Tree Recruitment in the Serengeti Ecosystem: the Role Of

TREE RECRUITMENT IN THE SERENGETI ECOSYSTEM: THE ROLE OF

RESOURCE AND DISTURBANCES

DEUSDEDITH M. RUGEMALILA,

A Dissertation Submitted to the Graduate Faculty of WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Biology May 2021

Winston – Salem, North Carolina

Approved By:

T. Michael Anderson, Ph.D., Advisor Lori Eggert, Ph.D., Chair

James Pease, Ph.D. Miles Silman, Ph.D Brian Tague, Ph.D. Clifford Zeyl, Ph.D.

Acknowledgments

I want to thank my adviser, Dr. T. Michael Anderson, for mentoring me throughout my

Ph.D. program. Having him as a mentor and academic advisor has changed my life for the better. I appreciate all your time and tireless support, and I do not know if I can ever repay.

I would also like to thank the rest of the committee – Drs. Lori Eggert, Miles Silman, Brian

Tague, Clifford Zeyl, and James Pease – for their overwhelming support from the beginning of my research proposal through my qualifying exams and the dissertation defense. My dissertation research would not have been possible without their guidance. I thank my collaborators, Dr. William K. Smith, and Scott Cory, who helped me develop skills related to understanding plant physiology.

Additionally, I am very appreciative of field assistants Jeremiah Sarakikya, Meshark

Mwitta, and many others who helped in data collection in the field.

I would like to thank my former and current labmates – Dr. Daniel Griffith, Dr. Kathylene

Quigley, Robert Baldwin, Rebecca Seifert, and Christopher Lopez, for their company and support.

I thank my sponsors, the Wake Forest University Department of Biology, through numerous small grants, the Vecellio Fund, and the Richter fund, for providing funding that helped me purchase research supplies and airline tickets for travel to my field station in

Tanzania.

I would like to thank the Tanzania Wildlife Research Institute (TAWIRI), the Serengeti

Wildlife Research Centre (SWRC), the Commission for Science and Technology

(COSTECH), the Tanzania National Parks (TANAPA), and the Ngorongoro Conservation

Area Authority (NCAA) for permission to conduct research in protected areas in Tanzania.

I would also like to thank all of my friends and family for their support and encouragement throughout my doctoral program, as they were my support system keeping me going.

Special thanks to my wife Joanitha Kiwanuka, my children (Benedict Rugemalila, Merian

Rugemalila, and Robert Rugemalila), and my niece Paris Carlo. Thanks to my mother

(Annamary Nsubuga) and mother-in-law (Merina Katambe), who came to visit my family here in the US and always prayed for my success.

iii

Table of Contents

Acknowledgements ...... ii

List of Tables...... vii

List of Figures ...... viii

List of Abbreviations ...... x

Abstract ...... xiii

General introduction ...... xiv

References ...... xvii

CHAPTER 1: Seed production in the Serengeti: The role of resources and disturbances across environmental gradients...... 23

Abstract ...... 23 Introduction ...... 24 Materials and Methods ...... 28 Study system and species ...... 28 Data collection ...... 29 Data analysis ...... 33 Results ...... 35 Discussion...... 37 Tables and Figures ...... 42 References ...... 55 CHAPTER 2: A short communication: Variability in Acacia seed germination potential sourced from the tree canopy, seedbank, and large ungulate dung...... 68

Introduction ...... 68

Materials and Methods ...... 70 Study location...... 70 Transects and study species ...... 71 Seed germination experiments ...... 72 Data analysis ...... 73 Results and discussion...... 73 Tables and Figures ...... 77 References ...... 82 CHAPTER 3: The role of microsite sunlight environment on growth, architecture, and resource allocation in dominant Acacia tree seedlings, in Serengeti, East Africa ...... 90

Abstract ...... 90 Introduction ...... 91 Materials and Methods ...... 94 Study system ...... 94 Statistical analyses ...... 98 Results ...... 99 Discussion...... 101 Acknowledgements ...... 107 Declarations ...... 108 Tables and figures ...... 109 Literature cited ...... 116 Supplemental Materials ...... 125 Appendix S1. Supplementary methods ...... 126 Supplemental figures ...... 127 Supplement references ...... 134 CHAPTER 4: Grass competition is more important in suppressing Acacia seedling growth than grass species type. For seedling survival, soil moisture's significance is tree species-specific...... 135

Abstract ...... 135 Introduction ...... 136

Materials and Methods ...... 140 Study location...... 140 Experimental setup ...... 142 Data collection ...... 143 Data Analysis ...... 143 Results ...... 145 Discussion...... 148 Tables and Figures ...... 152 Supplementary figures ...... 159 Cited references ...... 161 CV ...... 174

List of Tables

Table 1 Results from generalized linear mixed model fits by glmmTMB (AIC, the Akaike information criterion) testing the effect of alternative combination of resource and disturbance covariates on seed production for the two most common woody plant species in Serengeti (A. robusta and A. tortilis). Shown in bold are the parsimonious models..... 42

Table 2 Generalized mixed-effects model coefficients for the parsimonious model selection (Table 1) associating YEAR (for A. robusta) and BROWSERS (for A. tortilis) influence on seed production in Serengeti while controlling for tree size (CANOPY AREA). (SE = Standard Errors of the means, and p = p-value at 95% confidence interval) ...... 43

Table 3 Result of the ANOVA examining germination potential of A. robusta and A. tortilis seeds sourced from the tree canopy, seedbank, and animal dung after 180 days of laboratory germination...... 77

Table 4 Descriptive statistics table (means ±SE) for final germination percent (FGP) of scarified and unscarified A. robusta and A. tortilis seeds sourced from the tree canopy, seedbank, and large ungulate (elephant, giraffe, and impala) dung. T50 is the time (days) for cumulative germination to pass a 50% germination, and n is the number of seeds germinated (sample size). NA represents infinity T50...... 78

Table 5 Result of the ANOVA examining the effects of different sunlight levels and species and their interactions on growth, architecture and resource allocation traits in A. robusta and A. tortilis seedlings after 12 weeks of growth...... 109

Table 6 The generalized linear mixed-effects models fit (AIC, the Akaike Information criterion) for the effect of SPECIES-T, SPECIES-G, COMPETITION, SOIL MOISTURE, and their interaction on seedling diameter, height, H:D ratio, and survival...... 152

vii

List of Figures

Figure 1 Map showing the location of the Serengeti National Park in Tanzania. The filled circles show study site locations within the park ...... 44

Figure 2 Annual variation in seed production (mean ±SE) for A. robusta (=Acarob - blue bars) (n = 182) and A. tortilis (=Acator - red bars) (n = 226) for the years 2017, 2018, and 2019...... 45

Figure 3 Logistic regression coefficient plot showing the estimates for the association between tree reproduction probability and resources, disturbance and year variables for A. robusta (blue open circles) and A. tortilis (red squares)...... 46

Figure 4 Logistic regression coefficient plot showing the estimates for the association between tree reproduction probability and microsite environmental variables for A. robusta (blue open circles) and A. tortilis (red squares)...... 47

Figure 5 The relationship between canopy area and reproduction probability in A. robusta (solid blue line) and A. tortilis (dashed red line) fitted from a logistic regression model. 48

Figure 6 Variation in final germination percent (mean ± SE) for scarified (orange bars) and unscarified (light blue bars) in A. robusta (=Acarob) and A. tortilis (= Acator) seeds sourced from seedbank (top panels) and tree canopy (bottom panels)...... 79

Figure 7 Cumulative germination for scarified and unscarified A. robusta (blue lines) and A. tortilis (red lines) for seeds sourced from A: Tree canopy and B: Seedbank. The horizontal dashed line shows a 50% germination threshold...... 80

Figure 8 Variation in final germination percent (mean ± SE) for scarified (yellow bars) and unscarified (blue bars) A. tortilis seeds sourced from the tree canopy, seedbank, large ungulate dung (elephant, giraffe, and impala). The horizontal dashed line is the scarification-level treatment mean germination percent...... 81

Figure 9 Cumulative germination for scarified (yellow line) and unscarified (blue line) A. tortilis seeds sourced from the tree canopy, seedbank, and large ungulate dung (elephant, giraffe, and impala). The horizontal dashed line shows a 25% germination threshold. .... 82

Figure 10 Seasonal variation in mean photosynthetic active radiation (PAR) ratio (mean ± SE) for A. robusta (filled bars) (n = 186) and A. tortilis (open bars) (n = 318) seedlings microsites in the Serengeti. Lower PAR ratio values indicate a greater degree of light interception by the grass canopy and greater herbaceous biomass ...... 112

viii

Figure 11 Effect of sunlight levels on (mean ± SE) (a) relative growth rate RGR (g g- 1day-1) and (b) height growth AHG (mm day-1), (c) Silouehette to total leaf area ratio SPAR, and (d) unit leaf rate ULR (g m-2 day-1) in A. robusta (solid lines) and A. tortilis (dash lines) seedlings grown in a greenhouse...... 113

Figure 12 Relationship between ULR (g m-2 day-1) and SPAR in (a) A. robusta (n = 72) and (b) A. tortilis (n = 72) seedlings after 12 weeks of growth in the greenhouse. (r = regression coefficient (r2), p = p. value at α = 0.05). Symbols. Black squares + blue ellipse: full sunlight; black triangles + green ellipse: medium sunlight; black circles + red ellipse: low sunlight. [Color figure can be viewed on the online version] ...... 114

Figure 13 Effect of sunlight levels on (mean ± SE) (a) leaf mass fraction, LMF, (b) stem – mass fraction, SMF, (c) root – mass fraction, RMF and (d) Leaf Mass area (LMA) in A. robusta (solid lines) (n = 27) and A. tortilis (dash lines) (n = 27) seedlings as a function of variation in sunlight levels ...... 115

Figure 14 Effect of grass competition on maximum (mean ± SE), a) seedling stem diameter (cm), b) height (cm), and, c) height to diameter ratio in A. robusta (blue solid lines) and A. tortilis (red dashed lines) seedlings...... 154

Figure 15 Seedling survival of A. robusta (blue solid lines) and A. tortilis (red dashed lines) under a) dry condition and b) moist condition...... 155

Figure 16 Seedling survival under competition with clipped (soil lines) and unclipped (dashed lines) grasses only (-GRASS plots excluded, see survival data analysis) for a) A. robusta and b) A. tortilis...... 156

Figure 17 The relationship between seedling survival, a) stem diameter, and b) seedling height for A. robusta (blue solid lines) and A. tortilis (red dashed lines) species. The plots use results from the mixed effect logistic regression model showing variation in the relationship between species...... 158

List of Abbreviations

ABA: Abscisic Acid

AHG: Absolute Height Growth

AIC: Akaike Information Criteria

ANCOVA: Analysis of Covariance

ANOVA: Analysis of Variance

CHIRPS: Climate Hazards Center InfraRed Precipitation with Station

COSTEC: Commission for Science and Technology

DBH: Diameter at Breast Height

DIGMAC: Digitaria macroblephara

FGP: Final Germination Percent

GA: Gibberellin

GIS: Geographic Information System

GLMM: Generalized Linear Mixed Models

LMA: Leaf Mass Area

LMF: Leaf Mass Fraction

MAP: Mean Annual Precipitation

MGT: Mean Germination Time

MODIS: Moderate Resolution Imaging Spectroradiometer

NDVI: Normalized Differences Vegetation Index

PANMAX: Panicum maximum

PAR: Photosynthetic Active Radiation

PPF: Photosynthetic Photon Flux

PVC: Polyvinyl Chloride

RGR: Relative Growth Rate

RMF: Root Mass Fraction

RSR: Root Shoot Ration

SMF: Stem Mass Fraction

SPAR: Silhouette Projected Area Ratio

SWRC: Serengeti Wildlife Research Center

TANAPA: Tanzania National Parks

TAWIRI: Tanzania Wildlife Research Institute

TDR: Time Domain Reflectometer

THETRI: Themeda triandra

TWI: Topographic Wetness Index

ULR: Unit Leaf Rate

VIF: Variance Inflation Factor

VWC: Volumetric Water Content

WFU: Wake Forest University

xii

Abstract

Tree recruitment involves successful seed germination and establishment as seedlings that grow to become adult individuals. For decades, savanna ecologists have worked diligently to identify mechanisms that explain tree recruitment bottlenecks. Recruitment limitations are driven mainly by bottom-up (rainfall and nutrients) and top-down disturbances (herbivory and fire). However, the focus has been on how bottom-up and top-down drivers shape the tree – grass ratio, tree seedling transition to the adult tree stage, and the savanna woody cover. There is limited knowledge on the role of resources and disturbances on other parts of tree life history, such as seed production and the traits that contribute to the growth and survival of tree seedlings. For example, once seeds are mature on the parent tree, they can reach potential germination sites via various dispersal pathways. However, it is unclear whether the seed germination potential varies with seed pathways such as primary dispersal, seedbank, and endozoochory. The transition from a germinating seed to an established seedling is a challenging life-history stage because of high seedling mortality caused by competition, resource availability, and disturbances.

However, it is unclear what traits contribute to their growth and survival under various levels of resources, disturbances, and competition.

I conducted observational and experimental research utilizing the Serengeti National Park in Tanzania as a study ecosystem and to understand how resource variability influences pre and post dispersal processes that play a significant role in dominant tree species recruitment, such as fecundity (chapter 1), germination potential (chapter 2), seedling growth (chapter 3) and survival (chapter 4).

xiii

The results suggest that the role of resources and disturbance on seed production

(fecundity), growth, and survival is not uniform among tree species. Seed germination depended on scarification status and varied by dispersal pathway.

General introduction

Identifying mechanisms that explain tree recruitment bottlenecks has been a central focus among savanna ecologists (Sarmiento 1984, Scholes and Archer 1997, Lehmann et al.

2009, Wakeling et al. 2011). Tree recruitment failure in a given ecosystem can result from processes that occur before and after seed germination (Janzen 1971, Clark et al.

2007). Bottom-up (rainfall and nutrients) and top-down disturbances (herbivory and fire) are among the driving mechanisms for recruitment limitation (Scholes and Archer 1997,

Sankaran et al. 2005, Bond 2008, Staver et al. 2011, Voysey et al. 2020). However, there is a disproportionate focus on factors affecting tree seedling transition to the adult trees than the limitations at the seed stage (Goheen et al. 2004), consequently failing to establish between mortality filters along the tree life-history stage (Schupp and Fuentes

1995). Studying the early stages of tree recruitment, understanding the potential mortality filters, and addressing their variation across environmental gradients is crucial in addressing their role in tree recruitment.

Seed fate after maturity is dynamic and complicated as it varies between species (Hughes and Westoby 1992, Vander Wall et al. 2005, Muller-Landau et al. 2008), and in some cases, it involves a trade-off between dormancy and dispersal (Topham et al. 2017, Chen et al. 2020). Once seeds are mature on the parent tree, they can fall under the tree then establish a soil seedbank (temporal dispersal) (Witkowski and Garner 2000), and others

xiv remain on the tree until removed mechanically by browsers, birds, wind, or heavy rains

(spatial dispersal) (Salazar et al. 2011). Seeds have low survivorship when they establish under conspecific plants due to pathogen attacks and predation (Janzen 1971, Miller and

Coe 1993, Howe and Miriti 2004). Consequently, high seedling mortality near the parent plant makes dispersal a crucial mechanism to ensure sustainable gene flow and colonization of new habitats (Wang and Smith 2002, Kremer et al. 2012). Soil seedbank is a crucial component in plant community composition stability as it buffers seeds against unpredictable short-to-long term changes in environmental conditions such as precipitation and temperature (DeMalach et al. 2020). Previous research establishes that vertebrate seed dispersal increases seed survival because gut passage kills pests such as bruchid beetles and helping break seed dormancy imposed by hard seed coats (Miller and

Coe 1993, Miller 1996, Cochrane 2003). Despite our understanding of the seed fate pathways, it is unclear whether the seed germination potential varies with seed pathways such as primary dispersal, seedbank, and endozoochory. Herbivore-dominated ecosystems provide an opportunity for concurrently studying the germination potential of seeds sourced from different seed pathways (Dudley 1999, Blake et al. 2009, Campos-

Arceiz and Blake 2011, Albert et al. 2015, Caughlin et al. 2015).

The transition from a germinating seed to an established seedling denotes another critical bottleneck in savanna plant communities because of high seedling mortality caused by competition, resource availability, and disturbances (Higgins et al. 2000, Wiegand et al.

2006). Studies from both forest and savanna ecosystems show that water scarcity (Wilson and Witkowski 1998, Nagakura et al. 2004, El Atta et al. 2016), pathogens (Liang et al.

2016), canopy shade (Smith and Shackleton 1988), herbivory (Shaw et al. 2002) and fire

(Chidumayo 2012, Holdo 2013) drive seedling mortality. Nevertheless, light and water are the critical resources without which plant growth is stalled. Among other ecological and physiological functions, water helps plants convey nutrients and organic compounds throughout the plant, and light is an essential source of energy that facilitates and controls various plant developmental processes (Fankhauser and Chory 1997). In savannas, empirical studies on physiological mechanisms related to resource allocation as a function of microclimatic changes due to effects of drought and shade stress in concert with the competition with various grass species are limited.

Therefore, the overarching goal of this dissertation is to understand how resource variability influences pre and post dispersal processes that play a significant role in tree recruitment, such as seed production (chapter 1), germination (chapter 2), seedling growth (chapter 3) and survival (chapter 4). More empirical data are needed to link demographic bottlenecks at the early stages of tree recruitment and woody cover dynamics.

xvi

References

Albert, A., A. G. Auffret, E. Cosyns, S. A. Cousins, B. D'hondt, C. Eichberg, A. E.

Eycott, T. Heinken, M. Hoffmann, and B. Jaroszewicz. 2015. Seed dispersal by

ungulates as an ecological filter: a trait‐based meta‐analysis. Oikos 124:1109-

1120.

Blake, S., S. L. Deem, E. Mossimbo, F. Maisels, and P. Walsh. 2009. Forest Elephants:

Tree Planters of the Congo. Biotropica 41:459-468.

Bond, W. J. 2008. What Limits Trees in C4 Grasslands and Savannas? Annual Review of

Ecology, Evolution, and Systematics 39:641-659.

Campos-Arceiz, A., and S. Blake. 2011. Megagardeners of the forest–the role of

elephants in seed dispersal. Acta Oecologica 37:542-553.

Caughlin, T. T., J. M. Ferguson, J. W. Lichstein, P. A. Zuidema, S. Bunyavejchewin, and

D. J. Levey. 2015. Loss of animal seed dispersal increases extinction risk in a

tropical tree species due to pervasive negative density dependence across life

stages. Page 20142095 in Proc. R. Soc. B. The Royal Society.

Chen, S.-C., P. Poschlod, A. Antonelli, U. Liu, and J. B. Dickie. 2020. Trade-off between

seed dispersal in space and time. Ecology Letters 23:1635-1642.

Chidumayo, E. N. 2012. Effects of seed burial and fire on seedling and sapling

recruitment, survival and growth of African savanna woody plant species. Plant

Ecology 214:103-114.

Clark, C. J., J. R. Poulsen, D. J. Levey, and C. W. Osenberg. 2007. Are plant populations

seed limited? A critique and meta-analysis of seed addition experiments. Am Nat

170:128-142.

xvii

Cochrane, E. P. 2003. The need to be eaten: Balanites wilsoniana with and without

elephant seed-dispersal. Journal of Tropical Ecology 19:579-589.

DeMalach, N., J. Kigel, and M. Sternberg. 2020. The soil seed bank can buffer long-term

compositional changes in annual plant communities. Journal of Ecology, 109(3),

pp.1275-1283.

Dudley, J. P. 1999. Seed dispersal of Acacia erioloba by African bush elephants in

Hwange National Park, Zimbabwe. African Journal of Ecology 37:375-385.

El Atta, H. A., I. M. Aref, A. I. Ahmed, and P. R. Khan. 2016. Morphological and

anatomical response of Acacia ehrenbergiana Hayne and Acacia tortilis (Forssk)

Haynes sub sp. Raddiana seedlings to induced water stress. African Journal of

Biotechnology 11:10188-10199.

Fankhauser, C., and J. Chory. 1997. Light control of plant development. Annu Rev Cell

Dev Biol 13:203-229.

Goheen, J. R., F. Keesing, B. F. Allan, D. L. Ogada, and R. S. Ostfeld. 2004. Net effects

of large mammals on Acacia seedling survival in an African savanna. Ecology

85:1555-1561.

Higgins, S. I., W. J. Bond, and W. S. W. Trollope. 2000. Fire, resprouting and variability:

a recipe for grass–tree coexistence in savanna. Journal of Ecology 88:213-229.

Holdo, R. M. 2013. Effects of fire history and N and P fertilization on seedling biomass,

Specific Leaf Area, and root:shoot ratios in a South African savannah. South

African Journal of Botany 86:5-8.

Howe, H. F., and M. N. Miriti. 2004. When seed dispersal matters. BioScience 54:651-

660.

xviii

Hughes, L., and M. Westoby. 1992. Fate of Seeds Adapted for Dispersal by Ants in

Australian Sclerophyll Vegetation. Ecology 73:1285-1299.

Janzen, D. H. 1971. Seed predation by animals. Annual Review of Ecology and

Systematics:465-492.

Kremer, A., O. Ronce, J. J. Robledo-Arnuncio, F. Guillaume, G. Bohrer, R. Nathan, J. R.

Bridle, R. Gomulkiewicz, E. K. Klein, K. Ritland, A. Kuparinen, S. Gerber, and

S. Schueler. 2012. Long-distance gene flow and adaptation of forest trees to rapid

climate change. Ecol Lett 15:378-392.

Lehmann, C. E., L. D. Prior, and D. M. Bowman. 2009. Fire controls population structure

in four dominant tree species in a tropical savanna. Oecologia 161:505-515.

Liang, M., X. Liu, G. S. Gilbert, Y. Zheng, S. Luo, F. Huang, and S. Yu. 2016. Adult

trees cause density-dependent mortality in conspecific seedlings by regulating the

frequency of pathogenic soil fungi. Ecol Lett 19:1448-1456.

Miller, M. F. 1996. Dispersal of Acacia seeds by ungulates and ostriches in an African

savanna. Journal of Tropical Ecology 12:345-356.

Miller, M. F., and M. Coe. 1993. Is it advantageous for Acacia seeds to be eaten by

ungulates? Oikos 66:364-368.

Muller-Landau, H. C., S. J. Wright, O. Calderon, R. Condit, and S. P. Hubbell. 2008.

Interspecific variation in primary seed dispersal in a tropical forest. Journal of

Ecology 96:653-667.

Nagakura, J., H. Shigenaga, A. Akama, and M. Takahashi. 2004. Growth and

transpiration of Japanese cedar (Cryptomeria japonica) and Hinoki cypress

xix

(Chamaecyparis obtusa) seedlings in response to soil water content. Tree Physiol

24:1203-1208.

Salazar, A., G. Goldstein, A. C. Franco, and F. Miralles-Wilhelm. 2011. Timing of seed

dispersal and dormancy, rather than persistent soil seed-banks, control seedling

recruitment of woody plants in Neotropical savannas. Seed Science Research

21:103-116.

Sankaran, M., N. P. Hanan, R. J. Scholes, J. Ratnam, D. J. Augustine, B. S. Cade, J.

Gignoux, S. I. Higgins, X. Le Roux, F. Ludwig, J. Ardo, F. Banyikwa, A. Bronn,

G. Bucini, K. K. Caylor, M. B. Coughenour, A. Diouf, W. Ekaya, C. J. Feral, E.

C. February, P. G. Frost, P. Hiernaux, H. Hrabar, K. L. Metzger, H. H. Prins, S.

Ringrose, W. Sea, J. Tews, J. Worden, and N. Zambatis. 2005. Determinants of

woody cover in African savannas. Nature 438:846-849.

Sarmiento, G. 1984. The ecology of neotropical savannas. Harvard University Press.

Scholes, R. J., and S. R. Archer. 1997. Tree-grass interactions in savannas. Annual

Review of Ecology and Systematics 28:517-544.

Schupp, E. W., and M. Fuentes. 1995. Spatial Patterns of Seed Dispersal and the

Unification of Plant-Population Ecology. Ecoscience 2:267-275.

Shaw, M. T., F. Keesing, and R. S. Ostfeld. 2002. Herbivory on Acacia seedlings in an

East African savanna. Oikos 98:385-392.

Smith, T. M., and S. E. Shackleton. 1988. The Effects of Shading on the Establishment

and Growth of Acacia-Tortilis Seedlings. South African Journal of Botany

54:375-379.

Staver, A. C., S. Archibald, and S. A. Levin. 2011. The Global Extent and Determinants

of Savanna and Forest as Alternative Biome States. Science 334:230-232.

Topham, A. T., R. E. Taylor, D. Yan, E. Nambara, I. G. Johnston, and G. W. Bassel.

2017. Temperature variability is integrated by a spatially embedded decision-

making center to break dormancy in Arabidopsis seeds. Proceedings

of the National Academy of Sciences 114:201704745.

Vander Wall, S. B., P.-M. Forget, J. E. Lambert, P. E. Hulme, P. Forget, J. Labert, P.

Hulme, and S. Vander Wall. 2005. Seed fate pathways: filling the gap between

parent and offspring. Pages 1-8 in P.-M. Forget, J. E. Lambert, P. E. Hulme, and

S. B. Vander Wall, editors. Seed fate: Predation, dispersal and seedling

establishment. CABI Publishing, UK.

Voysey, M. D., S. Archibald, W. J. Bond, J. E. Donaldson, A. Carla Staver, and M.

Greve. 2020. The role of browsers in maintaining the openness of savanna grazing

lawns. Journal of Ecology, 109:913-926.

Wakeling, J. L., A. C. Staver, and W. J. Bond. 2011. Simply the best: the transition of

savanna saplings to trees. Oikos 120:1448-1451.

Wang, B. C., and T. B. Smith. 2002. Closing the seed dispersal loop. Trends in Ecology

& Evolution 17:379-385.

Wiegand, K., D. Saltz, and D. Ward. 2006. A patch-dynamics approach to savanna

dynamics and woody plant encroachment–Insights from an arid savanna.

Perspectives in Plant Ecology, Evolution and Systematics 7:229-242.

xxi

Wilson, T. B., and E. T. F. Witkowski. 1998. Water requirements for germination and

early seedling establishment in four African savanna woody plant species. Journal

of Arid Environments 38:541-550.

