Faculty of Bioscience Engineering
Academic year 2015 – 2016
Functional diversity study on an altitudinal forest transect in
Central Africa, Nyungwe National park, Rwanda
Cys Taveirne
Promotors: Prof. dr. ir. Pascal Boeckx & Prof. dr. ir. Landry Cizungu Ntaboba
Tutor: Marijn Bauters
Thesis submitted in the fulfilment of the requirements of the degree of
Master in Bio-Science Engineering
2
The authors and supervisors 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 from this thesis.
Ghent, June 2016
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.
Gent, Juni 2016
The promotors,
The author,
Prof. dr. ir. Pascal Boeckx
Cys Taveirne
Prof. dr. ir. Landry Cizungu Ntaboba
3
Aknowledgements
Writing now, I consider this thesis as an enrichment of my personal education and a good
impression of doing research into a scientific context. It all started with a field campaign in the
Tropics, continuing in the lab with the acquired data and finally summarizing everything in this
written paper. I was therefore accompanied by my colleague Dries Van der Heyden, with who I
shared all the adventures and challenges faced during this thesis as we were doing research in the
same forest. Above all, I would like to thank our tutor, Marijn Bauters. He was there from the
beginning, instructing my colleague and me on the field, helping us in the lab and providing the
necessary feedback in the writing process. Of course both our promotors, Pascal Boeckx and
Landry Cizungu Ntaboba, made this all possible by enabling us to do this research in Belgium and
Rwanda.
Next, I would like to thank all the persons who supported us during the field campaign in the
Tropics: James Kyalemaninwa, who drove us around everywhere, Fidelle, who knew almost every
plant in Rwanda while helping us with everything he could, Jean-Baptiste, who was there assisting
us from day one, despite he couldn’t understand a word of English nor French, ‘Sartier’, who
prepared our daily meal after a long day of work and of course all the other who were there to
support us full of enthusiasm.
I would also like to offer my thanks to Katja Van Nieuland who did all the crucial nutrient analyses
in the lab, Marie-leen Verdonck, who helped us during the field campaign, and last but not least the
two persons who read my thesis and gave my some useful advice, Lucie Fransen en Dirk Pottier.
4
Table of content
Aknowledgements ....................................................................................................................... 4
List of abbreviations..................................................................................................................... 7
Abstract / Samenvatting / Résumé .............................................................................................. 8
1.................................................................................................................................. Introduction
..................................................................................................................................................... 9
2........................................................................................................................Relevant literature
................................................................................................................................................... 11
2.1.
From forest to tropical mountain cloud forest .................................................................... 11
2.1.1.
Forests ....................................................................................................................... 11
2.1.2.
Tropical rainforest ...................................................................................................... 12
2.1.3.
Tropical Mountain Cloud Forest (TMCF).................................................................... 13
2.2.
Plant traits and functional diversity.................................................................................... 17
2.2.1.
Plant traits .................................................................................................................. 18
2.2.2.
Plant functional types................................................................................................. 22
2.2.3.
Functional Diversity.................................................................................................... 23
2.3.
Elevational transects ......................................................................................................... 26
2.3.1.
Introduction ................................................................................................................ 26
2.3.2.
Characteristics of elevational transects ..................................................................... 28
3.................................................................................................................Materials and methods
................................................................................................................................................... 30
3.1.
Experimental set-up .......................................................................................................... 31
3.1.1.
Site selection.............................................................................................................. 31
3.1.2.
Sample collection ....................................................................................................... 32
3.1.3.
Lab analysis ............................................................................................................... 33
3.2.
Data analysis..................................................................................................................... 34
3.2.1.
Taxonomical analysis................................................................................................. 34
3.2.2.
Functional diversity analysis ...................................................................................... 34
3.2.3.
Statistical analysis...................................................................................................... 35
4......................................................................................................................................... Results
................................................................................................................................................... 36
4.1.
Trait analysis ..................................................................................................................... 36
4.1.1.
Overview table ........................................................................................................... 36
4.1.2.
Correlation table......................................................................................................... 36
4.1.3.
Community Weighted Means ..................................................................................... 38
4.1.4.
Species-specific traits ................................................................................................ 39
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4.2.
Diversity analysis .............................................................................................................. 39
4.2.1.
Taxonomic diversity ................................................................................................... 39
4.2.2.
Functional diversity .................................................................................................... 40
5....................................................................................................................................Discussion
................................................................................................................................................... 44
5.1.
Functional tree traits response on elevational gradient..................................................... 44
5.1.1.
Functional leaf traits................................................................................................... 44
5.1.2.
Functional wood & whole plant traits ......................................................................... 46
5.1.3.
Species-specific traits ................................................................................................ 46
5.2.
Biodiversity response on elevational gradient................................................................... 47
5.2.1.
Taxonomic diversity ................................................................................................... 47
5.2.2.
Functional diversity .................................................................................................... 47
6................................................................................... General conclusions & recommendations
................................................................................................................................................... 51
7......................................................................................................................... List of references
................................................................................................................................................... 53
8...................................................................................................................................... Appendix
................................................................................................................................................... 62
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List of abbreviations
ARCOS: Albertine Rift Conservation Society
C: carbon
CO2: carbon dioxide
d13C: signature carbon isotope
d15N: signature nitrogen isotope
DBH: diameter at breast height
DNA: Deoxyribonucleic acid
FAO: Food and Agriculture Organization
FDis: functional dispersion indice
FDiv: functional divergence indice
FEve: functional evenness indice
FRic: functional richness indice
Ha: hectare
LA: leaf area
LNC: leaf nitrogen content on mass basis
LCC: leaf carbon content on mass basis
LNCa: leaf nitrogen content on area basis
LCCa: leaf carbon content on area basis
MASL: meters above sea level
N: nitrogen
PFT: plant functional type
PSP: permanent sample plots
RAINFOR: Rede Amazônica de Inventarios Florestais
RaoQ: Rao’s quadratic entropy index
SLA: specific leaf area
TH: tree height
TMCF: tropical montane cloud forest
UNFCCC: United Nations Framework Convention on Climate Change
UV: ultraviolet
WD: wood density
WUE: water use efficiency
WWF: World Wide Fund for Nature
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Abstract
The study of ecosystems and the mechanisms driving the processes behind it has been a subject
of long interest for scientists and, especially in times with the threat of global change, deserves
increased attention. In this paper, the ecology of a unique ecosystem, called cloud forests, on the
African continent was subjected to a functional diversity study along an elevational transect. It was
done on the basis of measurements in the national park Nyungwe in Rwanda, where we put out 20
permanent sample plots along a slope between 1700 m and 2950 m. An observed response from 9
out of 11 measured traits along the altitudinal transect, together with changes in several diversity
indexes, confirmed the altering environment towards more harsh conditions typically associated
with cloud forests. It could therefore be concluded there was a significant change in tree diversity
with decreasing niches and hence species towards the top, strongly suggesting an influence from
temperature, precipitation and an altered nitrogen-cycle. Thus, the use of functional diversity
indices can enhance ecological studies, especially when trait choice and efficient sampling method
could be improved.
8
1. Introduction
Forests always have had a central role in human society by supporting us in our subsistence.
Today’s ongoing changes in the world (like population growth, land-use intensification, global
change) however, have had an enormous impact on terrestrial ecosystems worldwide, resulting in
large-scale deforestations and degradation of remnant forests. Tropical forests have been mined
rather than managed for a very long time as they offer a wide range of economical interesting
products, thereby ignoring all the services a healthy well-managed system has to offer. These
services provide humans with both indirect - water regulation, erosion control and climate
modulation- and indirect –timber and non-timber products- economic values. Thereby we can’t
miss the more intrinsic ecological and social values (like biodiversity, recreation, cultural-historical
importance) and even the spiritual links encountered in many religions. Especially in the context of
global change, the importance of forests as carbon sinks, but as well as potential carbon sources,
has been more and more recognized and their role in carbon sequestration is being validated on a
global scale to point out their importance for the rising threats (Pan et al., 2011).
It is in this perspective ecologists try to understand the different mechanisms that drive the
species-rich combinations of a healthy and productive ecosystem. For decades, they have been
investigating population patterns assembled from common species pools in order to predict and
gather insight in specific community compositions. For a long time this was done on the basis of a
taxonomic system whereby the conclusions were relatively limited to the study sites and the
studies species, making it difficult to extend the conclusions to a larger scale. Therefore ecologists
came up with a more general system whereby a plant community is assessed as different groups
contributing to the ecosystem’s functioning instead of merely looking at all the species and their
phylogeny. These so called functional diversity studies focus on different plant traits and plant
strategies in order to understand crucial and viable set-ups that thrive the observed plant
assemblages. In addition, this pushes ecosystem research towards more general conclusions with
impact in a wider geographical and species range, and catalyzes an improved understanding on
how plant productivity and nutrient cycles vary among different systems (Diaz & Cabido, 2001). It
is important to note this is a more complementary system to the traditional taxonomic approach
than it is overriding. From both approaches different conclusions can be made, leading to a more
complete picture of the ecology of ecosystems.
The use of functional diversity has especially proven useful in tropical forests, which are among the
most biodiverse plant communities on the planet (Slik et al., 2015). The use of functional groups or
typology makes it possible to generalize the plant communities in terms of specific trait
combinations. An interesting part of these tropical forests are the shifts of forests along mountains
occurring in the landscape. It is already in 1805 that Von Humboldt discovered that mountains
have important influences on the biodiversity and ecosystem functioning thanks to their strongly
varying environmental conditions. This strong environmental gradient, on a relatively short
geographical range, renders as an open-air laboratory, which could greatly improve our knowledge
and understanding of future responses of forests on global change. Approximately a quarter of
Earth’s land surface is covered by mountains (Price et al., 2011), hosting at least one third of the
terrestrial plant species diversity and thereby making those tropical mountains extremely diverse.
Additionally, mountains have a significant role in the hydrological cycle of neighboring areas
9
because their increased elevation leads to an increase in precipitation. Now let this be one of the
most prominent characteristics of a specific kind of forest occurring at those tropical mountains,
called tropical mountain cloud forests. These ecosystems are known for their cloudiness, which
has next to the altered sunlight irradiance, a great influence on their water balance and the
surrounding areas.
It is in this context a thesis study was done to research the functional diversity along an elevational
transect in a tropical mountain cloud forest in Rwanda. This study is especially interesting as
ecological research on cloud forests has been executed chiefly in neotropical forest and Asian
tropical forest, while the African ecotype has been ignored over time. Additionally, this thesis was
executed in a scientific framework of an ecological comparison between an Amazonian elevational
transect and African elevational transect whereby three other master students contributed. One of
them investigated the functional diversity on the South-American continent, while the two others
went deeper into the nutrient cycles of both forest transects. So a better understanding of these
specific ecosystems could lead to an enhanced valorization by the local and even global
community, better management guidelines and veracious predictions towards the future confronted
with a changing climate.
As the main objective of this study was to explore the functional diversity in this cloud forest, some
main questions should be answered:
• How do the community-level traits respond on the change in altitude?
• How does the plant community diversity shifts along the transect?
• What are the consequences of these changes in trait values and community diversity?
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2. Relevant literature
2.1.
From forest to tropical mountain cloud forest
2.1.1. Forests
Forests covers approximately 39 900 210 km2 or 31% of the world’s earth surface (World Bank,
2015) and are thereby the most abundant terrestrial ecosystem. They account for 75% of terrestrial
gross primary production and 80% of Earth’s total plant biomass and contain more carbon in
biomass and soils than is stored in the atmosphere (Pan et al. 2013). This points to their role in the
global carbon cycle and implies that they are of major importance in the context of climate change.
So what exactly could be defined as a forest? Due to their different appearances and therefore
various uses around the world there is no simple answer for this question, but some definitions
may postulate a decent description for the term forest (FAO, 2002). One of them is defined by the
UNFCCC (2001):
“Forest is a minimum area of land of 0.05-1.0 hectares with tree crown cover (or equivalent
stocking level) of more than 10-30 per cent with trees with the potential to reach a minimum height
of 2-5 meters at maturity in situ. A forest may consist either of closed forest formations where trees
of various storeys and undergrowth cover a high proportion of the ground or open forest. Young
natural stands and all plantations which have yet to reach a crown density of 10-30 per cent or tree
height of 2-5 meters are included under forest, as are areas normally forming part of the forest
area which are temporarily unstocked as a result of human intervention such as harvesting or
natural causes but which are expected to revert to forest.”
This definition describes precisely the minimum dimensions and gives an idea of the possible looks
of a forest, but there is no reference to the versatile functions of these ecosystems. For example,
forests form habitats for an enormous array of organisms as they alone house approximately
already 80% of the world’s terrestrial biodiversity (IUCN, 2012). Further on, they serve in the
subsistence of roughly 60 millions indigenous people, while a rough 1.6 billion people around the
world are in some way directly depending for their livelihood on forest ecosystems (World Bank
2009). For a long time, forests were viewed merely as a production system serving humans by
delivering wood, fuel and other economic resources (Hubacek & Van Den Bergh, 2006). Luckily,
this archaic view is slowly evolving towards a broader one, with forests offering a much wider
range of ecosystem goods and services (e.g. water regulation, air purification, habitat creation,
food production, soil formation, etc.). To describe it in Diaz et al.’s (2007) words, “ecosystem
services are the key conceptual link between social evaluations of ecosystems an their properties”.
