Studying the Impact of Environmental Factors on Vegetation in Dahra, Senegal

THE SAHELIAN DRYLANDS UNDER PRESSURE: STUDYING THE IMPACT OF ENVIRONMENTAL FACTORS ON VEGETATION IN DAHRA, SENEGAL

Thomas Sibret Student number: 01102750

Promotor: Prof. dr. ir. Hans Verbeeck Tutor: MSc. Wim Verbruggen

A dissertation submitted to Ghent University in partial fulfilment of the requirements for the degree of Master of Science in Bioscience Engineering: Forest and Nature Management

Academic year: 2017 - 2018

Declaration of Authorship

“De auteur en de promotor geven de toelating deze scriptie voor consultatie beschikbaar te stellen en delen ervan te kopiëren voor persoonlijk gebruik. Elk ander gebruik valt onder de beperkingen van het auteursrecht, in het bijzonder met betrekking tot de verplichting de bron te vermelden bij het aanhalen van resultaten uit deze scriptie.”

“The author and the promotor give the permission to use this thesis for consultation and to copy parts of it for personal use. Every other use is subject to the copyright laws, more specifically the source must be extensively specified when using results from this thesis.”

Ghent, June 2018

The promotor, The tutor, The author, Prof. dr. ir. Hans Verbeeck MSc. Wim Verbruggen Thomas Sibret

Acknowledgements

Finally! I did it! After 2 months of field work in the oven of the Sahelian drylands between the wild dogs, camels and hundreds of scorpions, and nine months of intensive work, I can finally proudly say I accomplished this challenging adventure! However, this entire experience wouldn’t have been such a success without some people I’m willing to thank.

First of all I would like to thank my promotor Prof. dr. ir. Hans Verbeeck for offering me this unique opportunity and for the support and reviewing of my work.

Next, I would of course thank my tutor MSc. Wim Verbruggen for guiding me through this journey and correcting my thesis. I wish you the best to further complete this ambitious and most interesting project.

The entire fieldwork wouldn’t have been such a wonderful experience without two friendly collegues from KULeuven; thank you, Ilié Storms and MSc. Paulo Bernardino (alias “chicken”), you not only became precious colleagues but also real friends with whom I hope to get the opportunity to work again with in a close future! Furthermore, I would also thank Moustapha Mbaye and Ali Niang as well as their families for their warm hospitality and for sharing their culture! Jërë jëf samay xarit!

Special thanks to PhD Lore Verryckt, dr. ir. Marijn Bauters, dr. Ousmne Ndiaye, Prof. dr. ir. Jan Van den Bulcke and Hoaran Zhou for their very helpful advices that helped me reach my goals.

On a more personal point of view I would like to thank my beloved biologist Steffi Dekegel and my dear brother Mathieu Sibret, for their support and distractions in moments of need.

Last but not least, I would like to thank my dear mother for giving me the opportunity to go to university as well as for all the support she gave me during those fantastic but sometimes difficult years. This is why I would end with: Voilà maman, j’y suis arrivé! Merci pour tout! Summary

In this study, photosynthetic characteristics of Sahelian dryland species were examined in the Dahra field site, Senegal. During the field campaign, that took place from July until September 2017 (wet season), leaf gas-exchange measurements were performed in situ on all dominant species present on the site (trees and herbaceous plants). Furthermore, a complete site survey has been conducted and for each plant 24 leaves were collected in order to perform a leaf nitrogen content analysis.

First, the field survey results have been compared with those from previously executed fields surveys. This comparison enabled us to hypothesize that rainfall conditions and the introduction of new invasive species may play a determinant role in the composition of ground cover species in Sahelian drylands.

Then, the recorded photosynthetic response curves were fitted and various photosynthetic parameters were derived (e.g. dark respiration, maximal photosynthetic electron transport rate and maximal carboxylation rate). These photosynthetic parameters characterise a plant’s reaction to environmental factors (e.g. light and CO2) and form a basis for the parameterisation of vegetation models.

Afterwards, the differences in photosynthetic parameters between C3 and C4 plants measured within this study have been examined. The two different plant groups show significant differences for several photosynthetic parameters. Moreover, the photosynthetic parameters derived within this study were compared to those originating from studies effectuated within other regions. Here again, significant differences are observable. Given the extent of these differences it seems important to collect more field-data of Sahelian drylands species to provide an accurate parameterization for both C3 and C4 plants in vegetation models in order to get more precise predictions.

Finally, correlations between the different photosynthetic parameters and leaf nitrogen content were examined. This has been done for C3 and C4 plants separately. For C3 plants,

several correlations are observed when leaf nitrogen contents and photosynthetic parameters are expressed on leaf dry mass basis. For C4 plants no significant correlations were observed. Furthermore, different correlations were observed for the photosynthetic parameters and leaf nitrogen content with specific leaf area.

To further substantiate these conclusions and to improve the interpretations of the results, further research and data concerning photosynthetic parameters of Sahelian plant species is required.

Samenvatting

In deze studie worden fotosynthetische kenmerken van planten groeiende in het ariede klimaat van de Sahel onderzocht in Dahra, Senegal. Tijdens de veldcampagne, die plaats vond van juli tot september 2017 (regenseizoen), werden in situ fotosynthesemetingen uitgevoerd op alle dominante soorten die op het terrein aanwezig waren (zowel bomen als grassen). Bovendien werd er een volledige site-inventarisatie uitgevoerd en werden er voor elke plantensoort 24 bladeren verzameld om het stikstofgehalte in de bladeren te analyseren.

Eerst werd de site-inventarisatie vergeleken met eerder uitgevoerde inventarisaties van dezelfde site. Deze vergelijking leidde tot de conclusie dat neerslagomstandigheden en de introductie van nieuwe invasieve soorten een bepalende rol spelen in de soortensamenstelling van bodembedekkers in de ariede gebieden van de Sahel.

Vervolgens werden de opgenomen fotosynthetische responscurven gefit en werden hieruit verschillende fotosyntheseparameters afgeleid (bv. donkerrespiratie, maximale fotosynthetische elektronentransportsnelheid en maximale carboxylatiesnelheid). Deze fotosyntheseparameters karakteriseren de reactie van een plant op bepaalde omgevingsfactoren (bv. licht en CO2) en vormen de basis voor de parameterisatie van vegetatiemodellen.

Nadien werden de verschillen tussen fotosynthetische parameterwaarden voor C3 en C4 planten, die gemeten werden in dit onderzoek, onderzocht. De twee verschillende plantengroepen vertonen significante verschillen voor verschillende fotosyntheseparameters. Bovendien werden de fotosyntheseparameters die in dit onderzoek werden afgeleid ook nog eens vergeleken met deze van studies die in andere regio's (gematigd en tropisch klimaat). Ook hier zijn significante verschillen waarneembaar. Gezien de omvang van deze verschillen, lijkt het van belang om meer veldgegevens voor planten afkomstig uit ariede gebieden te verzamelen om zodanig een nauwkeurigere

parameterisatie te bieden voor zowel C3 als C4 planten in vegetatiemodellen en zo preciezere voorspellingen te krijgen.

Ten slotte werden de verbanden tussen de verschillende fotosyntheseparameters en het stikstofgehalte van de bladeren onderzocht. Dit werd afzonderlijk voor C3 en C4 planten uitgevoerd. Voor C3 planten werden meerdere correlaties waargenomen wanneer het gehalte aan bladstikstof en de verschillende fotosyntheseparameters werden uitgedrukt op bladdroge massa basis. Voor C4 planten werden geen significante correlaties waargenomen. Verder werden verschillende correlaties waargenomen voor de fotosyntheseparameters en het bladstikstofgehalte met specifiek bladoppervlak.

Om deze conclusies verder te ondersteunen en om de interpretaties van de resultaten te verbeteren, is verder onderzoek en een grotere toegankelijkheid tot gegevens betreffende fotosyntheseparameters van plantensoorten in de Sahel vereist.

Résumé

Dans cette étude, les caractéristiques photosynthétiques d’espèces originaires du climat aride du Sahel ont été examinées sur le site de Dahra, Sénégal. Durant le travail de terrain, qui se déroula de juillet à septembre 2017 (saison des pluies), des mesures d'échange gazeux des feuilles ont été réalisées in situ sur toutes les espèces dominantes présentes sur le site (arbres et plantes herbacées). De plus, une inventorisation botanique complète du site a été réalisée et pour chaque plante 24 feuilles furent prélevées afin d'effectuer une analyse de la teneur en azote des feuilles.

Tout d'abord, l’inventaire botanique effectuée a été comparée à d’autre inventorisations botaniques précédemment exécutées sur le même site. Cette comparaison a permis de conclure que les conditions pluviométriques et l'introduction de nouvelles espèces envahissantes jouent un rôle déterminant dans la composition des espèces de couverture végétale dans les zones arides sahéliennes.

Ensuite, les courbes de réponses photosynthétiques mesurées sur le terrain ont été ajustées et divers paramètres photosynthétiques ont été dérivés (ex. la respiration a l’obscurité, le taux maximal de transport d'électrons photosynthétiques et le taux maximal de carboxylation). Ces paramètres photosynthétiques caractérisent les réactions d'une plante à certain facteurs environnementaux (ex. la lumière et le CO2) et forment une base pour le paramétrage des modèles de végétation.

En outre, les différences de paramètres photosynthétiques entre les plantes C3 et C4 mesurées dans cette étude ont été examinées. Les deux groupes de plantes différents montrent des différences significatives pour plusieurs paramètres photosynthétiques. De plus, les paramètres photosynthétiques dérivés de cette étude ont été comparés à ceux provenant d'études effectuées dans d'autres régions (climat tempéré et tropicaux). Là encore, des différences significatives sont observables.

Compte tenu de l'ampleur de ces différences, il semble important de recueillir davantage de données sur les espèces sahéliennes pour fournir un paramétrage précis des plantes C3 et C4 dans les modèles de végétation afin d'obtenir des prédictions plus précises.

Finalement, les corrélations entre les différents paramètres photosynthétiques et la teneur en azote des feuilles ont été examinées. Cela a été fait séparément pour les plantes C3 et C4.

Pour les plantes C3, plusieurs corrélations positives ont été observées quand la teneurs en azote des feuilles et les paramètres photosynthétiques étaient exprimés en fonction de la masse sèche de la feuille. Pour les plantes C4, aucune corrélation significative n'a été observée. De plus, différentes corrélations ont été observées pour les paramètres photosynthétiques et la teneur en azote des feuilles avec la surface foliaire spécifique.

Afin d'étayer ces conclusions et d'améliorer l'interprétation des résultats, d'autres recherches ainsi que des données supplémentaires concernant les paramètres photosynthétiques sont nécessaires.

Table of contents

Chapter I ...... 15 Introduction ...... 15 1. Problem statement ...... 15 2. Goal ...... 15 Chapter II ...... 17 Literature review ...... 17 1. The Sahel ...... 17 1.1. Geographic position ...... 17 1.2. Climatic characteristics ...... 18 1.3. Flora ...... 19 1.4. Desertification or greening of the Sahel? ...... 20 1.4.1. Severe droughts ...... 20 1.4.2. Greening of the Sahel ...... 23 1.5. The uncertain future of the Sahelian climate ...... 24 2. Dynamic Global Vegetation Models ...... 26 3. Leaf photosynthetic models ...... 27 3.1. Light-response curves ...... 27 3.1.1. Theory of light-response curves ...... 27 3.1.2. Derivable parameters ...... 28 3.1.3. Fitting the light-response curve ...... 29 3.1.4. Temperature dependencies ...... 30 3.1.5. Differences among plants ...... 30 3.2. A-Ci response curves ...... 32 3.2.1. The theory of A-Ci response curves ...... 32 3.2.2. Derivable parameters ...... 36 3.2.3. Equations to fit the model to the data ...... 37 3.2.4. Temperature adjustments ...... 40 3.2.5. State of the art photosynthesis research in the Sahel ...... 40 4. Photosynthetic capacity and foliage nitrogen content ...... 42 Chapter III ...... 45 1. Site description ...... 45 2. Site inventory ...... 47 2.1. Tree inventory ...... 47 CONTENTS 13

2.2. Grass inventory ...... 49 3. Response curves ...... 49 3.1. In situ measurements practices ...... 49 3.2. Settings ...... 51 3.3. Post processing of response curves data ...... 52 3.3.1. Determination of measured area ...... 52 3.3.2. Adaxial stomata fraction ...... 52 3.4. Data Analysis ...... 53 4. Specific leaf area ...... 55 5. Leaf chemistry ...... 55 5.1. Grinding the leaves ...... 55 5.2. Foliage C and N content determination ...... 55 6. Statistical analysis ...... 56 Chapter IV ...... 57 Results & Discussion ...... 57 1. Inventory ...... 57 2. Response curves data ...... 62 2.1. Determination of the photosynthetic pathway ...... 62 2.2. Light-response curves measurements ...... 64 2.3. A-Ci curves measurements ...... 65 2.4. Critical evaluation on temperature dependencies adjustments ...... 66

3. Comparison between C3 and C4 plants ...... 73 4. Comparison of photosynthetic parameters with other climatic zones ...... 77 5. Correlations between photosynthetic parameters, foliage nitrogen content and Specific Leaf Area...... 80 Chapter V ...... 89 Conclusions ...... 89 Chapter VI ...... 91 Further research ...... 91 Chapter VII ...... 93 Bibliography ...... 93 Chapter VIII ...... 101 Appendix ...... 101

Abbreviations

A gross photosynthetic rate

AN net photosynthetic rate

Asat light saturated photosynthetic rate

CCM CO2 Concentrating Mechanism

Ci intercellular CO2 concentration

CO2comp CO2 compensation point DVGMs Dynamic Global Vegetation Models I photosynthetic photon flux density (µmol photons m-2s-1)

-2 -1 Icomp light compensation point (µmol m s )

Jmax maximum electron transport rate

Jmax25 standardised maximum electron transport rate (T=25°C) PEP phosphoenolpyruvate PEPc phosphoenolpyruvate carboxylase

Q10 the proportional increase in Rd with a 10°C rise in temperature R² coefficient of determination

Rd dark respiration

Rd25 standardised dark respiration (T = 25°C) RuBisCo Ribulose-1,5-Bisphosphate Carboxylase/oxygenase SLA Specific Leaf Area SST Sea Surface Temperature TPU Triose Phosphate Use

Vcmax maximum carboxylation rate

Vcmax25 standardised maximum carboxylation rate (T=25°C)

Vmax enzyme maximum velocity θ convexity factor

-1 ф0 maximum quantum yield (µmol CO2 µmol photons )

CHAPTER I: INTRODUCTION 15

Chapter I Introduction

1. Problem statement

The Sahelian drylands cover an area of 3.053.000 km² that stretches over a latitudinal band in North Africa and form a transition zone between the Sahara Dessert and the (sub-)humid tropics of equatorial Africa. The savannah landscape is characterised by natural pasture, with low-growing grass and tall, herbaceous perennials, thorny shrubs and various scattered tree species, such as acacia and baobab trees. The last decades however, this region has largely been under pressure due to global change, devastating climate extremes and unsustainable use of natural resources. In the 20th century, the Sahel has experienced one of the most striking climatic phenomena worldwide (Klönne, 2012); the devastating droughts of the mid 60’s, that had disastrous consequences on the local vegetation and even introduced the term of ‘desertification’ (Foley, Coe, Scheffer, & Wang, 2003; Hulme, 2001), have been followed by an increase in rainfall since the late 80’s that made many scientists wonder if a regreening of the Sahel is taking place (Herrmann, Anyamba, & Tucker, 2005a; Kelder, Nielsen, & Fensholt, 2013). All these drastic changes of the last decades even earned the Sahel the name of “hot spot” of global environmental change amongst scientists (Claussen, 2009a). Many researches have been focussing on the environmental changes occurring in this vast region (Herrmann et al., 2005a; Kelder et al., 2013; Klönne, 2012), but none led to a clear statement so far. A better comprehension on the impact of environmental factors on local vegetation could deliver a basis towards a better comprehension of these systems (Long & Drake, 1991).

2. Goal

This thesis concerns the measurement of various plant traits, with a focus on light- and CO2- response curves, within a semi-arid dryland ecosystem (Dahra, Senegal). 16

First, a complete field survey was performed in order to get a precise idea of the species composition. Next, gas exchange measurements were performed on the dominant species present on the field in order to gain insight in their photosynthetic characteristics. During the gas exchange measurements, photosynthesis was measured in situ to changing light (light- response curve) and to changing carbon dioxide concentrations (A-Ci curve), from which we can derive various photosynthetic parameters (e.g. dark respiration, maximal photosynthetic electron transport rate and maximal carboxylation rate). These photosynthetic parameters are not only vital in the scope of establishing a better understanding of these plants reactions to environmental factors, but they can also be related to leaf traits, such as leaf nitrogen content and specific leaf area (SLA). Furthermore, for future parameterisation of existing DVGMs (e.g. ED2), we aim to provide various model parameters, which will be used in the U- TURN project for simulating the vegetation in the Sahel, including the maximal carboxylation rate (Vcmax), derived from these gas exchange measurements. While studying photosynthetic characteristics of these plants, we aim not only to measure the C3 plants, but also to measure all the dominant C4 plants, of which less data is available, probably due to the lack of fitting tools for A-Ci curves of C4 plants. Quite recently, Zhou et al. however proposed a new fitting tool for C4 A-Ci curves (Zhou, Akcay, & Helliker, 2017). This fitting tool will thus be used in this study and the derived C4 parameters will then be compared to C3 parameters.

This thesis starts with a literature review, which provides more background about the Sahel and the ongoing changes that occurred during the last decades, followed by a theoretical description of the studied parameters. Furthermore, the study site characteristics and techniques used to collect the data will be presented in the second chapter, ‘Materials and methods’. In the ‘Results & discussion’ chapter, data and statistical results are illustrated and will be further discussed. Finally, the conclusion gives a brief summary of the findings of this thesis, followed by suggestions for further research.

CHAPTER II: LITERATURE REVIEW 17

Chapter II Literature review

This literature review can be divided in four main parts. First, a description of the Sahelian drylands will be given, followed by a general explanation of the climate changes that occurred in the Sahel during the last decades. In the second part, a brief description will be given about Dynamic Global Vegetation Models (DVGMs) and the role these could play in a better understanding and prediction of the Sahelian climate changes. In a third part, a complete description will be given about the study regarding the impact of environmental factors on vegetation by the use of leaf response models which can lead towards a better parametrisation of DVGMs. Finally, in the fourth part, the possible link between photosynthetic capacity and leaf nitrogen content will be discussed.

1. The Sahel

1.1. Geographic position The Sahel1 is a latitudinal region situated in the northern part of the African continent that stretches from the Atlantic Ocean in the West to the Red Ocean in the East (Klönne, 2012) (Figure 2.1.). From south to north this band roughly extends from 12° N to 18° N (Le Houerou, 1980), whereas it must be noticed that the defined longitudinal borders may differ from

Figure 2.1. Map of north Africa with the location of the Sahel

sāḥil) for shoreline, used to mean the southern fringe of the Sahara ,ساحل) The word Sahel is an Arabic term 1 desert; i.e. the shoreline of the desert. 18 source to source (Bader & Latif, 2003; Le Houerou, 1980). This is mainly because they are defined on the basis of precipitation values (100-600mm/year) or vegetation cover, which cannot be unequivocally defined since these are parameters that are subjected to large fluctuations over years (Klönne, 2012). The Sahel includes parts of several countries going from Senegal and Mauritania on the west, trough Mali, Burkina Faso, Niger, Nigeria, Chad and Sudan, to Ethiopia and Eritrea in the east.

1.2. Climatic characteristics The Sahel is a semi-arid area that forms a transitional zone between the dry arid Sahara Desert in the north (20° N) and the (sub-)humid tropics of equatorial Africa in the south (10° N). This position, between these two opposing climates, creates a strong north-south gradient in mean annual rainfall, ranging from 100-200 mm/year in the north to 400-600 mm/year in the south (Klönne, 2012). In the west this gradient is even steeper (Kelder et al., 2013). This rainfall gradient expresses itself on the ground by a subtle change in vegetation composition from the Saharan biome with very sparse vegetation cover -thorny shrubs interspersed between annual and perennial grasses- to the Sudanian and Guinean biomes, characterized by a higher amount of ground cover, taller vegetation and a greater proportion of woody species (White, 1983). Temperatures are relatively high: the average maximum rises to 40- 42°C with peaks of 45°C occurring rather regularly in April-May. The average minimum drops to 15°C in December-January with the absolute minimum rarely below 10°C (Le Houerou, 1980).

The Sahelian climate is also characterized by a pronounced seasonality, with a short rainy season and a long dry season (Bader & Latif, 2003). The rainy season takes place in the summer starting in June or July and ending in September with peak values in August. The rest of the year is dry and known as the dry season. This seasonality is associated with the seasonal movement of the Intertropical Convergence Zone (ITCZ), which is the low-pressure area that spans the Earth around the Equator where the northeast and southeast trade winds converge (Figure 2.2.). This causes large scale vertical upward movements of air, convection and heavy precipitation over the tropics. From space it is even visible as a band of clouds, usually thunderstorms, that encircle the globe near the equator. The location of the ITCZ varies with

CHAPTER II: LITERATURE REVIEW 19 the seasons, following the warmer waters northward (southward) when solar insolation reaches its maximum in the Northern (Southern) hemisphere in the boreal (austral) summer. When it reaches its northernmost position in the boreal summer it provides the Sahel with rain forming thus the rainy season. When it moves southward during the austral summer, it leaves the Sahel dry and under the influence of the subtropical high pressure belt forming the known long dry season (Herrmann, Anyamba, & Tucker, 2005b). As mentioned earlier, the vegetation cycle closely responds to the seasonality in rainfall, with virtually all biomass production taking place in the humid summer months (Figure 2.3.).

