a longer wavelength, and (2) earlier temporal coverage than Sentinel 1-A’s C-band SAR (Lucas et al. 2007; 2014). ALOS/PALSAR L-band HV imagery was employed to identify any significant change in mangrove biomass (L- band HV SAR backscatter amplitude) over 2007-2010 in identified areas of constant mangrove distribution (e.g. no loss or gain of extent; Section 8.3.4; potential for SLR resistance: Gilman et al. 2008) each study site (see Section 4.3; Cornforth et al. 2013).

Figure 5.10. ALOS/PALSAR L-Band HV backscatter amplitude scene over Ruvuma Estuary, Tanzania (22nd July 2010; Frame No. 6970) (JAXA/METI 2010).

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Table 5.2. ALOS/PALSAR scenes used within this thesis for the years 2007(-2008) and (2009-)2010.

2007 2010

Orbit+Scene Acquisition Off-Nadir Orbit+Scene Acquisition Off-Nadir Distributor Site Polarisation Frame No. Date Angle Polarisation Frame No. Date Angle (Collector) ESA EOLi-SA Saloum FBD (HH/HV) 081530260 5th Aug 2007 34.3 FBD (HH/HV) 249280260 28th Sept 2010 34.3 Delta, SN (JAXA/METI) ESA EOLi-SA FBD (HH/HV) 084010260 22nd Aug 2007 34.3 FBD (HH/HV) 245050260 30th Aug 2010 34.3 (JAXA/METI) Sherbro ESA EOLi-SA FBD (HH/HV) 075840140 27th June 2007 34.3 FBD (HH/HV) 230170140 20th May 2010 34.3 Bay, SL (JAXA/METI) Save River ESA EOLi-SA FBD (HH/HV) 075236760 23rd June 2007 34.3 FBD (HH/HV) 236276760 1st July 2010 34.3 Delta, MZ (JAXA/METI) Ruvuma ESA EOLi-SA FBD (HH/HV) 078296970 14th July 2007 34.3 FBD (HH/HV) 239336970 22nd July 2010 34.3 Estuary, TZ (JAXA/METI) Rufiji Delta, ESA EOLi-SA FBD (HH/HV) 121037020 2nd May 2008 34.3 FBD (HH/HV) 228397020 8th May 2010 34.3 TZ (JAXA/METI) Mahajamba, ESA EOLi-SA FBD (HH/HV) 092006860 16th Oct 2007 34.3 FBD (HH/HV) 253046860 24th Oct 2010 34.3 MG (JAXA/METI)

ASF DAAC Sundarbans, FBD (HH/HV) 072880420 7th June 2007 34.3 FBD (HH/HV) 180240420 12th June 2009 34.3 IN & BD (JAXA/METI) ASF DAAC FBD (HH/HV) 072880430 7th June 2007 34.3 FBD (HH/HV) 180240430 12th June 2009 34.3 (JAXA/METI) ASF DAAC FBD (HH/HV) 072880440 7th June 2007 34.3 FBD (HH/HV) 180240440 12th June 2009 34.3 (JAXA/METI)

5. Methodology

ALOS/PALSAR level 1.5 processed scenes were sourced from ESA’s EOLi-SA catalogue data access platform (https://earth.esa.int/web/guest/eoli; ESA 2013) under ESA Science Project “Planning for Change: Managing Mangroves in the Face of Climate Change” (ID 17441 and 23529) for Mahajamba, Madagascar; Rufiji Delta, Tanzania; Ruvuma Estuary, Tanzania; Saloum Delta, Senegal; Save River Delta, Mozambique; and Sherbro Bay, Sierra Leone (JAXA/METI 2010). Data for the Sundarbans, Bangladesh and India, were sourced and downloaded from Alaska Satellite Facility NASA Distributed Active Archive Centre (ASF DAAC)’s Vertex online data catalogue platform (https://vertex.daac.asf.alaska.edu/; ASF DAAC 2016; JAXA/METI 2009). Adjacent ALOS/PALSAR scenes for each year (2007 and 2010) were selected for low-tide acquisition dates as close as possible to neighbouring scenes (at least within the same annual season) for each study site (Cornforth et al. 2013; Table 5.2). Corresponding low-tide 2007 and 2010 scenes were similarly selected to as closely as possible match acquisition dates across the two time-periods. Due to limitations in data availability at some sites (e.g. date, season and tide-matching), scene coverage occasionally diverged from the 2007- 2010 time-period (e.g. 2008-2010 for Rufiji Delta, Tanzania, and 2007-2009 for the Sundarbans, India and Bangladesh; see Table 5.2).

5.4.1.3 Shuttle Radar Topography Mission Digital Elevation Model data NASA and USGS’s space borne Space Shuttle Endeavour (2000) Shuttle Radar Topography Mission (SRTM) DEM data (see Figure 5.11), processed from C-band SRTM SAR imagery, is currently the highest spatial resolution freely-available global DEM data (30m; USGS 2014). SRTM DEM data have been historically employed to map and monitor mangrove height and biomass (Simard et al. 2006; Fatoyinbo et al. 2008; Fatoyinbo & Simard 2013; Duncan et al. 2016). These data can also be employed to examine at-surface elevation (e.g. ground elevation + vegetation or anthropogenic structure; see Simard et al. 2006) within ecosystems. SRTM DEM data (30m) was employed here to calculate at-surface topography and slope within and behind mangrove areas at all study sites (see Section 4.3), to examine potential topographic barriers to mangrove landward expansion in response to the process of SLR (resilience; Gilman et al. 2008). SRTM DEM 1-Arc Second 30m data (USGS 2014) mosaics were extracted and downloaded from Google Earth Engine (https://earthengine.google.com/; Google Earth Engine Team 2016) for each study site (see Section 4.3).

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Figure 5.11. SRTM DEM elevation data over Ruvuma Estuary, Tanzania (2000; USGS 2014).

5.4.1.4 ENVISAT-MERIS Total Suspended Matter (TSM) data Satellite-derived coastal total suspended matter (TSM) is a good indicator proxy of suspended sediment inputs from surface and river run-off in coastal and estuarine waters (Blondeau-Patissier et al. 2014). TSM has been found to be highly correlated with mangrove sediment organic matter (and thus carbon content; Balke & Friess 2016), and with sediment surface accretion and elevation gain (Lovelock et al. 2015). It is thus a useful indicator of the ability of mangroves to keep pace with, and maintain productivity in the face of (resistance; Gilman et al. 2008), SLR (Lovelock et al. 2015).

Multispectral data from the MERIS instrument (390-1,040 nm) aboard ESA’s ENVISAT satellite has been processed under ESA’s DUE GlobColour Global Ocean Colour for Carbon Cycle Research project (http://www.globcolour.info/; ESA GLOBCOLOUR 2014) to provide repeated daily global oceanic TSM concentration (g m-3) over the years 2002-2012. Here freely-available level 3 processed 4km resolution global TSM concentration data at an eight-day composite interval was

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5. Methodology extracted from ACRI-ST (http://hermes.acri.fr/) over the years 2006-2010. These data were processed to produce average TSM (g m-3) across all global pixels for the time-period of ALOS/PALSAR data coverage (2006-2010 mean TSM; Figure 5.12).

Figure 5.12. ENVISAT-MERIS Total Suspended Matter (TSM; g m-3): mean over 2006-2010 (4km resolution) (ESA GLOBCOLOUR 2014).

Pixel-specific significant deseasoned trends in TSM (g m-3) over 2006-2010 were then calculated using the function significantTau in the ‘gimms’ R package (Detsch 2016). Pixels with significant (p<0.05) Mann Kendall tau coefficients across the time-series rasters were identified (all others attributed tau = 0) in order to establish the existence of any alterations to sediment inputs across the time-period of resilience investigation.

5.4.2 Satellite data pre-processing All satellite data pre-processing was conducted in R version 3.2.5 (R Development Core Team 2016: packages ‘raster’ [Hijmans et al. 2016]; ‘rgdal’ [Bivand et al. 2016a]; ‘rgeos’ [Bivand et al. 2016b]; ‘RStoolbox’ [Leutner & Horning 2016]; ‘sp’ [Pebesma et al. 2016]; and ‘waveslim’ [Whitcher 2015]), QGIS 2.14.0 (QGIS Development Team 2016) and ASF MapReady version 3.1.24 (ASF 2013). The following sections detail the satellite data pre-processing methodology stages, visualised in the schematic provided in Figure 5.13.

5.4.2.1 Landsat multispectral data pre-processing & indices calculation Atmospheric radiometric calibration and correction was applied to raw Landsat 5 TM and 8 OLI/TIRS optical bands (blue, green, red and near-infrared) for all scenes (Table 5.1; Figure 5.14). Top-Of-Atmosphere (T-o-A) reflectance was corrected to at-surface reflectance via simple Dark Object Subtraction (DOS) using the radCor function in the ‘RStoolbox’ R package (‘sdos’; Leutner & Horning 2016). Haze values

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Figure 5.13. Schematic of the satellite pre-processing methodology conducted in this thesis. DOS = Dark Object Subtraction atmospheric correction for multispectral imagery; SRTM DEM = Shuttle Radar Topography Mission Digital Elevation Model data; SAVI = Soil Adjusted Vegetation Index; AWEI = Automated Water Extraction Index (see Table 5.3). were selected for each band as the T-o-A reflectance of the darkest pixel (minimum reflectance) in that band. DOS relies on the presence of truly ‘dark’ (e.g. zero reflectance) pixels within a given multispectral scene (Wegmann et al. 2016). As all scenes covered either deep oceanic water bodies or dark anthropogenic structures (tarmacked roads), and thus haze estimates were considered here to be accurate (Wegmann et al. 2016). At-surface reflectance was then matched between adjacent scenes for each site via histogram matching (histMatch function in the ‘RStoolbox’ R package; Leutener & Horning 2016), and scenes were mosaicked using the mosaic function in the ‘raster’ R package using mean pixel values to produce site-specific mosaics for each time period (2007 and 2015; see Figure 5.15).

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Figure 5.14. Dark-Object-Subtraction (DOS) atmospheric correction on Landsat imagery. Left: raw Top-of-Atmosphere reflectance Landsat 8 OLI/TIRS Red-Green-Blue (RGB; Band 4-Band 3-Band 2) composite imagery over Ruvuma Estuary, Tanzania. Right: DOS atmospherically-corrected RGB imagery of the same Landsat 8 OLI/TIRS scene (9th June 2015; Path: 165; Row: 67) (USGS 2015).

Figure 5.15. Landsat imagery mosaicking for Saloum Delta, Senegal (2015). Left: Dark- Object-Subtraction atmospherically corrected Landsat 8 OLI/TIRS Red-Green-Blue (RGB; Band 4-Band 3-Band 2) composite scenes over the Saloum Delta, Senegal study site (imagery from February 2015; see Table 5.2; USGS 2015). Right: RGB composite of the histogram-corrected, mean mosaic of the left panel scenes.

In order to conduct later landcover classifications to identify and extract mangrove distribution at each site in the years 2007 and 2015, six of the total Landsat 5 TM and Landsat 8 OLI/TIRS bands were considered (blue, green, red, near-infrared [NIR], short-wave infrared [SWIR] 1 and 2; to ensure consistency between Landsat sensor information and remove the 60m resolution thermal infra-red bands). Calculation of vegetation, soil and water indices is essential to improving the

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5. Methodology predictive ability of land cover classification (Owen et al. 2015). Mangrove forests are highly visually distinguishable from surrounding coastal areas and from inland tropical forest due to their high productivity and vegetation and sediment moisture content. Accordingly, three vegetation indices highly correlated to primary productivity were employed (Table 5.3): the Normalised Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI) and the Soil Adjusted Vegetation Index (SAVI). In order to identify areas of high soil water content, four further soil and water indices were employed (Table 5.3): The Automated Water

Extraction Index (AWEIsh), the Modified Normalised Difference Water Index (MNDWI), the Normalised Difference Vegetation Index (NDWI) and the Normalised

Multi-Band Drought Index (soil; NMDIsoil). Figure 5.16 shows visualisation of all of the Landsat bands and indices employed in this study (Table 5.3).

5.4.2.2 ALOS/PALSAR data pre-processing The Level 1.5 processed ALOS/PALSAR scenes (JAXA/METI 2009; 2010; Table 5.2) were processed within ASF MapReady (ASF 2013). All raw scenes were radiometrically calibrated to backscatter amplitude, co-registered and terrain corrected to corresponding SRTM 30m DEM data (USGS 2014), and geocoded to WGS84 UTM zone map projections based on scene metadata in ASF MapReady (ASF 2013) (see Figure 5.10 for a processed ALOS/PALSAR L-Band HV backscatter amplitude image). Individual ALOS/PALSAR scenes can be spatially misaligned in image acquisition. In the absence of reliable ground control points at many sites, automated image co-registration was applied between adjacent scenes where more than one ALOS/PALSAR scene was used (Saloum Delta, Senegal and Sundarbans, India & Bangladesh; Table 5.2) to spatially align same-year scenes prior to mosaicking. The coregisterImages function of the ‘RStoolbox’ R package was used to co-register adjacent scenes (see Wegmann et al. 2016; Figure 5.17).

Adjacent same-year scenes were then mosaicked using the mosaic function in the ‘raster’ R package using mean pixel values to produce site-specific mosaics for each time period (2007 and 2010; Saloum Delta, Senegal and Sundarbans, India & Bangladesh; Figure 5.17; Table 5.2). ALOS/PALSAR scenes (or mosaics) for the 2010 time-period were then spatially co-registered to the 2007 imagery to align corresponding pixels for accurate change detection (Cornforth et al. 2013).

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Table 5.3. Vegetation, water and soil indices considered in this thesis.

Index Abbreviation Formula Citation

(푵푰푹 − 푹푬푫) Enhanced Liu & Vegetation EVI (푵푰푹 + 푪ퟏ ∗ 푹푬푫 − 푪ퟐ ∗ 푩푳푼푬 + 푳) Huete

Index L = 1; C1 = 6; C2 = 7.5; G = 2.5 (MODIS) 1995

Normalised (푵푰푹 − 푹푬푫) Rouse Difference 1973; NDVI (푵푰푹 + 푹푬푫) Vegetation Pettorelli Index NIR= near infrared band; RED = red band 2013

Soil Adjusted (푵푰푹 − 푹푬푫) ∗ (ퟏ + 푳) Huete Vegetation SAVI (푵푰푹 + 푹푬푫 + 푳) 1988 Index L = 0.5

푩푳푼푬 + ퟐ. ퟓ ∗ 푮푹푬푬푵 − ퟏ. ퟓ Automated ∗ (푵푰푹 + 푺푾푰푹ퟏ) − ퟎ. ퟐퟓ Feyisa et Water ∗ 푺푾푰푹ퟐ al. 2014; AWEIsh Extraction BLUE = blue band; GREEN = green band; Li & Gong Index SWIR1 = short-wave infrared 1 band; SWIR2 = 2016 short-wave infrared 2 band

Modified Normalised (푮푹푬푬푵 − 푺푾푰푹ퟏ) MNDWI Xu 2006 Difference (푮푹푬푬푵 + 푺푾푰푹ퟏ) Water Index

Normalised (푮푹푬푬푵 − 푵푰푹) McFeeters Difference NDWI (푮푹푬푬푵 + 푵푰푹) 1996 Water Index

Normalised Wang & Multi-Band (푵푰푹 − (푺푾푰푹ퟏ − 푺푾푰푹ퟐ)) Qu 2007; NMDIsoil ퟎ. ퟗ − Drought (푵푰푹 + (푺푾푰푹ퟏ − 푺푾푰푹ퟐ)) Wang et Index (Soil) al. 2008

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a) b) c)

d) e) f)

g) h) i)

j) k) l)

m) n)

Figure 5.16. Landsat bands and vegetation, soil and water indices considered in this thesis. a) Band 2 (Blue); b) Band 3 (green); c) Band 4 (red); d) Band 5 (near-infrared; NIR); e) Band 6 (short-wave infrared 1; SWIR1); f) Band 7 (short-wave infrared 2; SWIR2); g) Enhanced Vegetation Index (EVI); h) Normalised Difference Vegetation Index (NDVI); i)

Soil-Adjusted Vegetation Index (SAVI); j) Artificial Water Extraction Index (AWEIsh); k) Modified Normalised Difference Water Index (MNDWI); l) Normalised Difference Water

Index (NDWI); m) Normalised Multi-Band Drought Index (soil; NDMIsoil) (see Table 5.3). n) shows Red-Green-Blue (RGB; Band 4-Band 3-Band 2) composite imagery over the same site; Ruvuma Estuary, Tanzania (9th June 2015; Path: 165; Row: 67) (USGS 2015).

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Figure 5.17. ALOS/PALSAR scene misalignment: image co-registration and mosaicking. Top: Individual ALOS/PALSAR L-Band HV backscatter amplitude scenes over Saloum Delta, Senegal (August-September 2010; see Table 5.2; JAXA/METI 2010), showing misalignment of the adjacent scenes in the close-up view (left). Bottom: co-registered, mean mosaic of the HV backscatter amplitude scenes showing correct image alignment in the close-up view (left). 5.4.2.3 Satellite data filtering: wavelet transformation SAR imagery is subject to significant influence from the phenomenon of “speckle noise”. SAR speckle results in scenes with a granular appearance with random spatial variations in backscatter values, formed by surface interference with backscatter return (e.g. within-pixel local-scale topographic scatterers in the imaged surface scattering the SAR signal to produce increases or decreases in intercepted backscatter of neighbouring imaged pixels; Lopes et al. 1993; Lee et al. 1994; Gagnon & Jouan 1997). The presence of speckle in SAR images can affect image interpretability in image segmentation, classification and change detection. A number of filtering techniques for speckle noise reduction in SAR and other active remote sensing imagery have been developed (see Lee et al. 1994; Huang & van Genderen 1996). Traditional speckle filters employ moving window approaches (i.e. focal [central] pixel mathematical function calculation over e.g. 3x3, 5x5, 9x9 pixel windows) with a variety of statistical filtering methods (see Lee et al. 1994; Huang

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& van Genderen 1996). While such approaches improve the quality of SAR backscatter interpretation and applications, they are restricted to local denoising and between-landcover edge preservation can be compromised by e.g. mean pixel interpolation (Gagnon & Jouan 1997).

Some traditional speckle noise filters, such as the Refined Lee filter, perform more optimally, smoothing out noise in homogenous landcover areas effectively while better maintaining edge features (Lee et al. 1994). However, more recently the use of two-dimensional discrete wavelet transformation (2-D DWT), incorporating non- local image variation, has become common in image processing and de-noising (Richards 2012). The method deconstructs and characterises the frequency signal of an image via a mathematical wavelet function approximation of frequency (pixel values) and time (spatial location), and reconstructs the image as predicted from the calculated wavelet (see e.g. Dednath & Shah 2014 for detailed mathematical methods). Because of frequency and time signal characterisation and reconstruction across all image pixels (local-scale and long distance signal characterisation, in contrast to local moving-windows; see above), DWT has been found to be highly applicable to speckle noise reduction in SAR imagery, maintaining sharp edge transitions between landcover classes (Gagnon & Jouan 1997; Figure 5.18).

Figure 5.18. Raw and wavelet-transformed Landsat 8 OLI/TIRS Band 2 (Blue) and ALOS/PALSAR L-band HV backscatter amplitude imagery over Ruvuma Estuary, Tanzania. Landsat 8 scene from 9th June 2015 (Path: 165; Row: 67) (USGS 2015) and ALOS/PALSAR imagery from 22nd July 2010 (Frame No. 6970) (JAXA METI 2010). Because one of the aims of this thesis involves monitoring small changes to mangrove forest seaward and landward boundary distribution (Gilman et al. 2008),

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5. Methodology the maintenance of edge features in speckle noise reduction is essential. All ALOS/PALSAR L-band HV backscatter imagery was speckle filtered prior to further analysis via a Maximal Overlap Discrete Wavelet Transform using Daubechies orthonormal compactly supported wavelet (L = 8; Daubechies 1992; Figure 5.18), using the denoise.modwt.2d function in the ‘waveslim’ R package (Whitcher 2015). In order to smooth optical data and calculated vegetation, soil and water indices, the same wavelet transformations were also applied to all Landsat 5 TM and 8 OLI/TIRS raw bands and indices (Table 5.3; Figure 5.16) for each site (Figure 5.18).

5.4.2.4 Resolution resampling & cross-product image co-registration Prior to land cover classification, the 30m Landsat 5 TM and Landsat 8 OLI/TIRS imagery was resampled to 12.5m resolution (ALOS/PALSAR resolution) via bilinear interpolation via the resample function in the ‘raster’ R package (Hijmans et al. 2016; Figure 5.19). In the absence of ground control points (see Section 5.4.2.2), all sites ALOS/PALSAR L-band HV backscatter amplitude scenes and mosaics for the year 2007 (Table 5.2) were co-registered to the corresponding resampled (12.5m) 2007 Landsat 5 TM imagery (Table 5.1) in order to correctly align SAR to corresponding multispectral bands and indices using the coregisterImages function of the ‘RStoolbox’ R package (see Wegmann et al. 2016).

Figure 5.19. Landsat imagery resolution resampling. Left: Pre-processed, wavelet- transformed Landsat 8 OLI/TIRS Red-Green-Blue (RGB; Band4-Band 3-Band 2) composite imagery for the Ruvuma Estuary, Tanzania study site (9th June 2015; Path: 165; Row: 67) (USGS 2015). Right (top): Close-up of original 30m resolution imagery. Right (bottom): Close-up of bilinear interpolation resampled 12.5m resolution imagery for the same area.

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The Landsat imagery-calculated SAVI index was used to co-register HV backscatter amplitude, due to similarity between backscatter/reflectance information (see Figure 5.20). All 2010 ALOS/PALSAR HV backscatter amplitude imagery for each site was they co-registered to the corresponding 2007 imagery correctly aligned in the previous step.

Figure 5.20. ALOS/PALSAR L-Band HV backscatter amplitude comparison with Landsat 8 OLI/TIRS calculated Soil-Adjusted Vegetation Index (SAVI). Left: pre-processed, wavelet- transformed ALOS/PALSAR L-Band HV backscatter amplitude over Ruvuma Estuary, Tanzania (22nd July 2010; Scene Frame No. 6970). Right: Landsat 8 OLI/TIRS calculated Soil-Adjusted Vegetation Index (SAVI; see Table 5.3) over Ruvuma Estuary, Tanzania (9th June 2015; Path: 165; Row: 67) (USGS 2015).

5.4.2.5 Image masking: water and high elevation elimination All ALOS/PALSAR L-band HV backscatter amplitude and Landsat raw bands and calculated indices rasters (see Table 5.3) were cropped to site extents (minimum three pixels behind visually detected mangrove extent in the landward direction; see Figure 5.21). Coastal areas at >35m SRTM DEM elevation are considered to be beyond the conditions in which mangrove forests can grow (see Fatoyinbo et al. 2008). Accordingly, all pixels with SRTM DEM elevation greater than this threshold were eliminated from further analysis. Raw SRTM DEM elevation rasters were resampled to 12.5 m resolution via bilinear interpolation using the resample function in the ‘raster’ R package (Hijmans et al. 2016), and all pixels with elevation >35m were converted to NA values. All Landsat raw bands and calculated indices rasters were then masked by SRTM DEM NA values using the mask function in the ‘raster’ R package (Hijmans et al. 2016; Figure 5.21). All pixels covering water bodies (e.g. ocean, rivers and small creeks) were then masked from all Landsat raw bands and calculated indices rasters. The AWEIsh can be thresholded (AWEIsh > 0)

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5. Methodology to remove areas of permanent water, while retaining other wet coastal landcover classes (e.g. mudflats, beaches, seagrasses, saltmarsh, mangroves; Feyisa et al. 2014;

Li & Gong 2016). All AWEIsh raster values >0 at each site (2007 and 2015) were converted to NA values. All Landsat raw bands and calculated indices rasters were then masked by corresponding AWEIsh NA values using the mask function in the ‘raster’ R package (Hijmans et al. 2016; Figure 5.21). The resulting pre-processed Landsat bands and indices at each site (2007 and 2015) were then used for further analysis of mangrove resilience and resistance through time (see Section 5.4.3).

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tion tion

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Blue

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et et al

June June 2015; Path: 165; Row: 67) (USGS 2015). Middle: Pixels >35m

transformed, resampled (12.5m) Landsat 8 OLI/TIRS Red OLI/TIRS 8 Landsat (12.5m) resampled transformed,

(see Table(see 5.3; permanent water bodies) masked from further analysis (F

processed, wavelet processed,

pre

) data. Left: Cropped, Cropped, Left: data. )

Masking Landsat imagery with Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) and Automated Water Extrac Water Automated and (DEM) Model Elevation Digital (SRTM) Mission Topography Radar Shuttle with imagery Landsat Masking

(from 12.5m resampled SRTM DEM data) masked from further analysis (Fatoyinbo

Band Band 2) composite imagery for the Ruvuma Estuary, Tanzania study site (9

Figure 5.21 Figure Index(AWEI 3 elevation classification analysis with all pixels >0 AWEI 2016).

CHAPTER 6: Mangrove species and functional trait drivers of ecosystem services delivery

6.1 Introduction In recent years the growing pressures of global environmental change have driven a resurgence of interest into functional trait influences in community ecology (see Lavorel & Grigulis 2001). This is due to the recognition that, for example, some plant functional traits (e.g. specific leaf area; SLA) are directly correlated to processes such as photosynthetic rates and carbon sequestration (Shipley et al. 2005). “Soft” plant traits such as SLA, leaf toughness, plant height, wood density, longevity, seed mass and shape have indeed been recognised as “the plant traits that drive ecosystems” (Díaz et al. 2004). These traits have been found to control a multitude of ecosystem functions, from primary production, to soil organic matter and nutrient cycling (e.g. Díaz et al. 2004; Mokany et al. 2008; Laughlin 2011; Conti & Díaz 2013), and are thus essential for controlling multiple ecosystem services (ES) delivery (Diaz et al. 2007; Lavorel et al. 2011; 2013; Lavorel 2013; Balvanera et al. 2014).

Most exploration of functional trait influences on ecosystem functioning and ES delivery has been performed in terrestrial grassland and forest ecosystems (see e.g. Gamfeldt et al. 2015). In more species-poor, stressed ecosystem types, diversity in functional traits is predicted or observed to be low (Tomlinson 1986). However, it is also predicted that in such systems high complementarity should evolve between species (Warren et al. 2009) and a high proportion of facilitative interactions are expected (Brooker et al. 2008); where rare species may contribute more strongly to overall ecosystem functioning (Leitão et al. 2016). Accordingly, it may be predicted that, in addition to overall community functional structure (Mouillot et al. 2011), species richness contributes more greatly to ecosystem functioning in species-poor over more species-rich systems. Mangrove forests are an example of a water- and salinity-stressed ecosystem, where species diversity is often low and morphological convergence high (Tomlinson 1986; Field et al. 1998; Hogarth 2007). Studies into potential complementarity and facilitative interactions between mangrove flora and the influence that these may have on ecosystem functioning have been surprisingly minimal (Ellison 2000). However, experimental studies have recently emerged to shed light on the potential effects of species richness on mangrove ecosystem functioning. By manipulating planted seedling density and species composition,

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6. Mangrove species and functional trait drivers of ecosystem services delivery evidence has been identified for both intra- and interspecific facilitation between individuals, with increased biomass production at higher densities (Huxham et al. 2010) and at greater species richness (Huxham et al. 2010; Lang’at et al. 2012). These studies have proposed interspecific facilitative mechanisms including buffering from wave action and creation of favourable sediment microhabitats (Huxham et al. 2010), and potential complementarity between species arising from (1) differential propagule predation, and (2) differential sediment profile use (Huxham et al. 2010; Lang’at et al. 2012).

Yet many mangroves exist in near or complete monoculture (Field et al. 1998), and these can be among the most productive of global mangrove forests (e.g. West African mangroves: UNEP-WCMC 2007). The existence of high functioning near- monospecific mangrove forests suggests that the functional identity of dominant species (in the appropriate ecological settings; Tomlinson 1986; Hogarth 2007) may play an important role in driving ecosystem function (mass ratio hypothesis; Grime 1998; Loreau 2000; Fox 2005; Hooper et al. 2005; de Bello et al. 2010). Indeed, in all cases in the experimental mangrove seedling manipulation experiments above, a single species (A. marina) was found to boost plot-level biomass production (Huxham et al. 2010; Lang’at et al. 2012). In addition, irregular long-distance dispersal events mean that high intraspecific genetic diversity can exist in populations of mangrove species (Hogarth 2007; Triest 2008; Arnaud-Haond et al. 2009; Cerón-Souza et al. 2012; Albrecht et al. 2013; Kennedy et al. 2016). Preliminary findings suggest that differing genetic lineages of single mangrove species vary strongly in their functional traits (height, growth rate and canopy area) and controls on ecosystem functions, and that higher genetic diversity in populations may increase vegetation structure and productivity (Devlin & Proffitt 2016). Indeed, some mangrove species exhibit a relatively high capacity for phenotypic plasticity in structural traits that could enable high stem densities and structural complexity in monoculture (e.g. variable growth form: A. marina; Tomlinson 1986; Primavera et al. 2004; Hogarth 2007; Duke 2013). Thus, high intraspecific genetic diversity and plasticity could compensate for low species richness and interspecific functional diversity in driving mangrove ecosystem functioning and ES delivery (Field et al. 1998; Hughes et al. 2008; Barbour et al. 2009).

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But what about in hyper-diverse mangrove forests in the Indo-West Pacific region (Duke 1992; Duke et al. 1998; Field et al. 1998)? Findings from the aforementioned seedling planting experiments suggest that species richness may indeed play a key role in ecosystem functioning (Huxham et al. 2010; Lang’at et al. 2012). Despite early claims of high functional convergence between mangrove species (Tomlinson 1986; Field et al. 1998; Hogarth 2007), mangrove species have recently been found to be more functionally divergent in resource acquisition and structural traits than might be predicted under narrow intertidal niche availability (in particular between species with different inundation and salinity tolerances, and with different aerial rooting systems; Balun 2011). These differences are likely to be much greater than those produced by intraspecific genetic variation alone. The findings from multi- species experimental mangrove planting studies outlined above may thus reflect that, as in terrestrial systems (Roscher et al. 2012), both the functional identity of species and divergence in resource conservative-acquisitive use (fast vs. slow life histories: leaf, wood and height traits) and structural traits (root structure and height traits) may control multiple mangrove ES (e.g. timber production, carbon stocks; Huxham et al. 2010). The influence of mangrove functional traits on ecosystem functioning and multiple ES delivery has, however, never been tested, and the influence of diversity on ecosystem functioning in mature mangrove forests has received little attention. Furthermore, exploration of mangrove species and functional trait controls on ES more directly related to stem density and structural complexity than overall biomass production per se (storm surge attenuation; Alongi 2008; Bao 2011) has not been conducted to date. Yet, community- and species-level mangrove composition and attributes are considered to be important factors in coastal protection potential (Dahdouh-Guebas & Jayatissa 2009; Lee et al. 2014).

6.2 Hypotheses Together, the recent experimental and theoretical advances outlined in Section 6.1 open up the possibility of important species richness, functional divergence and identity effects on mangrove ES delivery. This chapter investigates potential diversity (mangrove species and functional traits) and functional trait dominance (mass ratio hypothesis; Grime 1998) drivers of multiple ES delivery in mature mangrove forests. It addresses the four main questions and four specific hypotheses (H6.1-H6.4) outlined in Section 3.6.1. These are reiterated again below.

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The main questions addressed in this chapter are: (1) Are functional traits (diversity and dominance) stronger drivers of mangrove ES over species richness per se in these relatively species-poor, functionally convergent systems? (2) Is diversity or dominance of functional traits (e.g. community functional structure) the main mechanism key to driving multiple mangrove ES? (3) Do different functional traits or mechanisms (diversity or dominance) drive different mangrove ES, and if so may these result in functional trait or mechanism (diversity or dominance)-based trade-offs between multiple mangrove ES delivery? And (4) some final ES (e.g. carbon stocks) are delivered via ecosystem functions operating within different ecosystem compartments (e.g. sediment nutrient cycling and vegetation productivity). Do different mangrove functional traits, or different mechanisms (diversity or dominance) operating on the same functional traits, predominantly drive ecosystem functions from different ecosystem compartments, and result in complexity in final ES delivery (see Duncan et al. 2015)? Specific hypotheses regarding the influences of mangrove species richness and functional traits on mangrove ES are:

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Cadotte et al. 2011; Lavorel 2013), it is expected, however, that functional trait diversity has a greater influence on the delivery of all ES than species richness per se.

6.3 Methodology and statistical analyses 6.3.1 Study sites used This study was conducted across the six mature natural mangrove forests in Panay Island, of varying species richness and functional diversity: Brgy. Ermita (N = 7 plots [one plot omitted as an outlier]), Brgy. Pedada (N = 8 plots), Brgy. Buntod (N

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= 8 plots), Brgy. Balaring (N = 8 plots), Bakhawan ecopark (N = 8 plots), KII ecopark (N = 6 plots x 5 vegetation zones = 30 plots) (see Section 4.2).

Figure 6.1. Location of mature natural mangrove sites considered in Chapter 6, and the location of Panay Island in Western Visayas (Region VI), Philippines.

6.3.2 Ecosystem services data used Six mangrove ES were considered: ES that may be primarily driven by species richness, and functional diversity in and/or dominance of functional traits associated with resource-use strategy (resource-acquisitive or resource- conservative; height, SLA, wood density traits) and structural adaptations of mangrove species (height, growth form, aerial root system). Field-derived estimates of harvestable kindling abundance (≥1.5 cm diameter), aboveground harvestable timber/charcoal production, vegetation carbon stocks (above- and belowground), sediment carbon stocks, total plot carbon stocks (vegetation + sediment + downed wood carbon stocks), and storm surge attenuation potential (effective vegetation length; Le at surge height of 2.7 m) were considered for each plot in this chapter (see Section 4.3).

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6.3.3 Mangrove diversity indices To explore hypotheses H6.1-H6.4, multiple taxonomic (species richness) and functional trait indices (functional diversity and dominant traits) were calculated from the community composition data from each surveyed plot (see Section 4.2). Species richness was calculated as the total number of mangrove species within each given plot (all individuals >1.37 m tall; i.e. seedling species not incorporated).

6.3.3.1 Functional trait data Species-level functional trait data were collected for all observed mangrove species (Table 6.1). Functional trait information for mangroves is sparse, particularly so for Philippines-specific trait estimates (see e.g. Thompson et al. 2014). Accordingly, and in the presence of fieldwork time constraints, species-specific trait data had to be collated from external sources. To reflect the acquisition vs. conservation resource- use axis of plant species (Díaz et al. 2004), leaf (SLA; leaf area to dry mass ratio; mm2 mg-1), wood (wood density; g cm-3) and growth-related (maximum height; m) traits were investigated. Data for these functional traits were taken from multiple sources. Species’ maximum height was taken from the field data used in this study (i.e. the tallest observed individual across all individuals at all sites). Wood density data was obtained from the Global Wood Density Database (Chave et al. 2009; Zanne et al. 2009). As wood density can vary strongly according to the latitudinal location of mangrove forests, average wood density estimates were taken across all studies from the Southeast Asian region (Oey Djoen Seng 1951; Anonymous 1971; Anonymous 1974; Kanisus 1981; Martawijaya et al. 1992; Desch 1996; Komiyama et al. 2005; Kauffman & Cole 2010; CAB International). SLA data was acquired from entries in the TRY database on plant traits (Kattage et al. 2011; estimates from Ball et al. 1988; Turner & Tan 1991; Choong et al. 1992; McPherson et al. 2004; Wright et al. 2004; Poorter et al. 2009; Vergutz et al. 2012). In addition, to reflect differences in further structural attributes that may influence storm surge attenuation potential (incl. maximum height), categorical functional traits of aerial rooting system type and growth form were investigated (see Table 6.1; data for Philippines mangrove species from Primavera et al. 2004).

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Table 6.1. Description of mangrove species functional trait data. Data sources are described in the text (Section 6.3.3.1). N.B. some trait estimates were sourced from other studies of attributed values for closely-related species in the absence of species-specific estimates (see table notation). Numbers in brackets: for maximum height, the minimum followed by mean observed heights; for wood density and SLA, the range and sample size. Pneum = pneumatophores. Units: max height = m; wood density = g cm-3; SLA = mm2 mg-1.