More recently, scientists even tried to link those ecosystem services to a monetary value, in a way
to be able to manage them in a more economic setting (de Groot et al., 2002). Two good examples
of this economic evaluation system are the Millennium Ecosystem Assessment (Millennium
Ecosystem Assessment, 2005) and the Economics of Ecosystems and Biodiversity (TEEB, 2010).
Both try to emphasize the value of ecosystems and biodiversity and their contribution to human
well-being. These evolutions will hopefully lead to a more respected position of our forests around
the world together with a better adapted forest management.
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2.1.2. Tropical rainforest
As already mentioned, forests occur with several appearances due to the different surroundings
and circumstances. The most determining factors of forest type are probably temperature and
precipitation, which were already used to define classes in one of the earliest classification
systems (Whittaker, 1962). However, a more recent classification for terrestrial ecosystems, with
respect to every distinctive biota and their complexity, is the one proposed by WWF, identifying 14
terrestrial biomes (Olson et al., 2001). These biomes are distinctive in vegetation structure and
environmental features and are further divided into 882 terrestrial ecoregions. The biome
encountered in this paper is ‘Tropical and subtropical moist broadleaf forests’ which comprises the
well-known tropical rainforest and the cloud forests studied in this paper.
Tropical rainforest usually occurs around the equator and more specific between 23.5°N (tropic of
Cancer) and 23.5°S (tropic of Capricorn), see figure 2.1. Tropical rainforests are characterized by a
low seasonality in temperature - which is usually high throughout the year (mean annual
temperature between 20° and 25°C) - and by high levels of rainfall (total annual precipitation
>1500mm). As temperature, light and water are overall sufficient available and so creating a more
or less stable climate, the main limiting factor is the deficiency of the nutrients in the soil
(Townsend & Asner, 2013). Of course other disturbances can create limiting circumstances (dry
seasons, wind, forest fires, etc.), but overall species distribution in the tropics is strongly influenced
by the nutrient availability (Condit et al., 2013). This species distribution is generally recognized as
being the most diverse amongst terrestrial biomes, but nevertheless specific numbers of this
biodiversity has only been measured very recently (Slik et al., 2015). They found out that both the
tropical American and Indo-Pacific regions are approximately equal in species numbers (40-53.000
tree species) while African tropics only enclose one fifth of this richness (4500-6000 tree species).
This number is still high in comparison with European temperate forests, which only have 124 tree
species (Slik et al., 2015).
Figure 2.1: World map with locations of tropical and subtropical moist broadleaf forests in green (figure
adopted from Google Images and edited by the author).
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Although tropical forests only occupy an average of 12% of all terrestrial surfaces, they are the
most productive ecosystems on earth. They account for 34% of the global terrestrial gross primary
production (Beer et al., 2010) and contain up to 50% off all the carbon in forests worldwide (Pan et
al., 2011), giving these forests a enormous influence on the global carbon cycle. When carbon
sinks and sources are examined, it even appears that the tropics have an even bigger impact as
they count for two third of the total carbon sinks in forests (Pan et al., 2011). This greater share as
sink is achieved by the combination of old-growth and especially new-growth forests. Ironically
enough, the land-use change of these forests resulting in large-scale deforestation counts for and
equal amount of carbon sources, making them neutral in the total carbon budget. Both their impact
on the world’s biodiversity and carbon budget makes tropical forests of global interest. Their
conservation should be prioritized and studies searching for crucial tipping points (as in critical
thresholds that are once passed, will push certain elements of the Earth system out of balance)
could be of major importance when planning towards the future (Nobre & Borma, 2009). New
findings could lead to more insights in the forests functioning, which could in turn lead to better
adapted management guidelines.
2.1.3. Tropical Mountain Cloud Forest (TMCF)
The appearance of elevated areas and mountains influence the vegetation composition of the
tropical forest as important environmental conditions – such as temperature - alter with altitude.
Therefore the delineation of four forest zones on a tropical mountainous slope is universally
accepted (Grubb, 1977; Ashton, 2003). It starts with the lowlands, followed by the lower montane
and upper montane zone to end finally in the subalpine zone which is delimited by the final tree
line. The transition from the lowlands zone to the lower montane forest zone can easily be
explained by temperature shift as this change is usually observed where the average minimum
temperature drops below 18°C (Bruijnzeel, 2001). When moving further upwards to the upper
montane zone, another explanation is needed. Usually this change occurs where the level of cloud
condensation becomes more persistent (Grubb & Whitmore, 1966). The last zonal transition can
again be explained by a temperature shift, more specific a temperature drop of the average
maximum temperature below 10°C. Interestingly, this altitudinal zonation occurs worldwide within
variable altitudinal ranges, which is better known as the mass elevation or telescoping effect
(Grubb, 1971) see figure 2.2. For example, the subalpine zone is found on lower altitudes when it
appears on a small isolated peak than when it’s found on bigger mountain masses (Flenley, 1995).
This phenomenon has two complementary explanations. On the one hand, small to mid-sized
mountains in a coastal area are confronted with humid oceanic air, which promotes cloud formation
and therefore results in a compression of the zonation. On the other hand, big mountain masses
have a bigger surface exposed to sunlight which leads to greater warming of the air resulting in the
higher occurrence of these forest zones (Bruijnzeel & Hamilton, 2000).
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Figure 2.2: The mass elevation effect, also known as telescoping effect or ‘Massenerhebung’ effect,
illustrating the different elevations of montane forest zones. (adopted from Flenley, 1995)
A subtype of tropical forests also associated with tropical slopes is the tropical montane cloud
forest (TMCF). Not every tropical forest appearing on a mountain is a TMCF, but every TMCF
appears on a mountain. Still, the name TMCF has been used for different subtypes of forest and
with different interpretations (Stadtmüller, 1987). So what exactly could be defined as cloud forest?
The main factor is that they differentiate themselves by the frequent or even permanent
appearance of ground-level clouds (Grubb, 1977). The altitude at which these clouds appear differs
between regions around the world, but is usually between 1200 m and 2500 m above sea level.
However extreme examples can be found in a range between 220 m and 5000 m above sea level,
again propagated by the previous explained mass elevation effect (Jarvis & Mulligan, 2011). The
cloud formation is mostly determined by various climatic and geographical factors (e.g. presence
and proximity of water currents at nearby seas, trade winds inversions, vegetation patterns, macroand micro-relief patterns of the mountain ranges, etc.), which can differ with each altitude
(Stadtmüller, 1987). In general, cloud forests differentiate themselves by the occurrence in either
coastal climates with a colder regime and higher altitude or they occur in regions with a higher
tendency of regular rainfall than other montane forests (Jarvis & Mulligan, 2011). It was estimated
by the FAO in 1993 that the total amount of cloud forest comprised 11% of all tropical forests.
Those TMCF’s can be found in the Amazon Basin, the Caribbean, Central and East Africa and the
Indo-Malayan Archipelagos.
The pertinent cloud cover in TMCF has an important influence on the ecosystem’s functioning. One
of the most obvious facts is probably the lowered sunlight irradiance due to frequent presence of
fog. The consequence is a lower leaf temperature combined with an expected reduction in
photosynthesis activity. However, photosynthesis capacities have been measured in cloud forests
and were within the range of non-pioneer lowland species (Bruijnzeel & Veneklaas, 1998),
nevertheless, leaf activity is despite the equal potential activity still lower due to less captured
sunlight in the cloud forests.
Another important effect of the cloud cover is the influence on the water balance of the forest. The
tree crowns intercept the water from the fog on their leaves and branches. This water falls on the
ground and helps the forests lack less water especially in dry periods. As a consequence,
observations have been done that deforestation causes overall water loss instead of the usual
increase in water runoff, thanks to this so called ‘cloud stripping’ or ‘horizontal precipitation’
14
phenomenon (Stadtmüller, 1987). The exact share of this cloud precipitation is very hard to
assess, but it’s sure it has a very important influence (Bruijnzeel, 2001). To go deeper into the
water balance of cloud forests, three interesting conclusions have been made by Zadroga (1981)
towards their relation with other tropical mountain forests:
1) an increased net precipitation
2) a reducted evapotranspiration rate
3) an altered regulation of the hydrological regime (especially in dry periods)
Indeed, high precipitation levels are observed in cloud forests, but the range is very large (from 500
mm/year up to 10 000 mm/year (Stadtmüller, 1987); and the rain can be all the year round or
strongly seasonal. Again, a big share of this precipitation is due to the intercepted rain from clouds.
An expected consequence of the reduction in evapotranspiration would be a lower uptake of
nutrients as the vapor pressure affects the water and nutrient uptake (Winneberger, 1958).
However, it has been observed that a lowered transpiration stream gets compensated by an
increase of nutrients in the xylem sap flow (Smith, 1991). Also one would expect a higher relative
humidity in comparison with tropical lowlands, but Grubb & Whitmore (1966) measured this in both
forest types and came to the conclusion that there is only a small insignificant difference in the
relative air humidity. The only significant difference they found was the overall higher availability of
liquid water in the forest. Finally, the altered regulation of the hydrological regime follows easily
from the previous two observations.
So it can be concluded that the cloud forests have a distinctive climate in comparison with other
ecosystems, however within TMCF there are still a lot of differences (wind regime, altitude, dry
season, etc.) which make it hard to provide an unequivocal description of these forests (Bruijnzeel
& Veneklaas, 1998; Jarvis & Mulligan, 2011).
2.1.3.1.
TMCF is a threatened ecosystem
The cloud forests fulfill important services as they protect soils from leaching and erosion, but as
well as they conserve an exclusive range of biodiversity. While they are as species-rich as their
lowland counterparts, if not richer, TMCF’s are especially known for their high percentage of
nationally and regionally endemic species (Hamilton, 1995). The mountain gorilla (Gorilla gorilla
beringei) of East Africa might be one of the best-known examples. Despite the small area of cloud
forests, they also have a strong effect on the geochemical cycle of a much bigger neighboring
area. This happens through mobilization and deposition of sediments and nutrients from up the
mountain to lower areas, making them of high interest for the people living in those areas (McClain
& Naiman, 2008).
Still, this type of forest is highly threatened as it is confronted with a constant decline in area.
Already in 1978, LaBastille and Pool concluded that cloud forests are the most rapid declining
forest type. Data from the FAO for the period 1981-1990 confirmed this by a decrease of tropical
mountain forest of 1.1% per year while tropical forests declined only 0.8% per year. Estimations
done in the year 2000 based on altitudinal data combined with forest cover data concluded that the
actual area was 215 000 km2 or 1.4% of the world’s tropical forest (Bruijnzeel et al., 2011). More
15
recently this number got updated with data acquired by remote sensing through observations of
frequently clouded areas, estimating that 14.2% of tropical forests are cloud forests. This seems an
enormous growth, but they admitted this percentage might be lower if they acquire more precise
data (Mulligan, 2010) and again this is highly dependent on the definition for cloud forest as
admitted by Mulligan himself. It might be that very fragmented areas were included and also not all
conditions for cloud forest were met. Anyway, it is sure that this subtype is one of the most
vulnerable and least represented ecosystems among the tropical forests.
This decline in forest area is, directly and indirectly, due to a lot of human activities. These
activities range from conversion to grazing land and cultivation, to wood logging for fuelwood or
extraction of non-wood products (orchids, bromeliads, medicinal plants, reptiles, amphibians, birds,
and mammals). In Rwanda, for example, tea and coffee cultivation is very common in the mountain
areas, while hunting for subsistence use and meat sale is a threat for the whole African continent
(Bubb et al. 2004). As this isn’t enough, humans created more endangering threats by organizing
touristic activities, introducing alien species and by (accidentally) causing fire in the seasonal dry
areas (Hamilton, 1995). One of the most obvious observed consequences might be the loss of
several animal species. For example, in Colombia, 31% of the bird species population living in a
cloud forest disappeared over a period of 50 years due to serious forest fragmentation (Kattan et
al., 1994). Thereby is the indirect effect of climate change very unpredictable.
Luckily, awareness has been raised and a lot of incentives (for example the Community Cloud
Forest Conservation) have been created to cope with the problem. The most common and effective
action is making the endangered areas protected, just like the National Park of Nyungwe in
Rwanda. In 2004, an estimated one-third of all cloud forests in the world were protected (Häger,
2006). This was at least partly made possible due to the early interests of scientists like Zadroga,
Bruijnzeel and Hamilton. Especially the latter has done great efforts by organizing the first
International Symposium on Tropical Montane Cloud Forests in 1993, which gave birth to more
research supplemented with the ‘Campaign for Cloud Forests’ by the International Union in 1995
(Bruijnzeel, 2001).
2.1.3.2.
TMCF in Africa
Approximately 16% of the world’s cloud forests are situated in Africa (Bruijnzeel et al., 2011),
whereas only 1.4% of the African tropical forests is TMCF (Bubb et al., 2004, see figure 2.3). The
African TMCF’s tend to be drier (average annual rainfall less than 1500mm) and more seasonal
than their Asian, Latin American and Caribbean counterparts, which experience a wider range of
rainfall conditions (Jarvis & Mulligan, 2011).
