Arts, Faculty of
Geography, Department of
DSpace
UBCV
Pletcher, Elise
Master of Science - MSc
University of British Columbia
Seasonally dry tropical forest in Southeast Asia is a complex mosaic of dry evergreen forest and deciduous dipterocarp “forest” (which are structurally and functionally savanna). These patchy savannas are threatened by fragmentation and forest-centric management practices. Understanding the ecological processes that govern the boundaries between forest and savanna is essential for their proper conservation. I analyzed patterns of remotely sensed tree cover and identified the key determinants of both tree cover and landscape mosaics across SE Asia. First, I examined distributions of tree cover for bimodality—evidence that forests and savanna exist as alternate stable states. I found no bimodality in tree cover regionally; however, bimodality was present within landscapes. Second, I analyzed the relative importance of environmental factors in shaping patterns of tree cover and landscape mosaics, using regression models. I found that fire was the strongest predictor of tree cover at the regional scale and that the prevalence of landscape mosaics is dependent on climate (rainfall and seasonality). These results reveal that the same environmental factors important in delineating the distribution of savannas globally drive patterns of tree cover and landscape mosaics across SE Asia. More broadly, this work shows a useful approach for parsing out which environmental factors maintain and drive forest-savanna mosaic formation. While global vegetation products (GVPs), such as those I employed in my first analysis, are powerful and easy to use, their applicability in highly heterogeneous landscapes such as savanna-forest mosaics is poorly understood. I therefore explored the limitations of global vegetation structure products, including those used in my primary analysis using Airborne Laser Scanning. I found that no GVP was a clear winner. MODIS VCF significantly underestimates tree cover in SE Asian savannas, relative to forests, and the Global Forest Change (GFC) product’s ability to accurately map tree cover within forest-savanna mosaics is limited. Overall, I argue that while datasets such as GFC allow ecologists to test theories at large spatial scales (such as alternate stable states), researchers should be mindful of where and what type of vegetation was used to validate the GVP before conducting research at regional and global scales.
A MULTI-SCALED APPROACH TO UNDERSTANDING THE ENVIRONMENTAL DRIVERS OF SOUTHEAST ASIAN FOREST-SAVANNA MOSAICS by Elise Pletcher B.Sc., University of Washington, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Geography) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2021 © Elise Pletcher, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: A multi-scaled approach to understanding the environmental drivers of Southeast Asian forest-savanna mosaics submitted Elise Pletcher in partial fulfillment of the requirements the degree of Master of Science in Geography Examining Committee: Dr. Naomi B. Schwartz, Geography, University of British Columbia Supervisor Dr. Greg Henry, Geography, University of British Columbia Supervisory Committee Member Dr. Carla Staver, Ecology and Evolutionary Biology, Yale University Supervisory Committee Member iii Abstract Seasonally dry tropical forest in Southeast Asia is a complex mosaic of dry evergreen forest and deciduous dipterocarp “forest” (which are structurally and functionally savanna). These patchy savannas are threatened by fragmentation and forest-centric management practices. Understanding the ecological processes that govern the boundaries between forest and savanna is essential for their proper conservation. I analyzed patterns of remotely sensed tree cover and identified the key determinants of both tree cover and landscape mosaics across SE Asia. First, I examined distributions of tree cover for bimodality—evidence that forests and savanna exist as alternate stable states. I found no bimodality in tree cover regionally; however, bimodality was present within landscapes. Second, I analyzed the relative importance of environmental factors in shaping patterns of tree cover and landscape mosaics, using regression models. I found that fire was the strongest predictor of tree cover at the regional scale and that the prevalence of landscape mosaics is dependent on climate (rainfall and seasonality). These results reveal that the same environmental factors important in delineating the distribution of savannas globally drive patterns of tree cover and landscape mosaics across SE Asia. More broadly, this work shows a useful approach for parsing out which environmental factors maintain and drive forest-savanna mosaic formation. While global vegetation products (GVPs), such as those I employed in my first analysis, are powerful and easy to use, their applicability in highly heterogeneous landscapes such as savanna-forest mosaics is poorly understood. I therefore explored the limitations of global vegetation structure products, including those used in my primary analysis using Airborne Laser Scanning. I found that no GVP was a clear winner. MODIS VCF significantly underestimates tree cover in SE Asian savannas, relative to forests, and the Global Forest Change (GFC) product’s ability to accurately map tree cover within forest-savanna mosaics is limited. Overall, I argue that while datasets such as GFC allow ecologists to test theories at large spatial scales (such as alternate stable states), researchers should be mindful of where and what type of vegetation was used to validate the GVP before conducting research at regional and global scales. iv Lay Summary Seasonally dry tropical forest in SE Asia is a complex landscape made up of distinct patches of dense evergreen forest and open, deciduous dipterocarp forest (also recognized as savanna). These patchy savannas have been historically unaccounted for in global studies as well as national management plans. Understanding the ecological processes that govern the boundaries between forest and savanna is essential. Remote sensing technology, specifically products derived from satellite imagery, make it possible to study patterns of tree cover in an ecologically meaningful way. In Chapter 3, I demonstrate how remotely sensed tree cover products can be used to explain how patterns of tree cover are influenced by climate and fire. In Chapter 4, I demonstrate how caution must be used when applying remotely sensed tree cover products to highly heterogeneous landscapes, such as in SE Asia. v Preface Naomi Schwartz was involved throughout concept formation and composition of Chapters 2, 3, and 4. A version of Chapter 3 was written for peer review and intended for publication. I was the lead author, and responsible for concept formation, data collection and analysis, as well as manuscript composition. Naomi Schwartz provided major contributions throughout the project in concept formation and manuscript composition. Carla Staver was also involved in concept formation and manuscript composition. Sarah Smith-Tripp provided feedback during the editing process. A version of Chapter 3 will shortly be submitted to an academic journal for peer review: Pletcher, Elise. Staver, A.C., and Schwartz, N.B. “The environmental drivers of tree cover across forest-savanna mosaics in SE Asia” 3-dimensional point clouds, used in Chapter 4 were developed from Airborne Lidar Scanning and acquired from and pre-processed by Damien Evans and the Cambodian Lidar Initiative. Sarah Smith-Tripp provided guidance on the methods for Chapter 4. The rest of this thesis is the original, unpublished, independent work by the author, Elise Pletcher. vi Table of contents ABSTRACT...................................................................................................................................iii LAY SUMMARY..........................................................................................................................iv PREFACE........................................................................................................................................v TABLE OF CONTENTS................................................................................................................vi LIST OF TABLES.......................................................................................................................viii LIST OF FIGURES........................................................................................................................ix ACKNOWLEDGEMENTS.............................................................................................................x CHAPTER 1: Introduction..............................................................................................................1 1.1 Interpreting the SE Asian forest savanna mosaic.....................................................................1 1.2 Project objectives......................................................................................................................3 CHAPTER 2: A review of tropical savanna ecology and evidence of savannas within SE Asia..................................................................................................................................................5 2.1 Defining savanna......................................................................................................................5 2.2 What determines global distributions of savannas?..................................................................5 2.3 What determines landscape scale forest-savanna dynamics?...................................................8 2.4 Site level savanna dynamics...................................................................................................12 2.5 Knowledge gaps…………......................................................................................................13 CHAPTER3: The environmental drivers of tree cover in savanna-forest mosaics in Southeast Asia………………………………………………………………………………………………15 3.1 Introduction.............................................................................................................................15 vii 3.2 Methods...................................................................................................................................19 3.3 Results.....................................................................................................................................23 3.4 Discussion...............................................................................................................................29 3.5 Conclusion..............................................................................................................................34 CHAPTER 4: Evaluating global gridded vegetation structure products for application in a forest-savanna mosaic…………………………......................................................................................36 4.1 Introduction............................................................................................................................36 4.2 Methods..................................................................................................................................38 4.3 Results....................................................................................................................................41 4.4 Discussion..............................................................................................................................47 4.5 Conclusion..............................................................................................................................49 CHAPTER 5: Conclusion and Recommendations for Future Work.............................................51 5.1 Key findings............................................................................................................................51 5.2 Implications.............................................................................................................................51 5.3 Recommendations for future work.........................................................................................52 REFERENCES..............................................................................................................................54 APPENDICES…………………………………………...............................................................66 APPENDIX A Supplementary Materials from Chapter 3.............................................................66 viii List of Tables Table 3.1 Summary statistics for environmental predictors and Tree cover ……...……………24 Table 4.1 Results from linear regression comparison of ALS derived and global vegetation structure products.……………………………..…………………………………………………42 ix List of Figures Figure 2.1 A diagram depicting the two alternate feedback cycles that maintain savanna and forest……………………………………………………………………………………………………...10 Figure 3.1 A conceptual diagram of the relationship between landscape patchiness, the distribution of percent tree cover, and average MAP across each landscape……………………18 Figure 3.2 The distribution of proportion of tree cover across the study region.………….……21 Figure 3.3 Map of bimodal landscapes…………….……………………………………………25 Figure 3.4 Standardized coefficients from the beta regression model used to model tree cover……………………………………………………………………………………………...26 Figure 3.5 Beta regression predictions at 5th (orange line) and 95th (blue line) percentiles of fire frequency…………………………………………………………………………………………26 Figure 3.6 Results of the linear regression used to model each patch metric.……………..……28 Figure 3.7 Model predictions for each patch metric ……………………………………………29 Figure 4.1 Map of the study region...……………………………………………………………41 Figure 4.2 Raster canopy cover and height models for each global vegetation product.…….…44 Figure 4.3 2D density plots demonstrating the relationship between vegetation products and ALS cover and height……………………………………………………………………………45 Figure 4.4 The distribution of differences of Global vegetation products from the ALS derived values of height and cover……….