Intertidal Growth Aerial Max. Species position form roots height Wood density SLA Aegiceras 7 0.510 7.194 Mid Shrub None corniculatum (2, 4) (N = 1) (6.795-7.967; N = 3) 11 0.567 6.570 Avicennia alba Low Tree Pneum (3, 7) (0.506-0.670; N = 4) (N = 1) Avicennia 17 0.650 7.376 Low Both Pneum marina (2, 5) (N = 1) (7.201-7.376; N = 2) Avicennia 19 0.605 7.920 Mid Tree Pneum officinalis (2, 7) (0.590-0.620; N = 2) (N = 1) Avicennia 27 0.650† 4.820 Mid Tree Pneum rumphiana (2, 7) (N = 1) (N = 1) Bruguiera Knee 10 0.735 8.750 Mid Tree cylindrica roots (2, 4) (0.720-0.749; N = 2) (N = 1) Camptostemon Knee 14 0.420 7.376‡ Mid Tree philippinense roots (2, 5) (N = 1) (N = 1) Ceriops 6 0.770 5.268• Mid Shrub Buttress decandra (2, 3) (N = 1) (N = 1) Knee 7 0.761 5.960 Ceriops tagal Mid Tree roots (2, 3) (0.746-0.780; N = 3) (5.300-6.720; N = 3) Excoecaria 10 0.416 12.330 Upper Tree None agallocha (2, 5) (0.379-0.480; N = 3) (N = 1) Nypa 7 4.702• Upper Palm None N/A fruticans (2, 3) (N = 1) Rhizophora Prop 20* 0.810 5.671 Low Tree apiculata roots (2, 3) (0.740-0.850; N = 2) (4.950-6.614; N = 3) Rhizophora Prop 14* 0.825 5.230 Low Tree mucronata roots (2, 5) (0.701-0.960; N = 5) (N = 1) Rhizophora Prop 11 0.840 4.467 Low Tree stylosa roots (2, 5) (N = 1) (3.640-5.285; N = 3) Scyphiphora 10* 0.685 7.510 Mid Shrub None hydrophylacea (3, 3) (0.680-0.690; N = 2) (6.440-8.580; N = 2) Sonneratia 24 0.568 6.445 Low Tree Pneum alba (2, 6) (0.387-0.780; N = 4) (6.190-6.700; N = 2) Terminalia 40Ө 0.460 13.470 Upper Tree None catappa (8, 8) (N = 1) (N = 1) Xylocarpus Plank 11 0.614 8.710 Mid Tree granatum roots (2, 4) (0.528-0.700; N = 2) (N = 1) Xylocarpus 8 0.636 8.710Ƨ Mid Tree Pneum moluccensis (2, 4) (0.531-0.740; N = 2) (N = 1) * Maximum height value taken from Primavera et al. (2004) due to small heights of surveyed individuals; Ө Maximum height value taken from Pacific Island Agroforestry (2006) for this non-mangrove tree species (T. catappa); † Wood density value for A. rumphiana taken from the closely-related A. marina in the absence of species-specific estimates in the Global Wood Density database (Chave et al. 2009; Zanne et al. 2009); ‡ In the absence of species-specific SLA estimates for C. philippinense, this species was allocated the SLA value for A. marina, based on morphological similarities between leaves of these species; • SLA values for C. decandra and N. fruticans taken from Balun (2011) in the absence of species-specific SLA estimates the TRY database (Kattge et al. 2011); Ƨ SLA value for X. moluccensis taken from the closely-related X. granatum.

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6.3.3.2 Functional trait indices Species richness does not always reflect diversity in the functional traits of ecological communities, and thus cannot always index potential complementarity effects between species in a community on ecosystem functioning (Petchey & Gaston 2002; Hooper et al. 2005). Functional trait diversity indices for single and multiple traits (Table 6.2) were thus also calculated for all plots. In order to reduce the number of models in the a priori candidate model lists (detailed below in Section 6.3.4; Burnham & Anderson 2013; Arnold 2010), two key leaf and growth traits were considered to represent functional diversity in the acquisition vs. conservation resource-use axis (Díaz et al. 2004) for single traits: SLA and maximum height. Single trait functional diversity was indexed using the 퐹퐷푣푎푟 measure of Mason et al. (2005); a widely-used measure of functional divergence which indexes the divergence of functional trait values, weighted by abundance (see Table 6.2). To index functional diversity in multiple traits (all traits in Table 6.1), reflecting diversity in the acquisition vs. conservation resource-use axis, as well as functional adaptation to mangrove intertidal position, the 퐹퐷푖푠 index of Laliberté & Legendre (2010) was calculated. This index was chosen as it is a close analogue of the single trait 퐹퐷푣푎푟 measure (indexing both functional richness and divergence), is mathematically independent of species richness (Laliberté & Legendre 2010), can be calculated for both continuous and categorical traits (see Table 6.2; Villéger et al. 2008; Laliberté & Legendre 2010), and finally is widely used in the ecosystem functioning literature (see Table 6.2).

To index the dominant (locally most abundant) functional traits reflecting the acquisition vs. conservation resource-use axis, the community-weighted mean (퐶푊푀; Garnier et al. 2004; Lavorel et al. 2008) in SLA and maximum height was calculated for each plot. All functional trait indices were calculated based on the basal area of each species in a plot, as opposed to the number of individuals, in order to represent the contribution of each species to overall tree biomass (Garnier et al. 2004). All calculations were conducted in R version 3.2.5 (R Development Core Team 2016). The 퐹퐷푖푠 and 퐶푊푀 measures were calculated via the ‘FD’ R package (Laliberté et al. 2014), while 퐹퐷푣푎푟 measures were calculated manually.

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Table 6.2. Description of the species and functional trait indices used to index species richness, and functional diversity (single [maximum height and SLA] and multiple traits [see Table 6.1]) and dominant trait values (community-weighted mean trait values [maximum height and SLA]).

Single-or Index Description Multi-trait Calculation Source Uses

The most widely used measure of species diversity A count of the total in ecosystem Species observed number of NA NA NA functioning richness species in a plot or study research: see e.g. area Cardinale et al. (2011); Lefcheck et al. (2015).

푁 2 2 퐹퐷푣푎푟 = arctan [5 x ∑[(ln 퐶푖 − ln̅̅̅푥̅) x 퐴푖]] An index of functional 휋 푖=1 e.g. Díaz et al. divergence; a measure of (2007); Mokany FDvar the variance in the where 퐶푖 = the trait value for the ith functional trait category, Mason et al. et al. (2008); (maximum functional traits in trait Single 퐴푖 = the proportional abundance of the ith functional trait (2005) Conti & Díaz height, SLA) space of species in a category, (2013); Grigulis community, weighted by ln̅̅̅푥̅ = the abundance-weighted mean of the natural logarithm of et al. (2013) abundance. trait values for the categories; or, the sum of trait proportional abundances multiplied by the natural logarithm of category trait values.

Table 6.2 (cont.). Description of the species and functional trait indices used to index species richness, and functional diversity (single [maximum height and SLA] and multiple traits [see Table 6.1]) and dominant trait values (community-weighted mean trait values [maximum height and SLA]).

Single-or Index Description Multi-trait Calculation Source Uses

∑ 푎 푥 푐 = 푗 푖푗 ∑ 푎푗 Functional dispersion: an where 푐 = the weighted centroid in the 푖-dimensional space index combining elements of (analogous to the community-weighted mean trait value but for e.g. Laliberté et al. functional richness (range of multiple traits in multidimensional (PCoA) trait space; see (2010); Ahumada et trait space filled by species in Laliberté & Legendre (2010) and Anderson (2006)), Laliberté & al. (2011); Morin et al. FDis a community) and functional Multi 푎푗 = the abundance of species 푗, Legendre (2011); Paquette & (all traits) divergence. The mean 푥푖푗 = the trait value of species 푗 for trait 푖. (2010) Messier (2011); distance in multidimensional Spasojevic & Suding trait space of individual ∑ 푎 푧 퐹퐷푖푠 = 푗 푗 (2012) species to the centroid of all ∑ 푎푗 species. where 푎푗 = the abundance of species 푗,

푧푗 = the distance of species 푗 to the weighted centroid 푐.

A measure of the dominant 푁 e.g. Díaz et al. (2007); Community- functional traits of species in 퐶푊푀 = ∑ 푝푖푥푖 Mokany et al. (2008); weighted a community. The mean 푖=1 Garnier et al. Laughlin et al. (2011); mean (CWM) functional trait value for a Single where 푝 = the relative abundance (or contribution to overall (2004) Conti & Díaz (2013); (maximum given trait within a 푖 see Lavorel et al. height, SLA) community, weighted by biomass) of species 푖 in the community, (2008) species relative abundances. 푥푖 = the trait value of species 푖.

6. Mangrove species and functional trait drivers of ecosystem services delivery

Inclusion of basal area-calculated 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 as a structural/resource- use strategy community functional trait predictor of vegetation carbon stocks and harvestable timber/charcoal production can result in a degree of circularity, as maximum tree height can be strongly related to above- and belowground biomass in forests (see Conti & Díaz 2013). Indeed, observed plot-specific maximum height was a strong predictor of allometrically-estimated (Komiyama et al. 2005) total plot biomass (top-left panel of Figure 6.2). However, total plot biomass was better explained by observed plot-specific maximum DBH (bottom-left panel of Figure 6.2), due to (1) allometric calculation via DBH measures (see Section 5.3.1), and (2) variable mangrove morphology compared to typical terrestrial tree species (Komiyama et al. 2005; Kauffman & Donato 2012), with low correlation between observed plot-specific tree height and DBH measures (Figure 6.3).

Figure 6.2. Plots of relationships between observed allometrically-estimated total plot biomass and plot-specific height and diameter at breast height (DBH) measures. Top-left: 푏푖표푚푎푠푠 = 6.00 + 1.12푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡; t = 7.33; R2 = 0.47; top-right: 푏푖표푚푎푠푠 = 6.63 + 0.21푚푒푎푛 ℎ푒푖𝑔ℎ푡; t = 2.95; R2 = 0.10; bottom-left: 푏푖표푚푎푠푠 = 6.66 + 0.03푚푎푥 퐷퐵퐻; t = 9.98; R2 = 0.61; bottom-right: 푏푖표푚푎푠푠 = 7.21 + 0.06푚푒푎푛 퐷퐵퐻; t = 3.92; R2 = 0.18.

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Figure 6.3. Plots of relationships between observed plot-specific diameter at breast height (DBH) and height measures. Top-left: 푚푎푥푖푚푢푚 퐷퐵퐻 = 0.98 + 2.41푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡; t = 4.86; R2 = 0.26; top-right: 푚푎푥푖푚푢푚 퐷퐵퐻 = 20.26 + 3.17푚푒푎푛 ℎ푒푖𝑔ℎ푡; t = 1.59; R2 = 0.04; bottom-left: 푚푒푎푛 퐷퐵퐻 = 3.06 + 0.24푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡; t = 1.49; R2 = 0.03; bottom- right: 푚푒푎푛 퐷퐵퐻 = −6.05 + 2.84푚푒푎푛 ℎ푒푖𝑔ℎ푡; t = 6.08; R2 = 0.36. Dashed regression lines depicted only for significant (p < 0.05) relationships.

퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 was calculated here based on species-specific, rather than individual-specific, maximum height (see Table 6.1). Accordingly, the employed 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 variable relects a given mangrove community’s potential to reach a tall maximum height (e.g. slow growth and a resource conservative life history strategy) rather than simply reflecting a measure of observed plot-specific observed height (see Figure 6.4). Nevertheless, due to calculation via basal area (see above), some correlation exists between calculated plot-specific 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 and plot-specific height variables here (see Figure 6.4), reflecting some degree of circularity in modelling of 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 as a predictor of plot- specific vegetation carbon stocks and harvestable timber/charcoal production (Conti & Díaz 2013).

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Figure 6.4. Plots of relationships between plot-specific basal area-calculated community weighted mean maximum height (퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡) and observed height and diameter at breast height (DBH) measures. Top-left: 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 = 13.24 + 0.44푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡; t = 4.72; R2 = 0.25; top-right: 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 = 13.20 + 1.38푚푒푎푛 ℎ푒푖𝑔ℎ푡; t = 4.11; R2 = 0.20; bottom-left: 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 = 16.42 + 0.08푚푎푥푖푚푢푚 퐷퐵퐻; t = 4.11; R2 = 0.20; bottom-right: 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 = 18.09 + 0.18푚푒푎푛 퐷퐵퐻; t = 2.40; R2 = 0.08.

6.3.4 Statistical analyses All statistical analyses were carried out in R version 3.2.5 (R Development Core Team 2016). The potential influences of the multiple diversity (species richness, 퐹퐷푣푎푟 푆퐿퐴, 퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡, 퐹퐷푖푠) and dominant trait indices (퐶푊푀 푆퐿퐴, 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡) on the delivery of all singular mangrove ES were modelled using a linear mixed effects modelling (lmer) framework (R package ‘lme4’; Bates et al. 2015). The singular mangrove ES considered were: harvestable kindling abundance [number of downed wood pieces ≥1.5 cm diameter; square root- transformed to stabilise the variance], harvestable timber/charcoal biomass [tonnes per plot; log-transformed to stabilise the variance], sediment carbon stocks

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[tonnes per plot to 150cm depth], vegetation carbon stocks [tonnes per plot; log- transformed to stabilise the variance], total plot carbon [tonnes per plot to 150cm depth], and storm surge attenuation potential [Le per plot to 2.7m; square root- transformed to stabilise the variance]).

Due to strong left-skew in the distributions of the 퐹퐷푣푎푟 푆퐿퐴 and 퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 variables, these fixed effects were square root transformed prior to analysis. All fixed effects were standardised (mean = 0, sd = 1; (x – x̅)/s.d.) prior to analysis, in order to produce standardised, comparable regression coefficients (Schielzeth 2010). The mangrove site at which a given plot was surveyed and the distance of the given plot from the seaward boundary of the forest were considered as random effects on the intercepts in all models. The geomorphology and hydrology of different mangrove forests have strong effects on their structure and function (Tomlinson 1986; Field et al. 1998; Hogarth 2007; Spalding et al. 2010), particularly as manifested in seafront versus basin mangrove sites (Ewel et al. 1998). In addition, while all sites sustain similar forms of human use, subtle differences in the magnitude of human influence on mangrove health and functioning (levels of cutting, nutrient and sediment loading etc.) between sites may remain. Thus, site (categorical variable) was considered as a random effect in order to control variation between similar sites, as well as larger differences between plots at basin mangrove sites (KII; N = 30 plots) versus seafront/fringing sites (Ermita, Pedada, Buntod, Balaring, Bakhawan; N = 39 plots). Plots at all seafront/fringing sites were established according to stratified sampling within 100m distance bands, in order to control for variation in geomorphological and hydrological factors which may affect mangrove community structure and ES delivery (see Section 4.1.2). The distance from plot centres to the seaward forest boundary for all plots within the KII site, which were established to represent variation in dominant vegetation communities (see Section 4.1.2), was estimated using existing site GIS shapefiles (see Section 4.1.2) and each plot was allocated a distance band accordingly. The 100m-wide distance band in which each plot was located (categorical variable) was also considered as a random effect, in order to control for differences in hydrology and the abundance and diversity of mangrove species at these varying intertidal positions.

An Information Theoretic AIC-based approach to model selection was adopted in these analyses (Akaike 1974; Burnham & Anderson 2013). An a priori candidate

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6. Mangrove species and functional trait drivers of ecosystem services delivery model list of 18 linear mixed effects models was established for each ES (Table 6.3), built to contain various models representing separately the diversity and dominance hypotheses (H6.1-H6.2; see Section 3.6.1), and those representing both hypotheses (Table 6.3, models 13-18). The goal of these analyses was not to establish one predictive “best-fitting” model for each ES, but rather to assess the relative importance of variables relating to H6.1-H6.2. Accordingly, the a priori candidate model list was developed to attempt represent each variable an approximately equal number of times within a reasonably small number of models (Burnham & Anderson 2013; maximum two variables within each additive model), in order that the relative variable importance of each could be assessed (Arnold 2010). There was some collinearity between functional diversity metrics across plots, and particularly between species richness and 퐹퐷푖푠 (Pearson’s r = 0.73), and 퐹퐷푖푠 and 퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 (r = 0.72; see Figure 6.5). Variance Inflation Factors (VIFs) were calculated from a full additive model and variables with VIFs >2.5 within this model were not considered within the same statistical models. 퐹퐷푖푠 and 퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 (VIF 4.61 and 4.06, respectively) were not considered within the same models (see Table 6.3).

Plausible models from the a priori candidate model lists for each ES were considered to be those with delta AIC (ΔAIC) < 4 (Burnham & Anderson 2013; Arnold 2010; Burnham et al. 2011). Model averaging based on Akaike weights was then conducted across all plausible models (ΔAIC < 4) for each ES (Burnham & Anderson 2013); via full model-averaging where the Akaike weight of the “best-fitting” model was <0.5 and via conditional model-averaging where the Akaike weight of the “best- fitting” model was ≥0.5 (Burnham & Anderson 2013; Arnold 2010). The model- averaged importance values (푊푖) and 85% confidence intervals (CIs) of each variable contained within the plausible model sets for each ES were assessed to explore their relative variable importance for each ES (Arnold 2010).

These analyses were then also conducted using only plots in the diverse KII site (N = 30 plots; see Section 4.2.2), in order to ensure consistency with findings from analyses conducted across all (low to high diversity) sites, and that results were not simply a product of high relative stem density (functional/species diversity) at the KII site.

In order to explore potential species identity/plasticity effects (e.g. growth form plasticity in A. marina; see Table 6.1) on delivery of the multiple ES considered (see

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Section 6.1), lms (linear models) were conducted exploring specific ES delivery across all “monoculture” (FDis = 0) plots dominated by either A. marina or S. alba as a function of the dominant species at each plot (N = 11 plots at the Brgy. Balaring, Brgy. Buntod, Brgy. Ermita and Brgy. Pedada sites).

Figure 6.5. Correlation plot of all diversity (species and functional trait) and dominance (community-weighted mean traits) indices (explanatory variables) across all mangrove plots considered in this Chapter (see Table 6.2). Values in the upper, right-hand panels are Pearson’s r correlation coefficients. SR = species richness; FDis = functional dispersion (all species-level functional traits: growth form, aerial rooting system type, maximum height, wood density and specific leaf area [SLA]; Table 6.1); FDvar Max. Height = functional divergence in maximum height; FDvar SLA = functional divergence in SLA; CWM Max. Height = community-weighted mean maximum height (m); CWM SLA = community- weighted mean SLA (mm2 mg-1).

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Table 6.3. A priori candidate model list of linear mixed effects models built for each ES (harvestable kindling abundance [number of downed wood pieces ≥1.5 cm diameter; square root-transformed], harvestable timber/charcoal biomass [tonnes per plot; log- transformed], sediment carbon stocks [tonnes per plot to 150cm depth], vegetation carbon stocks [tonnes per plot; log-transformed], total plot carbon [tonnes per plot to 150cm depth], storm surge attenuation potential [Le per plot to 2.7m; square root-transformed]). Both random effects are categorical variables modelled as random effects on the intercept.

No. Fixed effects Random effects

H1 1 Species richness Site + Distance class

H1 2 FDis (multi-trait) Site + Distance class

H1 3 FDvar max. height (square root- transformed) Site + Distance class

H1 4 FDvar SLA (square root-transformed) Site + Distance class

H1 5 Species richness + FDis (multi-trait) Site + Distance class

H1 Species richness + FDvar max. height (square root- 6 Site + Distance class transformed) H1 7 Species richness + FDvar SLA (square root-transformed) Site + Distance class

H1 8 FDis (multi-trait) + FDvar SLA (square root-transformed) Site + Distance class

H1 FDvar max. height (square root-transformed) + FDvar SLA 9 Site + Distance class (square root-transformed) H2 10 CWM max. height Site + Distance class

H2 11 CWM SLA Site + Distance class

H2 12 CWM max. height + CWM SLA Site + Distance class both 13 Species richness + CWM max. height Site + Distance class both 14 Species richness + CWM SLA Site + Distance class both 15 FDis (multi-trait) + CWM max. height Site + Distance class both 16 FDis (multi-trait) + CWM SLA Site + Distance class both 17 FDvar max. height (square root-transformed) + CWM SLA Site + Distance class both 18 FDvar SLA (square root-transformed) + CWM max. height Site + Distance class

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Table 6.4. Summary data of species and functional trait indices, and ES delivery across plots at all sites (means, and range in brackets). Included are mean species richness (SR), multi-trait functional diversity (퐹퐷푖푠; see Tables 6.1 & 6.2), single trait functional diversity measures (퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 & 퐹퐷푣푎푟 푆퐿퐴; see Tables 6.1 & 6.2), community-weighted mean for single traits (퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 & 퐶푊푀 푆퐿퐴; see Tables 6.1 & 6.2), harvestable kindling abundance, harvestable timber/charcoal abundance, vegetation carbon storage, sediment carbon storage and storm surge attenuation potential (see Chapter 5). Bakhaw = Bakhawan ecopark. All values are per plot (153.94m2).

Timber Storm surge /charcoal Sediment C attenuation FDvar max. CWM max. CWM SLA Kindling production Vegetation C (tonnes to potential 2 -1 Site SR FDis height FDvar SLA height (m) (mm mg ) (No.) (tonnes) (tonnes) 150cm) (푳풆)

2.00 0.03 0.0003 0.0001 17.31 7.30 8.43 2.32 1.50 3.30 0.46 Ermita (1-4) (0.00-0.13) (0.00-0.002) (0.00-0.001) (16.97-18.68) (6.92-7.38) (1-24) (1.51-3.21) (1.03-2.02) (1.67-4.68) (0.08-1.21)

1.88 0.02 0.0003 0.0001 21.44 6.78 9.88 3.87 2.40 3.23 0.80 Pedada (1-3) (0.00-0.09) (0.00-0.001) (0.00-0.0002) (16.95-24.00) (6.44-7.37) (3-24) (1.58-7.49) (0.97-4.58) (2.17-4.07) (0.44-1.41)

2.13 0.04 0.001 0.0001 18.12 7.20 2.12 1.34 0.87 0.90 0.24 Buntod (1-3) (0.00-0.12) (0.00-0.002) (0.00-0.001) (16.95-22.78) (6.61-7.38) (0-4) (0.34-6.25) (0.22-3.80) (0.70-1.19) (0.04-0.67)

1.75 0.03 0.001 0.0001 19.07 7.05 8.75 1.54 0.94 2.64 0.37 Balaring (1-3) (0.00-0.11) (0.00-0.01) (0.00-0.0002) (17.00-23.89) (6.46-7.38) (0-26) (0.71-2.34) (0.38-1.42) (1.31-4.64) (0.08-1.15)

3.63 0.09 0.001 0.0002 21.97 6.69 17.00 1.80 1.21 2.06 0.44 Bakhaw. (2-5) (0.02-0.13) (0.0004-0.002) (0.0001-0.0004) (20.00-23.60) (6.48-6.90) (7-24) (1.06-3.17) (0.74-2.10) (1.34-2.91) (0.15-1.05)

7.20 0.11 0.01 0.001 18.76 6.37 16.30 4.30 2.66 3.36 0.20 KII (3-11) (0.004-0.18) (0.0002-0.02) (0.001-0.003) (6.67-26.80) (4.86-8.80) (2-47) (0.66-19.17) (0.45-10.74) (1.50-5.20) (0.04-1.10)

6. Mangrove species and functional trait drivers of ecosystem services delivery

6.4 Results High variability existed in both ES delivery, and species and functional diversity (and dominance; CWM traits) within- and between-sites (Table 6.4). Some continuous species-specific functional traits (Table 6.1) varied according to their typical intertidal (or estuarine; see Figure 3.4; Duke 2006) position (Table 6.1). Species-specific SLA (mm2 mg-1) was lowest in low-intertidal zone species (intercept = 5.96 ± 0.52 [1 s.e.]), tended towards higher SLA in mid-intertidal zone species (also the greatest variability [Table 6.1; Figure 6.1]; estimate = 1.26 ± 0.66 [1 s.e.]; p = 0.076), and was greatest in upper-intertidal zone species (estimate = 6.94 ± 1.05 [1 s.e.]; p<0.001) (Figure 6.6).

Figure 6.6. Mangrove species-level specific leaf area (SLA) data according to intertidal position. Low-intertidal species-specific SLA (mm2 mg-1) ranged from 4.467-7.376, mid- intertidal species from 4.820-8.750, and upper-intertidal species [excluding Nypa fruticans, which is planted throughout the intertidal zone] from 12.330-13.470 (Table 6.1). Mean low- intertidal species-specific SLA was 5.96 ± 0.52 [1 s.e.]; mid-intertidal species tended toward higher SLA (estimate = 1.26 ± 0.66 [1 s.e.]; p = 0.076); and upper-intertidal species had significantly greater SLA than low-intertidal species (estimate = 6.94 ± 1.05 [1 s.e.]; p<0.001).

Species-specific wood density (g cm-3) was not significantly different between low- (intercept = 0.71 ± 0.05 [1 s.e.]) and mid-intertidal zone species (estimate = -0.07 ± 0.05 [1 s.e.]; p = 0.244), but was significantly lower in upper- over low-intertidal zone species (estimate = -0.27 ± 0.09 [1 s.e.]; p = 0.011). There was no significant difference in mean species-specific maximum height across intertidal zones (low-

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6. Mangrove species and functional trait drivers of ecosystem services delivery intertidal = 16.17 ± 0.05 [1 s.e.]; mid-intertidal estimate = -4.27 ± 4.16 [1 s.e.], p = 0.321; upper-intertidal estimate = 8.83 ± 6.58 [1 s.e.], p = 0.199).

6.4.1 Harvestable kindling abundance Eight of the 18 a priori models (Table 6.3) “plausibly” (ΔAIC≤4; see Section 6.3.4) explained variation in harvestable kindling abundance (Table 6.5). Within these models, little of the variation in harvestable kindling abundance (square root- transformed) was explained: marginal R2 from 0.06–0.13 and conditional R2 from 0.34–0.43. High model selection uncertainty existed (Akaike weight of the “best- fitting” model 9 = 0.31), and thus full model-averaging was conducted for all fixed effects variables within these models. Table 6.5. List of all plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot harvestable kindling abundance (number of downed wood pieces ≥1.5 cm diameter; square root-transformed). Included are the original number of each model within the a priori candidate model set, and the AIC, delta AIC (ΔAIC), Akaike weight, marginal (based on fixed effects only) and conditional R2 values (based on both fixed and random effects) for each model. Marginal and conditional R2 were calculated under the method of Nakagawa & Schielzeth (2013) and using R code developed by J.S. Lefcheck (available at: http://jonlefcheck.net/2013/03/13/r2-for-linear-mixed-effects-models/).

Akaike Marginal Conditional No. Fixed effects AIC ΔAIC weight R2 R2 FDvar max. height + 9 239.55 0.00 0.31 0.12 0.42 FDvar SLA 4 FDvar SLA 240.53 0.98 0.19 0.12 0.43 FDvar SLA + CWM 18 240.63 1.07 0.18 0.13 0.38 max. height Species richness + 7 242.47 2.91 0.07 0.12 0.43 FDvar SLA 8 FDis + FDvar SLA 242.50 2.95 0.07 0.12 0.43 FDis + CWM max. 15 242.55 3.00 0.07 0.08 0.34 height Species richness + 13 243.14 3.58 0.05 0.08 0.35 CWM max. height 1 Species richness 243.35 3.79 0.05 0.06 0.39

In support of H6.1, full model averaging revealed high relative variable importance of 퐹퐷푣푎푟 푆퐿퐴 (square root-transformed; 푊푖 = 0.83). 퐹퐷푣푎푟 푆퐿퐴 had a positive effect on harvestable kindling abundance, with high functional divergence in SLA being thus associated with an abundance of harvestable kindling (Table 6.6; Figure 6.7). The full model-averaged slope estimates for each fixed effect revealed a greater relative effect of 퐹퐷푣푎푟 푆퐿퐴 on harvestable kindling abundance (model-averaged b

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Figure 6.7. Plots of the relationships between plot harvestable kindling abundance (number of downed wood pieces ≥1.5 cm diameter) and all fixed effects included within the plausible linear mixed effects models (ΔAIC < 4; see Table 6.6). Regression lines depict model-averaged regression coefficients for those variables for which 85% CIs of model-averaged slope estimates do not cross zero (Arnold 2010). N.B. the y-axis (harvestable kindling production) is on a square root scale, and the x-axis for FDvar SLA and FDvar maximum height are also on a square root scale. All axes values for fixed effects variables have been back- transformed from standardised variables to their original values to enable interpretation.

6. Mangrove species and functional trait drivers of ecosystem services delivery

= 0.52 ± 0.34 [1 s.e.]). The 85% CIs for the model-averaged slope estimates show that 퐹퐷푣푎푟 푆퐿퐴 was the only variable with a consistent (positive) effect on harvestable kindling abundance.

Table 6.6. Model-averaged regression coefficients for all fixed effects contained within plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot harvestable kindling abundance (number of downed wood pieces ≥1.5 cm diameter; square root-transformed). Included is the full model-averaged intercept estimate and standard error (s.e.), and the full model-averaged slope estimates and standard errors, the importance values (푊푖), number of containing models and 85% confidence intervals (CIs) for each variable. Confidence intervals in bold highlight those variables for which the 85% CIs of the model averaged slope estimates do not cross zero.

Variable Estimate s.e. Wi No. models 85% CIs FDvar SLA 0.52 0.36 0.83 5 0.16 – 0.88 FDvar max. height -0.14 0.25 0.31 1 -0.40 – 0.12 CWM max. height 0.08 0.14 0.30 3 -0.07 – 0.22 Species richness 0.04 0.14 0.17 3 -0.11 – 0.19 FDis 0.02 0.12 0.14 2 -0.10 – 0.15

When these analyses were undertaken for only the KII site (see Section 4.2.2), seven of the 18 a priori models (Appendix 1 Table A1.1) plausibly explained variation in harvestable kindling abundance (square root-transformed). Full model averaging revealed high relative variable importance of 퐹퐷푣푎푟 푆퐿퐴 (square root- transformed) across plots at the KII site (푊푖 = 0.80), again with a positive effect on harvestable kindling abundance (see Appendix 1 Table A1.2). The 85% CIs for the model-averaged slope estimates indicate that, as across all plots, 퐹퐷푣푎푟 푆퐿퐴 was the only variable with a consistent (positive) effect on harvestable kindling abundance at KII (see Appendix 1 Table A1.3).

Across monoculture plots (퐹퐷푖푠 = 0), no significant difference was found between harvestable kindling abundance (square root-transformed) at plots dominated by A. marina (intercept = 1.88 ± 0.45 [1 s.e.]) and plots dominated by S. alba (estimate = 2.30 ± 1.05 [1 s.e.]; p = 0.056; R2 = 0.35).

6.4.2 Harvestable timber/charcoal production Five of the 18 a priori models (Table 6.3) plausibly explained variation in harvestable timber/charcoal production (log-transformed; Table 6.7). Within these models, fixed effects explained a reasonable amount of variation in harvestable timber/charcoal production (all model marginal R2 = 0.21), while explanatory power of the overall models remained low (conditional R2 0.45–0.47). High model

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6. Mangrove species and functional trait drivers of ecosystem services delivery selection uncertainty existed (Akaike weight of the “best-fitting” model 10 = 0.39), and thus full model-averaging was conducted across these models for all fixed effects variables.

Conversely to the findings for harvestable kindling abundance and in support of H6.2, full model-averaging revealed very high variable importance of

퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 (푊푖 = 1.00). It had a positive effect on harvestable timber/charcoal production (model-averaged b = 0.39 ± 0.08 [1 s.e.]), with a dominance of individuals with functional traits for tall vertical growth thus associated with high timber/charcoal production (Table 6.8; Figure 6.8). The 85% CIs for the full model-averaged slope estimates show furthermore that 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 was the only variable with a consistent (positive) effect on harvestable timber/charcoal production across the plausible models.

Table 6.7. List of all plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot harvestable timber/charcoal production (tonnes per plot; log-transformed). Included are the original number of each model within the a priori candidate model set, and the AIC, delta AIC (ΔAIC), Akaike weight, marginal and conditional R2 values for each model.

Akaike Marginal Conditional No. Fixed effects AIC ΔAIC weight R2 R2 10 CWM max. height 151.34 0.00 0.39 0.21 0.45 FDis + CWM max. 15 153.06 1.73 0.17 0.21 0.46 height Species richness + 13 153.22 1.88 0.15 0.21 0.47 CWM max. height CWM max. height + 12 153.33 1.99 0.14 0.21 0.45 CWM SLA FDvar SLA + CWM 18 153.34 2.00 0.14 0.21 0.45 max. height

When these analyses were conducted for just the KII site (see Section 4.2.2), five of the 18 a priori models (Appendix 1 Table A1.1) plausibly explained variation in harvestable timber/charcoal production (log-transformed; Appendix 1 Table A1.4). Within these models, fixed effects again explained a reasonable amount of variation in harvestable timber/charcoal production (all model marginal R2 = 0.48). Full model-averaging again revealed a high variable importance of

퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 at the KII site (푊푖 = 1.00), again with a positive effect on harvestable timber/charcoal production (b = 0.61 ± 0.12 [1 s.e.]; Appendix 1 Table A1.5). The model-averaged slope estimates for each fixed effect revealed a greater relative effect of 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 on harvestable timber/charcoal

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Figure 6.8. Plots of the relationships between plot harvestable timber/charcoal production (tonnes) and all fixed effects included within the plausible linear mixed effects models (ΔAIC < 4; see Table 6.8). Regression lines depict model-averaged regression coefficients for those variables for which 85% CIs of model- averaged slope estimates do not cross zero (Arnold 2010). N.B. the y-axis (harvestable timber/charcoal production) is on a log scale, and the x-axis for FDvar maximum height is on a square root scale. All axes values for fixed effects variables have been back-transformed from standardised variables to their original values to enable interpretation.

6. Mangrove species and functional trait drivers of ecosystem services delivery production (model-averaged b = 0.61 ± 0.12 [1 s.e.]). The 85% CIs for the full model-averaged slope estimates again showed that 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 was the only variable with a consistent (positive) effect on harvestable timber/charcoal production across the plausible models at the KII site (Appendix 1 Table A1.5).

Table 6.8. Model-averaged regression coefficients for all fixed effects contained within plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot harvestable timber/charcoal production (tonnes per plot; log-transformed). Included is the full model- averaged intercept estimate and standard error (s.e.), and the full model-averaged slope estimates and standard errors, the importance values (푊푖), number of containing models and 85% confidence intervals (CIs) for each variable. Confidence intervals in bold highlight those variables for which the 85% CIs of the model averaged slope estimates do not cross zero.

Variable Estimate s.e. Wi No. models 85% CIs CWM max. height 0.39 0.08 1.00 5 0.30 – 0.47 FDis -0.01 0.04 0.17 1 -0.05 – 0.04 Species richness -0.01 0.05 0.15 1 -0.06 – 0.04 CWM SLA 0.001 0.04 0.14 1 -0.04 – 0.04 FDvar SLA -0.0002 0.04 0.14 1 -0.05 – 0.05

Across monoculture plots (퐹퐷푖푠 = 0), significant lower harvestable timber/charcoal production was observed at plots dominated by A. marina (intercept = 2.26 ± 0.55 [1 s.e.]) than at those dominated by S. alba (estimate = 3.24 ± 1.29 [1 s.e.]; p = 0.034; R2 = 0.41).

6.4.3 Vegetation carbon stocks Five of the 18 a priori models (Table 6.3) plausibly explained variation in vegetation carbon stocks (log-transformed; Table 6.9). Within these models, fixed effects explained a relatively large amount of variation in vegetation carbon stocks (all model marginal R2 = 0.21), while the explanatory power of the overall models remained low (conditional R2 0.45–0.46). High model selection uncertainty existed within these models (Akaike weight of the “best-fitting” model 10 = 0.40), and thus full model-averaging was conducted for all fixed effects variables within these models.

As with the findings for harvestable timber/charcoal production and again in support of H6.2, full model-averaging revealed very high variable importance of

퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 (푊푖 = 1.00). It had a positive effect on vegetation carbon stocks (model-averaged b = 0.37 ± 0.08 [1 s.e.]), with a dominance of individuals belonging to species with functional traits for tall vertical growth thus associated

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6. Mangrove species and functional trait drivers of ecosystem services delivery with high vegetation carbon stocks (Table 6.10; Figure 6.9). The 85% CIs for the full model-averaged slope estimates showed that 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 was the only variable with a consistent (positive) effect on vegetation carbon stocks across the plausible models (Table 6.10). Table 6.9. List of all plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot vegetation carbon stocks (tonnes per plot; log-transformed). Included are the original number of each model within the a priori candidate model set, and the AIC, delta AIC (ΔAIC), Akaike weight, marginal and conditional R2 values for each model.

