Faculty of Bioscience Engineering Isotope Bioscience Laboratory - ISOFYS Academic year 2015 – 2016 Carbon storage and nutrient shifts along an altitudinal gradient in Nyungwe forest, Rwanda Dries Van der Heyden Promotors: Prof. dr. ir. Pascal Boeckx and prof. dr. Landry Cizungu Ntaboba Tutor: ir. Marijn Bauters Master dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Bioscience Engineering: Forest and Nature Management
Transcript
Faculty of Bioscience Engineering
Isotope Bioscience Laboratory - ISOFYS
Academic year 2015 – 2016
Carbon storage and nutrient shifts along an altitudinal
gradient in Nyungwe forest, Rwanda
Dries Van der Heyden
Promotors: Prof. dr. ir. Pascal Boeckx and prof. dr. Landry Cizungu Ntaboba
Tutor: ir. Marijn Bauters
Master dissertation submitted in partial fulfilment of the requirements for the
degree of Master of Science in Bioscience Engineering: Forest and Nature
Management
Copyright
The author and promoters 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.
De auteurs en promotors 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 uitdrukkelijk de bron te vermelden bij het aanhalen van resultaten uit
deze scriptie.
Ghent, August 2016
Prof. Dr. Ir. Pascal Boeckx Dries Van der Heyden
Acknowledgments
First of all I would like to express my gratitude to prof. Pascal Boeckx and prof. Landry Cizungu
for giving me the opportunity to participate in this research and for all their practical assistance. I
also want to thank Marijn Bauters for his professional help and personal guidance. Thanks to
Cys, for being a great companion and an efficient fieldworker.
Further, I would like to convey my appreciation to the Rwandese and Congolese co-workers:
James, ‘mon frère’, Fidele, Jean-Baptiste, Sartié and the very helpful guards and the staff in
Nyungwe. From them I learned the word ‘ubushakashatsi’, which is Kinyarwanda for ‘research’, a
word that expresses well the labour, envy and love I’ve put into this thesis.
I would also like to acknowledge the Belgian staff of Isofys and Geert Sioen of the INBO and
thank them for their help.
Lastly, I am very grateful for the support of my parents, brother, sister, friends and family during
this year and at university.
Table of contents
Abbreviations I
Abstract II
Résumé III
Samenvatting IV
1 Introduction 1
2 Literature review 2
2.1 Tropical forests 2
2.1.1 What and where? 2
2.1.2 Value and importance 3
2.2 Carbon cycling 5
2.3 Tropical montane forests 8
2.3.1 Characteristics 8
2.3.2 Altitudinal transects 11
2.3.3 Nutrient cycling and isotopes 13
2.3.4 Albertine rift montane forests 16
3 Research questions 17
4 Materials and methods 18
4.1 Study area 18
4.2 Altitudinal transect choice and site selection 19
4.3 Establishment and measurement of permanent sample plots 21
4.4 Sampling and processing of soil, litter, wood and leafs 22
4.5 Planimetric plot areas 23
4.6 Estimation of carbon stocks 24
4.6.1 Aboveground carbon 24
4.6.2 Carbon in litter and belowground carbon 25
4.7 Statistical analyses 25
5 Results 26
5.1 Planimetric plot area 26
5.2 Forest structure 26
5.3 Carbon stocks 28
5.4 Litter, fine roots and the O-horizon 31
5.5 Mineral soil 33
6 Discussion 36
6.1 The correction for planimetric plot areas and steep slopes 36
6.2 Changes in forest structure with altitude 36
6.3 Changing carbon stocks along an altitudinal gradient 36
6.3.1 Underestimated AGC in the lower strata 37
6.4 Mineral soil nutrient concentrations along an altitudinal gradient 37
6.5 How do the isotopic compositions change with altitude and what do they tell us? 38
7 General conclusion and future perspectives 39
8 List of references 40
9 Appendices 51
A. RAINFOR field work database codes for living trees 51
B. Lab protocol for soil bioavailable P 52
C. Graphs of the additional carbon stocks 53
D. Graphs of N & C in litter, O-horizon and roots 54
E. Graphs of δ13C and δ 15N in the soil at different depths 55
I
Abbreviations
AGB Aboveground Biomass
AGC Aboveground Carbon
BGC Belowground Carbon
DBH Diameter at Breast Height
FAO Food and Agriculture Organization of the United Nations
GPP Gross Primary Productivity
masl meters above sea level
NBP Net Biome Productivity
NEP Net Ecosystem Productivity
NPP Net Primary Productivity
POM Point of Measurement
RAINFOR Red Amazónica de Inventarios Forestales (Amazon Forest Inventory
Network)
SOM Soil Organic Matter
TMCF Tropical Montane Cloud Forest
TMF Tropical Montane Forest
UNEP United Nations Environment Programme
WCMC World Conservation Monitoring Centre
WRB World Reference Base for soil resources
WWF World Wildlife Fund/World Wide Fund for Nature
II
Abstract
Tropical montane forests sustain high biodiversity and deliver important ecosystem services such
as the provision of fresh water and carbon sequestration. Yet their functioning is understudied,
especially in Africa. The current process of climate warming encourages studying these
ecosystems which play a significant role in climate change mitigation but are under threat. An
altitudinal transect study was performed in a tropical montane forest in Rwanda. Twenty
permanent sample plots of 40x40m were established in 4 altitudinal strata (1800m, 2200m,
2500m and 2800m). Forest structure and carbon stocks were quantified and linked with
properties of soil, litter and roots. In accordance with the prevailing literature aboveground
carbon tended to decrease with increasing altitude, while carbon in the roots and the upper soil
layer demonstrated an inverse relation. . Increasing altitude was associated with a significant
increase in the C:N-ratio of leaves and litter and a significant decrease of the isotopic
composition δ15N of leaves, litter and soil, which indicates that at a higher altitude there is more
nitrogen limitation and an altered N cycle .These findings confirm nutrient availability to be a key
regulator of forest carbon cycling processes.
III
Résumé
Les forêts tropicales montagnardes assurent une biodiversité substantielle et livres des services
écosystémiques importantes, comme la provision d’eau pure et le stockage de carbone. Pourtant
le nombre d’études sur ce sujet est limité. Il y a surtout une manque d’études sur le
fonctionnement des forêts montagnardes en Afrique. Le réchauffement climatique actuel incite à
l’étude de ces écosystèmes, qui jouent un rôle majeur dans la mitigation, mais qui sont menacés.
Une étude transect de hauteur a été conduite dans une forêt tropicale montagnarde au Ruanda.
Vingt zones d’altitudes ont été établies à quatre hauteurs différentes (1800m, 2200m, 2500 en
2800m). La structure de la forêt et le stockage du carbone ont été quantifiés et les liens avec
certains caractéristiques du sol, la litière et les racines ont été établis. En ligne avec la littérature
existante la carbone dans la biomasse vivante au-dessus du sol tendait à diminuer avec l’altitude,
tandis qu’une relation inverse était observée pour la carbone dans les racines et la couche
supérieure du sol. L’augmentation de l’altitude était associée à l’augmentation significative du
ratio C-N des feuilles et de la litière, et la diminution significative de la composition isotopique
δ15N des feuilles, de la litière et du sol. Ceci indique qu’en grande altitude la quantité d’azote est
plus limitée et le cycle de l’azote est modifié. Ces observations confirment que la disponibilité des
nutriments jouent un rôle clé dans le cycle du carbone au sein des forêts
IV
Samenvatting
Tropische bergbossen waarborgen een hoge biodiversiteit en leveren belangrijke
ecosysteemdiensten zoals de provisie van zuiver water en koolstofopslag. Desondanks wordt hier
weinig onderzoek naar gedaan. Vooral het functioneren van de bergbossen in Afrika is nog
weinig onderzocht. De huidige klimaatopwarming zet aan tot het bestuderen van deze
ecosystemen, die een belangrijke rol spelen in de mitigatie van klimaatverandering maar wel
bedreigd zijn. Een hoogtetransect-onderzoek werd uitgevoerd in een tropisch bergbos in
Rwanda. Twintig permanente proefvlakken werden uitgezet in vier verschillende hoogtezones
(1800m, 2200m, 2500 en 2800m). De structuur van het bos en de koolstofstocks werden
gekwantificeerd en gelinkt aan bepaalde eigenschappen van de bodem, de strooisellaag en de
wortels. In overeenstemming met de meeste literatuur vertoonde de bovengrondse koolstof een
dalende trend met stijgende hoogte, waar de koolstof in de bovenste bodemlaag en in de wortels
net een omgekeerde relatie vertoonden. Toenemende hoogte was geassocieerd met een
significante stijging van de C:N-ratio van bladeren en strooisel en een significante daling van de
isotopensamenstelling δ15N van bladeren, strooisel en bodem, wat wijst op meer stikstoflimitatie
en een veranderde stikstofcylus naarmate de hoogte toeneemt. Deze bevindingen bevestigen dat
de beschikbaarheid van nutriënten een sleutelrol vervult in de koolstofcyclus van bossen.
1
1 Introduction
With the Paris climate conference (COP21) in December 2015, the huge threat of recent climate
change received renewed attention. In this respect, the important role of tropical forests is widely
acknowledged. Despite extensive research on the topic, the process-level understanding of
carbon and forest dynamics still needs to be improved (Bustamante et al., 2016). Furthermore it
remains largely unknown how tropical forests will respond and feedback to climate change at
ecosystem level (Zhou et al., 2013).
Advancements in the understanding of ecosystem ecology and functioning are often achieved
slowly and step by step. Therefore in a collaboration between CAVElab1 and ISOFYS2, Ghent
University, four master-students in Bioscience Engineering conducted research in tropical
montane forests (TMFs) in Rwanda and Ecuador. Two students studied functional diversity
while the other two studied carbon storage and nutrient shifts. Permanent sample plots were
established on an altitudinal transect since the environmental gradient offers a great experimental
setup. The same protocol was used, allowing for a prudent comparison between a central African
and an Andean TMF.