While already mentioning some of the threats for the cloud forests in the world, African TMCF are
mostly confronted with hunting, forest fires (caused by the trend of drier TMCF’s in Africa) and
firewood together with charcoal production (Bubb et al., 2004). Also in Africa incentives exist to
protect the forests. For example, the Albertine Rift Conservation Society (ARCOS) identifies
unprotected areas of cloud forest and supports effective management.
16
Figure 2.3: Map of Africa with potential cloud forest distributions and cloud forest site locations (adopted from
Bubb et al., 2004). TMCF occur in Angola, Burundi, Cameroon, Cote d'lvoire, DCR, Equatorial Guinea,
Ethiopia, Gabon, Guinea, Kenya, Liberia, Madagascar, Malawi, Mozambique, Nigeria, Rwanda, Sierra
Leone, Tanzania, Uganda + Bioko (Equatorial Guinea), the Canary Islands, the Comoros, Mauritius,
Réunion, São Tomé and Principe, and the Seychelles.
2.2.
Plant traits and functional diversity
Traditionally, ecological research question are approached within a taxonomical framework where
plants are classified according to their species or families (Cornelissen et al., 2003). While this
approach is still feasible in temperate forests where the researcher is only confronted with a
reasonable amount of species, it becomes quickly clear this method isn’t adapted for tropical
research. Especially when important ecological questions need to be studied and compared at the
scale of ecosystems, landscapes or even biomes. The taxonomic approach makes the findings
site-specific and the species list endlessly long, let alone the increased complexity by recent
discoveries of new taxonomic species (Gomez-Laurito & Gomez P., 1991). So the need for a more
universal framework is evident and can be found in the functional framework, which is generally
accepted in the ecological scientific community (Cornelissen et al., 2003). The idea behind this is a
projection of different plant traits in a functional space which delivers insights in different plant
strategies and life histories. This approach has been used for wood economic, leaf economic and
very recent even plant economic spectrums (Chave et al., 2009; Reich et al., 2014; Diaz et al.,
2015).
17
2.2.1. Plant traits
Functional diversity is approached by studying plant traits, which can be compared among different
species. A definition given by Reich on plant traits (2014):
“Traits, including functional traits, offer clues and insights regarding how and why a plant may
behave as it does, where it grows and where it does not, how it interacts with other plants, and how
it influences the abiotic and biotic environment around it.”
Through the study of functional plant traits, plants can be linked to their ecological strategy and
their life history can be better understood. It is recently proven by comparing a wide spread of taxa
and life histories that traits are constraint towards a small set of combinations (Diaz et al., 2015).
This suggests that only a certain array of trait combinations is competitive enough to sustain in the
planet’s ecosystems. Also it is proven that co-occurring species have on average a greater
functional similarity than they have phylogenetic similarities (Baraloto et al., 2012; Kraft et al.
2009), which means that plant strategies are of greater importance than their life history in
explaining their occurrence in the environment.
In order to learn something about the plant strategy, a good selection of traits should be made.
Hereby it should be kept in mind that the choice of the traits depends on the circumstances, like
study goal, practical feasibility, literature, familiarity with methods, etc. In order to provide a solid
basis and to make comparison possible across different regions and scales, several authors have
tried to find a good selection. Weiher et al. (1999) suggested several ‘soft’ core plant traits to
provide an answer for the most basic questions (dispersal, establishment and persistence) in plant
ecology studies (table 2.1). Soft means in this situation the traits that are relatively easy and quick
to quantify. Others proposed to sample some traits really intensive (for example the ‘soft’ traits)
while the other, ‘hard’ traits (opposite of soft traits and so relatively more labor-intensive), are only
measured on occasional basis (Baralato et al., 2010; Cornelissen et al., 2003). Some even stated
that only a couple of essential traits can provide a sufficient foundation for conclusions about the
plant strategies. For example, specific leaf area, leaf longevity, seed mass and height would
already capture the most important generalities following the study done by Westoby et al. (2002).
Also Diaz et al. (2015) leaned towards this idea by stating that plant size is of major importance
combined with the leaf economic spectrum, the balance between construction costs against growth
potential of the leaf, resulting in only 6 plant traits needed to capture the essence of plant form and
function.
In this perspective, it can be shortly noted that Lavorel et al. (2008) experimented and suggested
research towards a taxon-free rapid sampling method as an alternative on traditional taxonomic
sampling. This would make it possible to process data on a shorter time-span, while still being able
to make significant conclusions.
18
Table 2.1: list of core traits and associated plant function suggested by Weiher et al. (1999).
Once a good selection of the traits is made, the aimed vegetation can be submitted to a rigorous
study, which implies the traits are examined in a continuous pattern in order to clarify the steering
mechanisms. As the the plant variety in tropical forests is high due to different environmental
conditions and the selection is heavily influenced by the history of species arrivals (Fukami, 2005),
it remains a challenge to assess to which degree the functional characteristics measured are
reflected in those diverse conditions (Asner, 2014). In the lowlands this knowledge is already
growing, but little is known regarding their equivalences in montane areas as variability in
conditions is much higher (Apaza-quevedo et al, 2015). So a study of the plant traits can lead to a
better understanding of the relation between the cloud forest and its environment. In the following
part we will go deeper into those specific traits, starting with leaf traits, followed by a wood and a
whole-plant trait. Due to the fact there is an enormous array of traits, just referring to the TRY
database containing more than 1000 plant traits listed, this selection is rather a tip of the iceberg of
possible plant traits.
2.2.1.1.
Leaf traits
Leafs could be seen as the foundation of the terrestrial ecosystem as they are the main inputs of
energy. This is accomplished by capturing energy from the sun through photosynthetic activity and
thus forming a crucial energy and carbohydrate source for the ecosystem, and additionally the core
center of carbon exchange between the atmosphere and biosphere. Assessing leaf chemistry
allows us to assess different species strategies in this carbon sequestration process, so combining
measurements of leave characteristics at the species levels provides us with useful ecosystemlevel information (Asner & Martin, 2016; Diaz et al., 1998).
Just like the plant traits, the leaf traits do also have a limited number of possible combinations,
known as the plant economic spectrum (Wright et al., 2004). Plants are confronted with a major
trade-off between defensive, physiological and structural investment when producing leaf tissue.
On one side, there are quick-return species, which invest in high leaf nutrient concentrations, high
rates of photosynthesis and respiration, short leaf lifespan and low dry-mass per leaf area. On the
19
other side, there are the slow-return species with long leaf lifetimes, expensive and high dry-mass
per leaf area construction, low nutrient concentrations and low photosynthesis and respiration
rates. This supports the idea of a single spectrum of leaf economic variation around the world,
despite the biome or the climate. Other possible combinations are just out-selected by natural
selection.
One of the most assessed leaf traits is the leaf area per leaf dry mass (SLA) or leaf dry mass
per area (LMA), which is simply calculated by weighing the dry mass of a leaf and measuring the
area of a leaf and then dividing one through the other. It’s an assessment of the leaf dry-mass
investment per unit of light-intercepting leaf area deployed (Wright et al., 2004). A high SLA or low
LMA means a thick leaf or a denser tissue. This trait is highly correlated with the relative plant
growth rate as it is a reference for the photosynthetic capacity in the plant, but also related to the
leaf longevity which occurs usually in a situation with scarce nutrients (Kikuzawa, 1991; Reich et
al., 1992; 1998; Weiher et al., 1999).
Another useful trait is the leaf nitrogen concentration (LNC), which is the total amount of
nitrogen per unit of dry leaf mass (Cornelissen et al., 2003). Nitrogen is essential to proteins of the
photosynthetic machinery, especially Rubisco, and in all enzymatic activities. (Wright et al., 2004)
Nitrogen enters the ecosystem by fixation (valuable for species form the Fabaceae family) and
deposition and is recycled through mineralisation. Nitrogen has two stable isotopes, 15N and 14N,
which gives the possibility to another trait as ratios between both can be assessed. This ratio,
15
N:14N, is called the leaf nitrogen isotope signature (δ15N) and is calculated by the following
formula:
(15N /14 N) sample
δ N = 15 14
−1,(%)
( N / N) reference
15
The used standard is usually the ratio in air. Nitrogen’s stable isotope can shed more light on the
mechanisms of the N-cycle (Craine et al., 2009) while the content in the plant is highly affected by
mycorrhizal fungi, the€climate and microbial activity (Peri et al., 2012; Craine et al., 2015). Already
in 1999, Martinellie et al. observed a correlation between abundance nitrogen concentrations and
high δ15N concentrations in the leaves despite the fact they couldn’t explain these results. Later,
trends in foliar δ15N isotope discrimination have been observed with increasing annual precipitation
and decreasing mean annual temperature (Amundson et al., 2003; Craine et al., 2009), which
would be explained by the enhanced mobilization of the lighter isotope during wet and warm
conditions. The mycorrhizal fungi population also has a great influence on the foliar δ15N
discrimination as usually plants have lower concentrations than the soil, but in presence of this
population the δ15N foliar levels are even lower (Craine et al., 2009). Despite conclusions from
multiple disciplines, still more research is needed in order to integrate all observations and acquire
a broader view on its relation to ecosystem’s functioning (Craine et al., 2015).
Another interesting nutrient is the leaf carbon concentration (C), which is again the total amount
of carbon per unit of dry leaf mass. This is especially useful in determining the amount of biomass
in an ecosystem, but also when proportions with other nutrients are analyzed, for example C:N and
C:P. These proportions vary dramatically among forests, which means it can help a lot in the
understanding of different ongoing mechanisms. A very clear example is the difference between
20
geologically young and old soils, which are respectively limited by nitrogen and limited by phosphor
(Vitousek & Farrington, 1997). Additionally they can tell something about plant physiology and it’s
relation to the soil, as both proportions can be measured in the canopy and in the soil. Plants can
have a selected uptake or resorption of nutrients, which are reflected in C:N and C:P (McGroddy et
al., 2004). Just as it was the case with nitrogen, carbon also has two stable isotopes, namely 13C
and 12C. The latter one is the most common one as it accounts for 98.9% in the atmosphere while
13
C just fills up the rest. It has been observed that plants actively discriminate against 13C in two
places (Richard, 2006). First during the diffusion of CO2 from atmosphere into the sub-stomatal
cavities and second during the biochemical fixation of CO2 into sugar, resulting in a lower 13C:12C
ratio. This ratio is usually called the leaf carbon isotope signature (δ13C), which can be
calculated with the following formula:
(13 C /12 C) sample
δ C = 13 12
−1,(%)
( C / C) reference
13
As visible in the formula, the isotope signature is calculated with a reference, usually Pee Dee
Belemnite, which has a very high ratio resulting in negative δ13C values for natural materials.
€as an indication for the water use efficiency (WUE), which is a reference for
δ13C has been used
the amount of carbon assimilation to the rate of transpiration (Peri et al., 2012). In general WUE is
low when a site is moist, resulting in maximal stomatal conductance and finally in high
discrimination of 13CO2 during the carboxylation and thus low δ13C values. In contrary, dry or
water stress conditions will lead to less discrimination and a higher δ13C level in leaf tissue.
Additionally, Bonal et al. (2000) observed a lower δ13C in climax species compared with pioneer
species, independent from functional traits as both groups encompass light-demanding and shadetolerant species.
2.2.1.2.
Wood traits
Wood gives a tree the mechanical support to grow tall, while still providing in the need for water
transport and in the meantime acting as storage for nutrients. The main wood trait is wood
density, also known as wood specific gravity. It’s calculated by dividing the dry mass through
either the green, air-dry or oven-dry volume (Chave et al., 2009), which makes it easy to measure
and therefore widely available in databases and other studies. As there is the possibility to
measure the wood volume in three different ways, it is maybe necessary to agree upon a more
tying international standard, however different measurements might depend on the research
circumstances. The trait is a proxy for the balance between the mechanical and physiological
properties of the tree (or more specific the tree stability and the hydraulic efficiency), as it is
strongly determined by the amount of tissue and cell walls. Other things that could be deduced
from the trait is defense of the tree, architecture, carbon gain and potential growth (Poorter et al.,
2010). Finally, it is also an essential parameter to calculate the biomass of the tree.
Usually, fast growing trees, which have low construction costs are related to a low wood density,
while trees with a high wood density are more related to stressful conditions as they have a bigger
survival rate towards physical or biological damage (Poorter et al., 2010). It’s proven and easily
21
observed that phylogeny and age plays an important role in the density of wood. Still, apart from
phylogenetic links, interesting observations can be done concerning the influence of water
availability and temperature (Wiemann & Wiliamson, 2002; Swenson & Enquist, 2007).
2.2.1.3.
Whole-plant traits
The last treated trait is the maximum plant height or more specific in this case the maximum
tree height. It is the shortest distance between the plant at ground level and the upper part of the
plant (Cornelissen et al., 2003). This one is easily measured and is one of the core traits used not
only in scientific studies, but as well in forest management plans and in more economic-minded
activities. It is a good reference for the nutrient availability, amount of stress and light competition
(Koch et al., 2004). Again, it can be used to calculate the biomass of trees.
2.2.2. Plant functional types
Cornelissen et al.(2003) says the following about plant functional types (PFT’s):
“They can be defined as groups of plant species sharing similar functioning at the organismic level,
similar responses to environmental factors and/or similar roles in (or effects on) ecosystems or
biomes.”
