soil respiration in primary and secondary tropical montane rain

Theresa Simona Ibáñez

MSc Degree in Environmental Science, 45 ECTS creditsDepartment of Biological and Environmental Science

University of GothenburgNovember 2015

Examiner: Dr. Lennart BornmalmDepartment of Biological and Environmental Science

University of Gothenburg

Supervisor: Dr. Göran WallinDepartment of Biological and Environmental Science

University of Gothenburg

Soil Respiration in Primary and SecondaryTropical Montane Rain Forest

Nyungwe National Park, Rwanda

Soil Respiration in Primary and Secondary Tropical Montane Rain Forest Nyungwe National Park, Rwanda Master of Science Thesis Theresa Simona Ibáñez E-mail: [email protected], [email protected] Mobile phone: +46 (0)72 562 60 32 © Theresa Simona Ibáñez, 2015 All rights reserved. Department of Biological and Environmental Sciences University of Gothenburg SE 405 30 Göteborg Sweden Web page: www.bioenv.gu.se Telephone: +46 (0)31 786 00 00 Photographer: Theresa Simona Ibáñez, Nyungwe National Park, Rwanda, March 2015 Printed by University of Gothenburg Gothenburg, Sweden 2015

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Abstract An increasing number of forest landscapes are disturbed by mostly human influences, such as deforestation, and thereby transformed from primary into secondary forests. Such transformations and the subsequent release of CO2 from decaying vegetation and soil organics affect the world’s carbon and temperature balance, thus influencing global climate change. One of the most important paths of carbon fluxes from the ecosystem to the atmosphere is soil CO2 efflux originating from of root respiration and soil microorganisms. Ecological data on soil CO2 efflux in the tropics have been collected mainly in the South American Amazonian Basin and Asia, but are almost lacking for Africa. Therefore, this thesis studies soil respiration in a tropical montane rain forest in Rwanda within the Nyungwe National Park and examines the relation between soil CO2 efflux and several independent factors. Data was collected from 320 measuring positions distributed over 15 half-hectare plots along a 32 km transect of the forest, and repeated over two time periods. The results from this study revealed that the total amount of carbon flow from the soil (wet season in 2015) was 16.0 Mg C t h-1 yr-1 in primary forest and 19.7 Mg C h-1 yr-1 in secondary forest. High soil water content had a negative effect on soil CO2 efflux (SCE) during rain season due to oversaturated soil. Conversely, temperature and basal area had a positive affect on soil respiration. Though, SCE exhibited significant correlation with fine root production in secondary forest areas, pH and SCE did not show any significant relations. The thesis discusses the results against the background of the existing literature and identifies further areas of research. Keywords Tropical montane rain forest; Primary and secondary forest; Carbon stock; Soil respiration; Soil CO2 efflux (SCE); Soil and air temperature; Soil water content (SWC); pH; Fine root production (FRP); Basal area (BA).

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Sammanfattning Ett ökat antal skogslandskap störs mestadels av mänsklig påverkan, såsom avskogning, och förvandlas därmed från primär- till sekundärskog. Sådana förändringar orsakar frigörelse av CO2

genom nedbrytning av vegetation och organiskt material. Detta påverkar jordens kol- och temperaturbalans, vilket påskyndar den globala klimatförändringen. En av de viktigaste vägarna för kolflöden, från ekosystemet till atmosfären, är CO2 utflöde från marken, vilket huvudsakligen kommer från respiration av rötter och mikroorganismer. Basdata gällande utflöde av CO2 från marken i tropikerna har främst insamlats från Amazonas och Asien, medan däremot liknande data från Afrika till stor del saknas. Avsikten med föreliggande studie var därför att studera markrespiration i en tropisk bergsregnskog inom Nyungwe National Park i Rwanda, samt studera relationen mellan markens koloxidutflöde och flera andra oberoende faktorer. Under två perioder insamlades data från 320 mätpositioner fördelade över vardera 15 halva hektar, vilka låg i en 32 km transekt genom skogen. Resultaten visade att den totala mängden kolflöde från marken (under regnperioden 2015) var 16.0 Mg C h-1 yr-1 i primärskog och 19.7 Mg C h-1 yr-1 i sekundärskog. Hög markvattenhalt visade sig ha en negativ effekt på koloxidutflödet från marken under regnperioden på grund av att jorden var övermättad. Temperaturen och den basala arean påverkade markrespirationen positivt. Utflödet av koldioxid från marken var däremot bara signifikant i korrelation med finrotstillväxt i sekundärskog. Koldioxidutflödet visade däremot ingen signifikant relation med pH. Uppsatsen behandlar resultaten mot bakgrunden av befintlig litteratur och ger därmed uppslag till ytterligare forskningsområden.

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Preface This master thesis in Environmental Science at University of Gothenburg was conducted under the supervision of Dr. Göran Wallin. I would like to thank him for invaluable knowledge about the world of environmental and biological research, and support through the process of making this thesis. For this study, I performed fieldwork in Nyungwe National Park in Rwanda during two months. All data from 2015 in this study is therefore collected during that period of time. It would not have been possible to finish the fieldwork in time without the help from following three persons; Etienne Zibera, thank you very much for all the help with the preparation and implementation of the fieldwork and Innocent and Pierre, thank you for keeping us safe in the forest. Laboratory work were part of this study, and I would like to thank Dr. Donat Nsabimana for providing access to laboratory and other areas at the University of Rwanda in Butare. Thank you Dr. Beth Kaplin for letting us use your car for fieldwork. I would like to thank the Swedish International Development Cooperation Agency (SIDA) for financial support. Also I would like to thank the Rwanda Development Board (RDB) for permission to enter and perform research within the Nyungwe National Park. Special thanks go to Eric Mirindi Dusenge, for kindly introducing me to the Land of a Thousand Hills. Thank you Erasme Uyizeye for sharing your working space. You both helped me a lot within and outside the university during my stay in Rwanda. I would also like to thank Felicien Uwizeye Karekezi for fine root production data and for teaching me useful words in Kinyarwanda before my journey.

Thanks to my dear friends Josefin and Malin for encouraging me during the ups and downs through the process of this thesis. To Christian, thank you for everything. At last, an infinite thanks to my family, that is and will always be my core in life. Theresa Simona Ibáñez Gothenburg, October 2015

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List of abbreviations SCE; Soil CO2 efflux SWC; Soil water content FRP; Fine root production BA; Basal area T; Temperature SD; Standard deviation Forest type: Primary forest, Secondary forest Forest site: Primary 1 (plots 10 - 12), Primary 2 (plots 13 - 15), Secondary 1 (plots 1 - 3), Secondary 2 (plots 4 - 6), Secondary 3 (plots 7 - 9)

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Table of contents Abstract 1

Keywords 1 Sammanfattning 2 Preface 3 List of abbreviations 4 1. Introduction 6 2. Aim and Research Questions 9 3. Materials and Method 10

3.1 Site description 10 3.2 Soil CO2 efflux 11 3.3 Temperature, soil water content, and soil pH 12 3.4 Sampling of fine roots 13 3.5 Basal area 13 3.6 Data and statistical analysis 13 3.7 Literature study 14