In this framework, the present thesis deals with carbon storage and nutrient shifts in a montane
tropical rainforest in Rwanda. As the study site was located near the border with the Democratic
Republic of Congo and a partnership between ISOFYS and the Catholic University of Bukavu
(UCB) in eastern Congo already existed, a local collaboration was evident, resulting in the
A definition of tropical forest explains its two components: tropical, the climatic or geographic
aspect and forest, a land cover type. The geographic delineation of ‘tropical’ indicates the location
between the Tropic of Cancer and the Tropic of Capricorn, the tropical belt, whereas the climatic
delineation points to the regions where certain long-term meteorological conditions are fulfilled.
For instance according to the Köppen-Geiger classification system tropical climates where
allocated if a minimum average temperature of 18 °C was reached (at sea level or at low
elevations) during all twelve months of the year. Looking at the land cover type, numerous
definitions of forest exist. A clear and internationally accepted example, which is also used in this
literature review, is the one by the Food and Agriculture Organization of the United Nations
(FAO): Forest is a land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of
more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly
under agricultural or urban land use.
The FAO Global Forest Resources Assessment 2015 shows that forest nowadays covers around
3999 M ha of our planet’s surface (Keenan et al., 2015). 44% of this area is covered by tropical
forests, mainly in South America, West-Central Africa and Southeast Asia (Figure 1).
Figure 1. Global forest distribution. Tropical forests are indicated with orange to dark purplish red, according to the tree cover density. Map adopted from the Global Forest Resources Assessment (FAO, 2010).
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2.1.2 Value and importance
A first major value of tropical forests is their biodiversity. Of all terrestrial biomes they contain
the highest species diversity (Myers et al., 2000), with one-half to two-thirds of the Earth’s
terrestrial biodiversity to be found in these ecosystems (Gardner et al., 2010). Slik et al. (2015)
recently estimated the minimum number of tropical forest tree species to fall between ∼40.000
and ∼53.000, which is in sharp contrast with the 1166 species that make up the northern
temperate tree flora (Latham and Ricklefs, 1993). An additional value related to biodiversity is the
intactness, the way in which an area is unmodified by human impacts and how it is retaining its
natural character and influence. Although genuine pristine ecosystems are really scarce, in the
tropics there is still a quite extended area of old-growth forests that has experienced little to no
recent human disturbance. Important high-biodiversity ‘wilderness areas’ are Amazonia, the
Congo forests of Central Africa and the tropical rainforest of New Guinea (Mittermeier et al.,
2003). Gibson et al. (2011) found that biodiversity values are clearly lower in degraded forests
which encourages the protection of primary forests.
There are different ethical viewpoints on the value of nature and its biodiversity, with the three
main approaches being biocentrism, ecocentrism and anthropocentrism (Callicott, 2004). The
first two approaches respectively enfranchise morality to all living beings and to non-individual
environmental entities whereas the latter grants moral standing exclusively to human beings. The
anthropocentric environmental ethic considers nonhuman natural entities and nature as a whole
to be only a means for human ends, so the value of nature is derived from what people assign as
beneficial for human well-being. This approach is standing on traditional Western moral
philosophy and it is the most convenient and clear way to explain people the importance of
conservation and environmental protection. A quite recent anthropocentric approach that aims
to increase the appreciation and value of natural systems is the concept of ecosystem services.
Ecosystem services are the direct and indirect contributions of ecosystems that benefit human
wellbeing. It is a clear concept that is increasingly used on a global scale by policy makers as well
as scientists. It became widespread with the famous article of Constanza et al. (1997), ‘The value
of the world’s ecosystem services and natural capital’, and after the Millennium Ecosystem
Assessment of the United Nations in 2005 (MA 2005). The services ecosystems provide, are
generally classified in four groups: provisioning services, regulating services, supporting services
and cultural services. Forests, for instance, can provide wood and fresh water, regulate climate
and weather patterns, have a supporting function through soil formation and deliver a big
amount of cultural services such as recreation and beautiful landscapes (aesthetic value). It must
also be acknowledged that ecosystems can have a negative impact on people, the so-called
disservices. An example of a tropical forest disservice is crop damage from wild animals
(Sandbrook and Burgess, 2015). It is clear however that the benefits are generally much bigger.
Services can be of local importance and of global importance. E.g. wild bee populations in
tropical forests deliver an important value for local farmers through crop pollination (Ricketts et
al., 2004) and climate regulation via carbon sequestration can be seen as a more generic global
service.
4
Figure 2. Terrestrial organic carbon in soil and vegetation. Map adopted from the UNEP-WCMC report ‘Towards a global map of natural capital: key ecosystem assets’ (Dickson et al., 2014).
In 2014 the United Nations Environment Programme World Conservation Monitoring Centre
(UNEP-WCMC) created a report with the first attempt to give an overview of the global
distribution of ecosystem assets that have the capacity to generate a basket of ecosystem services
(Dickson et al., 2014). They identified and mapped five key assets of which two are predominant
in tropical forests: terrestrial biodiversity (adjusted for intactness) and terrestrial organic carbon in
vegetation and soil. The latter is depicted in Figure 2 and when compared with the distribution of
tropical forest in Figure 1, the overlap is quite clear. The role of tropical forests in the global
carbon cycle is more elaborately discussed in section 2.2.
Despite all the positive services and goods tropical forests deliver, the forest area in the tropics
decreased from 1966 M ha in 1990 to 1770 M ha in 2015 (Keenan et al., 2015). Another huge
problem in the tropical forests is forest degradation, an item that is even more difficult to
quantify but is also responsible for a lot of biodiversity and carbon stock losses. From pan-
tropical assessments of the importance of different deforestation and forest degradation drivers it
is clear that agriculture is the most prevalent deforestation driver whereas timber extraction and
logging is the most serious forest degradation driver (Geist and Lambin, 2002; Hosonuma et al.,
2012). An international programme which tries to tackle these problems by creating a financial
value to tropical forests and their carbon stocks is the United Nations Framework Convention on
Climate Change (UNFCCC) mechanism for Reducing Emissions from Deforestation and
Degradation in developing countries (REDD+). It also claims to go beyond deforestation and
forest degradation, by including the role of conservation, sustainable management of forests and
enhancement of forest carbon stocks. The well-functioning and results of this programme are
however subject of a lot of discussion.
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2.2 Carbon cycling
Carbon is in constant exchange between the oceans, the atmospheric pool and terrestrial
ecosystems via complex interactions that are increasingly understood (Falkowski et al., 2000).
Minor changes in one of these pools or of process determining parameters could have a huge
influence on both our planet’s biosphere or atmosphere. Just like other biogeochemical and
climatological processes, the global carbon cycle is heavily altered by human activities.
Anthropogenic carbon emissions lead to an atmospheric CO2 level unprecedented for at least 800
000 years (Pachauri and Meyer, 2014). Around 50% of these emissions between 1750 and 2011
originated in the last 40 years. Together with the emissions of other anthropogenic greenhouse
gasses it is extremely likely that they have been the dominant cause of the observed climate
warming since the mid-20th century. With the current understanding of the ocean carbon cycle, it
looks like the sink strength (the magnitude of net accumulation and storage of carbon from
atmospheric CO2) will probably weaken, leaving a potentially more important role to terrestrial
ecosystems (Falkowski et al., 2000).
In the terrestrial biosphere carbon is stored in vegetation, detritus and soil. Forests hold the
greatest share of these terrestrial C stocks and hence exert strong control on the evolution of
atmospheric CO2. The process at the basis of carbon sequestration is photosynthesis at the leaf-
level, where atmospheric CO2 is used to assimilate sugars or plant biomass by the use of solar
irradiance. In this process also water is used and oxygen gas is generated. Next to the basic
requirements for photosynthesis different abiotic and biotic controls determine forest
productivity (Hopkins and Hüner, 2004; Schulze et al., 2002):
the amount and light-use efficiency of foliage
water availability
ambient temperature
presence and availability of soil nutrients
adaptations of species to extreme temperatures
efficient use of water and nutrients
The photosynthetic input in ecosystems, the Gross Primary Productivity (GPP), is partially
compensated by the main natural carbon outputs: autotrophic respiration (Ra, i.e. from plants)
and heterotrophic respiration (Rh, i.e. from microbes and animals). In addition carbon is also
released from ecosystems by harvest and fire. The resultant quantity from photosynthesis minus
plant respiration, is termed Net Primary Productivity (NPP) and further subtraction of the
heterotrophic respiration results in the Net Ecosystem Productivity (NEP). NEP generally
accounts for a whole ecosystem in which the accumulation of carbon is calculated over a whole
season or other time period (IPCC, 2003). Carbon losses due to disturbances such as fire and
harvest are relatively infrequent and difficult to quantify at ecosystem scale (Boisvenue and
Running, 2006). However at the biome scale these disturbances can be considered processes that
occur on a regular basis in one area or another. The term used to refer to the net production of
organic matter in a region containing a range of ecosystems, including disturbances is the Net
Biome Productivity (NBP). It is calculated by summing NEPs over the region and subtracting the
losses due to disturbances. NBP seems to be the best way for analysing long-term, large-scale
changes in carbon but distinction among NPP, NBP and NEP is often unclear in literature.
6
Forests throughout the world are known to be a large and persistent sink. Pan et al. (2011)
estimated a total forest sink of 2.4 ± 0.4 Pg C year-1 for the period from 1990 to 2007. This
number has been contested by some scientists, e.g. by S. Joseph Wright (2013), but in general
there is little doubt about the occurrence and approximate magnitude of this substantial carbon
sink. The sink strength is highest in tropical forests, presumably because conditions for
photosynthesis are very favourable during most time of the year (Grace et al., 2014). The
difference with temperate forests is nevertheless not that big, because respiration is also very high
in the tropics and most temperate forests are in a relatively juvenile phase, being managed to be
productive with trees in their most active growth phase (Luyssaert et al., 2008). But then again the
significance of tropical forests also comes from the fact that they extent a vast area of ~1770 M
ha (Keenan et al., 2015). Approximately 45% of all terrestrial C is estimated to be stored in
tropical forests, accounting for a carbon stock as biomass that adds up to 271 ± 16 petagrams
(Pg) and a quantity of carbon as soil organic matter that is possibly even higher (Anderson-
Teixeira et al., 2016; Grace et al., 2014). However although they constitute the largest component
of the terrestrial C sink, carbon loss from deforestation, forest degradation, harvesting and fires
in tropical forests is estimated higher than the carbon gained from forest and woodland growth.
