Faculty of Bioscience Engineering Academic year 2015 2016
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Faculty of Bioscience Engineering Academic year 2015 – 2016 Climate response of Terminalia superba from the Mayombe forest (Democratic Republic of the Congo): intra-annual stable isotope analysis in tree rings Mirvia Angela Rocha Vargas Promotors: Prof. Dr. ir. Pascal Boeckx Dr. ir. Jan Van den Bulcke Tutor: ir. Tom De Mil Master’s dissertation submitted in partial fulfillment of the requirements for the degree of Master in Environmental Sanitation
Transcript of Faculty of Bioscience Engineering Academic year 2015 2016
Faculty of Bioscience Engineering
Academic year 2015 – 2016
Climate response of Terminalia superba from the Mayombe forest (Democratic Republic of the Congo):
intra-annual stable isotope analysis in tree rings
Mirvia Angela Rocha Vargas
Promotors: Prof. Dr. ir. Pascal Boeckx Dr. ir. Jan Van den Bulcke
Tutor: ir. Tom De Mil
Master’s dissertation submitted in partial fulfillment of the requirements for the degree of Master in Environmental Sanitation
Copyright
"The author and the promoter(s) give permission to make this master dissertation available
for consultation and to copy parts of this master dissertation for personal use. In the case of
any other use, the copyright terms have to be respected, in particular with regard to the
obligation to state expressly the source when quoting results from this master dissertation."
Ghent University, August 19, 2016
Promoter
Dr. ir. Jan Van den Bulcke
Promoter
Dr. ir. Pascal Boeckx,
The author
Mirvia Angela Rocha Vargas
Acknowledgement
I want to start thanking God for the amazing opportunity to be here and be able to increase
my knowledge in the environmental area.
In the same way, I thank my tutor ir. Tom De Mil for allow me to participated in this study and
learn more about the interesting area of tropical forest and isotopes. Also to Dr. ir. Jan Van
den Bulcke and professor Dr. ir. Pascal Boeckx, for their collaboration and guidance me in this
study.
Special thanks to Prof. Dr. Peter Goethals, and to Veerle Lambert and Sylvie Bauwens, for
the opportunity to be here and make possible such a terrific experience that will definitely
improve my professional career.
Finally, but not less important, I would like to thank the support of my mom, dad and brother
for their constant support, to my cousin Andrea, for always said exactly what I needed to hear
and to all my family and friend in Venezuela and Bolivia.
To Galo, Pau, Zara, Juan, Jeff and all my friends here in Belgium to be like a family to me and
always be there. A special thanks to Javier, despite everything and the distance, he always
supported me and is my strength to continue.
TABLE OF CONTENTS
List of abbreviations ............................................................................................................... i
Abstract..................................................................................................................................ii
1. Introduction ....................................................................................................................... 3
2. Literature review ............................................................................................................... 5
2.1. Climate change and tree response over the tropics .................................................... 5
2.1.2. Greenhouse Gases - Carbon Dioxide ................................................................... 5
2.1.3. Temperature, Rainfall and Relative Humidity (RH) ............................................... 6
2.2. Tropical forest ............................................................................................................. 8
2.2.1. Central African tropical forest ............................................................................... 9
2.3. Wood structure and tree rings ..................................................................................... 9
2.4. Dendrochronology .................................................................................................... 11
2.4.1. Tropical dendrochronology ................................................................................. 12
2.5. Stable Isotopes ......................................................................................................... 14
2.5.1. Stable carbon isotope (13C) ................................................................................ 15
Intrinsic Water Use Efficiency (WUEi)........................................................................... 18
2.5.2. Stable isotope oxygen 18O .................................................................................. 18
3. MATERIALS AND METHODS ........................................................................................ 21
3.1. Description of the area .............................................................................................. 21
3.2. Sample preparation .................................................................................................. 21
3.3. Cellulose extraction .................................................................................................. 24
3.3.1. Extraction ........................................................................................................... 25
3.3.2. Removal of the subsamples, homogenization and drying ................................... 25
3.3.3. Cleaning of cellulose extraction kit ..................................................................... 26
3.3.4. Isotope Analysis ................................................................................................. 26
4. Results and discussion ................................................................................................... 28
4.1. Climate data ............................................................................................................. 28
4.2. Stable carbon isotope 13C ......................................................................................... 30
4.2.1. Intra- and inter-annual variation of δ13C for Terminalia superba ......................... 30
Tree C .......................................................................................................................... 32
Tree B .......................................................................................................................... 34
Tree A. ......................................................................................................................... 36
4.3. Intrinsic Water Use Efficiency (WUEi) ....................................................................... 39
4.4. Stable oxygen isotope 18O ........................................................................................ 41
4.4.1. δ18O intra- and inter-annual variation for Terminalia superba ............................. 41
5. Conclusions .................................................................................................................... 45
6. Recommendations for further research ........................................................................... 46
References ......................................................................................................................... 47
Appendix ............................................................................................................................. 52
LIST OF FIGURES
Figure 1. Natural greenhouse effect (a). Enhance greenhouse effect (b) .............................. 5
Figure 2. Increase in concentration of CO2 levels in the atmosphere..................................... 6
Figure 3. Global mean annual temperatures (anomalies from 1961-90 mean) for the globe
(blue line) and for tropical areas (red line) ......................................................................... 7
Figure 4. Location of tropical forests ..................................................................................... 8
Figure 6. Tree wood structure ............................................................................................. 10
Figure 7. Section of a tree rings, conifer .............................................................................. 10
Figure 8. Distribution of seasonality in the tropics. Each number represents, 1= formation of
annual rings, 2= two rings per year and 0= no distinct growth ......................................... 14
Figure 9. Plant physiological and climate factor influencing the carbon isotope ratio of a tree
........................................................................................................................................ 15
Figure 10. Seasonal carbon isotope variations in cellulose of tree rings from Morus alba ... 17
Figure 11. Plant physiological and climate factors influencing the oxygen isotope ratio of a tree
........................................................................................................................................ 19
Figure 12. Isotopic fractionation between rainfall and the eventual tree ring cellulose ......... 20
Figure 13. Location of the Mayombe forest in the Democratic Republic of the Congo ......... 21
Figure 14. Difference in the location Tree A and B, C in the Luki reserve ............................ 22
Figure 15. Samples dimensions .......................................................................................... 23
Figure 16. Lintab dendrochronology station ........................................................................ 23
Figure 17. Subsampling the tree rings ................................................................................. 23
Figure 18. Subsamples of tree rings in Eppendorf vials ....................................................... 24
Figure 19. Cellulose extraction kit ....................................................................................... 25
Figure 20. BRANSON Sonifier ............................................................................................ 26
Figure 21. Mean historical monthly temperature and precipitation for Democratic Republic of
Congo, for the period 1900-2012 ..................................................................................... 28
Figure 22. Mean monthly temperature and precipitation during the growth period 2013 – 2014
(light) and 2014 – 2015 (dark) ......................................................................................... 29
Figure 23. Mean monthly Irradiance and Relative Humidity (RH) during the growth period 2013
– 2014 (dark) and 2014 – 2015 (light) .............................................................................. 29
Figure 24. Intra-annual δ13C pattern for Terminalia Superba tree C in function of time for
season (a) 2013-2014 and (b) 2014-2015 ....................................................................... 33
Figure 25. Effect of rainfall in the intra-annual δ13C composition tree C for the period 2014-
2015 ................................................................................................................................ 35
Figure 26. Intra-annual δ13C pattern for Terminalia Superba tree B in function of time for period
2014-2015 ....................................................................................................................... 36
Figure 27. Intra-annual δ13C pattern for Terminalia Superba tree A in function of time for
season (a) 2013-2014 and (b) 2014-2015 ....................................................................... 37
Figure 28. Effect of rainfall in the intra-annual δ13C composition tree A for the period 2014-
2015 ................................................................................................................................ 39
Figure 29. Relationship between WUEi and growth period (a) 2013-2014, period (b) 2014-
2015 ................................................................................................................................ 41
Figure 30. δ18O composition in function of tree A growth period (a) 2013-2014 and (b) 2014-
2015. Tree B growth period (c) 2014-2015. Tree C growth period (d) 2013-2014 and (e)
2014-2015 ....................................................................................................................... 44
LIST OF TABLES
Table 1. Solutions for cellulose extraction ........................................................................... 24
Table 2. General data stem disk Terminalia Superba .......................................................... 31
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LIST OF ABBREVIATIONS
GHG Greenhouse gases
WUEi Intrinsic Water Use Efficiency
δ13C Stable isotope composition of carbon
δ18O Stable isotope composition of oxygen
Δ13C Carbon isotope discrimination
RH Relative Humidity
EA-IRMS Elemental Analyzer - Isotope Ratio Mass Spectrometer
TC-EA-IRMS Thermal Conversion Elemental Analyzer - Isotope Ratio Mass
Spectrometer
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ABSTRACT
Dendrochronology is applied to assess the response of trees in a changing environment. For
tropical dendrochronology, the ring formation is predominantly caused by seasonal variation
of rainfall especially in Central Africa, remains understudied in this context, based on the ring
formation caused by the seasonal variation of rainfall. The study of δ13C and δ18O from
cellulose of tree rings are an important part of dendrochronology studies because they contain
continuous historical records of its variation over the years due to physiological processes
affected by weather conditions. With the δ13C and δ18O composition, it could be possible to
get an idea of the balance between stomatal conductance, photosynthetic rate, source water
and CO2 uptake due to the variation of weather conditions, such as rainfall and temperature.
