Patterns in C/N and parent material along an altitudinal gradient in Peruvian alpine grasslands

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Patterns in C/N and parent material along an altitudinal gradient in

Peruvian alpine grasslands

Analysing soil data of 10 years of fieldwork campaigns

Ulta Valley, Peru (Gravemaker, Meeteren and Scholz, 2016)

Student: Pieter Kramer

Supervisors: Erik L.H. Cammeraat, Anne Uilhoorn Date and place: 27-05-2021, Ilpendam

Bsc thesis: Future Planet Studies University of Amsterdam Words: 4533

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Abstract:

The neotropical grasslands of the Andes mountains, especially the Peruvian and Ecuadorian Andes, are characterized by large carbon (C) stocks. The large C stocks are crucial for ecosystem services such as agricultural production, water provision, and sustaining high biodiversity. The C stocks of high-altitude grasslands make the ecosystems vulnerable to environmental changes, as temperature rise and higher precipitation variability would lead to intensified releases of CO2 from the C stock. An important variable for key ecosystem services is the carbon to nitrogen (C/N) ratio. To fill in the knowledge gaps of the Peruvian grasslands compared to the Páramo grasslands, distributions of C/N ratios regarding altitude and parent materials were studied. This study used a data analysis of soil data of earlier conducted fieldworks, performed in different valleys in the Andean grasslands of Peru along an altitudinal gradient of 3000 to 5000 m. Linear regression models, t-tests and ANOVA’s were used to test the effects of altitude, pH and parent material on soil C, N, and C/N. Results indicated a significant, positive relationship between C/N and altitude, but only for the 2900-3750 m range, and not for the 3750-5000 m range. Mean C/N ratios were significantly higher on igneous bedrocks and glacial sediments than on soils on acid sedimentary bedrocks and fluviatile sediments. In addition, the more acid a soil, the higher the C/N. The Andean grasslands will face higher temperatures, and more water shortages as a result of climate change. This will increase decomposition, but might be balanced by higher plant productivity due to increased mineralized N. More studies are needed to examine the effects of climate change on soil properties and decomposition rates in the tropics.

Keywords:

Peruvian Andes, Neotropical grasslands, C/N ratio, Parent material, Lithology, Decomposition, Ecosystem services, Puna

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Soils play a key role in the global carbon (C) cycle and are the biggest pool of the terrestrial cycle (Lal, 2004; Scharlemann et al., 2014). They have the potential to limit human-induced climate change by storing atmospheric carbon into soil organic carbon (SOC) (Stockmann et al., 2013). In addition to its feedback to the atmospheric climate system, SOC plays a key role in many important biological, chemical, and physical processes in the soil and responses rapidly to environmental changes (Djukic et al., 2010; Blanco-Canqui et al., 2013). Therefore, SOC is an important connection between the terrestrial and atmospheric environment.

The SOC content of soils is not evenly distributed over terrestrial environments (Batjes, 1996). Ecosystems with large carbon stocks are the neotropical grasslands of the Andes mountains, of which the Peruvian and Ecuadorian Andes in particular (Muñoz García and Faz Cano, 2012; Rolando, Turin, et al., 2017). The Andes have humid and cool climatic conditions, which lower decomposition rates and lead to organic matter, and thus SOC accumulation (Oliveras et al., 2014). The large C stock is crucial for the ecosystem services neotropical grasslands provide, like agricultural production, water provision and sustaining high biodiversity (Rolando, Turin, et al., 2017). To maintain these ecosystem services, management of grasslands and soil is crucial (Yang et al., 2018).

Future soil and ecosystem management will even become more important, as the primary drivers of SOC storage are temperature and precipitation (Schmidt et al., 2011; Wiesmeier et al., 2019). An expected increase in temperature and higher precipitation variability as a result of climate change can alter soil microbial activity, decomposition rates and ecosystem C storage (Zak et al., 1999; Drenkhan et al., 2015). In other words, the large C pool of high-altitude grasslands makes the ecosystem vulnerable under changing environmental conditions (Leifeld et al., 2009).

In addition to climatic conditions, lithology (from now on parent material) has been observed to be an important driver of soil quality. It drives SOC and nitrogen (N) stocks (Prietzel and Christophel, 2014; Johnson, Xing and Scatena, 2015; Barré et al., 2017), but also soil texture, mineralogy, and fertility (Angst et al., 2018; Wiesmeier et al., 2019; Yang, Jansen, Absalah, et al., 2020). As a result, parent material can have a major impact on ecosystem, vegetation, and soil functioning (Jenny, 1994).

While most of the studies consider only the soil C cycle, the N cycle cannot be overlooked as soil C and N cycles are closely linked together (Sokolov et al., 2008; Tashi et al., 2016; Devi and Sherpa, 2019). The ratio between these two elements determines the decomposition and immobilization of organic matter, and therefore also the nutrient availability and nutrient leaching (Jarvis et al., 1996). Therefore, soil C/N ratio is an important variable for key ecosystem services (Peri et al., 2019). High C/N ratios, characterized by low N stocks relatively to the C stock, limit microbial activity due to a lack of nitrogen, and will result in lower mineralization rates (Ge et al., 2013; Peri et al., 2019). Because less organic matter is converted into nutrients, organic matter can accumulate quicker in soils with high C/N ratios. Lower C/N ratios on the contrary, increase the N mineralization by reducing microbial demand for N during decomposition, causing a net release of inorganic N to the soil solution (Bonito et al., 2003; Holland & Detling, 1990; Paul, 2006). Because organic matter is decomposed, a low C/N ratio is not conducive for carbon sequestration (Ge et al., 2013). Therefore, to study the C dynamics of soils, it is crucial to assess the C/N ratios (Finzi et al., 2006; Luo, Hui and Zhang, 2006).