Grace et al. (2014) reported this source to be 2.01 ± 1.1 petagrams of carbon per year (Pg C year-
1) and a tropical forest sink of 1.85 ± 0.09 Pg C year-1 for the period from 2005 to 2010.
Despite the major importance of tropical forests to the global C cycle, their ecosystem-level C
cycling is not as well understood as that of extra-tropical forests (Anderson-Teixeira et al., 2016).
Especially on the African continent information about the carbon balance is still sparse (Ciais et
al., 2009; Williams et al., 2007). Nevertheless progress has been made with the creation of
AfriTRON (the African Tropical Rainforest Observatory Network), a network of monitoring
plots which already lead to the first large-scale comparative study of above-ground biomass of
African tropical forests published in 2013 (Lewis et al., 2009, 2013).
Data on above-ground biomass and their spatial patterns acquired via long-term monitoring plots
are very valuable. They provide insights into how tropical forests function, deliver information
for estimating carbon losses from deforestation and forest degradation, assist calibrating and
validating carbon mapping exercises and are necessary for modelling tropical forests and their
response to a changing environment.
CO2
Plant
biomass
Medium- term
carbon storage
Long-
term
carbon
storage
Figure 3. Terrestrial ecosystems carbon uptake and storage. Figure adopted from Walker and Steffen (1997).
7
Notwithstanding important work by scientists such as Jobbágy & Jackson (2000), Schwartz &
Namri (2002) and Batjes (2008), robust estimates of soil organic carbon (SOC) pools and its
spatial and temporal variability continue to be lacking (Dieleman et al., 2013; Don et al., 2011).
Models that simulate the carbon cycle and vegetation dynamics are crucial to have an idea what to
expect in future global change scenarios and to identify possible tipping points. In tropical forests
anthropogenic deforestation will play a primary role in the evolution of the carbon cycle but
other carbon releases driven by climate change may not be neglected. Results for the twenty-first
century by Cramer et al. (2004), who applied a dynamic global vegetation model, confirm large
losses of carbon despite the uncertainty about deforestation rates. Additionally some climate
models produced large carbon fluxes due to increased drought stress caused by rising
temperatures and decreasing rainfall. However one climate model produced an additional carbon
sink. The magnitude of the effect of climate change on tropical forests remains quite uncertain
(Malhi et al., 2008) but coupled climate-carbon-cycle models often agree on two counteracting
effects on terrestrial carbon storage (Cox et al., 2013):
1. an increase due to the simultaneous enhancement of plant photosynthesis and water use
efficiency under higher atmospheric CO2 concentrations
2. a decrease due to higher soil and plant respiration rates associated with warming
temperatures
The balance between these effects varies enormously - up to a range of 330 gigatonnes in the
projected change by 2100 - according to the use of different models. Across multiple models Cox
et al. (2013) found an emergent linear relationship between the sensitivity of tropical land carbon
storage to warming and the sensitivity of annual growth rate of atmospheric CO2 to tropical
temperature anomalies. This provided a tight constraint on the sensitivity of tropical carbon to
climate change leading to an estimation of 53 ± 17 gigatonnes of carbon per degree Celsius
released over tropical land by warming alone. However these models depart from a (large) CO2
fertilization effect, increasing terrestrial carbon uptake due to increasing atmospheric CO2
concentrations, which is still being heavily debated (Battipaglia et al., 2015; van der Sleen et al.,
2014). This again demonstrates the need of more data on carbon cycling in tropical forests.
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2.3 Tropical montane forests
Tropical montane forest (TMF), being evolutionarily and ecologically distinct from tropical
lowland forest, is a type of forest that has been underrepresented in studies concerning ecosystem
ecology and biogeochemical cycles. In fact, TMFs are even more poorly researched than the
lowland tropics (Bubb et al., 2004). To better understand their role in the global carbon cycle,
there is a need of extra studies aimed at quantifying the overall carbon balance of TMFs
(Bruijnzeel and Veneklaas, 1998). A study by Spracklen and Righelato (2014) suggests that
current regional carbon stock estimates are too small since they are usually reported for the
planimetric area while the real surface area is 40 % greater due to the prevalence of steep slopes.
Malhi et al. (2010) clearly identified five major justifications for investing effort in studying
TMFs. Tropical elevations are for instance excellent sites for understanding past climate change
in the tropics and tropical mountains are potential arks for biodiversity in a century of rapid
warming. Furthermore environmental responses to global change drivers in TMFs may provide
an early indication of what the future holds for many of the world’s ecosystems (González et al.,
2013).
2.3.1 Characteristics
Altitudinal zonation
The large heterogeneity of the tropical forest biome encourages classification. Different
classification systems exist but an obvious recurrent distinction is the elevation. Elevation
influences climatic factors, such as temperature, incoming solar radiation, humidity and the
presence and frequency of clouds and fog, which are in its turn responsible for a lot of botanical
characteristics of the forest. In natural systems TMFs are delineated by the ecotone between
lowland tropical forest and lower montane tropical forest as lower limit and the treeline, where
the mountains are high enough, as upper limit. Ideally the lower mountain limit should be
defined climatically or according to the vegetation for every region over the whole world. Since
this is not feasible conventions are used. In the tropics the lower limit is usually set at 1000
m.a.s.l. (Körner et al., 2005).
While increasing in altitude different vegetation zones occur. This phenomenon, called altitudinal
zonation, quite early aroused the interest of several scientists. The famous Alexander von
Humboldt even wrote about it in the beginning of the 19th century (Humboldt as cited in Bach
and Gradstein, 2011). Different approaches to define this altitudinal belts have been used, based
upon abiotic as well as biotic factors. Troll (1959) for instance suggested a zonation based on
thermohygric conditions but most zonation schemes are contingent on biotic factors such as
vegetation structure and floristics (Bach and Gradstein, 2011). Four general forest zones on
tropical mountains have been recognised worldwide: lowland, lower montane, upper montane
and subalpine (Ashton, 2003). Classically the emphasis of research on altitudinal zonation was on
structural and physiognomic criteria, e.g. Richards (1952) and Grubb et al. (1963) but recent
studies focus mainly on floristics. The dependence on altitude of some physiognomic or
structural features such as the tree height and leaf size, are quite straightforward whereas others
such as the number of strata (vegetation layers) are much more discussable. Described characters
of structure and physiognomy for the altitudinal zones in tropical rain forest are presented in
Table 1.
9
Table 1. Characters of structure and physiognomy of the four general altitudinal forest zones (adopted from Ashton (2003))
An illustration of a more recent study on altitudinal zonation is the one by Andreas Hemp (2006).
Hemp produced the first statistically significant floristically defined altitudinal zonation of the
vegetation of Mt Kilimanjaro. On 600 plots over a range of 2400 m the entire vegetation (trees,
shrubs, epiphytes, lianas and herbs) was surveyed and classified by the phytosociological method
of Braun-Blanquet (1964). With his study Hemp showed clearly distinguishable community units
to be a result of stable and distinct ecological altitudinal differences. Although the term
“zonation” actually implies these altitudinal belts and distinct ecotones, whether a continuous
change over the whole range occurs or whether these changes are discontinuous, is still a matter
of debate. For instance Hamilton (1975) and Lovett (1998) did not observe a zonation of forests
on different East African mountains. Hemp on the other hand suggested that distinct differences
occur in all tropical montane forests and stated that not observing an altitudinal zonation of
forests may have resulted from different sampling methods and intensities or the mix of data
from different slopes. Obviously the transition between successive altitudinal belts can be much
more gradual in certain cases than in others but at least some show abrupt boundaries of forest
vegetation belts with very distinctive characteristics, e.g. bamboo zones.
Boundaries of altitudinal zones are mainly determined by factors closely related to altitude but
other factors (which are not always strongly correlated with altitude) such as rainfall and edaphic
soil conditions can be of major importance as well (see section 2.3.2). Soil conditions and
altitudinal nutrient shifts possibly responsible for vegetation changes will be elaborated in section
2.3.3.
Tropical montane cloud forests
A specific forest formation which also typically occurs in a relatively narrow altitudinal belt is
tropical montane cloud forest (TMCF) (Aldrich et al., 1997). However these cloud forests can be
quite extensive as well and therefore they are not seen as a zone occurring in tropical montane
forest but rather as a subtype of the latter (Bruijnzeel and Veneklaas, 1998). Numerous
definitions of this specific forest type exist but a broad generally accepted definition describes
TMCFs as tropical montane forests that are frequently covered in clouds or mist (Bruijnzeel and
Formation Tropical lowland evergreen rain forest
Tropical lower montane rain forest
Tropical upper montane rain forest
Tropical sub-alpine forest
Canopy height, emergent trees 24–45 m 15–33 m 1.5–18 m 1.5–9(15) m
Number of strata 3 2 1 (1)
Principal leaf size-class of woody plants Mesophyll Notophyll Microphyll Nanophyll
Non-vascular epiphytes Occasional Occasional to abundant
Often abundant Abundant
10
Hamilton, 2000). So in addition to rainfall, water droplets are captured directly from the low
cloud cover by condensation on the vegetation, strongly influencing the hydrology, ecology and
soil properties of these forests (Bubb et al., 2004). Other consequences of frequent cloud cover
are a reduced total solar radiation (Hamilton, 1995) but an increased insolation of UV-B light due
to the reflection from clouds (Flenley, 1995). All climatic conditions together are responsible for
the distinctive floristic and structured form of TMCF, e.g. small xerophyllic leafs, gnarled trunks,
distinctive species and a higher proportion of biomass as epiphytes.
The altitude at which cloud forests in tropical mountains occur, is influenced by the location. At a
greater distance from the equator, condensation will occur at lower altitudes due to lower average
temperatures (Bruijnzeel and Hamilton, 2000). At similar latitudes altitude will be influenced by
local effects such as the size and orientation of the mountain, sea surface temperatures and
exposure to the prevailing winds. The effect of the size of the mountain deserves special
attention. Cloud forests on small (isolated) mountains, especially on islands or in coastal areas,
generally occur at reduced altitudes in comparison with the altitude of cloud forest on larger
mountains away from the coast (Bruijnzeel and Hamilton, 2000; Bruijnzeel et al., 2011;
Bruijnzeel, 2001; Martin et al., 2011). This effect is called the ‘telescoping’, ‘Massenerhebung’ or
mass elevation effect. The first main reason to explain this phenomenon is that bigger mountains
have higher (overlying air) temperatures because their landmasses absorb more solar radiation.