Stable isotopes composition (δ13C and δ18O) of the intra-annual cellulose sections extracted
from the tree rings (2013-2014 and 2014-2015) was analyzed for three Terminalia superba
trees (Tree A, B and C) from different sites in the Luki reserve, Mayombe, Democratic Republic
of the Congo. This thesis is an exploratory study to perceive a possible climate and/or
physiological response to changes in weather conditions.
The δ13C composition profile for trees B and C, both located in an old secondary forest, were
similar, increasing at the beginning of the tree grow, followed by progressive decrease, gives
a similar profile as presented by Helle & Schleser (2004), Fichtler et al. (2010), and Verheyden
et al., (2004). Tree A, isolated in a coffee field, presented a different δ13C profile. It seems that
the trees located in secondary forest, event with variations in weather conditions for the period
2014-2015, maintained the mentioned profile, which is a combination of starch depletion of
the previous year and change to photosynthetically carbon for ring development. However, if
the trees are isolated (Tree A), as can be the case for trees in over-logged forests in the
tropics, the δ13C composition presented a different profile, more sensitive to weather
conditions. Therefore, the location of the trees could have an effect on the δ13C composition.
δ18O composition of the studied trees had less gradual change over the growth periods. The
flat line tendency of the δ18O composition of the studied trees could give the idea that, even
though the tree location and the environmental conditions were different, the source water
could be considered the same, probably coming from rainfall and the fog that is present in the
dry season. The intra-annual variation could be attributed to the mixing of source water from
weather changes or possible measurements errors.
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1. INTRODUCTION
The tropical forest has the capacity to influence the global environment and, at the same time,
be affected by the continuous changes in normal climate patterns. The way in which the trees
are affected, is registered in the wood under the form of growth rings. Therefore,
dendrochronology is applied to assess the response of trees over the changing climate.
Studies in dendrochronology have been developed in temperate zones since the 19th century,
and developed at the beginning of the 20th century for tropical areas, based on the ring
formation caused by the seasonal variation of rainfall (Worbes, 2002).
Dendrochronology is a method dating tree rings to generate high-resolution paleoclimatic data
and have a better understanding of the trees behavior by climate changes (Gebrekirstos et
al., 2014).
Isotopic composition of cellulose (δ13C and δ18O) of tree rings are an important part of
dendrochronology studies because they contain continuous historical records of its variation
over the years due to physiological processes or climate conditions.
Stable isotope composition of carbon (δ13C) records the balance between stomatal
conductance, photosynthetic rate and CO2 uptake from the atmosphere, influenced in tropical
areas by irradiance and temperature, also having an effect on the relative humidity and soil
moisture (Marshall et al., 2007; McCarroll et al., 2004; Poole et al., 2004)..
Stable isotope composition of oxygen (δ18O) depends mainly on the source water, the level of
evaporation in the leaf during the transpiration, affected by the rainfall, relative humidity,
temperature and soil moisture. In addition, biochemical fractionation due to the synthesis of
sucrose and the exchange between carbohydrate and xylem water during the synthesis of
cellulose affect the stable oxygen isotopes (Managave & Ramesh, 2012).
Although studies on stable isotopes in trees from temperate zones provide abundant
paleoclimatic data, tropical trees from tropical forest are still understudied (Fichtler et al.,
2010), especially at intra-annual resolution.
Climate studies in Central Africa and the rest of Africa, is currently the least developed on the
globe, meaning a limitation to understand the current and future climate variability
(Gebrekirstos et al., 2014). Therefore, innovated studies are being developed to identify the
climate response of tropical trees en Central Africa.
Hence, in the present study, stable isotopes composition (δ13C and δ18O) of intra-annual
cellulose samples extracted from the tree rings of Terminalia superba formed in 2013-2014
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and 2014-2015 were studied. It is an exploratory work to try to understand a possible climate
response to environmental changes.
This study is part of a larger project that integrates several aspects of tree response to climate,
through seasonal monitoring of Terminalia superba in the Mayombe forest (Democratic
Republic of the Congo), combining diel stem radius changes, phenology, intra-annual xylem
monitoring as well as wood anatomy and isotope composition of cellulose (De Mil et al., in
review). Terminalia superba was the tropical tree selected to be study because its
dendrochronology potential, including its climate sensitivity (De Ridder et. al., 2013).
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2. LITERATURE REVIEW
2.1. Climate change and tree response over the tropics
To understand the effect that a change in the climate can have on a tropical forest region, first
some concepts must be described. Climate is the average of the world’s regional climates,
where interaction between different components (soil, atmosphere, ocean, freshwater) occurs.
Earth's climate is always changing as a result of natural processes, going through warmer and
cooler periods. The Greenhouse effect is part of a natural process where shortwave solar
radiation release from the Earths’ surface, is absorbed or emitted to space by greenhouse
gases (GHG) in the atmosphere. This phenomenon makes the lower part of the atmosphere
to have an extra input of energy increasing the Earth’s surface temperature (Figure 1a). The
most important GHG is water vapor and the second is carbon dioxide (CO2) (IPCC, 1990; May,
2005).
a
b
Figure 1. Natural greenhouse effect (a). Enhance greenhouse effect (b) (Elder, n.d.)
The increasing population demands high quantities of products and services to fulfill the actual
needs. Therefore, more and more amounts of fossil fuels are being burned, increasing the
concentrations of GHG in the atmosphere, enhancing the natural greenhouse effect that
increases the temperature on Earth with 1.29°C according to GISTEMP Team et. al., (2016)
(Figure 1b). This phenomenon is called “Global warming”. “Climate change” is a more general
term that refers to a long term change in the change in the Earth’s climate, including many
climatic factors, such as temperature and rainfall, from a region or city (Elder, n.d.; May, 2005).
2.1.2. Greenhouse Gases - Carbon Dioxide
The high concentration of GHG in the atmosphere have being increasing since 1750: 40% for
carbon dioxide (CO2), 150% for methane (CH4) and 20% for nitrous oxide (N2O). Half of the
CO2 emissions have occurred in the last 40 years; these emissions come especially by fossil
fuel combustion, besides forestry and change in land use (Figure 2) (IPCC, 2014). CO2 is the
most important greenhouse gas that has the capacity to raise the earth’s temperature if it is
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present in high concentration in the atmosphere from 270 ppm (Ball, 2008), before the
industrial era, to the actual 400 ppm (Dlugokencky & Tans, n.d.), coming from the increase of
burning fossil fuels, adding the change in the land use.
Figure 2. Increase in concentration of CO2 levels in the atmosphere.
(To directly compared CO2 emissions to atmospheric CO2 levels, the concentrations are measure in Gigatonne of
CO2-equivalent per year (GtCO2/yr)) (IPCC, 2014)
It is known that the constant increase of CO2 in the atmosphere stimulates photosynthetic
rates of trees and its growth (Hikosaka et al., 2005). Besides the trees ability to use in an
efficient manner water and nutrients under a changing climate, it is possible for them to give
a response over the increasing concentration of atmospheric CO2. Nevertheless, there is still
uncertainty regarding the responses of stomatal conductance to elevated CO2. A decline in
stomatal conductance is predicted when plants are exposed to elevated CO2. If this decline
occurs in conjunction with an increase in carbon assimilation, this can change the gradient
between the atmospheric and internal CO2 in the leaf, improving the Intrinsic Water Use
Efficiency (WUEi) (Battipaglia et al., 2013).
In general, it is important to maintain a ratio between the CO2 uptake and the production of
oxygen of the trees in tropical forests, this ratio is what regulates the temperatures on earth
(Dlugokencky & Tans, n.d.).
According to Brett (n.d.), 35% of atmospheric carbon comes from the lack of its extraction from
the air due to deforestation and the concentration of oxygen in the atmosphere will decrease.
This alteration could feasibly change the global climatic norms and pose a serious problem to
Earth's population, plants and animal species.
2.1.3. Temperature, Rainfall and Relative Humidity (RH)
The tropics have been warming over the last century (Figure 3) to a lesser rate compared to
the rest of the earth. The tropics are projected to warm substantially over the coming 50 to
100 years like the rest of the world, depending on the greenhouse gas emissions (lowest
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emission scenarios around 1-2°C and high emission scenarios around 3-4°C by 2100)
(Trewin, 2012).
Figure 3. Global mean annual temperatures (anomalies from 1961-90 mean) for the globe
(blue line) and for tropical areas (red line) (Trewin, 2012)
Way and Oren (2010), mentioned by Ryan, (2010) found that increased temperature generally
increases tree growth, except for tropical trees. They suggest that this probably occurs
because temperate and boreal trees currently operate below their temperature optimum, while
tropical trees not. Therefore, it probably will not be possible to observe a direct influence of
the increasing temperature in tropical trees (Ryan, 2010). An indirect impact due to the
increase in air temperature in the tropics is the decrease of RH, thus, the chance of a fire
increases significantly (Brett, n.d.).
Current models indicated a low confidence in the possible change of rainfall in the tropics.
There are indications of an intensification of the seasonal rainfall cycle and a lengthening of
the monsoon season in many regions, with the wet season becoming wetter and the dry
season drier, as well as of an increase in extreme rainfall events at various timescales, but
uncertainties are large (Trewin, 2012). As rainfall patterns become more unpredictable as
climate changes, trees will be subjected to fluctuations in soil moisture availability, resulting
perturbations in the tree hydraulics or in its chemistry. Reduced soil water availability will
reduce water uptake, and also restrict nutrient uptake by roots and transport to the buds.
