A high-resolution paleoenvironmental study with...
115
FACULTEIT WETENSCHAPPEN Opleiding Master of Science in de geologie Academic year 2013–2014 Master thesis submitted in partial fulfillment of the requirements for the degree of Master in Science in de geologie Promotor: Prof. Dr. S. Louwye Tutor: W. Quaijtaal Jury: Prof. Dr. D. Van Rooij, Dr. E. Verleyen A high-resolution paleoenvironmental study with dinoflagellate cysts and organic geochemistry of the lower and middle Miocene from the Porcupine Basin (off southwestern Ireland): characterisation of Mi-events and the Middle Miocene Climatic Optimum. Ivo Van de Moortel
Transcript of A high-resolution paleoenvironmental study with...
FACULTEIT WETENSCHAPPEN
Opleiding Master of Science in de geologie
Academic year 2013–2014
Master thesis submitted in partial fulfillment of the requirements for the degree of Master in Science in de geologie
Promotor: Prof. Dr. S. Louwye Tutor: W. Quaijtaal Jury: Prof. Dr. D. Van Rooij, Dr. E. Verleyen
A high-resolution paleoenvironmental study
with dinoflagellate cysts and organic geochemistry of the lower and middle Miocene
from the Porcupine Basin (off southwestern Ireland): characterisation of Mi-events and the
Middle Miocene Climatic Optimum.
Ivo Van de Moortel
1
Content
1. Abstract In het Nederlands .............................................................................................................4
2. Introduction ...................................................................................................................................7
3. Events during the early and middle Miocene .................................................................................8
3.1 Miocene Isotope Events ............................................................................................................8
3.1.1 Stable Oxygen Isotope Curve ..............................................................................................8
3.1.2 Miocene Isotope Events......................................................................................................9
3.1.3 Eustatic Sea level Curve .................................................................................................... 10
3.2 Ice Sheets ................................................................................................................................ 10
3.2.1 Antarctic Ice Sheets .......................................................................................................... 10
3.2.2 Northern Hemisphere Glaciation ...................................................................................... 12
3.3 Vegetation .............................................................................................................................. 13
3.4 Milankovitch Cycles ................................................................................................................. 13
4. Climate Forcings ........................................................................................................................... 15
4.1 Paleoceanography ................................................................................................................... 15
4.1.1 Water Masses ................................................................................................................... 16
4.1.2 Miocene paleoceanography .............................................................................................. 17
4.1.3 Greenland-Scotland Ridge ................................................................................................ 18
4.1.4 East-Tethys Seaway .......................................................................................................... 20
4.1.5 Connection Paratethys and North Sea Basin ..................................................................... 21
4.1.6 Central American Seaway & Caribbean Sea ....................................................................... 21
4.1.7 Indonesian Seaway ........................................................................................................... 22
4.2 CO2 .......................................................................................................................................... 23
4.2.1 Orogenesis ....................................................................................................................... 24
4.2.2 Monterey Hypothesis ....................................................................................................... 25
4.2.3 Magmatic Events .............................................................................................................. 26
2
4.2.4 Controversy ...................................................................................................................... 26
5. Geological Setting ......................................................................................................................... 26
5.1 Location .................................................................................................................................. 26
5.2 Seismic Units ........................................................................................................................... 27
5.3 Sedimentary Units ................................................................................................................... 28
5.4 Dating ..................................................................................................................................... 29
6. Methodology ................................................................................................................................ 30
6.1 Material .................................................................................................................................. 30
6.2 Palynological measurements ................................................................................................... 30
6.2.1 Palynological processing ................................................................................................... 30
6.2.2 Taxonomy ......................................................................................................................... 31
6.2.3 Paleoenvironmental indices .............................................................................................. 32
6.3 Organic geochemistry .............................................................................................................. 34
6.3.1 𝑈37𝐾′- index .................................................................................................................... 34
6.3.2 𝑇𝐸𝑋 86𝐻 - index .............................................................................................................. 35
6.3.3 BIT- index ......................................................................................................................... 36
6.3.4 Organic Geochemical Processing ...................................................................................... 37
6.4 Statistical analysis of time-series ............................................................................................. 38
6.4.1 Cross-covariance............................................................................................................... 38
6.4.2 Pearson correlation coefficient ......................................................................................... 38
6.4.3 Auto regressive model analysis ......................................................................................... 38
7. Results .......................................................................................................................................... 39
7.1 Sedimentation rates ................................................................................................................ 41
7.1.1 Apparent sedimentation rate ............................................................................................ 41
7.1.2 Sample dimensions ........................................................................................................... 41
7.1.3 Sampling resolution .......................................................................................................... 41
7.2 Palynomorph assemblage ........................................................................................................ 41
3
7.2.1 Dinoflagellate cyst assemblage ......................................................................................... 41
7.2.2 Other marine palynomorphs ............................................................................................. 42
7.2.3 Biostratigraphical analysis................................................................................................. 43
7.2.4 Dinocysts per gram of dry-weight sediment ...................................................................... 43
7.2.5 Diversity ........................................................................................................................... 43
7.2.6 Sea-surface temperature .................................................................................................. 44
7.2.7 Neritic/Oceanic signal ....................................................................................................... 44
7.2.8 Continental influence ....................................................................................................... 45
7.2.9 Sea-surface Productivity ................................................................................................... 45
7.3 Organic geochemistry .............................................................................................................. 45
7.3.1 𝑈37𝐾′ - index ................................................................................................................... 45
7.3.2 𝑇𝐸𝑋 86 𝐻 - index ............................................................................................................. 46
7.3.3 BIT – index ........................................................................................................................ 46
7.4 Statistical analysis of time-series ............................................................................................. 47
7.4.1 Autocorrelation ................................................................................................................ 47
7.4.2 Pearson correlation coefficient ......................................................................................... 48
7.4.3 Auto regressive model ...................................................................................................... 48
8. Discussion ..................................................................................................................................... 50
8.1 Noise and Bias of Signals ......................................................................................................... 50
8.1.1 Random noise ................................................................................................................... 50
8.1.2 Sampled Interval ............................................................................................................... 50
8.1.3 Tuning of data .................................................................................................................. 50
8.1.4 Selective preservation ...................................................................................................... 51
8.2 Age model ............................................................................................................................... 51
8.2.1 Labyrinthodinium truncatum ............................................................................................ 51
8.2.2 The RD2 hiatus ................................................................................................................. 51
8.3 Linking the Paleoenvironmental Indices................................................................................... 52
4
8.3.1 SST and N/O ..................................................................................................................... 52
8.3.2 S/D ratio and BIT-index ..................................................................................................... 55
8.3.3 P/G and BIT ...................................................................................................................... 55
8.3.4 P/G, S/D and SST ............................................................................................................... 55
8.3.5 Diversity ........................................................................................................................... 56
8.3.6 Cyclopsiella granosa/elliptica ............................................................................................ 56
8.3.7 Dinocysts/gram ................................................................................................................ 57
8.3.8 Reworking ........................................................................................................................ 58
8.4 Forcings ................................................................................................................................... 58
8.4.1 Long term trend................................................................................................................ 58
8.4.2 Short term trend ............................................................................................................... 58
8.4.3 Introduction of NSDW....................................................................................................... 60
8.5 Hypothesis for Miocene paleoenvironment of the south-western Iris Shelf ............................. 60
9. Conclusion .................................................................................................................................... 62
10. References .................................................................................................................................. 63
1. ABSTRACT IN HET NEDERLANDS
Het Mioceen is een belangrijke link in de overgang van een warm klimaat tijdens het Krijt en Eoceen,
naar het Kwartair, gedomineerd door ijstijden. De twee meest opvallende paleoklimatologische
gebeurtenissen zijn het“Middle Miocene Climatic Optimum” (MMCO) en de “Middle Miocene
Climatic Transition” (MMCT). Het MMCO is een periode waarin het warme Miocene klimaat steeg
naar een maximum temperatuur. Deze periode werd gevolgd door de MMCT waarin de globale
temperatuur een sterke stapsgewijze afkoeling kende. Na het MMCT bereikte het klimaat nooit meer
de hoge temperatuur als voorheen. Bovendien is het Mioceen gekenmerkt door een aantal duidelijke
minima in de benthische stabiele zuurstof isotopen (δ¹⁸Obenthic). Deze “Miocene isotopic events” (Mi-
events) werden gerelateerd aaneen afkoeling van de diepzee en/of de dynamiek van de Antarctische
ijskappen. Maar er is nog veel onenigheid over welk mechanisme deze veranderingen stuurt.
Een eerste doel van deze thesis bestond eruit om meer duidelijkheid te scheppen over de mogelijke
mechanismen. Hiervoor is een literatuurstudie uitgevoerd om een aantal belangrijke Miocene
paleomilieu /klimatologische gebeurtenissen te definiëren. Daarna werd een is gedaan over de
mogelijke forcings.
5
Het hoofddoel van deze thesis is het uitvoeren van een paleomilieureconstructie en deze dan binnen
het grotere kader van de literatuurstudie te plaatsen. Er zijn hiervoor monsters genomen uit het
onderste deel van Site U1318 van the International Ocean Discovery Program (IODP) leg 307 ‘Modern
Carbonate Mounds: Porcupine Drilling. IODP leg 307 was oorspronkelijk bedoeld om de koud-water
koralen in Porcupine Basin te onderzoeken. Maar tijdens de reconstructie van de
afzettingsgeschiedenis door Louwye et al. (2008) werd aangetoond dat de Miocene lagen geschikt
waren voor een hoge-resolutie paleomilieu reconstructie. Tijdens deze studie is er een drastische
shift in dinoflagellaat cysten waargenomen. Dit werd gerelateerd aan de MMCT. Quaijtaal et al.
(2014) voerde het eerste hoge resolutie onderzoek uit op de bovenste Miocene lagen en
identificeerde drie Mi-events (Mi-3a, Mi-3b en Mi-4). Deze werden gelinkt met een afkoeling van de
oppervlakte wateren en een mogelijke zeespiegel daling. De hoge-resolutie record van Quaijtaal et
al. (2014) is aangevuld met samples uit het onderste deel van Site U1318B zodat de ondergrens van
deze record verder terug in de tijd wordt gelegd.
De paleomilieureconstructie is uitgevoerd met behulp van onderzoek op zowel dinocysten als
geochemische proxies. Verschillen in de gevonden dinocyst assemblages werden gebruikt voor een
kwalitatieve analyse van het paleomilieu. De gevonden species zijn onderverdeelt naargelang de
voorkeur voor specifieke milieus. Hierbij werd de onderverdeling van Quaijtaal et al. (2014) gevolgd.
De relatieve verhoudingen van die bepaalde species kunnen dan gebruikt worden als paleomilieu
indicator. Indexen zijn gebruikt voor: temperatuur van het oppervlaktewater (SST), primaire
productiviteit, kustnabijheid, relatieve input van terrestrisch materiaal. Bovendien is een onderzoek
gedaan naar de gevonden biodiversiteit en relatieve proportie van gevonden herwerkte species. De
data verworven met de gevonden dinocyst assemblages is aangevuld met data van geochemische
proxies. Er zijn tijdens dit onderzoek twee geochemische proxies gebruikt voor een schatting van de
absolute SST.De 𝑈37𝐾′
- index is gebaseerd op de relatieve verhouding van onverzadigde alkenonen.
Alkenonen zijn lange ketens van onverzadigde ketonen en worden geproduceerd door de
coccolithoforen. In deze thesis is de kalibratie-curve van Müller et al. (1998) gebruikt. De TEX86 index
is een relatief recente methode, die is voorgesteld in 2000. Deze proxy is gebaseerd op de
samenstelling van membraan-vetten uit niet-thermofiele Archaea. Deze membranen hebben een
unieke samenstelling en bevatten glycerol dialkyl glycerol tetraethers (GDGT). Er is een positieve
correlatie tussen de relatieve verhouding van GDGT met twee of meer cyclopentaan ringen en SST.
De meest recente kalibratie-methode gebruikt twee verschillede indexen, afhankelijk van de SST.
Hier is de index voor SST boven de 15°C gebruikt, de TEX 86𝐻 . De derde proxy (de BIT-index) is een
maat voor de relative input van terrestrisch organisch materiaal dat fluviatiel werd. Deze proxie is de
verhouding tussen Crenarcheol en vertakte GDGT vetten, afkomstig van het continent. De data over
de verschillende factoren van het paleomilieu is aanzien als signalen en een statistische analyse is
hierop uitgevoerd. De autocorrelatie geeft een indicatie voor de cycliciteit in de signalen en de
Pearson correlatiecoëfficiënt geeft op zijn beurt een maat voor in hoeverre de verschillende signalen
een gelijke tendens vertonen. Deze correlatie is geëvalueerd op significantie door het uitvoeren van
een autoregressief model van de eerste orde.
De paleomilieureconstructie heeft twee Mi-events (Mi-1b en Mi-2a) aan het licht gebracht. De
oudste samples vertonen een overgang van een fluctuerende koude periode naar een stabiele
warme periode. Dit is gecorreleerd met de transitie van Mi-1b naar het eerste deel van het MMCO.
Deze warme periode is onderbroken door nieuwe afkoeling. Deze koudere periode is gekenmerkt
door een hoge variabiliteit in paleomilieu, waarbij alle paleomilieu factoren sterk fluctueren. Dit is
6
gecorreleerd met de Mi-2a.De jongste samples bevatten de RD2 discordantie. Deze zijn gedateerd op
~16,3 Ma met behulp van het age-model van Quaijtaal et al. (2014). Het gerelateerde hiaat is geschat
op ~50 kyr. Dit bevestigt de veronderstelling van Louwye et al. (2008) dat dit hiaat maar van een
kleine orde was. Aangezien de RD2 is gelinkt met sterkere diep-water stromingen door de introductie
van Polair water in de Noord-Atlantische Oceaan, zijn de bovenliggende samples onderzocht op een
mogelijke trend. Er is geen directe shift in de signalen uit het sample net boven de RD2 gevonden.
Maar de effecten lijken wat trager op gang te komen. Er is voorgesteld dat de stijging in SST en daling
in primaire productiviteit het gevolg kan zijn van opwarming van de Noorse Zee door de introductie
van warmer water. Dit veroorzaakte mogelijks een noordwaartse migratie van het IJslandse
hogedrukgebied meer naar de huidige positie toe.
Een analyse van de mogelijke forcings wees op de invloed van zowel de ~172kyr als de 1,2 Myr
obliquiteitscycli. Er is verondersteld dat de beide Mi-events werden veroorzaakt door een minimum
in de 1,2 Myr obliquiteitscyclus. Een minimum in de obliquiteit modulus zorgt ervoor dat beide polen
gedurende een langere periode minder opgewarmd worden in de zomer. Dit zorgt er voor dat de
groei van de ijskappen tijdens de winterperiode niet volledig wordt afgesmolten tijdens de zomer.
Hierdoor blijven de ijskappen aangroeien doorheen de jaren. Door het albedo effect zal het continent
minder sterk opwarmen en smelten de ijskappen opnieuw wat minder af. Doorheen de jaren zullen
de ijskappen sneller aangroeien en kan een nieuwe ijstijd ontstaan.
De kortere ~172kyr obliquiteitscycli is veronderstelt om via eenzelfde mechanisme de SST af te
koelen tijdens de koudere periode. De SST fluctueert niet tijdens de warme periode. Dit kan dit er op
duiden dat de Antarctische ijskappen te gereduceerd waren in oppervlakte, zodat de ~172kyr
obliquiteitscycli er geen invloed op hadden. Een andere link is voorgesteld tussen de S/D en P/G
ratios enerzijds en de ~172kyr obliquiteitscycli anderzijds. Er is veronderstelt dat de S/D en P/G ratios
beïnvloed worden door fluctuaties in positie en/of intensiteit van de atmosferische cellen. Er wordt
aangenomen dat het IJslandse hogedrukgebied minder Noordelijk lag voor de introductie van warme
wateren van thermohaline circulatie in de Arctische Oceaan.
De hoge correlatie tussen de geochemische proxies voor SST en de W/C index bewijst de
bruikbaarheid van de gebruikte Warm/Cold index. Maar de W/C index toont een sterkere
variabiliteit wat wijst op een grotere foutmarge. Verder is er ook veronderstelt dat door gebruik te
maken van warme neritische species, de W/C index mogelijks een grotere invloed van het
continentale klimaat vertoont dan de geochemische proxies. De analyse op de Neritic/Oceanic index
toont aan dat de methode van in Quaijtaal et al. (2014), niet gebruikt kan worden tijdens het interval
onderzocht tijdens deze thesis. De reden hiervoor is de veel kleinere zeespiegel daling tijdens de Mi-
2a event dan tijdens het interval onderzocht door Quaijtaal et al. (2014). Bovendien komen een
aantal species gebruikt in de N/O index van Quaijtaal et al. (2014) niet voor in de samples van deze
studie. De species die wel voorkomen zijn te veel beïnvloed door zowel temperatuur als
kustnabijheid. Hierdoor is er veronderstelt dat de N/O index gebruikt hier, een duplicatie is van de
W/C index. Er wordt aangeraden om andere species te gebruiken indien men deze index wilt
toepassen op de onderzochte samples.
7
2. INTRODUCTION
It took several important steps to transform the early Cenozoic Greenhouse World into the glaciated
Icehouse World of the Quaternary. Near the end of last century, Miller et al. (1991) correlated 12
Oligocene and Miocene increases in benthic stable oxygen isotopes (δ¹⁸Obenthic) with erosional events
along continental margins from the sea level curve of Haq et al. (1987). These events were named as
the Miocene Isotopic Events (Mi-events; Miller et al., 1991). Over the years, higher-resolution
records allowed for the detection of new Mi-events and better dating (e.g. Miller et al., 1996; 1998;
Westerhold et al., 2005; Abels et al., 2005). Nowadays it is generally accepted that after the Early
Eocene Climatic Optimum the Earth gradually cooled down until the first ice sheets occurred on
Antarctica during the Eocene-Oligocene Transition (Zachos et al., 2001). It is also accepted (e.g.
Wright et al., 1992; Zachos et al., 2001; Shevenell & Kennett, 2004) that the following
δ¹⁸Obenthicaberrations are linked to changes in bottom water temperature and/or volume of ice-
sheets. However, there is still a lack of continuous, high-resolution paleoenvironmental records. This
causes the climate forcings to remain a subject of debate. The possible forcings include: changes in
greenhouse gas levels such as CO2 (Vincent & Berger, 1985; Raymo & Ruddiman, 1992), tectonically
induced changes in paleoceanography (Woodruff & Savin, 1989; Flower & Kennett, 1994) and semi-
periodic oscillations in the Earth’s orbit (Westerhold et al., 2004; Abels et al., 2005; Liebrand et al.,
2011).
A first goal of this thesis was to unravel some uncertainties concerning the forcings of late-early and
early-middle Miocene climatic events (Middle Miocene Climatic Optimum and Mi-events). Therefore,
a literature study was carried out to define some important Miocene
paleoenvironmental/paleoclimatic events. Afterwards a review was performed on the possible
climate forcings. Both literature studies can be found in Chapter 3 and 4 respectively.
The main goal of this thesis was to carry out a paleoenvironmental reconstruction and implement it
into the bigger picture of the literature study. Samples were taken from Site U1318 from the
International Ocean Discovery Program (IODP) leg 307 ‘Modern Carbonate Mounds: Porcupine
Drilling’. IODP leg 307 intended to investigate cold water coral mounds in the Porcupine Basin.
However, the reconstruction of the depositional history by Louwye et al. (2008) indicated that the
Miocene strata recovered below the mounds showed great promise for a high-resolution
paleoenvironmental study of the eastern North-Atlantic realm. Moreover, Louwye et al. (2008) linked
the relatively drastic shifts in dinoflagellate cyst (dinocyst) assemblages with the Middle Miocene
Climatic Transition. Not long afterwards, Quaijtaal et al. (2014) carried out a first high-resolution
study on the upper part of the Miocene strata and indentified three Mi-events (Mi-3a, Mi-3b and Mi-
4). They stated that these events were associated with cooling of sea-surface waters and a possible
drop in relative sea-level. The lowermost part of site IODP leg 307 site U1318 was sampled, in order
to extend the high-resolution paleoenvironmental record of Quaijtaal et al. (2014; unpublished)
further back in time. Chapter 5 handles the geological setting.
The high-resolution paleoenvironmental study uses both dinocysts and geochemical proxies for the
reconstruction of sea surface temperatures. Dinoflagellate cyst distribution in the aquatic realm is
controlled by several factors such as sea surface temperatures (SST), salinity (SSS), productivity (SSP)
and sea-level changes (Louwye, Head & De Schepper, 2004; Verleye & Louwye, 2010; Quaijtaal et al.,
2014). This makes them a useful tool for paleoenvironmental reconstruction. The data of dinocyst
8
assemblages was added with data on geochemical proxies for SST ( TEX 86𝐻 and 𝑈37
𝐾′ index ).
Afterwards, a statistical analysis was carried out in order to detect noise caused by random error
and/or extreme events. The different signals were correlated with one another and an
autoregressive process was performed to evaluate the significance of the correlation. The
methodology is presented in Chapter 6, while Chapter 7 handles the results. In Chapter 8 the results
are discussed and implemented in the literature review. A brief conclusion is given in Chapter 9.
3. EVENTS DURING THE EARLY AND MIDDLE MIOCENE
3.1 Miocene Isotope Events
3.1.1 Stable Oxygen Isotope Curve
Since early 1970, benthic stable oxygen (δ¹⁸Obenthic) isotopes derived from benthic foraminifers have
been used to reconstruct regional and global climate change (Zachos et al., 2001). Deep-sea δ¹⁸Obenthic
values reflect the ¹⁸O values of deep water masses. The ¹⁸O values of deep water masses are, in
turn, influenced by events in regions of deep water production (Miller et al., 1991). In order to derive
the effect of ice-volume changes from temperature changes ¹⁸O values of planktonic foraminifers
(¹⁸Oplanktonic) in low-latitude, non-upwelling regions are used. SST in these regions is considered to be
least affected by temperature changes (Miller et al., 1991 and references therein). A covariance
between δ¹⁸Obenthic and low-latitude ¹⁸Oplanktonic - signals is used as the best indication for a link with
changing ice volumes (Miller et al., 1987; 1991; 1996; Flower and Kennett, 1994; Shevenell et al.,
2008).
However, an increase in the δ¹⁸O values recorded by both benthic or planktonic foraminifera is a
function of both temperature and the sea water ¹⁸O value were the organisms lived (Miller et al.,
1991; Flower and Kennett, 1994; Shevenell et al., 2008). An increase in δ¹⁸O values derived from
foraminifers thus reflect several events (Miller et al., 1991): a decrease in water temperature, a
global increase in water ¹⁸O due to ice growth or, for planktonic foraminifera, a local increase in
water ¹⁸O due to enhanced evaporation or reduced precipitation (reflecting local salinities). The
uptake of ¹⁸O changes in foraminifers is species-dependant. So different species will provide
different ¹⁸O values. Some species even change their uptake throughout time, rendering certain
records useless (Miller et al., 1991).
9
Figure 3.1 (Adjusted from Zachos et al., 2001): Cenozoic benthic stable oxygen isotope curve. MMCO: Middle
Miocene Climatic Optimum; MMCT: Middle Miocene Climatic Transition.
3.1.2 Miocene Isotope Events
Miller et al. (1991) identified and dated 12 oxygen isotope zones throughout the Oligocene and
Miocene. The base of these isotopic zones were defined as the maximum benthic δ¹⁸Obenthic value.
They also recognised six of them in tropical or subtropical δ¹⁸Oplanktonic records. Moreover, they
correlated these events with sea level changes, derived from sequence boundaries on continental
margins (Haq et al., 1987). This led to the assumption that the increases in δ¹⁸Obenthic values reflected
Antarctic glaciation events. Considering the Miocene, the positive increases in δ¹⁸Obenthic related to
these zones were also referred to as Miocene isotopic events (Mi-events; e.g. Miller et al., 1996;
Quaijtaal et al., 2014). In the following years, new higher-resolution records allowed for the
detection of new Mi-events and better dating (Miller et al., 1996; 1998). Because of strong
correlation between changes in the ¹⁸O curve, including the Mi-events, and Milankovid-cycles (see
below -section 2.3), some researchers gave orbitally tuned ages for the largest aberrations in
δ¹⁸Obenthic (Westerhold et al., 2005; Abels et al., 2005).
There are 2 prominent features in the Miocene δ¹⁸Obenthic - curve. During the late early to early middle
Miocene there is a prominent minimum in δ¹⁸Obenthic, which is often referred to as the Mid-Miocene
Climatic Optimum (MMCO; Zachos et al., 2001). It represents a long warm period that can be
inferred from several proxies, such as the southward migration of tropical and subtropical fauna to
New-Zealand (~16 Ma; Hornibrook, 1992)or the appearance of warm water molluscs and mangroves
in Japan (Itoigawa & Yamanoi, 1990). The MMCO is followed by a sharp decrease in δ¹⁸Obenthic. In this
thesis, this event will be referred to as the Middle Miocene Climatic Transition (MMCT), following
10
some publications used during this thesis (e.g. Flower & Kennett, 1994; Hauptvogel & Passchier,
2012; Hamon et al., 2013). It represents a period of rapid ice-sheet growth and is associated with
drastic species turnover in both hemispheres (Flower & Kennett, 1994).
3.1.3 Eustatic Sea level Curve
Haq et al. (1987) created a first eustatic sea-level curve based on principles of sequence stratigraphy.
This pioneering work, although widely cited, contained some problems including higher amplitude
variations, when compared to other studies (Kominz et al., 2008; John et al., 2011). Kominz et al.
(2008), presented an eustatic curve, based on backstripping offshore New Jersey onshore cores. The
newest curve was published by John et al. (2011), which gives larger estimates for all eustatic sea-
level drops than the one of Kominz et al. (2008). The newest eustatic sea level seems to correlate
well with the δ¹⁸Obenthic curve, linking eustatic sea level falls with Mi-events.
Figure3.2 (Adjusted from John et al., 2011): Sealevel Curve. Red line marks the interval studied during this
thesis.
3.2 Ice Sheets
3.2.1 Antarctic Ice Sheets
During the last decades, several investigations (e.g. Miller et al., 1987; 1991; 1996) used analyses on
δ¹⁸Obenthic to develop a broad overview of Antarctic Ice Sheet fluxes in the Miocene. These datasets
were compiled into a review article of Zachos et al. (2001), which served as the backbone for the
following investigations of e.g. Shevenell et al., 2004a, 2004b, 2008; Westerhold et al., 2005; Pekar &
11
DeConto, 2006. A general trend is recognised, namely that Antarctic Ice Sheets were already present
from the early Oligocene onwards (Zachos et al., 2001). However, the Ice Sheets were of a more
temperate character (Hambrey et al., 1991; Flower & Kennett, 1994). A late Oligocene warming
reduced their extent until the Mi-1 cooling event at the Miocene-Oligocene boundary (Zachos et
al.,2001). During the Mi-1 event a strong increase in ice-volume occurred for a period of 200kyr
(Wilson et al., 2009). Afterwards, during the early Miocene, large fluctuations in the East-Antarctic
Ice Sheet volume occurred, suggesting a more dynamic cryosphere than during the Oligocene (Pekar
& DeConto, 2006).
Proceeding into the time range of interest for this thesis, an additional proxy for ice flow patterns
was added to the former proxies for ice volumes. Hauptvogel & Passchier (2012) used the heavy
mineral content of sediments in the Ross Sea, 10 km offshore Antarctica, as source rock indicators of
glacially eroded material. Since Passchier (2007) stated that the area of maximum glacial erosion is
located under the edges of glaciers, Hauptvogel & Passchier (2012) could determine the location of
ice margins throughout time. Before the MMCO, between 17,7 and 17,1 Ma, their reconstructed ice-
flow patterns pointed towards relatively large ice-sheets. This required a West-Antarctic ice sheet
during, at least, glacial periods. This correlates with the δ¹⁸Obenthic values of Pekar & DeConto (2006),
suggesting periodically larger Antarctic ice volumes than during recent interglacial. Hauptvogel &
Passchier (2012) did observe a significant calving event around 17,3 Ma, which was further discussed
by the authors. This could indicate shrinking Antarctic ice sheets. A second heavy mineral interval
was recognized between 17,1 and 15,5 Ma. The East-Antarctic ice sheets retreated towards the
Transantarctic Mountais and became oscillating outlet glaciers (Hauptvogel & Passchier, 2012). This
is consistent with δ¹⁸O values, suggesting a minimal ice cover on Antarctic during the MMCO (e.g.
Zachos et al., 2001). From 15,5 Ma onwards, the East-Antarctic ice sheet advanced towards the
Antarctic coastline (Hauptvogel & Passchier, 2012). This timing is in agreement with Shevenell et al.
(2008) who concluded that Antarctic cryosphere expansion began ~1 Ma prior to the globally
recognized climate step (MMCT; Mi-3a). It is also correlates well with the sea-level reconstruction of
John et al. (2011). A last phase from 14,3 Ma onwards, was characterized by the combining both the
West- and East-Antarctic ice sheets into one single ice sheet, larger than the present-day interglacial
configuration (Hauptvogel & Passchier, 2012).
Figure 3.3 ( Adjusted from Hauptvogel & Passchier, 2012): Antarctic Ice-sheet dynamics.
12
3.2.2 Northern Hemisphere Glaciation
Before the recent (2004) Integrated Ocean Drilling Program Arctic Coring Expedition (APEX ; IODP Leg
302), pre-Pleistocene material from the central Arctic Ocean was only rarely recovered (Moran,
2006). Although some researchers started to argue the contrary (Wolf-Welling et al., 1996), the
hypothesis of an asynchronous polar cryosphere development during the Cenozoic was generally
accepted (Flower and Kennett 1994; Zachos et al., 2001). It was thought that the Northern
Hemisphere was ice-free until the late Pliocene (Shackletonet al., 1984; Raymo et al., 1989).
asynchronous polar cryosphere development would have been caused by the absence of a polar
continent on which a continental ice sheet could develop (Thiede et al., 1989).
The IODP APEX recovered sediments from the Upper-Cretaceous to the Quaternary at the
Lomonosov Ridge and founde vidence for ice-rafted debris (IRD) was found at the Lomonosov Ridge
(Central Arctic Ocean) as early as ~45 Ma (Moran et al., 2006). Around 14 Ma a significant increase in
IRD was transported towards the Lomonosov Ridge (Moran et al., 2006). While Knies and Gaina,
(2008) found IRD being deposited in the Fram Strait. While Knies and Gaina (2008) found IRD being
deposited in the Fram Strait, suggesting transport of icebergs towards the Greenland-Norwegian Sea.
The latter authors investigated the deposited mineral assemblages and concluded that the IRD
originated from a calving ice sheet located in the uplifted northern Barents Sea. Frank et al., (2008)
found a continuous sea-ice cover over the Lomonosov Ridge during the last 12,3 Ma.