The second main raison is the higher atmospheric moisture content of oceanic air, leading to
lower cloud formation.
Distribution
Few accurate data on the distribution of tropical montane forests exist but Kapos et al. (2000)
estimated an area of 3 257 275 km2 which included altitudinal ranges from 300 m to above 4 500
m. More effort has been put in the distribution of cloud forests. Global forest cover assessments
together with global elevation data and regionally specific altitudinal limits were used to estimate
potential as well as actual areas of TMCF (Bruijnzeel et al., 2011). The potential area, the entire
area between the specified altitudinal limits regardless of actual forest cover, was estimated
around 380 000 km² whereas the actual area was estimated at 215 000 km². The actual area
estimated in this way constitutes 1.4% of the world’s tropical forest area and 6.6% of the area
covered by TMF as defined above. An indication of the locations of TMCFs is given by a map
with more than 560 sites with confirmed cloud forest presence (Figure 4). Of all TMCFs 46%
can be found in Asia and northern Australia, 43% in the Americas and the Hawaiian archipelago
and 16% in Africa.
Another approach in which a hydro-climatic definition of tropical montane cloud forest was
used, yielded a much greater global extent of 2.21 M km² (Bruijnzeel et al., 2011). This can be
explained because fragmented areas of forest and sparse woodland at 500-m resolution were
included and a wider range of conditions and structural features, e.g. certain lowland forests that
are significantly cloud-affected, was covered.
Both approaches compared actual distributions with potential distributions, showing great losses
due to deforestation. The first approach suggests that ~56% of the original forest still remains
whereas the second suggests that ~45% of its original distribution still remains. TMCFs are
undeniably rare and threatened ecosystems.
11
Figure 4. Tropical montane cloud forest sites by UNEP-WCMC.
Biodiversity and ecosystem functions
Tropical montane forests have unique characteristics of biodiversity and high endemism (Aldrich
et al., 1997; Spracklen and Righelato, 2014). Many areas serve as refugia for species which are
endangered by the transformation or destruction of ecosystems at lower elevations. Furthermore
TMFs are much appreciated for the provision of hydrological services and freshwater supply. For
instance the montane forests of Kenya are known as “Kenya’s Water Towers”, yielding possibly
more than 15800 million cubic meters of water per year (Crafford et al., 2012).
2.3.2 Altitudinal transects
From a scientific viewpoint TMFs are interesting to study because the mountainous area in which
they grow, displays a rapid rate of change in environmental characteristics with relatively short
geographic distances (González et al., 2013). The responses of ecosystems on these
environmental gradients are adequately studied by means of altitudinal transects. To illustrate the
diversity of altitudinal transects studies in TMFs some examples are presented in Table 2.
Furthermore TMFs show recurrent broad-scale ecological patterns that can be detected with
reasonable power and contrasted between different regions. But caution is required in studying
environmental conditions and its influence on the vegetation: it is very important to distinguish
between environmental changes that are physically tied to meters above sea level and those that
are not generally altitude specific (Körner, 2007).
12
Table 2. Different biological and ecological altitudinal transect studies in tropical montane forests. LAI= leaf area index.
Topic Country of study site Source
LAI and stand leaf biomass Ecuador Moser et al. (2007) Microbial biomass, fungal biomass and microbial community structure
Ecuador Krashevska et al. (2008)
Forest structure and AGB Brazil Alves et al. (2010) Moth diversity Australia Ashton et al. (2016) Vascular plant species richness Malaysia (Borneo) Grytnes & Beaman (2006)
A first altitudinal driver mainly impacting biodiversity and its evolution is the available land area.
With increasing altitude the land area shrinks, narrowing opportunities for life in concurrent
fragmented climatic mountain ‘islands’. The habitat diversity and spatial isolation as a
consequence are also enhancing segregation of populations and thus potentially causing
speciation. Next to this global altitude-related phenomenon four major climatic changes are
associated with altitude:
a decreasing total atmospheric pressure and partial pressure of all atmospheric gases,
a reduction of the atmospheric temperature,
an increasing radiation under a cloudless sky and
a higher fraction of UV-B radiation at any given total solar radiation.
Environmental changes that are not generally altitude specific exert strong influences in TMFs as
well and they have introduced confusion in scientific literature on altitude phenomena (Körner,
2007). Examples of the latter are precipitation, wind velocity, hours of sunshine, geology and
human land use. However the first two are discussable since orographic precipitation and wind
speed often significantly rise with increasing altitude although they are also very dependent on the
mountain topography (Hodkinson, 2005). Data collected along altitudinal gradients will always
reflect the combined effect of regional peculiarities and general altitude phenomena but the
distinction is crucial when the results of different studies are compared and actual physical
conditions must be documented as much as possible.
Temperature, being closely and inversely correlated with elevation, has been suggested as the first
and foremost explanatory variable in changing ecological and biogeochemical processes along an
altitudinal gradient. Vitousek et al. (1992) even used elevation as a surrogate for mean annual
temperature on the Mauna Loa in Hawaii. In the free atmosphere the temperature decrease is
generally taken to lie between 5.5 and 6.5 °C for each km of ascent, although it can vary
according to local conditions, topography and meteorological circumstances (Anslow and Shawn
as cited in Hodkinson, 2005). Raich et al. (2006) discussed the effect of a decreasing atmospheric
pressure and partial pressure of CO2 on forest carbon cycling processes but argued that
elevational gradients provide a useful basis to identify the effect of decreasing temperature on
forest ecosystems.
The temperature decrease with increasing altitude has also been put forward to explain the
existing paradigm that with increasing altitude aboveground carbon decreases while belowground
carbon increases (Leuschner et al., 2013). The reduced carbon stocks at higher altitude are a
consequence of a reduced NPP which was for instance observed in studies from Kitayama and
Aiba (2002) and Waide et al. (1998). An increasing belowground carbon is for example found in
the studies from Dieleman et al. (2013), Kitayama and Aiba (2002) and Leuschner et al. (2007).
13
The effect of temperature on NPP could be directly or indirectly due to the effect of temperature
on nutrient availability, driven by the rate of decomposition and nutrient mineralisation (Myers,
1975). Moreover Fernández-Martínez et al. (2014) recently demonstrated the crucial importance
of nutrient availability to carbon cycling. This aspect is elaborated in the next section. Other
explanations for a reduced NPP that have been suggested are: persistent cloud cover, high wind
velocity and waterlogging (Bruijnzeel and Veneklaas, 1998).
2.3.3 Nutrient cycling and isotopes
Nutrient cycling is an important aspect that influences the physiognomy of TMFs. While the
carbon cycle has been discussed in section 2.2, the link with nitrogen (N) and phosphorus (P)
availability needs some further explanation. Additionally the role of some cationic elements will
be touched upon and the use of isotopic ratios in biogeochemical and ecological research will be
addressed.
Carbon is taken out of the atmosphere under the form of CO2 (by photosynthesis, see earlier) but
other nutrients necessary for plant growth are absorbed by the roots. Nutrients are subsequently
transported and incorporated in plant tissue. When dead plant material falls on the forest floor,
decomposition makes recycling of these nutrients possible. In lowland tropical rainforest the hot
and humid conditions allow for a rapid decomposition and due to the high demand from the
vegetation most nutrients do not remain in the soil for long (Jones et al., 2013). Thanks to this
rapid consumption nutrients are also concentrated near the soil surface. In other climates and at
higher altitudes temperature, sunlight and/or water are much more limited (or too abundant, e.g.
waterlogging), influencing nutrient cycling and plant growth (Jolly et al., 2005). Nutrient cycling
of a certain element is also dependent upon the availability of other nutrients. Any part in the
biogeochemical process in which availability of one or more nutrients constraints the rate at
which another nutrient cycles, is referred to as nutrient limitation (Townsend et al., 2011). The
most important macronutriens that sustain life on earth are N and P. Their cycling within natural
ecosystems is explained more detailed below. Information on these processes were largely
retrieved from two handbooks: Global Biogeochemical Cycles in the Climate System, edited by
Schulze et al. (2001) and Soil Conditions and Plant Growth, edited by Gregory and Nortcliff
(2013).
Nitrogen and phosphorus cycling
In natural ecosystems atmospheric N enters the soil predominantly through biological fixation by
free-living bacteria or bacteria living in association with N-fixing plants such as legumes
(Fabaceae). Dinitrogen gas (N2), the most abundant atmospheric N form, is reduced to ammonia
(NH3) and incorporated into amino acids for protein synthesis. Smaller quantities enter the
system directly under the form of NH3, ammonium (NH4+), nitrate (NO3
-) and particulate and
organic N. These depositions originate from a variety of sources, most noteworthy for TMFs:
biomass burning, e.g. Velescu et al. (2016). Forest fire in the system itself, on the other hand,
causes an output of N through volatilization.
The largest pool in the soil is found as nitrogenous compounds in the organic matter as a result
from the decomposition of plant material and microorganisms. This organic N is made available
for plants through mineralisation by microbes and soil fauna, resulting in the release of NH4+. On
the contrary, microbial immobilisation reduces the availability of N to plants. Thus the rates of
these processes strongly control the soil N cycle and govern N transfer to the wider environment.
The released NH4+ is converted to nitrite (NO2
-) and subsequently to NO3- in a process called
14
nitrification which is carried out by bacteria and fungi. NO3- is bio-available and highly mobile.
When it is not taken up by plants or microbes, NO3- can be lost through leaching in the water
and through denitrification in the air. Denitrification is the microbial reduction of NO3- to
gaseous nitric oxide (NO), nitrous oxide (N2O) and N2. Plants are known to take up organic N
forms but mostly take up NH4+ and NO3
-.
In grazed ecosystems a substantial amount of the N taken up by plants is returned in the form of
dung and urine, often dislocated from the area of uptake. This sometimes leads to profound
effects on nutrient dynamics and bioavailability. In forests this role is rather small and not
accounted for on an ecosystem level.