Figure 2.2. Localisation of the ITCZ during the northern Figure 2.3. Vegetation cover and rainfall amount during hemisphere summer and during the southern hemisphere the months of Marche and September 2004 in The Sahel summer. region. Source: NASA Earth Observatory

1.3. Flora The Sahel is mostly covered in grassland and savannah (Figure 2.4.), with areas of woodland and shrubland. Grass cover is fairly continuous across the region, dominated by annual grass species such as Aristidia mutabilis, Cenchrus biflorus, Schoenefeldia gracilic and Eragrostis tremula. Species of Acacia are dominant trees, with Acacia tortilis being the most common, along with Acacia Senegal and Acacia laeta. Other tree species include Commiphora Africana, Balanites aegyptiaca, Faidherbia albida, and Boscia senegalensis. In the northern part of the Sahel, areas of desert shrub, including Panicum turgidum and Aristida sieberana, alternate with areas of grassland and savanna (Le Houerou, 1980; New World Encyclopedia contributors, 2012). During the long dry season, many trees lose their leaves and the predominantly annual grasses die. The hardy landscape of the Sahel is also known as one of Africa’s most productive crop regions.

Figure 2.4. 360° panoramic picture of a Sahelian savannah landscape. Taken at the Dahra field site (12/09/2017).

1.4. Desertification or greening of the Sahel? 1.4.1. Severe droughts Despite its productivity, the Sahel has a dark history of famine tied to highly erratic rainfall (Figure 2.5.). Although strong fluctuations in rainfall are natural in these regions (Hulme, 2001), there was an anomalously strong rainfall in the 1950s and early 1960s, followed in the mid 1960’s by several devastating droughts (Foley et al., 2003; Hulme, 2001) that drove millions of people to suffer from famine (Herrmann et al., 2005a; Nicholson, Tucker, & Ba, 1998; Olsson, 1993). This dry period lasted until the mid-1990s and was characterised by a 25% rainfall reduction in comparison to preceding decades (Hulme, 2001). Following this, many started to wonder if the Sahara was extending southwards, swallowing the arable land in the Sahel leading to the well-known term of “desertification2”.

4 3 2 1 0

cm/month -1 -2 -3 -4 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Year Figure 2.5. Rainfall averages anomalies of the rainy seasons (June through October) in the Sahel (20-10°N, 20°W-10°E) over the period of 1901-2017. The bars express the difference departure from the average calculated over the period of 1901- 2017. The trendline express the 5 year rolling average. Sources: Deutscher Wetterdienst Global Precipitation Climatology Centre data. 1.4.1.1. Causes of the drought in the Sahel In attempt to identify the causes of these droughts, two opposing hypotheses were proposed; (1) The first one, (Charney J.G., 1975), focused on anthropogenic factors such as overgrazing and conversion of woodlands to agricultural fields. Both of these processes tend to increase the surface albedo, which reduces surface heating and thus atmospheric heating. This all lessens convection and by such reduces the moisture supply to the atmosphere. They, as a result, lead to a decrease in precipitation and

2 Desertification is the process through which productive land becomes permanently non-productive (on a human time scale).

CHAPTER II: LITERATURE REVIEW 21

therefore to less favourable vegetation conditions. Resulting in a diminishing of the initial vegetation cover, amplifying in this way the initial conditions (Charney J.G., 1975; Hagos & Cook, 2008; Xue, Shukla, & Clim, 1993). This entire process is also known as the “Charney’s hypothesis”(Agnew & Chappell, 1999). (2) The second hypothesis, presented in the mid 80’s (Folland, Palmer, & Parker, 1986), invoked large-scale atmospheric circulation changes triggered by multidecadal variations in global sea surface temperature (SST). These variations in SST result in a change of the African Monsoon’s strength. Meaning that positive SSTs in the tropical Indian, Atlantic and Pacific Ocean lead to drought over the Sahel. Anomalously wet periods could consequently be associated with colder-than average SSTs (Agnew & Chappell, 1999). Recent studies showed that, even if they do play a role, nor the modest land use changes of the last 35 to 40 years (Taylor, 2002), nor the variations in global SST (Alessandra Giannini, Biasutti, Held, & Sobel, 2008) could fully explain the observed droughts. (1) Although the proponents of Charney’s theory did obtain changes in rainfall pattern when forcing their models with land surface conditions only, the magnitude of the models produced remained below the observed anomalies. And when they did reproduce the observed values, the prescribed land use change usually exceeded the real developments (Hagos & Cook, 2008). (2) Recent models (Agnew & Chappell, 1999; Lu & Delworth, 2005) did produce the observed interdecadal variability in precipitation by only forcing the SST. The amplitude as well as the spatial pattern of the simulated rainfall compares well with the observed values. But although these models successfully capture the decadal drying trend, they have up to now not been able to explain the rainfall in individual years (Brooks, 2004). According to Giannini et al. (A. Giannini, Saravanan, & Chang, 2003), only 25 to 35% of the observed rainfall (1930–2000) changes could directly be linked to the SST change. It is also very likely that SST change, natural vegetation processes and land use change have acted synergistically to produce the unusual drought in the Sahel (Agnew & Chappell, 1999). What is missing may also well be the land-atmosphere feedbacks such as the vegetation- feedback and the atmospheric dust feedback (Klönne, 2012; Taylor, 2002; Zeng, Neelin, Lau, & Tucker, 1999).

1.4.1.2. Is the desertification really occurring? As the area of the Sahel is far too vast to conduct the extensive ground measurements that would be needed to find out if the Sahel is becoming a desert, scientists started using satellite images to determine if the Sahel could still support plant life (NASA, 2004). Desertification can be identified in satellite images by comparing rainfall to vegetation growth. If plants grow after rain falls, then the land is still productive, and desertification has not happened. If plants fail to grow after rain, then the land might have become non- productive. If plants fail to grow after several years of rainfall, then the change may be permanent, and the land has been desertified (NASA, 2004).

In 2006, the Global Inventory Modelling and Mapping Studies (GIMMS) group, led by Compton Tucker at Goddard Space Flight Centre, released a twenty-four-year-long satellite- based vegetation record of Africa’s Sahel. They recorded the NDVI3, which is an indicator of the amount and state of vegetation cover on the ground (Myneni et al., 2001; Myneni & Tucker, 1999) and thus gives a direct measure of how much plants are growing. Studied in conjunction with rainfall, the vegetation record revealed that plants in the Sahel were still growing when the region received rainfall, meaning that desertification was not completely taking place (Hutchinson, Herrmann, Maukonen, & Weber, 2005).

On the contrary, further research has shown that rainfall has steadily increased since the mid- 1980s (Lebel & Ali, 2009), and a new re-greening debate largely replaced the previous degradation paradigm.

3 NDVI = Normalized Difference Vegetation Index; defined as the ratio of the difference between near-infrared reflectance and red visible reflectance to their sum.

CHAPTER II: LITERATURE REVIEW 23

1.4.2. Greening of the Sahel While some studies still have reported a decline in vegetation productivity (e.g. Millennium Ecosystem Assessment, 2005), plenty of other studies based on remotely sensing data show an increased vegetation productivity in the Sahel (Herrmann et al., 2005a; Kelder et al., 2013) reporting that the Sahel is actually expanding, retreating by such the southern border of the Sahara since the mid-1980s. In 2009 the Global Warming Policy Foundation even reported the following (GWPF, 2009): “There has been a spectacular regeneration of vegetation in northern Burkina Faso, which was devastated by drought and advancing deserts 20 years ago. It is now growing so much greener that families who fled to wetter coastal regions are starting to come back. There are now more trees, more grassland for livestock and a 70% increase in yields of local cereals such as sorghum and millet in recent years. Vegetation has also increased significantly in the past 15 years in southern Mauritania, north-western Niger, central Chad, much of Sudan and parts of Eritrea. In Burkina Faso and Mali, production of millet rose by 55 percent and 35 percent, respectively, since 1980.”

1.4.2.1. Causes of the greening Even if the underlying reasons of this greening aren’t completely known and understood, most of the studies agree on the fact that the main reason for the greening of the Sahara and the Sahel is the increase in rainfall since the mid-1980s (Hagos & Cook, 2008; Hickler et al., 2005). Besides the indispensability of rain for vegetation growth in the Sahel, this increase in rainfall also induced some positive feedback effects. The increase in rainfall has allowed more plants to grow, which in turn increases precipitation even more; ➢ Plants transfer moisture from the soil into the air by evapotranspiration from their leaves and hold water in the soil close to the surface, where it can also evaporate. ➢ The darker surface of plants compared with sand also decreases the albedo, and as such absorbs more solar radiation, which can create convection and turbulence in the atmosphere which might create rainfall. Vegetation effects account for as much as 30 percent of annual rainfall variation in the Sahel (Los et al., 2006).

However, the greening of the Sahel cannot be explained solely by the increase in rainfall. Olsson et al. compared 40 rainfall stations across the Sahel with complete time series with the trends in vegetation greenness. They identified some regions that had known a vegetation increases in areas where rainfall was decreasing, suggesting as such that another factor was responsible for the greening in these areas. (Olsson, Eklundh, & Ardö, 2005).

This other factor might be the rise of atmospheric CO2 levels (Hickler et al., 2005). Rising levels of atmospheric CO2 concentration is a trend occurring since the beginning of industrialisation

th (Keeling, 1960). On the 12 march of 2018 the global atmospheric CO2 concentration reached a value of 406.78 ppm while the air samples of Mauna Loa recorded a value of 315.98ppm in 1959 (“Atmospheric Carbon Dioxide Record from Mauna Loa,” 2018). This means that the

Atmospheric CO2 level has known an increase of 90.8ppm in less than 60 years and this value keeps increasing throughout the years; e.g. over the year of 2017 alone an increase of 2.38ppm was recorded (US Department of Commerce, NOAA, 2018).

The aerial fertilization effect of the ongoing rise in the air’s CO2 concentration increases greatly the productivity of plants. The more CO2 there is in the air, the better plants grow.

Rising atmospheric CO2 levels also allows the plants to achieve the same growth with a lower stomatal opening allowing them to reduce their transpiration, which enhances the water-use efficiency of plants and enables them to grow in areas that were once too dry (Battipaglia et al., 2013; Sibret, Aernouts, Devriendt, & de Walque, 2015).

1.5. The uncertain future of the Sahelian climate Climate scientists do not agree on how the future climate of the Sahel will look like. While some climate models simulate a decrease in rainfall, others predict an increase in rainfall (Buontempo, 2010; Haarsma, Selten, Weber, & Kliphuis, 2005) making North Africa the area of greatest disagreement among climate scientists (Claussen, 2009b).

Claussen explains that forecasting how global warming will affect the Sahel is complicated by the region’s vast size and the unpredictable influence of high-altitude winds that disperse monsoon rains (Claussen, Brovkin, Ganopolski, Kubatzki, & Petoukhov, 2003). By using a climate system model, he also considered the likelihood of a greening of the Sahara due to global warming and concluded that an expansion of vegetation into today’s Sahara is

CHAPTER II: LITERATURE REVIEW 25

possible as a consequence of the ongoing CO2 emissions. His climate models suggest that the rate of greening could be fast. Depending on the rate of atmospheric CO2 concentration increase, vegetation migration into the Sahara could cover up to 1/10th of the Saharan area per decade, but could not exceed a coverage of 45% (Claussen et al., 2003).

2. Dynamic Global Vegetation Models

The drastic changes in the Sahelian climate of the last decades have earned the Sahel belt of Africa to be the focus of much scientific attention. Some scientist even identified the Sahel as a “hot spot” of global environmental change (Hickler et al., 2005). But despites all this interest, the understanding of the roles of different climatic and anthropogenic forcing factors driving change in the region is still incomplete.

The improvement of Earth System Models (ESMs) could lead towards a better comprehension of the Sahelian dryland ecosystem and help to get more precise predictions (e.g. from IPCC, C4MIP) on the impact of future climate changes. A better representation of vegetation demography in ESMs has been repeatedly identified as a critical step towards a more realistic representation of biologically mediated feedback in modelling future climates (Fisher et al., 2018). Dynamic global vegetation models (DGVMs) are the components of land surface models (LSMs) that generally combine biogeochemistry-, biogeography-, plant physiology-, vegetation dynamics- and disturbance- (such as wildfires) sub-models to allow the modulation of vegetation demography and even simulate the effects of future climate change on natural vegetation and its carbon and water cycles (Prentice et al., 2004). Examples of DVGMs are: MOSES (Cox, 1999), ORCHIDEE (Krinner et al., 2005), IBIS (Kucharik et al., 2000), BETHY (Knorr, 2000), LPJ-GUESS (Smith et al., 2014), ED2 (Medvigy, Wofsy, Munger, Hollinger, & Moorcroft, 2009). Most DVGMs, such as those listed above, are based on leaf photosynthesis models (Fisher et al., 2018; Prentice et al., 2007) . Leaf photosynthetic models are based on the underlying biochemical processes of photosynthesis (S. Von Caemmerer, 2000) or accurately described by empirical equations (Ögren & Evans, 1993) and provide better insight in leaf gas-exchange measurements (S. Von Caemmerer, 2000). The simplicity of these models enables straightforward upscaling to canopy-, stand- and ecosystem level or can be used as an application in a climate model. In 1991, Long et al., already pointed out that predicting the responses of leaf photosynthesis to environmental factors was fundamental to projecting the impact of global change on the biosphere (Long & Drake, 1991). However, for successfully driving, evaluating and using the model, a significant amount of field data is required.

CHAPTER II: LITERATURE REVIEW 27

3. Leaf photosynthetic models

Photosynthesis in plants consists of different types of interconnected biological processes located in different compartments of photosynthesising eukaryotic cells. Both biophysical processes (e.g. CO2 transport through the leaf and stomata) and biochemical processes determine the net rate of CO2 assimilation (AN). These biophysical and biochemical processes, and environmental variables such as light intensity, atmospheric CO2 concentration and temperature can have a significant effect on CO2 assimilation by plants (A). This makes it difficult to predict how A is influenced by genetics, epigenetics and environmental factors (Sharkey, Bernacchi, Farquhar, & Singsaas, 2007). Hereby, biochemical models of leaf photosynthesis form an invaluable tool to both predict plant response to environmental factors and climate change and could even serve to identify potential targets to improve the efficiency of CO2 fixation (Sage & Kubien, 2007; Zhu, Long, & Ort, 2008). As mentioned above, these models are also used in DVGMs to estimate terrestrial CO₂ exchange at the canopy and earth system scale (Drewry et al., 2010; Rogers, Medlyn, & Dukes, 2014; B. J. Walker & Ort, 2015).

3.1. Light-response curves

The net photosynthetic light-response curve (AN/I curve, Figure 2.6.) describes the net CO2

-2 -1 assimilation by a plant leaf (AN; µmol CO2 m s ) as a function of the increase in the photosynthetic photon flux density (I; µmol photons m-2s-1) from the total absence of light to a high level of light, e.g. 2000 µmol photons m-2s-1 (Pereira et al., 2013; S. Von Caemmerer,

2000) and this at a constant CO2-concentration and constant temperature.

3.1.1. Theory of light-response curves This curve typically presents at least two distinguishable phases, i.e. the light-limited phase and the CO2-limited phase:

➢ Phase1: The light-limited phase occurs at a low photon flux density. In this phase the

CO2 build-up increases linearly with the light intensity. Sometimes, at the beginning, from complete darkness to the vicinity of the light

compensation point (Icomp), a rapid increase in AN with I can be noticeable. This is due

to the partial suppression of dark respiration (Rd) by light and is also called the Kok effect (Sharp, Matthews, & Boyer, 1984).

➢ Phase2: The CO2-limited phase, occurring at higher photon flux, is characterized by a

region of nonlinear die-off before AN reaches a semi-plateau. At this level an increase

in I does not provoke a proportional increase in AN (Ögren & Evans, 1993). The

progressive curvature in the ratio AN/I in this region can be described by a convexity factor (Ögren & Evans, 1993).

Sometimes, after reaching a maximum value of AN, a subsequent decrease in AN with I, referred to as photoinhibition, can be observed (Ye 2007).

3.1.2. Derivable parameters From this theoretical description some important parameters emerge that allow us to perform comparisons between specific light response curves of different plants (Figure 2.6.).

-2 -1 -2 -1 At complete darkness (I = 0 µmol photons m s ), the dark respiration (Rd; µmol m s ), which represents the CO2 production by non-photorespiratory respiration (Sharkey et al., 2007), can be derived. Where the straight section of the curve, located in the light-limited phase, passes through the

-2 -1 zero line, the light compensation point (Icomp; µmol m s ) is located. At this point, the net

CO2 build-up as a result of photosynthesis is equal to the CO2 production as a result of the sum of both dark and light respiration.

The slope of this linear section is also called the apparent maximum quantum yield (ф0; µmol

-1 CO2 µmol photons ). It indicates the amount of light that is needed to build in a certain amount of CO2. The higher the ф0, the more efficient the CO2 build-in is.

-2 -1 The Asat (µmol CO2 m s ), which is the highest reached AN value, reflects the light saturated photosynthetic rate of a leaf on the plant (Sharkey et al., 2007).

CHAPTER II: LITERATURE REVIEW 29

Figure 2.6. Typical response of net photosynthesis to irradiance with indication of different derivable photosynthetic parameters. The intercept with the x-axis and y-axis are respectively the light compensation point (Icomp) and the dark respiration (Rd), the slope of the line is the quantum yield (ф0), the curvature of the line is described by θ and the light- saturated photosynthetic rate is Asat. The first part of the curve, at low irradiance, is light limited and the second part, at high irradiance, CO2 limited.

3.1.3. Fitting the light-response curve Even if it is not yet possible to predict the photosynthetic light-response curve from the underlying biochemical properties of the leaf, the curve can accurately be described by empirical equations (Ögren & Evans, 1993). The equation which best describes light-response curves is a non-rectangular hyperbola (Fang et al., 2015), which is described by the solutions of the following quadratic equation:

2 θA − (ϕ0I + Asat)A + ϕ0IAsat = 0

-2 -1 where A is the total photosynthetic rate (µmol CO2 m s ); θ is the convexity (dimensionless), i.e. the rate of bending (curvilinear angle) of the nonrectangular hyperbola; φ0, the quantum

-1 -2 -1 yield at I = 0 (µmol CO2 µmol photon ); Asat (µmol CO2 m s ) the light-saturated assimilation rate and I is the absorbed irradiance.

AN = A - Rd; thus, the above formula can be converted to the following solution (Fang et al., 2015):

2 ϕ0I + Asat − √(ϕ0I + Asat) − 4Iϕ0θAsat A = − R N 2θ d

-2 -1 With Rd the dark respiration (µmol m s ).

3.1.4. Temperature dependencies The different parameters estimated from the analysis of a light response curve depend on the measurement temperature, therefore comparisons between different measurements are often made at a single reference temperature, mostly 25°C (John R. Evans & Schortemeyer, 2000; Pereira et al., 2013). Whereas the situation sometimes doesn’t allow to execute the measurements at a temperature of 25°C, representative temperature responses of the fitted parameters are used to adjust these values to a single temperature.

Standardised dark respiration values at a reference temperature of 25°C (289,16°K) (Rd25) can be obtained by using the Q10 function which is frequently used to describe temperature responses (Woodrow and Berry 1988; Collatz et al. 1991):

푅푑 푅푑25 = (푇−25)⁄10 푄10

With Q10 being the proportional increase in Rd with a 10°C rise in temperature [Slot et al., 2013].

3.1.5. Differences among plants As mentioned above, the different photosynthetic parameters allow to perform comparisons between specific light response curves of different plants. Plants are usually adapted to growth in direct sunlight or shaded conditions (Figure 2.7., left). Similar differences are observed among the leaves of large trees; those leaves that develop under the shade of other leaves are anatomically and metabolically different from those that grow on exposed canopy surfaces. Shade-type leaves are typically thinner, have more surface area, and contain more chlorophyll than those of sun leaves. As a result, shade-leaves often are more efficient in harvesting sunlight at low light levels; thus, shade-leaves mostly show a higher quantum yield value than sun-leaves. However, sun-leaves display a higher Asat value than shadow-leaves (Lambers, 1998).

Between C3 and C4 plants similar differences can be observed (Figure 2.7., right); plants capable of C4 photosynthesis carry on a more efficient form of photosynthesis. This leads C₄ plants to score higher Asat-values than C3 plants. C4 plants actually usually do not reach a clear

CHAPTER II: LITERATURE REVIEW 31

-2 -1 saturation. For many C3 plant the Asat values lie around the 20 μmol m s . For C4 plants this can go from 30 to 60 μmol m-2s-1 (Lambers, 1998). C₄ plants generally also show a higher quantum yield than C3 plants. On the other hand, the light compensation point of C4 plants is mostly lower than for C3 plants. These characteristics relate to the decarboxylation of malate in the bundle sheath cells typical for C4 plants, whereby higher CO2 concentrations are created, which allow higher photosynthetic rates (Lambers, 1998).

Figure 2.7. Comparison of light response curves between shade- and sun-leaves (left) and C3 and C4 plants (right). The characterising differences in quantum yield (slope of purple lines) and Asat values are indicated with yellow double-arrows.

3.2. A-Ci response curves 3.2.1. The theory of A-Ci response curves

A-Ci response curves plot the photosynthetic CO2 assimilation rate (A) versus the intercellular

CO2 concentration (Ci) (G. Farquhar & Raschke, 2016), this at a constant temperature and constant light irradiance.

Depending on the photosynthetic pathway of the plants, A-Ci response curves show some crucial differences (Figure 2.8.). For this reason, A-Ci curves of C3 and C4 are described separately in the sections below.

Figure 2.8. CO2 assimilation rate as a function of intercellular CO2 partial pressure, Ci , for Flaveria bidentis (C4; filled triangles), Flaveria floridana (C3–C4; filled dots) and Flaveria pringlei (C3; open dots). Measurements were made at an irradiance of 1000 µmol photons mol−1 and a leaf temperature of 25°C (S. Von Caemmerer, 2000).