Witkowski, E. T. F., and R. D. Garner. 2000. Spatial distribution of soil seed banks of

three African savanna woody species at two contrasting sites. Plant Ecology

149:91-106.

xxii

CHAPTER 1: Seed production in the Serengeti: The role of resources and disturbances across environmental gradients.

Prepared for submission in ecological engineering journal, with co-authors Ricardo M.

Holdo and T. Michael Anderson.

Abstract

Savanna ecosystems are spatially heterogeneous, consisting of patchy fire-tolerant trees, with a continuous layer of C4 grasses. Tree mortality filters in savannas affect different life-history stages, disproportionately disrupting transition from seed to seedling and ultimately influencing tree community assembly. Seed production is a critical phase in tree life history, and its variability comprises a significant consequence for seedbank dynamics, recruitment, and population viability. The role of environmental heterogeneity on seed production patterns is not well understood in savannas because the focus has been on seedling survival processes at the post-germination stages and survival differences between species or functional groups.

We quantified annual seed production patterns for the dominant Acacia (= Vachellia) tree species in the Serengeti National Park, Tanzania, Acacia robusta, and A. tortilis. In the

Serengeti, A. robusta trees dominate the mesic areas in the north and A. tortilis, the dry southern area. We assessed how seed production output (fecundity) and reproduction probability varies with resources, disturbances, and tree size. We used linear mixed- effects modeling and logistic regressions for our statistical analysis.

Seed production varied between species and years, with A. tortilis showing consistency among year trends but significant fluctuations in A. robusta. Tree reproduction

23 probability was negatively associated with browsers' disturbances in both species but positively with grazers for A. robusta. Moisture resources were essential for reproduction probability in A. tortilis but not in A. robusta. Our study suggests that seed production in the Serengeti tree community is spatially and temporally episodic and that seed production should play a major part in modeling recruitment bottlenecks in savanna ecosystems.

Introduction

Savanna ecosystems contrast with forest ecosystems in that they are spatially heterogeneous in tree cover, consisting of relatively regularly spaced fire-tolerant trees, with a continuous layer of C4 grasses (Scholes and Archer 1997, Ratnam et al. 2011).

Woody cover in savanna ecosystems is structured mainly by rainfall (Sankaran et al.

2005, Staver et al. 2011). Despite their extensive global distribution, contribution as a carbon sink (Chen et al. 2003, Grace et al. 2006), and significant direct benefits to the human population (Scholes and Archer 1997, Twine 2019), tree recruitment mechanisms, and adaptation to changing environment are still not well understood.

Recruitment bottlenecks in plant community ecology usually refer to a life-history stage that experiences significant ecological mortality filters or external factors (mortality filters; hereafter) that constrain developmental transition from one stage (e.g., seed) to another (e.g., seedling). These mortality filters include limited seed production, seed viability, seed predation, seed dispersal, and seedling survival (Harcombe 1987, Schupp and Fuentes 1995, Nathan and Muller-Landau 2000, Ashman et al. 2004, Clark et al.

2007, Salazar et al. 2011). Consequently, mortality filters at different life-history stages

24 disproportionately disrupt tree transition from seed to seedling establishment, compounding their effects on the overall recruitment process and ultimately influencing tree community assembly (Chambers and MacMahon 1994, Clark et al. 1998, Clark et al.

2007, Salazar et al. 2011).

Studies in forest ecosystems establish that each mortality filter's strength and importance depend on the species' life history and environmental conditions, e.g., temperature, soils, fire frequency, and herbivory (Wright 1992, Svenning and Wright 2005, Clark et al.

2007, Myers and Harms 2011). Despite extensive studies in savanna ecosystems on the recruitment limitation at the early life history stage, the focus has been on either seedling survival processes at the post-dispersal and germination stages (Goheen et al. 2004,

February et al. 2013) or survival differences between species or functional groups

(Brown et al. 2003, Walters and Milton 2003).

The maximum woody cover in savanna ecosystems is constrained by and increases linearly with mean annual precipitation (MAP) (Sankaran et al. 2005, Sankaran et al.

2008), which also drives species composition turnover (Egan and Williams 1996,

Williams et al. 1996, Rugemalila et al. 2016). The "demographic bottleneck" hypothesis suggests that savanna tree recruitment is rare and occasional due to high mortality rates at the early tree stage (Setterfield 2002, Sankaran et al. 2004, Bond 2008). However, most studies tend to focus on mortality filters at the seedling stage and less on pre-dispersal life history stages such as seed production.

A few studies in savanna ecosystems show temporal variation in seed production, with some years recording greater seed production than others (Coe and Coe 1987, Sabiiti and

Wein 1987, Walters and Milton 2003). However, this variation in seed production has

25 been related to tree size classes such that trees in small size classes produce fewer seeds than those with large sizes (Brown et al. 2003, Ward and Esler 2011). What remains unclear is whether environmental heterogeneity plays a role in influencing variability in seed production, especially in ecosystems with persistent environmental gradients and disturbances from fires and herbivory. The unclear role of the environmental gradient in influencing seed production patterns is an essential ecological knowledge gap given the necessity to model the fate of the produced seeds in terrestrial ecosystems as a function of abiotic and biotic factors (Chambers and MacMahon 1994, Balcomb and Chapman 2003,

Vander Wall et al. 2005, Goheen et al. 2010).

Seed production is a critical phase in tree life-history, and its variability comprises a significant consequence for seedbank dynamics, recruitment, and population viability. It is a vital stage in tree recruitment as it plays a crucial role in community structure, function, and population dynamics (Harper and Harper 1977, Greene and Johnson 1994).

However, it is among the less studied life-history stages, especially in savanna ecosystems (Eriksson and Ehrlen 1992, Svenning and Wright 2005), and in some cases, it is left out of the determinants of tree recruitment bottlenecks (Midgley and Bond 2001).

Studies show correlations between seed production and tree size in forest ecosystems in which larger trees tend to produce more seeds (Greene and Johnson 1994, Ashman et al.

2004). Similarly, other works show that trees in continuous (unfragmented) forests tend to produce more seeds than those in fragmented ones (Tomimatsu and Ohara 2002,

Burgos et al. 2008) and that increased seed production increases seedbanks in disturbed environments (Moody-Weis and Alexander 2007). Despite rainfall being an essential component in tree reproduction, empirical evidence from forest ecosystems provides

26 mixed results regarding seed production as a rainfall-dependent process. For example, some studies find a high correlation between seed production and rainfall patterns

(O'connor and Pickett 1992, Seghieri et al. 1995, Joubert et al. 2013), while others find no relationship (Greene and Johnson 1994, Salazar et al. 2011). With the patchy nature of savanna systems and rainfall as the main constraint of woody cover (Sankaran et al. 2005,

Sankaran et al. 2008), it is unclear if seed production follows similar trends.

A few studies in savannas have found higher variability between individual tree seed production linked to tree size differences (Brown et al. 2003, Walters and Milton 2003) and disturbance from herbivory, mainly by large browsers (Goheen et al. 2007, Goheen et al. 2010). However, it is unclear whether resources and disturbances individually or interactively influence seed production dynamics.

The overall goal of this study is to quantify annual seed production patterns for the dominant Acacia tree species in the Serengeti National Park (the Serengeti, hereafter) across a rainfall gradient. Our specific goal was to assess how fecundity varies with resources (rainfall), disturbances (fires and herbivory), and tree size. We also included topographical and environmental variables that explain site-level vegetation health

(NDVI), soil moisture availability and microsite conditions. Our second goal was to understand whether the factors influencing fecundity are predictive of individual tree reproduction probability (probability) in the Serengeti ecosystem. Our third goal was to test the alternative hypothesis that larger trees have a reproductive benefit over smaller ones by assessing a relationship between reproduction probability and tree size.

Materials and Methods

Study system and species

The study focused on two savanna tree species, Acacia (= Vachellia) robusta (Burch) and

A. tortilis (Forssk), in the Serengeti - Tanzania (Fig 1). The Serengeti lies between 1–2°S,

34–26°E, and has a total area of >14,000 km2. The two species dominate the ecosystem with extensive distribution across the Serengeti's rainfall gradient. Acacia tortilis is locally abundant in the Serengeti, dominates the dry sites, and produces indehiscent pods

(pods remain closed at maturity). The species is also common throughout Africa and the

Middle East (Noumi et al. 2010). Conversely, A. robusta dominates the wet sites, produces dehiscent pods (pods open to expose seeds while on the tree). The dry area of the Serengeti has mean annual precipitation (MAP) ranging between ~ 250 mm. yr-1 to ~

600 mm. yr-1 in the SE, while the wet sites' MAP ranges between 900 mm. yr-1 ~1100 mm. yr-1 in the NW near the SE part of Lake Victoria (Norton-Griffiths et al. 1975,

Sinclair 1979). Other common tree species found in Serengeti includes A. drepanolobium

(Harms ex Sjöstedt), A. Senegal (Senegalia senegal - Britton), Commiphora trothae

(Engl), and Balanites aegyptiaca (Delile) (Anderson et al. 2015). The perennial grasses such as Themeda triandra (Folsk), Digitaria macroblephara (Hack), Panicum maximum

(Jacq), Sporobolus fimbriatus (Trin.), and Pennisetum mezianum (Leeke) dominate

Serengeti’s understorey grass community (Sinclair 1979, Williams et al. 1998, Williams et al. 2016).

Data collection

For clarity, variables intended for further statistical analysis are both italicized and written in capital letters.

Transects

In 2016, we established eight (1km × 20 m = 2 ha) transects at selected sites (variable

SITE) across the Serengeti woodland (Holdo et al. 2020). In 2017, we added two more transects (Musabi and Kirawira, Fig 1) in a more mesic part of the Serengeti’s western corridor and then conducted a tree survey to map all trees in the transect. We identified all trees (> 2m) to a species level within 10 m of the transect centerline, mapped using

GPS, and measured their heights (variable HEIGHT), diameter at breast height (DBH, ~

1.3 m), basal diameter (BASAL), and mean canopy diameter (averaged over two measurements at right angles to each other). We assumed a circular shape for tree canopy

(with a caveat that canopy shapes vary) and calculated canopy area (CANOPY AREA).

We randomly selected and tagged ten focal trees (FTs) per transect per species using racetrack aluminum tags (Forestry Supplier Inc. USA). Two transects lacked one species

(i.e., Soit and Simiyu, Fig 1), and therefore, we tagged ten FTs. Six branches (~ 3 – 4 cm in diameter) located approximately in the mid-canopy on each tagged FT were selected and marked for subsequent seed production monitoring.

Seed production data

In Serengeti, mature pod availability tends to peak after the rainy season (Mduma et al.

2007), and therefore, we monitored seed production events for the tagged FTs once in two months from July 2017 to October 2019. We counted all mature pods on marked

29 branches (explained above) at each survey and collected a handful of dry pods for further seed investigations. We gently crushed all collected pods at the research center and recorded the number of seeds per pod (pooled to a tree level).

Rainfall data

We used satellite-derived precipitation estimates from the Climate Hazard Group

InfraRed Precipitation (CHIRP) for years corresponding to the data collection period. The

CHIRP data are global, with data collection beginning in 1981 (Funk et al. 2015). It contains daily rainfall data derived from a combination of satellite-derived precipitation estimates and weather stations at a 0.05×0.05 degree spatial resolution (Funk et al. 2015).

We performed CHIRP data acquisition and analysis using the R package chirps, which enabled us to collect daily rainfall data based on our transect coordinates (de Sousa et al.

2020). Our initial CHIRP data analysis suggested that rainfall peak was between

November – April, corresponding to historical patterns (Norton-Griffiths et al. 1975). We used the CHIRP data to obtain a six-month rainfall average between November – April before the dry season (variable CHIRP RAINFALL).

Fire, NDVI, and TWI data

We computed variation in fire frequencies per SITE, vegetation greenness (NDVI – an index of site-level productivity and greenness), and topographic wetness index (TWI – an index of topographic influences on hydrology) from data captured by NASA’s 250‐m resolution MODIS MOD09GQ product (Anderson et al. 2018) for all our FTs locations for the 2017 – 2019 period. We calculated mean fire frequencies, NDVI, and TWI across

30 the transect by assigning to a given tree location the fire frequency, NDVI, and TWI corresponding to its 250 – m MODIS pixel (Anderson et al. 2018).

Herbivore data

We estimated herbivore occupancy from motion-activated camera traps per transect

(HCO Scoutguard - Boly Inc., Santa Clara, CA) (Holdo et al. 2020). Two cameras (one in woodland and another in open habitat) were attached either to trees or metal poles at ~1.5 m above ground level, oriented parallel to the transect. Detailed camera trap information, settings, deployments, and image processing are elsewhere (Holdo et al. 2020). For our analysis, we processed and utilized image data collected in the woodland habitat. We then converted animal counts into binary data (presence/absence), assuming an equal animal capture probability (Palmer et al. 2017, Holdo et al. 2020). We aggregated our capture incidences by totaling the number of captures per camera per species and then grouped the animals into BROWSERS and GRAZERS categories before statistical analysis. We applied herbivore body mass estimates from Cumming and Cumming

(2003) and Wilmshurst et al. (2000) to standardize their effect based on species-specific metabolic rate (Holdo et al. 2020). We included impala (Aepyceros melampus), giraffe

(Giraffa camelopardalis), and elephants (Loxodonta africana) in the BROWSERS category and the remaining herbivores, such as warthog (Phacochoerus africanus),

Thomson's gazelle (Eudorcas thomsonii), grant's gazelle (Gazella granti), topi

(Damaliscus lunatus), hartebeest (Alcelaphus lichtensteini), wildebeest (Connochaetes taurinus), zebra (Equus burchelli), and African buffalo (Syncerus caffer) were in the

GRAZERS category.

Microsite environment

To characterize how variation in microsite environment potentially influences seed production patterns, we installed custom-built dataloggers connected to three sensors that collected soil moisture, light, and temperature data. Data loggers, sensors, and deployment details are in (Holdo et al. 2020). In summary, we deployed ten datalogger units per transect inserted in a custom-built waterproofed PVC pipe buried in the ground.

The dataloggers collected data on a 5-min scan interval and a 1-h logging interval. Soil moisture sensors (GS-1-Meter Group, Hopkins, CT) were buried horizontally at 10 cm depth, about 1 m from the datalogger. The light sensors (Quantum Flux, Apogee

Instruments Inc., Logan, UT) collected photosynthetically active radiation - PAR (μmol m-2 s-2) and were vertically attached to a tube clamped on a rebar piece so that the light sensor was 5 cm above ground level (Fig S1). We used a K-type thermocouple (Adafruit

Industries) to collect temperature data. To match FTs and neighboring sensors, we created a 30 m buffer around the sensor and used the function “spatial join” in ArcMap v10.8 to query and merge FTs to the sensors. The dataset from the sensors was summarized to obtain daily temperature (maximum – TMAX , minimum – TMIN, and mean

–TMEAN), PAR (maximum –PARMAX and mean-PARMEAN), and the proportion of time when the sites experienced adequate soil moisture (measured as soil water potential - ψ), i.e., ψ > -1.5 MPa (PROPORTION; see below).

Soil moisture sensors collected volumetric water content (VWC). However, a separate analysis simultaneously collected and evaluated the relationship between VWC and soil water potential (ψ) suggested site-specific retention curves (Holdo et al. 2020), meaning that ψ varies by soil types for a given value of VWC (Fig S2). We converted the site-

32 specific daily VWC for individual soil moisture sensors to ψ using the power-law function (Clapp and Hornberger 1978, Holdo et al. 2020) and then counted sensor- specific time series portions with temporally overlapping data. We estimated the proportion of time (days) across the soil moisture time series during which moisture stress conditions were unlikely to have occurred (ψ > −1.5 MPa) (Holdo et al. 2020). The values close to 0 indicate relatively higher soil moisture stress, and those close to 1 indicate the unlikely occurrence of soil moisture stress for the selected time window.

Data analysis

We performed all statistical analyses in R 4.0.3. To quantify seed production, we used seed count per pod (SEEDS) versus pod counts per branch (PODS) to fit a power relationship applying nonlinear least square regression implemented in R's nls function

(Johnson and Goody 2011). We then parameterized the power relationship as Y = a* X b, where X is the observed parameter (e.g., pods), Y is the predicted parameter (i.e., seeds), and a and b are parameters that define the shape of the relationship between X and Y. We computed the average number of seeds counted per branch per tree across our dataset.

We subset our dataset by species and performed a Poisson regression test for overdispersion using the AER package (Kleiber et al. 2020), the results of which rejected the null hypothesis, suggesting overdispersion in seed count data.

To understand how our predictor variables influence seed production events

(REPRODUCTION PROBABILITY), we converted all seed production counts into binary data with values > 0 assigned 1, otherwise 0.

We categorized our variables as 1) tree size (DBH, CANOPY, BASAL, and HEIGHT), 2) resources and disturbance (CHIRP, TWI, FIRE, BROWSERS, and GRAZERS), and 3) microsite condition (PARMEAN, PARMAX, TMEAN, TMIN, TMAX, and PROPORTION). We removed two sites (Kirawira and Musabi) from statistical analysis due to a lack of herbivores and microsite data. Preceding statistical analysis, we used the package

PerformanceAnalytics (Peterson et al. 2018) in R to perform Pearson's correlations, the results of which suggested strong correlations among tree size variables (Fig. S3). We then applied a machine-learning decision tree using the package rpart (Therneau et al.

2019) to determine the optimal variable that accounts for most variation in seed production. We performed collinearity diagnostics on microsite condition predictor variables by calculating the variance inflation factor (VIF) using package olsrr (Hebbali and Hebbali 2020), the results of which suggested omission of PARMEAN, TMEAN, and TMIN in future analyses due to multicollinearity (Daoud 2017).

We developed a set of 14 a priori candidate models (including an intercept-only model) relating seed production counts to different combinations of resources and disturbance covariates (Table 1). We used generalized linear mixed-effects models implemented in the glmmTMB function with a negative binomial distribution (Magnusson et al. 2017). In our models, we included an intercept only model, main effects, a resource' model

(CHIRP, TWI, and NDVI), a disturbance model (FIRE, GRAZERS, and BROWSERS), and a saturated model (i.e., with all the covariates) (Burnham and Anderson 2002). We also included the year of seed collection (variable YEAR) as a fixed effect because we were interested in detecting whether YEAR possesses a more substantial explanatory power in seed production trends than other biotic and abiotic factors. To facilitate model

34 convergence, we used the function rescale in R to normalize GRAZERS and BROWSERS predictor variables to have a mean and standard deviation of 0 and 1, respectively. We included the canopy area in all models (except the intercept) as a covariate to control for the effect of tree size. We treated resources and disturbances variables as fixed effects and SITE as a random effect. We used a model selection approach applying Akaike

Information Criterion (AIC) to compare model fits, in which the most parsimonious model had the lowest AIC values (Burnham and Anderson 2002).

We used multiple logistic regression analysis with a binomial distribution and logit link function in R’s glm function to model how resources (CHIRP RAINFALL, TWI, NDVI), disturbance (FIRES, BROWSERS, GRAZERS), time (YEAR), and microsite conditions

(PARMAX, TMAX, PROPORTION) associated with REPRODUCTION PROBABILITY for the two tree species. To avoid model overfitting (Babyak 2004), we created two separate multiple-logistic regression models: resource - disturbance continuum and microsite condition variables. To independently test between-species variation in tree reproduction as a function of tree size, we created a simple mixed-effects logistic regression model with SPECIES x CANOPY AREA interaction as a fixed effect and SITE as a random effect.

Results

Seed production in the Serengeti varied strongly between species and years, with A. tortilis showing an overall consistency among year trends but more significant fluctuations in A. robusta (Wald ꭓ2 = 10.4, p = 0.005, Fig. 2). The top model explaining the variation in seed production included YEAR in A. robusta and BROWSERS in A.

35 tortilis (Table 1). Further model examination suggested seed production for A. robusta was the lowest in 2018 and bounced back in 2019 (Fig 2, Table 2). Moreover, there was a decreasing trend in fecundity in A. tortilis associated with browsers (Table 2). Including canopy area as a covariate in our model (Table 2) showed that increased seed fecundity (

Fig. 2 and Fig S4) in both species was also associated with large tree canopy sizes (A. robusta: ꭓ2 = 11, p < 0.001; A. tortilis: ꭓ2 =21.9, p < 0.001, Table 2).

Reproduction probability also varied among species and years, and for A. robusta, the effect of browsers was negative (p < 0.01, Fig 3, Table S1) while that of grazers was positive (p < 0.05, Fig 3, Table S1). Reproduction probability for A. robusta was significantly greater in 2019 (p < 0.05, Fig 3, Table S1). For A. tortilis, while the tree reproduction was negatively associated with browsers as was in A. robusta (p < 0.05, Fig

3, Table S1), it was insignificantly associated with grazers (p > 0.05, Fig 3, Table S1).

Rainfall was positively associated with reproduction probability for A. tortilis (p < 0.01,

Fig 3, Table S1), but not A. robusta. CHIRP, TWI, NDVI, and FIRE did not influence reproduction probability in both tree species.

For microsite conditions, we found a positive association between seed production probability and light conditions for A. robusta trees (p < 0.05, Fig 4 Table S2), but a negative association with the PROPORTION (p < 0.05, Fig 4, Table S2). Understorey temperatures had no association (p > 0.05, Fig 4, Table S2). Tree reproduction probability in A. tortilis trees was independent of microsite conditions (p > 0.05, Fig 4,

Table S2).

Tree size influenced seed production probability in both trees (Wald ꭓ2 = 8.29, p < 0.005,

Fig. 5), suggesting a size-dependent benefit in tree reproduction.

Discussion

Seed production in the Serengeti varied vastly between species and years, with A. tortilis showing an overall consistency among year trends and more significant fluctuations in A. robusta (Fig. 2). Our study shows that seed production in the Serengeti tree community is spatially and temporally episodic (Fig. 2 and Fig S4). The role of resources and disturbances in influencing fecundity and reproduction probability varied between tree species.

Browsers were the important explanatory variable regulating reproduction probability in our study. In particular, they negatively influenced species' reproduction probability, but the impact was statistically significant in A. robusta but not in A. tortilis tree species (Fig

3, Table S1). The influence of grazers was notably positive on A. robusta’s reproductive probability but insubstantial on A. tortilis (Fig. 3, Table S1) because A. robusta dominates sites with high grass productivity and grazing intensity (McNaughton 1983,

Holdo et al. 2007). Grazing induces positive feedback on savanna tree growth by reducing below-ground competition (Riginos 2009), which eventually may facilitate reproduction potential (Goheen et al. 2010, Ward and Esler 2011). The positive association between grazers and reproduction probability (Fig 3) may explain why light conditions resulted in a similar association for A. robusta (Fig 4). The impact of browsers on tree mortality is well documented, especially in savanna ecosystems (Augustine and

McNaughton 2004, Noumi et al. 2010, Kohi et al. 2011, Morrison et al. 2015, Voysey et al. 2020). However, the magnitude to which different browser species (e.g., elephants, giraffe, and impala) affect the tree structure varies spatially, temporally, and is species-

37 specific. For example, a study in the same ecosystem reported that elephants tend to select A. tortilis more than A. robusta (Morrison et al. 2015). Suppose a selection by browsers (notably elephants) was towards A. tortilis; in that case, we should expect a negative association with reproduction probability for A. tortilis since elephants’ impact on the tree canopy is the most severe (Barnes 1983, Pringle 2008, Moe et al. 2009) compared to that of giraffes or impala (Strauss and Packer 2015). Our study found that the negative association with browsers is more robust for A. robusta than A. tortilis trees suggesting that the effect is notably related to the seasonal shift in diet preference observed mainly in elephants (Koch et al. 1995, Kohi et al. 2011). Past studies in

Serengeti (Pellew 1983, Strauss and Packer 2015) reported that elephants browse and damage A. robusta more frequently than A. tortilis, but giraffes tend to avoid A. robusta due to its low nutritive value compared to other trees in the vicinity. Large herbivore exclusion studies in comparable ecosystems have reported high herbivory costs to reproduction because when protected from extensive herbivory, tree reproduction doubles in some cases (Goheen et al. 2007, Young and Augustine 2007, Goheen et al. 2010).

Although moisture availability (with rainfall – Table 1) was not a strong predictor of variation in fecundity in both species, it was positively associated with reproduction probability in A. tortilis (Fig. 3), a species that dominates the ecosystem's dry sites

(Anderson et al. 2015, Rugemalila et al. 2016). The positive effect of rainfall and seed production was also observed in A. mellifera in Namibia (Joubert et al. 2013). However, at a microsite level (with the proportion of time under adequate soil moisture conditions -

Fig 4), the association was negative in Acacia robusta but neutral in A. tortilis. The negative association between soil moisture (higher proportion of time under adequate soil

38 moisture conditions) suggests that A. robusta species tend to devote resources towards growth and possibly away from reproduction (Monks and Kelly 2006). Studies in forest and miombo ecosystems have reported resource switching scenarios in which trees allocate resources towards growth and away from reproduction in high rainfall years

(Chidumayo 1997, Kelly and Sork 2002, Monks and Kelly 2006). Our study did not quantify tree growth parameters during the study period, but our CHIRP rainfall trends

(unpublished data) suggest that the scenario cannot be ruled out completely.

On the contrary, tree reproduction in Acacia tortilis was positively associated with rainfall despite the variability between years (Fig 3, Table S1), corroborating previous studies from sites with comparable rainfall (Joubert et al. 2013). Chidumayo (1997) reported little to no correlation between reproduction and rainfall for the leguminous species Isoberlinia angolensis and Julbernardi globiflora, probably due to species- specific ability to source water from deeper and reliable soil depths (Chidumayo 1997,

Barnes 2001).

We did not find a significant effect of fires on either fecundity or reproduction probability in this study. Fires are a crucial component of savanna tree recruitment as they influence animal movement (O'Kane and Macdonald 2018), assist with breaking seed dormancy

(Auld and O'Connell 1991, Hu et al. 2018), and consume grasses to ease competition, therefore facilitating tree recruitment (Van Langevelde et al. 2003, Bond and Keane

2017). Studies in Australian savannas have reported mass reproduction of Alpine Ash

(Eucalyptus delegetensis) following fires in south-eastern Australia (O'Dowd and Gill

1984). However, other studies have shown that the effect of fire on seed production depends on timing and intensity, such that early fires may have a positive association

39 with seed production, and late or frequent fires may be detrimental (Setterfield 1997,

Gawryszewski et al. 2020). Neither NDVI nor TWI had a strong association with seed production patterns in our study (Fig. 3). NDVI is a good indicator of productivity at regional scales (Wang et al. 2004, Chamaillé‐Jammes and Fritz 2009) and TWI is an essential determinant of soil moisture accumulation and distribution (Anderson et al.