Excessive rainfall can result in inundation of soil, reducing the partial pressure of oxygen
around the roots of trees, also reducing the water uptake. Such changes in the water status
will reduce the tree growth (Morison & Morecroft, 2006).
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2.2. Tropical forest
The biologist Normal Myers called the tropical forest, “the greatest celebration of life on Earth”
(Laurance & Bierregaard, 1998). With its diversity, ecology and complexity is one of the most
important ecosystem, with more than 50% of all living species on earth (Brett, n.d.).
The tropical forests are located around the Equatorial latitude of Asia, Africa and America. It
extension goes approximately 220 million ha in Asia, 750 million ha in America and 340 million
ha in Africa(Figure 4) (Dupuy et al.,1999).
Figure 4. Location of tropical forests (Bonenberger & Bonenberger, n.d.)
As it is mentioned by Montagnini & Jordan (2005), tropical forests have different functions,
from being a natural source of productive raw material (fiber, fuelwood, forest products), have
a climate function (climate regulation, carbon sequestration, reserve for biodiversity, soil and
water conservation) and influence in the social function (subsistence for local populations).
These natural resources can only be regenerated by themselves if the rate at which they are
consumed is not too high allowing the natural regeneration. Nevertheless, the actual demand
for these goods is high, making the tropical forest a sensitive area in losing its resources and
biodiversity. Unfortunately, parks or other conservation areas protect only five percent of the
world’s tropical forests (Brett, n.d.).
Tropical forests play a role in regulating temperature and the production of oxygen. A change
in the earth’s temperature could have an effect in the tropical forest and therefore in the world’s
ecosystems. The vital oxygen gas (O2) comes as a byproduct from the photosynthesis process
of the tree, where carbon dioxide (CO2) is taken from the atmosphere and with the effect of
sunlight in order to produce biomass, and thus carbon stock. The tropical forest can be
considered as the main source of extraction of CO2 on land from the atmosphere, because its
dense and has a high degree of untouched vegetation (Brett, n.d.).
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2.2.1. Central African tropical forest
The African tropical forest is very diverse and contains not fewer than fourteen vegetation
classes (UNESCO, 1973 mention by Pullan, 1988). They have fewer plant species than other
humid forests but they contain many thousands of endemic species (Pullan, 1988).
Characteristic tropical rainforest from Central Africa, grow in general in significantly drier
conditions compared with other continents tropical forest (Bonnefille, 2011). The tropical forest
humid climate contains annual variations in rainfall length, dry season and RH (Ridder et al.,
2013).
White (1983) cited by Bonnefille (2011), mentions that the seasonal rainfall in these areas is
far from being uniform, while the mean monthly temperature remains constant, possibly to
reach at least 18 °C during the coolest month. The duration of the dry season depends on the
distance from the equator, ocean and altitude (Bonnefille, 2011; Ridder et al., 2013).
Anyhow, with these climatic differences, as is mentioned by CTFT (1983) referenced by Ridder
et al. (2013), it is possible that the fewer plants species developed in this type of forest, can
have a broad distribution area and are able to grow in this diverse climate. These trees can
from annual rings and grow depending on the climate conditions. Tree growth is determined
by cambium activity during a specific period of the year resulting in the formation of growth
rings. In general, tropical trees grow are induced by seasonally alternating favorable and
unfavorable growth conditions (Shimamoto, Botosso, & Amano, 2016).
2.3. Wood structure and tree rings
Wood is composed of different carbohydrates to form its cellular structure, such as cellulose,
lignin, hemicellulose and extractives. Cellulose is the type of structural carbohydrate, made by
the plants to build their cell walls. The simplest and most common carbohydrate in a plant is
glucose. Plants make glucose (formed by photosynthesis) to use for energy or to store as
starch for later use. A plant uses glucose to make cellulose when it links many simple units of
glucose together to form long chains (Gifford, n.d.; UXL Encyclopedia of Science, 2002).
The wood is structured by individual cells that constitute the building blocks of the tree. These
structure cells form vessels, called xylem, which carry water and nutrients from the roots up
to the leaves, and phloem or inner bark, which carry nutrients from the leaves to the branches,
trunk and roots. Active xylem is called sapwood and old xylem, heartwood, which no longer
transports water and nutrients. Outer bark protects the tree from injury and the cambium is
located between the phloem and xylem, where new cells are formed each year, forming the
tree rings (Fritts, 1976) (Figure 6).
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A tree ring can be defined as a layer of wood cells produced by a tree in one year and is
determined by cambium activity during a specific period of grow (Grissino-Maye, 1996;
Shimamoto et al., 2016). Earlywood consists of thin walled cells formed early in the growing
season and latewood of thicker walled cells produced later in the growing season as a
consequence of low temperature, therefore, from the beginning of earlywood formation to the
end of the latewood formation spans one annual tree ring (Figure 7). It can be easily
determined in trees of temperate forests, however, in tropical trees, the growth rings are
usually less evident because the wood anatomy is much more complex and variable
(Shimamoto et al., 2016).
Figure 6. Tree wood structure (Virginia Department of Forest, n.d.)
Figure 7. Section of a tree rings, conifer (Fritts, 1976)
The cell structure of a tree can be subject to modification due to the environment. Size and
shape of cells may change, as well as the number of cell types and features, or specific
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features can occur that are normally not seen. These characteristics may vary under severe
conditions during the growing season such as shortage in water availability, tree injury or
variations in temperature (Wimmer, 2002).
These modifications lead to tree ring anomalies, such as false rings, diffuse boundaries, ring
wedging or even the absence of a tree ring. A false ring occurs as a consequence of an
extreme condition that reduces the growth rate for a certain period and after the limitation the
tree continues growing. A diffuse ring boundary is formed when the growing conditions are
optimal and the tree continues growing the remaining grow year. Ring wedging is a
phenomenon where a segment of the circumference radial growth in a different rate than
another segment (Fritts, 1976; Speer, 2011).
A point of debate is the existence of tree rings in the tropical trees. For trees in temperate
forests earlywood and latewood are easily identified, however, in tropical trees, the growth
rings are usually less evident because the wood anatomy is much more complex and variable.
Nevertheless, the reduction of rainfall in these areas could represent a dry season, even if it
is considered as an everwet or perhumid condition (Shimamoto et al., 2016; Worbes, 2002),
marking possible the formation of a tree ring.
2.4. Dendrochronology
Dendrochronology can be considered as one important climate recording able to determine
possible changes caused by a variety of natural climate processes. Dendrochronology could
be defined as the study of the chronological sequence of annual growth rings in trees. When
the radial growth of trees is affected by a common limiting factor or an unusual climate
phenomenon, dendrochronology can study these events through time providing reliable and
ubiquitous archives for dating past events and for paleoenvironmental reconstruction.
Therefore, the trees become an instrument for climate monitoring and as a long term bio-
indicator (Coulthard & Smith, 2013; Speer, 2011).
Several sources such as pollen, ice cores, lake varves, coral layers are used as so called
proxy records for climate records; nevertheless, according to Speer (2011), dendrochronology
provides the most reliable dating with the highest accuracy and precision of any of the proxies.
Dendrochronology follows some principles and concepts that govern its application:
1. Uniformitarian Principle states that the physical and biological processes occurring
today are the same of those occurring in the past affecting the climate conditions that
affect the regular radial tree growth (Speer, 2011) (Coulthard & Smith, 2013).
According to Fritts (1976) mentioned by Coulthard & Smith (2013) it does not mean
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that past conditions were the same as present ones, but rather that tree growth was
influenced in the same manner in the past as it is in the present.
2. Principle of crossdating is the basis of dendrochronology which states that individual
tree rings are assigned to an exact year of formation (Coulthard & Smith, 2013).
Without crossdating it is likely to erroneously date due to absent of false tree rings.
This principal is imperative when tree width measurements are compared to the annual
phenomena such as meteorological data or when reporting past events (Speer, 2011).
3. Principle of Limiting Factors mentions that the limiting of an climate factor controls the
annual tree ring growth (Speer, 2011). Rainfall, air temperature, snowpacking, soil
condition or insect infestation are all example of limiting factor. Only tree rings may be
cross dated if one or more climate factors becomes significantly limiting, persists long
enough, and acts over a wide enough geographical area (Coulthard & Smith, 2013).
4. Principle of Aggregate Tree Growth. Tree ring growth provides a record of everything
that affects the regular growth of the tree, therefore it is possible to provide a
conceptual model:
𝑅𝑡 = 𝐴𝑡 + 𝐶𝑡 + 𝐷1𝑡 + 𝐷2𝑡 + 𝐸𝑡 (1)
Where, R is the tree ring width in one year, A is a factor of age related growth trends,
C is climate influence, D1 is the endogenous disturbance, D2 is the exogenous
disturbance and E is the error for this variable (Coulthard & Smith, 2013; Speer, 2011).
5. Principle of Ecological Amplitude. Tree rings are sensitive to climate factors delimited
by the altitudinal and longitudinal conditions, such as topography, slope. These
conditions determine the microclimate of a specific site which affects the local
distribution of a specie (Coulthard & Smith, 2013; Speer, 2011).
6. Principle of Site Selection describes that the selection of a tree sample must be the
one that is highly variable and controlled by a limiting climate factor (Speer, 2011).