………………………………………………………………47 x Acknowledgements I am grateful to all those who made the completion of this thesis possible and for making my experience throughout the process a positive one. I would like to thank my Supervisor, Naomi Schwartz, for her guidance, wisdom, and flexibility throughout the process, and the other members of my committee, Carla Staver and Greg Henry, for taking the time to share their expertise and provide valuable feedback. I thank colleagues at the Cambodian Forest Administration and Institute of Forest and Wildlife Research and Development for making field work possible and for sharing local knowledge. I would also like to thank Tony and Prum, for their assistance in the field, and making my limited time in Cambodia a positive experience. I would also like to thank the entire Schwartz lab for their positivity and comradery, as well as their stellar online gaming skills (in Dominion in particular). Specifically, I’d like to thank Adrian Dwiputra for providing novel insights, positivity, and humor. I thank Sarah Smith-Tripp, for allowing her amazing and seemingly limitless energy to wear off on me. And I thank Tony Zhang, for always providing thoughtful feedback and great GIFS. A special thanks to a few amazing teachers and professors in biological sciences over the years—thank you for always treating your students as equals and inspiring me to pursue ecological research. Finally, I thank my family, whose support is fundamental to everything I do. 1 Chapter 1: Introduction 1.1 Interpreting the SE Asian forest savanna mosaic Southeast Asia was once home to large swaths of seasonally dry tropical forest (SDTF)—one of three major global expanses of tropical dry forest (Maxwell and Cox 2011). However, these forests are increasingly threatened by deforestation, fragmentation, and afforestation. As of 2006, 78% of tropical dry forest in SE Asia was considered highly fragmented (Miles et al. 2006). This raises concerns for the region’s diverse flora, dwindling populations of mega fauna throughout the region, and local communities dependent on this landscape for livelihoods (livestock fodder, wood for cooking fuel, etc.) (McShea et al. 2011). Conservation of tropical dry forest is hindered by a poor understanding of the ecology of this system, relative to other tropical forests. Further, conservation schemes of afforestation and fire suppression initiatives put forward by National governments, REDD++, and western-funded NGOs aimed at conserving densely wooded tropical rainforests have the potential to negatively impact tropical dry forest systems and other tropical grassy biomes (Parr et al. 2014). In response to these concerns, further research is needed to provide a better understanding of SE Asian forest ecology, necessary to ensure proper conservation. SE Asia’s tropical dry forest landscape is comprised of a patchy mosaic of deciduous dipterocarp (DDF), mixed deciduous (MDF), and dry evergreen (DEF) forest. These forests differ in structure, plant composition, and leaf habit and form distinct boundaries with each other. The environmental drivers influencing the distribution of these forest types is poorly understood, but frequent, low-intensity ground fires (both natural and anthropogenic) are thought to play a key role in maintaining the boundary between DDF and DEF (Stott 1986, Nguyen and Baker 2016, Nguyen et al. 2019). This hypothesis is a result of prior research, where fire frequently is responsible for the sharpness of boundaries between other ecosystem types, such as forest and savanna (Ratnam et al. 2011, Oliveras and Malhi 2016). Importantly, while descriptive information exists on fire’s influence on vegetation structure, empirical data are lacking (Stott 1986, Maxwell and Cox 2011). How different types of tropical dry forest respond to varying frequencies and intensities of fire is of critical importance for their proper management and conservation (McShea et al. 2011). 2 There is growing support and scientific evidence that DDF, while structurally resembling savanna (i.e, a grassy understory and open canopy), also shares important functional similarities (e.g. thick bark, ability to resprout) to savannas (Stott 1986, Ratnam et al. 2016, Nguyen et al. 2019, Khaing et al. 2019, Kumar et al. 2019). This distinction between forest vs. savanna has important management implications, as savannas are fire dependent and forests are fire senstive. While there has been a limited number of field-based studies conducted on the fire-resilient properties of DDF in SE Asia, to date, the distribution of savannas across SE Asia is also poorly understood. While forest and savanna often inhabit different climatic spaces and are maintained by different ecosystem processes, they also frequently exist within the same climatic space. Forest and savanna often co-exist as distinct patches across landscapes (Dantas et al. 2016). For this reason, forests and savannas have been identified as alternate stable states, in which, in the absence of stressors such as fire, savanna may shift to forest (Warman and Moles 2009, Staver et al. 2011b, Hoffmann et al. 2012a). Remote sensing is a powerful tool for biome delineation. It has been shown to provide proof of alternate stables states across entire continental regions (Staver et al. 2011b, Hirota et al. 2011, Beckett and Bond 2019), as well as delineate the savanna biome (Lehmann et al. 2011). In addition to the coarse scale (250 m) fractional land cover products frequently used in these studies (derived Terra MODIS satellite imagery), 30 m Landsat-derived tree cover is ideal for assessing finer scale patterns of tree cover. However, there are concerns about the application of these remote-sensing products in SDTF in general, and SE Asian SDTF in particular (Miettinen et al. 2014, Sanchez‐Azofeifa et al. 2017, Adzhar et al. 2021). Neither coarse nor medium scale products may be able to discern patterns of tree cover in this highly heterogeneous landscape. While 3-dimensional high resolution airborne laser scanning (ALS) is a well-accepted proxy for field-based structural measurements (Means et al. 2000, White et al. 2016), it is nowhere close to global coverage. Availability of ALS data is especially sparse in SE Asia. In addition to the need for regional and landscape scale studies analyzing patterns of SE Asian savannas, further parsing out the functional differences between composition and function of forest and savanna in SE Asia remain of the upmost importance. Evidence demonstrates that savannas in SE Asia are structurally ambiguous (relatively dense canopy cover), and therefore compositional and functional traits maybe more reliable evidence of savannas (Ratnam et al. 2011, 2016). These compositional and functional differences are currently difficult if not 3 impossible to discern with currently available remotely sensed products. Therefore, ideally the integration of both field-based and remote sensing techniques will help identify the key processes that occur across scales to define vegetation structure, composition, and function of SDTF in SE Asia. For the purpose of this thesis, I focus on studying patterns of tree cover at the regional and landscape scales. First, I identify the key determinants of both tree cover and landscape mosaics across SE Asia. Then I explore the limitations of global vegetation structure products, including those used in my primary analysis. 1.2 Project Objectives The overarching goal of this thesis is to investigate the environmental determinants of SE Asian savannas across scales. This thesis is broken up into a literature review and two data chapters. Chapter 2 is a literature review that introduces concepts discussed throughout the thesis. Within the literature review I build a framework for employing a multiscale approach to study SE Asian savannas. The three main objectives of the data chapters are as follows: Chapter 3 In order to address the urgent need to map the extent of SE Asian savannas to ensure their proper conservations and management, this first data chapter aims to understand how landscape mosaics of forest and savanna are shaped by climate and disturbance. This chapter focuses on both regional and landscape scale patterns of tree cover across SE Asia. Objective 1: First, I sought to demonstrate scale-dependent patterns of tree cover across SE Asia by asking, (a) to what degree do forests and savannas represent alternate stable states in SE Asia? and (b) how does spatial scale affect the interpretation of this distribution? Objective 2: Secondly, I asked, (2a) how do fire, climate, and soils influence average tree cover across SE Asian landscapes? and (2b) how do these same environmental factors influence the prevalence of landscape mosaics or patchiness? Chapter 4 There are challenges to understanding ecological phenomena at broad scales due to remote sensing data quality; therefore, I assess the accuracy of the global vegetation structure products 4 used in this study and many global analyses of vegetation structure using Airborne Laser Scanning. This chapter focuses on landscape scale patterns of tree cover within a forest-savanna mosaic of Northern Cambodia. Objective 3: Thirdly, I ask, what are the limitations of the application of gridded global forest structure datasets in assessing vegetation structure in the SDTF of continental SE Asia? 5 Chapter 2: A review of tropical savanna ecology and evidence of savannas within SE Asia The objective of this literature review is to synthesize research conducted on tropical savannas and fire-adapted systems globally, across regional, landscape, and local scales. Using this multiscale framework, I build a link between the extensive body of knowledge that exists on the ecological processes governing tropical savannas across South America, Africa, and Australia, and the limited work that exists on ecological processes within SE Asian savannas. Through this linkage, I identify major knowledge gaps to better understanding the ecological drivers of savannas in SE Asia in support of their proper conservation and management. 2.1 Defining the term savanna Savannas are characterized most frequently by a continuous C4 grass layer and an open, discontinuous canopy. They cover roughly 20% of the earth’s land surface and are responsible for 30% of terrestrial primary production (Grace et al. 2006). Several recent studies have highlighted the importance of defining a savanna ecosystem, beyond semantics, especially when distinguishing a heavily wooded savanna from a degraded forest (Ratnam et al. 2011, 2016). At the global scale, fractional tree cover derived from satellite imagery is frequently used to differentiate and classify regions of grassland, savanna, woodland, and forest (Miles et al. 2006, Staver et al. 2011a, Hirota et al. 2011, Venter et al. 2018, Beckett and Bond 2019). However, across continental regions percent tree cover in savannas varies greatly; anywhere from 15% percent to ~50% tree cover (Staver et al. 2011b, 2011a). This variation suggests that tree cover is not the only defining characteristic of savannas. Several studies maintain that vegetation structure is a key component of proper savanna classification, however emphasize the importance of co-dominance or co-existence of tree species and grass species (Lehmann et al. 2014, Oliveras and Malhi 2016, Ratajczak et al. 2017, Kumar et al. 2019). 2.2 What determines the global distributions of savannas? Using a combination of remote sensing techniques and field data, global distributions of savannas have been linked to various environmental factors including precipitation and its many dimensions, fire, soil quality, and topography (Aleman et al. 2020). Several studies have 6 assessed the distribution of savanna and fire at intermediate levels of precipitation, where probability of fire and savanna both peak at 1000 mm mean annual precipitation (MAP) (Sankaran et al. 2005, Lehmann et al. 2011, Staver et al. 2011b, Hirota et al. 2011, Bertram and Dewar 2013). This suggests that while savanna distribution is constrained climatically, fire also plays a major role in defining the savanna boundary within that climatic range. 2.2.1 Dimensions of rainfall While tree cover is strongly correlated with precipitation, it often increases discontinuously across this gradient, in which a treeless, savanna, and forest state form distinct peaks in the distribution of tree cover (Hirota et al. 2011). Tropical savannas exist anywhere from below 250 mm (in Africa and Australia) to upwards of 2,000 mm MAP (in South America) (Staver et al. 2011b, Lehmann et al. 2014). Savanna is often further defined as either arid or mesic, where mesic savanna experience >800 mm MAP, however this range does vary by continent (Hoffmann et al. 2012a). Forests also exist at intermediate rainfall—as low as 1,000 mm MAP (Staver et al. 2011b, Lehmann et al. 2014). At intermediate levels of rainfall, bimodality in tree cover emerges at regional scales—providing broad scale empirical evidence that savannas and forest often exist within the same climate space (Staver et al. 2011b, 2011a). There are regional differences in tree cover response to MAP—in which bimodality does not always occur. Tree cover in South America and Africa both exhibit strong bimodality at intermediate MAP (Staver et al. 2011b). Whereas tree cover in Australia is not bimodal; the Australian landscape is dominated by savannas at intermediate MAP (low tree cover), and rainfall seasonality is the main driver of savanna distribution (Staver et al. 2011b). Variability in precipitation (intensity, seasonality, interannual variation, etc.) is also an important determinant of tropical vegetation dynamics (Schwartz et al. 2020), and in particular, savanna vegetation (Xu et al. 2015, 2018), especially at the transition zone between mesic savanna and forest (Lehmann et al. 2011). At intermediate levels of rainfall, where mesic savanna occur, precipitation variability explains a significant amount of the variation in tree cover (Xu et al. 2018). This correlation between tree cover and precipitation variability is likely driven by tree-grass coexistence, where relative of advantages of the different water use strategies used by each growth form balance out (Xu et al. 2015). 7 SE Asian savannas exist at the higher end of the accepted savanna-MAP range (mesic) averaging around 1,000-1,500 mm across studies, and experience strong seasonality due to a monsoonal climate (4-6-month dry season) (Stott 1984, Bunyavejchewin et al. 2011, Ratnam et al. 2016, Nguyen et al. 2019). How tree cover in SE Asia compares to tree cover distributions across other continents is unknown. SE Asian savannas occupy similar climatic space to that of other savannas, and analysis of regional distributions of SE Asian savannas across gradients of rainfall could provide further insight into the extent that dimensions of rainfall drive savanna distributions in a global context. 2.2.2 Fire Fire plays a major role in shaping vegetation structure in many ecosystems, and its importance in maintaining savanna physiognomy has been well studied (Bond et al. 2004, Lehmann et al. 2009, Ratnam et al. 2011, Veenendaal et al. 2015, Oliveras and Malhi 2016, Werner and Peacock 2019). Ecosystems dominated by C4 grasses may burn as often as once per year, in which many fire tolerant plant and tree species have adapted to and thrived under frequent fire (Bond et al. 2004). While understory vegetation provides a major fuel source (Hoffmann et al. 2012b), climate also indirectly and directly influences fire occurrence. Xu et al. (2018) found that fire’s relative importance was highest compared to other environmental factors where MAP was high. Fire is more intense in mesic savannas vs. arid savannas, due to higher grass biomass (fuel) promoted by increased water availability (Sankaran et al. 2005, Govender et al. 2006, Nippert and Holdo 2015). Hoffman et al. (2012a) defines the average probability of burn in mesic savannas to be 0.33, or 1 burn every 3 years. By contrast, bordering forests (called gallery forests or evergreen forests depending on the system) burn extremely rarely (Gignoux et al. 2009). Since forest and savanna coexist at intermediate level of rainfall, the presence of savanna is dependent on the presence of fire (Bond et al. 2004, Lehmann et al. 2011), and fire is therefore often responsible for the sharpness of the boundaries between forests and savannas (Ratnam et al. 2011). 2.2.3 Soil Fertility While rainfall, seasonality, and fire explain the majority of the global distributions of savannas, soil fertility plays a smaller role in explaining the distribution of savannas; savanna presence declines with increased soil fertility (Lehmann et al. 2011). Lehmann et al. (2011) provide a 8 framework for how low soil fertility can influence vegetation structure: woody growth rates are reduced in low fertility environments, leading to an increased ‘time to closed canopy’, resulting in a greater likelihood of disturbance event occurring (such as fire). 2.3 What determines landscape scale forest-savanna dynamics? MAP dictates patterns of tree cover at regional scales; however, savanna and forest often coexist at a much finer scale within tropical landscapes (Lehmann et al. 2011). Therefore, savannas and forests often exist within the same climate space. Research demonstrating bimodal distributions of tree cover at intermediate rainfall suggest, empirically, that savannas and forest form alternate stable states (Staver et al. 2011a). In addition to forming stark boundaries with each other, forest and savanna frequently form a landscape-scale mosaic termed the “forest-savanna mosaic” (Dantas et al. 2016). 