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For alpine grasslands in the Andes, most studies have concentrated on SOC stocks of the volcanic soils of the Páramo ecosystem in northern Peru, Ecuador and Colombia (Yang, Jansen, Kalbitz, et al., 2020). Soils of central and southern Peru are more diverse and contain large SOC stocks as well, but have received less attention (Zimmermann et al., 2010; Muñoz García and Faz Cano, 2012; Rolando, Dubeux, et al., 2017; Yang, Jansen, Kalbitz, et al., 2020). For the C/N ratio in the Páramo grasslands, there is a positive relationship between C/N ratio and altitude, as a result of lower mineralization due to low soil temperature and high soil moisture (Ramsay, 1992; Zehetner and Miller, 2006; Schawe, Glatzel and Gerold, 2007). However, as parent material controls soil properties (Yang, Jansen, Kalbitz, et al., 2020), and Peru is characterized by a more heterogenous distribution of parent materials (Yang et al., 2018), the drawn relationships of the Páramo cannot directly be applied to the alpine

grasslands of Peru. In other words, the carbon and nitrogen distribution is still largely unknown in the Peruvian Andes.

Therefore, in this research, the C/N ratio will be studied above 3000 m in the Andean grasslands in Peru. By taking altitude and therefore temperature into account, this research can deliver crucial information regarding the temperature dependency of soil properties and thus also of the ecosystem services. Additionally, parent materials will be compared to study its influence on the C/N ratio. The research question is as follows:

How does the C/N ratio of neotropical grasslands change along an altitudinal gradient and how does this differ between parent materials in the Peruvian Andes?

This study will use a data analysis of collected soil data of earlier conducted fieldworks, performed in different valleys in the Andean grasslands of Peru along an altitudinal gradient of 3000 to 5000 m. Through a regression model, the relationships between C/N ratio and both altitude and parent materials can be established.

The fieldwork campaigns were done by a collaboration between the University of Amsterdam, the Netherlands and The Mountain Institute, Peru (TMI). TMI addresses the decline in traditional communities and biodiversity by supporting and protecting local populations and their ecosystems, culture, and economy. The obtained information of this study will be used to protect the ecosystem services of the Andean grasslands of Peru, also in times of climate change.

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2. Methodology

2.1 Study area

The study area contains 3 sites, being distributed over more than 800 km from the northwest to the centralwest of Peru (Fig. 1). The fieldwork campaigns have been conducted in alpine grasslands, located between the upper forest line (maximum of 3500 m) and the permanent snow line (5000 m). They are characterized as unique ecosystems and are water providers for people in lower Andean regions (Den Haan, 2013; Salvador et al., 2014). For a more detailed map of each sample area, see Appendix A.

The most northern fieldwork campaigns are performed in the department of Cajamarca, covering an area of 7°15–21’ S to 78°56–63’W. The area is elevated from 2900 to 4000 m and lies in the

neotropical region of the Jalca.

The Jalca has a drier climate than the Páramo region in Ecuador but is characterized by a more humid climate than the Puna grasslands in the south (Sanchez Vega et al., 2006). Therefore, the Jalca region is often seen as transition zone between the Páramo and the Puna (Buytaert, Cuesta-Camacho and Tobón, 2011).

The parent materials of the area consist of a basement of folded Mezosoic marine sediments, such as limestone, shale, marl, and quartzite (Yang, Jansen, Kalbitz, et al., 2020). This bedrock is intruded and overlain by igneous rocks due to volcanic activity, such as granite and ignimbrite (Reyes-Rivera, 1980; Yang, Jansen, Kalbitz, et al., 2020). Also, these bedrocks might be overlain by Quaternary deposits due to past glacial activity (Rodbell, 1993b; Seijmonsbergen et al., 2010; Den Haan, 2013).

More to the south, the Jalca grasslands have transitioned into the Puna grasslands. In the valleys of the Cordillera Blanca mountain range, near Huaraz, samples have been taken between 2900 and Fig. 1. Locations of the three main sites in the Peruvian

grasslands. Per site, several fieldwork campaigns could be performed. From Google Earth Pro (2021)

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4500 m (9°09-74’ S to 77°18-62’ W).

Due to the decreasing latitude, the area around Huaraz (3050 m) is characterized by a drier and cooler climate. The rainy season is only from December to April and throughout the year, diurnal variations cause daily freeze-thaw events (Rodbell, 1992; Young and Lipton, 2006).

In contrast to Cajamarca, in this area, there is a lack in volcanic activity as a result of a low-angle subduction (Ramos, 1999; Stern, 2004). The Cordillera Blanca is globally the most glaciated tropical mountain range (Kaser, Ames and Zamora, 1990), and is therefore heavily modified by glacier activity (Rodbell, 1993a). The parent materials, sedimentary rocks and less igneous rocks, will therefore be mainly affected by Quaternary sediments as a result of past glacial activity (Jonkman and Scholte, 2010; Yang, Jansen, Kalbitz, et al., 2020).

The southernmost located fieldwork area is the Nor Yauyas-Cochas reserve. This area is still part of the Puna grasslands, however the conditions are drier than the northern fieldwork sites. The fieldwork area ranges between 3300 and 4700 m and is characterized by steep valley slopes, caused by a combination of fluvial and glacial activity (Nijdam et al., 2019). Due to the low volcanic activity (Ramos, 1999; Stern, 2004), most parent materials will be sedimentary rocks affected by Quaternary sediments.