7. The last principle is the Principle of Replication, which states that to maximize climate
affecting signals and minimize the errors, more than one sample, more than one tree,
and/or several sites in a region must be collected (Coulthard & Smith, 2013).
2.4.1. Tropical dendrochronology
Studies on dendrochronology in tropical regions of Latin America and Asia have been
conducted more than in Africa (Worbes, 2002). The number of exactly dated tree-ring
chronologies from West and Central African species is limited (De Ridder et. al., 2013).
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In the 19th century Hartig (1853) mentioned by Worbes (2002) developed the theory on
periodic wood formation in trees focusing on temperate regions, while annual rings in tropical
trees was develop at the beginning of the 20th century.
The anatomical structures of the tree rings vary in response to fluctuating climate and climate
variables (Coulthard & Smith, 2013). A wide ring can mean: more growth, more photosynthesis
due to higher CO2 in air, which leads to a higher temperature, more rainfall and/or more
sunlight. And a small ring to: slower growth, less photosynthesis due to lower CO2 in air, which
leads to lower temperature, less rainfall and/or less sunlight (Wilson, 2014).
Worbes (2004) states that three different types of climatic seasonality are possibly affecting
annual tree growth:
1. Annual temperature variation with temperature near or below the freezing point in winter.
2. Annual flooding of the great river systems in the tropics, causing anoxic conditions in the
soil and disturbance in the root respiration and water uptake.
3. Tropic climate with variation of rainfall between rainy season and dry season.
Annual rings in tropical trees are induced by dry or flooding periods (Worbes, 1995).
Temperature, radiation, and rainfall can be considered as limiting factors. In the tropics the
temperature can be consider as constant over the year (see section 2.2.). Site conditions
influence the intensity of reaction to these factors (Worbes, 2004).
In general, the positive relation between the amount of rainfall, ring formation and its width is
shown for many parts of the tropics in Figure 8 (Worbes 1995). In Africa, the majority of tree
species presents the formation of a tree ring per year of growth, affected the rainfall.
The observation of Worbes (1995) and others, indicate that an annual dry season with a
length of 2 to 3 months and less than 60 mm monthly rainfall induce annual rings in tropical
trees.
Although a long history of climate reconstructions exists, there is still some uncertainty
regarding the specific effects of certain climate parameters, especially temperature and
rainfall, on tree ring width and wood anatomy (Vaganov et al., 2009). This is based on the
knowledge that seasonal and annual changes in tree rings are also controlled by physiological
drivers and the availability of storage products at the time of wood formation (Hemming et al.
2001 cited by Vaganov et al., 2009). In addition, the conditions for wood formation may be
different for solitary growing trees under extreme climate conditions than for trees in thick
forest, which can have a competition for water, nutrients and space (Vaganov et al., 2009).
14
Figure 8. Distribution of seasonality in the tropics. Each number represents, 1= formation of
annual rings, 2= two rings per year and 0= no distinct growth (Worbes, 1995)
2.5. Stable Isotopes
Changes in climate conditions support the use of stable carbon and oxygen isotope
dendrochronology. This is a new area of dendrochronology with which it is possible to study
tree response to climatic forcing factors that can affect isotopic fractionation. Nevertheless,
isotopic studies are limited to natural physiological mechanisms that control the concentration
of isotopes in a tree ring (Jansma et. al., 2004; Speer, 2011).
Carbon dioxide (CO2) and water from the soil and rainfall are the sources of carbon and
oxygen respectively needed for the development of the plant. The combination of isotopes
may provide a clear understanding of the physiological factors affecting tree development
(Jansma, 2004). The isotopic composition of wood differs from, either the atmosphere, water
or soil, so the trees do not collect and store these elements in the same composition.
Therefore, according to McCarroll et al. (2004), the wood of tree rings represents a sensitive
bio-indicator of the way trees have changed in response to the environments in which they
lived. The isotopic composition of the cellulose in tree rings is affected by the isotope
distribution of CO2 entering the tree and after photosynthesis (Managave et al., 2012).
The isotopic composition of the elements are conventionally shown as a Delta (δ) in parts per
thousand (‰):
δ = (𝑅𝑠𝑎𝑚𝑝𝑙𝑒
𝑅𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑− 1) ∗ 1000 (2)
where R sample and R standard are the ratios between the heavier and lighter isotopes (13C/12C
and/or 18O/16O) in the sample and standard, respectively. The carbon standard is the fossil
15
belemnite from the Pee Dee formation of South Carolina (PDB) with 13C/12C=0.0112372 and
the standard for oxygen is reported relatively to the Vienna Standard Mean Ocean Water
(VSMOW) with 18O/16O=0.0020052.
The stable isotope studies are performed on the cellulose fraction of the wood. Cellulose is
chosen because it does not exchange C and O isotopes after formation and it has varying
quantities of stable isotopes due to different biosynthetic pathways, compared to other wood
components such as lignin, hemicellulose and extractives. All these factors could skew the
final stable isotope analysis and the final interpretation of the signal (Hufkens, n.d., Gaudinski
et. al., 2005).
2.5.1. Stable carbon isotope (13C)
Stable carbon isotopes record the balance between stomatal conductance, photosynthetic
rate and CO2 uptake from the atmosphere, dominated at moist sites by irradiance and
temperature, whereas the relative humidity and soil moisture, and in dry sites relative humidity
and soil water status also have an effect (Figure 9). Plants contain less 13C than atmospheric
CO2 because of the discrimination or fractionation against the isotopic heavier carbon. The
degree of fractionation and the stable carbon isotope value of the plant are controlled to some
extent by the response of the tree to its environment. (Marshall et al., 2007; McCarroll et al.,
2004; Poole et al., 2004).
Figure 9. Plant physiological and climate factor influencing the carbon isotope ratio of a tree
(Managave & Ramesh, 2012)
16
Plants enzymatic demand and physical processes cause the depletion of 13C in favor of 12C.
According to Marshall et al. (2007), the fractionation process for C3 plants, such as trees,
begins with the diffusion of CO2 through the stomatal pores into the air spaces within the leaf.
Stomata are pores in the leaf that enables gas exchange between the atmosphere and plants
allowing the entry of CO2 into the leaf and the exit of water vapor. When stomata are open,
transpiration rates increase and when they are closed, transpiration rates decrease. There are
climate conditions that affect the stomatal behavior (Sterling, 2005) (Granados-Páez,
Delgado-Huertas, & Reyes, 2009) (Figure 9):
- Relative humidity (RH). When RH is low, there is less moisture in the atmosphere,
hence there is a greater driving force for transpiration, and the stomata tend to be
open. At high RH, more moisture in the atmosphere reduces the driving force for
transpiration, thus stomatal tend to close.
- Rainfall. Higher rainfall will tend to stomata open and vice versa. However, excessive
rain can harm the plant causing stress resulting in stomatal closure.
- Temperature. Warmer air is the driving force for transpiration, thus opening of stomata
is the result. Cooler air will decrease the driving force for transpiration, thus the closing
of stomata.
- Soil water content. The source of water for transpiration comes from the soil. If there
is a lack of water in the soil, the stomata close to avoid more loss of water
(transpiration) and wilting of the plant.
- Irradiance. Light makes stomata to open so CO2 is available for photosynthesis. During
trees, stomata are closed in the dark.
Upon stomatal closure an increasing amount of 13C is used in photosynthesis and this in turn
alters δ13C of plant tissue (Poole et al., 2004). The diffusion of 13C through the stomata has an
apparent fractionation of around 4.4‰ due to the slower motion of the heavier isotope. Inside
the leaf, the enzyme Ribulose-1,5-Bisphosphate Carboxylase/oxygenase (RuBisCO) diffuses
even more the 13C, around 27‰ (Marshall et al., 2007).
According to Farquhar et al. (1982) cited by Managave et al. (2012), the carbon isotope
discrimination in the plant (δ13Cplant) can be expressed in the following equation:
𝛿13𝐶 = 𝛿13𝐶 𝑎𝑡𝑚 − 𝑎 − (𝑏 − 𝑎) ∗ (𝐶𝑖
𝐶𝑎) (3)
where δ13Catm is the ratio of 13C/12C in the atmosphere (−8.1‰), a and b are constants that
represent the carbon isotope fractionations through the stomatal (4.4‰) and during enzymatic
fixation by RuBisCo (27‰) respectively and Ci/Ca is the ratio of the intercellular to atmospheric
17
CO2 (Figure 8). The δ13C values range from -23.6‰ and -27.9‰ (V-PDB) (Granados-Páez et.
al., 2009).
According to Helle & Schleser (2004) for temperate areas and to Fichtler et al., (2010) and
Verheyden et al., (2004) for tropical areas, the values of δ13C in the cellulose of an intra-annual
tree ring tend to increase at the beginning of the regular tree development, and then
progressively decreasing until the end of the annual growing period (Figure 10). In some rings,
the δ13C composition tends to increase again at the very end of the vegetation period. In the
Figure 10 it is described the mentioned δ13C profile for a temperate tree (Morus alba) were it
can be easily observed the earlywood (EW) and latewood (LW).
Figure 10. Seasonal carbon isotope variations in cellulose of tree rings from Morus alba
(EW= Earlywood, LW= Latewood) (Helle & Schleser, 2004)
At the beginning of the season, tree growth depends on reserves of the previous year(s),
mainly stored as starch. Assuming that the biochemical processes and kinetic isotope effects
involved in starch formation in the amyloplast are similar to those in the chloroplast, the starch
reserves stored in woody tissue during winter should be enriched in 13C in comparison with
sugars such as sucrose and hexoses, by about 3‰ (Helle & Schleser, 2004; Michelot et. al.,
2011).