In contrast to atmospheric N inputs, those of P are very limited. P in natural ecosystems is
almost exclusively derived from soluble primary minerals originating from soil parent material. In
the soil P processes are controlled by
the form in which P occurs (organic or inorganic),
inorganic adsorption and desorption reactions,
dissolution and precipitation reactions and
mobilisation and immobilisation by the microbial community.
Recycling from dead plant materials (and dung and urine) as explained for the N cycle also
accounts for P. The detailed explanation of these processes however is out of the scope of this
thesis.
More relevant is the link between the availability of P and N and carbon cycling. Studies on
nutrient cycling in forests span more than 100 years and have been dominated by the role of N as
a limiting factor, especially in temperate forests (Attiwill and Adams, 1993). However in his paper
ca. 30 years ago Vitousek (1984) first discussed at length the idea that phosphorus specifically
limits productivity of many tropical rainforests. It is clear that this idea cannot be generally
applied for all moist tropical forests but his statement seemed to be correct for lowland forests
on older soils (Herbert and Fownes, 1995; Vitousek and Farrington, 1997). These soils are highly
weathered and P has been leached, resulting in the observed limitation. In montane forests, in
contrast, where soils are less weathered and nitrogen mineralization occurs at slower rates, it is
suggested by Tanner et al. (1998) (among others) that nitrogen, rather than phosphorus, limits
productivity. This hypothesis has been adopted a lot and has been confirmed by some
experiments although not all transect studies looking at N limitation in TMFs provided much
evidence (Fisher et al., 2013; van de Weg et al., 2009). While a lot of studies looked at limitation
of single nutrients and compared between ecosystems, recent attention shifted somewhat towards
studying multiple nutrients that could limit growth and acknowledge that even within an
ecosystem different nutrients could be limiting (Velescu et al., 2016).
Cationic elements
Mineral elements needed for plant growth and fecundity are taken up at the root surface from the
soil solution in their cationic forms. Kaspari et al. (2008) showed that their role in ecosystems
may not be overlooked when studying nutrient limitation and biogeochemical processes in
ecosystems.
15
Isotopic ratios
First the principles and terminology concerning isotopes in this thesis will be explained. The
isotopic ratio R is the molar ratio of a minor (heavier) to a major (lighter) isotope. The relevant
isotopic ratios in this study are 15N/14N for nitrogen and 13C/12C for carbon. The isotopic
composition δ is the normalized departure of R from R*, its value in a standard reference
material, so: δ=R/R* - 1. The reference materials for the isotopic ratios of N and C are
respectively air, so atmospheric molecular N which is by convention set at 0 ‰, and Pee Dee
Belemnite (PDB). From here on the isotopic compositions will be noted as δ15N and δ13C.
Due to fractionation processes, δ15N of plants or soils can be used to infer N cycling but this has
to be done with great caution since it responds on multiple drivers. Our understanding
concerning δ15N in plants and soil is still incomplete but has improved a lot compared to a
decade ago and is extensively reviewed by Craine et al. (2015b). I refer to this article for the full
explanation, but some important influences on δ15N are given here:
Plant δ15N is greatly influenced by the role of mycorrhizal fungi
Plant δ15N as well as soil δ15N are said to be influenced by the role of climate but this is
discussable
Soil δ15N changes occur with microbial processing
Different forms of N have different signatures of δ15N
Higher δ15N in hot/dry systems refers to an open N cycle, while smaller δ15N in cold/wet
systems refer to a closed N cycle
Soil δ15N changes among soil fractions
δ15N is higher when more N is available and decreases when N is limited
δ13C in plants has been used extensively in studies on the carbon-water balance of plants and
ecosystems, being effected by short-term as well as longer term environmental conditions (Seibt
et al., 2008). For instance with independent estimates of gas exchange or environmental
conditions it can be used as proxy for water use efficiency. Furthermore δ13C in soil organic
matter (SOM) is useful as a constraint in carbon cycle models, although our understanding of the
mechanisms leading to 13C enrichments in SOM still needs to be improved (Ehleringer et al.,
2000). Additionally δ13C values can be very valuable in geochemical research (e.g. Watanabe et al.,
(2000)).
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2.3.4 Albertine rift montane forests
The Albertine Rift montane forests are tropical and subtropical moist broadleaf forests of a
mountain chain with exceptional faunal and moderate floral endemism (WWF, 2011). The area is
a priority ecoregion for the World Wildlife Fund (WWF) and it supports many endangered
species such as the mountain gorilla (Gorilla beringei beringei) and the eastern lowland gorilla (Gorilla
beringei graueri). These afromontane forests, stretching over the Democratic Republic of Congo
(70%), Uganda (20%), Rwanda (6%), Burundi (3%) and Tanzania (1%), form a part of the
Albertine Rift that extends from the northern tip of Lake Albert to the southern tip of Lake
Tanganyika. Containing the highest number of vertebrate species on the African continent, the
Albertine Rift is one of the most important regions for conservation in Africa (Plumptre et al.,
2007). Top priority sites for conservation in the Albertine Rift based upon the numbers of
endemic and globally threatened species are shown in Table 3.
Table 3. Top priority sites for conservation in the Albertine Rift based upon the paper by Plumptre et al. (2007); NP=National Park
Priority site Country
Virunga NP Democratic Republic of Congo Kahuzi Biega NP Democratic Republic of Congo
Itombwe Massif Democratic Republic of Congo
Bwindi Impenetrable NP Uganda Nyungwe NP Rwanda
Next to its biodiversity, the Albertine Rift montane forests are also very important for the
delivery of other ecosystem services, especially to the people living in the area. However despite
the forests’ high importance, much of them remain poorly studied. Furthermore civil wars and
international conflicts in the region hampered research and tourism development in the past
decades and in more peaceful areas the high density of people (up to 500-600 km-2) threatens
nature through farming activities and poaching.
17
3 Research questions
Altitudinal transect studies in TMFs concerning biogeochemical processes such as carbon storage
are relatively numerous in America and Australasia but largely lack in Africa. Yet the literature
review clearly illustrated its importance. This study aimed to estimate carbon stocks along an
altitudinal gradient in a montane tropical rainforest in Central-Africa while also assessing forest
structure and nutrient cycling to a certain extent. A particularly interesting approach was applied
by including the isotopic compositions of nitrogen and carbon. More specifically the study tried
to determine significant differences between altitudinal strata and examined if linear trends could
be found with increasing altitude. At its core this study can be brought to one question: how do
the carbon stocks change with the altitude and can a trend in nutrient availability be found?
Figure 5. Images of Nyungwe National Park. Upper: a landscape at the top of Bigugu, the highest mountain of the national park, with large heather vegetation occurring above the treeline; left corner: forests on hillsides at midlevel altitudes; right corner: a closer look at the vegetation at lower to midlevel altitudes (the pictures were taken in August at the end of the dry season)
18
4 Materials and methods
The study for this thesis is based on data collected during a field campaign in Nyungwe National
Park in August and September 2015.
4.1 Study area
Nyungwe National Park is located in southwestern Rwanda (2°17' – 2°49' S, 29°03'– 29°29' E)
(Figure 6,a) (Gharahi Ghehi et al., 2012). The National Park covers an area of about 1000 km²
with altitudes ranging from 1485 to 2950 masl. (Figure 6,b). In Nyungwe vast stretches of forest
are interrupted by two large swamps. It was gazetted as a forest reserve in 1933 and became a
National Park in 2004 (About Rwanda, 2016). Together with the contiguous Kibira National Park
in neighbouring country Burundi it forms one of the largest blocks of montane tropical forest in
Africa (Plumptre et al., 2002).
Figure 6. Location of the Nyungwe forest in Rwanda. (a) seven climate stations around the forest are presented by red stars, the recent climate station in the Nyungwe forest is presented by a yellow star; (b) elevation map; (c) parent material map of the Nyungwe forest: Q = quartzite; QI = quartzite intercalated with schists; IQm = quartzite intercalated with micaschists; GQ = granitic and quartzitic rocks; G = acid rocks (granite); Gm = micaceous acid rocks (granitoide); Im = micaschists; I = schists (figure adopted from Gharahi Ghehi et al. (2012)).
19
Climatologically Nyungwe is characterised as a humid tropical climate with a major dry season
between July and August and a small dry season occurring in December and January (Cizungu,
2014). Recent weather data for the surrounding region of the National Park are not easily
available but based upon seven climate stations in the vicinity of Nyungwe (Figure 6, a) useful
data for the period from 1974 to 1989 is at hand (Gharahi Ghehi et al., 2012). Annual
precipitation ranged from 1308 to 2071 mm with an average of 1660 mm and average annual
temperature was 17°C, with small seasonal variations. The average monthly minimum was 11°C
and the average monthly maximum 23°C. However one automatic climate station has been
established inside the forest near Uwinka visitor centre at 2465 m.a.s.l. (2°28′S, 29°12′E) in 2007
(Figure 6, a), demonstrating an average annual mean temperature of 14.5°C and an annual
precipitation of 1824.7 mm.
The main parent materials from which soils in Nyungwe have been developed are schists,
micaschists, quartzitic schists and granites (Figure 6, c). Micaschists are dominant in the eastern
part of the forest, mostly exceeding 2000 m.a.s.l., whereas schists are dominant in the western
part, featuring a larger area of lower altitudes. Dominating soil groups as defined by the World
Reference Base for soil resources (WRB; FAO, 2014) are Acrisols, Alisols and Cambisols (Van
Ranst et al., 2000a, 2000b, 2000c, 2000d). In addition to these Luvisols, Podzols, Regosols,
Leptosols and Ferralsols are associated with sloping terrain in Nyungwe and Histosols and
Gleysols occur in the swamps, small valleys and depressions.
In terms of biodiversity Nyungwe contains one of the most important montane tropical
rainforests in central Africa, especially regarding endemicity (Plumptre et al., 2002, 2007). For
instance 1105 different plants and more than 230 species of trees have been found in Nyungwe
and 13 species of primates are known to inhabit the forest, including chimpansees (Pan troglodytes
schweinfurthii) and the rare owl-faced guenons (Cercopithecus hamlyni). Furthermore the National
Park is also one of the most important sites for bird conservation in Africa with a total of 280
bird species of which 26 are endemic to the Albertine Rift. Concerning the distribution of this
biodiversity surveys by Plumptre et al. (2002) clearly showed that both species richness and
species diversity for most taxa are significantly higher in the western part of the forest than in the
eastern part. Few is known about other ecosystem services of Nyungwe National Park but as a
part of the Congo-Nile watershed, is it definitely also an important water catchment area,
delivering water for about 70% of Rwanda (USAID & WCS 2013).