CHAPTER II: LITERATURE REVIEW 33

3.2.1.1. Plants using the C3 photosynthesis pathway

According to the Farquhar et al. (1980) model for C3 plants of photosynthesis (G. D. Farquhar, Von Caemmerer, & Berry, 1980; G. Farquhar & Raschke, 2016) two phases (and one transition zone) can be distinguished in these curves, i.e. the Rubisco-limited photosynthesis and the RuBP-regeneration-limited photosynthesis. According to Sharkey even a third phase may be distinguished (Sharkey, 1985), i.e. the triose-phosphate-utilization-utilisation photosynthesis (Figure 2.9.):

➢ Phase 1: The Rubisco carboxylation photosynthesis, also known as the Rubisco-

limited photosynthesis typically occurs at low CO2 concentration, generally <20 Pa

(Sharkey et al., 2007). Here, the low [CO2] limits the carboxylation of ribulose-1,5- bisphophate(RuBP) by Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) and by this way also limits the rate of photosynthesis. The rate of photosynthesis can thus be predicted by the properties of Rubisco assuming a saturating supply of substrate, RuBP.

➢ Between 20 and 30 Pa, a transition, which varies with temperature, occurs from one limitation to the other (Yamori, Noguchi, Hikosaka, & Terashima, 2010).

➢ Phase 2: The RuBP-regeneration-limited photosynthesis typically occurs at >30 Pa

CO2. In this phase the photosynthetic rate is limited by the rate of regeneration of RuBP. This includes the conditions where light intensity limits the rate of photosynthesis but can also include conditions in which enzymes of the Calvin cycle (other than Rubisco) limit the rate of photosynthesis. Due to the fact that an increasing

[CO2] causes more RuBP to be carboxylated at the expense of oxygenation, RuBP-

regeneration-limited photosynthesis still increases as [CO2] increases.

➢ Phase 3: Finally, at >70 Pa CO2, a third state, that does not always occur (Leakey et al., 2009), takes place when the chloroplast reactions have a higher capacity than the capacity of the leaf to use the products of the chloroplast; primarily, but not exclusively, triose phosphate. This is the so called triose phosphate use (TPU) limitation (Sharkey et al., 2007). In this condition, photosynthesis does not increase

to increasing CO2. Actually, in this phase and when significant glycine or serine use

occurs, photosynthesis can decrease with an increase in [CO2] (Harley & Sharkey, 1991).

Figure 2.9. Modelled rate of CO2 assimilation as a function of internal CO2 concentration (Pa) of a C3 plant with indication of the different phases and derivable photosynthetic parameters. The intercept with the x-axis indicates the CO2 compensation point (CO2comp), the slope of the line is the maximum carboxylation rate (Vcmax) and the light- and CO2-saturated photosynthetic rate is Amax. The first part of the curve, at low internal CO2 concentration, is the Rubisco limited phase, the second part is the RUBP regeneration limited phase and the third part, at high irradiance, is the Triose Phosphate Use limited phase.

Figure 2.10. Scheme showing some of the processes that affect photosynthetic rate. For each of the three panels, any process in that panel will cause the photosynthetic rate to vary with [CO2] in the same way. Rubisco, ribulose 1·5-bisphosphate carboxylase/oxygenase. Source: (Sharkey et al., 2007)

CHAPTER II: LITERATURE REVIEW 35

3.2.1.2. Plants using the C₄ photosynthesis pathway

C4 plants, contrary to C3 plants, have a CO2-concentrating mechanism (CCM) that creates a high CO2 concentration in the vicinity of Rubisco. This mechanism effectively suppresses the oxygenase activity of Rubisco and hence photorespiration in C4 photosynthesis. This is accomplished by first fixing CO2 via phosphoenolpyruvate (PEP) carboxylase (PEPc) in mesophyll cells into C4 acids, which are then transported to bundle sheet cells where the C4 acids are decarboxylated and the released CO2 refixed by Rubisco in the C3 pathway of the Benson–Calvin cycle (Yin et al., 2011) (Figure 2.11.).

Following above description the A-Ci curves of C4 plants can be determined by two limitation states of the C4 cycle, and two limitation states of the C3 cycle (not taking the TPU-limited state in consideration) in the C4 photosynthesis model, thus resulting in four possible combinations of limitation states (Yin et al., 2011; Zhou et al., 2017):

➢ The RuBP carboxylation and PEP carboxylation limited assimilation (AEE), occurring

under very low Ci. This is the state at which CO2 is the limiting substrate, limiting by

such both C4 and C3 cycles in their enzyme activity.

The rate of PEP carboxylation (Vp) is here limited by the PEPC carboxylation rate (Vpc);

➢ The RuBP carboxylation and PEP regeneration limited assimilation (ATE). This is the

rate in which the C4 cycle is limited by e-transport and the C3 cycle is limited by enzyme activity;

Here, the rate of PEP carboxylation (Vp) is limited by the PEP regeneration (Vpr);

➢ The RuBP regeneration and PEP carboxylation limited assimilation (AET), being the

net rate where the C4 cycle is limited by enzyme activity and the C3 cycle is limited by e-transport;

Here, the PEP carboxylation (Vp) is again limited by the PEPC carboxylation rate (Vpc);

➢ The RuBP regeneration and PEP regeneration limited assimilation (ATT), takes place under high Ci. the rate when both C4 and C3 cycles are limited by e-transport.

Resulting once more in the rate of PEP carboxylation (Vp) being limited by the PEP

regeneration (Vpr).

A-Ci curves of C4 species can thus be described by at least two limitations states (AEE and ATT) and eventually by a third one (ATE/AET) (Yin et al., 2011; Zhou et al., 2017) (Figure 2.12).

Figure 2.11. Schematic diagram of C₃ and C₄ photosynthesis

3.2.2. Derivable parameters Plotting A against Ci and modelling the response allow researchers to determine some specific photosynthesis properties of a plant and thus helps to see how internal and external factors affect the components of photosynthesis.

Where the curve crosses the zero line we find the CO₂ compensation point. This point indicates at which CO₂ concentration the CO₂ build-in (via photosynthesis) is equal to the CO₂ production (via breathing and photorespiration) but is also seen as a measure of the photorespiration degree of a plant. The higher the CO₂ compensation point, the higher the light response. Furthermore, the A-Ci curve shows the CO₂ concentration above which the A values remain almost constant, this is the rate of CO₂ assimilation in light and CO₂ saturated conditions (Amax). The CO₂ value for this Amax is the maximum intercellular CO₂ concentration above which the plant can no longer achieve an increase in CO₂ sequestration (Bloomfield et al., 2014). Photosynthetic capacity is often described in plants by the maximum rate of carboxylation

-2 -1 (Vcmax; µmol m s ), which is in turn determined by the amount and activity of the enzyme ribulose 1·5-bisphosphate carboxylase/oxygenase (RuBisCO) (G. D. Farquhar et al., 1980). This

CHAPTER II: LITERATURE REVIEW 37 parameter is also known as the maximum carboxylation velocity (Bloomfield et al., 2014) or the maximum rubisco activity (Susanne Von Caemmerer, 2013).

For the C4 cycle a similar term exists for the carboxylation of PEP, namely the maximal PEP carboxylation rate (Vpmax). Finally out of this response curve one can also determine the Light saturated potential rate

-2 -1 of electron transport rate (Jmax; µmol m s ) (Bloomfield et al., 2014; G. D. Farquhar et al., 1980).

All of these values can be used for the characterization of the photosynthesis behaviour of a given plant.

3.2.3. Equations to fit the model to the data 3.2.3.1. The fitting of C3 A-Ci curves

The Rubisco-limited photosynthesis rate (Phase1), Ac, can be described by the following equation (S. Von Caemmerer, 2000):

퐶 − Г∗ 퐴 = V [ 푐 ] − 푅 푐 푐 푚푎푥 O 푑 퐶푐 + 퐾푐(1 + ) 퐾푂

-2 1 Where Vcmax (µmol m s- ) is the maximum RuBP carboxylation rate, Cc (µbar) is the CO2 partial pressure at Rubisco, Kc (µbar) is the Michaelis-Menten constant of Rubisco for CO2, Ko (mbar)

-2 -1 is the inhibition constant of Rubisco for O2, Rd (µmol m s ) is the CO2 production by non- photorespiratory respiration Rd and Γ* (µbar) is the CO2 photorespiratory compensation point.

The RuBP-regeneration-limited photosynthesis rate (Phase2), Aj, can be approached by (S. Von Caemmerer, 2000): ∗ 퐶푐 − Г 퐴푗 = J ∗ − 푅푑 4퐶푐 + 8Г

With J (µmol m-2s-1) the rate of electron transport.

For the photosynthesis rate limited by TPU, Ap, Sharkey suggested following equation (Harley & Sharkey, 1991; Sharkey et al., 2007):

퐴푝 = 3푇푃푈 − 푅푑

Where TPU is the rate of Triose Phosphate Use. However this last equation is also described as “not reliable enough to model” (Sharkey et al., 2007), and is thus mostly not considered in models describing A-Ci curves.

The basic photosynthesis model implies that the most limiting phase determines the net photosynthesis, A (µmol m-2s-1) (S. Von Caemmerer, 2000), implying that:

A = min{|퐴푐|,|퐴푗|,|퐴푝|}.

3.2.3.2. The fitting of C4 A-Ci curves:

Fewer estimates of photosynthetic parameters have been reported for C4 species, as there has been a lack of accessible C4 estimation methods (Bellasio, Beerling, & Griffiths, 2016; Yin et al., 2011). Some researches even estimated C4 parameters using C3 fitting tools (e.g. Killi et al., 2017) , which can lead to erroneous estimations of the different photosynthetic parameters.

However, quite recently Zhou et al. focussed on this lack of accessible C4 estimation methods and came up with a fitting tool for A-Ci for C4 species (Zhou et al., 2017). This fitting tool is based on the previous mentioned limitation states of the C₄ A-Ci curves wherein the assimilation rate under RuBP carboxylation rate, Ac, of C4 species can be modelled as:

푉 (퐶 −ɣ∗푂 ) 푉 퐾 퐴 = 푐푚푎푥 푏푠 푏푠 − 푅 in which ɣ∗ = 표푚푎푥 푐 푐 푂푏푠 푑 푉 퐾 퐶푏푠+퐾푐(1+ ) 푐푚푎푥 0 퐾0

∗ Where Vomax is the maximum oxygenation rate allowed by Rubisco. The parameter ɣ represents the specificity of Rubisco and is considered as a constant given temperature among C4 species and Cbs the CO2 concentration in bundle sheath cells.

CHAPTER II: LITERATURE REVIEW 39

Aj is given by (Sack, 2012; Vico & Porporato, 2008):

∗ (퐽 − 2푉푝)(퐶푏푠 − ɣ 푂푏푠) 퐴푗 = ∗ − 푅푑 4퐶푏푠 + 8ɣ 푂푏푠

With 2푉푝푟 = 푥퐽

Where 2Vp is deduced to account for the cost of CCM in the RuBP regeneration equation.

In both equations Vp = min(Vpc,Vpr), leading thus to the 4 described limitation states (see 3.2.1.2, Chapter II):

if (푉푝푐 < 푉푝푟), 퐴푐 = 퐴퐸퐸 and 퐴퐽 = 퐴퐸푇, otherwise 퐴푐 = 퐴푇퐸, 퐴푗 = 퐴푇푇

퐴푛 = min (퐴푐, 퐴푗) OR in other words:

퐴푛 = min (퐴퐸퐸, 퐴퐸푇, 퐴푇퐸, 퐴푇푇)

Figure 2.12. Fitting of C4 ACi data; Under very low Ci (CaL), CO2 is the limiting substrate, thus, Vp is limited by Vpc and A is given by Ac (AEE; the RuBP carboxylation and PEP carboxylation limited assimilation); under very high Ci (CaH) electron transport is limiting, thus, Vp is limited by Vpr and A is given by Aj (ATT; the RuBP regeneration and PEP regeneration limited ). The points between CaL to CaH are freely determined by AEE, ATE (The RuBP carboxylation and PEP regeneration limited assimilation), AET (The RuBP regeneration and PEP carboxylation limited assimilation) or ATT. Source: Zhou et al.(2018)

3.2.4. Temperature adjustments The parameters estimated from the analysis of an A-Ci curve also depend on measurement temperature, thus comparisons between different photosynthesis measurements are often made at a single reference temperature, mostly 25°C (John R. Evans & Schortemeyer, 2000; Sharkey et al., 2007).

Whereas the situation sometimes doesn’t allow to execute the measurements at a temperature of 25°C, representative temperature responses of the fitted parameters are used to adjust these values to a single temperature. The dependence of reaction rates on temperature is exponential and is described by a Arrhenius-type equation (Harley & Sharkey, 1991):

훥퐻 (푐− 푎) 푃푎푟푎푚푒푡푒푟 = 푒 푅.푇 (For Jmax, Vcmax, Vpmax, Rd) or

훥퐻 (푐− 푎) 푒 푅.푇 푃푎푟푎푚푒푡푒푟 = 훥퐻 (For TPU) (훥푆.푇− 푎) 1+푒 푅.푇

Where c is a scaling constant; ΔHa is an enthalpy of activation; R is the universal gas constant = 8.314 J mol-1 K-1 and T is the temperature in degree Kelvin (0°C = 273.15°K).

The scaling constant for the equations used to adjust the parameters is mostly chosen to cause the result to be 1 at 25 °C and the calculated value at other temperatures can be used to scale the parameter to 25 °C.

3.2.5. State of the art photosynthesis research in the Sahel Lots of data for photosynthetic parameters can be found for plants from temperate and tropical regions (e.g. Walker et al., 2014). Unfortunately, this is not the case for plants from drier climates such as the Sahelian drylands.

No research data has been found concerning light response curve parameters of Sahelian dryland species. However, due to the higher light-intensity and a low tree density of this

CHAPTER II: LITERATURE REVIEW 41 region, some might expect typical characteristics of extreme sun-light leaves including, as mentioned in section 3.1.6, high photosynthetic rates.

Concerning the A-Ci response curves of Sahelian dryland species, some data has been collected for Jmax and Vcmax by Dominigues et al. (Domingues et al., 2010). These datasets were collected on plants which typically grow in the Sahelian drylands (e.g. Acacia Senegal,

Combretum glutinosum, Adansonia digitata). The measured values for Vcmax25 ranged

-2 -1 - between 61.00 and 73.60 µmolCO2 m s , those for Jmax25 between 72.8 and 99.6 µmolCO2 m 2s-1. Unfortunately, no other published data were found. Yet, for the same reasons as highlighted for the light-response curves, one might expect high values for Jmax, Vcmax and Amax.

4. Photosynthetic capacity and foliage nitrogen content

As photosynthesis is based on biochemical processes (G. D. Farquhar et al., 1980), a correlation between the different photosynthetic parameters and the chemical composition of a plants leaf might be expected.

Further research (e.g. Reich, Oleksyn and Wright, 2009) has indeed shown a correlation between the photosynthetic properties of a leaf and its nutrient content (Figure 2.13.). Probably the most important of these relationships is a recognized strong and positive correlation between photosynthetic capacity (Amax) and leaf-Nitrogen (N) (Field & Mooney, 1986)(Figure.14). However, positive correlations were also found between other photosynthetic parameters such as Asat, Vcmax and Jmax and the leaf nitrogen content (Domingues et al., 2010; John R. Evans, 1989; P. B. Reich et al., 2009; A. P. Walker, Beckerman, Gu, Kattge, Cernusak, Domingues, Scales, Wohlfahrt, Wullschleger, Woodward, et al., 2014).

These strong relationships are mainly due to the fact that nitrogen can be found in various enzymatic components of the photosynthesis process, which consequently results in a correlation between the photosynthetic parameters and the nitrogen leaf content. More specific, Vcmax corresponds with the amount and activity of the enzyme ribulose 1,5- bisphosphate carboxylase/oxygenase (RuBisCo), which requires a high amount of nitrogen (Domingues et al., 2010; A. P. Walker, Beckerman, Gu, Kattge, Cernusak, Domingues, Scales,

Wohlfahrt, Wullschleger, & Woodward, 2014), while Jmax corresponds with the protein in the thylakoid membranes of the chloroplast (J. R. Evans, 1989).

These correlations between leaf traits, such as nitrogen foliage content, and photosynthetic parameters are so strong that relevant advances in leaf- and canopy- trait representations are gradually being incorporated into fine- and global-scale photosynthesis models in order to estimate photosynthetic parameters by means of empirical functions. (Kattge, Knorr, Raddatz, & Wirth, 2009; Sellers et al., 2010; Sitch et al., 2003; Xu, Gertner, & Scheller, 2009).

CHAPTER II: LITERATURE REVIEW 43

Despite the importance of globally representative datasets for global models, most of previously published photosynthesis studies have been carried out on model species or in temperate ecosystems, while some globally important areas remain under-represented (Kattge et al., 2009; I J Wright, Reich, & Westoby, 2001). This bias in field data potentially undermines the accuracy of modelling efforts that use leaf traits as a basis for prediction of photosynthesis. A better knowledge of these correlations within under-represented areas such as the Sahelian drylands could greatly improve the accuracy of these models.

However, substantial trait variability is evident within particular regions (e.g. Güsewell, 2004; Reich and Oleksyn, 2004), making it difficult at present to justify a universal set of scaling relationships that functions equally well for all terrestrial ecosystems (Kattge et al., 2009; Ian J. Wright, Leishman, Read, & Westoby, 2006).

Figure 2.13. Scatterplot of the relationship of Amax versus leaf nitrogen content, both expressed per unit dry leaf mass, for four biome groups as cited in Reich et al., 2009.

CHAPTER III: MATERIALS & METHODS 45

Chapter III Materials and methods

1. Site description

All measurements for this study were collected in the Dahra field site located about 7 km north-east of the town of Dahra in the semiarid central part of Senegal (15.403°N, 15.432°W) (Figure 3.1.). Situated in the Sahelian ecoclimatic zone the site has a hot semiarid climate with monthly average air temperature ranging between 23.62°C and 31.08°C, for respectively January and May. Rainfall is sparse in the region, with a mean annual sum of 290.19mm (2010 – 2012) and more than 95% of the rain falling during the rainy season occurring from July to October (Figure 3.2.).

Figure 3.1. Location of the Dahra field site, Senegal.

The site has a relatively short growing season, coinciding with the rainy reason, and is a typical low tree and shrub savanna environment dominated by annual grasses (e.g. Schoenefeldia gracilis, Digitaria gayana, Dactyloctenium aegypticum, Aristida mutabilis, and Cenchrus biflorus). Trees and shrubs are relatively sparse (∼3% of the land cover) and are largely dominated by three tree species namely: Acacia tortilis, Acacia senegal and Balanites aegyptiaca. All three tree species exist throughout the Sahel, and Balanites aegyptiaca and Acacia tortilis throughout most African arid and semiarid regions. The area around the station is sparsely populated and is grazed by cattle and sheep, while migrating camels from the northern Sahel feed on the leaves of the trees.

The Dahra field site is located in the ‘Centre de Recherche Zootechnique (CRZ)’ managed by the Institut Sénegalais de Recherche Agronomique (ISRA) and is used for hydrological and biogeochemical studies, and to validate remote sensing studies (University of Copenhagen, Karlsruhe Institute of Technology). To do so, the university of Copenhagen installed 2 towers; (1) a meteorological tower with meteorological, hydrological and radiation sensors, (2) a flux tower with an eddy covariance (EC) system for CO₂, H₂O and energy flux measurement. The site is also equipped with a rain collector, several lysimeters and a pluviometer which all belong to the Aerology laboratory of Toulouse (Figure 3.3.).

40 120 35 100

30 C) ° 80 25 20 60 15

40 Temperature Temperature (

10 (mm) Precipitation 20 5 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month

P(mm) Average Minimum Maximum

Figure 3.2. Annual climate pattern at Dahra (15.403°N, 15.432°W), Senegal. Bars are the means of monthly precipitation levels recorded from 2010 to 2012. Rainy season months are shaded. Average, minimum and maximum monthly temperatures were recorded for 2010-2012.

CHAPTER III: MATERIALS & METHODS 47

Figure 3.3. Map and photos of the Dahra field site and measured areas; (a) The map shows the location of Dahra field site with the location of the meteorological tower (red dot), Eddy tower (green dot) grass inventory sites (blue dots) and tree inventory site (green area); (b) photo of the meteorological tower; (c) photo of the EC tower; (d) photo of the pluviometer (e) photo of the rain collector; (f) photo of a lysimeter.

2. Site inventory

In order to get an accurate picture of the vegetation composition and cover of the site, a complete vegetation inventory has been carried out.

2.1. Tree inventory For the realisation of the tree inventory, the field team consisted of 3 persons: ➢ A local with knowledge of local vegetation who identified the tree species; ➢ A student who measured the GPS-coordinates and diameters of the trees; ➢ A second student who measured the height of the trees and took notes.

For the tree-inventory, all trees present within a circle of 250m around the fluxtower were taken into consideration. It should be mentioned that the site displayed a low tree-diversity, by such the identification went pretty smoothly. The delineation of this area was performed using a ‘Nikon® Forestry PRO Laser Rangefinder’ (Figure 3.4. (a)) which allows to measure the actual distance between the device and a pointed object, in this case the fluxtower. According to dr. Torbern Tagesson (University of Copenhagen) this area of 19.63 ha should represent an average of 50% of the fluxtower’s footprint. Tree species were determined based on the knowledge of locals and mainly based on the leaves and spines of the trees. Because the locals used vernacular names, the names had to be translated to the scientific names. This was done by using the book “Trees and shrubs of

48 the Sahel: Their characteristics and uses” by Hans-Jürgen von Maydell (Maydell & Brase, 1986). The use of this book also permitted an identification control.

For every single tree present in the tree inventory site the exact geographic position, described on the basis of the latitudinal and longitudinal coordinates, has been determined using a ‘GPSMAP® 64s I GARMIN’. The tree-height was determined with a ‘Nikon® Forestry PRO Laser Rangefinder/ Hypsometer’. This device features a three-point height measurement function which allows an accurate height-measurement (Figure 3.4.).

Figure 3.4. (a) Nikon® Forestry PRO Laser Rangefinder/Hypsometer (b) Proceeding of the Nikon® Forestry PRO Laser Rangefinder/Hypsometer: (1) once provided with a clear line of sight to a point on the stem, it calculates the horizontal distance, (2) captures two other points (e.g. basis and top of a tree) to create an angle and (3) accurately calculate the height.