2010, Raduła et al. 2018). Fitting a linear regression comparing transformed NDVI and

TWI values showed no relationship between the two variables (F406 = 3.03, p > 0.05).

Studies in desert ecosystems have reported a strong correlation between NDVI and TWI

(Lamchin et al. 2017). In savanna ecosystems, the NDVI strongly relates to rainfall, but mostly at dry and not mesic sites (Chamaillé‐Jammes and Fritz 2009). We associate the lack of influence of NDVI and TWI on seed production patterns to the strength of other disturbances such as herbivores in the ecosystem rather than, for example, utterly lacking association with reproduction.

Reproduction probability in both species was positively associated with tree size

(represented by the canopy area - Fig 5), suggesting a size-dependent reproductive benefit. Similar size-dependent benefits were found on Acacia mellifera in South Africa

(Ward and Esler 2011), with size-independent evidence in East Africa (Young and

Augustine 2007). However, Ward and Esler (2011) suggested that soil types may be the factor driving within-species variation in seed production, the hypothesis not tested in this study.

In conclusion, our study suggests spatial and temporal variation in fecundity and reproduction probabilities between dominant tree species in Serengeti, and therefore, the seed production stage should be a significant part when modeling savanna recruitment

40 hurdles (Midgley and Bond 2001). Variation among species could be related mainly to differences in water availability between sites, annual rainfall, browsers, and grazing intensity. The lack of fire association to reproduction patterns is not surprising, given that this ecosystem experiences extensive controlled and accidental fires and that the Acacia species exhibit tolerance to this form of disturbance (Holdo et al. 2009, Rugemalila et al.

2016, Probert et al. 2019). However, some adaptations could buffer extreme seed production hurdles such as long-distance dispersal, seed dormancy, high germination rates, and accumulation of persistent soil seedbank.

Tables and Figures

Tree species Acacia robusta Acacia tortilis Model** ΔAIC* df ΔAIC* df Intercept 35.2 3 17 3 YEAR 0 6 7.7 6 CHIRP RAINFALL 19.2 5 6.5 5 FIRE 32.6 5 6.3 5 NDVI 26.8 5 5.7 5 TWI 29.5 5 1.7 5 GRAZERS 32.6 5 6.5 5 BROWSERS 17 5 0 5 TWI + CHIRP RAINFALL + NDVI 22 7 4.7 7 FIRE + BROWSERS 32.8 6 8.2 6 GRAZERS + BROWSERS 33.9 6 8.4 6 CHIRP RAINFALL + FIRE 22.2 7 8.3 6 YEAR × CHIRP 5.1 9 6.9 7 CHIRP RAINFALL + BROWSERS 20.5 6 8.3 6 CHIRP RAINFALL + TWI + NDVI + FIRE + BROWSERS 28.3 8 22.8 8 ** See text for variable description; in all cases SITE was treated as random effect and tree canopy area a model covariate * Differences in AIC from the best (bold) model

AREA). (SE = Standard Errors of the means, and p = p-value at 95% confidence interval)

Species Variable Estimates SE Z-value p Acacia robusta (Intercept) -1.62 1.10 -1.48 0.14 YEAR (2018) -4.44 0.97 -4.60 <0.0001 YEAR (2019) 1.72 0.73 2.35 0.02 CANOPY AREA 0.09 0.02 3.57 <0.001

Acacia tortilis (Intercept) 0.26 1.88 0.14 0.89 BROWSERS -2.74 3.76 -0.73 0.47 CANOPY AREA 0.03 0.01 4.68 <0.0001

Figure 1 Map showing the location of the Serengeti National Park in Tanzania. The filled circles show study site locations within the park

Figure 2 Annual variation in seed production (mean ±SE) for A. robusta (=Acarob - blue bars) (n = 182) and A. tortilis (=Acator - red bars) (n = 226) for the years 2017, 2018, and

2019.

TMAX

Acacia robusta PAR MAX Acacia tortilis

PROPORTION

Estimate

Figure 5 The relationship between canopy area and reproduction probability in A. robusta

(solid blue line) and A. tortilis (dashed red line) fitted from a logistic regression model.

Supplemental figures and methods:

Soil moisture sensor cable

PAR sensor Sealed PVC pipe

Support rebar

Figure S 1 A sample image illustrating how sensors were set in the transects in Serengeti.

(Photo by Ricardo Holdo).

Figure S 2 Soil water potential variation as a function of soil volumetric water content and the fitted water retention curves for the woody habitat soils within each of eight 1‐km transects in Serengeti. The fitted lines are the best fits for the Clapp and Hornberger

(1978) power-law relationship between water potential and volumetric water content.

Figure S 3 Pearson's correlation matrix between tree size variables for a) Acacia robusta and b) Acacia tortilis. The top is the absolute value of the correlation, and the bottom panel is the bivariate scatterplots with fitted lines. Red stars show the strength of the correlation.

Figure S 4 Site-level variation in seed production (mean ±SE) for A. robusta (blue bars) and A. tortilis (red bars) in Serengeti.

Table S 1 Multiple logistic regression model coefficients relating tree reproduction probability to the year, resources, and disturbances variables in the Serengeti. (SE =

Standard Errors of the means, Z = Z scores, and p = p-value at 95% confidence interval)

Species Fixed effects Estimate SE Z p value Acacia robusta (Intercept) 8.39 7.70 1.09 0.28 CHIRP RAINFALL -7.42 4.04 -1.84 0.07 YEAR (2018) 4.05 3.75 1.08 0.28 YEAR (2019) 3.75 1.34 2.80 0.01 TWI -0.11 0.22 -0.49 0.63 NDVI -16.85 16.25 -1.04 0.30 FIRE -0.03 0.03 -0.96 0.34 BROWSERS -2.57 0.91 -2.84 0.00 GRAZERS 2.46 1.09 2.27 0.02

Acacia tortilis (Intercept) -3.91 5.09 -0.77 0.44 CHIRP RAINFALL 7.54 3.02 2.50 0.01 YEAR (2018) -6.44 2.39 -2.69 0.01 YEAR (2019) -2.16 1.02 -2.10 0.04 TWI 0.06 0.15 0.41 0.68 NDVI 1.21 10.26 0.12 0.91 FIRE 0.01 0.02 0.38 0.70 BROWSERS -2.07 1.06 -1.96 0.05 GRAZERS 0.82 0.90 0.92 0.36

Table S 2 Multiple logistic regression model coefficients relating tree reproduction probability to microsite environmental variables in the Serengeti. (SE = Standard Errors of the means, Z = Z scores, and p = p-value at 95% confidence interval)

Species Fixed effects Estimate SE Z p value Acacia robusta (Intercept) 3.67 2.12 1.74 0.08 Tmax -0.07 0.04 -1.60 0.11 PARmax 0.00 0.00 2.49 0.01 prop.time -3.79 1.54 -2.46 0.01

Acacia tortilis (Intercept) 5.04 6.39 0.79 0.43 Tmax -0.31 0.26 -1.21 0.23 PARmax 0.01 0.00 1.31 0.19 prop.time -0.95 2.50 -0.38 0.70

References

Anderson, T. M., J. G. Hopcraft, S. Eby, M. Ritchie, J. B. Grace, and H. Olff. 2010.

Landscape-scale analyses suggest both nutrient and antipredator advantages to

Serengeti herbivore hotspots. Ecology 91:1519-1529.

Anderson, T. M., T. Morrison, D. Rugemalila, R. Holdo, and D. Ward. 2015.

Compositional decoupling of savanna canopy and understory tree communities in

Serengeti. Journal of Vegetation Science 26:385-394.

Anderson, T. M., P. M. Ngoti, M. L. Nzunda, D. M. Griffith, J. D. M. Speed, F. Fossøy,

E. Røskaft, and B. J. Graae. 2018. The burning question: does fire affect habitat

selection and forage preference of the black rhinoceros Diceros bicornis in East

African savannahs? Oryx 54:234-243.

Ashman, T.-L., T. M. Knight, J. A. Steets, P. Amarasekare, M. Burd, D. R. Campbell, M.

R. Dudash, M. O. Johnston, S. J. Mazer, R. J. Mitchell, M. T. Morgan, and W. G.

Wilson. 2004. Pollen Limitation of Plant Reproduction: Ecological and

Evolutionary Causes and Consequences. Ecology 85:2408-2421.

Augustine, D. J., and S. J. McNaughton. 2004. Regulation of shrub dynamics by native

browsing ungulates on East African rangeland. Journal of Applied Ecology 41:45-

58.

Auld, T. D., and M. A. O'Connell. 1991. Predicting patterns of post-fire germination in

35 eastern Australian Fabaceae. Austral Ecology 16:53-70.

Babyak, M. A. 2004. What You See May Not Be What You Get: A Brief, Nontechnical

Introduction to Overfitting in Regression-Type Models. Psychosomatic Medicine

66:411-421.

Balcomb, S. R., and C. A. Chapman. 2003. Bridging the Gap: Influence of Seed

Deposition on Seedling Recruitment in a Primate–Tree Interaction. Ecological

Monographs 73:625-642.

Barnes, M. E. 2001. Seed predation, germination and seedling establishment of Acacia

erioloba in northern Botswana. Journal of Arid Environments 49:541-554.

Barnes, R. F. W. 1983. Effects of Elephant Browsing on Woodlands in a Tanzanian-

National-Park - Measurements, Models and Management. Journal of Applied

Ecology 20:521-539.

Bond, W. J. 2008. What Limits Trees in C4 Grasslands and Savannas? Annual Review of

Ecology, Evolution, and Systematics 39:641-659.

Bond, W. J., and R. Keane. 2017. Fires, ecological effects of. Reference Module in Life

Sciences. doi: 10.1016/B978-0-12-809633-8.02098-7.

Brown, J., N. J. Enright, and B. P. Miller. 2003. Seed production and germination in two

rare and three common co-occurring Acacia species from south-east Australia.

Austral Ecology 28:271-280.

Burgos, A., A. A. Grez, and R. O. Bustamante. 2008. Seed production, pre-dispersal seed

predation and germination of Nothofagus glauca (Nothofagaceae) in a temperate

fragmented forest in Chile. Forest Ecology and Management 255:1226-1233.

Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a

practical information-theoretic approach. Springer Science & Business Media.

Chamaillé‐Jammes, S., and H. Fritz. 2009. Precipitation–NDVI relationships in eastern

and southern African savannas vary along a precipitation gradient. International

Journal of Remote Sensing 30:3409-3422.

Chambers, J. C., and J. A. MacMahon. 1994. A Day in the Life of a Seed: Movements

and Fates of Seeds and Their Implications for Natural and Managed Systems.

Annual Review of Ecology and Systematics 25:263-292.

Chen, X., L. B. Hutley, and D. Eamus. 2003. Carbon balance of a tropical savanna of

northern Australia. Oecologia 137:405-416.

Chidumayo, E. N. 1997. Fruit Production and Seed Predation in Two Miombo Woodland

Trees in Zambia. Biotropica 29:452-458.

Clapp, R. B., and G. M. Hornberger. 1978. Empirical equations for some soil hydraulic

properties. Water resources research 14:601-604.

Clark, C. J., J. R. Poulsen, D. J. Levey, and C. W. Osenberg. 2007. Are plant populations

seed limited? A critique and meta-analysis of seed addition experiments. Am Nat

170:128-142.

Clark, J. S., E. Macklin, and L. Wood. 1998. Stages and Spatial Scales of Recruitment

Limitation in Southern Appalachian Forests. Ecological Monographs 68:213-235.

Coe, M., and C. Coe. 1987. Large Herbivores, Acacia Trees and Bruchid Beetles. South

African Journal of Science 83:624-635.

Cumming, D. H. M., and G. S. Cumming. 2003. Ungulate community structure and

ecological processes: body size, hoof area and trampling in African savannas.

Oecologia 134:560-568.

Daoud, J. I. 2017. Multicollinearity and regression analysis. Page 012009 in Journal of

Physics: Conference Series. IOP Publishing. de Sousa, K., A. H. Sparks, W. Ashmall, J. van Etten, and S. Ø. Solberg. 2020. chirps:

API Client for the CHIRPS Precipitation Data in R. Journal of Open Source

Software 5:2419.

Egan, J. L., and R. J. Williams. 1996. Lifeform distributions of woodland plant species

along a moisture availability gradient in Australia's monsoonal tropics. Australian

Systematic Botany 9:205.

Eriksson, O., and J. Ehrlen. 1992. Seed and microsite limitation of recruitment in plant

populations. Oecologia 91:360-364.

February, E. C., S. I. Higgins, W. J. Bond, and L. Swemmer. 2013. Influence of

competition and rainfall manipulation on the growth responses of savanna trees

and grasses. Ecology 94:1155-1164.

Funk, C., P. Peterson, M. Landsfeld, D. Pedreros, J. Verdin, S. Shukla, G. Husak, J.

Rowland, L. Harrison, and A. Hoell. 2015. The climate hazards infrared

precipitation with stations—a new environmental record for monitoring extremes.

Scientific data 2:1-21.

Gawryszewski, F. M., M. N. Sato, and H. S. Miranda. 2020. Frequent fires alter tree

architecture and impair reproduction of a common fire-tolerant savanna tree. Plant

biology 22:106-112.

Goheen, J. R., F. Keesing, B. F. Allan, D. L. Ogada, and R. S. Ostfeld. 2004. Net effects

of large mammals on Acacia seedling survival in an African savanna. Ecology

85:1555-1561.

Goheen, J. R., T. M. Palmer, F. Keesing, C. Riginos, and T. P. Young. 2010. Large

herbivores facilitate savanna tree establishment via diverse and indirect pathways.

J Anim Ecol 79:372-382.

Goheen, J. R., T. P. Young, F. Keesing, and T. M. Palmer. 2007. Consequences of

herbivory by native ungulates for the reproduction of a savanna tree. Journal of

Ecology 95:129-138.

Grace, J., J. San Jose, P. Meir, H. S. Miranda, and R. A. Montes. 2006. Productivity and

carbon fluxes of tropical savannas. Journal of Biogeography 33:387-400.

Greene, D. F., and E. A. Johnson. 1994. Estimating the Mean Annual Seed Production of

Trees. Ecology 75:642-647.

Harcombe, P. A. 1987. Tree Life-Tables. BioScience 37:557-568.

Harper, J. L., and J. L. Harper. 1977. Population biology of plants. JSTOR.

Hebbali, A., and M. A. Hebbali. 2020. Tools for Building OLS Regression Models.

Available at https://cloud.r-project.org/web/packages/olsrr/olsrr.pdf (Accessed on

2 January 2021).

Holdo, R. M., R. D. Holt, M. B. Coughenour, and M. E. Ritchie. 2007. Plant productivity

and soil nitrogen as a function of grazing, migration and fire in an African

savanna. Journal of Ecology 95:115-128.

Holdo, R. M., R. D. Holt, and J. M. Fryxell. 2009. Grazers, browsers, and fire influence

the extent and spatial pattern of tree cover in the Serengeti. Ecol Appl 19:95-109.

Holdo, R. M., D. A. Onderdonk, A. G. Barr, M. Mwita, and T. M. Anderson. 2020.

Spatial transitions in tree cover are associated with soil hydrology, but not with

grass biomass, fire frequency, or herbivore biomass in Serengeti savannahs.

Journal of Ecology 108:586-597.

Hu, D., J. M. Baskin, C. C. Baskin, X. Yang, and Z. Huang. 2018. Ecological role of

physical dormancy in seeds of Oxytropis racemosa in a semiarid sandland with

unpredictable rainfall. Journal of Plant Ecology 11:542-552.

Johnson, K. A., and R. S. Goody. 2011. The Original Michaelis Constant: Translation of

the 1913 Michaelis-Menten Paper. Biochemistry 50:8264-8269.

Joubert, D. F., G. N. Smit, and M. T. Hoffman. 2013. The influence of rainfall,

competition and predation on seed production, germination and establishment of

an encroaching Acacia in an arid Namibian savanna. Journal of Arid

Environments 91:7-13.

Kelly, D., and V. L. Sork. 2002. Mast Seeding in Perennial Plants: Why, How, Where?

Annual Review of Ecology and Systematics 33:427-447.

Kleiber, C., A. Zeileis, and M. A. Zeileis. 2020. Package ‘AER’. R package version 1.2

Koch, P. L., J. Heisinger, C. Moss, R. W. Carlson, M. L. Fogel, and A. K. Behrensmeyer.

1995. Isotopic Tracking of Change in Diet and Habitat Use in African Elephants.

Science 267:1340-1343.

Kohi, E. M., W. F. de Boer, M. J. S. Peel, R. Slotow, C. van der Waal, I. M. A.

Heitkonig, A. Skidmore, and H. H. T. Prins. 2011. African Elephants Loxodonta

africana Amplify Browse Heterogeneity in African Savanna. Biotropica 43:711-

721.

Lamchin, M., W. K. Lee, S. W. Jeon, J. Y. Lee, C. Song, D. Piao, C. H. Lim, A.

Khaulenbek, and I. Navaandorj. 2017. Correlation between Desertification and

Environmental Variables Using Remote Sensing Techniques in Hogno Khaan,

Mongolia. Sustainability 9:581.

Magnusson, A., H. Skaug, A. Nielsen, C. Berg, K. Kristensen, M. Maechler, K. van

Bentham, B. Bolker, and M. Brooks. 2017. glmmTMB: Generalized Linear

Mixed Models using Template Model Builder. R package version 0.1.3 2.

McNaughton, S. J. 1983. Serengeti Grassland Ecology - the Role of Composite

Environmental-Factors and Contingency in Community Organization. Ecological

Monographs 53:291-320.

Mduma, S. A. R., A. R. E. Sinclair, and R. Turkington. 2007. The role of rainfall and

predators in determining synchrony in reproduction of savanna trees in Serengeti

National Park, Tanzania. Journal of Ecology 95:184-196.

Midgley, J. J., and W. J. Bond. 2001. A synthesis of the demography of African acacias.

Journal of Tropical Ecology 17:871-886.

Moe, S. R., L. P. Rutina, H. Hytteborn, and J. T. du Toit. 2009. What controls woodland

regeneration after elephants have killed the big trees? Journal of Applied Ecology

46:223-230.

Monks, A., and D. Kelly. 2006. Testing the resource-matching hypothesis in the mast

seeding tree Nothofagus truncata (Fagaceae). Austral Ecology 31:366-375.

Moody-Weis, J., and H. M. Alexander. 2007. The mechanisms and consequences of seed

bank formation in wild sunflowers (Helianthus annuus). Journal of Ecology

95:851-864.

Morrison, T. A., R. M. Holdo, and T. M. Anderson. 2015. Elephant damage, not fire or

rainfall, explains mortality of overstorey trees in Serengeti. Journal of Ecology.

Myers, J. A., and K. E. Harms. 2011. Seed arrival and ecological filters interact to

assemble high-diversity plant communities. Ecology 92:676-686.

Nathan, R., and H. C. Muller-Landau. 2000. Spatial patterns of seed dispersal, their

determinants and consequences for recruitment. Trends in Ecology & Evolution

15:278-285.

Norton-Griffiths, M., D. Herlocker, and L. Pennycuick. 1975. The patterns of rainfall in

the Serengeti ecosystem, Tanzania. African Journal of Ecology 13:347-374.

Noumi, Z., B. Touzard, R. Michalet, and M. Chaieb. 2010. The effects of browsing on

the structure of Acacia tortilis (Forssk.) Hayne ssp raddiana (Savi) Brenan along a

gradient of water availability in arid zones of Tunisia. Journal of Arid

Environments 74:625-631.

O'connor, T., and G. Pickett. 1992. The influence of grazing on seed production and seed

banks of some African savanna grasslands. Journal of Applied Ecology:247-260.

O'Dowd, D. J., and A. M. Gill. 1984. Predator Satiation and Site Alteration Following

Fire: Mass Reproduction of Alpine Ash (Eucalyptus Delegatensis) in

Southeastern Australia. Ecology 65:1052-1066.

O'Kane, C. A. J., and D. W. Macdonald. 2018. Seasonal influences on ungulate

movement within a fenced South African reserve. Journal of Tropical Ecology

34:200-203.

Palmer, M. S., J. Fieberg, A. Swanson, M. Kosmala, and C. Packer. 2017. A 'dynamic'

landscape of fear: prey responses to spatiotemporal variations in predation risk

across the lunar cycle. Ecol Lett 20:1364-1373.

Pellew, R. A. P. 1983. The Impacts of Elephant, Giraffe and Fire Upon the Acacia-

Tortilis Woodlands of the Serengeti. African Journal of Ecology 21:41-74.

Peterson, B. G., P. Carl, K. Boudt, R. Bennett, J. Ulrich, E. Zivot, D. Cornilly, E. Hung,

M. Lestel, and K. Balkissoon. 2018. Package ‘performanceanalytics’. R Team

Cooperation.

Pringle, R. M. 2008. Elephants as agents of habitat creation for small vertebrates at the

patch scale. Ecology 89:26-33.

Probert, J. R., C. L. Parr, R. M. Holdo, T. M. Anderson, S. Archibald, C. J. Courtney

Mustaphi, A. P. Dobson, J. E. Donaldson, G. C. Hopcraft, G. P. Hempson, T. A.

Morrison, and C. M. Beale. 2019. Anthropogenic modifications to fire regimes in

the wider Serengeti-Mara ecosystem. Glob Chang Biol 25:3406-3423.

Raduła, M. W., T. H. Szymura, and M. Szymura. 2018. Topographic wetness index

explains soil moisture better than bioindication with Ellenberg’s indicator values.

Ecological Indicators 85:172-179.

Ratnam, J., W. J. Bond, R. J. Fensham, W. A. Hoffmann, S. Archibald, C. E. R.

Lehmann, M. T. Anderson, S. I. Higgins, and M. Sankaran. 2011. When is a

‘forest’ a savanna, and why does it matter? Global Ecology and Biogeography

20:653-660.

Riginos, C. 2009. Grass competition suppresses savanna tree growth across multiple

demographic stages. Ecology 90:335-340.

Rugemalila, D. M., T. M. Anderson, and R. M. Holdo. 2016. Precipitation and elephants,

not fire, shape tree community composition in Serengeti National Park, Tanzania.

Biotropica 48:476-482.

Sabiiti, E. N., and R. W. Wein. 1987. Fire and Acacia Seeds - a Hypothesis of

Colonization Success. Journal of Ecology 75:937-946.

Salazar, A., G. Goldstein, A. C. Franco, and F. Miralles-Wilhelm. 2011. Seed limitation

of woody plants in Neotropical savannas. Plant Ecology 213:273-287.

Sankaran, M., N. P. Hanan, R. J. Scholes, J. Ratnam, D. J. Augustine, B. S. Cade, J.

Gignoux, S. I. Higgins, X. Le Roux, F. Ludwig, J. Ardo, F. Banyikwa, A. Bronn,

G. Bucini, K. K. Caylor, M. B. Coughenour, A. Diouf, W. Ekaya, C. J. Feral, E.

C. February, P. G. Frost, P. Hiernaux, H. Hrabar, K. L. Metzger, H. H. Prins, S.

Ringrose, W. Sea, J. Tews, J. Worden, and N. Zambatis. 2005. Determinants of

woody cover in African savannas. Nature 438:846-849.

Sankaran, M., J. Ratnam, and N. Hanan. 2008. Woody cover in African savannas: the

role of resources, fire and herbivory. Global Ecology and Biogeography 17:236-

245.

Sankaran, M., J. Ratnam, and N. P. Hanan. 2004. Tree-grass coexistence in savannas

revisited - insights from an examination of assumptions and mechanisms invoked

in existing models. Ecology Letters 7:480-490.

Scholes, R. J., and S. R. Archer. 1997. Tree-grass interactions in savannas. Annual

Review of Ecology and Systematics 28:517-544.

Schupp, E. W., and M. Fuentes. 1995. Spatial Patterns of Seed Dispersal and the

Unification of Plant-Population Ecology. Ecoscience 2:267-275.

Seghieri, J., C. Floret, and R. Pontanier. 1995. Plant Phenology in Relation to Water

Availability - Herbaceous and Woody Species in the Savannas of Northern

Cameroon. Journal of Tropical Ecology 11:237-254.

Setterfield, S. A. 1997. The impact of experimental fire regimes on seed production in

two tropical eucalypt species in northern Australia. Austral Ecology 22:279-287.

Setterfield, S. A. 2002. Seedling establishment in an Australian tropical savanna: effects

of seed supply, soil disturbance and fire. Journal of Applied Ecology 39:949-959.

Sinclair, A. 1979. The Serengeti environment. Pages 31-45 in A. R. E. S. a. a. M. Norton-

Griffiths, editor. Serengeti: Dynamics of an ecosystem. The University of

Chicago Press.

Staver, A. C., S. Archibald, and S. Levin. 2011. Tree cover in sub-Saharan Africa:

Rainfall and fire constrain forest and savanna as alternative stable states. Ecology

92:1063-1072.

Strauss, M. K., and C. Packer. 2015. Did the elephant and giraffe mediate change in the

prevalence of palatable species in an East African Acacia woodland? Journal of

Tropical Ecology 31:1-12.

Svenning, J. C., and S. J. Wright. 2005. Seed limitation in a Panamanian forest. Journal

of Ecology 93:853-862.

Therneau, T., B. Atkinson, and B. Ripley. 2019. Package ‘rpart’. Available online:

https://cran.pau.edu.tr/web/packages/rpart/rpart.pdf (Accessed on 17 January

2021).

Tomimatsu, H., and M. Ohara. 2002. Effects of forest fragmentation on seed production

of the understory herb Trillium camschatcense. Conservation Biology 16:1277-

1285.

Twine, W. 2019. Socioeconomic Value of Savannas. Pages 151-179 Savanna Woody

Plants and Large Herbivores.

Van Langevelde, F., C. A. D. M. Van De Vijver, L. Kumar, J. Van De Koppel, N. De

Ridder, J. Van Andel, A. K. Skidmore, J. W. Hearne, L. Stroosnijder, W. J. Bond,

H. H. T. Prins, and M. Rietkerk. 2003. Effects of Fire and Herbivory on the

Stability of Savanna Ecosystems. Ecology 84:337-350.

Vander Wall, S. B., P.-M. Forget, J. E. Lambert, P. E. Hulme, P. Forget, J. Labert, P.