4. Results 15 4.1 Research question 1: SCE in primary and secondary forest (spatial) and temporal variations 15

4.1.1 SCE at plot level 16 4.2 Research question 2: Contributing factors to soil respiration 16

4.2.1 Soil and air temperature 17 4.2.2 Soil Water Content 19 4.2.3 SCE in relation to pH at different soil profiles 19 4.2.4 Basal Area and Fine Root Production 20

5. Discussion 21 5.1 Research question 1: Soil CO2 efflux in spatial and temporal variations 21 5.2 Research question 2: Parameters in relation to soil respiration 22

5.2.1 Soil and air temperature 22 5.2.2 Soil Water content 22 5.2.3 pH 22 5.2.4 Basal Area 23 5.2.5 Fine Root Production 23

5.3 Limitations for the thesis 23 6. Conclusions 24 7. References 26 8. Appendices 29

Appendix A 29 Table 1 29

Appendix B 30 Table 1 30 Table 2 31

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1. Introduction The world’s forests contribute to essential ecosystem services such as carbon uptake, improved soil and water quality through evapotranspiration and high biodiversity (Hansen et al., 2013; Riebeek, 2011). Due to their carbon storing capacity, forests are part of the global carbon cycle and play a major role in the climate regulation. The carbon cycle is a biogeochemical process that exchanges fluxes of carbon between different reservoirs, such as the atmosphere, geosphere, hydrosphere and pedosphere (Riebeek, 2011). The global terrestrial carbon stocks contain 2 011 Gt carbon (C) in soil to depths of one meter and 466 Gt C in vegetation. Tropical forests contain 216 Gt C in soil and 212 Gt C in vegetation (IPCC, 2014). The major part of the tropical ecological data is collected in terrestrial ecosystems such as the Amazon (the largest tropical rainforests) and Asia (the third largest tropical rainforests) (Malhi et al., 2013). So far, the African continent has not been a research priority, despite having the second largest area of tropical rain forests (Cao et al., 2001). Africa is also predicted to be among the worst affected areas by elevated CO2 exposure (changing climate) in the future (Pettorelli et al., 2012). When eco-physiological research is conducted within a forest, the area can be subdivided into groups based on the level of previous disturbance. Forests with no anthropogenic disturbance are generally defined as primary or pristine forests (Josefsson, 2009). Forests with anthropogenic impacts are categorized as secondary or disturbed forests. Primary forest can be affected by natural events such as wildfire and heavy storms that change the landscape. Natural disturbance is important for the maintenance of the forests and allows pioneer species to grow when canopy gaps are created. On the other hand, the world’s forest landscapes are increasingly transformed by anthropogenic impacts, turning large terrestrial areas from primary to secondary forest every year (Wright, 2005). Humans can affect the forest in several ways. Deforestation or forest fire that prevent the forest from recovering into its natural state are a few examples of human impact. The Nyungwe National Park in Rwanda consists of both primary and secondary forest. Forest fires caused by humans during and after the civil war in the 1990’s have changed the landscape and its vegetation (Pettorelli et al., 2012; E. Zibera, personal communication, March, 2015). Due to Rwanda’s growing population, the need of agricultural land for food production is increasing, causing deforestation. People also use wood from the forest as construction material and fuel for heating. Although today the forest is protected by the government and defined as a National Park, there is still small-scale illegal logging. To study the potential outcome of the anthropogenic impact in different forest areas, one can examine the soil CO2 efflux from the respiration in ground processes. Below ground processes are relevant and interesting research areas when observing the carbon cycle. This is because the soil CO2 efflux (SCE) from below ground respiration is one of the most important paths for fluxes of carbon from the ecosystem to the atmosphere (Adachi et al., 2006; Rustad et al., 2000; Sotta et al., 2004). Annually, The global soil respiration releases annually approximately 60 Pg C yr-1 from the soil, of which 24 Pg C yr-1 is from tropical forest soils (Giardina et al., 2014; Lee at al., 2006; IPCC, 2007; Nottingham et al., 2012; Schlesinger, 1992). The total soil respiration is dependent on autotrophic respiration by the roots supported by carbohydrate exports from the photosynthesis in the leaves and heterotrophic respiration by soil microorganism feeding on the soil organic matter. These processes are highly affected by factors

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such as soil water content (SWC) and temperature (Lloyd and Taylor, 1994; Lee et al., 2006; Lou et al. 2006; Rustad et al., 2000). Soil respiration can be calculated as FSR = FHR + FRR, where the soil respiration is equal to the sum of heterotrophic respiration and root respiration (autotrophic respiration) (Lee et al., 2006; Lou et al., 2006; Vargas et al., 2011). Soil respiration is a process that naturally occurs in the carbon cycle, creating viable conditions for life on earth. It is the anthropogenic emissions that have, since the industrial revolution with land use change and emissions from combustion of fossil fuels, rapidly increased the efflux of CO2 to the atmosphere and changed the balance within the carbon cycle (Eliasson et al., 2005; Rustad et al., 2000). The unbalanced carbon cycle is affecting ongoing climate change by the increasing atmospheric CO2 concentration. The resulting increase of the global temperature is a potential contributing factor for soil respiration (Raich and Schlesinger, 1992). The SCE’s relation with soil temperature can be explained with the Arrhenius type equation (temperature dependence of chemical reaction rate) and has been shown in different patterns, for example, in exponential functions or in linear regressions (Lloyd and Taylor, 1994; Reth et al., 2005). Soil respiration has a positive feedback loop with global climate. This means that soil respiration can be affected by climate change and respond back by reinforcing climate change (by elevated CO2 effluxes) (Friedlingstein, 2006; Rustad et al., 2000). Soil water content varies depending on soil type and the amount of precipitation. It also affects the rate of soil respiration (Rustad et al., 2000). Soil contains small pore spaces that are normally filled with CO2 rich air however, when the soil is saturated, the small pores with air are replaced by water, creating an efflux of CO2 out of the soil. The SCE can therefore increase drastically during heavy rainfall (Lee et al., 2002; Lou et al., 2006). Though, the soil can be saturated with too high percentages of water, which constrains the soil respiration. Highly saturated soil will over time prevent the aerobic and heterotrophic respiration; aerobic respiration because of an absence of O2, and heterotrophic respiration due to decreasing availability of organic carbon uptake for the microbes (Davidson et al., 2000). The soil quality is affected by pH, which can thus be a contributing factor to the soil respiration rate. A less acidic soil will increase the microbial activity, which simultaneously will raise the heterotrophic respiration (Andersson and Nilsson, 2011; Ellis et al., 1998). Elevated CO2 concentrations to a forest will increase the basal area, which influences the inflow of carbon to the soil in several ways (Hamilton, 2002). Uptake of atmospheric CO2 by the trees is performed by photosynthetic activities within leaves and transported through the trunk. It directly contributes to soil respiration through root respiration or indirectly through rhizosphere respiration (Bréchet et al., 2011). The inflow of carbon into the soil can also increase through the amount of litter fall. Higher tree density can contribute to more surface litter fall for organic matter breakdown (heterothrophic respiration) (Bréchet et al., 2011; Sotta et al., 2004). The number of trees also contributes to the amount of roots, and root growth will increase root respiration (autotrophic respiration). According a number of studies, the basal area and fine root production is predicted to have an impact on the total soil respiration (Bréchet et al., 2009; Katayama et al., 2009; Søe and Buchmann, 2005). This study is a part of a major project, where researchers from the University of Rwanda together with colleagues from the University of Gothenburg measure carbon stock and fluxes in the

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Nyungwe Forest, South West of Rwanda. The overarching objective of this research is to improve our understanding of carbon cycles within forests and the atmosphere and add knowledge and information to the Global Ecosystem Monitoring network (GEM). This thesis attempts to contribute to carbon stock research by studying soil respiration within Nyungwe tropical mountain rainforest. The project was funded by the Swedish International Development Cooperation Agency (SIDA) within the Minor Field Study (MFS) framework. The motion for MFS is to prepare Swedish students to operate in a global context by gathering knowledge about developing countries and development issues and to encourage interest and future actions within development countries (MFS, 2015).