So the combinations of several traits summarize the plant strategy and life history. It is possible to
divide plants, based on their place in the functional space –defined by their plant trait scores, into
plant functional types. Several authors have tried to make a good classification of these types, for
example, Box (1996) suggested a list with dominant plant types all over the world (table 2.2)
Table 2.2: Small selection from the list of dominant plant types suggested by Box (1996).
PFT's help to understand the response of plant traits to specific environmental factors (Cornelissen
et al., 2003) and it’s more general because it is an abstraction of the plant communities. Plant
functional types could be situated somewhere between the taxonomic framework and the
functional framework as still a discrete classification is used, but it is based on functional
characteristics (Smith et al., 1997). Despite the advantages, PFT’s still has some shortcomings
(Ordonez et al., 2009). Just like with the taxonomical classification, it does remain very context
dependent and site-specific even though a physiological basis is on the background. Second, the
functional space is continuous and so functions may overlap, while a classification uses
22
boundaries. This also implies that a changing climate might change the functional boundaries
demanding the classification to change to. Maybe the most important obstacle of using functional
groups is the arbitrary decision of the experimenter (Wright et al., 2006). To summarize, PFT’s are
a useful concept in order to help us understand different mechanics, but there should always be a
well elaborated background as for example using a functional space (Chave et al., 2009; Reich et
al., 2014; Diaz et al., 2015).
2.2.3. Functional Diversity
2.2.3.1.
From biodiversity to functional diversity indices
Diversity can be assessed on different scales: from genetic scale all the way to ecosystem and
even biome scale. In order to identify this diversity, mankind initially classified organism based on
their taxonomical identity. One of the first scientific relevant works is probably ‘Systema naturae’
from Carl Linnaeus published in 1735 (Linnaeus, 1735). The first methods of classifying were
mainly based on morphological characteristics, whereby it contained still some shortcomings
towards the phylogeny of species (Ereshefsky, 2008). It’s only with more recent technology, like
DNA sequencing, that a more precise taxonomical phylogeny could be used. This has proven to be
a powerful and important tool towards systematics, but it shouldn’t be seen as a substitute for
understanding and studying whole organisms as this taxonomical identity is not directly linked to
plant function or strategy and maybe even more important, it is very labor-intensive (Will &
Rubinoff, 2004).
While some conclusions are achieved with taxonomic studies, the real understanding of an
ecosystem’s functioning seems to be better declared by functional diversity (Diaz & Cabido, 2001;
Cornelissen et al., 2003; Mouchet et al., 2010). It is however a quite recent approach as the
number of publications about functional diversity has been rising since 1990 (Schleuter et al.,
2010). As quoted by Tilman (2001): ‘Functional diversity is a reference of the ecological importance
of the diversity that influences the ecosystem’. It can be achieved by studying values, ranges and
relative abundances of specific species traits, leading to the classification of functional groups of
organisms, which are groups that influence and are influenced by the functioning of an ecosystem.
This makes it a useful and more fundamental method, as it provides the possibility of comparing
different sites and habitat types in a more fundamental functional framework. Thereby there is no
need for difficult taxonomy, but just a simplification of reality by focusing on the traits related to
similar species (Weiher & Keddy, 1995).
Still the question remains how this functional diversity is to be calculated and interpreted. For this, it
is necessary to suppose that the mechanisms behind diversity are based on different usage of the
functional space, also known as niches, by organisms (Tilman et al., 2001). This means that
several groups of species use other sources and respond in their particular way to different
environmental conditions, what is called niche complementarity. Related to the concept of niches
are the assembly rules, saying that traits associated with competition are over-dispersed while
traits filtered by environmental barriers are under-dispersed (Weiher & Keddy, 1995). To provide a
meaningful interpretation for the functional diversity, it is thus necessary to assess the size of the
functional space and the distribution of species in that space (Mason et al., 2005). These
23
characteristics can finally be translated into indices, which will tell something about the
mechanisms behind functional diversity. Hence by splitting functional diversity into different
components (indices) it provides more details in examining those mechanisms (Villeger, 2008).
Now the question rises which indices can be used to interpret the traits and make valuable
conclusions. Many indices have been proposed, whereby some are highly redundant (Mouchet,
2010), however 3 categories of indices are mainly accepted among researchers: functional
richness, functional evenness and functional divergence, which are all independent of each other
(Mouchet, 2010; Mason et al., 2005; see fig 2.4):
A. Functional richness is a measure for the volume of the functional space occupied by the
species in the community.
B. Functional evenness is a measure for the uniformity of the distribution of abundance in this
volume.
C. Functional divergence is a measure for the degree of deviation in the distribution of
abundance in this volume.
Figure 2.4a: A one-dimensional representation of the functional diversity indices categories, whereby species
abundances are shown on the vertical axes. The first set of figures represents functional diversity and
functional evenness. A1 and A2 show the difference between high and low functional diversity while B and C
show the difference between high and low functional evenness.
24
Figure 2.4b: The second set of figures shows the different between high and low functional divergence
(figures adopted from Mason et al., 2005).
In the context of the three categories, Villeger (2008) proposed three complementary indices which
proved to have a high explanatory power of the ecological assembly rules in comparison with other
indices (Mouchet, 2010): FRic, FEve and FDiv. Species are hereby arranged according to their
traits in a multidimensional functional space. In 2010, Laliberté & Legendre added an extra indice
to this list, namely functional dispersion (FDis). This is a measure for the mean distance of the
species from the centroid of the volume, in other words the deviation from the average of the
volume. There is one last indice worth mentioning, called Rao’s quadratic entropy (RaoQ), which
was initially created as a diversity index (Rao, 1982), but was adapted as a functional diversity
indice by Zoltan (2005). This indice measures the mean functional distance between two randomly
chosen individuals and is labeled to be a measure for functional richness and divergence (Mouchet
et al., 2010).
Trait 1
Trait 1
Figure 2.5: A two-dimensional representation for the functional dispersion, species’ trait abundances are
represented by the size of the black dots, the centroid is represented by c. Left: FDis is calculated by the
taking the average of the distances between a species point and the centroid. Right, FDis is calculated
equally, but the centroid is moved because of weight towards the more abundant species points (figure
adopted from Laliberté & Legendre, 2010).
25
When calculating these indices, the impact of every individual species on the ecosystem’s
functioning could be assessed. Therefore community weighted means are being used (CWM),
which is supported by the mass ratio hypothesis (Grime, 1998) saying that the ecosystem is chiefly
controlled by dominant species in terms of biomass. Therefore the measured traits should be
weighted to the physical occurrence of the specie. The link between CWM and the ecosystem’s
properties gather more support through empirical evidence (Diaz et al., 2007) resulting in the use
of CWM in computation of the functional diversity indices.
So by combining functional diversity with complementary phylogenetic and taxonomic diversity, the
mechanisms behind the ecosystem functioning and the biodiversity are hopefully elucidated
(Mason et al., 2005; Mouchet et al., 2010).
2.2.3.2.
Functional diversity of forest vegetation
When we study the functional diversity on the scale of forests, it expresses ecological processes at
multiple geographic scales (Asner et al., 2014). It can go from the microhabitat of the tree all the
way to the complete ecosystem of a forest. The best way to get an idea of this last one is by
studying the trees in the forest. They are the cornerstones of the forests as they create endless
habitats for all kinds of flora and fauna and because they dominate the amount of biomass and
carbon storage. So by studying their characteristics and the direct surrounding, useful conclusions
could be made about functional groups and their ecological responses towards a changing
environment.
2.3.
Elevational transects
2.3.1. Introduction
The study of certain factors along a mountain slope with an environmental gradient is called an
elevational transect. Elevational transects are unique setups for the understanding of an
ecosystem’s functioning, as you can study the response of this ecosystem on a gradient of several
climate variables (Malhi et al., 2010): “The use of elevation gradients within the tropics is a
particularly powerful tool to further understanding of the influence of temperature on the
biodiversity, ecology, ecosystem function and global change response of forest ecosystems.” Apart
from the temperature, transects also show changes in the atmospheric pressure, atmospheric
composition, cloudiness, solar radiation and the fraction of UV. Of course there are even more
covarying parameters like moisture, hours of sunshine, wind, geology, etc., but these are less tied
to the amount of meters above sea level (Körner, 2007; van de Weg, 2010). Additionally, some of
these traits are altered even more due to the microclimate as result of local topography (Takyu et
al, 2002). Not all of this fluctuating factors are desired in a transect studies, but it should be kept in
mind when interpreting results from elevational transects. This highlights the importance of a
careful plot selection and a good documentation of all variables, especially in the tropics where
abrupt changes in these variables can occur over the timespan of just one day (Rundel et al.,
1994). On the other hand, some kind of similar study could be done in a laboratory where most
conditions are controlled meticulously. While they can be interesting towards the understanding of
some short-term responses, it isn’t a feasible alternative for studying the longer-term responses
26
and even less for studying responses on forest level. Briefly, altitudinal transects are unique openair laboratories to assess long-term forest community responses to environmental changes.
A potential worthy alternative for studying the influences of different climatic conditions is
comparing similar settings on different latitudes. Yet, the variable conditions which are also
encountered in the elevational transect would have an even bigger range in an altitudinal study, let
alone the possibility of new variables introduced (variation in day length, dormant intensity, weather
phenomena, mineral soil, etc.). Some relations get thereby more complex because of the latitude
approach. Where it would be possible to compare mean temperatures across regions, it would be
way more challenging to disentangle the effect of seasonality in the temperature while interpreting
the results (Malhi et al., 2010). Additionally, different biogeographic areas usually have a different
history which makes the available species pool less comparable to each other. On the contrary, the
potential species pool on elevational transects has had the time to mix, disperse and grow to their
capacities on the mountainous areas. Not forgetting the practical advantages of working in one
area instead of plots spread over the world.
Elevated ecosystems, and so the knowledge gathered with elevational studies, differ significantly
from their lowland relatives. The higher areas have experienced different and versatile conditions
which have had their impact on the evolution and ecology of the organisms (Malhi et al., 2010).
This resulted in unique and rich species compositions and currently unknown soil microbial
communities with large stocks of soil organic matter and litter. A recent estimation states that 70
000 endemic species can be found in just 11 hotspots on tropical mountains (Myers et al., 2010).
This is an important extra incentive promoting fundamental ecological research on altitudinal
transects. Altogether, insights into the mechanisms promoting these shifts in species and their
relation towards the abiotic environment are essential for our better understanding of the effect of
global climate change on tropical forest ecosystems.
An interesting question rising in the climate change topic is what the thermal niche of tropical
species is and where those species are situated in that range (Janzen, 1967). This would help to
predict if those species are able to adapt to climate change and if they experiencing any decline in
their function or fitness (Malhi et al., 2010). It would also support the idea that tropical forests are
more sensitive for climate change then temperate forests, causing potential climate changeinduced changes in this biome likely to occur earlier (Deutsch et al., 2008). However, the question
is complicated as the forest have to cope with an extinction debt or an immigration credit caused
by a changing environment (Jacksdon & Sax, 2010), causing an either positive or negative effect
on the species distribution.
In the case of a positive effect by climate change through an increased immigration, the cloud
forests could become important refugia’s, especially when lowlands become unlivable in a future
confronted with this changing climate. Already in the last glacial, the cloud forests probably played
an important role as refugia or as speciation centers (Ramirez-Barahona & Eguiarte, 2013). As
such, forests at higher elevations seem to have a lot of information of past climate changes in the
tropics and species distributions correlated with these changes (Hooghiemstra & van der Hammen,
2004), but making foolproof conclusions on the past millenias stays challenging. So research done
on elevational transects in the tropics could provide us knowledge and insight on the potential
27
changes in the future by comprehending the mechanics behind the functioning and by comparing
with previous and similar situations.
2.3.2. Characteristics of elevational transects
2.3.2.1.
Visual observations
Probably the most obvious observation done in the mountain forests and of course in TMCF is the
difference in canopy height. Starting from lowland, trees can have a size of 45 m, going up to an
altitude of 4000 m, where the trees are only left with 2 m in size (Grubb, 1977). It is visually clear
there is a shift from straight tall trees to trees with a much shorter and stunted stature (van de Weg
et al., 2009). Along with the decreasing stature, the stem density increases (Waide et al., 1998)
and the leaves get smaller, thicker and harder (briefly more ’xeromorphic’) with increasing altitude
(Bruijnzeel & Veneklaas, 1998). Additionally, there is also a strong increase in epiphyte abundance
(with observations of epiphytes on 96% of the trees) due to less water loss and more opportunities
for water uptake (Grubb et al., 1963).
2.3.2.2.
Biodiversity
The overall biodiversity of mountainous areas is very high, but on the slopes themselves, the
biodiversity decreases with increasing altitude, as observed in Ecuador (Homeier, 2009), Costa
Rica (Lieberman, 1996) and even in paleotropical forests (Gentry, 1988). However, these changes
aren’t merely driven by the difference in elevation, as topographical differences also affect the
environmental conditions. It is difficult to disentangle both influences from altitude and topography
and infer their share on the microclimate defining the potential species-pool (Takyu et al, 2002;
Edward Webb et al, 1999). Lippok et al. (2014) even observed that the topography effect can be
stronger than the elevational driver for vegetational patterns when assessing over a small
altitudinal transect. Anyhow, there are multiple studies, which observed a change in plant
composition with altering altitude (von Humboldt, 1805; Asner et al., 2014), suggesting that the
underpinning ecological processes are likely to be related to the altitude.
