FACULTEIT WETENSCHAPPEN

Opleiding Master of Science in de geologie

Academiejaar 2012–2013

Scriptie voorgelegd tot het behalen van de graad Van Master of Science in de geologie

Promotor: Prof. Dr. M. De Batist Co-promotor: Dr. S. Bertrand Begeleider: Dr. S. Bertrand Leescommissie: Prof. Dr. E. Van Ranst, Dr. K. Heirman

Nature of sediment sources to Laguna Parrillar (southern Chilean Patagonia, 53°S) and

implications for paleoclimate reconstructions

Clara Paesbrugge

Cover picture: View towards the Northwest of Laguna Parrillar. (Photo: S. Bertrand)

Acknowledgements

First and foremost I would like to thank Professor Dr. Marc De Batist and Dr. Sebastien

Bertrand for giving me the opportunity to participate in this project and to give me a chance

of performing my MSc thesis on a subject I am thoroughly interested in.

I would also like to express my thanks to the staff of the Renard Centre of Marine Geology

for allowing me to use their laboratory equipment as well as their delicious coffee.

Special thanks goes out to Dr. Sebastien Bertrand, who despite his own work, found the time

to explain the various techniques and answer every question that would spring to mind,

relevant or not. Moreover he also found the time to read, correct and reread the manuscript

I would send to him.

This thesis would not have been possible without the many expeditions to the far end of the

world. Therefore, I would like to thank the group of people who went to Chile multiple times

to collect the samples and data so that I could use them. In this regard, I would especially

like to thank Dr. Sebastien Bertrand and Zakaria Ghazoui, to both of whom I would also like

to express gratitude for the photographs.

The help I received from Dr. Nathalie Fagel at Liège University and Ir. Joel Otten regarding

the explanation about XRD, the use of the equipment and analyzing the data was vital for

the end result. I would therefore also like to thank them.

I would also like to thank Professor Dr. Peter Vandenhaute, Ingrid Smet and the Van Ranst

lab for letting me use their equipment to respectively crush and pulverise the rock samples.

Furthermore, I would also like to express gratitude towards the staff of UCDavis Stable

Isotope facility for performing the carbon and nitrogen analyses as early as possible.

For the second major objective of this thesis, I would like to thank Dr. Katrien Heirman for all

the hard work she has put into the analysis of the Parrillar core and for letting me us her

data.

Last but not least, I would like to thank my friends and family for supporting me through the

good and lesser times of this MSc thesis. Finishing this project would have been a lot harder

without their constant encouragement.

Thank you and have a nice read!

Table of Contents

1. Introduction........................................................................... 1

2. General Setting ...................................................................... 4

2.1. Andes and Southern Patagonia ............................................................. 4

2.2. Geological Setting ................................................................................. 5

2.2.1. Andean Cordillera ........................................................................................................ 5

2.2.2. Southern Patagonia ..................................................................................................... 7

2.2.3. Brunswick Peninsula .................................................................................................... 7

2.3. Climatic Setting .................................................................................... 9

2.3.1. Glaciations and Quaternary climate variability in Southern Patagonia ...................... 9

2.3.2. Current Climate Setting ............................................................................................. 14

3. Methods .............................................................................. 16

3.1. Digital Processing and Visualisation ..................................................... 16

3.1.1. Available Data ........................................................................................................... 16

3.1.2. Data Processing ......................................................................................................... 18

3.2. Sediment Analysis ............................................................................... 21

3.2.1. Available Samples ...................................................................................................... 21

3.2.2. Sample preparation ................................................................................................... 22

3.2.3. Sieving ........................................................................................................................ 24

3.2.4. Magnetic Susceptibility .............................................................................................. 27

3.2.5. Loss On Ignition (LOI) ................................................................................................. 28

3.2.6. Carbon (13C) and Nitrogen (15N) elemental and isotopic analysis ............................. 29

3.2.7. X-Ray Diffraction (XRD) .............................................................................................. 31

4. Results ................................................................................. 34

4.1. GIS of Parrillar’s Watershed ................................................................. 34

4.2. Sediment Analysis ............................................................................... 38

4.2.1. Sieving ........................................................................................................................ 38

4.2.2. LOI .............................................................................................................................. 38

4.2.3. Magnetic Susceptibility .............................................................................................. 38

4.2.4. XRD............................................................................................................................. 42

4.2.5. Total Organic Carbon ................................................................................................. 46

4.2.6. C/N Ratios .................................................................................................................. 46

4.2.7. 13C Istopes .................................................................................................................. 52

4.2.8. Filters ......................................................................................................................... 55

4.2.9. C/N vs δ13C ................................................................................................................ 57

5. Discussion ............................................................................ 58

5.1. Sources of inorganic particles .............................................................. 58

5.1.1. Rocks .......................................................................................................................... 58

5.1.2. Soils ............................................................................................................................ 59

5.1.3. River Sediment ........................................................................................................... 60

5.2. Sources of sedimentary organic matter ............................................... 60

5.2.1. Terrestrial organic matter ......................................................................................... 61

5.2.2. Lake productivity........................................................................................................ 62

5.2.3. Selection of geochemic values for aquatic and terrestrial end-members. ................ 63

5.3. Influence of grainsize on sediment composition .................................. 63

5.3.1. Mineralogy ................................................................................................................. 63

5.3.2. Organic carbon .......................................................................................................... 64

5.3.3. Magnetic susceptibility .............................................................................................. 66

5.4. Influence of Peat on water and sediment composition ........................ 66

5.5. Influence of peat bogs on Laguna Parrillar’s trophic status .................. 71

5.6. Implications for the Laguna Parrillar sediment record interpretation .. 72

5.6.1. Mineralogy and magnetic susceptibility .................................................................... 75

5.6.2. Organic matter .......................................................................................................... 78

6. Conclusions .......................................................................... 82

References ...................................................................................... I

Nederlandstalige Samenvatting (Dutch Summary) ........................ VII

Appendix .................................................................................... XIII

Introduction 1

1. Introduction

The present debate on global warming has started a world-wide discussion about climate

change in general. The recent change in temperature is not a unique event in time.

Throughout earth’s history climate has always been fluctuating between warmer and colder

periods. Understanding the cause of this variability requires a wide array of high-resolution

palaeoclimate records with geographical and chronological resolutions sufficient enough for

analysis of past patterns of climate dynamics (Bertrand and Ghazoui, 2011). Although

continental records from the northern hemisphere are widely available and extensively

documented since the start of paleoclimatic research, data from the ocean-dominated

southern half of the planet are far less at hand. Especially data from the mid and high

latitudes of the southern hemisphere are clearly insufficient, and this while recent

palaeoclimate findings provide evidence that the southern hemisphere is a key factor in

global climate change during the Quaternary. Understanding the climate of the Quaternary,

especially the Late Quaternary, is of particular importance to aid in placing the recent

changes in climate in context of the natural climate variability and to help improve climate

models (Bertrand and Ghazoui, 2011).

With regards to improving our understanding of past changes in climate and environment of

the Southern Hemisphere, research was done in framework of the FWO CHILT (CHILean Lake

Transect) project. This project has as objective the reconstruction of Late-Glacial and

Holocene climate variations recorded in lake sediments along a North-South transect

through the south-western part of South America. This transect runs from the Chilean Lake

District (39°S) in the north to Patagonia (53°S) in the south. This part of South America is

especially well suited to produce valuable palaeoclimate records for the study of spatial and

temporal patterns in climate variability in the southern hemisphere. It is the only landmass

extending this far south (followed by New-Zealand (up to 46°S)). This allows comparing

continental records from southern medium to high latitudes to those from Antarctica and

Sub-Antarctic Islands, but also with those from lower latitudes. The continuous distribution

of large lakes across a wide latitudinal belt, from 40-55°S, is more or less coincident with the

natural northern and southern boundaries of the “Southern Polar front” (fig.1.1) (source:

researchportal.be).

2 Introduction

Fig.1.1 Antarctic convergence, the yellow box indicates Patagonia (after: http://www.eoearth.org/view/article/150096/)

The two objectives of the CHILT project were to improve the understanding of the

deglaciation history of southern South America and to study the temporal and spatial

variability of rapid climate fluctuations that occurred during the transition between the Late

Glacial and the Holocene (source: researchportal.be)

One of the lakes of interest is Laguna Parrillar (53°24’S 71°17’W) in the centre of the

Brunswick Peninsula. Due to its location at the southern tip of southern South America, this

lake is perfectly located to reconstruct terrestrial climate at high latitudes of the southern

hemisphere during the Late-Pleistocene (Bertrand and Ghazoui, 2011). The lake sediments

from Laguna Parrillar can be used to determine the relative position, strength and cyclicity of

the global atmospheric circulation systems that affected and still affect large parts of the

southern hemisphere (Van Daele et al., 2009 – fig.1.1).

In framework of K. Heirman’s PhD thesis (Heirman, 2011), a long sediment core was

collected in the centre of the lake. This was sampled and submitted to multi-proxy analyses

including magnetic susceptibility, loss on ignition, bulk organic geochemistry and XRF core

Introduction 3

scanning (Bertrand and Ghazoui, 2011). These proxies display large variations during the last

45,000 years, which are interpreted as variations in lake level and wind speed (Heirman,

2011).

To improve the interpretation of the proxy records generated from Laguna Parrillar, it is

necessary to characterize the nature of the terrestrial and aquatic particles supplied to the

lake and to understand the factors that affect their composition. For that reason samples

were collected all over the lake’s watershed during expeditions in 2011 (Bertrand and

Ghazoui, 2011) and 2012 (De Vleeschouwer, 2012). These samples consist in rock, soil, river

sediment and suspended particles in both river and lake water. The analyses of these

samples as well as the interpretation of the results form the basis of this MSc thesis.

In order to allow comparison between the characteristics of the samples taken within the

watershed to those of the lake sediments within the Parrillar core, similar analyses were

performed. These include magnetic susceptibility measurements, Loss On Ignition (LOI), X-

Ray Diffraction (XRD) to determine the mineralogy, and bulk organic geochemistry (TOC, C/N

and δ13C). The results were then compared to those found by Heirman (2011) to see how the

influence of either sediment source changed over time.

This MSc thesis consists of six parts, including this introduction. The following part, chapter

2, describes the general setting of the area of the Magellan Strait, in particular the Brunswick

Peninsula and Laguna Parrillar watershed. Modern geology and climate are mentioned

alongside the palaeoclimatological evolution of southern Patagonia, the latter with special

focus on the extent of the glaciers during the Last Glacial Maximum and other (minor ice

ages).

Chapter 3 covers both the processing of the digital information and the methods used to

analyse the different samples mentioned above. Each method is briefly accounted for.

Chapter 4 follows a similar structure of the previous chapter as it lists the results of both the

GIS and sample analyses. The interpretation of these results as well as the discussion about

the links with the Parrillar core can be found in the fifth chapter. The last part, chapter 6, is

the conclusion.

At the end one can find the reference list as well as the appendices mentioned within the

text.

4 General Setting

2. General Setting

2.1. Andes and Southern Patagonia

The Andean Cordillera is a morphologically

continuous mountain chain along the

western margin of South-America. The more

than 7500 km long Cordillera is segmented

into regions with distinct pre-Andean

basement ages, Mesozoic and Cenozoic

geologic evolution, crustal thickness,

structural trends, active tectonics and

volcanism (fig.2.1). A simple division is the

one in Northern (12°N-5°S), Central (5-33°S)

and Southern (33-56°S) Andes (fig.2.1). The

Northern Andes of Colombia and Ecuador

follows a general northeast-southwest trend.

The Central Andes include the Northern

Central Andes, a northeast-southwest

trending Peruvian segment, and the Southern

Central Andes, the northern part of the

north-south trending Andes in Chile and

Argentina. The Southern Andes contains the

remaining part, including the area of interest:

Southern Patagonia (Stern, 2004).

It is in this part of Chile, around 53°25'S and 71°17'W, that Laguna Parrillar can be found in

the centre of the Brunswick Peninsula, which is in turn part of the far south of Chilean

Patagonia. The west coast of the peninsula looks out over Seno Otway, whereas the east and

south coast are part of the Strait of Magellan (fig.2.2), which cuts off any area further to the

south from the mainland and gave its name to the region: Magallanes y Antártica Chilena.

Its capital is Punta Arenas.

Fig.2.1 Map of South-America showing the different parts of the Andes (Stern, 2004)

General Setting 5

Fig.2.2. Satellite image of Southern Chile ©NASA/GSFC

2.2. Geological Setting

2.2.1. Andean Cordillera

Since the Middle Miocene, two oceanic plates have been subducting underneath the South-

American plate: the Nazca plate to the north and the Antarctic plate to the south. These

plates are separated by the active oceanic Chile spreading ridge, which is colliding with the

subduction zone at an angle of approximately 20°C (fig.2.3, D’Orazio et al., 2003). Although

6 General Setting

many of the main features of the Andes were acquired during the Miocene, Quaternary

neotectonic deformation had, and still has, a significant effect on topography.

The subduction results in

extensive volcanism which occurs

in four separate regions along the

Cordillera. These are named the

Northern (NVZ), Central (CVZ),

Southern (SVZ) and Austral (AVZ)

Volcanic Zones (fig.2.1). Active

volcanoes in the NVZ, CVZ and SVZ

result from subduction of the

Nazca plate, and those of the AVZ

from subduction of the Antarctic

plate (Stern, 2004).

The AVZ is formed by five stratovolcanoes (Lautaro, Viedma, Aguilera, Reclus, Burney) and

the small complex of Holocene domes and flows on Cook Island, the southernmost volcanic

centre in the Andes (fig.2.3, Stern, 2004; D’Orazio et al., 2003). Tephras linked to their

eruptions are used for tephrochronology and are easy to correlate because of their

characteristic composition compared to the volcanoes of the SVZ, e.g. the Hudson volcano

(Killian et al., 2003). Possible hazards associated with Andean volcanism include pyroclastic

lava flows, lahars, debris flows generated by sector collapse and tephra falls.

Large earthquakes are commonplace in Chile. More than ten events with magnitudes equal

to or greater than 8 have taken place during the twentieth century alone. Among these

earthquakes is the 1960 event, the largest earthquake ever recorded since the beginning of

instrumental seismology. Such extreme seismic activity is a result of the interaction of the

convergence of four plates in south-western South-America where Chile is located: the

earlier mentioned Nazca, Antarctic and South-America plate as well as the Scotia plate

(Barrientos, 2007).

South of the triple junction where the South-American, Nazca and Antarctic plate converge,

up to 57°S the Antarctic plate is being subducted underneath the South-American plate at

rates between 10 and 20 mm/yr, which is slower than the Nazca plate more north (Pelayo

Fig.2.3 Map of the Austral Volcanic Zone (Stern, 2004)

General Setting 7

and Wiens, 1989). The slow convergence rate and the type of subducted structures are

probably the main reason for the relative lack of a Benioff zone of seismic activity in the

region south of the Chile triple junction (Barrientos, 2007).

Historical earthquakes in 1879 and 1949, all of them with an estimated magnitude above 7,

are the largest earthquakes recorded in the south of the western end of the Magellan Strait

(c. 52°S). Here, the Antarctic plate is no longer being subducted under the South-American

plate, but under the Scotia plate (e.g. Pelayo and Wiens, 1989). Part of the convergence rate

is accommodated by the boundary between the South-American and Scotia plates along the

Magellan fault (fig.2.3, Barrientos, 2007).

2.2.2. Southern Patagonia

The geology of Southern Patagonia can be

roughly divided into three parts. From west

to east one can distinguish the western

Cordillera consisting mainly out of granites

and granodiorites (fig.2.4, 1), a sedimentary

sequence mixed with volcanic sediments

from Jurassic to Miocene age (2) and

Quaternary glacial sediments (3)

(SERNAGEOMIN, 2002).

2.2.3. Brunswick Peninsula

Brunswick Peninsula’s geology is characterised by thick sedimentary successions. The

general strike is SE oriented, with younger units towards the northeast (fig.2.5, Otero et al.,

2012). In accordance with ENAP (Empresa Nacional del Petróleo, 1977), the geological

sedimentary sequence that surfaces in the area corresponds with the formations of Fuentes

(Campanian), Rocallosa (Maestrichtian), Chorrillo Chico (Palaeocene) and Agua Fresca

(Lower and Middle Eocene). The first two belong to the Upper Cretaceous and the two later

ones to the Lower Tertiary (Dollenz, 1983).

Fig.2.4. Geological Map of Patagonia (after SERNAGEOMIN, 2002). Numbers explained in text.

8 General Setting

Fig.2.5 Geology of the Brunswick peninsula (Otero et al., 2012).

The Fuentes formation is composed of gray, somewhat silty mudstones with limestone

concretions, sometimes with a sandy member in the centre part. The Rocallosa formation

are clayey sandstones and sandy, glauconitic siltstones. The Chorrillo Chico formation

consists of well-stratified dark gray claystones, with interbedded siltstone and fine

glauconitic sandstone (Dollenz, 1983). Agua Fresca is an up to 2,000 m thick formation

consisting of marine deposits exposed in the eastern part of the Brunswick Peninsula, along

the Agua Fresca River (Otero et al., 2012). The Agua Fresca formation is a sequence of gray

claystones, partially glauconitic with limestone concretions (Dollenz, 1983).

The soils have been described by Pisano (1973 in Dollenz, 1983) as podzols, gray forest soils

that support the deciduous forests and bog soils corresponding to the Sphagnum peat bogs.

On the peaks and along the banks of the lake, sand and gravel can be found, sometimes

containing some organic material (Dollenz, 1983).

General Setting 9

2.3. Climatic Setting

2.3.1. Glaciations and Quaternary climate variability in Southern Patagonia

Glaciers worldwide have been adapting to an ever-changing climate. Their fluctuating sizes

have been extensively used in scientific research as a proxy for palaeoclimate. The record of

glacier fluctuations in southernmost South America is especially important because of three

reasons linked to its global location. Firstly it offers a terrestrial climate record in the

dominantly oceanic domain of the Southern Hemisphere. Secondly, it also spans the area of

the southern Westerlies, the dynamic component of the atmospheric circulation of the

Southern Hemisphere (i.a. fig.1.1). As such it holds the promise of linking climatic processes

in mid-latitudes with those of the Antarctic domain. Thirdly, its location is ideal for testing

alternative hypotheses about the mechanisms of climate change based on synchrony or

asynchrony between the hemispheres (Sugden et al., 2005).

Currently, ice fields and smaller ice caps exist on the higher massifs in the Andes, with

particular centres in the Cordillera Darwin (55°S) and the Northern and Southern Patagonian

Ice fields, Hielo Patagonico Norte (HPN) and Hielo Patagonico Sur (HPS), between latitudes

46-47,5° and 48-51° respectively (fig.2.6).

Fig.2.6 Current location of the major ice fields in Southern Patagonia (after: Glasser et al., 2004)

Studies following the first discovery of moraines as former glacial limits by Caldenius (1932 in

Clapperton et al., 1995), have made the stratigraphical, morphological and chronological

10 General Setting

glacial history of the southern Andes to be one of the most complete in the world (Rabassa

and Clapperton, 1990). Clapperton et al. (1995) renamed the moraine sequences first

described by Caldenius A, B, C, D and E, with A being the oldest and E the youngest. In the

Magellan Strait and Bahía Inútil the two moraines present are B and C (fig.2.7). Moraine

system D can be picked out by the continuity of meltwater channels in the Magellan area.

Evidence supports that fluctuations in climate on an orbital scale follow those of the

Northern Hemisphere, whereas millennial scale fluctuations during the last glacial-

interglacial transition seem out of phase between hemispheres (Sugden et al., 2005).

Another influence factor not to be disregarded, is the latitudinal migration of the Westerlies.

These winds have not always occupied their current position and their migration is sure to

influence precipitation. Below follows a summary of the different climatological fluctuations

Fig.2.7 Map showing the position of glacier advances A-E in central Magellan Strait (Clapperton, 1995)

General Setting 11

Fig.2.8. Extent of the Ice sheet during Glacial stage A, B and C (Sugden, 2009) The red arrow points towards Laguna Parrillar

and accompanying glacier retreats or advances. All radiocarbon dates mentioned in the text

below have been converted to calibrated years by using the IntCal9 calibration curve in the

OxCal 4.2 project application (Bronk Ramsey, 2009; Heaton et al., 2009).

2.3.1.1. Last Glacial Maximum 25-23 ka

Broken marine shells within the basal till of advance A (fig.2.8) at Cabo Porpesse and Parque

Chabunco, have been radiocarbon dated at 42,000 year BP, but this age is too close to the

dating limit of the 14C-method that it was deemed unreliable. Amino acid (isoleucine)

epimerisation dating of shells from the same places gave a minimum age of 90,000 year BP,

or 130,000 year BP when the effective diagenetic temperature was lowered to 2°C

(Clapperton et al., 1995). The authors propose advance A taking place during an early part of

the last glaciation (e.g., during MIS 5d, 5b or 4).

The moraines of stages B and C have been dated by Clapperton et al. (1995) to the LGM.

They demonstrate the existence of lobes of ice which reached their maximum around or

some time before 23-25ka in the Magellan area (fig.2.8, Sugden et al., 2005) .

12 General Setting

The overall structure of the LGM in Patagonia displays a “northern” signal. This means that

despite a maximum in summer insolation at these latitudes, Patagonian glaciers expanded as

a response to changes driven by the Northern Hemisphere. This response applies to the

timing of the maxima, their duration, the onset of deglaciation and the two-step nature of

deglaciation. At the time of the LGM, the glaciers discharged directly onto outwash plains

occupying the Strait of Magellan (Sugden et al. 2009).

The limits of this advance define a much smaller amount of ice than during advance A

(fig.2.8) as discrete lobes apparently diverged around the Brunswick Peninsula and left

central parts, including Laguna Parrillar, ice-free as can be seen in fig.2.8.A and fig2.8.B free

(Clapperton et al. (1995), Sugden et al. (2009), Heirman (2011)). As sedimentation in Laguna

Parrillar continued during the LGM, the sediment record is one of the few in the area which

allows for paleoenvironmental conditions of that time (Heirman, 2011). The advance related

to moraine D around 17.5 ka is the last advance of the LGM and is followed by an extremely

rapid deglaciation (Sugden et al., 2005).