4.2 Altitudinal transect choice and site selection
Since there was ample prior knowledge on the physical factors directly related with altitude, this
study tried to span the entire altitudinal range of Nyungwe forest in 4 more or less evenly
distributed strata (Table 4, Figure 7). Site location was selected on a certain altitudinal level based
on practical feasibility to work as well as basic forest characteristics. From a practical viewpoint
for instance slopes could not be too steep and too far from hiking trails or roads. Regarding the
forest characteristics we searched for relatively undisturbed old-growth forests with full canopy
closure. The latter was rather difficult, because a great part of Nyungwe forest seemed to show a
quite open structure, with large patches of open terrain dominated by a native but invasive liana,
Sericostachys scandens. As a consequence some plots contain small open places.
20
Table 4. Basic stratum characteristics. Targeted altitudes where chosen as rough guidelines for the plot altitudes based upon an approximate elevation map of Nyungwe National Park. The number of tree species per stratum was retrieved from Taveirne (2016) who elaborated on functional diversity in the same study site. PSP stands for permanent sample plot. Refecence soil groups were retrieved from Van Ranst et al. (2000a, 2000b, 2000c, 2000d).
Stratum 1 Stratum 2 Stratum 3 Stratum 4
PSP numbers 9,10,11,12,13 6,7,17,18,21 19,20,22,23,24 1,2,3,4,5 Targeted altitude (masl) 1800 2200 2500 2800 Mean altitude (masl) 1760 2200 2512 2865 Min. and max. altitude (masl) 1659, 1835 2141, 2293 2456, 2557 2767, 2937 Number of tree species 46 37 20 15 Reference soil groups (WRB) Cambisol Cambisol,
Alisol Cambisol,
Alisol Regosol, Leptosol (1,2)
Cambisol (3,4,5)
Figure 7. Map of the altitudinal transect in Nyungwe National Park with the dots representing the locations of the PSPs and the boxes giving details of the measured altitude per plot. The grey star is the location of the visitor’s center Uwinka (adopted from Taveirne (2016)).
21
4.3 Establishment and measurement of permanent sample plots
To make sure that the results can be compared with other studies, the establishment and
measuring of the permanent sample plots (PSPs) was done following a modified worldwide
standardised method drawn up by the Amazon Forest Inventory Network (RAINFOR, Y. Malhi
et al., 2002). The 2016 revised version of this protocol, the ‘Field Manual for Plot Establishment
and Remeasurement’ (Phillips et al., 2016) is available on the RAINFOR website.
Site conditions caused restrictions in
following the protocol and some adoptions
were made. Due to the extreme topography
an area smaller than the standard size of 1 ha
recommended by RAINFOR was chosen.
On the selected sites square plots of 40x40m
were accurately delineated by compass, for
the ease of working with the sides parallel
and perpendicular to the slope. The plot
consisted of four square subplots with sides
of 20m (Figure 8) and the slopes of all sides
where measured with a laser rangefinder
(Forestry Pro, Nikon, Japan). The plots were
made permanent by digging out the corners
and placing a brick in the centre. On every
corner and in the centre coordinates and
altitude were recorded by GPS (GPSMAP®
64s, Garmin, Taiwan).
Within these plots all living trees with a minimum diameter at reference height of 0.1 m were
tagged and identified by a local botanist after which diameters were measured. Each tree was also
given a status code according to the RAINFOR protocol (Appendix A). A tree was included in
the plot if more than 50% of the root system was situated within the plot boundaries. The
reference height and the point of measurement (POM) were 1.3 m, also referred to as diameter at
breast height (DBH), and tags were nailed in the stem at 1.6 m, or 0.3 m above the POM. If 1.3
m was not used as the POM in order to avoid abnormalities such as trunk deformities or buttress
roots, the height of the alternate POM was carefully recorded. The height at which diameters
were measured is not the vertical height above the ground but the straight line distance along the
trunk. On slopes the downhill side of the tree was used to measure 1.3 m and for leaning trees
this length was measured along the side of the stem closest to the ground. In case of climbing
vines or roots from other plants growing against the stem at DBH the measuring tape was passed
underneath. In the rare cases where lianas or stranglers were to firmly attached to the tree stem,
the diameter was estimated by holding the tape straight to the stem at the POM to optically
determine an approximation. If a tree had multiple stems, all stems with a DBH greater than or
equal to 0.1 m were measured, tagged and recorded. Furthermore both the total tree height and
tree bole length (the height until the lowest big branch) were measured with the Nikon Forestry
Pro for approximatively 20 % of the trees in each plot. We aimed to cover all diameter classes.
Figure 8. The permanent sample plot setup. A, B, C and D indicate the four corners, E is the plot centre. Subplots are indicated with the roman numbers I to
IV. A was always located in the northwest of the plot.
22
Lianas were tagged and measured if its diameters were greater than or equal to 0.1 m at any point
within 2.5 m of the ground, even if they had a diameter smaller than 0.1 m at 1.3 m from the
base. Each climbing liana stem that was in accordance with this criterion and that was separately
rooted, counted as one individual plant. Diameters were measured at three points:
at 1.3 m along the stem from the principal rooting point
at 1.3 m vertically above the ground
at the widest point on the stem within 2.5 m of the ground, including any deformity
The lianas were tagged 0.3 m above the POM, 1.3 m vertically above the ground. If lianas or trees
were clearly elliptic in cross-section at the POM, the stems were measured both conventionally
(with the measuring tape wrapped around the whole stem) and by measuring the linear distance
of the maximum dimension and the linear distance perpendicular to the latter after which the
geometric mean was calculated.
4.4 Sampling and processing of soil, litter, wood and leafs
At 5 places within each plot (1 near the centre and 4 randomly chosen in each subplot) the
mineral soil was sampled by drilling to a depth of 100 cm using a soil auger (Eijkelkamp). This
depth was sectioned into 6 segments: 0-5, 5-10, 10-20, 20-30, 30-50 and 50-100 cm. For each
depth the 5 samples were adequately mixed and the composite samples, just like all other samples
described hereafter, were sun-dried in the field as much as possible.
At 3 places within each plot, randomly chosen in 3 different subplots, litter, the O-horizon
(consisting of humus and roots) and the upper mineral soil - now for bulk density measurement -
were sampled. To collect a sample of the litter and the O-horizon, a square metal framework of
25x25 cm was used whereas the soil sample of the upper mineral layer was taken by means of a
100 cm³ steel Kopecky ring (height 5 cm, diameter 5.3 cm, Eijkelkamp).
Per stratum the most abundant tree species, covering 95% of the basal area, were determined and
used to sample leaves and wood cores. Per tree species at least 10 leaves of 3 randomly chosen
individuals in each stratum were collected by a climber. Preferably leaves receiving full sunlight
were collected but if this was not practically feasible, shade leaves were taken instead, while
carefully making notes of these light conditions. Wood samples were collected using an
increment borer (16”, Haglöf, Sweden) both on the uphill and on the downhill side of the trees,
approximately at DBH. Leafs were dried, stored and transported in paper envelopes whereas
paper straws were used for the wood core samples.
The soil samples of the upper mineral layer, needed to determine the soil bulk densities, were
dried and weighed in the lab of the Catholic University of Bukavu (Université Catholique de
Bukavu, UCB), Democratic Republic of Congo. All other samples were transported to Belgium
and dried for at least 48 hours in an oven at 60°C. After the fragmentation layer samples were
dried, they were sieved through a 1 cm screen, to roughly separate the extensive root system3
from the humus. To homogenize the samples, a planetary ball mill (PM 400, Retsch, Germany)
was used for the soil samples and an ultra centrifugal mill (ZM 200, Retsch, Germany) was used
for the litter, leaves, humus and roots with a diameter < 1 cm.
3 In all strata the fragmentation layer consisted of humus and an extensive system of fine roots that were highly connected
23
Wood samples were used to determine the wood density with the water displacement method.
First all samples were accurately weighed and secondly they were submerged in a small water
reservoir on a precision balance. When fully submerged the weight of the displaced water was
calculated, being equal to the volume of the wood sample.
Of the soil, humus, roots, litter and leaves subsamples were taken and carefully weighed on a
precision balance. These subsamples where then analysed with an elemental analyser (ANCA-SL,
SerCon, Crewe, UK) coupled to an isotope ratio mass spectrometer (20-20, SerCon, Crewe, UK)
(EA-IRMS). Through the former carbon and nitrogen concentrations were obtained, through the
latter δ15N and δ13C were obtained. Additionally soil samples for a depth of 0-5, 5-10, 10-20 and
20-30 cm were weighed and mixed to obtain samples for a depth of 0-30 cm for each plot. These
composite samples were consequently analysed for P concentrations and exchangeable cations.
Total P and cation concentrations were measured at the Forest & Nature Lab (ForNaLab,
UGent) in Gontrode. Total P was measured colorimetrically with malachite green after
destruction in acid while atomic absorption spectroscopy after extraction with barium chloride
was used to determine K, Al, Na, Ca and Mg concentrations. Bio-available P was measured using
spectrophotometry at a wavelength of 650 nm after extraction with hydrochloric acid and anion
exchange membrane (AEM) strips. The lab protocol for this analysis is given in Appendix B.
4.5 Planimetric plot areas
Per subplot the distance of each side on a
horizontal plane was calculated by
multiplying the actual distance (20m) with
the cosine of the slope angle θ, which was
measured in the field (Formula 1). Since the
projection of a square on a horizontal plane
has to be a parallelogram, the opposite sides
need to equal. Consequently the averages of
the opposite sides on the horizontal plane
were taken. To calculate the surface area of
the parallelogram (Formula 2), i.e. the
estimated planimetric area of a subplot, the basis now has been determined but the height is still
lacking. Therefore the mean height, i.e. the altitudinal difference between two points, of the
diagonal in the square subplot was calculated using Formula 3 on all sides, after which the
projection of the diagonal could be calculated using the Pythagorean theorem (Formula 4). After
calculating the angle α between the two sides of the parallelogram using the law of cosines
(Formula 5), the height was easily obtained (Formula 6) and the surface area of the parallelogram
could be calculated. The planimetric areas of the four subplots (parallelograms) were summarized
to obtain a value for the whole plot. Slope values for the whole PSPs were calculated using the
following formula: slope angle=arccos(planimetric area/surface area).