Akaike Marginal Conditional No. Fixed effects AIC ΔAIC weight R2 R2 10 CWM max. height 144.92 0.00 0.40 0.21 0.45 FDis + CWM max. 15 146.79 1.87 0.16 0.21 0.46 height Species richness + 13 146.90 1.99 0.15 0.21 0.46 CWM max. height FDvar SLA + CWM 18 146.91 2.00 0.15 0.21 0.45 max. height CWM max. height + 12 146.92 2.00 0.15 0.21 0.45 CWM SLA

Table 6.10. Model-averaged regression coefficients for all fixed effects contained within plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot vegetation carbon stocks (tonnes per plot; log-transformed). Included is the full model-averaged intercept estimate and standard error (s.e.), and the full model-averaged slope estimates and standard errors, the importance values (푊푖), number of containing models and 85% confidence intervals (CIs) for each variable. CIs in bold highlight those variables for which the 85% CIs of the model averaged slope estimates do not cross zero.

Variable Estimate s.e. Wi No. models 85% CIs CWM max. height 0.37 0.08 1.00 5 0.29 – 0.45 FDis -0.01 0.04 0.16 1 -0.05 – 0.03 Species richness -0.002 0.04 0.15 1 -0.05 – 0.04 FDvar SLA 0.001 0.04 0.15 1 -0.04 – 0.05 CWM SLA 0.0002 0.03 0.15 1 -0.04 – 0.04

When these analyses were undertaken for just the KII site (see Section 4.2.2), five of the 18 a priori models (Appendix 1 Table A1.1) plausibly explained variation in vegetation carbon stocks (log-transformed; Appendix 1 Table A1.6). Within these models, fixed effects again explained a reasonable amount of variation in vegetation carbon stocks (all model marginal R2 = 0.47). Full model-averaging again revealed a high variable importance of 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 at the KII site (푊푖 = 1.00),

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Figure 6.9. Plots of the relationships between plot vegetation carbon stocks (tonnes) and all fixed effects included within the plausible linear mixed effects models (ΔAIC < 4; see Table 6.10). Regression lines depict model-averaged regression coefficients for those variables for which 85% CIs of model-averaged slope estimates do not cross zero (Arnold 2010). N.B. the y-axis (vegetation carbon stocks) is on a log scale, and the x-axis for FDvar SLA is on a square root scale. All axes values for fixed effects variables have been back-transformed from standardised variables to their original values to enable interpretation.

6. Mangrove species and functional trait drivers of ecosystem services delivery again with a positive effect (model-averaged b = 0.57 ± 0.12 [1 s.e.]) on vegetation carbon stocks (Appendix 1 Table A1.7). The 85% CIs for the full model-averaged slope estimates again showed that 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 was the only variable with a consistent (positive) effect on vegetation carbon stocks across the plausible models at the KII site (Appendix 1 Table A1.7).

Across monoculture plots (퐹퐷푖푠 = 0), significant lower vegetation carbon stocks were observed at plots dominated by A. marina (intercept = 1.40 ± 0.33 [1 s.e.]) than at those dominated by S. alba (estimate = 2.00 ± 0.78 [1 s.e.]; p = 0.03; R2 = 0.42).

6.4.4 Sediment carbon stocks Five of the 18 models in the a priori candidate model list (Table 6.3) plausibly explained variation in sediment carbon stocks (Table 6.11). Within these models, fixed effects explained a small amount of the variation (marginal R2 0.07–0.11); however, the explanatory power of the overall models was high (conditional R2 0.56–0.64), suggesting strong control of the mangrove site conditions and distance of a plot from the front of the forest on sediment carbon sequestration and storage. The Akaike weight of the “best-fitting” model 12 for sediment carbon stocks was relatively high (0.53), and thus conditional model-averaging was conducted for all fixed effects within these models.

Table 6.11. List of all plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot sediment carbon stocks (tonnes per plot to 150 cm depth). Included are the original number of each model within the a priori candidate model set, and the AIC, delta AIC (ΔAIC), Akaike weight, marginal and conditional R2 values for each model.

Akaike Marginal Conditional No. Fixed effects AIC ΔAIC weight R2 R2 CWM max. height + 12 186.04 0.00 0.53 0.11 0.62 CWM SLA FDis + CWM max. 15 188.05 2.01 0.19 0.10 0.61 height 10 CWM max. height 189.17 3.13 0.11 0.07 0.56 FDvar max. height + 9 189.59 3.54 0.09 0.09 0.62 FDvar SLA 3 FDvar max. height 189.93 3.89 0.08 0.11 0.64

Conditional model-averaging revealed relatively high variable importance of both

퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 (푊푖 = 0.83) and 퐶푊푀 푆퐿퐴 on sediment carbon stocks (푊푖 = 0.53), in support of H6.2. Both variables had positive effects on sediment carbon

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6. Mangrove species and functional trait drivers of ecosystem services delivery stocks, with a dominance of individuals belonging to species with functional traits for tall vertical growth and investing in high leaf resource acquisition and turnover thus associated with high sediment carbon stocks (Table 6.12; Figure 6.10). The conditional model-averaged slope estimates were high for all fixed effect variables, reflecting their calculation only over models in which they are contained. The low relative variable importance of functional diversity indies (퐹퐷푖푠, 퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 [square root-transformed], and 퐹퐷푣푎푟 푆퐿퐴 [square root- transformed]; Table 6.12) and large spread in the relationships between these variables and sediment carbon stocks (Figure 6.9), however, reveal little support for their influence on sediment carbon stocks.

Table 6.12. Model-averaged regression coefficients for all fixed effects contained within plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot sediment carbon stocks (tonnes per plot to 150 cm depth). Included is the full model-averaged intercept estimate and standard error (s.e.), and the full model-averaged slope estimates and standard errors, the importance values (푊푖), number of containing models and 85% confidence intervals (CIs) for each variable. Confidence intervals in bold highlight those variables for which the 85% CIs of the model averaged slope estimates do not cross zero.

Variable Estimate s.e. Wi No. models 85% CIs CWM max. height 0.38 0.13 0.83 3 0.25 – 0.52 CWM SLA 0.27 0.11 0.53 1 0.15 – 0.38 FDis -0.22 0.12 0.19 1 -0.35 – -0.09 FDvar max. height -0.51 0.17 0.17 2 -0.68– -0.33 FDvar SLA 0.28 0.18 0.09 1 0.09 – 0.47

When these analyses were conducted at only the KII site (see Section 4.2.2), five of the 18 a priori models (Appendix 1 Table A2.1) plausibly explained variation (Appendix 1 Table A1.8). Within these models, fixed effects again explained a reasonable amount of variation in vegetation carbon stocks (model marginal R2 = 0.26-0.38). Conditional model-averaging again revealed a high variable importance of 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 (푊푖 = 0.91) and 퐶푊푀 푆퐿퐴 (푊푖 = 0.50), again with positive effects on sediment carbon stocks (Appendix 1 Table A1.9). All functional and species diversity indices contained within the plausible models at the KII site (퐹퐷푖푠, 퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 (square root-transformed) and species richness) had negative influences on sediment carbon stocks across all models, and their low relative variable importance again shows little support for their influence on sediment carbon stocks (Appendix 1 Table A1.9).

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Figure 6.10. Plots of the relationships between plot sediment carbon stocks (tonnes to 150 cm depth) and all fixed effects included within the plausible linear mixed effects models (ΔAIC < 4; see Table 6.12). Regression lines depict model-averaged regression coefficients for those variables for which 85% CIs of model- averaged slope estimates do not cross zero (Arnold 2010). N.B. the x-axes for FDvar SLA and FDvar maximum height are on a square root scale. All axes values for fixed effects variables have been back-transformed from standardised variables to their original values to enable interpretation.

6. Mangrove species and functional trait drivers of ecosystem services delivery

Across monoculture plots (퐹퐷푖푠 = 0), sediment carbon stocks were not found to be significantly different between plots dominated by A. marina (intercept = 2.57 ± 0.45 [1 s.e.]) and those dominated by S. alba (estimate = 0.22 ± 1.07 [1 s.e.]; p = 0.84; R2 = 0.005).

6.4.5 Total plot carbon stocks Five of the 18 models in the a priori candidate model list (Table 6.3) plausibly explained variation in total plot carbon stocks (vegetation + sediment + downed wood carbon stocks; log-transformed; Table 6.13). Within these models, fixed effects explained a moderate amount of variation (marginal R2 = 0.12–0.16). The explanatory power of the overall models was high (conditional R2 0.75–0.76), however, suggesting strong control of the mangrove site conditions and distance of a plot from the front of the forest on total plot carbon sequestration and storage. High model selection uncertainty existed (Akaike weight of the “best-fitting” model 15 = 0.34), and thus full model-averaging was conducted for all fixed effects variables within these models.

Table 6.13. List of all plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on total plot carbon stocks (tonnes per plot). Included are the original number of each model within the a priori candidate model set, and the AIC, delta AIC (ΔAIC), Akaike weight, marginal and conditional R2 values for each model.

Akaike Marginal Conditional No. Fixed effects AIC ΔAIC weight R2 R2 FDis + CWM max. 15 283.56 0.00 0.34 0.16 0.75 height 10 CWM max. height 284.35 0.79 0.28 0.12 0.76 Species richness + 13 285.22 1.66 0.15 0.15 0.76 CWM max. height FDvar SLA + CWM 18 285.44 1.88 0.14 0.15 0.76 max. height CWM max. height + 12 286.35 2.79 0.09 0.12 0.76 CWM SLA

As with the findings for vegetation and sediment carbon stocks and again in support of H6.2, full model-averaging revealed very high variable importance of

퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 (푊푖 = 1.00). 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 had a positive effect on total plot carbon stocks, with a dominance of individuals belonging to species with functional traits for tall vertical growth thus associated with high total plot carbon stocks (Table 6.14; Figure 6.11). The full model-averaged slope estimates for each fixed effect variable revealed a greater relative effect of 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ

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Figure 6.11. Plots of the relationships between total plot carbon stocks (tonnes) and all fixed effects included within the plausible linear mixed effects models (ΔAIC < 4; see Table 6.14). Regression lines depict model-averaged regression coefficients for those variables for which 85% CIs of model-averaged slope estimates do not cross zero (Arnold 2010). N.B. the x-axis for FDvar SLA is on a square root scale. All axes values for fixed effects variables have been back- transformed from standardised variables to their original values to enable interpretation.

6. Mangrove species and functional trait drivers of ecosystem services delivery on vegetation carbon stocks (model-averaged b = 1.01 ± 0.21 [1 s.e.]). Calculation of the 85% CIs for the full model-averaged slope estimates show furthermore that 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 was the only variable with a consistent (positive) effect on vegetation carbon stocks across the plausible models.

Table 6.14. Model-averaged regression coefficients for all fixed effects contained within plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on total plot carbon stocks (tonnes per plot). Included is the full model-averaged intercept estimate and standard error (s.e.), and the full model-averaged slope estimates and standard errors, the importance values (푊푖), number of containing models and 85% confidence intervals (CIs) for each variable. Confidence intervals in bold highlight those variables for which the 85% CIs of the model averaged slope estimates do not cross zero.

Variable Estimate s.e. Wi No. models 85% CIs CWM max. height 1.01 0.21 1.00 5 0.79 – 1.22 FDis -0.14 0.24 0.34 1 -0.39 – 0.11 Species richness -0.05 0.18 0.15 1 -0.24 – 0.13 FDvar SLA -0.04 0.15 0.14 1 -0.20 – 0.12 CWM SLA -0.001 0.08 0.09 1 -0.08 – 0.08

When these analyses were undertaken for only the KII site (see Section 4.2.2), five of the 18 a priori models (Appendix 1 Table A1.1) plausibly explained variation in total plot carbon stocks (log-transformed; Appendix 1 Table A1.10). Within these models, fixed effects again explained a reasonable amount of variation (model marginal R2 = 0.18-0.21). Full model-averaging again revealed a high variable importance of 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 at the KII site (푊푖 = 1.00), again with a positive effect on total plot carbon stocks (model-averaged b = 1.28 ± 0.30 [1 s.e.]; Appendix 1 Table A1.11). The 85% CIs for the full model-averaged slope estimates again showed that 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 was the only variable with a consistent (positive) effect on total plot carbon stocks across the plausible models at the KII site (Appendix 1 Table A1.11).

Across monoculture plots (퐹퐷푖푠 = 0), total plot carbon stocks were not found to be significantly different between plots dominated by A. marina (intercept = 3.97 ± 0.50 [1 s.e.]) and those dominated by S. alba (estimate = 0.22 ± 1.17 [1 s.e.]; p = 0.09; R2 = 0.29).

6.4.6 Storm surge attenuation potential Five of the 18 models in the a priori candidate model list (Table 6.3) plausibly explained variation in storm surge attenuation potential (square root-transformed;

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6. Mangrove species and functional trait drivers of ecosystem services delivery

Table 6.15). Within these models, fixed effects explained a large amount of the variation (marginal R2 0.30–0.36), and the explanatory power of the overall models was reasonable (conditional R2 0.43–0.52). The Akaike weight of the “best-fitting” model 16 for storm surge attenuation potential was relatively high (0.51), and thus conditional model-averaging was conducted for all fixed effects variables within these models.

Conditional model-averaging revealed high variable importance of 퐶푊푀 푆퐿퐴 (푊푖 =

0.67), 퐹퐷푖푠 (푊푖 = 0.51) and 퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 (square root-transformed; 푊푖 = 0.49) on storm surge attenuation potential, thus providing support for the influence of both the diversity (H6.1) and dominance (H6.2) hypotheses on storm surge attenuation. Lower values of 퐿푒 indicate a greater potential for storm surge attenuation (퐿푒 being a measure of the ratio of the volume of open space in a mangrove area to mangrove surface area in that same mangrove area). 퐶푊푀 푆퐿퐴 had a positive effect on 퐿푒 (and thus a negative effect on storm surge attenuation potential), with a dominance of individuals belonging to species with functional traits for investing in high resource conservation thus associated with high storm surge attenuation (Table 6.16; Figure 6.12).

Table 6.15. List of all plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot storm surge attenuation potential (퐿푒; square root-transformed). Included is the original number of each model within the a priori candidate model set, and the AIC, delta AIC (ΔAIC), Akaike weight, marginal and conditional R2 values for each model.

Akaike Marginal Conditional No. Fixed effects AIC ΔAIC weight R2 R2 16 FDis + CWM SLA -10.75 0.00 0.51 0.31 0.52 FDvar max. height + 17 -8.40 2.36 0.16 0.32 0.49 CWM SLA 3 FDvar max. height -7.74 3.01 0.11 0.30 0.43 Species richness + 6 -7.68 3.08 0.11 0.36 0.50 FDvar max. height FDvar max. height + 9 -7.66 3.09 0.11 0.35 0.49 FDvar SLA

Conversely, 퐹퐷푖푠 and 퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 had a negative effect on 퐿푒 (and thus a positive effect on storm surge attenuation potential), with greater multi-trait functional diversity and diversity in functional traits for vertical growth height thus associated with increased storm surge attenuation potential. The effects of species richness and 퐹퐷푣푎푟 푆퐿퐴 (square root-transformed) on 퐿푒 were also negative (and

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Figure 6.12. Plots of the relationships between plot storm surge attenuation potential (퐿푒) and all fixed effects included within the plausible linear mixed effects models (ΔAIC < 4; see Table 6.15). Regression lines depict model-averaged regression coefficients for those variables for which 85% CIs of model-averaged slope estimates do not cross zero (Arnold 2010). N.B. the y-axis (storm surge attenuation potential) is on a square root scale, and the x-axes for FDvar SLA and FDvar maximum height are on a square root scale. All axes values for fixed effects variables have been back-transformed from standardised variables to their original values to enable interpretation.

6. Mangrove species and functional trait drivers of ecosystem services delivery thus positive effects on storm surge attenuation potential); however, being contained within a small number of well-supported models and subsequent low relative variable importance for these variables suggests comparatively little support for their influence on storm surge attenuation potential (Table 6.16).

Table 6.16. Model-averaged regression coefficients for all fixed effects contained within plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot storm surge attenuation potential (퐿푒; square root-transformed). Included is the full model-averaged intercept estimate and standard error (s.e.), and the full model-averaged slope estimates and standard errors, the importance values (푊푖), number of containing models and 85% confidence intervals (CIs) for each variable. Confidence intervals in bold highlight those variables for which the 85% CIs of the model averaged slope estimates do not cross zero.

Variable Estimate s.e. Wi No. models 85% CIs CWM SLA 0.07 0.03 0.67 2 0.04 – 0.10 FDis -0.12 0.03 0.51 1 -0.15 – -0.09 FDvar max. height -0.13 0.04 0.49 4 -0.17 – -0.09 Species richness -0.06 0.04 0.11 1 -0.10 – -0.01 FDvar SLA -0.06 0.04 0.11 1 -0.11 – -0.02

Across all plots at all sites, plot-specific stem density (number of trees [>1.5cm DBH] 153.94m2; square-root transformed), had a significant negative relationship with 퐿푒 (square root-transformed; thus a positive relationship with storm attenuation potential) (intercept = 11.36 ± 0.70 [1 s.e.]; b = -7.94 ± 1.19 [1 s.e.]; p<0.001; R2 = 0.40); a non-significant negative relationship with 퐶푊푀 푆퐿퐴 (intercept = 11.72 ± 3.32 [1 s.e.]; b = -0.72 ± 0.49 [1 s.e.]; p = 0.149; R2 = 0.03); a significant positive relationship with 퐹퐷푖푠 (intercept = 4.70 ± 0.50 [1 s.e.]; b = 32.12 ± 5.54 [1 s.e.]; p<0.001; R2 = 0.33), and; a significant positive relationship with 퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 (square root-transformed; intercept = 4.90 ± 0.51 [1 s.e.]; b = 46.01 ± 8.59 [1 s.e.]; p<0.001; R2 = 0.30) (Figure 6.13). No significant relationship was found between plot-specific basal area (m2 153.94m2) and stem density, 퐿푒, 퐶푊푀 푆퐿퐴 or 퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 (all p >0.05), but a positive trend was found between plot-specific basal area and 퐹퐷푖푠 (intercept -5.83 ± 1.04 [1 s.e.]; b = 15.50 ± 7.81 [1 s.e.]; p = 0.052) (see Figure 6.13 legend).

When these analyses were undertaken for only the KII site (see Section 4.2.2), only one of the 18 a priori models (model 16: Appendix 1 Table A1.1) plausibly explained variation in storm surge attenuation potential. In support of H6.1 and H6.2, this model contained the negative influence of 퐹퐷푖푠 (intercept = 0.40 ± 0.02 [1 s.e.]; b

= -0.12 ± 0.02 [1 s.e.]; t = -5.06) on 퐿푒 (thus a positive influence on storm surge

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6. Mangrove species and functional trait drivers of ecosystem services delivery attenuation potential), and the positive influence of 퐶푊푀 푆퐿퐴 (b = 0.13 ± 0.02 [1 s.e.]; t = 5.69) 퐿푒 (thus a negative influence on storm surge attenuation potential). Within this model, a large amount of variation in storm surge attenuation potential was explained by the two functional trait indices fixed effects (marginal & conditional R2 = 0.64).

Figure 6.13. Relationships between plot-specific stem density (number of trees [>1.5cm 2 DBH] 153.94m ) and storm surge attenuation potential (퐿푒; Mazda et al. 1997) and all identified functional trait indices predictors of storm surge attenuation potential across all plots. Relationship between plot-specific stem density and (a) 퐿푒 (√푠푡푒푚 푑푒푛푠푖푡푦 = 2 11.08 − 7.94√퐿푒; p<0.001; R = 0.40); (b) 퐶푊푀 푆퐿퐴 (√푠푡푒푚 푑푒푛푠푖푡푦 = 11.72 − 0.72퐶푊푀 푆퐿퐴; p = 0.149; R2 = 0.03); (c) 퐹퐷푖푠 (√푠푡푒푚 푑푒푛푠푖푡푦 = 4.70 + 32.12퐹퐷푖푠; p<0.001; R2 = 0.31); (d) 퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 (√푠푡푒푚 푑푒푛푠푖푡푦 = 4.90 + 46.01√퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡; p<0.001; R2 = 0.30). No significant relationship was found between plot-specific stem density and plot-specific basal area (m2 153.94m2): intercept = -4.71 ± 0.99 [1 s.e.]; b = 0.47 ± 0.98 [1 s.e.]; p = 0.63). No relationship was found between plot-specific basal area (glms with quasipoisson error distribution) and (1) plot-specific stem density (intercept = -5.47 ± 1.29 [1 s.e.]; b = 0.15 ± 0.14 [1 s.e.]; p =

0.297); (2) 퐿푒 (intercept = -4.73 ± 1.14 [1 s.e.]; b = 0.72 ± 1.76 [1 s.e.]; p = 0.685); (3) 퐶푊푀 푆퐿퐴 (intercept = -4.04 ± 4.32 [1 s.e.]; b = -0.04 ± 0.64 [1 s.e.]; p = 0.947); (4) 퐹퐷푣푎푟 (intercept = -4.71 ± 0.87 [1 s.e.]; b = 7.56 ± 12.00 [1 s.e.]; p = 0.531). A positive trend was found with 퐹퐷푖푠 (intercept -5.83 ± 1.04 [1 s.e.]; b = 15.50 ± 7.81 [1 s.e.]; p = 0.052).

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In monoculture plots across all sites (퐹퐷푖푠 = 0), significantly lower 퐿푒 was found at plots dominated by A. marina (intercept = 0.39 ± 0.13 [1 s.e.]) than at plots dominated by S. alba (estimate = 0.72 ± 0.30 [1 s.e.]; p = 0.038; R2 = 0.40). Thus, storm surge attenuation potential was significantly greater in plots containing monoculture A. marina than plots containing monoculture S. alba.

6.5 Discussion This Chapter provides, at the time of writing, the first quantitative investigation of the species diversity and community functional structure controls on multiple ES delivery in mature mangroves. The findings provide support for both the diversity and mass ratio hypotheses (see Section 3.6.1) of community structure controls on ES delivery. In accordance with much of the terrestrial and marine B-EF literature, the findings moreover reveal a greater importance of community functional structure over species richness in driving ES delivery. The observed greater importance of functional dominance measures (primarily tall maximum height) in driving mangrove ES related to high biomass production (harvestable timber/charcoal production, carbon stocks), and controls of both functional dominance (low SLA) and diversity (multi-trait and size [maximum height]) on storm surge attenuation potential may point to a mechanism-driven trade-off in the delivery of key mangrove climate change mitigation and adapataion (CCMA) ES. These findings may have important implications regarding mangrove management and rehabilitation for CCMA project goals into the future, which are discussed in Chapter 9.

Despite the relatively species-poor, functionally convergent nature of mangrove flora (Tomlinson 1986; Field et al. 1998; but see Balun 2011), the modelling framework revealed no support for a positive influence of species richness on delivery of most ES (Tables 6.7-6.14; Appendix 1 A2-A6). Only for storm surge attenuation potential across plots at all sites was weak support (푊푖 = 0.11 within one candidate model) found for a positive effect of species richness (negative effect on 퐿푒; Tables 6.15 and 6.16). Thus, in support of sub-hypothesis H6.1.2, and as concluded in much of the recent theoretical and experimental B-EF literature (see Section 2.3), species richness per se was a less important predictor of ES delivery than the functional structure of mangrove communities (Díaz et al. 2001; 2004; 2007; Cadotte et al. 2011; Lavorel 2013). Previously observed positive species richness effects and high functional contribution of specific species to mangrove

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6. Mangrove species and functional trait drivers of ecosystem services delivery biomass production (Huxham et al. 2010; Lang’at et al. 2012) may thus be driven by functional trait complementary and identity, respectively.

Evidence was found for influences of both functional diversity (퐹퐷푣푎푟 and 퐹퐷푖푠) and functional trait dominance (퐶푊푀) indices on mangrove ES delivery. The specific functional traits and mechanisms (diversity vs. functional dominance; Tables 6.1 and 6.2) with the greatest controls, however, varied across the individual ES considered. 퐹퐷푣푎푟 푆퐿퐴 was the most supported index driving harvestable kindling abundance across all sites (Tables 6.4 and 6.5; Appendix 1 Table A1.3): greater downed wood production and ecosystem functioning with diversity in resource use strategies (see Sections 6.1 and 6.2). However, variation in tidal export of super-typhoon Yolanda-felled downed wood across sites (2013; all sites surveyed here; J.H. Primavera pers. comm.) may have influenced these findings, which must thus be viewed with some caution.

High storm surge attenuation potential (low 퐿푒; Mazda et al. 1997) across all sites was driven by both a dominance of individuals of species with low SLA (low 퐶푊푀 푆퐿퐴) and increasing multi-trait (퐹퐷푖푠) and maximum height (퐹퐷푣푎푟) diversity (Tables 6.15 and 6.16). Storm surge attenuation potential, 퐶푊푀 푆퐿퐴, 퐹퐷푖푠 and 퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 were all positively correlated with stem density, yet no significant relationship existed between stem density and basal area (Figure 6.13). The positive influence of 퐹퐷푖푠 and 퐹퐷푣푎푟 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 on storm surge attenuation potential (negative effect on 퐿푒) across all sites thus reflects a greater level of vegetation structural complexity (increased “space-filling” and surface area for drag force reduction alongside maintained vegetation volume [Mazda et al. 1997; Wolanski 2006; Dahdouh-Guebas & Jayatissa 2009; McIvor et al. 2011; Zhang et al. 2012; Ohira et al. 2013; Marois & Mitsch 2015]) in more functionally diverse communities. Diversity in structural and resource use traits could enable complementarity in horizontal and vertical space-use through variability in shade tolerance (Ball 1980; Saenger 2013), or in sediment and forest floor space-use by mangrove aerial and fine rooting systems (Huxham et al. 2010; Lang’at et al. 2012).

The negative influence of 퐶푊푀 푆퐿퐴 on storm surge attenuation potential suggests a dominance of slower-growing, resource conservative mangrove species (Díaz et al. 2004) could enable higher stem densities in the absence of outshading by large, fast-growing resource acquisitive species (e.g. Ball 1980; Saenger 2013). However, low mangrove SLA is caused by two factors: a resource conservative life history

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6. Mangrove species and functional trait drivers of ecosystem services delivery strategy (i.e. high investment into leaf defence and maintenance [e.g. Rhizophora and Ceriops spp.; Hogarth 2007]), and salinity tolerance strategy (e.g. specialised salt secreting glands [A. officinalis: Tan et al. 2013], or reduced surface area to minimise transpiration losses [succulence: e.g. Sonneratia and Pemphis spp. etc.]; Tomlinson 1986; Hogarth 2007). Low SLA-dominated mangrove areas may also sustain diversity in the same and other functional traits (e.g. 퐶푊푀 푆퐿퐴 negatively predicted 퐹퐷푣푎푟 푆퐿퐴 (square-root transformed) across all plots in this study: b = - 0.007 ± 0.002 [1 s.e.]; p = 0.003). This may occur predominantly in intertidal locations where salinity ranges enable persistence of highly salinity adapted species (e.g. low SLA; Table 6.1) and where functional diversity can be greatest (mid- intertidal zone: Figure 6.6; Table 6.1; Figure 3.4; Duke 2006). Together, these findings tentatively suggest that resource use and structural complementarity may enable simultaneous maintenance of aboveground biomass and high stem density in functionally diverse mid-intertidal mangroves (see Balun 2011). Some of the high level of variability in storm surge attenuation potential at low functional diversity (see Figure 6.12) was explained by species identity in monospecific communities (Section 6.4.6). Intraspecific variability in structural traits (growth form in A. marina; Table 6.1) may thus compensate for interspecific functional complementarity in some monospecific stands (sensu Devlin & Proffitt 2016).

Delivery of ES most directly linked to above- and belowground mangrove biomass production (harvestable timber/charcoal production and vegetation carbon stocks) was greatest in plots with high 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 (e.g. plots with a basal dominance of species able to reach tall heights; Tables 6.7-6.10; Appendix 1 Tables A1.2-A1.5). These findings are in accordance with B-EF studies in semi-arid terrestrial forests (Conti & Díaz 2013). The finding that dominant functional identity (퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡) was the main driver of vegetation biomass and carbon stocks contradicts previous manipulative species richness planting experiments, which have found higher root biomass yields in more diverse juvenile mangroves (Lang’at et al. 2012). Thus, while these findings do support previously observed juvenile mangrove species identity effects on biomass production (A. marina [Table 6.1]; Huxham et al. 2010; Lang’at et al. 2012), they may also suggest a reduced relative influence of complementarity and facilitative interactions in driving biomass production at mature stages.

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Again in corroboration with terrestrial forest B-EF studies (Conti & Díaz 2013) communities with high 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 and 퐶푊푀 푆퐿퐴 (resource acquisitive life history; Díaz et al. 2004) were associated with high sediment carbon stocks (Tables 6.11 and 6.12). The comparatively little-supported negative influences of functional diversity indices on sediment carbon stocks (Tables 6.11 and 6.12) are similarly in accordance with terrestrial plant functional diversity controls on sediment carbon stocks (Conti & Díaz 2013). However, where strong positive relationships have been found between 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 and sediment carbon stocks across terrestrial forests (R2 = 0.49; Conti & Díaz 2013), weak explanatory power and a high level of explained variation by random effects (site and distance to shoreline) were found for mangrove sediment carbon stocks in the current study (Tables 6.11 and 6.12). Within the KII site alone, a reasonable level of variation in sediment carbon stocks was explained by 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 and 퐶푊푀 푆퐿퐴 (R2 = 0.38; Appendix 1 A5), where a dominance of individuals of species able to achieve tall heights and with resource acquisitive life history traits (high SLA) enhanced sediment carbon stocks. Several hypotheses may be proposed to explain this finding: (1) net litter carbon inputs may be reduced in resource conservative dominated communities, whereby positive sediment carbon contributions from high C:N ratios and low leaf decomposition rates (e.g. Twilley et al. 1986; Alongi 2009; Chanda et al. 2016) may be mitigated by reduced relative litter fall rates (Fernando & Bandeira 2009; Mehlig et al. 2010); (2) fine root production may be lower in some more resource conservative species (Mehlig et al. 2010; Lang’at et al. 2012). However, most viable is a confounding of hydrological conditions and flora community structure within the KII site (Section 4.2.2.1; Table 4.2). Basin mangroves have high litter residence time and lower turnover than more tidal-influenced mangroves (Twilley et al. 1986), where on average ~50% of litter is removed via tidal export (Adame & Lovelock 2011; Alongi 2014). Physiological tolerances and sediment optima (Tomlinson 1986; Primavera et al. 2004; Hogarth 2007) mean high SLA-dominated communities occur toward the infrequently- inundated landward margins of the KII site. Accordingly, litter residence time and cycling to the sediment compartment is greater in such communities.

While no trade-off was found in the functional traits or mechanisms (diversity vs. dominance) controlling carbon stocks among vegetation and sediment compartments (H6.4), total plot carbon stocks were not well explained by mangrove flora community structure indices (퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡; Tables 6.11 and 6.12;

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Appendix 1 Tables A1.6 and A1.7). The low variation explained by flora community structure in total plot relative to vegetation and sediment carbon stocks at the KII site (Appendix 1 A4-A6) highlights incongruence in the spatial distribution of mangrove areas with high total carbon stocks (e.g. areas with high vegetation carbon stocks did not always sustain high sediment carbon stocks). Within the Indo- West Pacific region, and indeed globally, mangrove sediment carbon stocks are highly variable (e.g. Donato et al. 2011), and are strongly driven by local geomorphological and hydrological controls on freshwater and sediment inputs, and inundation frequency, duration and range (Twilley et al. 1986; 1992; Bouillon et al. 2008; McLoed et al. 2011). The high influence of site differences here reveals that the extreme niche and strong abiotic controls on mangrove sediment blue carbon stocks mean they are less predictable from flora community structure than in terrestrial forested systems (e.g. Conti & Díaz 2013). While no evidence was found for divergent flora community structure controls on carbon stocks from the different mangrove compartments (H6.4), the observed strong site-specific controls on sediment carbon stocks highlights the need for caution in the use of singular proxies of delivery for such complex ES in mangrove systems (Duncan et al. 2015).

There do remain some methodological caveats, as well as identified unknowns, associated with these analyses. First, some degree of circularity was found in modelling 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 as a predictor of harvestable timber/charcoal production and vegetation carbon stocks (see Section 6.3.3.2), potentially enabling underestimation of potential positive functional (and species) diversity drivers of vegetation biomass and carbon stocks here (see Huxham et al. 2010; Lang’at et al. 2012). However, the low correlation observed between plot-specific 퐶푊푀 푚푎푥푖푚푢푚 ℎ푒푖𝑔ℎ푡 and observed maximum height (R2 = 0.25; Figure 6.4), and higher association between high total plot vegetation and high plot-specific observed DBH than height (see Figure 6.2), suggest that CWM maximum height was an indeed appropriate proxy of a specific community’s potential to achieve tall maximum height. However, that said, the maximum 퐷퐵퐻 limits of the allometric equations employed here (49cm: Komiyama et al. 2005; Kauffman & Donato 2012) are more likely to have affected individuals of larger species, potentially enhancing positive bias in carbon stock estimates for plots with a dominance of species able to achieve tall maximum heights (due to calculation of functional trait indices via basal area; see Section 6.3.3.2). Investigation into the methodology employed to avoid extrapolation of biomass estimates beyond the allometric equation limits were,

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6. Mangrove species and functional trait drivers of ecosystem services delivery however, found to deflate the potential for this bias (see Section 5.3.1; Figure 5.3). Second, this study was dependent on the availability of published mangrove functional trait data (Chave et al. 2009; Zanne et al. 2009; Kattage et al. 2011; Table 6.1). While the traits considered (see Table 6.1) enable indexing of variation in resource use strategies and structural traits, and encompass a relatively data-rich resource, they do not enable detection of the specific mechanism by which complementarity and/or facilitative interactions (see Huxham et al. 2010; Lang’at et al. 2012) may be positively contributing to storm surge attenuation potential (e.g. shade and salinity tolerance, or differential sediment profile use). Further study into the biotic controls on mangrove ecosystem functioning and multiple ES delivery will necessitate further mangrove physiological and functional trait data collation. Third, the contribution of large and diverse mangrove faunal communities to ES delivery was not considered here, yet complex community interactions are likely to be key to multiple ES delivery (Cannicci et al. 2008; Lee 2008; Lee et al. 2014).

While the variance explained by community functional structure indices in the current study was marginal in comparison to that found in terrestrial forests (see Conti & Díaz 2013; reflecting the complex geomorphological and hydrological controls on mangrove ES delivery: e.g. Lee 2008), these findings do provide, at least for hyper-diverse Indo-West Pacific mangroves, a counter argument to the notion of mangroves as functionally convergent systems controlled by abiotic conditions (e.g. Field et al. 1998; see Balun 2011). The divergent control from different functional traits and mechanisms on ES delivery suggests a potential trade-off between the mangrove CCMA ES of carbon stocks and storm surge attenuation potential (H6.3). While carbon stocks were greatest in mangrove communities dominated by individuals of species with the capacity to reach large sizes (tall heights), the increased stem density, but maintained vegetation volume (Mazda et al. 1997; Bao 2011; McIvor et al. 2011), associated with increased diversity in resource use strategy and structural traits enhanced storm surge attenuation potential (“space filling”) in more functionally diverse mangrove communities. Interestingly, and in accordance with recent preliminary investigations (Devlin & Proffitt 2016), the greater storm surge attenuation potential observed in monospecific A. marina communities over S. alba communities indicates that intraspecific variability in structural traits (here growth form) may have the capacity to mitigate the identified CCMA ES trade-off. Further manipulative experimental studies will be required in order to establish the validity of this finding, the magnitude of potential intraspecific

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6. Mangrove species and functional trait drivers of ecosystem services delivery functional diversity compensation, and its potential importance in explaining mangrove biogeographical and productivity patterns (Duke et al. 1998; Spalding et al. 2010). Importantly, the functional community structure controls on specific mangrove ES observed here have applied implications for mangrove management and rehabilitation, particularly in the face of the potential trade-offs in CCMA ES gains revealed. These issues will be discussed, and specific recommendations made, in Chapter 9. In a broader sense, the findings of this study reveal that ES commonly assumed to be positively related in space may hold greater complexity to their delivery and community structure controls which must be accounted for in ecosystem modelling and ES prediction at a landscape scale.