The observed initial 13C enrichment requires an additional isotope effect. This can be attributed
to the fast formation of structural organic matter, which removes first the lighter carbon isotope
(12C) faster than the heavier one (13C). Towards the end of the vegetation period, the observed
decrease of δ13C could correspond to the change of the carbon source from storage material
to photosynthates that contain less 13C. adding an isotope partitioning between enriched
18
starch and depleted cell-wall tissue leads to the observed decrease of 13C (Helle & Schleser,
2004). Therefore, the δ13C composition depends on the physiological processes that are
influenced by the weather conditions.
Intrinsic Water Use Efficiency (WUE i)
The intrinsic Water Use Efficiency is a parameter that relates the net photosynthesis and
transpiration. It gives the ratio of carbon assimilation to stomatal conductance of water and is
considered a characteristic of plant tolerance to water stress and soil salinity (Granados-Páez
et al., 2009). Seasonal values of WUEi are calculated from the δ13C using the model of carbon
isotope discrimination (Δ13C) of Farquhar et al. (1982) mentioned by Michelot et al., (2011):
𝑊𝑈𝐸𝑖 =𝐶𝑎(𝑏−∆13𝐶)
1.6(𝑏−𝑎) with ∆13𝐶 =
𝛿13𝐶𝑎𝑡𝑚−𝛿13𝐶𝑟𝑖𝑛𝑔
1+𝛿13𝐶𝑟𝑖𝑛𝑔 (4)
where Ca is the atmospheric concentration of CO2 (mmol mol-1), a is the discrimination against
13CO2 during diffusion through stomata (= 4.4‰), b is the discrimination against 13CO2 during
RuBisCO carboxylation (= 27‰), Δ13C is the discrimination against 13C (‰), the value 1.6 is
the ratio of the stomatal conductance of water to that of CO2. δ13Catm is the isotope composition
of 13C in CO2 of the atmosphere and δ13Cring is the isotopic composition of cellulose.
2.5.2. Stable isotope oxygen 18O
Plants physiological and climate factors affect the composition of the stable isotopic
composition of oxygen (Figure 11). The δ18O depends mainly on the source water, the level
of evaporation in the leaf during transpiration, biochemical fractionation due to the synthesis
of sucrose and the exchange between carbohydrates and xylem water during the synthesis of
cellulose (Managave & Ramesh, 2012).
19
Figure 11. Plant physiological and climate factors influencing the oxygen isotope ratio of a
tree (Managave & Ramesh, 2012)
In the soil, the evaporation can modify the original isotopic ratio of the cellulose, the variations
in the δ18O of rainfall and in the depth from which roots access water, therefore, the residence
time of the soil water is important (Dawson, 1993; Dawson & Pate, 1996, Buhay & Edwards,
1995 cited by McCarroll & Loader, 2004). The uptake of water by the plant root does not cause
an isotope fractionation. However, in the leafs a critical fractionation occurs, where
evaporation (transpiration) leads to a loss of the lighter isotopes and a consequent enrichment
in 18O, which, according to Saurer et al., (1998) cited by McCarroll & Loader (2004), it can be
as much as 20%. The following expression gives the level of enrichment (18O discrimination)
of leaf water above source water at the sites of evaporation:
∆18𝑂𝑒 = 휀∗ + 휀𝑘 + (∆18𝑂𝑣 − 휀𝑘) 𝑒𝑎𝑒𝑖
⁄ (6)
Where ε* is the proportional depression of water vapor pressure by the heavier H218O, εk is the
fractionation of the water diffusion through the leaf boundary layer and. ∆18Ov is the oxygen
isotope composition of water vapor in the atmosphere (relative to source water), and ea and ei
are the ambient and intercellular vapor pressures (McCarroll & Loader, 2004). At constant
temperature, and where source (soil) water and atmospheric vapor have the same isotopic
signature, the degree of enrichment due to evaporation is linearly dependent on ambient and
intercellular vapor pressures (Barbour et al., 2002).
Further fractionation occurs in the process to form cellulose (Figure 12). As is described by
McCarroll & Loader (2004), 20% of the oxygen can exchange with water when sucrose splits
to form hexose phosphates. An inconvenience is that the proportion of the hexose phosphate
20
does not immediately form cellulose, instead it passes through a useless cycle which allows
further exchange (Hill et al., 1995 cited by McCarroll & Loader, 2004).
Figure 12. Isotopic fractionation between rainfall and the eventual tree ring cellulose (Roden
et al., 2000)
The δ18O is not a direct measure of the 18O of the source soil water due to is influenced by the
water evaporation in the leaf. The amount of evaporation depends on δ18O of moisture vapor
outside the leaf, the stomatal conductance and vapor pressure deficit, the last two are linked
to relative humidity (McCarroll & Loader, 2004).
21
3. MATERIALS AND METHODS
The present study comprises isotope analysis 18O and 13C from tree rings corresponding to
the growing seasons of 2013 - 2014 and 2014 - 2015 of Terminalia superba Engl. & Diels in
the Mayombe forest, Luki area.
3.1. Description of the area
The Mayombe Forest covers the western parts of the Democratic Republic of Congo (DRC).
The plantations of Terminalia superba were located at the southern border of the Mayombe
Forest (Figure 13), within a drier semi- evergreen Guineo-Congolian rainforest. In 1976, a relic
of the Mayombe Forest in Luki was assigned as a UNESCO Man and Biosphere Reserve. In
this reserve the Terminalia superba plantations were established 50 to 58 years ago (De
Ridder, 2013).
Terminalia superba trees are typically found in secondary forests. They are light-demanding,
semi-deciduous trees that shed their leaves during the dry season. Terminalia superba is a
valuable species given its dendrochronology potential, large distribution area and abundant
presence and its annual tree rings formation (De Ridder, 2013).
Figure 13. Location of the Mayombe forest in the Democratic Republic of the Congo (De Mil
et al., 2016)
3.2. Sample preparation
The Terminalia superba trees selected to the study were located in the Luki reserve (Figure
13) of the Mayombe forest and are part of a deeper study. These trees were located in an
open field (Tree A) and two trees (Tree B and C), who are adjacent to each other in a
secondary forest (Figure 14).
22
Figure 14. Difference in the location Tree A and B, C in the Luki reserve
In order to have a complete figure of the climate tree response, the selected trees were
equipped with several devices measuring following variables ((De Mil et al., 2016):
- Time Lapse Cameras for phenology monitoring;
- Climate sensors for RH, T and irradiance.
- Dendrometers for radial increment, and cambial pinnings for xylem formation
Based on the growth data given by the dendrometer series and using the Gompertz growth
model, it was possible to assign the isotopic composition in function of position within ring
width, to an actual timescale. The Gompertz function is a type of mathematical model for a
time series, where growth is slowest at the start and end of a given growing period (Scianna
& Preziosi, 2013).
The samples were extracted from different on 3-4 radii sections from the last two rings on the
circumference of a stem disk (Table 2), with a dimension of approximately 2.5 cm long and 1
cm width (Figure 15), next to the radius dendrometer position. From each sample, the tree
rings were identified and subsamples were cut to proceed with the cellulose extraction.
To cut the subsamples a Lintab Dendrochronology Station (Figure 16) was used, measuring
the total length of the last two tree rings and dividing it in as many samples as possible, making
small marks on the wood (Figure 17). It was possible to divide a tree ring in parts of 0.3mm
wide. Using a scalpel, the samples from the tree section were cut, extracted and placed in
Eppendorf vials (Figure 18).
23
Figure 15. Samples dimensions
Figure 16. Lintab dendrochronology station
Figure 17. Subsampling the tree rings
24
Figure 18. Subsamples of tree rings in Eppendorf vials
3.3. Cellulose extraction
The cellulose extraction consists of the removal of resins, fatty acids and tannins by a base
(NaOH), and lignin and different dyes, with an acid (NaClO2 with a pH 4-5) from the wood, so
that only cellulose remains to be analyzed.
Based on the cellulose extraction protocol of the ISOFYS lab of Ghent University, the wood
samples pass from an alkali to acid conditions inside a water bath with a temperature of 60°C.
The cellulose extraction kit has a capacity to treat 99 samples; therefore, all the solutions and
protocol were adapted to that quantity of samples. The next table describes the solutions and
quantity needed for the extraction of 99 samples.
Table 1. Solutions for cellulose extraction
Use Solution Solution for 99 samples
Removal of resins, fatty acids and
tannins
5% Sodium hydroxide (NaOH):
50g of NaOH in 1000ml distilled water
2000ml of 5% NaOH:
100g of NaOH in 2000ml of distilled water
Removal of lignin and different dyes
7.5% Sodium chlorite (NaClO2):
93.75g of 80% NaClO2 in 1000ml distilled water.
Add more or less 4ml of 100% acetic acid (CH3COOH) to reduce
the pH to 4-5.
Three times 400ml of 7.5% NaClO2:
37.5g of 80% NaClO2 in 400ml of distilled water.
Total volume needed of 1200ml 7.5% NaClO2
Cleaning of the cellulose extraction
glass vials
10g of potassium persulfate (K2S2O8) in 100ml of distilled water
1500ml of K2S2O8:
150g of K2S2O8 in 1500ml of distilled water
The cellulose extraction kit consists of 5 trays for 20 samples each, 99 glass vials and tubes
for the extraction of the solutions (Figure 19). To accomplish the extraction, a time of four days
is necessary using following methodology.