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2. Aim and Research Questions The aim of this thesis is to study potential effects of anthropogenic change in the forest landscape on the soil CO2 efflux by comparing measurements in primary and secondary forests. Two central research questions were developed under consideration of the literature presented in the introduction:

1. How much does the soil CO2 effluxes differ between primary and secondary forest in Nyungwe National Park? Are there temporal variations?

2. How is the soil CO2 efflux related to air and soil temperature, soil water content, pH, fine

root production and basal area? In relation to research question 1, the following hypothesis were derived from the section above:

1.1. Soil CO2 efflux will be significantly different between primary and secondary forest. In relation to research question 2, the following hypotheses were derived from the section above:

2.1 Elevated temperature will increase the rate of soil CO2 efflux.

2.2 The soil CO2 efflux will increase with increased soil water content.

2.3 The soil CO2 efflux will increase with increasing soil pH.

2.4 Higher values of basal area will result in higher soil CO2 efflux.

2.5 The soil CO2 efflux will increase with increasing fine root production.

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3. Materials and Method 3.1 Site description The research area is located in the Nyungwe forest, South West of Rwanda (2°17’- 2°50’S, 29°07’- 29°26’E) (Figure 1). The area is within the Nyungwe National Park, which was established in year 1999 and is now the best preserved montane rain forest in central Africa (Rwihaniza Gapusi, 2007). The National Park is the largest remaining tropical mountain rainforests in central eastern Africa, covering an area of 980 m2. It contains both primary forest (dominated by late succession species of which Syzigium guineense is most common) and secondary forest (dominated by pioneer species of which Macaranga kilimandscharica is most common). The Nyungwe forest has, as other parts of Rwanda’s landscape, many hills and different levels of altitude. The altitude within the forest is between 1 600- 2 950 m. There is a high diversity of both animal and plant species. Within the park’s boundary there are 1 068 different plant species and 275 different kinds of bird species. Thirteen primate species can be found in Nyungwe National Park, which is 25 % of the total amount of primates on the African continent. It is especially famous for its colonies of chimpanzees (Pan troglodytes).

Figure 1. Map of Rwanda and Nyungwe National Park. The mean annual day and night time air temperature during 2007- 2013 was 13.5 °C and 15.7 °C, respectively, with small seasonal variations. The average annual precipitation was 1 780 mm during the same period. During 2013- 2014 in Karamba (Primary 2) the temperature was on average approx. 1.5 °C higher over the whole year (Figure 2a). In general, precipitation was also higher at forest sites Secondary 1 and 2, and especially during rainy season that occur from September to April (Figure 2b).

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Figure 2. Mean air temperature (a) and precipitation (b) from June 2013 to May 2014. The forest has different measuring stations for air temperature and precipitation. Karamba is near Primary 2, Uwinka is close to Primary 1, Bigugu close to Secondary 3 and Uwagashihe is in between Secondary 1 and 2 (L. Hansson 2014). Fifteen plots were established in 2012 along a east-westerly transect of 32 km of the forest. Each plot was divided into 5 clusters creating 3 replicates in each. Three clusters represented secondary forest (Secondary 1, 2 and 3) and two represented primary forest (Primary 1 and 2). Each plot had an area of 0.5 ha (50 x 100 m) subdivided into 8 subplots (25 x 25 m) (Figure 6). The size of the plots was based on results from a pilot study which DBH (diameter at breast height) and species was determined for all trees > 5 cm DBH in 6 plots covering 1 ha each. The results showed that the basal area calculated from DBH saturated at 0.2 ha and number of species at about 0.5 ha (Figure 3).

Figure 3. Accumulated number of species in 1 ha. The three curves shows one plot each (G. Wallin, 2011). 3.2 Soil CO2 efflux SCE was measured at 360 positions during two measurement campaigns in August to November 2014 and February-March 2015. The measurements were conducted by using a closed chamber gas exchange system (Licor 6400-09, Nebraska, USA) (Figure 5). To prepare for the measurements, collars made of PVC tubes with an inner area of 75.4 cm2 or 80.5 cm2 were installed in July 2014 at three positions in each subplot (Figure 6). The instrument was installed to measure three cycles per collar position, where the CO2 concentration within the chamber was stabilized to an ambient concentration, 400 ppm, before every measuring cycle (Figure 4).

a b

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Figure 4. Three cycles of CO2 efflux measurements in position; plot 1, sub plot 1, collar position 3 in March 2015. 3.3 Temperature, soil water content and soil pH Simultaneous to the SCE measurements, the air and soil temperatures (C°) were measured with an additional thermistor probe, inserted 10 cm into the soil and at a distance of 10 – 20 cm from the soil chamber. SWC (v/v %) was measured with a soil moisture sensor (ML3 ThetaProbe, Delta T device, Cambridge, UK) at three positions with a distance of ca. 10 cm around each collar position. pH in the organic soil layer and upper mineral soil layer were measured in soil samples collected at every collar position (360 positions) at a distance of 50 cm from the collar. The soil samples were collected after measuring the CO2 efflux to minimize disturbance. Two mineral soil samples per plot were randomly collected at a depth of 30 cm with a soil auger. The pH of mineral soil was not expected to differ much at 30 cm, therefore, two samples were collected per plot in order to represent the deep soil pH value for the whole plot. The soil samples were stored in air sealed plastic bags in a refrigerator for a maximum of 7 days before measuring the pH. The soil samples were dried in open aluminium containers at room temperature for 24 hours. A 3 g sample of soil was put in bottles with 15 ml water, giving a soil to water ratio of 1:5 w/w. with 3 g of soil and 15 g (ml) of distilled water. The bottles were shaken and thereafter, the soil was allowed to settle for 30 min before the measurement (SIS, 1994). The pH and the sample temperature were determined in the water fraction with a combined pH and temperature electrode (HI 98128, Hanna Instrument, Sarmeola di Rubano, Italy). The pH electrode was calibrated before the measurements each day using buffer solutions with a pH 4 of and 7 (Steed and Reed, n.d.).

Figure 5. Li- COR 6400-09 for soil CO2 efflux measurement with a closed gas exchange system. An additional thermistor probe (seen in the middle) is for temperature measurements.