Hulme, and S. Vander Wall. 2005. Seed fate pathways: filling the gap between

parent and offspring. Pages 1-8 in P.-M. Forget, J. E. Lambert, P. E. Hulme, and

S. B. Vander Wall, editors. Seed fate: Predation, dispersal and seedling

establishment. CABI Publishing, UK.

Voysey, M. D., S. Archibald, W. J. Bond, J. E. Donaldson, A. Carla Staver, and M.

Greve. 2020. The role of browsers in maintaining the openness of savanna grazing

lawns. Journal of Ecology n/a.

Walters, M., and S. J. Milton. 2003. The production, storage and viability of seeds of

Acacia karroo and A. nilotica in a grassy savanna in KwaZulu‐Natal, South

Africa. African Journal of Ecology 41:211-217.

Wang, J., P. M. Rich, K. P. Price, and W. D. Kettle. 2004. Relations between NDVI and

tree productivity in the central Great Plains. International Journal of Remote

Sensing 25:3127-3138.

Ward, D., and K. J. Esler. 2011. What are the effects of substrate and grass removal on

recruitment of Acacia mellifera seedlings in a semi-arid environment? Plant

Ecology 212:245-250.

Williams, E. V., J. Elia Ntandu, P. Ficinski, and M. Vorontsova. 2016. Checklist of

Serengeti Ecosystem Grasses. Biodivers Data J:e8286.

Williams, K. J., B. J. Wilsey, S. J. McNaughton, and F. F. Banyikwa. 1998. Temporally

variable rainfall does not limit yields of Serengeti grasses. Oikos:463-470.

Williams, R., G. Duff, D. Bowman, and G. Cook. 1996. Variation in the composition and

structure of tropical savannas as a function of rainfall and soil texture along a

large‐scale climatic gradient in the Northern Territory, Australia. Journal of

Biogeography 23:747-756.

Wilmshurst, J. F., J. M. Fryxell, and C. M. Bergman. 2000. The allometry of patch

selection in ruminants. Proceedings of the Royal Society of London. Series B:

Biological Sciences 267:345-349.

Wright, S. J. 1992. Seasonal drought, soil fertility and the species density of tropical

forest plant communities. Trends Ecol Evol 7:260-263.

Young, T. P., and D. J. Augustine. 2007. Interspecific variation in the reproductive

response of acacia species to protection from large mammalian herbivores.

Biotropica 39:559-561.

CHAPTER 2: A short communication: Variability in Acacia seed germination

potential sourced from the tree canopy, seedbank, and large ungulate dung.

Prepared for publication in the African Journal of Ecology

Stylistic variations result from the journal’s demands.

Introduction

Seed germination is a critical life-history stage in plant recruitment as it marks a challenging life-history transition from seed to seedling (Midgley and Bond 2001). For decades, plant ecologists have worked diligently to understand the pathways that seeds follow to reach a conducive germination site (Janzen 1971, Hughes and Westoby 1992,

Midgley and Bond 2001, Vander Wall et al. 2005) and the factors affecting seed germination (Chachalis and Reddy 2000, Tangney et al. 2019). The seed fate research shows that mature seeds may fall to the ground beneath the parent tree or may be adopted for dispersal by wind or attract dispersers such as birds and browsers (Vander Wall et al.

2005, Walters et al. 2005, Muller-Landau et al. 2008). Seeds that fall to the ground can be removed by seed-caching rodents and get deposited to suitable sites if they survive predation (Miller and Coe 1993, Linzey and Washok 2000, Brewer and Rejmánek 2009) or get stored as seedbanks (Vander Wall et al. 2005, DeMalach et al. 2020). The seed germination potential after arriving at a new location depends on a multitude of factors

68 such as resource availability, abiotic factors, and interspecific interactions (Barnes 2001,

Tangney et al. 2019).

Past research suggests that seeds that fall under the parent tree canopy tend to have lower germination potential due to either physical damage or attack by pathogens from conspecifics (Janzen 1970, Wenny 2000a, Luo et al. 2013, Comita et al. 2014) and are most likely to be removed by various seed predators (Hulme 1994, Miller 1994a, Linzey and Washok 2000, Wenny 2000a). Other seeds may use dormancy to move through time and space, and external cues such as temperature and soil moisture regulated by abscisic acid (ABA) and gibberellin (GA) influence the timing of the transition to germination

(Topham et al. 2017). Some plant species depend on vertebrates to disperse their seeds to other suitable microsites away from the parent trees (Chambers and MacMahon 1994).

However, data on whether vertebrate dispersal enhances germination is mixed (Wenny

2001) because of considerable variation between species (Janzen 1983, Chapman 1995,

Traveset and Willson 1997, Wenny 2000b).

A definitive agreement in seed ecology is that vertebrates, specifically ungulates, benefit some trees through endozoochory - seed dispersal by consumption and passage through the ungulate gut (Miller and Coe 1993, Miller 1994b). Studies suggest that gut passage improves seed germination because of the direct effect of the animals' digestive fluids upon the seed coats (Krefting and Roe 1949, Pellew and Southgate 1984, Miller 1996b,

Cochrane 2003, Venier et al. 2012, Spanbauer and Adler 2015). Janzen (1971) suggested that vertebrate-adapted seeds have retarded germination if they do not pass through a vertebrate gut. Whether the seeds deposited by ungulates have a different germination potential than the seeds from other pathways such as the tree canopy and seedbank is

69 unclear. Many studies do not compare variability in seed germination potential as a function of their sources (Samuels and Levey 2005, Spanbauer and Adler 2015).

The goal of this experimental study was to assess seed germination potential as a function of pre-germination pathways. We asked the following questions: 1) Does dispersal pathway (tree canopy, seedbank, and endozoochory) affect germination success?; 2)

Does passage through the animal gut (endozoochory) improve germination potential of seeds with hard seedcoat?

Materials and Methods

Study location

We utilized seeds collected from a herbivore-dominated tropical savanna ecosystem - the

Serengeti National Park (the Serengeti hereafter), Tanzania, which lies between 1° to 2°

S and 34° and 36° E. The dominant tree species in the Serengeti are in the genera Acacia

(= Vachellia and Senegalia), co-occurring with a grass layer dominated by Themeda triandra, Digitaria macroblephara, Panicum maximum, Pennisetum meziunum, and various species of the genera Sporobolus. Other sub-dominant tree species in the

Serengeti includes Kigelia africana, Commiphora trothae, A. drepanolobium, A. gerardii,

A. senegal (=Senegalia senegal), and Balanites aegyptiaca (Sinclair et al. 2009,

Anderson et al. 2015). The most common herbivores in the ecosystem are wildebeest

(Connochaetes taurinus), zebra (Equus quagga), impala (Aepyceros melampus), elephants (Loxodonta africana), giraffe (Giraffa camelopardalis), buffalo (Syncerus

70 caffer), and the Thomson's gazelle (Eudorcas thomsonii). Others include Grant's gazelles

(Gazella granti), eland (Taurotragus oryx), hartebeests (Alcelaphus buselaphus), and warthogs (Phacochoerus africanus).

Transects and study species

This study focused on the two most abundant tree species in the Serengeti: Acacia

(=Vachellia) tortilis and A. robusta because of their large spatial extent and opposing dominance across the rainfall gradient (Rugemalila et al. 2016). We sourced seeds from eight transects established in 2016 for a separate study (see (Holdo et al. 2020)). The transects are 1km × 20 m (= 2 ha) and span across the Serengeti rainfall gradient (Holdo et al. 2020). We conducted a tree survey to identify and map all trees (> 2m in height) and then randomly selected and tagged (using racetrack aluminum tags - Forestry

Supplier Inc. USA) ten trees per transect per A. robusta and A. tortilis species. On each tagged tree, we collected canopy seeds and seedbanks using the procedure described below.

Canopy seeds: On each tagged tree, a sample of six branches (3 – 4 cm in diameter) was selected and tagged for a separate subsequent seed production monitoring study

(unpublished). Between July 2017 and December 2019, we collected, paper-bagged, and transported all mature, dry pods to the research center for further seed assessment.

Seedbank: We estimated seedbank from soil samples collected under the tagged trees during the dry seasons of 2017 and 2019. We used a square quadrant frame (30 cm × 30 cm) to collect six soil core samples to a depth of 5 cm, at 3 m intervals, oriented N-S from each tree stem. After soil collection, we thoroughly homogenized the soil cores and sieved them on-site through a 4 mm screen to retain the seeds. We counted the seeds

71 from the soil cores, stored them in coin envelopes, and transported them to the research center for further seed assessment.

Ungulate dung seeds: We mapped and collected dung deposited by elephants, giraffes, and impala along each transect during the dry seasons of 2017 and 2019, concurrent with seed monitoring campaigns, and placed it in paper bags (for giraffe and impala dung) and nylon sacks (for elephant dung). We transported collected dung samples to the research center, air-dried them, manually retrieved the seeds, counted them, and inspected for infestation and damage by bruchid beetles.

Uninfested seeds collected from the three sources (tree canopy, seedbank, and ungulate dung) were transported to Wake Forest University facilities for germination in a controlled environment to quantify their germination potential.

Seed germination experiments

We pooled our seeds to a site level before germination trials. Because of the large variability in the number of seeds collected per site per species, we treated sites as replicates, and for the seed sources that did not have enough replicates or seeds, we combined the seeds and germinated them in three replicates. Therefore, the number of seeds per treatment varied depending on the seed stock (Table 4). The first part of the experiment compared the four treatments; scarification (scarified vs. not scarified), species (A. robusta vs. A. tortlis), year of seed collection (2017 vs. 2019), and seed source

(seedbank vs. tree canopy). The second part of the experiment compared germination potential for A. tortilis seeds from animal dung to A. tortilis seeds from the first part of the experiment. Seed scarification was done by gently sanding the seed coat with

72 sandpaper to expose the seed mesocarp and enhance water permeability (Rugemalila et al. 2017).

We germinated all seeds in the seed germination trays containing a mix of organic soil

(Metromix 360 – P, Marysville, OH), sand and clay in a 2:1:1 ratio. We monitored seed germination at least two times a week, and a seed was considered viable and tallied after producing a cotyledon above the soil surface (Rugemalila et al. 2017). We ran the germination experiment for almost six- months (180 days).

Data analysis

We performed the statistical analyses using R 4.0.3. For each treatment, we computed two germination parameters; the time (days) for 50% of seeds to germinate (T50) and the final germination percentage (FGP). We used T50 rather than a widely applied mean germination time (MGT) (Sommerville et al. 2013, Zhang et al. 2014, Mwendwa et al.

2020) because two groups of seeds can have the same MGT or FGP but differ in the rate to reach a given threshold (e.g. T50) (Soltani et al. 2015). We compared FGP between treatments using the analysis of variance (ANOVA) after the angular transformation of percentage values to stabilize the variance (Gianinetti 2020).

Results and discussion

We found significant main and interaction effects of species and treatment on seed sourced from the tree canopy and seedbank, respectively (Table 3). Seeds sourced from seedbank and tree canopy had no differences in germination potential among years when

73 comparing the same scarification treatment (Table 4, Fig. 6). However, for A. robusta seeds (=Acarob in Fig. 6), scarification treatment only increased germination potential for seeds sourced from tree canopy but not from seedbank (Table 3, Fig. 6A and C). We also found that unscarified A. robusta seeds from tree canopy were the only group to exceed

50% germination achieved after 46 days (Fig.7, Table 3). No other unscarified seeds attained 50% germination within the 180 days study period (Fig. 7). We expected germination potential for scarified seeds to be higher in both species regardless of the seed sources because scarification accelerates water imbibition. The results suggest that the seedbank pathway does not improve A. robusta seed germination potential because a relatively soft seed coat (compared to A. tortilis) allowed for between-treatment similarities in germination potential. Acacia fruits can either be dehiscent (i.e., mature pods split open before the wind or gravity dispersal) or indehiscent (i.e., mature pods remain inside their pods until eaten or depredated) (Miller and Coe 1993). Even though the seedbank pathway hedges risk to germinants (Chen et al. 2020), it may expose the seeds to pathogens, elevated temperatures from fires, and proximately negatively affect dehiscent embryo viability (Merritt et al. 2007, Tangney et al. 2019).

As expected, the germination potential of scarified A. tortilis seeds from tree canopies and seedbanks was at least 3 – folds higher than the unscarified seeds (Table 3, Fig 6B &

D). The results are consistent with a previous study in the same ecosystem (Morrison et al. 2019), which reported that seed scarification is the dominant predictor of germination potential in the field. In comparison, the scarified seeds of A. tortilis and A. robusta attained T50 in less than a week (Table 3, Fig 7), and the FGP was equivalent for the seeds sourced from the tree canopy (Table 3, Fig 6 & 7). In contrast, scarified A. tortilis

74 seedbank germination was two-fold higher than A. robusta. The benefit low germination in unscarified A. tortilis and its improvement after scarification is that the seeds can persist in the soil seedbank and resistance to pathogens, ultimately contributing to competitive ability under harsh conditions (Jimenez-Alfaro et al. 2016). The A. tortilis germination results suggest that post dispersal physical scarification is key to its successful germination in the field. The agents of scarification for A. tortilis (and most

Acacias) are mainly bruchid beetles (Lamprey et al. 1974, Coe and Coe 1987, Miller

1994b, 1996a, Ward et al. 2010, Rugemalila et al. 2017), small rodents, and large ungulates (Miller 1996b, Goheen et al. 2010).

Surprisingly, unscarified seeds of A. tortilis sourced from ungulate dung had lower FGP than those from the tree canopy and seedbank (Table 3, Fig. 8) (except seeds sourced from impala dung) as they did not attain 25% germination in (Fig 9). The impala's unscarified seeds had a much higher germination potential than all other seed sources

(Table 3, Fig. 8). These results contradict a few previous studies because Miller (1994b) reported 1.5%, 49%, and 28% germination of uninfested seeds from the canopy, giraffe, and impala, respectively, implying gut passage advantage over other pathways. Other previous studies have shown that gut passage directly affects germination by either placement of seeds in microsites suitable for germination (Schupp 1993, Howe and Miriti

2000, Herrera 2002) or mechanical and chemical alteration of seed coat structure in the gut (Venier et al. 2012). Our results suggest that for A. tortilis, the former is more likely than the latter. However, after achieving scarification, ungulate sourced seed germination potential is slightly higher than canopy and seedbank sourced seeds (Fig. 8 and 9). The improved germination potential of seeds sourced from ungulate dung may be attributed to

75 the complete digestion of infested seeds, leading to the defecation of uninfested seeds

(Coe and Coe 1987, Miller 1994b).

Previous studies have reported positive correlations between seed germination and animal body size (Rohner and Ward 1999). However, this study shows that germination potential for unscarified seeds is higher for the small-sized impala and the lowest for elephants

(Fig 8), consistent with results reported by Grellier et al. (2012) and Tjelele et al.( 2015).

In conclusion, the simultaneous assessment of seed germination potential for different dispersal pathways in the same ecosystem sheds light on understanding each component's relative importance on tree recruitment and gene flow. Germination success involves a series of events that start from maturing seeds to movement away from a parent tree.

Despite the importance of various dispersing agents such as large ungulates, the action of seed predators such as bruchid beetles and small rodents may be critical to successful germination.

Tables and Figures

Table 3 Result of the ANOVA examining germination potential of A. robusta and A. tortilis seeds sourced from the tree canopy, seedbank, and animal dung after 180 days of laboratory germination.

Seed source Variables Df F value p-value Canopy Species 1 9.5 <0.01 Treatment 1 45.5 <0.0001 Year 1 0.4 0.52 Species × Treatment 1 3.1 0.09 Species × Year 1 0.9 0.35 Residuals 23

Seedbank Species 1 2.7 0.11 Treatment 1 12.6 <0.01 Year 1 0.1 0.75 Species × Treatment 1 31.1 <0.0001 Species × Year 1 0.2 0.68 Residuals 29

Endozoochory Animal species 2 1.3 0.30 Treatment 1 369.4 <0.0001 Animal species × Treatment 2 11.1 <0.01 Residuals 12

NA represents infinity T50.

Collection Percent germination Seed source Species Year Scarified n T50 Not scarified n T50

Tree canopy Acacia robusta 2017 87.7 ± 4.1 247 6 46.5 ± 10.9 124 NA Acacia tortilis 2017 87.3 ± 4.7 125 6 23 ± 8.7 260 NA Acacia robusta 2019 91 ± 6.6 200 4 60 ± 13.4 200 46 Acacia tortilis 2019 82.5 ± 17.5 128 4 16.7 ± 6 240 NA

Seedbank Acacia robusta 2017 40.3 ± 10.3 173 NA 39.0 ± 3.4 105 NA Acacia tortilis 2017 73.7 ± 5.1 86 6 13.3 ± 3.4 105 NA Acacia robusta 2019 33.6 ± 2.6 157 NA 47.1 ± 7.8 105 NA Acacia tortilis 2019 71.3 ± 8 100 7 21.0 ± 4.2 105 NA

Elephant Acacia tortilis 2017/19 95.7 ± 3 140 9 7.6 ± 1.3 210 NA Giraffe Acacia tortilis 2017/19 92.4 ± 3.4 105 9 10 ± 0.8 210 NA Impala Acacia tortilis 2017/19 86.7 + 4.8 105 9 32.4 ± 4.2 105 NA

References

Anderson, T. M., T. Morrison, D. Rugemalila, R. Holdo, and D. Ward. 2015.

Compositional decoupling of savanna canopy and understory tree communities in

Serengeti. Journal of Vegetation Science 26:385-394.

Barnes, M. E. 2001. Seed predation, germination and seedling establishment of Acacia

erioloba in northern Botswana. Journal of Arid Environments 49:541-554.

Brewer, S. W., and M. Rejmánek. 2009. Small rodents as significant dispersers of tree

seeds in a Neotropical forest. Journal of Vegetation Science 10:165-174.

Chachalis, D., and K. N. Reddy. 2000. Factors affecting Campsis radicans seed

germination and seedling emergence. Weed Science 48:212-216, 215.

Chambers, J. C., and J. A. MacMahon. 1994. A Day in the Life of a Seed: Movements

and Fates of Seeds and Their Implications for Natural and Managed Systems.

Annual Review of Ecology and Systematics 25:263-292.

Chapman, C. A. 1995. Primate seed dispersal: Coevolution and conservation

implications. Evolutionary Anthropology: Issues, News, and Reviews 4:74-82.

Chen, S.-C., P. Poschlod, A. Antonelli, U. Liu, and J. B. Dickie. 2020. Trade-off between

seed dispersal in space and time. Ecology Letters 23:1635-1642.

Cochrane, E. P. 2003. The need to be eaten: Balanites wilsoniana with and without

elephant seed-dispersal. Journal of Tropical Ecology 19:579-589.

Coe, M., and C. Coe. 1987. Large Herbivores, Acacia Trees and Bruchid Beetles. South

African Journal of Science 83:624-635.

Comita, L. S., S. A. Queenborough, S. J. Murphy, J. L. Eck, K. Xu, M. Krishnadas, N.

Beckman, Y. Zhu, and L. Gomez-Aparicio. 2014. Testing predictions of the

Janzen-Connell hypothesis: a meta-analysis of experimental evidence for

distance- and density-dependent seed and seedling survival. J Ecol 102:845-856.

DeMalach, N., J. Kigel, and M. Sternberg. 2020. The soil seed bank can buffer long-term

compositional changes in annual plant communities. Journal of Ecology n/a.

Gianinetti, A. 2020. Basic Features of the Analysis of Germination Data with

Generalized Linear Mixed Models. Data 5:6.

Goheen, J. R., T. M. Palmer, F. Keesing, C. Riginos, and T. P. Young. 2010. Large

herbivores facilitate savanna tree establishment via diverse and indirect pathways.

J Anim Ecol 79:372-382.

Grellier, S., S. Barot, J. L. Janeau, and D. Ward. 2012. Grass competition is more

important than seed ingestion by livestock for Acacia recruitment in South Africa.

Plant Ecology 213:899-908.

Herrera, C. M. 2002. Seed dispersal by vertebrates. Pages 185-208 Plant–animal

interactions: an evolutionary approach.

Holdo, R. M., D. A. Onderdonk, A. G. Barr, M. Mwita, and T. M. Anderson. 2020.

Spatial transitions in tree cover are associated with soil hydrology, but not with

grass biomass, fire frequency, or herbivore biomass in Serengeti savannahs.

Journal of Ecology 108:586-597.

Howe, H. F., and M. N. Miriti. 2000. No question: seed dispersal matters. Trends in

Ecology & Evolution 15:434-436.

Hughes, L., and M. Westoby. 1992. Fate of Seeds Adapted for Dispersal by Ants in

Australian Sclerophyll Vegetation. Ecology 73:1285-1299.

Hulme, P. E. 1994. Post-Dispersal Seed Predation in Grassland: Its Magnitude and

Sources of Variation. The Journal of ecology 82:645.

Janzen, D. H. 1970. Herbivores and the Number of Tree Species in Tropical Forests.

American Naturalist 104:501-+.

Janzen, D. H. 1971. Seed predation by animals. Annual Review of Ecology and

Systematics:465-492.

Janzen, D. H. 1983. Seed and pollen dispersal by animals: convergence in the ecology of

contamination and sloppy harvest. Biological Journal of the Linnean Society

20:103-113.

Jimenez-Alfaro, B., F. A. O. Silveira, A. Fidelis, P. Poschlod, and L. E. Commander.

2016. Seed germination traits can contribute better to plant community ecology.

Journal of Vegetation Science 27:637-645.

Krefting, L. W., and E. I. Roe. 1949. The role of some birds and mammals in seed

germination. Ecological Monographs 19:269-286.

Lamprey, H., G. Halevy, and S. Makacha. 1974. Interactions between Acacia, bruchid

seed beetles and large herbivores*. African Journal of Ecology 12:81-85.

Linzey, A. V., and K. A. Washok. 2000. Seed removal by ants, birds and rodents in a

woodland savanna habitat in Zimbabwe. African Zoology 35:295-299.

Luo, Y., F. He, and S. Yu. 2013. Recruitment limitation of dominant tree species with

varying seed masses in a subtropical evergreen broad-leaved forest. Community

Ecology 14:189-195.

Merritt, D. J., S. R. Turner, S. Clarke, and K. W. Dixon. 2007. Seed dormancy and

germination stimulation syndromes for Australian temperate species. Australian

Journal of Botany 55:336-344.

Midgley, J. J., and W. J. Bond. 2001. A synthesis of the demography of African acacias.

Journal of Tropical Ecology 17:871-886.

Miller, M. F. 1994a. The Fate of Mature African Acacia Pods and Seeds during Their

Passage from the Tree to the Soil. Journal of Tropical Ecology 10:183-196.

Miller, M. F. 1994b. Large African Herbivores, Bruchid Beetles and Their Interactions

with Acacia Seeds. Oecologia 97:265-270.

Miller, M. F. 1996a. Acacia seed predation by bruchids in an African savanna ecosystem.

Journal of Applied Ecology 33:1137-1144.

Miller, M. F. 1996b. Dispersal of Acacia seeds by ungulates and ostriches in an African

savanna. Journal of Tropical Ecology 12:345-356.

Miller, M. F., and M. Coe. 1993. Is it advantageous for Acacia seeds to be eaten by

ungulates? Oikos 66:364-368.

Morrison, T. A., R. M. Holdo, D. M. Rugemalila, M. Nzunda, and T. M. Anderson. 2019.

Grass competition overwhelms effects of herbivores and precipitation on early

tree establishment in Serengeti. Journal of Ecology 107:216-228.

Muller-Landau, H. C., S. J. Wright, O. Calderon, R. Condit, and S. P. Hubbell. 2008.

Interspecific variation in primary seed dispersal in a tropical forest. Journal of

Ecology 96:653-667.

Mwendwa, B. A., C. J. Kilawe, and A. C. Treydte. 2020. Effect of seasonality and light

levels on seed germination of the invasive tree Maesopsis eminii in Amani Nature

Forest Reserve, Tanzania. Global Ecology and Conservation 21:e00807.

Pellew, R. A., and B. J. Southgate. 1984. The Parasitism of Acacia-Tortilis Seeds in the

Serengeti. African Journal of Ecology 22:73-75.

Rohner, C., and D. Ward. 1999. Large mammalian herbivores and the conservation of

arid Acacia stands in the Middle East. Conservation Biology 13:1162-1171.

Rugemalila, D. M., T. M. Anderson, and R. M. Holdo. 2016. Precipitation and elephants,

not fire, shape tree community composition in Serengeti National Park, Tanzania.

Biotropica 48:476-482.

Rugemalila, D. M., T. Morrison, T. M. Anderson, and R. M. Holdo. 2017. Seed

production, infestation, and viability in Acacia tortilis (synonym: Vachellia

tortilis) and Acacia robusta (synonym: Vachellia robusta) across the Serengeti

rainfall gradient. Plant Ecology 218:909-922.

Samuels, I. A., and D. J. Levey. 2005. Effects of gut passage on seed germination: do

experiments answer the questions they ask? Functional Ecology 19:365-368.

Schupp, E. W. 1993. Quantity, Quality and the Effectiveness of Seed Dispersal by

Animals. Vegetatio 108:15-29.

Sinclair, A., C. Packer, S. A. Mduma, and J. M. Fryxell. 2009. Serengeti III: human

impacts on ecosystem dynamics. University of Chicago Press.

Soltani, E., F. Ghaderi-Far, C. C. Baskin, and J. M. Baskin. 2015. Problems with using

mean germination time to calculate rate of seed germination. Australian Journal of

Botany 63:631-635.

Sommerville, K. D., A. J. Martyn, and C. A. Offord. 2013. Can seed characteristics or

species distribution be used to predict the stratification requirements of herbs in

the Australian Alps? Botanical Journal of the Linnean Society 172:187-204.

Spanbauer, B. R., and G. H. Adler. 2015. Seed protection through dispersal by African

savannah elephants (Loxodonta africana africana) in northern Tanzania. African

Journal of Ecology 53:496-501.

Tangney, R., D. J. Merritt, J. B. Fontaine, and B. P. Miller. 2019. Seed moisture content

as a primary trait regulating the lethal temperature thresholds of seeds. Journal of

Ecology 107:1093-1105.

Tjelele, J., D. Ward, and L. Dziba. 2015. The effects of seed ingestion by livestock, dung

fertilization, trampling, grass competition and fire on seedling establishment of

two woody plant species. PLoS One 10:e0117788.

Topham, A. T., R. E. Taylor, D. Yan, E. Nambara, I. G. Johnston, and G. W. Bassel.

2017. Temperature variability is integrated by a spatially embedded decision-

making center to break dormancy in Arabidopsis seeds. Proceedings

of the National Academy of Sciences 114:201704745.