These recent studies imply a co-evolution of ice sheets on both poles, enhancing the albedo effect
and giving an improved positive feedback mechanism towards a global cooling event.
Figure 3.4: Left: Map eastern and central Arctic with the position of the ACEX Core (Frank et al., 2008); Right:
Reconstructed paleobathymetry at 14 Ma(Knies & Gaina, 2008). Yellow arrows show the proposed pathway
of icebergs. The location of Site 909, investigated by Kniess & Gaina (2008) is highlighted. HR: Hovgard Ridge;
GR: Greenland Ridge.
13
3.3 Vegetation
Pound et al. (2012) investigated the global distribution of vegetation throughout the middle and late
Miocene and concluded that the vegetation reflects globally warmer temperatures than today. They
also found significantly shallower latitudinal temperature gradients during the Langhian than today.
The latitudinal temperature gradients became progressively steeper throughout the remainder of the
Miocene. However when they compared the Northern with the Southern Hemisphere, the inferred
increase in latitudinal temperature gradient occurred at different paces. The Southern Hemisphere
already had a more modern-like gradient during the Serravallian, while in the Northern Hemisphere a
modern-like gradient was not reached until after the Messinian. Pound et al. (2012) also found
evidence for tundra vegetation on Antarctica during the Langhian. This vegetation became extinct
during the Serravallian, which is reflects the fully glaciated Antarctic continent. Pound et al. (2012)
agreed with Flower & Kennett (1994) that vegetation reflected drier conditions from the middle
Miocene onwards.
Zooming into North-western Europe, Utescher et al. (2012) found that the vegetation patterns of the
Lower Rhine Basin (NW Germany) correlated with Mi-events. These cooling events were mainly
expressed by decreased Coldest Month Mean temperature (CMM). However the latter is probably
caused by a bias: the vegetation-based estimates are more sensitive to winter season temperature,
because this is the most limiting factor for subtropical vegetation (Donders et al., 2009 and
references therein). Larsson et al. (2011) investigated palynomorph distribution in Denmark between
19 and 8 Ma and recognised 4 main Miocene climatic events. A first cooling event occurred ~19 Ma,
and was rapidly followed by a warming at 18,5 Ma. The warmest period in Neogene Denmark started
at ~17 Ma. They correlated this with the global MMCO and inferred the following temperatures: a
stable Warmest Month Mean temperature (WMM) of ~27°C; Mean Annual Temperatures (MAT)
between 18-17°C; CMM higher than 10°C. The warm period is interrupted by a short cooling
(expressed in MAT and CMM) and drying event barely older than 16 Ma, possibly reflecting the Mi-2
event. The MMCO was followed by a marked temperature decrease (expressed in all temperature
estimates: CMM; MAT; WMM) in the middle Serravallian (~13Ma). The latter decrease was
continued by WMM throughout the remainder of their interval, while the CMM and MAT showed a
high variability possibly linked to small-scale climate cycles. They suggested relatively high
precipitation rates during their entire interval, but inferred a link between drier conditions and
cooling events. Furthermore, they confirmed a very weak latitudinal temperature gradient during the
middle to early late Miocene of Europe.
The Icelandic vegetation patterns reflect a delay in cooling when compared to the global vegetation
patterns (Denk et al., 2013). No cooling occurred when considering the 15 and 12 Ma data points,
although short term cycles are not visible due to the low resolution. The Icelandic climate
deterioration occurred between 12 and 10 Ma, hereby offsetting the first signals of the global
MMCT. Denk et al. (2013) suggested a direct link with the effective heat transport of an enhanced
Atlantic Meridional Overturning Circulation.
3.4 Milankovitch Cycles
Due to the rapidly shifting nature of both the δ¹⁸Obenthic and the δ¹³Cbenthic curves, researchers try to
link these higher frequency changes with the semi-periodic oscillations in the Earth’s orbit (e.g.
14
Zachos et al., 2001; Westerhold et al., 2004; Abels et al., 2005; Liebrand et al., 2011). It appears that
linking the semi-periodic climate variability to orbital pacing was successful (Zachos et al., 2001). Due
to this success, several researchers (e.g. Westerhold et al., 2004; Abels et al., 2005; Holbourn et al.,
2014) started to tune their data to computed variations of the Earth’s orbit of, e.g.,Laskar et al.
(2004). After doing so, they obviously found even better matches (see disussion -section 8.1.4).
A review of articles published on this topic was given by Zachos et al. (2001). It seemed that when ice
sheets were present, the primary beat occurred in the obliquity band for both the higher and lower
frequency bands (41kyr - 1,2 Myr). This is caused by the high sensitivity of ice-sheets to high latitude
insolation changes, particularly when there was only an unstable configuration with partly ice-
covered polar regions. The opposite is true during the ice-free periods of the Cenozoic: when no ice-
sheet amplifier is present, there is almost no influence of the obliquity band. The eccentricity
oscillations (100Kyr & 400kyr) are another strong forcing on climate, both in frequency and
amplitude. The power of the 400kyr cycle is especially pronounced during the early Miocene. The
eccentricity forces minima in irradiation through minima in precession amplitude. However, this
needs a secondary mechanism that transports this effect towards high-latitudes and hereby
amplifying the Earth’s response through responses of the ice-sheet (Zachos et al., 2001).
The Mi-1 event was correlated with both long and short term eccentricity (2,4 Myr ; 400 kyr; 100 kyr)
and obliquity (1,2 Myr; 172kyr) minima (Zachos et al., 2001; Wilson et al., 2009; Liebrand et al.,
2011). Liebrand et al. (2011) suggested that during their investigated interval (up to 19 Ma) a
deglaciation occurred every 100kyr. On the other hand not every 400kyr cycle was connected with a
major ice sheet expansion. They proposed that the influence of the 100kyr cycle needed to be
suppressed before the 400 kyr cycle could get a grip on the Antarctic Ice sheet. Liebrand et al. (2011)
concluded that other forcings were needed to suppress the effect of the 100kyr cycles.
Spectral analysis from the MMCO comes from a high-resolution research near the equatorial Pacific
(Holbourn et al., 2014). Until 14,7 Ma, the δ¹⁸Obenthic and the δ¹³Cbenthic curves showed strong negative
correlation and were paced by the 100kyr eccentricity cycles. These were interpreted as eccentricity
paced carbonate dissolution events connected to 100kyr cycled warming periods. From 14,7 Ma
onwards (MMCT; Holbourn et al., 2014) a shortening of the dominant signal from 100kyr towards
41kyr rhythm occurred. This transition was coeval with increased carbonate preservation. It was
followed by a period with a more 400kyr-like signal. This is in correlation with Shevenell et al. (2008),
who found a 400kyr glacial-interglacial cycles. A sharp δ¹⁸O increase between 13,9 and 13,8Ma was
immediately followed by a last (and largest)orbitally paced carbonate maximum event (Holbourn et
al., 2014): a 400kyr δ¹³Cbenthic maximum, called the Monterey excursion.
Westerhold et al. (2004) found that the strong increase in δ¹⁸Obenthic linked with Mi-3 was correlated
with 400kyr and ~174kyr eccentricity minima, closely followed by a 1,2 Ma obliquity minimum. They
actually observed a spectral peak around 180kyr, but related this to the asymmetry of the ~174kyr
obliquity cycle. Abels et al. (2005) found a ~172kyr cycle, but suggested a similar correlation between
the drastic middle Miocene δ¹⁸Obenthic increase and the 1.2Myr; now ~172kyr; 400kyr and 100kyr
cycles. Abels et al. (2005) referred to the middle Miocene δ¹⁸Obenthic increase as the Mi-3b event,
following nomenclature of Miller et al. (1997). While Westerhold et al. (2004) referred to it as the
Mi-3 even. However, both authors did refer to the same event. Concerning the younger Mi-events:
both Westerhold et al. (2004) and Abels et al. (2005) found a strong coupling between nearly all
15
δ¹⁸Obenthic maxima and low obliquity and eccentricity modulations, except Mi-4. Mi-4 was not
recognised as a δ¹⁸Obenthic excursion by Abels et al. (2005), while Westerhold et al. (2004) found no
correlation. Only Mi-3 and Mi-6 were correlated with the 1,2 Myr obliquity cycle (Westerhold et al.,
2004).
As mentioned above, there is a causal link between climatic changes and Milankovid cycles. So it
seems obvious that they can be the trigger mechanism, either by providing a final and rapid push
across a threshold or as principal driving force. However, the orbitally related rhythms do oscillate
around a certain mean, which is caused by the Earth’s boundary conditions (Zachos et al., 2001).
Since there is no continuous history of Ice-ages throughout the Cenozoic (Zachos et al., 2001), these
boundary conditions (and with them the orbital mean) are changed by different mechanisms. A
modelling study by Langebroek et al. (2009) confirmed these statements by concluding that radiative
changes caused by the orbital cycles could not create the sharp decline at MMCT alone.
4. CLIMATE FORCINGS
4.1 Paleoceanography
Rearrangement of tectonic plates can seriously alter deepwater circulation patterns and invoke
climate changes. (Woodruff & Savin, 1989; Wright et al., 1992; Flower & Kennett, 1994; Zachos et al.,
2001; Hamon et al., 2013). However, there are different views on the possible effect of an altered
circulation. When comparing different views on the MMCT, the following hypotheses are found:
1. A decreased flux in Northern Component Water and Tethyan Indian Saline Water towards
the Southern Ocean induced a deterioration of the heat transport towards high latitudes.
Polar climates cooled down and got pushed towards enhanced glacial conditions (e.g.
Woodruff & Savin, 1989; Flower & Kennett, 1994).
2. Shevenell et al. (2008) found a substantial Antarctic ice sheet growth during the last part of
the MMCO, when the Southern Ocean was relatively warm. They stated that poleward
heat/moist supply was essential for the Antarctic cryosphere expansion.
3. The models of Hamon et al. (2013) suggested that a temperature drop related to the closure
of the East-Tethys was not the main cause of the MMCT. The modelled temperature
decrease probably amplified the cooling inferred from another mechanism.
4. Wright et al. (1992) did not observe a correlation between the million year glacial-interglacial
cycles and deepwater circulation changes.
16
4.1.1 Water Masses.
Water Mass Origin
Circum Polar Water
(CPW)
Water masses circling around Antarctica
Can be separated into a shallower (upper) and
deeper (lower) CPW
Antarctica
Mediterranean Outflow
Water (MOW)
Here: used for possible Miocene equivalent of
modern MOW as well
Tethys Sea /
Mediterranean Sea
Northern Component
Water (NCW)
Miocene equivalent of the North Atlantic Deep
Water (NADW)
North-Atlantic Ocean
Pacific Outflow Water
(POW)
Pacific return flow towards Southern Ocean at
intermediate depths
Pacific Ocean
Southern Component
Water (SCW)
Miocene equivalent of Antarctic Bottom Water
(AABW)
Antarctica
Southern Component
Intermediate Water
(SCIW)
Miocene equivalent of Antarctic Intermediate
Water
Antarctica
Tethyan Indian Saline
Water (TISW)
Miocene warm and saline water mass East-Tethys / Paratethys
17
Figure 4.1 (Adjusted from http://www.scotese.com/miocene.htm): The World during the Miocene. CAS:
Central American Seaway; ETS: East-Tethys Seaway; GSR: Greenland-Scotland Ridge; IS: Indonesian Seaway.
The yellow stark marks the location of the Porcupine Seabight.
4.1.2 Miocene paleoceanography
A first global overview of Miocene deep-water circulation patterns was created by Woodruff & Savin
(1989) and was later refined by Wright et al. (1992). They tried to link these circulation patterns with
the observed climatic changes, inferred from global changes in δ¹⁸Obenthic values. Wright et al. (1992)
followed the workflow of Woodruff & Savin (1989) by using δ¹³Cbenthic values to recognise deep water
circulation patterns. They assumed that “young” deep water masses with high δ¹³C values originated
from sinking surface waters (NCW and TISW). Only a small portion of the modern AABW (equivalent
to SCW) is composed of surface water, resulting in lower δ¹³C values. Due to the effect of deep water
aging on δ¹³C values, the modern Pacific deep waters have lower δ¹³C values. So, the return flow
towards the Southern Ocean at intermediate depths (POW), has low δ¹³C values (Wright et al., 1992).
Wright et al. (1992) used the deep Southern Ocean δ¹³Cbenthic record as a proxy for deep water
circulation patterns. Wright et al. (1992) distinguished the following pattern:
During the earliest Miocene (until ~20 Ma) there were equivalent δ¹³Cbenthic values between the North
Atlantic the Southern Ocean and the Pacific. There was a small south to north gradient with lowest
values at the Rockall Plateau (south of Greenland-Scotland Ridge; see below - section 4.1.3). This
indicates NCW production was little to non-existent and SCW filled the deep North Atlantic.
From ~20 Ma onwards δ¹³Cbenthic values of the Pacific and Southern Oceans started to diverge.
Moreover, between ~19 and ~16 Ma, the highest values were recorded in the North Atlantic Ocean,
intermediate values in the Southern Ocean and the lowest in the North Pacific. This indicates that
NCW was being produced and the North Pacific was the “end-of-the-line”, like modern configuration.
18
The δ¹³C values in the northern Indian Ocean were higher than in the Southern Ocean, similar to the
North Atlantic, and indicate TISW production.
From the middle Miocene onwards, the deep Southern Ocean records lacked sufficient resolution,
limiting their interpretation. Wright et al. (1992) used the inter-basin differences between the North-
Atlantic and Pacific Ocean from then onwards. These inter-basin differences declined during the early
middle Miocene (from ~15,5 onwards), which implies a shutdown, or significant decrease, of NCW
production. A small south to north δ¹³C gradient (0,2‰) indicates SCW was the main source of deep
water in the Atlantic-Ocean. However, they do admit that lower differences in δ¹³C values, could also
partially reflect lower oceanic nutrient levels. Due to the lack of sufficient resolution in the Southern
Ocean record, it is difficult to distinguish between these two. A stronger (0,7‰) south to north δ¹³C
gradient appeared in the Pacific Ocean, implying ventilation with deep water originating from the
Southern Ocean.
Between 13 and 12 Ma, interbasin differences between the Atlantic and the Pacific started to
increase again, suggesting NCW production resumed. The δ¹³Cbenthic gradient in the Atlantic Ocean
was from north to south again, confirming NCW production. The lowest δ¹³Cbenthic values were found
in both northern Indian and Pacific Oceans and intermediate waters in northern Indian Ocean had a
low value as well. This means that TISW had ceased to exist.
Consequently, Wright et al. (1992) concluded that there were 2 main modes of deep water
circulation throughout the Miocene. Deep waters were ventilated by only the SCW between 24 - 20
Ma and 16 - 12,5 Ma. While additional sources occurred between 20 -16 Ma (SCW, NCW,TISW) and
after 12,5 Ma (SCW, NCW). Moreover, they inferred no evidence for Pacific Deep water production
throughout the Miocene. They also suggested SCIW was present in the southern hemisphere
intermediate waters of Indian, Pacific and Atlantic Ocean. The SCIW had a relatively high δ¹³Cbenthic
value caused by air-sea exchange and/or biological stripping of nutrients (Wright et al., 1992).
4.1.3 Greenland-Scotland Ridge
Deep water connection between the North Atlantic and the Arctic Oceans across the Greenland-
Scotland Ridge (GSR) is an important regulator of modern paleoceanography (New et al., 2001). The
height and structural configuration of the GSR determines the volume of the southward flow (Stoker
et al., 2005). It is thought that a modern-day like exchange of water masses started during the
Miocene (Wright et al., 1992; Stoker et al., 2002; 2005; Laberg et al., 2005). The latter authors
considered the massive increase in contourite drift formation in both the eastern North Atlantic
Ocean and the Nordic Seas during the early Miocene to be evidence for the start of NCW production.
Stoker et al. (2005) found two major unconformities along the entire North Atlantic Ocean. The latter
authors called it the Base-Neogene and Intra-Miocene unconformities (during the early middle
Miocene). Both can be recognised in the Porcupine Basin (Stoker et al., 2005; Van Rooij et al., 2007).
Although the Base-Neogene reflector is not recognisable as an unconformity in the Porcupine Basin
(McDonnel & Shannon, 2001), it can be recognised in wells as an early Miocene hiatus (23-19 Ma;
Dobson et al., 1991). The Intra-Miocene unconformity is represented by the C20 reflector (Stoker et
al., 2001). This is recognised in the Porcupine Basin as the RD2 unconformity (Van Rooij et al., 2003).
These two unconformities represent 2 important changes in deep water circulation (Stoker et al.,
2005; Laberg et al., 2005). Moreover, the timing of changes in circulation patterns seems to be coeval
with a major phase of intraplate compressive tectonism as far-field stress effect from the Alpine
19
Orogeny and North-Atlantic spreading (Stoker et al., 2001; 2005). Models using the infinite element
method show that, due to flexural effects of the intraplate compression, deflections of the opposite
sign can be formed (Stoker et al., 2005). These submergences are relatively small, in order of a few
tens to hundreds of metres. Still, this is sufficient to create changes in circulation patterns (Stoker et
al., 2005). Stoker et al. (2005) argue that this mechanism formed the Faroe Conduit, creating a
deeper passageway across the Greenland-Scotland Ridge and enhancing the exchange of deep water
between the Nordic Seas and the North Atlantic Ocean. They suggest a start during the early
Miocene but with a maximum extent during the early-middle Miocene.
Stoker et al. (2005) suggested that the Southern (across the GSR) and the Northern (Fram Strait;
between Greenland and Svalbard) gateways opened during the same time period. This is in
agreement with Laberg et al. (2005). Laberg et al. (2005) proposed that the Fram Strait reached a
depth of approximately 2km during the middle Miocene. This allowed both surface and deep water
exchange between the Greenland-Norwegian Sea and the Arctic Ocean. The paleobathymetry model
of Knies & Gaina (2008) proposed a similar water depth of 2500-2800m around 14 Ma. The overall
submergence of the GSR seems to have an additional effect, allowing enhanced water exchange
between the North Atlantic and the Norwegian-Greenland Sea from around late-early to middle
Miocene (Laberg et al., 2005) and across the Denmark Strait (shallower then Faroe Conduit) during
the late Miocene (Stoker et al., 2005; Laberg et al., 2005). However changes in hot spot activity and
in eustatic sea level might have induced significant changes in Northern Component Water
production, causing it sometimes to cease (Laberg et al., 2005).
Figure 4.2 (Stoker et al., 2005): Map of NE-Atlantic Ocean. The yellow star marks the Porcupine Seabight. DS:
Denmark Strait; FC: Faroe Conduit; FS: Fam Strait; JM: Jan Mayen Ridge; RP: Rockall Plateau; RT: Rockall
Through.
20
4.1.4 East-Tethys Seaway
Throughout the early Miocene, there was a deep connection between the Indian Ocean and the
eastern Tethys, which allowed for deep water exchange between the Tethys and the Indian Ocean
(Woodruff & Savin, 1989; Wright et al., 1992; Flower & Kennett, 1994; Harzhauser & Piller, 2007;
Hamon et al., 2013). Because of high evaporation rate, low precipitation and low runoff from
neighbouring continents, the Paratethys was characterised by high salinities (Hamon et al., 2013).
Due to these high salinities, a salinity-driven bottom-current brought warm, saline deep water
southward towards the fresher Indian Ocean (Woodruff & Savin, 1989; Wright et al., 1992; Flower &
Kennet 1994; Hamon et al., 2013). Carbon and oxygen isotopic composition suggested that TISW was
transported through the south Indian Ocean towards the Southern Ocean, bringing along warm
waters (Wright et al., 1992). The presence of this warm water in the Southern Ocean could induce a
slower Antarctic Circumpolar Current (Woodruff & Savin, 1989).
The East-Tethys Seaway closed during the middle Miocene, hereby provoking the termination of
TISW. However, the exact timing of closure of the East-Tethys Seaway seems controversial. Most
authors agree on a timing between ~15 and ~14 Ma (Woodruff & Savin, 1989; Wright et al., 1992;
Flower & Kennett, 1994). This is based on isotopic and faunal proxies though, while a study of
foreland basins suggest a gateway along the northern margin of Arabia up to 11 Ma (Hüsing et al.,
2009). A recent modelling study performed by Hamon et al. (2013) investigated the effect of a
shallowing seaway. They concluded that deep water exchange terminated when the seaway was
~250 m deep. However, it must be mentioned that they assumed a reduction from 1000 m to 250 m
water depth between two computed steps. Moreover, they found a northward flow into the
Paratethys, which was induced by Indian Summer Monsoon winds along the African coast. Their
models show that this northward flow occurred in the upper 500m, while the counter-current was
restricted to the depths below. This indicates a possible termination before 250 m water depth. They
concluded a complete termination or enhanced restriction of deep water exchange between ~15 and
~14 Ma, while possibly a gateway was still open until ~11 Ma. Moreover, because of fluctuating sea
levels (Haq et al., 1987; Kominz et al., 2008; John et al., 2011) these restrictions/terminations must
have occurred periodically prior and around ~14 Ma (Woodruff & Savin, 1989).
The models presented by Hamon et al. (2013) showed other effects caused by the East-Tethys
Seaway closure. When deep water exchange occurred and there was a net outflow of the Paratethys,
their models showed an inflow into the western Tethys from the Strait of Gibraltar to adjust the net
loss of water mass to the Indian Ocean. No deep water outflow occurred towards the Atlantic Ocean,
due to the shallower conditions in the Strait of Gibraltar than in the East-Tethys Seaway. However,
with a shallow East-Tethys Seaway and restriction of deep water exchange towards the Indian Ocean,
intermediate water started to flow west into the Atlantic Ocean. They suggest more saline and
warmer Atlantic Intermediate waters and a strengthening of the Atlantic Meridional Overturning
Circulation (AMOC), similar to modern conditions. The models show a good correlation between the
closure of the East-Tethys and strengthening of the ACC, which is in agreement with Woodruff &
Savin (1989) as mentioned before.
21
Figure 4.3 (Harzhauser & Piller, 2007): Evolution of the East-Tethys Seaway.
4.1.5 Connection Paratethys and North Sea Basin
Throughout the Miocene there were periodic connections between the North Sea Basin and the
Tethys Sea through a large trans-European system, which connected the Rhine, the Rhône-Bresse
Grabens and the Alpine Foreland Basin (Ziegler 1994, Munsterman & Brinkhuis, 2004; Harzhauser et
al., 2007; Donders et al., 2009; Larson & Dybkjær, 2011). This allowed intermingling of shallow
marine biota from the North Sea with biota of the Tethys realm. This is confirmed by Donders et al.
(2009), who stated that dinocyst records of the Mediterranean are largely representative for the
North Sea Basin. However, their statement is based on Munsterman & Brinkhuis (2004), who used a
different argument to link both dinocyst assemblages. They stated that the “Mediterranean” was
strongly influenced by Atlantic circulation throughout the Cenozoic, hereby linking both dinocyst
records.
4.1.6 Central American Seaway & Caribbean Sea
The Panama Isthmus was formed during the Pliocene (Hoorn et al., 1995; Zachos et al., 2001).
However because of the shallowing of the Central American Seaway, closure of the intermediate
water connection already occurred during the middle Miocene (Roth et al., 2000). A first, but
temporary, complete closure occurred between ~10 and 9,5 Ma, related to an eustatic sea level fall
Roth et al. (2000) related carbonate dissolution episodes between ~12 and 10 Ma in the Caribbean
with the opening of seaways between the southern (Columbian) and northern (Yucatan) Caribbean
basins. This triggered the establishment of the Caribbean Current, enhancing the NCW. The
Caribbean Current was further strengthened by the gradual closing of the Central American Seaway.
22
4.1.7 Indonesian Seaway
The Indonesian Seaway is the last remaining equatorial gateway, allowing the Indonesian
Throughflow to transfer heat between from the Pacific towards the Indian Ocean (Hall et al., 2011).
Fluctuations in the West Pacific Warm Pool are influenced by variability in the Indonesian
Throughflow, hereby controlling inter-annual variability such as the El Niño-Southern Oscillation and
the Asian monsoon (Hall et al., 2011). However, the past evolution is not as well investigated as for
other important Gateways (Hall et al., 2011).
Before ~25 Ma a broad gateway existed between Australia and South-East Asia, allowing deep water
exchange between the Indian and Pacific Oceans (Kuhnt et al., 2004; 2011; Rong et al., 2012).
However from ~25 Ma onwards, the New-Guinea Block, part of Indo-Australian plate, started to
collide with the SE-Asian Block, part of Eurasian plate. Consequently deep water exchange already
was restricted before the start of the Miocene and only intermediate to surface water could pass this
gateway (Kuhnt et al., 2004; 2011; Rong et al., 2012). This is in agreement with findings of Woodruff
& Savin (1989) whose δ13C values suggested a better ventilated Indian Ocean between 23-14 Ma,
when compared to the Pacific Ocean, indicating that deep water exchange already was impeded
(Kuhnt et al., 2004). During the early Miocene, both the intermediate and deep water temperatures
increased in the Indian Ocean as cool bottom waters got restricted to the southern parts. Moreover,
there were no distinct differences difference in δ18O values between deep and intermediate waters
anymore (Kuhnt et al., 2004).
Around 15 Ma a wide Molucca Sea (see figure 4.4) still allowed the exchange of intermediate and
surface waters (Kuhnt et al., 2004; Rong et al., 2012). Hall et al. (2011) suggest that a deeper and
more open Indonesian gateway would allow the Western Pacific Warm Pool to move to the Indian
Ocean. These wind-driven waters would then “collide” with Africa, creating a counter-current
starting in these regions (Kennett et al., 1985). The exchange of surface and intermediate waters got
hindered as well during enhanced closure of the Molucca Sea between 15 and 10 Ma (Kuhnt et al.,
2004; Rong et al. 2012). This induced cooler SST in the Indian Ocean, which in turn got reflected in
increased aridity over eastern Africa (Kuhnt et al., 2004). Moreover, the latter authors argue that
decreased heat transport from the tropics could stimulate global cooling, possibly enhancing or
triggering the development of ice sheets.
The large eustatic sea level fall during the MMCT had serious consequences, periodically shutting
down all cross-flow (Kennett et al., 1985; Kuhnt et al., 2004; Rong et al., 2012). The warm equatorial
waters were deflected north- and southward creating/enhancing the Kuroshio and East-Australian
current respectively (Kennett et al. 1985; Kuhnt et al., 2004). Kennett et al. (1985) proposed that the
piling up of the warm surface waters created a first easterly flowing equatorial Undercurrent was
created. This was reflected in an increase in siliceous biogenic productivity associated with upwelling
of the Equatorial Undercurrent.
23
Figure 4.4 (Kuhnt et al., 2004): The Indonesian Seaway during the Middle Miocene.
4.2 CO2
Changes in greenhouse gas levels, such as CO2, will induce variation in its radiative forcing (Zachos et
al., 2001; Royer et al.; 2004; 2005, Langebroek et al., 2009). Early investigations used changes in past
δ¹³C values to infer qualitative changes in atmospheric CO2 levels (Vincent & Berger, 1985; Raymo &
Ruddiman 1992). Recently, other tools have been used to establish a past pCO2 curve: surface ocean
pH measurements derived from boron isotope ratios (δ¹¹B) in planktonic foraminifera (Pearson &
Palmer, 1999; 2000); the δ¹³C of long-chained alkenones in haptophyte algae (Pagani et al., 1999);
stomatal densities of fossil leaves (Royer et al., 2001; Kürschner et al., 2008; Grein et al., 2013).
The timing of the Mi-1 event coincides with a decrease in pCO2 (Pagani et al., 1999). Afterwards,
atmospheric CO2 levels remained relatively low. Zachos et al. (2001) suggested that with these low
pCO2 during the Miocene subtle changes in atmospheric concentrations could give an important
triggering-effect for climatic events. The following three sections will describe the most prominent
mechanisms influencing atmospheric pCO2. In the fourth section a different point of view, inferred
from recent pCO2 estimates, will be given.
24
FIGURE 4.5: ( Adjusted from Garzione, 2008): Cenozoic Seawater Sr and past pCO2 curve. The red circl: the
boron isotope records of Pearson & Palmer (2000); Yellow circles: pedogenic carbonate records; Bleu field:
alkenones records (Pagani et al., 1999; 2005); EOT: Eocene-Oligocene transition; MMCO: Middle Miocene
Climatic Optimum; MMCT: Middle Miocene Climatic Transition. First conifers on Tibet are supposed to
reflect surface uplift (Garzione, 2008).
4.2.1 Orogenesis
Mountain building is considered a forcing mechanism for possible drawdown of atmospheric CO2
(Raymo et al., 1988; Raymo & Ruddiman, 1992). Modelling of the feedbacks connected to the
erosion-hypotheses suggested the silicate weathering and carbon burial to be the primary
mechanisms connected to a decrease in pCO2 (Raymo et al., 1988; Garzione, 2008). Chemical
weathering of silicates consumes CO2 by following reaction: CaSiO3 + CO2 CaCO3 + SiO2. Due to
uplift and the related increase in mechanical erosion, more surface area of fresh minerals becomes
available which induces an enhanced chemical weathering (Raymo & Ruddiman, 1992). Mountain
building also enhances the erosion of terrestrial organic carbon (OC) derived from the biosphere,
which is then transported towards the deep marine depositional areas (Kao et al., 2014). However,
exhumation and oxidation of petrogenic OC increase with higher erosion rates as well (Kao et al.,
2014). This links orogens with a certain release in CO2. Although the oxidation of petrogenic OC is still
poorly understood, Kao et al. (2014) proposed the flux of CO2 burial to be higher than the flux of CO2
being released into the atmosphere due to oxidation of petrogenic OC.
Due to plate tectonic reorganisations, several phases of orogeny occurred during the Miocene. The
Alpine Orogeny proceeded into a third phase with the uplift of the Mont-Blanc Massif starting
around 22 Ma (LeLoup et al., 2005). The main uplift of the northeastern Andes started during the late
Oligocene due to a break-up of the Farallon Plate into the Nazca and Cocos plates and the coeval
creation of a new spreading centre (Hoorn et al., 1995). The latter resulted in thick successions of
25
molasse being deposited during the Miocene-Pleistocene and a new drainage pattern from the
Middle-Miocene onwards. Extensive wetlands were created, which periodically showed brackish
conditions caused by marine incursions during eustatic sea level rises (Hoorn et al., 1995 and
references therein). However, Raymo & Ruddiman (1992) believed the Tibetan uplift to be the main
contributor to the large proportion of dissolved load being transported by their drainage rivers. The
authors proposed that the uplift of the Tibetan Plateau since ~40 Ma created a long-term cooling-
trend which continued during the Miocene. Their theory is strengthened by an increase in seawater
⁸⁷Sr/⁸⁶Sr from ~40 Ma onwards (Figure 4.5), which is a commonly used proxy for continental
weathering (Garzione, 2008).