395

400

405

410

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420

0 50 100 150

CO

2 (pp

m)

Time (sec)

Cycle 1, 1.25x + 372.89, R² = 1.00

Cycle 2, 1.15x + 310.29, R² = 1.00

Cycle 3, 1.12x + 246.57, R² = 1.00

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3.4 Sampling of fine roots Data from the same plots, but collected by other researchers, was added to the study to improve the results and scientific reliability. Those data included fine root production (representative for the whole time period of study), soil CO2 efflux, temperature and soil water content from 2014. The fine root production (FRP) was estimated by using the in-growth core technique (Metcalf et al. 2007). One in-growth core (8 cm diameter, 40 cm depth) was installed at the centre of each sub-plot in October-November 2013 and harvested approximately every six months. For this study, the harvest from December 2014 was used, as it was the closest to the time period of the SCE measurements. The fine roots have, by definition, a root diameter of less than 2 mm (root dm2 < 2 mm) (Gaudinski et al., 2001; Lee, 2003). The root fractions were taken from two soil layers; organic soil (root litter) and mineral soil. The SCE data was analysed in relation to the sum of the fine roots in the organic and mineral soil layers. 3.5 Basal area Basal area (BA), defined as total cross sectional stem area per ground area, was determined by measuring the stem diameter at breast height (DBH) of all trees > 5 cm DBH in the plots. These measurements were made as part of the main project in 2012, but the change is likely to be minimal.

Figure 6. Schematic representation of one sub plot (25 x 25 m) with points for different measuring positions. 3.6 Data and statistical analysis Firstly, due to technical problems in field, air temperature values were missing from measurements in plot 7 during 2015. As replacement, the mean of surrounding and previous measurements was calculated and used. Secondly, since FRP was measured near collar position 2 only, the FRP would not be representative for the whole plot. Therefore, for the analysis of the FRP data in relation to SCE, only collar position 2 was used from the SCE data. Finally, the exact collar position for SCE data from plot 4 for 2014 was missing. Therefore, an average for the whole sub plot (8 sub plots in plot 4) was extrapolated to each FRP value for each sub plot. In an initial analysis, a number of outliers were identified in the original SCE and SWC data, where values seemed to have high deviation from the mean. However, a test of the outliers impact on the results of the analyses showed that the differences were insignificant and could therefore be ignored. Where possible, data was examined at collar position level. However, as some independent variables were only available on a subplot level, the dependent data (SCE) was aggregated via the average of the collar position values. The sample size used in the statistical analysis for SCE, SWC,

Collar for soil respiration measurements

In-growth core for roots

Sampling point for pH organic soil and upper layer mineral soil

Sampling point for pH mineral soil at depth of 30 cm

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soil and air temperature from both 2014 and 2015, came to a total of 704 replicates; 288 from primary forest and 416 from secondary forest. Replicates for pH (2015) in organic soil and pH in upper layer mineral soil were 360 respectively, and for the pH in mineral soil at the depth of 30 cm there were 15 replicates. FRP had 120 and BA had 240 replicates. BA was assumed as constant during this period and representative for both 2014 and 2015. Univariate, descriptive statistics were conducted to answer research question 1. Based on forest type (primary, secondary), forest site (Primary 1, Primary 2, Secondary 1, Secondary 2, Secondary 3) and temporal variations (2014, 2015), different samples were compared for similarities and differences of the means of SCE. The significance of those differences was tested via repeated measure analysis of variance (ANOVA) followed by Tukey’s Post hoc test, in order to find the eventual differences (α = 0.05). To examine research question 2, Pearson correlations between the dependent variable SCE and the different independent variables were calculated. The Pearson correlation is a bivariate measure of the relation between two variables. It is used to ask to what extent a change in a value in one variable is accompanied by a change in value in the other in the same direction. The Pearson correlation thereby assumes a linear relationship between the two values. The strength of the correlation is represented by the correlation coefficient R. If R = 1 (R = -1), this means the data is perfectly positively (negatively) correlated. R = 0 indicates that the data shows no relation at all. Often researchers use R2, which can be interpreted as the percentage of the variance in the dependent value (e.g. SCE) that is explained by the model. For the Pearson correlation and most other cases, R2 is in fact just the correlation squared and it depends on the research community, which of those two is more common. In environmental science, we mostly see R2, and it is also used here. For all tests, the differences are first set to be significant at p ≤ 0.01 and secondary at level p ≤ 0.05. Person correlation is sensitive to outliers (Anscombe, 1973), therefore it is generally recommended to also look at a graphical representation of the data, (in this study scatter plots were used). To increase readability, the graphs presented in this thesis were to utmost extent aggregated as means, in order to reduce the number of data points in the scatter plots. The programs used for statistical data analysis were Microsoft Excel 2010 and SPSS version 23. 3.7 Literature study The development of the research questions, hypotheses and analyses were informed by previous studies and other literature. Databases used when searching for material were Google Scholar and Web of Science. The search terms were mainly sentences containing words similar to “climate change”, “tropical montane rain forest”, “soil respiration”, “soil water content” and “fine root production”. In order to understand and be able to use the measuring instruments during fieldwork, manuals and literature to related machines were studied.

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4. Results The soil CO2 efflux data, collected during 2014, was from the same measuring positions as in 2015. The SCE data was moderately correlated (R2 = 0.24) between the two years (Figure 7a). By eliminating measurements for the plots 3, 4 and 8 that were taken during the dry season in 2014 but rain season in 2015 (see explanation on page 16), the correlation showed higher relation (R2 = 0.39) (Figure 7b).

Figure 7. Soil CO2 efflux in 2014 in relation to soil CO2 efflux in 2015. When SCE data from plots 3, 4 and 8 were included (a), the correlations was weaker than when the same plots were excluded (b). The plots 3, 4 and 8 were in 2014 measured during dry season and show therefore different results than for the rest of the plots, measured during rain season. 4.1 Research question 1: SCE in primary and secondary forest (spatial) and temporal variations The measurements related to the first research question “How much does the soil CO2 effluxes differ between primary and secondary forest in Nyungwe National Park? Are there temporal variations?” resulted in a higher soil CO2 efflux in secondary forest than in primary forest. In 2014, the SCE was 4.09 ± 0.87 (mean ± SD) µmol m-2 s-1 in primary forest and 4.56 ± 0.99 µmol m-2 s-1 in secondary forest. The average SCE was 4.22 ± 0.90 µmol m-2 s-1 in primary forest and 5.20 ± 0.86 µmol m-2 s-1

in secondary forest in 2015 (Appendix B, Table 1). However, the repeated measure ANOVA test showed that both spatial and temporal differences in SCE mean were not significant (p = 0.283). Hence, the total annual amount of carbon flow from the soil to the atmosphere was 15.7 Mg C h-1 yr-1 in primary forest and 18.3 Mg C h-1 yr-1 in secondary forest. The analysis of the mean SCE for the five sites (Table 1) shows that the Secondary 3 in 2015 had the highest SCE total of 5.7 ± 0.96 µmol m-2 s-1, and Primary 2 in 2015 had the lowest with 3.43 ± 0.21. The by far the highest temporal variation (2014 and 2015) was detected in the Secondary 3 plot, with an SCE mean difference of 1.29 µmol m-2 s-1 (21.6 %). Tukey’s Post hoc test showed no significance in SCE between the five sites in 2014. Though in 2015 all sites were significant to one or several of the other sites except from Secondary 1 (Table 1).