2.3.1.2. Antarctic Cold Reversal (15.4-12.9 ka) – Younger Dryas (13.2-12 ka)

Evidence for a glacier stage coinciding with the Antarctic Cold Reversal (ACR) is clearest in

the south of Patagonia and comes in the form of field evidence. Some of the moraine

systems, indicated as Stage E, lay inside the LGM limit, so the occurrence of a Late-Glacial

event or glacier expansion during ACR or YD cannot be discounted (Glasser et al., 2008).

Besides the moraines and sediments linked to proglacial lakes, there is limited additional

evidence for glacier conditions in southernmost South America coinciding with the time of

the ACR.

Overall the palaeoenvironmental record in Patagonia seems to indicate that the period

13,000–5000 14C years BP (15,548–5730 cal years BP) was relatively warm and dry (Rabassa

and Clapperton, 1990). The southward shift of the Westerlies and the corresponding

increase of precipitation in the Magellan Region at circa 12,300 14C yr BP (14,462-14,040 cal

years BP) (McCulloch et al., 2000) appears to contradict these conditions. However, it is

likely that most of the precipitation was distributed on the western flanks of the Andean

Cordillera, similar to today, and the area to the east lay in a rain shadow. The rain shadow

would have been intensified by the presence of the expanded Patagonian ice field along the

Andean Cordillera (fig.2.9). It is probable that the Magellan Glacier was responding to the

General Setting 13

increase in precipitation brought by the southerly migrating Westerlies (McCulloch and

Davies, 2001).

Fig.2.9 Extent of the ice sheet during Glacial Stage D and E (Sugden et al., 2009) Colour scale can be found in

fig.2.8

2.3.1.3. Mid to late Holocene (5,800 cal BP-present)

The established chronology of the Neoglacial fluctuations in the Patagonian Andes for the

last part of the Holocene is based on radiocarbon dates obtained from groups of moraines in

front of the outlet glaciers of the Northern and Southern Patagonian Icefields (i.a. Mercer,

1970). Based on these dates, Mercer proposed three Neoglacial advances since 5,000 14C

years BP (5800-5700 cal BP), namely those at 4700-4200 14C years BP (5500-4700 cal BP), at

2700-2000 14C years BP (2900-1900 cal BP) and during the Little Ice Age (LIA) of the last three

centuries. The first is also thought to be greatest of the three whereas this was a minor

event in the Northern Hemisphere (Mercer, 1970).

The strong cooling episode at 5000 14C years BP (5800-5700 cal BP) interrupted the climatic

amelioration of the Mid Holocene. This certainly continued after 3500 14C years BP (3828-

3725 cal years BP) with the extensive development of more extensive Holocene forests

suggesting greater effective moisture, probably related to cooler temperatures (Glasser et

al., 2004).

14 General Setting

Most analysis of the LIA fluctuation of glaciers comes from work carried out on the Northern

Patagonian Icefield (e.g. Glasser et al., 2002). It is clear that the outlet glaciers of the

Northern Patagonian Icefield receded from their late historic moraine limits at the end of the

19th century. This was a period when there was a poleward shift in precipitation (Lamy et al.,

2001) and winter precipitation was above the long-term mean (Villalba, 1994). The

coincidence of higher than average winter precipitation with a glacier advance suggests that

the advance may have been related to changes in precipitation rather than changes in

atmospheric temperatures (Glasser et al., 2004).

2.3.2. Current Climate Setting

Current climate is influenced by the neighbouring Pacific

and Atlantic oceans, as well as the proximity of the

Antarctic Peninsula. Based on the vegetation of the area

around Parrillar, climate can be categorised as being Trans-

Andean with degenerative steppe, equivalent to a

transitional form of extreme rainy and maritime weather in

the west to desert-like in the east, and isothermal tundra

(fig.2.11, Pisano, 1973 in Dollenz, 1983). The annual

precipitation varies between 650 and 700mm and the

predominating winds come from the west, which is to be

expected in the domain of the Westerlies (fig.1.1,

fig.2.10) (Dollenz, 1983).

The area of the lake’s watershed is covered by Sphagnum peat bogs and the surrounding

mountain ranges are covered with Fagaceae forests, mainly those of the Nothofagus kind

(fig.2.12).

Fig.2.10 Wind currents over South America: Tradewinds (yellow and

brown) and Westerlies (blue)

General Setting 15

Fig.2.11 Climate zones of Southern Patagonia (©

Institutio de Patagonia)

Fig.2.12 Vegetation of Southern Patagonia (Godley, 1960)

16 Methods

3. Methods

3.1. Digital Processing and Visualisation

A digital terrain model was created to, amongst other things, quantify the lake’s watershed

and locate the samples. In addition, combining the model with extra information such as

topographic or geological maps, allows for an easier interpretation of the spatial distribution

of the samples.

3.1.1. Available Data

3.1.1.1. SRTM Data

In order to delimitate the limits of the lake watershed and characterise elevation changes

within the watershed, SRTM (Shuttle Radar Topography Mission) data of the area were

downloaded from the server of the United States Geological Survey (http://dds.cr.usgs.gov).

These data consist of a digital height model and coordinate system assembled by satellites

scanning the earth’s surface with radar waves with a spatial resolution of 3 arc-seconds

(about 90 meter). The reference ellipsoid is the WGS84.

Despite Laguna Parrillar’s limited size compared to Brunswick Peninsula, all files necessary to

cover the entire peninsula were downloaded to ensure a complete overview. This resulted in

downloading 6 tiles between latitudes of 53°S and 55°S and the longitudes of 71°W and

73°W. As radar waves are strongly attenuated by water, they barely penetrate the water

surface. SRTM data therefore contain no bathymetrical information, resulting in flat areas

for the water’s surface (fig.3.1). Additional bathymetrical information is thus needed to

generate a complete digital elevation model.

This bathymetrical data of the lake’s subsurface was acquired during earlier expeditions

(Kilian et al., 2009) by use of a Parametric Echosounding System SES 96, from INNOMAR.

Methods 17

Fig.3.1 The SRTM data from the Parrillar Area as seen in Global Mapper. The lake is positioned in the centre.

3.1.1.2. Topographic and Geological Maps

The following maps were used:

- Topographic map Estuario Silva Palma 5300-7115 Section L N°24, scale: 1:100.000,

resolution 300dpi.

- Geological map of Chile, No. 4, 2003, Scale 1:100.000 (SERNAGEOMIN, 2002)

- Georectified Landsat7 false-colour image of Parrillar Area (53.24129°S-53.49811°S and

71.48765°W-71.23125°W)

3.1.1.3. Sample Coordinates

Samples were collected during the Chilean Lake Transect (CHILT) expedition in January 2011

(Bertrand, 2011). All the sampling sites were located with a GPS Garmin Etrex Vista HCx. The

different kinds of samples are labelled by the letter combination in their name, (table 3.1,

see section 3.2.1, table 3.2 for amount of samples).

PA11 Parrillar 2011 LW Lake Water RS River Sediment

PA12 Parrillar 2012 WS River Water SS Soil Sediment

RR Rock Samples

Table 3.1 Legend of Sample names

18 Methods

3.1.2. Data Processing

3.1.2.1. Coordinates of the lake margin

The coordinates of the lake margin were obtained by using the digital information loaded

into Global Mapper, which includes the satellite images and topographic maps. By using

these as reference, the lake’s margin is traced by using Global Mapper’s Digitizer tool.

3.1.2.2. Bathymetrical Model

The next step in creating the model was to edit the available data so that it could be used for

further processing. The original bathymetric data are in the xyz-format, corresponding to

latitude, longitude and depth. The latter was recalculated to height above sea level by

subtracting the depth value from the elevation of the lake’s surface, taken at 292m (value

found in Google Earth). Lake margin coordinates and bathymetrical data were then merged

into a single table, resulting in a total of 492 elevation data points.

This final table was then imported into Golden Software Surfer. To create a model from this

data, it is necessary to choose a gridding method. There are many options available, such as

Polynomial Regression, Nearest Neighbour, Triangulation, Kriging, which each result in a

(slightly) different model as each uses a different way of calculating a value at a certain point

(Appendix 1, see publications for detailed explanations). Kriging was eventually chosen as it

was often mentioned (in Geostatistical classes) as being one of the better methods.

Kriging calculates values for a rectangular area, and therefore it was necessary to cut out the

lake itself. This was done by creating a Blanking file (.bln) containing all data points

concerning the lake’s margin. Subsequently, the grid file (.grd) created earlier was combined

with this blanking file and loaded into Surfer to create the final bathymetrical map (Fig.3.2).

Methods 19

.

Fig.3.2. Blanked Grid file of lake, which includes the isobaths. The horizontal and vertical axes show longitude

and latitude respectively.

This blanked grid was finally imported into Global Mapper. The software reads it in the same

way as the SRTM data in the background, showing it as part of the height model in e.g.

profiles and 3D view, as can be seen in section 4.1. The SRTM elevation model and

bathymetrical data were combined in Global Mapper, to create a complete digital elevation

model of the area.

3.1.2.3. GIS of the watershed

a. Georeferencing

The topographical and geological maps were georeferenced using the Image Rectifier option

of Global Mapper.

The coordinates of distinctive places on the maps and georeferenced satellite images were

used for this process. Such places generally correspond to river-mouths or capes and bays

along the coastline. The coordinates were found by comparing the satellite images on

http://itouchmaps.com/latlong.html with the maps (fig.3.3). This was a bit more difficult in

the direct area around Laguna Parrillar and the southern margin of the peninsula as the

satellite images were rather clouded. But since the maps cover a rather large area, this

didn’t pose too much of a problem.

20 Methods

Fig.3.3 Georeferencing: left: satellite image online, centre: indicating the same point on the topographic map in

Global Mapper, right: once given the coordinates the point shows up on the SRTM data.

3.1.2.4. Digitising important linear and areal features

Once all images have been imported, rivers and watershed were digitized in Global Mapper,

using the Digitiser tool, which allows drawing point-, line- and area-features. An example of

an area-feature is the lake itself, drawn as mentioned earlier. Global Mapper calculates the

perimeter and enclosed area of every area-feature, which can be used as additional

information.

In a similar manner, the main rivers and their tributaries were digitised as well. Many

tributaries do not connect to the main river, as the areas through which they flow are

characterised by a (permanently) wet environment (swamps and peat bogs). To set them

apart (visually), the inflowing rivers were drawn in a different colour than the out-flowing

river (and its tributaries which have no effect on Laguna Parrillar).

The watershed itself is a combination of Area and Line features. The contour of the area was

drawn by connecting the areas with highest topography, using the topographic map at a

lowered opacity and the digital terrain model underneath it as reference. The eastern part

has little height differences and therefore it is difficult to draw the line with certainty (hence

the difference in colour). The end result was simply redrawn using the “Draw Area” option to

get the area feature.

Point features cover the sample locations. The final result can be seen in section 4.1.

Methods 21

3.2. Sediment Analysis

3.2.1. Available Samples

(Source: CHILT report Parrillar 2011).

As mentioned in section 3.1.1.3, the samples analysed were collected during a field

campaign conducted in the watershed of Laguna Parrillar as part of the Chilean Lake

Transect (CHILT) project in 2011 (Bertrand and Ghazoui, 2011). Five different kinds of

samples were collected according to the methods described below: river sediment samples

(RS), rock samples (RR), soil samples (SS), suspended particles in the lake (F-LW) and rivers

(F-WS) (table 3.1, 3.2). Besides the location, characteristics of the samples were written

down in a field notebook. Sampling locations are represented in fig.4.5.

3.2.1.1. River Sediment Samples (RS)

The river sediment samples were collected in the two main rivers that flow into the lake.

Three samples were taken along the river profile of the western river and two along the

northern one (fig.4.5). Samples were collected with a small hand shovel from the river

shores with a focus on fine-grained sediment particles.

3.2.1.2. Rock Samples (RR)

Rock samples were taken at the Northern shore of the lake, which is the only location where

small rock outcrops were observed (fig.3.4).

Fig.3.4 Rocks occurring at the Northern shore of Laguna Parrillar. Photo: Z. Ghazoui

(Bertrand and Ghazoui, 2011)

22 Methods

3.2.1.3. Soil Samples (SS)

Soil Samples were taken with a small shovel on top of soil profiles, mainly located on river

embankments, along the lake shore, or on fresh road cuts.

3.2.1.4. Suspended Particles (F)

Both river water samples and lake water samples were collected in pre-rinsed water bottles

and then filtered on glass fibre filters (GF-F) until near-total saturation of the filters. Two

filters were used for most water samples in order to collect enough particles for geochemical

analysis.

Name Amount

RS 5

RR 5

SS 10

F (lake - LW) 7

F (river - WS) 8

Table 3.2: Amount of samples

3.2.2. Sample preparation

3.2.2.1. Soil Samples (SS) and River Sediment (RS)

After arriving from Chile, the samples were stored in the freezer. This causes the porewater

to freeze and bacterial activity to stop. This ensures sample conservation with as little as

possible alteration between time of sampling and time of analysis. The frozen samples were

then freezedried using a Labconco Freezedry system/Freezone 4.5 freezedrier (fig.3.5).

The plastic bags containing the samples were put in a glass beaker covered by a filter to

prevent cross-contamination. The opening of the bag was made as large as possible,

meaning a larger surface of frozen sample is in contact with the air. This allows for a more

efficient drying process.

The freeze-drying process itself goes as follows: the chamber containing the samples is

placed under vacuum using an Edwards vacuum pump. Under low pressure and low ambient

Methods 23

temperature (about -40°C) the frozen porewater starts

to sublimate. The damp is then returned to ice in the

condensing chamber positioned underneath the first

chamber. This is to prevent the damp from entering the

vacuum pump and damaging it. Once enough time has

passed (usually set at three days), most of the

porewater has been removed from the sample, leaving

nothing but solid particles in the bag. Freeze-drying is

better than drying in the oven because it removes the

water without changing the physical structure of the

sediment much.

3.2.2.2. Rock Samples (RR)

Prior to analysis, the rock samples were pulverised using a Jawbreaker. By repeatedly

throwing the (broken) rock pieces between two massive iron plates, one moving and one

static, the size of the pieces is gradually reduced until most to all pieces fit through a five

millimetre sieve. As the Jawbreaker cannot handle pieces of rock larger than approx. 5cm,

these were broken into smaller pieces with a hammer first.

Further crushing was done with help of the Pulverisette (fig.3.6). This machine uses agate

balls in an agate cup to grind down rock particles. Each cup contains 5 agate balls and a part

of the finer fraction of the grinded rock samples. The machine then rotates the cup with a

velocity of 350RPM for six minutes: 2 minutes clockwise, 2 minutes counterclockwise and

once again 2 minutes clockwise. This process was repeated until the contents are pulverised

(fig.3.7).

Fig.3.5 River Sediment samples drying in the freeze dryer

24 Methods

Fig.3.6. The pulverisette Fig.3.7. The starting size (in agate cup) and end result of pulverising

in the Pulverisette

3.2.2.3. Filters (LW and WS)

Filters required little preparation. They will only be used for C/N isotope analysis (see 3.2.6).

But as they are too large to put in the tin cups in their entirety, the rim which does not

contain any particles was cut off. This reduces the size from 4.5cm to approx. 3.7cm.

3.2.3. Sieving

River sediments and soil sediments were separated in different grainsize fractions through

sieving. This was done to separate coarser and finer fractions as analyses will mainly be

conducted on the finer fractions, which is the material that effectively reaches the lake.

3.2.3.1. Determining the sieve-size

Before starting the sieving sequence, it was necessary to determine the grain size fraction

that is the most representative of the sediments deposited in the lake. Astrid Bartels (2012)

had opted for an 88µm sieve in her thesis for soil sediments. It needed to be verified if this

applies to the Parrillar samples as well. Gradistat, an excel application (Blott and Pye, 2001),

calculates sample statistics (mode, mean, Φ-values, grain size distribution, sorting, etc...) for

a given sieving sequence based on the aperture (µm) and the class weight retained (g or %).

It calculates these values in two ways: Arithmetic and Geometric. This was done for all

samples of the sediment core taken in Laguna Parrillar during an earlier campaign (Heirman,

2011). The variation of the mean grain size down the sediment core as determined by

Katrien Heirman can be seen in fig.3.8.

Methods 25

Fig.3.8 Downcore variation of mean grainsize variation as determined in Heirman, (2011)

The average grainsize distribution of the core as determined by Gradistat can be seen below

(fig.3.9). It is clear that in the end, a 3.5φ (~90mm) sieve is a nice choice as it removes most

of the course grained fraction, yet allows for a decent amount of the finer fraction to be

retained. This is important because it is the finer fraction that will be analysed as this is what

actually reaches the lake.

Fig.3.9 The average grainsize distribution of the core as determined by Gradistat. Also included is the grainsize

distribution of the soil samples determined by Bartels (2012). This shows that 88µm is a good aperture size to

separate the finest fractions from the rest.

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

0.5

,

24

,

48

,

72

,

92

,

10

4.2

,

11

6.2

,

12

8.2

,

14

0.2

,

15

2.2

,

16

4.2

,

17

6.2

,

18

8.2

,

21

2.3

,

25

2.3

,

28

6.3

,

33

2,

37

2,

40

6.8

,

45

2.6

,

49

2.6

,

52

6.6

,

57

2.6

,

61

2.6

,

64

6.6

,

69

2.6

,

Me

an G

rain

size

(µ

m)

Arithmetic

Geometric

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

0.001 0.01 0.1 1 10 100 1000 10000

Cla

ss W

eigh

t (%

)

Particle Diameter (µm)

GRAINSIZE DISTRIBUTION

Heirman2011

Bartels2012

88 µm

26 Methods

3.2.3.2. Sieving

All Soil Samples (SS) and River Sediment Samples (RS)

were sieved at 90µm to discard the coarser particles that

are not representative of the fraction that reaches the

lake. Only the < 90µm fraction was used for analysis.

Prior sieving of the samples at 1mm ensures the removal

of coarse elements (plant debris and pebbles) and

facilitates the sieving step at 90µm.

In addition, two selected river sediment samples were

used to see whether measured characteristics (see

sections 3.2.4 to 3.2.7) differ between grainsize fractions.

These samples were sieved at 1mm, 710µm, 500µm,

355µm, 250µm, 180µm, 125µm, 90µm, 63µm, 45µm and

32µm, in addition to 1mm and 90µm as mentioned

above. To differentiate from the latter from the former

they were named ZRS.

Sieving was done by shaking the Retsch Vibratory Sieve Shaker AS 300 (largest aperture at

the top, lowest at the bottom, fig.3.10) for ten minutes with an amplitude of 1mm/”g”. Then

the fraction retained in each sieve was collected in labelled plastic vials for storage and/or

future use.

3.2.3.3. Atterberg Column

To separate fractions finer that 32µm another method was in order as no sieves exist for

these grainsizes. An adequate alternative is the Atterberg-Stokes column (fig.3.11, Loring

and Rantala (1992)). This method relies on the formula of Stokes (1) to calculate the time it

takes for a certain grainsize to sink to the bottom of a (glass) column of a given height filled

with water. All data necessary for the calculations were found within methods in

sedimentary petrology (Müller, 1995).

Fig.3.10 Sieve Shaker

V = Particle Velocity (cm/s²) g = gravity acceleration = 981 (cm/s²) D1= density of falling sphere (g/cm³) D2 = density of liquid or gas (g/cm³) η = viscosity of liquid g/(cm s) r = radius of sphere (cm)

Methods 27

As the viscosity of water (η) is temperature dependent, the settling times were calculated for

the most common room temperatures. Depending on the temperature in the lab, the

correct time was taken.

Approximately 10g of the sediment saturated with

deionised water was poured into the glass column

before filling it to the marker line. Bubbles were

removed from the water as much as possible

because these tend to catch some of the settling

grains, giving rise to errors.

Conform to the law of Stokes larger grains sink faster to the bottom than smaller ones. Once

the calculated time had passed, the water above the bottom marker line was siphoned,

taking the finer fraction with it and leaving the coarser fraction in the glass column.

However, finer grains that started near the bottom of the column, would already have

settled before the indicated time passed. Therefore the process needed to be repeated

several times until the water above the marker line was clear when the calculated time is

over. This process is the same for each grainsize to be analysed. For convenience, separation

was made first at 8µm and then 16µm.

It was important that the samples were dry before moving on to the next steps in the

analysis. Drying was done by placing the samples in the oven at 50-60°C (any temperature

higher would start to alter the organic composition of the samples) after centrifuging to

remove the majority of water.

3.2.4. Magnetic Susceptibility

3.2.4.1. Soil Samples, River Samples and Rock Samples

Magnetic susceptibility (MS) was measured to determine the sensitivity of material to the

surrounding magnetic field. It is often used as a way to determine the presence of iron-

bearing minerals, e.g. in volcanic material. MS is also used as a means of classifying soils:

hydric soils, characterised by anaerobic conditions, show deterioration of both detrital

magnetite and soil-formed ferrimagnetics and hence lower MS values (Grimley et al., 2004).

Fig.3.11. The Atterberg-Stokes column. Coarser grains are collected at the bottom, whereas finer grains are collected in other containers.

28 Methods

The magnetic susceptibility (χ – chi) was measured with a Bartington MS2G sensor (fig.3.12).

A 1cc plastic tube was filled with sample to the marker line at a height of 2.5 cm. When the

capsule isn’t entirely filled a correction factor needs to be applied, as less sample means the

measured value is lower than what it would be for a full tube. The value of that factor is

automatically applied by the program itself based on the height of the sediment fill. Before

and after every sample measurement, a blank measurement is done. This is to remove the

effects of the plastic container and environment, which might cause errors. All samples are

measured several times (minimum twice)

until the error between serial

measurements is less than two percent.

Then the average of all measurements is

taken as the final result.