Gaillardet and Galy (2008) revised evidence for degassing of CO2 in orogenic zones, questioning
whether the Himalaya is a net carbon source or sink. The underlying idea is that organic-rich
sediments and limestone became buried because of tectonic loading. The subsequent metamorphic
reactions free CO2 from the mineral content, which is later on released into the atmosphere by
degassing in hot springs (Kerrick & Caldeira, 1993). Evans et al. (2008) found that a considerable flux
of CO2 is being degassed in hot springs near the main deformation zone of the Himalaya. They
extrapolated their findings towards the whole Himalaya and proposed a significant amount of CO2
(up to a quarter of the global uptake due to silicate weathering; Gaillardet & Galy, 2008) being
injected into the atmosphere. These assumptions significantly reduce the impact of orogeneses as a
carbon sink. However, Gaillardet & Galy (2008) concluded that whether orogenesis are a net source
or sink depends on the considered time scale. The degassing measured by Evans et al. (2008), was a
relatively short event compared to the whole orogeny. Moreover, the degassed CO2 can be
consumed by silicate weathering and the resulting carbonates can be buried and degassed again.
This loop of 20 to 50 Myr nullifies the net effect of metamorphic CO2. If this loop is interrupted and
carbon is re-injected into the mantle, CO2-levels will be decreased for geologically longer periods
(Gaillardet & Galy, 2008).
4.2.2 Monterey Hypothesis
The Monterey Hypothesis was originally proposed by Vincent and Berger (1985) and was quickly
accepted by other authors (e.g. Flower & Kennett, 1994). They argued for a drawdown of CO2 due to
extensive deposits of organic-rich sediments, such as the Monterey Formation near California. Their
main argument was the co-occurrence of a positive shift in carbonate δ¹³C values with deposition of
the Monterey Formation, which was followed by an increase in δ¹⁸O values. They suggested that an
initial cooling phase enhanced coastal upwelling, which lead to an increase in coastal productivity. A
recent very high-resolution study (1,5 - 3kyr) carried out by Holbourn et al. (2014), confirmed the
Monterey Hypothesis. They found a close relation between δ¹³C and δ¹⁸O values, suggesting that
upwelling in the Eastern Equatorial Pacific enhanced CO2 drawdown. They concluded a tight coupling
of climate systems in the high and low latitudes: cooling in the high latitudes increases convection of
Si-rich waters, promoting productivity and giving a positive feedback to the initial cooling event.
Considering another basin, Armstrong & Allen (2008) proposed intensive carbon storage in the
Paratethys basins after closure of the East-Tethys seaway. A modelling study performed by Hamon et
al. (2013) showed an increase in stratification that could indeed favour carbon storage.
26
4.2.3 Magmatic Events
Hodell and Woodruff (1994) linked the MMCO with enhanced atmospheric CO2 concentrations
caused by the Columbia River volcanism. A second, minor, source could be the Central European
Volcanism (Kürschner et al., 2008). Both were occurring between 17 and 15 Ma, which corresponds
with the timing of the MMCO (Schwartz, 1997).
4.2.4 Controversy
Most of the recent pCO2 -reconstructions (Pagani et al., 1999; Pearson & Palmer, 1999; 2000; Royer
et al., 2001; Grein et al., 2013), provide a different point of view on a greenhouse gas driven climate
change during the Miocene. These publications seem to indicate that low levels of pCO2 were
common (figure 4.5). Pagani et al. (1999), Royer et al. (2001) and Grein et al. (2013) suggest that the
MMCO was not the direct result of large scale changes in pCO2. Estimates with general circulation
modals on global mean surface temperatures suggest that other factors are required to explain this
warm period (Royer et al., 2001). Moreover, there is no clear consensus about a distinct drop in pCO2
near the MMCT (figure 4.5). Pagani et al. (1999) suggested that low pCO2 levels could invoke a higher
sensitivity of the climate system towards an oceanographic control. Moreover, the latter authors
suggested that the low latitudinal temperature gradients could have been promoted by the
combination of low pCO2 levels and the lack of large ice sheets.
There is, however, one measurement that uses stomatal densities of fossil leaves and proposes a
complete different pattern in pCO2 evolution (Kürschner et al., 2008; Kürschner & Kvaček, 2009).
They suggest higher pCO2 levels, up to 500 ppmv, during the MMCO. Afterwards their trend shows a
decrease towards 300 ppmv correlating with the MMCT.
5. GEOLOGICAL SETTING
5.1 Location
The Porcupine Seabight (PSB) is located southwest of Ireland (Figure 5.1). It forms a deep
embayment into the Irish Shelf (Figure 5.1). The embayment is enclosed by four shallow platforms:
Porcupine Bank to the west, Slyne Ridge in the north, the Irish mainland Shelf to the east and the
Goban Spur in the South (Figure 5.1). All barriers consist of metamorphic Precambrian and Palaeozoic
rocks (Van Rooij et al., 2003). Because of this configuration there is only one narrow passage towards
the North Atlantic Ocean, located in the southeast between the Porcupine Bank and the Goban Spur.
The PSB is the expression of an underlying Middle to Late Jurassic failed-rift: the Porcupine Basin. The
rift was related to the opening of the proto-North Atlantic Ocean (Moore & Shannon, 1992). The
central part of the PSB is covered in 10 km of Mesozoic and Cenozoic sediments (Shannon, 1991).
These were deposited during the post-rift period, which was mainly characterized by thermal
subsidence. Most sediment is derived from the Irish and Celtic shelves, while input from the
Porcupine Bank is lacking (Rice et al., 1991).
The PSB gained renewed interest thanks to the discovery of large, deep water carbonate mounds,
which are located in distinct provinces (Henriet et al., 1998; De Mol et al., 2002; Huvenne et al.,
2003). The International Ocean Discovery Program (IODP) leg 307 ‘Modern Carbonate Mounds:
27
Porcupine Drilling’ focussed on one of these mounds: the Challenger Mound (Figure 5.2), which is
located in the Belgica mound province on the eastern slope of the PSB. Site U1318 ( 51°26,16’ N;
11°33.0’ W) was drilled upslope from the Challenger mound at a water depth of 409 m. The site is
located on the upper slope region near the shelf edge. Due to this deeper location, no eustatic sea
level fall was large enough to allow for subaerial erosion (Haq et al., 1987; Kominz et al., 2008).
Three holes were drilled at site U1318: 1318A, 1318B and 1318C. In order to drill Hole 1318B
(51°26,148’ N; 11°33,019’ W), the drillship was offset 20 m south of Hole 1318A. Hole 1318C was
drilled to overcome the poor recovery in core 1318B 15X and 1318B 16X. Hole 1318C (51°26,148’ N;
11°33,019’ W) was drilled after offsetting the drillship 25 m south of Hole 1318B.
Figure 51 (Adjusted from Raddatz et al., 2011): Paleobathymetric map. The yellow star marks site U1318; The
red line marks the 100m water depth contour; The numbered areas marked the different Cold-water Mound
provinces: Belgica (1), Magellan (2), Hovland (3), Viking (4) and Enya (5) Mound Provinces.
5.2 Seismic Units
High-resolution seismic profiles through the Belgica mound province revealed 3 seismic units (De Mol
et al., 2002; Van Rooij et al., 2003). The lowermost unit P1 consists mostly of gentle basinwards
dipping parallel strata. However, in a certain interval sigmoidal deposits were observed by De Mol et
al. (2002). These were interpreted as upslope migrating sediment waves within a sediment drift unit,
28
suggesting the presence of a bottom current flow (Van Rooij et al., 2003). Shannon et al. (2007)
suggested them to be caused by late Oligocene to early Miocene along-slope sediment transport.
Unit P1 is bounded at top by an erosional surface and was correlated by Van Rooij et al. (2003) to an
early middle Miocene erosional event, called RD2 (Stoker et al. 2002). This event can be correlated
throughout the entire NW European Atlantic margin. The RD2 is related to the strengthening of
bottom current activities due to the introduction of Norwegian Sea Water into the North Atlantic
Ocean (See above - section 4.1.3).
On top of the RD2 lies an acoustically transparent layer with low-amplitude reflectors: unit P2. This
unit is deeply incised by a basin-wide erosive event called RD1 (Van Rooij et al., 2003). This
unconformity was linked with strong bottom currents caused by the reintroduction of MOW in the
North Atlantic Ocean and the effects of glacial-interglacial events on the deep water circulation. Van
Rooij et al. (2009) discovered a previously unmapped unconformity near the Enya mound cluster
(Figure 5.2) and redefined the RD1 unconformity to be a composite event of RD1a and RD1b. Unit P2
is incised by a late Miocene erosional event, the RD1b. This unconformity was covered by a Pliocene
contourite drift unit P2bis (U2bis, Van Rooij et al. 2009). A last Intra-Pliocene RD1a erosional event
removed most of the P2bis deposits and masked the former RD1b. The composite event, RD1, is
overlain by the uppermost Unit P3, which consists of drift deposits.
5.3 Sedimentary Units
Expedition 307 scientists (2006) distinguished 3 lithostratigraphical units (Figure 5.3). Unit 1 consists
mostly of silty clay. Unit 1 is subdivided in 3 subunits with boundaries that can be correlated with
shifts in the magnetic susceptibility record. The base of Unit 1C is defined by a sharp erosive
boundary at 82 meters below seafloor (mbsf). The greater part of Unit 2 are olive-grey sands which
show a fining upward trend. At the base of this unit a 5-10 cm thick conglomerate with black pebbles
and granules occurs. Lithological units 1 and 2 correlate with the seismic unit P3 and are separated
from lithological Unit 3 by the RD1 at 86,2 mbsf. Lithological unit 3 is subdivided in 3 subunits with
different carbonate contents. The upper 10cm of subunit 3A is a bivalve bed, which overlies silty-
clays interbedded with fine-grained sand and silt. Subunit 3A is divided from 3B by a sharp erosive
boundary at 127,3 mbsf in Hole U1317C. However the erosive boundary was not recovered in Hole
U1318B. Subunit 3B is a homogeneous greenish grey silty clay. Seismic unit P2 corresponds with both
subunits 3A and 3B. The RD2 separates subunit 3B and 3C at a depth of 190,3 mbsf (~196,15 mcd).
Subunit 3C was correlated with seismic unit P3. It consists of greenish grey silty clay and has some
lithified layers occurring in the middle, which are partly dolomitised.
29
Figure 5.2 (Adjusted from Louwye et al., 2008 and Quaijtaal et al., 2014): Overview sedimentary and Seismic
units. The red lines represent the sampled interval.
5.4 Dating
A first relative dating was carried out during the IODP Expedition 307. A biostratigraphical analysis on
core catcher samples of Holes U1318A and U1318B was performed using calcareous nannofossils.
Hole U1318A was studied as well, with both planktonic and benthic foraminifers. Louwye et al.
(2008) refined these former age assessments through dinocyst biostratigraphy.
Louwye et al. (2008) also interpreted the shipboard paleomagnetic measurement. However, these
shipboard measurements on inclination could have been overprinted during drilling. Background
noise in the cryogenic magnetometer could further impede a correct correlation with the
geomagnetic polarity timescale. The result is an artificial magnetic inclination pointing downward
(Expedition 307 Scientists, 2006). In order to overcome these limitations, Louwye et al. (2008) added
measurements on discrete samples. However, no discrete samples were taken from Hole 1318C that
covers the core gap of Hole 1318B. The magnetic data was combined with the biostratigraphy and a
Strontium-isotopic analysis of Kano et al. (2007) to create an age-depth-diagram.
Quaijtaal et al. (2014) used new biostratigraphical data to update the age-depth-plot and converted
the depths in metres below seafloor (mbsf) into metres composite depth (mcd). The magnetic
reversal points were provided by Louwye et al. (2008). However, the reversal points covering the
core gap, from Hole U1318C, were excluded because of the lack of discrete samples. The result was a
first age model in which the part below 122 mcd was shifted by two magnetochrons towards an
older age (Figure 5.3). Reason for this is the difference between the age provided by new
biostratigraphical data and the former age-depth-plot of Louwye et al. (2008).
30
Figure 5.3 (Quaijtaal et al., 2014): The difference in age-models between Louwye et al. (2008) and Quaijtaal
et al. (2014)
6. METHODOLOGY
6.1 Material
Sediments were obtained from cores of IODP Leg 307, Site U1318, hole B stored at the IODP Core
Repository in MARUM, University of Bremen, Germany. A total of 51 samples were taken at a regular
interval of ~1m, with the exception of 3 samples (see Appendix 1). Thickness of all samples is ~2 cm.
The sampled interval runs from core 1318B 21X (upper depth limit: 186,36 mbsf; 192,51 mcd) until
the lowermost core 1318B 27X (lower depth limit: 241,325 mbsf; 247,475 mcd).
6.2 Palynological measurements
6.2.1 Palynological processing
The standard palynological preparation technique from the Research Unit for Palaeontology of Ghent
University was used (Quaijtaal et al. 2014). The samples were oven-dried at 60°C. Approximately 10
to 14 gram of dry sediment was used. Before further treatment of the samples, two Lycopodium
clavatum tablets (Batch 1031, X=20848 ±2186) were added for determination of the number of
palynomorphs per gram sediment following the methodology of Stockmarr (1971). The first step
involves the removal of carbonates with hydrochloric acid (HCl, 2N). After a few cycles of settling and
rinsing of the remaining sediment with demineralised water, a pH between 6 and 7 was achieved.
The second step consisted of the removal of silicates and heavy minerals, such as pyrite. First, the
sediments were etched for 4 hours with hydrofluoric acid(HF, 40%), and afterwards the heavy
minerals were disposed of through rinsing cycles consisting of adding demineralised water and
31
decanting. The remaining silicates were removed by adding HF (40%). In normal lab procedure these
are heated up in a ~65°C bath for 2 days. However, due to lack of time and free spots in the warm-
water baths, all samples from number IV 21 onwards were treated with cold HF (40%) for a minima
of 3 days. The assemblage is not altered by a cold HF treatment, however the removal of silicates
does take more time. The reaction was halted by adding demineralised water and subsequent
decantation. After settling of the residue, 2N HCl was added for the removal of the fluorosilicates and
heated for two days in a hot bath at ~65°C. The HCl was replaced after one day. The residue was
decanted one last time in order to make it less acidic. This was performed for personal safety. To
counter and prevent clustering of amorphous organic matter, the residue was given an ultrasonic
bath for 30 seconds. The residue was sieved over a nylon screen with a mesh size of 10 µm and
transferred to a plastic vial. Several drops of CuSO4 were added to the residue in the vials to prevent
fungal growth.
The vials were centrifuged using the following program: speed up until 2000 rpm, which was
maintained for 5 minutes. Then, the centrifuge slowed down to 0 rpm without performing a sudden
brake, so the residue remained at the bottom of the vials and the supernatant could be removed.
Microscope slides were made by mixing the homogenised residue with a droplet of liquid glycerol
gelatine. The slides were sealed with nail polish. All samples and microscope slides are stored in the
collection of the Research Unit for Palaeontology of Ghent University, Belgium.
The slides were analysed at 400x magnification on a Zeiss AxioImager and Zeiss Axioskop 2
transmitted light microscope and photomicrographs were taken with an AxioCamMRc5. The slides
were scanned along parallel traverses to avoid overlap, until a total of 300 dinocysts were counted. A
count of 300 specimens is considered relevant for generating reliable diversities and absolute
abundances (Mertens et al., 2009). The recorded palynomorphs included dinoflagellate cysts,
acritarchs, chlorophytes, organic linings of foraminifers and pollen. The area used for counting
species was doubled and this extra area was scanned for rare species. These rare species were not
included into the count.
6.2.2 Taxonomy
The complete taxonomy can be found in Appendix 5.
Taxonomy follows Fensome et al. (2008). Some dinocyst taxa were grouped in complexes.
Batiacasphaera deheinzelinii and B. hirsuta were grouped together because they are morphological
similar and were hard to distinguish. Batiacasphaera minuta, B. micropapillataand B. sphaerica were
groupedinto the Batiacasphaera complex for the same reasons. Spiniferites membranaceus and
mirabilis were grouped due to similar morphology as well. This did not alter the Warm/Cold index,
since they have the equal ecological preferences (Zonneveld et al., 2012). The
Spiniferites/Achomosphaera cpx contained all species of Spiniferites that were not identified onto
species level. Capisocysta lata and C. lyellii were grouped since the tabulation pattern of the hypocyst
was not visible and this is necessary for determination.
Spiniferites hyperacanthus was not found. However it must be admitted that, because of the
inexperience of the investigator, this species was probably included into the
Spiniferites/Achomosphaera- complex.
32
6.2.3 Paleoenvironmental indices
The data assembled during this thesis were supplemented with data from Quaijtaal et al. (2014) and
Quaijtaal et al. (unpublished). In order to make a comparison between the different sections of Site
U1318 hole B, all species used to calculate the indices were taken from Quaijtaal et al. (2014).
Species used as indicator coastal proximity and SST are presented in Table 6.1.
Table 6.1: Species used for the W/C and N/O indices
6.2.3.1 Sea-surface temperature
The Warm / Cold index (W/C), is used to perform a qualitative analysis of past SST (Versteegh, 1994).
The index is calculated using Versteegh (1994):
W/C = 𝑛𝑊
𝑛𝑊 +𝑛𝐶
Where n = number of specimens counted; W = species indicating warm sea surface water (ssw)
conditions; C = species indicating cold ssw conditions.
6.2.3.2 Neritic/Oceanic signal
The occurrence of certain dinocysts is associated with coastal proximity (Tabel 6.1). This leads to two
indices: the inner neritic/outer neritic and the Neritic/Oceanic (N/O) index (Versteegh, 1994). Here,
we use the Neritic/Oceanic (N/O) index because Site U1318 is located on the transition between
SpeciesWarm Cold Neritic Oceanic
Bitektatodinium tepikiense X
Bitektatodinium raedwaldii X
Capisocysta lata/lyelli X X
Cleistosphaeridium placacanthum X
Dapsilodinium pseudocolligerum X
Dinopterygium cladoides X
Homotryblium tenuispinosum X
Impagedinium pallidum X X
Impagedinium paradoxum X X
Impagedinium strialatum X X
Impagedinium spp. X
Lingulodinium machaerophorum X X
Melitasphaeridium choanophorum X
Nannobarbophora gedlii X
Nematosphaeropsis labyrinthus X
Operculodinium spp. X
Operculodinium israelianum X X
Polysphaeridium zoharyi X X
Spiniferites/Achomosphaera X
Spiniferites membranaceus / mirabilis X X
Spiniferites solidago X X
Tectatodinium pellitum X X
Tubercolodinium vancampoae X X
W/C N/O
33
oceanic and neritic conditions (Quaijtaal et al., 2014). This index is also a proxy for the degree of
transport of species coming from the shelf (Louwye et al. 2008). The N/O was calculated as followed
(Versteegh, 1994):
N/O = 𝑛𝑁
𝑛𝑁 +𝑛𝑂
Where n = number of specimens counted; N = species that indicate neritic environments; O = species
indicating oceanic conditions.
6.2.3.3 Continental influence
The amount of terrestrial (spores and pollen) versus marine (dinoflagellates and marine acritarchs)
palynomorphs can be used as a proxy for coastal proximity: the Sporomorph/Dinocyst ratio (S/D;
Versteegh, 1994):
S/D = 𝑛𝑆
𝑛𝑆+𝑛𝐷
Where n = number of specimens counted; S = pollen and spores; D = dinocysts and marine acritarchs.
6.2.3.4 Sea-surface Productivity
The upwelling of water masses and enhanced river discharge will increase the nutrient supply for
phytoplankton. So these nutrient-rich areas are often associated with an increase in sea-surface
productivity. Diatoms are often the largest group within these phytoplankton associations (Louwye et
al., 2008). Now, since most peridinioid species have a heterotrophic or mixothrophic feeding
strategy, their numbers will increase with higher primary production (Dale,1996). This lead to the
Peridinioid / Gonyaulacoid (P/G) ratio as a proxy for increased sea surface productivity and in turn for
enhanced nutrient supply (Reichart & Brinkhuis, 2003). The proxy is calculated by (Versteegh, 1994):
P/G = 𝑛𝑃
𝑛𝑃 +𝑛𝐺
Where n = number of specimens counted; P = peridinioid species; G = all other dinocysts except for
the goniodomacean, the polykrikacean species and the organic membranes of calcareous cysts.
However, oxidation of sediments can lead to selective degradation of peridinioid species so the
complete absence of peridinoids might indicate post-depositional oxidation (Reichart & Brinkhuis,
2003).
6.2.3.5 Diversity
Dinoflagellate diversity has a positive correlation with temperature and availability of macronutrients
(Chen et al., 2011). There are multiple ways to evaluate diversity, here we use three: Richness (S); the
Shannon-Wiener index (H’) and Evenness (EH). Richness equals the amount of different species found
during counting and scanning of the slide.
The Shannon-Wiener index (H’) is used to perform a qualitative analysis on biodiversity. It gives a
measure of entropy in the system. A higher Shannon-Wiener index indicates a higher degree of
entropy in the system. H’ will approach zero when a certain species takes a higher proportion of the
total system, while others are rare (Krebs, 1998). It is calculated by the following equation (Shannon,
1948):
34
𝐻′ = − 𝑝𝑖 ∗ 𝑙𝑛 (𝑝𝑖)
𝑆
𝑖=1
Where S = total number of species; pi = proportion of total sample belonging to species i.
The Evenness (EH) gives a measure how equal the representation of each species is. EH can be seen
as a normalised version of the Shannon-Wiener index. EH goes towards zero when the assemblage is
unevenly distributed: dominated by a few species and some rare ones. On the other hand EH goes
towards one with an even distribution: when all species are equally represented. It is calculated by
using the following equation (Heip, 1974):
EH =𝐻′
ln(𝑆)
Where H’ = Shannon-Wiener index; S = total amount of species.
6.2.3.6 Dinocysts per gram of dry-weighted sediment
An amount of dinocysts per gram of sediment could indicate favourable conditions for dinocyst
production (e.g. Quaijtaal et al., 2014°. The assumption is made thath this might represent a better
preservation of organic material in the sediments or just less input of non-biogenic particles. The
ratio is calculated by using Lycopodium clavatum tablets (Stockmarr, 1971):
𝐷
𝑔𝑟𝑎𝑚=
𝑛𝐷𝑖𝑛𝑜𝑐𝑦𝑠𝑡𝑠
𝑛𝐿𝑦𝑐𝑜𝑝𝑜𝑑𝑖𝑢𝑚𝑠𝑝𝑜𝑟𝑒𝑠∗𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝐿𝑦𝑐𝑜𝑝𝑜𝑑𝑖𝑢𝑚 𝑠𝑝𝑜𝑟𝑒𝑠
𝑡𝑎𝑏𝑙𝑒𝑡∗
𝑡𝑎𝑏𝑙𝑒𝑡𝑠
𝑔𝑟𝑎𝑚𝑜𝑓𝑑𝑟𝑦 − 𝑤𝑒𝑖𝑔𝑡𝑒𝑑 𝑠𝑒𝑑𝑖𝑚𝑒𝑛𝑡
6.2.3.7 Reworking
The relative proportion of reworked palynomorphs to the total amount of in situ palynomorphs can
be used as a proxy for sea-level variations (Sturrock, 1996). A high amount of reworked dinocysts
might correlate with a low sea level because the base level of erosion lowers (e.g. Louwye et al.,
2008).
6.2.3.8 Cyclopsiella granosa/elliptica
Cyclopsiella granosa/elliptica is a shallow marine and possibly low-salinity tolerant acritarch
(Matsuoka & Head, 1992; Brinkhuis et al., 1994). The abundance of this species was investigated
since the assumption is made that this could indicate an increase in precipitation and/or run off.
6.3 Organic geochemistry
6.3.1 𝑈37𝐾 ′
- index
The 𝑈37𝐾 ′
index is based on long-chain unsaturated ketones (alkenones) derived from haptophyte
algae, mostly the coccolithophorids (Müller et al., 1998). The method evolved from the observation
that certain phytoplankton of the class Prymnesiophyceae synthesise alkenones with the extent of
unsaturation linked with growth temperature (Müller et al., 1998). Brassel et al. (1986) originally
proposed to use the ketone unsaturation index (𝑈37𝐾 ) as a temperature proxy using the relative
abundances of C37 methyl alkenones containing 2 and 4 double bonds. However, the tetra-
unsaturated alkenones (C37:4) are not always present in sediments, so the simplified version of the
index (𝑈37𝐾 ′
) is used more often (Müller et al., 1998). It was Prahl & Wekeham (1987) who validated
35
the 𝑈37𝐾 ′
proxy with a temperature-controlled study on cultures of the haptophyte Emiliania huxleyi.
The ratio is calculated using following equations (Müller et al., 1998):
𝑈37𝐾 ′
= 𝐶37:2
𝐶37:2 + 𝐶37:3
T = 𝑈37
𝐾 ′− 0,044
0,033 (r ² = 0,958)
The working range for the calibration study of Müller et al. (1998) lies between 0°C and 29°C. Müller
et al. (1998) suggested that a deviation from linearity might occur at high and low temperatures.
They derived a systematic flattening at warmer temperatures (>25°C). However, they stated that this
could also be because of difficulties in measuring the C37:3 alkenone concentrations.
Using the method described in section 4.3.1 (see below), an analytical error of 0.2°C is achieved
(Schouten et al., 2007). The standard error of estimate (calibration error) is ±1,5°C (Müller et al.
1998).
6.3.2 𝑇𝐸𝑋 86𝐻 - index
The TEX86 index is a relatively new organic seawater temperature proxy, originally proposed by
(Chaler et al., 2000). This proxy is based on membrane lipids of a non-thermophilic group of Archaea,
the Crenarcheota (Karner et al., 2001), which has recently been classified under the novel phyllum
Thaumarchaeota (Brochier-Armanet et al., 2008; Spang et al., 2010). These membrane lipids have a
unique composition and contain glycerol dialkyl glycerol tetraether (GDGT) lipids. Apart from GDGTs
with zero to three cyclopentyl moieties (SCHOUTEN et al., 2007), Crenarcheota also biosynthesise
crenarcheol, which has four cyclopentyl moieties and one cyclohexyl moiety. They also biosynthesise
small quantities of GDGT 4’, which is a regioisomer of crenarcheol. Chaler et al. (2000) found a
correlation between higher sea surface temperatures and an increase in the relative amount of
GDGTs with two or more cyclopentyl moieties. So by measuring the relative amounts of GDGTs in the
sediment, an estimate can be made of past sea surface temperature. The most recent calibration was
obtained by Kim et al. (2010). They proposed 2 different indices and calibrations and suggested to
apply the TEX 86𝐻 when SST exceeds 15°C. TEX 86
𝐻 and SST are calculated by using the following
equations (Kim et al., 2010):
TEX 86𝐻 =
𝐼𝐼𝐼 + 𝐼𝑉 + [𝑉𝐼′]
𝐼𝐼 + 𝐼𝐼𝐼 + 𝐼𝑉 + [𝑉𝐼′]
(numbered GDGTs refer to figure 7.1 )
T = 68,4 * Log ( TEX 86𝐻 ) + 38,6 (r² = 0,87)
Using the method described below ( section 6.3.4), an analytical error of 0.2°C is achieved (Schouten
et al., 2007). The standard error of estimate (calibration error) is ±2,5°C (Kim et al., 2010).
36
Figure 7.1 (Weijers et al., 2006b): Branched and Isoprenoid Tetraethers
6.3.3 BIT- index
Another group of GDGT membrane lipids contains branched (VII – IX; figure X) instead of alkyl chains.
Weijers et al. (2006a) proposed these tetraethers to be of bacterial, rather than archaeal origin.
Reason for this was the branched alkyl chains and the bacterial 1,2-di-O-alkyl-sn-glycerol
stereochemical configuration at the C-2 in the glycerol backbone. Weijers et al. (2006b) analysed a
diverse collection of soil samples and confirmed their widespread occurrence in the terrestrial
environment. The Branched and Isoprenoid Tetraether (BIT) index is the ratio between crenarcheol
(IV; figure X) and three branched GDGT lipids (VII – IX; figure X) in marine and lacustrine sediments. It
is used as proxy for the relative fluvial input of organic matter, derived from soils, in marine
sediments (Hopmans et al., 2004; Weijers et al., 2006b). The BIT-index can be used as a proxy for
relative terrestrial organic input. It is calculated using following equation (Weijers et al., 2006b):
𝐵𝐼𝑇 = 𝑉𝐼𝐼 + 𝑉𝐼𝐼𝐼 + [𝐼𝑋]
𝑉𝐼𝐼 + 𝑉𝐼𝐼𝐼 + 𝐼𝑋 + [𝑉𝐼]
(numbered GDGTs refer to figure 7.1 )
Donders et al. (2009) found a reproducibility of 0.01 for the BIT-index while using the same lab
procedure (in the same lab; NIOZ institute).
The isoprenoid GDGTs containing cyclopentyl moieties (II-IV) which are used for the TEX 86𝐻 index,
are detected in soils as well (Weijers et al., 2006b). Weijers et al. (2006b) found that isopenoid
GDGTs in soils have different relative proportions which depends on the difference in abundances of
different methanogenic Euryarchaeota groups. A significant input of terrestrial derived isoprenoid
GDGTs will bias the TEX86 measurement, hereby altering the inferred SST. The bias increases
substantially in a non-linear way. Weijers et al., 2006b suggested to evaluate the relative input of
fluvial terrestrial organic material so a possible bias can be determined. Furthermore, they stated
that the temperature deviation strongly depends on the difference in TEX86 signal of marine versus
terrestrial end member: places with cool ocean waters will likely generate the largest temperature
deviations since soils generally contain relatively warm TEX86 signals. Weijers et al. (2006b) obtained
37
the following relation by performing a simple two end member mixing model in the eastern
equatorial Atlantic equatorial region: the analytical error of TEX 86𝐻 was reached at a BIT ratio of 0.2-
0.3, while a deviation of > more than 2°C was reached at a BIT ratio of 0,4. This region is different
from the area investigated during this thesis. However the assumption is made that when
considering the high SST and high temperatures on land near the eastern equatorial Atlantic, the
Miocene NW-Europe had similar characteristics. So during this investigation, the numbers found by
Weijers et al. (2006b) are used as a limit from where a significant bias in the TEX 86𝐻 has to be
evaluated.