Based on their height trees were then divided into 2 groups; (1) Trees smaller than 1.30m, considered as saplings, for which no more measurements were taken and (2) trees taller than 1.30m for which some additional measurements were taken; The DBH (Diameter at Breast Height, at 1.3m) and the DSH (Diameter at Stump Height, at 0.2m) were derived out of the circumference which was measured using a measuring tape. The state of every tree has also been registered as being 1 of three following classes: (1) good state, (2) bad state, (3) Dead. During the first visit of the site an abundant presence of lianas, which were rapidly identified as being Leptadenia hastata, was noticed. In order to get an idea of the abundance of the trees covered with lianas and at the same time complete the inventory, the presence or absence of lianas in canopies was also taken into consideration during the tree inventory.

CHAPTER III: MATERIALS & METHODS 49

2.2. Grass inventory This work does not attempt to give a complete inventory of the grasses present on the site but only tries to identify the most dominant species. Five circular subplots with a diameter of 2m were randomly taken within 250m of the flux tower. Within those subplots the most dominant grasses (covering > 30%) as well as the well represented grasses (covering > 5 %) were determined. The determination was once more based on the knowledge of local people and afterwards confirmed by Dr. Ousmane, doctor in Pastoral Ecology (ISRA/CRZ of Dahra).

3. Response curves

3.1. In situ measurements practices Leaf gas-exchange measurements were taken in situ, i.e. on the tree itself without cutting the branches, using a Portable Photosynthesis System (CIRAS-3) equipped with a Universal Parkinson leaf chamber (PLC-3, 18x25mm window) (Figure 3.5. and Figure 3.6.). In situ measurements were selected over ex situ measurements, since this latest technique could result in introducing cavitated vessels, i.e. breaking of the water column within a vessel, a phenomenon which is preferably avoided as it may have an influence on measurements. All measurements were performed on young (presumably more photosynthetically active), fully expanded sun-leaves and took place during the rainy season which, as mentioned before, takes place from July to October.

Figure 3.5. Portable Photosynthesis System (CIRAS-3, PP-System) equipped with a Universal Parkinson leaf chamber (PLC-3, 18x25mm window)

The tree density being relatively low, the use of a tripod sufficed to enable the PLC3 to reach the sun-leaves. The tripod being able to maximally reach a height of 1.8m, measured plants were selected in function of their accessibility.

For each tree species (3) and the liana species (1) present on the site, three light response curves and three A-Ci curves were measured on different individuals. For the grasses two light response curves and two A-Ci curves were taken for the most dominant species and one light response curve and one A-Ci curve were taken for the well represented species, accounting for a total of 26 light response curves and 26 A-Ci curves (Table 3.1.). Aiming to monitor the plants during their most active photosynthetic activity and to avoid the midday depression in stomatal conductance, all measurements were performed between 7h30 and 11h30.

Table 3.1. Number of measured curves per species measured in the Dahra field site and growth form (tree/grass/liana) of each species.

n° Species Growth form #Light response curves #A-Ci curves 1 Acacia tortilis tree 3 3 2 Acacia senegal tree 3 3 3 Balanites aegyptiaca tree 3 3 4 Leptadenia hastata liana 3 3 5 Zornia glochidiata grass 2 2 6 Diodia sp. grass 2 2 7 Cenchrus biflorus grass 2 2 8 Chloris prieurii grass 2 2 9 Alysicarpus ovalifolius grass 1 1 10 Ipomoea coptica grass 1 1 11 Tribulus terrestris grass 1 1 12 Dactyloctenium aegyptium grass 1 1 13 Unknown grass 1 1 14 Unknown grass 1 1 Total - 26 26

Figure 3.6. Pictures illustrating performed in situ measurements. Picture of the set up used to reach the leaves (here of Balanited aegyptiaca) (left) and a close-up of the PLC3 measuring a leaf (of Leptadenia hastata).

CHAPTER III: MATERIALS & METHODS 51

3.2. Settings During measurements, the different parameters were selected so that chamber conditions were representative for the actual external microclimate and were the same for every measurement.

Following general CIRAS-3 settings were selected: (1) cuvette temperature (Tcuv) of 38°C, held slightly above ambient air temperature to avoid problems of condensation; Relative humidity remained to ambient (100%). Light distribution was set on 38/37/25 (red/green/blue); The rate of air flow to the cuvette was set on 300 cc./min while the Analyzer Flow was set on 100 cc/min.

For the light-response curves specifically, the CIRAS-3 settings for atmospheric CO2 concentration was set at 400 μmol mol-1 and following light irradiance step sequence: 2500, 2250, 2000, 1750, 1500, 1250, 1000, 800, 600, 500, 400, 300, 200, 100, 50 and 0 μmol m-2s-1 was used to perform a light-response curve. Three measurements were taken at each step, with an acclimation time of 120 seconds between the steps.

For the A-Ci measurements the CIRAS-3 settings for photosynthetic active radiation (PAR) are the same for all species and were set on 1500 μmol m-2s-1, which is the average saturating light level, determined with preliminary light response curves. Measurements were made on fourteen stepwise atmospheric CO2 concentrations; Cref = 400, 300, 200, 100, 50, 10, 400, 600, 800, 1000, 1200, 1500, 1700 and 2000 μmol mol-1.

The infrared gas analysers (IRGA) of CIRAS-3 measure the absolute CO2 and H2O concentrations for both a reference and analysis gas stream. CO2 and H2O concentrations are measured by mid-infra-red wavelengths produced by the source and pulsed through a gold plated cell. The infrared source, received by the detector, is absorbed by the target gas molecule, whereby both CO2 and H2O molecules have diverse absorption spectra, 2.6 and 4.26 µm, respectively. The higher the target gas concentration, the lower the infrared signal received at the detector. The detection of concentration differences of CO2 and H2O in the

−2 −1 Reference and Analysis IRGA allows an estimation of the CO2 assimilation (A, µmol m s ),

−2 −1 −1 transpiration (E, mmol m s ), intracellular CO2 concentration (Ci, µmol mol ) and stomatal

52 conductance (gs, mmol m−2s−1) of the sampled leaf, as well as leaf temperatures obtained from energy balance equations.

3.3. Post processing of response curves data

3.3.1. Determination of measured area Most of the species present on the site carry relatively small leaves, making it difficult/impossible to completely fill the measuring chamber (Universal Parkinson leaf chamber; PLC-3, 18x25mm window). During gas exchange measurements, the measured area was set on 4.5 cm², which is a standard setting. As the measured CO2 assimilation is expressed in μmol m-2s-1 the exact measured surface must be determined and used to post process the results of CIRAS-3 measurements using a recalculation file of PP-systems. The exact measured surface of each measured leaf was determined by following the steps described in Figure 3.7..

Figure 3.7. The determination of the exact measured leaf area; A) Picture of the leaf-position in the measuring chamber; B) In field ‘reconstruction’ of the leaf’s position in the measuring chamber; C) Ex situ ‘reconstruction’ of leaf’s position with measure; D) processed image of C with ‘ImageJ’ used to determine the surface of measured leaf area.

3.3.2. Adaxial stomata fraction One of the initial settings of CIRAS-3 is the Adaxial Stomata Fraction (ASF), describing the proportion of adaxial stomata in comparison to the total amount of stomata of a leaf (adaxial plus abaxial). During gas exchange measurements, ASF was set at 50%, which was a standard setting. Therefore, the ASF of each measured species had to be determined and used to post process the results of CIRAS-3 measurements with the recalculation Excel script provided by PP-systems (“PP Systems - CIRAS-3 Portable Photosynthesis System,” 2018). For each species three clear nail varnish imprints of both the abaxial and adaxial side of leaves were made, peeled off with scotch tape and when dried, stuck on a microscope slide. Subsequently, with a VANOX-S microscope (OLYMPUS) the stomata could be counted. To

CHAPTER III: MATERIALS & METHODS 53 ensure an unbiased counting, the dissector method has been applied (See Figure). In this method a counting frame is positioned on the image with the size chosen to have enough visible objects of interest, here being stomata, that still allows a simple counting. When an object is situated out of the counting frame or crossed by a rejection line (Figure red line) the object is not counted. whereas the object is counted when fully situated in the counting frame or crossed by an acceptance line (Figure 3.8., green line). For each nail varnish imprint, the number of stomata was counted in six of these counting frames. Using following formula, the stomatal density of each sample could be determined: 1 푁푠푡표푚푎푡푎 = 푁푐표푢푛푡푠 × ( ) 푁퐶 × 푆퐴

With Nstomata being the unbiased estimate of number of stomata (stomata/mm²), Ncounts the number of counted stomata, NC the number of counting frames and SA the size of the counting frames (mm²). Finally using underlying equation, the ASF was determined for each species, and the CIRAS-3 measurements could be post processed with the recalculation file of PP-system.

푁푎푑푎푥푖푎푙.푠푡표푚푎푡푎 퐴푆퐹 = 푁푎푑푎푥푖푎푙.푠푡표푚푎푡푎 + 푁푎푏푎푥푖푎푙.푠푡표푚푎푡푎

Figure 3.8. Example of a counting frame on a microscopic image with objects of interest (=stomata) included (green check mark) or rejected (red crosses) 3.4. Data Analysis

The light-response curves and related parameters, i.e. dark respiration (Rd), intrinsic quantum yield (Φ0), light compensation point (lc) and light-saturated photosynthesis (Asat), are obtained by using a fitting tool (Pereira et al., 2013) using the non-rectangular hyperbola model conform to Fang et al. (2015). Afterwards, standardised dark respiration values at a reference temperature of 25°C (289.16 K) (Rd25) were calculated by using equation mentioned previously (Chapter II, Section 3.1.4), with a Q10 value of 2.46 (S. Von Caemmerer, 2000).

To analyse the A-Ci curves and related parameters, i.e. Amax, Vcmax, Jmax, the choice of the used fitting tool depended on the photosynthetic pathway (C3 or C4) of the concerned plant.

For the measured plants using the C3 photosynthetic pathway the R package 'plantecophys', which is a toolkit used to analyse and model leaf gas exchange data, was used (Duursma, 2015). This fitting tool fits the Farquhar-Berry-von Caemmerer (FvCB) model of leaf photosynthesis to measurements of photosynthesis and intercellular CO2 concentration (Ci).

It also estimates Vcmax, Jmax, Rd and their standard errors. The fitting tool also includes a temperature correction for Vcmax and Jmax using the Arrhenius-type equation mentioned in Chapter II (Section 3.2.4) and the values given in Table 3.2.

-1 Table 3.2. Parameter values for activation energy (J mol ; ΔHVcmax for Vcmax, ΔHJmax for Jmax) used in the R package ‘plantecophys’. Parameter Value

ΔHVcmax 58550 ΔHJmax 29680

Because plants using the C4 photosynthetic pathway do represent a non-negligible part of the measured plants at the Dahra field site, it was considered important to include those plants in the data analysis and to determine their photosynthetic parameters as well. To do so the new fitting tool of Zhou et al. (Zhou et al., 2017) has been applied. This fitting tool estimates

C4 photosynthetic parameters using solely A-Ci curves, which was perfect for this research. The fitting tool relies on the formulas described in Chapter II, Section 3.2.3.2. The values used for the different parameters are given in Table 3.3.

Table 3.3. Parameter values (at 25°C) used in the fitting tool for C4 plants (Zhou et al.). Kc, Michaelis-Menten constant of Rubisco activity for CO2 (Pa); Ko, Michaelis-Menten constant of Rubisco activity for O2 (kPa); γ*, specificity of Rubisco; Kp, Michaelis-Menten constant of PEPC activity for CO2 (Pa); ΔHVcmax, ΔHVpmax , ΔHJmax the enthalpies of activation for respectively -1 Vcmax, Vpmax and Jmax (J mol ) (Zhou et al., 2017).

Parameter Value Kc 75.06 K 35.82 o ɣ* 0.0002442

Kp 8.5455 ΔHVcmax 51890 ΔHVpmax 65690.5

ΔH 69246 Jmax

CHAPTER III: MATERIALS & METHODS 55

4. Specific leaf area

The determination of the leaf area had to take place as fast as possible after the removal from the twigs to avoid dehydration of the leaves, which may lead to shrinkage of the leaves and therefore somewhat unreliable measurements of the leaf area. Of every measured species at the Dahra field site, 24 leaves were taken and photographed on a white background with a scale next to it. Using ‘imageJ’ the pictures were then processed in order to determine the total leaf area of each leaf. Afterwards, the leaves were dried in a dry oven at 65°C for two days and subsequently weighed. Finally, the leaf area per unit leaf mass was estimated for each measured species, or in other words, the specific leaf area (SLA) was estimated.

5. Leaf chemistry

5.1. Grinding the leaves Before the actual grinding, the leaves were dried in a dry oven at 65°C for a total duration of 48 hours. The dried leaves were then ground with a Retsch ZM-200 centrifugal mill with a sieve of mesh width 25µm (Figure 3.9.) and set on 14000 rotations per minute (RPM). After each pulverization, the machine was carefully cleaned to avoid contamination.

Figure 3.9. Retsch ZM-200 centrifugal mill 3.5.2

5.2. Foliage C and N content determination Subsequently, chemical analyses were conducted at the Isotope Bioscience Laboratory (ISOFYS) at Ghent University. For the determination of the mass-based leaf carbon (LCC) and leaf nitrogen (LNC) concentrations in the leaves, samples were prepared by weighing

56 duplicate 1.20 ± 0.12 mg (1.08-1.32 mg) subsamples into tin foil capsules (Sercon Ltd, Standard Weight 8 x 5mm). The sample were then analysed with an elemental analyser: the ANCA-SL (Automated Nitrogen Carbon Analyser - Solids and Liquids) interfaced with a SerCon 20-22 IRMS (SysCon electronics). As references sorghum flour and wheat flour were used. Further isotope values were determined, for carbon calibrated to IAEA-CH-6 and for nitrogen to IAEA-N-1.

6. Statistical analysis

Statistic relationships were analysed in R-3.4.1 (R Core Team, 2017) and considered significant at P < 0.05. Differences between the measured photosynthetic parameters of this study and photosynthetic parameters of plants from other climate zones, and between C3 and C4 plants from this study were analysed by a Mann-Whitney U test. Correlations between the different photosynthetic parameters, the foliage nitrogen content and SLA were analysed with a Pearson product-moment correlation coefficient.

CHAPTER IV: RESULTS & DISCUSSION 57

Chapter IV Results & Discussion

1. Inventory

A botanical inventory has been carried out in order to get a precise idea of the site’s vegetation composition. For the trees present in the inventory site, species, abundance, mean height, mean DBH, mean DSH and liana abundance have been determined (Table 4.1. and Table 8.2. (Chapter VIII, Appendix)). For the ground vegetation the most dominant and well represented grasses and herbaceous species were identified (Table 4.2.). The dominant tree species were Balanites aegyptiaca, Acacia tortilis and Acacia senegal in decreasing number of abundance. The total tree abundance was of 18.54 trees ha-1, the mean height ranged from 4.48 to 5.20 m, the DBH from 15.23 to 21.78 cm and the DSH from 18.39 to 25.29 cm. Lianas (Leptadenia hastata; vernacular name: Thiakhate) were present on 22.25% of the trees present on the inventoried site. The dominant grasses and herbaceous species present on the site during the growing season of 2017 were Zornia glochidiata, Diodia sp., Cenchrus biflorus and Chloris virgate. Other, well represented species were Alysicarpus ovalifolius, Ipomoea coptica, Tribulus terrestris, Dactyloctenium aegyptium and two other species that could not be determined.

Table 4.1. Dominant tree species at the Dahra field site in 2017 and their vernacular names (wolof), mean heights (m), mean diameters at breast height (DBH; cm), mean diameters at stump height (DSH; cm) and liana abundance in tree canopies (%).

Liana Vernicular Abundance Mean Mean DBH Mean DSH Species PFT abundance (trees ha -1) height (m) (m) (cm) name (%) Balanites aegyptiaca Sump evergreen 11.97 4.48 17.26 21.68 24.68 Acacia tortilis Seing deciduous 5.19 5.2 21.78 25.29 19.61 Acacia senegal Werek deciduous 1.38 4.52 15.23 18.39 11.11 Total - - 18.54 4.69 18.38 22.45 22.25

Table 4.2. Dominant and well represented grasses and herbaceous species composition at the Dahra field site during the growing season of 2017 with their vernacular names.

Species Family Vernacular name Dominant species

Zornia glochidiata Fabaceae Ndéngué Diodia sp. Rubiaceae Douti doute Cenchrus biflorus Poaceae Khakhame Chloris virgate Poaceae Diorokhane

Well represented species Alysicarpus ovalifolius Fabaceae Bamate Ipomoea coptica Convolvulaceae Serwent Tribulus terrestris Zygophyllaceae Ndague Dactyloctenium aegyptium Poaceae Ndéngalare Ind 13 (Unknown) - Ngiapantane Ind 14 (Unknown) - Sidioume khathe

In 2008, Rasmussen et al. (Rasmussen et al., 2011), effectuated a tree inventory of the Dahra field site as well. Out of this field survey, they reported the same 3 dominant tree species: Balanites aegyptaica (29.0 trees ha-1), Acacia tortilis (7.3 trees ha-1) and Acacia Senegal (2.0 trees ha-1). Even if a similar trend in abundance can be observed, it is clear that the absolute abundance values differ from those recorded during our research. These dissimilarities in abundance could be explained by the difference in inventory methods used in the different studies; while our study effectuated a complete inventory within circular plot (250m radius) around the tower, Rasmussen et al. (2011) worked with a total of 26 plots (50mx50m), situated around the tower. These plots were selected based on a number of criteria including proximity to the tower, perceived representativeness, and their spatial distribution (Rasmussen et al., 2011). Some of these plots were situated over 250 m away from the flux tower, covering by such other areas than the one included in our research, having presumably different characteristics. Rasmussen et al. (2011) also reported the sporadic presence of 4 other species that were not reported in this research, namely: Acacia seyal (0.3 trees ha-1), Calotropis procera (0.3 trees ha-1), Combretum glutinosum (0.3 trees ha-1) and Sclerocarya birrea (0.3 trees ha-1). The fact that these species were identified in Rasmussen et al.’s inventory and not in ours could also be explained by the use of different survey methods. Moreover, the strong similarity between Acacia tortilis and Acacia seyal (Figure 4.1.), could possibly have led to an incorrect identification of some Acacia seyal individuals in our research

CHAPTER IV: RESULTS & DISCUSSION 59 into Acacia tortilis, neglecting by such this species and leading to a slight overestimation for Acacia tortilis.

Concerning the ground cover vegetation, a drastic change can be observed between the dry and the rainy season. Figure 4.2. shows three 360°panoramic pictures of the site taken at 3 different moments around the start of the rainy season. In less than 2 months a clear increase in ground vegetation can be observed. Error! Reference source not found. gives a summary o f all herbaceous inventory surveys conducted at the Dahra field site since 2006 (Rasmussen et al., 2011, This research). The ground cover composition of 2017 clearly differs from most years (2008-2012) that mostly have Aristida adscensionis as the dominant species. The ground cover composition of 2017, however, shows strong similarities with the ground cover composition of 2006. Tagesson (Tagesson et al., 2015) already discussed this unusual ground cover composition of 2006. They described it as the year with the lowest diversity in species composition (within the years 2006, 2008-2012). Like the year of 2017, the dominant species in 2006 was an annual legume, Zornia glochidiata of the Fabacea family, and, in comparison to the other years, only few of the dominant species were annual grasses of the Poaceae family. Most of the years had a dominant species of the Zornia genus. However, under certain rainfall conditions Zornia glochidiata can become very dominant and it can produce continuous mats of leaf layers during the rainy season (Burkill, 1985). Out of all the years recorded by Tagesson the year 2006 was the driest of all years and it also had a so called false start of the rainy season (there was rainfall on ~14 June, 28 days before the main part of the rainy season started) (Tagesson et al., 2015). A false start of the rainy season can strongly influence the plant community because some species (the dominant for the other years) are specialized to grow quickly at the beginning of the rainy season (Elberse & Breman, 1990; Mbow, Fensholt, Rasmussen, & Diop, 2013), and possibly these species did not survive the dry period after the false start of the rainy season. A plausible explanation for the herbaceous vegetation composition of 2017, which is divergent from most recorded years (2008-2012), might thus be a similar rainfall condition as the one that occurred during the year of 2006. And indeed, when analysing the relatively sparse precipitation pattern of 2017, a ‘false start’ of the rainy season may be observed (Figure 4.3.).

Another dissimilarity between the ground species composition of 2017 and other recorded years is the sudden presence of Chloris virgate as dominant species. This non-native species is known as being an invasive species (“www.cabi.org,” 2018). It is thus plausible that this plant, completely absent on previously executed field surveys, managed to colonise the grounds of the Dahra field site in less than 5 years to become one of the most dominant species of the site. Concerning Diodia sp., no plausible explanation can be given due to the fact that the plant hasn’t fully been determined. However, as this species is also completely absent from previous executed field surveys, a similar scenario as for the one suggested for Chloris virgate might be conceivable.

In order to improve the accuracy of the DGVMs, it might thus be interesting to get a better knowledge on the different factors, such as rainfall conditions and the invasive species, that might have an influence on the species compositions of the areas under study.

Figure 4.1. Pictures of plant part (leaves and flowers) of Acacia seyal (left) and Acacia tortilis (right).

Figure 4.2. 360° Panoramic pictures of the Dahra field site; (a) on the 31th July of 2017, (b) the 22th August of 2017 and (c) the 12th September of 2017.