3.2.5. Loss On Ignition (LOI)

The next part is to determine the chemical composition. To get an estimate of the water and

carbon content, both organic and inorganic, of the sediment, the LOI method was applied

(Heiri et al., 2001). This implies combusting the sample at different temperatures to reveal

its contents. First and foremost some sample is taken apart for the LOI. This is about 0.5

grams for the soil and river samples, and about 1 gram for the rock samples. This is because

rock samples generally contain less water and carbon. More material means a more reliable

result. SS05 is not included as it did not contain enough material. Then these samples were

put into porcelain crucibles and placed in the oven at 105°C overnight. The difference in

weight is an indication for the water content. Next step was to repeat the process at 550°C

for 4 hours and subsequently at 950°C for 2 hours. The first step oxidises organic carbon to

carbon dioxide, whereas the second step affects the inorganic carbon in the form of

carbonate (Heiri et al., 2001). By weighing each sample after each step and subtracting the

final weight from the initial weight, one can estimate the water content, inorganic matter

and inorganic carbon content.

Fig.3.12. Measuring of MS: the sensor containing

the plastic capsule in front is connected to the

computer in the back which manages the

measurements.

Methods 29

3.2.6. Carbon (13C) and Nitrogen (15N) elemental and isotopic analysis

Further analysis concerns the determination of carbon and nitrogen concentrations and

stable isotopes. The C/N ratio is used, in addition to δ13C analysis to distinguish between

sources of organic matter. Aquatic and land sources have distinctively different C/N ratio

compositions related to algae and vascular plants respectively: algae typically have C/N

ratios of 4 to 10, whereas vascular plants have C/N ratios ranging between 30-40 (fig.3.13;

Meyers, 1994; Meyers and Teranes, 2001).

The preparation of the samples starts with weighing an amount of sample that depends on

its estimated carbon content. The exact amount necessary was calculated based on the

results from the LOI at 550°C. Based on the percentage of organic matter, the sediment

needed for 0.8-1g of carbon was calculated. This amount is then taken from the <90µm

fraction from the soil (SS), rock (RR) and river sediment (ZRS) samples. For the river sediment

(RS) samples sieved in different grain-size fractions, for which LOI was not measured, the

amount was estimated in a different way. Weights for the fractions between 500µm and

32µm were estimated at 120-130% of the values calculated for the ZRS. As there is no LOI

data available for the finer (<32 µm) and coarser (>500µm), the sediment taken was based

on the principle that these fractions generally contain more organic matter. As more organic

matter means less material necessary for the same carbon content, only 70-80% (or 50% for

very organic rich material) was taken for the coarser fractions, 80% for 32-16µm, 65% for 16-

8µm and 50% for <8µm fraction.

Fig.3.13 Representative elemental and isotopic composition of Lacustrine Algae, C3 and C4 Land Plants (Meyers and Teranes, 2001)

30 Methods

The amount of sediment was then put into small silver cups, weighed, and treated with 50µl

deionised water and 50µl 5% HCl. The acidification removes inorganic carbon without

significantly altering the composition of the samples (Jaschinski et al., 2008).

The dried cups are folded to become smaller than 8x8 mm (size limitation defined by the

analysing equipment). During the entire procedure the silver cups cannot be touched to

avoid contamination. Weighing, filling and subsequent folding is therefore done by using

tweezers. Filters were treated the same way. As the larger cups are made of tin, acidification

wasn’t possible. This would not be a problem as the filters were expected to not contain any

carbonate.

Analysis was done at the UCDavis Stable Isotope Facility (SIF) located in California, using an

elemental analyser interfaced to a continuous flow isotope ratio mass spectrometer (EA-

IRMS). The procedure entails combustion of the samples at 1000°C in a reactor filled with

copper oxide and lead chromate. After combustion, oxides are removed in a reduction

reactor (reduced copper at 650°C). The helium carrier then flows through a water trap

(Magnesium perchlorate). N2 and CO2 are separated using a molecular sieve adsorption trap

before entering the IRMS (fig.3.14). The isotope ratio is measured relative to reference gases

after which it is corrected based on the known values of included laboratory standards. The

standard used was standard G-18 Nylon 5, which has a δ13C of -27.72. The long term

standard deviation is 0.2‰ for 13C, which is consistent with the -27.72±0.06 precision

reported. The final delta values are expressed relative to the international V-PDB (Vienna

PeeDee Belemnite) standard and air for carbon and nitrogen, respectively.

(Source: http://stableisotopefacility.ucdavis.edu/13cand15n.html)

Methods 31

Fig.3.14. Simple schematic diagram of an EA-IRMS for the determination of δ13

C and δ15

N.

(Carter and Barwick, 2011)

3.2.7. X-Ray Diffraction (XRD)

The mineral content of each sample was determined by XRD. To minimise peak broadening

(by large particle size) or scattering and/or expulsion from the sample (by small particle

sizes), the particle size of the RS-fractions larger than 90µm and rock samples was reduced

by grinding about 2 to 3 grams of sediment. Sample holders (fig.3.15) were then filled using

the “Backside” method, as recommended by Brown and Brindley (1980, p. 310). This method

comes down to filling the centre of a PVC ring positioned on the flat surface of a glass plate

with sediment. When the centre is filled, the ring is closed off with a black lid and turned

around. After removing the glass plate from the ring, the bare sediment surface should be

even and ready for analysis. This method offers a way to avoid orientation of the grains,

which might influence the diffraction results.

Actual XRD analysis was done at the University of Liège (ULg, Belgium) as this is where

Katrien Heirman analysed the sediment samples from Laguna Parrillar. Using the same

equipment, a Bruker D8-Advance diffractometer, in the same environment allows a direct

comparison of the results obtained on different types of samples.

The instrument uses CuKα radiations with a wavelength of 0.15418 nm. These are generated

due to an electron from the “L-shell” occupying a vacancy in the “K-shell” of a Cu-atom. The

difference in energy between the two states can be emitted as X-rays. These X-rays hit the

32 Methods

sample’s surface and scatter on the atoms of the crystals in the particles. Although most of

these scattered rays cancel each other out, some amplify each other. The angle at which this

happens is characteristic for each mineral. By rotating the detector from 2° to 45° 2θ, with θ

being the diffraction angle, most common minerals can be identified (table 1 in Cook et al.,

1975).

Fig.3.15 Samples ready for XRD analysis

The resulting diffractograms (appendix 2) were then interpreted using the Bruker EVA-

software. With this program, the mineral content was determined by comparing the position

of the peaks with the theoretical d-value of minerals commonly found in sediments. The

reference minerals can be found in the table 3.3.

By measuring the peak heights, the minerals could also be semi-quantified. It is necessary,

however, to multiply the maximum values by a correction factor (Cook et al., 1975), as not

every mineral generates a peak as high and well-defined as quartz, usually the highest peak

in the diffractogram (factor equals 1).

Mineral Range of D-spacings (Å) Correction Factor

Quartz 3.37-3.31 1.00

Plagioclase (Albite) 3.21-3.16 2.80

K-Feldspar (Orthoclase) 3.26-3.21 4.30

Pyroxene (Augite) 2.99-3.00 5.00

Amphibole (Hornblende) 8.59-8.27 2.50

Olivine (Forsterite) 2.45 5.00

Methods 33

Calcite 3.04-3.01 1.65

Aragonite 1.96-1.97 9.30

Total Clays 4.5 20

Amorphes* – 20

Table 3.3 Principle peaks of common minerals and their correction factors (after Cook et al., 1975)

*The amorphous material is determined by applying an additional background correction after the initial

background and noise correction. The maximum height of this secondary background correction was taken as

the quantity of the amorphous material.

34 Results

4. Results

4.1. GIS of Parrillar’s Watershed

As mentioned in section 3.1, the perimeter and area of the lake and its watershed (table 4.1)

were calculated using Global Mapper.

Perimeter Area

Watershed 57.173 km 77.627 km²

Lake 13.811 km 9.727 km²

Table 4.1 Perimeter and area of the lake and its watershed as calculated by Global Mapper

The following figures (fig.4.1-fig.4.5) depict some of the results of the work done in Global

Mapper: bathymetric modelling, georeferencing and digitising.

Fig.4.1 shows that the bathymetric model of the lake generated in Surfer is treated by Global

Mapper as part of the SRTM data in the background. One can see that the transect through

the lake shows the lake’s subsurface relief instead of the flat surface of the SRTM data. The

bathymetric model also aided in calculating several morphometric features (table 4.2).

Average Water Depth ( ) 4.64 m

Maximum water depth(zmax) 24.09 m

Depth Ratio (zmax/ ) 0.1925

Watershed / Lake area 7.98

Lake area / Watershed 0.1253

Volume ( xALake) 0.045 km³

Maximum Length 3.924 km

Maximum Width 3.719 km

Table 4.2 Lake morphometrics based on the bathymetry model

Results 35

Fig.4.1. Profile through the bathymetrical model as seen in Global Mapper

Global Mapper also allows creating a 3D view of the loaded data. Fig.4.2 shows such a 3D

view (in Flight Mode) with a vertical exaggeration of 7. The figure below it, a similar 3D view

is shown with water level set at 292m a.s.l., which represents the actual water level. .

Fig.4.2. 3D view on the lake (left) with water level at 292m (right).

Fig.4.3 shows the digitised watershed of Parrillar with the topographic map and terrain

model as background. It was this setting that was used to draw the watershed’s features as

the contour lines can then be easily combined with the terrain model to find the highest

points. The geological map (Fig.4.4) shows that Parrillar’s entire watershed is located in the

geological unit denoted as Ks1mp, which corresponds to paralic and marine sedimentary

sequences of the Upper Cretaceous (Campanian to Maestrichtian) consisting of arenites and

siltstones. In the region of Laguna Parrillar these are the formations of Fuentes, Rocallosa,

Tres Pasos, Cerro Cuchilla and Dorotea (SERNAGEOMIN, 2002). The first two correspond to

the formations mentioned by Dollenz (1983) and Otero et al. (2012).

36 Results

Fig.4.3. View on the watershed of the lake after digitising as seen in Global Mapper with topographic map as

background layer. Red = certain boundary of watershed, pink: uncertain boundary of watershed, light blue:

river inflow, purple: outflowing river, dark blue: tributaries of outflowing river.

Fig.4.4. idem previous figure with but with geological map as background layer

Fig.4.5. shows a Landsat7 image of the watershed, which depicts the vegetation of the area.

It shows that most of the area is covered by forests (green). Major peat bogs (purple brown)

can be found over the entire length of the value of the western river, whereas these are

limited to where the northern valley opens up towards the lake. There is also major

concentration of peat bogs on the southern and south-eastern shores of the lake. Uncovered

bedrock (magenta) can be seen on several places above elevations of 600 m.a.s.l. Sample

locations are also added.

Results 37

Fig.4.5. Location of the samples with Pseudo-coloured LandSat7 image as background (green: forest, purple

brown: peat bogs, magenta: bare rock)

Fig.4.6. Detail of sample locations, red and black inserted frames show details

38 Results

4.2. Sediment Analysis

4.2.1. Sieving

As sieving is not important for the interpretation and discussion, the results of the sieving

analysis will not be elaborated upon. The results can be found in Appendix 3.

4.2.2. LOI

Fig.4.7.A-C show the results of the LOI analysis. Soils have the highest organic matter (OM)

content (24.19±13.75%) and inorganic carbonate (IC=LOI900x1.3; Heiri et al., 2001) content

(2.033±0.94%) compared to the river and rock samples. Rivers have about half of the OM-

content of the soil samples (12.20±3.50%), rocks about one eighth (3.14±0.56%). Not

including the IC-LOI900 peak for PA11-RR02, rocks have a little more inorganic carbon than

rivers (1.93±0.48% compared to 1.82±0.94%).

Of the soil samples, PA11-SS03 contains the most OM (50.049%) and PA11-SS02 the least

(7.686%). Inorganic carbon is more distributed, with PA11-SS04 having the most IC (3.012%)

and PA11-SS03 the least (0.812%). No LOI data is available for PA11-SS05 as there was too

little material to perform all analyses. River samples have less variety in their organic matter

content, which never exceeds 20% (average 12.02±3.50%) as opposed to the soil samples

which are much richer in OM (see above). Their IC content is also fairly evenly distributed,

around 1.82±0.17%. PA11-RS04 has the highest OM of the measured river samples (18.11%).

Whereas the five rock samples have comparable amounts of OM, PA11-RR02 has a

distinctively different amount of IC compared to the other four (43.71% compared to an

average of 1.93±0.48% without PA11-RR02).

4.2.3. Magnetic Susceptibility

Fig.4.8 shows the mass-specific magnetic susceptibility (MSMS) of the measured samples.

Globally, soils have the highest average MSMS (448.38·10-6±1,525.86·10-6 SI/kg) and rocks

the lowest (166.22·10-6±167.41·10-6 SI/kg). Soil samples also show the greatest variability in

MSMS values (Fig.4.8.C). The river samples do not show these larger variations in MSMS

values, as can be seen in fig.4.8.A. PA11-RS03 has the highest value whereas PA11-RS02 and

PA11-RS04 the lowest. Fig.4.8.B indicates the MSMS measurements for rock samples. PA11-

Results 39

RR01, PA11-RR02 and PA11-RR03 show similar MSMS values, whereas PA11-RR04 shows the

highest value.

As can be seen in fig.4.8.C, it is clear that PA11-SS01 and PA11-SS07 show considerable

higher MSMS values than other samples of the same kind. PA11-SS05 shows the least

response for the MS measurements. Fig.4.8.D depicts the magnetic response of the different

grainsize fractions of PA11-RS02. It shows a gradual rise and decline from finer fractions to

coarser ones, with a maximum for grainsize fraction 63-90µm. Fractions 8-16µm and 180-

>1000 µm all have an MSMS between 100·10-6 SI/kg and 200·10-6 SI/kg. The average MSMS

of RS02 is 250.51·10-6 SI/kg. Fig.4.8.E depicts the same as fig.4.8.D, but for PA11-RS05. One

can see that the general trend for both river samples is similar, with one fraction, 16-32µm,

having much higher values in the PA11-RS05 sample. PA11-RS05 shows a higher response

than PA11-RS02, with the average being 342.73·10-6 SI/kg (275.53·10-6 SI/kg without the

outlier of the 16-32µm fraction).

40 Results

Fig.4.7. LOI (550 – red, 900 – blue) results of A) River Samples with a grainsize fraction <90µm, B) Rock Samples,

C) Soil samples <90µm (WR = Western River, NR = Northern River)

Fig 4.8 Mass-specific Magnetic Susceptibility of A) River Samples with a grainsize fraction <90µm, B) Rock Samples, C) Soil samples <90µm, D)Different grainsize fractions of PA11-

RS02, E) Different grainsize fractions of PA11-RS05 (WR = Western River, NR = Northern River)

42 Results

4.2.4. XRD

Fig.4.9 depicts the results from the XRD analysis of each sample. The three graphs (fig.4.9.A-

C) show that the kinds of minerals which can be found in river, soil and rock samples are

similar. Quartz, plagioclase and clays are present in every sample, as well as amorphous

matter. Rock samples contain the least amount of total clays (22.96±10.02%, without PA11-

RR02), as opposed to the soil samples which contain the most (38.50±6.04% without PA11-

SS05). The same can be said about the amorphous material (respectively 7.96±3.26% versus

13.87±4.96). Plagioclase is most abundant in rock samples (27.73±11.19%) and least in soils

(13.42±4.47%) and rivers (15.98±1.37%). Quartz is more or less evenly distributed over the

three kinds of samples, though it is less present in soils (29.84±4.80%) than in rivers

(36.22±5.82%) and rocks (37.07±3.99%). K-feldspar is the mineral which is the least present

of the main five, with a maximum of 6.75±4.12% in the rivers (though it is barely present in

PA11-RS02 (0.22±0.80%)).

River samples of both rivers show a similar mineral content, although PA11-RS04 has no K-

feldspar (fig.4.9.A). One thing that can be noted immediately from the result of the rocks

(fig.4.9.B) is that PA11-RR02 has a completely different composition than the other four rock

samples: limited quartz and plagioclase, but a large amount of calcite and pyroxene.

Fig.4.9.C shows that the soil samples have a varying composition, but most soil samples

seem to vary little in quartz and plagioclase content (average without PA11-SS05 is

29.84±4.79% and 13.42±4.47% respectively). The diffractogram of PA11-SS05 (fig.4.9.C-

Appendix 2, fig.A.5) shows little to no response of the common minerals. There is however a

large peak (absolute peak height is 158) at d=3.69 and a minor one at d=2.6 (absolute peak

height 24.2). Based on these d values, the minerals that might correspond to these peaks

were identified as possibly being a combination of an aluminium hydroxide (e.g. Diaspore)

and a Magnesium(-Iron) hydroxycarbonate (e.g. Nesquehonite, Fougerite, etc....).

Similar peaks can be seen in PA11-SS08 (d=3.7 and d=2.54) which could correspond to an

iron-bearing hydroxide mineral. Possible candidates are Melanterite, Berthierine (a mineral

common in polar soils (http://webmineral.com/data/Berthierine.shtml; Kodama and

Foscolos, 1981)) or Ferrihydrite.

As mentioned earlier, PA11-RS02 has barely any K-feldspar in it except for grainsize fraction

125µm-90µm (2.90% - fig.4.9.D). On average, PA11-RS02 contains more amorphous material

(9.03±2.72%) and quartz (37.77±5.82%) than PA11-RS05 (8.59±2.21% and 32.44±5.07%),

whereas PA11-RS05 contains more K-feldspar (3.89±4.08%) and total clays (31.64±7.51%).

Results 43

The amount of plagioclase doesn’t differ that much (23.31±8.85% for PA11-RS02 and

23.44±11.94% for PA11-RS05), but its distribution is not consistent with grainsize for both

rivers (fig.4.10.A-B). Finest fractions in PA11-RS02 and PA11-RS05 contain about 10%

plagioclase whereas the intermediate fractions contain more plagioclase. Fig.4.10.A and

Fig.4.10.B show that the plagioclase is more evenly distributed in the intermediate to

coarser fractions in PA11-RS02 than it is for PA11-RS05.

44 Results

Fig.4.9 Mineral content as defined by XRD of A) River Samples with a grainsize <90µm, B) Rock Samples, C) Soil

samples <90µm. Legend of A) applies to B) and C) as well. * indicates that other minerals are present.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

PA11-RS05

PA11-RS04

PA11-RS03

PA11-RS02

PA11-RS01

A - River Samples <90µm

Amorphes Quartz Plagioclase K-Feldspar Pyroxene

Amphibole Olivine Calcite Aragonite Total Clays

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

PA11-RR05

PA11-RR04

PA11-RR03

PA11-RR02

PA11-RR01

B - Rock Samples

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

PA11-SS10

PA11-SS09

*PA11-SS08

PA11-SS07

PA11-SS06

*PA11-SS05

PA11-SS04

PA11-SS03

PA11-SS02

PA11-SS01

C - Soil Samples <90µm

Results 45

Fig.4.10 Mineral content as defined by XRD of D) Different grainsize fractions of PA11-RS02 (WR), E) Different

grainsize fractions of PA11-RS05 (NR). Legend of D) applies to E) as well.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

<8µm

8-16µm

16-32µm

32-45µm

45-63µm

63-90µm

90-125µm

125-180µm

180-250µm

250-355µm

355-500µm

500-710µm

710-1000µm

A - PA11-RS02

Amorphes Quartz Plagioclase K-Feldspar Pyroxene

Amphibole Olivine Calcite Aragonite Total Clays

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

<8µm

8-16µm

16-32µm

32-45µm

45-63µm

63-90µm

90-125µm

125-180µm

180-250µm

250-355µm

355-500µm

500-710µm

710-1000µm

>1mm

B - PA11-RS05

46 Results

4.2.5. Total Organic Carbon

The variation in total organic carbon (TOC) is shown in fig.4.11 for the <90µm grainsize

fraction of the river and soil samples and of the rocks. Soils have an average of 10.26±7.63%

TOC, which is much more than the river samples (4.60±1.57%) and rocks (0.42±0.17%). Of

the river samples (fig.4.11.A) PA11-RS03 has the least TOC (2.92%) and PA11-RS04 the most

(7.11%). PA11-RS02 and PA11-RS04 show a similar TOC content with respectively 4.70% and

4.51%. The TOC of the rock samples is depicted in fig.4.11.B. PA11-RR03 has the most TOC

(0.64%). PA11-RR04 and PA11-RR05 have equal TOC with 0.28%. There are no data available

for PA11-RR02 as the carbon content could not be measured. Soil samples show large

variations in TOC content, which fluctuates between 2.12% for PA11-SS02 and 26.39% for

PA11-SS03.

Variation of the TOC with grainsize for the two river samples PA11-RS02 and PA11-RS05 can

be seen in fig.4.12.A and fig.4.12.B. PA11-RS02 shows a general increase from 1.41% in the

1000-710µm fraction to 9.43% in the finest fraction (so a decrease with grain-size). This

trend is interrupted with fraction 32-16µm showing a sudden decrease to 1.10%. PA11-RS05

shows a higher TOC content than PA11-RS02 for the coarser fractions. It isn’t until the 180-

125µm fraction that PA11-RS05 shows the same rising trend as PA11-RS02, including the

decrease in TOC for the 32-16µm fraction (to 1.47%).

4.2.6. C/N Ratios

Fig.4.13 shows the different C/N ratios for the river samples <90µm, the rocks and soil

samples <90µm. Rivers and soils have the highest C/N ratios, with respectively 17.89±0.71

and 17.68±4.62, whereas the rocks only have a ratio of 9.55±2.06. River samples (fig.4.13.A)

vary between 16.88 for PA11-RS01 and 18.65 for PA11-RS04. Of the rock samples, as shown

in fig.4.13.B, PA11-RR05 has the highest C/N ratio (11.72) and PA11-RR01 the lowest (7.61).

Soil samples show a varying C/N ratio, which ranges from 12.25 to 26.48 (fig.4.13.C). The

highest ratio belongs to PA11-SS03 and the lowest can be found in PA11-SS09.

Fig.4.14 shows the variation of the C/N ratio with grainsize fraction for the two river samples

that underwent the full sieving sequence. PA11-RS02 shows a rise in C/N ratio from the

coarser fractions to the 180-125µm fraction, from where the C/N ratio more or less stabilises

around 18 for the finer fractions. PA11-RS05 shows higher C/N ratios for the coarser

Results 47

fractions than PA11-RS02 does. Although the coarsest fractions (>1000µm-500µm) are

absent in the latter, this is evident in the 500µm-180µm fractions and the average C/N ratio

of both samples (16.69±2.27 for PA11-RS02 and 20.97±4.98 (18.67±2.21% without fractions

>1000µm-500) for PA11-RS05). As in PA11-RS02 the C/N ratio stabilises around 17-18 for the

fractions finer than 180µm.