Traveset, A., and M. F. Willson. 1997. Effect of birds and bears on seed germination of

fleshy-fruited plants in temperate rainforests of southeast Alaska. Oikos 80:89-95.

Vander Wall, S. B., P.-M. Forget, J. E. Lambert, P. E. Hulme, P. Forget, J. Labert, P.

Hulme, and S. Vander Wall. 2005. Seed fate pathways: filling the gap between

parent and offspring. Pages 1-8 in P.-M. Forget, J. E. Lambert, P. E. Hulme, and

S. B. Vander Wall, editors. Seed fate: Predation, dispersal and seedling

establishment. CABI Publishing, UK.

Venier, P., C. C. Garcia, M. Cabido, and G. Funes. 2012. Survival and germination of

three hard-seeded Acacia species after simulated cattle ingestion: The importance

of the seed coat structure. South African Journal of Botany 79:19-24.

Walters, M., S. J. Milton, M. J. Somers, and J. J. Midgley. 2005. Post-dispersal fate of

Acacia seeds in an African savanna.

Ward, D., I. Musli, K. Or, T. Gbenro, and O. Skutelsky. 2010. Bruchid seed infestation

and development time in three host species of Acacia (Coleoptera, Bruchidae).

Zoology in the Middle East 51:95-103.

Wenny, D. G. 2000a. Seed Dispersal of a High Quality Fruit by Specialized Frugivores:

High Quality Dispersal?1. Biotropica 32:327-337.

Wenny, D. G. 2000b. Seed dispersal, seed predation, and seedling recruitment of a

neotropical montane tree. Ecological Monographs 70:331-351.

Wenny, D. G. 2001. Advantages of seed dispersal: A re-evaluation of directed dispersal.

Evolutionary Ecology Research 3:51-74.

Zhang, C., C. G. Willis, L. T. Burghardt, W. Qi, K. Liu, P. R. Souza-Filho, Z. Ma, and G.

Du. 2014. The community-level effect of light on germination timing in relation

to seed mass: a source of regeneration niche differentiation. New Phytol 204:496-

506.

CHAPTER 3: The role of microsite sunlight environment on growth, architecture, and resource allocation in dominant Acacia tree seedlings, in Serengeti, East Africa

Originally published in the Plant Ecology journal:

Rugemalila, D. M., Cory, S. T., Smith, W. K., & Anderson, T. M. (2020). The role of microsite sunlight environment on growth, architecture, and resource allocation in dominant Acacia tree seedlings, in Serengeti, East Africa. Plant Ecology, 221(12), 1187-

1199.

Stylistic variations result from the journal’s demands.

Abstract

Seedling establishment is a critical life history stage for savanna tree recruitment due to variability in resource availability. While tree – grass competition for water is recognized as an important driver of tree seedling mortality, the importance of sunlight exposure on tree seedling performance has received little attention in savanna ecosystems despite variable seedling light environment caused by heterogeneity in the biomass of the grass canopy. We studied the seasonal sunlight micro-environment for two dominant East

African tree species (Acacia =Vachellia), robusta (Burch) and A. tortilis (Forssk), under natural field conditions. In the Serengeti National Park, Tanzania, A. robusta trees occur in tall grasslands of the north (shady) and A. tortilis in the southern short grasslands

(lighter). We designed a greenhouse experiment to quantify sunlight effects on growth, architecture, and resource allocation traits in seedlings. In the field, A. robusta seedlings were associated with lower understory sunlight during the wet season compared to A.

90 tortilis, with the trends switching for the dry season. In the greenhouse experiments, plant growth, architecture, and resource allocation were influenced by sunlight exposure.

Under low sunlight, A. robusta gained height faster than A. tortilis. Self-shading of canopy leaves was evident in A. tortilis and not in A. robusta.

Trends for their biomass allocation to leaves, stems, and roots differed at different light environments suggesting variable strategies to cope with the sunlight resource availability. Our study suggests that microsite sunlight variability should be part of models developed to understand spatial and temporal variability in savanna tree recruitment.

Keywords: coexistence, incident sunlight, light interception, seedling recruitment, savanna.

Introduction

A fundamental goal in plant ecology is to understand how plants coexist and persist under variable and limited resource availability (Foster and Tilman 2003; Tilman 1997).

Because of high mortality, the tree seedling stage may be a critical demographic bottleneck for plants in highly seasonal environments (Brodersen et al. 2019; Holdo et al.

2014). Savannas are highly seasonal, and fire dependent ecosystems characterized by a grass-dominated understory and a spatially heterogeneous distribution of adult trees

(Lehmann et al. 2014; Ratnam et al. 2011). Within the grass canopy, first-year tree seedlings experience high rates of mortality due to stressful abiotic and biotic conditions

(Setterfield et al. 2018; Tomlinson et al. 2019), including potentially strong competition

91 from grasses. However, the nature and magnitude of this grass/tree seedling competition remains unclear (Cramer et al. 2012; Morrison et al. 2018).

Establishment depends on a seedling’s ability to survive and optimize the ‘persistence niche’ despite limiting resources and microclimatic conditions (Brodersen et al. 2019;

Cramer and Bond 2012; Cramer et al. 2012). However, much of the research on tree seedlings in savannas focuses on growth and survival as a function of disturbance regimes such as herbivory and fire (Gignoux et al. 2016; Wigley et al. 2009). While some research has focused on seedling growth responses to abiotic drivers such as soil moisture

(Kebbas et al. 2015; Maraghni et al. 2010; Wilson and Witkowski 1998), little attention has been paid to seedling responses to sunlight variability in the grass understory.

Understanding the influence of light levels is important because the grass understory can impose strong competition for above ground resources (Davis et al. 1999; Dickie et al.

2007). Theory suggests that plant species in seasonal environments can utilize plasticity in architecture and resource allocation to enhance growth and survival (Valladares et al.

2007; Valladares and Niinemets 2007). However, knowledge about the extent to which savanna seedling growth, architecture, and biomass allocation respond in concert to changes in grass cover remains unclear.

East African savannas are dominated by species in the genus Acacia (= Vachellia), two of the most common being Acacia robusta (Burch.) and A. tortilis (Forssk.). In Serengeti

National Park, Tanzania (Serengeti—hereafter), A. robusta dominates northern areas comprised of characteristic tall grasslands whereas A. tortilis dominates the short grasslands in the southern areas of the park (McNaughton 1983; Rugemalila et al. 2016).

Despite their dominance in the canopy, seedlings of these species in Serengeti are

92 relatively rare in the herbaceous layer (Anderson et al. 2015), due to a strong demographic bottleneck limiting establishment (Holdo et al. 2014). Thus, the spatial and life stage distribution of adults and seedlings are uncoupled – possibly as a result of a historical shift in the conditions suitable for seedling establishment or because of fundamentally low recruitment rates (Anderson et al. 2015). If the latter hypothesis is supported (low background recruitment rates), we expect these species to exhibit variability in traits that promote establishment under harsh and variable environments

(Valladares et al. 2007). Examples include growth and architectural traits that maximize light interception under low light or above- and belowground resource allocation traits that will vary in response to resource limitation (Durigan et al. 2012).

Increasing precipitation in savannas typically leads to herbaceous communities dominated by taller grass species, which may impose strong light limitations on young, understory tree seedlings (McNaughton 1985; Morrison et al. 2018). Therefore, the purpose of this study was to combine field observation and a controlled experiment to (1) evaluate the current distribution of A. robusta and A. tortilis seedlings in relation to sunlight exposure in the field, and (2) link those distributions to variation in seedling growth, architectural, and resource allocation trait responses to sunlight exposure under controlled conditions. As our focus was on young seedlings, we conducted our study based on two main hypotheses; (1) that photosynthetic active radiation (PAR) at microsite

(below grass canopy) level would reflect the distribution pattern of the Acacia seedlings in the field i.e. A. robusta trees occur in the taller grasslands of the north (less light) and

A. tortilis in the shorter grasslands in the south (more light) (Anderson et al. 2015;

Rugemalila et al. 2016). (2) We also hypothesized that traits related to seedling growth,

93 architecture and resource allocation for both species will vary according to the light micro-environment in which they occur, i.e. seedlings of A. robusta (which are successful under higher productivity conditions) should either be tolerant to limited sunlight or grow faster as a mechanism to outcompete grasses and access light. In contrast, we expect A. tortlis seedlings to show traits that increase tolerance to high sunlight levels as a mechanism to persist in open sites.

Materials and Methods

Our research combined observational studies of seedling distributions of two dominant tree species in Serengeti with experiments in a controlled greenhouse setting. In the field, we surveyed and assessed the light conditions of naturally germinated seedlings in two different seasons across eight sites. For the controlled experiment, we germinated seeds, quantified seedling traits and analyzed the extent to which the observed trait patterns were important in the field conditions in Serengeti.

Study system

Serengeti is a tropical savanna located in northern Tanzania (34.85 o E, 2.44 o S) and is classified as a mix of Acacia-Commiphora woodland and edaphic grasslands on volcanic soils (i.e., the Serengeti plains). Field observations were conducted at eight study sites located in the central woodlands of Serengeti where our focal species overlap in their distributions. Mean annual rainfall for these sites is ~ 750 mm yr-1 (range: 626 – 814 mm yr-1). The minimum, maximum and average distance between sites are ~ 6, 100 and 49 kilometers respectively. More details about the sites can be found in Holdo et al. (2019).

Seedling distributions: To understand landscape-level variability in the microsite light environments of A. tortilis and A. robusta under field conditions, we used eight established 1- kilometer transects located at our study sites distributed across Serengeti

(see Holdo et al. (2019)). Within each transect, we mapped the location of > 100 seedlings belonging to 14 different tree species (mean ± se = 124 ± 6.1 seedlings per transect; A. robusta 18% ± 1.4% and A. tortilis 24% ± 11.3%) per transect. During both the wet (May) and dry (October) seasons of 2018, we measured the effect of herbaceous understory on photosynthetically active radiation (PAR; μmol m-2s-1) at the location of every target seedling. We measured PAR using a ceptometer (AccuPar LP-80, Decagon

Devices, Inc., Pullman, WA) held i) above the grass canopy and ii) at ground level

(below the grass canopy) adjacent to each seedling (avoiding shading of the ceptometer by the seedling itself). The light ratio (bottom/top) of the two measurements provides an index of light interception, with lower values indicating a greater degree of light interception by the grass canopy and greater herbaceous biomass (Gibson 2014).

Greenhouse experiments: Seeds of A. robusta and A. tortilis were haphazardly collected from multiple individuals in central Serengeti, mixed within species, and transported to

Wake Forest University, USA for further study in a controlled environment.

A 2:1:1 potting mixture of organic (Metromix 360 -P, Marysville, OH), sand and clay soils was prepared and used for germination in seedling trays and in pots for the main experiment. Prior to planting, seeds were scarified by gently scratching the seed coat with sandpaper to expose the mesocarp and enhance water permeability. Seeds were germinated in plastic trays for seven days after which newly emerged seedlings were transplanted into 10.2 x 10.2 x 35.6 cm tree pots. Light levels of 100% (high), ~50%

(medium), and ~25% full sunlight (low) were created using neutral-density shade cloth.

Cumulative daily photosynthetic photon flux (PPF) was measured using Li-Cor LI-190

PAR sensors connected to a HOBO H8 datalogger (Onset Computer, Bourne, MA). The mean daily maximum photosynthetic photon flux (PPF) in the greenhouse for each light treatment were 1009 ± 115 mol m-2 (high; n = 53); 594 ± 44 mol m-2 (medium, n = 36); and 337 ± 31 mol m-2 (low, n = 18). Watering of seedlings in the greenhouse was done once a week. Throughout the greenhouse study, the mean daily minimum and maximum temperature was 23.5 ºC and 29.1 ºC respectively.

Seedling growth data: To quantify seedling growth, we focused on structural traits that potentially influence light capture and above ground plant competitiveness (Ford 2014;

Freschet et al. 2018). We quantified relative growth rate (RGR, based on seedling total dry mass) to understand resource acquisitive strength of our study species under variable sunlight environment (Reich et al. 2003a; Tomlinson et al. 2014). We measured absolute height growth (AHG) to understand whether growing under limited sunlight triggers rapid height gain (Anten 2004; Archibald and Bond 2003; Hunt 1990) as a mechanism to increase light acquisition and competitive ability.

To estimate RGR, a separate set of seedlings were germinated (A. tortilis: n = 35; A. robusta: n = 23) and harvested after 7 days, washed free of soil, placed in a drying-oven for 24 h at 65 °C and weighed to establish an average initial seedling dry biomass. RGR was estimated using the following general formula:

푅퐺푅 = 푙푛퐵2−푙푛퐵1……………………………………………………………….. (i) 푇2 − 푇1

Where T1 and T2 represent initial (i.e., seedling transplant) and final (i.e., harvest) times, while lnB1 and lnB2 represent the log-transformed average initial and final dry biomass values, respectively. We used seedling height to estimate AHG. We compared values of seedling vertical height (measured perpendicularly to the soil) and seedling length

(measured along stem length - as some seedlings grew in an oblique inclination) and used the maximum value to represent seedling height parameter. AHG was calculated as the:

퐴퐻퐺 = 퐻2−퐻1……………………………………………………………………… (ii) 푇2 − 푇1

Where T1 and T2 represent initial and final times, and H1 and H2 are the seedling height at

T2 and T1, respectively.

Seedling canopy architecture: To quantify seedling architecture, we focused on a trait linked to foliage exposure and light capture efficiency: the ratio of seedling canopy silhouette area to projected leaf area (SPAR; see supplementary methods S1). SPAR reflects architectural adjustments at the whole-plant scale and measures the leaf surface area in the seedling canopy that is directly absorbing sunlight when the canopy leaves are in their natural orientation at a given time of day. Lower values of SPAR represent more self-shading within the canopy which lowers light interception efficiency (Cory et al.

2017; Ishii et al. 2012; Oker-Blom and Smolander 1988). To understand how leaf exposure affects seedling respiration, we utilized unit leaf rate (ULR) equation (Poorter et al. 2009) to analyze the relationship between SPAR and ULR. Unit leaf rate determines the daily seedling respiration per unit leaf area (Poorter et al. 2009; Poorter and Remkes

1990).

Biomass allocation to leaves, stems and roots: For resource allocation, seedlings were destructively harvested to measure below- and above-ground biomass after 56 days of growth. To estimate dry biomass, the harvested seedlings were washed free of soil, placed in a drying-oven for 48 hours at 70 °C and weighed. To understand relative allocation to different plant parts, harvested seedlings were divided into leaves, stems and roots. Variation in biomass allocation to leaves, stems, and roots was calculated with standard metrics (Poorter et al. 2012), including root-to-shoot ratio (RSR; root dry mass/shoot dry mass), leaf mass fraction (LMF; leaf dry mass/total dry mass), stem mass fraction (SMF; stem dry mass/total dry mass), root mass fraction (RMF; root dry mass/total dry mass). In addition, we measured LMA (Leaf dry Mass per projected leaf

Area) of a sample of 2-4 leaves from each seedling to understand the association between biomass allocation and light resource acquisition per leaf area which has important implication on leaf life-span (Enrique et al. 2016; Wright et al. 2004).

Statistical analyses

All data analyses were performed in the R statistical language (R Development Core

Team 2011) version 3.5.1. Data were tested for normality prior to analysis. Non-normally distributed data, and all ratios, were log-transformed. Otherwise, no adjustments were made (Baum 2008; O’hara and Kotze 2010; Warton and Hui 2011).

For the field study, we first analyzed mean PAR ratio per site to understand how PAR ratio varied between species and seasons. To understand how PAR environment influences species incidence in different seasons, we developed a set of five (including intercept only) competing generalized linear mixed models. In our models, variable site

98 was used as random effect while SEASON and SPECIES were used as fixed factors (see

Table S3 for models and interactions). We then employed a model selection approach using Akaike Information Criterion (AIC) in which the final superior model had the lowest AIC values (Burnham and Anderson 2002). For the greenhouse study a two-way analysis of variance (ANOVA) implemented in the R package car (Fox 2002) was used to test for the effects of species and sunlight levels on RGR, AHG, SPAR, RSR, LMF,

SMF, RMF and LMA. To test for the effect of species and sunlight on ULR, analysis of covariance (ANCOVA) was used with variable SPAR as our covariate. As our goal was to understand whether our study species respond similarly or differently to sunlight, we diverted our attention to main effects of species and sunlight only when the ANOVA results showed no interaction effects. For significant interaction (α ≤ 0.05), a Tukey HSD post hoc tests were used to identify species treatment effects.

Results

Seedling distributions in Serengeti woodlands: For the field study, the mean microsite

PAR ratio differed significantly between species and seasons (X2 = 152, df = 1, p <

0.0001, n = 504; Fig. 10). The distribution of both species in the field was potentially influenced by the light environment in that during the dry season, percent difference in light incidence was almost 20% with A. robusta associating with higher PAR ratio than A. tortilis (A. robusta-dry season: mean±SE:0.66 ± 0.03, n = 80 | A. tortilis-dry season: mean±SE:0.54 ± 0.02, n = 149, Fig.10). During the wet season however, percentage difference in light incidence was 35% with A. robusta experiencing lower PAR ratio than

A. tortilis (A. robusta-wet season: mean ± SE: 0.31 ± 0.02, n = 106 | A. tortilis-wet season: mean ± SE: 0.44 ± 0.01, n = 169, Fig. 10).

Seedling growth and canopy architecture under variable light conditions: Over the course of the experiment, the RGR of A. robusta was on average about 9% lower than that of A. tortilis (A. robusta: mean±SE:0.078gg day-1 ± 0.001gg day-1, n = 27 | A. tortilis: mean ± SE: 0.86 gg day-1 ± 0.002 gg day-1, n = 27, Table S4, Fig. 11a) with both species experiencing an average RGR increase of about 20% when treatment level was increased from low to full sunlight (Fig. 11a). For seedling height growth, reduced sunlight condition resulted in about 22% difference with A. robusta attaining greater AHG values than A. tortilis (low sunlight: A. robusta: mean±SE:3.2mm day-1 ± 0.1mm day-1, n = 20 |

A. tortilis: mean ± SE: 2.5mm day-1 ± 0.2mm day-1, n = 20, Table S4, Fig. 11b) with no significant differences between species under full sunlight treatments.

Acacia tortilis consistently had lower SPAR values than those of A. robusta (Fig. 11c), with on average A. robusta expressing 10% ± 0.005% of its leaf area and A. tortilis expressing 7% ± 0.003% suggesting increased self-shading in A. tortilis compared to A. robusta seedlings (Table S4, Fig. 11c). Ancova results on ULR when controlling for

SPAR showed the main effects of species and sunlight levels (Table 5). Daily seedling unit leaf rate (ULR) increased by more than two-fold in A. tortilis and three-fold in A. robusta as a function of sunlight levels (Fig. 11d). However, among the two species, while the variable ULR and SPAR were found to be negatively correlated in A. robusta

(r(70) = -0.36, p = 0.002, Fig. 12a), the correlation was weak for A. tortilis (r(70) = -0.23, p < 0.06; Fig. 12b).

100

Resource allocation: Both species increased their resource allocation to roots (RSR) in response to increasing sunlight exposure with no significant differences between species

(Table 5, Fig S7). Despite these apparent similarities in RSR trends between species, the patterns of biomass allocation to above ground tissues (leaves and stems) were different

(Table S5). While A. robusta resulted in a fixed proportion of leaf mass fraction (LMF)

(mean ± se; 0.32 ± 0.02, n = 27), A. tortilis had a considerable decrease in LMF of about

45% with increasing sunlight exposure (low sunlight: mean ± SE; 0.41 ± 0.02, n = 8; full sunlight: mean ± SE; 0.23 ± 0.03, n = 9; Table S5, Fig. 13a). In addition, as A. tortilis seedlings resulted in almost static proportion of stem biomass (SMF) (mean ± se; 0.23 ±

0.02, n = 27) at all light levels, the proportion in A. robusta decreased by 36% when grown under full sunlight levels (Fig. 13b). Both species had an average root biomass proportion of about (mean ± SE; 0.39 ± 0.02; n = 17) under low sunlight condition which increased to (mean ± SE; 0.54 ± 0.03; n = 17; Table S5, Fig. 13c) under full sunlight.

Furthermore, while A. robusta doubled its low sunlight LMA values (low sunlight: mean

± SE; 31gm-2 ± 2gm-2, n = 9; full sunlight: mean ± SE; 78gm-2 ± 3gm-2, n = 8), A. tortilis values increased gradually by 59% (low sunlight: mean ± SE; 31gm-2 ± 4gm-2, n = 8; full sunlight: mean ± SE; 49gm-2 ± 10gm-2, n = 9; Table S5, Fig. 13d).

Discussion

The results of our study demonstrate (1) the seasonal and spatial variability in the proportion of sunlight intercepted by the grass canopy, which correlates to the distributions of our two study species, and (2) that these species differ in the mechanisms by which they optimize resource acquisition under different light regimes. Taken

101 together, these findings suggest that light-gathering strategies of our study species are an important factor for savanna seedling establishment, growth and success (Liu et al. 2006;

Salazar et al. 2012; Valladares et al. 2016).

Seasonal variation in the proportion of sunlight intercepted by the grass layer (Fig. 10) corresponds to the characteristic high herbaceous biomass that occur in mesic sites of

Serengeti (Holdo et al. 2014; McNaughton 1985). During the dry season, mesic sites of

Serengeti experience increased fires and grazing pressure which removes most of the grass biomass and exposes the surviving seedlings to substantially more sunlight

(McNaughton 1979; McNaughton 1985). High values of PAR ratio during the dry season at A. robusta locations supports the notion that mesic sites where A. robusta species is abundant have higher grass biomass productivity but become more open during the dry seasons because of increased grazing and fire intensity (Probert et al. 2019). The effect of large grass canopy on growing seedlings has been reported in other ecosystems as they impose substantial changes in the light and moisture environment which may influence inter-species interactions (Iacona et al. 2012; Whitecross et al. 2017).

In the greenhouse experiment, the effect of sunlight exposure was apparent, implicating an important role of sunlight during the early stage of seedling establishment. Limited sunlight reduced relative growth rates (RGR) of both species by an average of ~ 20% of full sunlight level (Fig. 11a) suggesting a resource-driven (in this case, light) reduction in carbon gain when conditions are not favourable (Reich et al. 2003b; Tomlinson et al.

2014). However, rapid aboveground height growth (AHG) under low sunlight in A. robusta (Fig. 11b) suggests a phenotypic response aimed at avoiding low sunlight, increase stem elongation to capture more sunlight and possibly increase competitiveness

102 with neighbor vegetation (Freschet et al. 2018; Irving 2015; Liu and Su 2016; Valladares et al. 2016). Investment in shoot elongation at early stage in A. robusta, nevertheless, may come at a cost of being unsustainable as young seedlings may bear thinner and weaker stems which would make the plant structure more susceptible to mechanical failure (Anten 2004; Archibald and Bond 2003; Liu et al. 2006; Valladares et al. 2016) or result in higher mortality under severe fires (Bond and Keeley 2005; Hoffmann et al.

2020; Probert et al. 2019). Previous studies suggest that for such seedlings to advance to a sapling stage, longer fire-free periods may be the crucial survival option (Higgins et al.

2000; Hoffmann et al. 2020). Rarity of these fire-free periods in most savanna ecosystems may be linked to low recruitment rates and substantial decoupling of species composition between understory and overstory tree communities (Anderson et al. 2015;

Armani et al. 2018).

Our study found experimental evidence of plant level canopy adjustments that might benefit both species in different ways in optimizing light absorption surface area to balance the costs of possible too much sun exposure. At first, lower SPAR values for A. tortilis (Fig. 11c) suggest increased self -shading which may ultimately enable its seedlings to minimize leaf damage and eventually acclimate to prolonged periods of high light environments (Irving 2015). In contrast, higher SPAR values in A. robusta suggest reduced leaf overlap and self-shading, presumably as a way to maintain efficient photosynthetic capabilities per unit leaf area under constistent limited sunlight (Ishii et al.

2012; Valladares and Niinemets 2007; Valladares et al. 2002). However, our data suggests that photosynthesis output of seedlings represented by unit leaf area rate (ULR) is light-dependent as it increases with sunlight in both species (Fig. 11d). Moreover, at a

103 given SPAR value, plants grown under high sunlight light were able to achieve higher

ULR (and thus, higher Net Photosynthesis) than plants grown under low light (Fig. 12a- b). Additionally, plants grown under high sunlight tend to have lower SPAR values suggesting that high-light plants can build canopies with lower SPAR (which is good for reducing leaf temperatures, and evaporation), but still achieve high high Net

Photosynthesis. This is further suggesting a balance between light-absorbing area vs the costs of too much sun exposure (Ishii et al. 2012). Comparing species, A. tortilis seedlings tended to have lower SPAR values within each particular light level (Fig. 12b) suggesting the ability to achieve similar ULR to A. robusta (Fig. 12a), despite having overal lower leaf exposure to high incident sunlight (Falster and Westoby 2003; Poorter et al. 2009; Valladares and Niinemets 2007).

Both species increased investment towards light gathering structures when sunlight was limited (Fig. S7) - consistent with theory and previous emperical studies (Freschet et al.

2018; Smith and Shackleton 1988). Due to a constrained balance between the amount of biomass invested in roots, stems and leaves (Poorter and Nagel 2000; Reynolds and

Pacala 1993), each species however, showed different biomass allocation strategy between respective plant parts (Fig. 13 a-c). Biomass allocation to leaves in A. robusta was almost constant at all light levels (Fig. 13a), suggesting an adaptation for effiecient exploitation of low irradiance which requires consistent investment in light acquiring struture for assured photosynthetic capability (Durigan et al. 2012; Tomlinson et al. 2019;

Tomlinson et al. 2012) given high grass productivity in areas where the species occur.

Overall, most of A. robusta biomass was allocated to roots regardless of sunlight conditions (low sunlight: mean ± se; 42% ± 1%, full sunlight: 52% ± 2%, Fig. 13c),

104 implying that despite canopy architecture adjustment for sunlinght acquision, roots are central organs of biomass conservation (Reich et al. 2003b; Tomlinson et al. 2012). On the contrary, while most biomass in A. tortilis was allocated to leaves under low sunglight

(mean ± se; 41% ± 2%, Fig. 13a), the trend switched to allocation to roots under full sunlight (mean ± se; 56% ± 3%, Fig. 13c) leaving the stems with only ~ 21%, fig. 13b.