2.2 Fieldwork campaigns

In total, 11 fieldwork campaigns are included in the dataset. The fieldwork campaigns followed a similar procedure. Transects were used to cross all different landforms, parent materials and soils to garantuee systematic random sampling. To describe the sites and samples, all fieldwork campaigns used a standard field form (Appendix B). Soil profiles were classified using World Reference Base (WRB) (FAO, 2006), however, in depth of soil sampling, there was no consistency. While some groups sampled the top 10 cm of the soil, other groups sampled only the A horizon or sampled every 10 cm until the C horizon was reached. In this latter case, only the top 10 cm was used to secure the highest consistency between the sampled soil depths.

2.3 Data analysis

After all data was collected, this needed to be adjusted to a dataset in which the content of all columns corresponded. This was done in MS Excel version 2103. When all single datasets were merged to the overall dataset, this dataset was exported to R studio version 1.1.463 (Appendix C).

Table 1. Overview of the contents of the dataset, with the used variables in bold.

Variable Description

Sample ID Number of the sample. Given by 1, 2, …, n

Group The location of the conducted fieldwork of the sample Coordinate S Longitude in the coordinate system

Coordinate W Latitude in the coordinate system Altitude Elevation of taken sample. Given in m

Geomorphology Classification of the most dominant land formation processes Landuse Classification of the most dominant land use

Parent material Indicates the most abundant rocks. This might be bedrock, but can also be rocks deposited by alluvial fans

Soil texture Classification of the texture of the soil

Soil type Classification of the type of soil, based on the WRB (FAO, 2006) Main soil class The main soil class of the soil type

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Electrical conductivity Measures the ability of the soil of conducting electricity

pH Measures the soil acidity

Bulk Density Measures the compaction of the soil, in g/cm3 Water Deep Percolation

Time

Measured in seconds

N(%) Concentration of soil nitrogen C(%) Concentration of soil carbon

C/N Carbon-to-nitrogen ratio

Grouped parent material Parent materials grouped to the terms Acid sedimentary bedrock, Calcareous sedimentary bedrock, Igneous bedrock, Glacial sediment, and Fluviatile sediment Soil acidity Soil acidity grouped to Acid (ph < 5.5) or Near-neutral-to-alkaline (ph ≥ 5.5)

The parent materials were grouped, to compare whether there were differences between the results of the single and grouped parent materials (Table 2). Also, the pH values were used to group these into acid (pH < 5.5) and near-neutral-to-alkaline (pH ≥ 5.5) groups to test the differences in group means.

Table 2. The classification of the parent materials into the grouped parent materials. Grouped parent materials Parent materials

Acid sedimentary bedrock Sandstone/Shale, Shale, Quartzite Calcareous sedimentary bedrock Limestone, Marl

Fluviatile sediment Alluvium

Glacial sediment Ablation till, Subglacial till Igneous bedrock Granite, Granodiorite, Ignimbrite

From the dataset, missing values were detected and removed by listwise deletion for the variables altitude and C/N ratio and pairwise deletion for parent material and pH. It is important to note that there are a lot of missing values, causing listwise deletion to remove entire or large parts of datasets. Pairwise deletion was used to keep as much observations as possible, but caused also the use of an inconsistent dataset of observations, which have to be taken into account. The locations of the missing values are represented in Appendix D.

Outliers were removed manually, and only the unreliable values, such as negative C/N ratios or cases in which N was equal to C.

To test the effects of pH and altitude on C/N, linear regression models were used. Between C/N, parent material, and pH, Kruskal-Wallis tests were used as non-parametric alternative to the one-way ANOVA. This test was followed by Dunn’s post-hoc test when the results turned out significant (p < 0.05). The Wilcoxon test was performed to test mean differences between acid and alkaline soils. The non-parametric tests were used since the observations within groups were not normally distributed by using the Shapiro-Wilk test (p < 0.05).

For the linear regression models, the entire dataset was used, and normality was assumed to be true considering the high number of observations. All tests were conducted in Rstudio and all tables and graphs were imported from Excel.

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3. Results

3.1 The impact of parent material and soil acidity on the C/N ratio

In Table 3, a concise overview of the dataset is shown after the listwise deletion of the missing values. A more detailed view on this dataset is available in Appendix F.

The C/N ratios lie in a range of 2.51 to 24.00, with an average of 11.87 ± 0.13. The mean C/N ratio differs significantly between fieldwork locations (p < 0.001), from 13.03 ± 0.22 as highest in Cajamarca, North Peru, to 10.29 ± 0.16 as lowest in the Ulta Valley, Central-North Peru.

The table shows also the most dominant parent materials. Around Cajamarca, volcanic bedrocks are most present, while more to the south, bedrocks are often overlain by glacial or fluviatile sediments.

Table 3. Summary of the results of the dataset. Fieldwork location n of

fieldwork campaigns

n of observations Range in altitude

Mean altitude Most dominant group of parent material

Cajamarca 2 75 3373 – 3901 m 3612.22 m Igneous bedrock

Ulta 3 264 2933 – 4950 m 3572.24 m Glacial sediment &

Igneous bedrock Nor Yauyas-Cochas 1 92 3356 – 4676 m 4142.59 m Fluviatile sediment &

Glacial sediment

Huaraz 2 52 3254 – 4446 m 3791.17 m Glacial sediment

All 8 483 2933 – 4950 m 3779.56 m

Fieldwork location Range in pH Mean pH Range in C/N Mean C/N SE Cajamarca 4.17 – 7.76 5.58 9.82 – 19.38 13.03 0.218465 Ulta 3.90 – 7.09 5.35 2.51 – 19.90 10.29 0.160182 Nor Yauyas-Cochas 4.10 – 8.70 6.35 5.40 – 24.00 11.25 0.323714 Huaraz 4.38 – 5.85 5.37 10.18 – 18.00 12.92 0.198337 All 3.90 – 8.70 5.66 2.51 – 24.00 11.87 0.125819

For all three soil properties – C/N ratio, carbon concentration and nitrogen concentration –, the impact of parent material on mean values was significant (p < 0.001, Fig. 2). There were significant differences in C/N mean values between shale and subglacial till (p < 0.01), alluvium and subglacial til (p < 0.001, quartzite and shale (p < 0.05), and quartzite and alluvium (p < 0.01).