CHAPTER IV: RESULTS & DISCUSSION 61

Table 4.3. Dominant and well represented grasses and herbaceous species composition at the Dahra field site during the growing season of 2006, 2008-2012 (Rasmussen et al., 2011) and 2017 (this study)

Dominance 2006 2008 2009 2010 2011 2012 2017 Dominant Zornia glochidiata, Aristida adscensionis, Aristida adscensionis, Aristida adscensionis, Aristida adscensionis, Aristida adscensionis, Zornia glochidiata, species Cenchrus biflorus Cenchrus biflorus, Cenchrus biflorus, Zornia latifolia Dactyloctenium Cenchrus biflorus, Diodia sp., Cenchrus Dactyloctenium Digitaria velutina aegyptium, Zornia Dactyloctenium biflorus, Chloris aegyptium, Eragrostis latifolia aegyptium, Zornia prieurii tremula, Zornia latifolia latifolia Well Aristida adscensionis, Alysicarpus ovalifolius, Alysicarpus ovalifolius, Cenchrus biflorus, Cenchrus biflorus, Eragrostis tremula, Alysicarpus ovalifolius, represented Merremia pinnata, Digitaria velutina, Ctenium elegans, Dactyloctenium Dactyloctenium Cenchrus biflorus Ipomoea coptica, species Tephrosia purpurea Ipomeae coptica, Zornia latifolia aegyptium, Eragrostis aegyptium, Eragrostis Tribulus terrestriis, Merremia pinnata tremula tremula Dactyloctenium aegyptium, Ind 13, Ind 14

Figure 4.3. Precipitation patterns of the Dahra field site of the years 2012 (left) and 2017 (right) with indication of a ‘false start’ of the rainy season in the year of 2017 (red). Data source: http://fluxnet.fluxdata.org/

2. Response curves data

2.1. Determination of the photosynthetic pathway As described in the Materials & Methods section, it is important to determine the photosynthetic pathway of the different measured species before fitting the A-Ci response curve data. Due to a lack of knowledge concerning the photosynthetic pathway of the measured plants, this last one had to be determined by analysing measured data.

It is known that C4 plants typically show low values for the CO2comp compared to C3 plants (Lambers, 1998; Stoy, 1969). This distinctive characteristic was by default thus used to determine the photosynthetic pathway of each measured species within this research.

By analysing the different CO2 compensation point values, two distinct groups may be differentiated (Table 4.4 and Figure 4.4). Based on these observations, one might consider Acacia tortilis, Acacia Senegal, Balanites aegyptiaca, Leptadenia hastata, Zornia glochidiata,

Diodia sp., Alysicarpus ovalifolius, Ipomoea coptica and Ind 14 as being C3 plants while Cenchrus biflorus, Chloris virgate, Tribulus terrestris, Dactyloctenium aegyptium and Ind 13 might be considered being C4 plants.

Of course, the analysis of one single parameter cannot completely ensure a correct determination of the photosynthetic pathway. However, other distinctive characteristics of

C3 and C4 plants, such as the shape of their respective A-Ci data and the different values of the photosynthetic parameters do confirm these findings (see later on). Thus, in order to be able to continue this research, we will further assume the determined pathways as being correct.

CHAPTER IV: RESULTS & DISCUSSION 63

Figure 4.4. CO2 compensation point values of all measured species at the Dahra field site. All species having relatively low CO2 -1 compensation values (<20µmol mol ) are considered C4 plants (dark green), all other species are considered C3 plants (light green). The name of each plant corresponding to the indicated number on the x-axis can be found in table 4.4..

Table 4.4. CO2 compensation point values of all measured species in the Dahra field site. All species having relatively low CO2 -1 compensation values (<20µmol mol ) are considered C4 plants, all other species are considered C3 plants.

n° Species name CO2comp C3/C4

1 Acacia tortilis 92.12 C3 2 Acacia senegal 89.52 C3 3 Balanites aegyptiaca 101.62 C 3 4 Leptadenia hastata 80.03 C3

5 Zornia glochidiata 85.07 C3

6 Diodia sp. 84.27 C3

7 Cenchrus biflorus 10.42 C4 8 Chloris virgate 10.95 C4 9 Alysicarpus ovalifolius 79.36 C3

10 Ipomoea coptica 52.87 C3

11 Tribulus terrestris 7.60 C4

12 Dactyloctenium aegyptium 6.27 C4 13 Unknown 14.93 C4 14 Unknown 76.37 C 3

2.2. Light-response curves measurements Light-response curves were measured for all the dominant and well represented plant species in the Dahra site and a nonrectangular hyperbola model was used to fit the measured data. The coefficient of determination (R²) for each fit is represented on the graph, the closer the

R² to 1, the better the fit (Figure 4.5.). Four light-response parameters, dark respiration (Rd,

−2 −1 −2 −1 µmol CO2 m s ), light-saturated photosynthesis (Asat, µmol CO2 m s ), light compensation

−2 −1 −1 point (lc, µmol photons m s ) and the intrinsic quantum yield (φ0, µmol CO2 µmol photons ) were extracted from the nonrectangular hyperbola model. The standardised dark respiration

−2 −1 value (Rd25, µmol CO2 m s ) was determined using the Q10 function (Section 3.1.4., Chapter II).

−2 −1 Dark respiration ranges from 2.82 to 14.93 µmol CO2 m s , the standardised dark respiration

−2 −1 from 0.88 to 4.63 µmol CO2 m s , the light-saturated photosynthesis rate from 18.77 to

−2 −1 123.59 µmol CO2 m s and the intrinsic quantum yield from 0.5781 to 0.9837 µmol CO2 µmol photons−1 (Table 4.5.). All the light response curves of the studied species showed high light-compensation points

−1 ranging between 100.63 to 417.20 µmol CO2 µmol photons . Furthermore, the light-response curve showed no (or only at very high photon flux density values (>1400 µmol mol-1)) saturation. Both these characteristics indicate that the measured leaves were extreme sun- leaves.

Table 4.5. Light-response curve parameters of the different plant species measured in the Dahra field site. Dark respiration −2 −1 −2 −1 (Rd, µmol CO2 m s ), standardised dark respiration (25 ◦C) (Rd25, µmol CO2 m s ), light-saturated photosynthesis (Asat, µmol −2 −1 −2 −1 −1 CO2 m s ), light compensation point (lc, µmol photons m s ) and Intrinsic quantum yield (Φ0, µmol CO2 µmol photons ).

n° Species name Rd Rd25 Asat Icomp φ0 1 Acacia tortilis 3.93 1.22 40.08 120.26 0.58

2 Acacia senegal 3.43 1.06 36 124.17 0.77 3 Balanites aegyptiaca 2.82 0.88 18.77 103.96 0.94 4 Leptadenia hastata 3.32 1.03 28.39 100.63 0.91 5 Zornia glochidiata 5.07 1.57 30.28 159.58 0.97 6 Diodia sp. 4.42 1.37 42.78 123.02 0.82 7 Cenchrus biflorus 7.58 2.35 73.04 143.53 0.98 8 Chloris virgate 9.95 3.09 123.59 116.98 0.96 9 Alysicarpus ovalifolius 4.98 1.55 44.91 151.19 0.85 10 Ipomoea coptica 7.29 2.26 28.58 275.6 0.9 11 Tribulus terrestris 7.6 2.36 68.2 190.18 0.95 12 Dactyloctenium aegyptium 6.27 1.95 83.46 114.64 0.98

13 Unknown 14.93 4.63 55.29 417.2 0.98 14 Unknown 2.9 0.9 35.45 120.09 0.86

CHAPTER IV: RESULTS & DISCUSSION 65

2.3. A-Ci curves measurements

A-Ci curves could be established with the Farquhar model of photosynthesis for C3 plants (G.

D. Farquhar et al., 1980) and the fitting tool of Zhou et al. for C4 plants (Zhou et al., 2017).

For the C3 plants the first phase of the A-Ci curves (Fig 4.6.), the Rubisco limited photosynthesis, is described by the red line while the second phase, the RuBP-regeneration limited photosynthesis, is defined by the blue line. If present, the third phase, the TPU-limited photosynthesis, is described by a grey dashed line (Figure 6; a, b, c, d, e, f, I, j, n).

Every individual reaches saturation at different Ci values. The transition zone varies among

−1 the individuals but lies in a range of 180 to 500 µmol mol . Amax ranges between 23.33 and

−2 −1 −2 −1 78.08 µmol CO2 m s , Vcmax25 from 31.00 to 137.14 µmol m s and Jmax25 between 137.12

−2 −1 and 664.37 µmol m s . For C4 plants the maximal PEP carboxylation rate (Vpmax25, µmol m−2s−1) is also determined and ranges between 268.52 and 797.41 µmol m−2s−1 (Table4.6.).

-2 -1 Table 4.6. Rate of CO₂ assimilation in light- and CO₂-saturated conditions (Amax, µmol CO2 m s ), maximum carboxylation −2 −1 −2 −1 rate (Vcmax, µmol m s ), standardised maximum carboxylation rate (Vcmax25, µmol m s ), maximal photosynthetic electron −2 −1 −2 −1 transport rate (Jmax, µmol m s ), standardised maximal photosynthetic electron transport rate (Jmax25, µmol m s ) and CO2 compensation point (ppm) derived out of the measured A-Ci curves for all plants. For C4 plants the maximal PEP carboxylation −2 −1 rate (Vpmax, µmol m s ) is also given.

n° Species name Amax Vcmax Vcmax25 Jmax Jmax25 Vpmax Vpmax25 CO2comp 1 Acacia tortilis 35.42 216.48 49.63 217.69 181.01 X X 92.12 2 Acacia senegal 59.12 356.23 81.61 546.39 429.63 X X 89.52 3 Balanites aegyptiaca 23.33 178.14 37.33 142.58 137.12 X X 101.62 4 Leptadenia hastata 60.91 499.84 128.82 620.95 481.87 X X 80.03 5 Zornia glochidiata 41.14 324.95 81.85 300.92 252.43 X X 85.07 6 Diodia sp. 59.24 514.27 90.89 504.14 579.12 X X 84.27 7 Cenchrus biflorus 70.43 102.5 40.67 538.5 398 797.41 267.15 10.42 8 Chloris virgate 76.03 108.5 45.5 591 427 275.45 136.48 10.95 9 Alysicarpus ovalifolius 48.71 450.52 168.1 370.6 517 X X 79.36 10 Ipomoea coptica 68.68 438.21 101.65 448.34 425.42 X X 52.87 11 Tribulus terrestris 47.34 74 31 381 276 290.78 144.07 7.6 12 Dactyloctenium aegyptium 78.08 108 45 582 421 268.52 133.04 6.27 13 Unknown 49.58 123 51 446 323 337.02 166.99 14.93 14 Unknown 44.5 91.15 83.34 255.74 270.09 X X 76.37

2.4. Critical evaluation on temperature dependencies adjustments Subsequently, it is important to make some critical remarks on the parameters used for temperature dependency adjustments and their implications on the results.

First, to be able to standardise dark respiration values, the respective Q10 values should be available, which is the temperature sensitivity of respiration that can be described as the proportionally increase in respiration per 10°C. This parameter is also known to vary in relation to leaf functional traits, and among plant functional types (Slot, Wright, & Kitajima, 2013). However, as these data were not yet available for any of the measured species within this study, we assumed a Q10 value of 2.4, which is a value proposed by Von Caemmerer (2000) for the standardisation of dark respiration values. This value originates from the Farquhar C3 model plant (G. D. Farquhar et al., 1980) and thus is not plant specific.

The same issue applies on the standardisation of Jmax and Vcmax, which were standardised by an Arrhenius-type equation. The different values for enthalpy of activation (ΔHa) used in this research are given in Table 3.2 and Table 3.3. These values were incorporated in the used A- Ci curve fitting tools. The source of these values however is not clearly indicated. Nevertheless, there is an important lack of species-specific temperature sensitivity parameters data for plants in the Sahelian dryland. This lack of data may potentially be introducing quite some extra uncertainties in the standardisation of photosynthetic parameter values and thus in the modelling of DGVMs in these regions.

CHAPTER IV: RESULTS & DISCUSSION 67

(a) Light response curves of Acacia tortilis

(b) Light response curves of Acacia senegal

(d) Light response curves of Leptadenia hastata

(e) Light response curves of Zornia glochidiata

(f) Light response curves of Diodia spp.

(g) Light response curves of Cenchrus biflorus

(h) Light response curves of Chloris virgate

(i) Light response curve of Alysicarpus ovafolius (j) Light response curve of Ipomoea coptica

(j) Light response curve of Tribulus terrestris (i) Light response curve of Dactyloctenium aegyptium

CHAPTER IV: RESULTS & DISCUSSION 69

(k) Light response curve of Species 15 (n) Light response curve of Species 14

Figure 4.5. Light-response curves of the different plant species at the Dahra field site measured with a CIRAS-3 instrument and fitted with a non-rectangular hyperbola model (Fang et al., 2015). R² indicates the fit of the model to the data.

(a) A-Ci curves of Acacia tortilis

(b) A-Ci curves of Acacia senegal

(d) A-Ci curves of Leptadenia hastata

(e) A-Ci curves of Zornia glochidiata

CHAPTER IV: RESULTS & DISCUSSION 71

(f) A-Ci curves of Diodia spp.

(g) A-Ci curves of Cenchrus biflorus

(h) A-Ci curves of Chloris virgate

(i) A-Ci curve of Alysicarpus ovafolius (j) A-Ci curve of Ipomoea coptica

(k) A-Ci curve of Tribulus terrestris (l) A-Ci curve of Dactyloctenium aegyptium

(m) A-Ci curve of Species 13 (n) A-Ci curve of Species 14

Figure 4.6. A-Ci curves of the different plant species measured at Dahra field site with CIRAS-3, fitted with the Farquhar model of photosynthesis for C3 plants (Farquhar et al., 1980; a, b, c, d, e, f, I, j, n) and the fitting tool of Zhou et al. for C4 plants (Zhou, Akcay, & Helliker, 2017; g, h, k, l, m). For the C3 plants the Rubisco limited photosynthesis is described by the red line, the RuBP-regeneration limited photosynthesis is defined by the blue line and if present the TPU-limited photosynthesis is described by a grey dashed line. For C4 plants the RuBP carboxylation and PEP carboxylation limited assimilation (AEE) is indicated by a dark red line, the RuBP carboxylation and PEP regeneration limited assimilation (ATE) by a red line, the RuBP regeneration and PEP carboxylation limited assimilation (AET) by a light green line and the RuBP regeneration and PEP regeneration limited assimilation (ATT) by a dark green one.

CHAPTER IV: RESULTS & DISCUSSION 73

3. Comparison between C3 and C4 plants

A first distinct difference that can be observed between C3 and C4 plants, is the shape of the

A-Ci curves (Figure 4.7.); as described by Lambers (Lambers, 1998), the A-Ci curves of the C4 plants saturate abruptly, due to PEP-carboxlyase (phosphoenolpyruvate) activity resulting in a reduction of photorespiration, while the A-Ci curves of the C3 plants start to saturate only at higher CO2 concentrations. These distinctive differences confirm the previously performed determination of photosynthetic pathways based on the CO2 compensation points.

Figure 4.71. CO2 assimilation rate as a function of intercellular CO2 partial pressure, Ci (ppm), for Acacia senegal (C3; light green dots) and Dactyloctenium aegyptium (C4; dark green triangles), both measured at the Dahra field site.

Further, a comparison between the different derived photosynthetic parameters of C3 and C4 plants measured at the Dahra field site has been effectuated by subdividing the different photosynthetic parameters over 2 boxplots according to the photosynthetic pathway of the plant (C3 or C4) (Figure 4.8.).

-2 -1 With median values of 35.45 and 73.04 µmol CO2 m s for Asat and 83.34 and 144.07 µmol

−2 −1 m s for Vmax25 (being Vcmax25 for C3 plants and max(Vcmax25, Vpmax25) for C4 plants), for C3 and

C4 plants respectively, the values for Asat and Vmax of C4 plants are all significantly (P<0.001 for

Amax and P < 0.05 for Vmax25) higher than those of the C3 plants. Even if not significant (P >

0.05), the measured Amax values of the C4 plants were also higher than those of C3 plants with

−2 −1 median values of 48.71 and 70.43 µmol CO2 m s for C3 and C4 plants respectively. These observed differences are typical differentiating characteristics for C3 and C4 plants (Lambers,

1998) and confirm by the same way the previously executed photosynthetic pathway’s determination.

For the light compensation point however, which is known to be typically higher for C3 plants than for C4 plants (Lambers, 1998; Stoy, 1969), the median value of the C3 plants was lower than the median value of the C4 plants, which were respectively 120.09 and 143.53 µmol photons m-2s-1. However, these differences were not significant (P > 0.05).

The standardised dark respiration values of the measured C4 plants, with a median value of

-2 -1 2.36 µmol CO2 m s , are also significantly higher (P < 0.01) than those of C3 plants with a

-2 -1 median value of 1.22 µmol CO2 m s . Higher standardised dark respiration rates for C4 plants compared to C3 plants were also reported in other studies (Fukuyama, Yamada, Harada, & Imai, 1973; Ramamurthy Naidu, Rajendrudu, & Das, 1980). However, no clear explanation has been found to describe this finding.

For Vcmax25, the observed values of C3 plants were significantly higher than those of the C4

−2 −1 plants with recorded median values of 83.34 and 45.00 µmol m s for C3 and C4 plants respectively. As this parameter plays a determinant role in the parametrisation of DVGMs, it might be interesting to investigate why C4 plants do show a lower Vcmax25 value then C3 plants do.

−2 −1 For Jmax25, with median values of 425.42 and 398.00 µmol m s , for C3 and C4 plants respectively, no significant difference has been found.

Based on these findings alone, it is clear that C3 and C4 plants do show different characteristics concerning their photosynthetic parameters. Given the extent of these differences it seems important to collect more field-data of C4 plants originating from Sahelian drylands to provide an accurate parameterization for C4 plants in DVGMs in order to get more precise predictions.

CHAPTER IV: RESULTS & DISCUSSION 75

Table 4.7. The medians, first (25%) and third (75%) quantiles of the photosynthetic parameters: dark respiration (Rd25), light- saturated photosynthesis (Asat), Light compensation point (Ic), light- and CO2-saturated photosynthesis (Amax), standardised maximum carboxylation rate (Vcmax25), standardised maximal photosynthetic electron transport rate (Jmax25) and CO2 compensation point (CO2comp) of C3 and C4 plants measured at the Dahra field site.

C3 C4 Q1 Median Q3 Q1 Median Q3 R 1.03 1.22 1.55 2.35 2.36 3.09 d25 A 28.58 35.45 40.08 68.2 73.04 83.46 sat I 120.09 123.02 151.19 116.98 143.53 190.18 comp A 41.14 48.71 59.24 49.58 70.43 76.03 max Vcmax25 81.61 83.34 101.65 40.67 45 45.5

Jmax25 252.43 425.42 481.87 323 398 421

CO2comp 79.36 84.27 89.52 7.6 10.42 10.95

Figure 4.8. Comparison of the photosynthetic parameters: dark respiration (Rd25), light-saturated photosynthesis (Asat), Light compensation point (Ic), light- and CO2-saturated photosynthesis (Amax), maximum carboxylation rate (Vcmax25), maximal photosynthetic electron transport rate (Jmax25) and CO2 compensation point (CO2comp) of C3 and C4. The boxplots represent the different photosynthetic parameters of C3 (light green) and C4 (dark green) plants measured at the Dahra field site (C3 and C4) (nC3 = 9, nC4 = 5).

Table 4.8. Mann-Whitney U test between the photosynthetic parameters: dark respiration (Rd25), light-saturated photosynthesis (Asat), Light compensation point (Ic), light- and CO2-saturated photosynthesis (Amax), standardised maximum carboxylation rate (Vcmax25), standardised maximal photosynthetic electron transport rate (Jmax25) and CO2 compensation point (CO2comp) of C3 and C4. Significance levels: ns , P > 0.05; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

p-value

Rd25 2.00E-03 ** Asat 9.99E-04 *** Icomp 6.06E-01 ns

Amax 8.29E-02 ns

Vcmax25 1.90E-02 *

Jmax25 8.98E-01 ns

CO2comp 9.99E-04 ***

CHAPTER IV: RESULTS & DISCUSSION 77

4. Comparison of photosynthetic parameters with other climatic zones

To enable a comparison between the photosynthetic parameters measured within this study and those originating from other regions, supplementary data was acquired from literature. For the temperate region 13 measurements were collected, for the tropical 30 (Table 4.9.).

These datasets comprise data for Vcmax25 and Jmax25 of different C3 plants. Since these datasets only contain data of C3 plants, only the C3 data of the dataset collected within this study will be used for the comparison.

Table 4.9. Supplementary acquired published datasets from different regions (Temperate and tropical); The table gives the climate zones of the measured plants, author of the datasets and number of datasets taken out of each paper. these datasets comprise data for Vcmax25 and Jmax25.

Climate zone Author n° of individuals Temperate Bauer et al. 2001 4 Carswell et al. 2005 8

Calfapietra 2005 1

Tropical Cernusak et al. 2011 10 Domingues et al 2010 20

Every studied variable has been subdivided over 3 boxes according to the region from which the measured plant originates (Temp= Temperate climate, Trop= Tropical climate, Dahra= Dahra field site).

−2 −1 For Vcmax25 the measured medians are 42.90, 53.03 and 81.61 µmol m s for Temp, Trop and

Dahra respectively (Figure 4.9.). The Vcmax25 values measured at the Dahra field site are significantly higher (P<0.01) than the collected values for Temperate and Tropical regions (Table 4.11.).

A similar but more pronounced trend can be observed for Jmax25 with median values of 57.28, 101.54, 481.87 µmol m−2s−1 for Temp, Trop and Dahra respectively (Figure 4.10. and Table 4.11.). Medians and quartiles of the photosynthetic parameters originating from the different regions are shown in Table 4.10. Both these observations clearly indicate that the measured Sahelian drylands plant species carry higher values for Vcmax25 and Jmax25 than plants originating from temperate or tropical regions do.

However, no clear explanation has been found to explain those higher values. The quite extreme difference in value may even raise some doubts on the reliability of the measured parameters during this research. Unfortunately, due to a clear lack of data originating from similar conditions as the Dahra field site, it is practically impossible to check whether the derived results are correct or overestimated.

Figure 4.9. Comparison of the standardised maximal carboxylation rate (Vcmax25) of C3 plants originating from different regions. The boxplots represent the Vcmax25 of plants within a temperate climate (Temp, light green), a tropical climate (Trop, Dark green) and the Dahra field site (Dahra, yellow). (nTemp = 13, nTrop= 30 and nDahra = 9).

Figure 4.10. Comparison of the maximal electron transport rate (Jmax25) of C3 plants originating from different regions. The boxplots represent the Jmax25 of plants within a temperate climate (Temp, light green), a tropical climate (Trop, Dark green) and a the Dahra field site (Dahra, yellow) (nTemp= 13, nTrop= 30 and nDahra = 9).