2.3.2.3.
Net primary production
Increasing altitude usually leads to a decrease of net primary productivity (Weaver & Murphy,
1990; Waide et al., 1998; Kitayama & Aiba, 2002; Soethe et al., 2008). For example did Moser et
al. (2011) observed of a decrease in leaf biomass, stemwood mass and total aboveground
biomass by 50% to 70% on a transect of 2000m in Ecuador . There are several factors that could
cause these decreases, and different studies report on the potential drivers of these observations
of the lower productivity on higher altitudes:
+ The direct effect of low temperature on plant growth leading to a lower photosynthetic activity
(Grubb, 1977; Berry & Bjorkman, 1980; Johnson & Thornley, 1985).
+ The indirect effect of temperature on the nutrient availability as temperature influences the rate of
decomposition and nutrient mineralisation (Myers, 1975).
28
+ The persistent presence of clouds resulting in a lower percentage of PAR (photosynthetic active
radiation) which leads to lower photosynthetic activity (Bruijnzeel & Veneklaas, 1998; Lets &
Mulligan 2005).
+ Increased UV-B radiation which causes damage to the photosynthetic apparatus in elevated
areas (Flenley, 1995).
+ Exposure to strong winds can have a significant effect on the stature and the allocation of
nutrients in plants (Cordero, 1999; Lawton, 1982), however wind speed might differ strongly in
every region.
+ Waterlogging could be an important factor, but is excluded by most researcher to have a serious
influence (Grubb, 1977; Bruijnzeel et al., 1993). Still it causes a lower availability of oxygen
availability in the soil which leads to lower root respiration (Silver et al., 1999).
+ Nutrient limitation (Vitouesk & Sanford, 1986; Tanner et al., 1998).
+ The presence of younger soils (Raich & Russel, 1997). The age of soils is strongly correlated
with altitude as younger soils are more often found on higher elevation and older ones usually
more in the lowlands (Porder et al., 2007).
+ High concentrations of phenolic compounds (sometimes accompanied with aluminium) in the soil
organic matter are observed on high altitudes (Bruijnzeel et al., 1993), which has a negative
effect on plant growth (Kuiters, 1990). However it is affecting the productivity, it is rather a
consequence caused by the different conditions related to elevation.
Despite the observed decline in aboveground biomass, researchers discovered more recently a
slightly compensating increase in below ground biomass. For example did Moser et al. (2011)
observed an impressive increase in the coarse, large and fine root biomass, accompanied with
almost a doubled fine root production, which is contrary to the altitudinal patterns perceived for
above ground biomass. Also both Leuschner et al. (2007) and Kitayama & Aiba (2002) observed a
carbon allocation shift from above ground to below ground with increasing elevation.
This leads to the realization of the potential importance of an altitudinal gradient study in cloud
forests. Still, montane ecosystems are less studied than the lowland equivalences (Bubb et al,
2004) leaving open more questions for TMCF’s, especially towards functional diversity which is a
rather new aspect in tropical montane forests (Diaz et al., 2007).
29
3. Materials and methods
The actual study was done in the Nyungwe Forest National Park of Rwanda (2°28'42.0"S
29°12’00.6”E, coordinates from the visitor’s center Uwinka, centrally located in the park). The area
covers 1015 km2, mainly consisting of montane rainforest, and is located in the South-East of
Rwanda, adjacent to Burundi’s National Park Kibira (Briggs, 2006). In addition, it is located on the
Albertine Rift which divides Congo from Uganda, Rwanda, Burundi and Tanzania. During the last
ice age, this rift seemed largely unaffected by the drying up of the lowland why it is expected to act
as a big refuge for fauna & flora (Levinsky et al., 2013). Most of the cloud forest is found in the
wetter South-Western zone. The highest peak called Mt. Bigugu (2950m) is located in the mid-west
of the forest (see figure 3.1).
As the park provides up to 70% of the countries water supply, houses a remarkably high level of
biodiversity (for example observations have been done for 13 primate species, approximately 280
bird species with 28 Alberitine rift endemics, up to 230 tree species, etc.) and is ranked high for
Albertine endemics (Plumptre et al., 2007), the park is of valuable importance for the Rwandese
people and global biodiversity (Aldrich et al., 1997; Briggs, 2006). During the past, the forest has
been confronted with expanding agriculture, gold mining activities and the subsistence use of
locals (like food, fuel and material), which fragmented the park up to the date of 1984, when a
coordinated forest protection plan was made to assure the conservation of the park. This attributes
to the almost 10% of protected areas in the country which is a rare number on a global scale
(Aldrich et al., 1997). Thanks to the 50 km long concrete road, which divides the park into two, and
the wide availability of hiking trails, the park is reasonably good accessible for scientific research.
The western section of the park is characterized by the presence of a very dense forest at 17002000m, while the eastern section has much more secondary forest and high occurrence of
clearings, situated higher at an average of 2200-2500m (Aldrich et al., 1997) (see figure 3.1). The
forest is dominated by strongly leached, acid soils with a high diversity in silt, sand and clay
content (N. G. Ghehi, 2012). The underlying parent material is from different origins ranging from
schist, micaschists, quartzitic schists and granites. Climate data has been gathered very recently
as weather measurements started in the Uwinka climate station in 2007 (daily precipitation and
daily min and max temperatures). This resulted in an average annual mean temperature and
precipitation of respectively 14.5°C and 1824.7mm.
30
Figure 3.1: Altitudinal map of Nyungwe, with marks for the ranger posts which were used as base camps
during the field work.
3.1.
Experimental set-up
The field campaign was conducted during the months August and September, 2015. The protocol
followed was mainly based on the RAINFOR protocol which tries to contribute to a worldwide
standardized research method for tropical forests (Malhi et al., 2002).
3.1.1. Site selection
The main goal was to understand the effect of the altitudinal gradients in ecological processes by
assessing species composition and determining leaf traits. Therefore permanent sample plots
(PSP’s) were established in 4 altitudinal strata along the transect ranging from 1700 m to 2950 m.
Five different plots clustered around the same altitude (1800 m, 2200 m, 2500 m and 2800 m) were
established for every strata. This resulted in 20 permanent sample plots, each measuring 40x40 m
and orientated parallel and perpendicular to the direction of the slope. Although the protocol
advises to work with one-hectare plots, the smaller ones proved to be more efficient and practically
feasible, especially in a mountainous area characterized by an extreme topography. The focus on
the site selection was to look for relatively undisturbed and old-growth forest patches with a
(nearly) closed canopy, lacking ravines or rivers. The reason for this was to avoid forest edges as
these show different attributes in measured traits (Ewers et al., 2007). Also the accessibility and
the slope of the plots was something to be taken into account as the objective of these plots is to
31
make them permanent. Each square plot of 40x40m, was divided into 4 subplots to facilitate further
measurements. The principle axes together with the center point were registered by measuring
geographic coordinates and altitude with a GPS (GPSMAP® 64s, Garmin, Taiwan). Finally, the
topography of every plot was recorded by measuring the slope of the four borders of each subplot.
The field campaign was completed by making the plots permanent for further studies by digging
out the corners of the plot and by placing a brick in the center of each plot. An overview of the
obtained permanent square plots can be seen at figure 3.2.
2158
2141
2937
2240
2456
2293
2167
2522
2879
2500
2875
2767
2761
1760
1659
2523
1835
1799
2557
1753
Figure 3.2: Map of the Nyungwe Forest National Park with the dots representing the locations of the
permanent sample plots and the boxes giving details of the measured altitude per plot. The grey star is the
location of the visitor’s center Uwinka (figure acquired with Google Maps).
3.1.2. Sample collection
To begin, all living trees with a DBH > 10 cm were tagged with a numeration tag 30 cm above the
measured DBH (which was at 1.30 m), each facing the same direction per subplot. Multiplestemmed trees are tagged only on the stems with diameter >10 cm at 1.30 m height, while it was
noted carefully these stems consist of the same tree. A tree was included in the plot the moment >
50% of the root system was inside the plot border. All diameters were measured at DBH, 1.30 m
straight line distance along the trunk at the downhill side, except if needed to be adjusted for
deformations or buttresses to a higher point on the trunk. Climbing vegetation was excluded out of
the diameter measurement. Along with the diameter, tree species and other characteristics like
bole form and adjusted points of measurements were noted, following accurately the RAINFOR
protocol. For the species identification was relied on the knowledge of a local botanist.
32
For 20% of all the trees per plot, the total tree height and the height of the lowest (heavy) branch
got measured with a laser rangefinder (Forestry Pro, Nikon, Japan). For the most abundant tree
species per strata (covering 95% of the basal area), leaf and wood samples were collected.
Practically, 3 randomly chosen trees belonging to the required species got sampled per stratum. In
addition, two tree species occurring along the studied range of the transect (Psychotria mahonii
and Syzygium guineense) were sampled 5 times per stratum in order to look for an infraspecific
response. If a tree was not adequate to be measured (not climbable, insufficient diameter, not
accessible, etc…) another potential tree from the same species was selected to be sampled in the
neighboring area, if possible in the same plot. Concerning the leaf samples, preferentially sun
leaves were collected, but if not possible lower leaves were gathered. At least 10 leaves per tree
were collected and stored in paper envelopes. After collecting the leaves, the contours of the
leaves were drawn on a A4 blank paper. Two wood samples were collected on both sides of the
tree (uphill and downhill) with an increment borer (16”, Haglöf, Sweden) and stored in paper
straws. Soil samples were gathered from the fragmentation layer and the mineral layer together
with a bulk density measurement of the mineral layer. Litter samples were collected from an area of
25x25 cm on 3 random places in each plot, together with a composite sample of the mineral soil
from 5 places within each plot, consisting of 6 different soil layers (0-5 cm, 5-10 cm, 10-20 cm, 2030 cm, 30-50 cm and 50-100 cm). In addition, bulk densities were taken with a Kopecky ring from
the mineral soil on 3 random places per plot. Finally all samples were air dried in the sun before
transport to Belgium and Congo for further analysis.
3.1.3. Lab analysis
The collected samples were pre-processed for analysis. This included the drying of all samples for
at least 48 hours in an oven at a temperature of 60° C, followed by a homogenizing process for leaf
and soil samples. Leaf samples were grinded with an ultra centrifugal mill (ZM 200, Retsch,
Germany) and soil samples with a planetary ball mill (PM 400, Retsch, Germany). Thereafter, both
sample collections, leaves and soil, were weighted with a precision balance.
In a next step, leaf and soil samples got analysed for total carbon, nitrogen, δ13C and δ15N, using
an elemental analyzer (ANCA-SL, PDZ Europa, UK) coupled to an IRMS (20-22, SerCon, UK).
Therefore test samples needed to be analyzed prior to the actual analysis in order to know the
exact amount of C and N in each sample type to ensure the highest result accuracy. Once known,
the exact amounts where measured with a precision balance and collected in round cylindrical
cups, which were then introduced into the elemental analyzer and IRMS. These machines gave as
result the percentage of total carbon and nitrogen as well as both stable isotopes referred
percentages. The isotopes are expressed in ratios, using the reference material consisting of a lab
standard (Pee Dee Belemnite for carbon and air for nitrogen) with an isotopic composition of -27.01
+- 0.04% for δ13C and 2.69 +- 0.15% for δ15N.
To calculate the leaf areas, all contour drawings were first scanned and edited with Irfanview to
correct for mistakes entered by the scanning process and to fill op the contours. Thereafter the
edited leaf images were analyzed in ImageJ with the following actions in sequence (implemented in
a macro): setting the scale (using the dimensions of an A4 paper in cm), setting a threshold
(convert all background pixels to white and all object pixels to black), applying a despeckle median
33
filter (correct further for the mistakes resulting from the scanning process resulting in a solid object)
and analyzing the particles (calculate the area of each object, using a minimum area as a threshold
to ignore insignificant anomalies). Once the leaf areas were known, the specific leaf area could
be calculated by dividing the areas through the leaf weights.
Wood density got determined with the water displacement method as suggested by Chave
(2005). Therefore the wood samples were first weighted and afterwards submerged in a body of
water placed on a precision scale, immediately after they were dried in order to prevent diffusion of
water from the air into the samples. After the balance is tarred and the wood sample submerged
without touching the container holding the water, the balance records the weight of the displaced
water, which is equal to the sample’s volume (due to water’s density of 1 g cm-3). An additional
standard correction is needed for the displaced volume owing to the pincet holding the wood
sample, which is determined beforehand. From both the displaced weight in water, which can be
converted to a volume of water and hence the sample volume, and the dry weight, the wood
density can by calculated.
3.2.
Data analysis
Prior to the analyses, the acquired data from the fieldwork was enriched with some external data.
Firstly, the tree heights of all trees are estimated, using plot-level allometric diameter-height
relations (done in the parallel thesis study by Van der Heyden, 2016). Secondly, the wood densities
were complemented with the global wood densities from the DRYAD global wood density database
(Chave et al., 2009; Zanne et al., 2009). In addition, more traits could be derived from the base
traits assembled with the sample collection (LNC, LCC, d15N, d13C and LA). As the foliar nutrients
were initially measured on mass basis, an extra calculation was done to also have the leaf carbon
content per area (LCCa) and the leaf nitrogen content per area (LNCa). To complete the trait set,
the ratio between carbon and nitrogen was calculated (C:N). All these traits together with the
previous calculated SLA and WD resulted in 11 functional leaf traits (SLA, LA, WD, TH, LCC,
LCCa, dC13, LNC, LNCa, d15N and C:N) which were used for the functional diversity analysis.