25
Figure 19. Cellulose extraction kit
3.3.1. Extraction
Before starting of the extraction itself, a solution of 2000ml of 5% NaOH was prepared, the
extraction kit armed (Figure 19), the subsamples of the tree rings were transfered from the
Eppendorfs to the glass vials in the trays and the trays were put in a transfer them to a water
bath at 60°C in a vented hood.
Approximately 400ml of 5% NaOH was added to the subsamples in the glass vials, repeating
this procedure five times with an interval of two hours between each run. After this, the
subsamples were washed with boiling distilled water approximately six times to neutralize the
pH and a solution of 7.5% NaClO2 was added, approximately 400ml. This solution must be
prepared just before usage due to its limited reactivity of only 10 hours. To remove all the
lignin and different dies, this step must be repeated two more times in an interval of 10 to 12
hours between them, having a total of three rounds of NaClO2. Finishing these rounds, the
subsamples must be washed with boiling distilled water approximately nine times until reach
a neutral pH.
3.3.2. Removal of the subsamples, homogenization and drying
When the cellulose extraction was finished, the remaining cellulose fibers were removed from
the glass vials and placed in Eppendorf vials.
Tweezers were used to transfer the cellulose. Because cellulose tends to stick to the tweezers,
0.5ml of distilled water was added to the Eppendorf vials helping to loosen the cellulose from
the tweezers. If necessary, a small quantity of distilled water must be squeezed in the
tweezers, inside the Eppendorf vials to remove the sticky cellulose. The tweezers had to be
rinsed with distilled water between each subsample manipulation and the empty glass vials
were put in a beaker with distilled water. After the removal of the samples, the cellulose fibers
were ready to be homogenized.
26
The homogenization was done with a BRANSON sonic homogenizer (Figure 20). A small
mixer tip was installed putting it inside the Eppendorf vials. The homogenization was done in
one minute cycles. Ice was used to avoid the increase of temperature of the Eppendorf vials.
At the end of the homogenization, as a quality check, the cellulose fibers should not contain
big pieces and sometimes it can show a gel like appearance.
Figure 20. BRANSON Sonifier
To dry the cellulose fibers, the Eppendorf vials were put in an oven at 65°C for around 72
hours, covered by tin foil with small holes, allowing the moist air to escape.
3.3.3. Cleaning of cellulose extraction kit
The glass vials had to be cleaned by putting them into a solution of K2S2O8 at 90°C for one
hour. To reach the temperature and to dissolve the salt, the solution had to be heated for a
period of approximately 1.5 hours. This procedure had to be done in a vented hood due to the
formation of noxious fumes. The trays and others pieces were rinsed with distilled water. Once
the glass vials were cleaned, they were rinsed with distilled water and let them dried at ambient
air.
3.3.4. Isotope Analysis
To analyze the stable isotopes (13C and 18O) from the extracted cellulose of the tree rings, the
subsamples were weighed and packed in tin cups for 13C measurement (around 1mg of
cellulose) and silver cups for 18O measurement (around 0.75mg of cellulose).
27
The isotope analyzes were made at the Isotope Bioscience Laboratory (ISOFYS) of the
Faculty of Bioscience Engineering of Ghent University in Belgium.
For the 13C isotope analysis the EA-IRMS (Elemental Analyzer - Isotope Ratio Mass
Spectrometer) was used. Solid materials are analyzed using a PDZ Europa ANCA-GSL
elemental analyzer interfaced with a Sercon 20-20 IRMS with SysCon electronics (SerCon,
Crew, UK). The samples are measured relative to laboratory standards, which are adjusted to
the sample size and have been calibrated against international standards by Iso-Analytical.
The final delta unit is expressed relative to international standards VPDB (Vienna PeeDee
Belmenite).
For the analysis of 18O, the TC-EA-IRMS (Thermal Conversion Elemental Analyzer - Isotope
Ratio Mass Spectrometer) was used. Solid materials are analysed using a SerCon high
temperature elemental analyzer interfaced with a SerCon 20-20 IRMS with SysCon
electronics (Sercon, Crew, UK). The final delta unit is expressed relative to international
standards VSMOW2 (Vienna Standard Mean Ocean Water).
28
4. RESULTS AND DISCUSSION
4.1. Climate data
The climate data comes from several devices installed in the Mayombe forest, Luki area for
the period from 2013 to 2015. In this region, corresponding to the southern hemisphere,
summer is from December to March and winter is from June to September. In this tropical
region, a regular year (Figure 21) has a dry season from June to September and the rest of
the year a variable rainy season. The months April and November have the highest rainfall
values up to around 200 mm.
Figure 21. Mean historical monthly temperature and rainfall for Democratic Republic of
Congo, for the period 1900-2012 (The World Bank Group, 2016).
The climate data: rainfall (mm), temperature (°C) (Figure 22), irradiance (W/m2) and relative
humidity (%) (Figure 23), for the years of the studied tree rings (2013 to 2015), were plotted
from August, corresponding to the beginning of leaf foliation until July, the end of season.
29
Figure 22. Mean monthly temperature and rainfall during the growth period 2013 – 2014
(light) and 2014 – 2015 (dark)
Figure 23. Mean monthly Irradiance and Relative Humidity (RH) during the growth period
2013 – 2014 (dark) and 2014 – 2015 (light) (No irradiance data for December-14 and January-15)
30
According to Figure 22, the mean rainfall for the period 2013 – 2014 was around 1043.6 mm,
and 1188.4 mm for the period 2014 – 2015. As the historical monthly data (Figure 21), the
rainy season, April 2014 was the rainiest month for the 2013 – 2014 period with around 200
mm; followed by December 2013 with around 177 mm. In 2014 – 2015 period, the rainy
months started one month earlier, March had the highest rainfall of the two periods, around
317 mm, followed by November, with around 189 mm. December 2014, had a lower mean
rainfall (≈87 mm) compared to the previous year (177 mm) and the mean historical monthly
data (206 mm).
Figure 23 presents the insolation and RH for both seasons. The months February, March and
April had higher insolation values for each year, being 176, 173, 172 W/m2 respectively for the
period 2013 – 2014, and 155, 159, 159 W/m2 respectively for the period 2014 – 2015.
The RH tended to decrease and varied not more than 10% from February to April, in the same
months of higher insolation. During the dry season, a thick cloud cover coming from the
Atlantic Ocean covers the study area.
4.2. Stable carbon isotope 13C
4.2.1. Intra- and inter-annual variation of δ13C for Terminalia superba
The δ13C composition from the subsamples of the extracted cellulose from the last two tree
rings (2013-2014 and 2014-2015 rings) of Terminalia superba trees varies considerably within
the tree ring. The general data of the stem disks and tree rings are described in the Table 2.
The tree phenology (leaf flushing) started around August each year, but the tree growth itself,
wood formation according to cambial pinning data, started in December for tree B and C and
October for tree A. Therefore, using the dendrometer data, cambial pinning and using the
Gompertz growth model, it was possible to related the δ13C composition relation with growth
period (months). The δ13C from each sample at a position within the tree ring width is
presented in Appendix 1.
31
Table 2. General data stem disk Terminalia superba
Stem disk Sample Tree ring width (mm) δ13C average (‰) Standard Deviation
2013 - 2014 2014 - 2015 2013 - 2014 2014 - 2015 2013 - 2014 2014 - 2015
A
Isolated tree next to
coffee field 47 years
A1 5.80 6.42 -23.35 -25.61 0.29 0.41
A2 3.40 3.95 -24.85 -24.62 0.24 0.16
A3 9.25 13.80 -24.94 -24.51 0.40 0.51
A4 4.80 6.05 -24.99 -24.76 0.35 0.12
B
Tree in secondary
forest Older tree 67 years
B1 Missing ring 7.19 Missing ring -24.78 Missing ring 0.88
B2 Missing ring 3.07 Missing ring -25.78 Missing ring 0.43
C
Tree in secondary
forest 60 years
C1 6.00 7.25 -24.87 -24.94 0.71 0.91
C2 7.80 9.30 -24.71 -24.82 0.88 0.90
C3 4.80 8.20 -23.35 -25.13 0.75 0.82
32
To have a better understanding of tree response to carbon isotope composition during tree
growth in relation to climate conditions, results of tree C are presented first. This tree
presented a similar pattern of δ13C as identified by Helle & Schleser (2004) for temperate
areas and Fichtler et al., (2010) and Verheyden et al., (2004) for tropical areas. The intra-
annual δ13C variations comes from variance within the year of physiological and weather
factors (Figure 22 and 23).
Tree C
As is described in Table 2, tree C is located in a thick secondary forest, surrounded by other
trees of the same species, including tree B. The δ13C relation with growth period (months) is
presented in the Figure 23a and 23b, both correspond to the last two tree rings from December
to July 2013-2014 and 2014-2015. The Gompertz growth model was used to relate δ13C with
the growth periods.
In the Figure 24a and 24b, it can be observed that δ13C for the samples from both growth
periods show the tendency to increase (i.e. become less negative), at the beginning of the
growth period up to the end of February; from then they turn into more negative values around
March up to the end of the growth period. In our samples, this behavior is expressed explicitly
for sample C3 and to a lesser extent for sample C1 for the tree growth period from 2013-2014.
All samples for the 2014-2015 present a similar tendency.