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CHAPTER 7: Ecosystem services delivery by rehabilitated and natural mangrove forests

7.1 Introduction To combat mangrove losses, and to enhance climate change mitigation and adaptation (CCMA) efforts in the tropics, rehabilitation is an essential management tool (Ellison 2000; Kairo et al. 2001; Lewis 2005; Primavera & Esteban 2008; Primavera et al. 2012b). Rehabilitated mangrove blue carbon-based Payments for Ecosystem Services (PES) projects are emerging (Wylie et al. 2016), and governments are increasingly recognising the significance of mangrove coastal protection (Marois & Mitsch 2015), with national coastal greenbelt replanting programmes now widespread following recent natural disasters (see Section 3.5; Dahdouh-Guebas et al. 2005; Primavera et al. 2014). Recent studies on potential blue carbon PES schemes have concluded that projects would benefit from inclusion of “bundled services” to offset low voluntary carbon market prices (Locatelli et al. 2014; Thompson et al. 2014), with particular reference to coastal protection (Kairo et al. 2009; Locatelli et al. 2014). However, mangrove rehabilitation efforts have historically seen low successes (e.g. Primavera & Esteban 2008; Dale et al. 2014; Bayraktarov et al. 2015; but see Arnaud-Haond et al. 2009; Goessens et al. 2014), and where established, longer-term monitoring of functionality has been minimal (Bosire et al. 2008). Where this has been monitored, the structure, productivity and ES delivery (e.g. carbon stocks) of rehabilitated mangroves can be comparable to adjacent natural stands (Kairo et al. 2001; Bosire et al. 2008; Ren et al. 2010; Salmo et al. 2013; Nam et al. 2016); however, their relative potential for high multiple ES delivery is mostly unknown (but see Rönnbäck et al. 2007 and Nam et al. 2016). We thus currently lack quantitative information on the combined CCMA potential of current mangrove rehabilitation efforts.

There are two major potential sources of variation in the ability of rehabilitated mangroves to deliver high multiple CCMA ecosystem services (ES). First, ignorance of, or noncompliance to, scientific guidelines has driven many rehabilitation efforts to take place in low-intertidal seafront areas where sub-optimal hydrological conditions limit survival and growth of replanted mangroves (Iftekhar 2008; Primavera & Esteban 2008; Primavera et al. 2012b; 2012b; 2014). Rehabilitation in such areas may result in low relative mangrove biomass and density, and associated carbon stocks and coastal protection potential, particularly where rehabilitation

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7. Ecosystem services delivery by rehabilitated and natural mangrove forests failure has historically been high. Second, site areal configuration may heavily impact the potential ES delivery of rehabilitated mangroves. Rehabilitated mangrove carbon stocks may be expected to increase linearly with site area, while coastal protection rapidly increases with mangrove greenbelt width (Koch et al. 2009). Indeed, low-intertidal rehabilitated mangroves exist primarily in monospecific narrow-fringing stands (Ellison 2000; Iftekhar 2008; Primavera et al. 2012b; 2012b), with potentially severely limited ability to deliver effective coastal protection (Ewel et al. 1998; Barbier et al. 2008; 2011; Koch et al. 2009). Larger rehabilitation sites in the mid- to upper-intertidal zone may thus be expected to deliver much higher multiple CCMA benefits than narrow, low-intertidal rehabilitated mangroves. However, the spatial configuration and area of suitable land for mangrove rehabilitation is often constrained by land tenure conflicts and complexities in the coastal zone (e.g. competition with agri- and aquaculture); often the major driver for prioritizing low-intertidal zone rehabilitation (Iftekhar 2008; Primavera & Esteban 2008; Primavera et al. 2012a; 2014). CCMA arguments for rehabilitation actions may be key in future decision-making and spatial planning. To guide effective coastal zone management in the face of climate change, there is thus a need to identify and prioritise rehabilitation locations in which high CCMA gains may co-occur with minimal tenure issues (Locatelli et al. 2014; Primavera et al. 2014; Thompson et al. 2014).

7.2 Hypotheses This chapter examines the CCMA potential of mangrove rehabilitation in abandoned aquaculture ponds relative to low-intertidal, seafront areas across Panay Island, Philippines. It addresses the five main questions and five specific hypotheses (H7.1- H7.5) outlined in Section 3.6.2. These are reiterated below.

The main questions addressed in this chapter are: (1) how well do rehabilitated mangroves replicated the CCMA ES delivery (carbon stocks and coastal protection potential) of natural mangrove areas? (2) is CCMA ES delivery from rehabilitated mangroves greater in mid- to upper-intertidal zone rehabilitated abandoned fishponds over low-intertidal seafront rehabilitated areas? (3) how much coastal greenbelt rehabilitation is required for effective coastal protection potential in Panay Island? (4) what are the CCMA benefits that could be derived from adopting a strategy of targeted abandoned fishpond reversion? and (5) is a strategy of targeted abandoned fishpond reversion for coastal greenbelt protection feasible

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7. Ecosystem services delivery by rehabilitated and natural mangrove forests under current land tenure in the Philippines? The specific hypotheses regarded these questions that are investigated herein are:

H7.2: Site-specific carbon stocks. Site-specific vegetation and sediment carbon stocks are greatest in natural mangrove areas (greater biomass and carbon burial with mangrove age; large areal coverage; Bosire et al. 2008; Alongi 2011), followed by rehabilitated abandoned fishpond areas (mid-intertidal location in former mangrove sediment; large areal coverage), and are lowest in rehabilitated low- intertidal seafront areas (low-intertidal location; small areal coverage; Iftekhar 2008; Iftekhar 2008; Primavera & Esteban 2008; Primavera et al. 2012b; 2012b; 2014).

H7.5: Abandoned fishpond tenure status. A high proportion of Fishpond Lease Agreement (FLA) leaseholds in the Philippines (see Section 4.2; Primavera et al.

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2014) means tenure status of a large proportion of abandoned fishponds will be favourable for reversion to former mangrove forest.

The methodology undertaken within this chapter then takes a step-wise approach to address the questions and hypotheses above. The relative vegetation and sediment carbon stocks, and coastal protection potential of rehabilitated mangrove areas (mid- to upper-intertidal abandoned fishpond and low-intertidal seafront areas) is first quantified, against mature natural reference mangrove stands, to explore the ES potential of these different rehabilitation strategies. A municipality- specific (Dumangas, Iloilo, Western Visayas) case study is then conducted to model the potential CCMA benefits of targeted abandoned fishpond reversion, with specific reference to current coastal greenbelt rehabilitation efforts. Finally, a recent tenure assessment of fishponds in the municipality (Primavera et al. 2014) is employed to assess the tenure status of identified abandoned fishponds, thus examining the feasibility of prioritising abandoned fishpond reversion for CCMA goals under current fishpond tenure status across the municipality.

7.3 Methodology and statistical analyses 7.3.1 Study sites used The analysis for this chapter was conducted across two mangrove forest sites containing natural, mature mangrove stands and low-intertidal seafront rehabilitated areas, and two rehabilitated abandoned fishpond sites in Panay Island. The two natural mangrove and seafront rehabilitation sites were Brgy. Ermita and Bakhawan ecopark (see Section 4.2 and 4.3). The two abandoned fishpond sites were Brgy. Nabitasan abandoned fishpond and Dumangas abandoned fishpond (see Section 4.3). A depiction of the location of these study sites is provided in Figure 7.1.

7.3.2 Climate change mitigation and adaptation ecosystem services data used Two mangrove CCMA ES were considered: vegetation and sediment carbon stocks, and coastal protection potential. Plot-level vegetation and sediment carbon stocks were calculated (N = 8 plots at each site/sub-site) using the methodology outlined in Sections 5.2, 5.3.1 and 5.3.2. Contrary to the analysis in Chapter 6, which assessed storm surge protection potential at a plot-level, here site-specific coastal protection potential from regular waves (3m height; Section 5.3.6; Bao 2011) was considered across the rehabilitated and natural sites. This was in order to index the “total

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7. Ecosystem services delivery by rehabilitated and natural mangrove forests ecosystem” coastal protection potential of differing forms of rehabilitation site against reference natural stands.

Figure 7.1. Location of rehabilitated (low-intertidal seafront and abandoned fishpond) and natural mangrove sites considered in Chapter 7, and the location of Panay Island in Western Visayas (Region VI), Philippines. Filled circles donate rehabilitated abandoned fishpond sites, while half-filled circles donate sites containing natural mangrove areas and rehabilitated low-intertidal seafront areas.

7.3.3 Site-specific climate change mitigation and adaptation ecosystem services delivery

7.3.3.1 Site-specific carbon stocks All rehabilitation sites were highly heterogeneous in vegetation biomass distribution; remote sensing methods were thus employed to index spatial variability in site-specific carbon stock estimates. A technique similar to that derived by Simard et al. (2006) and Fatoyinbo et al. (2008) using Shuttle Radar Topography Mission (SRTM) near-global digital elevation (DEM) data (30m resolution; Rodriguez et al. 2006) was employed. SRTM pixel values (SRTM-derived height; m) were first extracted for each temporary field plot location. C- and X-band

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SRTM radar scattering is influenced by vegetation density, biomass and canopy closure, and error exists between observed SRTM DEM height and true canopy height (Simard et al. 2006; Fatoyinbo et al. 2008). A linear regression was thus established to predict field plot mean height (m) from corresponding SRTM DEM pixel height (m) across all sites. Plot-level mean height data was used to derive (1) a mean height-aboveground biomass relationship, and (2) a mean height- belowground biomass (plot level Mg [tonnes], extrapolated to Mg 900m2) relationship with the allometrically-estimated biomass values for each plot (Komiyama et al. 2005; Section 5.3.1), using linear regression. These equations were then applied across all SRTM DEM pixels at each site, and summed (biomass value × proportion of pixel within the site boundary) to estimate site-level biomass. Site- level above- and belowground biomass estimates were then multiplied by carbon concentration values of 0.464 (Donato et al. 2011) and 0.39 (Kauffman & Donato 2012) respectively to obtain vegetation carbon stock estimates.

Site-level sediment carbon stock estimates were calculated from the plot-level estimates from each site (Mg 153.94m2; Section 5.3.2). The area of each site was divided by plot area (153.94m2) to create ‘sub-site’ areas, and an estimate of sediment carbon stock for each ‘sub-site’ was drawn from a normal distribution created from the mean and standard deviation of plot-level estimates for each depth interval. These ‘sub-site’ estimates were then summed to create total site sediment carbon stock estimates for each depth interval at each site (mean over 1,000,000 runs).

For consistency with site-specific estimates, mean per hectare above- and belowground vegetation carbon stocks were calculated across all pixels with ≥50% of their area within the site boundary, instead of from the mean of the raw plot-level data (Section 5.3.1). Linear regressions (lms) were applied to model differences in vegetation and sediment carbon estimates (Mg ha-1) across sites (with Site Name as a predictor variable) to test H1.

7.3.3.2 Site-specific coastal protection potential The required mangrove greenbelt width of the site-specific mangrove vegetation for effective coastal protection potential from 3m high regular waves (퐵푊; Section

5.3.6; Bao 2011) was compared to the median actual landward width (퐵퐴푐푡푢푎푙; m) to determine the current coastal protection potential of each site. Landward and

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7. Ecosystem services delivery by rehabilitated and natural mangrove forests seaward boundaries of the polygon outline of each site were extracted in QGIS version 2.14.0 (QGIS Development Team 2016) and the median distance between seaward and landward boundary vertices (N ≈ 2,000) were calculated for each site using the function gDistance in the R package ‘rgeos’ (Bivand et al. 2016b). This median distance provided the estimate of median landward width (퐵퐴푐푡푢푎푙; m) for each site.

7.3.4 Targeted abandoned fishpond reversion? Dumangas municipality Fishpond density in Dumangas, Iloilo Province, Panay Island is among the highest in Western Visayas (4,282.7ha; Primavera et al. 2014). The distribution of abandoned fishponds (mangrove vegetation present) in Dumangas was mapped from high- resolution satellite imagery in Google Earth from the year 2015 (Google Earth 2016). SRTM-derived vegetation biomass estimates (Mg ha-1) from the two abandoned fishpond sites (Nabitasan and Dumangas; Section 4.3) were used to project potential vegetation carbon stock accumulation across Dumangas’ abandoned fishponds over a period of 6.5 years (mean abandoned fishpond site age) via random sampling from the observed distribution of vegetation carbon stock (Mg ha-1) estimates (mean over 1,000,000 runs). This method was employed to produce a conservative estimate, as some abandoned fishponds are currently in relatively advanced stages of mangrove re-colonisation (Primavera et al. 2014). Potential sediment carbon accretion was estimated using a conservative accretion rate of 25mm y-1 for the Indo-Pacific Region (Lovelock et al. 2015; 1.63cm surface accretion over 6.5 years) and random sampling from the observed distribution of sediment carbon stock (Mg ha-1) for the 0–50cm depth interval (mean over 1,000,000 runs). Each mapped abandoned fishpond in the municipality was then cross-referenced against a recent survey of tenure status in Dumangas (Primavera et al. 2014).

Mapped sea-facing abandoned fishponds were then subsetted and the median distance between landward and seaward boundary vertices was calculated for each abandoned fishpond as above (Section 7.3.3.2). Vertices with median distance to landward boundaries greater than estimated required greenbelt widths to attenuate three metre initial waves (퐵푊; estimated for the abandoned fishpond sites as above; Bao 2011) were retained to calculate the length of Dumangas’ coastline with potential for effective coastal protection from regular wind waves following abandoned fishpond reversion. The tenure status (Primavera et al. 2014) of

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7. Ecosystem services delivery by rehabilitated and natural mangrove forests identified sea-facing abandoned fishponds with potential for effective coastal protection from regular wind waves following reversion was then assessed.

7.4 Results 7.4.1 Vegetation structure Mean vegetation basal area, DBH, height and biomass were greatest in the natural mangrove areas, followed by the rehabilitated seafront areas, and abandoned fishponds (Table 7.1). Stem density was highly variable across natural and rehabilitated sites, being greatest at the rehabilitated seafront area at Bakhawan (11,839.18 stems ha-1) and lowest at the rehabilitated seafront area at Ermita (1,916.36 ha-1) (Table 7.1).

Table 7.1. Mean field-derived structural parameters across plots (N = 8) in all study sites. Biomass refers to mean total (above- and belowground) biomass. FP = abandoned fishpond.

Stem Canopy Basal area density closure Mean Biomass Site (m2 ha-1) (N ha-1) (%) 푫푩푯 (cm) height (m) (Mg ha-1) Bakhawan 27.68 6,496.12 88.10 5.76 6.16 177.88 Natural Bakhawan 16.52 11,839.18 84.45 2.71 3.78 79.27 Rehab Ermita 33.16 2,151.84 84.85 18.0 6.67 241.62 Natural Ermita 10.75 1,916.36 87.19 6.86 4.63 52.88 Rehab Nabitasan 3.38 2,379.21 63.60 3.27 1.99 15.25 FP Dumangas 8.17 6,950.85 84.00 3.98 2.89 37.96 FP

7.4.2 SRTM-based vegetation biomass modelling A positive linear relationship was found between mean plot-level vegetation height (m; log-transformed) and SRTM-derived height (m; square root-transformed): R2 = 0.43; root-mean-square error (RMSE) = 0.41 (Figure 7.2). Allometrically-estimated (Section 5.3.1) plot-level above- (linear; R2 = 0.73; RMSE = 0.50; Figure 7.3a) and belowground biomass (quadratic; R2 = 0.89; RMSE = 0.44; Fig. 7.3b), both extrapolated to Mg 900 m2, showed a positive relationship with mean plot-level vegetation height (m; log-transformed). These equations were applied across SRTM tiles for Panay to predict above- (Figure 7.4) and belowground biomass across sites.

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Figure 7.2. Regression of observed plot-level mean mangrove height (m; log- transformed) against SRTM DEM-derived elevation (m; square root-transformed). log(푚푒푎푛 ℎ푒푖𝑔ℎ푡) = 1.31 + 0.23√푆푅푇푀 푒푙푒푣푎푡푖표푛; R2 = 0.43; RMSE = 0.41.

7.4.3 Carbon stocks Mean above- and belowground vegetation carbon stocks (Figure 7.5) were greatest at the two natural areas (Ermita: 50.41 Mg ha-1; Bakhawan: 46.95 Mg ha-1), followed by the rehabilitated seafront areas (Ermita: 41.01 Mg ha-1; Bakhawan: 30.42 Mg ha- 1), and were lowest in the abandoned fishponds (Dumangas: 25.68 Mg ha-1; Nabitasan: 5.17 Mg ha-1). Both rehabilitated seafront sites had significantly lower SRTM-derived vegetation carbon stocks (Mg ha-1) than adjacent natural areas (log(vegetation carbon); intercept = 4.46 ± 0.10 [1 s.e.]; Bakhawan rehab: -1.08 ± 0.18 [1 s.e.], p <0.001; Ermita rehab: -1.35 ± 0.18 [1 s.e.], p <0.001). Contrary to expectations under H7.1, both abandoned fishponds had significantly lower SRTM- derived vegetation carbon stocks (Mg ha-1) than rehabilitated seafront areas (log(vegetation carbon); intercept = 3.25 ± 0.13 [1 s.e.]; Dumangas: -0.58 ± 0.23 [1 s.e.], p = 0.02; Nabitasan: -1.42 ± 0.23 [1 s.e.], p <0.001).

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Figure 7.3. Best-fitting regressions (highest variance explained; R2) of (a) above- and (b) belowground biomass (allometric estimation from field 퐷퐵퐻 data; Komiyama et al. 2005) against observed mean mangrove height (m; log-transformed). Allometrically-estimated above- and belowground biomass has been scaled from plot- to pixel-level estimates (900m2; log-transformed). The relationship between allometrically-estimated aboveground biomass and observed mean mangrove height was linear in form (log(푎푏표푣푒𝑔푟표푢푛푑 푏푖표푚푎푠푠) = 2.14log(푚푒푎푛 ℎ푒푖𝑔ℎ푡) – 1.84; R2 = 0.73; RMSE = 0.50), while the relationship for allometrically-estimated below-ground biomass was a quadratic relationship (log(푏푒푙표푤𝑔푟표푢푛푑 푏푖표푚푎푠푠) = –1.05log(푚푒푎푛 ℎ푒푖𝑔ℎ푡)2 + 6.13log(푚푒푎푛 ℎ푒푖𝑔ℎ푡) – 6.25; R2 = 0.89; RMSE = 0.44).

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Figure 7.4. Predicted above-ground mangrove biomass (Mg 900m2) across the two SRTM DEM tiles on Panay Island. Blue pixels denote areas of low biomass, while red areas denote higher biomass. Dark blue pixels indicate active aquaculture pond areas, and black pixels denote areas with biomass >10 Mg 900m2. N.B. This figure illustrates predictions of areas outside of the distribution of mangroves on Panay Island (e.g. beach forest and terrestrial forest and plantation areas), which were not included in the analyses of this chapter.

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Figure 7.5. Mean mangrove vegetation and sediment carbon stocks across all study sites (Mg ha-1). Mean above- and below-ground vegetation carbon stocks (Mg ha-1) were calculated as the mean SRTM DEM-predicted above- and below-ground carbon stocks across all pixels with >50% of their area within each given study site. Mean sediment carbon stock estimates (Mg ha-1) were calculated across all plot-level measurements (N = 8 per site). Error bars depict one standard deviation from the mean of each estimate at each site. FP = abandoned fishpond. Sediment OC estimates for the 0–50cm depth interval ranged from 0.71% (Ermita Rehab) to 4.88% (Dumangas FP), and for the 50–150cm depth interval ranged from 0.82% (Bakhawan Rehab) to 4.59% (Dumangas FP) (Table 7.2). Bulk density ranged from 0.37–0.38 g cm-3 at the Dumangas abandoned fishpond to 0.85–0.91 g cm-3 at the Ermita seafront rehabilitation site (Table 7.2). Mean total sediment carbon stocks were similarly variable (Figure 7.5), being lowest at the two seafront rehabilitated areas (Bakhawan: 120.85; Ermita: 131.14 Mg ha-1), followed by the natural area at Bakhawan (204.47 Mg ha-1), the Nabitasan abandoned fishpond (207.04 Mg ha-1), and the natural area at Ermita (324.85 Mg ha-1). The largest mean total sediment carbon stock occurred at the Dumangas abandoned fishpond (684.67 Mg ha-1) (Figure 7.5). Both rehabilitated seafront sites had significantly lower plot- level total sediment carbon stocks than adjacent natural areas (log(total sediment carbon); intercept = 5.52 ± 0.10 [1 s.e.]; Bakhawan rehab: -0.86 ± 0.17 [1 s.e.], p <0.001; Ermita rehab: -0.67 ± 0.17 [1 s.e.], p <0.001). Plot-level total sediment carbon stocks at the Nabitasan abandoned fishpond were not significantly different

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7. Ecosystem services delivery by rehabilitated and natural mangrove forests than at the two natural areas (log(total sediment carbon); intercept = 5.52 ± 0.09 [1 s.e.]; Nabitasan: -0.23 ± 0.16 [1 s.e.], p = 0.16), while those at the Dumangas abandoned fishpond were significantly larger (Dumangas: 0.92 ± 0.16 [1 s.e.], p <0.001).

Table 7.2. Data summary from sediment analysis, organised by site and sediment depth interval. Values represent mean values across all plots at each site (N = 8) ± one standard deviation. Sediment profile depth values denote the range of mean depths across all plots (see Section 5.2 for mean sediment profile depth estimation methodology).

Depth interval Bulk density Sediment depth Site (cm) (g cm-3) OC (%) (cm)

Bakhawan 0-50 0.60 ± 0.07 1.01 ± 0.29 207–238 Natural 50-150 0.67 ± 0.08 1.61 ± 0.80

Bakhawan 0-50 0.65 ± 0.16 0.85 ± 0.33 181–226 Rehab 50-150 0.68 ± 0.16 0.82 ± 0.55

Ermita 0-50 0.70 ± 0.11 1.84 ± 1.11 163–391 Natural 50-150 0.63 ± 0.07 2.33 ± 0.83

Ermita 0-50 0.91 ± 0.07 0.71 ± 0.24 159–172 Rehab 50-150 0.85 ± 0.06 1.02 ± 0.32

Nabitasan 0-50 0.62 ± 0.04 1.72 ± 0.60 146–250 FP 50-150 0.63 ± 0.03 1.70 ± 0.51

Dumangas 0-50 0.38 ± 0.08 4.88 ± 0.97 380–400 FP 50-150 0.37 ± 0.12 4.59 ± 1.41

7.4.4 Site-specific carbon stocks The largest site-level total carbon stock occurred at the Dumangas abandoned fishpond (vegetation carbon = 1,176.12 Mg; sediment carbon = 31,482.69 Mg), followed by the natural area at Bakhawan (vegetation carbon = 1,813.37 Mg; sediment carbon = 7,888.11 Mg), the seafront rehabilitated area at Bakhawan (vegetation carbon = 599.06 Mg; sediment carbon = 2,358.92 Mg), and the Nabitasan abandoned fishpond (vegetation carbon = 46.82 Mg; sediment carbon = 1,870.91 Mg). Owing to small areal coverage (2.34 ha and 0.50 ha respectively), total site carbon stock was low at both the natural (vegetation carbon = 114.03 Mg; sediment carbon = 760.10 Mg) and seafront rehabilitated areas at Ermita (vegetation carbon = 7.19 Mg; sediment carbon = 64.60 Mg) (Table 7.3).

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Table 7.3. Results of site-level carbon stock analysis (units are in Mg per site).

Sediment Site Aboveground Belowground Sediment C below area Site vegetation C vegetation C C 0-50 cm 50 cm Total (ha) Bakhawan 1,267.27 546.10 1,176.94 6,711.17 9,701.48 38.59 Natural Bakhawan 430.18 168.88 563.18 1,795.74 2,957.98 19.52 Rehab Ermita 79.30 34.73 140.31 619.79 908.86 2.34 Natural Ermita 5.05 2.14 15.56 49.04 71.79 0.50 Rehab Nabitasan 41.35 5.47 477.97 1,392.94 1,917.73 9.04 FP Dumangas 856.90 319.22 4,315.52 27,167.17 32,658.81 45.99 FP

7.4.5 Site-specific coastal protection potential Variable vegetation structure (Table 7.1) produced variable potential coastal protection and required greenbelt width (퐵푊) across the sites (Table 7.4). Both natural and rehabilitated areas at the Bakhawan site provide relatively strong protection (protection categories III and II respectively) and require narrow greenbelt widths (퐵푊 = 201 and 270m respectively). This is within the median actual greenbelt width (퐵퐴푐푡푢푎푙 = 842m) (Table 7.4).

Table 7.4. Results of coastal greenbelt protection analysis. FSI = Forest Structure Index and the associated protection category (min = I; max = V), 퐵푊= required greenbelt width (Bao 2011), 퐵퐴푐푡푢푎푙 = median actual greenbelt width calculated from GIS distance analysis (Bivand et al. 2015a).

Protection Site FSI category 푩푾 (m) 푩푨풄풕풖풂풍 (m) Age (years) Bakhawan 0.012 III 201 842 – Natural Bakhawan 0.009 II 270 842 8 Rehab Ermita 0.011 III 229 81 – Natural Ermita 0.007 II 331 81 7 Rehab

Nabitasan FP 0.001 I 2,405 268 5

Dumangas 0.006 II 370 827 8 FP

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At the Ermita site, despite high FSI and protection category (III and II respectively), the required 퐵푊 for adequate coastal protection potential (229–331m) is not achieved, due to a narrow fringing area available for rehabilitation (median 퐵퐴푐푡푢푎푙 = 81m; Table 4). Both abandoned fishpond sites had high median actual greenbelt width (퐵퐴푐푡푢푎푙; Nabitatasan = 268m and Dumangas = 827m). High stem density and vertical growth (Table 7.1) translated to a higher protection category for the Dumangas abandoned fishpond site (II) and adequate current coastal protection potential (퐵푊 = 370m). However, low vegetation density and height (Table 7.1) at the Nabitasan abandoned fishpond site resulted in a low protection category (I) and high required greenbelt width (퐵푊 = 2,405m).

7.4.6 Targeted abandoned fishpond reversion? Dumangas municipality

An area of 377.25ha of abandoned fishponds (see Section 7.3.4 for identification methodology) was identified in Dumangas (8.8% of total current aquaculture area; Primavera et al. 2014; Figure 7.6). Potential total vegetation carbon stock accumulation across this area over 6.5 years was estimated at 4,691.73 Mg. Estimated potential sediment carbon accretion (1.63cm; 0.25 cm yr-1) over 6.5 years was 911.32 Mg.

Figure 7.6. Identified distribution of abandoned fishponds (mangrove vegetation present) across Dumangas municipality, Iloilo, Western Visayas, Philippines from Google Earth imagery (Google Earth 2016).

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This analysis was extended to calculate potential carbon stock accumulation based on only vegetation and sediment carbon stock estimates (Mg ha-1) from the partially-banked Dumangas abandoned fishpond site in order to model potential abandoned fishpond reversion carbon gains under seaward dike management for high propagule and organic matter retention (see Section 4.3). Under this scenario, potential total vegetation carbon accumulation in Dumangas municipality’s abandoned fishponds over 6.5 years was estimated at 6,564.49 Mg and sediment carbon accretion at 1,166.41 Mg. Of the 377.25ha of abandoned fishponds identified in Dumangas, 167.59ha (44.4%) are currently leased under FLAs (Figure 7.7; Primavera et al. 2014).

Figure 7.7. Map of fishpond tenure status across Dumangas municipality, Iloilo, Western Visayas, Philippines. Fishponds outlined in blue are held under private titled tenure, yellow fishponds are held under tenure for tax declaration purposes, green fishponds are areas that are currently listed as non-fishpond land use and purple fishponds are those which are entirely undocumented in available official government records. Red fishponds are those currently tenured under Fishpond Lease Agreements (FLAs) on public mangrove lands (Primavera et al. 2014). A length of 8.59km of Dumangas’ coastline (30.5% of total coastline) was identified as currently fringed by abandoned fishponds. All abandoned fishponds within this 8.59km had an estimated median landward width >100m (current Philippines greenbelt mandate). 3.66km of coastline had an estimated median landward width

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>370m (estimated required bandwidth (퐵푊) based on the Dumangas abandoned fishpond site; Table 7.4), translating to 13.0% of the Dumangas municipality coastline with potential adequate coastal protection from abandoned fishpond mangroves over eight years from reversion (Dumangas abandoned fishpond site age; Table 7.4). Of this coastline, 3.45km (96.7%) is fringed by abandoned fishponds in breach of FLAs (Figures 7.7 and 7.8; Primavera et al. 2014).

Figure 7.8. Distribution of identified sea-facing abandoned fishponds containing coastline with a potential greenbelt width greater than that required (>370m) for effective coastal protection potential from regular waves after eight years of rehabilitation across Dumangas municipality, Iloilo, Western Visayas, Philippines.

7.4 Discussion This chapter provides a quantitative analysis of relative CCMA ES delivery by different mangrove rehabilitation areas and adjacent natural stands. While per hectare carbon stocks were variable across both rehabilitated and natural areas, rehabilitation for enhanced CCMA goals appears more promising in abandoned fishponds. Despite currently lower per hectare biomass production, carbon-rich sediments and large areal coverage enhanced the overall carbon stocks and coastal protection potential of rehabilitated abandoned fishponds. The Dumangas municipality-specific case study revealed that overlap may exist between areas of high rehabilitation potential for CCMA goals and low competing opportunity costs, with 96.7% of the identified wide sea-facing abandoned fishponds currently in

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7. Ecosystem services delivery by rehabilitated and natural mangrove forests breach of lease agreements (FLAs) on public lands. The results may also have implications regarding reverted abandoned fishpond management for high carbon stocks, with specific reference to fishpond bank maintenance.

All rehabilitated stands exhibited structural parameters (stem density, DBH, biomass) within observed trajectories to maturity (15–20 years; Bosire et al. 2008; Alongi 2011; Table 7.1), suggesting good rehabilitation status to date. However, contrary to expectations according to their mid- to upper-intertidal position (H7.1), both abandoned fishponds had comparatively low per hectare vegetation carbon stocks (Table 7.1; Figure 7.5). This may in part reflect methodological limitations that could have underestimated true biomass and canopy height: first, shrubby A. marina was dominant at both sites, and biomass calculation for multi-stemmed individuals (Fu & Wu 2011) may have underestimated biomass; second, SRTM-DEM data were acquired prior to rehabilitation of these areas (Rodriguez et al. 2006). Application of new high resolution TanDEM-X global DEM data (2014) to be released for scientific use in late 2016–2017 will enhance the application of SAR- derived DEM data to recent mangrove rehabilitation monitoring (Zink et al. 2015). However, the SRTM DEM-related limitation also applied to the seafront rehabilitated areas, where estimated vegetation carbon stocks remained high (Figure 7.5). High heterogeneity across both abandoned fishponds, due to on-going natural recolonization at the Dumangas site and high pre-rehabilitation erosion at the Nabitasan site, likely contributed to low average per hectare vegetation carbon stocks. However, lower relative biomass production may also indicate possible hydrological or fishpond effluent constraints to biomass production in abandoned fishponds (Lewis 2005; Matsui et al. 2010; Primavera et al. 2014). These results suggest further active rehabilitation may be necessary to enhance mangrove functioning in reverted abandoned fishponds. Indeed, the Bakhawan seafront site had the highest per hectare vegetation carbon stocks of all rehabilitated sites (Figure 7.5). This may reflect a combination of active replanting and hydrological rehabilitation (see Lewis 2005) to encourage high natural re-colonisation as an effective strategy in low- to mid-intertidal areas (sensu Matsui et al. 2010), and a possible positive influence of multi-species rehabilitation (Lang’at et al. 2012).

As observed in other studies from the region (Thompson et al. 2014), carbon stocks in natural areas (Figure 7.5) were at the lower end of estimates for Indo-Pacific mangroves (Donato et al. 2011). The abandoned fishponds were either not different

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(Nabitasan) or had significantly greater (Dumangas) plot-level sediment carbon stocks than natural areas, and greater than seafront rehabilitated areas (Figure 7.5; H7.1). This reflects their position on former mangrove sediments, and highlights their greater potential over seafront rehabilitation sites in reforestation PES schemes that recognise existing stocks (Locatelli et al. 2014). The large sediment carbon stocks at the Dumangas fishpond are particularly notable (mean 684.67 ± 263.39 Mg ha-1), as these are among the highest recorded in Panay. This includes a large ancient basin mangrove (mean sediment carbon stock 372.60 ± 128.61 Mg ha- 1; data from Chapter 6). A possible driver is site configuration: much of the seaward bank is retained and reduces complete tidal flushing, trapping organic matter in partially-waterlogged sediments. Conversely, near-complete loss of the Nabitasan fishpond seaward bank before recolonization has resulted in extensive erosion at the seaward margin. While these observations are limited to only two abandoned fishponds, they suggest that retaining partial seaward banks may be important for preventing erosion, retaining sediment carbon stocks and improving propagule establishment in recovering abandoned fishponds (similar to breakwater interventions: Hoang Tri et al. 1998; Hasim et al. 2010; Kamali et al. 2011; Tamin et al. 2011; Primavera et al. 2012b). Relatively high bulk densities and low OC at seafront rehabilitation sites compared to adjacent natural areas (Table 7.2) suggest low rates of soft sediment accretion, and slow gains in potential sequestration- oriented PES schemes (Locatelli et al. 2014). Further surveying of abandoned fishpond areas, and study of accretion rates in abandoned fishponds and seafront plantations, will be required to investigate these findings further and establish relative sediment carbon sequestration rates.

Overall, the comparatively large size, mid- to upper-intertidal position and high sediment carbon of the abandoned fishponds translated to high relative total ecosystem carbon stocks (Table 7.3; H7.2). Moreover, high vegetation and sediment carbon sequestration gains were predicted over 6.5 years of potential future rehabilitation of abandoned fishponds in Dumangas municipality (5,809.95- 7,995.75 Mg). It is important to note that these estimates are preliminary and likely underestimates; they employ basic (but conservative) accretion rates and do not account for important processes such as carbon burial (Lee et al. 2014). Large area translates to landward widths available for rehabilitation at abandoned fishpond sites (Table 7.4) that are considerably greater than at rehabilitated seafront areas

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(Bakhawan: 186m; Ermita: 41m). Seafront rehabilitation sites were bordered at their landward margins by existing natural mangrove, increasing effective greenbelt width (Table 7.4). In the unique case of Bakhawan ecopark (see Section 2.1), the combination of rehabilitated and natural forest makes the total greenbelt well above that estimated for effective coastal protection potential (Table 7.4). However, for Ermita, a site more typical of the remaining seafront mangroves in the Philippines, the combined greenbelt width of both rehabilitated and natural areas is far below that required (Table 7.4; H7.3). Repeated LGU planting in adjacent areas to the Ermita site since the early 2000s have seen no survival. The existence of a coastal road between these now fringing areas and the former deltaic mangrove (now active fishponds) inland further prohibits development of an effective greenbelt in the area.

In contrast, both abandoned fishponds had large median landward widths (Nabitasan: 268m; Dumangas: 827m; H7.3). Low vegetation density caused very large estimates of required greenbelt width for Nabitasan abandoned fishpond mangroves (Table 7.4). However, saplings (high density at Nabitasan) were not considered in these analyses, and can contribute to protection from regular wind waves (Mazda et al. 1997). Furthermore, rehabilitated mangroves at this site are comparatively young (Table 7.4), and assisted rehabilitation by the Leganes LGU is on-going. The landward greenbelt width of the Dumangas abandoned fishpond may provide ample coastal protection, even while in relatively young stages of development (Table 7.4; Alongi 2011). In direct conflict with current greenbelt rehabilitation programmes in Dumangas (National Greening Program [NGP] low- intertidal seafront planting), a large length of coastline (3.66km; H7.4) with adequate future protection potential from abandoned fishpond mangroves (>370m wide) eight years post-rehabilitation was identified (Figure 7.8). While this wide greenbelt requirement will reduce as rehabilitated abandoned fishpond mangroves mature (Alongi 2008; Bao 2011; McIvor et al. 2012; Lee et al. 2014), it is important to note that: (1) these estimates are derived for protection from regular waves (Bao 2011), while greenbelt requirements for storm surge attenuation will be substantially greater (Koch et al. 2009; McIvor et al. 2012), and (2) natural stands were estimated here to have required greenbelt widths of >200m (Table 7.4). This suggests that current Philippines greenbelt laws (50–100m) may be inadequate for coastal protection in typhoon-prone areas. Reallocation of Philippines’ NGP funds

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7. Ecosystem services delivery by rehabilitated and natural mangrove forests and capacity away from seafront planting toward targeted reversion and assisted rehabilitation of large sea-facing abandoned fishponds is thus advisable if effective mangrove greenbelt reestablishment and integrated CCMA goals are to be realised (Blankespoor et al. 2016).