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ab b ab ab _ab _ab ab a ab _b a 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 C /N r ati o p < 0.001 abc abc abc abc c ab ab abc abc bc a 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 C [%] p < 0.001 ab ab ab ab b ab ab ab ab ab a 0.00 0.20 0.40 0.60 0.80 1.00 1.20 N [%] p < 0.001

Fig. 2. The mean C/N, C, and N values per parent material, tested by a Kruskal-Wallis test (significance in bold). Lower case letters indicate significant differences among groups (Dunn’s post-hoc test, p < 0.05. P.adj: Holm)

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Fig. 3. The mean C/N, C, and N values for each grouped parent material, tested by a Kruskal-Wallis test (significance in bold). Lower case letters indicate significant differences among groups (Dunn’s post-hoc test, p < 0.05. P.adj: Holm)

a b a b ac 0 2 4 6 8 10 12 14

Glacial sediment Fluviatile sediment

Igneous bedrock Acid sedimentary bedrock Calcareous sedimentary bedrock C /N r ati o p < 0.001 ab ab b b a 0 2 4 6 8 10 12

Igneous bedrock Acid sedimentary bedrock Calcareous sedimentary bedrock C [%] p < 0.01 a ab b ab ab 0.00 0.20 0.40 0.60 0.80 1.00

Igneous bedrock Acid sedimentary bedrock Calcareous sedimentary bedrock N [%] p < 0.01

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Again, for each soil property there was at least one significant difference in group mean values (p < 0.001, Fig. 3). However, for the C/N ratio, this influence was more significant than for the C and N concentrations (p < 0.01). Between groups, significant differences in average C/N ratio were found between acid sedimentary bedrock and glacial sediment (p < 0.01), acid sedimentary bedrock and igneous bedrock (p < 0.01), fluviatile sediment and glacial sediment (p < 0.001), and fluviatile sediment and igneous bedrock (p < 0.001).

For the N concentration, glacial sediment was significantly higher than igneous bedrock (p < 0.05). For C, the mean value of calcareous sedimentary bedrock was significantly higher than those of acid sedimentary bedrock and igneous bedrock (p < 0.05).

In addition to the parent materials, the impact of soil pH was tested. The mean C/N of acid soils 11.55 ± 0.16 differed significantly from the mean of the more alkaline soils 10.74 ± 0.21 (p < 0.01, Fig. 4). For both the C and N concentrations, averages were slightly higher in alkaline soils than in acid soils, but these were not significant (p = 0.90 and p = 0.87 respectively, Appendix F).

There is a clear and significant relationship between C/N and soil pH (Fig. 5, Table 4, p < 0.001). The more alkaline a soil, the lower the C/N ratio. Yet, only 2% of the variance of observations is described by pH. This makes pH a significant, but a weak predictor variable. Considering the regression analysis for the C and N concentrations, it has resulted in the same results as above; no relationship between pH and C and N concentrations (C: p = 0.53, R2_{= 0.003, N: p = 0.44, R}2_{= 0.002, Appendix F).}

Fig. 4. The mean C/N ratio of both acid and alkaline soils, tested by the Wilcoxon test (significance in bold). y = -0.4352x + 13.547 R² = 0.02 0.00 5.00 10.00 15.00 20.00 25.00 30.00 3.80 4.80 5.80 6.80 7.80 8.80 C /N pH p < 0.01

Fig. 5. Scatter plot between pH and C/N, with the significance level at the top left, and the regression equation and R-squared at the top right.

p < 0.01 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 Acid Alkaline C /N r ati o

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There is a significant difference in mean pH among grouped parent materials (p < 0.001, Fig. 6). However, only the mean pH of soils on igneous bedrocks was found to be significantly different than the pH of the other parent materials.

Fig. 6. The mean pH per parent material, tested by the Kruskal-Wallis test (significance in bold). Lower case letters indicate significant differences among groups (Dunn’s post-hoc test, p < 0.05. P.adj: Holm).

3.2 Relationships between altitude, C/N ratio and C and N concentrations

The relationship between altitude and the soil properties are mainly tested by regression models. For C/N, the relationship was significant (p < 0.001), but weak (6% of the observations were described by the model). Two trends could be observed which indicates that the distribution is not completely linear (Fig. 7). First, the values increase until an elevation of 3750 m is reached. The model of this first part of observations explains the observations better, and the model has become more significant (R2 = 0.09, p < 0.001, Fig. 7). For the 3750-5000 scale, a very weak, and unsignificant correlation can be observed (R2 _{= 0.002, p = 0.62, Fig. 7).} a a b a a 0 1 2 3 4 5 6 7

Igneous bedrock Acid sedimentary bedrock Calcareous sedimentary bedrock

pH

p < 0.001

Fig. 7. The altitudinal distribution of C/N. On the right side, the difference in distribution between 2900-3750 m and 3750-5000 m. In each graph, the significance level is at the top left, and the regression equation and R-squared at the top right.

y = 0.0017x + 4.9187 R² = 0.06 0.00 5.00 10.00 15.00 20.00 25.00 2800 3300 3800 4300 4800 C /N r ati o Altitude (m) p < 0.001

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The regression models of the C and N concentrations show a different pattern compared to the regression model of C/N. These resulted in very significant and strong regression models, also for the observations between 3750 and 5000 m (C: p < 0.001, R2_{= 0.20, N: p < 0.001, R}2_{= 0.21). Still, these} observations are also in this case harder to explain than the lower range (C: p < 0.01, R2 = 0.05, N: p < 0.05, R2 = 0.04).