6.3.4 Organic Geochemical Processing
All samples were prepared and measured following the standard technique used at the NIOZ, Royal
Netherlands Institute for Sea Research. Approximately 5 gram of sediment was selected for each
sample, freeze-dried and homogenized with a mortar and pestle and. These samples were extracted
by the accelerated solvent extraction (ASE 350 system, Dionex) with a mixture dichloromethane
(DCM) / methanol (MeOH) (9:1; v:v). This technique allows for a fast and highly automated extraction
with values identical within the error as other methods (Figure 4, Schouten et al., 2007). The
extraction then was dried with a stream of nitrogen. The residue was dissolved in DCM and brought
onto a sodium sulphate (Na2SO4) column for the removal of the last traces of H20. An aliquot of the
remaining total lipid extract was separated in an apolar, a ketone and a polar fraction through a small
column chromatography with activated aluminium oxide. The eluents used for the apolar, ketone
and polar fractions were hexane/DCM (9:1; v/v), hexane/DCM (1:1; v/v) and DCM/MeOH (1:1; v/v),
respectively. All fractions were dried using a stream of nitrogen.
The ketone fraction was dissolved in ethyl acetate and 1 µl was injected into a Gas Chromatography
(GC, Agilent Technologies 6890N Network GC system). The column was 50 m long and 320 µm thick
with a 0,12 µm film thickness (Agilent CP-Sil 5 CB 50 x 0,32 [0,12]) and contained 100%
dimethylpolysiloxane. A constant pressure program at 14,500 PSI with a run time of 71,5 minutes
was performed. The temperature program started at 70°C, below the boiling point of the injected
solvent. Afterwards the temperature was increased with 20°C/min until 200°C. The temperature was
increased with 3°C/min until 320°C and was maintained for 25 minutes. Relative concentrations of
long-chain alkenones were measured with a flame ionization detector at a data rate of 10 Hz. The
data acquisition was performed with the software from Thermo Scientific™ Atlas Chromatography
Data System.
The polar fraction was dissolved in hexane/2-propanol (99:1; v/v) and filtered using a
polytetrafluoroethylene (PTFE) 0,45 µm filter in order to prevent blocking of the liquid
chromatography column. A 5 µl aliquot was injected into an (ultra) high-performance liquid
chromatography / mass spectrometry detector ((U)HPLC/MSD). This was a conjunction of the Agilent
1290 and 1260 Infinity series matched with a single quadrupole (Agilent’s 6100 Series Single
Quadrupole LC/MS System). The separation of the GDGTs from the other polar constituents, was
attained with a Prevail Cyano column with a length of 150mm a width of 2,1 mm and a packing of
3µm. A flow rate of 0,2 ml/min and a temperature of 30°C was maintained and gave a pressure
between 35 and 40 bar. During the first 5 minutes the samples were eluted isocratically with a
mixture of 99% A (hexane) and 1% B (2-propanol). The mass spectrometry detection of the
protonated molecules ( *M+H+⁺ ) of the GDGTs started after 5 minutes and positive ion spectra were
generated by scanning m/z 950-1450 in 1,9 s. At the same time gradient elution started with a 98% A
38
and 2% B until 44 minutes. From 44 minutes onwards, back-flush occurred for 10 minutes with 90% A
and 10% B, for cleaning the column. The MSD stopped measuring at 45 minutes.
6.4 Statistical analysis of time-series
A statistical analysis was performed by programming a code in Matlab® R2011a (see appendix 7). The
following sections give a short overview of the tools applied. All tools were applied on and between
all signals. Information on these subjects was obtained during the course ‘Geomodelling’ at Aarhus
University. This course was taught by both Prof. Dr. David L. Egholm and Prof. Dr. Mads F. Knudsen.
The following sections handle the methods used in my Matlab-code. References are made to papers
concerning the appropriate topics.
6.4.1 Cross-covariance
A cross-covariance gives an idea how similar two signals behave (Everitt, 2002; Orfanidis, 2007). With
similar behaviour one understands the correlation of maxima and minima. If both series tend to have
a similar behaviour, the sign of covariance is positive and vice versa. A cross-covariance was
calculated by (Everitt, 2002):
Cov ( X , Y ) = E [ ( X - μ x ) * ( Y - μ y ) ]
Where X and Y are both signals; μ is the mean value; E is the expected value operator.
This cross-covariance was calculated for different time displacements, so a lead or lag, due to lag of
signal on forcing, could be determined. A special case of cross-covariance is applied when comparing
a signal with a time-shifted version of itself. This is called the auto-correlation and could give an idea
of cyclicity. The auto-correlation was necessary for the auto regressive analysis (see below - section
5.4.3).
6.4.2 Pearson correlation coefficient
The Pearson correlation coefficient (PCC) is a normalised version of the covariance. This allows for
comparison between the analysis of different signals. It is calculated by (Everitt, 2002):
PCC = Cov ( X , Y ) / (σ𝑥*σy )
Where: Cov ( X , Y ) is the cross-covariance between signal X and Y; σ𝑥 and σy are the standard
deviation of signal X and Y respectively.
The Pearson correlation coefficient was calculated for both the entire interval and with a 20 point
running window. The latter was performed to evaluate if signals showed a good correlation during a
certain period, while in a different period an anti-correlation was achieved.
6.4.3 Auto regressive model analysis
During the Auto regressive model analysis of order 1 (AR1) a number (here 1000) of random
synthetic datasets were created which resemble the signal on which it was performed. These 1000
datasets of a certain signal were then correlated with the other signals, so the significance the
appropriate PCC could be calculated. For example: a PCC of 0,4 was obtained between signal X and Y.
To assess whether 0,4 is a good or bad correlation, the quantiles of PCC between the 1000 random
39
datasets and signal Y were calculated. If none of these random data achieved a PCC of 0,4 or higher,
the correlation can be considered as significantly high.
The Autoregressive model is a representation of a certain type of random process, where the values
of this process depend linearly on its own values (Everitt, 2002; Orfanidis, 2007). It states that the
next value is equal to the sum of a mean value (here taken to be zero), a memory of the previous
deviation from the mean value which is damped by a memory factor (Φ), and a random disturbance
which gives the innovation for the new value.
The memory factor was calculated by: Φ = ( Cov ( X tj , X ti ) / variance) (1 / ( j-i ) )
Where Cov ( X tj , X ti ) is the covariance of a time-shifted version of itself; j – i is the shift in time. The
choice of the time-shift (j-i) is based on a visual representation of the auto-covariance: the best
assumption for calculating the memory factor is to take the time-shift where the peak in auto-
correlation starts to broaden up. Or in other words: where the next value is less dependent on the
previous value.
The random disturbance is assumed to be White Gaussian noise (WGN; Everitt, 2002; Orfanidis,
2007). Gaussian noise is a spectrum of noise with a certain mean and variance. White Gaussian noise
requires the mean to be zero. The variance of WGN was calculated using tau (τ):
τ = Δt / ln (1/ Φ)
Where Φ is the same memory factor calculated before and Δt is the time-lapse between two
different data points of the signal. Since there is a difference in time-lapse and computing the correct
time-lapse for the appropriate interval is beyond the scope of this thesis, the average time-lapse (29
kyr ; see below - section 6.1.1.3) is used.
The variance of the WGN was calculated by: Variance (WGN) = 1 – e ( - 2 * Δt / τ )
WGN itself is calculated by: standard deviation (square root of variance) multiplied by a random
number between 0 and 1. This creates a normally distributed noise with a mean equal to zero and
with the appropriate variance.
Now, the last step is to create the random dataset. This is done by randomly choosing a value out of
the signal as new first value. The next values are created with the following equation (Everitt, 2002;
Orfanidis, 2007):
X(ti) = c + Φ * X (ti-1) + WGN (ti)
Where c is the mean value (here taken to be zero); X (ti-1) is the previous value; Φ is the memory
factor; WGN (ti) is the White Gaussian Noise, which is different with each calculation of the following
value.
7. RESULTS
The table containing all results can be found in Appendix 1. For the graphs of each index is referred
to Appendix 2. Appendix 3 and 4 hold the results of the autocorrelation and PCC respectively.
40
Figure 7.1: Combined lot of indices. Left: Index versus Depth (in mcd); Right: Index versus age (mcd). Blue
lines represent the samples.
41
7.1 Sedimentation rates
7.1.1 Apparent sedimentation rate
Sedimentation rates were calculated by integrating depths in mcd with the ages obtained from the
Age model of Quaijtaal et al. (2014). These are not the true sedimentation rates because compaction,
due to burial, reduces the thicknesses of the layers. This in turn will lead to an underestimation of the
derived sedimentation rates when just integrating them from a time versus depth curve. However,
from now onwards this apparent sedimentation rate will be called sedimentation rate for the
convenience.
The average sedimentation rate is 8,4 cm/kyr, but there is a wide spectrum (Appendix 1). Highest
sedimentation rates occur in the oldest part until ~17,61 Ma (~241 mcd) with an average rate of 20,4
cm/kyr. After this interval, sedimentation rates drop towards 4,1 cm/kyr and they keep decreasing
towards the absolute minimum of 1,15 cm/kyr between ~17,16 (~226 mcd) and ~16,77 Ma (~221
mcd). After this interval sedimentation rates generally increase towards the top of the section, with
the exception of a drop between ~16,43 and ~16,33 Ma (~198 and ~196 mcd).
7.1.2 Sample dimensions
All samples taken had a thickness of around 2cm. By using the inverse of sedimentation rates, it was
possible to estimate how many years were compressed in this relatively small interval. The higher
the sedimentation rate, the less time will be comprised into one 2-cm-thick sample. The average
sample contained ~130 years/2cm. The lowermost and uppermost parts represent 2 minima with
~24yrs/2cm and ~30yrs/2cm respectively. A maxima of ~435 yrs/2cm was obtained for the interval
between 17,17 and 16,77 Ma (~225 and 222 mcd).
7.1.3 Sampling resolution
Sampling resolution can be found in Appendix 1 under “Age differences” and is plotted in Appendix
2. The Y-axis shows the age difference between a sample and the previous sample. The average
sampling resolution is 0,029 Ma (29 kyr). Lowest sampling resolution is 0,108 Ma and is in between
~17,72 and ~17,61 Ma (~237 and ~242 mcd). Two other minima occurred between ~16,48 till ~16,33
and ~17,34 till ~16,82 Ma (~229 till ~221 mcd and ~200 till ~197 mcd). The highest sampling
resolution was obtained near both the upper and lower boundary and between ~16,72 and ~16, 49
Ma (between ~201 and ~220 mcd). These resolutions are 0,006; 0,005 and around 0,01 Ma
respectively.
7.2 Palynomorph assemblage
7.2.1 Dinoflagellate cyst assemblage
The Spiniferites/Achomosphaera–complex is the most abundant group in all samples, also Spiniferites
membranaceous/mirabilis and “Paucisphaeridium sp. B” of Quaijtaal (unpublished) are present in
relatively high numbers throughout the entire interval. Other taxa showing a relatively high
abundance throughout the entire interval are: Batiacasphaera-complex, Cleistosphaeridium
placacantha, Pauisphaeridium sp.B of Quaijtaal et al. (2014) and the Peridiniae-group.
Cousteaudinium aubryae is present in high numbers in the lower part, with a peak of 73 at ~ 17,53
42
Ma (~234 mcd). The presence of C. aubryae decreases from ~16,70 Ma (~219 mcd) onwards, from
where it shows a more “common”-like appearance.
Common taxa throughout the entire interval are: Batiacasphaera deheinzelini/hirsuta, Dapsilodinium
pseudocolligerum, Kallosphaeridium sp. Of Head and Westphal., Lingulodinium machaerophorum,
Polysphaeridium zoharyi (with both subspecies: P. zoharyi zoharyi and P. zoharyi ktana) and
Reticulatosphaera actinocoronata. The presence of Cribroperidinium tenuitabulatum and
Dinopterygium cladoides is more punctuate, becoming common only once in a while.
Labyrinthodinium truncatum is rare in the oldest interval until a peak between ~16,77 and ~16,71 Ma
(~221-220 mcd), from where its presence fluctuates between rare and common. Labyrinthodinium
truncatum truncatum shows a punctuate appearance between its lowest occurrence at ~229 mcd
(~17,34 ) and ~226 mcd (~17,17 Ma). From ~226 mcd onwards, it shows a continuous presence.
Labyrinthodinium truncatum modicum is present before the LO of L. truncatum truncatum. It shows a
very punctuate appearance.
Impagidinium patulum was not found during this investigation, even though it was found by Louwye
et al (2008) while investigating the same cores. However, their abundance found in Louwye et al
(2008) can be considered as very rare, mostly occurring outside the count of ~400 to ~500 dinocysts.
Reworking of dinocysts remains low with an average of 1,67%, while the amount of reworked
dinocysts never exceeds 4% of the total dinocyst assemblage including the reworked specimens. The
highest flux of reworked specimens is 4% and occurs around ~17,17 Ma (~226 mcd). Although, there
is a large variability between following samples, one might distinguish a general trend: more
reworking is found in the lowest sample with a general decrease towards a minimum between
~16,82 and ~16,48 Ma (between 220 and 200,52 mcd). The lack of younger data hinders a proper
evaluation of the youngest part. Reworked species included some which must have originated from
nearby Cretaceous (large, thick-walled Cretaceous species of Spiniferites) or Oligocene/Eocene
deposits (Emmerocysta urnaformis and a large specie of Cleistosphaeridium, possibly
diversispinosum).
One species was called cf. Labyrinthodinium truncatum sensu de Verteuil & Norris (1996) (mini)
(Figure 3.p; Appendix 5), because of its small size and possible relation to the Labyrinthodinium
truncatum as presented in de Verteuil & Norris (1996). However the species presented in de Verteuil
& Norris had an erratic definition (Quaijtaal et al., personal communication). It was not grouped as
Paucisphaeridium sp.B of Quaijtaal (Unpublished) due to its morphological differences.
7.2.2 Other marine palynomorphs
Several acritarchs were found and determined until species level. Some of them will be published by
Quaijtaal (2014, Unpublished).
Lavradosphaera crista, Nannobarbophora gedlii and skolochorate acritarchs are found in relatively
high abundance throughout the entire interval. Cymatiosphaera spp., Leiosphaeridia rockhallensis,
Platycystidia sp. II (Manum et al., 1976) and Paralecaniella indentata occur in lower abundances and
their presence throughout the interval is less continuous.
43
Cyclopsiella granosa/elliptica is present throughout most of the interval. However, the abundance
ranges from rare to abundant. The maximum value of 247 is reached in a peak at ~17,74 Ma (~246
mcd). Between ~17,24 and ~16,69 Ma (~227 and ~218 mcd) the amount of Cyclopsiella
granosa/elliptica is relatively higher than average, while a last peak of 25 is reached at ~16,30 Ma
(~196 mcd).
7.2.3 Biostratigraphical analysis
A recent biostratigraphical analysis on the same Hole 1318B was published by Louwye et al. (2008)
and updated by Quaijtaal et al. (2014). Here, the biostratigraphy of subunit 3C is updated with new
biostratigraphical data obtained by a higher sampling resolution. These data were then correlated
with the new age-model of Quaijtaal et al. (2014).
Louwye et al. (2008) found a LO of Labyrinthodinium truncatum in section 1318B-26X-2 and derived
the transition from the Cousteaudinium aubryae Interval Zone (DN3; de Verteuil 1996; 1997; de
Verteuil & Norris 1996) to the Distatodinium paradoxum Interval Zone (DN4; de Verteuil 1996; 1997;
de Verteuil & Norris 1996) between sections 1318B-27-X-2 and 1318B-26X-2. Louwye et al. (2008)
found a HO of Distatodinium paradoxum in section 1318B-21X-7. They placed the transition from
DN4 to the Batiacasphaera sphaerica Interval Zone (DN5; de Verteuil 1996; 1997; de Verteuil &
Norris 1996) between sections 1318B-22X-7 and 1318B-21X-5.
Here, the LO of Labyrinthodinium truncatum was found in section 1318B-26X-2 as well. The transition
from DN3 to DN4 was found within section 1318B-26X-2, between 230,94 and 229,92 mbsf (237,09
and 236,07 mcd; ~17,61 till ~17,59 Ma). The HO of Distatodinium paradoxum was found in section
1318B-21X-6, hereby extending its occurrence with one core-section. The transition from DN4 to
DN5 is placed between sections 1318B-21X-6 and 1318B-21X-5, between 193,40 and 192,25 mbsf
and (199,56 and 198,40 mcd; ~16,47 till ~16,43 Ma).
7.2.4 Dinocysts per gram of dry-weight sediment
No dinocysts/gram of dry-weighted sediment was obtained for the lowermost three samples (245,5-
247,5 mcd) because no Lycopodium clavatum tablets were added as a spike. The average amount is
17489 dinocysts/gram with a minimum of 7023/gram around ~16,53 Ma (~205 mcd) and a maximum
of 41019 at ~16,29 Ma (~193 mcd). From the bottom until ~17,17 Ma (~226 mcd) there is a higher
amount of dinocysts/gram although it does fluctuates with high amplitude. Between ~17,08 Ma
(~225 mcd) and ~16,39 Ma (~198 mcd) the dinocysts / gram is relatively low. This minimum is
interrupted by three sharp peaks at ~16,72; ~16,54 and ~16,50 Ma ( ~220; ~206 and ~203 mcd). The
third peak at 16,50 Ma, is the only one defined by multiple data points. Minima occur at ~16,77;
~16,66; ~16,60; ~16,53 and ~16,39 Ma (~221; ~216; ~212; ~206 and ~198 mcd). The youngest 5 data
points show an increasing trend.
7.2.5 Diversity
Three indices used to express biodiversity show good coherence in their trend. The species richness
is relatively high with an average of 40 species per sample. Both the Shannon-Wiener index and
Evenness indicate a relatively diverse, more evenly distributed assemblage with a mean value of 2,58
and 0,70 respectively. The somewhat higher Shannon-Wiener index and Evenness indicate, that even
though all assemblages are dominated by the Spiniferites/Achomosphaera – complex, there are still
other species with a relatively high abundance. Both indexes have a strong negative correlation with
44
the relative proportion of the total assemblage belonging to the Spiniferites/Achomosphaera–
complex. However it is obvious that when counting up to 300 species, less species of this complex
will be counted if there is a higher abundance of other species.
The general trend shows a drop from ~17,59 Ma (~235 mcd) onwards towards a minimum in
biodiversity near ~17,53 Ma (~232 till ~234 mcd). Here, richness drops beneath the 30 species, while
the lower values of Shannon-Wiener index and Evenness (2,06 and 0,61 respectively) suggest a
relatively low diverse and unevenly distributed assemblage. These assemblages are dominated by
three species: Spiniferites/Achomosphaera–complex Cousteaudinium aubryae and
Cleistosphaeridium placacantha. Afterwards the trend steadily increases towards a maximum
biodiversity between ~16,61 and ~16,48 Ma (~213 and ~200 mcd) with a subsequent drop towards
the youngest sample.
7.2.6 Sea-surface temperature
The Warm/Cold-index shows a high variability. Apart from 2 minima around ~16,61 and ~16,69 Ma
(~212 and ~218 mcd), all values fluctuates between 0,80 and 1,00 with an average ratio of 0,92.
These values indicate relatively warm SST. The record can be split up into an older interval with
somewhat lower variability and a younger interval with higher variation from ~16,72 Ma (~220 mcd)
onwards. Considering the older part: An increase might be interrupted by a small decrease between
17,74 and 17,73 Ma (~247 till ~245 mcd). Afterwards the increase continues until a relatively stable
warm period with a W/C ratio of ~0,95 to ~1 between ~17,56 and ~17,24 Ma (~235 till ~227 mcd).
Then, the ratio drops towards a minima of 0,857 at ~17,17 Ma (~226 mcd). A subsequent increase
brings the start of a second relatively stable warm period between ~16,99 and ~16,72 Ma (~224 mcd
and ~220 mcd). The youngest interval starts with a distinct drop towards a minimum of 0,750 at
~16,69 till ~16,68 Ma (~218 till ~217 mcd) and subsequent increase towards a peak between ~16,66
and ~16,64 Ma (~216 till ~214 mcd). The second minimum of 0,769 is reached between ~16,62 and
~16,61 Ma (~213 and ~212 mcd). Values are somewhat higher between ~16,60 and ~16,49 Ma (~206
and ~202 mcd). Two last minima occur at ~16,43 and ~16,30 Ma (~198 and ~196 mcd) followed by a
last increase with peak at ~16,29 Ma (~194 mcd).
7.2.7 Neritic/Oceanic signal
The N/O index (Figure 7.1 / Appendix 2) clearly indicates a strong neritic signal, with an average ratio
of 0,975. The data can be divided in 3 intervals. The oldest interval is relatively stable and shows a
high signal, dropping only twice below the mean N/O ratio: between ~17,73 and ~17,72 and at
~16,90 Ma (~245 till ~242 mcd and 223 mcd) to values of 0,971 and0,973 respectively. With
fluctuations of 1 to 2%, the signal to noise ratio might be low and the general trend has to be
interpreted with caution. The trend shows a drop from higher values in the oldest samples, towards
the first minimum around ~17,7 Ma. The signal increases towards a stable maximum between ~17,61
and ~17,24 Ma (~237 and ~227 mcd). Afterwards the N/O index decreases towards the second
minimum of this interval at ~16,90 Ma. The oldest interval ends with an increase towards a second
maximum at ~16,77 Ma (~221 mcd), reaching a fully neritic signal of 1,00.
The middle interval, starting at ~16,71 Ma (~220 mcd), shows fluctuations with greater variability.
Minima occur at: ~16,70 - 16,68 Ma; ~16,62; ~16,58 and ~16,48 Ma (~219 -~217; ~213; ~210 and
~201 mcd). The drops at ~16,62 and ~15,8 Ma represent the largest drops towards values of 0,912
and 0,905 respectively. However the extreme values are only characterised by one data point, while
45
the decreases at ~16,70 - 16,68 Ma and ~16,48 Ma are relatively smaller (0,960; 0,938 respectively)
but are characterised by more data points. This indicates that the two largest drops have to be
interpreted with caution. The youngest interval starts with an increase from the minimum values at
~16,48 Ma (~200 mcd) towards peak values of 0,995 at ~16,33 Ma (~197 mcd). Afterwards the N/O
ratio decreases towards 0,964 at ~16,29 Ma (~195 mcd) followed by a possible increase.
7.2.8 Continental influence
The S/D ratio generally shows a high variability, fluctuating around an average ratio of 0,17. When
looking at the S/D versus depth curve (Figure 7.1 / Appendix 2), one can say that there is a general
increase in amplitude with time. Looking at the S/D versus age curve (Figure 7.1 / Appendix 2), the
record can be divided in 3 parts based on amplitude and frequency in oscillations. In the oldest part,
there are 2 cycles with similar amplitude and minima occurring at ~17,74; ~17,53 and ~17,34 Ma
(~246; ~234 and ~229 mcd). After the last minimum, we enter a second mode with a more uniform
increase until a stable maximum between ~17,2 and ~16,9 Ma (~226 and ~223 mcd). From ~16,9 Ma
onwards, S/D values decrease to a next minimum at ~16,72 Ma (~220 mcd). The second interval
however, is sampled in a relatively low resolution compared to the other intervals. The youngest
part, starting from the last minimum in S/D, shows the highest variability. Both maxima and minima
occur in this youngest part. The three highest peaks were dated on ~16,69; ~16,56 and ~16,37 Ma
(~218, ~209 and ~198 mcd) with ratios of 0,30; 0,48 and 0,47 respectively. The lowest ratio is ~0,25
and is dated at ~16,29 Ma (~194 mcd).
Large ratios are mostly dominated by bisaccate pollen, probably from Pinus. Other abundant genera
are the Baculatisporites spore and Ilex pole. Most pollen and spores, however, were not determined
since this is beyond the scope of this thesis.
7.2.9 Sea-surface Productivity
The P/G – index fluctuates heavily around a mean of 0,14 with values ranging from 0,03 to 0,39. The
P/G versus time curve (Figure 7.1 / Appendix 2) generally shows 3 cycles. The oldest interval between
~ 17,75 and ~17,61 Ma (~247 and ~237 mcd) show a relatively large variability with three peaks in
P/G. P/G values drop towards a first minimum between ~17,56 and ~17,42 Ma (235 and 231 mcd).
From then onwards, a second interval with fluctuations starts. A second minimum occurs between
~16,90 and ~16,72 Ma (~223 and ~220 mcd). The third and youngest interval in this studied interval
shows a much higher amplitude and shorter period in its fluctuations. 3 sharp peaks occur at ~16,62;
~16,56 and ~16,49 Ma (~213; ~209 and ~202 mcd). This interval ends with decreasing P/G values
towards a last minimum at 16,29 (~195 mcd). There seems to be a positive correlation between
sample resolution and increasing variation.
7.3 Organic geochemistry
7.3.1 𝑈37𝐾′
- index
All temperatures derived from the 𝑈37𝐾′ - index approach the upper temperature range of the
calibration study performed by Müller et al. (1998). Indeed there were difficulties in measuring the
C37:3 alkenone concentrations. Peaks were difficult to distinguish from background noise. A sample
was run on a coupled GC-MS in order to determine which peak belonged to the C37:3 alkenones.
Although the peak approached the detection limit of the GC-MS, thanks to the experienced staff at
46
the NIOZ the appropriate peak could be determined. See (Figure 7.1 / Appendix 2). The assumption is
made that a deviation of linearity in the T versus the 𝑈37𝐾′
signal, occurred. This suppresses the
derived temperature differences, which will cause an underestimation of the difference in
paleotemperature between two samples.
Most temperatures lie between 28±1,5°C and 29±1,5°C with an average temperature is 28,4°C. The
standard error is probably higher because of deviation from the calibration curve, so the 1,5°C is only
a minimum measure of standard error. All temperatures lie in a range smaller than the calibration
error. This could suggest that all variability in this signal is due to noise. The following trend might be
derived: the earliest interval until ~16,72 Ma (~220 mcd) is generally warmer with peaks at ~17,56
and ~17,46 Ma (~235 and ~232 mcd). Temperatures start to drop from ~16,72 Ma (~220 mcd)
onwards, starting a relatively colder period with higher variability. During this period with higher
variability minima occur at ~16,70; ~16,62; ~16,58 and ~16,50 Ma (~219; ~213; ~210 and ~203 mcd).
Peaks occur at ~16,68; ~16,60; ~16,56 till ~16,51 and ~16,48 Ma ( ~217; ~212; ~209 till ~204 and
~201-200 mcd). A temperature increase might be distinguished near the uppermost part of the
section, starting near ~16,33 Ma (~196 mcd). A small drop and subsequent increase in temperature
could be inferred in the lowermost part although similar reasons as mentioned before could be the
cause of this.
7.3.2 𝑇𝐸𝑋 86 𝐻 - index
The temperatures derived from the 𝑇𝐸𝑋 86 𝐻 - index indicate warmer conditions than during present
times. The temperature ranges between 22±2,5°C and 27±2,5°C with an average temperature is
24°C. The 𝑇𝐸𝑋 86 𝐻 - index does show lower temperatures with a larger variability than the 𝑈37
𝐾′ -
index, but both general trends seems to correlate well. A possible small temperature decrease and
subsequent increase occurs as well in the lowermost part. Afterwards temperatures increase
towards a maximum around ~17,42 Ma (~231 mcd). A relatively warmer period lasts until ~16,77 Ma
(~221 mcd), after which temperatures drop towards a minimum between ~16,6 and ~16,3 Ma (~213
and ~195 mcd). There is a relatively higher variability from the temperature-drop. This variability is
shows several distinct minima around: ~16,70; ~16,60 till ~16,58; ~16,49; ~16,43 till ~16,39 and
maybe around ~16,30 Ma (~219; ~212 till ~210; ~202; around ~198 mcd and maybe ~196 mcd). Peak
temperatures occur at ~16,68 till ~16,66; ~16,56 till ~16,51 and ~16,48 Ma (~217 till ~216; ~209 till
~204 and ~199 till ~201 mcd). The uppermost part of the section witnesses a sharp increase in
temperature starting around 16,30 Ma (~195 mcd).
7.3.3 BIT – index
The BIT values range from 0,079 till 0,234, with an average value of 0,140. Since almost all BIT values
remain below 0,2, no strong bias in the SST derived from 𝑇𝐸𝑋 86 𝐻 measurements occurred (See
section 5.3.4). The only times BIT values pass 0,2 is around ~16,69 and ~17,46 Ma (~218 and ~232
mcd). no distinct increase in SST derived from 𝑇𝐸𝑋 86 𝐻 can be correlated with these speaks. This
indicates that no strong bias for these data points occurred neither.
What can be inferred is a higher variability in the bottom part, followed by a relatively stable period
between ~17,34 and ~16,77 Ma (~229 and ~221 mcd). The youngest part shows the highest
variability with a minimum in BIT values around ~16,28 Ma (~195 to ~193 mcd). Other minima occur
at ~16,65; ~16,58; ~16,48; ~16,33 Ma (~215; ~210; ~200 and ~197 mcd).
47
7.4 Statistical analysis of time-series
Statistical analysis was performed on both the raw data and a three-point running average. The
three-point running average was used in order to reduce natural variation and random error. This
resulted in different Pearson correlation coefficients (see below - section 7,1,4,2). This section
handles mainly the filtered signal. The results on the raw data can be found in Appendix 2.
A small note: All analyses were performed on the temperatures derived from both TEX 86 𝐻 and 𝑈37
𝐾′.
However, for convenience and readability the temperature estimates of TEX 86 𝐻 and 𝑈37
𝐾′ will be
shortened towards just TEX 86 𝐻 and 𝑈37
𝐾′.
7.4.1 Autocorrelation
All Graphs showing the results of the auto-correlation can be found in Appendix 6. Note the artificial
higher auto-correlation when comparing the data from the 3-point running average with the raw
data. As mentioned above, nearly all samples are taken over a constant interval of ~1m. This means
that, when while shifting the signals over a certain amount of data points, the autocorrelation will
show cyclicities in depth-intervals. The periodicities when considering time-intervals were derived by
subsampling all signals at a certain resolution and performing an autocorrelation on these “new”
signals.
Only two signals show a relatively strong cyclic pattern: the S/D and BIT index, of which the S/D ratio
shows the strongest cyclic pattern.
The autocorrelation of the S/D index shows shifts between correlation and anti-correlation at a
relatively high frequency, although the amplitude decreases with a larger shift. Maxima in correlation
occur at a shift of 9-10, 20, 27 and 40 points (0,26-0,29; 0,58; 0,78 and 1,15 Myr) . Minima occur at a
shift of 4, 15, 24 and 32 points (0,115; 0,44; 0,68 and 0, 93 Myr). This could suggest a semi-periodic
signal with a period of approximately 10 data points (~0,20-0,25 Myr), with some big discrepancies).
The autocorrelation of the BIT-index produces maxima in correlation occur at a shift of 6-7, 13, 21, 27
and 33 points ( 0,2; 0,38; 0, 61; 0,78 and 0,96 Ma), of which the shift of 27 ( 0,78Ma) produces the
highest autocorrelation. Minima occur at a shift of 4, 10, 17, 24, 30-31 and 37 points (0,12; 0,30;0,50;
0,70; 0,90 and 1,1 Ma) with shifts of 17 and 37 points (0,5 and 1,1 Ma), producing the largest anti-
correlations. This pattern a combination of two semi-periodicities. A cycle of around 6-7 points (~0,20
Ma) produces smaller amplitude variations, while a cycle of around 20 points (~0,50-0,60 Ma)
produces the largest variations.