2.3.1 The forest-savanna mosaic The boundary between savanna and forest is often stark (2 m wide) (Gignoux et al. 2009). Although, in a study that compared the savanna-forest transition zone using the field collected data, compiled from all major continental regions of savanna, suggests that the transition from forest to savanna is much more continuous when intermediate shrubby vegetation is included in measurements of structure and biomass (Veenendaal et al. 2015). Nevertheless, patchy mosaics of forest and savanna have been reported within all major regions where forest and savanna form boundaries with each other (Dantas et al. 2016). Similarly, vegetation in SE Asia is characterized by a mosaic of three dominant vegetation types across the landscape—deciduous dipterocarp forest (DDF), mixed deciduous forest (MDF), and dry evergreen forest (DEF)—which differ in structure, composition, and function (Stott 1990, Bunyavejchewin et al. 2011, Khaing et al. 2019). DDF are structurally and functionally savannas, with grassy understories and open canopies, and are slowly gaining support for classification as mesic savanna (Stott 1984, Ratnam et al. 2016, Nguyen et al. 2019). While the climatic drivers and environmental constraints of savannas are well documented (Spessa et al. 2005, Sankaran et al. 2005, Staver et al. 2011b, Lehmann et al. 2011, 2014, Alvarado et al. 2020), few studies have demonstrated how landscape-level mosaics of forest and savanna may be similarly constrained by disturbance and climate. Therefore, landscape mosaics 9 in SE Asia present a valuable opportunity to further parse out the drivers of landscape mosaics globally. 2.3.2 Alternate stable states Forest and savanna are frequently regarded as alternate stable states, representing opposite successional pathways. The theory of alternate stable states postulates that multiple stable states may exist within a landscape under the same set of conditions, in which ecosystem states represent opposite basins of attraction (May 1977). In a stable state, an ecosystem (forest, for example) will remain forest even after small perturbations (fire) but may shift to an alternate state (savanna), if the perturbation is large enough (frequent and/or large fires) (Beisner et al. 2003). In savannas, a flammable grassy understory promotes fire, suppresses recruitment of tree seedlings, and maintains an open light environment. In forests, closed canopies exclude flammable grasses and thereby exclude fire from the understory (Fig. 2.1) (Sankaran et al. 2005, Govender et al. 2006, Staver et al. 2011a, Warman and Moles 2009, Hoffmann et al. 2009, Murphy and Bowman 2012, Trauernicht et al. 2012, Staal et al. 2016, Ratajczak et al. 2017). Evidence from the field demonstrates that in the absence of fire, a savanna state will transition to a forested one, once a critical “fire suppression threshold” is reached (Hoffmann et al. 2012a). Outside of SE Asia, bimodal distributions of remotely sensed tree cover have demonstrated wide-ranging evidence and prompted the acceptance that forest and savanna often represent alternate stable states, though the degree of bimodality varies geographically (Staver et al. 2011a, 2011b, Hirota et al. 2011, Beckett and Bond 2019). That is, at intermediate levels of rainfall, both high and low tree cover dominate and intermediate tree cover is scarce—with fire playing a dominate role in predicting patterns of tree cover (Staver et al. 2011a). To date, there has been no regional-level assessment of the distribution of tree cover in SE Asia, however a bimodal distribution of tree cover would provide further support that forest and savanna are two, distinct states within SE Asia. Whether or not forests and savannas exist as alternate stable states has important management implications. While the significance of fire in maintaining the boundary between DDF and DEF in SE Asia has been suggested, there is no clear evidence to date (Stott 1986, Baker et al. 2008, Ratnam et al. 2016, Khaing et al. 2019). If fire does indeed play a key 10 role in maintaining the boundary between savanna (DDF) and forest (DEF), with fire suppression we would expect to see a blurring of this boundary and an invasion of forest into savanna. Figure 2.1: A diagram depicting the two alternate feedback cycles that maintain savanna and forest. Savanna is maintained by a feedback in which an open canopy promotes a highly flammable grassy understory, which promotes frequent fire and in turn limits tree recruitment, maintaining an open canopy. Forests are maintained by a closed canopy, which excludes light and shade intolerant grasses from the understory, thereby reducing the fuel load and fire occurrence. 2.3.3 Further ecosystem-level dynamics in the ecotone Microclimate also varies greatly between adjacent forest and savanna; savannas experience higher wind speeds, lower relative humidity, and greater fluctuations in day/night-time temperatures (Hoffmann et al. 2012b). However, low bulk fuel loads represented in the grass layer of savannas still explain a majority of the variation in flammability between the two systems (Hoffmann et al. 2012b). Microclimate conditions may vary less within SE Asia, where savannas tend to have a more substantial canopy. SE Asian DDF species may also contribute further to the fuel load (in addition to grasses) by shedding their leaves during the dry season. 11 2.3.4 Woody encroachment Woody encroachment occurs when savanna is replaced by forest and is widespread across tropical savannas (Stevens et al. 2017, García Criado et al. 2020). However, the numerous studies conducted on forest encroachment within the forest-savanna ecotone have not been representative of the global distribution of savannas—a majority were conducted in Northern Australia, Africa, and the Brazilian Cerrado (Stevens et al. 2017). Additionally, the rate of encroachment was found to be highly context dependent and varied by continent. Clearly, trends in the rate and direction of woody change are regionally dependent, and further research is needed to determine how these trends may play out in under-represented savanna regions of the world such as SE Asia. 2.3.5 Limitations of remote sensing data in analyzing patterns of forest and savanna Many global analyses rely on remotely sensed tree cover to assess distributions of forest and savanna as well as evidence for ecosystem-level processes, such as alternate stable states. However, analyzing regional and landscape scale patterns of tree cover and vegetation in relation to ecological processes is not without challenges. While global forest and vegetation cover products derived from satellite imagery, such as MODIS Vegetation Continuous Fields (VCF) or Global Forest Change, are powerful and easy to use, these products were developed with a focus on accurately mapping temperate zones. In areas of relatively high intermediate tree cover, and complex heterogeneous landscapes, such as SDTF in SE Asia, it is unclear how accurately these global products map tree cover (Miettinen et al. 2014). Furthermore, land cover products such as MODIS VCF are unlikely to capture fine-scale heterogeneity in tree cover—a wide-spread phenomenon across the humid tropics—in which patches of structurally distinct forest and savanna form landscape-scale mosaics, and often exist in patches smaller than the cover product’s 250 m resolution. Recently, Adzhar et al. (2021) demonstrated that while tree cover in forested areas was generally accurately mapped, tree cover in savanna sites was significantly underestimated, and caution was recommended when using MODIS VCF to map tropical savannas. The Global Forest Change (GFC) dataset developed by Hansen et al. (2013) derived from 30 m Landsat data, offers a finer scale alternative to the MODIS VCF developed by DiMiceli et al. (2015) derived from 250 m Terra MODIS satellite data. However, how accurately this data set 12 maps tree cover in SE Asia is unknown, and further research is needed to validate the GFC’s application within SE Asia’s mosaicked landscape. 2.4 Site level savanna dynamics Tree density within savannas generally increases with rainfall (Ratnam et al. 2011), where wetter savannas can be heavily wooded, and therefore may be difficult to distinguish from forest based on structure alone (Ratnam et al. 2016). However, forest and savannas exhibit major compositional differences and species demonstrate functional trade-offs between competitive ability and disturbance tolerance (Gignoux et al. 2009, Rossatto et al. 2009, Gignoux et al. 2016, Nguyen et al. 2019). These distinctions between composition and function present valuable opportunities for comparative studies between forest and savanna, in which research can inform proper management of each respective ecosystem. For example, observed patterns of woody encroachment, based on structure alone, do not necessarily indicate community-level invasion by forest species, however, shifts in species composition have been observed in savannas and other grassy biomes (DeSantis et al. 2011; Geiger et al. 2011). In SE Asia, DDF within Vietnam was shown to exhibit a compositional mismatch between dominants of the regenerating seedling layer, and dominants of the adult tree layer, suggesting a potential shift in the future composition of these forests (Nguyen and Baker 2016). 2.4.1 Diverging growth strategies and functional traits at the seedling stage Forest and savanna species represent a stark trade-off between resistance and growth (Gignoux et al. 2009), in which forest species are unlikely to survive the seedling recruitment stage within savannas, and savannas species are unlikely to out-compete forest species within a low light forest environment (Hoffmann et al. 2004). At the seedling stage, investing in a larger root to shoot ratio allows savanna tree seedlings to survive top-kill (e.g. fire), and quickly re-sprout and recover after disturbance (Bond and Midgley 2001, Hoffmann et al. 2004). Whereas forest tree species tend to invest in leaves and upward growth, and exhibit shade tolerance due to light competition within dense forest understory (Gignoux et al. 2016). The ability to re-sprout after fire is an essential survival strategy of fire-resilient tree species. However, at the stand level, a recruitment bottleneck forms in which few seedlings escape the fire trap and reach a fire-resistant sapling stage (>2 m in height) (Gignoux 1997, Hoffmann et al. 13 2012a). Strides to demonstrate how savanna seedling dynamics map onto recruitment patterns of DDF have been made. In particular, evidence suggests that DDF systems in SE Asia exhibit patterns of fire-mediated recruitment bottlenecks (Nguyen et al. 2019). 2.4.2 Diverging growth strategies and functional traits at the adult stage Bark thickness is the most consistent indicator of fire resistance across species (Gignoux 1997, Gignoux et al. 2009, 2016, Geiger et al. 2011). Deciduous savanna species in South Asia exhibit a suite of characteristics (thicker bark, high wood density, and lower specific leaf area) representative of slower, conservative growth strategies expected of disturbance tolerant species (Ratnam et al. 2019). While it has been suggested that DDF species in SE Asia exhibit thicker bark than evergreen forest species (Stott 1984, Nguyen et al. 2019), this has yet be quantitatively measured. To understand to what extent fire tolerance vs. fire sensitivity should be a management concern within DDF and SE Asian forest-savanna mosaics, further research should consider comparative studies focused on functional traits between the two vegetation types. 2.4.3 Understory Composition A continuous C4 grass or C3 understory is perhaps the most important identifier of the savanna ecosystem (Lehmann et al. 2014, Oliveras and Malhi 2016, Ratajczak et al. 2017). Grass is highly productive, shade intolerant, and highly flammable (Lehmann et al. 2011). In addition to indirectly influencing seedling recruitment through fire promotion, grass provides intense light competition to tree seedlings <2-3 m in height (Hoffmann et al. 2012a, Stevens et al. 2018). Evidence shows that relative to climatic, environmental, and geographic factors such as latitude, elevation, rainfall, and temperature, grass competition has the largest impact on seedling survival within savannas (Stevens et al. 2018). This research suggests that the continuous grass layer that is also present in DDF likely plays a large role in maintaining an open canopy as well as tree regeneration and seedling recruitment. 2.5 Knowledge gaps Within the last five years there has been a growing acceptance and urgency in classifying a substantial portion of SDTF in SE Asia as savanna, with a number of studies having emphasized structural, compositional, functional, and palaeoecological evidence (Ratnam et al. 2016, Nguyen et al. 2019, Khaing et al. 2019, Kumar et al. 2019, Hamilton et al. 2020). Through this 14 literature review I have highlighted key savanna characteristics across scales—creating a case for how SE Asian savannas fit into the picture at each relevant scale. At the site level, DDF do exhibit some functional similarities to savanna, although further inquiry into the specific mechanisms that govern seedling recruitment, disturbance resistance, and other growth strategies are needed, and could provide insight into more general knowledge of savanna dynamics. There is conflicting evidence of whether fire sensitivity and resilience differ between open and closed forest formations in SE Asia (Stott 1984, Baker et al. 2008, Khaing et al. 2019). This has implications for both species level and ecosystem level resilience. At the landscape level, it is clear that SDTF in SE Asia form distinct mosaics of vegetation—that vary greatly in structure, composition and leaf habit (Stott 1990, Bunyavejchewin et al. 2011, Khaing et al. 2019). Mosaics are a signature of forest-savanna boundaries globally (Dantas et al. 2016), however this phenomenon remains understudied. Research into the environmental factors influencing the distribution of landscape mosaics within SE Asia, will provide key contributions to the study of mosaic phenomena globally. At the regional scale, to date there has been no regional level assessment of the distribution of savannas across SE Asia. The climatic envelope that encompasses savannas varies by continent (Lehmann et al. 2014, Ratnam et al. 2016), and SE Asia represents a unique climatic envelope with relatively high rainfall, punctuated by high seasonality. Ideally, the integration of both site level (field) and regional scale (remote sensing) techniques will help identify the key environmental processes within SE Asian savannas and provide further evidence of global savanna characteristics and distributions, in addition to informing local management decisions. Fire suppression dominates management policies at the national level throughout SE Asia. However, fire plays a large role in vegetation structure in many ecosystems and may play a key role in maintaining the structural, compositional, and functional differences within SE Asia’s mosaicked landscape. 