Fig. 8. The altitudinal distribution of C and N concentrations with the significance level at the top left, and the regression equation and R-squared at the top right.

Below, grouped parent materials are included in the relationship between C/N and altitude. The parent materials are quite clustered along the altitudinal gradient. Sedimentary and igneous bedrocks are especially present below 3800 m, while above, the parent materials are mainly dominated by fluviatile and glacial sediments.

y = 0.0006x - 1.6222 R² = 0.21 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 2800 3300 3800 4300 4800 N [%] Altitude (m) p < 0.001 y = 0.0082x - 23.459 R² = 0.20 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 2800 3300 3800 4300 4800 C [%] Altitude (m) p < 0.001

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Fig. 9. The altitudinal distribution of C/N for each grouped parent material.

4. Discussion

4.1 The impact of altitude, parent material and pH on C/N

There was a significant, positive relationship between C/N ratios and altitude. Especially between 2900 and 3750 m, a clear and significant increase in C/N ratio was observed. For lower regions, this would mean higher decomposition rates, as a consequence of relatively high soil N concentrations (Bonito et al., 2003; Paul, 2006). At high elevations, however, low temperature and high soil moisture are seen as the primary drivers behind the lack of decomposition, instead of the C/N ratio (Ramsay, 1992; Zehetner and Miller, 2006; Schawe, Glatzel and Gerold, 2007). Under cool and wet conditions, decomposition is decelerated more than biomass production, accumulating organic matter (Raich et al., 2006; Zimmermann et al., 2009). In this study, this was shown in the significant increase in C and N concentrations with altitude, supported by other studies of alpine grasslands (Zehetner and Miller, 2006; Soethe, Lehmann and Engels, 2007; Yang et al., 2018). As a consequence of decomposition and organic matter accumulation, C/N tends to increase (Marrs et al., 1988). So, instead of C/N ratio regulating decomposition, decomposition seems to regulate C/N ratios. Because in the Andes temperature decreases and precipitation increases with altitude (Zehetner and Miller, 2006), and therefore organic matter accumulation increases with altitude, the found C/N ratio distribution is supported by literature.

This relationship stopped above 3750 m. C and N concentrations kept rising, while C/N ratios stopped increasing and seemed to reach a constant value. Between 2900 and 3750 m, the rise in C/N was explained by a steeper decline in decomposition than in biomass production. There is no sign this changed, considering the increase in C and N concentrations between 3750 and 5000 m. This indicates that there is a different underlying process that drives C/N for the upper elevation ranges. Interestingly, parent material is found to be related with altitude. At higher altitudes – above 3750 m –, most sites were affected by fluviatile and glacial sediment, while at lower altitudes igneous, acid, and calcareous were present. This is caused by higher coverage of former glaciers at higher

elevations. Whether parent material therefore influences the altitudinal distribution of C/N – and

0.00 5.00 10.00 15.00 20.00 25.00 2800 3300 3800 4300 4800 C /N r ati o Altitude

Lithology

Acid sedimentary bedrock Calcareous sedimentary bedrock Fluviatile sediment Glacial sediment Igneous bedrock

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therefore the lack in C/N increase above 3750 m – or that altitude influences the C/N values per parent material remains uncertain.

Parent materials are key factor controlling SOM stabilization and distribution (Heckman, 2009; Yang, Jansen, Kalbitz 2020). SOC stocks and C/N ratios have been found to be significantly lower on calcareous bedrocks than on igneous bedrocks (Heckman et al., 2009; Yang, Jansen, Absalah, et al., 2020). These findings are supported by findings of this research, however, the difference in C/N ratio was not significant. The significant differences associated with glacial and fluvial sediments have not been previously reported. The results – in which the C/N values of glacial sediments were

significantly higher than fluvial sediments – could not be explained by altitude, as both were similarly distributed along the altitudinal gradient.

In addition to altitude and parent material, a significant, positive relationship between C/N and soil acidity was observed. This is in line with lower C/N values found on higher pH soils in the Andes (Yang, Jansen, Absalah, et al., 2020). Soil acidity controls organic matter decomposition by lowering the degrading activity of enzymes (Kok and Van Der Velde, 1991; Leifeld, Zimmermann and Fuhrer, 2008; Zimmermann et al., 2009). This supports the theory C/N ratios increase with low

decomposition. The impact of pH on C/N is probably not due to underlying parent material, which was reported by Yang et al. (2018). From the grouped parent materials, only igneous bedrocks drove soils to significantly lower pH values.

4.2 Other factors influencing soil properties in tropical grasslands

There are more factors that have not been researched in this study, but are of significant influence on soil properties and have to be taken into account. First, amorphous soils have been studied. They develop in volcanic soils under humid conditions and limit decomposition by their stabilizing effects on organic matter (Parfitt, Russell and Orbell, 1983; Dahlgren, Shoji and Nanzyo, 1993; Zehetner and Miller, 2006). Especially on high altitudes (above 2700 m), more amorphous soils can be found, as a result of higher rainfall at high altitudes (Zehetner and Miller, 2006). By their stabilizing effects on organic matter, amorphous soils cause C stocks and C/N ratios to be higher in volcanic areas (Marrs et al., 1988; Zehetner and Miller, 2006). Because Peru is not characterized by the homogenous volcanic ash soils as in the Páramo, this effect will be limited for the Peruvian grasslands. However, the role of amorphous soils have to be taken into account when comparing volcanic regions with non-volcanic regions.