48 Results

Fig.4.11 Total organic carbon in A) River sediments samples of the <90µm grainsize fraction, B) Rock samples and C) soil samples of the <90µm grainsize fraction. (WR = Western River, NR = Northern River)

Results 49

Fig.4.12 Variation of TOC with grainsize in river samples A) PA11-RS02 and B) PA11-RS05

50 Results

Fig.4.13 C/N ratios of A) river sediments samples of the <90µm grainsize fraction, B) rock samples and C) soil

samples of the <90µm grainsize fraction.

Results 51

Fig.4.14. The variation of the C/N ratio with grainsize fraction in river samples A) PA11-RS02 and B) PA11-RS05.

52 Results

4.2.7. 13C Istopes

The variation in δ13C for the river, rock and soil samples can be seen in fig.4.15. Rivers have

the highest average δ13C (-29.18±0.41‰). Rocks and soils have comparable δ13C values with

respectively -27.32±0.97‰ and -27.33±0.54‰.

Of the river samples <90µm PA11-RS04 has the highest δ13C (-28.81±0.06‰) and PA11-RS04

the lowest (-29.86±0.06‰). PA11-RS01 and PA11-RS03 have comparable δ13C values, namely

-28.96±0.06‰ and -28.99±0.06‰. The rocks samples show a variety in their δ13C going from

-25.97±0.06‰ for PA11-RR03 to -28.18±0.06‰ for PA11-RR04. The grainsize fraction <90µm

of the soil samples shows no particular value applicable to all soil samples. Instead δ13C

varies between -24.48±0.06‰ for PA11-SS04 and -28.15±0.06‰ for PA11-SS03. PA11-SS01,

PA11-SS07 PA11-SS09, however show similar δ13C values between -27.26±0.06‰ and -

27.08±0.06‰. This can also be said for PA11-SS02 and PA11-SS08 (-27.89±0.06‰ and -

27.76±0.06‰).

As can be seen in fig.4.16.A, the δ13C of PA11-RS02 shows a slight decline from the coarser

fractions to the finest fraction: from -28.75±0.06‰ in the 1mm-710µm fraction to -

29.26±0.06‰ for the <8µm fraction. This declining trend is interrupted in the 500-355µm

and 32-16µm fractions, which both show an increase to around -28.56‰ (respectively -

28.58±0.06‰ and -28.55±0.06‰). Fig.4.16.B makes clear that PA11-RS05 shows this same

decreasing trend with a slight increase in the 32-16µm fraction to -28.19±0.06‰. Despite

this general similar trend, the values are slightly more positive for PA11-RS05 (-

28.59±0.56‰) than they are for PA11-RS02 (-28.95±0.23‰). The biggest difference,

however, is the δ13C value of 1mm-710µm which is much more negative than any of the

other values measured (-30.05±0.06‰).

Results 53

Fig.4.15 δ13

C values of A) River Samples <90µm, B) Rock Samples and C) soil samples <90µm. Error bars have

the same size as the data points (WR = Western River, NR = Northern River)

54 Results

Fig.4.16 Variation of δ13

C values with grainsize fraction for A) PA11-RS02, B) PA11-RS05 <90µm. Error bars have

the same size as the data points

Results 55

4.2.8. Filters

The results of the carbon-nitrogen analyses can be seen in fig.4.17, where lake and river

filters are positioned next to each other. The first thing to be noted is that the

measurements of the lake filters show less variation than those of the rivers, regardless of

which measurement. The organic carbon concentration (Fig.4.17.A) of the lake averages at

523.24±17.34µg/L and that of the rivers at 451.64±170.08µg/L. PA11-LW03 has the highest

carbon concentration with 541.77µg/L of the lake filters and PA11-LW02 the lowest with

503.27µg/L. PA11-LW01 and PA11-LW04 have a carbon concentration of respectively

515.05µg/L and 532.89µg/L. PA11-WS02 resembles the lake filters the most with a carbon

concentration of 508.23µg/L. PA11-WS01 has the highest carbon concentration of

642.17µg/L. PA11-WS03 has less than half of that with 236.61µg/L. PA11-WS02 is

somewhere in between with 508.28µg/L.

The variation in atomic C/N ratios can be seen in fig.4.17.B. Lake C/N ratios average around

11.11±0.33. The highest ratio can be found in PA11-LW02 (11.45) and the lowest in PA11-

LW04 (10.62). PA11-LW01’s C/N ratio of 11.35 is comparable to that of PA11-LW02. The C/N

ratio of PA11-LW03 (11.00) is closer to the one of PA11-LW04. The C/N of the particles

suspended in the river water once again shows more variation than what’s found in the

filters of the lake. With an average of 12.36±4.49 the C/N ratio of the lake is slightly higher

than that of the rivers. As was the case with the TOC, this average is the result of the sum

that includes a low value (attributed to PA11-LW01’s C/N ratio of 7.96) and higher values of

which PA11-LW04’s C/N ratio (14.79) is the highest. PA11-LW02 and PA11-LW03 both have

C/N ratios between 13 and 14, 13.09 and 13.60 respectively.

The δ13C values are shown in fig.4.17.C. These average at -28.17±0.11‰ for the lake. PA11-

LW04 has the least negative δ13C value (-28.02±0.042‰). PA11-LW02 and PA11-LW03 have

comparable δ13C values with -28.25±0.042‰ and -28.27±0.042‰ respectively. PA11-LW01

has a δ13C value of -28.13‰. Because of the measurements of PA11-WS03 (-29.48‰) and

PA11-WS04 (-29.16‰) the average δ13C value of the rivers is more negative than that of the

lake: -28.73±0.71‰. PA11-WS01 and PA11-WS02 are comparable to the readings of the

lake: -28.31‰ and -27.96‰.

56 Results

Fig.4.17. A – Particulate organic carbon, B – C/N and C - δ13

C of Lake (blue) and River (red) water filters

Results 57

4.2.9. C/N vs δ13C

As C/N combined with δ13C gives a good impression of the source of the organic material

present in each sample, the two were plotted against each other. As can be seen in fig.4.18,

samples plot between -25‰ and -30‰ for δ13C. Of the three different kinds of samples,

river samples seem to be the most consistent in their values. They appear grouped together

between C/N ratios of 15 and 20 and between δ13C values of -28‰ and -30‰. Soil samples

show a much larger range of C/N values, with the majority situated in the same range as the

river samples. Rock samples plot at a lower C/N range of 7 to 12. Lake water filters plot

grouped together around -28‰ for their δ13C and between 10 and 13 for their C/N ratios.

River water filters plot around the same δ13C value, but show a larger variety in their C/N,

with one sample having a C/N ratio lower than 10.

Fig.4.18 C/N ratio of the different samples versus the δ13

C values of soil, river and rock samples, as well as the

filters. Values for Freshwater Algae and C3 Landplants after Meyers and Teranes (2001).

58 Discussion

5. Discussion

5.1. Sources of inorganic particles

5.1.1. Rocks

Rocks form the most important source of material available for erosion within the

watershed. Most of the rock samples available for this study originate from the north shore

of Laguna Parrillar, as this is one of the few accessible places where the bedrock is exposed.

With the exception of sample PA11-RR04, which was collected in the northern valley

(fig.4.5), there is no knowledge of other rock outcrops within the lower reaches of the

watershed. Bare rocks can be found on the higher peaks surrounding the lake, but their

access is relatively complicated (fig.4.5).

The majority of the rock samples contain quartz, plagioclase and K-feldspar, as well as clays

and amorphous matter (fig.4.9). This is a composition typical of most siliciclastic rocks

(Boggs, 2006). This is consistent with the geological map of the area which states the

presence of arenites and siltstones (SERNAGEOMIN, 2002) as well as the lithological

descriptions made by Dollenz (1983; section 2.2.3). Mineralogically, PA11-RR02 differs quite

a lot from the other rock samples. It was described as a white nodule in metamorphic rocks.

It contains a large amount of calcite (44.93%) and pyroxene (23.44%) (fig.4.9) and LOI-900

values are high (32.14%). These characteristics match the limestone concretions described

by Dollenz (1983).

The rock samples show no significant difference in their particulate inorganic carbon (PIC)

content, except for the carbonate nodule (PA11-RR02; fig.4.7.B). The latter has a

considerable amount of inorganic carbon, due to its mineralogy (fig.4.9.B).

Although the magnetic susceptibility measurements of the rock samples found near the

shore of the lake show some variation, they are relatively similar to each other when

compared to PA11-RR04 (fig.4.8), which was found within the valley of the northern river.

The latter one has a considerably higher magnetic susceptibility response than the lake

rocks, in particular PA11-RR01 and PA11-RR03. As the XRD results (fig.4.9) show no

significant differences, it must be that the composition of PA11-RR04 must differ from the

other rock samples in the minerals not identified with XRD.

Discussion 59

5.1.2. Soils

Because of their unconsolidated nature, soils represent one of the most readily available

sources of sediment to Laguna Parrillar. The ten soil samples analysed in this study show a

much larger diversity in most of the measured variables than the rocks. This is to be

expected as they come from different locations within the watershed of the lake. At first

glance, the XRD results of the soil samples (fig.4.9.C) look comparable to those of the rock

samples (fig.4.9.B). The 25-40% quartz, 10-20% plagioclase and occasional 10% plagioclase

are indeed in the same range as the rocks. This suggests that at least these minerals are

derived from the bedrock. The combination of these minerals is often found in podzols

(which are present in the area, section 2.2.3). The 6-25% amorphous material and 30-50%

total clays means the soils are enriched in these components compared to the rocks. This

can be explained through clay (neo)formation and incorporation of degraded organic

material (litter from the forests and peat bogs). The diversity of these samples is most

apparent in the magnetic susceptibility measurements (fig.4.8.C). It appears that the

samples taken in the vicinity of the lake (PA11-SS03, PA11-SS04, PA11-SS05, PA11-SS06;

fig.4.5) have the lowest MSMS. This is especially true for PA11-SS05 and PA11-SS03. In case

of PA11-SS05 this low susceptibility seems to be related to its high organic carbon content,

which is very weak or negative in magnetic susceptibility (Dearing, 1999). This sample was

indeed described on the field as a peaty soil sample, representative of the soils that occur

along the south-southwestern shore of the lake (fig.4.5). This is also reflected in the XRD

data, which show a near-100% amorphous material response (fig.4.9.C). Besides containing

plenty of organic material peat bog environments tend to be very acidic, often with a pH<4

according to Holden (2005). Such acid environments promote the dissolution of Fe-bearing

minerals (Williams, 1992) and limit the rate of ferromagnetic mineral generation (Maher,

1998). This is also reflected in the XRD data (fig.4.9.C), which show the presence of

unidentified minerals. As these are not present in other samples, it is likely that these are

newly formed minerals. Although a correct semi-quantification of these minerals is not

possible without the correction factor, the peak height indicates that they make up a

considerable part of the sample’s mineralogical composition. The reason mainly aluminium-

bearing minerals were suggested is that aluminium is amongst the least mobile major

elements due to low solubility of Al2O3 between pH 5 and 8 (McQueen, 2009). This implies

that minerals based on aluminium oxides will be amongst the last ones to disintegrate in

acid environments.

60 Discussion

The other soil samples in the vicinity of the lake (PA11-SS03, PA11-SS04 and PA11-SS06)

show MSMS values comparable to those of the rock samples. This is especially the case for

PA11-SS04 and PA11-RR03 which differ a mere 8% in their MSMS (fig.4.8). As there

mineralogical compositions are also comparable, it is plausible to assume that the soils are

formed through erosion of the rock and still show some traces of their origin. The high

magnetic susceptibility of PA11-SS01 and PA11-SS07 is most likely due to a source rock with

a MSMS within the same range. This source rock, however, is unknown as there are no

known outcrops in the vicinity of these samples (see earlier).

5.1.3. River Sediment

Unlike the soil sediments, river sediments show less variation in the listed variables. For

starters, the XRD results show a pretty consistent mineralogical composition over the five

river samples. With 30-40% quartz, 14-18% plagioclase, 6-10% K-feldspar (PA11-RS04 not

included), the composition of the river sediments is closest to that of the soil sediments

(fig.4.9). This implies that the river sediment mostly originates from soil erosion. The limited

variation is also reflected in the inorganic carbon content (fig.4.7.A), which is comparable to

that of the soil samples themselves.

The magnetic susceptibility results show that the river sediment samples have comparable

MSMS, which is unlike the much more varying MSMS of the soil (fig.4.8). This indicates that

the high variety of soil MSMS is averaged out when the soil material ends up in the river.

5.2. Sources of sedimentary organic matter

Determinations of the amount and origin of organic matter in sedimentary records are an

important part of palaeolimnology. Organic matter in lake sediments in general originates

from sources within the water column and sources in the watershed, referred to as the

aquatic and terrestrial end-members respectively (Meyers and Teranes, 2001). These two

can be distinguished by the characteristic C/N atomic ratios of algae and vascular plants: 4 to

10 for phytoplankton opposed to vascular plants which usually have C/N ratios higher than

20 (see also fig.3.13, Meyers and Teranes, 2001).

Discussion 61

5.2.1. Terrestrial organic matter

5.2.1.1. Soils

Soils show a high variability dependent on their respective location. The high carbon content

of PA11-SS03 and PA11-SS06 (fig.4.7.C, fig.4.11.C) might be a remnant of organic matter

accumulation on the past lake terraces (Heirman, 2011). The intermediate results of PA11-

SS10 and PA11-SS08 could be linked to the peat bogs. The C/N ratios measured on the soil

samples average at 17.67±4.62. This is comparable to the 16-20 which Post et al. (1985)

suggested as common C/N ratios of soils within cold humid climate. The variety in soil

samples is also reflected in the C/N vs. δ13C plot (fig.4.18) which shows samples plotted

against the general δ13C and C/N of the aquatic and terrestrial end-members. It should be

noticed that the δ13C of the soils shows less variety than the C/N. Soils have an average δ13C

of -27.33±0.55‰, which is comparable to the value of soil organic matter of C3 plants

Kendall et al. (2001) mention in their text (-27‰).

5.2.1.2. River sediment

The average TOC of the river sediments of the western river is 3.79±0.89%, which is

comparable to the value of 3.60±2.10%, obtained on the soil samples for that part of the

watershed (fig.4.11). For the northern river the TOC is somewhat larger (5.81±1.84%), which

could be explained by the higher TOC of soils in that part of the watershed (9.85±4.77%). The

organic matter of the river is combination of terrestrial sources (soil organic matter and leaf

debris) and sources within the river (phytoplankton, algae and macrophytes). These sources

all contribute to the C/N and δ13C measured on the river system, but according to Kendall et

al. (2001) plankton has the largest contribution to river systems in general. The river samples

plot together on the C/N vs. δ13C plot (fig.4.18). This plot shows that the organic carbon of

the river sediment is derived from C3-land plants, but with a hint of freshwater plankton.

The average C/N ratio of the river samples (17.89±0.71) is not significantly different than

that of the soil samples (17.67±4.62), suggesting that the river sediments hold a considerable

amount of eroded soil material. In case of the δ13C values, the average of the river sediments

(-29.18±0.41‰) is more depleted than that of the soil sediments (-27.33±0.55‰). This

shows that the river sediment is not an exact replica of the soil sediment, but instead is more

of a mixture of soil sediments and plankton (which is more depleted in δ13C according to

Kendall et al., 2001).

62 Discussion

5.2.1.3. Suspended river particles

As can be deduced from fig.4.17.A the water of the northern river (PA11-WS03, PA11-WS04,

contains less particulate organic carbon (328.06 µg/L) than the western one (575.22%).

Although this seems contradictory to what was mentioned earlier, this is not the case as the

suspended particles represent the finest fraction of the river sediment which shows this

same response (see section 5.3.2). The C/N ratios of the suspended particles (12.36±4.49)

are lower than for river sediment (17.89±0.71) and soils (17.68±4.62). The lower C/N ratios

of the suspended particles might be the result of the plankton in the river water column,

which has C/N ratios generally lower than 10 (Kendall et al., 2001) and, as mentioned before,

contributes largely to the C/N of the river. This is especially reflected in PA11-WS01 which

has a C/N ratio of 7.96. The C/N vs. δ13C plot (fig.4.18) puts this sample within the area of the

freshwater algae. This plot also shows that the river water’s suspended particles are closer in

their C/N ratios and δ13C to some of the soil samples than they are to the river sediment.

This suggests that components of suspended material are mainly determined by the

composition of soils in combination with the biological activity within the water. This is also

agreed upon by the δ13C (average -28.73‰) which indicates a mixture of soil organic matter

(average -27‰) and plankton (average -30 per mil) (Kendall et al., 2001). The latter are

divided in phytoplankton, which has the most depleted values (up to -37‰), and epiphytic

algae, which have values between -23 and -33‰ (Hamilton and Lewis, 1992) and are the

ones that form the reference for freshwater algae in fig.4.18.

5.2.2. Lake productivity

Particles collected on filtrates of lake waters are generally assumed to represent the organic

matter produced within the lake itself (e.g., Hecky et al., 1993). For Laguna Parrillar, fig.4.17

shows that particulate organic carbon (POC) within the lake averages at 523±17µg/L. This

value does not differ significantly from that obtained for the western river (575±94µg/L). By

comparison, the northern river is relatively poor in POC (328±129µg/L). Based on these

values, one can conclude that there’s little need for organic carbon productivity within the

lake.

This interpretation is confirmed by the bulk organic geochemical values obtained on the lake

POM. C/N ratio and δ13C wise, the composition of the lake (respectively 11.11 and -28.16‰)

is closer to that of the western river (10.53 and -28.41‰) than it is to the northern river

(14.20 and -29.32‰). As before, these values confirm there is little contribution of lake

productivity to the C/N and δ13C. Based on the values proposed by Kendall et al. (2001), a

Discussion 63

significant contribution of plankton (C/N: 5-8, δ13C: -30‰) would most likely result in lower

C/N and more depleted δ13C values. It can therefore be said that currently, the western river

has the most influence on the lake.

5.2.3. Selection of geochemic values for aquatic and terrestrial end-members.

Although rivers are the main contributors of sediment to the lake, analyses of C/N and δ13C

show that organic carbon of river sediment is influenced by aquatic plankton. Therefore

these cannot be interpreted as representing the pure terrestrial end-member. Instead, as

soil organic matter seems to contribute largely to the river sediment, using the soils as

representatives for the terrestrial end-member would make more sense. The C/N ratio of

the terrestrial member is therefore 17.67±4.62. Following the same logic, the δ13C best

representing the terrestrial end-member would be that of the soils: -27.33±0.55‰.

The low lake productivity and high influx of terrestrial carbon input make the C/N and δ13C

of the filters unsuitable to use as representatives for the pure aquatic end-member.

Therefore we propose using the value of 7.7 that Bertrand et al. (2010) found in a part of

Lake Puyehue in South-Central Chile which was shielded from any direct influence of

terrestrial organic carbon. The accompanying δ13C value is -28.2‰.

5.3. Influence of grainsize on sediment composition

Comparing both rivers made clear that the river sediments showed differences in

composition. The two samples that were sieved in different grainsize fractions allow

comparing the characteristics of the two rivers and distinguishing which of these are

affected by grainsize. Determining the influences of grainsize on sediment composition will

allow changes due to grainsize to be detected next to those because of other parameters

such as sediment source when interpreting the Parrillar core.

5.3.1. Mineralogy

Even though the mineralogical components of the river samples are similar (fig.4.9.A), their

uneven distribution can be seen in fig.4.10.

Although both PA11-RS02 and PA11-RS05 contain K-feldspar, the XRD data of the separate

grainsize fractions (fig.4.10) show that this mineral is concentrated in the 90-125µm fraction

in the case of PA11-RS02, whereas it’s more scattered over several grainsize fraction in

PA11-RS05. These river samples follow the general trend mentioned by Pettijohn et al.

64 Discussion

(1972) in which the total clay fraction increases with decreasing grainsize (fig.4.10). This is

most obvious in PA11-RS02, where the amount of total clay increases to 45% in the <8µm

fraction. This trend is less linear in PA11-RS05, but the amount of clays still increases when

grainsize gets smaller. Plagioclase also follows the trend described by Pettijohn et al. (1972):

the amount decreases in finer grainsize fractions. Quartz does not really show any trend with

grainsize. Neither does amorphous matter, except perhaps a slight enrichment in the finer

fractions, but this is not significant.

Comparing the XRD results of the <90µm fraction with the results of each grainsize fraction

<90µm, tells us that the bulk mineralogical composition resembles mostly that of the finest

grainsize fractions for PA11-RS02. This is more ambiguous for PA11-RS05 where the

resemblance between the bulk mineralogy and grainsize fractions is mineral dependent.

5.3.2. Organic carbon

According to the LOI-500 data the northern river (PA11-RS04 and PA11-RS05 in fig.4.7.A)

contains more organic carbon than the western river does (PA11-RS01, PA11-RS02 and

PA11-RS03 in fig.4.7.A). This is also visible in the TOC for the <90µm fraction data

(fig.4.11.A). When only taking PA11-RS02 and PA11-RS05 into consideration, however, one

can see that the former is slightly enriched in TOC (4.70% to 4.51%). Fig.4.12, however,

shows that this is mainly due to an effect of grainsize: coarser fractions contain more organic

material in PA11-RS05 than those in PA11-RS02. This could also be noticed in the

unprocessed samples themselves. PA11-RS05 contains visibly more organic material (twigs,

grass, leave, etc.) than the unsieved sample of PA11-RS02 (fig.5.1). Both rivers show a

distinct minimum in %TOC for the 16-32µm fraction and a maximum for the finest fractions

when the coarser fractions of PA11-RS05 are not included as these are missing from PA11-

RS02’s data (Fig.4.12). This is consistent with what Ungureanu et al. (2004) found for the

Danube River.

Discussion 65

Fig.5.1. Untreated samples of PA11-RS02 (left) and PA11-RS05 (right).