The Dumangas municipality-specific case study revealed that 96.7% of the coastline identified as having rehabilitation potential for effective coastal protection within eight years is currently tenured under and in breach of FLA terms (H7.5; Figures 7.7 and 7.8). As such, reversion (Bureau of Fisheries and Aquatic Resources of the Department of Agriculture [DA-BFAR]) and rehabilitation (Department of Environment and Natural Resources [DENR]; see Section 4.2.1) of these areas is a legal requirement, is not in direct conflict with opportunity costs of fishpond leaseholders, and could provide substantial benefits to inland coastal communities from coastal protection and fisheries enhancement (Walton et al. 2006; Primavera et al. 2012b).

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CHAPTER 8: Resilience and resistance to sea level rise in global mangrove forests

8.1 Introduction Global sea level rise (SLR) presents a major threat to mangrove forests, where associated salinity, tidal inundation and erosion increases may lead to seaward mangrove die-off and subsequent coastline retreat (Ellison 1993; Woodroffe 1995; Cahoon et al. 2006; Gilman et al. 2008; Ellison 2015). Increased physiological stress and die-off, and shoreline mangrove retreat under SLR thus threaten mangrove climate change mitigation and adaptation (CCMA) ecosystem services (ES) potential from carbon sequestration and storage (reduced vegetation biomass production) and storm surge attenuation potential (both reduced vegetation biomass production and greenbelt width) (Alongi 2011; Donato et al. 2011; Koch et al. 2009; McIvor et al. 2012). The potential future success of mangrove management and rehabilitation activities to enhance CCMA in the tropics thus hinges on adaptive management under variable climatic projections (IPCC 2013) to reduce the vulnerability of natural and rehabilitated mangrove forests and their CCMA ES to SLR. In order to plan for change, information on the relative ability of mangrove forests to maintain functionality in the face of SLR, and on the abiotic factors principally driving their vulnerability, is required. Quantitative and semi- quantitative site-specific information on mangrove exposure, sensitivity and adaptive capacity to SLR (see Section 3.4; Ellison 2015) currently enable categorisation of global mangrove forests according to relative vulnerability. However, these assessments rely upon assumptions of the relative importance of factors influencing mangrove vulnerability (i.e. additive ranking), and their semi- quantitative nature means that fine scale variation through time cannot be accounted in future monitoring (Ellison 2015).

As discussed in Section 3.4, the resilience of mangrove forests to SLR can be broken down in to two components: resistance (capacity for maintained functionality [e.g. productivity]) and resilience (capacity for maintained areal coverage [e.g. landward migration]; Gilman et al. 2008). This distinction is key, as the underlying processes (abiotic conditions and anthropogenic influences) affecting the capacity of mangroves to exhibit resilience and resistance may vary (Gilman et al. 2008). Resistance is driven predominantly through sediment accretion and surface elevation maintenance relative to rates of SLR (sediment and freshwater input

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8. Resilience and resistance to sea level rise in global mangrove forests regimes) and cryptic degradation with maintained areal coverage (e.g. selective cutting and pollution) (Dahdouh-Guebas et al. 2005; Lee et al. 2014; Lovelock et al. 2015). Through associated impacts on potential coastline erosion and sediment accretion, sediment and freshwater input regimes may also impact mangrove resilience to SLR (e.g. influencing levels of seaward die-back). However, the ability of mangroves to migrate landward under SLR is dependent principally on (1) the landscape topography behind back mangroves (which influences the hydro- ecological conditions affecting landward seedling recruitment), and (2) the presence of physical barriers to landward seedling recruitment (anthropogenic structures: buildings, dikes, sea walls, roads, and agri- and aquaculture) (Gilman et al. 2008; Ellison 2015). Management to enhance mangrove resilience and resistance thus necessitates different approaches (e.g. actions to increase sediment inputs and reduce cutting in stressed systems [resistance], or to zone landward areas to facilitate landward expansion [resilience]: see Gilman et al. 2008; Ellison 2015). Accordingly, to prioritise management approaches into the future where resources and capacity are limited, combined examination of the relative resilience and resistance of mangrove forests is required.

The potential for mangroves to keep pace with, and their potential vulnerability to, SLR has received much scientific attention in recent years. However, due largely to the complexities of field data collection in mangrove ecosystems, studies are often limited in geographic scope (one or few mangrove forests [Ellison 1993; Gilman et al. 2007; Ellison & Zouh 2012], or comprise regional studies based on assumptions of uniform anthropogenic context [the Indo-Pacific region; Lovelock et al. 2015]), and do not consider both mangrove resilience and resistance to SLR, and their drivers, separately in analyses. For example, a recent Indo-Pacific-wide study using Rod Surface Elevation Table Marker Horizon (R-SET) measurements found high vulnerability to SLR (insufficient sediment surface elevation gain) in the majority of mangroves studied, and that Indo-Pacific mangroves with low tidal ranges and sediment inputs could be submerged by the end of the 21st century under SLR (Lovelock et al. 2015). However, the study did not assess the potential for landward migration, or indeed for erosion-driven coastline retreat, and thus may have under- or overestimated vulnerability in the mangroves considered. Conversely, using field- and satellite remote sensing-based methodologies, Ellison & Zouh (2012) assessed mangrove resilience (seaward and landward boundary change) and resistance (sediment accretion) in the mangroves of Douala Edea, Cameroon,

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8. Resilience and resistance to sea level rise in global mangrove forests enabling assessment of vulnerability for the area. However, the influences of human development and potential cryptic degradation from mangrove cutting, and thus associated potential management needs, were not assessed (Ellison & Zouh 2012). Accordingly, we still lack systematic, repeatable methods to assess the capacity for mangrove CCMA ES resilience and resistance to SLR, as well as the predominant factors driving these under current anthropogenic activities and management (Gillman et al. 2008).

8.2 Hypotheses This chapter employs multi-product satellite remote sensing imagery and products to develop a method for monitoring resilience and resistance of mangrove carbon storage (aboveground biomass) and coastal protection potential (aboveground biomass and areal coverage [greenbelt width]) CCMA ES in multiple tropical mangrove sites. It aims to index resilience and resistance across sites and assess the site-specific relative landscape-level and anthropogenic factor controls on these two components of vulnerability to SLR. The chapter addresses the five main questions and three specific hypotheses (H8.1-H8.3) outlined in Section 3.6.3. These are reiterated again below.

The main questions addressed in this chapter are: (1) how resilient are mangrove forests to SLR (maintained areal coverage through landward migration) under current use and management? (2) how resistant are mangrove forests to SLR (maintained biomass/productivity through time) under current use and management? (3) what are the landscape-level topographic constraints to the resilience of mangrove forests? (4) what are the sediment load constraints to mangrove resilience and resistance? and (5) to what extent does anthropogenic development constrain mangrove forest resilience and resistance? The specific hypotheses relating to these main questions are:

H8.2: Sediment load constraints to resilience and resistance (Alongi 2008; Gilman et al. 2008; Krauss et al. 2014; Lovelock et al. 2015). Seaward mangroves with higher satellite-derived coastal total suspended matter (TSM; Blondeau-Patissier et al. 2014) will have greater resistance to SLR. These mangroves will have a higher potential for sediment trapping in their root systems, thus having higher surface

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8. Resilience and resistance to sea level rise in global mangrove forests accretion and elevation gain, and will have lower susceptibility to erosion, which will enable them to keep pace with SLR.

8.3 Methodology and statistical analyses 8.3.1 Study sites used The sites included in this chapter are outlined in Section 4.3. The geographic location and characteristics of each site considered in this chapter are depticted in Figure 8.1.

Figure 8.1. Mangrove forests considered in Chapter 8, and their location from West Africa to South Asia. Displayed imagery is Landsat 5 TM and 8 OLI/TIRS imagery (USGS 2015). (a) Saloum Delta, Senegal; (b) Sherbro Bay, Sierra Leone; (c) Save River Delta, Mozambique; (d) Ruvuma Estuary, Tanzania; (e) Rufiji Delta, Tanzania; (f) Mahajamba, Madagascar; (g) The Sundarbans, India and Bangladesh (see Section 4.3).

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The study sites comprise seven mangrove forests located throughout the tropics (West Africa to South Asia) chosen to represent estuarine and deltaic mangroves (with low topography and thus the ability to migrate landward; Gilman et al. 2008) of large areal coverage (>5,000 ha; potential for resistance), experiencing medium to substantial SLR (5-15cm over a 15 year period, according to sea surface height data: Beckley et al. 2010; GSFC 2013), and with appropriate satellite imagery availability (see Section 4.3).

8.3.2 Satellite remote sensing data used The methodology undertaken within this chapter utilises multiple sources of raw product satellite imagery and developed data products to assess mangrove resistance (biomass change) and resilience (distribution change [landward migration]; Gilman et al. 2008; Ellison 2015), and landscape-level (topographic slope and sediment availability) and anthropogenic development. Landsat 5 TM (2007) and Landsat 8 OLI/TIRS (2015) multispectral imagery (Section 5.4.1.1; Table 5.1; USGS 2015) processed bands and calculated indices (Section 5.4.2.1) were employed to assess mangrove distribution and landward migration and anthropogenic development over 2007-2015. ALOS/PALSAR L-Band HV backscatter amplitude data (Sections 5.4.1.2 and 5.4.2.2; Table 5.2; JAXA/METI 2009; 2010; ESA 2013; ASF DAAC 2016) were employed to assess biomass change over the intertidal mangrove zone and in the seaward boundary (coastline retreat). SRTM DEM elevation data (Section 5.4.1.3; USGS 2014) were used to assess landscape-level topographic constraints to mangrove resilience and resistance, and ENVISAT-MERIS TSM data (Section 5.4.1.4; Figure 5.12; ESA GLOBCOLOUR 2014) used to assess sediment availability. The following subsections outline the methodology framework employed in this chapter, which can be visualised in the schematic provided in Figure 8.2.

8.3.3 Landcover classification in 2007 and 2015 All analyses were conducted in R version 3.2.5 (R Development Core Team 2016), QGIS 2.14.0 (QGIS Development Team 2016), and SENTINEL-1 Toolbox version 4.0.0 (Array Systems Computing Inc. 2016). A schematic of the different stages of resilience and resistance analysis can be visualised in Figure 8.2.

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Figure 8.2. Schematic of the analyses conducted to assess resilience and resistance of mangroves at all study sites (see Section 4.3). PCA = principal components analysis; resistance = the maintenance of ecosystem processes (biomass/productivity) under the process of sea level rise; resilience = adapting ecosystem distribution (landward migration) under the process of sea level rise (Gilman et al. 2008).

Mangrove distribution in the years 2007 and 2015 was classified from pre- processed Landsat 5 TM and 8 OLI/TIRS raw bands and calculated indices rasters for each site (Tables 5.1 & 5.3; Figure 5.16). Landcover classification identifies landcover classes from satellite imagery based on differences in spectral (or other) signatures (Wegmann et al. 2016). Owing to the high spectral distinctiveness of mangrove forests (Kuenzer et al. 2011), and the low complexity of landcover in the coastal study sites considered in this thesis, unsupervised landcover classification was applied (Wegmann et al. 2016).

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Prior to unsupervised landcover classification, Principal Components Analysis (PCA) was conducted across all potential input variables at each site for each time period (2007 and 2015; all Landsat raw spectral bands and calculated indices; see Section 5.4.2.1; Table 5.3; Figure 5.16). This was undertaken in order to reduce the number of potentially redundant variables (e.g. similar spectral characteristics among bands resulting in higher computational reliance on particular spectral information) within final landcover classifications, and to improve multi-temporal (2007 and 2015) classifications (Townshend et al. 1985; Eklundh & Singh 1993; Benedetti et al. 1994; Wegmann et al. 2016). Unstandardized PCA was applied using all Landsat raw bands and calculated indices across all imagery pixels using the rasterPCA function in the ‘RStoolbox’ R package (Leutner & Horning 2016). The resulting PCA loadings plots were visually inspected for all sites for both time periods (2007 and 2015) and variables with high correlations to others were removed from further analyses (see Figure 8.3).

(3)

(1) (2)

Figure 8.3. Example of Principal Components Analysis (PCA) plots for Landsat 5 TM raw bands and calculated indices (Ruvuma Estuary, Tanzania [2007]; representative for all study sites). Left: PCA on all 13 raw bands and indices, showing the high correlation between: (1) Enhanced Vegetation Index (EVI) and Soil-Adjusted Vegetation Index (SAVI); (2) Normalised Multi-Band Drought Index (NMDI), and raw short-wave infrared 1 (SWIR1) and short-wave infrared 2 (SWIR2); and (3) raw Blue, Green and Red bands. Right: PCA on the final nine bands considered within further landcover classifications (EVI, NMDI, Blue and Green removed; see details in the text). The final set of variables considered for further analyses following PCA included nine of the initial 13 Landsat raw band and calculated indices variables (see Section 5.4.2.1; Table 5.3; Figure 5.16; Figure 8.3). These were: Red, NIR, SWIR1, SWIR2,

AWEIsh, MNDWI, NDWI, NDVI and SAVI (Figure 8.3). High correlation between the EVI and SAVI resulted in the removal of EVI, which does not correct for distinctive

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8. Resilience and resistance to sea level rise in global mangrove forests soil conditions of mangrove forests (Table 5.3; Figure 8.3). High correlation among the Blue, Green and Red raw Landsat bands across all imagery resulted in the Red band only being maintained within the final variable set due to its high association with vegetation characteristics and lower Rayleigh scattering (Pettorelli 2013; Jensen 2014; Table 5.3; Figure 8.3). Finally, high correlation between the NMDI and the two SWIR (SWIR1 and SWIR2) raw bands resulted in the removal of NMDI from further analysis (see Figure 8.3).

Unsupervised classifications were conducted on the site-cropped nine variables via kmeans clustering (Hartigan-Wong) into spectrally similar pixel classes (Wegmann et al. 2016). These pixel-based unsupervised landcover classifications were conducted using the unsuperClass function in the ‘RStoolbox’ R package (Leutner & Horning 2016), using random sampling of 10,000 pixels and 100 model iterations over 25 random starts. Unsupervised classifications were performed initially at each site for each time period (2007 and 2015) to identify 10 landcover classes, which were subsequently reduced (removal of one class at each interval) via visual inspection for meaningful landcover classes (Zhu 2016) against high resolution Google Earth imagery at each site (2006-2008 and 2014-2016 imagery for each time period respectively; Google Earth 2016). The optimal number of landcover classes across most sites was established to be five, which corresponded to mangrove (wet vegetation), wet bare ground (mudflats; aquaculture ponds), dry bare ground (sand, salt pans, aquaculture pond banks, agricultural land, cleared and urban areas), dry terrestrial forest and dry terrestrial shrub/grass/agricultural -land (see Figure 8.4).

Sites with the presence of clouds in parts of the Landsat imagery (Saloum Delta, Senegal in 2007) had six landcover classes: those above and clouds. In the Sundarbans study site, areas of unhealthy, defoliated mangroves suffering top dying disease (Heritiera fomes; Iftekhar & Islam 2004) had unique spectral reflectance qualities within the multispectral imagery employed (see also Giri et al. 2007). As a result, for this site mangroves were described by two of the five landcover classes: defoliated and healthy mangroves (other classes: wet bare ground; dry bare ground and dry agricultural areas; terrestrial vegetated areas). The presence of multiple mangrove types at the Mahajamba, Madagascar site resulted in the detection of two mangrove landcover classes (shrubby, low-productivity mangrove and tall, dense mangrove); thus, the optimal number of landcover classes here was six. The classes

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8. Resilience and resistance to sea level rise in global mangrove forests for shrubby and tall mangroves were then combined into a single mangrove class following unsupervised classification (Giri 2016; Zhu 2016).

Figure 8.4. Unsupervised kmeans landcover classification for the Ruvuma Estuary, Tanzania study site in 2007. See text for details regarding landcover classification methodology.

Landcover classifications can often result in misclassification of isolated pixels within other landcover areas, which must be smoothed out in post-classification filtering (Giri 2016; Wegmann et al. 2016; Zhu 2016). Because one of the aims of this thesis is to assess small changes to the landward and seaward boundaries of mangrove distribution through time (Gilman et al. 2008), it was not appropriate to apply post-classification filtering to all classified pixels here, due to the potential for smoothing of some mangrove edge pixels (Wegmann et al. 2016). Accordingly, only larger, contiguous mangrove pixels (>3,600 m2; four initial Landsat pixels at 30m resolution) were considered within further analyses (see Figure 8.5).

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Figure 8.5. Classified mangrove extent at the Ruvuma Estuary, Tanzania study site: close-up of removal of small classified patches (≤3,600 m2). Left: classified mangrove forest extent for the year 2007. Right: classified mangrove forest extent with all small clusters of “mangrove” pixels (≤3,600 m2) removed. See text for detail on this methodology. Post-classification landcover change detection (e.g. comparison of separately classified multi-date imagery) can have low accuracy due to the introduction of classification error at two stages, and instead assessment of pixel-specific change across time periods is advocated (Deng et al. 2008; Hussain 2013; Giri 2016). In order to ensure that changes (loss or gain) in mangrove distribution in the unsupervised landcover classifications over 2007-2015 were due to actual changes in mangrove cover rather than representing classification errors, further pixel- specific post-classification change detection was conducted. Across each study site, the pixel-specific percentage difference in NDVI (vegetation index; see Section 5.4.2.1) for 2007 and 2015 was calculated. Pixels attributed as representing mangrove loss or gain in the previous step were only maintained as such if they also had ≥25% loss or gain in NDVI respectively over the 2007-2015 period (in order to index e.g. open canopy or dwarf mangroves converted to agriculture, or bare ground with established young mangrove by 2015). All “loss” or “gain” pixels with <25% change in NDVI over the time period were considered to represent classification error in the 2015 unsupervised landcover classification, and were assigned the same landcover (i.e. mangrove or other) as the 2007 landcover classification.

In the absence of ground truth data on mangrove distribution at all sites, the accuracy/validity of classified mangrove distribution in each year at each site was visually checked against reference high resolution historical optical satellite imagery in Google Earth (Google Earth 2016; see Fritz et al. 2009; Duncan et al.

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2014; Owen et al. 2015 for applications). For the years 2007 and 2015, classified mangrove distribution rasters for each site were cropped to the distribution of available high resolution optical imagery within Google Earth Pro (2006-2008 and 2014-2016; Google Earth 2016). Pixel centres of all mangrove landcover pixels with available Google Earth imagery were then converted to point shapefiles using the rasterToPoints function in the ‘raster’ R package (Hijmans et al. 2016). For each site at each time period, 200 points within the reduced classified mangrove distribution were randomly selected from these point shapefiles as drawn from a uniform probability distribution. The resulting shapefiles were buffered to create a 12.5×12.5 m square polygon corresponding to the pixel extent in the classified resampled Landsat imagery (see above; Figure 8.6).

Figure 8.6. Landcover classification validation from high resolution multispectral imagery (Google Earth 2016): examples of correctly classified mangrove landcover pixels (>50% mangrove cover), mixed pixels (≤50% mangrove cover), and misclassified pixels (0% mangrove cover; top: sandy riverbank; middle: creek; bottom: mudflat). The pixel centre points and extent polygons were then converted to .kml format in QGIS 2.14.0 (QGIS Development Team 2016) and visually assessed for true landcover in their respective years of acquisition (2007 and 2015) in the high-

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8. Resilience and resistance to sea level rise in global mangrove forests resolution Google Earth imagery (Google Earth 2016). Classified mangrove pixel polygons were scored into three categories: (1) true mangrove (>50% of the polygon contained mangrove cover), (2) misclassified (the polygon did not contain mangrove cover), or (3) mixed pixel (the polygon contained ≤50% mangrove cover) (Fisher 1997) in Google Earth imagery in the year of acquisition (Figure 8.6). This was undertaken in order to assess the accuracy of the landcover classification algorithm across sites, and to assess the potential impact of mixed pixels (de Jong & van der Meer 2007) on boundary extent and change through time (2007-2015) in these analyses. It is acknowledged that this approach does not assess misclassification of true mangrove landcover as other landcover classes (wet bare ground [mudflats], dry bare ground [sand, salt pans, cleared and urban areas], dry terrestrial forest, or dry terrestrial shrub/grass/agricultural -land).

8.3.4 Mangrove landward distribution change 2007-2015 (resilience) Following accuracy assessment, change in mangrove distribution at each site across 2007-2015 at each site was assessed. Pixels with mangrove cover in both the 2007 and 2015 landcover classifications were extracted into a new raster to establish the constant mangrove distribution across the time period. Pixels with mangrove cover in 2007 but not in 2015 were then extracted into a new raster to establish pixels of mangrove loss across the time period. Finally, pixels with no mangrove cover in 2007 but new mangrove cover in 2015 were extracted into a new raster to establish pixels of mangrove gain across the time period (Figure 8.7). Total area of gain and loss (ha) and percentage change in mangrove distribution over 2007-2015 were calculated in order to assess mangrove resilience (maintained areal coverage; Gilman et al. 2008) to sea level rise under current anthropogenic threats and management.

8.3.5 Mangrove seaward boundary change 2007-2010 (resilience and resistance) Change in the seaward boundary of mangrove distribution at all sites between 2007 and 2010 was assessed using pre-processed ALOS/PALSAR L-Band HV backscatter amplitude data (see Sections 5.4.1.2 and 5.4.2.2; Cornforth et al. 2013). Waterways (oceanic bodies, rivers and small creeks) display very low levels of ALOS/PALSAR L-Band HV backscatter amplitude due to specular microwave reflection, and thus may be reliably extracted from imagery (Cornforth et al. 2013). Following the methodology employed by Cornforth et al. (2013), waterways were determined using a threshold value (34) on a 7×7 moving window Refined Lee filter (Lee et al.

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1994: conducted in SENTINEL-1 Toolbox version 4.0.0 with a default backscatter amplitude edge variance threshold of 5,000; Array Systems Computing Inc. 2016) on the square root of the sum of backscatter amplitude values between the co- registered 2007 and 2010 ALOS/PALSAR L-Band HV imagery (conducted in QGIS version 2.14.0; QGIS Development Team 2016) (see Figure 8.8). This method enables detection and extraction of pixels that were land (e.g. mangrove) in one scene and water in the other, such that changes in coastline extent and retreat can be identified and biomass changes therein calculated (Cornforth et al. 2013).

Figure 8.7. Spatial overlay of mangrove distribution change over 2007-2015 at the Ruvuma Estuary, Tanzania study site. Inserts depict areas of distribution loss and gain. Green = constant mangrove cover over 2007-2015. Red = loss of mangrove landcover pixels from 2007 to 2015. Yellow = gain of mangrove landcover pixels from 2007 to 2015.

At each site, all seaward mangrove pixels of within five pixels’ buffer distance (62.5m) from the combined 2007-2010 coastline edge were maintained and analysed for coastline retreat (significant biomass change over 2007-2010; see Section 8.3.6; see Cornforth et al. 2013). Comparison of coastline retreat and

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8. Resilience and resistance to sea level rise in global mangrove forests landward boundary shift (landward mangrove distribution gain as established in Sections 8.3.4 and 8.3.7.1) was then conducted at each site to assess overall resilience of each mangrove stand.

Figure 8.8. Combined 2007-2010 coastline extraction from ALOS/PALSAR L-Band HV backscatter amplitude at Ruvuma Estuary, Tanzania. Left: 7x7 moving window Refined Lee filtered HV backscatter amplitude imagery for 2007; middle: 7x7 moving window Refined Lee filtered HV backscatter amplitude imagery for 2010; right: thresholded square root of the sum of the values from the left and middle panels, revealing the combined 2007-2010 coastline edge. See text for further details. 8.3.6 Mangrove biomass change 2007-2010 (resistance) The ability of mangrove forests to maintain functionality (e.g. biomass productivity; resistance: Gilman et al. 2008) was assessed here through change detection on pre- processed ALOS/PALSAR L-Band HV backscatter amplitude (Sections 5.4.1.2 and 5.4.2.2; aboveground biomass proxy: Proisy et al. 2003; Lucas et al. 2007; Cornforth et al. 2013; Fatoyinbo & Simard 2013) between 2007 and 2010 imagery. Change in L-Band HV backscatter amplitude was assessed within the area of constant mangrove distribution over 2007-2010 (see Section 8.3.4; Figure 8.9). Change in the sum of total HV backscatter amplitude between the years 2007-2010 across the constant mangrove distribution was then calculated to assess total mangrove biomass change (e.g. a measure of current relative resistance of each mangrove forest in the face of SLR; Gilman et al. 2008). Pixel-specific percentage change in ALOS/PALSAR L-Band HV backscatter amplitude within the constant 2007-2010 mangrove distribution was then calculated from the raw backscatter change calculations shown in Figure 8.9. In order to produce conservative estimates of significant biomass change over the study period, areas of ≥15% increase or decrease in backscatter amplitude were extracted, as this is greater than the threshold (≥10%) that can correspond to a significant change in aboveground biomass on the ground (Lucas et al. 2009; Mitchard et al. 2011; Carreiras et al. 2012; Cornforth et al. 2013).

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Figure 8.9. Raw pixel-specific change in ALOS/PALSAR L-Band HV backscatter amplitude between 2007 and 2010 imagery at the Ruvuma Estuary, Tanzania study site. Purple pixels show decreasing backscatter (e.g. aboveground biomass) and green pixels increasing backscatter values between the two time periods. Total area and proportion of pixels with significant increase and decrease in mangrove biomass was then calculated to further assess current relative resistance of each mangrove forest in the face of SLR (Gilman et al. 2008). SLR may enhance inundation frequency and salinity ranges of affected mangrove areas (Lovelock et al. 2015), which may in turn restrict mangrove growth and productivity (resistance) through time (Gilman et al. 2008; Krauss et al. 2014). Simultaneously, anthropogenic alterations to freshwater, sediment and nutrient inputs, extraction of mangrove timber and clearing in landward mangrove areas may impede levels of mangrove resistance to SLR (Lee et al. 2014). Accordingly, these analyses reflect current relative resistance of mangroves to SLR under existing environmental conditions and anthropogenic disturbance regimes (Gilman et al. 2008).

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8.3.7 Drivers of mangrove resistance & resilience All analysis of drivers of mangrove resilience and resistance was conducted in R version 3.2.5 (R Development Core Team 2016), QGIS 2.14.0 (QGIS Development Team 2016), and Google Earth (Google Earth 2016). The following sections detail the stages of analysis of the further drivers of mangrove resilience and resistance (e.g. drivers of total significant biomass change [Section 8.3.6] are assumed here to be a combination of both SLR and anthropogenic processes).

8.3.7.1 Topographic controls on resilience To assess determinants of potential landward shifts in mangrove distribution (resilience; Gilman et al. 2008) from 2007-2015 at all sites, pixels immediately adjacent to the 2007 classified mangrove distributions were extracted and analysed. The 2007 classified mangrove distribution rasters were first converted to polygons using the gdal_polygonizeR function (J. Baumgartner; https://johnbaumgartner. wordpress.com/2012/07/26/getting-rasters-into-shape-from-r/) calling GDAL (Warmerdam 2008; GDAL 2016) from R. The resulting 2007 mangrove distribution polygons were then buffered by 9m (just larger than diagonal pixel edge to centre distance) using the gBuffer function in the ‘rgeos’ R package (Bivand et al. 2016b), and the perimeter pixels extracted using the gDifference function from the ‘rgeos’ R package (Bivand et al. 2016b) applied to the original and buffered 2007 mangrove distribution polygons. AWEIsh rasters for 2007 and 2015, calculated from the Landsat 5 TM and Landsat 8 OLI/TIRS imagery respectively (see Sections 5.4.1.1, 5.4.2.1 and 5.4.2.5), were then used to remove seaward perimeter pixels from the buffered 2007 mangrove distribution layer. Pixels for which the sum of AWEIsh in 2007 and 2015 was >0 (e.g. water present in both years; Feyisa et al. 2014; Li & Gong 2016) were extracted and the created 2007 mangrove distribution perimeter polygon was masked with the extracted water pixels. This method did not remove seaward pixels within the 2007 mangrove distribution adjacent perimeter polygons that did not comprise waterbodies (e.g. pixels behind mudflats, sandbanks or other geological features). Accordingly, the 2007 mangrove distribution adjacent perimeter polygons were further manually edited in QGIS to create a final landward 2007 mangrove distribution perimeter polygon (Figure 8.10).

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Figure 8.10. Example of the extracted 2007 mangrove distribution adjacent perimeter at Ruvuma Estuary, Tanzania. The 2015 mangrove distribution rasters were then cropped to the landward 2007 mangrove distribution perimeter polygons (e.g. all pixels immediately adjacent [one-pixel distance] from mangrove distribution in 2007). This resulted in a 2015 adjacent perimeter raster layer containing pixels with mangrove cover in 2015 (1s) and pixels with no mangrove cover in 2015 (0s). The number, length and proportion of landward perimeter pixels containing mangrove cover in 2015 were extracted for each site to index landward migration at each site. Pixels within the 2015 adjacent mangrove perimeter raster layer (1’s and 0’s for landward shift) were then analysed for their topographic information. SRTM DEM elevation data (30m) was resampled to the extent and spatial resolution (12.5m) of the landcover classifications produced for each study site via bilinear interpolation using the resample function in the ‘raster’ R package (Hijmans et al. 2016). The topographic slope of each resampled SRTM DEM pixel relative to all eight neighbouring pixels was then

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8. Resilience and resistance to sea level rise in global mangrove forests calculated using the terrain function of the ‘raster’ R package (Hijmans et al. 2016) (Figure 8.11). The SRTM DEM slope values for each adjacent mangrove perimeter raster pixel were then extracted.

Figure 8.11. Example of SRTM DEM slope data used to assess topographic controls on mangrove resilience at Ruvuma Estuary, Tanzania. Left: SRTM DEM elevation (m) resampled to 12.5m resolution; right: calculated topographic slope in each SRTM DEM pixel (eight neighbours). An Integrated nested Laplace approximation (INLA) approach for latent Gaussian Markov random field models (Rue et al. 2009) was employed to assess the potential effect of topographic slope on mangrove landward migration into the 2007 mangrove distribution adjacent perimeter by 2015 at each site. An INLA approach was conducted in order to control for spatial structure (spatial autocorrelation) in 2015 landward migration across the adjacent mangrove perimeter raster pixels, using a Stochastic Partial Differential Equation (SPDE) approach (Lindgren et al. 2011; Blangiardo et al. 2013). INLA SPDE models for binomial response data (1 = mangrove in 2015; 0 = no mangrove in 2015), using a Delauney triangulation mesh

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8. Resilience and resistance to sea level rise in global mangrove forests with a minimum distance between observations of 2.5 km and a maximum triangle edge length of 125,000 km (reduced computational resources in areas with no observations; Blangiardo & Cameletti 2015; Lindgren & Rue 2015), were employed. INLA SPDE models were conducted over a random sample of 20,000 adjacent mangrove perimeter pixel centres (to reduce computational resource requirements) at each site to assess site-specific potential topographic slope controls on landward mangrove migration over 2007-2015. At each site, single INLA SPDE models including topographic slope (%; square root-transformed to stabilise the variance) as a predictor variable, alongside the SPDE spatial structure field across the created Delauney triangulation meshes for each site.

8.3.7.2 Sediment input load controls on resistance The ability of mangroves to keep pace with SLR (resilience and resistance; Gilman et al. 2008) is dependent on sediment surface accretion and elevation gain, which is impacted by mangrove root biomass production and sediment trapping (Alongi 2008; Gilman et al. 2008; Krauss et al. 2014; Lovelock et al. 2015). As discussed in Section 5.4.1.4, satellite-derived TSM is a good indicator proxy of suspended sediment inputs from surface and river run-off in coastal and estuarine waters (Blondeau-Patissier et al. 2014), and correlates well with sediment surface accretion and elevation gain (Lovelock et al. 2015).

Mangrove timber extraction and clearing is assumed here to be more frequent in the more easily-accessible middle to landward portions of the mangrove forests examined. In order to assess potential sediment input load effects on within-site mangrove resilience (seaward boundary change) and resistance (biomass change) to SLR, seaward pixels within five pixels from the combined 2007-2010 coastline edge detected from pre-processed ALOS/PALSAR L-Band HV backscatter amplitude data (see Section 8.3.5) were analysed relative to local ENVISAT-MERIS-derived TSM (g m-3) at each site. Pixel centres of all seaward pixels at each site were converted to point shapefiles using the rasterToPoints function in the ‘raster’ R package (Hijmans et al. 2016). Pixel centres of all mean TSM (g m-3) and significant Kendall’s tau in TSM (g m-3) 2006-2010 rasters were converted to point shapefiles in the same manner (see Section 5.4.1.4). The mean TSM and Kendall’s tau of the closest pixels to each seaward ALOS/PALSAR L-Band HV pixels were then extracted using the gDistance function in the ‘rgeos’ R package (Bivand et al. 2016b).

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INLA SPDE models for Gaussian response data were then employed to assess the potential effects of mean TSM (2006-2010) and Kendall’s tau in TSM (2006-2010) on ALOS/PALSAR L-Band HV backscatter amplitude change (proportional change over 2007-2010) within all seaward boundary pixels at each site. As in the previous analyses (Section 8.3.7.1), Delauney triangulation meshes with a minimum distance of 2,500 km between observations and a maximum triangle edge of 125,000 km (Blangiardo & Cameletti 2015; Lindgren & Rue 2015) were employed for each site. Three INLA SPDE models were conducted over a random sample of 20,000 seaward boundary pixels at each site to assess site-specific potential sediment input load controls on proportional change in biomass over 2007-2010. The three models included: (1) mean TSM (g m-3) 2006-2010 only, (2) Kendall’s tau in TSM (g m-3) 2006-2010 only, and (3) mean TSM (g m-3) + Kendall’s tau in TSM (g m-3) as predictor variables, alongside the SPDE spatial structure field across the calculated Delauney triangulation meshes for each site. INLA SPDE models at each site were assessed for significant effects using the 97.5% quantiles of the slope estimates for each predictor variable contained, and compared using the Deviance Information Criterion (DIC; Spiegelhalter et al. 2002) and Watanabe-Akaike Information Criterion (WAIC; Watanabe 2010) for Bayesian hierarchical models. Best-fitting models were selected as those with four WAIC greater than other fitted models (Burnham & Anderson 2013; Arnold 2010; Burnham et al. 2011).

8.3.7.3 Anthropogenic controls on resilience The ability of mangroves to migrate landwards in response to SLR is hindered by the presence of physical anthropogenic barriers (e.g. roads, embankments, buildings, and other coastal infrastructure; Gilman et al. 2008). For all pixels within the adjacent mangrove perimeter raster layer identified in the previous step (Section 8.3.7.1), landcover classes in the years 2007 and 2015 were extracted to form two new 2007 and 2015 adjacent mangrove perimeter landcover raster layers. Within these rasters, all pixels classified as dry bare ground landcover (e.g. sand, salt pans, aquaculture pond banks, agricultural land, roads, embankments, cleared and urban areas; see Section 8.3.3) were extracted as an index of potential anthropogenic landcover in the landward direction. The number and relative proportion of adjacent mangrove perimeter pixels containing potential anthropogenic landcover were then calculated as a proxy for the density of potential anthropogenic barriers to mangrove landward migration.

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Indexing potential anthropogenic landcover as only the dry bare ground landcover class comes with two caveats: (1) dry bare ground areas (e.g. exposed rock, sand banks, salt pans) can be relatively common landscape features and do not represent adjacent anthropogenic landcover, and (2) not considering wet bare ground in assessment of potential adjacent anthropogenic landcover means that some aquaculture development (shallow brackish water aquaculture with less developed banks) may not be well accounted. However, while this method may cause some spurious estimation of anthropogenic landcover, it is assumed that changes to the distribution of natural dry bare ground landcover (exposed rock, sand banks, salt pans) over the time period are unlikely to be significant, and that aquaculture development through time will be indexed by dry bare ground landcover where banks are sufficiently well developed. Thus, any temporal changes in the density and proportion of dry bare ground landcover are assumed to reflect increases in anthropogenic development at landward mangrove margins alone. Accordingly, the increase in adjacent potential anthropogenic landcover from 2007-2015 was employed to assess the level of anthropogenic control on potential landward mangrove migration (resilience) and biomass change (resistance) across sites.