Secondly, effects of grazing have been studied and are often included in soil dynamics reports in the Andes. Andean grasslands have been used for hundreds of years for extensive grazing purposes (Sarmiento and Frolich, 2002) by farmers, burning vegetation to encourage new, and more nutritious growth for livestock (Ramsay and Oxley, 1996). Grazing causes soil trampling, reduced vegetation cover and higher soil density, which increases the soil temperature and therefore enhances soil organic matter decomposition (Zimmermann et al., 2010; Muñoz García and Faz Cano, 2012). Together with the human-induced fires, grazing lead to enhanced nutrient cycling and will lower N stocks and increase C/N ratios (Hofstede, 1995; Harris et al., 2007; Zimmermann et al., 2010). Also without fires, C/N ratios seems to maintain or increase their value in a consistent trend, suggesting potential N limitation under grazing conditions (Pineiro et al., 2010). Whether the

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4.3 Differences in C/N values between Puna, Páramo, and Apolobamba grasslands

Studies in Páramo grasslands, conducted between 3000 and 4200 m, described C/N values of 16 ± 1 (Tonneijck et al., 2010), 16.3 ± 0.1 (Podwojewski et al., 2006) and 19.3 ± 0.3 (Farley, Kelly and Hofstede, 2004). In the Puna grasslands, for the same altitudinal range, a mean ratio of 10.97 ± 0.11 was found. This means that, considering the wetter climate and the presence of amorphous soils of the Páramo grasslands (Tonneijck et al., 2010; Den Haan, 2013), temperature – which is lower in the Puna region – has a lower impact on decomposition rates than the combined effect of wet and amorphous soils.

In the Apolobamba grasslands of Bolivia, values between 11.3 ± 0.2 and 14 ± 0.9 were found for different degradation states between 4500 and 5500 m (Muñoz García and Faz Cano, 2012). In this study, a mean value of 12.04 ± 0.49 was found between 4500 and 5000 m. Although the warmer and wetter conditions in Peru (Muñoz García and Faz Cano, 2012; Podvin, Cordero and Gómez, 2014), this value is quite similar to the study in the Apolobamba grasslands, in which 7 of the 8 zones found mean ratios close to 12 (Muñoz García and Faz Cano, 2012). That would confirm our results that at higher altitudes, C/N ratios are constant and not driven to increase.

4.4 The future

The Tropical Andes are expected to be one of the most affected by climate change over the next 100 years (Gonzalez et al., 2010). The Andean Tropics are a global biodiversity hotspot (Myers et al., 2000), and over 100 million people in the Andes rely on natural resources and ecosystem services, such as water supply, carbon sequestration and fuel production (Buytaert, Cuesta-Camacho and Tobón, 2011), which will be affected by increasing temperatures and higher precipitation variability. There is doubt whether this higher precipitation varaibility will result in a decrease (Magrin et al., 2014), or increase in precipitation (Buytaert and Bievre, 2012). Still, it is very likely that it will result in longer or stronger dry seasons (Buytaert, Cuesta-Camacho and Tobón, 2011). Together with

temperature increase, this will cause problematic water supply in dry seasons, as they decrease water provision by glacier melting and wetland drying (Magrin et al., 2014; Rolando, Turin, et al., 2017). This will affect people and industries in lower, arid Andean regions with lower water supply in dry seasons, but will also affect agriculture in the highlands, since 70% of this agriculture is rainfed (Rolando, Turin, et al., 2017).

Temperature rise and decreased water supply will also change soil conditions. First, a higher soil temperature will accelerate microbial acitivity, leading to faster decomposition rates (Buytaert, Cuesta-Camacho and Tobón, 2011). Drier soil conditions, induced by expected longer and/or stronger dry seasons, will also intensify decomposition (Buytaert, Cuesta-Camacho and Tobón, 2011).

Moreover, slow decomposing pools of organic matter, such as the Andean highlands (Rolando, Turin, et al., 2017), are more sensitive to such temperature change than fast pools, especially those without protection by amorphous soils, such as Peruvian grasslands (Zehetner and Miller, 2006).

It is uncertain how this intensified decomposition will affect Andean grasslands and their ecosystem services. In the tropics, accelerated decomposition rates will increase mineralized soil N, which will enhance plant productivity and C storage (Melillo et al., 2002; Sokolov et al., 2008). This would indicate a negative feedback in which C loss due to decomposition is balanced by C storage. Whether this also applies for dried, vulnerable soils, such as the Peruvian grasslands, is not known and needs to be studied further to examine the effects of climate change on C and N stocks of these highlands.

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5. Conclusion

Between 2900 and 3750 m, our results have found a signficant, positive relationship for C/N ratio and altitude. This was driven by cooler and wetter conditions at higher altitudes, decelerating

decomposition more than biomass production, increasing organic matter accumulation. This underlying process, however, cannot explain the lack in relationship between C/N and altitude for the observations between 3750 and 5000 m. Whether these constant values are driven by the dominance of fluviatile and glacial sediments need to be studied further.

Among parent materials, significant differences in mean C/N were found. The mean C/N ratio on igneous bedrocks and glacial sediments were significantly higher than on acid sedimentary bedrocks and fluviatile sediments. For calcareous sedimentary bedrocks, no significant differences were found. In addition to altitude and parent material, soil acidity was the third driver of soil C/N ratios. Values increased with soil acidity, which corresponds with the lower decomposing activity of enzymes in acid soils.