The main difference of the C/N atomic ratio of the sediments of the two rivers lies within the

coarser grain fractions (Fig.4.14). High values up to 30 suggest an important contribution of

terrestrial leaf debris, which is stated to have a C/N ratio >15 (Kendall et al., 2001). The

lower values for the coarser fractions of PA11-RS02 suggest more of a contribution from soil

organic matter (C/N of 8-15, Kendall et al. (2001)). For the finer fractions C/N ratios stabilise

around 17 for both rivers. This is comparable to soil samples PA11-SS10 (C/N ratio of 16.19)

and PA11-SS08 (C/N ratio of 16.26) - close to the river sample PA11-RS05 - hinting that for

this river, the finer fraction of the soil is an important factor. This might also be the case for

the northern river, but it is not reflected in the soil samples taken alongside this river. The

combination of these values with the δ13C suggests another factor of influence, namely peat.

When only considering the grainsize fractions finer than 710µm, the δ13C of the river

sediments averages around -28.97‰ and -28.57‰ for PA11-RS02 and PA11-RS05

respectively. This is close to the δ13C value of peat (-28.7‰) suggested by Ertel and Hedges

(1984) and the range that Broder et al. (2012) found for ombrotrophic (“precipitation-fed”)

Patagonian bogs (-26.5±2‰), but lower values have been observed in minerotrophic (“river-

fed”) bogs (Knorr et al., 2008). The possible peaty signature of river sediment δ13C combines

well with an average C/N ratio for peat of 17.3 (Ertel and Hedges, 1984). The coarser

fractions of PA11-RS05 most likely show a δ13C influenced by macrophytes and soil organic

matter, in accordance with what was mentioned earlier. Considering the δ13C of the bulk

river samples (<90µm), we can see that these are closest to the δ13C of the finest fraction

(<8µm): -29.27‰ for the bulk of PA11-RS02 and -29.26‰ for the <8µm fraction and -28.7

and -28.81‰ for respectively the bulk and <8 fraction of PA11-RS05. For the C/N this is

66 Discussion

different. Where the bulk C/N of PA11-RS02 (18.42) is closest to that of the 16-8µm fraction

(17.95), it is the C/N of the 63-45µm fraction (17.96) which is closest to the bulk C/N (17.95)

of PA11-RS05. Soils, on the other hand show little resemblance to the finer fraction of the

river: soils (PA11-SS02, PA11-SS10 and PA11-SS09) in direct vicinity of both river sediment

and filter samples, generally have lower C/N and less negative δ13C than the bulk river

sediment samples. Taking the δ13C and C/N of the filter particles in reconsideration, the

values found for PA11-WS04 seem to follow up to the descending trend that the δ13C and

C/N show in the finest fractions of PA11-RS05. Therefore it might be possible that for the

northern river the filters simply represent the finest fraction of the river sediment, instead of

a strong influence of biological activity. The very low C/N of PA11-WS01, which does not

follow the trend of the finest fractions of PA11-RS02, suggests however that this probably

isn’t the case in the western river.

5.3.3. Magnetic susceptibility

The variations in magnetic susceptibility with grainsize for PA11-RS02 and PA11-RS05, as

seen in fig.4.8, can be summarised as follows: maxima of the magnetic susceptibility can be

found within the (very) fine sand (125-63µm) to (coarser) silt fractions (63-32µm). This is

consistent with what Thompson and Morton (1979) found in the Lomond drainage basin

(Scotland): the bulk of the total susceptibility occurs in the sand-sized fractions (size) and in

the medium to coarse silt fractions (size). The high peak at 16-32µm in the analysis of PA11-

RS05 (northern river) is probably due to highly magnetic material in the source of the

sediment. Although PA11-SS07 is situated downstream of PA11-RS05’s location (fig.4.5), it

indicates that there are certainly soils in the watershed whose fine fractions have similar

magnetic susceptibility readings.

5.4. Influence of Peat on water and sediment composition

As can be seen on the Landsat figure (fig.4.5) the rivers are surrounded by extended peat

bogs. Their character and effects on river sediments and river waters can be easily deduced

when comparing samples upstream from the peat bogs to those downstream. Samples of

the western river include PA11-WS02, PA11-RS03, WS02, PA11-RS03, PA11-SS02 and PA12-

WS04 before the peat bog, and PA11-SS01, PA11-WS01, PA11-RS01, PA11-RS02, PA12-

WS03, PA12-WS05 situated more downstream. This same figure also shows that the sample

Discussion 67

distribution of the northern river is as follows: PA11-WS03, PA11-SS08, PA11-SS09, PA11-

RS04, PA11-RR04, and PA12-WS01 upstream of the peat bog and PA11-RS05, PA11-SS10,

PA11-WS04 and PA12-WS02 downstream. Fig.5.2 gives a summary of how the composition

of these soil, river sediment and river water samples changes in respect to their position to

the peat bogs.

Peat has no influence on the mineralogy of the sediments. Significant changes in mineral

composition, like K-feldspar (fig.5.2), are mostly related to local variations in source. Peat

does however influence the carbon flux of the rivers: PIC (particulate inorganic carbon), POC

(particulate organic carbon), DIC (dissolved inorganic carbon) and DOC (dissolved organic

carbon) all increase once the water has passed through the peat (fig.5.2).

The C/N and δ13C show different relative changes for both rivers. Therefore, peat doesn’t

seem to have any consistent and significant on C/N and δ13C. As is the case for the

mineralogy of the samples, these characteristics seem to be mostly influenced by variations

in local conditions such as a possible high N productivity by plankton.

68 Discussion

Fig.5.2 Summary of how composition of soil, river sediment and river water samples changes in respect to the

position to the peat bogs. Coloured circle indicate that the change is significant (i.e. larger than the error), with

red circles indicating negative changes and green positive ones.

Discussion 69

The rise in POC and DOC is largely explained by the fact that peat consists mainly of

(degraded) organic material. A large amount of this organic material ends up in the river

which results in an increase in the organic carbon component of the river water. Although

not the only factor in play, the peat is also indirectly responsible for the rise in DIC. DIC is

defined as being the sum of [CO2], [H2CO3], and [HCO3-] and [CO3

2-]. The reactions between

these components are heavily pH-dependent. The acidity of peat bog environments (section

5.2; Holden, 2005) could possibly affect the water’s pH when it passes through peaty

environment. The river water has an average pH around 7.5 (personal communication). In

environments of this pH, HCO3- is the main species in solution (fig.5.3).

Fig.5.3. Change in carbonate system of water with pH (after Andersen, 2002)

This most likely comes from dissolution of carbonate rocks. As mentioned in section 5.1

these are definitely present within the lake’s catchment, proven by sample PA11-RR02.

Although CO2 is also a key player within the carbonate system, the CO2-generating reaction

at pH<8 (H2CO3

CO2 + H2O) is much slower than the reactions involving HCO3- (CO3

2-+ H2O

HCO3- + OH-) (Sjöberg and Rickard, 1984). The majority of the generated CO2 is transmitted

to the atmosphere by degassing of the rivers (Holden, 2005). This would lead to an excess of

HCO3- (and H2CO3). It is this excess that gives higher readings for DIC values.

Total Dissolved Solid (TDS) is a measurement for dissolved materials in water. The main

inorganic anions dissolved in water include carbonate, chlorides, sulphates and nitrates. The

principal cations are sodium, potassium, calcium and magnesium (Quality criteria for water,

1986). The average of TDS found after analysis of river water averages at around 90mg/L

(89.18±6.29mg/L for all five filters, 91.8±2.66 without outlier PA11-WS01). This last sample

70 Discussion

has a considerably lower TDS (78.70mg/L) than the other samples which are all situated

closer to the average.

Samples from both rivers situated after the peat bogs have more TDS than those before

them. It should be noted, however that the change in TDS is only significant in the western

river. TDS is said to increase from the head of the river to its mouth, because more particles

end up in the water and dissolve on the course of the river. The low value of PA12-WS04 can

be explained by the sample’s location. It’s located relatively close to the head of the river,

unlike the samples of the western river which are already further down the river course. By

combining PA11-WS03 and PA12-WS04 we can see that this particular location shows the

lowest values for DOC, POC and DIC as well.

Despite the partial correlation in the suspended particles within river waters, only the

western river sediment shows this same trend for both its POC (particulate organic carbon)

and PIC (particulate inorganic carbon). Values of PIC are low (~1%, fig.4.7.A) in river

sediment samples and therefore these probably contain little to no inorganic carbon, which

causes the changes listed in fig.5.2 to be of little importance. POC of the western river also

shows this insignificant rise and is slightly higher closer to the lake. Concerning the northern

river the POC of the river sediment sample is lower right after the peat bog. This might,

however, be due to a local variation in environment causing PA11-RS04 to have a very high

%TOC (both visible in TOC – fig4.11.A and LOI-500°C – fig.4.7.A) as the %TOC of PA11-RS05

shows comparable values to PA11-RS02 of the western river.

Peat seems to have little effect on the river sediment’s MSMS, although iron-bearing

magnetite is prone to dissolution in acidic peat environments, which could lower the overall

magnetic response of the sediment (Williams, 1992).

As can be seen in fig.4.5 the northern river flows through the peat bog, unlike the western

river which flows around it. Initially the appearance of the northern river does not differ that

much of the northern river (fig.5.4). Once it enters the peat bog, however, the flow path

diverges within the peat. This would cause a sudden drop in flow velocity, causing the

majority of the suspended particles to settle at the bottom. This would imply that the water

leaving the peat bog (fig.5.5) is deprived of sediment particles originating from upstream

areas. The sediment of the river is then only influenced by the soils surrounding that part of

the river. In this area close by the lake, fine clay particles are immediately swept away by

the current. This would explain why the amount of total clays downstream (PA11-RS05) is

considerably lower than upstream from the peat (PA11-RS04), which resembles the soils

Discussion 71

(PA11-SS08 and PA11-SS09) nearby (fig.4.9.A). This effect of a sediment trap also explains

why the river sediment of the western river shows less significant changes in the measured

characteristics than that of the northern river (fig.5.2).

Fig.5.4. view on the mouth of the western river where the water flows into the lake (Photo: Z. Ghazoui)

Fig.5.5. Post-peat bog part of the northern river (Photo: Z. Ghazoui)

5.5. Influence of peat bogs on Laguna Parrillar’s trophic status

The low lake productivity described in section 5.2.2 would

imply that Laguna Parrillar is an oligotrophic lake. The bottom

waters of oligotrophic lakes often support many fish species,

like the lake trout, which require cold, well oxygenated

waters. This is the case as the oxygen levels are relatively

constant over the entire depth of the lake (fig.5.6).

Fig.5.6 Oxygen profile of Laguna Parrillar (after Van Daele, 2009)

72 Discussion

That this fish species thrives well in the lake is reflected in the recreational fishing for brook

trout (Salvelinus fontinalis) from October to March. This fish species has no problem with

extreme pH values (although mostly 5.0 to 7.5).

The dissolved organic carbon, however, is much higher than what would be expected from

oligotrophic lakes. With an average DOC of 9238±116µg/L it far exceeds the 2400µg/L that

Jordan and Likens (1975 in Thurman, 1985) found for an oligotrophic lake in New Hampshire

(USA) and is instead closer to the values of peat bogs which have DOC between 3 and

10mg/L (Thurman, 1985). This is not a surprise as the high carbon influx results from the

peat bogs surrounding the rivers. This high influx of carbon turns the water of the lake

brown and causes it to have a low transparency. Although the exact Secchi depth is

unknown, the condition of the lake water suggests a shallow one, or at least shallower than

the >8m needed for an oligotrophic lake. Instead, it would be closer to the 1-2m which

characterises eutrophic conditions (Carlson and Simpson, 1996).

The two rivers have a different contribution to the carbon influx. Although the northern river

has the lowest DOC and POC of the two rivers, the lake water closest to its mouth show the

highest DOC. As there are no POC values available close to the river mouth of the western

river, not much can be said about the water entering the lake through the western river.

Thus can be said that the trophic state of the lake is two-sided: the low lake productivity

suggests an oligotrophic state, whereas the high carbon influx gives it more the appearance

of a eutrophic lake.

5.6. Implications for the Laguna Parrillar sediment record interpretation

In her thesis, Heirman (2011) defined three units within the 695cm long sediment core

collected in the centre of Laguna Parrillar based on the results of the characteristics she

measured. Unit I (695-195cm, fig.5.7, 5.8, 5.9) is characterised by low H2O-content, a stable

gamma-density and magnetic susceptibility. The transition to Unit II is rather diffuse and

characterised by rusty colouring of the sediment laminae. Unit II (195-96cm, fig.5.7, 5.8, 5.9)

is composed of very fine laminae which contain more sand than further downcore. This unit

has the highest gamma-density and magnetic susceptibility measurements, and either shows

a sudden shift at the beginning and end of this unit. Unit II has the lowest H2O-content and

shows more variation in δ13C and C/N than the other unit. The beginning of Unit III (96-0cm,

fig.5.7, 5.8, 5.9) is characterised by a sudden colour change, the same rusty colouring which

Discussion 73

was witnessed at the transition of Unit I to Unit II. The youngest unit shows the most

variation in its characteristics, with H2O, TOC and C/N all showing a general increase and δ13C

a general decrease compared to previous units (Heirman, 2011). By comparing the

characteristics of each unit to the findings of the samples within the watershed of Laguna

Parrillar, one can deduce how lake sediment sources changed through the 44-45 ka that the

core covers (Heirman, 2011).

Through her analyses, Heirman (2011) found that grainsize seemed to significantly control

magnetic susceptibility variables and mineralogy, especially the amount of clay. Therefore,

downcore changes in these parameters must be interpreted with caution, taking changes in

grainsize in consideration. She also makes mention of how changes in C/N and δ13 reflect

changes in the surrounding vegetation, especially the establishment of the moorland.

74 Discussion

Fig.5.7. Downcore variation of Parrillar sediment core

Left: Litholog (Heirman, 2011)

Above: H2O-content, magnetic susceptibility and γ-density (After Heirman, 2011)

Discussion 75

5.6.1. Mineralogy and magnetic susceptibility

Unit I (44800 – 29600 cal a BP), as defined by Heirman (2011), shows a mineralogical

composition of 16-28% quartz, 7-18% plagioclase, 4-9% pyroxene and 30-53% Total clays

(fig.5.8). In se, this composition does not differ significantly from the erodible material found

within the watershed. The lake sediment does, however, seem to be slightly depleted in

quartz content compared to the soils, rocks and river sediments analysed in this work. The

closest match in quartz content is the finer fraction (<8µm) of the northern river. This is

consistent with the fine grainsize found in this unit (mean: 4.40-8.03µm, fig.5.8). This is

supported by the soils of the northern valley which have slightly lower quartz contents than

those of the western valley. Plagioclase is also closest to those finer fractions, although the

bulk <90µm sediment samples of either river are possible as well. The background

contribution of pyroxene to the mineralogy can be either due to erosion of carbonate

nodules found around the lake, as these are the only samples to show a significant pyroxene

content, or due to erosion of volcanic material (in combination with olivine) of which there is

no sample available.

Grainsize shows little variation throughout this unit. What little variation there is does not

seem to influence mineralogy in a consistent way. Therefore it is likely that mineralogy and

magnetic susceptibility might be determined by changes in sediment provenance. For

example, a peak in quartz is often accompanied by a slight decrease in magnetic

susceptibility, which might indicate more influence of the less magnetic, but more quartz-

rich western river.

Unit II (22600 – 17500 cal a BP) is more variable in its mineralogy than Unit I (fig.5.8)

although the same minerals are present, the ratios are slightly different then they were in

Unit I. Without including the extreme values for plagioclase (45%) and pyroxene (33%) this

summarises to: 14-30% quartz, 8-29% plagioclase, 3-12% pyroxene and 23-45% total clays.

Although grainsize shows more variability then the previous unit (fig.5.8), the unit can be

divided into two sub-units, of which the lower one is coarser than the upper one. The

difference between the two sub-units is also reflected in the magnetic susceptibility having

higher readings in the lower half, as well as quartz, plagioclase and pyroxene showing larger

variations in the lower half than in the upper half. In the lower sub-unit, MS and quartz seem

to follow the general trend of grainsize, where an increase in grainsize coincides with an

increase in both parameters. The upper half only has quartz following the trend set by

grainsize.

76 Discussion

The overall magnetic susceptibility of Unit II is higher than that of Unit I. When the values of

the tephra layers are not included in the magnetic signal of the core, the highest readings

can be found in Unit II, as well as some readings which are similar to those of Unit I. High

magnetic readings in the watershed samples can be found along both rivers (e.g. PA11-SS01

alongside the western river and PA11-SS07 in the northern valley). But in general, the

strongest magnetic signal comes from the northern river. So is the river sediment of the

northern river closest to the lake (PA11-RS05) slightly higher in MSMS than its counterpart in

western river and the rock sample PA11-RR04 gives out the highest reading in MSMS.

The higher readings of Unit II are all accompanied by a slight coarsening in grainsize: instead

of the average grainsize finer than 16µm, the average grainsize becomes larger than 16µm

(up to 22.6µm, fig.5.8). This is coincidentally the grainsize fraction which has the strongest

magnetic response in PA11-RS05. It is therefore very likely that these strongly magnetic

readings link to a time when the sediment supply was dominantly coming from the northern

river. The overall higher magnetic response of Unit II would then possibly link to a combined

sediment supply of the highly magnetic northern river and less magnetic western river. This

is conform the mineralogy corresponding mainly to the composition of soils and rocks and

variations being due to changes in sediment source rather than grainsize.

One such period of higher magnetic susceptibility (140-110cm) has been dated to coincide

with the Last Glacial Maximum. Although it has a higher MS, the grainsize shows no

coarsening. Heirman (2011) suggests an effect of a prolonged ice cover which prevents

coarser material from entering the lake.

If the reconstructions by Sugden et al. (2009, fig.2.8) are true, that would mean that during

this time the glaciers of the Patagonian ice field extended to the northern valley, but left the

western valley mostly ice-free. The lower magnetic readings similar to those of Unit I would

then indicate a time when the northern river lost its dominance to the western river.

Although there is no data available about glacier fluctuations prior to the LGM, it could be

that glacier advances and retreats are reflected within the magnetic and grainsize signal.

Discussion 77

Fig.5.8. Downcore variation of grainsize, magnetic susceptibility and mineralogy (After Heirman, 2011).

78 Discussion

The mineralogy of the youngest unit (Unit III, 17500 – 0 cal a BP) is not significantly different

from the older units (fig.5.8). With 14.31-29.29% quartz it resembles unit II’s quartz content.

The average plagioclase content of the third unit is insignificantly lower than Unit II’s, while

pyroxene is insignificantly higher. What sets this unit apart from the older units are i.a. the

decreasing magnetic susceptibility and general coarser grainsize. Although the latter shows

quite some variability, the variations in magnetic susceptibility and mineralogy do not seem

to follow these changes. Therefore the latter must be influenced by a change in sediment

provenance rather than a change in grainsize.

This is supported by the mineralogy of the content of the unit, which shows mixed influence

of the surrounding area. The quartz content is mostly comparable to that of the rocks and

soils situated around the lake, as the quartz content of either river is too high. Another

possibility are the soils around the western river, of which some have a low quartz content

not too different from that of the lake sediment. Plagioclase gives no definitive answer as

either source could contribute to the plagioclase content of the lake sediment: low values

are comparable to those of the soils close to the lake, whereas the intermediate and higher

values are characteristic to both rivers, with highest values (+30%) linking to the northern

river.

Despite the overall magnetic response of Unit III being comparable to that of Unit I, unit III

shows more variability in magnetic responses. There are episodes where the MS is higher,

more like the values found in Unit II, and there are episodes with MS readings which are the

lowest in the entire core. Such low magnetic responses can be found in soils and rocks in the

direct vicinity of the lake. This would suggest periods with increased local erosion, in

addition to the influx of river sediment, as none of either rivers shows exceptionally low

magnetic susceptibility in the 16-32µm grainsize fraction – the range of grainsizes in this

unit.

5.6.2. Organic matter

The C/N ratios of Unit I vary between 7.40 and 9.20 (average 8.32, fig.5.9), which Heirman

interpreted as being typical for lacustrine algae, in accordance with the 5-8 listed by Kendall

(2001). The δ13C varies between -25.97 and -26.40‰ (fig.5.9). These values are less depleted

than what the samples of the watershed generally show (-27 to -30‰ for both the northern

and western river). Although they are not depleted enough for the aquatic-member (-

28.2‰), the fT (fig.5.10) suggests that the major contributor to organic material is in fact

aquatic in origin. This is most likely due to epiphytic algae which can have δ13C up to -23‰.

Discussion 79

The lower values of this unit’s C/N atomic ratios (7.29 to 17.03, average 8.83) tell the same

story as in the Unit I. The sudden shift associated with the value of 17.03 possibly represent

faulty measurements (Heirman, 2011) and care should be taken to see if these actually

represent a sudden increase in influence by the terrestrial end-member. This is also the case

for the δ13C values of Unit II, which vary between -25.77 and -28.42‰ (average -26.29‰).

The latter value seems to be an outlier measured on the same sample which gave the

outliers in C/N. Therefore, a more representative range would be between -25.77 and -

26.56‰ (fig.5.9). This still hints a dominant influence by aquatic algae, although the fT

(fig.5.10) clearly shows that the terrestrial influence starts to increase during the second half

Unit II.

The youngest unit, Unit III, has δ13C varying between -25.95 and -27.51‰ (average -26.86,

fig.5.9), which makes it the unit with the most depleted values. This range of δ13C suggests

an increasing influence of the terrestrial end-member next to contribution of algae. This mix

of algae and increasing influence of the terrestrial end-member is also reflected in the C/N

(7.62 to 16.12)

Throughout the majority of Unit I and Unit II, the most important contributors to organic

carbon are planktonic in origin (fig.5.10). It isn’t until half-way unit II that the terrestrial end-

member starts to show influence over the aquatic end-member. It is certain, however, that

from the transition to the third unit the terrestrial end-member becomes increasingly

important. The onset of this constant increase in C/N and fT (fig.5.9-5.10) at 84cm (~13ka) is

most likely due to establishment of the moorland vegetation, as Heirman (2011) suggested.