The abundance of Cyclopsiella elliptica/granosa shows a peak at a time shift of 15 data points
(0,44Ma). However this peak is not repeated after a time shift of 30 data points. Moreover, when
looking at the signal itself ((Figure 7.1 / Appendix 2)), it is indeed difficult to correlate this with any
kind of semi-periodicity.
All other signals ( TEX 86 𝐻 ; 𝑈37
𝐾′ ; W/C ; N/O ; P/G ; the diversity indices; reworking and
dinocysts/gram) do not show any strong cyclic pattern neither, but there is a consensus among the
strongest anti-correlation. This minima in correlation always occurs near a shift of 20 points (around
0,60 Ma), after which the autocorrelation shows a steady recovery. The best examples are TEX 86 𝐻
48
and 𝑈37𝐾′ and reworking. The autocorrelation of W/C largely reflects both TEX 86
𝐻 and 𝑈37𝐾′ , although
small amplitude variations do occur. Maxima are situated at a shift of: 5, 13, 21-22 and 28 points
(0,14; 0,4; 0,65 and 0,81 Ma). Peaks in anti-correlation occurs at 10, 16, 24 and around 35 points
(0,32; 0,48; 0,70 and 1 Ma). The N/O has maxima occurring at 9 and a small peak at 25 (0,26 and 0,73
Ma), while minima in autocorrelation at a shift of 6, 22 and 27 point (0,16; 0,65 and 0,78Ma).
The P/G ratio shows maxima in autocorrelation at a shift of: 11, 16, 27 and 37 points (0,32; 0,47; 0,79
and 1,07 Ma), of which the peak at 16 (0,47 Ma) is very small. Minima occur at 8, 15, 20 and 31
points (0,23; 0, 43; 0,56 and 0,9 Ma), while the drop at 15 (0,43 Ma) is of minor magnitude. Richness
shows peaks at shifts of 9, 16, 27 and maybe 38 (26; 0,47; 76; -- Ma), while minima occur at 6, 14, 21
and 30 (0,18; 0,41; 0,62 and 0,88 Ma). The Shannon-Wiener index has maxima at 10-11, 27, 38 (0,30;
0,80; and 1,1 Ma) and minima at 7-8, 19 and 34 (0,22; 0,60 and 0,90). The evenness largely reflects
the pattern of the Shannon-Wiener index. Dinocysts/ gram shows peaks in autocorrelation at 5, 13,
20-21 and 28 (0,15; 0,38; 0,60 and 0,80 Ma).
A detrending of the data has been performed by substracting the best fitting second order
polynome. This resulted in the enhancement of the ~10 data points cycle in all signals. Even the
geochemical temperature proxies started to show some hints of this cycle.
7.4.2 Pearson correlation coefficient
All Pearson Correlation Coefficients (PCC) of the investigated relations can be found in Appendix 4.
As expected, the highest correlations are found between Richness, the Shannon-Wiener index and
Evenness and between the TEX 86 𝐻 and 𝑈37
𝐾′. What is more interesting, is the high anti-correlation
between TEX 86 𝐻 and 𝑈37
𝐾′ on the one hand and Richness, the Shannon-Wiener index and Evenness
on the other. The PCC between the P/G index and TEX 86 𝐻 and 𝑈37
𝐾′indicates a strong anti-correlation
while a strong correlation was found between P/G and the diversity indices. The N/O ratio shows an
opposite correlation: good correlation with TEX 86 𝐻 and 𝑈37
𝐾′ and a strong anti-correlation with the
diversity indices. All other PCC need to be investigated by comparing them to the PCC of synthetic
datasets.
7.4.3 Auto regressive model
All parameters characterising the AR(1) processes are given in Appendix 3 For most signals it was
relatively easy to choose the time-shift necessary for defining the memory factor, since it was easy to
derive the peak starts to broaden up. This proved more difficult other signals ( TEX 86 𝐻 ; 𝑈37
𝐾′ ; S/D; BIT
and the abundance of Cyclopsiella granosa/elliptica) this was hindered by a steady decease in
correlation until an anti-correlation was reached. If this was the case the time-shift was chosen as the
last shift resulting in a positive correlation (see Appendix X; figures Autocorrelation). All time-shifts
chosen are given in Appendix 3.
As mentioned above (in section 6.4.3) the significance of a PCC between two signals was found by
comparing them to the PCC of the 1000 synthetic datasets. What is looked at is the proportion of
synthetic datasets that have a PCC lower than the PCC between two signals. However the synthetic
datasets were made randomly. This produces a certain range in PCC, which can vary within a certain
range. The following assumptions were considering significance of PCC:
49
1. When more than 95% of the synthetic datasets have a PCC lower than the investigated
signal, the PCC is considered to be significant.
2. The range of PCC between 5 and 10% is considered as a gray zone, close to 95%, with
plausible correlation.
3. A PCC below 10% of the PCC of the synthetic datasets is questionable and is not taken into
account.
The significance found for all PCC can be found in table Appendix 3.. The calculated significances
suggest the proxies to be linked in groups. The proxies within one group show very significant
correlations with one another.
The first group of proxies contains the temperature proxies and the N/O index. The second group
contains the three diversity indices and the productivity signal (P/G). All proxies from group one
show a significant anti-correlation with the other group.
The S/D ratio shows a significantly good correlation with the BIT-index. However, both indices show a
different relation towards other indices. The S/D shows a significant negative correlation with the
first group containing temperature proxies, although the anti-correlation with N/O is inferred as
plausible. Considering group 2, a plausible to questionable positive correlation is inferred with the
S/D. The BIT-index on the other hand does not show any significant correlation with neither of the
two groups.
Reworking shows similar correlations as the proxies of group one. Because of the questionable
significance of its correlation with the W/C index, it was not included into the first group of proxies.
Reworking has significantly high correlation with group two and the S/D ratio.
The abundance in Cyclopsiella granosa/elliptica can only be correlated with the signal of reworking.
An anti-correlation with the S/D ratio is very questionable.
The random data produced during the AR(1) process for the Dinocysts/graom, gave PCC which lie
within a very small range. There are three possible reasons for this:
1. A error is made in the Matlab program, although this was investigated thoroughly and none
were found.
2. The AR(1) model is not suited for a process
3. The significances derived are correct
If the significances derived are correct, the following relations can be made: significant correlation
with group one, a strong anti-correlation with group two without evenness and a strong anti-
correlation with the S/D ratio.
50
8. DISCUSSION
8.1 Noise and Bias of Signals
8.1.1 Random noise
Mertens et al. (2009) proved that a count of 300 dinocysts is relevant for generating reliable
diversities and absolute abundances. Still, the cold water species and the oceanic species have a
relatively low abundances in all samples due to the dominance of warm and neritic species. This
creates a low signal-to-noise ratio, which means that sudden small shifts in both the N/O and W/C
index need to be interpreted with caution. However, when different indices show a similar shift in
the same sample, the probability for this to be caused by noise decreases. This is especially the case
when the shift is visible in both palynological and geochemical indices. The highest peaks in the S/D
and P/G ratio are defined by only one sample. They do represent significant chances in the
assemblages, which cannot be solely derived from noise.
The intervals between the oldest sample and ~17,4 Ma and between ~16,7 and ~16,5 Ma roughly
correlate with the highest sampling resolution in time. These intervals are characterised by the
highest amplitude in variation, but most cycles are defined by multiple data points. This reduces the
chance that these trends reflect noise. Two exceptions are the W/C and N/O indices, which fluctuate
with larger amplitude.
Considering the autocorrelation, it is highly unlikely that longer cycles with periodicities of e.g. 10 or
more data points are related to random noise. It is possible though, that noise could mask a certain
oscillation by offsetting the period from the central frequency.
8.1.2 Sampled Interval
A sample of 2 cm will consist out of a certain time interval (e.g. section 7.1.2). All samples were
homogenised, so measurements were performed on the average value of this relatively small time
range. The smaller the sampled time interval, the more influence one extreme event (e.g. extreme
floodings) can have on the measured interval. These extreme events can have a similar effect on data
as random noise, but probably with a higher amplitude. Especially during the lowermost and
uppermost intervals with minima in the sampled time-interval of around ~25 to ~30yrs/cm, one
extreme event can have a lot of influence on the average value of a sample. The high peaks in P/G
and S/D ratio at ~16,56 and in S/D at ~16,39 Ma, which are defined by only one data point, could be
the result of such an extreme event. However they do seem to be part of a larger cycle, defined by
multiple data points. The interpretation here is based on these cycles, not on these extreme values.
As an example: even without the extreme values, the amplitude in variation of most cycles in the
upper part is higher than before.
8.1.3 Tuning of data
As mentioned before, many researchers (e.g. Westerhold et al., 2004; Abels et al., 2005; Holbourn et
al., 2014) tune the ages of their data to the computed Milankovitch cycles of Laskar et al. (2004). It
might be that a large proportion of fluctuations in data are forced by Milankovitch cycles. If so,
tuning the data will result in enhanced dating of the data points. However since most datasets can
51
contain some errors due to random noise, extreme events or measure errors, this tuning will
artificially create a better correlation.
A second effect of tuning is the artificial reduction in lead or lag. This lead or lag could be the result of
another mechanism, or could indicate that other factors have a stronger influence on the signal. So,
again, one has to be aware of these reductions when making conclusions.
No tuning has been performed during this thesis. This means that the correlations were not
artificially enhanced and suggests that a significant PCC does indicate a connection between the
signals. On the other hand, because no tuning occurred, the semi-periodic cycles could be masked by
the uncertainty in dating.
It is acknowledged that tuning could give a better view of the real relations. However, the suggestion
is made to perform a similar analysis as presented in this thesis to evaluate the significance of the
found correlations.
8.1.4 Selective preservation
Peridinioid dinocysts are present throughout the entire interval, so the assumption is made that no
post-depositional oxidation has occurred. This indicates that the assemblages found are not biased
by selective degradation.
Moreover, the dinocysts/gram signal does not show a positive correlation with the P/G ratio, but
more like an anti-correlation. This strengthens the assumption that there were no significant changes
in preservation of organic matter.
8.2 Age model
8.2.1 Labyrinthodinium truncatum
If the new age-model of Quaijtaal et al. (2014) is applied, the LO of Labyrinthodinium truncatum is
correlated with an age of ~17,6 Ma. In the southern North Sea Basin, this LO is placed at 15,8 Ma
(Brinkhuis & Munsterman, 2004). Williams et al. (2004) gave a global overview and based the LO of
Labyrinthodinium truncatum at 16,5 Ma. The latter date was based on de Verteuil & Norris (1996)
who dated the LO on the New Jersey Coastal plain in the western North-Atlantic. The age derived
from the new-age model of Quaijtaal et al. (2014) is offset by 1 to 1,5 Ma. However, as mentioned in
Louwye et al. (2008), Munsterman did suspect a delay entry of this species in the southern North
Sea.
The presence of Labyrinthodinium truncatum is very punctuate until core 1318B-24X-1 at 245,47
msbf (221 mcd; ~16,82 Ma). This sample marks the start of a distinct increase in abundance, with a
peak between ~16,77 and ~16,72 Ma (221 – 220 mcd). This age is in close resemblance with the age
found by de Verteuil & Norris (1996). This indicates that Labyrinthodinium truncatum has a longer
range in time, than previously expected. So caution has to be made when using the LO for
biostratigraphy.
8.2.2 The RD2 hiatus
Scientists expedition 307 (2006) placed the unconformity between lithostratigraphic unit 3B and 3C
in core 1318B-21X-3 at 114 cm. This position correlates with 196,39 mcd. The expedition 307
52
scientists (2006) correlated this transition with the RD2 unconformity of Van Rooij et al. (2003). Here,
the unconformity is dated at ~16,33 Ma and occurs just before the data point of ~16,33 Ma. This age
correlates well with the age of 15-16 Ma (NN4-5) presented by Stoker et al. (2001). The latter authors
dated the C20 reflector at DSDP site 610 on the western side of the Rockall Through (North-Atlantic
Ocean).
A distinct feature is seen in the sedimentation rates (Appendix 2). From the middle part towards the
top of the investigated interval, the sedimentation rates show an increasing trend. This trend is
interrupted by a distinct drop to a minimum between 198,40 and 196,54 mcd (16,43 and 16,33 Ma).
These dates were derived by linear interpolation between two magnetostratigraphic tie points
(Quaijtaal et al., 2014). These two magnetic reversals characterise the top and bottom of C5Cn.2n
and are dated on 16,303 and 16,472 Ma (Hilgen et al., 2012). Quaijtaal et al. (2014) correlated them
with the magnetic reversals at 196,05 and 199,150 mcd respectively. This drop in sedimentation
rates probably reflects the hiatus. The hiatus was estimated following the steps described above
(section 7.1.1). This results in an estimation of 0,049 Ma or 49 kyr, which confirms the statement of
Louwye et al. (2008) that the hiatus is probably of minor magnitude.
8.3 Linking the Paleoenvironmental Indices
8.3.1 SST and N/O
The relatively high PCC calculated between the W/C-ratio and the SST estimates ( 𝑇𝐸𝑋 86 𝐻 - index,
𝑈37𝐾′ - index), confirms the usefulness of the W/C-index as a proxy for SST. No perfect match was
found. This shows that the W/C has to be interpreted with caution since other factors hamper the
signals response to SST changes, although noise could play a big role, since only two species are used
as cold water indicators.
N/O shows a strong correlation with SST, even higher than the W/C, suggesting a similar forcing
enhancing SST and the relative abundance in neritic species found. The latter relation was also found
by Quaijtaal et al. (2014) and Donders et al. (2006), although both settings differ. Donders et al.
(2006) investigated a proximal, inner shelf setting, very near to the coast, while Quaijtaal et al. (2014)
investigated the same site U1318 as this thesis, which has a distal, oceanic setting. Still, both
publications found a similar relationship and found a comparable solution to this counter-intuitive
positive correlation. Donders et al. (2006) stated that the tectonic blocks surrounding their location
got flooded during sea level rises, hereby increasing the area where shallow marine conditions occur.
Quaijtaal et al. (2014) proposed that a eustatic sea level lowering would significantly reduce the shelf
in area, hereby significantly suppressing the abundance of neritic species living on the entire shelf.
Here a different mechanism is proposed, at least for the interval and location studied during this
thesis.
The eustatic sea level reconstruction of John et al. (2011) shows a maximum eustatic sea level drop
of 28 m during their Mi-2a event. One could argue that this curve is derived from a location on the
Southern Hemisphere (Marion Plateau, offshore Australia) and suggest that John et al. (2011) made a
wrong estimate of the eustatic sea level curve, by not sufficiently reducing the impact of local sea
level changes. Looking at the methodology used (see John et al., 2011) this is highly unlikely.
However if the assumption is made that the eustatic sea level curve of John et al. (2011) contains
some errors, their estimates for magnitudes of eustatic sea level drops are still larger than when
53
considering Kominz et al. (2008). There might be a gravitational influence of ice build-up at specific
locations, on sea level curves from passive continental margins (e.g. Hauptvogel & Passchier 2012).
Whether or not the eustatic sea level curve of John et al. (2011) or Kominz et al. (2008) is the most
appropriate curve for our investigated region, the largest estimation for the eustatic sea level drop is
used.
Figure 5.1 shows the current bathymetry offshore Ireland, with the 100m water depth curve
highlighted in red. If recent bathymetry is considered, a decrease in water depth of 28 m will not
have any significant impact on the shelf in area at all. It is of course not advisable to base the
assumptions on current situations, especially because some thermal subsidence did occur (Naylor &
Shannon, 2011). The main magmatic body is situated in the centre of the Porcupine Basin (O’Reilly &
Hauser, 2006; Naylor & Shannon, 2011). The assumption is made that the thermal subsidence was
located near the centre of the basin and that it had only minor influences on the south-western Irish
shelf. Figure 8.1 from Ziegler (1994) shows a rough estimate of the Miocene-Pliocene
paleogeography. This map shows a large shelf, equivalent in area to the modern shelf. This indicates
that a maximum sea level drop of 28m did not significantly reduce the shelf in area. It suggests that,
in the interval investigated during this thesis, a different mechanism forced the positive correlation
between N/O and SST.
Figure 8.1 (Ziegler, 1994): Miocene map of North-western Europe. Yellow star marks the location of interest.
Table 8.1 shows all species used for both calculating the W/C and N/O ratios. A cross marks when the
species is used in both ratios during both this thesis and Quaijtaal et al. (2014). It is clear that species
used for neritic conditions have strong ties with warm conditions as well. Operculodinium spp.
includes Operculodinium israelianum, which was used as an indicator for warm SST.
54
Spiniferites/Achomosphaera includes the warm SST indicator: Spiniferites membranaceus /mirabilis.
Also, as mentioned before, there is a high possibility that Spiniferites hyperacanthus was included
into the Spiniferites/Achomosphaera-complex, which is used as a warm water indicator in Quaijtaal et
al. (2014). Moreover, the Principal Component Analysis performed by Quaijtaal et al. (2014), plots
the Spiniferites/Achomosphaera-complex on the right side of the y-axis (see figure 7, Quaijtaal et al.,
2014). This strongly suggests that species connected with warmer environments were included into
the complex. An even stronger link was found when investigating other publications. Head &
Westphal (1999) found a link between Dapsilodinium pseudocolligerum and a tropical to warm-
temperate paleoenvironment ranging from neritic to oceanic. This environment suggests that
Dapsilodinium pseudocolligerum shows a stronger link with warmer temperatures than with neritic
conditions. Strauss et al. (2001) linked Cleistosphaeridium placacanthum with warm inner neritic
conditions. Considering the oceanic species, some were linked with warm: Impagidinium paradoxum
and Impagidinium strialatum are linked with warmer conditions, while Impagidinium pallidum is
linked with cold conditions. Of all species belonging to the genera Impagidinium, Impagidinium
pallidum clearly is the most dominant (Appendix 1).
Table 8.1: Species Neritic/Oceanic conditions with possible relation to Warm or Cold SST. X: Used in the
indices resented here. H: Head & Westphal (1999); S: Strauss et al. (2001). * : possibly including Spiniferites
hyperacanthus.
These are strong indications for a bias in the relationship between N/O and SST, with neritic species
having strong preferences for warm environments as well. The higher PCC of N/O with SST estimates
when compared to the PCC of the W/C index,could reflect a stronger influence of temperature drops
on warm, neritic species. A cooling of the continent will influence species living in shallow, near
coastal waters the most.
Species Warm ColdCapisocysta lata/lyelli X
Cleistosphaeridium placacanthum S
Dapsilodinium pseudocolligerum H
Dinopterygium cladoides - -
Homotryblium tenuispinosum - -
Lingulodinium machaerophorum X
Operculodinium spp. - -
Operculodinium israelianum X
Polysphaeridium zoharyi X
Spiniferites/Achomosphaera *
Spiniferites membranaceus /mirabilis X
Tectatodinium pellitum X
Tubercolodinium vancampoae X
Impagidinium pallidum X
Impagidinium paradoxum X
Impagidinium strialatum X
Impagidinium spp. - -
Nematosphaeropsis labyrinthus - -
Ner
itic
Oce
anic
55
To conclude, at least in the interval studied during this thesis, the N/O does not show any significant
link with sea level fluctuations, but is a reflection of variations in SST. This conclusion is strengthened
by the relation between N/O and both the S/D ratio and the BIT-index. Since, when the setting
becomes more proximal, a stronger terrestrial signal should be obtained. However, the N/O shows
an anti-correlation with the S/D ratio, while no link could be made with the BIT index.
8.3.2 S/D ratio and BIT-index
The significant positive correlation between S/D shows and BIT shows that, at least a proportion, of
both signals are influenced by a similar forcing, although not all peaks correlate well. The highest
correlation is obtained for the detrended data, this is seen by the enhancement of the cycle with a
semi-period of ~10 points. The averaged BIT data shows the lowest PCC with the S/D, probably by
suppressing the same semi-period.
The PCC of the raw BIT- data probably provides a better evaluation of its relationships with other
proxies. Reason for this is that averaging the small fluctuations of the BIT-index will produce a nearly
monotone signal. One can state that, by not averaging the data the signal-to-noise ratio remains too
high. However, because of the relatively low concentrations of GDGT’s in terrestrial organic matter, a
small increase in BIT is already considered as significant (Weijers et al., 2006b). Also, Donders et al.
(2009) found a reproducibility in lab-procedure of 0,01. As stated above (section 6.3.3), the same
methodology and lab were used during this thesis as Donders et al. (2009). The assumption is made
that an equivalent reproducibility was achieved.
Since the BIT index does not trace aeolian transported terrestrial organic matter (Hopmans et al.,
2004), other forcings than wind must be at work. A second possibility is coastal proximity. This would
enhance the input of both fluvial and aeolian transported terrestrial material. As presented above
(section 8.3.1), it is suggest that a maximum sea level drop of 28m will not significantly reduce the
distance to the coast. This suggest that sea level changes do not have significant influence on the
input of fluvial and aeolian transported material. A third possible link is an increase in fluvial
transported material due to enhanced precipitation and runoff.
There still is a proportion of variability in both indices that does not correlate. This might reflect a
combination of random variability and the fact that winds only influence S/D and not the BIT index.
8.3.3 P/G and BIT
The insignificant PCC between the P/G ratio and the BIT-index suggests that enhanced primary
productivity is primarily driven by enhanced nutrient supply transported by rivers. There are some
exceptions such as the peak around ~218 mcd, which is the second highest peak in the BIT-signal. No
correlation is obtained for the highest peak in the BIT-index. The assumption is made that fluvial
transported nutrients are not the main driving mechanism for P/G ratios. Instead, it probably
enhances the effect of another mechanism.
8.3.4 P/G, S/D and SST
There is a strong anti-correlation between P/G and the temperature proxies, pointing towards a
common forcing mechanism.
56
One possibility could be that a sea level drop brings the investigated location closer to the coast,
hereby enhancing the input of terrestrial material. However, as presented above (in section 8.3.2),
the maximum sea level drop of 27m will not significantly enhance the input of fluvial and aeolian
transported material. Moreover, reworking correlates stronger with warmer temperatures, while an
anti-correlation is shown with both S/D and P/G ratios. This suggests that enhanced P/G ratios are
not influenced by small sea level fluctuations.
Another mechanism connecting decreased SST and enhanced primary production is upwelling. This
brings nutrients and colder deep waters towards the surface of the ocean. Upwelling could be
induced by oceanic currents, with deep and intermediate water masses being forced into a confined
basin and getting deflected towards the surface. However, when we look at the uppermost samples,
above the RD2, the P/G ratio does not show a sudden increase, but more like a steady decrease.
Since the RD2 is related to a strengthening of the AMOC (see above sections 4.1.3 and 5.2), this
counters the current-induced upwelling hypothesis. A second possibility is wind-driven upwelling,
with a strong wind bringing along more pollen and spores and inducing upwelling. This hypothesis is
strengthened by the significant correlation between S/D and P/G, while S/D and temperature show
an equivalent anti-correlation. Stronger winds could also enhance the input of dust, which in turn
increases the input of nutrients. North-western Europe was inferred to have a subtropical to tropical
climate with relatively high mean annual precipitation, throughout most of the Miocene (Larsson et
al., 2011; Utescher et al., 2012). A subsurface densely covered with vegetation does not allow for a
large input of dust.
The SST only shows some cyclicity during the colder event, while the P/G and S/D seem fluctuate
throughout the entire interval. This points to a decoupling of SST and P/G and S/D during the stable
warm period or that upwelling has no influence at all on the SST.
The P/G and S/D seem to fluctuate with lower amplitude throughout the middle part (17,4-16,7 Ma).
It could be that this reflects the lower sampling resolution. Another possibility is that these
fluctuations are mainly due to noise, while the mechanism forcing P/G and S/D is suppressed during
this interval.
8.3.5 Diversity
The good correlation between the diversity indices and the P/G indices suggests an increase in
biodiversity when more nutrients are available. There is also a strong anti-correlation with the
temperature proxies. This might confirm the upwelling-hypothesis.
However, the latter relation might also reflect a cooling in the proximal SST. This suppresses the
input of dominant warm, neritic species, so more rare species are found within the count of 300
dinocysts.
8.3.6 Cyclopsiella granosa/elliptica
Since, Cyclopsiella granosa/elliptica is a shallow marine and low-salinity tolerant species, an increase
in abundance might indicate an increase on precipitation and/or runoff. The abundance in
Cyclopsiella granosa/elliptica only shows a significant PCC with reworking and a questionable one
with S/D. Their abundance does not show a true cyclic pattern, but is mostly represented by one
discrete event. The highest peak at ~246 mcd (~17,74-17,72 Ma ) was found in only two samples. This
57
sudden increase seems to correlate with a peak in reworking, P/G, a small increase in BIT-index and a
distinct drop in SST and S/D-ratio.
Because of the correlation with reworking, the peak in abundance in Cyclopsiella granosa/elliptica
might be a distinct erosional event. No sudden decrease in the natural gamma ray log of Hole
U1318B is visible(Expedition 307 scientists, 2006). So no sudden increase in coarse grained sediment
occurred. This significantly reduces the possibility of a tsunami-like event. The coincidence with a
distinct drop in all temperature proxies could point to a eustatic sea level drop due to a glaciation. A
sea level drop could cause incision of rivers in their previously deposited fans. Moreover, the sea
level curve of Kominz et al. (2008) does show a distinct eustatic sea level drop of around 12m (see
figure 8.2; the curve John et al., 2011 does not extend this far back in time). This information
strengthens the hypothesis of a relatively small (~12m) eustatic sea level drop. However, an eustatic
sea level drop due to Antarctic ice-sheet growth which only lasts ~20kyr is quite remarkably. This
would suggest an extreme event. Due to the correlation with an apparent eustatic sea level drop
derived from sediments in the eastern Atlantic Ocean, this must reflect a basin-wide event. The
eustatic sea level curve of John et al. (2011) does not cover this time period, so future research is
needed to investigate whether this is a global or basin-wide event. It might be related to global
climates proceeding into the MMCO.
Figure 8.2 (Adjusted from Kominz et al., 2008). The timing of this distinct sea level drop is highlighted.
8.3.7 Dinocysts/gram
The signal in dinocysts/gram shows large amplitude oscillations with a high frequency. It does
roughly correlate with the temperature proxies, which is confirmed by the significant PCC. This could
strengthen the hypothesis that dinocysts/gram relates to favourable conditions for dinocysts
production. However, the dinocysts/gram signal shows a significant anti-correlation with the P/G
index. It seems that the amount of dinocysts/gram largely reflects the relative abundances of the
dominant warm, neritic species. This is confirmed by the strong anti-correlation with evenness and
58
the other diversity indices. The assumption is made that at least during this interval, the
dinocysts/gram roughly corresponds largely with the input of warm, neritic species.
8.3.8 Reworking
The relative proportion in Reworked dinocysts fluctuates with large amplitude. It is assumed that this
signal mostly represent noise.
8.4 Forcings
8.4.1 Long term trend
Most indices suggest a semi-periodic cyclicity of ~40 data points (~1,10 to 1,20 Myr). These indices
show minima in autocorrelation at ~20 points (around 0,5 – 0,6 Myr), after which the autocorrelation
gradually restores. Even though the S/D index mainly fluctuates with a ~10 point (~0,20 Myr) cycle,
the amplitudes of these oscillations do roughly follow the ~40 data points cycle. The BIT-index seems
to be influenced by a combination of two semi-periodic mechanism. The first period of ~10 Ma
correlates well with the S/D index, hereby confirming the hypothesis of a similar forcing. The second
fluctuation proves more difficult to correlate with a certain period. Looking at the signal itself (figure
X.X), a similar relation as the S/D index is proposed.
The ~40 data points cycle roughly correlates with ~1,1 to ~1,2 Myr cycle. This suggests a link with the
lower frequency obliquity band (1,2 Myr). It is admitted though, that the investigated interval is too
short to make a strong statement about cyclicity with such a long periodicity. Further investigation
on a longer time-scale is needed.
The ~1,2 Myr cycle reflects similar conditions during ~17,7-17,6 Ma as during ~16,6-16,4 Ma. These
periods correlate well with eustatic sea level drops presented in John et al. (2011) (Figure 3.2). The
oldest period probably reflects the last effects of the Mi-1b event (Miller et al., 1996), while the
second period coincides with the Mi-2a event (John et al., 2011). The link with decreasing global
¹⁸O-values and eustatic sea level drops were interpreted as periods of Antarctic glaciations. The ~1,2
Myr obliquity cycle has been proven to influence other Mi-events (Zachos et al., 2001; Westerhold et
al., 2004; Abels et al., 2005; Liebrand et al., 2011). The results presented here suggest that a similar
forcing has influenced the glaciation events during Mi-1b and Mi-2a.
The results indicate the long-term trend to be forced by the ~1,2 Myr Milankovitch cycle, which
influenced Antarctic Ice-sheet growth. This in turn changes SST, continental climates, and sea level
drops.
8.4.2 Short term trend
The ~10 points (~0,2 Myr) semi-periodicity is best reflected in the S/D and BIT-indices. Other cycles
show only hints of a similar cyclicity in the autocorrelation. When suppressing the long term trend,
these cycles become more visible and the periods become more centralised around 0,2 Myr. No
spectral peak is found for a frequency with a ~100kyr period, this counters a hypothesis of
eccentricity driven changes. The 0,2 Myr semi-periodicity might be a reflection of the ~172 kyr
obliquity cycle. Even after tuning their data, Westerhold et al. (2004) found a spectral peak around
180 kyr and linked it with the asymmetry of a ~174 kyr (actually 172 kyr e.g. Abels et al., 2005)
obliquity cycle. The data obtained during this thesis was not tuned, but still point towards the ~172
59
kyr obliquity cycle as a possible forcing. The discrepancy with the ~172 kyr cycle can point to the
asymmetry of a ~172 kyr cycle (e.g. Westerhold et al., 2004). It is probably also related to a
difference between the estimates and true ages of the samples. The ages were obtained by a linear
interpolation of the magnetostratigraphic ages provided by the age-model of Quaijtaal et al. (2014).
A second possibility is that another factor delays the influence of the ~172 kyr cycle. Same factors
might delay the signal provided by Westerhold et al. (2004). These factors could have been overseen
by Westerhold et al. (2004) because of tuning of their data.