15 Chapter 3: The environmental drivers of tree cover across forest-savanna mosaics in SE Asia 3.1 Introduction While the savanna biome extends across large continuous expanses of Africa, South America, and Australia, savannas also frequently form landscape-scale mosaics with forest. This savanna-forest mosaic phenomenon has been reported within all major continental tropical regions (Dantas et al. 2016). However, while the climatic drivers and environmental constraints of savannas are well documented (Spessa et al. 2005, Sankaran et al. 2005, Staver et al. 2011b, Lehmann et al. 2011, 2014, Alvarado et al. 2020), few studies have demonstrated how landscape mosaics of forest and savanna may be similarly constrained. Although evidence indicates disturbance regimes (e.g., fire and herbivory), or abiotic factors (e.g. rainfall, seasonality, soil fertility and topography) maintain the abrupt boundaries between forest and savanna (Warman and Moles 2009, Hoffmann et al. 2012a, Staal et al. 2016, Beckett and Bond 2019), the influence of environmental factors on both the distribution and presence of landscape mosaics remains a major research gap. SE Asian savannas remain absent from global analyses of the climatic and environmental determinants of savannas (e.g. Staver et al., Lehmann et al., others), possibly due to their sole existence within landscape-scale mosaics (Stott 1990, Bunyavejchewin et al. 2011, Khaing et al. 2019), in which they are too “highly fragmented and poorly defined” (Alvarado et al. 2020). Global biome maps identify large swaths of deciduous dipterocarp forest (DDF) in SE Asia as seasonally dry tropical forest (Miles et al. 2006). DDF are structurally savanna, with grassy understories and open canopies. In addition to structural similarities, these ‘forests’ are exposed to frequent, low-intensity surface fires, and dominant species display functional traits indicative of fire tolerance (e.g., thick bark, ability to resprout) (Stott 1986, Ratnam et al. 2016, Nguyen et al. 2019, Kumar et al. 2019). To my knowledge there has been no regional-level assessment of the distribution of savannas across SE Asia. By studying forest-savanna mosaics in SE Asia we can apply a similar framework more broadly, to study how landscape mosaics are shaped by climate and disturbance. 16 Savannas and forests co-occur, often in mosaics, at intermediate levels of rainfall, between 1,000-2,000 mm/yr (Staver et al. 2011b). Savanna distributions are constrained by mean annual precipitation (MAP), precipitation seasonality (henceforth seasonality), and to a lesser extent, soil fertility (Lehmann et al. 2011, Xu et al. 2018). Savannas exist anywhere from below 250 mm (in Africa and Australia) to upwards of 2,000 mm MAP (in South America) (Staver et al. 2011b, Lehmann et al. 2014). SE Asian forest-savanna mosaics fit within this climate niche, occurring between 1,000-1,500 mm MAP (Stott 1984, Bunyavejchewin et al. 2011). Identifying drivers of forest-savanna mosaics in SE Asia, therefore, may shift our understanding of where climate conditions support mosaics elsewhere. While savannas are limited by climate, fire also plays a major role in shaping vegetation—due to fire (and other disturbance events), savanna may persist in areas that climatically support forest (Bond et al. 2004, Oliveras and Malhi 2016). Forests and savannas are frequently regarded as alternate stable states, representing opposite successional pathways. In savannas, a flammable grassy understory promotes fire, suppresses recruitment of tree seedlings, and maintains an open light environment, whereas in forests, closed canopies exclude flammable grasses and thereby excludes fire from the understory (Warman and Moles 2009, Hoffmann et al. 2009, Staver et al. 2011a, Ratajczak et al. 2017). An alternate approach to accurately mapping savanna within SE Asia is urgently needed to protect these systems. Forest-savanna mosaics harbor high levels of biodiversity, where savanna patches may account for up to half of the biodiversity present in the landscape mosaic (Bond and Parr 2010). However, many conservation policies promote reforestation of degraded landscapes, in which low tree cover patches, such as DDF, are at risk of being neglected, mismanaged, and/or termed degraded landscapes and subjected to forest-centric management practices, such as fire suppression (Ratnam et al. 2011, 2016, Parr et al. 2014, Joshi et al. 2018, Kumar et al. 2019). Research into the distribution and determinants of SE Asian forest-savanna mosaics will also provide a more complete picture of how tropical savannas are distributed globally and a useful approach for assessing the influence of climate and disturbance on the distributions of forest-savanna mosaics across the tropics. Remotely sensed tree cover is a powerful tool for both biome delineation and modeling the stability of these forest and savanna boundaries. Outside of SE Asia, bimodal distributions of remotely sensed tree cover demonstrate further evidence of 17 alternate stable states, though the degree of bimodality varies geographically (Staver et al. 2011a, 2011b, Hirota et al. 2011, Beckett and Bond 2019). This work uses remotely sensed tree cover in order to outline the determinants and distribution of SE Asian forest-savanna mosaics. First, we sought to demonstrate scale-dependent patterns of tree cover across SE Asia by asking, (1a) to what degree do forests and savannas represent alternate stable states in SE Asia? and (1b) how does spatial scale affect the interpretation of this distribution? To answer my first questions, I assessed whether remotely sensed tree cover follows a bimodal distribution. However, due to the prevalence of landscape-scale mosaics within SE Asia, bimodality may not be evident at coarser resolutions. Therefore, in addition to analyzing the distribution of tree cover at coarser resolutions (0.05°), I analyzed the distribution of tree cover within landscapes—that is—within the grid cells (each raster dataset is made up of grid cells or pixels) used in my dataset at high resolution (30 m). I predicted that a bimodal distribution may not be apparent at coarser resolutions, however, at higher resolutions, bimodal distributions will emerge. Secondly, I asked, (2a) how do fire, climate, and soils influence average tree cover across SE Asian landscapes? And (2b) how do these same environmental factors influence the prevalence of landscape mosaics or patchiness? To answer these questions, I used regression models to interpret the relative importance of environmental factors in predicting both tree cover and the prevalence of patchiness (i.e., the distribution of patch sizes and cover type) across mainland SE Asia. I predicted that tree cover would be higher in places with high rainfall, but lower in more seasonal climates with frequent fire. I predicted soil sand content would have a minor influence on tree cover, and that tree cover would be higher where soil sand content was low. Similar to my predictions for low tree cover, I expected landscape mosaics, or patchiness, would be most prevalent in drier (relative to the region’s range; Fig. 3.1) and highly seasonal areas with high fire frequency. I predicted soil sand content would have a minor influence, but that patchier landscapes would exhibit higher soil sand content. 18 Figure 3.1: A conceptual diagram of the relationship between landscape patchiness, the distribution of percent tree cover, and average MAP across each landscape. At low levels of MAP, I expect the landscape to be dominated by low levels of tree cover and at high levels of MAP I expect high levels of tree cover to dominate. Whereas, at intermediate levels of rainfall, I would expect a bimodal distribution of tree cover, indicative of high levels of patchiness. 1000 m and 2000 m represent the intermediate levels of rainfall shown to support bistability of savannas and forests. 19 3.2 Methods Scope My analysis covered continental SE Asia including Myanmar, Thailand, Cambodia, Vietnam, and Laos (4.85-29.55 degrees Latitude and 90.95-110 Longitude). The region is comprised of both seasonally dry tropical forest and moist/rain forests. The seasonally dry tropical forests consist of a patchy mosaic of 3 distinct vegetation types: deciduous dipterocarp forests, dry evergreen forests, and mixed deciduous forests (Olson et al. 2001, Bunyavejchewin et al. 2011). Due to the tolerance of tree species to frequent fire and a relatively open canopy and grassy understory, deciduous dipterocarp forests have been more recently recognized as savannas (Stott 1984, Ratnam et al. 2016, Nguyen et al. 2019) Datasets Fire frequency was derived from the MCD64A1.006 MODIS Burned Area Monthly Global product (500 m resolution) (Giglio et al. 2018). I derived fire frequency from 2000-2019 by summing the number of times each pixel burned during that time period. Mean annual precipitation (MAP) was derived from both CHIRPS Daily: Climate Hazards Group InfraRed Precipitation with Station Data (0.05° resolution) (Funk et al. 2015) and TRMM 3B43: Monthly Precipitation Estimates (0.25° resolution) (Huffman et al. 2007), and found similar results to the CHIRPS derived values, and therefore report CHIRPS, due to its finer resolution. Precipitation seasonality was derived using CHIRPS precipitation data to calculate a relative seasonality index as described by Schwartz et al. (2020) and Feng et al. (2013), 𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒚𝒚 = �� 𝒑𝒑�𝒎𝒎𝒔𝒔𝒔𝒔𝒍𝒍𝟐𝟐 �𝒑𝒑�𝒎𝒎𝒒𝒒𝒎𝒎�𝟏𝟏𝟐𝟐𝒎𝒎=𝟏𝟏 � ∙ 𝑹𝑹�/𝑹𝑹�𝒎𝒎𝒔𝒔𝒎𝒎 Where 𝒑𝒑�𝒎𝒎 is the long-term mean monthly rainfall, normalized by mean annual rainfall (𝑹𝑹�) for each month (𝒎𝒎), relative to a normal distribution, where 𝒒𝒒𝒎𝒎 is equal to 𝟏𝟏𝟏𝟏𝟐𝟐. 𝑹𝑹𝒎𝒎𝒔𝒔𝒎𝒎 is the observed maximum annual rainfall in the region (within CHIRPS date range) (Feng et al. 2013, Schwartz et al. 2020). Relative seasonality is high where precipitation is concentrated over a few months, whereas a uniform distribution of annual precipitation returns a low index of seasonality. Soil sand content was extracted from Harmonized World Soil Database (30 arc seconds resolution) (FAO/IIASA/ISRIC/ISS-CAS/JRC 2012). 20 I characterized tree cover using the Global Forest Change v1.7 (2000-2019) product which contains high resolution (30 m) maps of tree cover (%) from the year 2000 (Hansen et al. 2013). To avoid including areas where fire is attributable to deforestation, I masked areas classified as ‘loss’ in this dataset from all further analyses. A percent natural vegetation layer was created to minimize anthropogenic influences using 30 m resolution regional land cover from SERVIR (2018). The SERVIR product provides land cover data classified into 22 separate classes. Land-cover types were re-assigned as either anthropogenic (orchard/plantation forest, urban, cropland, rice, mining, barren and aquaculture) or natural/semi-natural vegetation (e.g. forest, mixed forest, evergreen broadleaf, shrubland, and grassland). Any grid cell with >25% anthropogenic land cover was then masked. This natural vegetation mask was applied to each raster dataset. Since there was still likely some anthropogenic influence in grid cells with <25%, but > 0% anthropogenic land cover, a raster of percent natural vegetation was also exported and included in the model. The soil and tree cover datasets were aggregated to mean values within 0.05° grid cells, and all raster datasets were exported at 0.05° resolution. This resulted in a dataset comprised of 3,889 grid cells (Fig. 3.2). I consider each of the 3,889 0.05° grid cells as a ‘landscape’. 1,000 landscapes were randomly subsampled, and 30 m resolution tree cover was collected for each (see Appendix S1 in Supporting Information). Raster datasets, unless otherwise noted, were processed in Google Earth Engine. All raster datasets were then imported into R (version 4.0.2) for statistical analyses. 21 Figure 3.2: The distribution of proportion of tree cover across the study region. Data comes from 30 m tree cover, aggregated to 0.05° degrees. Tree cover data was filtered and masked to only include grids cells with <25% non-natural vegetation. The distribution of tree cover peaks around 90% tree cover. Assessing distributions of tree cover First, I examined the distributions of tree cover across spatial scales—averaged at 0.05° resolution (across landscapes), and 30 m resolution (within each landscape)—in order to assess scale dependent bimodality, as evidence for alternate stable states. To assess whether these distributions were bimodal, I extracted the number of local maxima from the density function for each distribution of tree cover. I defined maxima as peaks in the distribution, with a density proportional at least 0.1 of the density of the tallest peak, separated by more than 10% tree cover from neighboring peaks (see Appendix S2). Peaks were extracted using the ‘ggpmisc’ package in R (Aphalo 2020). Assessing environmental factor’s role in predicting tree cover I built models to assess how the same environmental factors important in determining the distributions of savannas globally–fire, climate, and soil quality—influence average tree cover at the regional scale (0.05°) in SE Asia. Models were created using beta regressions with the ‘betareg’ package in R (Cribari-Neto and Zeileis 2010). Predictor variables included MAP, 22 seasonality, fire frequency, soil sand content, and percent natural vegetation. Percent natural vegetation was included as a predictor, in order to account for the effect of anthropogenic land use on average tree cover. Predictors were z-transformed in order to compare the relative effects of each predictor. I also used backward selection to test various models with AIC, to identify the best fit model. A matrix of Pearson correlation coefficients was calculated between all predictors to assess collinearity (see Appendix S3). All correlation coefficients were below the threshold value in which collinearity is high (>0.7)(Dormann et al. 2013). The correlation between seasonality and MAP were the highest (0.7), and all other correlations between predictors were below 0.4. Assessing the relative influence of environmental factors on the prevalence of landscape mosaics To determine the role that environmental factors play in mediating the occurrence of landscape mosaics, I calculated 4 metrics of patchiness in which the size, shape, and number of patches were calculated for both forest and savanna patches in each landscape. I then built linear models to assess whether MAP, seasonality, fire frequency, and soil sand content have strong influences over patchiness. Within each landscape, 30 m tree cover data was classified as either forest (tree cover >65%), savanna (tree cover <65%), or anthropogenic land cover. This cut off was chosen based on previous analyses (Staver et al. 2011a, Hirota et al. 2011) that classified savannas as tree cover greater than 5%, but less than 60%, and based off the lowest point in the frequency distribution in percent tree cover, which occurred at ~68% in my dataset (see Appendix S4). I also reran analyses with thresholds at 60% and 70% tree cover and found that results were not qualitatively different (see Appendix S5). Prior to calculating patchiness metrics for each landscape, patches smaller than 5 pixels in size were filtered and reassigned to the surrounding class to reduce the influence of noise in the underlying data. I chose four landscape metrics to represent different aspects of patchiness: number of patches, mean patch area, Shannon evenness, and landscape shape index. Number of patches equals the number of either forest or savanna patches combined. Mean patch area is the mean area (m2) of all patches (both forest and savanna) in each landscape. Shannon evenness is calculated as the 23 ratio between the actual Shannon’s diversity index (which accounts for the abundance and evenness of all land cover types) and the theoretical maximum diversity index, ranging between 0 and 1; a value of 1 would represent a completely even proportion of forest and savanna land cover classes. Landscape shape index (LSI) is calculated as the ratio between the actual length of a patch and the hypothetical minimum length of that patch; a patch with a minimum patch length will have an LSI of 1. LSI represents the aggregation of patches and increases as the length of patch edges increase. All four patch metrics were calculated for each individual landscape using the ‘landscapemetrics’ package in R (Hesselbarth et al. 2019). Landscapes with a large number of patches, low mean patch size, high Shannon evenness, and high landscape shape index are considered patchier, and represent mosaicked landscapes. Landscape metrics were log-transformed because values were highly right skewed. Linear regressions were used to model relationships between each patchiness metric and all five environmental predictors (MAP, fire frequency, sand content, seasonality). To account for the influence of anthropogenic fragmentation on patch metrics, percent natural vegetation was also included as a predictor in each of these models. 