This is the time that the expansion of forests begins in Tierra del Fuego and Patagonian

tundra is converted into forest lands (Markgraf, 1992). Many records from the high latitudes

come from peat bogs with basal dates between 15.000 and 14.000BP (e.g. Markgraf, 1992

and Pendall et al., 2001). It might be that prior to this time, conditions were less favourable

for peat growth (Markgraf, 1992). The C/N values also hint to an increasing importance of

the C3-vegations as opposed to freshwater algae. Fig.5.10 also shows that the changes in

TOC closely follow those of fT, which indicates that the changes in TOC are largely due to

changes in terrestrial organic matter.

When comparing the results of the core analyses to the results found for the northern river,

the western river and the lake surroundings, one can see that for Unit III, these seem to be

mostly influenced by material from the surroundings close to the lake and from the western

river. Although δ13C does not allow for a distinction between which factor (northern river,

80 Discussion

western river or lake vicinity) is the most important one, TOC and C/N both are close to the

sediments for the western river and the soils and rocks around the lake than they are to the

sediment of the northern river. The western river being the dominant one in the most recent

unit (which includes modern conditions), is consistent with the findings of the river and lake

suspended particles (section 5.2.2)

Fig.5.9 Downcore variation of δ13

C, TOC and C/N (After Heirman, 2011)

Discussion 81

Fig.5.10 Down core variation of fraction of terrestrial organic carbon (fT), Total Organic Carbon (TOC) next to the age-depth model (after Heirman, 2011)

82 Conclusion

6. Conclusions

(1) There are two major sediment sources in the watershed of Laguna Parrillar: rocks and soils.

River sediment is derived from the previous two. Each of these contributes to the sediment within

the lake and has its own characteristics in mineralogy and organic components.

(2) The rocks within the watershed show two distinctively different compositions: one which

indicates a siliciclastic origin and one a carbonate origin. Siliciclastic rocks are the dominant type

within the watershed, while the other kind occurs as carbonate nodules. Their main differences lay

within the mineralogical composition (quartz, plagioclase and K-feldspar, as opposed to calcite and

pyroxene) and the inorganic carbon content, which is much higher for the carbonate rocks.

(3) Although soils seem to be much more variable than rocks in their composition, their

mineralogical content is still resembling that of the rocks. Their variation is mainly determined by

local factors such as the presence of peat soils. An average C/N atomic ratio of 17.67±4.62 for the

soils is within the range of cold humid climate soils. Their high variability is also reflected within

the magnetic susceptibility, which can be used as in indication of the provenance of the soil. The

unconsolidated nature of the soils makes them the most readily available sediment source in the

watershed.

(4) River sediments are much less variable than soils and this is primarily reflected in their

mineralogy. The five river samples show a pretty consistent mineralogical composition: 30-40%

quartz, 14-18% plagioclase and 6-10% K-feldspar with exception of PA11-RS04. As is the case for

the mineralogy, magnetic susceptibility of river sediments is the average of all material which ends

up in the river.

(5) The effect of grainsize on sediment composition is mostly limited to mineralogy and

magnetic susceptibility. So is the amount of K-feldspar, clays and plagioclase in river sediment

dependent on the grainsize fraction. In general, the bulk composition is determined by the finest

grainsize, although this can be mineral-dependent. The bulk of the total susceptibility occurs

within the (very) fine sand (125-63µm) to (coarser) silt fraction (63-32µm). %TOC seems to be

slightly affected by grainsize as well as there is a distinct minimum in the 16-32µm fraction and

finest fractions tend to be enriched in organic material compared to coarser fractions. C/N of the

coarsest grainsize fractions shows the most variation depending on the presence of vegetational

litter (leaves, twigs, grass, etc...). Finer grainsize fractions (<125µm) show a more or less constant

C/N value. This is also the case for δ13C, where the coarsest grainsize fractions may vary and finer

(<710µm) grainsizes show a slightly decreasing trend with a less negative value in the 16-32µm

fraction.

Conclusion 83

(6) Peat has no influence on the mineralogy of the sediment. It does however influence the

carbon flux of the rivers, with exception of C/N and δ13C. POC and DOC are influenced by the

surplus of degraded organic material coming from the peat. DIC is indirectly influenced by the

control of peat on environmental pH, which in turn affects the solubility of carbonate nodules.

Dissolution in acid pH might influence magnetic minerals, but peat has no distinct influence on MS

besides that. TDS is unaffected by peat as well, as the variations in TDS are determined by the

sampling distance from the source of the river.

(7) The total organic carbon supply of the lake consists of terrestrial material and aquatic

material. The aquatic end-member is defined by the lake-production, which according to the POM

of the lake is low in Laguna Parrillar. As there is a high influx of organic material from both rivers,

samples taken within the lake itself are not representative of the pure aquatic end-member.

Instead a C/N of 7.7 and a δ13C of -28.3‰ found in a different lake were taken as representatives

for the aquatic end-member. The terrestrial end-member is represented by the soil organic

matter, as this is the second largest contributor to river organic matter besides the planktonic

supply. This is reflected within the mineralogy and magnetic susceptibility as well as C/N and δ13C

of river sediment samples. This gives a C/N of 17.67±4.62and a δ13C of -27.33±0.55‰ for the

terrestrial end-member.

(8) The low lake productivity, but high terrestrial organic matter influx gives Laguna Parrillar a

two-faced trophic state. The former indicates an oligotrophic lake whereas the latter classifies it as

a eutrophic lake.

(9) Generally speaking the mineralogy of the Parrillar core does not differ from what is found

within the watershed, although the sediment seems to be somewhat depleted in quartz compared

to the bulk mineralogy of the watershed samples.

(10) The fine grainsize and quartz content of Unit I (44800 – 29600 cal a BP) indicate sediment

supply dominated by the northern river. The variation in magnetic susceptibility hints an

occasional increase in supply by the western river. The organic matter of this Unit is determined by

the aquatic end-member. The average C/N ratios of 8.35 and δ13C values of -26.19‰ as well as the

(near-)zero fT suggest dominant organic matter coming from plankton and epiphytic algae.

(11) Unit II (29600 – 17500 cal a BP) shows more variety in grainsize and mineralogy than the

Unit I, though quartz and MS seem to be the only factors changing with grainsize. The overall MS

of unit II is the highest within the Parrillar core. The highest readings within this unit are linked to

an increase in grainsize resembling the grainsize of the highest magnetic fraction of sediment from

the northern river. The variation in magnetic susceptibility would then indicate an alternation of

dominant sediment supply from the northern river and a combined supply from both rivers.

84 Conclusion

Although the organic material of the lower half of unit II clearly still shows a dominance of the

aquatic end-member, the second half shows an increase in terrestrial organic matter supply.

(12) Unit III (17500 – 0 cal a BP) shows a general decrease in MS accompanied by a coarsening

in grainsize. This trend does not seem to affect general mineralogy, however. This would indicate

that variations in mineralogy are due to a change in sediment provenance, rather than grainsize.

The mineralogical composition suggests an increased local sediment supply or contribution of

western river sediment, rather than the northern river. This is confirmed by TOC and C/N which

are comparable to the sediments of the western river. This is conform the result of the suspended

particles. Local sediment input is supported by the low magnetic susceptibility of this unit. The

constant increase in C/N and decrease of δ13C, along with the steady increase in fT indicate the

establishment of the forests and moorland vegetation. The most significant increase starts around

13ka, which is consistent with the age given in literature.

(13) Future research in Parrillar’s watershed is possible. The remaining river samples can be

separated in different grainsize fractions to see if the trends found for PA11-RS05 and PA11-RS02

also apply to samples taken before the peat bog. This would allow to i.a. better establish the effect

of peat on specific grainsizes. Other options consist of looking at the nitrogen chemistry of the

watershed samples and see how they change and the effects on the Parrillar core.

It might be possible to identify the minerals which were unidentifiable the peaty samples with

other methods such as e.g. XRF and optical methods.

If possible extra samples could be taken to enhance the interpretation of the parameters. Extra

lake filter samples, such as one closer to the mouth of the western river, would allow for a better

characterisation of the organic material of the western river that enters the lake. Also possible

would be to look for further evidence of colder periods before the LGM.

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Images

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Fig.2.11:http://www.umag.cl/facultades/instituto/climatologia/clima_magallanes.php

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Weblinks

http://www.researchportal.be/en/project/reconstructing-late-glacial-and-holocene-

climate-change-in-the-southern-hemisphere-from-a-south-chilean-lake-transect-chilt--

%28VUB_1000000000019496%29/

http://dds.cr.usgs.gov/srtm/version2_1/SRTM3/South_America/

http://stableisotopefacility.ucdavis.edu/13cand15n.html

www.webmineral.com

Nederlandstalige samenvatting VII

Nederlandstalige Samenvatting (Dutch Summary)

Inleiding

Klimaatswijzigingen zijn er geweest sinds het begin van de geschiedenis van de aarde. Het

bestuderen en begrijpen van deze klimaatswijzigingen is van cruciaal belang om de huidige

klimaatsverandering in perspectief te brengen alsook om de huidige klimaatsmodellen te

verbeteren. Hiervoor zijn echter records nodig van hoge resolutie (Bertrand and Ghazoui, 2011).

Hoewel continentale records veelvuldig voorkomen en uitgebreid bestudeerd zijn in het noordelijk

halfrond, zijn vergelijkbare gegevens van uit de hogere breedtegraden van het zuidelijk halfrond

veel minder voorhanden. Nochtans laat dit deel van de wereld toe om de gegevens van de lage en

middelhoge breedtegraden te vergelijken met deze van Antarctica en de Sub-Antarctische

eilanden. Patagonië is hiervoor uitermate geschikt omdat het de meest zuidelijke landmassa is in

een halfrond gedomineerd door water. Hier treft men een aantal grote meren aan die gelegen zijn

langsheen een traject dat de natuurlijke noord- en zuidgrens van het “Zuidelijk Polair Front”

verbindt. Als onderdeel van CHILT (CHIlean Lake Transect) laten de meersedimenten toe om de

deglaciatie geschiedenis van zuidelijk Zuid-Amerika te begrijpen, alsook om de tijdsgebonden en

ruimtelijke variatie van snelle klimaatsveranderingen ten tijde van de overgang van het Laat-

Glaciaal naar het Holoceen te bestuderen. (bron: researchportal.be).

Een van deze meren is Laguna Parrillar (53°24’S 71°17’W) in het centrum van het Brunswick

schiereiland. Deze positie zorgt ervoor dat dit meer uiterst geschikt is om het continentaal klimaat

in de hoge breedtegraden van de zuidelijke atmosfeer ten tijde van het Laat-Pleistoceen te

reconstrueren (Bertrand and Ghazoui, 2011). De sedimenten van Laguna Parrillar kunnen gebruikt

worden om de relatieve positie, kracht en cycliciteit van het globale atmosferische

circulatiesysteem dat in werking is in dit deel van de zuidelijke hemisfeer te bepalen (Van Daele et

al, 2009). Voor haar doctoraatsthesis heeft Heirman (2011) de sedimentkern genomen in het

diepste punt van het meer, aan multiproxy onderzoek onderworpen met behulp van onder andere

XRD, magnetische susceptibiliteit (MS) en koolstofanalyses (Bertrand and Ghazoui, 2011). Om de

interpretatie van deze data te verbeteren is het noodzakelijk om de aard van de terrestrische en

aquatische deeltjes die in het meer eindigen te karakteriseren alsook de factoren die hun

samenstelling beïnvloeden. Met dat doel voor ogen werden tijdens twee expedities in 2011

(Bertrand and Ghazoui, 2011) en 2012 (De Vleeschouwer, 2012) stalen verzameld in het

stroombekken van het meer. Het gaat om gesteentefragmenten, stalen van bodems en

riviersediment. Er werd ook rivier- en meerwater gefilterd waarbij gesuspendeerde deeltjes op de

filters achter bleven. Het water zelf werd ter plaatse geanalyseerd op o.a. DOM (dissolved organic

VIII Nederlandstalige samenvatting

matter) en pH. Tijdens een eerdere expeditie in 2009 (Kilian et al., 2009) was de bathymetrie van

het meer opgemeten.

Methoden

Naast de geplande analyses van de stalen ook een GIS (Geografisch informatiesysteem) van het

stroombekken van het meer gemaakt. Hiervoor moet eerst een digitaal terreinmodel gemaakt

worden om onder andere de locaties van de stalen weer te geven en het bekken te kwantificeren

(o.a. diepte ratio, volume, meer oppervlak, omtrek van het meer, etc.…) De SRTM (Shuttle Radar

Topography Mission) data die de basis vormen voor dit hoogtemodel werden gedownload. Deze

bestaan uit het digitaal hoogtemodel en een coördinatensysteem verzameld door satellieten die

het oppervlak van de aarde scannen met een ruimtelijke resolutie van 3 arcseconden. De

radargolven die hiervoor gebruikt worden dringen niet door water en daarom moet het

dieptemodel van het meer zelf toegevoegd worden. Dit wordt gedaan door de bathymetrische

data te verwerken in Golden Software Surfer computerprogramma. Dit programma gebruikt

statistische methodes om een model te maken dat geëxporteerd kan worden naar andere

programma’s. Omdat de gebruikte methode, Kriging, een rechthoekig gebied berekent, is het

nodig om eerst de achtergrond te verwijderen. De uiteindelijke grid-file kan dan in Global Mapper

ingeladen worden, waarna dit bathymetrisch model als deel van het hoogtemodel wordt

behandeld. Hieraan werden nog gegeorefereerde kaarten zoals onder andere de topografische en

geologische kaarten van het gebied, toegevoegd om het model te vervolledigen met ontbrekende

informatie. Als laatste stap werden belangrijke vormen en lijnen overtrokken.

De analyses van de stalen zelf worden voorafgegaan door een voorbereidende stap. De

bodemstalen en riviersedimentstalen werden eerst gevriesdroogd om het poriewater uit de

sedimenten te verwijderen. Daarna werden gesteentefragmenten gebroken en vervolgens tot fijn

poeder vermalen. De overtollige rand werd van de filters geknipt om ze klein genoeg te maken

voor C/N isotopen analyse. Vervolgens worden de sedimentstalen gezeefd door middel van een

zeeftoren welke voorzien is van een 1mm en 90µm zeef om het fijne materiaal van het grove te

scheiden. Twee van de riviersedimentstalen werden ook gezeefd op 1mm, 710µm, 500µm,

355µm, 250µm, 180µm, 125µm, 90µm, 63µm, 45µm en 32µm. Om de fijnere korrelgroottefracties

van 16 and 8µm te scheiden werd gebruik gemaakt van een Atterbergkolom, welke werkt volgens

de wet van Stokes. De scheiding in verschillende korrelgroottefracties wordt gedaan om de

invloed van korrelgrootte op de samenstelling te kunnen vaststellen.

Voor de verdere analyses worden enkel de gesteentefragmenten, de <90µm fracties van de

bodem- en riviersedimentstalen gebruikt alsook de volledige zeefsequentie van beide rivierstalen.

Nederlandstalige samenvatting IX

De eerste stap in de analyse is het bepalen van de magnetische susceptibiliteit (MS). Deze

methode wordt gebruikt om de gevoeligheid van het materiaal aan het omgevend magnetisch

veld vast te stellen omdat deze een maat is voor de ijzerhoudende mineralen in het sediment. MS

wordt ook gebruikt om bodems te classificeren omdat bijvoorbeeld hydrische (slecht doorlatende)

bodems vaak een lage MS hebben omdat magnetiet en andere ferrimagnetische mineralen er niet

stabiel blijven (Grimley et al., 2004).

De chemische analyses gaan van start met Loss On Ignition (LOI) om het watergehalte (LOI-105°C)

te bepalen naast de hoeveelheid organische (LOI-500°C) en anorganische (LOI-900°C) koolstof

(Heiri et al., 2001). De LOI-500°C worden onder andere gebruikt om de benodigde hoeveelheid van

een staal te berekenen dat nodig is voor de koolstof (C) en stikstof (N) analyses. Naast het bepalen

van de respectievelijke concentraties worden ook de stabiele isotopen 13C en 15N bepaald. De C/N

verhouding wordt samen met de δ13C gebruikt om het onderscheid te maken tussen verschillende

bronnen van organisch materiaal. Aquatische en terrestrische bronnen zijn duidelijk

onderscheidbaar op basis van hun C/N ratio’s aangezien deze afhankelijk zijn van het voorkomen

van algen of vasculaire planten. Algen hebben typisch een C/N ratio van 4 op 10, terwijl vasculaire

planten eerder C/N ratio’s vertonen tussen 30 en 40 (Meyers, 1994; Meyers and Teranes, 2001).

De mineraalinhoud van elk staal wordt bepaald met behulp van X-stralen diffractie (XRD). XRD

werkt volgens het principe dat X-stralen afkomstig van een koperatoom uitgezonden worden naar

het monster. Het oppervlak van het staal verstrooit de X-stralen en hoewel de meeste stralen

elkaar zullen opheffen, zullen sommige elkaar versterken. De hoek waaronder dit gebeurt is

specifiek voor elk mineraal. De resulterende diffractogrammen werden geïnterpreteerd door

middel van de Bruker EVA-software, die toestaat om door middel van gekende diffractiepatronen

mineralen te identificeren aan de hand van de diffractiepieken. Om de mineralen te semi-

kwantificeren is het nodig de absolute piekhoogte te vermenigvuldigen met een

mineraalspecifieke correctiefactor (Cook et al., 1975).

Resultaten en Discussie

Uit de analyses blijkt dat de gesteentefragmenten in te delen zijn in twee soorten. De ene soort,

die het meest voorkomt, bestaat vooral uit kwarts, plagioklaas en K-veldspaat, een samenstelling

die wijst op een siliciclastische oorsprong (Boggs, 2006). De andere bestaat vooral uit calciet en

pyroxeen, wat eerder typisch is voor een carbonaatgesteente (Boggs, 2006). De rotsfragmenten in

de directe omgeving van het meer hebben in het algemeen een lage magnetische susceptibiliteit,

terwijl het ene staal uit de noordelijke vallei duidelijk een ander signaal geeft. Dit duidt erop dat

MS gebruikt kan worden om de herkomst van het afgeleide erosiemateriaal te bepalen.

X Nederlandstalige samenvatting

Door hun losse structuur vormen bodems de meest voor de hand liggende bron van

erosiemateriaal. De gemeten parameters tonen meer variatie dan deze van de

gesteentefragmenten. Desondanks is de mineralogie van de bodems in het algemeen vrij

gelijkaardig aan deze van de gesteenten. Dit suggereert dat de bodems afgeleid zijn van

onderliggend gesteente. De aanrijking van amorf materiaal en klei in bodems komt door

kleivorming en incorporatie van organisch materiaal afkomstig van afval van de omliggende

bossen en veen. De variatie van de bodemstalen is het duidelijkst zichtbaar in de resultaten van

MS. Net zoals de gesteentefragmenten hebben de bodemstalen rondom het meer de laagste MS-

waarden. Naast de kenmerken overgedragen door erosie van de gesteenten, komt dit onder

andere door een grote hoeveelheid organisch materiaal (afkomstig uit veen of oude

meerterrassen) dat meestal een lage MS heeft. De bodemstalen genomen in veen vertonen

mineralen die niet voorkomen in andere stalen. Waarschijnlijk gaat het hier om

aluminiumhoudende mineralen, aangezien aluminium een van de meest stabiele elementen is

(McQueen, 2009).

In tegenstelling tot de geanalyseerde bodemstalen, vertonen de riviersedimenten minder variatie

in hun parameters. Zo is de mineralogie vrij gelijkaardig bij de vijf stalen van het riviersediment.

Deze is vergelijkbaar met die van de bodems, wat erop duidt dat de riviersedimenten afgeleid zijn

van de bodems. De MS vertoont, net zoals de mineralogie, een waarde die het gemiddelde is van

alles wat in de rivier terecht komt.

Het effect van korrelgrootte op de samenstelling van de sedimenten is voornamelijk beperkt tot

de mineralogie en magnetische susceptibiliteit. Zo is de hoeveelheid K-veldspaat, klei en

plagioklaas in riviersediment afhankelijk van de korrelgroottefractie. In het algemeen wordt de

bulk samenstelling bepaald door de fijnste korrelgroottefractie. Het grootste deel van de totale

susceptibiliteit komt voor in de (heel) fijne zandfractie (125-63µm) tot (grovere) siltfractie (63-

32µm). Daarnaast wordt %TOC (Total Organic Carbon) mogelijk ook beïnvloed door korrelgrootte

aangezien er een duidelijk minimum in %TOC zichtbaar is in de 16-32µm fractie en omdat fijnere

fracties vaak aangerijkt zijn met organisch materiaal. Korrelgrootte beïnvloedt ook de C/N in die

zin dat de grofste korrelgroottefacties variaties in C/N vertonen die afhankelijk zijn van de

aanwezigheid van plantaardig afval. Fijnere korrelgroottefracties (<125µm) vertonen min of meer

een constant C/N waarde. Een soortgelijk effect is ook zichtbaar voor de δ13C, waar de grofste

fracties kunnen variëren van staal tot staal en fracties fijner dan 710µm een zacht dalende trend

vertonen met een minder negatieve waarde in de 16-32µm fractie.

In tegenstelling tot de korrelgrootte heeft veen geen invloed op de mineralogie van het sediment.

Het heeft vooral een effect op de koolstofflux van de rivieren, hoewel C/N en δ13C hierop een

Nederlandstalige samenvatting XI

uitzondering vormen. DOC (dissolved organic carbon) en POC (particulate organic carbon) worden

beïnvloed door de extra toevoer van (half)vergaan organisch materiaal waaruit veen voornamelijk

bestaat. De invloed van veen op de pH van de omgeving heeft een gevolg voor de oplosbaarheid

van de carbonaatknollen wat dan weer de DIC (dissolved organic carbon) wijzigt. Zure pH kan ook

magnetische mineralen aantasten, maar veen heeft verder geen duidelijke invloed op MS. Veen

heeft ook geen effect op TDS (total dissolved solids), aangezien dit bepaald wordt door de afstand

van het genomen staal tot de bron van de rivier. De organische koolstof die uiteindelijk in het

meer belandt, bestaat uit materiaal van terrestrische en aquatische afkomst. Aquatisch organisch

materiaal is afkomstig van productie (door o.a. plankton) in het meer zelf. Aangezien deze

productie op basis van de POM (particulate organic matter) in de waterkolom laag is en er veel

terrestrisch materiaal in het meer eindigt, kunnen de data van Laguna Parrillar niet gebruikt

worden om het aquatische eindlid te definiëren. Daarom worden de data van Lake Puyehue die

veel minder beïnvloed zijn door terrestrisch materiaal, gebruikt: een C/N van 7.7 en een δ13C van -

28.3‰. Omdat op basis van XRD en MS alsook C/N en δ13C bodems de grootste bron van

organisch materiaal zijn naast plankton, vormen deze een goede vertegenwoordiger voor het

terrestrische eindlid. Daarom heeft het dit als C/N 17.67±4.62 en als δ13C of -27.33±0.55‰.