Nevertheless, our results suggest that Acacia seedlings prioritize biomass allocation to resource acquiring organs (leaves and roots) at the expense of stems (Poorter et al. 2012).

The functional value of biomass allocated to leaves can be evaluated through LMA trends because species with low LMA have larger leaf surface area for light capture per unit leaf mass (Enrique et al. 2016; Poorter et al. 2009; Wright et al. 2002). Similar values of LMA in both species (Fig. 13d) suggest equal photosynthetic capacity when light is limiting.

However, variable values LMA at full sunlight suggests variable strategy for photosynthetic processes and leaf longevity. As pointed out earlier, A. robusta seedlings produce fewer leaves than A. tortilis but maintain about 32% of its biomass in leaves.

This suggests that A. robusta leaves may be capable of maintaining thicker leaf blades and/or denser tissue which might be good for both photosynthetic processes (Wright et al.

2004; Wright et al. 2002) and leaf longevity at variable light conditions (Osnas et al.

2013). As for A. tortilis, low LMA values (compared to A. robusta) at full sunlight may be related to apparent low LMF (~ 22%). With lower than A. robusta LMA values, efficient exploitation of high irradiance may require increased leaf overlap (indicated by low

SPAR). This is because lowering LMA under excessive sunlight exposure may reduce seedling leaf longevity (Osnas et al. 2013; Tong and Ng 2008) which may require increased leaf turnover in order to replace the shed leaves.

105

In conclusion, tree seedling - grass competition studies have mostly focused on variation in water use without distinguishing between competition for water and light - which are correlated as herbaceous biomass increases (Holdo and Brocato 2015). Variation in light availability imposes variable conditions to the growing seedlings (Iacona et al. 2012;

Valladares et al. 2016), which may influence future abundance and range shifts.

Periodically, ecologists do not incorporate both the above- and below-grass canopy light, hence posing a possibility of underrating the level at which sunlight interception influences seedling recruitment and distribution (Niinemets 2010; Stave et al. 2006).

Moreover, seedling recruitment is a function of multiple biotic and abiotic factors

(Hoffmann 2000) and arguably requires multi-variable analysis to understand combined effects at different life stages. Studies of recruitment bottlenecks are still paramount, especially on combined resource limitation and to evaluate possible trade-offs between responses (e.g., combined nutrient, moisture, and light availability). The study of recruitment bottlenecks and trade-offs will significantly improve our understanding of life-history strategies and management of globally important savanna ecosystems.

106

Acknowledgements

We thank, the Serengeti Wildlife Research Centre (SWRC), the Tanzania Wildlife

Research Institute (TAWIRI), the Commission for Science and Technology (COSTEC), the Tanzania National Parks (TANAPA) for permission to conduct research in Tanzania.

We are grateful to Jeremia Sarakikya and Meshark Mwita for excellent assistance in the field, and to Wake Forest University undergraduate students Robyn Corapi, Noemie

Kloucek and Aidan Galloway for their help in the greenhouse. Wake Forest University and National Science Foundation grants to TMA supported this work. We confirm that we have no conflicts of interest.

107

Declarations

Funding: This study was funded by the department of Biology, Wake Forest University, through Vecellio award to DMR. This study was also funded by the National Science

Foundation (NSF BCS 1461728) to TMA.

Conflicts of interest: We confirm that we have no conflicts of interest

Availability of data and material: Data available from the Dryad Digital Repository, upon acceptance.

Authors' contributions: DMR and SC designed the experiment, collected and performed data analysis. TMA and WKS provided editorial advice. DMR wrote first draft of the manuscript, and all authors contributed critically to subsequent drafts.

108

Tables and figures

Seedling growth and architecture Resource allocation traits

traits

Analysis Factor d.f F P Analysis Factor d.f F P

RGR g g-1 day-1 SPECIES 1 14.02 0.0005 RSR (g g-1) SPECIES 1 1.61 0.211

LIGHT 2 26.84 <0.000 LIGHT 2 10.01 0.0002

1 3

SPECIES x 2 1.23 0.3 SPECIES x 2 0.64 0.53

LIGHT LIGHT

Residuals 48 Residuals 48

AHG mm day-1 SPECIES 1 4.97 0.028 LMF (g g-1) SPECIES 1 0.03 0.85

LIGHT 2 3.48 0.034 LIGHT 2 8.27 0.008

109

SPECIES x 2 3.58 0.031 SPECIES x 2 6.85 0.002

LIGHT LIGHT

Residuals 114 Residuals 48

SPAR SPECIES 1 31.18 <0.000 SMF (g g-1) SPECIES 1 8.97 0.004

LIGHT 2 1.88 0.16 LIGHT 2 6.97 0.002

SPECIES x 2 0.25 0.78 SPECIES x 2 5.42 0.008

LIGHT LIGHT

Residuals 150 Residuals 48

ULR (g cm-2 SPECIES 1 6.75 0.013 RMF (g g-1) SPECIES 1 2.61 0.11 day-1)

LIGHT 2 19.8 <0.000 LIGHT 2 15.67 <0.000

1 1

110

SPAR 1 4.85 0.03 SPECIES x 2 2.47 0.095

LIGHT

SPECIES x 2 1.01 0.37 Residuals 48

LIGHT

Residuals 40

LMA (g m- SPECIES 1 5.58 0.022

LIGHT 2 9.94 0.0003

SPECIES x 2 2.24 0.12

LIGHT

Residuals 48

111

Figure 10 Seasonal variation in mean photosynthetic active radiation (PAR) ratio (mean ±

SE) for A. robusta (filled bars) (n = 186) and A. tortilis (open bars) (n = 318) seedlings microsites in the Serengeti. Lower PAR ratio values indicate a greater degree of light interception by the grass canopy and greater herbaceous biomass

112

Figure 11 Effect of sunlight levels on (mean ± SE) (a) relative growth rate RGR (g g-

1day-1) and (b) height growth AHG (mm day-1), (c) Silouehette to total leaf area ratio

SPAR, and (d) unit leaf rate ULR (g m-2 day-1) in A. robusta (solid lines) and A. tortilis

(dash lines) seedlings grown in a greenhouse.

113

114

Figure 13 Effect of sunlight levels on (mean ± SE) (a) leaf mass fraction, LMF, (b) stem

– mass fraction, SMF, (c) root – mass fraction, RMF and (d) Leaf Mass area (LMA) in A. robusta (solid lines) (n = 27) and A. tortilis (dash lines) (n = 27) seedlings as a function of variation in sunlight levels

115

Literature cited

Anderson TM, Morrison T, Rugemalila D, Holdo R (2015) Compositional decoupling of

savanna canopy and understory tree communities in Serengeti. Journal of

Vegetation Science 26:385-394. doi: 10.1111/jvs.12241

Anten NP (2004) Optimal photosynthetic characteristics of individual plants in vegetation

stands and implications for species coexistence. Annals of Botany 95:495-506

Archibald S, Bond WJ (2003) Growing tall vs growing wide: tree architecture and

allometry of Acacia karroo in forest, savanna, and arid environments. Oikos

102:3-14

Armani M, Van Langevelde F, Tomlinson KW, Adu-Bredu S, Djagbletey GD,

Veenendaal EM (2018) Compositional patterns of overstorey and understorey

woody communities in a forest–savanna boundary in Ghana. Plant Ecology &

Diversity 11:451-463

Baum CF (2008) Stata tip 63: Modeling proportions. Stata Journal 8:299

Bond WJ, Keeley JE (2005) Fire as a global ‘herbivore’: the ecology and evolution of

flammable ecosystems. Trends in Ecology & Evolution 20:387-394

Brodersen CR et al. (2019) Seedling survival at timberlines critical to conifer mountain

forest elevation and extent. Frontiers in Forests and Global Change 2:9

Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a

practical information-theoretic approach. Springer Science & Business Media

116

Cory ST, Wood LK, Neufeld HS (2017) Phenology and growth responses of Fraser fir

(Abies fraseri) Christmas trees along an elevational gradient, southern

Appalachian Mountains, USA. Agricultural and Forest Meteorology 243:25-32

Cramer MD, Bond WJ (2012) N-fertilization does not alleviate grass competition induced

reduction of growth of African savanna species. Plant and Soil 366:563-574. doi:

10.1007/s11104-012-1456-4

Cramer MD, Wakeling JL, Bond WJ (2012) Belowground competitive suppression of

seedling growth by grass in an African savanna. Plant Ecology 213:1655-1666

Davis MA, Wrage KJ, Reich PB, Tjoelker MG, Schaeffer T, Muermann C (1999)

Survival, growth, and photosynthesis of tree seedlings competing with herbaceous

vegetation along a water-light-nitrogen gradient. Plant Ecology 145:341-350

Dickie IA, Schnitzer S, Reich P, Hobbie S (2007) Is oak establishment in old‐fields and

savanna openings context dependent? Journal of Ecology 95:309-320

Durigan G, Melo AC, Brewer JS (2012) The root to shoot ratio of trees from open-and

closed-canopy cerrado in south-eastern Brazil. Plant Ecology & Diversity 5:333-

343

Enrique G, Olmo M, Poorter H, Ubera JL, Villar R (2016) Leaf mass per area (LMA) and

its relationship with leaf structure and anatomy in 34 Mediterranean woody

species along a water availability gradient. PloS one 11:e0148788

Falster DS, Westoby M (2003) Leaf size and angle vary widely across species: what

consequences for light interception? New Phytologist 158:509-525

Ford ED (2014) The dynamic relationship between plant architecture and competition.

Frontiers in plant science 5:275

117

Foster BL, Tilman D (2003) Seed limitation and the regulation of community structure in

oak savanna grassland. Journal of Ecology 91:999-1007

Fox J (2002) An R and S-Plus companion to applied regression. Sage

Freschet GT, Violle C, Bourget MY, Scherer‐Lorenzen M, Fort F (2018) Allocation,

morphology, physiology, architecture: the multiple facets of plant above‐and

below‐ground responses to resource stress. New Phytologist

Gibson DJ (2014) Methods in comparative plant population ecology. Oxford University

Press

Gignoux J, Konaté S, Lahoreau G, Le Roux X, Simioni G (2016) Allocation strategies of

savanna and forest tree seedlings in response to fire and shading: outcomes of a

field experiment. Scientific reports 6

Higgins SI, Bond WJ, Trollope WSW (2000) Fire, resprouting and variability: a recipe

for grass–tree coexistence in savanna. Journal of Ecology 88:213-229

Hoffmann WA (2000) Post‐Establishment Seedling Success in the Brazilian Cerrado: A

Comparison of Savanna and Forest Species 1. Biotropica 32:62-69

Hoffmann WA, Sanders RW, Just MG, Wall WA, Hohmann MG (2020) Better lucky

than good: How savanna trees escape the fire trap in a variable world.

Ecology:e02895

Holdo RM, Anderson TM, Morrison T (2014) Precipitation, fire and demographic

bottleneck dynamics in Serengeti tree populations. Landscape Ecology 29:1613-

1623

Holdo RM, Brocato ER (2015) Tree–grass competition varies across select savanna tree

species: a potential role for rooting depth. Plant Ecology 216:577-588

118

Holdo RM, Onderdonk DA, Barr AG, Mwita M, Anderson TM (2019) Spatial transitions

in tree cover are associated with soil hydrology, but not with grass biomass, fire

frequency, or herbivore biomass in Serengeti savannahs. Journal of Ecology

Hunt R (1990) Absolute growth rates. Basic growth analysis. Springer, pp 17-24

Iacona GD, Kirkman LK, Bruna EM (2012) Experimental test for facilitation of seedling

recruitment by the dominant bunchgrass in a fire-maintained savanna. PloS one 7

Irving LJ (2015) Carbon Assimilation, Biomass Partitioning and Productivity in Grasses.

Agriculture 5:1116

Ishii H, Hamada Y, Utsugi H (2012) Variation in light-intercepting area and

photosynthetic rate of sun and shade shoots of two Picea species in relation to the

angle of incoming light. Tree physiology 32:1227-1236

Kebbas S, Lutts S, Aid F (2015) Effect of drought stress on the photosynthesis of Acacia

tortilis subsp. raddiana at the young seedling stage. Photosynthetica 53:288-298

Lehmann CE et al. (2014) Savanna vegetation-fire-climate relationships differ among

continents. Science 343:548-552

Liu W, Su J (2016) Effects of light acclimation on shoot morphology, structure, and

biomass allocation of two Taxus species in southwestern China. Scientific reports

6:35384

Liu Y, Schieving F, Stuefer JF, Anten NP (2006) The effects of mechanical stress and

spectral shading on the growth and allocation of ten genotypes of a stoloniferous

plant. Annals of Botany 99:121-130

Maraghni M, Gorai M, Neffati M (2010) Seed germination at different temperatures and

water stress levels, and seedling emergence from different depths of Ziziphus

119

lotus. South African Journal of Botany 76:453-459. doi:

https://doi.org/10.1016/j.sajb.2010.02.092

McNaughton S (1979) Grazing as an optimization process: grass-ungulate relationships

in the Serengeti. The American Naturalist 113:691-703

McNaughton S (1983) Serengeti grassland ecology: the role of composite environmental

factors and contingency in community organization. Ecological monographs

53:291-320

McNaughton S (1985) Ecology of a grazing ecosystem: the Serengeti. Ecological

monographs 55:259-294

Morrison TA, Holdo RM, Rugemalila DM, Nzunda M, Anderson TM (2018) Grass

competition overwhelms effects of herbivores and precipitation on early tree

establishment in Serengeti. Journal of Ecology

Niinemets Ü (2010) A review of light interception in plant stands from leaf to canopy in

different plant functional types and in species with varying shade tolerance.

Ecological Research 25:693-714

O’hara RB, Kotze DJ (2010) Do not log‐transform count data. Methods in ecology and

Evolution 1:118-122

Oker-Blom P, Smolander H (1988) The ratio of shoot silhouette area to total needle area

in Scots pine. Forest science 34:894-906

Osnas JL, Lichstein JW, Reich PB, Pacala SW (2013) Global leaf trait relationships:

mass, area, and the leaf economics spectrum. Science 340:741-744

120

Poorter H, Nagel O (2000) The role of biomass allocation in the growth response of

plants to different levels of light, CO2, nutrients and water: a quantitative review.

Functional Plant Biology 27:1191-1191

Poorter H, Niinemets Ü, Poorter L, Wright IJ, Villar R (2009) Causes and consequences

of variation in leaf mass per area (LMA): a meta‐analysis. New phytologist

182:565-588

Poorter H, Niklas KJ, Reich PB, Oleksyn J, Poot P, Mommer L (2012) Biomass

allocation to leaves, stems and roots: meta‐analyses of interspecific variation and

environmental control. New Phytologist 193:30-50

Poorter H, Remkes C (1990) Leaf area ratio and net assimilation rate of 24 wild species

differing in relative growth rate. Oecologia 83:553-559. doi:

10.1007/BF00317209

Probert JR et al. (2019) Anthropogenic modifications to fire regimes in the wider

Serengeti-Mara ecosystem. Global Change Biology 25:3406-3423. doi:

10.1111/gcb.14711

R Development Core Team (2011) R: A language and environment for statistical

computing R foundation for Statistical Computing. R Foundation for Statistical

Computing, Vienna, Austria

Ratnam J et al. (2011) When is a ‘forest’ a savanna, and why does it matter? Global

Ecology and Biogeography 20:653-660. doi: 10.1111/j.1466-8238.2010.00634.x

Reich PB et al. (2003a) Variation in growth rate and ecophysiology among 34 grassland

and savanna species under contrasting N supply: a test of functional group

differences. New Phytologist 157:617-631

121

Reich PB et al. (2003b) The evolution of plant functional variation: traits, spectra, and

strategies. International Journal of Plant Sciences 164:S143-S164

Reynolds HL, Pacala SW (1993) An analytical treatment of root-to-shoot ratio and plant

competition for soil nutrient and light. The American Naturalist 141:51-70

Rugemalila DM, Anderson TM, Holdo RM (2016) Precipitation and Elephants, Not Fire,

Shape Tree Community Composition in Serengeti National Park, Tanzania.

Biotropica. doi: 10.1111/btp.12311

Salazar A, Goldstein G, Franco AC, Miralles‐Wilhelm F (2012) Differential seedling

establishment of woody plants along a tree density gradient in Neotropical

savannas. Journal of Ecology 100:1411-1421

Setterfield SA, Clifton PJ, Hutley LB, Rossiter-Rachor NA, Douglas MM (2018) Exotic

grass invasion alters microsite conditions limiting woody recruitment potential in

an Australian savanna. Scientific reports 8

Smith T, Shackleton SE (1988) effects of shading on the establishment and growth of

Acacia tortilis seedlings. South African journal of botany: official journal of the

South African Association of Botanists= Suid-Afrikaanse tydskrif vir plantkunde:

amptelike tydskrif van die Suid-Afrikaanse Genootskap van Plantkundiges

Stave J, Oba G, Nordal I, Stenseth NC (2006) Seedling establishment of Acacia tortilis

and Hyphaene compressa in the Turkwel riverine forest, Kenya. African Journal

of Ecology 44:178-178. doi: 10.1111/j.1365-2028.2006.00614.x

Tilman D (1997) Community invasibility, recruitment limitation, and grassland

biodiversity. Ecology 78:81-92

122

Tomlinson KW, Poorter L, Bongers F, Borghetti F, Jacobs L, van Langevelde F (2014)

Relative growth rate variation of evergreen and deciduous savanna tree species is

driven by different traits. Annals of botany 114:315-324

Tomlinson KW, Sterck FJ, Barbosa ER, de Bie S, Prins HH, van Langevelde F (2019)

Seedling growth of savanna tree species from three continents under grass

competition and nutrient limitation in a greenhouse experiment. Journal of

Ecology 107:1051-1066

Tomlinson KW et al. (2012) Biomass partitioning and root morphology of savanna trees

across a water gradient. Journal of Ecology 100:1113-1121

Tong P, Ng F (2008) Effect of light intensity on growth, leaf production, leaf lifespan and

leaf nutrient budgets of Acacia mangium, Cinnamomum iners, Dyera costulata,

Eusideroxylon zwageri and Shorea roxburghii. Journal of Tropical Forest

Science:218-234

Valladares F, Gianoli E, Gómez JM (2007) Ecological limits to plant phenotypic

plasticity. New phytologist 176:749-763

Valladares F, Laanisto L, Niinemets Ü, Zavala MA (2016) Shedding light on shade:

ecological perspectives of understorey plant life. Plant Ecology & Diversity

9:237-251

Valladares F, Niinemets U (2007) The architecture of plant crowns: from design rules to

light capture and performance. Functional plant ecology. 2nd Edition (Books in

soils, plants and the …

123

Valladares F, Skillman JB, Pearcy RW (2002) Convergence in light capture efficiencies

among tropical forest understory plants with contrasting crown architectures: a

case of morphological compensation. American journal of Botany 89:1275-1284

Warton DI, Hui FK (2011) The arcsine is asinine: the analysis of proportions in ecology.

Ecology 92:3-10

Whitecross M, Witkowski E, Archibald S (2017) Savanna tree-grass interactions: A

phenological investigation of green-up in relation to water availability over three

seasons. South African Journal of Botany 108:29-40

Wigley B, Cramer M, Bond W (2009) Sapling survival in a frequently burnt savanna:

mobilisation of carbon reserves in Acacia karroo. Plant Ecology 203:1

Wilson T, Witkowski E (1998) Water requirements for germination and early seedling

establishment in four African savanna woody plant species. Journal of Arid

Environments 38:541-550

Wright IJ et al. (2004) The worldwide leaf economics spectrum. Nature 428:821-827

Wright IJ, Westoby M, Reich PB (2002) Convergence towards higher leaf mass per area

in dry and nutrient‐poor habitats has different consequences for leaf life span.

Journal of ecology 90:534-543

124

Supplemental Materials

Title: The role of microsite sunlight environment on growth, architecture, and resource

allocation in dominant Acacia tree seedlings, in Serengeti, East Africa

Deusdedith M. Rugemalila1 *, Scott Cory1, William K. Smith1 and T. Michael Anderson1

1Department of Biology, Wake Forest University, Winston-Salem, NC, USA,

*Corresponding author; e-mail: [email protected]

125

Appendix S1. Supplementary methods

Calculation of the silhouette projected area ratio (SPAR)

SPAR was collected at three times (0900, 1200 and 1600 hours) on day 51 of the experiment. The process involved photographing each seedling using a Nikon D50 DSLR digital camera (Nikon, Melville, NY, USA) with 55mm lens, from a position nearly parallel to the sun beam (Fig. S5). A small disk of a known area was placed adjacent to each seedling for calibration purposes (Fig. S6- (a)). Images were digitally processed using Adobe Photoshop CC 2017 (Adobe Systems Inc., San Jose, CA) to create a uniform background leaving a clear black seedling silhouette for further analysis (Fig.

S6- (b)). The total single-side projected leaf area was estimated by scanning a sub-sample of leaves (range 3 – 5), averaging the projected area per leaf across individuals and multiplying by the total number of leaves at the time of the silhouette area measurements for the whole seedlings. The automated software program Black Spot (Varma and Osuri

2013) was used to estimate leaf area from images to calculate pixels from seedling canopy silhouette images and projected disks. SPAR was obtained by dividing the silhouette area of each seedling by the total estimated projected area of all leaves (Carter and Smith 1985; Smith et al. 1991).

126

Supplemental figures

0900 1200 1500

TIME (hrs.)

Figure S 5 Illustration of camera positions used to determine silhouette area at different times of the day.

127

(a) (b)

Figure S 6 Illustration of digital image acquisition and processing. Picture taken by a camera; background has a calibration circular shape (a) and processed using photoshop software (b). Images similar to B were used by a black spot (Varma and Osuri 2013) software to calculate the pixel numbers.

128

Figure S 7 Effect of sunlight levels on (mean ± SE) root to shoot ratio (RSR) in A. robusta (solid lines) and A. tortilis (dash lines) seedlings grown in a greenhouse.

129

Table S 3 Model fits for the effect of SPECIES and SEASON, and their interaction on

PAR ratio using generalized linear mixed- effects models.

Analysis Fixed effects♣ ΔAIC* df PAR ratio Intercept only 114.9 3 SPECIES 112 4 SEASON 36.4 4 SPECIES + SEASON 43.6 5 SPECIES X SEASON 0 6 ♣ See text for variable description; in all cases SITE was treated as a random effect * Models with the strongest support have lower values and are shown in bold

130

Table S 4 Descriptive statistics table (means ±SE) for growth and canopy architecture trait response variables of A. robusta and

A. tortilis seedlings as a function of variation in sunlight levels. The numbers in brackets are treatment level sample size for each species. Means with different letters are significantly different (Tukey’s HSD, p < 0.05)

Species Light RGR (g g-1 day-1) AHG (mm day - SPAR ULR (g m-2 day-1)

levels 1)

Acacia Low 0.069 ± 0.001 (9)a 3.2±0.1 (20)a 0.102± 0.002 (27)a 6.79± 0.32 (27)a robusta

Medium 0.077 ± 0.003 (10)a 2.8±0.1 (20)ab 0.091±0.001 (27)a 15.72±0.96 (27)b

Full 0.087 ± 0.001 (8)b 2.5±0.1 (20)b 0.072±0.0006 (18)a 21.61±0.61 (18)b

Acacia Low 0.074 ± 0.003 (8)a 2.5 ± 0.1 (20)a 0.075±0.008 (18)b 6.86±0.55 (18)a tortilis

Medium 0.088 ± 0.002 (10)b 2.7 ± 0.1 (20)a 0.066±0.007 (27)b 11.93±0.62 (27)b

Full 0.092 ± 0.002 (9)b 2.4 ± 0.2 (20)a 0.057±0.004 (27)b 16.88±1.67 (27)b

131

Table S 5 Descriptive statistics table (means ±SE) for resource allocation trait response variables of A. robusta and A. tortilis seedlings as a function of variation in sunlight levels. Number in brackets are treatment level sample size. Means with different letters are significantly different (Tukey’s HSD, p < 0.05)

Species Light RSR (g g-1) LMF (g g-1) SMF (g g-1) RMF (g g-1) LMA (g m-2)

levels

Acacia robusta Low 0.74 ± 0.05 (9)a 0.33±0.02 (9)a 0.24±0.01 (9)a 0.42±0.01 (9)a 31.35±1.64 (9)a

Medium 0.97±0.08 (10)a 0.31±0.02 (10)a 0.19±0.01 (10)ab 0.48±0.02 (10)a 56.72±6.64

(10)ab

Full 1.13±0.1 (8)a 0.32±0.01 (8)a 0.15±0.01 (8)b 0.52±0.02 (8)a 77.52±2.94 (8)b

Acacia tortilis Low 0.59 ±0.08 (8)a 0.41±0.02 (8)a 0.22±0.02 (8)a 0.36±0.03 (8)a 30.55±4.3 (8)a

Medium 0.94±0.13 (10)ab 0.33±0.03 (10)a 0.25±0.01 (10)a 0.42±0.03 (10)a 47.52±5.92

(10)a

132

Full 1.38±0.24 (9)b 0.22±0.03 (9)b 0.21±0.2 (9)a 0.56±0.03 (9)b 48.73±9.96 (9)a

133

Supplement references

Carter GA, Smith WK (1985) Influence of shoot structure on light interception and

photosynthesis in conifers. Plant Physiology 79:1038-1043

Smith W, Schoettle A, Cui M (1991) Importance of the method of leaf area measurement

to the interpretation of gas exchange of complex shoots. Tree physiology 8:121-

127

Varma V, Osuri AM (2013) Black Spot: a platform for automated and rapid estimation of

leaf area from scanned images. Plant ecology 214:1529-1534

134

CHAPTER 4: Grass competition is more important in suppressing Acacia seedling growth than grass species type. For seedling survival, soil moisture's significance is

tree species-specific.

Prepared for submission in Austral Ecology journal, with co-Authors Jeremiah Sarakikya and T. Michael Anderson and

Stylistic variations result from the journal’s demands.