3.2.1. Taxonomical analysis
As already mentioned, functional diversity analysis was first preceded by an assessment of the
taxonomic diversity, initiated by comparing tree and species numbers per plot and per stratum. In
addition, strata were compared by using Shannon diversity index, Gini-Simpson diversity (opposite
of Simpson similarity index) index and Bray-Curtis dissimilarity index (actually the opposite of
Sørenson similarity index). Shanon-index quantifies the uncertainty that a random individual equals
a certain species and the Gini-Simpson index is the chance that two random picks from the
population are different species (Tuomisto, 2012). The Bray-Curtis gives a reference for the
dissimilarity between 2 populations.
3.2.2. Functional diversity analysis
The actual functional diversity analysis was done with a distance-based approach as suggested by
Laliberté & Legendre (2010). To resolve this, a species-functional trait matrix and a plot-species
34
abundances matrix were assembled. For the trait matrix, all traits were averaged per species,
except the tree height whose average was calculated with the share of the 10% tallest trees in
order to represent maximum tree height. The abundances matrix the basal area derived from the
DBH was used to calculate the share of every tree species in each plot. Subsequently, the principal
coordinate analysis ordinations (PCoA) were calculated for the total trait matrix, leading to a
species-by-species distance matrix. This PCoA provides a Euclidean trait space (were all
distances are actual distances between species) and the obtained scores of the PCoA are then
used as ‘traits’ to compute the indices Fric, FEve, FDiv (Villager et al., 2008) and FDis (Laliberté &
Legendre, 2010). However, if the calculated PCoA axes are negative, corrections should be used
to be able to represent the species-by-species distance matrix in the Euclidean space (possible
corrections are square-root, cailliez or lingoes). A fifth indice, RaoQ (Botta-Dukát, 2005), can be
calculated from the uncorrected species-by-species distance matrix. Finally the community-level
weighted means of the trait values is computed by weighing the mean trait values of the species by
their relative abundances.
The functional diversity indices were assessed for multiple traits as well as for single traits. The
latter should shed more light on the results of the multiple trait analysis, if not functioning as more
informative and ecological relevant indices (Lepš et al., 2006) because communities can have
different levels of diversification for every trait. When calculating functional diversity with multiple
traits, it is important to use traits representing different ecological characteristics, as otherwise
double weight can be allocated to a single characteristic (represented by two different traits).
Therefore, the functional traits were tested for a linear correlations using the Pearson productmoment correlation coefficient. Also a principal component analysis was done on all functional
traits to have a more visual representation of the correlations and relative weights between each
other. These correlations can be a good reference for the trait selection, but is not conclusive as
correlated traits can have ecological and functional differences (Lepš et al., 2006). That’s why
maybe the best approach is to start with good thought-out a priori selection.
3.2.3. Statistical analysis
To compare the results of different strata with each other, a Kruskal-Wallis test (also known as a
one-way ANOVA on ranks) is used to test wether they have the same distribution. When a
significant difference is found, a pairwise Mann-Whitney U test can be consulted to compare each
stratum individually to the others. To assess the relation of indices and indexes with the elevation,
a simple linear regression was used to look for a possible trend.
All calculations were done in the freely available statistical software environment R (R Core Team,
2015), whereby the basis for the functional diversity analysis was mainly done with the FD package
(Laliberté & Legendre., 2010; Laliberté et al., 2014). It is a package, which helps computing
different multidimensional functional diversity (FD) indices and the community weighted means. Of
course also other functions were used to calculate diversity indices, using the vegan package
(Oksanen et al., 2016).
35
4. Results
4.1.
Trait analysis
4.1.1. Overview table
The main functional traits mentioned in Material & Methods are reported in an overview table below
(table 4.1). Maximum mean values for SLA, LA, LNC and δ15N are observed for the lowest plots on
Stratum 1, while minimum values for the same traits are observed for the highest plots on Stratum
4. Similarly, a minimum value for δ13C is observed in Stratum 1 and a maximum value for LCC and
δ13C in Stratum 4. The extreme values in SLA observed in the lowest stratum are caused by the
tree species Alangium chinense, while Podocarpus latifolius is responsible for the minimum values
of SLA registered in the highest stratum. The high numbers (N) for WD and TH are attributable to
the adopted data as described in Material & Methods.
4.1.2. Correlation table
The complete list of functional traits averaged per species was used to perform a correlation table
(table 4.2). Prior to this, LNCa and LCCa were log-transformed because their distribution without is
very skewed (see 8.1 in the Appendix), these logarithms are kept in the further analyses to have a
better distribution. SLA was positively correlated with LNC (R = 0.75; p < 0.001) and negatively
with C:N ratio (R = 0.69, p < 0.001), while LA is also positively correlated with LNC (R = 0.57, p <
0.001) and δ15N (R = 0.53, p < 0.001) and negatively with logLCCa and logLNCa (R = 0.83 and
0.87 respectively; both with p < 0.001). Thereby was the log of LCC per area positively correlated
with log of LNC per area (R = 0.96; p < 0.001). C:N ratio showed the highest negative correlation
with LNC (R = 0.93; p < 0.001) while in comparison C:N correlation with LCC was insignificant. A
correlation between LNC and its isotope was also significant (R = 0.63, p < 0.001). Finally it’s
notable that TH, nor δ13C showed any significant correlation with any other trait. The correlations
were also visualized in a principal component analysis (see 8.2 in the Appendix).
36
Table 4.1: Main functional traits overview with specific leaf area (SLA), leaf area (LA), wood density (WD), tree height (TH), leaf carbon content (LCC), leaf carbon
isotope signature (δ13C), leaf nitrogen content (LNC), leaf nitrogen isotope signature (δ15N) and carbon-nitrogen ratio (C:N). Mean values, standard deviations,
Statistical Group
ranges and statistical groups are calculated per stratum: M±SD (minimum-maximum)
.
Table 4.2: Correlation table of all functional traits: specific leaf area (SLA), leaf area (LA), wood density (WD), tree height (TH), leaf carbon content (LCC), leaf
carbon content per area (LCCa) , leaf carbon isotope signature (δ13C), leaf nitrogen content (LNC), leaf nitrogen content per area (LNCa), leaf nitrogen isotope
signature (δ15N) and carbon-nitrogen ratio (C:N). P-values indicated as: *** p < 0.001, ** p < 0.01, * p < 0.05.
SLA (cm2 g-1) LA (cm2)
LA (cm2)
0.40 *
WD (g cm3)
0.11
TH (m)
-0.06
LCC (%)
LCCa (g
cm2)
δ13C (%)
LNCa (g
cm2)
δ15N (%)
C:N
0.14
0.29 *
-0.12
-0.37 *
-0.84 ***
0.27 *
-0.17
-0.23
-0.13
-0.28 *
0.19
-0.15
0.27 *
0.42 **
-0.69 ***
LCCa (g cm2) δ13C (%) LNC (%)
LNCa (g cm2) δ15N (%)
-0.34 *
-0.47 **
-0.17
LCC (%)
-0.34 *
-0.27 *
0.75 ***
LNC (%)
WD (g cm3) TH (m)
0.54 ***
-0.79 ***
0.54 ***
-0.48 **
0.27 *
0.41 **
0.04
-0.13
0.25
-0.52 **
-0.18
-0.12
0.39 *
0.96 ***
-0.09
0.22
-0.34 *
-0.64 ***
-0.27 *
0.63 ***
-0.50 ***
0.10
-0.23
0.63 **
0.27 *
-0.93 ***
0.40 **
0.16
0.22
-0.27
i
-0.67 ***
37
4.1.3. Community Weighted Means
The results of the community weighted mean analysis were used in a linear regression with the
altitude (Figure 4.2). This resulted in 11 different plots for each functional trait. All traits, except
LCC and TH, showed a significant trend with the altitude. This trend is negative for SLA, LA, WD,
LNC en δ15N, while it was positive for δ13C, C:N and both log-transformed LCCa and LNCa. The
LCC is way better explained by the height on an area basis (r2 = 0.756, p < 0.001) than on mass
basis (insignificant). The latter was also true for LNC, but the difference was negligible small.
Figure 4.1: Linear regressions of the community weighted means of the functional traits: specific leaf area
(SLA), wood density (WD), tree height (TH), leaf carbon content (LCC), log of leaf carbon content per leaf
area (logLCCa), leaf carbon isotope signature (dC13), leaf nitrogen content (LNC), log of leaf nitrogen
content per leaf area (logLNCa), leaf nitrogen isotope signature (dN15), leaf area (LA) and C:N ratio.
38
4.1.4. Species-specific traits
From the 301 Psychotria mahonii trees in the sampled plots, 20 were measured on their functional
traits. The only trait which showed a significant response with the change in altitude for Psychotria
mahonii was the plot averaged δ15N (r2 = 0.668, p < 0.001 – figure 4.2), whereby a negative trend
was found.
From the 232 Syzygium guineense trees within the plots, also 20 trees were measured. Also for
this tree species the plot averaged δ15N was the only trait showing a significant trend (r2 = 0.425, p
< 0.05).
Figure 4.2: Linear regressions of the relation between δ15N, measured for the species Psychotria mahonii
(A.) and Syzygium guineense (B.) and elevation.
4.2.
Diversity analysis
4.2.1. Taxonomic diversity
In total, 66 tree species were encountered amongst the 1931 trees measured in this study (table
4.2). This was almost 29% of the total presence of 230 tree species noted for the Nyungwe forest,
which was well surveyed in comparison with other forests on the Albertine rift (Plumptre et al.,
2007). In the two highest strata nearly the double amount of tree individuals were registered in
comparison with the two lower ones. In contrary, the maximum amount of tree species were found
in the lowest stratum, while this amount decreases steadily in every higher stratum.
39
Table 4.3: Overview of the observed tree numbers and species numbers in total, per stratum and per plot.
The most dominant species in the lower plots were Cleistanthus polystachyus (15.5% of the
observations in in stratum 1 and 13.2% in stratum 2), Grewia mildbraedii (13.8% only appearing in
stratum 1) and Strombosia scheffleri (6.3% and 8.9% respectively). Dominance of species rised in
the third stratum with Syzygium guineense counting for 30.7% and Psychotria mahonii with 15.6%.
The share for the dominant species reached its maximum in the last stratum when 63% of the total
trees in this sites was represented by just two species Podocarpus latifolius and Psychotria
mahonii (33.0% and 30.3% respectively).
When looking at the Shannon diversity index and the Gini-Simpsons diversity index between strata,
similar indices were found (table 4.3). The Shannon index decreases for every next strata, while
the Gini-Simpson first reaches a maximum in the second stratum before it declines further with the
higher strata. Still the difference between the first two strata for both indexes was very small.
Finally Bray-Curtis dissimilarity indexes shed more light on the differences between the different
strata. Indeed, the smallest dissimilarity was found between stratum 1 and 2 (0.49), while the
differences with stratum 3 and 4 were much higher or both (respectively, 0.92 and 0.94 for 1, 0.82
and 0.92 for 2). Also stratum 3 and 4 seemed to be slightly more related to each other as their
dissimilarity gave a value of 0.65, which was still higher than the value for the first two strata.
Table 4.4: Overview of the diversity indices per stratum. H stands for Shannon Index and S stands for GiniSimpson Index.
4.2.2. Functional diversity
4.2.2.1.
Multiple traits
The distance-based functional diversity analysis done with SLA, WD, C:N and d δ13C resulted in 5
indices for the elevational transect study (table 4.5). Both FRic and FDiv rely on finding a minimum
convex hull to include all species whereby a dimensionality reduction is required when there are
more species than traits. This is the case for plot 18 with only 4 species, leading to the removal of
1 PCoa axis from the 4 in total, resulting in a quality of the reduced-space of 0.93, which can be
interpreted as a r2-like ratio (Laliberté et al., 2010). FRic reached a maximum in the first stratum
40
and was lowest on the highest stratum. This highest stratum also had the maximum average for
FEve, FDiv, FDis and RaoQ. While the trend for RaoQ was similar as the one for FDis, the
absolute numbers were higher.
A linear regression approved that the functional richness can be explained by the height (r2 =
0.407, p < 0.01; fig 4.4). Thereby an extreme value of 19.18 was observed for the lowest plot,
which will be further investigated in the single trait analysis. Also linear regressions for FDis and
RaoQ were significant with the height (r2 = 0.307, p < 0.05 and r2 = 0.268, p < 0.05 respectively),
whereby both showed very similar trends. It can already be noted however that responses of the
indices on the height change inevitable with the chosen traits.
4.2.2.1.
Single trait
To gather deeper insights in the observed trends with the multiple trait analysis, the same distancebased analysis can be done with only one functional trait. This implies the functional richness is
measured as a range instead as a convex hull volume, whereby functional divergence can’t be
calculated.
Specific leaf area:
A maximum value of 5.41 for FRic is found for the lowest plot, parallel with a maximum average for
the first stratum (table 4.6). As with the multiple traits, SLA also reached maximum values in the
highest stratum for FEve, FDis and RaoQ. No significant linear regressions were found for any of
the indices.