CHAPTER IV: RESULTS & DISCUSSION 79

Table 4.10. The medians, first (25%) and third (75%) quantiles of the photosynthetic parameters: standardised maximum carboxylation rate (Vcmax25) and standardised maximal photosynthetic electron transport rate (Jmax25) of plants (subdivided in the region of origin: temperate, tropical and Dahra).

Vcmax25 Q1 Median Q3 Temp 38.98 42.9 53.13 Trop 42.49 53.03 63.39 Dry 81.61 81.61 83.34

J Q1 Median Q3 max25 Temp 48.53 57.28 70.63 Trop 74.66 101.54 117.34 Dry 252.43 425.42 481.87

Table 4.11. Mann-Whitney U test between the Vcmax25 and Jmax25 values of plants from temperate, tropical and the Dahra region climates. Significance levels: ns, P > 0.05; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Vcmax Trop Dahra Temp 1.50E-01 ns 4.08E-03 ** Trop X 3.93E-03 ** Jmax Trop Dahra

Temp 6.89E-05 *** 4.02E-06 *** Trop X 1.17E-05 ***

5. Correlations between photosynthetic parameters, foliage nitrogen content and Specific Leaf Area.

In this part correlations between the foliage nitrogen content, the specific leaf area and the different derived photosynthetic parameters will be analysed. Table 4.12. lists the stable isotopes and chemical composition (N and C) expressed on mass and area basis of the measured species in the Dahra field site. Total nutrient contents for nitrogen and carbon on mass (Nmass and Cmass) and area basis (Narea and Carea) respectively ranged between 26.19 to 41.24 mg N g−1 and 343.67 mg C g−1, 1.29 to 4.03 g N m−2 and 14.24. to 57.78 g C m−2. Carbon isotopes, with Pee Dee Belemnite (PDB) as standard, ranged from - 28.78 to -11.91 ‰ and nitrogen isotopes, with air used as standard, from 1.44 to 7.85 ‰. Table 4.13. gives the determined SLA of every measured plant. These ranged from 75.01 to 304.38 cm2 g-1.

-1 By comparing the leaf nitrogen content on dry leaf mass basis (Nmass, mg g ) data collected within this study with those collected in previously mentioned studies (Table 4.9.), a similar pattern as those observed for the Vcmax25 and Jmax25 comparisons is found (Figure 4.11.). As several researches describe positive correlations between Nmass and different photosynthetic parameters, such as Vcmax25 and Jmax25 (Evans, 1989; Meir, Grace and Miranda, 2001; Reich, Oleksyn and Wright, 2009; Domingues et al., 2010; Walker et al., 2014; Pausenberger, 2016), the high values for Vcmax25 and Jmax25 of the C3 plants originating from the Dahra field site could partially be explained by the high Nmass values recorded for these plants. However, before exploring this hypothesis, a plausible correlation between Nmass and the different photosynthetic parameters recorded within this study should first be verified.

CHAPTER IV: RESULTS & DISCUSSION 81

Table 14.12. Leaf nutrients (N and C) expressed on mass (mg g−1) and area (g m−2) basis, carbon (δ13C in ‰ versus AIR), nitrogen (δ15N in ‰ versus AIR) isotopes and the C:N ratio of all measured species in the Dahra field site.

15 13 n° Species C3/C4 Nmass Cmass Narea Carea δ N δ C C:N

1 Acacia tortilis C3 28.58 435.30 2.65 40.38 4.31 -26.42 15.23

2 Acacia senegal C3 36.35 429.93 2.98 35.21 5.61 -26.86 11.83

3 Balanites aegyptiaca C3 26.19 433.40 3.49 57.78 6.26 -27.18 16.55

4 Leptadenia hastata C3 32.82 365.07 2.01 22.41 7.57 -28.16 11.12

5 Zornia glochidiata C3 36.72 408.00 2.03 22.57 1.44 -27.88 11.11

6 Diodia sp. C3 30.05 366.15 2.13 25.89 5.31 -28.58 12.18

7 Cenchrus biflorus C4 35.74 426.25 2.10 25.10 6.32 -11.91 11.93

8 Chloris prieurii C4 39.38 438.20 2.35 26.13 7.85 -13.55 11.13

9 Alysicarpus ovalifolius C3 39.38 411.50 1.29 13.52 4.87 -13.50 10.45

10 Ipomoea coptica C3 41.24 357.20 2.38 20.59 2.33 -28.78 8.66

11 Tribulus terrestris C4 34.71 347.62 1.42 14.24 7.15 -27.38 10.01

12 Dactyloctenium aegyptium C4 36.01 443.68 4.03 49.70 6.92 -13.49 12.32

13 Unknown C4 30.98 343.67 1.58 17.55 4.69 -28.55 11.09

14 Unknown C3 27.61 408.40 1.90 28.05 5.17 -13.75 14.79

-1 Figure 4.11. Comparison of the leaf nitrogen content expressed on dry leaf mass basis (Nmass, mg g ) of C3 plants originating from different regions. The boxplots represent the Nmass of plants within a temperate climate (Temp, light green), a tropical climate (Trop, Dark green) and the Dahra field site (Dahra, yellow). (nTemp = 13, nTrop= 30 and nDahra = 9).

Table 4.13. Specific Leaf Area (SLA, cm2 g−1) of measured species at the Dahra field site.

n° Species name SLA 1 Acacia tortilis 107.80 2 Acacia senegal 122.09

3 Balanites aegyptiaca 75.01

4 Leptadenia hastata 162.94

5 Zornia glochidiata 180.77 6 Diodia sp. 141.40 7 Cenchrus biflorus 169.83 8 Chloris virgate 167.68 10 Alysicarpus ovalifolius 304.38 11 Ipomoea coptica 173.45 12 Tribulus terrestris 244.20

13 Dactyloctenium aegyptium 89.28

14 Unknown 195.83

15 Unknown 145.60

First, Pearson correlations were tested between the leaf nitrogen contents and the different photosynthetic parameters for the C3 and C4 plants separately.

For the measured C3 plants, several photosynthetic parameters showed some positive significant correlations with the foliage nitrogen content when all expressed on dry mass basis: Amax and Rd25 showed strong significant positive correlations (P<0.01) while Vcmax25 and

Jmax25 also showed clear significant positive correlations (P<0.05) with leaf nitrogen content (Table 4.14. and Figure 4.12.). These results thus tie in with a large number of other studies (Domingues et al., 2010; John R. Evans, 1989; Meir, Grace, & Miranda, 2001; Pausenberger, 2016; P. B. Reich et al., 2009; A. P. Walker, Beckerman, Gu, Kattge, Cernusak, Domingues, Scales, Wohlfahrt, Wullschleger, Woodward, et al., 2014).

Since significant positive correlations have been found between both Vcmax25 and Nmass and

Jmax25 and Nmass values when the different parameters are expressed on dry leaf mass basis, the relatively high values for Nmass might indeed offer an explanation for the relatively high derived Vcmax25 and Jmax25 values for C3 plants measured within this study. Yet, then again, one might ask why these plants do have such high values for Nmass. Investing the reasons for these high values could therefore form an interesting research topic.

CHAPTER IV: RESULTS & DISCUSSION 83

On the other hand, when expressed on a leaf area basis, no significant positive correlations were found between the foliage nitrogen content and any of the derived photosynthetic parameters (Table 4.14.). Then again, most papers do find stronger correlations between photosynthetic parameters and foliage nitrogen content when expressed on dry mass basis than when expressed on leaf area basis (John R. Evans, 1989; Meir et al., 2001; Pausenberger, 2016; A. P. Walker, Beckerman, Gu, Kattge, Cernusak, Domingues, Scales, Wohlfahrt,

Wullschleger, Woodward, et al., 2014). However, the fact that Narea and Vcmax25 show a significant negative correlation (Table 4.14.) is rather unusual to observe. No clear explanation has been found for this unexpected result.

For the measured C4 plants no significant correlations (P>0.05) were found between the photosynthetic parameters and the foliage nitrogen content, nor when expressed on mass basis, nor when expressed on area basis (Table 4.15.).

Afterwards, Pearson correlations were tested between measured Specific Leaf Areas and the photosynthetic parameters. This has first been performed for C3 plants alone and then for all the measured plants together (C3 and C4 plants).

Both for C3 plants alone and all plants together SLA showed strong positive significant correlations with all studied photosynthetic parameters (Table 4.16., Figures 4.13. and 4.14.). This was also observed by Meir et al. (2001) and Domingues et al. (2010). This indicates that SLA is an important factor influencing or related to the photosynthetic parameters, whereas the implementation of SLA in regression models to predict photosynthetic parameters could also contribute to improvement of the modelling of DVGMs (Domingues et al., 2010; Meir et al., 2001).

Finally, Pearson correlations were tested between measured Specific Leaf Areas and the foliage nitrogen contents. This has, once more, first been performed for C3 plants alone and then for all the measured plants together (C3 and C4 plants)(Table 4.16., Figure 4.13 and 4.14.).

Only for the C3 plants alone a significant positive correlation (P<0.05) was observed between the SLA and the foliage nitrogen content (Table 4.16. and Figure 4.13).

Table 4.14. Pearson’s correlation coefficients between photosynthetic parameters (light-saturated net photosynthetic rate (Asat), light- and CO2-saturated net photosynthetic rate (Amax), standardised maximal carboxylation rate (Vcmax25), standardised maximal photosynthetic electron transport rate (Jmax25) and leaf nitrogen content on leaf dry mass (Nmass) and area (Narea) basis). Significance levels: ns, P > 0.05; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

C3 Asat Rd25 Amax Vcmax25 Jmax25 -1 -1 Photosynthetic parameters expressed on a leaf mass basis (nmol g s ) -1 0.53 ns 0.84 ** 0.81 ** 0.71 * 0.82 * Nmass (mg g ) -2 -1 Photosynthetic parameters expressed on a leaf area basis (µmol m s ) -2 Narea (g m ) -0.60 ns -0.32 ns -0.37 ns -0.82 * -0.54 ns

Table 4.15. Pearson’s correlation coefficients between photosynthetic parameters (light-saturated net photosynthetic rate (Asat), light- and CO2-saturated net photosynthetic rate (Amax), maximal carboxylation rate (Vcmax25), maximal photosynthetic electron transport rate (Jmax25) and leaf nutrients on area basis and leaf mass basis: total nitrogen content per unit leaf area (Narea) and per unit leaf dry mass (Nmass); and total carbon content per unit leaf area (Carea) and per unit leaf dry mass (Cmass). Significance levels: ns , P > 0.05; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

C4 Asat Rd25 Amax Vcmax25 Vpmax25 Jmax25 Photosynthetic parameters expressed on a leaf mass basis (nmol g -1 s -1 ) -1 Nmass (mg g ) 0.56 ns -0.62 ns 0.36 ns -0.50 ns -0.32 ns 0.10 ns Photosynthetic parameters expressed on a leaf area basis (µmol m -2 s -1 ) -2 Narea (g m ) -0.52 ns 0.73 ns 0.35 ns 0.79 ns 0.28 ns -0.26 ns

CHAPTER IV: RESULTS & DISCUSSION 85

−2 −1 −2 −1 Figure 4.12. Linear regression of relationships between photosynthetic parameters (Asat, µmol m s ; Amax, µmol m s ; -2 −1 −1 −1 −1 Vcmax25, µmol m s ; Jmax25, µmol m s ) and leaf nitrogen content on leaf dry mass basis (Nmass, mg g ).

Table 4.16. Pearson’s correlation coefficients between photosynthetic parameters (light-saturated net photosynthetic rate (Asat), standardised dark respiration (Rd25), light- and CO2-saturated net photosynthetic rate (Amax), standardised maximal carboxylation rate (Vcmax25), standardised maximal photosynthetic electron transport rate (Jmax25) ) and leaf nitrogen content -1 2 −1 on dry leaf mass basis (Nmass, mg g ) on one hand and Specific Leaf Area (SLA, cm g ) on the other hand for the C3 plants measured in the Dahra field site. Significance levels: ns, P > 0.05; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Asat Rd25 Amax Vmax25 Jmax25 Nmass

Measured C 3 plants SLA (cm2 g-1) 0.95*** 0.89** 0.89** 0.97*** 0.91*** 0.69*

All measured plants (C 3 and C 4 plants) SLA (cm2 g-1) 0.62** 0.66* 0.82*** 0.85*** 0.84*** 0.50 ns

−2 −1 −2 −1 Figure 4.13. Linear regression of relationships between photosynthetic parameters (Asat, µmol m s ; Rd, µmol CO2 m s −2 −1 -2 −1 −1 −1 -1 Amax, µmol m s ; Vcmax25, µmol m s ; Jmax25, µmol m s ) and leaf nitrogen content on dry leaf mass basis (Nmass, mg g ) 2 −1 on one hand and Specific Leaf Area (SLA, cm g ) on the other hand for the C3 plants measured in the Dahra field site.

CHAPTER IV: RESULTS & DISCUSSION 87

−2 −1 −2 −1 Figure 4.14. Linear regression of relationships between photosynthetic parameters (Asat, µmol m s ; Rd, µmol CO2 m s −2 −1 -2 −1 −1 −1 -1 Amax, µmol m s ; Vcmax25, µmol m s ; Jmax25, µmol m s ) and leaf nitrogen content on dry leaf mass basis (Nmass, mg g ) 2 −1 on one hand and Specific Leaf Area (SLA, cm g ) on the other hand for all the plants (C3 and C4) measured in the Dahra field site.

CHAPTER V: CONCLUSIONS 89

Chapter V

Conclusions

Rainfall conditions and the introduction of new invasive species seem to play a determinant role in the composition of ground cover species in Sahelian drylands. We managed to determine the photosynthetic pathway of each dominant and well represented plant species (trees and herbaceous plants) present at the Dahra field site during the rainy season of 2017 by analysing the respective CO2 compensation points. For those same species we also analysed the photosynthetic response curves (light response and A-Ci response curves) and derived different photosynthetic parameters. This was not only performed on C3 plants but also on C4 plants, thanks to the A-Ci fitting tool for C4 plants of Zhou et al. (2017). This allowed us to perform a comparison between photosynthetic parameters of C3 and C4 plants, which show significant differences in characteristics. Given the extent of these differences it seems important to collect more field-data of C4 plants originating from Sahelian drylands to provide an accurate parameterization for C4 plants in DVGMs in order to get more precise predictions.

Values for the standardised maximum carboxylation rate (Vcmax25) and standardised maximum electron transport rate (Jmax25) derived from C3 plants measured within this study showed significantly higher values than those derived from C3 plants originating from temperate and tropical region obtained in other studies. A plausible explanation for these higher values might be the higher leaf nitrogen contents which are known to be positively correlated to the different photosynthetic parameters when expressed on leaf dry mass basis, such as was the case in this study. Another even stronger positive correlation was found between the different photosynthetic parameters and the SLA of the respective plants. This indicates that SLA is an important factor influencing or related to the photosynthetic parameters, whereas the implementation of SLA in regression models to predict different photosynthetic parameters could also contribute to improvement of the parameterization of DVGMs.

CHAPTER VI: FURTHER RESEARCH 91

Chapter VI

Further research

During this research several topics have been identified as potentially interesting for further research. Firstly, further periodical monitoring of soil vegetation in dryland sites could support the hypothesis of the influence of ‘false start’ of the rainy season and invasive plants on the composition of the ground vegetation in dryland ecosystems. Moreover, there is a clear lack of data concerning photosynthetic response curves of Sahelian dryland plants and of C4 plants in general. A greater accessibility to such data could help verify whether or not Sahelian dryland species always do show such high photosynthetic parameter values compared to other regions and even lead to a better comprehension of these high values. Further, there is need of more data for Sahelian dryland species concerning temperature responses of photosynthetic parameters (e.g. Rd, Vcmax and Jmax), in order to convert the different parameters more accurately to standardised parameters (at T=25°C). All of this could help towards a better comprehension of the Sahelian drylands vegetation and its reactions to environmental factors which could lead towards a better modelling of these ecosystems.

CHAPTER VII: BIBLIOGRAPHY 93

Chapter VII

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Chapter VIII

Appendix

Table 8.1. Adaxial stomatal density (stomata/mm2), abaxial stomatal density (stomata/cm2) and adaxial stomatal fraction (-) of all dominant species of the Dahra field site. n° Species adaxial abaxial ASF

1 Acacia tortilis 123.19 123.19 0.50

2 Acacia senegal 312.73 266.30 0.54

3 Balanites aegyptiaca 254.53 281.70 0.47

4 Leptadenia hastata 84.24 224.64 0.27

5 Zornia glochidiata 250.00 175.72 0.59

6 Diodia sp. 284.42 144.93 0.66

7 Cenchrus biflorus 157.61 121.38 0.56 8 Cloris virgate 94.20 40.76 0.70 9 Alysicarpus ovalifolius 525.36 418.48 0.56 10 Ipomoea coptica 144.93 175.72 0.45 11 Tribulus terrestris 206.52 237.32 0.47 12 Dactyloctenium aegyptium 294.84 234.38 0.58 13 Unknown 353.26 240.94 0.59 14 Unknown 135.87 204.71 0.40

Table 8.2. Complete tree inventory data of the Dahra field site (all trees within 250m of the fluxtower) executed during the rainy season of 2017. Following details are given: latitudinal coordinates, longitudinal coordinates, species, state (tree or shrub), height (m), Diameter at Breast Height (cm), Diameter at Stump Height, presence/ absence of liana’s (species: Leptadenia hastata; 0=absent, 1=present and alive, 2=present but dead).

ID coordinates coordinates Species t/s Height dbh dsh Liana N W [m] [cm] [cm] 1 15° 24. 193 15° 26. 064 Acacia tortilis t 3.8 14.64 23.68 1 2 15° 24. 193 15° 26. 049 Acacia tortilis t 4.8 25.21 27.50 0 3 15° 24. 185 15° 26. 051 Acacia tortilis t 4.7 13.88 18.08 1 4 15° 24. 186 15° 26. 048 Acacia tortilis t 4.2 11.33 13.24 0 5 15° 24. 181 15° 26. 049 Balanites aegyptiaca t 5.6 27.63 27.12 0 6 15° 24. 187 15° 26. 051 Acacia tortilis t 2.2 3.57 4.07 0 7 15° 24. 191 15° 26. 037 Acacia senegal t 3.4 9.42 14.51 2 8 15° 24. 187 15° 26. 034 Acacia senegal t 5.2 12.61 14.64 2 9 15° 24. 183 15° 26. 032 Balanites aegyptiaca t 3 9.68 12.10 0 10 15° 24. 176 15° 26. 034 Acacia tortilis t 6.2 16.30 18.33 1

102

11 15° 24. 177 15° 26. 034 Acacia tortilis t 4.5 12.35 14.26 0 12 15° 24. 178 15° 26. 032 Acacia tortilis t 3.8 10.95 11.33 2 13 15° 24. 186 15° 26. 020 Acacia tortilis t 4.8 15.28 16.81 0 14 15° 24. 187 15° 26. 014 Acacia tortilis t 5.8 18.33 23.68 2

15 15° 24. 190 15° 26. 016 Balanites aegyptiaca t 1.5 4.84 0 16 15° 24. 194 15° 26. 018 Balanites aegyptiaca s 2.8 7.13 10.06 2

17 15° 24. 190 15° 26. 009 Balanites aegyptiaca s 0

18 15° 24. 188 15° 26. 008 Balanites aegyptiaca s 0

19 15° 24. 198 15° 26. 006 Balanites aegyptiaca s 0 20 15° 24. 185 15° 25. 991 Acacia tortilis t 6.1 29.28 31.83 2 21 15° 24. 180 15° 25. 986 Acacia tortilis t 5 14.01 18.72 0

22 15° 24. 189 15° 25. 989 Balanites aegyptiaca s 0 23 15° 24. 186 15° 25. 981 Balanites aegyptiaca t 2.8 5.35 10.57 0 24 15° 24. 190 15° 25. 977 Balanites aegyptiaca t 6.8 22.41 29.41 0 25 15° 24. 185 15° 25. 975 Balanites aegyptiaca t 1.6 4.58 10.19 1 26 15° 24. 190 15° 25. 960 Acacia tortilis t 4.4 11.97 18.59 2 27 15° 24. 178 15° 25. 952 Balanites aegyptiaca t 6.2 14.26 21.01 0 28 15° 24. 178 15° 25. 951 Balanites aegyptiaca t 4.2 14.90 17.32 0 29 15° 24. 180 15° 25. 949 Balanites aegyptiaca t 4.1 12.22 20.88 1 30 15° 24. 162 15° 25. 933 Balanites aegyptiaca t 7.6 19.35 22.66 0 31 15° 24. 165 15° 25. 930 Balanites aegyptiaca t 6.2 21.39 14.26 0 32 15° 24. 172 15° 25. 949 Balanites aegyptiaca t 2.4 4.33 8.53 2 33 15° 24. 169 15° 25. 957 Balanites aegyptiaca t 3.6 11.71 16.17 0 34 15° 24. 164 15° 25. 961 Balanites aegyptiaca t 5.4 14.77 17.57 2 35 15° 24. 164 15° 25. 969 Balanites aegyptiaca t 5.3 13.24 18.08 0 36 15° 24. 166 15° 25. 970 Acacia senegal t 3 9.42 12.48 2

37 15° 24. 159 15° 25. 980 Acacia tortilis s 1 38 15° 24. 169 15° 25. 989 Acacia tortilis t 5.4 11.84 17.57 0 39 15° 24. 167 15° 25. 990 Acacia tortilis t 3.8 5.98 12.99 2 40 15° 24. 169 15° 25. 993 Balanites aegyptiaca t 4.4 11.71 13.24 0

41 15° 24. 169 15° 25. 992 Acacia tortilis s 0

42 15° 24. 168 15° 25. 994 Balanites aegyptiaca s 0

43 15° 24. 164 15° 25. 994 Balanites aegyptiaca s 0 44 15° 24. 169 15° 26. 003 Acacia tortilis t 3.5 9.55 15.02 0 45 15° 24. 174 15° 26. 003 Balanites aegyptiaca t 4.1 14.01 17.06 2 46 15° 24. 153 15° 25. 998 Acacia tortilis t 6.4 30.43 32.34 2