Implicaties voor Laguna Parrillar

Door de lage productiviteit wordt het meer geclassificeerd als een oligotrofisch meer. Dit strookt

niet met de hoge instroom van organisch materiaal afkomstig van het veen dat het water een

bruine kleur geeft en weinig helder is. Hierdoor krijgt het meer eerder een eutrofisch uitzicht

(Carlson and Simpson, 1996).

De sedimentkern genomen in Laguna Parrillar werd door Heirman (2011) onderverdeeld in drie

eenheden op basis van de resultaten van de analyses. Door deze resultaten te vergelijken met de

analyses van de stalen uit het stroomgebied is het mogelijk de verandering in sedimentbronnen

doorheen de tijd vast te stellen.

Eenheid I (44800 - 29600 cal BP) wordt gekenmerkt door een fijne korrelgrootte en een lage

kwartsinhoud. Dit kan duiden op dominante invloed van de noordelijke rivier. Variaties in

magnetische susceptibiliteit kunnen wijzen op toenemende invloed van de westelijke rivier.

Hoewel de δ13C van -26.19‰ niet negatief genoeg zijn om gelinkt te worden aan het aquatische

eindlid, duiden de C/N (8.35) en lage fT (fractie terrestrisch materiaal) erop dat de dominante

invoer van organisch materiaal afkomstig is van plankton en epifytische algen, welke vaker δ13C

waarden vertonen tot -23‰ (Hamilton and Lewis, 1992).

XII Nederlandstalige samenvatting

Unit II (29.600-17500 cal a BP) vertoont meer variatie dan Unit I, wat zichtbaar is in korrelgrootte

en mineralogie. Enkel kwarts en magnetische susceptibiliteit lijken de variaties in korrelgrootte te

volgen. De hoogste MS-waarden van deze eenheid zijn gemeten daar waar de korrelgrootte

toeneemt tot een korrelgrootte die overeenkomt met de fractie met de hoogste MS in sediment

van de noordelijke rivier. De variatie in MS duidt dan weer op een afwisseling van een dominante

sediment invoer uit de noordelijke rivier met minder magnetische sediment uit de westelijke

rivier. Hoewel het onderste deel van deze eenheid nog steeds bepaald wordt door organisch

materiaal afkomstig van het aquatische eindlid, vertoont de tweede helft een toenemende invloed

van terrestrisch materiaal.

Unit III (17500 - 0 cal a BP) wordt gekenmerkt door een algemene afname van MS samengaand

met een vergroving van de korrelgrootte. Deze vergroving lijkt geen effect te hebben op de

mineralogie van deze eenheid. Dit laatste suggereert een toename van lokale sediment-toevoer of

een contributie van de westelijke rivier. Dit wordt bevestigd door TOC en C/N die vergelijkbaar zijn

met sediment uit de westelijke rivier, terwijl de lage MS-waarden eerder duiden op lokale input.

Unit III vertoont een voortdurende toename in C/N, de δ13C daarentegen neemt af. Deze trend en

de verdere toename van terrestrisch materiaal die in Unit II begon, duiden op een verandering van

de vegetatie. Het is rond deze tijd, ongeveer 13ka, dat bossen aan hun verspreiding begonnen en

de steppevegetatie de Patagonische toendra vervingen (Markgraf, 1992).

Appendix XIII

Appendix

What follows below are the tables of data which were used in the creation of graphs found in

chapter 4. Also added are extra figures for further information.

1. Bathymetric model of Laguna Parrillar

Fig.A.1 Polynomial Regression – Quadratic Surface Fig.A.2 Nearest Neighbour

Fig.A.3 Triangulation Fig.A.4 Kriging

XIV Appendix

2. XRD diffractograms

Fig.A.5. Raw diffractogram of PA11-RS01

Fig.A.5. Raw diffractogram of PA11-RR02. Calcite is the largest peak on the left, whereas pyroxene is a smaller peak on the right of the calcite peak. The two peaks overlap.

Fig.A.5. Raw diffractogram of PA11-SS05 with unidentified peaks

0

50

100

150

200

250

300

0 5 10 15 20 25 30 35 40 45 50

PA11-SS01

-50

0

50

100

150

200

250

300

0 5 10 15 20 25 30 35 40 45 50

PA11-RR02

0

50

100

150

200

250

0 5 10 15 20 25 30 35 40 45 50

PA11-SS05

Quartz

Plagioclase

Calcite

Pyroxene

Quartz

Quartz

Appendix XV

3. Grainsize distribution of PA11-RS02 and PA11-RS05

Table.A.1.Grainsize distribution of PA11-RS02 Table.A.2.Grainsize distribution of PA11-RS05

PA11-RS02 Initial weight: 240.49g

Grainsize Sediment (g) %

>1000µm 23.0564 9.68

1000µm - 710µm 35.1611 14.76

710µm - 500µm 37.6471 15.81

500µm - 355µm 29.0319 12.19

355µm - 250µm 20.2589 8.51

250µm - 180µm 15.6813 6.58

180µm - 125µm 19.2172 8.07

125µm - 90µm 21.4733 9.02

90µm - 63µm 17.6593 7.42

63µm - 45µm 6.4345 2.7

45µm - 32µm 5.7853 2.43

32µm - 16µm 2.6837 1.13

16µm - 8µm 3.5071 1.47

<8µm 0.5418 0.23

Total= 238.1389 100

PA11-RS05 Initial weight: 148.19g

Grainsize Sediment (g) %

>1000µm 18.5647 12.53

1000µm - 710µm 10.869 7.34

710µm - 500µm 11.2975 7.63

500µm - 355µm 13.5654 9.16

355µm - 250µm 16.1612 10.91

250µm - 180µm 18.6291 12.58

180µm - 125µm 21.4982 14.52

125µm - 90µm 16.9249 11.43

90µm - 63µm 11.1313 7.52

63µm - 45µm 3.6433 2.46

45µm - 32µm 2.6988 1.82

32µm - 16µm 1.1187 0.76

16µm - 8µm 1.3887 0.94

<8µm 0.6189 0.42

Total= 148.1097

0 2 4 6 8

10 12 14 16 18

Grainsize distribution of PA11-RS02

0 2 4 6 8

10 12 14 16

Grainsize distribution of PA11-RS05

XVI Appendix

4. LOI

Sample Crucible Sediment H2O content Organic Matter Carbonate

Nr Weight Weight Crucible + Sedi

Sedi ∆ LOI105% Crucible + Sedi

Sedi ∆ LOI550 (%) Crucible + Sedi

Sedi ∆ LOI900 (%)

PA11-SS01 70 21.5724 0.5029 22.0642 0.4918 0.0111 2.207 21.988 0.4156 0.0762 15.15 21.9774 0.405 0.0106 2.11

PA11-SS02 71 20.4354 0.5035 20.9338 0.4984 0.0051 1.013 20.8951 0.4597 0.0387 7.69 20.888 0.4526 0.0071 1.41

PA11-SS03 72 20.9993 0.5023 21.4777 0.4784 0.0239 4.758 21.2263 0.227 0.2514 50.05 21.2233 0.224 0.003 0.6

PA11-SS04 73 21.2552 0.5012 21.7398 0.4846 0.0166 3.312 21.6523 0.3971 0.0875 17.46 21.6412 0.386 0.0111 2.21

PA11-SS05 PA11-SS06 74 20.7636 0.5018 21.2408 0.4772 0.0246 4.902 21.0471 0.2835 0.1937 38.6 21.0392 0.2756 0.0079 1.57

PA11-SS07 75 21.0981 0.5019 21.59 0.4919 0.01 1.992 21.5012 0.4031 0.0888 17.69 21.4918 0.3937 0.0094 1.87

PA11-SS08 76 21.8116 0.5 22.2965 0.4849 0.0151 3.02 22.1401 0.3285 0.1564 31.28 22.1308 0.3192 0.0093 1.86

PA11-SS09 77 21.4046 0.5024 21.9005 0.4959 0.0065 1.294 21.8372 0.4326 0.0633 12.6 21.8294 0.4248 0.0078 1.55

PA11-SS10 79 20.694 0.5006 21.1847 0.4907 0.0099 1.978 21.0483 0.3543 0.1364 27.25 21.0395 0.3455 0.0088 1.76

PA11-ZRS01 61 20.9478 0.5008 21.4419 0.4941 0.0067 1.338 21.3904 0.4426 0.0515 10.28 21.384 0.4362 0.0064 1.28

PA11-ZRS02 62 20.7027 0.5018 21.2 0.4973 0.0045 0.897 21.1388 0.4361 0.0612 12.2 21.1311 0.4284 0.0077 1.53

PA11-ZRS03 63 21.0313 0.5031 21.5316 0.5003 0.0028 0.557 21.4858 0.4545 0.0458 9.1 21.4798 0.4485 0.006 1.19

PA11-ZRS04 64 19.8043 0.5013 20.299 0.4947 0.0066 1.317 20.2082 0.4039 0.0908 18.11 20.2016 0.3973 0.0066 1.32

PA11-ZRS05 65 19.5867 0.5013 20.082 0.4953 0.006 1.197 20.0253 0.4386 0.0567 11.31 20.0185 0.4318 0.0068 1.36

PA11-RR01 51 20.6861 1.0008 21.6766 0.9905 0.0103 1.029 21.6448 0.9587 0.0318 3.18 21.6309 0.9448 0.0139 1.39

PA11-RR02 52 19.9668 1.0001 20.9659 0.9991 0.001 0.1 20.936 0.9692 0.0299 2.99 20.6146 0.6478 0.3214 32.14

PA11-RR03 53 19.8117 1.0008 20.7957 0.984 0.0168 1.679 20.7568 0.9451 0.0389 3.89 20.7381 0.9264 0.0187 1.87

PA11-RR04 54 20.7325 0.9998 21.7215 0.989 0.0108 1.08 21.6868 0.9543 0.0347 3.47 21.6768 0.9443 0.01 1

PA11-RR05 56 20.6329 1.002 21.6248 0.9919 0.0101 1.008 21.601 0.9681 0.0238 2.38 21.5869 0.954 0.0141 1.41

20.6329 21.6259 0.993 0.009 0.898 21.601 0.9681 0.0249 2.49 0.954 0.0141 1.41

Table A.3. LOI-150, LOI-500 and LOI-900 results

Appendix XVII

5. Magnetic susceptibility

Name Weight Vial

Vial + Sediment

Sediment

Vol. Susc. Meas. in SI Average volumeteric

MS

Standard deviation

Mass specific MS

g g g Run1 Run2 Run3 Run4 Run 5 Run 6 Run 7 Run 8 Run 9 x10^-6

PA11-SS01 0.7000 1.2345 0.5345 8.32E-04 8.37E-04 8.34E-04 3.41E-06 1561.11

PA11-SS02 0.7032 1.3662 0.6630 3.26E-04 3.28E-04 3.27E-04 1.63E-06 493.45

PA11-SS03 0.6837 1.1981 0.5144 3.31E-05 3.39E-05 3.35E-05 5.31E-07 65.08

PA11-SS04 0.6991 1.2920 0.5929 9.79E-05 9.81E-05 9.80E-05 1.19E-07 165.26

PA11-SS05 0.7029 0.8162 0.1133 2.82E-06 5.17E-06 3.99E-06 1.66E-06 35.25

PA11-SS06 0.6840 1.2667 0.5827 1.09E-04 1.10E-04 1.09E-04 5.11E-07 187.68

PA11-SS07 0.7058 1.2230 0.5172 5.82E-04 5.82E-04 5.82E-04 1.99E-07 1125.56

PA11-SS08 0.6950 1.0936 0.3986 8.94E-05 8.72E-05 8.83E-05 1.57E-06 221.5

PA11-SS09 0.6834 1.2797 0.5963 2.01E-04 2.05E-04 2.03E-04 2.59E-06 340.15

PA11-SS10 0.6934 1.2228 0.5294 1.52E-04 1.54E-04 1.53E-04 1.23E-06 288.76

PA11-RR01 0.7000 1.5100 0.8100 9.39E-05 9.72E-05 9.72E-05 9.61E-05 1.91E-06 118.64

PA11-RR02 0.6842 1.7528 1.0686 1.55E-04 1.58E-04 1.67E-04 1.68E-04 1.62E-04 6.37E-06 151.85

PA11-RR03 0.7043 1.5053 0.8010 8.41E-05 8.57E-05 8.73E-05 8.57E-05 1.59E-06 106.94

PA11-RR04 0.6942 1.5232 0.8290 2.30E-04 2.27E-04 2.26E-04 2.27E-04 1.93E-06 274.36

PA11-RR05 0.6959 1.7232 1.0273 1.84E-04 1.82E-04 1.86E-04 1.84E-04 2.10E-06 179.32

PA11-ZRS01 0.6980 1.3383 0.6403 2.37E-04 2.36E-04 2.37E-04 5.37E-07 369.55

PA11-ZRS02 0.6983 1.2999 0.6016 2.06E-04 2.10E-04 2.08E-04 3.04E-06 346

PA11-ZRS03 0.6949 1.4094 0.7145 3.18E-04 3.22E-04 3.20E-04 2.93E-06 447.97

PA11-ZRS04 0.6965 1.1685 0.4720 1.67E-04 1.68E-04 1.68E-04 7.89E-07 355

PA11-ZRS05 0.7015 1.2686 0.5671 2.31E-04 2.32E-04 2.32E-04 7.77E-07 408.3

RS02

<8µm 0.6979 1.0446 0.3467 7.91E-05 8.13E-05 8.30E-05 8.37E-05 8.18E-05 2.02E-06 235.81

8-16µm 0.6899 1.2091 0.5192 9.25E-05 9.47E-05 9.57E-05 9.43E-05 1.65E-06 181.63

16-32µm 0.6986 1.4647 0.7661 2.18E-04

2.22E-04 2.24E-04

32-45µm 0.6965 1.2216 0.5251 1.55E-04 1.56E-04 1.52E-04 1.54E-04 1.54E-04 1.65E-06 293.29

45-63µm 0.6845 1.3270 0.6425 2.73E-04 2.75E-04 2.76E-04 2.75E-04 1.69E-06 427.31

63-90µm 0.6961 1.3392 0.6431 3.12E-04 3.11E-04 3.16E-04 3.13E-04 2.77E-06 487.18

90-125µm 0.6888 1.3801 0.6913 2.47E-04 2.48E-04 2.49E-04 2.48E-04 9.73E-07 358.84

125-180µm 0.6907 1.4401 0.7494 1.66E-04 1.67E-04 1.70E-04 1.68E-04 1.85E-06 223.92

180-250µm 0.7008 1.3088 0.6080 1.18E-04 1.17E-04 1.17E-04 1.17E-04 1.38E-07 193.18

250-355µm 0.6961 1.5196 0.8235 1.35E-04 1.37E-04 1.38E-04 1.37E-04 1.49E-06 166.14

500-710µm 0.6934 1.6321 0.9387 1.58E-04 1.60E-04 1.61E-04 1.60E-04 1.41E-06 170.13

710-1000µm 0.7049 1.6741 0.9692 1.73E-04 1.78E-04 1.78E-04 1.76E-04 2.95E-06 181.83

>1000µm 0.7051 1.5622 0.8571 1.15E-04 1.22E-04 1.21E-04 1.19E-04 3.88E-06 139.06

XVIII Appendix

Name Weight Vial

Vial + Sediment

Sediment

Vol. Susc. Meas. in SI Average volumeteric

MS

Standard deviation

Mass specific MS

g g g Run1 Run2 Run3 Run4 Run 5 Run 6 Run 7 Run 8 Run 9 x10^-6

<8µm 0.6948 1.0899 0.3951 6.49E-05 6.72E-05 6.65E-05 6.62E-05 1.18E-06 167.47

8-16µm 0.6962 1.1790 0.4828 6.62E-05 6.63E-05 6.81E-05 6.80E-05 6.95E-05 6.97E-05 7.07E-05 6.84E-05 1.73E-06 141.59

16-32µm 0.6814 0.8581 0.1767 2.21E-04 2.10E-04 2.13E-04 2.15E-04 5.78E-06 1,216.28

32-45µm 0.6980 1.1678 0.4698 1.15E-04 1.16E-04 1.18E-04 1.19E-04 1.21E-04 1.21E-04 1.18E-04 2.62E-06 251.93

45-63µm 0.6820 1.2358 0.5538 1.96E-04 1.97E-04 2.00E-04 2.02E-04 2.00E-04 2.01E-04 1.99E-04 2.22E-06 359.89

PA11-RS05 0.6863 1.3222 0.6359 3.66E-04 3.69E-04 3.69E-04 3.78E-04 3.75E-04 3.78E-04 3.73E-04 5.08E-06 585.86

90-125µm 0.6841 1.3921 0.7080 3.34E-04 3.45E-04 3.50E-04 3.34E-04 3.32E-04 3.30E-04 3.38E-04 8.15E-06 476.71

125-180µm 0.7086 1.2985 0.5899 1.62E-04 1.59E-04 1.66E-04 1.65E-04 1.63E-04 1.57E-04 1.50E-04 1.60E-04 5.42E-06 271.78

180-250µm 0.6802 1.2509 0.5707 1.06E-04 1.09E-04 1.11E-04 1.13E-04 1.12E-04 1.04E-04 1.04E-04 1.09E-04 3.75E-06 190.2

250-355µm 0.6853 0.9660 0.2807 5.58E-05 5.57E-05 5.44E-05 5.56E-05 5.40E-05 5.46E-05 5.59E-05 5.59E-05 5.52E-05 7.79E-07 196.77

355-500µm 0.6991 1.0547 0.3556 9.52E-05 9.38E-05 8.89E-05 8.81E-05 8.69E-05 8.27E-05 8.43E-05 8.33E-05 8.79E-05 4.64E-06 247.2

500-710µm 0.7017 0.9402 0.2385 8.46E-05 8.15E-05 8.29E-05 8.40E-05 8.92E-05 7.98E-05 8.37E-05 3.23E-06 350.88

710-1000µm 0.6834 0.9767 0.2933 5.57E-05 5.53E-05 5.54E-05 5.76E-05 6.22E-05 6.28E-05 5.82E-05 3.47E-06 198.28

>1000µm 0.6866 0.9397 0.2531 3.65E-05 3.46E-05 3.45E-05 3.64E-05 3.77E-05 3.80E-05 3.63E-05 1.48E-06 143.32