All plots y = 0.54x + 2.49

R² = 0.24

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0 2 4 6 8 10

SCE

2015

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ol m

-2 s-1

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SCE 2014 (µmol m-2 s-1)

a Plots ≠ 3, 4, 8

y = 0.69x + 1.53 R² = 0.39

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0 2 4 6 8 10 SC

E 20

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mol

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s-1)

SCE 2014 (µmol m-2 s-1)

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Table 1. Soil CO2 efflux (µmol m-2 s-1) in five forest sites in Nyungwe National Park, including primary and secondary forest. SCE means at plot level from 2014 and 2015. Multiply comparison in mean values are indicated by different letters a < ab < b. p < 0.05. Forest site SCE 2014 SCE 2015 Primary 1 4.66 ± 0.85a 5.00 ± 0.35a

Primary 2 3.51 ± 0.42a 3.43 ± 0.21b

Secondary 1 4.22 ± 1.05a 4.55 ± 0.56ab

Secondary 2 4.45 ± 1.40a 5.09 ± 0.37a

Secondary 3 4.68 ± 0.82a 5.97 ± 0.96a

4.1.1 SCE at plot level The results above indicated that there were higher variations between forest types than between seasons. SCE results were also compared at plot level, 2014 and 2015 together, in order to see how the efflux varies between the 15 plots. The ANOVA test showed that the 15 plots differed significantly (p < 0.01). The highest SCE was measured in plot 7 with 6.04 ± 2.00 µmol m-2 s-1. The lowest SCE was measured in plot 13 (Primary 2) with an efflux of 3.26 ± 1.57 µmol m-2 s-1. When comparing the plots between 2014 and 2015, only the plots 3, 4, 8, 10 (p < 0.01) and 7 (p < 0.05) varied significantly between the two years. Variations were highest in plot 4 (43.2%) and plot 8 (40.4%). With the exception of plot 10, all of those plots represent secondary forest. Therefore it becomes apparent, that the plots within primary forest tend to show much less variation between the two years than the plots in secondary forest (Appendix 1). A potentially relevant aspect to explain those results is that for 2014, the data in plots 3, 4 and 8 (secondary forest) as well as plot 13 (primary forest) were all measured in August. This month is the month just before the rainy season begins. However, the other plots were measured during rain season later in 2014. One could therefore assume that conditions between those plots and the others are quite different. In plot 13 in year 2015 there were sub plots with very high SWC due to that the mineral soil were mixed with clay. This can be an explanation as to why there was little difference in SCE between the two years in this plot. The SWC was higher in plot 13 year 2015 than in 2014 and therefore also resulted in a lower SCE. In some parts of the presented results the data is tested excluding plots 3, 4 and 8 but including plot 13, because of above mentioned explanations. Generally it is worth noting that the standard deviation within a plot varied substantially between the different plots (Appendix 1, Table 1). The largest coefficient of variation (the standard deviation in relation to the mean) was 62 % in plot 11 in 2014, the lowest 20 % in plot 15 in 2104. This underlines the importance of examining the impact of additional factors than forest type and season on soil CO2 efflux. In the following, this thesis will present some of those factors. 4.2 Research question 2: Contributing factors to soil respiration The second research question “How is the soil CO2 efflux related to air and soil temperature, soil water content, pH, fine root production and basal area?” showed both expected and unexpected results. In general, differences in SCE could be explained by spatial environmental conditions rather than temporal.

17

4.2.1 Soil and air temperature In 2014, the mean soil temperature was 15.7 ± 0.7 °C in primary and 14.8 ± 0.7 °C in secondary forest, respectively. In 2015, the soil temperature was somewhat higher with 16.0 ± 0.7 °C and 15.1 ± 0.5 °C in primary and secondary forest, respectively. The same pattern for soil temperature between the two forest types was shown for air temperature, where the air temperature was slightly lower in secondary forest. The air temperature in primary forest in 2014 was 21.8 ± 1.6 °C and in secondary forest 20.9 ± 2.2 °C. In 2015, it was 22.3 ± 1.7 °C in primary forest and 21.1 ± 1.8 °C in secondary forest (Appendix B, Table 1). Figure 8 illustrates that all soil temperature data (2014 and 2015) was positively correlated with air temperature data (R2 = 0.37). This indicates that increased air temperature will increase the soil temperature.

Figure 8. Soil temperature correlated to air temperature shows that increasing air temperature also increases the soil temperature. In primary forest, there was no significant correlation between SCE and soil temperature in 2014 and 2015. However, in secondary forest the relation between SCE and soil temperature was significant on the p < 0.01 level in both years. The correlation was positive with R2 = 0.09 (Figure 9a) for 2014 and R2 = 0.26 in 2015 (Figure 9b). Air temperature showed somewhat contrasting results. In 2014, SCE showed no significant relation for either type of forest (Figure 9c). In 2015 though (Figure 9d), SCE was negatively correlated with air temperature (R2 = 0.14) in primary forest, but positively correlated in secondary forest (R2 = 0.13), significant at p < 0.01 level.

y = 0.24x + 10.17 R² = 0.37

14

14,5

15

15,5

16

16,5

17

16 18 20 22 24 26

T Soi

l °C

TAir°C

18

Figure 9. Primary forest is represented in blue scatter plots and trend line and secondary forest in red. Means at sub plot level. Results of soil CO2 efflux in relation to soil temperature in 2014 (a), in 2015 (b), which shows similar patterns; negative correlation in primary forest and positive correlation in secondary forest. The relation between soil CO2 efflux and air temperature in 2014 (c) shows a negative linear pattern in primary forest and no pattern in secondary forest. In 2015 (d) there was a contrasting result; primary forest had a negative correlation and secondary forest had a positive correlation. By looking at the mean values of temperature at plot level in 2015 it becomes clear that there are temperature differences. The primary plots are located at a lower altitude and had a warmer mean temperature (1 - 2° C). The temperature responses towards SCE in plots 10, 11 and 12 (Primary 1) and plots 13, 14 and 15 (Primary 2) were tested separately, because Primary 2 had higher mean temperature. The results showed that the soil temperature responses within primary forest were positively correlated, R2 = 0.25 in Primary 1 and R2 = 0.02 in Primary 2. As in primary forest in total 2015, the correlations within Primary 1 and Primary 2 were not significant. A variant of the test was made for the data in secondary forest, where plots 3, 4 and 8 (measured in dry season in August 2014) were excluded. Plot 13 (primary forest) was also measured in August but had different ambient conditions and was therefore not particularly affected by the dry season. The differences were indicated in plots 3, 4 and 8, for example, by the lower mean SCE in these plots. In addition, the correlation between SCE and temperature becomes stronger when plots 3, 4 and 8 were excluded, with a R2 value increasing from 0.09 to 0.20 (SCE vs. TSoil) and 0.01 to 0.17 (SCE vs. TAir). However, the air and soil temperatures were not differing much, even if there were

Primary forest y= -0.44x + 10.97

R² = 0.046

Secondary forest y = 0.70x - 5.96

R² = 0.09

0

2

4

6

8

10

13 14 15 16 17 18

Soil

CO

2 effl

ux (µ

mol

m-2

s-1)

TSoil °C

a. 2014

Primary forest y = -0.50x + 12.21

R² = 0.07


R² = 0.26

0

2

4

6

8

10

13 14 15 16 17 18

Soil

CO

2 ef

flux

(µm

ol m

-2 s-1

)