47 15° 24. 151 15° 26. 006 Acacia tortilis s 1 48 15° 24. 151 15° 26. 011 Balanites aegyptiaca t 7 21.01 23.43 2 49 15° 24. 157 15° 26. 015 Acacia tortilis t 2.5 ? 8.28 0 50 15° 24. 168 15° 26. 034 Balanites aegyptiaca t 3 ? 11.71 1 51 15° 24. 158 15° 26. 076 Acacia tortilis t 2.8 9.17 10.70 0 52 15° 24. 156 15° 26. 038 Balanites aegyptiaca t 3.2 13.75 19.10 2 53 15° 24. 145 15° 26. 041 Acacia tortilis t 8.2 38.20 35.91 0 54 15° 24. 144 15° 26. 032 Balanites aegyptiaca t 4.8 19.99 25.72 2

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55 15° 24. 154 15° 26. 059 Balanites aegyptiaca t 4.4 17.32 20.88 0 56 15° 24. 154 15° 26. 059 Balanites aegyptiaca t 3.6 12.22 18.33 0 57 15° 24. 150 15° 26. 062 Balanites aegyptiaca t 6.8 24.70 25.34 0

58 15° 24. 156 15° 26. 063 Balanites aegyptiaca s 0 59 15° 24. 160 15° 26. 070 Balanites aegyptiaca t 3.6 10.95 13.75 1 60 15° 24. 157 15° 26. 071 Acacia tortilis t 4.6 21.90 25.97 1 61 15° 24. 157 15° 26. 072 Balanites aegyptiaca t 2.2 7.64 15.28 1 62 15° 24. 179 15° 26. 054 Balanites aegyptiaca t 3.2 ? 8.40 0 63 15° 24. 138 15° 26. 063 Balanites aegyptiaca t 2.2 ? 6.11 0

64 15° 24. 132 15° 26. 058 Balanites aegyptiaca s 1 65 15° 24. 111 15° 26. 051 Balanites aegyptiaca t 7.2 23.94 26.74 0 66 15° 24. 107 15° 26. 033 Acacia tortilis t 4.1 11.46 16.68 2 67 15° 24. 113 15° 26. 024 Acacia tortilis t 4.4 10.95 13.24 1 68 15° 24. 118 15° 26. 031 Acacia tortilis t 2.2 4.07 4.84 0 69 15° 24. 135 15° 26. 037 Balanites aegyptiaca t 6.2 20.12 22.41 2

70 15° 24. 119 15° 26. 042 Balanites aegyptiaca s 1

71 15° 24. 118 15° 26. 043 Balanites aegyptiaca s 0

72 15° 24. 135 15° 26. 038 Balanites aegyptiaca s 0 73 15° 24. 128 15° 26. 023 Balanites aegyptiaca t 5 14.39 20.12 2 74 15° 24. 132 15° 26. 015 Acacia tortilis t 3.8 17.19 19.61 2 75 15° 24. 137 15° 26. 016 Balanites aegyptiaca t 4 17.57 23.17 0

76 15° 24. 132 15° 25. 995 Balanites aegyptiaca s 0

77 15° 24. 132 15° 25. 993 Acacia tortilis s 0 78 15° 24. 138 15° 25. 987 Balanites aegyptiaca t 5.8 23.17 27.76 1 79 15° 24. 149 15° 25. 973 Balanites aegyptiaca t 6.2 23.17 23.68 0

80 15° 24. 148 15° 25. 974 Balanites aegyptiaca s 0 81 15° 24. 156 15° 25. 938 Balanites aegyptiaca t 3.8 10.95 13.93 2 82 15° 24. 153 15° 25. 956 Balanites aegyptiaca t 5.2 18.59 19.99 0 83 15° 24. 153 15° 25. 953 Balanites aegyptiaca t 3.2 6.88 13.75 2 84 15° 24. 155 15° 25. 948 Balanites aegyptiaca t 4.2 17.32 24.70 0 85 15° 24. 159 15° 25. 949 Balanites aegyptiaca t 3.4 10.13 14.26 0 86 15° 24. 147 15° 25. 944 Acacia senegal t 3.2 10.19 17.32 0 87 15° 24. 147 15° 25. 936 Acacia senegal t 3.8 7.38 11.59 0 88 15° 24. 136 15° 25. 938 Acacia tortilis t 6.9 22.15 22.41 0 89 15° 24. 145 15° 25. 954 Acacia senegal t 2.4 4.84 10.19 0 90 15° 24. 132 15° 25. 951 Balanites aegyptiaca t 4.6 ? 23.68 0 91 15° 24. 143 15° 25. 961 Acacia tortilis t 5 24.96 30.56 0

92 15° 24. 132 15° 25. 955 Balanites aegyptiaca s 0 93 15° 24. 130 15° 25. 971 Balanites aegyptiaca t 6.2 19.74 21.52 0 94 15° 24. 129 15° 25. 971 Balanites aegyptiaca t 6.1 22.03 25.97 2 95 15° 24. 126 15° 25. 982 Balanites aegyptiaca t 5.2 22.03 26.23 2

96 15° 24. 119 15° 25. 986 Balanites aegyptiaca s 0

97 15° 24. 118 15° 25. 986 Balanites aegyptiaca s 0 98 15° 24. 119 15° 25. 987 Balanites aegyptiaca t 5.7 21.65 23.17 2

104

99 15° 24. 110 15° 25. 966 Acacia tortilis t 4.4 24.70 26.23 2 100 15° 24. 109 15° 25. 988 Balanites aegyptiaca t 6.2 24.96 37.69 1 101 15° 24. 123 15° 25. 995 Balanites aegyptiaca t 3.2 ? 12.61 1

102 15° 24. 094 15° 26. 019 Balanites aegyptiaca s 1

103 15° 24. 076 15° 25. 997 Acacia tortilis s 1

104 15° 24. 072 15° 26. 001 Balanites aegyptiaca s 0 105 15° 24. 069 15° 25. 981 Balanites aegyptiaca t 4.2 14.01 22.41 1 106 15° 24. 066 15° 25. 977 Balanites aegyptiaca t 3.4 ? 16.42 1 107 15° 24. 069 15° 25. 970 Balanites aegyptiaca t 4.5 ? 22.28 0 108 15° 24. 069 15° 25. 963 Balanites aegyptiaca t 2.8 10.82 16.55 0 109 15° 24. 062 15° 25. 963 Balanites aegyptiaca t 4.2 16.55 21.70 2 110 15° 24. 061 15° 25. 968 Balanites aegyptiaca t 4.1 20.24 22.03 0 111 15° 24. 083 15° 25. 982 Balanites aegyptiaca t 8.1 25.97 26.99 0

112 15° 24. 085 15° 25. 978 Balanites aegyptiaca s 1

113 15° 24. 082 15° 25. 977 Balanites aegyptiaca s 0

114 15° 24. 082 15° 25. 972 Acacia tortilis s 0

115 15° 24. 091 15° 25. 971 Acacia tortilis s 0 116 15° 24. 079 15° 25. 952 Acacia tortilis t 3.6 12.99 19.99 2 117 15° 24. 074 15° 25. 935 Acacia tortilis t 4.5 18.08 18.72 1 118 15° 24. 057 15° 25. 936 Acacia senegal t 4.6 11.97 15.02 0 119 15° 24. 052 15° 25. 940 Acacia senegal t 3.6 11.71 12.99 1 120 15° 24. 051 15° 25. 947 Balanites aegyptiaca t 2.6 5.86 12.48 1 121 15° 24. 086 15° 25. 931 Balanites aegyptiaca t 5.6 15.79 18.08 0

122 15° 24. 087 15° 25. 932 Balanites aegyptiaca s 0

123 15° 24. 103 15° 25. 944 Acacia tortilis s 1

124 15° 24. 108 15° 25. 941 Balanites aegyptiaca s 0

125 15° 24. 113 15° 25. 949 Balanites aegyptiaca s 0 126 15° 24. 115 15° 25. 946 Balanites aegyptiaca t 5.4 21.52 22.92 0

127 15° 24. 116 15° 25. 945 Balanites aegyptiaca s 0 128 15° 24. 121 15° 25. 953 Acacia tortilis t 3.5 7.89 9.80 0 129 15° 24. 118 15° 25. 937 Acacia tortilis t 4.2 18.97 21.01 0

130 15° 24. 118 15° 25. 937 Balanites aegyptiaca s 0

131 15° 24. 126 15° 25. 924 Acacia tortilis s 0

132 15° 24. 126 15° 25. 923 Balanites aegyptiaca s 0 133 15° 24. 136 15° 25. 920 Acacia tortilis t 6 13.37 20.63 2 134 15° 24. 140 15° 25. 919 Acacia tortilis t 6.4 20.50 22.59 2 135 15° 24. 143 15° 25. 922 Acacia tortilis t 4.5 12.99 14.64 2 136 15° 24. 144 15° 25. 917 Acacia senegal t 4 8.66 15.02 0 137 15° 24. 147 15° 25. 912 Acacia tortilis t 5.6 13.37 15.53 0 138 15° 24. 147 15° 25. 912 Acacia tortilis t 5.8 15.28 18.59 0 139 15° 24. 152 15° 25. 917 Acacia senegal t 4.2 9.17 18.33 0 140 15° 24. 117 15° 25. 918 Balanites aegyptiaca t 3.6 9.80 16.55 1

141 15° 24. 115 15° 25. 923 Acacia tortilis s 1

142 15° 24. 106 15° 25. 929 Balanites aegyptiaca s 1

CHAPTER VIII: APPENDIX 105

143 15° 24. 102 15° 25. 916 Balanites aegyptiaca t 3.8 9.42 13.75 1 144 15° 24. 086 15° 25. 904 Acacia tortilis t 6.8 26.10 28.52 1 145 15° 24. 072 15° 25. 915 Acacia tortilis t 7 31.19 33.36 1

146 15° 24. 104 15° 25. 917 Acacia tortilis s 0

147 15° 24. 101 15° 25. 914 Balanites aegyptiaca s 1 148 15° 24. 113 15° 25. 911 Balanites aegyptiaca t 5.2 16.42 17.83 0 149 15° 24. 116 15° 25. 907 Balanites aegyptiaca t 4.2 15.79 18.59 2

150 15° 24. 121 15° 25. 809 Balanites aegyptiaca s 0 151 15° 24. 061 15° 25. 809 Balanites aegyptiaca t 5.6 12.73 28.52 1 152 15° 24. 049 15° 25. 919 Balanites aegyptiaca t 8.2 26.74 32.59 1 153 15° 24. 047 15° 25. 919 Balanites aegyptiaca t 4.8 16.30 18.59 1 154 15° 24. 045 15° 25. 918 Balanites aegyptiaca t 2.6 10.44 15.53 0 155 15° 24. 044 15° 25. 917 Balanites aegyptiaca t 5.2 16.42 17.32 0

156 15° 24. 040 15° 25. 924 Balanites aegyptiaca s 1 157 15° 24. 040 15° 25. 924 Balanites aegyptiaca t 8.6 33.36 35.91 0

158 15° 24. 034 15° 25. 915 Balanites aegyptiaca t 16.30 21.65 1

159 15° 24. 037 15° 25. 919 Balanites aegyptiaca s 1

160 15° 24. 038 15° 26. 020 Balanites aegyptiaca s 0 161 15° 24. 036 15° 25. 924 Balanites aegyptiaca t 4.4 12.73 14.26 1

162 15° 24. 035 15° 25. 925 Balanites aegyptiaca s 0 163 15° 24. 040 15° 25. 926 Balanites aegyptiaca t 5.2 ? 22.66 1

164 15° 24. 040 15° 25. 927 Balanites aegyptiaca s 0

165 15° 24. 040 15° 25. 929 Balanites aegyptiaca s 0

166 15° 24. 041 15° 25. 931 Balanites aegyptiaca s 0

167 15° 24. 041 15° 25. 931 Balanites aegyptiaca s 1

168 15° 24. 043 15° 25. 932 Balanites aegyptiaca s 1 169 15° 24. 081 15° 25. 922 Balanites aegyptiaca t 2 ? 12.78 1 170 15° 24. 131 15° 25. 913 Acacia senegal t 4 13.24 16.30 0 171 15° 24. 107 15° 25. 898 Balanites aegyptiaca t 3.5 10.44 12.48 1 172 15° 24. 110 15° 25. 896 Balanites aegyptiaca t 4 13.24 15.53 1 173 15° 24. 112 15° 25. 892 Balanites aegyptiaca t 6.4 17.06 21.39 0 174 15° 24. 113 15° 25. 891 Balanites aegyptiaca t 3.8 20.37 22.41 0 175 15° 24. 115 15° 25. 886 Balanites aegyptiaca t 5 16.30 19.35 0 176 15° 24. 115 15° 25. 884 Balanites aegyptiaca t 6.4 22.41 23.43 2 177 15° 24. 118 15° 25. 883 Balanites aegyptiaca t 5.8 22.41 23.68 0

178 15° 24. 106 15° 25. 887 Balanites aegyptiaca s 0 179 15° 24. 105 15° 25. 880 Balanites aegyptiaca t 3.2 12.22 16.81 1

180 15° 24. 084 15° 25. 885 Acacia tortilis s 0 181 15° 24. 073 15° 25. 916 Acacia senegal t 6.6 33.87 36.41 1 182 15° 24. 048 15° 25. 890 Balanites aegyptiaca t 5.6 14.77 21.14 0

183 15° 24. 051 15° 25. 885 Balanites aegyptiaca s 0

184 15° 24. 050 15° 25. 884 Balanites aegyptiaca s 0

185 15° 24. 048 15° 25. 882 Balanites aegyptiaca s 0 186 15° 24. 055 15° 25. 881 Balanites aegyptiaca t 2.6 11.71 14.77 1

106

187 15° 24. 057 15° 25. 884 Balanites aegyptiaca t 3.6 10.95 15.79 0 188 15° 24. 066 15° 25. 881 Balanites aegyptiaca t 3.2 6.11 18.08 1 189 15° 24. 061 15° 25. 866 Balanites aegyptiaca t 4.2 15.53 17.06 1 190 15° 24. 054 15° 25. 861 Balanites aegyptiaca t 2 6.37 8.40 0

191 15° 24. 054 15° 25. 860 Balanites aegyptiaca s 0 192 15° 24. 056 15° 25. 860 Balanites aegyptiaca t 2.1 7.38 15.02 0

193 15° 24. 055 15° 25. 856 Balanites aegyptiaca s 0 194 15° 24. 061 15° 25. 859 Balanites aegyptiaca t 5.2 14.51 17.83 1 195 15° 24. 065 15° 25. 858 Balanites aegyptiaca t 3.6 12.99 14.51 0

196 15° 24. 065 15° 25. 851 Balanites aegyptiaca s 0 197 15° 24. 054 15° 25. 852 Balanites aegyptiaca t 2.3 6.11 7.89 1

198 15° 24. 044 15° 25. 859 Acacia tortilis s 1

199 15° 24. 054 15° 25. 844 Acacia tortilis s 0

200 15° 24. 062 15° 25. 841 Acacia tortilis s 1

201 15° 24. 064 15° 25. 839 Acacia tortilis s 0

202 15° 24. 085 15° 25. 861 Acacia tortilis s 1 203 15° 24. 088 15° 25. 854 Acacia tortilis t 5.8 19.86 23.17 0

204 15° 24. 102 15° 25. 866 Acacia tortilis s 1 205 15° 24. 176 15° 25. 927 Acacia senegal t 6 18.46 24.19 0 206 15° 24. 169 15° 25. 912 Acacia senegal t 4.6 22.92 37.88 0 207 15° 24. 068 15° 25. 910 Balanites aegyptiaca t 2.8 12.10 17.19 0 208 15° 24. 163 15° 25. 906 Acacia tortilis t 4 13.37 16.87 0

209 15° 24. 167 15° 25. 905 Balanites aegyptiaca s 0 210 15° 24. 157 15° 25. 908 Acacia tortilis t 4.6 16.55 21.01 0 211 15° 24. 157 15° 25. 897 Balanites aegyptiaca t 3.8 16.55 20.37 1 212 15° 24. 137 15° 25. 899 Balanites aegyptiaca t 6.8 24.83 39.15 1

213 15° 24. 158 15° 25. 913 Balanites aegyptiaca s 0

214 15° 24. 142 15° 25. 905 Balanites aegyptiaca s 0 215 15° 24. 127 15° 25. 887 Balanites aegyptiaca t 5.4 13.37 17.83 1 216 15° 24. 140 15° 25. 878 Balanites aegyptiaca t 7.4 40.11 38.20 1 217 15° 24. 117 15° 25. 870 Balanites aegyptiaca t 3 11.14 16.55 2 218 15° 24. 120 15° 25. 873 Balanites aegyptiaca t 4.6 24.83 28.65 1 219 15° 24. 123 15° 25. 863 Balanites aegyptiaca t 4.2 28.65 27.06 0 220 15° 24. 115 15° 25. 855 Balanites aegyptiaca t 7.6 34.38 39.47 0

221 15° 24. 117 15° 25. 854 Balanites aegyptiaca s 0 222 15° 24. 120 15° 25. 852 Balanites aegyptiaca t 6 34.06 36.61 2 223 15° 24. 108 15° 25. 863 Balanites aegyptiaca t 4.2 15.92 17.51 1

224 15° 24. 101 15° 25. 855 Balanites aegyptiaca s 0 225 15° 24. 096 15° 25. 851 Acacia tortilis t 3.6 20.05 18.14 1 226 15° 24. 102 15° 25. 842 Balanites aegyptiaca t 3 10.19 12.73 1

227 15° 24. 111 15° 25. 841 Balanites aegyptiaca s 0 228 15° 24. 107 15° 25. 833 Acacia tortilis t 4 12.41 14.01 1 229 15° 24. 080 15° 25. 835 Acacia tortilis t 5.2 25.15 29.28 1

230 15° 24. 085 15° 25. 828 Balanites aegyptiaca s 0

CHAPTER VIII: APPENDIX 107

231 15° 24. 085 15° 25. 829 Acacia tortilis t 3 15.92 15.60 1

232 15° 24. 078 15° 25. 832 Balanites aegyptiaca s 0 233 15° 24. 093 15° 25. 817 Balanites aegyptiaca t 3.2 17.51 14.96 1

234 15° 24. 107 15° 25. 801 Balanites aegyptiaca s 0 235 15° 24. 095 15° 25. 805 Acacia tortilis t 4.6 16.55 21.33 0 236 15° 24. 112 15° 25. 805 Balanites aegyptiaca t 5.6 29.92 31.83 0 237 15° 24. 113 15° 25. 810 Balanites aegyptiaca t 4.8 24.19 26.42 0 238 15° 24. 118 15° 25. 815 Balanites aegyptiaca t 5.6 36.29 40.74 0 239 15° 24. 119 15° 25. 819 Balanites aegyptiaca t 4 15.92 20.37 0 240 15° 24. 121 15° 25. 823 Balanites aegyptiaca t 4 16.23 12.10 0

241 15° 24. 117 15° 25. 820 Balanites aegyptiaca s 0 242 15° 24. 124 15° 25. 833 Acacia tortilis t 4.2 20.69 30.24 0 243 15° 24. 129 15° 25. 845 Balanites aegyptiaca t 3.4 15.92 24.51 0 244 15° 24. 130 15° 25. 850 Balanites aegyptiaca t 3.4 20.37 24.19 0 245 15° 24. 128 15° 25. 850 Balanites aegyptiaca t 5 25.46 31.83 0 246 15° 24. 134 15° 25. 858 Acacia tortilis t 5.4 23.24 23.24 0 247 15° 24. 151 15° 25. 858 Acacia tortilis t 4.2 14.32 18.46 1 248 15° 24. 166 15° 25. 861 Acacia tortilis t 5 29.60 42.97 0 249 15° 24. 168 15° 25. 853 Balanites aegyptiaca t 7.2 43.93 43.61 0 250 15° 24. 169 15° 25. 841 Acacia tortilis t 9.4 46.15 47.43 0 251 15° 24. 164 15° 25. 841 Acacia tortilis t 3 16.55 22.28 0 252 15° 24. 173 15° 25. 842 Balanites aegyptiaca t 6.6 23.24 25.78 2

253 15° 24. 174 15° 25. 844 Balanites aegyptiaca s 0

254 15° 24. 176 15° 25. 843 Balanites aegyptiaca s 0

255 15° 24. 176 15° 25. 842 Balanites aegyptiaca s 1

256 15° 24. 173 15° 25. 842 Balanites aegyptiaca s 0

257 15° 24. 161 15° 25. 838 Acacia senegal s 0 258 15° 24. 155 15° 25. 843 Acacia senegal t 4.6 13.05 14.96 0 259 15° 24. 150 15° 25. 840 Acacia tortilis t 4.8 20.37 23.55 0 260 15° 24. 151 15° 25. 830 Acacia senegal t 4.4 14.01 14.01 0 261 15° 24. 148 15° 25. 819 Balanites aegyptiaca t 5.2 18.78 19.10 0 262 15° 24. 138 15° 25. 827 Acacia tortilis t 3.2 16.55 20.69 0 263 15° 24. 156 15° 25. 816 Acacia tortilis t 4.4 31.19 30.56 0 264 15° 24. 148 15° 25. 815 Acacia tortilis t 3.6 16.23 16.23 0 265 15° 24. 158 15° 25. 824 Acacia tortilis t 4.8 37.88 43.29 0 266 15° 24. 127 15° 25. 797 Acacia tortilis t 10.2 36.29 38.20 0 267 15° 24. 136 15° 25. 799 Acacia senegal t 5.2 16.55 18.46 0 268 15° 24. 138 15° 25. 803 Acacia senegal t 4.2 14.64 19.42 0 269 15° 24. 139 15° 25. 803 Acacia senegal t 6.6 19.10 21.65 0 270 15° 24. 143 15° 25. 795 Acacia senegal t 5.2 19.10 22.92 0