3.3 Results MAP and relative precipitation seasonality vary greatly across the region. MAP ranges from 500-5,300 mm across all of SE Asia, however, post implementation of my natural vegetation mask, the remaining naturally vegetated areas ranged from 1,200-4,300 mm MAP. A majority of the region experiences 1,000-3,000 mm, whereas MAP greater than 3,000 mm is isolated to the western coastlines and northern Myanmar. Relative seasonality (generated from the seasonality index developed by Feng et al. (2013)) ranges from low seasonality, 0.04, in some sub-regions, highly seasonal, 0.74, in others (table 1). Similar to MAP, the western coastlines of SE Asia experience the highest levels of seasonality, and generally areas that experienced lower rainfall, also experienced lower seasonality. Mean fire frequency was 0.68 (+/- 2.66 sd) but ranged widely–from 0-18 (table 1). High fire frequency was concentrated the southeastern part of study region, with nearly yearly fires occurring Northeastern Cambodia, and along the border between Cambodia and Vietnam. A clustering of high fire frequency also occurred in Northern Thailand. 24 Post masking of anthropogenic land use, mean tree cover was 87% (+/- 2.66 sd) (table 1). Clusters of the remaining natural vegetation were located in northern and southeastern regions of study area, including northern Myanmar, northeastern Cambodia, central Vietnam, southwestern Cambodia, and along the west coast of Myanmar and Thailand (see Appendix S1). Areas of low/intermediate tree cover were concentrated in northeastern Cambodia and central Myanmar—where fire frequency was high, and MAP and seasonality were intermediate. All other remaining large clusters of natural tree cover were characterized by high tree cover. Table 3.1: Summary statistics for environmental predictors and Tree cover Variable mean sd max min 5th percentile 95th percentile fire frequency 0.68 2.66 18.49 0 0 3.57 MAP (mm) 2503.38 667.8 4302.76 1020.1 1518.56 3566.63 sand content (%) 48.61 6.76 78 15 41 52 seasonality 0.28 0.12 0.74 0.04 0.13 0.49 proportion tree cover (%) 0.87 0.14 0.98 0.07 0.57 0.96 Evidence for bimodality and alternate stable states depends on scale The distribution of tree cover was not bimodal at the regional scale (aggregated at 0.05°) (Fig. 3.2). However, 17% of landscapes demonstrated bi- or multi-modality (2 or more maxima) in distributions of tree cover (Fig. 3.3). Geographically, bimodal landscapes were present across SE Asia. Bimodal landscapes occurred where I would expect landscape mosaics to form based off climate (low MAP) and disturbance (high fire frequency). On average, bimodal landscapes occurred in areas of lower average (82% vs. 89%; p < 0.001) tree cover and MAP (2240 vs. 2600; p < 0.001), and higher fire frequency (1.12 vs. 0.388; p < 0.001) than unimodal landscapes. No significant difference was found between seasonality or soil sand content in bimodal vs. unimodal landscapes. 25 Figure 3.3: Map of bimodal landscapes. Colored squares represent the 1,000 raster grid cells that were subset for the analysis of patch metrics. Yellow squares represent the landscapes with a unimodal distribution of tree cover and purple squares represent the landscapes with a bimodal distribution of tree cover. The environmental drivers of average tree cover After using backward selection to test various models with AIC, I determined that the full model, with all predictors present, was the best fit, with a delta AIC greater than 7, relative to the next best model (including percent natural vegetation as the control variable; see Appendix S6). The full model had an R2 of 0.67. Tree cover ~ MAP * fire frequency + seasonality + sand content + percent natural vegetation Fire frequency was the strongest predictor of tree cover. As expected, tree cover was lower where fire was more frequent (Fig. 3.4). At high fire frequency (95th percentile), which was roughly one fire every five years (a fire frequency of 0.28), average tree cover was 37%. Where fire was absent, the average tree cover was 91%. Accordingly, model predictions differed where fire frequency was high vs. low. The positive relationship between tree cover and MAP became 26 weaker where fire was high (Fig. 3.5). MAP was the second most important predictor of tree cover, in which tree cover was higher in wetter regions (Fig. 3.4). Seasonality was also found to be significant predictor of tree cover, with lower tree cover in areas of higher seasonality (Fig. 3.4). As predicted, tree cover was higher in areas within less anthropogenic land cover. However, percent natural vegetation was not as strong a predictor as fire, MAP, or seasonality (Fig. 3.4). Figure 3.4: Standardized coefficients from the beta regression model used to model tree cover. Error bars represent 2.5% and 97.5% confidence intervals. All predictors, except soil sand content were found to be significant. Figure 3.5: Beta regression predictions at 5th (orange line) and 95th (blue line) percentiles of fire frequency. Model predictions are plotted on top of full data set, representing MAP and proportion tree cover for each grid cell, prior to stratified sampling. Data points are also colored based off a threshold of low vs. fire frequency, where high fire (orange) is equal to greater than the 95th percentile of fire frequency. The environmental drivers of patchiness MAP, fire frequency, seasonality, soil sand content, and percent natural vegetation (as a control) were used to model all four landscape metrics (see Appendix S7). The R2 for each modelled landscape metric were as follows: log number of patches, 0.237; log mean patch area, 0.291; Shannon evenness index, 0.471; log landscape shape index, 0.222. 27 Across all four landscape metrics, MAP was the strongest environmental predictor of patchiness. As predicted, landscapes were patchier in drier places (Fig. 3.6 and 3.7). At low MAP, there were more small-sized patches (Fig. 3.7a, b), with complex, disaggregated shapes (Fig. 3.7c), and the proportion of forest and savanna was more even (Fig. 3.7d). Contrary to my predictions, fire frequency was not a significant predictor of patchiness. However, there were some confounding weak influences of fire on each patch metric. Fire had a positive influence on patchiness based on the number of patches and landscape shape index (Fig. 3.6a and 3.6d). Whereas it had a negative influence on patchiness, based on mean patch area and Shannon evenness, patches were slightly larger and less even with higher fire (Fig. 3.6b and 3.6c). Seasonality was also a significant predictor of patchiness in three out of the four landscape metrics, with highly seasonal regions supporting patchier landscapes (Fig. 3.6a-d). Sand content was not found to be a significant predictor in any of the landscape-metric models (Fig. 3.6a-d). As expected, percent natural vegetation was an important predictor in landscape heterogeneity and was the most important predictor of both Shannon evenness and mean patch area. Landscapes with less anthropogenic land cover were less patchy. When comparing patch metrics to average tree cover for each landscape, number of patches, Shannon evenness, and landscape shape index all show a peak between 60-70% average tree cover, providing evidence that landscapes with intermediate levels of tree cover are patchier than landscapes with exceptionally low or high tree cover (see Appendix S8). 28 Figure 3.6: Results of the linear regression used to model each patch metric. Open circles represent non-significant results closed circles represent significant results. Across the board, percent natural vegetation and MAP were strong predictors. MAP had negative influences on number of patches (a), Shannon evenness index (c), and landscape shape index (d), and a positive influence on mean patch area. Seasonality was also a significant predictor of ¾ metrics. Fire and soil sand content were not significant predictors of landscape patchiness. 29 Figure 3.7: Model predictions for each patch metric, log transformed number of patches (a), log transformed mean patch area (b), Shannon evenness index (c), and log transformed landscape shape index (d), plotted across the range of MAP. R squared values reported were for full linear model with all environmental predictors included as coefficients. 3.4 Discussion While large continuous regions of savanna do not currently exist across SE Asia, my results suggest that distinct vegetation assemblages do emerge and exist at fine scales; thus demonstrating that a significant proportion (17%) of landscapes sampled exhibit bimodality in tree cover. Further, I show that both average tree cover and landscape patchiness are strongly linked to precipitation and seasonality—landscapes exhibited lower tree cover and were patchier in drier more highly seasonal subregions. In addition, while climate does dictate patterns of tree cover across scales, fire also plays a major role in shaping vegetation structure at the regional scale. Put together, these results demonstrate the same environmental drivers important in delineating savannas globally, are also key drivers of tree cover and forest-savanna mosaics within SE Asia. More broadly, I provide a framework for identifying the drivers of forest-savanna mosaics across the tropics. 30 Evidence for bimodality differs at regional and landscape scales The lack of bimodality at regional scales is likely the result of several factors. For one, there are 4 major formations of savannas in SE Asia: deciduous dipterocarp savannas, mixed broadleaf savannas, pine savannas, and fine leafed/spiny savannas, each with varying climatic extents and likely varying structure and tree cover density (Ratnam et al. 2016). It follows that variation in tree cover and forest type between these different formations of savanna may limit the detection of two distinct peaks in the distribution. Secondly, the absence of a peak in low cover at the regional level may also be due to anthropogenic land use as DDF and vegetation across the central lowlands in SE Asia may be especially affected by current rates of deforestation (Sodhi et al. 2010, Wohlfart et al. 2014). My results show, however, that the presence of both forest and savanna within landscapes may be masked out by the resolution used in my regional-level analysis (0.05°) and by the resolution commonly used in other regional analyses, 0.1°-0.5° (Staver et al. 2011b, Lehmann et al. 2011, Alvarado et al. 2020). By aggregating tree cover at coarser resolutions, I am computing an average for every grid cell. By computing averages across highly heterogeneous landscapes, such as forest-savanna mosaics, I mask local-scale heterogeneity. Here, I demonstrate that bimodality in tree cover is lost, and therefore evidence of alternate stable states between savannas and forests via remote sensing is also lost. These results differ from those of Staver et al. (2011a) which demonstrate that bimodal distributions in tree cover were independent of scale. This may explain why a peak in low tree cover is missing in SE Asian savannas—because they do not exist across continuous expanses, in addition to mosaics, as they do in other regions. Forests and savannas as alternate stable states in SE Asia My findings of bimodality at fine scales provide further evidence that forest and savannas do indeed form alternate stable states. Processes that maintain alternate stable states function at the ecosystem level (e.g., recruitment bottlenecks, fire, light availability), and my results provide clear evidence that distinct assemblages of open and closed canopy formations co-occur within landscapes. My findings also support field-based findings that demonstrate the mechanisms that maintain forest and savanna alternate stable states are at work in SE Asia (e.g. recruitment bottlenecks and high rates of post-fire resprouting; (Nguyen et al. 2019)). A key requirement for the presence of alternate stable states is stability of each state over time, and recent 31 palaeoecological records demonstrate rapid reversibility between DDF and closed canopy forest over the last 4,000 yrs, when stressors (increased fire frequency and decreased precipitation) were eliminated (Hamilton et al. 2020). However, I argue that low rainfall and frequent fire are essential for savannas and forest to form alternate stable states with each other, and in their absence forest cover will dominate. Many grassy dominated systems are already experiencing wide-spread woody encroachment as a result of shifts in management practices and climate (Kumar et al. 2019, García Criado et al. 2020, Conradi et al. 2020, Scheiter et al. 2020). In SE Asia, the existence of savannas is disputed. Sparsely wooded deciduous dipterocarp forests may appear as degraded forests, solely based on structure, however fossil evidence and the coexistence of fire-adapted tree species and grasses point to deciduous dipterocarp forests as a distinct ecosystem and form of savanna (Ratnam et al. 2011, 2016). Both forests and savannas represent distinct structural, compositional, and functional diversity from one another, each hosting their own unique species assemblage and own set of valuable ecosystem services (Stott 1984, 1990, Bunyavejchewin et al. 2011, Nguyen et al. 2019, Khaing et al. 2019) and together form structurally diverse mosaics, which host high-levels of biodiversity (Bond and Parr 2010, Fair et al. 2020, Pausas and Bond 2020). Climate drives patterns of tree cover and landscape patchiness Savannas in SE Asia exist at the high end of the accepted range of rainfall within savannas: deciduous broadleaf savannas exist up to 2,100 mm and pine savannas have been found to exist up to 3,200 mm MAP (Ratnam et al. 2016). Stott et al. (1984) define the range of MAP of SE Asian savannas as 1,000-1,500 mm MAP. My dataset, derived from remotely sensed data, demonstrates that MAP of areas with low tree cover (i.e. <65%) ranges from a low of 1,200 mm, up to 3,000 mm. The climatic envelope that encompasses savannas varies drastically across continents. Savannas within South America exist between ~1,000-2,500 mm, similar to that of my analyses within SE Asia, while savannas in Australia and Africa rarely exist above ~1,500 mm MAP (Spessa et al. 2005, Lehmann et al. 2014). Vegetation in SE Asia is defined by strong patterns of seasonality throughout the region (Bunyavejchewin et al. 2011). My results demonstrate that sub-regions with high levels of seasonality are more likely to support low tree cover and landscape mosaics. These results are in line with past research showing that seasonality or rainfall intensity drives the co-dominance of 32 trees and grasses in savannas, and is responsible for the open canopy structure of savannas (Xu et al. 2015). Similar to low tree cover, landscape patchiness was highest at the region’s lower rainfall limit, ~1,200 mm MAP, and in more seasonal subregions. Previous works demonstrates that transitions in tree cover are abrupt at the boundary between forest and savanna, across an increasing rainfall gradient, but that tree cover is highly variable at this boundary (Staver et al. 2011b, Li et al. 2019). At the boundary (around ~1,000-2,000 MAP), savannas and forests represent alternate stable states and form patchy mosaics with each other (Staver et al. 2011a, 2011b, Li et al. 2019). I provide empirical evidence that landscapes do form distinct patches of tree cover at intermediate levels of rainfall, and