8.4 Results 8.4.1 Site-specific exposure: trend in mean sea level anomaly The Permanent Service for Mean Sea Level (PSMSL) tide gauge time-series data (Holgate et al. 2013; PSMSL 2016) were downloaded and analysed to assess the trend in SLR for each study site (Table 8.1; see Appendix 2). Analysis of the closest long-term tide gauges for each study site within the PSMSL database revealed an increasing trend in mean sea level anomaly (mm; SLR) at all sites (Table 8.1; see Appendix 2). The greatest mean positive trend in mean sea level anomaly was observed at the Save River Delta, Mozambique (0.05 mm month-1), followed by the Sundarbans, India and Bangladesh (0.027 mm month-1), Saloum Delta, Senegal and Sherbro Bay, Sierra Leone (0.025 mm month-1), Mahajamba, Madagascar (0.02 mm month-1), and Ruvuma Estuary and Rufiji Delta, Tanzania (0.003 mm month-1).

8.4.2 Site-specific landscape features (elevation, slope and sediment inputs) Study sites varied in their topographic characteristics, with most sites having average elevation above mean sea level of ~11.5-14.5m (Table 8.2), However, the Save River Delta and Sundarbans study sites represented lower-lying mangrove areas (mean elevation [m] 4.84 and 6.27 respectively) (Table 8.2). Save River Delta

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8. Resilience and resistance to sea level rise in global mangrove forests also represented the site with the lowest mean topographic slope across pixels (0.003), followed by the Sundarbans, Ruvuma Estuary, Rufiji Delta, Mahajamba, Saloum Delta and Sherbro Bay (Table 8.2).

Table 8.1. Tide gauge record time-series data for each study site. The names of tide gauges employed at each site, the tide gauge record time period coverage, the intercept, slope and p value of linear regressions of mean sea level anomaly (mm; see Appendix 2), and mean site-specific slopes for trends in mean sea level anomaly (mm) are provided. Numbers in brackets represent standard deviations of the estimates provided.

Tide gauge Intercept Slope record (mean sea level (mean sea level Mean Site Tide gauge coverage anomaly; mm) anomaly; mm) p value slope Saloum Delta, Dakar 2 1992-2014 -39.22 (9.79) 0.04 (0.003) <0.001 0.025 SN Palmeira 2000-2014 -139.70 (18.98) 0.01 (0.001) <0.001 Sherbro Bay, Dakar 2 1992-2014 -39.22 (9.79) 0.04 (0.003) <0.001 0.025 SL Palmeira 2000-2014 -139.70 (18.98) 0.01 (0.001) <0.001 Save River Maputo 1994-2001 -483.45 (107.70) 0.05 (0.01) <0.001 0.05 Delta, MZ Ruvuma Zanzibar 1984-2014 -32.49 (7.46) 0.003 (0.001) <0.001 0.003 Estuary, TZ Rufiji Delta, Zanzibar 1984-2014 -32.49 (7.46) 0.003 (0.001) <0.001 0.003 TZ Mahajamba, Dzadoudzi 2008-2015 -253.30 (120.07) 0.02 (0.01) 0.038 0.02 MG Charchanga 1979-2000 -166.50 (26.15) 0.03 (0.003) <0.001 Sundarbans, Hiron Point 1983-2003 -76.00 (22.75) 0.01 (0.003) <0.001 0.027 IN & BD Khepupara 1977-2000 -267.8 (24.94) 0.04 (0.003) <0.001

Sediment availability, as indexed from ENVISAT MERIS-derived TSM data (Section 8.3.7.3) across all study site pixels, similarly revealed variability across sites. Mean site TSM (g m-3) was greatest at the Sundarbans (20.90), followed by Mahajamba (13.08) and Saloum Delta (10.41), and was lower across the remaining sites, being lowest at Sherbro Bay (5.32) (Table 8.2). As assessed on 95% CIs, no significant change in sediment availability across all site pixels was observed at Save River Delta and Ruvuma Estuary (Mean Kendall’s tau across all pixels; Table 8.2). However, a weak positive trend in sediment availability was observed across pixels at Sherbro Bay (mean tau = 0.006; 95% CIs = 0.003-0.008), and negative trends were observed at Rufiji Delta (mean tau = -0.04; 95% CIs = -0.05--0.02), Saloum Delta (mean tau = -0.02; 95% CIs = -0.02--0.01), Mahajamba (mean tau = -0.005; 95% CIs = -0.009--0.001) and the Sundarbans (mean tau = -0.04; 95% CIs = -0.007- -0.002).

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Table 8.2. Summary of SRTM DEM-derived topographic data and ENVISAT MERIS-derived total suspended matter (TSM; g m-3) data across all pixels at each study site. Figures in bold represent significant mean Kendall’s tau TSM (g m-3) trend estimates based on bootstrapped 95% confidence intervals (CIs).

Mean Mean Mean TSM Mean elevation slope (g m-3; 95% CIs; Kendall’s tau TSM Site (m; range) (%; range) range) (95% CIs; range) 10.41 -0.02 Saloum Delta, 12.86 0.04 (10.01-10.80; (-0.02--0.01; SN (0.00-34.99) (0.00-0.94) 0.03-37.84) -0.40-0.26) 5.32 0.006 Sherbro Bay, 11.57 0.05 (4.93-5.69; (0.003-0.008; SL (0.00-34.99) (0.00-0.74) 0.04-21.28) -0.09-0.28) 9.92 0.001 Save River Delta, 4.84 0.003 (9.07-10.77; (-0.001-0.003; MZ (0.00-27.02) (0.00-0.45) 0.12-19.66) -0.08-0.09) 5.77 -0.01 Ruvuma Estuary, 11.72 0.04 (3.93-7.42; (-0.02-0.004; TZ (0.00-34.99) (0.00-0.51) 0.12-17.47) -0.21-0.00) 9.69 -0.04 Rufiji Delta, 12.17 0.04 (7.70-11.59; (-0.05--0.02; TZ (0.00-34.99) (0.00-0.69) 1.00-27.39) -0.21-0.02) 13.08 -0.005 Mahajamba, 14.55 0.04 (11.70-14.45; (-0.009--0.001; MG (0.00-34.99) (0.00-0.79) 0.20-34.88) -0.25-0.12) 20.90 -0.004 The Sundarbans, 6.27 0.03 (20.40-21.40; (-0.007--0.002; IN & BD (0.00-34.93) (0.00-0.61) 1.05-32.55) -0.18-0.20)

8.4.3 Unsupervised landcover classification Accuracy assessment of the unsupervised landcover classifications at randomly- selected classified mangrove pixels from high resolution Google Earth Pro imagery (Google Earth 2016) revealed good levels of accuracy in mangrove landcover classification across sites and time periods (mean true positives = 90.8%; Table 8.3). The mean proportion of false positive mangrove pixels across sites at both time periods was 3.89%, while the mean proportion of mixed pixels was 6.00% (Table 8.3). Lower overall mangrove landcover classification accuracies (82.5% and 83.0%, and 87.0% and 87.5%) were found at the Save River Delta and the Saloum Delta in both 2007 and 2015 respectively, and at the Rufiji Delta in 2015 (86.5%; Table 8.3). Relatively high false positive rates were observed in both the 2007 and 2015 mangrove landcover classifications for the Save River Delta and the Saloum Delta respectively: 9.0% and 7.0%, and 6.5% and 7.5% (Table 8.3). High mangrove

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8. Resilience and resistance to sea level rise in global mangrove forests landcover classification accuracy according to true positive detection was found in both years at all other sites (91.5-94.0%; Table 8.3).

Table 8.3. Accuracy assessment of unsupervised landcover classifications at each site for 2007 and 2015, based on visual inspection of 200 randomly selected mangrove classified pixels in each year from Google Earth (2016) imagery (see Section 8.3.3). ‘True positives’ represent pixels with >50% mangrove cover within their extent, ‘false positives’ represent pixels with 0% mangrove cover, and ‘mixed pixels’ represent pixels with 0-50% mangrove cover.

2007 2015

True False Mixed True False Mixed positive positive pixel positive positive pixel Site (%) (%) (%) (%) (%) (%)

Saloum, SN 87.0 9.0 4.0 87.5 7.0 5.5

Sherbro, SL 91.5 1.0 7.5 91.5 5.0 3.5

Save, MZ 82.5 6.5 11.0 83.0 7.5 9.5

Ruvuma, TZ 93.5 1.0 5.5 93.0 2.0 5.0

Rufiji, TZ 93.0 1.5 5.5 86.5 4.5 9.0

Mahajamba, 94.0 2.5 3.5 93.0 3.5 3.5 MG Sundarbans, 93.0 1.5 5.5 92.5 2.0 5.5 IN & BD

8.4.4 Mangrove landward distribution change 2007-2015 (resilience) All sites exhibited variation (both gain and loss) in mangrove distribution between 2007 and 2015 (Table 8.4). Four out of seven sites showed stable distributions (<1% change) –Ruvuma Estuary, Rufiji Delta, the Sundarbans and Saloum Delta – all showing marginal increases in mangrove area over the time period (0.64- - 0.09%) (Table 8.4; Appendix 3 Figures A3.1-A3.4). An overall loss of mangrove area was observed at only one study site: a -2.46% loss of coverage at Sherbro Bay, (4,822.50 ha; Table 8.4; Appendix 3 Figure A3.5). Conversely, net increases in mangrove area of 4.55% (2,737.23 ha) and 12.09% (8,696.70 ha) were observed at Save River Delta and Mahajamba (Table 8.4; Appendix 3 Figures A3.6 and A3.7).

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Table 8.4. Results of mangrove distribution change assessment (spatial overlay of 2007 and 2015 unsupervised mangrove landcover classifications) at each site between 2007-2015.

2007 2015 Loss Gain Constant 2007-2015 2007-2015 Site (ha) (ha) (ha) (ha) (ha) change (ha) change (%)

Saloum, SN 472,177.40 471,728.80 644.48 195.92 471,532.90 -448.60 -0.09

Sherbro, SL 196,085.30 191,262.80 4,831.97 10.80 191,252.00 -4,822.50 -2.46

Save, MZ 60,182.05 62,919.28 483.27 3,220.5 59,698.78 2.737.23 4.55

Ruvuma, TZ 13,700.62 13,787.84 37.14 124.36 13,663.48 87.22 0.64

Rufiji, TZ 43,000.92 43,157.88 651.69 808.64 42,349.23 156.96 0.36

Mahajamba, MG 71,953.86 80,650.56 1,784.50 10,481.20 70,169.36 8,696.70 12.09

Sundarbans, IN & 638,198.60 639,528.40 82.25 1,411.98 638,116.40 1,329.80 0.21 BD

8. Resilience and resistance to sea level rise in global mangrove forests

All study sites exhibited some mangrove landward migration into the 2007 mangrove distribution adjacent landward perimeter by 2015 (Table 8.5). The proportion of 2007 mangrove distribution adjacent landward perimeter pixels that contained mangrove cover in 2015 was, however, variable across sites (Table 8.5). The greatest levels of landward migration were observed in three of the sites with among the greatest mean sediment supplies: the Sundarbans, Mahajamba and Save River Delta (11.48-31.11%; Table 8.3; Section 8.4.2). However, among the lowest levels of landward migration in 2015 were observed at Saloum Delta (0.02%), as well as Sherbro Bay in West Africa (Table 8.5). Low to moderate levels of landward migration were observed in the two Tanzanian study sites: Ruvuma Estuary (6.54%) and Rufiji Delta (1.85%) (Table 8.5).

Table 8.5. Results of mangrove landward migration assessment over 2007-2015. Landward migration was assessed from the unsupervised classified 2015 mangrove distribution within a one-pixel wide landward perimeter of the 2007 classified mangrove distribution (see Section 8.3.7.1).

Landward pixels Proportion of landward Total landward pixels migrated 2007-2015 pixels migrated 2007- Site (No. & length; km) (No. & length; km) 2015 (%)

6,192,415 1,116 Saloum, SN 0.02 77,405.19 13.95

1,240,781 69 Sherbro, SL 0.01 15,509.76 0.86

380,145 79,768 Save, MZ 20.98 4,751.81 997.10

49,165 3,215 Ruvuma, TZ 6.54 614.56 40.19

243,415 4,501 Rufiji, TZ 1.85 3,042.69 56.26

Mahajamba, 384,666 119,651 31.11 MG 4,808.33 1,495.64

Sundarbans, 442,867 50,889 11.48 IN & BD 5,535.84 636.11

8.4.5 Mangrove seaward boundary change 2007-2010 (resilience and resistance) ALOS/PALSAR HV imagery availability over 2007-2010 restricted total site coverage, and biomass and seaward boundary biomass change detection was conducted across reduced site extents. Total constant mangrove area coverage for

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HV backscatter change detection analyses varied from 11.51% at Saloum Delta to 99.45% Ruvuma Estuary (Tables 8.4 & 8.5). Positive seaward biomass change was observed at two of the sites with the highest mean sediment supplies: Saloum Delta (5.92% seaward biomass increase) and Mahajamba (3.32% seaward biomass increase) (Table 8.6). However, the greatest loss of seaward biomass was observed in the Sundarbans (-7.97%) (Table 8.6). All other sites exhibited decreases in seaward boundary biomass over 2007-2010, ranging from a marginal decrease of - 1.88% at Sherbro Bay to a substantial loss of -7.63% at Rufiji Delta (Table 8.6).

8.4.6 Mangrove biomass change 2007-2010 (resistance) ALOS/PALSAR HV backscatter change detection in the constant mangrove distribution was variable across sites (Table 8.7). Biomass loss over 2007-2010 was observed in most sites, with the greatest percentage loss in total HV backscatter occurring in the East African study sites (Ruvuma Estuary [-5.56%]; Save River Delta [-7.19%]; Rufiji Delta [-7.37%]; Table 8.7; Appendix 3 Figures A3.8-A3.10). High proportions of significant biomass loss (≥15%) were also observed across all East African study sites (31.33-35.30%; Table 8.7). Lower percentage biomass loss was observed in all other sites, ranging from -2.71% in the Saloum Delta to -1.37% in Sherbro Bay (Appendix 3 Figures A3.11-A3.13) (Table 8.7). In contrast, an increase in mangrove biomass of 1.44% was observed at Mahajamba (Appendix 3 Figure A3.14; Table 8.7).

8.4.7 Topographic controls on landward migration (resilience) The low proportions of landward shifted pixels at Saloum Delta and Sherbro Bay (0.01-0.02%; Table 8.5) excluded statistical exploration potential topographic controls on landward migration. Results of the INLA SPDE analyses revealed that the probability of pixel landward migration in 2015 was significantly related to topographic slope at all remaining (N = 5) sites (see Table 8.8). The probability of landward migration was negatively associated with topographic slope in all cases: e.g. landward migration was less probable where topographic slope at the landward boundary was steeper (see Table 8.8). The strongest negative relationship between the probability of landward migration and topographic slope was observed at Mahajamba (b √푆푅푇푀 푠푙표푝푒 = -2.30 ± 0.25 [1 s.d.]), and the effect size was weakest at Rufiji Delta (b √푆푅푇푀 푠푙표푝푒 = -0.66 ± 0.28 [1 s.d.]) (Table 8.8).

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Table 8.6. Results of the ALOS/PALSAR HV backscatter amplitude change detection within the seaward boundary (five-pixels distance of the combined 2007- 2010 coastline edge; Sections 8.3.5 and 8.3.6) of the identified constant mangrove distribution for each site over 2007-2010. Significantly increasing/decreasing pixels over the time period were determined as those with ≥15% HV backscatter change over the time period (Lucas et al. 2009; Mitchard et al. 2011; Carreiras et al. 2012; Cornforth et al. 2013). The total seaward boundary 2007-2010 HV backscatter (biomass) change (%) is also provided: change in the sum of HV backscatter across all seaward pixels. Area and proportion of total constant mangrove area denotes the area and proportion of total constant mangrove area classified over 2007-2015 within the available ALOS/PALSAR imagery considered in these analyses.

Area (ha) and proportion of total Seaward significantly Seaward significantly Seaward significantly Seaward significantly 2007-2010 total constant mangrove increasing decreasing increasing decreasing seaward biomass Site area (%) (ha) (ha) (%) (%) change (%) 54,285.45 Saloum, SN 1,454.02 755.14 38.24 19.86 5.92 11.51 51,600.06 Sherbro, SL 432.08 506.00 23.06 27.00 -1.88 26.98 31,617.58 Save, MZ 290.30 520.67 20.11 36.06 -7.36 52.96 13,591.00 Ruvuma, TZ 175.77 332.23 19.14 36.18 -7.37 99.45 34,710.97 Rufiji, TZ 200.83 415.39 16.72 34.59 -7.63 81.96 Mahajamba, 19,987.47 272.70 167.97 39.62 24.40 3.32 MG 28.48 Sundarbans, 216,747.30 286.89 597.63 16.93 35.26 -7.97 IN & BD 33.97

Table 8.7. Results of the ALOS/PALSAR HV backscatter amplitude change detection within the identified constant mangrove distribution for each site over 2007- 2010. Significantly increasing/decreasing pixels over the time period were determined as those with ≥15% HV backscatter change over the time period (Lucas et al. 2009; Mitchard et al. 2011; Carreiras et al. 2012; Cornforth et al. 2013). The total 2007-2010 HV backscatter (biomass) change is provided: change in the sum of HV backscatter across all constant mangrove distribution pixels within the ALOS/PALSAR imagery over 2007-2010. Area and proportion of total constant mangrove area denotes the area and proportion of total constant mangrove area classified over 2007-2015 within the available ALOS/PALSAR imagery considered in these analyses.

Area (ha) and proportion of total Significantly Significantly Significantly Significantly 2007-2010 total constant mangrove increasing decreasing increasing decreasing biomass change Site area (%) (ha) (ha) (%) (%) (%) 54,285.45 Saloum, SN 11,313.03 13,985.12 20.84 25.76 -2.71 11.51 51,600.06 Sherbro, SL 11,532.41 12,142.22 22.35 23.53 -1.37 26.98 31,617.58 Save, MZ 5,860.44 11,159.64 18.54 35.30 -7.19 52.96 13,591.00 Ruvuma, TZ 2,546.23 4,257.66 18.73 31.33 -5.56 99.45 34,710.97 Rufiji, TZ 5,269.42 11,705.89 15.18 33.72 -7.37 81.96 Mahajamba, 19,987.47 5,712.23 4,302.40 28.58 21.53 1.44 MG 28.48 Sundarbans, 216,747.30 37,263.08 40,356.98 17.19 18.62 -1.58 IN & BD 33.97

8. Resilience and resistance to sea level rise in global mangrove forests

Table 8.8. INLA SPDE models of the effect of within-site SRTM DEM-derived topographic slope on the probability of landward migration into the 2007 mangrove landward distribution boundary in 2015 within each study site (excluding Saloum Delta and Sherbro Bay; see text for further details). WAIC = Watanabe’s Akaike Information Criterion (Watanabe 2010); DIC = Deviance Information Criterion (Spiegelhalter et al. 2002); s.d. = standard deviation; b = regression slope estimate.

b Intercept √푺푹푻푴 풔풍풐풑풆 97.5% Site (± 1 s.d.) (± 1 s.d.) quantiles WAIC DIC

Save, MZ -1.03 (0.14) -1.59 (0.32) -2.22–-0.97 20892.09 20895.32

Ruvuma, TZ -5.93 (15.31) -1.21 (0.28) -1.77–-0.66 20972.87 20974.84

Rufiji, TZ -1.33 (0.30) -0.66 (0.28) -1.21–-0.10 20394.90 20398.34

Mahajamba, -0.003 (0.23) -2.30 (0.25) -2.80–-1.81 22704.18 22707.95 MG

Sundarbans, -1.82 (0.19) -1.10 (0.40) -1.88–-0.33 13871.36 13871.12 IN & BD

8.4.8 Sediment input load controls on seaward biomass change (resistance) In four of the seven study sites (Saloum Delta, Save River Delta, Rufiji Delta, Mahajamba) no significant effect of mean sediment load or trend in mean sediment load on within-site variation in seaward biomass change over 2007-2010 was found. The best-fitting INLA SPDE model of seaward boundary biomass change included a positive effect of mean TSM (g m-3) on percentage change in biomass (ALOS/PALSAR L-Band HV backscatter amplitude change) at Ruvuma Estuary and Sherbro Bay (Table 8.9). Conversely, the best-fitting INLA SPDE model of seaward boundary biomass change at the Sundarbans included a negative effect of mean TSM on percentage change in biomass (Table 8.9).

8.4.9 Anthropogenic barriers to landward migration (resilience) The index of dry bare ground landcover employed to index change in anthropogenic development in the 2007 adjacent mangrove landward perimeter between 2007 and 2015 was highly variable across sites, being greatest in both years in the more arid sites containing higher natural dry bare ground landcover (Saloum Delta, Sherbro Bay and Save River Delta; Table 8.10). An increase in dry bare ground landcover density within the 2007 adjacent mangrove landward perimeter was

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8. Resilience and resistance to sea level rise in global mangrove forests observed in most sites, excepting the two West African study sites (Saloum Delta and Sherbro Bay; Table 8.10). The largest increases in dry bare ground landcover density (potential anthropogenic development; Section 8.3.7.2) within the 2007 adjacent mangrove landward perimeter were observed in the three East African study sites in 2015: a further 38.00% (1,155.77 km) of landward boundary with potential anthropogenic development at Rufiji Delta, a further 1.176% (34.51 km) of landward boundary with potential anthropogenic development at Save River Delta, and a further 0.73% (7.21 km) of landward boundary with potential anthropogenic development at Ruvuma Estuary (Table 8.10). Low potential anthropogenic development was observed at Mahajamba and the Sundarbans in 2015 (Table 8.10).

Table 8.9. Best-fitting INLA SPDE models of the effects of within-site total suspended matter (mean TSM; g m-3) and Kendall’s tau in TSM (2006-2010) on percentage change in biomass within the seaward boundary (change in ALOS/PALSAR L-Band HV backscatter amplitude; 2007-2010) for each study site. s.d. = standard deviation; b = regression slope estimate; WAIC = Watanabe’s Akaike Information Criterion (Watanabe 2010); DIC = Deviance Information Criterion (Spiegelhalter et al. 2002). Italicised text indicates unexpected relationships (H8.2; Section 8.2).

b Intercept estimate(s) 97.5% Site Variable(s) (± 1 s.d.) (± 1 s.d.) quantiles WAIC DIC

Sherbro -3.49 1.50 0.60- Mean TSM 183827.72 183816.43 Bay, SL (4.06) (0.46) 2.41

Ruvuma -8.59 0.38 0.14- Mean TSM 185143.09 185142.28 Estuary, TZ (2.66) (0.12) 0.61

Sundarbans, -5.31 -0.29 -0.48-- Mean TSM 181963.21 181960.93 IN & BD (3.64) (0.10) 0.101

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Table 8.10. Results of landward anthropogenic development analyses. Anthropogenic landcover was indexed via the number and proportion of dry landcover class pixels (determined via unsupervised landcover classification) within one-pixel wide landward perimeter of the 2007 classified mangrove distribution in the years 2007 and 2015 (see Section 8.3.7.2). “Anthropogenic landward pixels” thus do not represent absolute levels of anthropogenic landcover (also encompassing e.g. exposed rock, sand banks, salt pans), but the change in proportion of dry bare ground landcover is assumed here to represent an increase in anthropogenic landcover only (see Section 8.3.7.2).

2007 2015

Change Anthropogenic Proportion Anthropogenic Proportion (in total landward Total landward pixels landward pixels (No. anthropogenic landward pixels (No. anthropogenic length [%]; length Site (No. & length; km) & length; km) landward (%) & length; km) landward (%) increase [km]) 6,192,415 609,029 541,368 -1.10 Saloum, SN 9.84 8.74 77,405.19 7,612.86 6,767.10 (-845.76) 1,240,781 280,775 241,447 -3.17 Sherbro, SL 22.63 19.46 15,509.76 3,509.69 3,018.09 (-491.60) 380,145 302 3,063 0.73 Save, MZ 0.08 0.81 4,751.81 3.78 38.29 (34.51) 49,165 2 579 1.18 Ruvuma, TZ 0.004 1.18 614.56 0.03 7.24 (7.21) 243,415 19,527 111,989 38.00 Rufiji, TZ 8.02 46.02 3,042.69 244.09 1,399.86 (1,155.77) Mahajamba, 384,666 0 69 0.36 0.00 0.02 MG 4,808.33 0.00 0.86 (0.86) Sundarbans, 442,867 16,267 16,882 0.14 3.67 3.81 IN & BD 5,535.84 203.34 211.03 (7.69)

8. Resilience and resistance to sea level rise in global mangrove forests

8.5 Discussion This chapter develops a remote monitoring methodology for mangrove resilience and resistance to SLR, and provides a quantitative analysis of current capacity for and drivers of resilience and resistance across seven mangrove forests from West Africa to South Asia. Regardless of site-specific experienced rates of SLR, landscape- level topography, sediment availability and rates of anthropogenic development appeared to be the proximal drivers of resilience and resistance capacity. The capacity for mangrove resilience (landward migration) along shallow topographic slopes was evident at all sites, and observed resilience was generally moderate to high. Resilience in the seaward boundary was variable, suggesting that sediment availability controls on elevation capital relative to rates of SLR generally operate at the ecosystem scale where background erosion levels are not high and heterogeneous. Low resistance was associated with low site-specific sediment availability, as well as cryptic degradation of mangrove biomass with maintained areal cover where anthropogenic development was high. Overall, only one mangrove forest (Mahajamba, Madagascar) exhibited high levels of both SLR resilience and resistance. The findings of this chapter highlight important variability in processes of resilience and resistance across mangrove forests. The methodology developed enables ranking of current mangrove capacity for resilience and resistance to SLR, and trends in their drivers, to inform assessment of management priorities to minimise mangrove CCMA ES vulnerability to SLR.

Contrary to expectations (Section 8.2), neither greater resilience nor resistance was associated with lower exposure to SLR (lower relative trend in mean sea level anomaly; see Section 8.4.1). Instead, variation in landscape topography, sediment supply and rates of anthropogenic development across sites drove differential levels of resilience and resistance capacity. The findings regarding variation in levels of mangrove resilience and resistance are addressed in turn hereafter.

Most of the studied mangrove forests exhibited the capacity for SLR resilience, with some landward migration evident at all sites (Table 8.5). Landward migration enabled maintained areal coverage in six of the seven sites (Table 8.4), and rates of landward boundary change were greater than or equal to rates of biomass loss in the seaward boundary in five sites (excl. Sherbro Bay and Rufiji Delta; Tables 8.3 & 8.4). As was expected (Section 8.2), landward migration at all sites occurred into refuge areas of low topographic constraints (e.g. along shallow topographic slopes,

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8. Resilience and resistance to sea level rise in global mangrove forests and, presumably, into neighbouring areas with favourable hydrological and sediment conditions; see Section 8.4.4). However, minimal landward migration was observed at the West African sites, (Table 8.5), suggesting low fecundity and recruitment, which likely reflects competition with inland saltmarsh communities (Spalding et al. 2010; Ndour et al. 2011). Importantly, alterations to freshwater input regimes under desertification in the region (Saenger & Bellan 1995; Ndour et al. 2011) may be adding physiological salinity stress to these mangroves, reducing their capacity for resilience to SLR at the landward margins.

Again, in accordance with expectations, mangrove seaward boundary resilience was greatest (seaward biomass increase) in two sites with high relative sediment availability (Saloum Delta and Mahajamba; Section 8.4.2; Table 8.6), and seaward biomass loss was observed in sites with lower relative sediment availability (Ruvuma Estuary, Sherbro Bay, Rufiji Delta and Save River Delta; Section 8.4.2; Table 8.6). Greater sediment availability within sites was only associated with lower biomass loss in two of seven sites (positive relationships between within-site sediment availability and [positive] biomass change; Section 8.4.8), suggesting that sediment availability controls on elevation capital relative to rates of SLR generally operate at the ecosystem scale where background erosion levels are not high and heterogeneous. These associations and relationships support the assertion that the observed losses of mangrove biomass are associated with a loss of relative elevation capital (see McKee et al. 2007a; Krauss et al. 2014; Ellison 2015; Lovelock et al. 2015; Ward et al. 2016), and highlight that this can be detected from satellite- derived SAR imagery. However, findings from the Sundarbans deviated from expectations and from observations across the other study sites, with high rates of seaward biomass loss despite high sediment availability (Section 8.4.2; Table 8.6), and within-site seaward biomass losses spatially associated with high sediment loads (Section 8.4.8). These results are surprising, and likely reflect highly heterogeneous background erosion processes occurring throughout the large delta (see e.g. Rahman & Islam 2010; Rahman et al. 2011; Cornforth et al. 2013). This finding highlights potential limitations with inferences on the origin of remotely sensed coastal sediment loads (i.e. terrestrial vs. coastal sediment erosion sources), and the importance of on-the-ground site information and calibration with field- derived data in remote monitoring of coastal ecosystems. These analyses further revealed that simultaneous monitoring of resilience processes at seaward and landward margins is essential to assess the future trajectories of mangrove extent

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(see Ellison & Zouh 2012). For example, the observed low lansward boundary resilience in West Africa may not currently be key to overall resilience in the high sediment fed (Section 8.4.2) Saloum Delta (seaward biomass gain: Table 8.6), where rehabilitation (planting) projects at landward boundaries may also enhance the capacity for landward expansion (Alexandris et al. 2013). Yet potential physiological barriers to landward expansion and low sediment availability at Sherbro Bay may eventually result in coastal squeeze in response to SLR into the future (Section 8.4.2; Tables 8.2 and 8.4).

The observed ubiquitous negative relationships between topographic slope and the probability of landward migration suggest no extensive anthropogenic barrier disruptions to the natural process of mangrove expansion in the sites considered at present (see Section 8.2). However, in the Rufiji Delta site, where indexed potential anthropogenic development over 2007-2015 was high (a further 38% [1,155.77 km] of the landward boundary with potential anthropogenic development; see also Appendix 2 Figure A2.2), low landward migration (1.85% landward boundary length) was observed relative to the other East African study sites (Table 8.5). Spatial heterogeneity in the distribution of anthropogenic development (evident only in the north) enabled maintained areal cover across the period of investigation (Table 8.4); however, landward migration is currently being outpaced by estimated biomass losses in the seaward boundary (7.63% loss). Thus, while current resilience appears to be sufficient (maintained areal coverage), into the future mangroves such as Rufiji Delta may begin to suffer coastal squeeze if landward migration cannot increase around existing barriers. While barriers to expansion in the Save River Delta (greater landward migration than seaward biomass loss) and Ruvuma Estuary (similar rates of landward migration and seaward biomass loss) do not appear to be currently hindering resilience, these sites simultaneously experienced high seaward boundary biomass losses and among the most rapid increases in potential anthropogenic development (Table 8.10), suggesting a potential decline of SLR resilience in these sites into the future.

While resilience to SLR was generally moderate to high in the mangroves studied, the observed capacity for resistance was lower. Six of seven study sites exhibited marginal to substantial losses of biomass over the study period, with the greatest losses occurring in the East African study sites (Table 8.7). As expected (Section 8.2), biomass loss was lower (e.g. stable or increasing) in sites with higher relative

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8. Resilience and resistance to sea level rise in global mangrove forests sediment availability (Mahajamba, the Sundarbans and Saloum Delta; Section 8.4.2). Marginal biomass loss (e.g. stable biomass) was also observed at the Sherbro Bay site (Table 8.7), owing to biomass gains in estuarine island centres (Appendix 3 Figure A3.13). Excluding this site, biomass change was generally evenly distributed within the constant mangrove distribution across sites (Appendix 3 Figures A3.8- A3.14). Under the assumption that anthropogenic disturbance to mangrove aboveground biomass (e.g. cutting) occurs more readily in the more accessible, upper-intertidal landward portions of mangroves (see e.g. Webb et al. 2014), the observed biomass changes may thus appear to be driven by alterations to ecosystem productivity associated with increased physiological stress (e.g. Krauss et al. 2014).

In contrast, and as expected under H8.3, resistance capacity was lowest at all East African study sites, where rates of potential anthropogenic development were greatest (Tables 8.5 and 8.6). Furthermore, there was some evidence of anthropogenic influences on resistance at some sites (e.g. cryptic degradation from mangrove cutting; Dahdouh-Guebas et al. 2005; Lee et al. 2014). As mentioned above, heavy anthropogenic development and clearing were observed in the north of the Rufiji Delta (see Appendix 3 Figure A3.2), with mangrove replacement for agricultural development and urbanisation (Google Earth 2016). Within the northern delta, constant mangrove distribution biomass loss (-7.96%; 35.44% of pixels significantly decreasing) was greater than throughout the remainder of the site (-6.73%; 31.64% of pixels significantly decreasing). Accordingly, anthropogenic development and associated cryptic degradation (Mukherjee et al. 2014) appears to provide an important component of potential future resistance to SLR at the site (Gilman et al. 2008). Relative to the other mangrove sites considered here, the capacity for resistance, and indeed seaward boundary resilience (see above), to SLR appears to be a major concern for East African mangroves. Management to minimise unsustainable extractive activities near populated centres, as well as watershed management to maintain sediment supplies (Section 8.4.2), should be a priority for these sites into the future (see Gilman et al. 2008; Ellison 2015; Ward et al. 2016). Biomass loss is both a consequence and driver of mangrove vulnerability to SLR. As mangrove biomass is lost through reduced productivity, the capacity for sediment trapping and elevation gain in productive root systems also decreases (McKee et al. 2007a). Monitoring of these processes remotely using SAR imagery provides a key tool to inform necessary management in such low resistance sites.

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This chapter has shown that monitoring of both mangrove resilience and resistance is possible via a relatively simple multi-product satellite remote sensing methodology. The global and repeat-pass coverage of optical (Landsat archive; USGS 2015) and SAR imagery (e.g. historical ALOS/PALSAR data archives: JAXA/METI 2010; ASF DAAC 2016; ESA 2016a; future ESA Copernicus Sentinel 1-A C-band SAR data: Copernicus 2016a) will enable repeatable monitoring of mangrove responses to SLR, and potential future changes to SLR resilience and resistance and their drivers. This may provide a key means to prioritise areas for conservation efforts and the need for any active management and monitoring for vulnerability reduction within e.g. blue carbon Payments for Ecosystem Services (PES) and greenbelt rehabilitation programmes into the future under projected climate change. These issues are discussed in Chapter 9. A multi-product remote sensing approach is key to rapidly indexing components of both mangrove resilience and resistance (Gilman et al. 2008), as both are required to gain a complete understanding of the CCMA ES impacts of SLR. For example, in the East African sites studied here, if resilience alone were monitored the conclusion drawn may be that CCMA ES are currently stable (e.g. maintained greenbelt area and forest carbon stocks). However, in these sites overall biomass, and thus aboveground carbon stocks, are declining, also potentially compromising greenbelt protection potential despite maintained areal coverage (see Dahdouh-Guebas et al. 2005; McIvor et al. 2012; Lee et al. 2014; Chapter 6).

Importantly, separately indexing components of mangrove forest resilience and resistance, as well as trends in the factors driving them, enables ranking of management priorities for individual sites (see Table 8.11). For example, at present the Mahajamba, Madagascar site appears to have both high resilience and resistance, and no active management to reduce vulnerability is currently required. In contrast, at the Rufiji Delta site, identifiable management priorities include mitigation of trends in anthropogenic development to enhance both resilience and resistance at the site (Table 8.11). Other sites are variable in their relative resilience and resistance, and in trends in anthropogenic barriers to resilience (Table 8.11).

While the analyses conducted within this chapter make use of what are currently the best freely-available information on vegetation distribution and biomass change, and landscape-level topography and sediment availability, certain caveats do remain. First, the Landsat imagery (USGS 2015) employed to assess mangrove

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8. Resilience and resistance to sea level rise in global mangrove forests distribution, while widely used and effective for mangrove distribution monitoring at appropriate spatial scales (Giri et al. 2011; see Kuenzer et al. 2011), is of moderate spatial resolution (30m) and small patches of mangrove (particularly along coastlines and riverbanks; Giri et al. 2011) cannot be captured at this resolution. Furthermore, the pixel-based classification method employed (kmeans clustering; Wegmann et al. 2016) relies upon the assumption that all landcover contained within a given pixel is uniform. At the edges of landcover patches, and where small heterogeneous patches of a given landcover exist, the likelihood of inclusion of multiple landcover types contained within a given pixel (mixed pixels; Fisher 1997) at 30m resolution is high (see also Knight & Lunetta 2003). Indeed, mangrove landcover classification accuracy was reduced in this study by a relatively high proportion of mixed pixels across sites (see Table 8.3), which may have influenced detection of mangrove landward migration between years. However, recent analysis has revealed that 30m Landsat imagery has higher accuracy in mangrove distribution mapping over RapidEye multispectral imagery (5m) at delta-wide scales (A. Stephani & M. Wegmann unpublished data). Combining the historical Landsat imagery archive with the recently-launched ESA Sentinel-2 satellite multispectral imagery (12.5m resolution) may yet enhance possibilities for mangrove resilience monitoring into the future (Copernicus 2016b).