Abstract

We experimentally examined the influence of competition, water availability, and grass species type (taxa) on the growth and survival of two dominant tree species (Acacia (=

Vachellia) robusta and A. tortilis) of the Serengeti National Park in Tanzania. Despite being in the same ecosystem, the two tree species have an opposing overstorey dominance across a rainfall and nutrient gradient, with A. robusta dominating the mesic nutrient-poor sites and A. tortilis, the dry nutrient-rich sites. We aimed to unravel the potential role of different grass species identity characterized by variable physiological traits, habitat, and response to defoliation in influencing the growth and survival of

Acacia seedlings. We examined seedling stem growth (diameter and height) and seedling survival as a function of grass competition (- Grass, +Grass clipped, and +Grass unclipped), and soil moisture (-WATER vs. +WATER). We found no effect of soil

135 moisture or grass species identity on the growth of seedlings but did find a significant grass competition effect. Seedling survival increased with decreasing grass competition

(higher when grown without grasses vs. lower under unclipped grasses). Competition with grasses had a more severe effect on growth and survival of A. tortilis than A. robusta significantly affecting stem diameter growth. Acacia tortilis seedlings showed a more remarkable improvement in the growth and survival under clipped grasses than unclipped, with no significant effect in A. robusta suggesting that the effect of reduced competition via grazing has variable magnitude among savanna tree species.

Our study is important in advancing our understanding of tree-grass competition involving tree species occupying a variable environmental gradient. It also suggests that while grass competition is more important in suppressing Acacia seedling growth than grass species type, soil moisture's significance in survival is tree species-specific.

Introduction

Savanna ecosystems are biomes characterized by a continuous grass layer intermixed with a patchy tree network (Scholes and Walker 1993, Ratnam et al. 2011). The tree- grass dichotomy is primarily shaped by the balance between top-down disturbances (fire and herbivory) and bottom-up (water and nutrients) factors (Scholes and Walker 1993,

Bond 2008). The individual roles of fire, herbivory, soil moisture, soil types, and nutrients on tree-grass coexistence have been extensively documented (Sankaran et al.

2004, Archibald et al. 2005, Bond 2008, Sankaran et al. 2008, Kambatuku et al. 2011,

Staver et al. 2011) but are still an active area of research. Successful recruitment of woody species into adult size class denotes a critical bottleneck in savanna plant

136 communities because of high seedling mortality (Higgins et al. 2000, Wiegand et al.

2006) in part caused by the intense grass competition (Davis et al. 1999, Riginos 2009).

Previous studies have shown that the effect of competition between plants depends on the competitive power of adjacent interacting species in concert with a mosaic of abiotic stressors (Maestre et al. 2006), which may lead to a shift from competition to facilitation

(Bertness and Callaway 1994, Callaway and Walker 1997).

In the absence of disturbance from fire and herbivory, the suitability of a microsite location for successful tree recruitment depends on the interaction between multiple factors such as soil moisture, light, and the type of neighboring vegetation (Duncan and

Chapman 2003, Salazar et al. 2012, Bhadouria et al. 2016). Previous studies have shown conflicting effects of grass competition on woody plants (Bhadouria et al. 2016). While most studies have shown an adverse effect of grass competition on tree seedlings (Davis et al. 1999, Higgins et al. 2000, Sankaran et al. 2004, Hoffmann et al. 2009, Cramer et al.

2012, Tomlinson et al. 2019), others suggest neutral and positive effects via microsite stress amelioration by neighboring vegetation (Duncan and Chapman 2003, Maestre et al.

2006, Anthelme and Michalet 2009, Palmer et al. 2017) and low density of competing neighbors (Beckage et al. 2000, Heinemann et al. 2000, Beckage and Clark 2003,

Maestre et al. 2005).

Classic studies show that grazing reduces the magnitude of grasses’ competition, facilitating niche expansion for woody plants (McNaughton et al. 1998, Goheen et al.

2010). For example, clipping Themeda triandra (a dominant C4 grass in African savannas) negatively affects its growth (McNaughton et al. 1983, Coughenour et al. 1984,

Wallace et al. 1984, Coughenour et al. 1985, McNaughton 1985, McNaughton et al.

137

1998), and persistent defoliation may eliminate the species from the ecosystem (O'connor and Pickett 1992). The negative effect of clipping on the growth of T. triandra suggests that defoliation reduces its ability to acquire resources via reduced photosynthetic rates, stomatal conductance, and transpiration rates (Williams et al. 1998, Anderson et al. 2006,

Anderson et al. 2007b). While excessive defoliation is presumed to reduce the grasses' competitiveness against woody plant seedlings, mainly through lower carbon assimilation and root growth (Chirara et al. 1998), it is unclear whether those traits impose variable trends of growth and survival of tree seedlings.

In savannas, microsite light and water availability are spatially and temporally heterogeneous, and this heterogeneity provides a plant biomass productivity gradient in different ecosystems (Norton-Griffiths et al. 1975, Pennycuick and Norton-Griffiths

1976, Sinclair 1979, MacFadyen et al. 2018). For example, the Serengeti ecosystem in

Tanzania - East Africa has a rainfall and nutrients gradient that influences woody cover, ungulate grazing patterns, and plant community composition (Holdo et al. 2009, Gaughan et al. 2013, Rugemalila et al. 2016). While light and nutrients are the most limiting resources at the upper end of the productivity gradient, soil moisture is the most limiting resource at the lower end. The understorey woody species composition is decoupled from overstorey, with species of genus Acacia (= Vachellia) dominating the latter. In contrast, woody species in the genus Ormocarpum dominate the understorey, suggesting low tree recruitment or high mortality rates of the Acacias (Anderson et al. 2015, Morrison et al.

2019). The grass layer is also heterogeneous, with the wet sites dominated by tall grasses while the short grasses dominate the dry sites (Sinclair et al. 2007). Heterogeneity in the spatial distribution of grasses can result from the evolutionary influence of historical fire

138 and grazing pressure, ultimately influencing woody species composition (Forrestel et al.

2015, Simpson et al. 2016, Solofondranohatra et al. 2018). While studies have made considerable efforts to understand seedling mortality as a function of fire and herbivory, germinating seedlings' response to variable grass species identity in concert with resource heterogeneity is not well understood.

The current study's overarching goal is to understand the effect of grass species identity, competition, and soil moisture variability on the growth and survival of dominant tree species in the Serengeti National Park – Tanzania (hereafter – the Serengeti). We transplanted the Serengeti’s dominant tree seedlings on plots containing grasses of different species and subjected them to different competition and soil moisture levels.

The grass species involved in this experiment (see methods below) are abundant in the

Serengeti but have variable physiological traits, distributions, and responses to defoliation. We assessed seedling growth by computing the maximum seedling stem diameter and height. We performed seedling survival analyses to understand the probability of seedling survival beyond the experiment period (equivalent to the dry season span) as a function of treatment combinations. We based our research on the following two questions: (1) what are the effects of grass identity, competition level, and soil moisture on the growth and survival of Acacia seedlings? (2) How do seedling growth parameters influence survival probability under various growing conditions?

We hypothesized reduced growth and survival of seedlings grown under grasses characterized by high water utilization and transpiration rates. We predicted that tree seedling species dominating the mesic and productive sites would have better growth and survival when grown with tall grasses than seedlings adapted to dry sites (Anthelme and

139

Michalet 2009). We also predicted that competition level would have adverse effects on growth and survival, such that seedlings under undefoliated grasses will have lower growth and survival compared to those grown under defoliated or removed grasses.

However, we predicted growth and survival under moisture conditions would vary between tree species and that the seedling size would associate with survival probabilities

(Hoffmann et al. 2020).

Materials and Methods

Study location

We performed the experiment in an open nursery at the Serengeti Wildlife Research

Center (SWRC) located in the middle of the Serengeti, where annual rainfall averages ~

750 mm (Williams et al. 1998). The Serengeti ecosystem is a tropical savanna located between 33° 30’-35°30’E and 1°15’ - 3°30’S, and it comprises a productivity gradient with historical mean annual precipitation increasing from the south (SE) (~ 450mm) to the north (NW) (~ 1100 mm) near Lake Victoria (Norton-Griffiths et al. 1975, Sinclair

1979, Anderson et al. 2008, Reed et al. 2009). A significant proportion of the annual rain falls during the "dry" season in the mesic NW, while there is almost none in the SE during that period (Williams et al. 1998). However, grazing intensity is high in the SE part of the ecosystem during the wet season (McNaughton 1979, 1985).

In the Serengeti, two dominant tree species, Acacia robusta and A. tortilis, dominate sites with varying annual rainfall (Anderson et al. 2015, Rugemalila et al. 2016). The distribution of these species is antagonistic, meaning that their dominance overlaps, with

140

A. robusta dominating the mesic sites while A. tortilis dominates the dry sites of the ecosystem. Other overstorey tree species in the Serengeti are in the genera Acacia and include A. senegal (=Senegalia senegal), A. gerardii and, A. drepanolobium (Anderson et al. 2015).

Much of the Serengeti understorey plant community consists of medium-height perennial

C4 grasses dominated by Themeda triandra (Folsk) (THETRI, hereafter), Digitaria macroblephara (Hack) (DIGMAC, hereafter), Panicum maximum (Jacq) (PANMAX, hereafter), Sporobolus fimbriatus (Trin.), and Pennisetum mezianum (Leeke) (Sinclair

1979, Williams et al. 1998). This study utilized THETRI, DIGMAC, and PANMAX because of their variable dominance in the ecosystem, transpiration rates, and tolerance to defoliation (Anderson et al. 2007b).

THETRI is a perennial forage grass of medium stature (0.5 m – 1.2 m) and dominates most of the open grasslands receiving 600-800 mm of MAP (McNaughton 1983).

THETRI is very sensitive to clipping, and unlike other grass species, defoliation negatively affects its root biomass and crown production because of an inherent low tiller density and a sparse canopy (Lock and Milburn 1971, McNaughton 1983, Coughenour et al. 1985). DIGMAC co-occur with short grasses in arid plains of the southern Serengeti characterized by shallow nutrient-rich volcanic soils which tend to have low moisture retentivity (Anderson and Talbot 1965, Banyikwa et al. 1990). DIGMAC is a characteristic water spender where shoot growth is stimulated by clipping with increased photosynthetic rate and stomatal conductance (Anderson et al. 2006). PANMAX, on the other hand, is a tall grass that occurs in high rainfall areas (900 – 1100 mm), and in the

141

Serengeti, it occurs in mesic, riverine sites with wet soils and within woodlands

(Williams et al. 2016).

Experimental setup

Between January and June 2016, corresponding with the rain season, tillers of THETRI,

DIGMAC, and PANMAX were sourced around SWRC and planted in a randomized experiment inside three 6 m × 10 m herbivore exclusion blocks. Nine tillers (equally spaced) of each grass species were clonally propagated inside a one-meter square plot

(~25 cm apart) and maintained for two years. The maintenance involved weeding, fire protection, watering, and herbivore exclusion.

In May and June 2018, A. robusta and A. tortilis seeds were collected from within the

Serengeti and germinated under standard water and light environments for twenty-one

(21) days. In July 2018, corresponding with the beginning of the dry season, we transplanted six tree seedlings (3 per tree species) in a 1 m2 plot inside the herbivore and fire exclusion blocks (n = 378). Watering of transplanted seedlings continued for fourteen days until seedlings were fully established. We randomly assigned two levels of moisture

(+WATER vs. -WATER), three grass competition levels (+CLIP = grasses clipped vs. -

CLIP = grasses unclipped vs. -GRASS = grasses removed) on our plots containing different grass species. We initiated water manipulation treatment by watering 10 liters of rain – harvested water in each of the 1m2 twice a week for the water addition (+WATER) and once a month for the drought (-WATER) treatment. For competition levels, grasses clipping was performed at the height of ~5 cm from the ground to maintain below-ground

142 competition for soil moisture and minimize sunlight competition. Plots were maintained regularly by uprooting encroaching vegetation inside and around the blocks.

Data collection

For the duration of the experiment, we measured the understory micro-environment by recording soil moisture (volumetric water content – VWC) and sunlight (photosynthetic active radiation - PAR) using a handheld time domain reflectometer (TDR) soil moisture probe (Model CS659 – Campbell Scientific Inc®) and an AccuPAR LP‐80 ceptometer

(Decagon Devices, Pullman, WA) respectively. Soil moisture and light data (collected below and above grass canopy – see Rugemalila et al. (2020) for procedure) were collected preceding the seedling survey at two random locations on each plot.

All seedlings were subsequently surveyed monthly between 2 July and 1 December 2018

(> 4 months = 146 days). Data collected on tree seedlings on each survey included seedling height, stem diameter, and survival.

Data Analysis

Moisture and Light environment

We first evaluated volumetric water content (VWC) and light environment (PAR) variation by computing treatment means for VWC and PAR ratios. PAR ratio (PAR below/PAR above) is an index of light interception by the grasses. Lower PAR ratio values represent a high level of light interception or greater grass biomass, and higher values represent open or low light interception by grasses (Borer et al. 2014, Gibson

2014). We applied analysis of variance (ANOVA) to compare treatment means with the

143 alternative hypothesis that plots with unclipped PANMAX grasses will intercept more sunlight than DIGMAC and THETRI. We also analyzed the -CLIP and +CLIP grass species' effect on soil moisture retention, applying similar statistical methods.

Seedling growth analysis

To quantify how stem diameter and seedling height varied as a function of grass species identity, soil moisture, and competition levels, we computed the maximum values for each seedling's height and stem diameter. To understand the effect of our experimental treatments on seedling investment in stem diameter and height, we computed the seedling height-to-diameter ratio (H:D ratio). The H:D ratio is a crucial index of plant structural support (Haase 2008). Prior to statistical analysis, we performed appropriate data transformation to meet normality assumptions. We then developed eight linear mixed- effects (including an intercept-only) models for stem diameter and height (Table 6) and applied generalized linear mixed-effects models (GLMMs) fit using maximum likelihood implemented in function lmer (package lme4) (Bates et al. 2014) of the R statistical software. We treated seedling tree species (variable SPECIES-T), grass identity

(SPECIES-G), competition (COMPETITION), and soil moisture level (MOISTURE) (and their interactions) as fixed effects, and BLOCK as a random effect (Table 6). We used a model selection approach applying Akaike Information Criteria (AIC) to select the best- supported model. We performed model ranking using function AICctab with a correction for small sample size implemented in the R package bbmle, where the best-supported model had the lowest change in AIC (Burnham and Anderson 2002).

144

Seedling survival

We conducted two separate seedling survival analysis. In the first analysis, we quantified the non-parametric survival probability for each seedling from transplanting (T1) to the experiment's termination (T2) as a function of our treatment combinations. The seedling survival probability analysis was computed based on the survival function, which estimates the probability that a seedling dies between two surveys or survives beyond the last survey (Beckage and Clark 2003, Klein and Moeschberger 2006). To achieve this, we developed eight Cox-proportional survival regression models (in the same order as in the growth analysis above, Table 6) implemented in the R package “coxme” (Therneau and

Therneau 2015). We used the seedling death (variable EVENT) and time (days) of recording the EVENT (variable TIME) as dependent variables and BLOCK as a random effect. We applied model selection using an automated stepwise procedure that retained model terms based on Akaike's Information Criterion (AIC) as described above.

The second survival analysis applied logistic modeling to fit mixed-effect logistic regression- MELR (function glmer in R) with binomial error distribution and logit link function to predict the survival probability as a combination of seedling species and size

(diameter and height) (Jaeger 2008). In the MELR, SPECIES-T, stem diameter, and height parameters were used as fixed factors, and BLOCK as a random factor.

Results

We confirmed a significant reduction in the level of sunlight caused by grass identity

(variable SPECIES-G). Unclipped PANMAX grasses intercepted 26% more sunlight compared to THETRI, and DIGMAC (PAR ratio; mean ± SE; PANMAX: 0.13 ± 0.01, n =

145

18, THETRI; 0.22 ± 0.01, n = 18, DIGMAC; 0.17 ± 0.01, n = 20, p < 0.0001, Fig S8).

We also confirmed a significant reduction in soil moisture level caused by variable

MOISTURE in which, on average, +WATER plots retained ~ 22% more moisture between the watering events as we recorded soil moisture one day before the next water addition (VWC: moist; mean ± SE: 4.5 ± 0.1, n = 65; dry, 3.6 ± 0.1, n = 58, p <0.0001,

Fig S9). However, we found no between SPECIES-G variability in VWC (p >0.05, Fig

S9).

Seedling growth

Model selection results suggested a significant interactive effect between SPECIES-T and

COMPETITION (Wald χ2 = 8.2, p = 0.02, Fig. 6), which resulted from similar stem diameter values when grasses were either removed or clipped, but a ~ 50% difference in maximum stem diameter under competition with unclipped grasses (Fig. 14a). Acacia robusta seedlings grown under unclipped grasses attained higher maximum stem diameter than A. tortilis (Unclipped grass: A. robusta: mean ± SE: 0.83 cm ± 0.05 cm, n = 85; A. tortilis: 0.5 cm ± 0.04 cm, n = 82, p <0.0001, Fig 14a). Acacia robusta seedling attained higher seedling height values than A. tortilis under all competition levels (height:

A. robusta: mean ± SE: 6.7 cm ± 0.3 cm, n = 189; A. tortilis: 3.9 cm ± 0.2 cm, n = 182,

Fig. 14b). However, the difference was definitive, with about 59% difference in species height in the absence of competition with grass (Fig. 14b). The extent to which the two seedling species invested in stem and height growth was different, with A. robusta seedlings attained a consistent investment in height growth per unit stem diameter at all levels of competition. On the other hand, for A. tortilis, competition with grasses

146 adversely affected stem growth investment as it attained a significantly higher height to diameter (HD) ratio (Fig 14c).

Seedling survival

The interaction between SPECIES-T × MOISTURE best-explained variability in survival probabilities between tree seedlings (Fig. 15, p < 0.001) as water addition reduced mortality hazard for both seedling species by 51% (Hazard ratio = 0.49). While the survival probability beyond the 146 days of the experiment for A. robusta was significantly higher than that of A. tortilis in a moist environment (moist condition: A. robusta; S[146] = 0.44 ± 0.05; A. tortilis, (S[146] = 0.2 ± 0.04, p < 0.001, Fig 15b), there was no between species difference under a dry environment (dry condition: A. robusta;

S[146] = 0.13 ± 0.04; A. tortilis, S[146] = 0.09 ± 0.03, Fig 15a). The exclusion of plots with no grass competition (-GRASS) from the analysis revealed a stronger effect of grass competition on the survival of A. tortilis but not A. robusta seedlings (Wald χ2 = 4.41, p < 0.05, Fig 16). Growing A. tortilis seedlings under unclipped grass conditions significantly increased their mortality hazard by a factor of 1.7 compared to growth under clipped grasses with no significant variability on A. robusta seedlings (A. robusta: unclipped grass: S[146] = 0.22 ± 0.04; clipped grass, S[146] = 0.29 ± 0.05, p>0.05, Fig

16a: A. tortilis: unclipped grass: S[146] = 0.04 ± 0.02; clipped grass, S[146] = 0.2 ± 0.05, p < 0.0001, Fig 16b).

Mixed-effects logistic regression results showed that seedling survival probability was positively associated with stem diameter (β = 2.35, se = 0.5, z = 4.77, p < 0.0001, Fig

17a) and height (β = 0.57, se = 0.1, z = 5.32, p < 0.0001, Fig 17b), suggesting a stronger

147 effect of stem diameter (β = 2.35) on seedling survival probability than that of seedling height (β = 0.57).

Discussion

Our study's most apparent result is that the grass identity did not best explain Acacia seedlings' growth and survival as we anticipated, but grass competition level was most important (Riginos 2009, Morrison et al. 2019, Pillay and Ward 2020). While grass competition imposed an abrupt decrease in stem diameter in both tree species, the effect was slight for A. robusta and more substantial for A. tortilis, primarily when grown under competition with unclipped grasses (Fig 14a). Acacia robusta seedlings showed a morphological advantage as they always resulted in taller seedlings with more emphasis on consistent stem height growth per unit stem diameter than A. tortilis (Fig. 14b&c). In the absence of grass competition, Acacia tortilis seedlings invested more in stem diameter growth relative to their height increase, but that investment was severely affected by grass competition (Fig 14c). The importance of soil moisture was more apparent in the overall survival of A. robusta than in A. tortilis seedlings because the difference in survival probability for the two species in a dry environment was not as pronounced as in a moist environment (Fig 15). Our study supports the notion that grasses' competitive effects in concert with various environmental factors on tree recruitment are species-specific (Kraaij and Ward 2006).

Our hypothesis that variability in grass identity would impose variable response on seedling growth was not supported. In the Serengeti plant community, the three grass species used in this study (THETRI, DIGMAC, and PANMAX) have variable spatial

148 dominance and physiological traits (Sinclair 1979, Williams et al. 1998, Anderson et al.

2006, Anderson et al. 2007a). With sunlight (PAR ratio) data confirming higher interception by PANMAX than other grass species, we expected PANMAX to impose a facilitative effect on the seedling growth by ameliorating stressful conditions, primarily under dry conditions (Hoffmann 1996, Anthelme and Michalet 2009, Salazar et al. 2012).

Previous studies show that grass tussocks store humidity and provide nutrients in the form of litter, consequently facilitating tree seedling growth, especially when water is scarce (Maestre et al. 2001, Brooker et al. 2008, Maestre et al. 2009). A study by

(Anthelme and Michalet 2009) in northern Niger, in an ecosystem receiving rainfall below 100 mm, found a facilitative effect of Panicum turgidum (Forssk) on the growth of

A. tortilis seedlings. Another study by (Maestre et al. 2003) in the Mediterranean semiarid grassland ecosystem receiving annual rainfall below 370 mm found an increased survival of Pistacia lentiscus tree seedlings in the presence of perennial grass Stipa tenacissima. Our study's lack of grass species identity effects suggests that the facilitation effect is essential when plants have variable rooting zones (Holdo 2013, Ward et al.

2013). When tree seedlings and grasses share the same rooting depth, the grasses’ competitive magnitude is more robust than tree seedlings as grasses draw water resources sufficiently more rapidly than tree seedlings (February et al. 2013, Wakeling et al. 2015).

The planting of six tree seedlings per 1 m2 plot in our study subjected tree seedlings to an intra-specific competition, which is expected to be stronger than interspecific competition

(Adler et al. 2018). The improved growth and survival of tree seedlings in the absence of grasses competition support the notion that apart from grasses' fast resource extraction, they occupied a large proportion of the rooting space (non-resource competition), thereby

149 limiting seedling establishment by spatial exclusion (McConnaughay and Bazzaz 1991,

Pillay and Ward 2014, Pillay and Ward 2020).

Reduced grass cover by clipping (simulates grazing) imposed variable investment in stem and height growth, suggesting that A. robusta seedlings prioritize light acquisition while

A. tortilis prioritize stem stability (Fig 14c). For A. tortilis, which occupy habitats dominated by short grasses, reduced grass competition may be a crucial process for its successful recruitment (Roques et al. 2001) because seedling survival probability was higher under clipped compared to unclipped grasses (Fig. 16b). The influence of grazing on savanna tree recruitment has been reported previously, although the effect can be positive or negative. When grazers dominate the ecosystem, grazing diminishes belowground competition, making resources available for tree seedlings (Brown and Archer

1999, Roques et al. 2001, Kraaij and Ward 2006, Wiegand et al. 2006). On the contrary, when browsers dominate the ecosystem, their effect on seedling survival is adverse

(Voysey et al. 2020). The characteristic intensive wet-season grazing in the southern part of the Serengeti ecosystem (McNaughton 1983, 1985, Ruess and Seagle 1994) might also be necessary for the establishment of A. tortilis as they do not perform well under competition with unclipped grasses (Fig. 14a & 16b).

Soil nutrients promote grass biomass productivity, indirectly imposing stronger competition (Vadigi and Ward 2013). As A. tortilis dominates the dry sites and A. robusta the mesic sites of the Serengeti (Anderson et al. 2015, Rugemalila et al. 2016), we predicted soil moisture condition to reflect this variation in tree distribution via increased seedling growth and survival in wet conditions for A. robusta and dry condition for A. tortilis regardless of competition level. However, soil moisture's importance was

150 significant for A. robusta survival as predicted but not for A. tortilis, suggesting that the role of soil moisture in savanna tree recruitment is species-specific and not uniform among savanna tree species. Previous studies have indicated that both nutrients and water availability affect competition between grasses and tree seedlings, but the effect may vary between interacting tree-grass species and their physiological properties (Cohn et al.

1989, Van Der Waal et al. 2009, Cramer and Bond 2012, Tomlinson et al. 2019). The growth and survival of A. tortilis seedlings exclusively depended on reduced grass competition, emphasizing the vital role of grasses competition in explaining tree-grass coexistence (Riginos 2009, February et al. 2013).

In conclusion, our study has shown that grass competition is more important than grass species identity and soil moisture in explaining variability in growth and survival of co- occurring tree seedlings. The survival probability for A. tortilis seedlings to the most part depends on reduced competition with grasses. Perennial grass species, especially those with tall stature and large tussocks, have been used in A. tortilis restoration projects to facilitate seedling survival by ameliorating the effect of low moisture availability

(Anthelme and Michalet 2009, Palmer et al. 2017). Our study suggests that care must be taken as the facilitative effect may be important when the neighboring species occupy different rooting zones (Holdo 2013, Ward et al. 2013)

151

Tables and Figures

Table 6 The generalized linear mixed-effects models fit (AIC, the Akaike Information criterion) for the effect of SPECIES-T,

SPECIES-G, COMPETITION, SOIL MOISTURE, and their interaction on seedling diameter, height, H:D ratio, and survival.

Height: Seedling Seedling Seedling Model Diameter

diameter height survival ratio

Fixed effects♣ *ΔAIC df *ΔAIC df *ΔAIC df *ΔAIC df

Intercept only 53.9 3 101 3 19.4 3 70.6 1.9

SPECIES-T 47 4 6.9 4 21.2 4 50.9 2.9

SPECIES-T × COMPETITION 0 8 0 8 0 8 12.5 6.9

SPECIES-T × SPECIES-G 36.6 10 7.2 10 30.1 10 37.4 8.9

SPECIES-T × MOISTURE 58 6 16.9 6 29.8 6 40.6 4.9

SPECIES-T × COMPETITION + SPECIES-T × MOISTURE 11.6 10 10.2 10 8.9 10 0 8.9

152

SPECIES-T × SPECIES-G + SPECIES-T × MOISTURE 48.1 12 17.4 12 38.8 12 26 10.9

SPECIES-T × COMPETITION + SPECIES × SPECIES-G 22.5 12 16.3 12 8.1 12 62.1 9

♣ See text for variable description; in all cases, BLOCK was treated as a random effect

* Models with the strongest support have lower values and are shown in bold

153

a) b)

Species

154

a) Dry condition b) Moist condition

Figure 15 Seedling survival of A. robusta (blue solid lines) and A. tortilis (red dashed lines) under a) dry condition and b) moist condition.