TSoil °C

b. 2015


R² = 0.07

Secondary forest y = -0.06x + 5.61

R² = 0.01

0

2

4

6

8

10

15 17 19 21 23 25 27

Soil

CO

2 effl

ux (µ

mol

m-2

s-1)

TAir °C

c. 2014


R² = 0.14


R² = 0.13

0

2

4

6

8

10

15 17 19 21 23 25 27

Soil

CO

2effl

ux (µ

mol

m-2

s-1)

TAir °C

d. 2015

19

two seasons (wet and dry). It was the soil CO2 efflux that was lower in these plots (3, 4 and 8), which can indicate that there were one or several other factors than temperature that were contributing to the regulation of efflux. 4.2.2 Soil Water Content The mean percentage of SWC was 24.9 ± 11.9 % for primary forest in 2014 and 26.5 ± 11.6 % in 2015. Secondary forest obtained lower SWC, 23.9 ± 14.3 in 2014 and 19.6 ± 11.6 in 2015 (Appendix B, Table 1). The lowest SWC values were found in plots 4 and 8 with plot means of 9.5 - 11.6 ± 4.5 - 3.3. With 48.4 ± 15.1 %, plot 5 showed and exceptionally high SWC in 2014. This could be due to temporary heavy rainfall during the measuring session. Plots 13, 14 and 15 that constitute the area Primary 2 all had high mean SWC values, 33.7 - 32.9 ± 9.3 - 12.9 %. This can be explained by their close location to Karamba, where the highest annual precipitation was measured (Figure 2b). In primary forest in 2014, SCE and SWC resulted in a significant negative correlation (R2 = 0.25), but in secondary forest the result was contradictory with a significant positive correlation (R2 = 0.19) (Figure 10a). In 2015, SCE in primary forest was significant and negatively correlated with SWC (R2 = 0.28). The result for secondary forest in 2015 was not significant, but SCE had a weak negative relation to SWC (Figure 10b). Thus, SCE was decreasing with increasing SWC, with the exception of secondary forest in 2014, where the SCE increased with increasing SWC. The analysis was repeated without plots 3, 4 and 8 measured in dry season and consequently the correlation was no longer apparent.

Figure 10. The graphs show the relation of soil CO2 efflux and soil water content in 2014 (a) and 2015 (b). SCE was decreasing with increasing SWC in all cases except from in secondary forest in 2014 where the SCE was increasing with decreasing SWC. Blue scatter plots and trendline indicates primary forest and red indicates secondary forest. Means at sub plot level. 4.2.3 SCE in relation to pH at different soil profiles The pH values in primary forest were 3.9 ± 0.5 in the upper layer mineral soil, 3.9 ± 0.5 in organic soil and 4.1 ± 0.3 in mineral soil at a depth of 30 cm. In secondary forest, the pH was slightly lower revealing 3.7 ± 0.2 in upper layer mineral soil, 3.8 ± 0.3 in organic soil and 3.9 ± 0.1 in mineral soil at 30 cm. All pH values were measured in 2015.

Primary forest y = -0.06x + 5.5,

R² = 0.25

Secondary forest y = 0.04x + 3.46

R² = 0.19

0

2

4

6

8

10

0 20 40 60 80

Soil

CO

2 effl

ux (µ

mol

m-2

s-1)

Soil water content (%)

a. 2014


R² = 0.28

Secondary forest y = -0.04x + 5.93

R² = 0.04

0

2

4

6

8

10

0 20 40 60

Soil

CO

2 effl

ux (µ

mol

m-2

s-1)


b. 2015

20

Figure 11. Graph a shows the pH in upper layer mineral soil (blue) and organic soil (red) correlated to pH in mineral soil at 30 cm. In both cases the pH in deeper profile is consistent with the surface pH. Means at plot level. In graph b, the soil CO2 efflux is decreasing with increasing pH in organic soil (red). SCE had a weak negative correlation with pH in mineral soil at 30 cm (green) and upper layer mineral soil (blue). Means at sub plot level and data from 2015. The pH in upper layer mineral soil strongly correlated to the pH in mineral soil at the depth of 30 cm (R2 = 0.47). There was a weak positive but not significant correlation between organic soil and mineral soil at 30 cm (Figure 11a). Though the positive trend indicates that increased pH in deeper soil layer are consistent with increasing pH in surface soil and organic soil layer. Soil CO2 efflux in relation to pH did not show any significant correlation in either soil level (organic soil, upper layer mineral soil and mineral soil at 30 cm (Figure 11b). The only exception was found in secondary forest plots, where SCE correlated positively with mineral soil pH at 30 cm (R2 = 0.1). 4.2.4 Basal Area and Fine Root Production The average basal area in primary forest (33.8 ± 16.5 m2 ha-1) was higher than in secondary forest (25.8 ± 11.6 m2 ha-1). The highest mean value by forest site was in Primary 1 with 40.6 ± 19.9 m2 ha-1 and the lowest value was in Secondary 1 with 22.9 ± 11.7 m2 ha-1. Figure 12a shows that primary forest had several subplots that had a high BA, between 50 - 80 m2 ha-1. Though, in secondary forest, the basal area is more or less equally distributed within a basal area span of 1 - 50 m2 ha-1. SCE was weakly but positively correlated (p < 0.01) to BA in both primary (R2 = 0.09) and secondary forest (R2 = 0.04) (Figure 12a). It might be worth noting that the correlation is higher in 2015 than in 2014 for both primary and secondary forest. The total annual fine root production within the Nyungwe forest was 4.5 ± 2.9 Mg ha-1 yr-1. The mean of FRP in primary forest was 3.9 ± 3.3 Mg ha-1 yr-1 and in secondary forest 4.8 ± 2.6 Mg ha-1 yr-1 (Appendix B, Table 2). The amount of soil CO2 efflux did not show any direct relation to the fine root production. While SCE was not significantly correlated with FRP in primary forest, the data showed a weak positive correlation in secondary forest (R2 = 0.10) (Figure 12b).

Upper layer mineral soil y = 0.53x + 1.97

R² = 0.47

Organic soil y = 023x + 31

R² = 0.05

3,4

3,6

3,8

4

4,2

4,4

4,6

3 3,5 4 4,5 5

pH

pH Mineral soil 30 cm

a

Upper layer mineral soil

y = -0.93x + 8.33 R² = 0.09

Organic soil y = -1.94x + 12.26

R² = 0.25

Mineral soil 30cm y = -0.58x + 7.11

R² = 0.02

0

2

4

6

8

3 3,5 4 4,5 5

Soil

CO

2 effl

ux (µ

mol

m-2

s-1)