Second, while the Landsat imagery was seasonally-matched as closely as possible (see Section 5.4.1.1; Table 5.1) and imagery pre-processing stages were conducted to minimise atmospheric noise differences between both adjacent and multitemporal scenes (see Section 5.4.2.1; Wegmann et al. 2016), slight differences in reflectance characteristics according to factors that include climatic conditions (vegetation water content) and growing season (vegetation productivity) are possible. A reasonable level of accuracy in mangrove distribution classification was ascertained through the validation method employed at most study sites across years (Table 8.3). However, the accuracy assessment involved validation of only mangrove landcover true and false positives (e.g. false negatives [true mangrove classified as alternate landcover] were not assessed; see Owen et al. 2016) and thus mangrove detection accuracy may have been overestimated. Accuracy assessment was further limited by the availability of historical high resolution optical imagery in Google Earth Pro (Google Earth 2016), and only portions of each study site could be validated in both 2007 and 2015. Going forward, the incorporation of field- derived ground truth data and aerial photographs for combination with satellite

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8. Resilience and resistance to sea level rise in global mangrove forests remote sensing assessment of mangrove resilience and resistance is required. This process is underway at present, with limited ground truth data on historical mangrove distribution now obtained for the Sundarbans study site (K. Upham unpublished data).

Table 8.11. Ranking of resilience and resistance of the mangrove forests assessed in this chapter to inform adaptive management. Resilience = observed current capacity for maintained areal coverage under SLR (Sections 8.4.3 & 8.4.4); resistance = observed current capacity for maintained productivity under SLR (Section 8.4.5); potential change (resilience) = observed trend in potential anthropogenic development at each site (Section 8.4.7); potential change (resistance) = observed mean trend in sediment availability (mean Kendall’s tau in ENVISAT MERIS-derived TSM (g m-3) over 2006-2010; Section 8.4.2; ESA GLOBCOLOUR 2014). Potential Potential change change (resilience; (resistance; Site Resilience Resistance %) tau) Maintained area Saloum Delta, Marginal biomass -1.10 -0.02 SN Landward stable & loss Seaward increasing Decreasing area Sherbro Bay, Marginal biomass -3.17 0.006 SL Landward migration loss < Seaward loss Save River Increasing area Delta, Landward migration Biomass loss 0.73 - MZ > Seaward loss Ruvuma Maintained area Estuary, Landward migration Biomass loss 1.18 - TZ ≈ Seaward loss Maintained area Rufiji Delta, Biomass loss 38.00 -0.04 TZ Landward migration < Seaward loss Increasing area Mahajamba, Biomass gain 0.36 -0.005 MG Landward & Seaward increasing Maintained area Sundarbans, Marginal biomass 0.14 -0.004 IN & BD Landward migration loss ≈ Seaward loss

Third, biomass change detection using SAR imagery is a relatively untested approach, and the ability of L-Band HV SAR to detect mangrove aboveground biomass is affected by stand structural complexity (Proisy et al. 2003; Lucas et al. 2007; Cohen 2014). While a strong positive correlation exists between L-Band HV backscatter and aboveground mangrove biomass, this relationship can saturate above 120-150 Mg ha-1 (Lucas et al. 2007; Mitchard et al. 2011), and backscatter can

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8. Resilience and resistance to sea level rise in global mangrove forests be reduced at very high canopy densities (>200 Mg ha-1; Lucas et al. 2007; Kuenzer et al. 2011). Furthermore, double-bounce and direct surface scattering of SAR can enhance backscatter in short, open canopy mangroves (Cohen 2014 [Radarsat C- Band SAR]; C. Duncan unpublished data [Sentinel 1-A C-band SAR; Panay Island, Philippines]), potentially limiting the ability to detect biomass change in heavily degraded areas. However, aboveground biomass in most of the mangroves considered is below the threshold for saturation (Table 4.3; but Ruvuma Estuary and Rufiji Delta: 136 Mg ha-1; Mahajamba: 121 Mg ha-1), and heavy degradation was not anticipated in the mangrove forests considered herein (see Section 4.3). Thus, the limitation of L-Band HV backscatter-aboveground biomass relationships was not expected to have significantly influenced the findings of this chapter.

Fourth, high variability in the areal extent of the study sites considered in this chapter resulted in differences in proportional site representation within ALOS/PALSAR imagery (Tables 8.2 & 8.4-8.5). Assessment of overall biomass change and seaward boundary change at larger sites may represent true site resilience and resistance less well. This may have hindered accurate interpretation at these sites (Table 8.7). However, the full opening of the ESA ALOS/PALSAR archives in the fourth quarter 2016/first quarter 2017 will enable near-complete assessment of resistance for all study sites, and indeed enhance the applicability of L-BAND SAR data for the accurate monitoring of biomass change in global mangroves (e.g. the Philippines and Southeast Asia; ESA 2016b). Lastly, the methodological framework employed here did not incorporate the potential effects of other anthropogenic and natural disturbance processes affecting mangroves (in particular storm damage and freshwater pollution: Spalding et al. 2010; McIvor et al. 2012; Ellison 2015; Ward et al. 2016), which may also impact mangrove ecosystem health and capacity for resilience and resistance to SLR. For example, the relatively low overall biomass change observed in the Sundarbans was indexed over a period affected by both Heritiera fomes (dominant mangrove species) top-dying disease and damage from cyclone Sidr (Giri et al. 2007; Cornforth et al. 2013; central portions of the employed ALOS/PALSAR imagery; Appendix 3 Figure A3.12). This suggests currently good resistance where sediment supplies are high, despite these and myriad other threats (see Giri et al. 2007). Moreover, heavy metal pollutants entering East African mangroves may be in part responsible for the high rates of biomass loss observed here (see UNEP-WCMC 2003). Going forward, further development of the developed methodological framework to include mangrove

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8. Resilience and resistance to sea level rise in global mangrove forests responses to and trends in exposure to these key processes, as well as field-derived information on mangrove accretion and elevation capital (e.g. R-SET information; see Lovelock et al. 2015), will be important to clarify the conclusions drawn herein and enhance the applicability of the current methods to informing management on the ground.

This chapter has shown that mangrove resilience and resistance to SLR is variable across different forests around the globe. Furthermore, it has revealed that CCMA ES vulnerability to SLR is not uniform regarding resilience and resistance processes at individual mangroves. The findings suggest that there can be high capacity for mangrove forest resilience through extended landward boundaries where anthropogenic, topographic and ecological barriers to expansion are minimal, even over relatively short time periods. Thus, while a less empirically explored component of mangrove vulnerability (see Rogers et al. 2014; Lovelock et al. 2015; Ward et al. 2016; but see Cohen & Lara 2003; Gilman et al. 2007; Ellison & Zouh 2012; Di Nitto et al. 2014; Runting et al. 2016), landward migration can provide an important component of adaptability to SLR. However, the results also urge caution in the assessment of resilience via areal coverage alone, where reduced productivity and cryptic degradation may cause the cryptic loss of functionality and CCMA ES delivery. The employment of a multi-product satellite remote sensing approach provided an open access, repeatable means of monitoring resilience and resistance and threat processes across sites, providing detailed information to inform adaptive management into the future under global climate change. The mangrove study sites considered herein represent mangroves with low comparative anthropogenic threats (Spalding et al. 2010). The capacity for SLR resilience and resistance in the wider tropics (e.g. Southeast Asia) is likely to be substantially lower, where alterations to sediment availability, cryptic degradation through cutting and anthropogenic development at landward margins are extensive (see Lovelock et al. 2015; Richards & Friess 2016), and in small islands (Sasmito et al. 2016a). The landscape-level and landward anthropogenic constraints to mangrove resilience and resistance demonstrate that future management to enhance these processes must incorporate both marine and terrestrial government departments and Non- Governmental Organisations, as well as existing mechanisms under, for example, MPA designation and management (Friess et al. 2016). The specific implications of these findings for the future implementation of conservation activities for mangroves are discussed in more detail in the following chapter.

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CHAPTER 9: Discussion, conclusions and recommendations

9.1 In summary Mangrove forests are among the world’s most important ecosystems, in terms of both their key ecological role in the coastal zone and the ecosystem services (ES) they provide to humanity. The precipitous anthropogenically-driven historic declines in mangrove cover and integrity pose an economic and livelihoods risk to dependent tropical coastal communities (Walton et al. 2006; van Oudenhoven et al. 2015), and have contributed significantly to global carbon dioxide emissions (Donato et al. 2011; Siikamaki et al. 2012). While rates of mangrove loss have reduced, deforestation and degradation is on-going (Hamilton & Casey 2016; Richards & Friess 2016), and the extent of enforced management and protection is unknown even within the ~41% of mangrove area currently contained in the global Protected Area network (Long et al. 2013; UNEP 2014). Furthermore, climate change – in particular sea level rise (SLR) – now brings added pressure to vulnerable coastal areas and further complicates coastal zone and mangrove management considerations (Ellison 2015; Lovelock et al. 2015). As the global human population grows, it will become increasingly challenging to predict the future status of mangrove forests and their ES, and to develop and implement appropriate policies for their protection. While hydro-ecological rehabilitation and replanting afford opportunities for the revival and enhancement of mangrove ES (Lewis 2005; Bosire et al. 2008; Primavera et al. 2012b), current contradictory rehabilitation activities, particularly in the Southeast Asian region, involve wasteful and often damaging practices such as planting in seagrass and mudflat areas (Primavera & Esteban 2008). There is furthermore little evidence to suggest that current management under projected SLR (UNEP 2014; Ellison 2015; Lovelock et al. 2015) and common practices in rehabilitation (Primavera & Esteban 2008; Bayraktarov et al. 2016; Duncan et al. 2016; Villamayor et al. 2016) have sufficient potential for the effective delivery of climate change mitigation and adaptation (CCMA) ES. Going forward under global climate change, it becomes increasingly important to enhance and promote the ecological toolkit for informed mangrove management and rehabilitation for CCMA goals.

The majority of the literature on mangrove CCMA ES delivery has focused on quantification, valuation and projection of stocks (e.g. Koch et al. 2009; Barbier et al. 2011; Donato et al. 2011; McIvor et al. 2012; Thompson et al. 2014), as well as on

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9. Discussion, conclusions and recommendations stakeholder preferences regarding specific ES (e.g. Walters 2000; 2003; 2004; Walton et al. 2006), and prediction of ES delivery under varying land use regimes (e.g. van Oudenhoven et al. 2015). However, the potential implementation and success of ecosystem based adaptation and mangrove conservation projects for CCMA in the coastal zone depend also on (1) an understanding of the community ecology of specific CCMA ES delivery, (2) overcoming spatial- and tenure-specific constraints to effective CCMA greenbelt implementation, and (3) understanding, monitoring and mitigation of key future threats from and responses to climate change (in particular SLR). Through a dual focus on mangroves in Panay Island, Philippines and throughout West Africa to South Asia, this thesis has addressed some key questions regarding these issues.

Focussing on key functional traits of mature natural and diverse mangroves of Panay Island, this thesis has shown for the first time that, in addition to site-specific attributes, the functional structure of mangrove communities is a significant driver of ES delivery over species richness per se (Chapter 6). Examining multiple mangrove ES, the findings revealed control of functional dominance (i.e. dominance of large-bodied species) on mangrove ES related to biomass production (harvestable timber/charcoal production and carbon stocks), control of functional diversity on harvestable kindling abundance (diversity in leaf traits; specific leaf area), and control of both functional dominance (i.e. dominance of species with low specific leaf area; SLA) and diversity (multiple traits and size) on storm surge attenuation potential. The latter findings point to the role of diversity in life history strategies and mechanisms of complementarity in space and resource use (Fox 2005; Huxham et al. 2010; Lang’at et al. 2012) in enabling greater stem densities and vegetation surface area for storm surge attenuation where functional diversity is greater. They furthermore demonstrate the importance of mid- to upper- intertidal zone mangroves, where the potential for high functional diversity is greatest (Duke et al. 1992), for the maintenance of overall mangrove ecosystem functioning, as well as for structural greenbelt strengthening. Crucially, the observed trade-off in functional drivers of carbon stocks and storm surge attenuation potential demonstrates that ES commonly assumed to be positively related in space may hold greater complexity to their delivery. This cautions against simplistic assumptions of ES delivery (Adame et al. 2014; van Oudenhoven et al. 2015), and highlight an area for active management for high multiple CCMA ES delivery in rehabilitated mangrove stands.

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The thesis then examined the mangrove CCMA ES delivery of rehabilitated seafront and abandoned fishpond areas relative to reference natural stands in Panay Island, and the potential land tenure constraints to rehabilitation for effective CCMA goals (Chapter 7). While per hectare carbon stocks were variable across both rehabilitated and natural mangrove stands (see also Donato et al. 2011; Nam et al. 2016), rehabilitation for enhanced CCMA goals was more promising in abandoned fishponds due to their large landward coverage and deep, carbon-rich former mangrove sediments. In the absence of landward natural mangrove (due to clearing and infrastructure development), current standard practice of seafront rehabilitation (e.g. under the Philippines Government’s National Greening Programme [NGP]; Section 4.2.1) was found to not return effective coastal protection potential, and low areal coverage resulted in comparatively low ecosystem carbon stocks. In terms of coastal protection potential, analysis of forest structure in mature, natural reference mangroves demonstrated that current Philippines greenbelt mandates (50-100m greenbelt width) are currently too narrow for effective coastal protection from regular waves and for storm surge protection in typhoon-prone areas (Bao 2011; McIvor et al. 2012). This highlights the importance of up-to-date forest structure information and quantitative assessments of greenbelt requirements to inform national coastal zone management for CCMA. A municipality-specific case study revealed overlap between abandoned fishponds with high rehabilitation potential for CCMA goals (13% of coastline with potential for effective mangrove coastal protection from abandoned pond reversion) and low competing opportunity costs. Importantly, despite current mangrove government rehabilitation practice, minimal legal tenure constraints to a strategy of targeted abandoned fishpond reversion for CCMA greenbelt rehabilitation were observed (~97% of identified abandoned fishpond priorities for effective greenbelt coastal protection potential held under breached lease agreements). These findings support a strategy of quantitative spatial prioritisation for mangrove rehabilitation, and operational and tenure inventories of aquaculture in many South and Southeast Asian countries where aquaculture regulation is low and pond abandonment high.

Finally, the thesis explored the capacity for resilience and resistance to SLR, and the landscape-level and anthropogenic factors driving these processes, across mangrove forests from West Africa to South Asia (Chapter 8). The results revealed variability in processes of resilience (maintained areal coverage, landward

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9. Discussion, conclusions and recommendations migration and seaward boundary biomass maintenance) and resistance (total mangrove biomass maintenance) between mangrove forests. Similar variability was identified in the capacity for these processes at specific forests (Ellison 2015). Variation in site-specific resilience and resistance processes was not associated with trends in mean sea level anomaly. Instead, with some site-specific exceptions, the capacity for resilience and resistance in mangroves appeared to be controlled by landscape-scale factors and anthropogenic activity at the landward margins. The multi-product remote sensing methodology enabled detection of capacity for resilience via landward migration along shallow topographic slopes (Gilman et al. 2007; 2008; Ellison & Zouh 2012) at all sites, revealing the importance of this often empirically overlooked component of mangrove resilience to SLR. These findings advocate for considered coastal zone spatial planning for managed retreat into the future, and advise against infrastructural and agri-/aquaculture development in low-sloping refuge areas landward of vulnerable mangroves (Gilman et al. 2008; Rogers et al. 2014; Ellison 2015; Runting et al. 2016). High seaward boundary resilience and overall resistance was generally associated with higher relative sediment inputs, highlighting the importance of maintained sediment loads to maximise maintenance of mangrove elevation capital relative to rates of SLR (McKee et al. 2007a; Krauss et al. 2014; Ellison 2015; Lovelock et al. 2015; Ward et al. 2016). Lower mangrove resistance capacity to SLR was observed in areas with greater rates of landward anthropogenic development, revealing the importance of monitoring and managing cryptic mangrove degradation (Dahdouh-Guebas et al. 2005; Lee et al. 2014) to enhance resistance. Critically, the multi-product remote sensing methodology developed herein provides a repeatable monitoring framework to assess resilience and resistance processes in mangrove forests at local to global scales, and changes to their relative drivers, simultaneously into the future. Monitoring of these combined processes is essential to tease out key areas for concern and prioritise management actions to reduce overall mangrove vulnerability to SLR (Ellison 2015).

The main findings outlined above have key implications for management for mangrove CCMA ES delivery, which may also have positive implications for mangrove ES delivery beyond CCMA. To explore this futher, the following subsections address the main thesis questions and objectives outlined in Chapter 1 with respect to future research and management challenges and directions that

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9. Discussion, conclusions and recommendations have been highlighted by the key findings described in the thesis. Specific management recommendations are then outlined in Section 9.5.

9.2 What kinds of mangrove do we need to protect and rehabilitate for high CCMA ES delivery? Overall, the findings of this thesis demonstrate that, with the aim of carbon stock enhancement alone in mind (e.g. in blue carbon Payments for Ecosystem Services [PES] schemes), monoculture plantations of large-bodied mangrove species may be sufficient to meet mangrove management or rehabilitation goals. However, similarly to the situation for biomass production in early juvenile mangrove stages (Huxham et al. 2010; Lang’at et al. 2012), mangrove conservation efforts for multiple CCMA goals should prioritise protection and rehabilitation of functionally and structurally diverse mangrove stands where possible. Specifically, that is, to maintain cover of mature, large-bodied mangrove species to maximise carbon storage potential, while also maintaining functionally diverse “space-filling” individuals (e.g. Bosire et al. 2006) to enhance coastal storm surge attenuation potential. These findings provide further evidence against single species mangrove planting in the tropics, which, as well as having low survival rates (Primavera & Esteban 2008; Bayraktarov et al. 2016), and negative impacts on other vulnerable coastal ecosystems (seagrasses; Primavera et al. 2012b), will have low relative potential for greenbelt protection in typhoon-prone tropical countries (see also Villamayor et al. 2016). Importantly, these results also support enforcement of management to minimise unsustainable species-specific mangrove extraction and cryptic degradation of rarer mangrove species (Dahdouh-Guebas et al. 2005; Lee et al. 2014; Polidoro et al. 2014). This provides much-needed further justification for rare species-specific mangrove protection, rehabilitation and management beyond biodiversity conservation alone, where a high proportion of global mangrove species are now under elevated risk of global extinction (~1/6 species; Polidoro et al. 2010; 2014). Protection and rehabilitation of species and functionally diverse mangrove stands may also be key to enhancing mangrove resilience, as variation in physiological tolerance ranges may enhance the occurrence and rate of landward migration in response to SLR (Di Nitto et al. 2014; Polidoro et al. 2014). However, there remain certain key areas for future investigation regarding protection and rehabilitation of mangroves for CCMA.

First, the findings of this thesis expose important future research questions regarding (1) context-dependency in functional diversity controls on mangrove

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CCMA ES delivery, and (2) the specific mechanisms by which functional diversity is associated with high storm surge attenuation potential. In the first instance, and in light of the greater structural complexity observed in the more variable growth form Avicennia marina versus Sonneratia alba monocultures, further research effort into mangrove functional traits and CCMA ES delivery across wider geographic regions, including more species-poor assemblages and distributional physiological extremes (e.g. dwarf mangroves), will serve to elucidate potential context dependency in the biodiversity-ecosystem services (B-ES) relationships observed herein (see e.g. Balvanera et al. 2014; Duncan et al. 2015). In the second instance, while the results of this thesis hint at resource and space use complementarity in driving mangrove storm surge attenuation potential, further experimental planting research involving mangrove species’ vertical and horizontal sediment profile use and shade tolerance traits will be required to validate these findings and clarify the precise mechanisms of potential complementarity between species.

A second further research challenge to inform protection and rehabilitation of functionally diverse mangroves regards analysis of the capacity for natural dispersal and establishment of diverse species assemblages in degraded parts of the global mangrove distribution. For many, particularly mid- to upper-intertidal, mangrove species, long distance dispersal capacity is minimal (Duke et al. 1998). With extensive mangrove clearing and degradation, mature propagule source populations are increasingly geographically isolated from degraded sites (see e.g. Camptostemon philippinense; Primavera et al. 2004; Polidoro et al. 2010). While natural colonisation from adjacent areas can result in the establishment of relatively diverse mangrove stands (e.g. Matsui et al. 2010; Nam et al. 2016), enhancing functional diversity in sites with anthropogenically-degraded propagule availability and connectivity may require active planting of rarer species, as well as inclusion of these species within project nurseries (see e.g. Primavera et al. 2012b). Future tidal and current-based modelling of potential propagule connectivity (e.g. Wood et al. 2014) between source populations and degraded sites will be key to prediction of future mangrove species and functional diversity (see Record et al. 2013). This approach may also provide vital information for the refinement of species-specific extinction risk estimates (Polidoro et al. 2010). Such research should be employed to inform potential extension of the current Protected Area network to include important propagule source populations (UNEP 2014) to maximise the meta- maintenance of mangrove functional diversity and CCMA ES delivery.

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Third, a currently active research area that it was not possible to address within the remit of this thesis relates to the connections between macrobenthos and wider food web and community structure and mangrove ecosystem functioning (see Cannicci et al. 2008; Lee 2008; Lee et al. 2014). Investigations of the interactions between mangrove flora functional diversity and macrobenthos communities (bioturbation, herbivory; Cannicci et al. 2008) will be essential to improve our understanding of mangrove nutrient transfer and carbon sequestration. Where this wider community structure is considered, the association between low functional diversity and high carbon stocks observed within this thesis may begin to breakdown and reveal overall greater biodiversity controls on mangrove carbon cycling. The capacity for return of faunal communities in rehabilitated mangroves is variable (Field 1998; Adite et al. 2013; Andradi-Brown et al. 2013). Research into the functioning and CCMA ES delivery of rehabilitated and natural mangroves according to faunal community structure will be key to informing measures of success in mangrove rehabilitation efforts.

Indeed, variation in macrobenthos community structure may in some part explain the high variability in sediment carbon stocks observed across Panay Island in this thesis (Chapter 6). However, a fourth key area of potential research focus going forward lies in the establishment of relative controls of mangrove flora and faunal community structure and diversity and wider coastal land use management (e.g. aquaculture inputs, riverine sediment input regimes) on mangrove sediment carbon storage (see e.g. Bhomia et al. 2016; Sasmito et al. 2016b), in order to identify any non-linearities or thresholds to multiple CCMA ES delivery. These questions are essential to enhance understanding of wider terrestrial connectivity controls on mangrove ecosystem functioning, and to inform whole watershed and ridge-to-reef management to maximise CCMA efforts. On a related note, better incorporation of wider landscape and ecosystem connectivity regarding the associated roles of reefs and seagrass beds in providing additional storm surge attenuation potential in coastal areas is required, as positive interactions between these connected systems may enhance multiple CCMA benefits (Gillis et al. 2014). Key questions in this area involve the degree to which seagrass and coral reef areal coverage and structural complexity could compensate for mangrove coverage and structural and functional diversity in providing coastal protection ES, to assist in identifying priority areas for mangrove management for storm surge attenuation.

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The fifth future research challenge in identifying the optimal community structure of mangrove forests for protection and rehabilitation for CCMA lies in their capacity for simultaneous fisheries enhancement. Data availability constraints meant that it was not possible to assess relationships between mangrove community structure and fisheries resources within this thesis. Current scientific opinion supports that greater mangrove species (and functional) diversity may also enhance wider ecosystem functioning, enabling microhabitat variation to support greater diversity at higher trophic levels in wider coastal and marine food webs (Lee et al. 2014; Polidoro et al. 2014). Accordingly, mangrove forest management for high functional diversity and CCMA ES delivery may also be associated with simultaneously enriched coastal fisheries, thus enhancing potential benefit transfer to myriad local to global stakeholders (Folke et al. 2007; Adame et al. 2014; Máñez et al. 2014; Needles et al. 2015). There is evidence for greater fish abundance and diversity in mangrove versus non-mangrove coastal areas (e.g. Mumby et al. 2004; but see Huxham et al. 2004), and in intact (and rehabilitated) versus degraded mangrove (e.g. Adite et al. 2013) (for a global data review see Carrasquilla-Henao & Juanes 2016). However, quantitative exploration of relationships between the specific structure and diversity of mangrove assemblages and their capacity to support abundant and diverse fisheries assemblages is lacking and challenging to address, as the fluidity of the marine environment makes identification of optimal spatial scales of investigation difficult to ascertain. Going forward, these are key questions to begin to unpick for both an improved understanding of the role of mangroves in supporting livelihoods, and in identification of synergies in stakeholder preferences for the potential successful implementation of mangrove protection and rehabilitation-based CCMA activities.

Indeed, as well as the ecological considerations to CCMA ES delivery, the spatial distribution of multiple stakeholder preferences for ES delivery must be considered in coastal zone management. The perceived economic costs incurred by storm/flood damage (e.g. dependent on the density of adjacent infrastructure and agri- /aquaculture) do not always co-vary spatially with optimal areas for, for example, carbon stock potential (Atkinson et al. 2016). Thus, as well as mangrove community structure drivers of carbon stock and coastal protection ES trade-offs identified herein, coastal zone management for singular CCMA ES enhancement may also result in spatial trade-offs in CCMA for different coastal beneficiaries (see Atkinson et al. 2016). In the case of mangrove rehabilitation, as identified in Chapters 6 and

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7, low-diversity, narrow low-intertidal mangrove planting can be associated with enhancing carbon stocks; yet, is also associated with low coastal protection potential and likely low fisheries enhancement for vulnerable coastal communities (Polidoro et al. 2014). In developing countries such as the Philippines, where much of the coastal zone is densely populated largely by vulnerable low-income fishing communities and much natural mangrove has been lost, coastal zone spatial planning must include the requirements of the full multitude of coastal ES stakeholders to ensure optimal CCMA benefit transfer. Prioritisation for protection of remaining large and functionally diverse natural mangrove stands, and rehabilitation to return wide (full intertidal zone) greenbelts for high functional diversity in areas affected by historical aquaculture development, should now be a priority in CCMA efforts. Research into multiple stakeholder preferences, and how these may co-vary with opportunities for multiple mangrove CCMA ES delivery, including rehabilitation potential, will be required to inform effective spatial prioritisation.

Finally, the findings of this thesis show that, through careful mangrove forest management for maintenance and rehabilitation of both large-bodied and functionally diverse “space-filling” species, synergies in CCMA ES delivery within advocated potential multiple PES schemes (Kairo et al. 2009; Locatelli et al. 2014; Thompson et al. 2014) are possible. However, the trade-offs in preferences and requirements of different ES beneficiaries outlined above further confers complications to the implementation of such multiple CCMA PES projects. The long- term Mikoko Pamoja project in Gazi Bay, Kenya has demonstrated the financial benefits that can be transferred to coastal communities through voluntary carbon markets under mangrove PES schemes, mitigating otherwise low preferences for mangrove carbon stocks and reversing trends in degradation (Huxham et al. 2012). However, the identification of donors for additional CCMA ES (i.e. coastal protection) to be incorporated within PES schemes to mitigate currently low voluntary carbon market prices remains a key issue to the future of mangrove PES schemes (see Naeem et al. 2015). Resolutions in this area require collaboration between local and national government institutions, NGOs, and in particular the aquaculture industry, as well as multidisciplinary socio-economic and ecological research to elucidate networks of multiple stakeholder benefits in terms of mangrove conservation into the future.

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9.3 What are the intertidal location-specific constraints to rehabilitation of CCMA ES? The findings of this thesis clearly reveal that, while mangrove rehabilitation can return per unit area CCMA ES delivery (carbon stocks), current standard practice low-intertidal planting activities have extremely low capacity to achieve CCMA ES goals. Indeed, rehabilitation efforts must be considered in terms of the costs of implementation versus the benefits derived from successful project implementation (e.g. Bayraktarov et al. 2016). In the absence of blue carbon PES schemes for successfully established low-intertidal seafront mangroves, their low CCMA ES delivery suggests limited net gain from avoided infrastructure damage, and thus wasteful rehabilitation practices (Primavera & Esteban 2008). Conversely, while substantial shifts in political will and capacity will be required to target CCMA rehabilitation in abandoned fishponds, the observed high greenbelt capacity suggests that substantial benefit will also be gained in terms of reduced risk to lives, housing and infrastructure (e.g. the aquaculture industry). The findings of this thesis thus advocate for a shift away from government-led low-intertidal rehabilitation, and away from short-term action toward long-term CCMA gains. A strategy of targeted (e.g. spatially prioritised for high CCMA ES delivery) abandoned fishpond reversion to former mangrove forest should now be a national priority in the Philippines and across those Southeast Asian countries with similar rates of aquaculture abandonment (see below).

Prioritisation of abandoned fishpond reversion over low-intertidal planting furthermore may serve to increase mangrove resilience to SLR via landward migration in areas with high anthropogenic development at landward margins and high rates of SLR (e.g. the Philippines; see Figure 3.7; Gillman et al. 2008; Ellison 2015). Furthermore, targeting wide sea-facing abandoned fishponds, as well as those landward of remaining mangrove stands, for mangrove rehabilitation will serve to expand current greenbelt widths beyond currently insufficient legal mandates in the Philippines (50-100m). A potential identified tool to assist in abandoned fishpond rehabilitation for high CCMA ES delivery may lie in the partial maintenance of former seaward pond banks, as built structures mitigate tidal action and enhance sediment trapping (Hoang Tri et al. 1998; Hashim et al. 2010; Kamali et al. 2011; Tamin et al. 2011; Primavera et al. 2012b), reducing the on-the-ground capacity requirements for active rehabilitation. Adoption of such techniques may further serve to stabilise seaward mangrove margins, increasing their relative

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9. Discussion, conclusions and recommendations capacity for resilience to SLR. The driver of intertidal zone constraints to successful mangrove CCMA rehabilitation across Southeast Asia is the existence of strong tenure conflict across the mid- to upper-intertidal zone. The findings of this thesis reveal opportunities for identification and inventory of low-conflict abandoned aquaculture areas, and have important implications for reversal of trends in mangrove CCMA ES loss in much of Southeast Asia. However, key research is required to enhance incorporation of these and similar findings into global mangrove conservation activities for CCMA.

First, the findings of this thesis and our future ability to predict CCMA gains and trajectories in reverted abandoned fishpond sites are currently limited by a lack of quantitative information regarding the carbon dynamics of aquaculture ponds. This includes unvegetated fishpond carbon stocks, carbon sequestration into pond sediments from faunal inputs, sediment respiration and trapping within fishpond banks (Alongi 2011). Existing data show low sediment carbon stocks in unvegetated abandoned aquaculture ponds relative to rehabilitated and natural mangroves (52 Mg ha-1; Bhomia et al. 2016), suggesting that rehabilitated abandoned fishpond mangrove vegetation and sediment accretion has the potential to substantially increase carbon stocks, as was reinforced by the results of this thesis. However, the absence of wider geographical data coverage hinders the potential for monitoring of carbon stock trajectories from rehabilitated abandoned fishponds, and thus prediction of potential emissions reductions from local to global abandoned aquaculture reversion into the future. These data will be required to inform emissions reduction forecasts of any potential future afforestation-based blue carbon PES schemes in rehabilitated abandoned aquaculture areas, and will be key to development of methodologies to enable inclusion of sediment stocks into blue carbon projects. Similarly, information availability regarding the successes and failures of mangrove rehabilitation activities for CCMA ES delivery beyond initial area or numbers of propagules planted are disparate both within and between regions (see e.g. Bayraktarov et al. 2016), and indeed information regarding the existence and extent of global rehabilitation projects is sparse. Efforts to enhance data sharing on global mangrove rehabilitation activities are greatly needed. Moreover, systematic reviews of global mangrove rehabilitation trajectories, including carbon stock development and coastal protection potential, will be required in order to improve the current capacity for predicting CCMA gains from global mangrove rehabilitation.

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Second, as previously discussed, often the key obstacle to ecologically-led mid- to upper-intertidal zone mangrove rehabilitation is tenure conflict in the coastal zone. As such, coastal zone spatial planning and prioritisation exercises for multiple CCMA ES greenbelt rehabilitation (based on, for example, coastal vulnerability assessment) should first aim to evaluate the existence of formally-recognised tenure gaps (e.g. abandonment, unproductive land, or here tenure breaches; Primavera et al. 2014), in order to evaluate the benefit to cost ratios of CCMA rehabilitation efforts in any currently-tenured areas. Such approaches may have particular relevance in other South and Southeast Asian countries where fishpond abandonment is high: e.g. Malaysia (60%; Choo 1996), Thailand (50-80%; Hossein & Lin 2001) and Sri Lanka (60-90%; Jayakody et al. 2012; Bournazel et al. 2015), Vietnam and Taiwan (Stevenson 1997). Detailed spatial land tenure information is often unavailable or is labour-intensive and difficult to collect (Primavera et al. 2014), yet is essential, in combination with ecological factors and exposure and impact assessment, for the development of spatial prioritisation models for mangrove rehabilitation management for CCMA goals. Going forward, research into context-dependency in the level of conflict between optimal areas for mangrove rehabilitation and formal coastal tenure, and potential opportunities to overcome these in coastal zone management will be indispensible. Furthermore, future spatial modelling exercises for mangrove greenbelt rehabilitation priorities should also seek to incorporate wider biodiversity benefits (e.g. connectivity to other coastal ecosystems; seagrasses and reefs), in order to maximise potential benefit transfer from CCMA activities.

Third, the issue of evaluating mangrove coastal protection and storm surge attenuation potential is an area that warrants significant research attention. Current methods to assess coastal protection ES, such as those employed in this thesis, have been developed in comparatively narrow contexts (see McIvor et al. 2012), limiting accurate prediction across global mangrove forests. Furthermore, tropical storms and typhoons are associated with both high wind speeds and storm surges, factors that must both be assessed in the context of mitigation of their impacts by mangrove forests. The impacts of these separate components to mangroves, and their ability to mitigate incoming energy, may vary according to forest structure and species composition. Indeed, heavy storm damage from high typhoon winds is associated with tall but narrow mangrove species (e.g. Rhizophora spp.; Villamayor et al. 2016), while resistance to storm surges is associated with high aboveground

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9. Discussion, conclusions and recommendations biomass and surface area in mature, well-rooted mangroves (Alongi 2008). Development of hydrological spatial models of typhoon trajectories through coastal areas and into mangroves (e.g. Zhang et al. 2012) according to these multiple components is required to improve our ability to predict future coastal protection impacts under climate change, and the potential greater mangrove community structure-specific complexities to storm surge attenuation. Such models will be important in informing spatially explicit greenbelt requirements under current exposure, enabling prioritisation of greenbelt rehabilitation activities. Furthermore, as outlined in Section 9.2, it will be important to better incorporate the combined impacts of adjacent seagrass and coral reef cover and structural complexity into such modelling approaches (Gillis et al. 2013).

Finally, this thesis assessed rehabilitation potential in terms of CCMA ES benefits alone. While the rehabilitation of large areas of abandoned fishpond relative to narrow low-intertidal seafront mangrove is likely to have had greater positive influences on adjacent coastal intertidal faunal communities (Mumby et al. 2004; Adite et al. 2013), access to these resources may vary according to the responsible management body and tenure of rehabilitated areas. For example, at the Nabitasan abandoned fishpond ecopark site considered within this thesis, all extractive activities by local communities (e.g. crab and shellfish extraction) are currently banned by the Local Government Unit (LGU) management while the rehabilitated mangrove continues to establish. To date, the Dumangas abandoned fishpond study site has not had its tenure status legally reverted by the responsible government agency, the Bureau of Fisheries and Aquatic Resources of the Department of Agriculture (DA-BFAR). Accordingly, the rehabilitated mangrove land remains under “private” tenure, meaning no current extractive access by adjacent communities. Conversely, direct community involvement at both seafront rehabilitation sites (Bakhawan ecopark and Ermita, Dumangas; see Section 4.2.2) means that gleaning activities and sustainable mud crab fisheries provide additional mangrove-associated livelihood opportunities to coastal community stakeholders. A combination of (1) ecological research into mangrove faunal rehabilitation, (2) socio-economic research into perceptions of and benefits derived from abandoned fishpond reversion, (3) greater LGU incorporation of coastal community needs into greenbelt rehabilitation plans, and (4) collaboration between government departments (DA-BFAR and Department of Environment and Natural Resources [DENR] in the Philippines) for implementation of breached tenure reversion are

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9. Discussion, conclusions and recommendations required to maximise multiple stakeholder benefits and the potential successful realisation of CCMA activities surrounding abandoned fishpond reversion.