The Icelandic Low pressure zone around the 60th parallel north represents the border between the
Polar and Ferrel cells, called the Polar front, in Northwestern Europe (Seagar et al., 2002). North of
this border, the Polar cell brings cold polar air towards the south. South of the 60th parallel north,
warm air is brought from the subtropics towards the north. The Icelandic Low has a more northerly
position than the North-Pacific equivalent (Seagar et al., 2002). This is probably a reflection of
different conditions, such as no warming of the Norwegian Sea, different bathymetry and different
distribution of landmasses (e.g. Seagar et al., 2002). The AMOC mainly affects the winter
temperatures in the Norwegian Sea (Seagar et al., 2002). If no AMOC was present the Norwegian Sea
probably was cooler and the Icelandic Low was positioned further to the south. This is the case
during most of the investigated interval. Only during the upper part, enhanced inter-basinal
exchange between the Norwegian Sea and the North-Atlantic Ocean occurred (Stoker et al., 2005;
Laberg et al., 2005). If the low-pressure zone was situated more to the south, the Polar front might
be situated close to Site U1318 (51°26,1616’ N; 11°33.0184’ W).
The Antarctic ice sheets grow faster during the minima in ~172 kyr obliquity modulation (Westerhold
et al., 2005), which in turn might decrease global SST. Colder SST and cooler high-latitudes can create
stronger latitudinal temperature gradients (Flower & Kennett, 1994; Hamon et al., 2013), which will
induce a contraction and intensification of the Hadley cell (Flower & Kennett, 1994; Hamon et al.,
2013). This could enhance Polar cell, hereby strengthening the northerlies, which in turn enhances
upwelling. These stronger periods of upwelling will then give a positive feed-back to a decreasing
SST. This hypothesis might explain the increase in amplitude of S/D and P/G oscillations during
periods of lower SST.
The SST does only show some cyclicity during the cool intervals. This could suggest that the ~172 kyr
cycles only start to get a grip on the Antarctic ice sheets when they reach a certain extent. However,
this is in contradiction with the observed cycles in P/G and S/D. W/C and N/O indices do show
stronger hints of these short term cycles.
1. All these ~172 kyr cycles during are mainly due to noise. The assumption is made that the
cycles in the lower samples do not reflect mere noise.
2. The ~172 kyr obliquity cycles do force the Antarctic Ice sheet, but do not force a drop in SST.
The small fluctuations in W/C and N/O could be noise. Or reflect temperature changes in
near coastal environments, caused by cooler continental climates due to stronger
northerlies.
3. The ~172 kyr obliquity cycles force the high latitudes of the Northern-hemisphere, suggesting
the presence of some Arctic ice sheets. Since there is strong inter-basinal exchange between
the Arctic Oceans and the North Atlantic, this will not influence Atlantic SST. Even though
Moran et al. (2006) found indications of IRD around ~45 Ma. Stronger indications for
60
enhanced Arctic glaciations are only present from ~14 Ma onwards (Moran et al., 2006; Knies
& Gaina, 2008).
Further research is needed to get a better view on this topic.
It is difficult though to connect higher precipitation rates with both enhanced SST or a stronger
northerlies. One solution might be that during dryer continental climates, the continent will be less
densely covered by vegetation. This in turn allows for more erosion of the substrate and will result in
more terrestrial material being transported by rivers. Larson et al. (2011) found a cooling and drying
event in the eastern part of the North-Sea Basin (Denmark), just prior to 16 Ma. This event might be
the Mi-2a event. Even though the continental climates became more arid, there still was enough
precipitation to transport sediments towards the ocean. The drying event might have been severe
enough to induce aeolian transport, which fertilise the oceans with dust. However this possibility
remains highly unlikely. It is admitted though that this hypothesis is not based on strong arguments,
so the possible influence of the ~172kyr cycle on the BIT index needs to be further investigated.
8.4.3 Introduction of NSDW
Most indices show a continuation of their trend across the RD2. The largest differences occur
between the first two samples just above the RD2: between 194,57 and 195,52 mcd (16,30 and 16,29
Ma). The increase in reworked dinocysts might represent erosion due to the intensification of bottom
currents. However, the signal is assumed to be mainly influenced by noise. The BIT and P/G indices
drop to a minimum, while the temperatures increase to a new maximum. This could reflect the
northward migration of the Icelandic Low towards a similar latitude as during recent times. This
would strengthen the southerlies, hereby warming up continental climates and stopping the wind-
driven upwelling. However a further investigation of younger samples is needed to confirm this
hypothesis
8.5 Hypothesis for Miocene paleoenvironment of the south-western Iris Shelf
The investigated interval probably contains two Mi-events. The temperature increase from the oldest
samples until ~17,5 Ma represents the transition from the Mi-1b event (Miller et al, 1996) towards
the Middle Miocene Climatic Optimum. The trend is interrupted by a discrete event at ~17,74 Ma.
This event is characterised by a distinct drop in temperatures, an extreme increase in abundance of
Cyclopsiella granosa/elliptica together with a small increase in reworked dinocysts. This distinct
event is seems to be related with the relatively small (~12m) eustatic sea level drop presented in the
sea level curve of Kominz et al. (2008). This might represents an eustatic sea level drop or a basin-
wide event related to climates proceeding into the MMCO. Further research is needed to confirm
this hypothesis. After this distinct event, the eustatic sea level raised (Kominz et al., 2008) as the
Antarctic ice sheets melted (Hauptvogel & Passchier, 2012). When the Norwegian Sea was not
warmed by the thermohaline circulation, the Icelandic Low could have been located further to the
south, closer to Site U1318B (e.g. Seagar et al., 2002). The ~172 kyr obliquity cycle could
strengthened the Polar Cell, hereby influencing the climates of north-western Europe. This is
reflected in the S/D, BIT and possibly the W/C index.
Afterwards a relatively stable warm period is maintained between ~17,5 Ma and ~16,7 Ma. This is
considered to represent the first part of the MMCO (e.g. Zachos et al., 2001). The influence of the
61
~172 kyr obliquity cycle on most indices was significantly suppressed. Some hypotheses were made,
but need to be further investigated.
From ~16,7 Ma onwards minima in the ~1,2 Myr obliquity cycle forced global climates into a new
cold period, the Mi-2a event (John et al., 2011). Antarctic ice sheets grew, which was reflected by an
eustatic sea level drop (John et al., 2012). During this event the influence of the ~172 kyr obliquity
cycle on the climate was enhanced in several ways.
1. When the Antarctic ice sheets had a certain extent, the ~172 kyr cycle had a stronger grip on
the albedo effect, forcing the ice sheets to wax and wane at a second, higher frequency. This
decreased SST.
2. Under the influence of decreased SST, latitudinal temperature gradients increased, hereby
intensifying the atmospheric cells (e.g. Flower & Kennett, 1994; Hamon et al., 2013). The
enhanced Polar Cell could bring stronger northerlies towards the south-western Irish Shelf,
inducing wind-driven upwelling. This in turn, gave a positive feed-back to the cooler SST
offshore Ireland.
3. The North-western European climates cooled and dried, which correlates with the findings in
the eastern North-Sea Basin (Larson et al., 2011).
4. A link between the drying event and enhanced fluvial transport of terrestrial organic material
needs to be further investigated, but the BIT-index does correlate with lower SST and a
higher S/D ratio.
From ~16,35 Ma onwards, temperatures started to increase again when global climates proceeded
out of the Mi-2a event. A basin-wide erosional event marks the intensification of bottom current
activities, due to the introduction of Norwegian Sea Water into the North Atlantic Ocean (Stoker et
al., 2005; Laberg et al., 2005). The RD2 (Van Rooij et al.,2003 ) unconformity is dated on ~16,33 Ma
and occurs just before the data point of ~16,33 Ma. The hiatus connected with this event is
estimated on to last ~49 kyr. The youngest increase in temperature might be linked with the
strengthening of the AMOC, bringing more heat towards the high latitudes and pushing the Icelandic
Low further to the north towards its position during modern times (e.g. Seagar et al., 2002). This in
turn, might have stopped or reduced the wind-driven upwelling.
62
9. CONCLUSION
A high-resolution paleoenvironmental study was performed using both dinoflagellate cysts and
geochemical proxies on the lowermost samples of IODP Leg 307 Site U1318. Two Miocene Isotopic
events were identified. The lowermost samples contain the effects of the Mi-1b event. A stable warm
period between ~17,5 and ~16,7 Ma was correlated with a first interval of the Middle Miocene
Climatic Optimum. The MMCO was interrupted by a second cooling event, which lasted from ~16,7
until ~16,3 Ma. This event was linked with the Mi-2a event. It is suggested that these two Mi-events
were forced by the 1,2 Myr obliquity cycle. However, a spectral analysis on longer time-scale is
needed to confirm this hypothesis. Autocorrelation performed on the data shows cycles of ~200kyr.
These were linked with the ~172kyr obliquity cycle. The discrepancy in ages was related to noise,
error on data and possibly a second factor delaying the effect of the ~172kyr obliquity cycle. More
research is needed to characterise this delay, but it is possible that other investigations might show a
similar relation.Several hypotheses concerning the influence of the ~172kyrs cycle on the S/D, P/G
and SST were made but need to be further investigated.
The RD2 unconformity is dated on ~16,3 Ma. The hiatus connected with the RD2 is estimated to last
~50 kyr in the Porcupine Basin. This confirms the hypothesis of Louwye et al. (2008) that this hiatus
was only of minor magnitude. The unconformity, related to the introduction of Norwegian Sea Water
in the North-Atlantic, is not correlated with a immediate and sudden shift in SST, primary production
or input of terrestrial organic material. The effects seem to be delayed. It is proposed that an
enhanced AMOC pushed the Icelandic Low northwards towards a more modern-like position.
Furthermore, an analysis on the paleoenvironmental indices proved the usefulness of the
Warm/Cold index. However, by using warm, neritic species the W/C index shows a greater influence
of continental climates than the geochemical temperature estimates. It is proposed that the
Neritic/Oceanic index with the species used here, does not track sea level changes during the
investigated time-interval.
It is suggested to implement a similar analysis as presented in this thesis when the data are tuned, in
order to evaluate the significance of the correlation.
63
10. REFERENCES
ABELS, H.A., HILGEN, F.J., KRIJGSMAN, W., KRUK, W., RAFFI, I., TURCO, E. & ZACHARIASSE, W.J. (2005). Long-period orbital control on middle Miocene global cooling: Integrated stratigraphy and astronomical tuning of the Blue Clay Formation on Malta. Paleoceanography 20, 1-17.
BROCHIER-ARMANET, C., BOUSSAU, B., GRIBALDO, S. & FORTERRE, P. (2008). Mesophilic crenarchaeota: proposal for a third archaeal phylum, the Thaumarchaeota. Nat Rev Microbiol 6, 245–252
DE MOL, B., VAN RENSBERGEN, P., PILLEN, S., VANHERREWEGHE, K., VAN ROOIJ, D., MCDONNELL, A., HUVENNE, V., IVANOV, M., SWENNEN, R.&HENRIET, J.-P.( 2002). Large deep-water coral banks in the
Porcupine Basin, southwest of Ireland. Marine Geology 188, 193–231. DENK, T., GRIMM, G.W., GRIMSSON, F. & ZETTER, R. (2013). Evidence from Köppen signatures of
fossil plant assemblages for effective heat transport of Gulf Stream to subarctic North Atlantic during the Miocene cooling. Biogeosciences 10, 7927-7942.
DE VERTEUIL, L. & NORRIS, G. (1996). Miocene dinoflagellate stratigraphy and systematics of Maryland and Virginia. Micropaleontology Supplement 42, 1–172.
DONDERS, T.H., WEIJERS, W., MUNSTERMAN, D.K., KLOOSTERBOER-VAN-HOEVE, M.L., BUCKLES, L.K., PANCOST, R.D., SCHOUTEN, S., SINNINGHE DAMSTE, J.S. & BRINKHUIS, H. (2009). Strong Climate coupling of terrestrial and marine environments in the Miocene of northwest Europe. Earth and Planetary Science Letters 281, 215-225.
EXPEDITION 307 SCIENTISTS: FERDELMAN, T.G., KANO, A., WILLIAMS, T., HENRIET, J-P. (2006). Proceedings IODP 307.doi:10.2204/iodp.proc.307.105.2006
EVERITT, B. S. (2002).Cambridge dictionary of statistics. FENSOME, R.A. McRAE, R.A. & WILLIAMS, G.-L. (2008). DINOFLAJ, Version 2, American Association of
Stratigraphic Palynologists (Data Series no.1). FLOWER, P.F. & KENNETT, J.P. (1994). The middle Miocene climatic transition: East Antarctic ice
sheet development, deep ocean circulation and global carbon cycling. Paleoceanography, Paleoclimatology, Paleoecology 108, 537-555.
FRANK,M., BACKMAN, J., Jakobsson, M., MORAN, K. & GARBE-SCHÖNBERG, D. (2008) Beryllium isotopes in central Arctic Ocean sediments over the past 12.3 million years: Stratigraphic and Paleoclimatic implications. Paleoceanography 23, 1-22.
GAILLARDET, J. & GALY, A. (2008). Himalaya-Carbon sink or source. Atmospheric science 320, 1727-1728.
GARZIONE, C.N. (2008). Surface uplift of Tibet and Cenozoic global cooling. Geology 36, 1003-1004. GREIN, M., OEHM, C., KONRAD, W., UTESCHER, T., KUNZMANN, L. & ROTH-NEBELSICK, A. (2013)
Atmospheric CO2 from the lalte Oligocene to early Miocene based on photosynthesis data and fossil leaf characteristics. Pal., Pal., Pal. 374, 41-51.
HALL R., COTTAM., M.A. & WILSON, M.E.J. (2011) The SE Asian gateway: history and tectonics of the Australian-Asia collision. Geological Society of London, Special Publications 355, 1-6.
HAMON, N., SEPULCHRE, P., LEFEBRE, V. & RAMSTEIN, G. (2013). The Role of East-Tethys seaway closure in the middle Miocee climatic transition (ca. 14 Ma). Climate of the Past Discussions 9, 2115-2152.
HAUPTVOGEL, D.W. & PASSCHIER, S. (2012). Early-Middle Miocene (17-14 Ma) Antarctic ice dynamics reconstructed from the heavy mineral provenance in the AND-2A drill core, Ross Sea, Antarctica. Global and Planetary Change 82-83, 38-50.
HARZHAUSER, M. & PILLER, W.E. (2007). Benchmark data of a changing sea – Paleogeography, Paleobiogeography and events in the Central Paratethys during the Miocene. Pal, Pal, Pal 253, 8-31.
64
HAQ, B., HARDENBOL, J. & VAIL, P.R. (1987) Chronology of Fluctuating Sealevels since the Triassic. Science 235, 1156 -1167.
HEIP, C. (1974). A new index measuring evenness. Journal of the Marine Biological Association of the United Kingdom, 54, 555-557.
HENRIET, J. P.,DE MOL, B., PILLEN, S.,VANNESTE, M., VAN ROOIJ, D., VERSTEEG, W., CROKER, P. F., SHANNON, P. M., UNNITHAN, V.,BOURIAK, S. & CHACHKINE, P. 1998.Gas hydrate crystals may help build reefs. Nature 391,648–9.
HOORN, C., GUERRERO, J., SARMIENTO, G. & LORENTE, M.A. (1995). Andean tectonics as a cause for changing drainage patterns in Miocene northern South America. Geology 23 (3), 237-240.
HOLBOURN, A., KUHNT, W., LYLE, M., SCHNEIDER, L., ROMERO, O. & ANDERSEN, N. (2014). Middle Miocene climate cooling linked to intensification of eastern equatorial Pacific upwelling. Geology 42, 19-22.
HORNIBROOK, N.D.B. (1992). New Zealand Cenozoic marine paleoclimates: a review based on the distribution of some shallow water and terrestrial biota. In: R. Tsuchi & J. Ingle, Pacific Neogene Environments, Evolution and Events. Univ. Tokyo Press, Tokyo, pp. 83 106.
HÜSING, S.K., ZACHARIASSE, W.-J., VAN HINSBERGEN, D.G., KRIJGSMAN, W., INCEÖZ, M., HARZHAUSER, M., MANDIC, O. & KROH, A. (2009): Oligocene–Miocene basin evolution in SE Anatolia, Turkey: constraints on the closure of the eastern Tethys gateway, Geological Society,London, Special Publications, 311, 107–132.
HUVENNE, V. A. I., CROKER, P. F. & HENRIET, J. P.2002. A refreshing 3-dimensional view of an ancient sediment collapse and slope failure. Terra Nova 14, 33–40.
JOHN, C.M., KARNER, G.D., BROWNING, E., LECKIE, R.M., MATEO,Z., CARSON, B. & LOWERY, C. (2011). Timing and magnitude of Miocene eustasy derived from the mixed siliciclastic-carbonate stratigraphy record of the northeastern Australian margin. Earth and Planetary Science letters 304, 455-467.
KAO, S.-J., HILTON, R.G., SELVARAJ, K. & HOVIUS, N. (2014). Orogenesis as a carbon dioxide source or sink? New insights from the organic carbon cycle of Taiwan. In: Earth Surface Dynamics 2, 127-139.
HOPMANS, E.C., SHOUTEN, S., PANCOST, R.D., VAN DER MEER, M. & SINNINGHE DAMSTE, J.S. (2000). Analysis of intact tetraether lipids in archaeal cell material and sediments by high performance liquid chromatography/atmospheric pressure chemical ionization mass spectrometry. Rapid communications in mass spectrometry 14, 585-589.
HOPMANS, A.C., WEIJERS, J.W.H., SCHEFUB, E., HERFORT, L., SINNINGHE DAMSTE, J.S. & SCHOUTEN, S. (2004). A novel proxy for terrestrial organis matter in sediments based on branched and isoprenoid tetraether lipids.. Earth and Planetary Science Letters 224, 107-116.
ITOIGAWA, I. & YARRANOI, T. (1990). Climatic optimum in the mid-Neogene of the Japanese Islands. ln: R. Tsuchi (Editor), Pacific Neogene Events: Their Timing, Nature and Interrelationship. Univ. Tokyo Press, Tokyo, pp. 3 14
KREBS, C. (1989). Ecological Methodology. HarperCollins. KANO, A., FERDELMAN, T.G., WILLIAMS, T. & THE INTEGRATED OCEAN DRILLING PROGRAM
EXPEDITION 307 SCIENTISTS (2012). Age constraints on the origin and growth history of a deep-water coral mound in the northeast Atlantic drilled during integrated ocean drilling program expedition 307. Geology 35 (11), 1051-054.
KARNER, M., DELONG, E. F.& KARL, D. M. (2001). Archaeal dominance in the mesopelagic zone of the Pacific Ocean. Nature 409,507-510.
KIM, J.H., VAN DER MEER, J., SCHOUTEN, S., HELMKE, P., WILLMOTT, V., SANGIORGI, F., KOÇ, N., HOPMANS, E.C. & DAMSTÉ, J.S.S. (2010). New indices and calibrations derived from the distribution of crenarchaeol isoprenoid tetraether lipids: Implications for past sea surface temperature reconstructions. Geochimica et Cosmochimica Acta 74, 4639-4654.
KNIES, J. & GAINA, C. (2008). Middle Miocene ice sheet expansion in the arctic: Views from the Barents sea. Geochemistry, Geophysics, Geosystems 9 (2). 1-8.
65
KOMINZ, M.A., BROWNING, J.V., MILLER, K.G., SUGARMAN, P.J., MIZINTSEVA, A. & SCOTESE, C.R. (2008). Late Cretaceous to Miocene sea-level estimates from the New Jersey and Delaware coastal plain coreholes: an error analysis. Basin Research 20, 2211-226.
KUHNT W., HOLBOURN, A., HALL, R., ZUVELA, M. & KÄSE, R. (2004). Neogene History of the Indonesian Throughflow. Geophysical monograph series 149, 299 – 320.
KURSCHNER, W., KVECEK, Z. & DILCHER, D. (2008). The impact of Miocene atmosphereic carbon dioxide fluctuations on climate and the evolution of terrestrial ecosystems. PNAS 105 (2), 449-453.
KURSCHNER, W. & KVACEK, Z. (2009). Oligocene-Miocene CO2 fluctuations, climatic and palaeofloristic trends inferred from fossil plant assemblages in central Europe. Bulletin of geosciences 84 (2). 189-202.
LABERG, J.S., STOKER, M., DAHLGREN, T., DE HAAS, H., HAFLIDASON, H., HJELSTUEN, B., SHANNON, P.M. & CERAMICOLA, S. (2005). Cenozoic alongslope processes and sedimentation of the NW European Atlantic Margin. Marine and Petroleum Geology 22, 1069-1088.
LANGEBROEK, P.M., PAUL, A. & SCHULTZ, M. (2009) Antarctic ice-sheet response to atmospheric CO2 and insolation in the Middle Miocene. Climate of the Past 5, 633-646.
LIEBRAND, D., LOURENS, L.J., HODELL, D.A., DE BOER, B., VAN DE WAL, W. & PÄLIKE, H. (2011). Antarctic ice sheet and oceanographic response to eccentricity forcing during the early Miocene. Climate of the Past 7, 869-880.
LARSSON, L.M., DYBKJAER, K., RASMUSSEN, E.S., PIASECKI, S., UTESCHER, T. & VAJDA V. (2011). Miocene climate evolution of northern Europe: A palynological investigation from Denmark. Pal, Pal, Pal 309, 161-175.
LOUWYE, S., HEAD, M.J., DE SCHEPPER, S. (2004). Dinoflagellate cyst stratigraphy and palaeoecology of the Pliocene in the northern Belgium, southern North Sea Basin. Geological Magazine 141, 353-378.
LOUWYE, S., FOUBERT, A., MERTENS, K., VAN ROOIJ, D. & THE IODP EXPEDITION 307 SCIENTIFIC PARTY (2008a). Integrated stratigraphy and palaeoecology of the lower and Middle Miocene of the Porcupine Basin. Geological Magazine. 321-344.
LOUWYE, S., MERTENS, K.N. & VERCAUTEREN, D. (2008b).New dinoflagellate cysts from the Miocene of the porcupine basin, offshore southwest Ireland. Palynology 32 , 131-142.
MANUM, S. B. 1976. Dinocysts in tertiary Nowegian–Greenland Sea sediments (Deep Sea Drilling project leg 38), with observations on palynomorphs and palynodebris in relation to environment. In Initial Reports ofthe Deep Sea Drilling Project, vol. 38 (eds M. Talwani & G. Udintsev), pp. 897–919. Washington D.C.: U.S. Government Printing Office.
McDonnell, A., Shannon, P.M., 2001. Comparative Tertiary basin development in the Porcupine and Rockall Basins. In: Shannon, P.M., Haughton, P.D.W., Corcoran, D.V (Eds.), The Petroleum Exploration of Ireland’s Offshore Basins. Geological Society, London, Special Publication,188 324–344
MERTENS, K.N., VERHOEVEN, K., VERLEYE, T., LOUWYE, S., AMORIM, A., RIBEIRO, S., DEAF, A., HARDING, A., DE SCHEPPER, S. GONZALEZ, C., KODRANS-NSIAH, M., DE VERNAL, A., HENRY, M., RADI, T., DYBKJAER, K., POULSEN, N.E., FEIST-BURKHARDT, S., CHITOLIE, J., HEILMANN-CLAUSEN, C., LONDEIX, L., TURON, J.-L., MARRET, F., MATTHIESSEN, K., McCARTHY, F., PRASAD, V. POSPELOVA, V., HUGHES, J., RIDING, J;, ROCHON, A., SANGIORGI, F., WELTERS, N., SINCLAIR, NATALIE, THUN, C., SOLIMAN, AL;, VAN NIEUWENHOVE, N., VINK, A. & YOUJNG, M. (2009). Determining the absolute abundance of dinoflagellate cysts in recent marine sediments: The Lycopodium marker grain method put to the test. Revieuw of Palaeobotany and Palynology 157, 238-252.
MILLER, K.G., WRIGHT, J.D. & FAIRBANKS, R.G. (1991). Unlocking the Ice House: Oligocene-Miocene Oxygen Isotopes, Eustacy, and Magin Erosion. Journal of geophysical research 96 (84), 6829-6845.
66
MILLER, K.G., MOUNTAIN, G.S. & LEG SHIPBOARD PARTY AND MEMBERS OF THE NEW JERSEY COASTAL PLAIN DRILLING PROJECT (1996). Drilling and Dating New Jersey Oligocene-Miocene Sequences: Ice Volume, Global Sea Level, and Exxon Records. Science 271, 1092-1095.
MILLER, K.G., MOUNTAIN, G.S., BROWNING, J.V., KOMINZ, M., SUGARMAN, P.J., CHRISTIE-BLICK, N., KATZ, M.E.& WRIGHT, J.D., (1998). Cenozoic global sea level, sequences, and the New Jersey transect; results from coastal plain and continental slope drilling. Reviews of Geophysics 36, 569–601.
MORAN, K., BACKMAN, J. & BRINKHUIS, H. (2006). The Cenozoic palaeoenvironment of the Artic Ocean. Nature 441, 601-605.
MOORE, J. G. & SHANNON, P. M. 1992. Palaeocene–Eocenedeltaic sedimentation, Porcupine Basin, offshore Ireland– a sequence stratigraphic approach. First Break 10(12),461–9.
MULLER, P.J., KIRST, G., RUHLAND, G., STORCH, I & ROSSEL-MELE, A. (1998) Calibration of the alkenone paleotemperature index Uk37 based on core-tops from the eastern South Atlantic and the global ocean (60°N-60°S). Geochimica et Cosmochimica Acta 10, 1757-1772.
MUNSTERMANN, D.K. & BRINKHUIS, H. (2004). A Southern North Sea Miocene dinoflagellate cyst zonation. Netherlands Journal of Geosciences / Geologie en Mijnbouw 83 (4), 267-285.
NAYLOR, D. & SHANNON, P. M. 1982. The Geology of Offshore Ireland and West Britain. London: Graham &Trotman Ltd, 161 pp
NEW, A.L., BARNARD, S., HERRMAN, P. & MOLINES, J.M. (2001). On the origin and pathway of the saline inflow to the Nordic seas: insights from models. Progress in oceanography.
ORFANIDIS, S.J. (2007). Optimal Singal processing. PAGANI, M., ARTHUR, M.A. & FREEMAN, K.H. (1999). Miocene evolution of atmospheric carbon
diocide. Paleoceanography 14 (3), 273-292. PEKAR, S.F. & DECONTO, R.M. (2006) High-resolution ice-volume estimates for the early Miocene:
Evidence for a dynamic ice sheet in Antarctica. Pal, Pal, Pal 231, 101-109. POUND, J.M., HAYWOOD, A.M., SALZMANN, U. & RIDING, J.B. (2012). Global vegetation dynamics
and latitudinal temperature gradients during the Late Miocene (15.97 – 5.33 Ma). Earth-Science Reviews 112, 1-22.
QUAIJTAAL, W., DONDERS, T.H., PERSICO, D. & LOUWYE, S. (2014). Characterising the middle Miocene Mi-events in the Eastern North Atlantic realm: A fist high-resolution marine palynological record from the Porcupine Basin. Paleogeography, Paleoclimatology, Paleoecology 399, 140-159.
RADATZ, J., RUGGEBERG, A., MARGRETH, S., DULLO, W.-C. & THE IODP 307 Scientific Party (2011). Paleoenvironmental reconstruction of Challenger Mound initiation in the Porcupine Seabight, NE Atlantic. Marine Geology 262, 79-90.
RADDATZ, J., RUGGEBERG, A., LIEBETRAU, V., FOUBERT, A., HATHORNE, E.C., FIETZKE, J., EISENHAUER, A. & DULLO, W.-C; (2014). Environmental boundary conditions of cold-water mound growth over the last 3 million years in the Porcupine Seabight, Northeast Atlantic. Deep-Sea Research II 99, 227-236.
RAYMO, M.E. & RUDDIMAN, W.F. (1992). Textonic forcing of late Cenozoic climate. Nature 359, 117-1222.
RONG, Y.U., TIANYU, C., HONGFEI, L. (2012). Late Cenozoic History of deep water circulation in the western Northe Pacific: Evidence from the Nd isotopes of ferromanganese crusts. Chinese Sience Bulletin 57 (31), 4077-4086.
ROTH, M., DROXLER, A.W. & KAMEO, K. (2000).The Caribbean carbonate crash at the middle to late Miocene transition linkage to the establishment of the modern global ocean conveyor. In: Proceedings of the ocean drilling program. Scientific results 165, 249-272.
ROYER, D.L., WING, S.L., BEERLING, D.L., JOLY, D.W. & AL, E. (2001). Paleobotanical evidence for near present-day levels of atmospheric CO(2) during part of the tertiary. Science 292 (5525), 2310-2313.
ROYER, D.L., BERNER, R.A., Montanez, I.P., TABOR, N.J., BEERLING, D.J. (2004). CO2 as a primary driver of Phanerozoic climate. GSA today.
67
ROYER, D.L. (2006) CO2-forced climate thresholds during the Phanerozoic. Geochimica et Cosmochimica70, 5665-5675.
SEAGER, R., BATTISTI, D.S., YIN, J., GORDON, N., NAIK, N., CLEMENT, A.C. & CANE, M.A. (2002). Is the Gulf Stream responsible for Europe’s mild winters? Quarterly Journal of the Royal Meterological Society 128 (586). 2563-2586.
SCHOUTEN, S., HOPMANS, A.C., SCHEFUB, E., SINNINGHE DAMSTE, J.S. (2002) Distributional variations in marine crenarchaeotal membrane lipids: A new tool for reconstructing ancient sea water temperatures. Earth and Planetary Science 204, 265-274.
SCHOUTEN, S., HUGUET, C., HOPMANS, E.C., KIENHUIS, V.M. & SINNINGHE DAMSTE, J.S. (2007). Analytical methodology for TEX86 paleothermometry by high-perforance liquid chromatograpy/Atmospheric pressure chemical ionization-mass spectrometry. Analalytical Chemistry 79, 2940-2944.
SHANNON, C.E. (1948). A mathematical theory of communication. The Bell System Technical Journal 27, 379–423, 623–656.
SHANNON, P.M., McDONNEL & A., BAILEY, W.R.(2007). The evolution of the Porcupine and Rockall basins, offshore Ireland: the geological template for carbonate mound development. Int.J.Earth Sci. (Geol. Rundsch) 96, 21-35.
SHANNON, P. M. 1991. The development of Irish offshore sedimentary basins. Journal of the Geological Society, London 148, 181–9.
SHEVENELL, A.E. & KENNETT, J.P. (2004). Paleoceanographic Change During the Middle Miocene Climatic Revolution: An Antarctic Stable Isotope Perspective. Geophysical Monograph series 151, 235 -251.
SHEVENELL, A., KENNETT, J.P. & LEA, D.W. (2008). Middle Miocene ice sheet dynamics, deep-sea temperatures, and carbon cycling: A Southern Ocean perspective. Geochemistry, Geophysics, Geosystems 9 (2), 1-14.
SCHWARTZ, T. (1997). Aterite bauxite in central Germany and implications for Miocene paleoclimate. Pal, Pal, Pal 129, 37-50.