271 15° 24. 145 15° 25. 797 Acacia tortilis s 0 272 15° 24. 148 15° 25. 803 Acacia senegal t 4 15.60 18.46 0 273 15° 24. 162 15° 25. 808 Balanites aegyptiaca t 3 ? 19.10 1 274 15° 24. 166 15° 25. 811 Balanites aegyptiaca t 3.8 24.83 25.78 2

108

275 15° 24. 155 15° 25. 692 Balanites aegyptiaca s 1 276 15° 24. 158 15° 25. 792 Balanites aegyptiaca t 3.8 14.64 16.87 1 277 15° 24. 163 15° 25. 794 Balanites aegyptiaca t 4.2 22.28 27.69 1 278 15° 24. 161 15° 25. 694 Balanites aegyptiaca t 3.8 13.05 14.96 0

279 15° 24. 159 15° 25. 796 Balanites aegyptiaca s 0

280 15° 24. 163 15° 25. 792 Balanites aegyptiaca s 0

281 15° 24. 167 15° 25. 798 Balanites aegyptiaca s 0 282 15° 24. 172 15° 25. 797 Acacia tortilis t 7.2 22.60 26.10 0 283 15° 24. 170 15° 25. 802 Balanites aegyptiaca t 4.2 13.69 24.51 1 284 15° 24. 170 15° 25. 810 Balanites aegyptiaca t 5 27.06 32.79 0 285 15° 24. 178 15° 25. 798 Acacia senegal t 4.4 20.05 19.74 0 286 15° 24. 185 15° 25. 798 Acacia tortilis t 7 44.56 45.52 0 287 15° 24. 192 15° 25. 810 Acacia tortilis t 6.6 43.29 42.02 0 288 15° 24. 196 15° 25. 824 Acacia tortilis t 10.6 50.93 55.70 0 289 15° 24. 193 15° 25. 820 Balanites aegyptiaca t 2.2 15.92 18.14 0

290 15° 24. 197 15° 25. 835 Acacia senegal s 0 291 15° 24. 184 15° 25. 851 Acacia tortilis t 6.4 29.28 33.74 1 292 15° 24. 173 15° 25. 870 Balanites aegyptiaca t 5 25.46 30.24 0 293 15° 24. 171 15° 25. 900 Balanites aegyptiaca t 5.6 37.88 30.56 0 294 15° 24. 172 15° 25. 907 Balanites aegyptiaca t 5.2 25.46 26.42 0 295 15° 24. 172 15° 25. 898 Balanites aegyptiaca t 4 17.19 20.37 0 296 15° 24. 170 15° 25. 894 Balanites aegyptiaca t 6.2 32.79 32.79 0

297 15° 24. 174 15° 25. 892 Balanites aegyptiaca s 0 298 15° 24. 183 15° 25. 890 Balanites aegyptiaca t 4.6 14.01 30.88 0 299 15° 24. 182 15° 25. 887 Balanites aegyptiaca t 7 45.52 39.47 0 300 15° 24. 186 15° 25. 878 Balanites aegyptiaca t 5 17.83 24.19 0 301 15° 24. 208 15° 25. 820 Balanites aegyptiaca t ? 37.88 40.74 0 302 15° 24. 219 15° 25. 833 Acacia tortilis t 9.4 44.56 47.43 0

303 15° 24. 210 15° 25. 828 Balanites aegyptiaca s 0

304 15° 24. 210 15° 25. 831 Balanites aegyptiaca s 0

305 15° 24. 209 15° 25. 832 Balanites aegyptiaca s 0

306 15° 24. 208 15° 25. 834 Balanites aegyptiaca s 0

307 15° 24. 206 15° 25. 835 Balanites aegyptiaca s 0 308 15° 24. 213 15° 25. 831 Balanites aegyptiaca t 3.2 10.50 15.92 0 309 15° 24. 216 15° 25. 835 Balanites aegyptiaca t 2.2 6.68 11.14 0 310 15° 24. 215 15° 25. 836 Balanites aegyptiaca t 2.2 3.50 7.96 0 311 15° 24. 210 15° 25. 839 Balanites aegyptiaca t 4 27.06 29.92 0 312 15° 24. 213 15° 25. 841 Balanites aegyptiaca t 4 10.50 15.60 0

313 15° 24. 212 15° 25. 844 Balanites aegyptiaca s 0 314 15° 24. 216 15° 25. 843 Balanites aegyptiaca t 3.8 15.92 18.46 1 315 15° 24. 216 15° 25. 844 Balanites aegyptiaca t 5 17.19 20.69 1 316 15° 24. 220 15° 25. 839 Balanites aegyptiaca t 4.8 14.96 16.55 0 317 15° 24. 228 15° 25. 836 Balanites aegyptiaca t 6 23.87 24.83 1

318 15° 24. 226 15° 25. 842 Balanites aegyptiaca s 0

CHAPTER VIII: APPENDIX 109

319 15° 24. 228 15° 25. 843 Balanites aegyptiaca s 0

320 15° 24. 226 15° 25. 845 Balanites aegyptiaca s 0

321 15° 24. 225 15° 25. 846 Balanites aegyptiaca s 0

322 15° 24. 227 15° 25. 849 Balanites aegyptiaca s 0 323 15° 24. 233 15° 25. 841 Acacia tortilis t 5 18.46 35.01 1 324 15° 24. 244 15° 25. 851 Balanites aegyptiaca t 5.2 19.10 20.37 2 325 15° 24. 245 15° 25. 851 Balanites aegyptiaca t 5.2 35.01 36.29 0 326 15° 24. 249 15° 25. 850 Balanites aegyptiaca t 8.8 36.61 51.57 0 327 15° 24. 237 15° 25. 867 Balanites aegyptiaca t 4.4 14.64 17.51 1 328 15° 24. 235 15° 25. 868 Acacia tortilis t 7.6 35.33 41.70 0 329 15° 24. 235 15° 25. 870 Acacia tortilis t 5.2 27.69 32.79 0 330 15° 24. 219 15° 25. 857 Acacia tortilis t 5.4 21.01 25.46 0 331 15° 24. 214 15° 25. 851 Balanites aegyptiaca t 2.8 6.37 9.87 0 332 15° 24. 212 15° 25. 853 Balanites aegyptiaca t 3 12.41 14.32 1

333 15° 24. 217 15° 25. 851 Balanites aegyptiaca s 0 334 15° 24. 213 15° 25. 859 Balanites aegyptiaca t 3 11.78 13.69 1 335 15° 24. 214 15° 25. 863 Balanites aegyptiaca t 4 17.19 21.96 0 336 15° 24. 214 15° 25. 878 Acacia tortilis t 6 27.06 28.65 2 337 15° 24. 209 15° 25. 898 Acacia tortilis t 4.6 26.42 27.69 0 338 15° 24. 204 15° 25. 874 Acacia tortilis t 7.8 27.06 29.60 0 339 15° 24. 204 15° 25. 868 Balanites aegyptiaca t 2.2 14.96 17.51 0 340 15° 24. 204 15° 25. 869 Acacia tortilis t 4.4 12.41 15.28 0

341 15° 24. 198 15° 25. 872 Acacia tortilis s 0 342 15° 24. 203 15° 25. 880 Acacia tortilis t 3.8 15.92 35.97 0 343 15° 24. 210 15° 25. 880 Acacia tortilis t 6.8 24.83 25.46 0 344 15° 24. 211 15° 25. 886 Acacia tortilis t 3.4 23.24 30.24 0 345 15° 24. 205 15° 25. 889 Balanites aegyptiaca t 3.6 6.37 12.73 0

346 15° 24. 204 15° 25. 891 Balanites aegyptiaca s 0 347 15° 24. 191 15° 25. 903 Balanites aegyptiaca t 5.8 25.78 29.28 2 348 15° 24. 189 15° 25. 907 Balanites aegyptiaca t 4 8.28 16.87 0 349 15° 24. 184 15° 25. 903 Acacia tortilis t 5.6 16.55 29.28 0 350 15° 24. 184 15° 25. 923 Acacia tortilis t 5 22.92 22.92 0

351 15° 24. 189 15° 25. 914 Balanites aegyptiaca s 0

352 15° 24. 190 15° 25. 914 Balanites aegyptiaca s 0

353 15° 24. 191 15° 25. 908 Balanites aegyptiaca s 0

354 15° 24. 192 15° 25. 909 Balanites aegyptiaca s 0

355 15° 24. 196 15° 25. 905 Balanites aegyptiaca s 0

356 15° 24. 196 15° 25. 904 Balanites aegyptiaca s 0

357 15° 24. 199 15° 25. 904 Balanites aegyptiaca s 0

358 15° 24. 201 15° 25. 907 Balanites aegyptiaca s 0

359 15° 24. 202 15° 25. 900 Balanites aegyptiaca s 0

360 15° 24. 209 15° 25. 903 Balanites aegyptiaca s 0 361 15° 24. 220 15° 25. 892 Acacia tortilis t 4.4 19.42 21.96 0

362 15° 24. 221 15° 25. 990 Acacia tortilis s 0

110

363 15° 24. 218 15° 25. 884 Acacia tortilis t 6 28.01 28.33 0

364 15° 24. 253 15° 25. 865 Balanites aegyptiaca s 0 365 15° 24. 256 15° 25. 861 Balanites aegyptiaca t 6.4 24.51 3.50 0 366 15° 24. 260 15° 25. 867 Balanites aegyptiaca t 7.8 9.87 20.37 1 367 15° 24. 262 15° 25. 866 Balanites aegyptiaca t 2.4 11.78 12.41 1 368 15° 24. 264 15° 25. 851 Acacia tortilis t 2 8.59 14.32 1 369 15° 24. 264 15° 25. 874 Balanites aegyptiaca t 1.4 4.46 7.64 1 370 15° 24. 265 15° 25. 874 Balanites aegyptiaca t 2.8 12.41 15.28 0 371 15° 24. 263 15° 25. 875 Balanites aegyptiaca t 2.8 15.28 14.96 0 372 15° 24. 259 15° 25. 882 Balanites aegyptiaca t 4.8 23.24 26.74 0 373 15° 24. 266 15° 25. 884 Balanites aegyptiaca t 2.2 7.32 9.87 0 374 15° 24. 271 15° 25. 880 Balanites aegyptiaca t 4.2 21.33 25.46 0 375 15° 24. 275 15° 25. 881 Balanites aegyptiaca t 2.8 7.00 10.50 0 376 15° 24. 278 15° 25. 886 Balanites aegyptiaca t 5.6 27.06 26.42 1

377 15° 24. 263 15° 25. 895 Balanites aegyptiaca s 0 378 15° 24. 263 15° 25. 898 Balanites aegyptiaca t 4.8 25.78 28.33 0 379 15° 24. 264 15° 25. 900 Balanites aegyptiaca t 4.2 21.65 23.55 0 380 15° 24. 265 15° 25. 903 Balanites aegyptiaca t 4.2 14.32 19.42 0 381 15° 24. 275 15° 25. 910 Balanites aegyptiaca t 4.6 19.74 31.51 0 382 15° 24. 275 15° 25. 912 Balanites aegyptiaca t 4.8 17.83 24.19 0

383 15° 24. 282 15° 25. 896 Balanites aegyptiaca s 0 384 15° 24. 284 15° 25. 899 Acacia tortilis t 4 14.32 17.83 1 385 15° 24. 284 15° 25. 910 Balanites aegyptiaca t 6.4 28.33 32.15 0 386 15° 24. 284 15° 25. 915 Acacia tortilis t 5.6 28.01 27.69 0 387 15° 24. 280 15° 25. 925 Acacia tortilis t 6.8 32.47 33.74 0

388 15° 24. 277 15° 25. 924 Balanites aegyptiaca s 0 389 15° 24. 246 15° 25. 931 Acacia senegal t 3.2 14.01 14.96 0 390 15° 24. 284 15° 25. 931 Acacia senegal t 3.6 11.14 14.64 0 391 15° 24. 286 15° 25. 934 Acacia senegal t 7.2 35.65 25.46 1 392 15° 24. 268 15° 25. 933 Balanites aegyptiaca t 4.8 26.74 27.69 0

393 15° 24. 263 15° 25. 925 Balanites aegyptiaca s 0 394 15° 24. 257 15° 25. 932 Acacia tortilis t 8 40.11 44.25 0 395 15° 24. 256 15° 25. 931 Balanites aegyptiaca t 6.2 27.37 28.65 0 396 15° 24. 254 15° 25. 932 Balanites aegyptiaca t 6.2 18.78 22.28 1 397 15° 24. 256 15° 25. 936 Balanites aegyptiaca t 5.2 17.19 20.37 0 398 15° 24. 256 15° 25. 936 Balanites aegyptiaca t 4.6 12.41 20.05 0

399 15° 24. 253 15° 25. 937 Balanites aegyptiaca s 0

400 15° 24. 250 15° 25. 936 Acacia tortilis s 0 401 15° 24. 248 15° 25. 937 Balanites aegyptiaca t 4.4 9.87 15.92 0

402 15° 24. 252 15° 25. 915 Acacia tortilis s 0 403 15° 24. 248 15° 25. 917 Acacia tortilis t 3.2 7.32 12.10 0 404 15° 24. 250 15° 25. 910 Balanites aegyptiaca t 3 7.96 14.64 1 405 15° 24. 246 15° 25. 895 Balanites aegyptiaca t 2.4 7.00 9.55 0 406 15° 24. 243 15° 25. 898 Balanites aegyptiaca t 6 22.60 31.19 0

CHAPTER VIII: APPENDIX 111

407 15° 24. 245 15° 25. 901 Balanites aegyptiaca t 2.2 5.73 11.78 0 408 15° 24. 239 15° 25. 900 Balanites aegyptiaca t 2.6 7.64 9.55 0 409 15° 24. 239 15° 25. 896 Balanites aegyptiaca t 4.4 17.19 22.60 0 410 15° 24. 237 15° 25. 895 Balanites aegyptiaca t 4.2 20.37 22.92 0

411 15° 24. 227 15° 25. 802 Acacia tortilis s 0 412 15° 24. 227 15° 25. 805 Acacia tortilis t 4.4 21.65 26.42 1 413 15° 24. 240 15° 25. 822 Acacia tortilis t 6.8 22.92 27.06 1 414 15° 24. 228 15° 25. 919 Acacia tortilis t 3.8 18.46 18.46 0 415 15° 24. 225 15° 25. 916 Acacia tortilis t 4.8 21.33 24.51 0 416 15° 24. 219 15° 25. 915 Balanites aegyptiaca t 3.6 11.78 15.92 0 417 15° 24. 206 15° 25. 911 Balanites aegyptiaca t 4.4 15.92 23.87 1 418 15° 24. 198 15° 25. 917 Balanites aegyptiaca t 3.2 14.01 19.10 0 419 15° 24. 198 15° 25. 917 Balanites aegyptiaca t 3.4 9.55 17.83 0 420 15° 24. 198 15° 25. 920 Balanites aegyptiaca t 3.4 14.01 16.87 0 421 15° 24. 199 15° 25. 922 Balanites aegyptiaca t 3.6 11.78 24.51 0 422 15° 24. 201 15° 25. 922 Balanites aegyptiaca t 4.2 15.28 23.24 0 423 15° 24. 205 15° 25. 916 Balanites aegyptiaca t 6.6 17.51 23.87 0 424 15° 24. 204 15° 25. 918 Balanites aegyptiaca t 6.8 33.42 35.33 0 425 15° 24. 208 15° 25. 918 Balanites aegyptiaca t 4.8 32.15 31.83 0 426 15° 24. 206 15° 25. 923 Balanites aegyptiaca t 5.2 15.92 22.28 0 427 15° 24. 204 15° 25. 923 Balanites aegyptiaca t 6.6 16.87 21.01 0 428 15° 24. 208 15° 25. 927 Balanites aegyptiaca t 5 12.73 16.23 0 429 15° 24. 200 15° 25. 926 Balanites aegyptiaca t 4 14.96 26.10 0 430 15° 24. 196 15° 25. 930 Balanites aegyptiaca t 3.4 8.59 11.78 1 431 15° 24. 190 15° 25. 931 Balanites aegyptiaca t 3.4 14.64 16.55 2

432 15° 24. 185 15° 25. 930 Balanites aegyptiaca s 0 433 15° 24. 203 15° 25. 943 Balanites aegyptiaca t 3.2 17.83 18.78 2

434 15° 24. 204 15° 25. 944 Acacia tortilis s 0 435 15° 24. 203 15° 25. 944 Balanites aegyptiaca t 3.2 7.32 11.14 0 436 15° 24. 202 15° 25. 947 Balanites aegyptiaca t 4.6 36.61 35.01 0 437 15° 24. 206 15° 25. 947 Balanites aegyptiaca t 4.2 11.14 16.23 0 438 15° 24. 225 15° 25. 958 Balanites aegyptiaca t 5.2 19.74 25.78 0

439 15° 24. 221 15° 25. 955 Balanites aegyptiaca s 0 440 15° 24. 226 15° 25. 942 Acacia tortilis t 5.2 25.15 30.24 2 441 15° 24. 230 15° 25. 945 Acacia tortilis t 5 23.24 23.87 2 442 15° 24. 242 15° 25. 961 Acacia tortilis t 6.2 40.43 42.34 0 443 15° 24. 249 15° 25. 959 Balanites aegyptiaca t 4.6 31.19 29.92 0 444 15° 24. 256 15° 25. 957 Acacia tortilis t 6.4 36.29 37.88 2

445 15° 24. 267 15° 25. 955 Acacia tortilis s 0 446 15° 24. 275 15° 25. 963 Acacia tortilis t 6.6 29.60 36.92 0 447 15° 24. 274 15° 25. 970 Balanites aegyptiaca t 6 23.24 27.37 0 448 15° 24. 272 15° 25. 972 Balanites aegyptiaca t 3.6 19.74 27.06 0 449 15° 24. 265 15° 25. 987 Balanites aegyptiaca t 7.2 31.51 35.33 0 450 15° 24. 258 15° 25. 983 Balanites aegyptiaca t 4 17.19 24.51 0

112

451 15° 24. 257 15° 25. 985 Balanites aegyptiaca t 4 20.69 23.55 0 452 15° 24. 274 15° 25. 983 Acacia tortilis t 4.4 19.10 21.96 1 453 15° 24. 247 15° 25. 991 Balanites aegyptiaca t 5.8 22.28 29.92 0

454 15° 24. 255 15° 25. 989 Acacia tortilis s 0

455 15° 24. 245 15° 25. 986 Acacia tortilis s 0 456 15° 24. 245 15° 25. 997 Acacia senegal t 4.4 15.28 14.64 0 457 15° 24. 252 15° 26. 002 Acacia senegal t 6.4 19.10 20.37 0 458 15° 24. 235 15° 26. 010 Balanites aegyptiaca t 6.6 34.38 37.56 0 459 15° 24. 237 15° 26. 005 Balanites aegyptiaca t 5.8 26.10 29.60 0 460 15° 24. 235 15° 26. 005 Balanites aegyptiaca t 4.4 18.46 21.65 0 461 15° 24. 237 15° 26. 022 Acacia tortilis t 7.2 25.46 29.92 0 462 15° 24. 232 15° 26. 017 Balanites aegyptiaca t 3.6 16.23 19.10 1 463 15° 24. 228 15° 26. 010 Balanites aegyptiaca t 3.6 21.01 20.05 1 464 15° 24. 230 15° 26. 026 Balanites aegyptiaca t 4.8 21.33 26.74 0 465 15° 24. 222 15° 26. 026 Balanites aegyptiaca t 5 28.65 31.19 0 466 15° 24. 220 15° 26. 027 Balanites aegyptiaca t 3.8 8.91 12.73 0 467 15° 24. 220 15° 26. 026 Balanites aegyptiaca t 3.4 18.46 35.01 0 468 15° 24. 213 15° 26. 023 Acacia tortilis t 5.4 48.06 49.66 0 469 15° 24. 209 15° 26. 023 Balanites aegyptiaca t 5 23.24 36.29 0 470 15° 24. 207 15° 26. 031 Balanites aegyptiaca t 5 20.69 22.92 0 471 15° 24. 206 15° 26. 017 Acacia tortilis t 6.8 26.10 36.61 0 472 15° 24. 204 15° 26. 016 Balanites aegyptiaca t 4.8 20.37 37.56 0 473 15° 24. 213 15° 25. 998 Acacia tortilis t 6.8 37.56 31.19 0 474 15° 24. 209 15° 25. 991 Balanites aegyptiaca t 4.2 18.78 25.46 0 475 15° 24. 209 15° 25. 986 Acacia tortilis t 4.4 14.64 17.51 0 476 15° 24. 236 15° 25. 968 Balanites aegyptiaca t 3.6 26.42 30.56 0 477 15° 24. 229 15° 25. 964 Balanites aegyptiaca t 4.8 17.83 23.55 0 478 15° 24. 220 15° 25. 955 Balanites aegyptiaca t 4.6 15.92 22.28 0 479 15° 24. 213 15° 25. 960 Balanites aegyptiaca t 6 23.55 24.19 0 480 15° 24. 220 15° 25. 967 Balanites aegyptiaca t 2.2 12.73 16.23 0 481 15° 24. 216 15° 25. 965 Balanites aegyptiaca t 3 13.37 25.78 0 482 15° 24. 221 15° 25. 975 Balanites aegyptiaca t 5.4 22.92 23.87 0 483 15° 24. 212 15° 25. 960 Balanites aegyptiaca t 3.4 11.14 14.96 1 484 15° 24. 207 15° 25. 970 Balanites aegyptiaca t 5.4 32.15 29.92 0 485 15° 24. 200 15° 25. 972 Acacia tortilis t 5.4 20.05 21.65 0 486 15° 24. 190 15° 25. 962 Balanites aegyptiaca t 4.8 15.60 22.60 0 487 15° 24. 187 15° 25. 978 Balanites aegyptiaca t 4.6 13.37 18.46 0 488 15° 24. 178 15° 25. 953 Balanites aegyptiaca t 5.6 28.33 26.74 0 489 15° 24. 178 15° 25. 951 Balanites aegyptiaca t 5.8 21.65 21.96 1 490 15° 24. 178 15° 25. 950 Balanites aegyptiaca t 4.4 22.92 27.69 0

CHAPTER VIII: APPENDIX 113