further, the level of patchiness decreases with increasing rainfall. Based off these findings, the prevalence of landscape patchiness or mosaics, along forest-savanna boundaries, may similarly vary across climatic gradients in other continental regions. Models built to examine how mosaics may shift given future climate scenarios, demonstrate that forest-savanna mosaics are vulnerable to increases in forest cover due to climate-change-induced increases in precipitation (Fair et al. 2020). Therefore, understanding the distribution of forest-savanna mosaics along current climatic gradients may shed light on future distributions of mosaics, given climate change. The importance of fire in determining tree cover and landscape patchiness While my results demonstrate that rainfall and seasonality are important factors in dictating tree cover, fire also plays a major important role in determining tree cover at the regional scale in SE Asia. Similarly, in addition to climate, fire is recognized as a strong predictor of tree cover globally—the probability of savanna is highest when the probability of annual fire is also high (Lehmann et al. 2011). Fire facilitates the occurrence of savannas in areas in which climate would otherwise support closed forest—and is the determinant by which forests and savannas form alternate stable state with each other (Bond et al. 2004, Hoffmann et al. 2012a). Savanna species assemblages are fire-adapted (Gignoux et al. 2009, Geiger et al. 2011), including in SE Asia where deciduous dipterocarp forest species exhibit fire tolerant traits and coexistence strategies (Nguyen et al. 2019). Contrary to my predictions and my findings at the regional scale, fire frequency did not have a significant influence over patchiness—limiting evidence that fire plays a role in maintaining 33 boundaries between forest-savanna mosaics in SE Asia. These results support previous research conducted within the study region, comparing DDF to closed-canopy forests (Huai Kha Khaeng, Thailand and Chattin, Myanmar Wildlife Sanctuaries), which found no relationship between vegetation structure and burn frequency (Baker et al. 2008, Khaing et al. 2019). While fire may not play a key role in maintaining forest-savanna boundaries within landscape mosaics, it is clear that fire still plays a strong role in patterns of tree cover at the regional scale. However, the role of fire may be limited to homogeneously low and high tree cover landscapes—which would account for the weak positive relationship I saw between patch size and fire frequency. Alternatively, the lack of a relationship between patchiness and fire may be due to the lower accuracy of the MODIS burned area product in detecting low intensity, ground fires which may be obscured by overstory vegetation (Roy et al. 2008), and which are characteristic of fires within the region (Stott 1986, Khaing et al. 2019). Fire maybe detected in a grid cell that did not homogenously burn, and this could confound the effects of fire within heterogeneous landscapes. Whereas I would expect homogenously low tree cover to burn regularly, and homogenously high tree cover to remain unburnt—which, maybe driving patterns of fire and tree cover at the regional scale. Previous studies demonstrate that edaphic qualities may provide a partial explanation for the current distributions of savannas globally (Murphy and Bowman 2012). And research shows that DDF exhibit soils composed of high sand fraction (Stott 1984). This is in opposition to my results, which showed high sand content soils—aligned with poor soil quality—supported higher tree cover, albeit weakly. However, the number of field samples collected within SE Asia used to populate harmonized world soil database is sparse. It is possible differences in soil are in part responsible for maintaining the boundaries between distinct vegetation types within SE Asia, however more detailed soil data is needed. Knowledge Gaps and future directions The vast majority of the research done on the bistability of savanna and forests has been done within a few well-studied systems, (e.g. research by Hoffman et al. in Brazilian Cerrado, and Staver et al. done in African savanna). While it may be difficult to analyze patterns of natural vegetation in the face of rapid anthropogenic change, as of 2011, 156,000 km2 of deciduous dipterocarp forest still exist within SE Asia – roughly 1/6 of the remaining forested area within 34 the region (Wohlfart et al. 2014). Not only are Southeast Asian savannas often left out of global analyses of savanna distributions, but it is likely that other forest-savanna mosaics elsewhere within the tropics, maybe additionally be left out. While regional analyses like this are extremely valuable for showing emerging patterns of tree cover and environmental drivers across landscapes, the majority of these analyses focus solely on structure (i.e. percent tree cover), whereas savannas and forests also exhibit key functional differences (e.g. fire resistance, resproutability, shade tolerance, etc.) (Gignoux et al. 2016, Ratajczak et al. 2017, Nguyen et al. 2019). In addition to this regional analysis, research is needed studying not only the structure, but the compositional and functional traits of savannas across SE Asia. 3.5 Conclusion I demonstrate that distinct assemblages of vegetation that are not visible in the distribution of tree cover at regional scales emerge at the landscape scale–providing evidence that forests and savannas do exist as alternate stable states in SE Asia. This work also provides further evidence that MAP, seasonality, and fire, the same environmental drivers important in maintaining savannas globally, are strong predictors of tree cover in SE Asia. This is important because fire is considered a major threat to seasonally dry tropical forests—the current designation of savannas in SE Asia (Miles et al. 2006). Misconceptions about the origin and history of both savanna landscapes and forest-savanna mosaics are not uncommon across the tropics, as western colonists in Brazil and Africa viewed these landscapes as degraded forests, negatively impacted by anthropogenic land use and fire (Joshi et al. 2018). However, evidence shows that some forest-savanna mosaics have remained more or less unchanged for decades and are remnants of long-term shifts in climate and hydrology that have occurred over the last 4,000 years—not changing human settlement patterns (Goetze et al. 2006, Ekblom 2008, Ngomanda et al. 2009). I argue that a mosaic framework is crucial to understanding patterns of savanna across SE Asia—a framework that can also be applied to the widespread, mosaicked landscapes, that may not fit into homogeneous regional biome classifications. This analysis provides a much-needed look into how environmental drivers related to climate and disturbance may be influencing patterns of tree cover and forest-savanna mosaics throughout SE Asia, a first step toward parsing 35 out the complex system of feedback between climate, fire, and vegetation within a very underrepresented region of savanna. 36 Chapter 4: Evaluating global gridded forest structure datasets for application in a forest-savanna mosaic 4.1 Introduction In recent decades, the application of remote sensing products in ecological studies has grown. Increases in both spectral and spatial resolution of remote sensing datasets are rapidly expanding the range of opportunities to tackle ecological and biological questions integral to the conservation of threatened ecosystems and even individual species (Turner et al. 2003). Developed from satellite imagery and spaceborne lidar, global vegetation structure products (GVPs) are powerful and easy to use, and are frequently used to assess deforestation, habitat fragmentation, and land use change (DiMiceli et al. 2021). However, the application of GVP’s extends beyond solely mapping vegetation change. For example, MODIS Vegetation Continuous Fields (VCF) collection 6 developed by DiMiceli et al. (2015) derived from 250 m Terra MODIS satellite data has been used to delineate biomes as well as study shifting fire regimes, climate buffering capacities of forests, and biodiversity loss and conservation (Miles et al. 2006, Staver et al. 2011b, Davis et al. 2019, DiMiceli et al. 2021). The Global Forest Change dataset developed by Hansen et al. (2013) derived from 30 m Landsat data has been used to map and analyze patterns of ecosystem productivity, soil erosion, secondary succession, and restoration potential (Poorter et al. 2016, Liang et al. 2016, Borrelli et al. 2017, Bastin et al. 2019). In addition to products that map fractional canopy cover, some products incorporate lidar data, such as the Global Canopy Height model recently developed by Potapov et al. (2021) derived from Global Ecosystems Dynamics Investigation (GEDI) spaceborne waveform Lidar and 30 m Landsat data. These provide a more complete picture of 3-dimensional vegetation structure, essential for accurately mapping forest degradation and assessing carbon sequestration. However, these tree cover products were developed with an emphasis on calibrating in and validating to temperate zones. In areas of relatively high intermediate tree cover and complex heterogeneous landscapes, such as seasonally dry tropical forest (SDTF) in SE Asia, it is unclear how accurately these products map tree cover (Miettinen et al. 2014). Furthermore, land cover products such as MODIS VCF are unlikely to capture fine-scale heterogeneity in tree cover—a wide-spread phenomenon across tropical biomes—in which patches of structurally distinct forest and savanna form landscape-scale mosaics and often exist in patches smaller than the cover 37 product’s 250 m resolution. Recently, Adzhar et al. (2021) demonstrated that while tree cover in forested areas was generally accurately mapped, tree cover in savanna sites was significantly underestimated, and caution was recommended when using MODIS VCF to map tropical savannas. Sanchez-Azofeifa et. al. (2017) note that pixel sizes around 250-1000 m cannot be used to effectively discern ecological processes due to substantial spectral mixing. Improvements in spatial resolution, however, have vastly improved this problem. Independent verification is a valuable method for understanding how global estimates of tree cover may diverge from local scale estimates. Many global analyses rely on remotely sensed tree cover to assess shifts in vegetation in relation to climate. Southeast Asian savannas are threatened by woody encroachment (Kumar et al. 2019) and the region is expected to shift toward taller evergreen trees (Scheiter et al. 2020). However, documenting these shifts will be difficult without accurate maps of tree cover. In addition, there are concerns that the variation between open and closed canopy vegetation types may be easily confounded by anthropogenic disturbance such as selective logging or shifting cultivation practices (Miettinen et al. 2014). Three-dimensional point clouds produced through airborne laser scanning or ALS have become a widely accepted proxy for field collected vegetation structural metrics, such as canopy height and cover (Means et al. 2000, White et al. 2016). Beyond basic structural characteristics, they can be used to calculate area-based metrics such as above-ground biomass (White et al. 2016). Accurate records of these metrics are widely used to study ecosystem structure and function in order to inform conservation policy and silviculture alike (Means et al. 2000, Lefsky et al. 2002). Due to its high level of accuracy and known levels of error, ALS-derived structural data has been used to evaluate the quality of other remotely sensed data products (Tompalski et al. 2021). The overarching goal of this study is to assess the accuracy of GVPs in mapping vegetation structure in seasonally dry tropical forests of continental SE Asia. First, I derive canopy structural metrics including percent canopy cover and canopy height across the study area using ALS. Second, I use both ALS-derived canopy cover and canopy height models to evaluate the product accuracy of several satellite products commonly used in ecological studies: MODIS Vegetation Continuous Fields (VCF) (DiMiceli et al. 2015), Global Forest Change (Hansen et al. 2013), and Global Forest Canopy Height Model (Potapov et al. 2021). I expected that the global CHM values would be the most accurate, and close to observed ALS CHM values, with strong 38 linear correlation. I predicted that the GFC dataset would perform better than the MODIS VCF, due to its higher spatial resolution and highly heterogeneous nature of the landscape. 4.2 Methods Study Region This study took place within Preah Vihear, Cambodia, across a 164km2 area (Fig. 4.1). The study area sits at ~130 m above sea level and is relatively flat. Climatically, the region receives ~2,100 mm of rainfall per year, which is punctuated by high seasonality. During the wet season, the region receives an average of 2000 mm rainfall (May through October), while the dry season experiences less than 200 mm (November through April). The study area is dominated by two forest types that differ in structure, function, and leaf habit: (1) deciduous dipterocarp forest (DDF), which is characterized by open canopy and grassy dominated understory and (2) dry evergreen forest (DEF), which is characterized by a closed canopy. DDF and DEF form distinct boundaries with each other, often existing within landscape-scale mosaics (Fig. 4.1). A portion of the study area is cultivated. I include these cultivated areas in the model to test each GVP’s ability to accurately map fragmentation. Lidar acquisition Lidar was acquired by PT McIlhanney Indonesia using an AS350 B2 helicopter equipped with a Leica ALS70 ALS sensor, during April 2015. Additionally, digital photos were acquired simultaneously and GPS survey ground support was provided by Trimble R8 GNSS receivers. Flight was carried out between 900 m-1350 m above ground with a flight speed of 80 knots. Laser scanning was conducted with a swath width of 670 m, achieving a point density of 16 points per m2, with 30% overlap of data acquisition between adjoining flight lines. Processing Lidar data All ALS point clouds were preprocessed by PT McIlhanney Indonesia after carrying out the Lidar acquisition. Terrascan/Terrasolid were used to classify and separate ground and non-ground returns, using a semi-supervised process. Lidar point clouds were then processed using both LAStools (Isenburg 2020) and the lidR package in R (see below for further explanation of these methods) (Roussel et al. 2020, Roussel 2021). 