SPANG, A., HATZENPICHLER, R., BROCHIER-ARMANET, C., RATTEI, T., TISCHLER, P., SPIECK, E., STREIT, W., STAHL,D.A., WAGNER, M. &SCHLEPER, C.(2010). Distinct gene set in two different lineages of ammonia-oxidizing archaea supports the phylum Thaumarchaeota. Trends Microbiology 18, 331–340.
STROKER, M.S., NIELSEN, T., VAN WEERING, T.C.E. & KUIJPERS, A. (2002).Towards an understanding of the Neogene tectonostratigraphic framework of the NE Atlantic margin between Ireland and the Faroe Islands. Marine geology 188, 233-248.
STROKER, M.S., HOULT, R.J.,NIELSEN, T. & McDonnel, A. (2005). Sedimentary and oceanographic responses to early Neogene compression of the NW European margin. Marine and petroleum geology 22 , 1031-1044.
UTESCHER, T., ASHRAF, A.R., DREIST, A., DYBKJAER, K., MOSBRUGGER, V., PROSS, J. & WILDE, V. (2012). Variability of Neogene Continental Climates in Northwest Europe – A detailed Study Based on Microfloras. Turkish Journal of Earth Sciences 21, 289-314.
VAN ROOIJ, D., DE MOL, B., HUVENNE, V., IVANOV, M. &, M. & HENRIET, J.-P. (2003). Seismic evidence of current-controlled sedimentation in the Belgica mound province, upper Porcupine slope, southwest of Ireland. Marine Geology 195, 31-53.
VAN ROOIJ, D., BLAMART, D., KOZACHENKO, M. & HENRIET, J.-P. (2007). Small mounded contourite drifts associated with deep-water coral banks, Porcupine Seabight, NE Atlantic Ocean. In: Geological Society London, Special Publications (VIANA, A.R. & REBESCO, M., eds.), Vol. 276, pp. 225-244. Economic and Palaeoceanographix Significance of Contourite Deposits.
VAN ROOIJ, D., HUVENNE, V.A.I., BLAMART, D., HENRIET, J.-P.., WHEELER, A. & DE HAAS, H. (2009) The Enya mounds: a lost mound-drift competition. Int. J. Earth Sci. (Geol. Runsch.), 98849-98863. DOI: 0.1007/s00531-007-0293-9
VINCENT, E. & BERGER, W.H. (1985). Carbon diocide and Polar Cooling in the Miocene: The Monterey Hypothesis. Geophysical Monograph Series 32. 455-468.
68
WESTERHOLD, T., BICKERT, T. & RÖHL, U. (2005). Middle to late Miocene oxygen isotope stratigraphy of ODP site 1085 (SE Atlantic): new constraints on Miocene climate variability and sea-level fluctuations. Paleogeography, Paleoclimatology, Paleoecology 217, 205-222.
WEIJERS, J.W.H.,, SCHOUTEN, HOPMANS, E.C., GEENEVASEN, J.A., DAVID, O.R.P., COLEMAN, J., PANCOST, R.D. &, SINNINGHE DAMSTE, J.S. (2006a). Membrane lipids of mesophilic anaerobic bacteria thriving in peats have typical archaeal traits. Environmental Microbiology 8 (4), 648-657.
WEIJERS, J.W.H.,, SCHOUTEN, S., SPAARGAREN, A.C., SINNINGHE DAMSTE, J.S. (2006b). Occurrence and distribution of tetraether membrane lipids in soils: Implications for the use of the TEX86 proxy and the BIT index. Organic Geochemistry 37, 1680-1693.
WILSON, G.S., PEKAR, S.F., NAISH, T., PASSCHIER, S. & DECONTO, R. (2009). The Oligocene-Miocene Boundary – Antarctic Climate Response to Orbital Forcing. Developments in Earth & Environmental Sciences 8, 373-405.
WOODRUFF, F. & SAVIN, S.M. (1989) Miocene Deepwater Oceanography. Paleoceanography 4 (1), 87-140.
WRIGHT, J.D., MILLER, K.G & FAIRBANKS, R.G. (1992). Early and Middle Miocene Stable Isotopes: Implications for Deepwater Circulation and Climate. Paleoceanography 7 (3), 357-389.
ZACHOS, J., PAGANI, M., SLOAN, L., THOMAS, E. & BILLUPS, K. (2001). Trends, Rhytms and Aberrations in Global Climate 65 Ma to Present. Paleoclimate 292, 686-693.
Appendix 1
Appendix 1: Results dinocyst assemblages and geochemical proxies
Core
Sect
ion
Top
sam
ple
inte
rval
(cm
)
Bott
om sa
mpl
e in
terv
al (c
m)
Core
dep
th(m
bsf)
Com
posit
e de
pth
(mcd
)
Age
(Ma;
ATN
TS 2
012)
Sam
ple
naam
Dino
flage
llate
cyst
s
Apte
odin
ium
spiro
ides
Apte
odin
ium
tect
atum
Bars
idin
ium
plio
ceni
cum
Batia
casp
haer
a sp
. A
Batia
casp
haer
a de
hein
zelin
ii / h
irsut
a
Batia
casp
haer
a ed
war
dsia
e
Batia
casp
haer
a -c
ompl
ex
Bite
ctat
odin
ium
raew
aldi
i
Bite
ctat
odin
ium
tepi
kien
se
Capi
socy
sta
lata
/lyel
li
Cere
broc
ysta
pou
lseni
i
Clei
stos
phae
ridiu
m p
laca
cant
ha
21X 1 26 28 186,36 192,51 16,28 IV 1 2 2 11 4 2 3
21X 1 128 130 187,38 193,53 16,29 IV 2 1 2 4 11 6 1 2
21X 2 82 84 188,42 194,57 16,29 IV 3 1 3 8 2 1 6
21X 3 27 29 189,37 195,52 16,30 IV 4 1 23 11 1 3 8
21X 3 129 131 190,39 196,54 16,33 IV 5 5 1 8 11 2 1 14
21X 4 84 86 191,44 197,59 16,39 IV 6 1 6 21 2 4
21X 5 64,5 66,5 192,25 198,40 16,43 IV 7 2 2 1 7 21 1 6 13
21X 6 80,5 82,5 193,41 199,56 16,48 IV 8 3 1 6 7 6
21X 7 27,5 29,5 194,38 200,53 16,48 IV 9 1 3 4 12 1 2 8
21X 7 128 130 195,38 201,53 16,49 IV 10 2 2 2 7 18 1 9
22X 1 68,5 70,5 196,39 202,54 16,50 IV 11 1 3 1 3 13 1 13
22X 2 20 22 197,40 203,55 16,51 IV 12 3 1 11 11 1 1 13
22X 2 120 122 198,40 204,55 16,52 IV 13 X 1 1 3 4 14 1 15
22X 3 75,5 77,5 199,46 205,61 16,53 IV 14 1 7 7 1 23
22X 4 64 66 200,34 206,49 16,54 IV 15 2 1 11 16 1 19
22X 5 75 77 201,45 207,60 16,55 IV 16 4 1 4 19 1 13
22X 6 20 22 202,40 208,55 16,56 IV 17 1 2 5 1 6 1 18 3 11
22X 6 120 122 203,40 209,55 16,58 IV 18 1 3 5 23 1 1 2 7
22X 7 67,5 69,5 204,38 210,53 16,59 IV 19 X 1 8 2 2 21 1 X 12
22X 9 19 21 205,45 211,60 16,60 IV 20 X 1 4 4 19 8
23X 1 63,5 65,5 205,84 211,99 16,61 IV 21 1 2 1 3 10 1 9
23X 2 20 22 206,90 213,05 16,62 IV 22 X 2 2 20 3 5
23X 2 120 122 207,90 214,05 16,64 IV 23 2 2 8 3 10
23X 3 71 73 208,91 215,06 16,65 IV 24 3 2 5 13 2 18
23X 4 20 22 209,90 216,05 16,66 IV 25 2 3 5 14 1 17
23X 4 120 122 210,90 217,05 16,68 IV 26 1 1 3 13 1 1 20
23X 5 68 70 211,88 218,03 16,69 IV 27 1 7 16 1 1 14
23X 6 17 19 212,87 219,02 16,70 IV 28 1 1 6 7 20
23X 7 3 5 213,93 220,08 16,72 IV 29 5 1 1 22
23X 8 18,5 20,5 214,84 220,99 16,77 IV30 1 19 1 3 1 20
24X 1 77 79 215,47 221,62 16,82 IV31 1 12 4 2 1 9
24X 2 18 20 216,38 222,53 16,90 IV 32 2 8 5 2 22
24X 2 117 119 217,37 223,52 16,99 IV 33 1 1 13 13 1 22
24X 3 69,5 71,5 218,40 224,55 17,08 IV 34 1 1 7 5 1 1 23
24X 4 20 22 219,40 225,55 17,17 IV 35 1 7 5 2 3 16
24X 4 120 122 220,40 226,55 17,24 IV 36 2 1 2 5 29
24X 6 63 65 222,83 228,98 17,34 IV 37 1 2 6 2 2 1 15
25X 1 18 20 223,88 230,03 17,38 IV 38 7 4 10
25X 1 117 119 224,87 231,02 17,42 IV 39 1 4 4 1 16
25X 2 69,5 71,5 225,90 232,05 17,46 IV 40 1 7 9 4 22
25X 3 20 22 226,90 233,05 17,50 IV 41 3 4 4 5 22
25X 4 20 22 227,74 233,89 17,53 IV 42 3 8 12 20
26X 1 58 60 228,88 235,03 17,56 IV 43 1 5 10 1 20
26X 2 11 13 229,92 236,07 17,59 IV 44 2 1 6 17 2 1 17
26X 2 112,5 114,5 230,94 237,09 17,61 IV 45 1 1 5 5 1 3
27X 1 40 42 235,40 241,55 17,72 IV 46 2 14 11 1 2 1
27X 1 128 130 236,28 242,43 17,72 IV 47 1 2 8 4 2 7
27X 3 41,5 43,5 238,42 244,57 17,73 IV 48 1 8 3 14
27X 3 140 142 239,40 245,55 17,74 IV 49 1 1 1 5 9 1 10
27X 4 89,5 91,5 240,40 246,55 17,74 IV 50 1 1 6 3 1 19
27X 6 25,5 27,5 241,33 247,48 17,75 IV 51 1 1 2 3 1 23
1 2 2 2 7 1 10 1 2 1 2 14Mean:
Appendix 1
Age
(Ma;
ATN
TS 2
012)
Sam
ple
naam
Din
ofla
gella
te c
ysts
Cous
teau
dini
um a
ubry
ae
Cord
osph
aerid
ium
min
imum
Crib
rope
ridin
ium
tenu
itabu
latu
m
Crist
atod
iniu
m c
rista
tose
rrat
um
Daps
ilidi
nium
pse
udoc
ollig
erum
Dino
pter
ygiu
m c
lado
ides
Dist
atod
iniu
m p
arad
oxum
Hete
raul
acac
ysta
cam
panu
la
Hom
otry
bliu
m te
nuits
pino
sum
Hyst
richo
kolp
oma
rigau
diae
Hyst
richo
spha
erop
sis o
bscu
ra
Impa
gedi
nium
ara
chni
on
Impa
gedi
nium
pal
lidum
Impa
gedi
nium
par
adox
um
Impa
gedi
nium
stria
latu
m
Impa
gedi
nium
vel
orum
Impa
gedi
nium
spp.
Kallo
spha
erid
ium
sens
u He
ad
Laby
rinth
odin
ium
tr. t
runc
atum
Laby
rinth
odin
ium
tr. m
odic
um
16,28 IV 1 1 6 1 1 1 1 1 6 5
16,29 IV 2 1 1 5 1 1 1 5 2 1
16,29 IV 3 4 2 8 1 2 2 1 5
16,30 IV 4 3 2 3 4 2 1 3
16,33 IV 5 15 5 4 1 3 1 1 4 1
16,39 IV 6 4 4 1 10 2 1 2 2 1 9 1 12
16,43 IV 7 10 10 9 1 1 3 5 1 10
16,48 IV 8 2 1 6 6 2 1 3 6 7 4
16,48 IV 9 1 4 6 2 5 1 5 4
16,49 IV 10 7 3 2 1 1 2 1 1 3 8
16,50 IV 11 2 1 5 2 1 4 1 3 1 2 1
16,51 IV 12 9 1 9 1 1 2 X 4
16,52 IV 13 9 6 3 2 1 8 2
16,53 IV 14 4 X 5 1 2 1 1 1 6 4
16,54 IV 15 4 1 7 1 3 1 4 8 4
16,55 IV 16 5 10 2 1 3 1 12 1
16,56 IV 17 8 1 5 2 1 1 2 1 7 2
16,58 IV 18 4 1 1 4 1 1 3 2 2 1 3
16,59 IV 19 5 8 6 1 2 1 2 1 4 2 4
16,60 IV 20 9 10 9 2 X 2 X 1 2 2 4
16,61 IV 21 3 5 5 1 1 3 1 5 1 6 2
16,62 IV 22 1 5 2 2 1 3 1 1 2 7 2 1 2
16,64 IV 23 6 10 2 1 1 2 4 7
16,65 IV 24 6 5 2 1 4 4 1 1 3 5
16,66 IV 25 3 5 1 1 4 1 1 3 1
16,68 IV 26 7 14 1 4 1 7 1 5 1 1 1 1
16,69 IV 27 9 12 2 7 5 1 6 4
16,70 IV 28 4 9 1 2 1 2 1 2 2 1 1 1 3
16,72 IV 29 28 1 9 5 1 8 2 1 1 3 16
16,77 IV30 10 3 4 3 12 2 22
16,82 IV31 11 1 4 3 4 3 1 1 1 1 1 6
16,90 IV 32 13 1 4 4 1 1 1 3 1 9
16,99 IV 33 42 7 5 3 1 2 3 1 3
17,08 IV 34 26 7 1 7 2 8 1 2
17,17 IV 35 15 8 1 3 1 5 1 3 1 4 1
17,24 IV 36 12 11 2 11 1 1 1
17,34 IV 37 29 2 1 1 1 1 1 8 1
17,38 IV 38 14 2 8 7 1 1 2 11
17,42 IV 39 4 4 6 5 4 2 7
17,46 IV 40 20 1 3 8 1 2
17,50 IV 41 36 4 4 1 2
17,53 IV 42 73 5 7 1 1 2 1 1
17,56 IV 43 15 1 4 3 1 1 3
17,59 IV 44 13 2 1 6 2 1 1 1
17,61 IV 45 15 11 4 1 1 6
17,72 IV 46 26 2 1 1 5
17,72 IV 47 41 3 1 6 1 1 3 3
17,73 IV 48 7 4 6 15 5 1 3 1
17,74 IV 49 29 1 3 7 11 1 1
17,74 IV 50 20 1 5 6 1 1 1
17,75 IV 51 53 1 3 7 5 3 1 1 3 1 2
##### 17,74 14 1 5 1 5 4 3 2 2 1 1 1 3 1 1 1 1 4 3 6
Appendix 1
Age
(Ma;
ATN
TS 2
012)
Sam
ple
naam
Din
ofla
gella
te c
ysts
Laby
rinth
odin
ium
spp
.
Leje
unoc
ysta
cha
lleng
eren
sis
Leje
unoc
ysta
cf.
cato
mus
Leje
unoc
ysta
con
vexa
Leje
unoc
ysta
mar
ieae
Leje
unoc
ysta
spp
.
Ling
ulod
iniu
m m
acha
erop
horu
m
Ling
ulod
iniu
m m
ultiv
irgat
um
Mel
itasp
haer
idiu
m c
hoan
opho
rum
Nem
atos
phae
rops
is la
byrin
thus
Ope
rcul
odin
ium
? bo
rger
holte
nse
Ope
rcul
odin
ium
cen
troc
arpu
m
Ope
rcul
odin
ium
eiri
kani
anum
Ope
rcul
odin
ium
gig
ante
um
Ope
rcul
odin
ium
isra
elia
num
Ope
rcul
odin
ium
long
ispi
nige
rum
Ope
rcul
odin
ium
pia
seck
ii
Ope
rcul
odin
ium
pla
citu
m
Ope
rcul
odin
ium
sp.
3
Pala
eocy
stod
iniu
m g
olzo
wen
se
Pala
eocy
stod
iniu
m m
ioca
enic
um
16,28 IV 1 5 1 1 7 2 1 2 1 2 4 4 1 1
16,29 IV 2 3 1 1 7 5 3 3 8 1 1 12 1
16,29 IV 3 5 1 14 4 3 7 2 8 2 1
16,30 IV 4 0 1 11 1 1 1 3 3 1
16,33 IV 5 1 1 10 2 1 2 3 1
16,39 IV 6 13 1 4 10 1 1 1 2 2
16,43 IV 7 11 3 3 1 3 2 1 1
16,48 IV 8 4 1 3 4 6 3 1 2 1 2 4 1
16,48 IV 9 4 2 3 4 1 3 1 3 1 3
16,49 IV 10 8 2 3 9 3 4 1 2 1 1 2
16,50 IV 11 1 X 1 3 8 7 3 1 3 1 5
16,51 IV 12 4 1 6 2 2 1 1 4 2
16,52 IV 13 2 X 1 4 1 2 5 5 4 1 1
16,53 IV 14 4 2 7 2 2 2 1
16,54 IV 15 4 3 5 2 1 1 1 2
16,55 IV 16 1 5 1 1 2 5
16,56 IV 17 2 1 3 10 1 2 5 3 2 2 1 1
16,58 IV 18 3 1 3 2 2 1 7 6 1 2 1 1 1
16,59 IV 19 4 1 1 7 2 1 3 3 1 1 2
16,60 IV 20 4 X X 4 X 5 2 3 6
16,61 IV 21 2 2 1 1 6 1 2 2 1 15
16,62 IV 22 2 1 2 2 4 1 4 1 1 2 19
16,64 IV 23 7 9 1 1 1 3
16,65 IV 24 5 3 5 2 4 1 3
16,66 IV 25 1 3 1 1 5 1 3 1
16,68 IV 26 1 1 6 1 1 1 1 2 1
16,69 IV 27 0 2 9 1 2 3 1 7 1 6
16,70 IV 28 3 2 2 11 1 1 2 1 4
16,72 IV 29 16 13 3 1 1 5 3 2
16,77 IV30 22 1 14 1 3 1
16,82 IV31 6 1 11 4 1 2 1
16,90 IV 32 0 2 11 2 1 3 1 3
16,99 IV 33 0 1 2 5 1 1 2 5 2
17,08 IV 34 0 1 1 1 9 1 7 2 1 1
17,17 IV 35 5 7 1 1 3 2 2 1 2
17,24 IV 36 1 1 8 1 1 4 2 6 5
17,34 IV 37 1 1 2 12 3 1 1 2
17,38 IV 38 0 2 3 10 3 3 8 3
17,42 IV 39 0 1 13 2 3 4 2 2 2
17,46 IV 40 0 5 2 6 4 4
17,50 IV 41 0 1 2 2 1 2 1 3 1
17,53 IV 42 1 3 1 1 1 1
17,56 IV 43 0 4 1 2 1 1 3 2 1 1
17,59 IV 44 1 4 1 1 6 1 1 3 1 1
17,61 IV 45 0 1 3 4 13 1 1 1 2 1 1
17,72 IV 46 0 2 9 2 2 2 3 2
17,72 IV 47 0 1 1 2 3 1 1 1 1 1 2 2 3
17,73 IV 48 0 5 1 2 2 1 4 2
17,74 IV 49 0 2 1 2 1 2 1 3 1 10
17,74 IV 50 0 1 1 2 8 1 1 1 13 2
17,75 IV 51 0 2 3 1 1 7 1 2
##### 17,74 3 2 1 1 2 2 7 2 2 2 2 3 1 1 2 3 2 1 1 3 1
Appendix 1
Age
(Ma;
ATN
TS 2
012)
Sam
ple
naam
Din
ofla
gella
te c
ysts
Sum
atra
dini
um h
amul
atum
Sum
atra
dini
um s
ouco
uyan
tiae
Tect
atod
iniu
m p
ellit
um
Tube
rcol
odin
ium
van
cam
poae
Trin
ovan
tedi
nium
? fe
rugn
omat
um
Trin
ovan
tedi
nium
glo
rianu
m
Trin
ovan
tedi
nium
har
pago
nium
Trin
ovan
tedi
nium
hen
rietii
Trin
ovan
tedi
nium
pap
ulum
Trin
ovan
tedi
nium
xyl
ocho
poru
m
Echi
nidi
nium
eua
xum
Echi
nidi
nium
sle
ipne
rens
is
Perid
inia
e in
det.
Tota
al
Din
ocys
t ind
et.
Lyco
podi
um-s
pore
Bisa
ccat
e po
llen
spor
en
polle
n
Tota
al p
olle
n en
spo
ren
16,28 IV 1 1 2 1 1 10 300 40 26 21 3 50
16,29 IV 2 1 1 1 1 1 5 300 3 22 6 3 1 10
16,29 IV 3 2 2 300 1 45 3 6 1 10
16,30 IV 4 1 1 2 1 1 10 300 25 27 10 1 38
16,33 IV 5 2 1 1 4 11 300 57 39 19 6 64
16,39 IV 6 2 1 1 3 20 300 89 95 60 17 172
16,43 IV 7 2 1 1 1 10 300 2 75 48 52 11 111
16,48 IV 8 5 1 2 1 28 300 58 37 10 12 59
16,48 IV 9 5 2 1 2 16 300 1 51 46 14 5 65
16,49 IV 10 2 8 1 1 1 1 2 7 40 300 49 36 28 4 68
16,50 IV 11 1 7 1 2 1 4 1 32 300 2 39 39 17 1 57
16,51 IV 12 4 1 2 1 1 1 1 22 300 60 32 22 2 56
16,52 IV 13 2 2 1 19 300 109 50 6 4 60
16,53 IV 14 5 1 18 300 164 72 20 5 97
16,54 IV 15 1 3 4 2 2 30 300 46 82 19 1 102
16,55 IV 16 1 4 1 1 1 20 300 98 70 18 4 92
16,56 IV 17 1 5 1 1 2 4 2 44 300 88 137 28 165
16,58 IV 18 X 1 2 X 1 X 3 1 33 300 2 93 40 30 3 73
16,59 IV 19 5 X X 2 19 300 2 135 60 22 4 86
16,60 IV 20 6 1 1 2 1 25 300 142 53 12 7 72
16,61 IV 21 6 1 1 4 5 2 1 22 300 72 44 26 4 74
16,62 IV 22 4 2 1 1 2 4 1 29 300 110 26 13 3 42
16,64 IV 23 3 2 1 1 3 20 300 83 22 10 32
16,65 IV 24 1 1 1 1 1 17 300 1 111 24 15 3 42
16,66 IV 25 1 1 2 1 14 300 156 40 9 2 51
16,68 IV 26 2 1 1 1 1 26 300 88 68 23 1 92
16,69 IV 27 2 1 26 300 75 67 32 3 102
16,70 IV 28 2 1 1 1 22 300 95 30 16 4 50
16,72 IV 29 1 1 1 2 7 300 50 19 6 1 26
16,77 IV30 1 2 1 19 300 127 25 11 1 37
16,82 IV31 1 2 2 1 10 300 1 85 36 13 2 51
16,90 IV 32 2 1 11 300 1 77 30 19 3 52
16,99 IV 33 1 2 15 300 109 52 19 3 74
17,08 IV 34 1 2 1 2 1 2 24 300 107 48 21 3 72
17,17 IV 35 1 1 2 21 300 44 53 26 2 81
17,24 IV 36 3 1 1 1 1 34 300 104 35 21 2 58
17,34 IV 37 1 2 12 300 42 15 12 4 31
17,38 IV 38 1 2 20 300 55 31 13 4 48
17,42 IV 39 1 1 5 300 41 29 22 3 54
17,46 IV 40 1 8 300 1 59 41 17 21 79
17,50 IV 41 1 1 7 300 103 28 16 17 61
17,53 IV 42 10 300 1 46 14 4 10 28
17,56 IV 43 2 1 3 1 11 300 1 89 29 16 10 55
17,59 IV 44 4 1 8 1 23 300 49 26 22 3 51
17,61 IV 45 1 1 7 1 19 300 56 25 8 8 41
17,72 IV 46 4 2 2 17 300 1 70 51 16 29 96
17,72 IV 47 2 1 1 1 22 300 1 86 36 15 4 55
17,73 IV 48 4 1 1 14 300 54 30 17 3 50
17,74 IV 49 4 1 1 1 1 2 23 300 1 25 16 4 45
17,74 IV 50 5 1 1 1 1 2 13 300 30 23 3 56
17,75 IV 51 2 1 1 6 300 21 15 1 37
##### 17,74 1 3 1 1 1 1 1 1 1 1 2 1 18 300 1 78 40 18 5 63
Appendix 1
Age
(Ma;
ATN
TS 2
012)
Sam
ple
naam
Din
ofla
gella
te c
ysts
Fora
min
ifera
lini
ng
Crus
tace
a re
mai
ns
Tota
al A
crita
rcha
e
Cycl
opsie
lla e
llipt
ica/
gran
osa
Cym
atio
spha
era
sp.
Cym
atio
spha
era
baffi
nens
is
Lavr
ados
phae
ra c
rista
Leio
spha
erid
ia ro
ckha
llens
is
Nan
noba
rbop
hora
ged
lii
Para
leca
niel
la in
dent
ata
Spp.