Formula 1 Lp=cos(θ)*20 Formula 2 areap=bp*hp Formula 3 h=sin(θ)*20 Formula 4 Dp=√(20²+20²-hD²) Formula 5 α=arccos((bp²+zp²-Dp)/(2*bp*zp)) Formula 6 hp=sin(α)
Table 5. Formulas needed to calculate an estimation of the planimetric plot areas. Subscript p refers to the parallelogram; subscript D refers to the diagonal; b, z and h are respectively the basis, adjacent side and height.
24
4.6 Estimation of carbon stocks
4.6.1 Aboveground carbon
As a first step in estimating aboveground carbon (AGC) in the plots, the improved pan-tropical
allometric relation for aboveground biomass (AGB) of individual trees by Chave et al. (2014) was
used on all measured trees. Subsequently these values were divided by 2, to obtain an estimation
of the AGC per tree, and summarized per plot. The formula by Chave et al. (2014) provides the
most accurate general estimation of AGB in tropical trees at hand. It relies on trunk diameter
(DBH), total tree height (H) and wood specific gravity (or wood density, ρ):
𝐴𝐺𝐵𝑡𝑟𝑒𝑒 = 0.0673 ∗ (𝜌 ∗ 𝐷𝐵𝐻2 ∗ 𝐻)0.976
where ρ is in g cm-3, DBH in cm and H in m. All DBH’s in the plots were measured in the field,
but measurements of all total tree heights and wood densities would have been too time-
consuming. Tree heights of the lacking trees were therefore determined using 16 published
diameter-height relationships (Table 6). First non-linear least-square estimates of the parameters
(a,b and c) were performed. Secondly the best relationship per plot was chosen according to the
Akaike Information Criterion (AIC). Consequently the lacking height values for the remaining
trees were estimated.
Table 6. Height-diameter models used for the performance test and total tree height estimates.
6 forest structure variables are summarised per stratum in Table 8 and visualised in Figure 9.
Dominant tree height, here defined as the average of the three highest trees measured in each
plot, was taken as a proxy for canopy height and decreased 1.5 fold from stratum 1 to stratum 4.
The average diameter at breast height and the tree basal area also showed a decreasing trend,
however with lower significance (0.01<p<0.05 in contrast to p<0.001 for dominant tree height).
Remarkably, no significant differences were found for these variables between the two lower
strata and the two higher strata. Slenderness and stand basal area displayed no trends or
significant differences between any strata whereas the stem density showed an increase from an
average of 436 trees per ha in stratum 1 to 773 trees per ha in stratum 4. Values varied strongly
between plots of the same stratum and a significant difference was only found between stratum 2
and 3. Additionally Taveirne (2016) also reported an increase in tree dominance at the higher
strata. Dominant tree species and their share per stratum are given in Table 9.
27
Figure 9. Average forest structure variables per PSP were plotted against the altitude. When significant (p<0.05) linear regressions were visualised and R² and p-values were given. (a) Dominant tree height in m; (b) Diameter at breast height in cm; (c) Slenderness, the ratio of tree height over DBH in m cm
-1; (d) Tree basal area in m²; (e) Stand basal area in m² ha
-1; (f)
Stem density, the number of trees per ha.
Table 8. Summary of the chosen variables that quantify the forest structure per stratum. DBH=diameter at breast height; slenderness=tree height/DBH; TBA=tree basal area; BA=(stand) basal area; KW gives the significance of the Kruskal-Wallis test: * = p<0.05, ** = p<0.01, *** = p<0.001; the letters between brackets indicate the results of the Mann-Whitney U test.
Table 9. Tree species dominance per stratum. Data retrieved from Taveirne (2016).
The carbon stocks in ton ha-1 per PSP were estimated according to the methods described in
section 4.6. The Kruskal-Wallis test indicates significant differences for AGC and root carbon.
AGC decreases from stratum 2 to stratum 4. By contrast, there is a significant (p<0.05) increase
in C in the roots with rising altitude (Table 10). The estimates for AGC and BCG in ton ha-1 per
PSP were plotted relative to the altitude. Figure 10 shows the AGC estimations and demonstrates
a slight, although just not significant, declining trend in carbon. However, BGC estimations do
not show a correlation to altitude (Figure 12). Nevertheless Table 11 elaborates in more detail on
the soil C in the different increment layers and here a significant increase is seen in the 10-20cm
layer from stratum 2 to stratum 4. The table also demonstrates a decrease of C with depth. The
soil C values are also visualized in Figure 13 for three different depth increments (0-5cm, 10-
20cm and 50-100 cm). A strong correlation is shown with in the first depth with an R2 of 0.241.
The carbon stocks of the humus layer roots are visualized in Figure 11. A highly significant
regression shows that the ton carbon per hectare in the roots correlates to the altitude with an R2
of 0.444. The carbon stocks for the O-horizon, litter and BGC from 0-30 cm show no trends and
are given in Appendix C.
Table 10. Summary of the estimated carbon stocks per stratum in ton ha-1
(mean ± SD). KW gives the significance of the Kruskal-Wallis test: * = p<0.05, ** = p<0.01, *** = p<0.001; the letters between brackets indicate the results of the Mann-Whitney U test.
Table 11. Belowground carbon in g kg-1
for all depth increment layers (mean ± SD). The letters between brackets indicate the results of the Mann-Whitney U test.
KW Stratum 1 Stratum 2 Stratum 3 Stratum 4
AGC ** 120.88 ± 10.82 (a) 178.80 ± 26.74 (b) 98.50 ± 35.98 (ac) 89.49 ± 9.60 (c) C in Litter n 3.80 ± 0.68 3.87 ± 0.62 3.46 ± 0.71 3.24 ± 0.55 C in O-horizon n 28.81 ± 12.60 (a) 48.43 ± 10.50 (ab) 52.21 ± 19.08 (b) 44.24 ± 19.30 (ab)
Figure 10. Aboveground carbon estimations per PSP in ton ha-1
for each plot-altitude. A linear regression was tested and showed a decreasing trend with an R² of 0.148 but was just not significant at p=0.05.
Figure 11. Carbon stocks of the sampled roots in the humus layer in ton ha-1
per plot. The linear regression had an R² of 0.444 and was highly significant.
30
Figure 12. Belowground carbon estimates per PSP in ton ha-1
plotted against altitude. No significant linear trend could be found.
Figure 13. Belowground carbon in g kg-1
for each PSP plotted against altitude for three different depth increment layers: 0-5 cm (red triangles), 10-20 cm (green circles) and 50-100 cm (blue squares). A linear regression was visualised (with the R² and p-value) as p < 0.05.
31
5.4 Litter, fine roots and the O-horizon
The following paragraph summarizes the findings of Tables 12, 13 and 14. These tables show the
litter layer, the roots and the O-horizon per stratum regarding the succeeding four variables:
weight (g), C/N ratio, δ15N (‰) and δ13C (‰).
Only the mass of the roots differs significantly between the strata and more than triples with
altitude. The C/N ratio differs significantly for both the litter layer and O-horizon. Both δ15N
and δ13C show significant differences between strata. While δ15N demonstrates a decrease, δ13C
shows an increase with the altitude. Linear regressions are shown in Figures 14 and 15.
Figure 14 shows a significant linear increase of C/N ratio with altitude for the foliage and litter
layer, with a respective R2 of 0.825 and 0.209. However, no significant linear trend is found for
the O-horizon and roots. The C and N concentrations with changing altitude are plotted in
Appendix D.
Table 12. Summary of the chosen variables that quantify properties of the litter layer per stratum (mean ± SD). Weight (g); C/N ratio; δ
15N in ‰; δ
13C ‰; KW gives the significance of the Kruskal-Wallis test: * = p<0.05, ** = p<0.01, *** =
p<0.001; the letters between brackets indicate the results of the Mann-Whitney U test
Table 14. Summary of the chosen variables that quantify properties of the roots per stratum (mean ± SD). Weight (g); C/N ratio; δ
15N in ‰; δ
13C ‰; KW gives the significance of the Kruskal-Wallis test: * = p<0.05, ** = p<0.01, *** =
p<0.001; the letters between brackets indicate the results of the Mann-Whitney U test.
32
Figure 14. C/N ratios of the foliar, litter, O-horizon and root layer along an altitudinal gradient. (a) foliar C/N; (b) litter C/N; (c) O-horizon C/N; (d) root C/N. Linear regressions were visualized and R
2
values were given when p<0.05. (data for foliar CN was provided by (Taveirne, 2016))
Figure 15. δ15
N and δ13
C of the litter, O-horizon and roots in ‰ for different altitudes. (a) litter δ15
N in ‰ (b) litter δ
13C in ‰ (c) O-horizon δ
15N in ‰ (d) O-horizon δ
13C in ‰ (e) root δ
15N in ‰ (f) root δ
13C
in ‰. Linear regressions were visualized and R2
values were given when p<0.05.
33
5.5 Mineral soil
Table 15 shows a significant difference between stratum 3 and stratum 4 for total P
concentration. No further significances are given. Soil N also does not significantly differ
between the different strata. However, a decrease in N concentration with soil depth can be
observed (Table 16).
Table 17 and Table 18 display results on the isotope distribution at different soil depths. Table 17
shows significant differences in δ15N (‰) for different depths per stratum, with a high
significance (p<0.01) for 20-30 cm. Except for the 30-50 cm layer, a decrease with altitude can be
observed starting from stratum 2. There are only significant differences in δ13C (‰) for the 30-50
cm and 50-100cm layers. The Mann-Whitney U test did show significant differences between
some strata except for the 20-30 cm layer (Table 18). However, the 0-5 cm layer does show a
significant linear regression, with slightly increasing δ13C for rising altitudes (Figure 18).