C/N ratios in the Puna grasslands were found to be lower than those in the Páramo grasslands. This reflects the higher impact of wet and amorphous soils on decomposition rates than a low

temperature. When comparing the Puna grasslands with the higher elevated Apolobamba, similar values were found, which would indeed suggest a constant C/N value above 3750 m.

In the future, soil dynamics will change as the Andes will probably face more water shortages and longer or stronger dry seasons. Drier and warmer soils will increase decomposition rates and result in soil C losses, endangering ecosystem services that rely on this soil property. However, this might be balanced by a negative feedback, in which decomposition can also result in enhanced plant

productivity due to higher mineralized N.

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Acknowledgements

I would like to thank all students, researchers, and local communities and institutions involved in the fieldwork campaigns from 2010 to 2019. I would especially like to thank the members of The Mountain Institute (TMI) for the kind collaboration with the University of Amsterdam and making these fieldworks possible. I am also grateful to Erik Cammeraat and Anne Uilhoorn for supervising and reviewing this project. Finally, I would like to thank Brandino Verhaak, my fellow student, for the helpful cooperation regarding the organisation and analysis of the data and intrepreting the results.

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Appendices

Appendix A: Fieldwork areas

Appendix A1: Cajamarca (Jonkman and Scholte, 2010; Den Haan, 2013)

Appendix A2: Cajamarca (Yang, Jansen, Absalah, et al., 2020)

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Appendix A4: Huaraz (Yang, Jansen, Absalah, et al., 2020)

Appendix A5: Ulta (Gravemaker, Meeteren and Scholz, 2016; Scholten, Dalmijn and Ustinov, 2016; Nannenberg, 2019)

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Appendix A6: Olleros (Radakovich and Chambers, 2012)

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Appendix C: Dataset

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Appendix E: R-script

#Removing Global Environment rm(list=ls())

#Load the dataset library(readxl)

De_Dataset <- read_excel("No_Outliers_Data.xlsx",

col_types =c("numeric", "text", "text", "text", "numeric", "text", "text", "text", "text", "text", "text", "numeric",

"numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "text", "text", "text", "numeric"))

#Create a subset with the columns you want

Thesis<-data.frame(De_Dataset$`Sample ID`,De_Dataset$Group, De_Dataset$Altitude, De_Dataset$pH, De_Dataset$Lithology, De_Dataset$`C/N`)

Thesis_pH<- data.frame(De_Dataset$`Sample ID`, De_Dataset$Group, De_Dataset$Altitude, De_Dataset$pH, De_Dataset$`C/N`, De_Dataset$`Soil Acidity`)

Thesis_Lithology<-data.frame(De_Dataset$`Sample ID`, De_Dataset$Group, De_Dataset$Altitude, De_Dataset$Lithology, De_Dataset$`C/N`)

#Rename the column names

names(Thesis)[names(Thesis)=="De_Dataset..Sample.ID."]<-"Sample ID"

names(Thesis)[names(Thesis)=="De_Dataset.Group"]<-"Group"

names(Thesis)[names(Thesis)=="De_Dataset.Altitude"]<-"Altitude"

names(Thesis)[names(Thesis)=="De_Dataset.pH"]<-"pH"

names(Thesis)[names(Thesis)=="De_Dataset.Lithology"]<-"Lithology"

names(Thesis)[names(Thesis)=="De_Dataset..C.N."]<-"C.N"

names(Thesis_pH)[names(Thesis_pH)=="De_Dataset..Sample.ID."]<-"Sample ID"

names(Thesis_pH)[names(Thesis_pH)=="De_Dataset.Group"]<-"Group"

names(Thesis_pH)[names(Thesis_pH)=="De_Dataset.Altitude"]<-"Altitude"

names(Thesis_pH)[names(Thesis_pH)=="De_Dataset.pH"]<-"pH"

names(Thesis_pH)[names(Thesis_pH)=="De_Dataset..C.N."]<-"C.N"

names(Thesis_pH)[names(Thesis_pH)=="De_Dataset..Soil.Acidity."]<-"Soil Acidity"

names(Thesis_Lithology)[names(Thesis_Lithology)=="De_Dataset..Sample.ID."]<-"Sample ID"

names(Thesis_Lithology)[names(Thesis_Lithology)=="De_Dataset.Group"]<-"Group"

names(Thesis_Lithology)[names(Thesis_Lithology)=="De_Dataset.Altitude"]<-"Altitude"

names(Thesis_Lithology)[names(Thesis_Lithology)=="De_Dataset.Lithology"]<-"Lithology"

names(Thesis_Lithology)[names(Thesis_Lithology)=="De_Dataset..C.N."]<-"C.N"

Thesis_lithology_NA<-na.omit(Thesis_Lithology) Thesis_pH_NA<-na.omit(Thesis_pH)

#Entire dataset summary(De_Dataset)

#Where are the missing values? vis_miss(Thesis)

gg_miss_var(Thesis, facet = Lithology, show_pct =TRUE)

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plot(De_Dataset$Altitude, De_Dataset$`C/N`)

plot(De_Dataset$Altitude, De_Dataset$`C(%)`) plot(De_Dataset$Altitude, De_Dataset$`N(%)`) plot(De_Dataset$`N(%)`, De_Dataset$`C(%)`)

#Standarderror of C/N

std <-function(x)sd(x)/sqrt(length(x)) std(De_Dataset$`C/N`)

#Regression model C/N~Altitude with assumptions

Mod<-lm(De_Dataset$`C/N`~De_Dataset$Altitude)#Also for C and N ~ Altitude summary(Mod)

Model<-lm(`C/N`~Altitude, data= De_Dataset) install.packages("ggfortify")

library(ggfortify) autoplot(Model)

install.packages("gvlma")

library(gvlma)

model_gvlma<-gvlma(Model)

summary(model_gvlma)

#Is the regression linear?