pH

b

21

Figure 12. Soil CO2 efflux in relation with basal area resulted in a positive pattern in both primary (blue) and secondary (red) forest (a). Graph b shows SCE versus fine root production. Primary forest (blue) did not show any specific relation, though in secondary forest (red) it was a positive correlation. SCE mean of data from 2014 and 2015 at sub plot level. 5. Discussion 5.1 Research question 1: Soil CO2 efflux in spatial and temporal variations The amount of carbon flow from the soil to the atmosphere in 2015, where all plots were measured during wet season, was 16.0 Mg C h-1 yr-1 in primary forest and 19.7 Mg C h-1 yr-1 in secondary forest. That is 18.8 % higher soil CO2 efflux in secondary forest than in primary forest. Between the two data collection periods, the SCE in 2015 was 8.3 % higher than in 2014. A likely explanation for the differences in SCE between the two years is that some secondary plots in 2014 were measured during the dry season, compared to the rest that were measured during the wet season. This is supported by Nsabimana and Wallin’s (2009) study of temporal variations in primary forest in Nyungwe National Park that revealed higher soil CO2 efflux during wet season and lower SCE during dry season. The total annual mean SCE in primary forest (dry and wet season) was 10.2 Mg C ha-1 yr-1. They suggest that the main factor for seasonal variation is the difference in precipitation and hence water content within the soil. Contrary to the results of this thesis, Zhou et al. (2013) measured a 9.7 % higher soil respiration in primary (16.73 ± 0.87 Mg C ha-1 yr-1) than in secondary (15.10 ± 0.26 Mg C ha-1 yr-1) tropical montane rain forest (Hainan Island) during wet season. They suggest, that the vegetation structure and species composition were the explanations for the total soil respiration. The variation between primary and secondary forest was said to be caused by the actual microclimate. Thus, the overall differences in total SCE between primary and secondary forest in the Nyungwe forest could be explained by the amount of biomass. It is assumed to be less above ground biomass in secondary forest, though the growth rate of trees can be higher than in primary forest. There is yet no data collected about the total amount of below ground biomass in the Nyungwe forest, but it is likely that there are differences between the two types of forest. Hence, there are independent factors (differently distributed biomass) that in a small scale (plot level) can explain the SCE, but are together resulting in a net flow of CO2 that is similar on a large scale (primary and secondary forest).

Primary forest y = 0.02x + 3.36

R² = 0.09


R² = 0.04

0

2

4

6

8

10

0 20 40 60 80

Soil

CO

2effl

ux (µ

mol

m-2

s-1)

Basal area (m2 ha-1)

a


R² = 0.01


R² = 0.10

0

2

4

6

8

10

12

0 2 4 6 8 10 12

Soil

CO

2 effl

ux (µ

mol

m-2

s-1)

Fine root production (t h-1 yr-1)

b

22

However, in this data, the difference between the mean SCE was neither temporally nor between primary and secondary forest statistically significant. Hypothesis 1.1, suggesting such differences, could therefore not be confirmed. 5.2 Research question 2: Parameters in relation to soil respiration 5.2.1 Soil and air temperature Within the secondary forest, there is a significant, positive effect of soil temperature on soil CO2 efflux. In the primary forest, however, the relationship is reversed, but weaker. The main difference between the secondary forest plots and the primary forest is that some of the primary plots are located at a lower altitude and had an air temperature that is approximately 2 degrees warmer than the average of all other plots. Since an increased temperature stimulates respiration, it could be anticipated that the soil CO2 efflux at the lower-altitude plots would be larger than at higher altitudes. By looking at Primary 1 and Primary 2 separately, SCE and soil temperature had a positive (but not significant) relation within Primary 2. The observed pattern may nevertheless indicate that temperature is not the main driver of soil respiration. The respiration at lower altitudes may instead have acclimated to long-term higher temperatures. According to Zheng et al. (1993), air temperature is positively correlated to soil temperature. Hypothesis 2.1 that suggested an increasing SCE with increasing temperature is confirmed by the results. 5.2.2 Soil Water content The result in this study shows that soil water content had a negative impact on SCE. The soil CO2 efflux mean values in some plots in secondary forest, measured during wet season in 2014, were significantly higher in 2015 than in 2014. The SWC was only slightly lower in 2014 than 2015. Thus, the unexpected high soil water content values in 2014 were the result of occasional rainfall during measuring procedures and not due to longer exposure to precipitation in the area. This is contradictory to the results of Nsabimana (2009), in which found that SWC had a significant positive influence on SCE. During wet seasons the SCE was higher when compared to dry seasons. According to Nsabimana (2009) the soil in Nyungwe forest was saturated when SWC > 28 % and that the relation between SCE and SWC will normally be shown as an optimum curve. Though by only looking at SWC < 28 % there was only a weak positive correlation in secondary 2014 and in the rest there were still negative correlations. Other studies have shown results that were equivalent to the result in this thesis. For example, Adachi’s et al. (2006) study resulted in negative correlations between SCE and SWC, in both primary and secondary forest. The authors claim that the high soil water content was decreasing the gas diffusiveness and biotic activity within the soil. Hypothesis 2.2 of this study predicting that SCE will increase when soil water content is increasing must therefore be rejected. 5.2.3 pH The Rwandan soil originates from strongly acidic soils (Verdoodt and Van Ranst, 2003). The soil samples from Nyungwe National Park in 2015 resulted in low pH values, which were equivalent to Nsambimana and Wallin’s pH measurements from 2009. Soil CO2 efflux was not strongly correlated to either soil profile. However, figure 11b showed a weak negative trend towards decreasing SCE with increasing pH. This indicative result is in contrast to previous studies (such as

23

Reth et al., 2005 and Zhou et al., 2013) that showed a positive correlation between SCE and pH, But in total, there was no statistical evidence for either a negative or positive correlation. Hypothesis 2.3 suggesting that SCE will increase with increasing soil pH can therefore not be accepted in this study. 5.2.4 Basal Area The basal area in Nyungwe National Park showed very low positive correlations to SCE in both primary and secondary forest (0.04 ≤ R2 < 0.1). Katayama et al. (2009) showed a stronger correlation between SCE and BA (R2 = 0.37). Other studies, for example Sotta et al. (2004), measuring SCE in tropical forest in Amazonas, did not find any correlation between SCE and BA. They suggested that the soil surface in tropical forests normally has a thinner layer of litter in the end of the rain seasons. The thin layer of litter does not alter the rate of SCE, because there is less decomposed organic matter (heterotrophic respiration). Thus, another factor other than basal area (the CO2 uptake from atmosphere thorough photosynthesis) can explain the inflow of carbon into the soil and therefore the efflux of CO2 through heterotrophic respiration. This is observed together with regulating factors such as temperature, soil water content and pH. In this study in the Nyungwe forest, data for total amount soil organic matter (SOM) from, inter alia, leaf litter fall was not included. The correlation between SCE and basal area in the Nyungwe forest was weakly positive, but significant. Therefore, hypothesis 2.4 stating that increasing SCE will increase BA, can be confirmed. 5.2.5 Fine Root Production The fine root production was higher in secondary forest, in which 10 % of the soil CO2 efflux was dependent on the growth respiration. The relation between SCE and FRP was not strong, although previous research suggests that FRP has an impact on SCE (e.g. Søe and Buchmann, 2005). This study in the Nyungwe forest, used external data for FRP that was collected after approximately 3 months of growth. The SCE was spatially measured (not over a time period) and not at the same time as roots were extracted. The sampling position for fine roots were located two meters from measuring position for soil CO2 efflux and the physical root structure can differ greatly at that distance. Data for maintenance respiration (total amount of roots) is missing for the study that could, together with growth respirations (fine root growth), explain the autotrophic respiration. Altogether, these factors could explain the weak correlation between SCE and FRP. As a result, the hypothesis 2.5 (increasing FRP will increase the SCE) returned mixed results, as it is supported in secondary forest but not in primary forest, but on a general level. The hypothesis therefore has to be rejected. 5.3 Limitations for the thesis Despite the already mentioned limitations, there are some aspects to consider for further studies. Firstly, the SCE measurements were not performed at the same diurnal time, e.g. one plot could be measured in the morning and another one in the afternoon. According to Nsabimana and Wallin (2009), the annual average of diurnal variations in SCE and temperature resulted in a bimodal