Wood density:
FEve had it’s maximum in stratum 4, while FDis and RaoQ were both at maximum in stratum 1
(table 4.6). A linear regression with the altitude was negative and significant for wood density’s
FRic (p < 0.01, r2 = 0.411; fig 4.5 - A), opposite with the other indices that didn’t show any
significance.
C:N ratio:
The C:N ratio showed as only trait for FEve a significant difference in the statistical groups. Stratum
4 differed namely with stratum 1 and 3 (p < 0.05 and p < 0.01 respectively). Thereby, it had a
maximum value in the fourth stratum (table 4.6). Also FDis and RaoQ had a different distribution for
stratum 4 in comparison with the others (p < 0.01 for all of them). A linear regression of FRic
showed a positive significant trend with the altitude (r2 = 0.451, p < 0.01; fig 4.4 - B), and a positive
significance for FDis related to the altitude (r2 = 0.239, p < 0.05; fig 4.4 - C). For the other indices
there were no significances with the altitude.
Leaf carbon isotope signature:
Maximum values above 4 were noted for three plots in the lowest stratum. Again, FEve had its
maximum in stratum 4 (table 4.6). The FRic of δ13C was negatively related to the altitude (r2 =
0.451, p < 0.01; fig 4.4 - D), other indices were not.
41
Table 4.5: Overview table of average functional diversity indices for the different strata: FRic (functional
richness), FEve (functional evenness), FDiv (functional divergence), FDis (functional dispersion) and RaoQ
(Rao’s quadratic entropy). Mean values, standard deviations, ranges and statistical groups are calculated per
Statistical Group
stratum: M±SD (minimum-maximum)
.
Table 4.6: Overview table of the single trait functional diversity indices: FRic (functional richness), FEve
(functional evenness), FDis (functional dispersion) and RaoQ (Rao’s quadratic entropy). Mean values,
standard deviations, ranges and statistical groups are calculated per stratum: M±SD (minimummaximum)Statistical Group.
42
Figure 4.3: Overview of significant linear regressions of functional diversity indices with height: A. functional
richness, B. functional dispersion, C. Rao’s quadratic entropy index.
Fig 4.4: Overview of the significant linear regressions from the single trait functional diversity indices with the
altitude: A. functional richness of wood density, B. functional richness of C:N, C. functional richness of δ13C
and D. functional dispersion of C:N.
43
5. Discussion
5.1.
Functional tree traits response on elevational gradient
5.1.1. Functional leaf traits
The results suggest a significant response from the functional tree traits on a changing elevation in
the national park Nyungwe. The observed responses corroborate with results from other similar
scientific studies in tropical montane forests, however equivalent experiments in TMCF’s are
among the least represented, leading to a gap in the understanding of trait responses in these
specific ecosystems (Van de Weg et al., 2009).
Differences in SLA and LA were both well explained by the altitude, which is in line with similar
observations done in other scientific studies in tropical montane forests (Homeier et al., 2010; Van
de Weg et al., 2009; Soethe et al., 2008; Moser et al., 2007; Kitayama & Aiba, 2002; VelazquezRosas et al., 2002), but also in temperate forests (Van de Weg et al., 2009). SLA is related to the
product of leaf density and leaf thickness and is mostly influenced by daily sun radiation, available
nutrients (especially nitrogen), water and temperature (Poorter et al., 2009). So the observed
decrease in SLA could be a reaction to increased UV-B irradiance caused by the frequent cloud
cover whereby a thicker leaf contains more protective compounds (Flenley, 1995) and to the lower
temperature at high altitudes resulting in compacter cells causing the smaller SLA’s. Another
explaining factor for the decreased SLA can be found in the altered water availability because the
exuberant availability of water at higher areas leads to waterlogged soils which result in less water
availability for the plants, leading to a decrease in SLA. Altogether, SLA could be an important
indicator for plant strategies, what implies that a low SLA is an indicator for slow growing species
with leafs with a long life-span, which has in turn an effect on the nutrient retention time (Westoby
et al., 2002; Ordoñez et al., 2009). This suggests that higher altitudes are confronted with a slowergrowth situation resulting in the decreasing SLA. Additionally, it has also been suggested that this
phenomena might be caused by thicker cell walls in order to protect them from fungal infections in
the cold and moist climate of TMCF’s (Edwards and Grubb, 1982). LA on the other hand might
have similar explanations as it is well correlated with SLA, however this trait is also highly
subjected to the age of the tree and position of the leaf in the total canopy (light versus shadow
leafs). Additionally, LA is strongly influenced by wind exposure as smaller leaves are less prone to
wind damage (Dolph & Dicher, 1980), which might also declare the decline in LA. Altogether, LA
seems a less suitable species-specific trait to use as compared to SLA.
Just as SLA, LNC showed a negative trend with the altitude. Together with the fact they have a
correlation of 0.77, this leads to the idea of SLA serving as a proxy for LNC. This correlation is also
observed in worldwide comparisons (Reich et al., 1997; Diaz et al., 2004; Wright et al., 2004) and
Diaz et al. (2004) already adduced the idea of using SLA (which is a soft trait) as a proxy for LNC
(which is a hard trait). In fact this means that both leaf area and the amount of nitrogen have the
same proportion/relation with the leaf weight. The foliar nitrogen concentration on area basis is
slightly better explained by the elevation than the concentration on mass basis, but the difference
is negligible.
44
As was the case for SLA, the LNC findings corroborate with other studies done in tropical mountain
areas (Van de Weg et al., 2009; Soethe et al., 2008; Moser et al., 2007; Reich et al., 2004;
Kitayama & Aiba, 2002). The decreasing trend with altitude might point in the direction of nutrientconservative species as was already suggested by the SLA results. The lower foliar N levels on
those higher altitudes could be explained by the lower mineralization rates in the soil caused by the
lower temperature (Tanner et al., 1998), high water saturation typical for TMCF (Cavelier et al.,
2000) or even changes in the decomposer communities (Richardson et al., 2005). So indeed the
LNC could be a reaction on the lower nutrients availability in the soil. Again, Edwards and Grubb’s
theory (1982) of a counter strategy against fungal infestation in the TMCF’s by the development of
thicker cell walls could also contribute to the lower LNC.
Interestingly enough the absolute levels of measured SLA and LNC in other tropical elevation
gradient studies are sometimes 50% lower than the ones measured in this study (see overview
table of measured traits), parallel with results from Van de Weg et al. (2009). Differences in both
SLA and LNC might be due to other environmental conditions (Asner et al. 2015) or might be
caused by a difference in soil fertility (Asner et al., 2016). Differences in the LNC could be
explained by other temperature regimes and hence mineralization or even by the proximity of large
scale biomass burning leading to a larger nitrogen deposition (Fisher et al., 2013). Still the main
focus of this study was to look at changes within this altitudinal gradient and less to compare
absolute values of the measured traits, whereby the observed trends carried the most useful
information.
Nitrogen was also assessed in ratio with the carbon content as the C:N ratio. A significant raising
C:N ratio was observable due to a more or less stable LCC and a decreasing LNC. A
supplementary ratio between nitrogen and phosphor would be an interesting trait, but phosphor
measurements are lacking in this study. Other studies in TMCF’s however found a typical decrease
in N:P which suggests that nitrogen becomes more limiting on higher altitudes in proportion with
phosphor (Tanner et al., 1998; Fisher et al., 2013; Nottingham, 2015). The greater availability of
phosphor would be declared by emerging phosphor sources through tectonic uplift, erosion and
landslide activities (Porder et al., 2007), while nitrogen input on the other hand is lowered through
lowered decomposition and mineralization rates, a decreased nutrient uptake because of low
temperatures and less mycorrhizal fungi in the soil which enhance the nutrient uptake (Leuschner
et al., 2007). As a consequence, general foliar nutrients in TMCF’s are lower than in lowlands
leading to dilution of nutrients in the leaf dry matter (Soethe et al., 2008). It is also expected that a
greater altitudinal range would lead to an increase in LCC with altitude, which could be explained
by a slightly increase in ‘non-structural carbon’ like starch, lipids and low molecular sugars (Zhao et
al., 2014).
Finally, both leaf δ13C and leaf δ15N showed significant trends. Nitrogen’s isotope is correlated with
LNC and shows as well a decreasing trend with increasing altitude. As it is expected that the
isotope would be discriminated in cold and wet conditions (Craine et al., 2009), this is confirmed by
the wet and colder conditions in TMCF’s leading to the lowered share of δ15N. The results might
also suggest a larger mycorrhizal fungi population as they increase the discrimination of the
isotope (Craine et al., 2009). The most interesting interpretation however from the δ15N value could
be about the N-cycle in ecosystems (Pardo et al., 2006). It is suggested that a more open N-cycle
(soils with more nitrification, mineralization and leaching) have a higher enrichment of the isotope.
45
This would mean that TMCF have a tendency to a more closed N-cycle, which could be another
possible explanation for the decreasing nitrogen levels. Despite the mechanisms behind the δ15N
discrimination might not be completely cleared out, it is significant that it is strongly related to the
foliar nitrogen level and an important reference for the N-cycle.
Carbon’s stable isotope, δ13C, on the other hand is much better understood in a way it can be seen
as a proxy for WUE, however in this study the trait was measured on changing altitudinal regions
whereby factors like leaf morphology, water availability, CO2’s partial pressure & photosynthetic
capacity where all variable. This complicates the observed trends in δ13C in response to the WUE
and hence this trait can only tell something about the stomatal conductance. Because δ13C shows
a clear increasing response with altitude, this means a lower uptake of CO2 at higher elevations
leading to lower discrimination of the isotope. This decrease in stomatal conductance could be
caused by the increased water availability in the soil (waterlogged situations) as well as the higher
vapor pressure encountered in TMCF’s.
Despite these conclusions, it needs to be kept in mind that the measured leaf traits aren’t a one on
one response to the abiotic environment. Differences in site-specific conditions (like micro-relief)
leading to a certain microclimate together with intra-specific trait variability also influence the
canopy values. Still there are some strong arguments pointing in the direction of a plant strategy
which is adapted to the harsh conditions of the TMCF. It seems that the plant community is
constrained in their nitrogen availability as visible in LNC, δ15N, SLA and C:N trends, which could
be explained by the altered N-cycle caused by lowered temperatures. Together with more UV-B
irradiance and higher risks on fungal infestation, this leads to the xeromorphic leaves confirming
the idea of a conservative plant strategy.
5.1.2. Functional wood & whole plant traits
Despite the variable ranges in wood densities among species, the trait did show a significant
decreasing response on the elevation. This observation corroborates with the results from Chave
et al. (2006), but contradicts Culmsee et al. (2010), which have found increasing wood densities in
relation with increasing elevation. The small decrease observed in this study could be due to more
investment in the hydraulically efficiency of the tree at the expense of less mechanical stability. The
latter one is possible as trees tend be smaller at higher altitudes wherefore is less invested in
mechanical support, while the former could be explained by the humid conditions in TMCF’s
resulting in a lower vapor pressure and therefore a bigger need of this hydraulically efficiency.
Tree height on the other hand didn’t show a significant trend as this data leant upon allometric
diameter-height relations and wasn’t therefore a trait measured per species. It is however highly
expected and even visually observed in the field that the tree height decreases with rising altitude.
Thereby, a declining trend is shown in many other studies (Grubb, 1977, Fisher & Malhi, 2013;
Homeier et al., 2010; Moser et al., 2008) and possible mechanisms are probably similar to the
ones explaining a lowered net primary production listed in 2.3.2.3 Net primary production.
5.1.3. Species-specific traits
46
Both Psychotria mahonii and Syzygium guineense functional traits didn't show any significant
trends with a change in elevation, with the exception for δ15N. This corroborates the observation
that chemical diversity within communities is driven by the differences between species rather than
by plasticity within species (Asner & Martin, 2016). As chemical characteristics appeared to be
correlated with some functional traits like SLA, this might explain why no significant trends were
observed. The observed decrease in δ15N on the other hand might suggest a lower nitrogen
availability in the soil and therefore indirectly lowered LNC, however this trend isn’t significant for
both species. It is rather a reference for the nitrification as this is better reflected in δ15N than it is in
LNC (Pardo et al., 2006). Of course a lack of significance could be due to the rather small
elevational range or even the small sample pool used in this study.
5.2.
Biodiversity response on elevational gradient
5.2.1. Taxonomic diversity
The results in the tree species diversity are reflected in general findings about plant diversity of
tropical montane forests (Kitayama, 1992; Vazquez & Givnish, 1998; Homeier et al., 2010; Soethe
et al., 2008), whereas all observed a decrease in species numbers with increasing altitude. The
range of plant species is determined by both the biotic and abiotic environment and by the arrival
of species as a result of migrations in a context of historical climate changes. A high number of
endemics strengthens the idea of plant diversity caused by high speciation rates (Homeier et al.,
2010), which is indeed the case for the Nyungwe forest with 137 Albertine endemics for the 230
noted tree species in the park (Plumptre et al., 2007). The decline in species towards the
mountaintops could be explained by the fact that these habitats comprise smaller areas which are
more isolated from similar habitats resulting in less migration (Vázquez & Givnish, 1998), but is of
course also due to the harsher environment which is further discussed in the functional diversity.