Model2<-lm(`C/N`~Altitude+Altitude_sq, data= De_Dataset)

summary(Model2)

anova(Model, Model2)#Model2 describes the observations better than Model #Which regression model explains best?

Mod<-lm(De_Dataset$`C/N`~De_Dataset$Altitude+De_Dataset$pH+De_Dataset$Lithology)#also for Altitude+pH and Altitude+pH+Geology

summary(Mod)

#Low and High altitude

#Separation of <3750 and >3750 m

HighAlt<-De_Dataset[De_Dataset$Altitude >3749,] LowAlt<-De_Dataset[De_Dataset$Altitude <3749,]

#Linear regression models

Mod_lowAlt_CN<-lm(LowAlt$`C/N`~LowAlt$Altitude)#also for HighAlt summary(Mod_lowAlt_CN)

#pH, single parent material (`Lithology`), and grouped parent material (`Geology`) #Regression models pH

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summary(Mod)

#t-test C/N~pH

Acid<-Thesis_pH_NA[Thesis_pH_NA$`Soil Acidity`=="Acid",]

Calcareous<-Thesis_pH_NA[Thesis_pH_NA$`Soil Acidity`=="Calcareous",]

#Assumptions

shapiro.test(Calcareous$C.N)# not normally distributed, thus Wilcoxon wilcox.test(Calcareous$C.N, Acid$C.N)

#Non-parametric ANOVA C/N~lithology, C/N~geology, and pH~geology

kruskal.test(`pH`~ `Geology`, data= De_Dataset)#also for mean C/N, C and N values for `Lithology` and `Geology` and for pH~Geology

#Dunns post hoc test install.packages("FSA")

library(FSA)

dunnTest(`C/N`~ Lithology, data= De_Dataset)#also for C/N~Geology (grouped parent material) #Altitude summary for grouped parent materials

Acid_Sed<-na.omit(De_Dataset$Àltitude`[De_Dataset$`Geology`=="Acid sedimentary bedrock"]) Calc_Sed<-na.omit(De_Dataset$Àltitude`[De_Dataset$`Geology`=="Calcareous sedimentary bedrock"]) Igneous_Sed<-na.omit(De_Dataset$Àltitude`[De_Dataset$`Geology`=="Igneous bedrock"])

Glacial_Sed<-na.omit(De_Dataset$`Altitude`[De_Dataset$`Geology`=="Glacially eroded"]) Fluvial_Sed<-na.omit(De_Dataset$`Altitude`[De_Dataset$`Geology`=="Fluvially eroded"])

quantile(Igneous_Sed)#for each grouped parent material (`Geology`) mean(Igneous_Sed)#for each grouped parent material (`Geology`) #Altitude boxplots for grouped parent materials

boxplot(De_Dataset$`Altitude`~ De_Dataset$Geology, main="Altitude boxplots for grouped parent materials", xlab="Grouped parent materials",

ylab="Altitude", col="white", border="black"

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Appendix F: Graphs and models for C and N concentrations and soil acidity

Appenix F1: Mean C and N concentrations for acid and alkaline soils

Appendix F2: Table with regression models between pH and C, N, and C/N

Appendix G: Table with regression models between altitude and C, N, and C/N

Regression R2 _p C/N 2900-3750 m C/N = -5.4203 + 0.0047 * Altitude 0.09 <0.001 3750-5000 m C/N = 10.301 + 0.0004 * Altitude 0.002 0.62 2900-5000 m C/N = 4.9187 + 0.0017 * Altitude 0.06 <0.001 C [%] 6.2 6.4 6.6 6.8 7 7.2 7.4 7.6 Acid Alkaline C [%] p = 0.90 0.48 0.5 0.52 0.54 0.56 0.58 0.6 0.62 Acid Alkaline N [%] p = 0.87 Regression R2 p C/N C/N=13.5468-0.4352*pH 0.02 <0.01 C[%] C=4.4737+0.4295*pH 0.003 0.53 N[%] N=0.4316+0.0214*pH 0.002 0.44

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2900-3750 m C = -18.666 + 0.0067 * Altitude 0.08 <0.001 3750-5000 m C = -26.277 + 0.0088 * Altitude 0.05 <0.01 2900-5000 m C = -23.06 + 0.0080 * Altitude 0.18 <0.001 N [%] 2900-3750 m N = -1.023 + 0.00041 * Altitude 0.06 <0.001 3750-5000 m N = -1.251 + 0.00050 * Altitude 0.04 <0.05 2900-5000 m N = -1.622 + 0.00058 * Altitude 0.21 <0.001

Appendix H: Summary table with altitudinal information of each grouped parent

mateiral

Table 4. Summary of the altitudinal information of each grouped parent material

Parent material Min 25% Median 75% 100% Altitudinal range Mean Acid sedimentary bedrock 3025 3327 3511 3818 4665 3025-4665 3661.8 Calcareous sedimentary bedrock 3423 3529 3614 3686 3877 3423-3877 3621.6 Fluviatile sediment 2933 3654 3974 4049 4485 2933-4485 3832.5 Glacial sediment 2963 3562 4020 4391 4950 2963-4950 3949.2 Igneous bedrock 2979 3466 3585 3659 4690 2979-4690 3542.3

Appendix I: Data repository

Data repository Pieter

Patterns in C/N and parent material along an altitudinal gradient in Peruvian alpine grasslands