24

pattern with lower values in the morning and increasing values until around 3 p.m in the afternoon. This could therefore affect the results. Secondly, both measurement periods were performed during the rainy season, except for plots 3, 4, 8, and 13 that were performed in August in 2014. August is at the end of the dry season and therefore the SWC is expected to be lower. This affects SCE in particular, but also the other parameters in the system. Thirdly, the model for this study is bivariate and to achieve a more realistic picture of the dependent factors for SCE, a multivariable analysis is required, where all factors are put in relation to each other. A bivariate model cannot explain factors that, for example, are not directly affecting the independent variable and also ignores effects (intercorrelations) between the different independent factors. 6. Conclusions This study examined soil CO2 efflux (SCE) and contributing factors in tropical montane rain forest in the Nyungwe National Park, Rwanda. SCE was measured both in 2014 and 2015 on five different forest sites representing primary and secondary forest types. In terms of spatial variation, higher SCE were found in secondary forest plots than in primary forest. This may be due to the difference in biomass between the two forest types that could have a significant impact on soil CO2 efflux. With regard to temporal variations, the results of this thesis showed that SCE was higher in 2015 than in 2014. A reasonable explanation is that some plots in 2014 were measured during the dry season, where the SCE is normally low. The rest of the data was collected during the rainy season, where the SCE is high. However, despite this effect, neither the temporal nor spatial variations of the mean SCE over the two forest types were statistically significant. Thus, the differences between the average SCE in large areas might be insignificant compared to the variation within a small area. Further research should therefore focus on examining variations of soil respiration in microclimates. Further, data was collected and tested in relation to its contribution to the soil respiration rate. This included soil and air temperature, soil water content, pH, basal area and fine root production. Temperature had, in general, a positive effect on soil respiration. However, the effect was only significant in secondary forest plots. In addition, in some primary forest areas at low altitude and significantly higher temperatures, the SCE was even lower than in the rest of the forest. This observed pattern may indicate that temperature is not the main driver of soil respiration. For example, the respiration at the lower altitude may instead have acclimated to a long-term higher temperatures, which would suggest that the strength of the effect of temperature on SCE varies under different conditions. This result stresses again the potential importance of additional research that accounts for variations in the microclimate. Soil water content (SWC) had a negative effect on SCE, which can be seen as a somewhat surprising result. It is likely due to the fact that many samples had very high SWC values and might thus reach a saturation point in terms of SCE. pH could not be identified as a driving force to soil respiration. Basal area on the other hand showed a significant effect. Finally, the fine root production was observed to have a weak correlation with SCE, but only in secondary forest.

25

Therefore, it can be concluded that although the factors examined here are important in explaining SCE variations, they are not enough to develop a satisfying model. For a better understanding of soil respiration and carbon stock, methodological improvements and further studies are required that include a broader set of independent factors, for example soil organic matter (SOM) and maintenance respiration. To further contribute to the overarching objective of this research and eventually be able to calculate the carbon balance in tropical montane rain forest, a deeper knowledge about the autotrophic and heterotrophic components in soil respiration is needed within the Nyungwe National Park in Rwanda.

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8. Appendices Appendix A Table 1: Soil CO2 efflux (µmol m-2 s-1) in plots from 2014 and 2015. Standard deviation from collar position level.

Year Plot# Mean N SD 2014 1 4.41 24 2.25

2 5.17 24 2.31 3 3.09 24 1.20 4 3.12 8 0.72 5 5.91 24 2.43 6 4.31 24 1.01 7 5.40 24 1.51 8 3.79 24 1.95 9 4.86 24 1.83

10 3.68 24 1.03 11 5.17 24 3.23 12 5.13 24 1.98 13 3.33 24 1.48 14 3.21 24 1.15 15 3.10 24 0.80

Total 4.36 344 2.00 2015 1 4.23 24 1.56

2 5.20 24 2.61 3 4.21 24 1.31 4 5.49 24 1.78 5 4.99 24 1.94 6 4.77 24 1.29 7 6.68 24 2.18 8 6.36 24 3.02 9 4.87 24 2.28

10 5.30 24 1.80 11 5.09 24 1.34 12 4.62 24 2.02 13 3.20 24 1.68 14 3.50 24 1.66 15 3.60 24 1.03

Total 4.81 360 2.09

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Appendix B Table 1: Mean of dependent and independent variables separated by year and forest type. The standard deviation is from collar position level.

Year Forest type Soil CO2

efflux (µmol m-2 s-1)


Soil temperature

(C°)

Air temperature

(C°)

2014

Primary

Mean 4.09 24.87 15.74 21.76

N 144 144 144 144

Std. Deviation 1.94 11.93 0.67 1.61

Secondary

Mean 4.56 23.90 14.81 20.87 N 200 200 200 200

Std. Deviation 2.01 14.34 0.72 2.22

Total

Mean 4.36 24.30 15.20 21.25 N 344 344 344 344

Std. Deviation 2.00 13.38 0.83 2.038

2015

Primary

Mean 4.22 26.45 16.03 22.29 N 144 144 144 144

Std. Deviation 1.79 11.56 0.74 1.71

Secondary

Mean 5.20 19.65 15.08 21.06 N 216 216 216 216

Std. Deviation 2.19 8.73 0.52 1.82

Total

Mean 4.81 22.37 15.46 21.55 N 360 360 360 360

Std. Deviation 2.09 10.49 0.773 1.87

Total

Primary

Mean 4.15 25.66 15.88 22.03

N 288 288 288 288

Std. Deviation 1.87 11.75 0.72 1.68

Secondary

Mean 4.89 21.69 14.95 20.97

N 416 416 416 416

Std. Deviation 2.12 11.95 0.64 2.025

Total

Mean 4.59 23.31 15.33 21.40

N 704 704 704 704

Std. Deviation 2.06 12.02 0.81 1.96

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Table 2: Mean of contributing factors of on soil respiration. For pH values at upper layer mineral soil and pH values for organic soil, the standard deviations are at collar position level and for pH in mineral soil at 30 cm, plot level. The standard deviations for basal area and fine root production are at sub plot level. All pH and basal area data is from 2015 and fine root production data is representing both 2014 and 2015.

Forest type pH upper

layer mineral soil

pH organic soil

pH Mineral soil (30 cm

depth)

Basal Area (m2 ha-1)

Fine root production

(Mg ha-1 yr-1)

Primary

Mean 3.87 3.89 4.15 33.82 3.91

N 144 144 6 48 94

Std. Deviation 0.54 0.46 0.30 16.64 3.30

Secondary

Mean 3.7353 3.80 3.90 25.82 4.84 N 216 216 9 72 144

Std. Deviation 0.21 0.32 0.15 11.59 2.59

Total

Mean 3.79 3.84 4.00 29.02 4.48 N 360 360 15 120 238

Std. Deviation 0.38 0.38 0.25 14.31 2.92

soil respiration in primary and secondary tropical montane rain

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