The Shannon-index and the Gini-Simpson-index suggested a decrease in species diversity with
increasing elevation. This observation is similar with other studies in cloud forests where they
calculated the Shannon-index and found minimum values for the highest elevated plant
communities (Weaver et al., 1986; Aida & Kitayama, 1999; Oosterhoorn & Kappelle, 2000). As
both indexes are references for both the amount of species and their distribution, they could serve
as a prospection of the functional diversity. It is however already suggested by Hurlbert in 1971
that these indices are somewhat dubious in explaining ecological mechanisms by a mere analysis
of the number count. The comparison with the Bray-Curtis index suggests a similarity between the
plant communities of respectively the first two and the last two strata, which might suggest a
delineation of the plant communities around the middle of the transect range, but more information
is needed to confirm this.
5.2.2. Functional diversity
It is rather hard to compare the observations of functional diversity indices along the transect with
literature because these indices haven’t been used regularly for the study of ecological processes
in forests, but more for the theoretical development of the understanding of these indices. Merely
three papers were found that used similar indices, all to assess disturbances in tropical forests
47
(Carreño-Rocabado et al., 2012; Magnago et al., 2014, Apaza-quevedo et al., 2015), despite
another study used real vegetation data, it rather tried to validate the functional diversity indices
(Bello, 2009). In addition, Mouchet et al. (2010) did some research with artificial data, but again the
focus was more on the functional indices validation. This is actually not surprising as the indices
are rather new (Mason et al., 2005; Villager et al., 2008; Laliberte et al., 2010) and their projections
are still in an early-development stage (Diaz et al., 2007). Awareness needs to be raised thereby
because functional diversity values could be influenced by the species richness, which is inevitable
for elevational studies (Mouchet et al., 2010). Still, it is evident that the observed functional
diversity indices values can lead to some conclusions towards the potential niches of the tree
vegetation (Mason et al., 2005; Kraft et al., 2008).
The functional diversity was studied by the use of 4 functional traits, but as already mentioned, the
choice of functional traits has a high influence on the observed results in the functional diversity
analysis. So a good a priori selection of the most ecologically relevant traits is needed (Lepš et al.,
2006), resulting in the choice of SLA, WD, CN and δ13C. It needs to be taken into account that
some traits are highly correlated by nature as they represent the same character, resulting in a
double weight for this character in the analysis. Therefore it was preferred to use C:N to represent
the foliar concentrations instead of LCC, LNC, LCCa or LNCa and only SLA instead of both SLA an
LA. Tree height isn’t used in the model as this trait wasn’t sampled on species occurence, but on
diameter classes, making it not representable for the functional diversity of the observed trees.
Finally δ15N is considered to be site-specific rather than a species-specific trait and hence less
relevant as a functional trait. This is also clearly confirmed in the intra-specific relations with
altitude whereby δ15N was the only trait giving a significant response for an individual species
suggesting greater plasticity for this trait.
Because the functional richness decreases with increasing altitude, either the abundance of
different niches is larger on lower altitudes supporting more functional groups (Kraft et al., 2008) or
these lower altitudes are closer to their maximum potential of different functional groups while on
the higher altitudes the population is still under-represented. The latter explanation could be due to
either more migration in the lower parts throughout history resulting in a more diverse use of the
environment (Ramirez-Barahona & Eguiarte, 2013) or by low extinction rates which supports more
speciation (Homeier et al., 2010). These possible explanations for the decline in functional richness
could be elucidated by a deeper study of the environment’s ecology, a better understanding of the
past glacials in the area and more insight in the phylogenetic relations between regional
populations. However, when looking at the functional evenness, there is no significant difference
between different strata, meaning the distribution of the functional trait values is rather equal. The
similar distributions of traits over different strata suggest that the niches related to these traits are
exploited on a similar level of resource utilization (Schleuter et al., 2010), which would mean that
the decline in functional richness is in response of lower availability of different niches instead of
under-represented populations at higher alitudes. Namely, if the functional evenness would for
example decline with rising altitude, this would rather suggest that some niches in the higher strata
are under-utilized or it would confirm that they have less diverse niches to offer. The observations
for functional richness and evenness are concurrent with the observed functional dispersions since
for the first strata there is a gradual rise while there is a strong increase for the highest stratum. As
functional dispersion is a measure for the deviation of the average value or position in the trait
space, this would suggest more heterogeneous niches or at least strong discrepancies between
48
the niches at those higher altitudes. Functional divergence could also be seen as the relative
functional richness (Laliberté & Legendre, 2006) which means indeed more speciation. The
extreme rise for the most elevated stratum could be explained by the dominance of a species with
extreme values, which is in this case Podocarpus latifolius having a low SLA, LNC and δ13C in
comparison with the second most dominant species Psychotria mahonii having higher values.
Finally the values of functional divergence increase again for the highest altitudes, which is equal
to a larger deviation in the distribution of the niches at the higher stratum, suggesting bigger
differences in the occupied trait space, which is reflected in more discreteness of niches.
Functional divergence is also seen as a proxy for the multi-functionality of the population (Mouillot
et al., 2001), again pointing in the direction of more specialization, but as well owing to the
dominance of two species at this altitude. Rao’s quadratic entropy index is been seen as a
combination between functional richness and functional divergence and is performing with the
exact same trends as the functional dispersion indice, which is in fact already predicted by
Laliberté & Legendre (2010), who admitted they give similar results, despite the fact they’re
computed in a different manner. RaoQ is a measure for the mean distance between two randomly
chosen species and could be seen as the Gini-Simpsons dissimilarity index for functional diversity
(Lepš et al., 2006) and so confirming the previously observed difference with the highest stratum
and the lower ones. It is therefore suggested no extra information could be acquired from this index
when using the other indices.
The single trait analysis could shed some more light on the behavior of a single trait in the
environment as not every trait may respond in the same extent, whereby information could be lost
in the multiple trait analysis. The observed results for WD and δ13C did confirm for example the
negative altitudinal response of the multiple trait functional analysis. On the other hand did the
functional richness of SLA not respond at all while C:N had a positive trend with rising altitude. This
means that towards the top, the C:N range enlarges with altitude and more different strategies are
probably used to cope with the lowered nitrogen availability. Also some differences were found in
the functional evenness and functional dispersion for C:N further suggesting respectively an overutilization of some low-nitrogen strategies and more speciation towards strategies related to
nitrogen. Yet, functional evenness is strongly depending on the species richness, which is
significantly smaller at the highest altitude (Mouchet et al., 2010), but also visible in maximum
values of functional evenness for every trait.
Using the niche model (Tilman, 2001), greater habitat heterogeneity and an associated higher
potential biodiversity leads towards a greater productivity. Indeed, multiple sources concluded a
decrease of net primary production in TMCF’s (Kitayama & Aiba, 2002; Soethe et al., 2008), which
is consistent with less different niches at higher altitude. Also the niche model, consisting of the
theories of environmental filtering and niche differentiation (Kraft et al., 2008), could be applied on
these conclusions. The environmental filtering says that co-occuring species converge in plant
strategy because of barriers imposed by the abiotic environment. This could be observed for TMCF
where the harsh conditions narrow the viable plant strategies reflected in the decline of functional
richness indice. On the other hand there is the niche filtering, which says that co-occuring species
diverge in plant strategy to prosper co-existence of species. This could be reflected in the increase
of the functional divergence indice (Mouchet et al., 2010) whereby TMCF species show more
specialized plant strategies.
49
By combining both conclusions from the taxonomic approach and the functional diversity approach,
a wider picture can be formed about the TMCF. As the characteristics of this ecosystem already
suggest, it is a harsh environment for plant communities, which have therefore developed adapted
plant strategies. There is a lower availability in different niches what is reflected in a decrease of
biodiversity at higher altitudes and in more specialized species. This is visible in the xeromorphic
leafs, containing less nitrogen and having a lower exchange of CO2 and moisture with the
atmosphere, which is also a response on the lower availability of nitrogen. Altogether, this
perceptions lead to the idea of more conservative plant strategies in TMCF, whereby nutrients
have a longer retention time and plant investments are in context of survival, leading to slower
growing trees which is in turn reflected in smaller statures combined with a decrease in net primary
production.
These forests are at the risk of being outcompeted by better adapted species in a potential global
change context. As the temperature would rise, several processes like mineralization and
decomposition are being influenced leading to an accelerated nitrogen cycle, resulting in altered
conditions for these ecosystems. The formerly isolated species are then slowly confronted with an
increased competition of the other migrating species, which might dominate the old vegetation in
time. So it is sure that an altered global climate will have its impact on our forest, but we can only
try to enhance our understanding and predict potential events in advance of the real change
50
6. General conclusions & recommendations
The elevational transect study in the Nyungwe national park showed a visible response from the
plant community and their traits with different elevations. More niches and therefore more plant
strategies are available in lower areas, while these factors decline when going higher on the slope.
Our data suggests that this is mainly caused by lower nitrogen availability at this elevated areas, as
well as harsher conditions, leaving fewer possibilities for a plant to be viable and as such invoking
a stronger environmental filtering. The lowered sunlight irradiance and hence changed light
spectrum, lower temperatures and high water availability pushes the plants further towards more
niche filtering. The plants adopt thereby a more resource-conservative strategy in order to have a
higher rate of survival in these harsh conditions. This corroborates with previous observations and
expectations of TMCF from other contitnents, but this study sheds more light on different aspects
of this ecosystem, which hadn’t been studied with a functional diversity approach. Also it
contributes to the research of the African TMCF’s, which are currently underrepresented.
The use of the designated indices for functional diversity is still in an early-development stage
creating some uncertainty on the different conclusions. The results are for example very dependent
on the chosen traits, emphasizing the importance of a good trait selection during the actual field
work or during choice of data from databases. It could be that some important traits are missed or
not recognized yet as valuable for the ecosystem’s functioning. When calculating functional
diversity, averages of species are used resulting in a loss of potential intra-specific plasticity.
However, the data suggests that this variability is of minor importance here as was shown for the
two examined species.
Concerning the initial questions, there is indeed a certain response of the community-level traits on
the change in altitude. This was the case for almost every measured and calculated functional trait
with the exception of LCC and TH. A maybe small range in elevation could cause the former while
the latter is probably due to an alternative study goal during the sample stage. The other functional
traits on the other hand suggested an increased share of leaf carbon with altitude, linked with a
smaller share of nitrogen, as was visible for LNC(a), LCC(a) and δ15N. The SLA did also shifted
well as the leaves became smaller and thicker at higher altitudes. Finally δ13C also suggested a
lower uptake of CO2 caused by the higher elevation forest.
Also the different measures of diversity along the transect showed a response as the amount of
species was consistently smaller at higher altitudes. The dissimilarity between the high and low
elevation were very clear, but some overlap is natural between abutting strata. Especially the
differences at the top of the mountain where very clear, as was the case for functional diversity
indices. These extremes in functional diversity indices could however be influenced by the smaller
species diversity associated with this elevation. The stable values for functional evenness suggest
that al niches are exploited at the same levels and no relative differences are found in over- or
under-utilization. As the functional richness points to a narrower trait space at a higher altitude,
both components suggest more niche filtering at the highest altitude, which is only confirmed with
extremes for functional divergence and dispersion.
51
Altogether, plants in TMCF have adopted a well-developed resource-conservative strategy under
harsh environmental conditions, which can be observed by changing traits on the elevational
transect. A functional diversity approach is useful for ecological studies, but a deeper insight is
achieved by combining this with traditional taxonomic diversity.
This study can hopefully contribute to the growing knowledge of cloud forests and might initiate a
wider interest towards the African share of them. Hopefully the appreciation of the local community
can thereby be enhanced by the rising attention from a global scientific community, leading to a
better management system whereby respect and protection for the TMCF play a central role. Also
emphasis on a better integration of similar ecosystems through a wider network of connected
forests could enhance the resilience of these precious forests in the context of an altered climate
whose impact is not known yet. If possible, further investigations should also implement wider
elevational ranges as thereby less sensitive responses of some traits could be discovered. This
points to the demand of a better insight of important functional traits and their different shares in
the mechanisms steering the population assembly. Also their response on potential climate change
can be integrated in models predicting the future changes. Ecology studies remain a complex field
as large-scale knowledge is gathered from small-scale experiments. Luckily some have been
coping with this problem by implementing tele-detection techniques or new taxon-free sampling
techniques. It is maybe hard to admit, but every research set-up probably has its shortcomings
whereby experience needs to be gathered and shared.
These forests are at the risk of being outcompeted by better adapted species in a potential global
change context. As the temperature would rise, several processes like mineralization and
decomposition are being influenced leading to an accelerated nitrogen cycle, resulting in altered
conditions for these ecosystems. The formerly isolated species are then slowly confronted with an
increased competition of other migrating species, which might dominate the old vegetation in time.
So it is sure that an altered global climate will have its impact on our forests, but we can only try to
enhance our understanding and predict potential events in advance of the real change.
52
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8. Appendix
Figure 8.1: QQ-plots for the distribution of LCCa & LNCa before and after log-transformation.
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Figure 8.2: PCA to valuate possible correlations between functional traits and their weights.
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Figure 8.3: Barplots to visualize the functional diversity indices for the multiple trait analysis.
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