39 ALS-derived Canopy Cover I created a digital terrain map (DTM) at 1 meter resolution, by which all non-ground returns were then normalized using the DTM. I then created the canopy cover model using the normalized non-ground points by calculating the ratio of Z values (point height) above a height threshold of 5 meters (to match the threshold used in GFC and MODIS VCF products) divided by the total number of Z values (above and below the threshold) and aggregated to mean values within 30 m grid cells (to match the resolution of GFC) (Tompalski et al. 2021). I created a second canopy cover model aggregated to 250 m grid cells to match MODIS VCF resolution. ALS-derived Canopy Height Model I created the canopy height model using the normalized non-ground points, aggregating mean canopy height within 1 m grid cells, and then further aggregating to 30 m grid cells, based off mean, 85th percentile, 90th percentile, 95th percentile, maximum, and standard deviation of mean height values. The 90th height percentile is recognized as aligning most closely to true canopy height values, as mean values do not accurately represent the height of canopies in area of open or heterogeneous canopies, with tall trees (Potapov et al. 2021). Global vegetation structure datasets The MODIS Vegetation Continuous Fields product represents fractional vegetation cover for three land classes, tree cover, non-tree cover, and non-vegetated ground at 250 m resolution. It is produced using the seven available bands, from multi-day composites of Terra MODIS (DiMiceli et al. 2015, 2021). Here I extract and evaluate the tree cover class only. Hansen Global Forest Change v1.8, henceforth GFC, is a medium resolution map of global tree cover loss and gain since year 2000. This map is the result of a time-series analysis of Landsat images, in which tree cover is defined as canopy closure for all vegetation taller than 5 m in height (Hansen et al. 2013). Additionally, this dataset uses MODIS VCF as an input in development (Hansen et al. 2003, 2013, DiMiceli et al. 2021). The Global Forest Canopy Height Model developed by Potapov et. al. (2021), henceforth Global CHM, was created by integrating canopy structural metrics gathered from Global Ecosystem 40 Dynamics Investigation (GEDI) with Landsat time-series. Specifically, relative height metrics for the 95th percentile of the GEDI wave-form returns were used due to high correlation with ALS-derived canopy height. The number of GEDI samples within the Asian tropics was quite low, compared to sub-tropical, temperate, and some other tropical locations. ALS validation data was collected from forests in USA, Mexico, DRC, and Australia. ALS point clouds were aggregated into 30x30 m grid cells based off the 90th percentile of Z values (point height) and used in model calibration. While the calibration data set does represent a diverse set of forest types, it does not include heterogeneous landscapes. For further information see Potapov et. al. (2021). I accessed and processed all global vegetation structure data using Google Earth Engine—a cloud processing platform which allows large-scale geospatial data to be processed, in which all three datasets were present in a public data catalogue. For MODIS VCF and Global CHM data, I collected the most recent year of tree cover/height data and clipped each raster to the study region. For GFC, all tree cover lost between 2000-2020 is represented as ‘loss’ in a separate band. For all areas that lost tree cover, I reclassified tree cover to 0%, to represent the most recent year of tree cover and clipped to the raster to the study region. I exported datasets at the same resolution and extent as ALS-derived canopy cover and canopy height models and imported them into R (version 4.0.2) for analysis. Statistical Analyses I used linear regression equations to evaluate global vegetation structural data vs. ALS data. I evaluated GFC and MODIS VCF twice, once with only non-zero values and once with zero values included. In these datasets, zero values represent areas of both cleared land and naturally occurring 0% tree cover (e.g., grasslands). Excluding zero values allows us to evaluate how each dataset does at distinguishing details about structure where tree cover does exist, while zero values simply distinguish where trees do not occur. I report R2, mean absolute difference, root mean square error, and mean difference—commonly recorded metrics for evaluating the accuracy of remote sensing datasets (Gatziolis et al. 2010, Ota et al. 2015, Graham et al. 2019, DiMiceli et al. 2021). 41 Figure 4.1: Map of the study region. Lidar data was acquired near Chhaeb, within the Preah Vihear Province of Cambodia. Seasonally dry forests extend across much of the landscape in Northern Cambodia and is composed of three major formations: dry evergreen forest, deciduous dipterocarp forest, and mixed deciduous forest. Here, dry evergreen forest forms stark boundaries with deciduous dipterocarp forest, which are visible in both aerial and ground photography. 4.3 Results Here I use ALS-derived values of canopy cover and height in lieu of field collected measures of canopy cover and height to evaluate each GVP’s applicability within a seasonally dry tropical forest mosaic. Based off the ALS data, the study region is subject to varying intensities of anthropogenic land use, with the northern portion of the study region experiencing moderate anthropogenic land use and land clearing. The seasonally dry tropical forest mosaic in this region is made of patches of both DEF (high tree cover) and DDF (intermediate-to-low tree cover), and 42 both formations are distinctly visible via aerial imagery, the ALS derived canopy cover, and all three GVP’s (Fig. 4.1, Fig. 4.2a-c). When GVP’s were compared to the ALS-derived canopy cover and height models for the Chhaeb region, there were major deviations across the board—height deviated by 7 m and cover by 18-24% (table 4.1). Contrary to my predictions, Global CHM had the weakest agreement with the ALS data, while MODIS VCF had the highest agreement (table 4.1). Table 4.1: Results from linear regression comparison of ALS derived and global vegetation structure products. Linear regression equations, R2, mean differences, and RMSE are all reported. GFC and MODIS VCF products were model results and errors were reported twice, once with only non-zero values included and once with all values, including zero values, included. Equation (x = ALS) R2 Mean absolute difference Mean difference RMSE Hansen GFC (only non-zero values included) GFC = 0.81x + 19.85 0.49 15.54% 8.70% 20.60% Hansen GFC (zero values included) GFC = 0.94x + 5.59 0.59 15.93% 2.92% 23.65% MODIS VCF (only non-zero values included) VCF = 0.76x – 2.76 0.76 16.20% -14.68% 19.20% MODIS VCF (zero values included) VCF = 0.73x – 0.47 0.77 15.69% -13.45% 18.74% Global CHM 90th GCHM = 0.40x + 8.35 0.19 5.80 m -5.14 m 7.26 m Bias was spatially aggregated GFC’s bias was somewhat spatially aggregated (Fig. 4.2a). For example, GFC predicted low to no cover accurately in areas where land clearing was apparent, whereas canopy cover was both under (negative bias) and over predicted (positive bias) across the boundary between DEF and DDF (Fig. 4.2a). Negative bias was somewhat clustered within DDF, indicating that canopy cover within DDF might be more likely to be underpredicted by GFC than canopy cover within 43 DEF (Fig. 4.2a). Overpredictions by GFC, on the other hand, were common across both DDF and DEF (Fig. 4.2b). Spatial aggregation of bias was even more visible across MODIS VCF than GFC (Fig. 4.2b). Unlike GFC, MODIS VCF overpredicted areas of low-to-no cover in the northern portion of the study area (Fig. 4.2b), demonstrating that GFC does better at detecting changes in cover than MODIS VCF. More so than GFC, MODIS VCF’s negative bias was concentrated in areas of intermediate tree cover—where the DDF formations occur (Fig. 4.2b). Areas of high tree cover—where DEF formations occur—appear to show lower negative bias (Fig. 4.2b). While there appear to be substantial differences between the ALS CHM and Global CHM, the bias is relatively spatially disaggregated, with both areas of high and low tree cover—DEF and DDF, respectively—subject to negative bias (Fig. 4.2c). 44 a) b) c) Figure 4.2: Raster canopy cover and height models for each global vegetation product. (a) 30 m canopy cover (%), (b) 250 m canopy cover, and (c) 30 m canopy height models. Each GVP is visualized alongside ALS derived values (at the equivalent resolution), and the GVP’s bias, calculated as GVP - ALS. 45 The shape of distribution of tree cover varies by product The distribution and range of values across each dataset also varied. GFC frequently reported 100% tree cover, whereas the ALS canopy cover rarely did (Fig. 4.3a). Both datasets do, however, show peaks at around 75-80% cover. Both GFC and Global CHM demonstrated multimodality in the distribution of tree cover and height, respectively, whereas the ALS-derived values of cover and height were unimodal (Fig. 4.3a, c). This suggests the shift from closed to open canopy cover maybe more continuous than is implied by either global cover or height models. Interestingly, when the ALS-derived cover values were aggregated at 250 m, three distinct peaks formed—one at very low cover, one at intermediate cover, and one at high cover (Fig. 4.3a and b). MODIS VCF, on the other hand, peaked at ~20% cover, and was noticeably missing a peak at intermediate cover (Fig. 4.3c). Figure 4.3: 2D density plots demonstrating the relationship between vegetation products and ALS cover and height. For each subplot, the grey line represents the fit linear regression for all non-zero values, the yellow line represents linear regression for all values (including zeroes), and the black dashed line represents 1:1. Subplots represent ALS derived canopy cover vs. a) Hansen Global Forest Change, b) MODIS VCF, and c) represents ALS derived canopy height aggregated into 30x30 m grid cells based off the 90th percentile of height values plotted against the Global CHM dataset. 46 MODIS VCF had the highest level of agreement I ran linear models and calculated root mean square error (RMSE), mean difference, and absolute mean difference to quantify the observed deviations between the ALS-derived values and the GVPs (table 4.1). The global vegetation structure dataset with the highest agreement with ALS data was MODIS VCF with an R2 of 0.76, whereas Global CHM performed the worst, with an R2 of 0.19 and a RMSE of 7.26 m. GFC was intermediate, scoring lower in accuracy than MODIS VCF both in terms of explaining less variation (R2 0.49 vs. 0.76) and a higher RMSE (20.6% vs. 19.20%) (table 4.1). While the linear regression was best fit to MODIS VCF, the relationship appears to be non-linear (Fig. 4.3b). I also note that when zero values were included in the linear models used to evaluate global cover products the agreement between GFC and ALS was substantially improved (from R2 0.49 to R2 0.59), however the RMSE did increase (20.60 to 23.65) (table 4.1). MODIS VCF was only slightly improved with zero values included (table 4.1). Further trends in deviations To further parse out how and where each GVP may diverge from ALS values of height and cover mean difference and absolute mean differences were calculated. Since GFC and MODIS VCF are both models of canopy cover they are directly comparable. While MODIS VCF more accurately predicted tree cover, MODIS VCF and GFC deviated to roughly the same degree, around 16% cover (table 4.1). However, the direction in which each product deviated was quite different (table 1, Fig. 4.4a-b). While MODIS VCF underpredicted canopy cover by an average of -14.68%, GFC overpredicted tree cover by ~8% on average (table 4.1). In agreement with the results from my linear regression (Fig. 4.3b), areas of intermediate tree cover were significantly underpredicted by MODIS VCF (Fig. 4.4b). Divergence of GFC from ALS appears to be driven by an overall high level of noise (100% variance is common) within the model’s predictions (Fig. 4.4a). Global CHM substantially underpredicted height by 5 m on average, especially where canopy height was high (table 1, Fig. 4.4c). 47 Figure 4.4: The distribution of differences of Global vegetation products from the ALS derived values of height and cover. ALS canopy cover and height are binned by whole values, and each boxplot represents the average strength and direction of bias for a given cover or height value. (a) the difference between Hansen Global Forest Change - ALS derived canopy cover, (b) MODIS VCF - difference between ALS derived canopy cover, (c) Global CHM - ALS derived canopy height based off 90th percentile of height, and (d) Global CHM - ALS derived canopy height based off mean height value. 4.4 Discussion Unsurprisingly, all three global vegetation products had major deviations in canopy cover and height from observed values. While MODIS VCF had the highest level of agreement based off the results of the linear regressions, the product clearly underestimates intermediate tree cover typical of DDF formations within the SE Asian SDTF mosaic. GFC on the other hand does a worse job of predicting tree cover overall, yet raises fewer systematic red flags, with observed deviations stemming from an overall noisy dataset that both under and overpredicts tree cover. Overall, Global CHM did substantially worse in predicting observed values, underpredicting tree cover by several meters on average. Based off these results I encourage researchers to exercise caution when applying any of these datasets to the SE Asian SDTF mosaic landscape or other highly heterogenous landscapes. 48 MODIS VCF under predicts tree cover in open canopy ecosystems When evaluating MODIS VCF, I found RMSE results for Chhaeb region were higher than reported values for other regions. For example, DiMiceli et al. (2021) reported RMSE values of 9.47% and 10.4% tree cover for ground evaluation sites in Maryland and Brazil, compared to my reported value 16.20%. My results are in agreement with other studies that demonstrate MODIS VCF under predicts tree cover in open canopied systems, such as savannas (Gaughan et al. 2013, Staver and Hansen 2015, Adzhar et al. 2021). Conversely, evaluation work conducted along the boreal-tundra transition zone demonstrated that the first iteration of MODIS VCF overestimated tree cover in sparsely forest areas (Montesano et al. 2009). In general, previous research suggests that caution should be used when analyzing patterns of MODIS VCF tree cover below 20-30%, a pattern which continues to persist in the most recent version (Staver and Hansen 2015, Adzhar et al. 2021). My study further demonstrates that intermediate tree cover within DDF (recognized as savanna) is also under threat of being underpredicted in global tree cover datasets. Evaluation of GFC and its applicability within seasonally dry tropical forest I found that the GFC dataset does not appear to have any major systematic differences across the two vegetation types. This is in contrast to Cunningham et al. (2019) who found that GFC did well at predicting small forest patches and narrow forest elements, but underestimated tree cover in tropical dry forest in Costa Rica. Past research points to tropical dry forest’s deciduous leaf habit as a likely culprit of underestimations of tree cover—cloud free days, necessary for accurate satellite imagery occur in the dry season when deciduous vegetation has senesced (La Barreda-Bautista et al. 2011, Cunningham et al. 2019). Further research also demonstrates that GFC exhibits an aridity bias where tree cover is underestimated in areas of precipitation below 2,270 mm mean annual precipitation (Cunningham et al. 2019, Fagan 2020). This discrepancy between precipitation values may explain why I did not see a strong underestimation of tree cover in my region, as Chhaeb exists on the higher end of the accepted range of rainfall for tropical dry forest, experiencing 2,100 mm precipitation per year. However, other regions of SE Asia do experience less than 2,000 mm precipitation, and under estimations of tree cover within dry tropical forest may be more prevalent within those regions. While I find no systematic differences related to percent cover within Chhaeb study region, I did find (contrary to my predictions) that GFC’s increased 30 m resolution did not increase the 49 product’s accuracy relative to MODIS VCF’s 250 m resolution. This suggests that while GFC does provide a finer scale dataset to work with, it does not necessarily provide valuable additional detail (beyond binary values of forest loss and gain). GVP datasets utilized at global scales contain millions of data points, and therefore require a large amount of computing power. As datasets increase in resolution, the number of available grid cells/data points increases rapidly. In my small study region alone, the 30 m dataset contains 165,000 grid cells vs. 3,000 grid cells in the 250 m dataset. For researchers facing computational limitations, it might make sense to stick with MODIS VCF’s 250 m resolution. These findings suggest that while GFC may be well suited for studying fragmentation and landscape connectivity, it is less well suited for studies focused on mapping patterns of tree cover in seasonally dry tropical forest. Complications with the Global CHM While agreement between Global CHM and ALS values was low compared to reports by the development team (R2 0.19 vs. R2 0.61), I found similar magnitudes of error, with a mean absolute error of 6-7 m (Potapov et al. 2021). Care was taken to validate the Global CHM using ALS data collected from diverse ecosystem types in Mexico, Australia, USA, and DRC, however the developers do note that their global model still underestimates tree cover within heterogeneous landscapes (Potapov et al. 2021). While this dataset holds promise, its use within highly heterogeneous seasonally dry tropical forests of SE Asia may be best placed on hold until later iterations of the product are released, which will hopefully improve based on refined and newly available GEDI observations incorporated into model calibration (Potapov et al. 2021). 