Table A.4. Magnetic Susceptibility measurements

Appendix XIX

6. XRD

a) Rocks

RAW Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

PA11-RR01 3.79 370 63.2 16.3 0 0 0 0 0 10.9

PA11-RR02 5.63 47.2 15.4 0 43.2 0 0 251 0 4.43

PA11-RR03 4.97 277 48.3 0 0 0 0 0 0 14.8

PA11-RR04 3.01 304 122 20.2 0 0 0 0 0 6.43

PA11-RR05 2.29 396 132 0 0 0 0 0 0 8.48

Corrected Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

Correction factor: 20 1 2.8 4.3 5 2.5 5 1.65 9.3 20

PA11-RR01 75.8 370 176.96 70.09 0 0 0 0 0 218

PA11-RR02 112.6 47.2 43.12 0 216 0 0 414.15 0 88.6

PA11-RR03 99.4 277 135.24 0 0 0 0 0 0 296

PA11-RR04 60.2 304 341.6 86.86 0 0 0 0 0 128.6

PA11-RR05 45.8 396 369.6 0 0 0 0 0 0 169.6

% Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

PA11-RR01 8.32 40.62 19.43 7.70 0.00 0.00 0.00 0.00 0.00 23.93

PA11-RR02 12.22 5.12 4.68 0.00 23.44 0.00 0.00 44.93 0.00 9.61

PA11-RR03 12.31 34.30 16.75 0.00 0.00 0.00 0.00 0.00 0.00 36.65

PA11-RR04 6.53 33.00 37.08 9.43 0.00 0.00 0.00 0.00 0.00 13.96

PA11-RR05 4.67 40.37 37.68 0.00 0.00 0.00 0.00 0.00 0.00 17.29

Table A.5 Raw, corrected and % XRD analyses of rock samples

XX Appendix

b) Soils RAW Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

SS01 4.65 205 31.9 0 0 0 0 0 0 11.8

SS02 3.24 289 68.1 12.5 0 0 0 0 0 20.2

SS03 6.32 315 37.4 14.8 0 0 0 0 0 15.4

SS04 3.65 161 26.9 10.7 0 0 0 0 0 15.8

SS05 24.2

SS06 5.35 131 12 9.87 0 0 0 0 0 6.93

SS07 4.76 166 40.7 0 0 0 0 0 0 13.8

SS08 5.75 165 22.2 0 0 0 0 0 0 14.6

SS09 3.62 284 51.9 0 0 0 0 0 0 11.3

SS10 5.39 252 31.4 21.8 0 0 0 0 0 16.3

Corrected Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

Correction factor: 20 1 2.8 4.3 5 2.5 5 1.65 9.3 20

PA11-SS01 93 205 89.32 0 0 0 0 0 0 236

PA11-SS02 64.8 289 190.68 53.75 0 0 0 0 0 404

PA11-SS03 126.4 315 104.72 63.64 0 0 0 0 0 308

PA11-SS04 73 161 75.32 46.01 0 0 0 0 0 316

*PA11-SS05 484 0 0 0 0 0 0 0 0 0

PA11-SS06 107 131 33.6 42.441 0 0 0 0 0 138.6

PA11-SS07 95.2 166 113.96 0 0 0 0 0 0 276

*PA11-SS08 115 165 62.16 0 0 0 0 0 0 292

PA11-SS09 72.4 284 145.32 0 0 0 0 0 0 226

PA11-SS10 107.8 252 87.92 93.74 0 0 0 0 0 326

% Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

PA11-SS01 14.92 32.89 14.33 0.00 0.00 0.00 0.00 0.00 0.00 37.86

PA11-SS02 6.47 28.84 19.03 5.36 0.00 0.00 0.00 0.00 0.00 40.31

PA11-SS03 13.77 34.32 11.41 6.93 0.00 0.00 0.00 0.00 0.00 33.56

PA11-SS04 10.87 23.98 11.22 6.85 0.00 0.00 0.00 0.00 0.00 47.07

*PA11-SS05 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

PA11-SS06 23.64 28.94 7.42 9.38 0.00 0.00 0.00 0.00 0.00 30.62

PA11-SS07 14.62 25.49 17.50 0.00 0.00 0.00 0.00 0.00 0.00 42.39

*PA11-SS08 18.13 26.02 9.80 0.00 0.00 0.00 0.00 0.00 0.00 46.05

PA11-SS09 9.95 39.03 19.97 0.00 0.00 0.00 0.00 0.00 0.00 31.06

PA11-SS10 12.43 29.05 10.14 10.81 0.00 0.00 0.00 0.00 0.00 37.58

Table A.6 Raw, corrected and % XRD analyses of soil samples

Appendix XXI

c) River Sediment

RAW Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

PA11-RS01 3.84 316 54.7 18.8 0 0 0 0 0 11.8

PA11-RS02 4.23 287 45.4 12.9 0 0 0 0 0 10.8

PA11-RS03 5.76 348 46.7 13.4 0 0 0 0 0 11.8

PA11-RS04 4.35 235 37.9 0 0 0 0 0 0 15.3

PA11-RS05 4.47 268 43.8 18.5 0 0 0 0 0 9.25

Corrected Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

Correction factor: 20 1 2.8 4.3 5 2.5 5 1.65 9.3 20

PA11-RS01 76.8 316 153.16 80.84 0 0 0 0 0 236

PA11-RS02 84.6 287 127.12 55.47 0 0 0 0 0 216

PA11-RS03 115.2 348 130.76 57.62 0 0 0 0 0 236

PA11-RS04 87 235 106.12 0 0 0 0 0 0 306

PA11-RS05 89.4 268 122.64 79.55 0 0 0 0 0 185

% Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

PA11-RS01 8.90 36.62 17.75 9.37 0.00 0.00 0.00 0.00 0.00 27.35

PA11-RS02 10.98 37.26 16.51 7.20 0.00 0.00 0.00 0.00 0.00 28.05

PA11-RS03 12.98 39.21 14.73 6.49 0.00 0.00 0.00 0.00 0.00 26.59

PA11-RS04 11.85 32.01 14.46 0.00 0.00 0.00 0.00 0.00 0.00 41.68

PA11-RS05 12.01 35.99 16.47 10.68 0.00 0.00 0.00 0.00 0.00 24.85

Table A.7 Raw, corrected and % XRD analyses of river samples

d) River Sediment – PA11-RS02 and PA11-RS05

RAW Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

RS02 <8µm 4 224 19.1 0 0 0 0 0 0 15.1

RS02 16-8µm 5.11 236 28.5 0 0 0 0 0 0 17.2

RS02 32-16µm 3.16 385 71.5 0 0 0 0 0 0 11.8

RS02 45-32µm 2.53 347 64.9 0 0 0 0 0 0 15.1

RS02 63-45µm 2.82 271 50.2 0 0 0 0 0 0 9.58

RS02 90-63µm 3.41 244 94.3 0 0 0 0 0 0 7.14

RS02 125-90µm 2.71 440 104 7.19 0 0 0 0 0 12.4

RS02 180-125µm 4.9 276 47.8 0 0 0 0 0 0 14.4

RS02 250-180µm 4.17 278 67.4 0 0 0 0 0 0 8.84

RS02 355-250µm 3.8 296 105 0 0 0 0 0 0 11.3

RS02 500-355µm 3.22 279 115 0 0 0 0 0 0 12.4

RS02 710-500µm 3.29 536 64.7 0 0 0 0 0 0 12.9

RS02 1mm-710µm 4.04 307 77.9 0 0 0 0 0 0 9.47

RS02 >1mm

XXII Appendix

Corrected Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

Correction factor: 20 1 2.8 4.3 5 2.5 5 1.65 9.3 20

<8µm 80 224 53.48 0 0 0 0 0 0 302

8-16µm 102.2 236 79.8 0 0 0 0 0 0 344

16-32µm 63.2 385 200.2 0 0 0 0 0 0 236

32-45µm 50.6 347 181.72 0 0 0 0 0 0 302

45-63µm 56.4 271 140.56 0 0 0 0 0 0 191.6

63-90µm 68.2 244 264.04 0 0 0 0 0 0 142.8

90-125µm 54.2 440 291.2 30.917 0 0 0 0 0 248

125-180µm 98 276 133.84 0 0 0 0 0 0 288

180-250µm 83.4 278 188.72 0 0 0 0 0 0 176.8

250-355µm 76 296 294 0 0 0 0 0 0 226

355-500µm 64.4 279 322 0 0 0 0 0 0 248

500-710µm 65.8 536 181.16 0 0 0 0 0 0 258

710-1000µm 80.8 307 218.12 0 0 0 0 0 0 189.4

% Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

PA11-RS02 <8µm 12.13 33.97 8.11 0.00 0.00 0.00 0.00 0.00 0.00 45.79

PA11-RS02 16-8µm 13.41 30.97 10.47 0.00 0.00 0.00 0.00 0.00 0.00 45.14

PA11-RS02 32-16µm 7.15 43.53 22.64 0.00 0.00 0.00 0.00 0.00 0.00 26.68

PA11-RS02 45-32µm 5.74 39.37 20.62 0.00 0.00 0.00 0.00 0.00 0.00 34.27

PA11-RS02 63-45µm 8.55 41.09 21.31 0.00 0.00 0.00 0.00 0.00 0.00 29.05

* PA11-RS02 90-63µm 9.48 33.93 36.72 0.00 0.00 0.00 0.00 0.00 0.00 19.86

PA11-RS02 125-90µm 5.09 41.34 27.36 2.90 0.00 0.00 0.00 0.00 0.00 23.30

PA11-RS02 180-125µm 12.31 34.68 16.82 0.00 0.00 0.00 0.00 0.00 0.00 36.19

PA11-RS02 250-180µm 11.47 38.24 25.96 0.00 0.00 0.00 0.00 0.00 0.00 24.32

PA11-RS02 355-250µm 8.52 33.18 32.96 0.00 0.00 0.00 0.00 0.00 0.00 25.34

PA11-RS02 500-355µm 7.05 30.55 35.25 0.00 0.00 0.00 0.00 0.00 0.00 27.15

PA11-RS02 710-500µm 6.32 51.49 17.40 0.00 0.00 0.00 0.00 0.00 0.00 24.78

PA11-RS02 1mm-710µm 10.16 38.60 27.43 0.00 0.00 0.00 0.00 0.00 0.00 23.81

Table A.8 Raw, corrected and % XRD analyses of PA11-RS02 samples

RAW Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

RS05 <8µm 5.17 232 25.45 17 0 0 0 0 0 16.5

RS05 16-8µm 3.61 270 35.05 19.1 0 0 0 0 0 18.5

RS05 32-16µm 4.08 367 74 0 0 0 0 0 0 11.2

RS05 45-32µm 3.69 311 58.3 0 0 0 0 0 0 20.6

RS05 63-45µm 3.62 311 153 0 0 0 0 0 0 13.8

RS05 90-63µm 3.2 268 61.9 0 0 0 0 0 0 10.9

RS05 125-90µm 3.75 394 132 20.8 0 0 0 0 0 14.1

RS05 180-125µm 4.64 316 135 0 0 0 0 0 0 11.5

RS05 250-180µm 3.31 302 78.9 16.4 0 0 0 0 0 14.9

RS05 355-250µm 3.52 277 226 0 0 0 0 0 0 14.4

Appendix XXIII

RS05 500-355µm 4.86 288 38.4 15.2 0 0 0 0 0 17.1

RS05 710-500µm 5.51 301 52.5 0 0 0 0 0 0 16

RS05 1mm-710µm 3.83 279 52.4 15 0 0 0 0 0 16.3

RS05 >1mm 3.1 355 57.7 14.1 0 0 0 0 0 11.3

Corrected Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

Correction factor: 20 1 2.8 4.3 5 2.5 5 1.65 9.3 20

<8µm 62 355 161.56 60.63 0 0 0 0 0 226

8-16µm 76.6 279 146.72 64.5 0 0 0 0 0 326

16-32µm 110.2 301 147 0 0 0 0 0 0 320

32-45µm 97.2 288 107.52 65.36 0 0 0 0 0 342

45-63µm 70.4 277 632.8 0 0 0 0 0 0 288

63-90µm 66.2 302 220.92 70.52 0 0 0 0 0 298

90-125µm 92.8 316 378 0 0 0 0 0 0 230

125-180µm 75 394 369.6 89.44 0 0 0 0 0 282

180-250µm 64 268 173.32 0 0 0 0 0 0 218

250-355µm 72.4 311 428.4 0 0 0 0 0 0 276

355-500µm 73.8 311 163.24 0 0 0 0 0 0 412

500-710µm 81.6 367 207.2 0 0 0 0 0 0 224

710-1000µm 72.2 270 98.14 82.13 0 0 0 0 0 370

>1mm 103.4 232 71.26 73.1 0 0 0 0 0 330

% Amorphes Quartz Plagioclase K-Feldspar Pyroxene Amphibole Olivine Calcite Aragonite Total Clays

PA11-RS05 <8µm 7.166056011 41.03145 18.67335 7.007709 0 0 0 0 0 26.12143

PA11-RS05 16-8µm 8.57955691 31.2493 16.43332 7.224301 0 0 0 0 0 36.51352

PA11-RS05 32-16µm 12.54839444 34.27465 16.73878 0 0 0 0 0 0 36.43817

PA11-RS05 45-32µm 10.79904009 31.99716 11.9456 7.261577 0 0 0 0 0 37.99662

PA11-RS05 63-45µm 5.551174894 21.84198 49.89749 0 0 0 0 0 0 22.70935

PA11-RS05 90-63µm 6.912827367 31.53586 23.06921 7.363936 0 0 0 0 0 31.11817

PA11-RS05 125-90µm 9.126671912 31.07789 37.17545 0 0 0 0 0 0 22.61998

PA11-RS05 180-125µm 6.19814221 32.56091 30.54444 7.391491 0 0 0 0 0 23.30501

PA11-RS05 250-180µm 8.848089366 37.05137 23.96173 0 0 0 0 0 0 30.1388

PA11-RS05 355-250µm 6.655635227 28.58981 39.38224 0 0 0 0 0 0 25.37231

PA11-RS05 500-355µm 7.687179701 32.39448 17.00346 0 0 0 0 0 0 42.91488

PA11-RS05 710-500µm 9.27483519 41.71403 23.55081 0 0 0 0 0 0 25.46033

PA11-RS05 1mm-710µm 8.089907784 30.25312 10.99645 9.20255 0 0 0 0 0 41.45798

PA11-RS05 >1mm 12.76921557 28.65046 8.800138 9.027366 0 0 0 0 0 40.75282

Table A.9 Raw, corrected and % XRD analyses of PA11-RS05 samples

XXIV Appendix

7. C and N Analyses

Sample Organic Matter Sediment needed for 0.8-

1g C Average Cup Sediment in

LOI550 (%) 0.0008g 0.001g g <90µm mg g g

PA11-SS01 15.15211772 0.01055958 0.0132 0.0119 11.87953 0.0397 0.0123

PA11-SS02 7.686196624 0.02081654 0.0260 0.0234 23.4186 0.0397 0.0234

PA11-SS03 50.04977105 0.00319682 0.0040 0.0036 3.59642 0.0391 0.0038

PA11-SS04 17.45810056 0.0091648 0.0115 0.0103 10.3104 0.04 0.0108

PA11-SS05

PA11-SS06 38.60103627 0.00414497 0.0052 0.0047 4.663087 0.0402 0.0047

PA11-SS07 17.69276748 0.00904324 0.0113 0.0102 10.17365 0.0393 0.0104

PA11-SS08 31.28 0.00511509 0.0064 0.0058 5.754476 0.0401 0.0058

PA11-SS09 12.59952229 0.01269889 0.0159 0.0143 14.28626 0.0401 0.0146

PA11-SS10 27.24730324 0.00587214 0.0073 0.0066 6.606158 0.0389 0.0066

PA11-RS01 10.28354633 0.01555883 0.0194 0.0175 17.50369 0.0403 0.0173

PA11-RS02 12.19609406 0.01311895 0.0164 0.0148 14.75882 0.0401 0.0148

PA11-RS03 9.103557941 0.01757555 0.0220 0.0198 19.77249 0.041 0.0198

PA11-RS04 18.11290644 0.00883348 0.0110 0.0099 9.937665 0.0403 0.0098

PA11-RS05 11.31059246 0.01414603 0.0177 0.0159 15.91429 0.0398 0.016

PA11-RR01 3.177458034 0.05035472 0.0629 0.0566 56.64906 0.0392 0.0566

PA11-RR02 2.98970103 0.05351706 0.0669 0.0602 60.20669 0.0398 0.0604

PA11-RR03 3.886890488 0.04116401 0.0515 0.0463 46.30951 0.0401 0.0464

PA11-RR04 3.470694139 0.04610029 0.0576 0.0519 51.86282 0.0399 0.0516

PA11-RR05 2.375249501 0.06736134 0.0842 0.0758 75.78151 0.0406 0.0762

2.48502994 0.06438554 0.0805 0.0724 72.43373 0.0395 0.0724

% of <90µm fraction needed

PA11-RS02 PA11-RS05

Sediment needed (g)

in (g) Sediment

needed (g) in (g)

>1mm

70-80% / 50%

0.0111-0.0074 0.0110 0.0119-0.0080 0.0079

1mm- 710µm

0.0111-0.0074 0.0110 0.0119-0.0080 0.0081

710µm - 500µm

0.0111-0.0074 0.0110 0.0119-0.0080 0.0080

500-355µm

120-130%

0.01845 0.0184 0.01989 0.0199

355µm - 250µm

0.01845 0.0184 0.01989 0.0199

250µm - 180µm

0.01845 0.0184 0.01989 0.0199

180µm - 125µm

0.01845 0.0184 0.01989 0.0199

125µm- 90µm

0.01845 0.0185 0.01989 0.02

90µm - 63µm

0.01845 0.0184 0.01989 0.0198

63µm - 42µm

0.01845 0.0185 0.01989 0.0199

42µm - 32µm

0.01845 0.0185 0.01989 0.0198

32µm-16µm 80% 0.01181 0.0119 0.01273 0.0128

16µm-8µm 65% 0.00959 0.0096 0.01034 0.0103

<8µm ~50% 0.00738 0.0074 0.00796 0.008

Table A.10 Calculations for determining how much material was necessary to have enough carbon in each sample.

Left: Soil, River and Rock samples, Left: PA11-RS02 and PA11-RS05

Appendix XXV

Sample ID d13C C Amount (ug)

d15N N Amount (ug)

Amount (mg)

%C %N C/N (atomic)

PA11-SS01 < 90µm -27.26 625.63 12.78 31.65 12.3 5.09 0.26 23.06

PA11-SS02 < 90µm -27.89 496.83 6.74 46.01 23.4 2.12 0.20 12.60

PA11-SS03 < 90µm -28.15 1002.91 5.07 44.18 3.8 26.39 1.16 26.48

PA11-SS04 < 90µm -26.48 661.24 12.74 47.50 10.8 6.12 0.44 16.24

PA11-SS06 < 90µm -26.72 769.40 4.99 53.14 4.7 16.37 1.13 16.89

PA11-SS07 < 90µm -27.14 698.55 7.75 42.63 10.4 6.72 0.41 19.12

PA11-SS08 < 90µm -27.76 767.63 5.60 55.07 5.8 13.24 0.95 16.26

PA11-SS09 < 90µm -27.46 641.66 6.02 61.13 14.6 4.39 0.42 12.25

PA11-SS10 < 90µm -27.08 787.07 5.15 56.72 6.6 11.93 0.86 16.19

PA11-ZRS01 < 90µm -28.96 650.56 4.11 44.96 17.3 3.76 0.26 16.88

PA11-ZRS02 < 90µm -29.27 695.00 3.34 44.02 14.8 4.70 0.30 18.42

PA11-ZRS03 < 90µm -28.99 577.44 3.74 38.36 19.8 2.92 0.19 17.56

PA11-ZRS04 < 90µm -29.86 696.78 2.65 43.57 9.8 7.11 0.44 18.66

PA11-ZRS05 < 90µm -28.81 721.61 3.21 46.89 16 4.51 0.29 17.95

PA11-RS02 1mm-710µm -28.75 155.58 6.79 11 1.41

PA11-RS02 710-500µm -28.87 118.96 8.41 11 1.08

PA11-RS02 500-355µm -28.58 265.56 5.33 25.03 18.4 1.44 0.14 12.38

PA11-RS02 355-250µm -28.85 271.01 5.04 23.03 18.4 1.47 0.13 13.73

PA11-RS02 250-180µm -28.80 381.57 4.28 28.81 18.4 2.07 0.16 15.45

PA11-RS02 180-125µm -28.96 622.06 3.15 40.30 18.4 3.38 0.22 18.01

PA11-RS02 125-90µm -29.13 500.42 3.59 32.32 18.5 2.70 0.17 18.07

PA11-RS02 90-63µm -29.15 718.06 2.38 42.52 18.4 3.90 0.23 19.70

PA11-RS02 63-45µm -29.09 680.79 3.36 44.62 18.5 3.68 0.24 17.80

PA11-RS02 45-32µm -29.20 807.78 3.05 52.65 18.5 4.37 0.28 17.90

PA11-RS02 32-16µm -28.55 131.30 6.96 11.9 1.10

PA11-RS02 16-8µm -29.18 680.94 2.77 44.25 9.6 7.09 0.46 17.95

PA11-RS02 < 8µm -29.26 698.02 1.35 51.10 7.4 9.43 0.69 15.94

PA11-RS05 >1000µm -27.30 559.20 6.02 21.31 7.9 7.08 0.27 30.61

PA11-RS05 1mm-710µm -30.05 1018.63 1.87 38.67 8.1 12.58 0.48 30.73

PA11-RS05 710-500µm -28.85 941.09 2.27 44.64 8 11.76 0.56 24.60

PA11-RS05 500-355µm -28.45 907.51 2.38 52.04 19.9 4.56 0.26 20.35

PA11-RS05 355-250µm -28.55 1585.12 1.55 77.36 19.9 7.97 0.39 23.91

PA11-RS05 250-180µm -28.65 942.62 2.47 54.36 19.9 4.74 0.27 20.23

PA11-RS05 180-125µm -28.46 486.91 3.61 32.98 19.9 2.45 0.17 17.22

PA11-RS05 125-90µm -28.56 562.34 3.72 38.06 20 2.81 0.19 17.24

PA11-RS05 90-63µm -28.59 685.60 2.89 45.41 19.8 3.46 0.23 17.61

PA11-RS05 63-45µm -28.66 953.29 2.40 61.92 19.9 4.79 0.31 17.96

PA11-RS05 45-32µm -28.57 1064.03 1.95 69.64 19.8 5.37 0.35 17.83

PA11-RS05 32-16µm -28.19 187.68 4.68 12.8 1.47

PA11-RS05 16-8µm -28.65 713.53 2.37 47.57 13 5.49 0.37 17.50

PA11-RS05 < 8µm -28.70 789.29 1.63 54.63 8 9.87 0.68 16.85

PA11-RR01 -27.26 277.45 5.29 42.54 56.6 0.49 0.08 7.61

PA11-RR03 -25.97 295.02 4.40 36.96 46.4 0.64 0.08 9.31

PA11-RR04 -28.18 144.53 4.95 51.6 0.28

PA11-RR05 -27.85 210.17 4.26 20.93 76.2 0.28 0.03 11.72

Table A.11 Results of Carbon and Nitrogen analyses of soil, river and rock samples as presented by UCDavis SIF

XXVI Appendix

Sample ID d13C C Amount (ug)

C conc µg/L d15N N Amount

(ug)

µg/L Amount (mg)

V water sampled (ml)

%C %N C/N (atomic)

PA11-LW01 -28.23 476.56 501.65 -0.23 46.97 49.44 2.6 950 18.33 1.81 11.84

PA11-LW01 -28.04 475.60 528.45 0.42 51.07 56.75 2.6 900 18.29 1.96 10.86

PA11-LW02 -28.22 465.97 517.75 -0.16 47.50 52.78 2.5 900 18.64 1.90 11.44

PA11-LW02 -28.27 439.91 488.79 0.04 44.73 49.70 2.4 900 18.33 1.86 11.47

PA11-LW03 -28.24 410.86 547.81 0.42 42.70 56.94 2.2 750 18.68 1.94 11.22

PA11-LW03 -28.30 455.37 535.73 0.16 49.32 58.02 2.5 850 18.21 1.97 10.77

PA11-LW04 -28.01 485.22 539.13 0.98 52.14 57.93 2.5 900 19.41 2.09 10.86

PA11-LW04 -28.03 447.64 526.64 1.26 50.33 59.21 2.4 850 18.65 2.10 10.38

PA11-WS01 -28.14 329.92 659.83 2.38 59.38 118.76 1.6 500 20.62 3.71 6.48

PA11-WS01 -28.31 312.25 624.51 1.19 38.59 77.19 1.5 500 20.82 2.57 9.44

PA11-WS02 -27.99 384.62 512.82 1.28 33.57 44.76 2.6 750 14.79 1.29 13.37

PA11-WS02 -27.93 377.80 503.73 2.58 34.37 45.83 3.3 750 11.45 1.04 12.82

PA11-WS03 -29.48 946.44 236.61 1.23 81.14 20.28 5.2 4000 18.20 1.56 13.61

PA11-WS04 -29.12 452.47 452.47 0.31 33.95 33.95 1.7 1000 26.62 2.00 15.55

PA11-WS04 -29.19 386.56 386.56 2.49 32.13 32.13 1.9 1000 20.35 1.69 14.04

Table A.12 Results of Carbon and Nitrogen analyses of filter samples as presented by UCDavis SIF