155

Figure 16 Seedling survival under competition with clipped (soil lines) and unclipped

(dashed lines) grasses only (-GRASS plots excluded, see survival data analysis) for a) A. robusta and b) A. tortilis.

156

157

158

Supplementary figures

Figure S 8 Variation in mean photosynthetic active radiation (PAR) ratio (mean ± SE) between the unclipped Themeda triandra (= thetri), Digitaria macroblephala (= digmac), and Panicum maximum (= panmax) grass species.

159

Figure S 9 Variation in volumetric water content ( = VWC) (mean ± SE) between the unclipped Themeda triandra (= thetri), Digitaria macroblephala (= digmac), and

Panicum maximum (= panmax) grass species for the moist (yellow circles) and drought

(blue circles) treatments.

160

Cited references

Adler, P. B., D. Smull, K. H. Beard, R. T. Choi, T. Furniss, A. Kulmatiski, J. M. Meiners,

A. T. Tredennick, and K. E. Veblen. 2018. Competition and coexistence in plant

communities: intraspecific competition is stronger than interspecific competition.

Ecology Letters 21:1319-1329.

Anderson, G. D., and L. M. Talbot. 1965. Soil Factors Affecting the Distribution of the

Grassland Types and their Utilization by Wild Animals on the Serengeti Plains,

Tanganyika. The Journal of ecology 53:33.

Anderson, T. M., J. Dempewolf, K. L. Metzger, D. N. Reed, and S. Serneels. 2008.

Generation and maintenance of heterogeneity in the Serengeti ecosystem.

Serengeti III: human impacts on ecosystem dynamics:135-182.

Anderson, T. M., Y. A. N. Dong, and S. J. McNaughton. 2006. Nutrient acquisition and

physiological responses of dominant Serengeti grasses to variation in soil texture

and grazing. Journal of Ecology 94:1164-1175.

Anderson, T. M., K. L. Metzger, and S. J. McNaughton. 2007a. Multi‐scale analysis of

plant species richness in Serengeti grasslands. Journal of Biogeography 34:313-

323.

Anderson, T. M., T. Morrison, D. Rugemalila, R. Holdo, and D. Ward. 2015.

Compositional decoupling of savanna canopy and understory tree communities in

Serengeti. Journal of Vegetation Science 26:385-394.

Anderson, T. M., W. T. Starmer, and M. Thorne. 2007b. Bimodal root diameter

distributions in Serengeti grasses exhibit plasticity in response to defoliation and

soil texture: implications for nitrogen uptake. Functional Ecology 21:50-60.

161

Anthelme, F., and R. Michalet. 2009. Grass-to-tree facilitation in an arid grazed

environment (Aïr Mountains, Sahara). Basic and Applied Ecology 10:437-446.

Archibald, S., W. Bond, W. Stock, and D. Fairbanks. 2005. Shaping the landscape: fire–

grazer interactions in an African savanna. Ecological Applications 15:96-109.

Banyikwa, F. F., E. Feoli, and V. Zuccarello. 1990. Fuzzy set ordination and

classification of Serengeti short grasslands, Tanzania. Journal of Vegetation

Science 1:97-104.

Bates, D., M. Mächler, B. Bolker, and S. Walker. 2014. Fitting linear mixed-effects

models using lme4. arXiv preprint arXiv:1406.5823.

Beckage, B., and J. S. Clark. 2003. Seedling Survival and Growth of Three Forest Tree

Species: The Role of Spatial Heterogeneity. Ecology 84:1849-1861.

Beckage, B., J. S. Clark, B. D. Clinton, and B. L. Haines. 2000. A long-term study of tree

seedling recruitment in southern Appalachian forests: the effects of canopy gaps

and shrub understories. Canadian Journal of Forest Research 30:1617-1631.

Bertness, M. D., and R. Callaway. 1994. Positive interactions in communities. Trends

Ecol Evol 9:191-193.

Bhadouria, R., R. Singh, P. Srivastava, and A. S. Raghubanshi. 2016. Understanding the

ecology of tree-seedling growth in dry tropical environment: a management

perspective. Energy, Ecology and Environment 1:296-309.

Bond, W. J. 2008. What Limits Trees in C4 Grasslands and Savannas? Annual Review of

Ecology, Evolution, and Systematics 39:641-659.

Borer, E. T., E. W. Seabloom, D. S. Gruner, W. S. Harpole, H. Hillebrand, E. M. Lind, P.

B. Adler, J. Alberti, T. M. Anderson, J. D. Bakker, L. Biederman, D. Blumenthal,

162

C. S. Brown, L. A. Brudvig, Y. M. Buckley, M. Cadotte, C. Chu, E. E. Cleland,

M. J. Crawley, P. Daleo, E. I. Damschen, K. F. Davies, N. M. DeCrappeo, G. Du,

J. Firn, Y. Hautier, R. W. Heckman, A. Hector, J. HilleRisLambers, O. Iribarne, J.

A. Klein, J. M. Knops, K. J. La Pierre, A. D. Leakey, W. Li, A. S. MacDougall,

R. L. McCulley, B. A. Melbourne, C. E. Mitchell, J. L. Moore, B. Mortensen, L.

R. O'Halloran, J. L. Orrock, J. Pascual, S. M. Prober, D. A. Pyke, A. C. Risch, M.

Schuetz, M. D. Smith, C. J. Stevens, L. L. Sullivan, R. J. Williams, P. D. Wragg,

J. P. Wright, and L. H. Yang. 2014. Herbivores and nutrients control grassland

plant diversity via light limitation. Nature 508:517-520.

Brooker, R. W., F. T. Maestre, R. M. Callaway, C. L. Lortie, L. A. Cavieres, G. Kunstler,

P. Liancourt, K. Tielbörger, J. M. Travis, and F. Anthelme. 2008. Facilitation in

plant communities: the past, the present, and the future. Journal of Ecology:18-34.

Brown, J. R., and S. Archer. 1999. Shrub invasion of grassland: recruitment is continuous

and not regulated by herbaceous biomass or density. Ecology 80:2385-2396.

Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a

practical information-theoretic approach. Springer Science & Business Media.

Callaway, R. M., and L. R. Walker. 1997. Competition and Facilitation: A Synthetic

Approach to Interactions in Plant Communities. Ecology 78:1958.

Chirara, C., P. Frost, and V. Gwarazimba. 1998. Grass defoliation affecting survival and

growth of seedlings of Acacia karroo, an encroaching species in southwestern

Zimbabwe. African Journal of Range & Forage Science 15:41-47.

163

Cohn, E., O. Van Auken, and J. Bush. 1989. Competitive interactions between Cynodon

dactylon and Acacia smallii seedlings at different nutrient levels. American

Midland Naturalist:265-272.

Coughenour, M., S. McNaughton, and L. Wallace. 1984. Modelling primary production

of perennial graminoids 3$ ̄uniting physiological processes and morphometric

traits. Ecological Modelling 23:101-134.

Coughenour, M. B., S. J. McNaughton, and L. L. Wallace. 1985. Responses of an African

graminoid (Themeda triandra Forsk.) to frequent defoliation, nitrogen, and water:

a limit of adaptation to herbivory. Oecologia 68:105-110.

Cramer, M. D., and W. J. Bond. 2012. N-fertilization does not alleviate grass competition

induced reduction of growth of African savanna species. Plant and Soil 366:563-

574.

Cramer, M. D., J. L. Wakeling, and W. J. Bond. 2012. Belowground competitive

suppression of seedling growth by grass in an African savanna. Plant Ecology

213:1655-1666.

Davis, M. A., K. J. Wrage, P. B. Reich, M. G. Tjoelker, T. Schaeffer, and C. Muermann.

1999. Survival, growth, and photosynthesis of tree seedlings competing with

herbaceous vegetation along a water-light-nitrogen gradient. Plant Ecology

145:341-350.

Duncan, R. S., and C. A. Chapman. 2003. Tree-Shrub Interactions During Early

Secondary Forest Succession in Uganda. Restoration ecology 11:198-207.

164

February, E. C., S. I. Higgins, W. J. Bond, and L. Swemmer. 2013. Influence of

competition and rainfall manipulation on the growth responses of savanna trees

and grasses. Ecology 94:1155-1164.

Forrestel, E. J., M. J. Donoghue, M. D. Smith, and J. Newman. 2015. Functional

differences between dominant grasses drive divergent responses to large

herbivore loss in mesic savanna grasslands of North America and South Africa.

Journal of Ecology 103:714-724.

Gaughan, A. E., R. M. Holdo, and T. M. Anderson. 2013. Using short-term MODIS time-

series to quantify tree cover in a highly heterogeneous African savanna.

International Journal of Remote Sensing 34:6865-6882.

Gibson, D. J. 2014. Methods in comparative plant population ecology. Oxford University

Press.

Goheen, J. R., T. M. Palmer, F. Keesing, C. Riginos, and T. P. Young. 2010. Large

herbivores facilitate savanna tree establishment via diverse and indirect pathways.

J Anim Ecol 79:372-382.

Haase, D. L. 2008. Understanding forest seedling quality: measurements and

interpretation. Tree Planters’ Notes 52:24-30.

Heinemann, K., T. Kitzberger, and T. T. Veblen. 2000. Influences of gap

microheterogeneity on the regeneration of Nothofagus pumilio in a xeric old-

growth forest of northwestern Patagonia, Argentina. Canadian Journal of Forest

Research 30:25-31.

Higgins, S. I., W. J. Bond, and W. S. W. Trollope. 2000. Fire, resprouting and variability:

a recipe for grass–tree coexistence in savanna. Journal of Ecology 88:213-229.

165

Hoffmann, W. A. 1996. The effects of fire and cover on seedling establishment in a

neotropical savanna. Journal of Ecology:383-393.

Hoffmann, W. A., R. Adasme, M. Haridasan, M. T. de Carvalho, E. L. Geiger, M. A.

Pereira, S. G. Gotsch, and A. C. Franco. 2009. Tree topkill, not mortality, governs

the dynamics of savanna–forest boundaries under frequent fire in central Brazil.

Ecology 90:1326-1337.

Hoffmann, W. A., R. W. Sanders, M. G. Just, W. A. Wall, and M. G. Hohmann. 2020.

Better lucky than good: How savanna trees escape the fire trap in a variable

world. Ecology 101:e02895.

Holdo, R. M. 2013. Revisiting the two-layer hypothesis: coexistence of alternative

functional rooting strategies in savannas. PLoS One 8:e69625.

Holdo, R. M., R. D. Holt, and J. M. Fryxell. 2009. Opposing rainfall and plant nutritional

gradients best explain the wildebeest migration in the Serengeti. Am Nat 173:431-

445.

Jaeger, T. F. 2008. Categorical data analysis: Away from ANOVAs (transformation or

not) and towards logit mixed models. Journal of memory and language 59:434-

446.

Kambatuku, J. R., M. D. Cramer, and D. Ward. 2011. Savanna tree–grass competition is

modified by substrate type and herbivory. Journal of Vegetation Science 22:225-

237.

Klein, J. P., and M. L. Moeschberger. 2006. Survival analysis: techniques for censored

and truncated data. Springer Science & Business Media.

166

Kraaij, T., and D. Ward. 2006. Effects of rain, nitrogen, fire and grazing on tree

recruitment and early survival in bush-encroached savanna, South Africa. Plant

Ecology 186:235-246.

Lock, J., and T. Milburn. 1971. The seed biology of Themeda triandra Forsk. in relation

to fire. In ‘The scientific management of animal and plant communities for

conservation’. Blackwell Scientific Publications: London.

MacFadyen, S., N. Zambatis, A. J. Van Teeffelen, and C. Hui. 2018. Long‐term rainfall

regression surfaces for the Kruger National Park, South Africa: a spatio‐temporal

review of patterns from 1981 to 2015. International Journal of Climatology

38:2506-2519.

Maestre, F. T., S. Bautista, and J. Cortina. 2003. POSITIVE, NEGATIVE, AND NET

EFFECTS IN GRASS–SHRUB INTERACTIONS IN MEDITERRANEAN

SEMIARID GRASSLANDS. Ecology 84:3186-3197.

Maestre, F. T., S. Bautista, J. Cortina, and J. Bellot. 2001. Potential for using facilitation

by grasses to establish shrubs on a semiarid degraded steppe. Ecological

Applications 11:1641-1655.

Maestre, F. T., R. M. Callaway, F. Valladares, and C. J. Lortie. 2009. Refining the stress‐

gradient hypothesis for competition and facilitation in plant communities. Journal

of Ecology 97:199-205.

Maestre, F. T., F. Valladares, and J. F. Reynolds. 2005. Is the change of plant–plant

interactions with abiotic stress predictable? A meta‐analysis of field results in arid

environments. Journal of Ecology 93:748-757.

167

Maestre, F. T., F. Valladares, and J. F. Reynolds. 2006. The stress‐gradient hypothesis

does not fit all relationships between plant–plant interactions and abiotic stress:

further insights from arid environments. Journal of Ecology 94:17-22.

McConnaughay, K., and F. Bazzaz. 1991. Is physical space a soil resource? Ecology

72:94-103.

McNaughton, S., L. L. Wallace, and M. B. Coughenour. 1983. Plant adaptation in an

ecosystem context: effects of defoliation, nitrogen, and water on growth of an

African C4 sedge. Ecology 64:307-318.

McNaughton, S. J. 1979. Grazing as an Optimization Process - Grass Ungulate

Relationships in the Serengeti. American Naturalist 113:691-703.

McNaughton, S. J. 1983. Serengeti Grassland Ecology - the Role of Composite

Environmental-Factors and Contingency in Community Organization. Ecological

Monographs 53:291-320.

McNaughton, S. J. 1985. Ecology of a Grazing Ecosystem - the Serengeti. Ecological

Monographs 55:259-294.

McNaughton, S. J., F. F. Banyikwa, and M. M. McNaughton. 1998. Root biomass and

productivity in a grazing ecosystem: The Serengeti. Ecology 79:587-592.

Morrison, T. A., R. M. Holdo, D. M. Rugemalila, M. Nzunda, and T. M. Anderson. 2019.

Grass competition overwhelms effects of herbivores and precipitation on early

tree establishment in Serengeti. Journal of Ecology 107:216-228.

Norton-Griffiths, M., D. Herlocker, and L. Pennycuick. 1975. The patterns of rainfall in

the Serengeti ecosystem, Tanzania. African Journal of Ecology 13:347-374.

168

O'connor, T., and G. Pickett. 1992. The influence of grazing on seed production and seed

banks of some African savanna grasslands. Journal of Applied Ecology:247-260.

Palmer, T. M., C. Riginos, R. E. Damiani, N. Morgan, J. S. Lemboi, J. Lengingiro, J. C.

Ruiz-Guajardo, and R. M. Pringle. 2017. Influence of neighboring plants on the

dynamics of an ant–acacia protection mutualism. Ecology 98:3034-3043.

Pennycuick, L., and M. Norton-Griffiths. 1976. Fluctuations in the rainfall of the

Serengeti ecosystem, Tanzania. Journal of Biogeography:125-140.

Pillay, T., and D. Ward. 2014. Competitive effect and response of savanna tree seedlings:

comparison of survival, growth and associated functional traits. Journal of

Vegetation Science 25:226-234.

Pillay, T., and D. Ward. 2020. Grass competition is more important than fire for

suppressing encroachment of Acacia sieberiana seedlings. Plant Ecology:1-10.

Ratnam, J., W. J. Bond, R. J. Fensham, W. A. Hoffmann, S. Archibald, C. E. R.

Lehmann, M. T. Anderson, S. I. Higgins, and M. Sankaran. 2011. When is a

‘forest’ a savanna, and why does it matter? Global Ecology and Biogeography

20:653-660.

Reed, D. N., T. M. Anderson, J. Dempewolf, K. Metzger, and S. Serneels. 2009. The

spatial distribution of vegetation types in the Serengeti ecosystem: the influence

of rainfall and topographic relief on vegetation patch characteristics. Journal of

Biogeography 36:770-782.

Riginos, C. 2009. Grass competition suppresses savanna tree growth across multiple

demographic stages. Ecology 90:335-340.

169

Roques, K., T. O'connor, and A. R. Watkinson. 2001. Dynamics of shrub encroachment

in an African savanna: relative influences of fire, herbivory, rainfall and density

dependence. Journal of Applied Ecology 38:268-280.

Ruess, R. W., and S. W. Seagle. 1994. Landscape Patterns in Soil Microbial Processes in

the Serengeti National-Park, Tanzania. Ecology 75:892-904.

Rugemalila, D. M., T. M. Anderson, and R. M. Holdo. 2016. Precipitation and elephants,

not fire, shape tree community composition in Serengeti National Park, Tanzania.

Biotropica 48:476-482.

Rugemalila, D. M., S. T. Cory, W. K. Smith, and T. M. Anderson. 2020. The role of

microsite sunlight environment on growth, architecture, and resource allocation in

dominant Acacia tree seedlings, in Serengeti, East Africa. Plant Ecology.

Salazar, A., G. Goldstein, A. C. Franco, and F. Miralles-Wilhelm. 2012. Differential

seedling establishment of woody plants along a tree density gradient in

Neotropical savannas. Journal of Ecology 100:1411-1421.

Sankaran, M., J. Ratnam, and N. Hanan. 2008. Woody cover in African savannas: the

role of resources, fire and herbivory. Global Ecology and Biogeography 17:236-

245.

Sankaran, M., J. Ratnam, and N. P. Hanan. 2004. Tree-grass coexistence in savannas

revisited - insights from an examination of assumptions and mechanisms invoked

in existing models. Ecology Letters 7:480-490.

Scholes, R. J., and B. H. Walker. 1993. An African savanna: synthesis of the Nylsvley

study. Cambridge University Press.

170

Simpson, K. J., B. S. Ripley, P.-A. Christin, C. M. Belcher, C. E. R. Lehmann, G. H.

Thomas, and C. P. Osborne. 2016. Determinants of flammability in savanna grass

species. Journal of Ecology 104:138-148.

Sinclair, A. 1979. The Serengeti environment. Pages 31-45 in A. R. E. S. a. a. M. Norton-

Griffiths, editor. Serengeti: Dynamics of an ecosystem. The University of

Chicago Press.

Sinclair, A. R., S. A. Mduma, J. G. Hopcraft, J. M. Fryxell, R. Hilborn, and S. Thirgood.

2007. Long-term ecosystem dynamics in the Serengeti: lessons for conservation.

Conserv Biol 21:580-590.

Solofondranohatra, C. L., M. S. Vorontsova, J. Hackel, G. Besnard, S. Cable, J. Williams,

V. Jeannoda, and C. E. Lehmann. 2018. Grass functional traits differentiate forest

and savanna in the Madagascar central highlands. Frontiers in Ecology and

Evolution 6:184.

Staver, A. C., S. Archibald, and S. Levin. 2011. Tree cover in sub-Saharan Africa:

Rainfall and fire constrain forest and savanna as alternative stable states. Ecology

92:1063-1072.

Therneau, T. M., and M. T. M. Therneau. 2015. Package ‘coxme’. Mixed effects cox

models. R package version 2.

Tomlinson, K. W., F. J. Sterck, E. R. M. Barbosa, S. de Bie, H. H. T. Prins, and F. van

Langevelde. 2019. Seedling growth of savanna tree species from three continents

under grass competition and nutrient limitation in a greenhouse experiment.

Journal of Ecology 107:1051-1066.

171

Vadigi, S., and D. Ward. 2013. Shade, nutrients, and grass competition are important for

tree sapling establishment in a humid savanna. Ecosphere 4:1-27.

Van Der Waal, C., H. De Kroon, W. F. De Boer, I. M. Heitkönig, A. K. Skidmore, H. J.

De Knegt, F. Van Langevelde, S. E. Van Wieren, R. C. Grant, and B. R. Page.

2009. Water and nutrients alter herbaceous competitive effects on tree seedlings

in a semi-arid savanna. Journal of Ecology:430-439.

Voysey, M. D., S. Archibald, W. J. Bond, J. E. Donaldson, A. Carla Staver, and M.

Greve. 2020. The role of browsers in maintaining the openness of savanna grazing

lawns. Journal of Ecology n/a.

Wakeling, J., W. Bond, M. Ghaui, and E. February. 2015. Grass competition and the

savanna-grassland ‘treeline’: A question of root gaps? South African Journal of

Botany 101:91-97.

Wallace, L., S. McNaughton, and M. Coughenour. 1984. Compensatory photosynthetic

responses of three African graminoids to different fertilization, watering, and

clipping regimes. Botanical Gazette 145:151-156.

Ward, D., K. Wiegand, and S. Getzin. 2013. Walter’s two-layer hypothesis revisited:

back to the roots! Oecologia 172:617-630.

Wiegand, K., D. Saltz, and D. Ward. 2006. A patch-dynamics approach to savanna

dynamics and woody plant encroachment–Insights from an arid savanna.

Perspectives in Plant Ecology, Evolution and Systematics 7:229-242.

Williams, E. V., J. Elia Ntandu, P. Ficinski, and M. Vorontsova. 2016. Checklist of

Serengeti Ecosystem Grasses. Biodivers Data J:e8286.

172

Williams, K. J., B. J. Wilsey, S. J. McNaughton, and F. F. Banyikwa. 1998. Temporally

variable rainfall does not limit yields of Serengeti grasses. Oikos:463-470.

173

DEUSDEDITH M. RUGEMALILA

Ph.D. Candidate, Wake Forest University

048 Winston Hall, Winston-Salem 27109, N.C. USA

[email protected] | www.deusrugemalila.weebly.com

EDUCATION

2021 Ph.D. in Biology (ABD) Wake Forest University, Winston Salem,

2015 M.A in Biological Sciences, University of Missouri, Columbia,

2015 Graduate Certificate (GIS Applications) University of Missouri

2008 Bsc Forestry Sokoine University of Agriculture, Tanzania

GRANTS, FELLOWSHIPS, AND AWARDS

Teaching Assistantship (Wake Forest University): Full Tuition and Stipend (> $360,000)

Alumni Travel Award, WFU, Graduate School of Arts & Sciences: $600

Cocke Travel Award, (Wake Forest University) (2016-2017): $1000.

Vecellio awards Wake Forest University (2015 - 2020): $ 5,500.00

Richter Award fund (Wake Forest University) – 2019: $ 4,350.00

174

The Peter Ashton Prize (for the outstanding paper published by a student in Biotropica)

(2016): $250

Research Assistantship (University of Missouri – Columbia: Full Tuition and Stipend

Teaching Assistantship (University of Missouri – Columbia: Full Tuition and Stipend

RESEARCH PUBLICATIONS

Under peer review

8. Rugemalila, D. M., Holdo R. M., and Anderson, T. M., Seed production in the

Serengeti: The role of resources and disturbances across environmental gradients.

Prepared for Ecological Engineering (awaiting co-author approval)

7. Rugemalila, D.M., and Anderson. T.M. Grass competition is more important in

suppressing Acacia seedling growth than grass species type. For seedling survival,

soil moisture's significance is tree species-specific. Prepared for Austral Ecology

(awaiting co-author approval)

6. Holdo, R.M., Donaldson J., Rugemalila, D.M., and Anderson. T.M. Spatial

variation in bottom-up constraints interacts with spatially - uniform top-down

drivers to generate recruitment thresholds in Serengeti tree seedlings. Submitted

to Ecology letters

Published

5. Rugemalila, D. M., Cory, S. T., Smith, W. K., & Anderson, T. M. (2020). The

role of microsite sunlight environment on growth, architecture, and resource

allocation in dominant Acacia tree seedlings, in Serengeti, East Africa. Plant

Ecology, 1-13. https://doi.org/10.1007/s11258-020-01074-5

175

4. Morrison, T. A., Holdo, R. M., Rugemalila, D. M., Nzunda, M., & Anderson, T.

M. (2019). Grass competition overwhelms effects of herbivores and precipitation

on early tree establishment in Serengeti. Journal of Ecology, 107(1), 216-228.

https://doi.org/10.1111/1365-2745.13010

3. Rugemalila, D. M., Morrison, T., Anderson, T. M., & Holdo, R. M. (2017). Seed

production, infestation, and viability in Acacia tortilis (synonym: Vachellia

tortilis) and Acacia robusta (synonym: Vachellia robusta) across the Serengeti

rainfall gradient. Plant Ecology, 218(8), 909-922. https://doi.org/10.1007/s11258-

017-0739-5

2. Rugemalila, D. M., Anderson, T. M., & Holdo, R. M. (2016). Precipitation and

elephants, not fire, shape tree community composition in Serengeti National Park,

Tanzania. Biotropica, 48(4), 476-482. https://doi.org/10.1111/btp.12311

1. Anderson, T. M., Morrison, T., Rugemalila, D., & Holdo, R. (2015).

Compositional decoupling of savanna canopy and understory tree communities in

Serengeti. Journal of Vegetation Science, 26(2), 385-394.

https://doi.org/10.1111/jvs.12241

JOURNALS REVIEWED FOR

African Journal of Ecology, Ecology and Evolution, Forest Research, Frontiers in

Ecology and Evolution, PeerJ, Journal of Plant Research, and Tropical Ecology

TEACHING EXPERIENCE

University Course Level

Wake Forest University Evolutionary and Ecological Biology Undergraduate

176

Genetics and Molecular Biology Undergraduate

Plants and People Undergraduate

Comparative Physiology Undergraduate

Biology and the Human Condition Undergraduate

Ecology Undergraduate

University of Missouri General Biology Undergraduate

General Ecology Undergraduate

PROFESSIONAL PRESENTATIONS

2017 - The role of microsite sunlight environment on growth, architecture, and resource

allocation in dominant Acacia tree seedlings, in Serengeti

-TAWIRI biennial scientific conference, Arusha, Tanzania

2016 - Antagonistic variation in tree height and seed mass of Acacia robusta and Acacia tortilis trees across the Serengeti soil moisture gradient

-101st ESA Annual Meeting, Fort Lauderdale, Florida, USA

2015 - Drivers of tree community composition and seed demography in Serengeti national park

- TAWIRI biennial scientific conference, Arusha, Tanzania

2015 - The role of seed limitation in Serengeti savanna tree recruitment

-100th ESA Annual Meeting, Baltimore, Maryland, USA.

2013 - College of African Wildlife Management – Mweka (guest lecturer)

2013 - School for Field Studies (SFS) – Wildlife Management Studies (guest lecturer)

177

PROFESSIONAL MEMBERSHIPS

British Ecological Society (BES)

Ecological Society America (ESA)

Tropical Biology Association (TBA)

DATA ANALYSIS SKILLS

Spatial analysis using ArcMap

Statistical analysis in R language/software

178