4.5 Conclusion Global vegetation products provide a valuable time- and cost-effective tool to scientists interested in studying ecological phenomena across regional and global scales. In this study I demonstrate that caution should be used when applying these products to seasonally dry tropical forests of SE Asia. My results corroborate previous research demonstrating that MODIS VCF exhibits relatively poor agreement in regions of low to intermediate tree cover. Specifically, I see an underestimation of tree cover within DDF as opposed to DEF. DDF makes up 1/6th of the remaining forest in SE Asia, and estimates of tree cover within this region may be drastically 50 underestimated (Wohlfart et al. 2014). This is especially concerning as DDF is especially threatened by rampant rates of deforestation throughout the region (Sodhi et al. 2010, Wohlfart et al. 2014). While 30 m resolution is ideal for assessing finer scale patterns of tree cover, I found that GFC (30 m) did not explain variation in tree cover better than MODIS VCF (250 m). This limits the applicability of using GFC to map percent cover in a complex landscape mosaic, where percent tree cover varies at fine scales. In conclusion, I encourage researchers to exercise caution when applying GVPs to highly heterogenous landscapes, and to consider the pros and cons of each GVP where multiple are available. 51 Chapter 5: Conclusion and Recommendations for Future Work 5.1 Key findings The overarching goal of this thesis was to investigate the environmental determinants of SE Asian savannas across scales. I accomplished this goal by first demonstrating that patterns of tree cover are scale dependent—while large continuous regions of savanna do not currently exist across SE Asia, my results suggest that distinct vegetation assemblages do emerge and exist at fine scales. Secondly, by mapping patterns of tree cover in SE Asia at both landscape and regional scales, I show that the same environmental drivers important in delineating the distribution of savannas globally (fire, seasonality, and precipitation), are also key drivers of tree cover (at the regional scale) and forest-savanna mosaics (at the landscape scale) within SE Asia. While regional analyses like these are valuable, remote sensing data quality presents challenges to understanding ecological phenomena at broad scales. In order to achieve my third objective, I assessed the accuracy of global vegetation products used in many global analyses of vegetation structure. All three global vegetation products evaluated had major deviations in canopy cover and height from observed values. I demonstrate that while there was no clear ‘best global vegetation product’ for use in SE Asian SDTF, GFC developed by Hansen et al. (2013) did not exhibit the same systematic bias as MODIS VCF developed by DiMiceli et al. (2015) in underestimation of intermediate tree cover typical of DDF formations within the SE Asian SDTF mosaic. 5.2 Implications The vast majority of the research done on the bistability of savanna and forests has been done within a few well-studied systems, (e.g. research by Hoffman et al. in Brazilian Cerrado, and Staver et al. done in African savanna). While it may be difficult to analyze patterns of natural vegetation in the face of rapid anthropogenic change, as of 2011, 156,000 km2 of deciduous dipterocarp forest still exist within SE Asia – roughly 1/6 of the remaining forested area within the region (Wohlfart et al. 2014). Chapter 3, therefore, provides a much-needed look into which environmental factors are responsible for driving patterns tree cover and forest-savanna mosaics throughout SE Asia, a first step toward parsing out the complex system of feedback between climate, fire, and vegetation within a very underrepresented region of savanna. 52 In addition to SE Asian savannas, it is likely that forest-savanna mosaics elsewhere in the tropics are similarly left out of global analyses due to their fine scale nature. My analysis of forest-savanna mosaics presents a useful framework for parsing out the environmental factors driving forest-savanna mosaic formation. While I did not find evidence that fire plays a major role in forest-savanna mosaic formation within SE Asia (Stott 1986), my results do show that landscape mosaics are most prevalent in areas of low precipitation and high seasonality. Impending shifts in climate may, therefore, exacerbate woody encroachment within SE Asian savannas (Kumar et al. 2019). Threats to SE Asian savannas also threaten the structure of the broader landscape, as forests and savannas form structurally complex mosaics, and host high-levels of biodiversity and mega-fauna combined (Bond and Parr 2010, Fair et al. 2020, Pausas and Bond 2020). Documenting woody encroachment in Southeast Asian savannas (Kumar et al. 2019, Scheiter et al. 2020) will be difficult without accurate maps of tree cover. My results corroborate previous research demonstrating that MODIS VCF exhibits relatively poor agreement in regions of low to intermediate tree cover (Montesano et al. 2009, Gaughan et al. 2013, Staver and Hansen 2015, Adzhar et al. 2021). Specifically, I see an underestimation of tree cover within DDF as opposed to DEF. Previous research provides evidence that GFC may similarly underestimate tree cover within SDTF and arid biomes (Cunningham et al. 2019, Fagan 2020), however my results do not exhibit this same level of bias. The patterns of GFC’s bias across an aridity gradient appear to be widespread at country and global scales (Cunningham et al. 2019, Fagan 2020). One possible explanation for this divergence maybe the smaller scale of my study and its high level of precipitation in relation to the accepted range of rainfall for SDTF (~2,100 mm/yr). In order to further understand GFC’s ability to accurately map tree cover with in SE Asian SDTF further evaluation along precipitation gradients within the region is necessary. In conclusion, I encourage researchers to exercise caution when applying GVPs to highly heterogenous landscapes, and to consider the pros and cons of each GVP where multiple are available. 5.3 Recommendations for future work The following recommendations outline two key avenues of future study which I believe are essential for continued work parsing out the environmental determinants of SE Asian savannas across scales. My thesis research demonstrated that although analyzing patterns of tree cover at regional and landscape scales maybe difficult in the face of rapid anthropogenic land use and 53 land change, meaningful ecological patterns can still be gleaned. However, as I show in Chapter 4, there are limitations to the remotely sensed products used in my analyses as well as other broad scale studies. To combat the error and bias that global vegetation products exhibit, the integration of remote sensing and field-based methods is ideal. In chapter 2, I build a framework for how a multi-scale approach can be used to parse out the complex set of ecological processes governing the boundary between forest and savanna in SE Asia. I believe that this chapter is a valuable starting point for future researchers hoping to study DDF and DEF boundaries. Specifically, in the “site level dynamics section”, I highlight several avenues of study in which comparative work studying the compositional and functional differences between DDF and DEF will provide key insights into the role that fire may play in shaping community level dynamics and individual species’ growth strategies. In chapter 3, contrary to my predictions, I did not find evidence that the prevalence of landscape mosaics is driven by fire. While these results do support past findings which find no relationship between vegetation structure and burn frequency across DDF and DEF boundaries (at local scales) (Baker et al. 2008, Khaing et al. 2019), my analysis also clearly demonstrates that fire plays a strong role in dictating patterns of tree cover at regional scales. Further research into this discrepancy between fire’s varied impact on tree cover across scales is needed. Additionally, it is well-documented that low intensity ground fires (characteristic of fires within the region) are underrepresented in the MODIS burned area product (Roy et al. 2008, Giglio et al. 2018). Therefore, I recommend that alternative methods for mapping fire within SE Asian SDTF be implemented. 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S2: Locations of local maxima were extracted from the density function. 67 S3: Pearson correlation coefficients between predictors for the full beta regression model used to predictor regional tree cover. Regression Model: Tree cover ~ MAP * fire frequency + seasonality + sand content + % natural vegetation MAP seasonality Fire frequency Sand content seasonality 0.70 Fire frequency -0.23 -0.11 Sand content -0.10 -0.16 0.19 Natural vegetation 0.33 0.24 -0.12 0.01 S4: The distribution of percent tree cover at 30 m resolution, combined across landscapes, filtered to various maximum MAP levels. Across several levels of MAP, there is a consistent frequency minimum at ~68% tree cover. 68 S5: Results of Landscape patch analysis with high and low tree cover thresholds at 60% (S5.1) and 70% (S5.2) were not qualitatively different than the chosen threshold of 65% tree cover, in which tree cover low tree cover is tree cover less than 65% and high tree cover is greater. Figures depict results of the linear regression used to model each patch metric, in which each coefficient has been scaled, to determine the relative influence of each environmental predictor. S5.1: Results of the linear regression used to model metrics of landscape patchiness. Here, tree cover was classified as either high, >60% tree cover or low, <60% tree cover. R2 for each modelled metric were as follows: log number of patches, 0.236; log mean patch area, 0.298; Shannon evenness index, 0.499; log landscape shape index, 0.226. S5.2: Results of the linear regression used to model metrics of landscape patchiness. Here, tree cover was classified as either high, >70% tree cover or low, <70% tree cover. R2 for each modelled metric were as follows: log number of patches, 0.209; log mean patch area, 0.268; Shannon evenness index, 0.418; log landscape shape index, 0.205. 69 S6: After using backward selection to test various models with AIC, I determined that the full model, with all predictors present, was the best fit (including percent natural vegetation as the control variable): Tree cover ~ MAP * fire frequency + seasonality + sand content + percent natural vegetation The model with all predictors, except for sand content present, scored the highest with AIC model comparison, however, the full model’s delta AIC was less than 2), so sand content retained in the model. Predicting Tree Cover model df logLik AICc delta weight a ~ natveg.z + sand.z + seasonality.z + firefreq.z * map.z 8 1720.531 -3424.923 0 0.97789932 a ~ natveg.z + seasonality.z + firefreq.z * map.z 7 1715.726 -3417.3434 7.579603 0.02210061 a ~ natveg.z + firefreq.z * map.z 6 1701.973 -3391.8664 33.05667 6.49E-08 a ~ firefreq.z + map.z + natveg.z + sand.z + seasonality.z 7 1690.703 -3367.2982 57.62488 3.00E-13 a ~ firefreq.z + map.z + natveg.z + seasonality.z 6 1689.018 -3365.956 58.96705 1.53E-13 a ~ firefreq.z * map.z 5 1676.927 -3343.7968 81.12625 2.37E-18 a ~ maprecip.z + natveg.z 4 1209.936 -2411.8343 1013.089 1.00E-220 a ~ natveg.z 3 1108.675 -2211.328 1213.595 2.89E-264 70 S7: Backward selection to test various models with AIC for each patch metric reported below. Shannon Evenness (a) model R2 df logLik AICc delta weight a ~ map.z + natveg.z 0.335196 4 -1399.14 2806.312 0 0.370067 a ~ natveg.z + firefreq.z * map.z 0.337286 6 -1397.63 2807.348 1.035268 0.220534 a ~ firefreq.z + map.z + natveg.z + season.z 0.337164 6 -1397.72 2807.524 1.211141 0.201969 a ~ natveg.z + season.z + firefreq.z * map.z 0.337502 7 -1397.47 2809.067 2.754198 0.093371 a ~ firefreq.z + map.z + natveg.z + sand.z + season.z 0.337245 7 -1397.66 2809.438 3.125084 0.077567 a ~ natveg.z + sand.z + season.z + firefreq.z * map.z 0.337609 8 -1397.4 2810.946 4.633157 0.036492 a ~ natveg.z 0.237466 3 -1464.7 2935.415 129.1028 3.42E-29 a ~ firefreq.z * map.z 0.118914 5 -1533.77 3077.603 271.2904 4.55E-60 Number of patches (a) model R2 df logLik AICc delta weight a ~ firefreq.z + map.z + natveg.z + sand.z + season.z 0.241477 7 -1581.84 3177.787 0 0.43575 a ~ firefreq.z + map.z + natveg.z + season.z 0.239309 6 -1583.26 3178.613 0.826726 0.288215 a ~ natveg.z + sand.z + season.z + firefreq.z * map.z 0.241578 8 -1581.77 3179.686 1.899818 0.168538 a ~ natveg.z + season.z + firefreq.z * map.z 0.239351 7 -1583.24 3180.586 2.799215 0.107497 a ~ map.z + natveg.z 0.207714 4 -1603.61 3215.264 37.47776 3.17E-09 a ~ natveg.z + firefreq.z * map.z 0.208867 6 -1602.88 3217.851 40.06469 8.70E-10 a ~ firefreq.z * map.z 0.17548 5 -1623.55 3257.163 79.37669 2.53E-18 a ~ natveg.z 0.036834 3 -1701.26 3408.551 230.7645 3.38E-51 Landscape Shape Index (a) model R2 df logLik AICc delta weight a ~ firefreq.z + map.z + natveg.z + season.z 0.225479 6 -1106.44 2224.974 0 0.376032 a ~ firefreq.z + map.z + natveg.z + sand.z + season.z 0.226964 7 -1105.49 2225.083 0.109252 0.356042 a ~ natveg.z + season.z + firefreq.z * map.z 0.225504 7 -1106.43 2226.969 1.995354 0.138656 a ~ natveg.z + sand.z + season.z + firefreq.z * map.z 0.226969 8 -1105.48 2227.109 2.135533 0.12927 a ~ map.z + natveg.z 0.195091 4 -1125.69 2259.414 34.44008 1.25E-08 a ~ natveg.z + firefreq.z * map.z 0.198271 6 -1123.71 2259.5 34.52592 1.20E-08 a ~ firefreq.z * map.z 0.156627 5 -1149.03 2308.115 83.14076 3.32E-19 a ~ natveg.z 0.048353 3 -1209.42 2424.863 199.8889 1.48E-44 Mean Patch Area (a) model R2 df logLik AICc delta weight a ~ natveg.z + season.z + firefreq.z * map.z 0.295454 7 -1290.08 2594.275 0 0.477641 a ~ natveg.z + firefreq.z * map.z 0.293369 6 -1291.56 2595.201 0.926336 0.300573 a ~ natveg.z + sand.z + season.z + firefreq.z * map.z 0.295454 8 -1290.08 2596.307 2.032066 0.17292 a ~ firefreq.z + map.z + natveg.z + season.z 0.290249 6 -1293.76 2599.607 5.331758 0.033214 a ~ firefreq.z + map.z + natveg.z + sand.z + season.z 0.290273 7 -1293.74 2601.601 7.3261 0.012253 a ~ map.z + natveg.z 0.284116 4 -1298.06 2604.166 9.891589 0.003398 a ~ natveg.z 0.196238 3 -1355.96 2717.936 123.661 6.71E-28 a ~ firefreq.z * map.z 0.118398 5 -1402.17 2814.41 220.1347 7.54E-49 71 S8: Patch metrics plotted against average tree cover within each landscape. Number of patches (a), landscape shape index (b), and Shannon evenness (c) peak around 60%. Whereas mean patch area is highest at high levels of tree cover.
Thesis/Dissertation
eng
Geography
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Schwartz, Naomi B.
A multi-scaled approach to understanding the environmental drivers of Southeast Asian forest-savanna mosaics
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