Helle & Schleser (2004), Fichtler et al. (2010), and Verheyden et al., (2004) (section 2.3.1.),
expressed a similar behavior. They attribute the initial increase of δ13C to the use of enriched
13C starch reserves of the previous season (Michelot et. al., 2011), and the fast formation of
structural organic matter, which removes the lighter carbon (12C) faster than the heavier (13C),
enriching the cellulose over time (Helle & Schleser, 2004).
From March until July (end growth period), the observed decrease of δ13C, could correspond
to a change in carbon source, from storage material to current photosynthates that contain
less 13C. In addition, assuming that currently produced assimilates (sugars) are more or less
depleted in 13C, in comparison with the remaining sugar reserves, their gradual incorporation
into newly formed cell wall material leads to the observed subsequent decline of lower δ13C
composition (Fichtler et al., 2010; Helle & Schleser, 2004).
Therefore, the δ13C composition is affected directly by the physiological grow processes of the
tree, being this affected by the weather conditions of the current year and the year before.
33
Figure 24. Intra-annual δ13C pattern for Terminalia superba tree C in function of time for
season (a) 2013-2014 and (b) 2014-2015 (Note: the last value for C2 was not take into account due to a
value outside the trend of δ13C)
The average δ13C values (Table 2) for tree C are around -23 ‰ to -25 ‰, with the later value
one delta unit lower than the profile of Terminalia superba (-23 ‰ to -24 ‰) from Biakoa,
Cameroon, for the years 1927–1936 (Fichtler et al., 2010). This decrease could be influenced
34
by the rise of lighter carbon (12C) and decreasing of heavier carbon (13C) coming from the
actual increasing CO2 in the atmosphere from the burning of fossil fuels (Granados-Páez et
al., 2009), this outcome is known as the Suess Effect (Earth System Research Laboratory,
n.d.).
As it was observed in Table 2, the C2 sample has the largest ring width, 7.8 mm and 9.3 mm,
for both tree growth periods, respectively. Thus, this tree section had better growth conditions
(Helle & Schleser, 2004) compared to the other samples further along the circumference.
The slight intra-annual variations of δ13C in the different samples between both years, as it
was mentioned by Fichtler et al. (2010), is possible due to the influence of external weather
conditions. weather conditions affecting the carbon isotope composition are irradiance,
rainfall, RH, temperature, soil moisture (Managave & Ramesh, 2012).
Based on the weather data collected for this study, as it was presented in section 4.1., it could
be possible to say that the intra-annual δ13C variation for the period 2014-2015 is especially
influenced by rainfall. A decrease in the δ13C values can be expected in a period of higher
rainfall were the stomata tend to be open, for example the faster decrease of δ13C in the month
of March 2015 due to a higher rainfall (Figure 25). Even though the weather conditions are
variable, the general physiological processes had the tendency to shape the δ13C composition
curve (Figure 23). At the end of the grow period, rainfall, i.e. the start of the dry season, an
increase would be expected in the δ13C values due to the stress condition, making the stomata
to close and have less 13C discrimination. Nevertheless, the physiological processes have
reduced the δ13C towards the end of the season.
Tree B
Tree B is located in an old secondary forest and it can be considered the oldest tree of the
dataset (Table 2). As for the previous tree, the Gompertz growth model was used to relate
δ13C with the growth period 2014-2015 (months), which is presented in Figure 26. The growth
period of 2013-2014 corresponded to a missing ring and was not taken into account.
A similar pattern as tree C is observed (section 2.5.1.). In Figure 26, the δ13C composition of
the two samples tend to increase at the beginning of the growth period up to February, for
then decrease up to July, the end of the growth period. Average δ13C values for tree B goes
around -24 ‰ to -25 ‰ (Table 2), similar to the tree C.
35
Figure 25. Effect of rainfall in the intra-annual δ13C composition tree C for the period 2014-
2015
Seasonal variation due to weather conditions and location in the stem disk could be the reason
for the difference in the δ13C composition between samples B1 and B2 as is observed in the
Figure 25. Sample B1 has higher values of δ13C than sample B2, probably implying the use of
a richer 13C starch, or intermittent flushes of growth due to the influence of the weather
conditions (Helle & Schleser, 2004).
Similar to tree C, the intra-annual δ13C variation can be related with the change of the weather
conditions, such as rainfall. However, it is not that evident to observe the effect of the climate
data in the intra-annual δ13C variation for tree B, as it was for tree C (Figure 26).
36
Figure 26. Intra-annual δ13C pattern for Terminalia superba tree B in function of time for
period 2014-2015
Overview
In general, tree B and C were located in the same area of a secondary forest. The thickness
of this type of forest probably resulted in a low but constant rainfall reaching the forest floor
and having a RH and temperature variation that is less pronounced over the studied periods.
The upper layer of the soil is rich in nutrients that become available from the decomposition of
fallen dead leaves and other organic litter, as Terminala superba has shallow roots, the tree
could had a continuous source of carbon.
The RH is a factor that is highly related to the stomatal conductance, nevertheless, it small
variation does not seem to have an influence in the δ13C composition for these trees.
An increase of irradiance in the months of February to April, also accompanies the profile of
13C, where the higher irradiance implies a higher rate of photosynthesis and therefore a
reduction of δ13C.
Tree A
δ13C for tree A, in function of growth period (months) is presented in Figure 27 for the period
(a) 2013 – 2014 and (b) 2014 – 2015. For this tree, the growth period started in October until
June for each year. Table 2 indicated that tree A was not located in the secondary forest as
the others trees; instead, it was located isolated surrounded by coffee fields.
37
It can be observed in Figure 27a and 27b, that the δ13C composition of tree A follows a different
profile compared to tree B and C (Figure 24 and 26). Instead, for the period 2013 – 2014 the
δ13C tends to increase during the entire growth period, and follows a generally flat pattern for
the growth period of 2014 -2015, except for sample A3.
Figure 27. Intra-annual δ13C pattern for Terminalia superba tree A in function of time for
season (a) 2013-2014 and (b) 2014-2015
38
The main difference between this tree A and tree B and C is the location. Tree A is located
surrounded by coffee fields, not influenced by the same factors as the rest of the studied trees,
i.e. soil moisture, soil organic matter, could have an effect on the earlier growth of tree A
(October, instead of December for tree C and B). Therefore, it is possible that tree A is not
only influenced by physiological factors, but also by the weather ones, affecting directly the
isotopic composition of the tree, as it was mentioned by McCarroll & Loader (2004), where
“the degree to which a single climatic parameter controls the isotopic ratios depends on the
site in which the tree grew”. Also probably, the age of the tree A can influenced the δ13C
composition, being younger than the rest of the trees.
Based on the previous statement, the continuous increase of δ13C for the period 2013 – 2014,
after the use of the starch reserves, probably could be attributed to the weather conditions. In
this area, as the tree is not influenced by others because is not located in a forest, so probably
in the dry season the soil moisture is affected, stressing the tree and increasing the δ13C.
Even though the rainfall increase in the month of April and the increase of irradiance can
probably directly influence the evapotranspiration (Palmeri et al., 2013) resulting in the closure
of the stomata, reducing the 13C discrimination, increasing the δ13C composition (Managave
& Ramesh, 2012). In addition, during the dry season, fog cover the forest , therefore, a
reduction in the photosynthesis rate, followed by stomatal closure and less 13C discrimination
(Helle & Schleser, 2004), increasing the δ13C observed in the Figure 26b.
For the period 2014 – 2015, the δ13C has a very different profile; the composition tends to
remain flat with regular seasonal intra-annual variations (Figure 26b). The variation of rainfall
in this period (Figure 21), as for the other trees, could influence the intra-annual variation of
δ13C, e.g. especially for sample A3 (Figure 28), it can be observed an increase in the δ13C,
probably for the decrease in the rainfall of December, affecting the stomata closure and
therefore the mentioned δ13C increase. With this it can be corroborate the difference in the
δ13C profiles between the radii sections of the tree mentioned by Helle & Schleser (2004).
Even the values of δ13C are similar to the other trees, the location of the tree A influenced
significantly in the δ13C (Figure 27a and 27b).
39
Figure 28. Effect of rainfall in the intra-annual δ13C composition tree A for the period 2014-
2015
In order to have an idea if there is a correlation between the climate data and the isotopical
composition (δ13C and δ18O); the Coefficient of Determination (R2) was determined. Appendix
3 has the data for R2 calculated for δ13C and appendix 4 the R2 calculated for δ18O. The plotted
graphs did not present an accurate relationship with the observed weather influence in the
δ13C composition, probably because there are too little values to work with. Therefore, a
mechanistic explanation was applied to assess the weather effect on the δ13C and δ18O
composition.
40
4.3. Intrinsic Water Use Efficiency (WUEi)
The intrinsic Water Use Efficiency (section 2.5.1.) is a parameter that relates the net
photosynthesis and transpiration. Due to its relation with δ13C, it is possible to associate the
WUEi with the carbon isotope discrimination (Δ13C) occurring in the leafs that affect the
composition of carbon in the wood (Granados-Páez et al., 2009).
Figure 29 described the WUEi is related with the growth period, slightly increasing at the end
of growth period for tree B and C, but reducing for tree A due to the continues increase in δ13C
for the period 2013 – 2014 and a constant WUEi for the period 2014 -2015. Therefore, even
the δ13C composition change over the tree growth, the WUEi is fairly constant.
Tree B presented a higher WUEi for the period 2014-2015, probably due to the values of δ13C
presented in the tree, meaning a better used of the source water for this tree.