9.4 What is the capacity for global mangrove forest resilience and resistance to SLR? The findings detailed in Chapter 8 of this thesis reveal variability in resilience and resistance processes across mangrove forests. They furthermore caution against assumptions of positive co-linearity between levels of resilience and resistance at individual mangroves, instead highlighting the need for careful breakdown of resilience and resistance processes and their drivers in order to inform management priorities to minimise overall vulnerability. While capacity for resilience via landward expansion along shallow topographic gradients was evident at all sites, the capacity for resistance (i.e. maintained productivity and biomass) was comparatively low. These findings are in accordance with regional studies on relative mangrove elevation capital in the face of SLR (e.g. Lovelock et al. 2015; Sasmito et al. 2016a). The thesis findings regarding capacity for landward migration provide support for strategies of managed retreat of mangrove forests for CCMA under future SLR (Rogers et al. 2014; Runting et al. 2016), particularly in areas where rates of seaward biomass loss associated with low sediment availability are also high. Sediment supplies are declining through freshwater extraction and damming in many parts of the world (e.g. Walling & Fang 2003). Thus, along with observed anthropogenically-driven cryptic mangrove degradation where landward development is high, trends in global mangrove resistance to SLR are likely to be negative into the future (Lovelock et al. 2015; Sasmito et al. 2016a). Global monitoring of mangrove resilience and resistance processes will be crucial into the future, and satellite remote sensing offers an unprecedented opportunity to access monitoring information at national to regional scales. However, while estimable from remote sensing indices as employed here, monitoring of trends in drivers of resilience and resistance processes in collaboration with on-the-ground Rod Surface Elevation Table Marker Horizon (R-SET) measurements and wider hydrological and sediment input changes will be required to better substantiate mangrove vulnerability to SLR (see e.g. Jones et al. 2015; Madagascar). Important future research and management avenues regarding mangrove resilience and resistance to SLR are discussed below.

First, assessment of potential mangrove resistance to SLR is currently geographically limited in focus. Particularly lacking are field data on trends in

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9. Discussion, conclusions and recommendations relative elevation capital (R-SET monitoring) across South and Southeast Asia, Africa and South America (see Sasmito et al. 2016a). While the methodology employed in this thesis expands current geographic scope for SLR resistance estimation from West Africa to South Asia, the methods employed rely on remote inference of relationships between sediment availability and change in biomass alone. Thus, satellite remote sensing methodologies such as those employed here must expand geographical coverage with future data availability (ESA ALOS/PALSAR archive opening [ESA 2016b], ALOS-2/PALSAR [see Lucas et al. 2015] and Sentinel-1 [Copernicus 2016a] imagery for biomass change monitoring; MODIS Terra [Miller & McKee 2004] for sediment load monitoring; TanDEM-X for topographic changes [Zink et al. 2015]; and the long term Landsat imagery archive [USGS 2015] for mangrove cover change estimation), while the global R-SET network must too expand beyond its current geographical limits (Sasmito et al. 2016a). Combining these datasets will enable calibration of Synthetic Aperture Radar (SAR)-derived biomass change with trends in surface elevation change and capital relative to SLR, enhancing the applicability of global monitoring methodologies for SLR vulnerability into the future.

Second, current management activities and enforcement of protection in many of the world’s mangrove forests are currently unknown (UNEP 2014). Accordingly, it is currently problematic to align trends in mangrove resilience and resistance potential to specific management needs on-the-ground. Future research into SLR threats to mangrove systems should begin to evaluate globally how processes of resilience and resistance co-vary with different management regimes, and how interventions (e.g. temporary built structures for erosion reduction [Hashim et al. 2010], planning for managed retreat [Rogers et al. 2014; Runting et al. 2016], policy interventions to reduce sediment input alterations [Gilman et al. 2008], landward mangrove rehabilitation [Alexandris et al. 2013]) could serve to reverse negative trends. These questions will be of significance to the Southeast Asian region, where heavy anthropogenic alterations in the coastal zone spell out highly limited present capacity for resilience and resistance to SLR (Lovelock et al. 2015).

Finally, while there is much evidence to support high mangrove vulnerability to SLR (Ellison 2015; Lovelock et al. 2015; Sasmito et al. 2016a; Ward et al. 2016), mangrove conservation activities and CCMA interventions in the tropics rarely consider future SLR impacts in their planning stages. Yet these considerations are

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9. Discussion, conclusions and recommendations essential to enhancing the potential success of these activities, and may in some cases incur financial or legal complications. For the implementation of blue carbon PES schemes for mangrove conservation activities, forecasted carbon emissions reductions (e.g. through avoided deforestation or mangrove afforestation) must be estimated in order to generate marketable credits (Locatelli et al. 2014; Wylie et al. 2016). In the case of carbon credit mechanisms such as the Verified Carbon Standard (VCS), afforestation-based PES projects can receive ex-ante credits based on generated emissions reductions forecasts, rather than on final project results (VCS 2016). If potential low SLR resilience and resistance (cf. also typhoon impacts) is not incorporated into carbon forecasting, such projects may fail to meet agreed legal PES targets, and may incur substantial unexpected costs of project implementation from, for example, unforeseen construction of erosion reduction structures. Indeed, potential SLR impacts are not currently addressed within coastal zone management, spatial planning and Protected Area designation (e.g. Runting et al. 2014; Rogers et al. 2016). Yet, in order to maximise CCMA efforts into the future, these are key areas for both research and management attention. Specific areas that are lacking include (1) development of methodologies to incorporate SLR impacts into blue carbon PES forecasting, (2) identification of when and where managed retreat is the best CCMA option over enhancing elevation capital, (3) identification of alterable barriers to landward expansion (e.g. removal of disused seawalls and dikes), and (4) research to address the potential impacts of SLR on greenbelt rehabilitation in vulnerable tropical countries. In the latter instance, it is likely that focused rehabilitation on abandoned lands (e.g. aquaculture) presents a viable option for enhancing the resilience of rehabilitated mangroves to SLR. These are key research questions going forward, in order to inform the cost-effective allocation of often limited CCMA funds.

9.5 Management recommendations Based on the findings of this thesis, and on the discussion of the scientific literature and current management practices above, specific management guidelines regarding mangrove management for CCMA ES delivery are suggested in the following subsections. These fall under five main areas: (1) Mangrove greenbelt rehabilitation for coastal protection; (2) Blue carbon PES; (3) Coastal zone management and spatial planning; (4) Protected Area designation and zoning, and; (5) Governance and policy as regards the previous four areas.

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9.5.1 Mangrove greenbelt rehabilitation for coastal protection 1. Greenbelt rehabilitation activities should aim to maximise mangrove functional diversity where possible. In areas at distance from diverse adjacent natural mangroves, and in particularly typhoon-vulnerable areas, this may require nursery rearing and out planting of species less able to naturally re-colonise.

2. Legal and hydrological reversion of abandoned aquaculture ponds should now be a priority over low-intertidal propagule planting for mangrove-based CCMA.

3. Legal greenbelt requirements may be too narrow for effective coastal protection. Site-specific estimates should instead be determined from mangrove structural parameters. (Philippines).

4. Temporary maintenance of partial seaward aquaculture pond banks (and other temporary built structures; e.g. wave breaks) is advisable to enhance mangrove greenbelt establishment and sediment carbon stocks, and reduce erosion.

5. Monitoring of greenbelt rehabilitation trajectories beyond initial project implementation (i.e. incremental survival, growth and species diversity data) is necessary to assess project success and inform wider mangrove rehabilitation.

6. Greenbelt rehabilitation projects should seek to involve the requirements of all stakeholders (including coastal communities) in legal abandoned fishpond reversion, including in rehabilitation pond identification for maximised ES delivery, and in rehabilitated mangrove forest stewardship and access rights.

9.5.2 Blue carbon PES 1. In avoided deforestation-based PES schemes aimed at both maintenance of blue carbon stocks and coastal protection, projects should target wide, functionally diverse mangroves with an abundance of mature, large-bodied mangrove species.

2. In afforestation-based PES schemes aimed at both enhancement of blue carbon stocks and coastal protection, projects should focus assisted natural colonisation (e.g. hydrological restoration) and out planting activities on the development of mangrove communities with an abundance of mature, large-bodied mangrove species and high functional diversity of “space filling” species. These projects may be best-targeted at large sea-facing abandoned aquaculture sites.

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3. Multiple ES PES schemes aimed at both blue carbon stocks and coastal protection must identify and establish mechanisms for industry (e.g. aquaculture) markets for coastal protection, alongside established voluntary mechanisms for carbon credit sales.

4. In afforestation-based blue carbon PES schemes aimed at abandoned aquaculture areas, preliminary site assessments of pre-project sediment carbon stocks will be required, in the absence of representative existing estimates.

5. Blue carbon PES project carbon emissions reduction forecasts must consider climate change-related pressures at project sites. Vulnerability assessments of potential seaward erosion, capacity for landward expansion, and trends in sediment elevation maintenance relative to SLR should be conducted prior to site selection. Forecasts of avoided emissions or potential emissions reductions must incorporate potential loss via SLR impacts in vulnerable sites.

9.5.3 Coastal zone management and spatial planning 1. Coastal zone management for CCMA must incorporate data and knowledge on both exposure, potential for CCMA ES delivery, ecological connectivity to seagrass beds and coral reefs, and multiple stakeholder needs in order to manage potential spatial trade-offs in ES delivery and benefit transfer.

2. Coastal zone management in regions of high aquaculture development should quantify and include aquaculture tenure, abandonment and potential for future CCMA ES delivery into spatial planning, in order to identify key areas for legal reversion to former mangrove forest.

3. Spatial planning and scenario modelling, including potential disruptions to coastal protection, should be conducted prior to the designation of any new aquaculture development. Mangrove-friendly aquaculture, at least achieving a 4:1 mangrove to pond ratio, should be advocated for all existing or novel aquaculture.

4. Coastal zone management should consider future changes to mangrove CCMA benefits under SLR. Spatial planning for infrastructural development should avoid extensive development in low-lying, shallow-sloping areas landward of existing mangroves, to facilitate landward expansion under SLR. The density of infrastructure development along mangrove landward boundaries, as a potential

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barrier to landward expansion and associated unsustainable extractive impacts on adjacent mangroves, should be considered in decision-making.

5. In areas with low mangrove seaward boundary resilience (e.g. mangrove loss) and resistance to SLR, upstream impacts on freshwater inflows and sediment availability should be considered and addressed with regard to their negative impacts on CCMA in the coastal zone.

9.5.4 Protected Area designation and zoning 1. Greater coverage of mangroves with enforced protection (and monitoring) within the global Protected Area network is required in order to reduce rates of mangrove loss and degradation.

2. Creation of legislation, and management enforcement, to reduce cryptic degradation of rare and functionally distinct mangrove species within Protected Areas is required to conserve mangrove diversity and coastal protection ES.

3. (Marine) Protected Area expansion to cover important source mangrove species populations is necessary to enhance future functional diversity and coastal protection potential in degraded hyper-diverse regions. This may further more serve to enhance the potential resilience of these regions to SLR into the future.

4. The Protected Area network should be expanded to include refuge areas landward of existing mangrove forests, to enhance resilience to SLR.

5. Protected Area zoning (either temporal or spatial) may be required in some areas to reduce species-specific extractive pressures and to enhance mangrove forest resistance to SLR.

9.5.4 Governance and policy 1. Closing the legislative gap. Mangrove forests are intertidal ecosystems that should be governed as such; that is, they should fall under the remit of both marine and terrestrial government environmental and resource management departments. Inclusion of mangroves within terrestrial planning departments is required to ensure that potential landward expansion in response to SLR is included within coastal policy initiatives.

2. Between marine and terrestrial departments, and between environmental and natural resources, departments (e.g. aquaculture and fisheries), collaboration for joint management of mangrove ecosystems and resources is required. This may

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require the development of umbrella regulatory or advisory boards to facilitate effective management.

3. National, regional and local governance in the coastal zone should work in partnership with coastal communities to ensure optimal coastal zone planning for CCMA that maximises benefit transfer to all stakeholders.

9.6 In conclusion This thesis has provided a quantitative assessment of mangrove forest CCMA ES biodiversity drivers, rehabilitation, and resilience to climate change (SLR), focusing on both the Philippines and mangroves across West Africa to South Asia. It has emphasised the role of mangrove community structure in driving potential trade- offs between CCMA ES delivery, but in particular of functional diversity for driving high greenbelt coastal protection potential. Furthermore, it has exposed the low CCMA capacity of current standard practice but unscientifically-founded practices for mangrove rehabilitation, highlighting instead key CCMA ES strings to the bow of prioritisation of abandoned aquaculture reversion. Finally, it has developed a remote monitoring framework for monitoring mangrove forest resilience and resistance processes to SLR, enabling systematic examination of key areas for concern for future management to minimise vulnerability. The findings and management recommendations of this thesis hold promise for supporting diverse, resilient mangrove forests, which will be of increasing importance into the future under elevated pressures on coastal systems from global climate change (IPCC 2013; Blankespoor et al. 2016).

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APPENDIX 1. Results of analyses of functional trait and species diversity drivers of ecosystem services at the KII site (Chapter 6).

A1 A priori candidate linear mixed effects models for the KII site Table A1.1. A priori candidate model list of linear mixed effects models built for each ES (harvestable kindling abundance [number of downed wood pieces ≥1.5 cm diameter; square root-transformed], harvestable timber/charcoal biomass [tonnes per plot; log- transformed], sediment carbon stocks [tonnes per plot to 150cm depth], vegetation carbon stocks [tonnes per plot; log-transformed], total plot carbon [tonnes per plot to 150cm depth], storm surge attenuation potential [Le per plot to 2.7m; square root-transformed]) modelled in analyses conducted for only plots at the KII site (N = 30 plots; see Section 4.2.2). The random effect of distance class (from plot centre to shoreline) is a categorical random effect on the model intercept.

No. Fixed effects Random effect

1 Species richness Distance class

2 FDis (multi-trait) Distance class

3 FDvar max. height (square root- transformed) Distance class

4 FDvar SLA (square root-transformed) Distance class

5 Species richness + FDis (multi-trait) Distance class

6 Species richness + FDvar max. height (square root-transformed) Distance class

7 Species richness + FDvar SLA (square root-transformed) Distance class

8 FDis (multi-trait) + FDvar SLA (square root-transformed) Distance class

FDvar max. height (square root-transformed) + FDvar SLA (square 9 Distance class root-transformed)

10 CWM max. height Distance class

11 CWM SLA Distance class

12 CWM max. height + CWM SLA Distance class

13 Species richness + CWM max. height Distance class

14 Species richness + CWM SLA Distance class

15 FDis (multi-trait) + CWM max. height Distance class

16 FDis (multi-trait) + CWM SLA Distance class

17 FDvar max. height (square root-transformed) + CWM SLA Distance class

18 FDvar SLA (square root-transformed) + CWM max. height Distance class

A2 Harvestable kindling production at the KII site

Table A1.2. List of all plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot harvestable kindling abundance (number of downed wood pieces ≥1.5 cm diameter; square root-transformed) at the KII site. Included are the original number of each model within the a priori candidate model set, and the AIC, delta AIC (ΔAIC), Akaike weight, marginal and conditional R2 values for each model. Marginal and conditional R2 were calculated under the method of Nakagawa & Schielzeth (2013) and using R code developed by J.S. Lefcheck (available at: http://jonlefcheck.net/2013/03/13/r2-for-linear-mixed- effects-models/).

Akaike Marginal Conditional No. Fixed effects AIC ΔAIC weight R2 R2 4 FDvar SLA 103.48 0.00 0.25 0.12 0.55 FDvar max. height + 9 103.67 0.19 0.23 0.15 0.54 FDvar SLA 8 FDis + FDvar SLA 104.86 1.38 0.13 0.14 0.59 FDis + CWM max. 15 105.09 1.60 0.11 0.12 0.59 height FDvar SLA + CWM 18 105.40 1.91 0.10 0.12 0.54 max. height Species richness + 7 105.48 2.00 0.09 0.12 0.55 FDvar SLA 2 FDis 105.60 2.12 0.09 0.08 0.57

Table A1.3. Model-averaged regression coefficients for all fixed effects contained within plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot harvestable kindling abundance (number of downed wood pieces ≥1.5 cm diameter; square root-transformed) at the KII site. Included is the full model-averaged intercept estimate and standard error (s.e.), and the full model-averaged slope estimates and standard errors, the importance values (푊푖), number of containing models and 85% confidence intervals (CIs) for each variable. CIs in bold highlight those variables for which the 85% CIs of the model averaged slope estimates do not cross zero.

Variable Estimate s.e. Wi No. models 85% CIs FDvar SLA 0.41 0.29 0.80 5 0.11 – 0.72

FDvar max. height -0.07 0.17 0.33 3 -0.26 – 0.11

FDis 0.12 0.24 0.23 1 -0.12 – 0.37

CWM max. height 0.04 0.14 0.21 2 -0.11 – 0.20

Species richness 0.002 0.08 0.09 1 -0.08 – 0.08

A3 Harvestable timber/charcoal production at the KII site

Table A1.4. List of all plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot harvestable timber/charcoal production (tonnes per plot; log-transformed) at the KII site. Included are the original number of each model within the a priori candidate model set, and the AIC, delta AIC (ΔAIC), Akaike weight, marginal and conditional R2 values for each model. Marginal and conditional R2 were calculated under the method of Nakagawa & Schielzeth (2013) and using R code developed by J.S. Lefcheck (available at: http://jonlefcheck.net/2013/03/13/r2-for-linear-mixed-effects-models/).

Akaike Marginal Conditional No. Fixed effects AIC ΔAIC weight R2 R2 10 CWM max. height 66.21 0.00 0.40 0.48 0.48 Species richness + 13 68.14 1.94 0.15 0.48 0.48 CWM max. height FDis + CWM max. 15 68.18 1.97 0.15 0.48 0.48 height CWM max. height + 12 68.20 2.00 0.15 0.48 0.48 CWM SLA FDvar SLA + CWM 18 68.21 2.00 0.15 0.48 0.48 max. height

Table A1.5. Model-averaged regression coefficients for all fixed effects contained within plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot harvestable timber/charcoal production (tonnes per plot; log-transformed) at the KII site. Included is the full model-averaged intercept estimate and standard error (s.e.), and the full model- averaged slope estimates and standard errors, the importance values (푊푖), number of containing models and 85% confidence intervals (CIs) for each variable. CIs in bold highlight those variables for which the 85% CIs of the model averaged slope estimates do not cross zero.

Variable Estimate s.e. Wi No. models 85% CIs CWM max. height 0.61 0.12 1.00 5 0.48 – 0.74

Species richness -0.005 0.05 0.15 1 -0.05 – 0.05

FDis 0.003 0.05 0.15 1 -0.05 – 0.06

CWM SLA 0.001 0.05 0.15 1 -0.05 – 0.05

FDvar SLA 0.001 0.05 0.15 1 -0.05 – 0.05

A4 Vegetation carbon stocks at the KII site

Table A1.6. List of all plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot vegetation carbon stocks (tonnes per plot; log-transformed) at the KII site. Included are the original number of each model within the a priori candidate model set, and the AIC, delta AIC (ΔAIC), Akaike weight, marginal and conditional R2 values for each model. Marginal and conditional R2 were calculated under the method of Nakagawa & Schielzeth (2013) and using R code developed by J.S. Lefcheck (available at: http://jonlefcheck.net/2013/03/13/r2-for-linear-mixed-effects-models/).

Akaike Marginal Conditional No. Fixed effects AIC ΔAIC weight R2 R2 10 CWM max. height 63.02 0.00 0.40 0.47 0.47 FDis + CWM max. 15 64.96 1.93 0.15 0.47 0.47 height Species richness + 13 65.02 1.99 0.15 0.47 0.47 CWM max. height FDvar SLA + CWM 18 65.02 2.00 0.15 0.47 0.47 max. height CWM max. height + 12 65.02 2.00 0.15 0.47 0.47 CWM SLA

Table A1.7. Model-averaged regression coefficients for all fixed effects contained within plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot vegetation carbon stocks (tonnes per plot; log-transformed) at the KII site. Included is the full model-averaged intercept estimate and standard error (s.e.), and the full model-averaged slope estimates and standard errors, the importance values (푊푖), number of containing models and 85% confidence intervals (CIs) for each variable. CIs in bold highlight those variables for which the 85% CIs of the model averaged slope estimates do not cross zero.

Variable Estimate s.e. Wi No. models 85% CIs CWM max. height 0.57 0.12 1.00 5 0.45 – 0.69

FDis 0.005 0.05 0.15 1 -0.05 – 0.06

Species richness -0.001 0.04 0.15 1 -0.05 – 0.04

FDvar SLA 0.0005 0.04 0.15 1 -0.05 – 0.05

CWM SLA 0.0003 0.05 0.15 1 -0.05 – 0.05

A5 Sediment carbon stocks at the KII site

Table A1.8. List of all plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot sediment carbon stocks (tonnes per plot to 150 cm depth) at the KII site. Included are the original number of each model within the a priori candidate model set, and the AIC, delta AIC (ΔAIC), Akaike weight, marginal and conditional R2 values for each model. Marginal and conditional R2 were calculated under the method of Nakagawa & Schielzeth (2013) and using R code developed by J.S. Lefcheck (available at: http://jonlefcheck.net/2013/03/13/ r2-for-linear-mixed-effects-models/).

Akaike Marginal Conditional No. Fixed effects AIC ΔAIC weight R2 R2 CWM max. height + 12 78.18 0.00 0.50 0.38 0.39 CWM SLA FDis + CWM max. 15 80.36 2.17 0.17 0.33 0.34 height 10 CWM max. height 80.71 2.52 0.14 0.27 0.27 Species richness + 13 81.39 3.20 0.10 0.30 0.30 CWM max. height 3 FDvar max. height 81.70 3.51 0.09 0.26 0.30

Table A1.9. Model-averaged regression coefficients for all fixed effects contained within plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on plot sediment carbon stocks (tonnes per plot to 150 cm depth) at the KII site. Included is the conditional model- averaged intercept estimate and standard error (s.e.), and the conditional model-averaged slope estimates and standard errors, the importance values (푊푖), number of containing models and 85% confidence intervals (CIs) for each variable. CIs in bold highlight those variables for which the 85% CIs of the model averaged slope estimates do not cross zero.

Variable Estimate s.e. Wi No. models 85% CIs CWM max. height 0.55 0.18 0.91 4 0.37 – 0.73

CWM SLA 0.34 0.15 0.50 1 0.18 – 0.50

FDis -0.25 0.16 0.17 1 -0.41 – -0.08

Species richness -0.17 0.15 0.10 1 -0.33 – -0.02

FDvar max. height -0.49 0.16 0.09 1 -0.66 – -0.33

A6 Total plot carbon stocks at the KII site

Table A1.10. List of all plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on total plot carbon stocks (tonnes per plot) at the KII site. Included are the original number of each model within the a priori candidate model set, and the AIC, delta AIC (ΔAIC), Akaike weight, marginal and conditional R2 values for each model. Marginal and conditional R2 were calculated under the method of Nakagawa & Schielzeth (2013) and using R code developed by J.S. Lefcheck (available at: http://jonlefcheck.net/2013/03/13/r2-for-linear- mixed-effects-models/).

Akaike Marginal Conditional No. Fixed effects AIC ΔAIC weight R2 R2 10 CWM max. height 129.40 0.00 0.48 0.19 0.78 CWM max. height + 12 130.70 1.30 0.16 0.18 0.81 CWM SLA FDis + CWM max. 15 131.14 1.74 0.13 0.21 0.77 height FDvar SLA + CWM 18 131.36 1.96 0.12 0.18 0.79 max. height Species richness + 13 131.39 1.98 0.11 0.20 0.78 CWM max. height

Table A1.9. Model-averaged regression coefficients for all fixed effects contained within plausible linear mixed effects models (ΔAIC < 4) for the influence of mangrove species and functional diversity, and community dominant functional traits on total plot carbon stocks (tonnes per plot) at the KII site. Included is the full model-averaged intercept estimate and standard error (s.e.), and the full model-averaged slope estimates and standard errors, the importance values (푊푖), number of containing models and 85% confidence intervals (CIs) for each variable. CIs in bold highlight those variables for which the 85% CIs of the model averaged slope estimates do not cross zero.

Variable Estimate s.e. Wi No. models 85% CIs CWM max. height 1.28 0.30 1.00 5 0.96 – 1.59

CWM SLA 0.33 0.37 0.16 1 -0.15 – 0.25

FDis -0.17 0.33 0.13 1 -0.16 – 0.12

FDvar SLA 0.07 0.32 0.12 1 -0.11 – 0.12

Species richness -0.05 0.33 0.11 1 -0.12 – 0.11

APPENDIX 2. SLR in mangrove forests from West Africa to South Asia (Chapter 8).

Tide gauge data was extracted for all study sites considered within Chapter 8 (see Section 4.3) from all long-term (1970-present; minimum eight years’ time-series data) tide gauge stations adjacent to each study site held within the Permanent Service for Mean Sea Level (PSMSL) dataset (Holgate et al. 2013; PSMSL 2016; available at: www.psmsl.org/data). Monthly mean sea level (mm) data for each tide gauge were extracted from the PSMSL database and processed in R version 3.2.5 (R Development Core Team 2016) to determine site-specific temporal trends in mean sea level. First, monthly mean sea level (mm) data for each tide gauge time-series were deseasoned using a 12-month seasonal cycle window to remove seasonal patterns in mean sea level data using the deseason function within the ‘remote’ R package (Appelhans et al. 2016), creating monthly mean sea level anomaly (mm) time-series data for each tide gauge. Linear models (lms) were then applied to model monthly mean sea level anomaly (mm) as a function of date (continuous predictor variable) at each tide gauge. Mean significant linear regression slopes for monthly mean sea level anomaly (mm) over the time-series data for all site-specific tide gauges were then compared to assess relative trend in SLR across study sites.

Adjacent site-specific tide gauges with sufficient recent long-term mean sea level height data (1970-present; minimum eight years of time-series data) for each site were: Dakar 2, Senegal and Palmeira, Cape Verde for the Saloum Delta, Senegal and Sherbro Bay, Sierra Leone sites (Figures A4.1 and A4.2); Maputo, Mozambique for the Save River Delta, Mozambique study site (Figure A4.3); Zanzibar, Tanzania for the Ruvuma Estuary and Rufiji Delta, Tanzania study sites (Figure A4.4); Dzadoudzi, Mayotte for the Mahajamba, Madagascar study site (Figure A4,5), and; Charchanga, Hiron Point and Khepupara, Bangladesh for the Sundarbans, India and Bangladesh study site (Figure A4.6-A4.8).

Figure A2.1 Monthly mean sea level anomaly (mm) at the Dakar 2, Senegal tide gauge: 1992- 2014 (Holgate et al. 2013; PSMSL 2016). Linear regression: intercept = -39.22 ± 9.79; b = 0.04 ± 0.003; p<0.001.

Figure A2.2 Monthly mean sea level anomaly (mm) at the Palmeira, Cape Verde tide gauge: 2000-2014 (Holgate et al. 2013; PSMSL 2016). Linear regression: intercept = -139.70 ± 18.98; b = 0.01 ± 0.001; p<0.001.

Figure A2.3 Monthly mean sea level anomaly (mm) at the Maputo, Mozambique tide gauge: 1994-2001 (Holgate et al. 2013; PSMSL 2016). Linear regression: intercept = -483.45 ± 107.70; b = 0.05 ± 0.01; p<0.001.

Figure A2.4 Monthly mean sea level anomaly (mm) at the Zanzibar, Tanzania tide gauge: 1984-2014 (Holgate et al. 2013; PSMSL 2016). Linear regression: intercept = -32.49 ± 7.46; b = 0.003 ± 0.001; p<0.001.

Figure A2.5 Monthly mean sea level anomaly (mm) at the Dzadoudzi, Mayotte tide gauge: 2008-2015 (Holgate et al. 2013; PSMSL 2016). Linear regression: intercept = -253.30 ± 120.07; b = 0.02 ± 0.01; p = 0.038.

Figure A2.6 Monthly mean sea level anomaly (mm) at the Charchanga, Bangladesh tide gauge: 1979-2000 (Holgate et al. 2013; PSMSL 2016). Linear regression: intercept = -166.50 ± 26.15; b = 0.03 ± 0.003; p<0.001.

Figure A2.7 Monthly mean sea level anomaly (mm) at the Hiron Point, Bangladesh tide gauge: 1983-2003 (Holgate et al. 2013; PSMSL 2016). Linear regression: intercept = -76.00 ± 22.75; b = 0.01 ± 0.003; p<0.001.

Figure A2.8 Monthly mean sea level anomaly (mm) at the Khepupara, Bangladesh tide gauge: 1977-2000 (Holgate et al. 2013; PSMSL 2016). Linear regression: intercept = -267.8 ± 24.94; b = 0.04 ± 0.003; p<0.001.

APPENDIX 3. Results of boundary and biomass change detection analyses in West African to South Asian mangrove forests: Figures (Chapter 8).

Figure A5.1 Mangrove distribution change (spatial overlay) over 2007-2015 at the Ruvuma Estuary, Tanzania study site. Green = constant mangrove cover from 2007 to 2015 (13,663.48 ha). Red = loss of mangrove landcover pixels from 2007-2015 (37.14 ha). Yellow = gain of mangrove landcover pixels from 2007 to 2015 (124.36 ha). Total mangrove cover in 2007: 13,700.62 ha; total mangrove cover in 2015: 13,787.84 ha; 2007- 2015 percentage change: 0.64%.

Figure A5.2 Mangrove distribution change (spatial overlay) over 2008-2015 at the Rufiji Delta, Tanzania study site. Green = constant mangrove cover from 2008 to 2015 (42,349.23 ha). Red = loss of mangrove landcover pixels from 2008-2015 (651.69 ha). Yellow = gain of mangrove landcover pixels from 2008 to 2015 (808.64 ha). Total mangrove cover in 2008: 43,000.92 ha; total mangrove cover in 2015: 43,157.88 ha; 2008-2015 percentage change: 0.36%.

Figure A5.3 Mangrove distribution change (spatial overlay) over 2007-2015 at the Sundarbans, India and Bangladesh study site. Green = constant mangrove cover from 2007 to 2015 (638,116.40 ha). Red = loss of mangrove landcover pixels from 2007-2015 (82.25 ha). Yellow = gain of mangrove landcover pixels from 2007 to 2015 (1,411.98 ha). Total mangrove cover in 2007: 638,198.60 ha; total mangrove cover in 2015: 639,528.40 ha; 2007-2015 percentage change: 0.21%.

Figure A5.4 Mangrove distribution change (spatial overlay) over 2007-2015 at the Saloum Delta, Senegal study site. Green = constant mangrove cover from 2007 to 2015 (471,532.90 ha). Red = loss of mangrove landcover pixels from 2007-2015 (644.48 ha). Yellow = gain of mangrove landcover pixels from 2007 to 2015 (195.92 ha). Total mangrove cover in 2007: 472,177.40 ha; total mangrove cover in 2015: 471,728.80 ha; 2007-2015 percentage change: -0.09%.

Figure A5.5 Mangrove distribution change (spatial overlay) over 2007-2013 at the Sherbro Bay, Sierra Leone study site. Green = constant mangrove cover from 2007 to 2013 (191,252.00 ha). Red = loss of mangrove landcover pixels from 2007-2013 (4,831.97 ha). Yellow = gain of mangrove landcover pixels from 2007 to 2015 (10.80 ha). Total mangrove cover in 2007: 196,085.30 ha; total mangrove cover in 2013: 191,262.80 ha; 2007-2015 percentage change: -2.46%.

Figure A5.6 Mangrove distribution change (spatial overlay) over 2007-2015 at the Save River Delta, Mozambique study site. Green = constant mangrove cover from 2007 to 2015 (59,698.78 ha). Red = loss of mangrove landcover pixels from 2007-2015 (483.27 ha). Yellow = gain of mangrove landcover pixels from 2007 to 2015 (3,220.50 ha). Total mangrove cover in 2007: 60,182.05 ha; total mangrove cover in 2015: 62,919.28 ha; 2007- 2015 percentage change: 4.55%.

Figure A5.7 Mangrove distribution change (spatial overlay) over 2007-2015 at the Mahajamba, Madagascar study site. Green = constant mangrove cover from 2007 to 2015 (70,169.36 ha). Red = loss of mangrove landcover pixels from 2007-2015 (1,784.50 ha). Yellow = gain of mangrove landcover pixels from 2007 to 2015 (10,481.20 ha). Total mangrove cover in 2007: 71,953.86 ha; total mangrove cover in 2015: 80,650.56 ha; 2007-2015 percentage change: 12.09%.

Figure A5.8 Significant pixel-specific ALOS/PALSAR L-Band HV backscatter amplitude change between 2007 and 2010 (aboveground biomass; ≥15% change) at the Ruvuma Estuary, Tanzania study site (99.45% of total constant mangrove distribution coverage). Purple pixels denote pixels with a significant decrease in biomass from 2007 to 2010 (31.33% of pixels), and green pixels denote pixels with a significant increase in biomass from 2007 to 2010 (18.73% of pixels). Total HV backscatter amplitude (aboveground biomass) change over 2007-2010 was -5.56%.

Figure A5.9 Significant pixel-specific ALOS/PALSAR L-Band HV backscatter amplitude change between 2007 and 2010 (aboveground biomass; ≥15% change) at the Save River Delta, Mozambique study site (52.96% of total constant mangrove distribution coverage). Purple pixels denote pixels with a significant decrease in biomass from 2007 to 2010 (35.30% of pixels), and green pixels denote pixels with a significant increase in biomass from 2007 to 2010 (18.54% of pixels). Total HV backscatter amplitude (aboveground biomass) change over 2007-2010 was -7.19%.

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Figure A5.10 Significant pixel-specific ALOS/PALSAR L-Band HV backscatter amplitude change between 2008 and 2010 (aboveground biomass; ≥15% change) at the Rufiji Delta, Tanzania study site (81.96% of total constant mangrove distribution coverage). Purple pixels denote pixels with a significant decrease in biomass from 2008 to 2010 (33.72% of pixels), and green pixels denote pixels with a significant increase in biomass from 2008 to 2010 (15.18% of pixels). Total HV backscatter amplitude (aboveground biomass) change over 2008-2010 was -7.37%.

Figure A5.11 Significant pixel-specific ALOS/PALSAR L-Band HV backscatter amplitude change between 2007 and 2010 (aboveground biomass; ≥15% change) at the Saloum Delta, Senegal study site (11.51% of total constant mangrove distribution coverage). Purple pixels denote pixels with a significant decrease in biomass from 2007 to 2010 (25.76% of pixels), and green pixels denote pixels with a significant increase in biomass from 2007 to 2010 (20.84% of pixels). Total HV backscatter amplitude (aboveground biomass) change over 2007-2010 was -2.71%.

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Figure A5.12 Significant pixel-specific ALOS/PALSAR L-Band HV backscatter amplitude change between 2007 and 2009 (aboveground biomass; ≥15% change) at the Sundarbans, India and Bangladesh study site (33.97% of total constant mangrove distribution coverage). Purple pixels denote pixels with a significant decrease in biomass from 2007 to 2009 (18.62% of pixels), and green pixels denote pixels with a significant increase in biomass from 2007 to 2009 (17.19% of pixels). Total HV backscatter amplitude (aboveground biomass) change over 2007-2009 was -1.58%.

Figure A5.13 Significant pixel-specific ALOS/PALSAR L-Band HV backscatter amplitude change between 2007 and 2010 (aboveground biomass; ≥15% change) at the Sherbro Bay, Sierra Leone study site (26.98% of total constant mangrove distribution coverage). Purple pixels denote pixels with a significant decrease in biomass from 2007 to 2010 (23.53% of pixels), and green pixels denote pixels with a significant increase in biomass from 2007 to 2010 (22.35% of pixels). Total HV backscatter amplitude (aboveground biomass) change over 2007-2010 was -1.37%.

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Figure A3.14 Significant pixel-specific ALOS/PALSAR L-Band HV backscatter amplitude change between 2007 and 2010 (aboveground biomass; ≥15% change) at the Mahajamba, Madagascar study site (28.48% of total constant mangrove distribution coverage). Purple pixels denote pixels with a significant decrease in biomass from 2007 to 2010 (21.53% of pixels), and green pixels denote pixels with a significant increase in biomass from 2007 to 2010 (28.58% of pixels). Total HV backscatter amplitude (aboveground biomass) change over 2007-2010 was 1.44%.