Figure 16 visualizes the belowground N (g kg-1) in three different soil layers, similar to
belowground carbon in Figure 13. It shows a significant linear increase of N in the first layer as
the altitude rises, with an R2 of 0.166. The bioavailable P and total amount of P are both plotted
against altitude in Figure 17, but both do not demonstrate any trends. Finally, Figure 18 shows
the δ13C and δ15N in ‰ for a soil depth of 0-5 cm. A slight linear increase of δ13C with altitude
can be seen, accompanied by an R2 of 0.172. δ15N on the other hand, displays a linear decrease
with rising altitude and an R2 of 0.635. Appendix E visualizes the δ13C and δ15N in ‰ for all soil
depths separately.
Table 15. P and micronutrients (mg kg-1
) per stratum (mean ± SD). Presin=Bioavailable phophorus; Ptot=Total phosphorus; Mg=Magnesium; Ca=Calcium; Na=Sodium; Al=Aluminum; K=Potassium; KW gives the significance of the Kruskal-Wallis test: * = p<0.05, ** = p<0.01, *** = p<0.001; the letters between brackets indicate the results of the Mann-Whitney U test.
for each PSP plotted against altitude for three different depth layers: 0-5 cm (red triangles), 10-20 cm (green circles) and 50-100 cm (blue squares). A linear regression was visualised (with the R² and p-value) as p < 0.05.
Table 17. Summary of the δ15
N in ‰ in the soil at different depths per stratum (mean ± SD). KW gives the significance of the Kruskal-Wallis test: * = p<0.05, ** = p<0.01, *** = p<0.001; the letters between brackets indicate the results of the Mann-Whitney U test.
C in ‰ in the soil at different depths per stratum (mean ± SD). KW gives the significance of the Kruskal-Wallis test: * = p<0.05, ** = p<0.01, *** = p<0.001; the letters between brackets indicate the results of the Mann-Whitney U test.
or substantial reductions in nutrient uptake rates of the roots because carrier activity in the
plasma membranes in temperature sensitive (Leuschner et al., 2007b).
6.3.1 Underestimated AGC in the lower strata
The AGB in the first stratum was significantly lower than in the second stratum. This could be an
effective representation of the reality or this could be an underestimation. If really lower, this
could be due to soil fertility, steeper slopes (the steepest slope was in the lowest stratum) or
because the lower stratum was near a village, so more accessible to illegally cut wood. However it
is also possible that an underestimation was made because too few large diameter trees were
measured. Often about 30 to 40 % of the AGB can be found in large-diameter trees (DBH>70
cm) (Brown and Lugo, 1992; Brown et al., 1997). We found 25 trees with a DBH>70 and of
those 4 were measured. The assigned values were always much lower than the measured values,
up to a difference of 24 m. The mean total tree heights are plotted in Figure 19 and the mean
values ± SD are given in the caption with the p of the kruskal wallis test and the letters indicating
the Mann whitney u test. It is assumed that the mean values of the first strata are actually higher.
Figure 19. Mean total tree heights per plot 13.03 ± 0.58 (ab), 13.68 ± 0.42 (a), 12.15 ± 0.88 (b), 12.27 ± 0.97 (b) (KW: p=0.02.
6.4 Mineral soil nutrient concentrations along an altitudinal gradient
N is a measure for organic material and this demonstrated an increase in the first 5 cm of the soil,
consistent with soil C. Total P is a measure for weathering and no significant trend was found.
Neither did the cations show any trends and neither did bio-available P show a trend, indicating
that P was not limiting.
38
6.5 How do the isotopic compositions change with altitude and what do they tell us?
The most significant altitudinal trends to be found in this thesis resulted from the isotopic
analysis. δ15N decreased significantly with altitude in the litter layer, the O-horizon, the roots and
in all depth increment layers of the soil. In addition this trend was also detected in the canopy
foliage by Taveirne (2016). Different interpretations to explain variations in δ15N of plants and
soil exist and have been reviewed by Craine et al. (2015b). Since it is a single response variable
with multiple drivers no fully conclusive answer can be given. Yet several results point in the
same direction. A possible explanation for the observed trend could be that the higher values of
δ15N are due to a more ‘open’ nitrogen cycle at higher altitudes, with higher N availability and
greater losses via fractioning pathways, while the lower values indicate a more closed N limiting
cycle at higher altitudes. This is probably indirectly caused by the decreasing temperature.
Increasing N limitation from lowland to montane tropical forest has been stated by many (e.g.
Tanner et al. (1998); Townsend et al. (2008)). The hypothesis concerning ‘open or closed N
cycles’ was also confirmed by Martinelli et al. (1999) who compared δ15N between temperate and
tropical forests. Craine et al. (2015a) however, state that soils in warmer ecosystems possibly have
soil organic matter with higher δ15N values as a result of greater decomposition of organic matter
and/or greater concentrations of clay. The latter is then responsible for a greater share of their N
in mineral-associated organic matter instead of having a greater proportion of N being lost to
fractionating pathways. Next to this protection of mineral association, δ15N in soil organic matter
is possibly enriched by microbial processing. The higher N availability in a lot of low-altitude
ecosystems may also reduce the share of N lost in gaseous forms relative to leaching (Perakis et
al., 2011). Nevertheless in favour of the first hypothesis Rütting et al. (2015) recently found very
high N losses at the altitude of the lowest stratum in the same study site, Nyungwe Forest.
Furthermore the counter-arguments may be valuable in discussing the trend in δ15N in the soil
and O-horizon but a decreasing trend was also found in foliar δ15N which has been shown clearly
to reflect N availability (e.g. Choi et al. (2005); Garten and Van Miegroet, (1994)). At last, there
are still other results indicating a shift to a more closed N cycle and N limitation at higher
altitudes:
the increased C:N-ratio in leaves and litter (more C per unit N is assimulated), and
a shift towards more nutrient conservative forest communities with increasing altitude as
shown by Taveirne (2016).
δ13C shows a clear highly significant increasing trend in the litter, the O-horizon and the roots but
a weak trend in the first increment layer of the soil (0-5 cm) and no trends in deeper layers. A
general increase of δ13C with increasing altitude has been found in plants all over the globe
(Körner et al., 1988) and is probably caused by changing partial pressures of O2 and CO2 and
changing temperatures (Körner et al., 1991). The long term effect of the environment, i.e. the
altitudinal gradient, led to an adapted leaf size and structure which altered the mesophyll
conductance and explains the observed trend (Seibt et al., 2008). However δ13C in plants is also
effected by short-term environmental conditions dependent on the evaporative demand but this
was not relevant for the present study.
39
7 General conclusion and future perspectives
All results pointed in one direction: with increasing altitude N limitation prevails and the N cycle
evolves from an open to a more closed cycle. With the limitation of N, productivity decreases,
resulting among others in a decrease of AGB. This is important in the light of future global
change. How will TMFs respond on a rising temperature, changing weather patterns and an
increased reactive N deposition? I recommend to recensus the PSPs in Nyungwe in 10 years to
evaluate the evolutions.
40
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9 Appendices
A. RAINFOR field work database codes for living trees
a = alive normal
b = alive, broken stem/top &resprouting, or at least phloem/xylem. Note at what height stem is broken.
c = alive, leaning by ≥ 10%
d = alive, fallen (e.g. on ground)
e = alive, tree fluted and/or fenestrated
f = alive, hollow
g = alive, rotten
h = multiple stemmed individual (each stem > 99 mm gets a number), always use with another code – e.g. if a tree is normal and with multiple stems, use ‘ah’, etc.
i = alive, no leaves/few leaves
j = alive, burnt
k = alive snapped < 1.3 m
l= alive, has liana >=10cm d on stem or in canopy
m=covered by lianas (note only in the case where canopy is at least 50% covered by lianas, even where no individual liana reaches 10 cm d)
n = new (recruit), always use with another code – e.g. if a tree is normal and new the code = ‘an’, if a tree is broken and new the code is ‘bn’, etc.
o= lightning damage
p= cut
q= bark loose or flaking off
s= has strangler
t = is a strangler
z = alive, declining productivity (nearing death, diseased etc.)
Note, tree status codes can be used together in whatever combination is necessary! Thus, for example, a multiple stemmed, leaning and broken tree would be coded bch.
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B. Lab protocol for soil bioavailable P
The resin P method is an adequate method that enables the extraction of biological available
forms of inorganic P (Pansu and Gautheyrou, 2006). It makes use of anion exchange membranes
(AEM). Before starting the method, it is very important to clean the AEM strips. Therefore, the
strips were washed 3 times with milli-Q® water (MerckMillipore, Darmstadt, Germany), making
sure all residual P, from possible previous uses, has been removed. Subsequently, the strips were
‘activated’, i.e. regenerated into the bicarbonate form by placing the strips into 0.5 M NaHCO3
(pH 8.5).
To measure the available P, 1 g of soil was weighed into a plastic jar containing 2 activated AEM
strips and 30 ml of milli-Q® water. This jar was shaken end-over-end for 16 hours at room
temperature. Consequently, the membranes were removed, cleaned with as little milli-Q® water
as possible to remove soil particles, placed into another plastic jar with 20 ml of 0.5 M HCL, and
shaken for another 16 hours. Subsequently, the strips were removed and the HCl desorption
solution was ready for P analysis.
As the desorption solution was too acid to measure with the auto-analyser, a phosphate
colorimetric kit (Sigma-Aldrich) was used to measure absorbances at a wavelength of 650 nm. In
the end bio-available P concentrations were determined via a calibration curve.
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C. Graphs of the additional carbon stocks
Figure 20. Carbon estimations per PSP n ton ha-1
for each plot-altitude. (a) BGC* is the belowground carbon in the top 30 cm (b) is the carbon in the O-horizon and (c) is the carbon in the Litter. No linear regressions and R
2 values are shown as
all p>0.05.
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D. Graphs of N & C in litter, O-horizon and roots
Figure 13. Concentrations of nitrogen and carbon in g g-1
. (a) and (b) are for litter, (c) and (d) are for the O-horizon and € and (f) are for the roots. Linear regressions were visualized and R
2 values were given when p<0.05.
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E. Graphs of δ13C and δ 15N in the soil at different depths