41
Figure 29. Relationship between WUEi and growth period (a) 2013-2014, period (b) 2014-
2015
4.4. Stable oxygen isotope 18O
As it is was apply for δ13C values, using the dendrometer data, cambial pinning and using the
Gompertz growth model, it was possible to related the δ18O with growth period (months). The
δ18O from each sample at a position within the tree ring width is presented in Appendix 2.
42
4.4.1. δ18O intra- and inter-annual variation for Terminalia superba
The oxygen isotope ratio composition (δ18O) for the last two tree rings, 2013-2014 and 2014-
2015 of Terminalia superba trees, describing the general data of the tree rings and stem disk
in the table 2.
According to Managave & Ramesh (2012), the δ18O of plants varies due to weather conditions
and physiological processes, depending mainly on the water source used by the tree, the level
of evapotranspiration and biochemical fractionation associated with the synthesis of sucrose
in the leaf, and the exchange between carbohydrate and xylem water during cellulose
synthesis. Poussart et.al., (2004), described that at tropical latitudes an inverse relationship
was observed between the δ18O and the amount of rainfall, low δ18O in wet season and vice
versa.
The δ18O composition for the Terminalia superba tree A, B and C and its samples have a
general flat pattern and tend to fluctuate intra annually over the tree growth period (Figure 30).
It is difficult to determine the direct effect of physiological processes or the weather conditions
on intra-annual δ18O composition for the two growth periods: none of the profiles showed a
profile that could be related with a specific physiological process or to a weather event, i.e.
months of higher rainfall. The same effect was observed by Barbour et al., (2002), where the
intra-annual profiles of δ18O were not as consistent as for δ13C.
Some possible factors that could affect intra-annual variation of the samples from the trees A
(Figure 30a and 30b), B (Figure 30c), and C (Figure 30d) and 30e), could be:
The location of the samples in the stem disk. Some areas of the same tree can receive
more or less influence of the δ18O in the exchange between carbohydrate and xylem
water during cellulose synthesis.
In the dry season, the area is surrounded by fog. The isotopic signature of fog tends
to be more enriched in the 18O than the rainfall, due to differences in condensation
temperature. Therefore, even the rainfall is reduced in the dry season, the fog could
maintain the δ18O composition (Scholl, Eugster, & Burkard, 2011).
Other factor is a possible inaccuracy in the measurement of 18O isotope values. As is
described in the methodology section 3.3.4., the samples, before being packed in silver
cups, must be dried in an oven to avoid any external 18O contamination. Therefore, as
the samples are sensitive, the exposure to outdoor air could give a false value of 18O
and consequentially a false δ18O ratio.
43
44
Figure 30. δ18O composition in function of tree A growth period (a) 2013-2014 and (b) 2014-
2015. Tree B growth period (c) 2014-2015. Tree C growth period (d) 2013-2014 and (e)
2014-2015
45
5. CONCLUSIONS
The study of intra-annual stable isotope analysis in tree rings for the growth periods 2013-
2014 and 2014-2015 of Terminalia superba from the Mayombe forest, can give an idea of how
a tropical tree can respond to the surrounded weather conditions.
With the δ13C and δ18O composition, it could be possible to get an idea of the balance between
stomatal conductance, photosynthetic rate, source water and CO2 uptake due to the variation
of weather conditions, such as rainfall and temperature.
The δ13C composition profile of the trees A, B and C indicated how the trees behaved due to
their location. Trees B and C were located in a thick secondary forest where it was possible to
notice that even though the environmental condition differed between the growth periods
2013-2014 and 2014-2015, the δ13C composition was similar, thus, it could be determined by
physiological processes related to starch reserves from the previous years. Intra-annual δ13C
variation could be attributed to the slightly positive correlation between temperature and
changes in the rainfall.
The δ13C composition of the tree A presented a different profile compared to the rest of the
trees. Tree A was not located in a secondary forest; instead, it was located in a separate area
without trees, in a coffee field. Therefore, the δ13C could indicate that not only the physiological
processes affected the composition; also, the changing in the weather conditions could had
an effect over the isotope composition.
Therefore, the location of the studied trees could have an effect on the δ13C composition of
the tree. It seems that the trees located in secondary forest, with change in weather conditions
for the period 2014-2015, maintained the profile presented by Helle & Schleser (2004), Fichtler
et al. (2010), and Verheyden et al., (2004). However, if the trees are isolated, as can be the
case for trees in over-logged forests in the tropics, the δ13C composition presented a different
profile. This study thus shows that there can be a large intra-annual variability in the δ13C
profile between trees of the same species.
δ18O composition of the studied trees had less gradual change over the growth periods. The
intra-annual variation could be attributed to the mixing of source water from the changes in
the weather or to measurements errors. However, the flat line tendency of the δ18O
composition of the studied trees could give the idea that even the tree location and the
difference in the weather conditions over the growth periods (rainfall), the source water for
those trees could be considered the same, probably coming from rainfall and the fog presented
in the dry season.
46
6. RECOMMENDATIONS FOR FURTHER RESEARCH
The present study focusses on the δ13C and δ18O composition of Terminalia superba for the
last two tree rings, corresponding to the periods 2013-2014 and 2014-2015, giving an idea
how this tropical tree can be influenced by the physiological and climate processes.
Nevertheless, to improve the knowledge about the δ13C and δ18O composition of the
Terminalia superba or other species, and its response due to changes in climate conditions, it
is recommended to measure the CO2 concentration in the surrounded atmosphere and the
stomatal conductance. In addition, the measurement of soil water potential could give a better
idea on the effect of rainfall in e.g. the δ13C composition of tree A.
To have a better understanding of the tree response over the years due to variation in the
climate conditions, it could be recommended to study the intra-annual isotopical composition
(δ13C and δ18O) of a larger dataset of crossdated tree ring series.
On the other hand, as climate science studies in Central Africa and the rest of Africa are
currently the least developed, a selection of other tree species with dendrochronology potential
and climate sensitivity, could be added. This could improve and expand the knowledge about
how the isotopical composition (δ13C and δ18O), and therefore tropical trees, can be affected
by the change in the surrounded climate.
On important area that should be taken into account for future researches is the location of
the studied trees, because it was possible to observed a difference in the δ13C composition in
the trees located in secondary forest and the one isolated.
47
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APPENDIX
Appendix 1
Relation tree ring width (mm) - δ13C (‰) for Tree A period 2013 - 2014
Relation tree ring width (mm) - δ13C (‰) for Tree A period 2014 - 2015
53
Relation tree ring width (mm) - δ13C (‰) for Tree B period 2014 – 2015
Relation tree ring width (mm) - δ13C (‰) for Tree C period 2013 – 2014
54
Relation tree ring width (mm) - δ13C (‰) for Tree C period 2014 – 2015
55
Appendix 2
Relation tree ring width (mm) - δ18O (‰) for Tree A period 2013 - 2014
Relation tree ring width (mm) - δ18O (‰) for Tree A period 2014 - 2015
56
Relation tree ring width (mm) - δ18O (‰) for Tree B period 2014 – 2015
Relation tree ring width (mm) - δ18O (‰) for Tree C period 2013 – 2014
57
Relation tree ring width (mm) - δ18O (‰) for Tree C period 2014 – 2015
58
Appendix 3
Coefficient of Determination (R2) for δ13C
Period Sample Rainfall Temperature Relative Humidity Irradiance
2013-2014
A1 0.0025 0.166 0.0123 0.3077
A2 2.00E-08 0.2271 0.00002 0.1959
A3 0.0201 0.0655 0.0037 0.1573
A4 0.1581 0.1138 0.2406 0.1811
B1 Missing ring Missing ring Missing ring Missing ring
B2
C1 0.4848 0.5639 0.355 0.7742
C2 0.0099 0.0007 0.0063 0.0638
C3 0.3784 0.5754 0.7059 0.885
2014-2015
A1 0.0146 0.3686 0.121 0.9935
A2 2.00E-08 0.1503 4.00E-05 0.943
A3 0.1295 0.2173 0.2012 0.0624
A4 0.4961 0.3287 0.0082 0.1192
B1 0.3145 0.5125 0.2745 0.4638
B2 0.3887 0.6488 0.5684 0.9133
C1 0.5111 0.7162 0.4819 0.8966
C2 0.4547 0.7997 0.4726 0.9466
C3 0.5454 0.7527 0.4611 0.8841
59
Appendix 4
Coefficient of Determination (R2) for δ18O
Period Sample Rainfall Temperature Relative Humidity Irradiance
2013-2014
A1 0.2658 0.1582 0.3353 0.1664
A2 0.2029 0.267 0.0683 0.4348
A3 0.1656 0.1425 0.118 0.1063
A4 0.2137 0.371 0.3199 0.3136
B1 Missing ring Missing ring Missing ring Missing ring
B2
C1 0.0395 0.2061 0.0548 0.0225
C2 0.0638 0.0562 0.0072 0.1445
C3 0.0816 0.2739 0.1214 0.2985
2014-2015
A1 0.00006 0.2977 0.0434 0.9687
A2 0.2029 0.1827 0.003 0.7462
A3 0.4757 0.3994 0.3985 0.6375
A4 0.6246 0.4765 0.0213 0.2693
B1 0.0948 0.0756 0.0286 0.3904
B2 0.0744 0.0008 0.0042 0.2855
C1 0.1901 0.0122 0.0049 0.0353
C2 0.0195 0.054 0.0188 0.0303
C3 0.4056 0.5731 0.15 0.6632