Herw
erkt
e
Emat
rocy
sta
urna
form
is (K
rijt)
Spin
iferit
es "
gran
ulaa
t" (K
rijt)
Spin
iferit
es "
grot
e he
rwer
kte"
(Krij
t)
Spin
iferit
es h
erw
erkt
Spin
iferit
es h
erw
erkt
Clei
stos
phae
ridiu
m p
laca
cant
ha /
dive
rsisp
inos
um (E
ocee
n) d
e vr
eem
de
16,28 IV 1 8 4 147 7 16 2 49 1 36 5 1 1 3
16,29 IV 2 5 5 145 13 15 1 43 1 36 6 1 3 2
16,29 IV 3 8 6 94 12 1 13 2 34 16 5 3 1 1
16,30 IV 4 7 8 76 25 2 7 5 1 18 3 2 1
16,33 IV 5 9 7 65 4 5 7 9 20 7 1 3 3
16,39 IV 6 22 9 104 1 20 15 34 3 1 2
16,43 IV 7 14 6 161 4 32 29 48 2 1 1
16,48 IV 8 26 4 130 1 1 2 11 1 33 13 34 6 1 1 3 1
16,48 IV 9 22 3 111 4 3 10 1 26 9 29 3 3
16,49 IV 10 6 6 84 7 8 2 15 10 21 0
16,50 IV 11 6 7 107 9 2 12 18 4 31 0
16,51 IV 12 27 13 113 5 1 5 4 15 17 33 2 1 1
16,52 IV 13 30 1 86 1 1 4 5 9 16 25 0
16,53 IV 14 27 5 79 1 2 7 24 5 20 0
16,54 IV 15 12 2 39 2 1 9 1 6 6 7 4 1 3
16,55 IV 16 19 2 66 2 1 10 1 8 6 19 0
16,56 IV 17 25 6 56 6 1 3 15 3 4 2 11 0
16,58 IV 18 4 3 60 4 4 8 1 10 3 15 0
16,59 IV 19 4 3 67 5 10 8 14 2 14 4 4
16,60 IV 20 13 3 81 7 5 11 11 1 23 4 4
16,61 IV 21 5 2 54 3 3 9 4 5 2 14 0
16,62 IV 22 4 3 58 4 5 13 4 4 14 0
16,64 IV 23 2 7 67 4 1 8 2 10 2 20 2 1 1
16,65 IV 24 10 4 89 2 1 5 16 1 32 6 1 5
16,66 IV 25 14 2 85 4 4 6 1 25 1 22 6 2 4
16,68 IV 26 14 4 59 13 3 8 1 17 2 2
16,69 IV 27 5 9 55 23 2 4 13 1 1
16,70 IV 28 7 3 40 8 2 4 4 11 1 1
16,72 IV 29 4 10 31 6 1 8 8 1 1
16,77 IV30 10 8 74 17 4 5 12 18 3 2 1
16,82 IV31 17 14 117 24 3 2 26 31 2 1 1
16,90 IV 32 8 8 102 40 1 2 21 19 5 1 4
16,99 IV 33 7 21 54 29 3 4 4 7 3 1 1 1
17,08 IV 34 13 12 80 49 3 2 14 6 3 2 1
17,17 IV 35 9 12 108 34 2 10 14 24 12 1 1 10
17,24 IV 36 7 7 66 43 1 2 1 5 7 6 1 1 1 1 1 1
17,34 IV 37 11 7 55 3 2 8 22 10 3 1 1 1
17,38 IV 38 21 8 52 2 6 24 12 4 6 3 3
17,42 IV 39 17 6 77 2 2 33 14 13 2 2
17,46 IV 40 8 18 82 3 6 28 13 16 5 3 1 1
17,50 IV 41 15 25 60 1 2 18 7 16 7 3 3 1
17,53 IV 42 17 18 51 2 1 17 17 7 6 2 1 2 1
17,56 IV 43 13 10 104 4 3 43 6 24 10 1 4 1 2 2
17,59 IV 44 11 6 81 2 1 11 21 6 20 5 3 1 1
17,61 IV 45 10 13 76 4 1 6 29 10 13 6 1 3 1 1
17,72 IV 46 36 12 70 14 11 19 8 9 6 1 1 3 1
17,72 IV 47 23 10 78 20 2 4 18 6 14 6 1 5
17,73 IV 48 4 2 177 116 11 18 2 15 11 1 2 7 1
17,74 IV 49 10 4 309 247 8 14 20 9 2 2 5
17,74 IV 50 10 10 90 26 10 1 15 2 18 6 1 1 3 1
17,75 IV 51 6 5 66 6 3 8 9 20 5 1 1 1 2
##### 17,74 13 8 87 18 1 3 8 2 17 6 19 4 1 2 2 1 2 2
Appendix 1
Age
(Ma;
ATN
TS 2
012)
Sam
ple
naam
Din
ofla
gella
te c
ysts
W/C
tex8
6-h
uk37
N/O
Eury
halin
e sp
ecie
s
S/D
BIT
P/G
Rich
ness
Shan
non-
Wie
ner I
ndex
Even
ness
Din
ocys
ts/g
ram
sed
imen
t
Rew
orke
d di
nocy
sts
16,28 IV 1 0,931 26,267 28,495 0,977 14 0,122 0,099 0,070 45 2,436 0,640 24148 1,667
16,29 IV 2 0,984 26,550 28,537 0,971 20 0,024 0,079 0,062 46 2,570 0,671 41019 2,000
16,29 IV 3 0,954 26,282 28,529 0,964 26 0,026 0,081 0,028 38 2,364 0,650 20736 1,667
16,30 IV 4 0,840 23,410 28,588 0,982 36 0,106 0,135 0,085 38 2,387 0,656 35211 1,000
16,33 IV 5 0,931 23,761 28,357 0,995 14 0,186 0,104 0,116 39 2,434 0,665 17915 2,333
16,39 IV 6 0,895 22,468 28,144 0,974 10 0,465 0,153 0,174 43 2,686 0,714 10442 1,000
16,43 IV 7 0,813 22,331 28,158 0,962 5 0,269 0,149 0,106 41 2,727 0,734 12945 0,667
16,48 IV 8 0,891 24,301 28,309 0,953 10 0,149 0,096 0,224 46 3,005 0,785 16501 2,000
16,48 IV 9 0,889 23,945 28,280 0,938 8 0,170 0,134 0,151 42 2,790 0,746 19316 1,000
16,49 IV 10 0,969 22,534 28,004 0,959 18 0,187 0,120 0,380 48 2,940 0,760 21115 0,000
16,50 IV 11 0,936 23,240 27,822 0,950 18 0,152 0,126 0,303 49 2,920 0,750 25324 0,000
16,51 IV 12 0,929 24,378 28,325 0,987 14 0,147 0,141 0,144 41 2,674 0,720 15397 0,667
16,52 IV 13 0,939 23,546 28,327 0,988 6 0,166 0,151 0,107 40 2,644 0,717 11408 0,000
16,53 IV 14 0,960 23,655 28,309 0,994 9 0,273 0,167 0,105 35 2,467 0,694 7023 0,000
16,54 IV 15 0,857 24,111 28,306 0,976 9 0,307 0,163 0,191 39 2,622 0,716 26924 1,333
16,55 IV 16 0,923 23,705 28,220 0,987 11 0,265 0,158 0,121 38 2,602 0,715 11841 0,000
16,56 IV 17 0,933 23,987 28,310 0,932 18 0,478 0,170 0,394 53 3,085 0,777 13589 0,000
16,58 IV 18 0,875 22,398 27,830 0,905 6 0,212 0,114 0,202 53 2,795 0,704 13184 0,000
16,59 IV 19 0,875 22,596 28,196 0,966 13 0,244 0,142 0,163 46 2,773 0,724 9056 1,333
16,60 IV 20 0,958 23,108 28,367 0,980 11 0,201 0,163 0,194 43 2,724 0,724 8382 1,333
16,61 IV 21 0,769 22,756 28,127 0,957 10 0,218 0,154 0,327 47 2,886 0,750 16578 0,000
16,62 IV 22 0,794 23,747 27,953 0,912 8 0,122 0,144 0,365 48 2,950 0,762 11062 0,000
16,64 IV 23 0,959 24,284 28,330 0,988 13 0,092 0,126 0,127 36 2,591 0,723 15192 0,667
16,65 IV 24 0,920 24,446 28,217 0,966 5 0,117 0,112 0,100 39 2,632 0,718 10717 2,000
16,66 IV 25 0,977 25,284 28,443 0,990 7 0,140 0,121 0,089 38 2,302 0,633 7565 2,000
16,68 IV 26 0,769 25,182 28,516 0,960 19 0,269 0,157 0,140 41 2,466 0,664 13991 0,667
16,69 IV 27 0,750 24,881 28,380 0,959 32 0,298 0,202 0,174 37 2,640 0,731 15301 0,333
16,70 IV 28 0,926 23,907 28,327 0,965 19 0,152 0,150 0,143 42 2,553 0,683 12540 0,333
16,72 IV 29 0,971 25,269 28,601 0,988 19 0,080 0,143 0,051 39 2,618 0,715 24770 0,333
16,77 IV30 0,940 25,889 28,632 1,000 31 0,104 0,118 0,092 32 2,517 0,726 8954 1,000
16,82 IV31 0,967 25,901 28,634 0,989 35 0,132 0,128 0,058 38 2,401 0,660 14462 0,667
16,90 IV 32 0,914 26,049 28,653 0,973 51 0,136 0,125 0,056 35 2,408 0,677 15162 1,667
16,99 IV 33 0,970 26,134 28,673 0,986 34 0,213 0,137 0,104 36 2,660 0,742 10705 1,000
17,08 IV 34 0,909 25,947 28,609 0,988 58 0,193 0,127 0,171 43 2,599 0,691 10835 1,000
17,17 IV 35 0,857 25,777 28,617 0,983 41 0,211 0,115 0,107 39 2,424 0,662 26323 4,000
17,24 IV 36 1,000 26,022 28,564 0,989 51 0,162 0,133 0,212 35 2,490 0,700 11862 2,000
17,34 IV 37 0,963 25,935 28,666 1,000 15 0,090 0,109 0,071 36 2,420 0,675 29664 1,000
17,38 IV 38 1,000 26,601 28,680 1,000 12 0,138 0,137 0,127 34 2,592 0,735 22017 2,000
17,42 IV 39 0,969 27,182 28,688 0,989 16 0,151 0,176 0,040 35 2,464 0,693 28197 0,667
17,46 IV 40 0,962 26,817 28,969 0,989 8 0,216 0,234 0,042 29 2,338 0,694 21041 1,667
17,50 IV 41 1,000 26,828 28,554 1,000 3 0,177 0,161 0,032 30 2,064 0,607 11403 2,333
17,53 IV 42 1,000 26,338 28,522 0,986 3 0,081 0,138 0,046 26 2,239 0,687 26412 2,000
17,56 IV 43 1,000 25,871 28,967 0,988 3 0,147 0,139 0,089 37 2,386 0,661 13948 3,333
17,59 IV 44 0,941 25,233 28,491 1,000 8 0,141 0,156 0,253 41 2,671 0,719 25126 1,667
17,61 IV 45 0,979 24,071 28,469 0,993 17 0,113 0,137 0,186 41 2,520 0,679 20561 1,993
17,72 IV 46 0,976 24,966 28,518 0,987 23 0,269 0,145 0,112 33 2,405 0,688 17647 2,000
17,72 IV 47 0,912 24,790 28,512 0,972 23 0,151 0,141 0,153 44 2,557 0,676 13315 2,000
17,73 IV 48 0,900 23,453 28,110 0,971 121 0,108 0,178 0,097 35 2,530 0,712 22666 3,667
17,74 IV 49 0,913 22,950 28,270 0,986 250 0,076 0,169 0,225 41 2,710 0,730 3,000
17,74 IV 50 0,929 23,680 28,342 0,994 34 0,151 0,144 0,132 41 2,614 0,704 2,000
17,75 IV 51 0,833 24,334 28,540 0,975 10 0,107 0,145 0,059 39 2,491 0,680 1,667
##### 17,74 0,921 24,649 28,412 0,976 25 0,175 0,140 0,143 40 2,584 0,703 17489 1,307
Appendix 1
Age
(Ma;
ATN
TS 2
012)
Sam
ple
naam
Dino
flage
llate
cyst
s
sedi
men
tatio
n ra
te (c
m/k
yr)
diffe
renc
e in
dep
th (m
)
Age
diffe
renc
e (k
yrs)
# yr
s / 2
cm
16,28 IV 1 16,857 2,7 30
16,29 IV 2 16,857 1,02 6,1 30
16,29 IV 3 16,857 1,04 6,2 30
16,30 IV 4 16,857 0,95 5,6 30
16,33 IV 5 1,834 1,02 29,9 273
16,39 IV 6 1,834 1,05 57,2 273
16,43 IV 7 1,834 0,81 43,9 273
16,48 IV 8 11,268 1,16 44,8 44
16,48 IV 9 11,268 0,97 8,6 44
16,49 IV 10 11,268 1,01 8,9 44
16,50 IV 11 11,268 1,01 8,9 44
16,51 IV 12 11,268 1,02 9,0 44
16,52 IV 13 11,268 1,00 8,9 44
16,53 IV 14 11,268 1,05 9,4 44
16,54 IV 15 11,268 0,89 7,9 44
16,55 IV 16 7,472 1,11 11,9 67
16,56 IV 17 7,472 0,95 12,7 67
16,58 IV 18 7,472 1,00 13,4 67
16,59 IV 19 7,472 0,97 13,0 67
16,60 IV 20 7,472 1,07 14,4 67
16,61 IV 21 7,472 0,39 5,2 67
16,62 IV 22 7,472 1,07 14,3 67
16,64 IV 23 7,472 1,00 13,4 67
16,65 IV 24 7,472 1,01 13,5 67
16,66 IV 25 7,472 0,99 13,2 67
16,68 IV 26 7,472 1,00 13,4 67
16,69 IV 27 7,472 0,98 13,1 67
16,70 IV 28 7,472 0,99 13,2 67
16,72 IV 29 7,472 1,06 14,2 67
16,77 IV30 1,148 0,91 51,6 436
16,82 IV31 1,148 0,63 55,3 436
16,90 IV 32 1,148 0,91 79,3 436
16,99 IV 33 1,148 0,99 86,2 436
17,08 IV 34 1,148 1,02 89,3 436
17,17 IV 35 1,148 1,01 87,6 436
17,24 IV 36 2,550 1,00 77,5 196
17,34 IV 37 2,550 2,43 95,3 196
17,38 IV 38 2,550 1,05 41,2 196
17,42 IV 39 2,550 0,99 38,8 196
17,46 IV 40 2,550 1,02 40,2 196
17,50 IV 41 2,550 1,01 39,4 196
17,53 IV 42 2,550 0,84 32,9 196
17,56 IV 43 4,076 1,14 28,8 123
17,59 IV 44 4,076 1,04 25,5 123
17,61 IV 45 4,076 1,02 24,9 123
17,72 IV 46 20,435 4,47 107,6 24
17,72 IV 47 20,435 0,88 4,3 24
17,73 IV 48 20,435 2,13 10,4 24
17,74 IV 49 20,435 0,99 4,8 24
17,74 IV 50 20,435 0,99 4,9 24
17,75 IV 51 20,435 0,93 4,6 24
##### 17,74 8,378 1,10 28,8 132
Appendix 2
Appendix 2: Plots of the Data
The three-point average is showed as the black striped line
Appendix 2
Appendix 2
Appendix 2
Appendix 2
Appendix 2
Appendix 2
Appendix 2
Appendix 2
Appendix 2
Appendix 2
Appendix 2
Appendix 2
Appendix 2
Appendix 2
Appendix 3
Appendix 3: Results AR(1) model
Parameters AR(1) Model
Result signicifances AR(1) Model
Figure 2: The numbers represent how many of the 1000 synthetic datasets had a lower PCC than
the signal itself
W/C tex86-h uk37 N/O S/D BIT P/G
time-shift used: 2 2 2 2 2 2 3
Cov ( X tj , X ti ) 0,004 58,306 1,390 0,004 0,049 0,005 0,001
Variance 0,200 94,171 2,968 0,022 0,397 0,038 0,400
memory factor 0,147 0,787 0,684 0,402 0,352 0,354 0,141
tau 0,151 1,210 0,764 0,318 0,277 0,279 0,148
Richness Shannon-Wiener Evenness Reworking Cyclopsiella ell/gran Dinocysts/gram
time-shift used: 3 3 3 2 2 2
Cov ( X tj , X ti ) 337 0,349 0,005 15 4821 0,004
Variance 1568 2,038 0,071 49 72065 0,192
memory factor 0,599 0,555 0,419 0,548 0,259 0,138
tau 0,566 0,493 0,333 0,482 0,214 0,146
W/C tex86-h uk37 N/O S/D BIT P/GRaw data Averaged Raw data Averaged Raw data Averaged Raw data Averaged Raw data Averaged Raw data Averaged Raw data
W/C 1000 1000 99 99 99 95 1000 99 95 95 80 10 95
tex86-h 99 99 1000 1000 1000 1000 1000 1000 1000 1000 0 0 1000
uk37 99 95 1000 1000 1000 1000 1000 1000 50 1000 0 0 1000
N/O 1000 99 1000 1000 1000 1000 1000 1000 80 90 30 40 1000
S/D 95 95 1000 1000 50 1000 80 90 1000 1000 99 95 95
BIT 80 10 0 0 0 0 30 40 99 95 1000 1000 40
P/G 95 95 1000 1000 1000 1000 1000 1000 95 90 40 5 1000
Richness 95 95 1000 1000 1000 1000 1000 1000 80 80 80 70 1000
Shannon-Wiener 95 95 1000 1000 1000 1000 1000 1000 90 80 0 10 1000
Evenness 80 90 1000 1000 1000 1000 99 99 90 90 70 10 1000
Reworking 80 80 1000 1000 1000 1000 95 95 90 90 50 10 95
Cyclopsiella ell/gran 10 10 70 0 30 0 40 40 70 80 60 30 30
Dinocysts/gram 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 0 0 1000
Richness Shannon-Wiener Evenness Reworking Cyclopsiella ell/gran Dinocysts/gramRaw data Averaged Raw data Averaged Raw data Averaged Raw data Averaged Raw data Averaged Raw data Averaged
W/C 95 95 95 95 80 90 80 80 10 10 1000 1000
tex86-h 1000 1000 1000 1000 1000 1000 1000 1000 70 0 1000 1000
uk37 1000 1000 1000 1000 1000 1000 1000 1000 30 0 1000 1000
N/O 1000 1000 1000 1000 99 99 95 95 40 40 1000 1000
S/D 80 80 90 80 90 90 90 90 70 80 1000 1000
BIT 80 70 0 10 70 10 50 10 60 30 0 0
P/G 1000 1000 1000 1000 1000 1000 95 90 30 10 1000 1000
Richness 1000 1000 1000 1000 99 1000 1000 1000 40 5 0 1000
Shannon-Wiener 1000 1000 1000 1000 1000 1000 99 95 5 10 1000 1000
Evenness 99 1000 1000 1000 1000 1000 95 95 10 10 0 0
Reworking 1000 1000 99 95 95 95 1000 1000 95 95 1000 1000
Cyclopsiella ell/gran 40 5 5 10 10 10 95 95 1000 1000 0 0
Dinocysts/gram 0 1000 1000 1000 0 0 1000 1000 0 0 1000 1000
Appendix 4: Pearson Correlation Coefficients
Table: Pearson correlation coefficients are given for both raw data and the Averaged data
W/C tex86-h uk37 N/O S/D BITRaw data Averaged Raw data Averaged Raw data Averaged Raw data Averaged Raw data Averaged Raw data Averaged
W/C 1,000 1,000 0,454 0,629 0,393 0,597 0,540 0,651 -0,308 -0,431 -0,200 -0,061
tex86-h 0,454 0,629 1,000 1,000 0,814 0,927 0,454 0,657 -0,351 -0,461 -0,112 -0,024
uk37 0,393 0,597 0,814 0,927 1,000 1,000 0,644 0,789 -0,238 -0,402 0,062 0,035
N/O 0,540 0,651 0,454 0,657 0,644 0,789 1,000 1,000 -0,243 -0,374 0,075 0,136
S/D -0,308 -0,431 -0,351 -0,461 -0,238 -0,402 -0,243 -0,374 1,000 1,000 0,455 0,364
BIT -0,200 -0,061 -0,112 -0,024 0,062 0,035 0,075 0,136 0,455 0,364 1,000 1,000
P/G -0,304 -0,478 -0,558 -0,727 -0,623 -0,792 -0,552 -0,712 0,376 0,396 0,095 0,022
Richness -0,369 -0,578 -0,579 -0,784 -0,634 -0,831 -0,699 -0,851 0,238 0,367 -0,257 -0,278
Shannon-Wiener -0,362 -0,537 -0,627 -0,789 -0,666 -0,849 -0,642 -0,797 0,319 0,395 0,001 -0,095
Evenness -0,251 -0,408 -0,485 -0,667 -0,513 -0,733 -0,444 -0,645 0,293 0,364 0,191 0,076
Reworking 0,246 0,374 0,328 0,391 0,406 0,509 0,381 0,529 -0,310 -0,421 -0,136 -0,053
Cyclopsiella ell/gran -0,053 -0,071 -0,113 -0,056 -0,049 0,023 0,086 0,168 -0,202 -0,293 0,160 0,112
Dinocysts/gram 0,087 0,469 0,275 0,423 0,201 0,398 0,106 0,332 -0,390 -0,483 -0,194 -0,272
P/G Richness Shannon-Wiener Evenness Reworking Cyclopsiella ell/gran Dinocysts/gramRaw data Averaged Raw data Averaged Raw data Averaged Raw data Averaged Raw data Averaged Raw data Averaged Raw data Averaged
W/C -0,304 -0,478 -0,369 -0,578 -0,362 -0,537 -0,251 -0,408 0,246 0,374 -0,053 -0,071 0,087 0,469
tex86-h -0,558 -0,727 -0,579 -0,784 -0,627 -0,789 -0,485 -0,667 0,328 0,391 -0,113 -0,056 0,275 0,423
uk37 -0,623 -0,792 -0,634 -0,831 -0,666 -0,849 -0,513 -0,733 0,406 0,509 -0,049 0,023 0,201 0,398
N/O -0,552 -0,712 -0,699 -0,851 -0,642 -0,797 -0,444 -0,645 0,381 0,529 0,086 0,168 0,106 0,332
S/D 0,376 0,396 0,238 0,367 0,319 0,395 0,293 0,364 -0,310 -0,421 -0,202 -0,293 -0,390 -0,483
BIT 0,095 0,022 -0,257 -0,278 0,001 -0,095 0,191 0,076 -0,136 -0,053 0,160 0,112 -0,194 -0,272
P/G 1,000 1,000 0,701 0,807 0,819 0,879 0,686 0,803 -0,360 -0,430 0,064 -0,074 -0,169 -0,311
Richness 0,701 0,807 1,000 1,000 0,784 0,890 0,414 0,671 -0,370 -0,484 -0,046 -0,064 -0,053 -0,254
Shannon-Wiener 0,819 0,879 0,784 0,890 1,000 1,000 0,888 0,934 -0,463 -0,574 0,014 -0,070 -0,142 -0,347
Evenness 0,686 0,803 0,414 0,671 0,888 0,934 1,000 1,000 -0,413 -0,562 0,048 -0,073 -0,160 -0,368
Reworking -0,360 -0,430 -0,370 -0,484 -0,463 -0,574 -0,413 -0,562 1,000 1,000 0,398 0,545 0,215 0,375
Cyclopsiella ell/gran 0,064 -0,074 -0,046 -0,064 0,014 -0,070 0,048 -0,073 0,398 0,545 1,000 1,000 0,063 0,002
Dinocysts/gram -0,169 -0,311 -0,053 -0,254 -0,142 -0,347 -0,160 -0,368 0,215 0,375 0,063 0,002 1,000 1,000
Appendix 5
Appendix 5: Taxonomy
Apteodinium spiroides Benedek, 1972
Apteodinium tectatum; Piasecki, 1980 (Figure 1.a - 1.b)
Barsidinium pliocenicum Head, 1993 (Figure 1.c)
Batiacasphaera sp.A Quaijtaal (unpublished) (Figure 1.d – 1.f)
Batiacasphaera hirsuta Stover, 1977 (Figure 1.g – 1.i)
Batiacasphaera deheinzelinii/hirsuta Louwye et al., 1999 / Stover 1977 (Figure 1.g)
Batiacasphaera edwardsiae Louwye et al., 2008 (Figure 1.h-1.i)
Batiacasphaera complex (Figure 1.j)
minuta Matsuoka, 1983
micropapillata Stover, 1977
sphaerica Stover, 1977
Bitectatodinium raedwaldii Head, 1977 (Figure 1.k – 1.l)
Bitectatodinium tepikiense Wilson, 1973 (Figure 1.m – 1.o)
Capisocysta lata//lyellii Head, 1988 (Figure 1.p – 1.q)
Cerebrocysta poulsenii de Verteuil & Norris, 1996 (Figure 1.r - 1.t)
Cymatiosphaera baffinensis Head et al., 1989
Cleistosphaeridium placacantha (Deflandre & Cookson, 1955) (Figure 2.a-2.c)
Piece of reworked species / (Figure 2.d)
Cousteaudinium aubryae de Verteuil & Norris, 1996 (Figure 2.e – 2.f)
Cordosphaeridium minimum (Morgenroth, 1966)
Cribroperidinium tenuitabulatum (Gerlach, 1961) (Figure 2.g – 2.h)
Cristatodinium cristatoserratum Head, 1989
Cyclopsiella elliptica/granosa Drugg and Loeblich Jr., 1967 / (Figure 2.i – 2.j)
elliptica Drugg and Loeblich Jr., 1967
granosa Matsuoka, 1983
Dapsilidinium pseudocolligerum (Stover, 1977) ( Figure 2.k – 2.l)
Melitasphaeridium choanophorum (Deflandre & Cookson 1955) (Figure 2.m)
Appendix 5
Dinopterygium cladoides Deflandre, 1935 (Figure 2.n)
Distatodinium paradoxum (Brosius, 1963) (Figure 2.o – 2.p)
Echinidinium euaxum (Head, 1993) (Figure 2.q – 2.r)
Heteraulacacysta campanula Druff & Loeblich Jr., 1967 (Figure 2.s)
Homotryblium tenuispinosum Davey & Williams, 1966 (Figure 2.t - 3.a)
Hystrichokolpoma rigaudiae Deflandre & Cookson, 1955 (Figure 3.b – 3.c)
Hystrichosphaeropsis obscura Habib, 1972 (Figure 3.c – 3.d)
Impagidinium arachnion de Verteuil & Norris, 1996 (Figure 3.e – 3.f)
Impagidinium pallidum Bujak, 1984 (Figure 3.g)
Impagidinium paradoxum (Wall, 1967) (Figure 3.h – 3.j)
Impagedinium velorum Bujak, 1984 (Figure 3.k – 3.l)
Kallosphaeridium sensu Head & Westphal, 1999 (Figure 3.m)
Labyrinthodonium truncatum Piaseckii, 1980 (Figure 3.n – 3.o)
Cf. Labyrinthodonium truncatum sensu de Verteuil & Norris (1996) (mini) (Figure 3.p)
Lavradosphaera crista De Schepper & Head, 2008 (Figure3.q – 3.r)
Leiosphaeridia rockhallensis Head & Norris, 2003
Lejeunocysta challengerensis Louwye et al., 2008
Lejeunocysta cf. catomus Lentin & Williams, 1993
Lejeunocysta convexa Matsuoka & Bujak, 1988
Lejeunocysta mariae (Harland et al., 1991)
Lingulodinium machaerophorum (Deflandre & Cookson 1955) (Figure 3.s – 3.t)
Lingulodinium multivirgatum de Verteuil & Norris 1996 (Figure 4.a – 4.b)
Melitasphaeridium choanophorum (Deflandre & Cookson, 1955) (Figure 4.c – 4.d)
Nannobarbophora gedlii Head, 2003 (Figure 4.e)
Nematosphaeropsis labyrinthus (Ostenfeld, 1903) ( Figure 4.g – 4.h)
Operculodinium? borgerholtense Louwye, 2001 ( Figure 4.i – 4.j)
Operculodinium centrocarpum (Deflandre & Cookson, 1955) (Figure 4.k)
Operculodinium longispinigerum Matsuoka, 1983 (Figure 4.l - 4.m)
Operculodinium ? eirikanianum Head et al., 1989 (Figure 4.n – 4.o)
Operculodinium giganteum Wall, 1967
Appendix 5
Operculodinium israelianum (Rossignol, 1962)
Operculodinium piaseckii Strauss & Lund, 1962 (Figure 4.p)
Operculodinium? placitum Drugg & Loeblich Jr., 1967
Palaeocystodinium golzowense Alberti, 1961 (Figure 4.q)
Paleocystodinium miocaenicum Strauss et al., 2001
Paralecaniella indentata (Deflandre & Cookson 1955) (Figure 4.r – 4.s)
Paucisphaeridium cylindricatum Islam, 1983 (Figure 4.t – 5.a)
Paucisphaeridium inversibuccinum (Davey & Williams, 1966) (Figure 5.b)
Paucisphaeridium sp.B Quaijtaal (Unpublished) (Figure 5.c – 5.d)
Pentadinium latincinctum Gerlach, 1961 (Figure 5.e – 5.f)
Polykrikos kofoidii / Schwartzii Bütschli , 1873
Polysphaeridium zoharyi zoharyi Rossignol, 1962 (Figure 5.g – 5.h)
Polysphaeridium zoharyi ktana (Rossignol, 1964) (Figure 5.i – 5.j)
Pyxidenopsis tuberculata Versteegh, 1995 (Figure 5.k – 5.l)
Reticulatosphaera actinocoronata (Benedek, 1972) (Figure 5.m – 5.n)
Round Brown Cyst / (Figure 5.n)
Selenopemphix brevispinosa Head et al., 1989 (Figure 5.o – 5.p)
Selenopemphix nephroides Benedek, 1972 (Figure 5.q)
Selenopemphix porcupensis Louwye, 2008 (Figure 5.r – 5.s)
Selenopemphix quanta (Bradford, 1975)
Spiniferites / Achomosphaera cpx / (Figure 5.t – 6.a)
Spiniferites membranaceus / mirabilis (Rossignol, 1964)
Spiniferites septa Quaijtaal (Unpublished)
Spiniferites solidago de Verteuil & Norris, 1996 (Figure 6.b – 6.c)
Spiniferites “spongy” Quaijtaal (Unpublished) (Figure 6.d – 6.f)
Spiniferites membranaceus / mirabilis (Rossignol, 1964)
Sumatradinium soucouyantiae de Verteuil & Norris, 1992 (Figure 6.g)
Sumatradinium druggii Lentin et al., 1994
Sumatradinium hamulatum de Verteuil & Norris, 1996
Tectatodinium pellitum Wall, 1967 (Figure 6.h – 6.i)
Appendix 5
Tuberculodinium vancampoae Rossignol , 1962
Trinovantedinium ferugnomatum de Verteuil & Norris, 1992 (Figure 6.j)
Trinovantedinium glorianum Head et al., 1989 (Figure 6.k – 6.l)
Trinovantedinium harpagonium de Verteuil & Norris, 1992 (Figure 6.m)
Trinovantedinium henrietii Louwye et al., 2008
Trinovantedinium papulum de Verteuil & Norris, 1992
Trinovantedinium? xylochoporum de Verteuil & Norris, 1992 (Figure 6.n – 6.p)
Appendix 5
Figure 1
1.a
1.e 1.f 1.g 1.h
1.i 1.j 1.k 1.l
1.m 1.n 1.o 1.p
1.q 1.r 1.s 1.t
1.b 1.c 1.d
Appendix 5
Figure 2
2.a 2.b
2.e 2.f 2.g 2.h
2.i 2.j 2.k 2.l
2.m 2.n 2.o 2.p
2.q 2.r 2.s 2.t
2.c 2.d
Appendix 5
Figure 3
3.a 3.b
3.e 3.f 3.g 3.h
3.i 3.j 3.k 3.l
3.m 3.n 3.o 3.p
3.q 3.r 3.s 3.t
3.c 3.d
Appendix 5
Figure 4
4.a 4.b
4.e 4.f 4.g 4.h
4.i 4.j 4.k 4.l
4.m 4.n 4.o 4.p
4.q 4.r 4.s 4.t
4.c 4.d
Appendix 5
Figure 5
5.a 5.b
5.e 5.f 5.g 5.h
5.i 5.j 5.k 5.l
5.m 5.n 5.o 5.p
5.q 5.r 5.s 5.t
5.c 5.d
Appendix 5
Figure 6
6.a 6.b
6.e 6.f 6.g 6.h
6.i 6.j 6.k 6.l
6.m 6.n 6.o 6.p
6.c 6.d
Appendix 6
Appendix 6: curves autocorrelation
Left: autocorrelation obtained after shifting in data points; Right: autocorrelation obtained after subsampling
and shifting in age-points
Appendix 6
Appendix 6
Appendix 6
Appendix 7: Matlab code
close all; clear all; close all; clc;
%%% INPUT OF DATA % Import the file sheetName='Blad1'; [numbers, strings, raw] = xlsread('Matlab indexen.xlsx', sheetName); if ~isempty(numbers) newData1.data = numbers; end if ~isempty(strings) newData1.textdata = strings; end
if ~isempty(strings) && ~isempty(numbers) [strRows, strCols] = size(strings); [numRows, numCols] = size(numbers); likelyRow = size(raw,1) - numRows; % Break the data up into a new structure with one field per column. if strCols == numCols && likelyRow > 0 && strRows >= likelyRow newData1.colheaders = strings(likelyRow, :); end end
% Create new variables in the base workspace from those fields. vars = fieldnames(newData1); for i = 1:length(vars) assignin('base', vars{i}, newData1.(vars{i})); end
Depth = data(:,1); % Depth in mcd Age_Dat = data(:,2); % Age in Ma [row_data,col_data] = size(data);
Dat = data(:,3:col_data); [r,c] = size(Dat);
%%% Subsample the dataset at a certain resolution in time % Delete or place the "%" in order to apply/not apply while running. % Taking average sampling resoluton of 0,029 Ma as calculated during thesis % Resolution = 0.029; DATA =zeros(r-2,c); [row,col] = size(DATA);
Age_before= Age_Dat; Data_before=Dat; Age = (min(Age_Dat):resolution:max(Age_Dat)); DATA = zeros(length(Age),col); Age = Age'; for i=1:col DATA(:,i) = interp1(Age_before, Data_before(:,i), Age);
figure; plot (Age,DATA(:,i))
end
%%% CALCULATE 3-POINT RUNNING AVERAGE DATA =zeros(r-2,c); [row,col] = size(DATA); % Age of 3-point running average is set as the first age of the running % window Age=Age_Dat(1:r-2);
for i= 1:c-1 for j= 1:r-2 DATA(j,i) = mean(Dat(j:j+2,i)); end end
for j=1:r-5 DATA(j,col)= mean(Dat(j:j+2,col)); End
%%% DETRENDING THE DATA %Tick on/off by deleting/placing "%"
%N = 2; %trend = zeros(row,col); %detrend = zeros(row,col);
%for i = 1: col % [ trend(:,i), detrend(:,i) ] = DETRENDING( Age, DATA(:,i), N ); %end
%DATA=detrend;
%%% AUTO-COVARIANCE %Preallocation auto_cov = zeros(((2*row)-1),col); lag_auto = zeros(col,(2*row)-1); variance = zeros(1,col);
for i = 1 : col-1 [auto_cov(:,i),lag_auto(i,:)] = xcov(DATA(:,i)); variance(i) = max(auto_cov(:,i)); end
% Geen cunt lycos -_- [auto_cov_c,lag_auto_c] = xcov(DATA((1:row-3))); variance(1,col) = max(auto_cov_c);
% Figure %for i = 1: (col-1) % figure;
% plot(lag_auto(i,:),auto_cov(:,i),'b'); %end %figure; %plot(lag_auto_c,auto_cov_c,'b');
% Figures when considering time-shifts (so for the resampled curve) %%% tag on or off by removing/placing "%" respecctively Age_lag = lag_auto*0.029; Age_lag_C= lag_auto_c*0.029; for i = 1: (col-1) figure; plot(Age_lag,auto_cov(:,i),'b'); end
figure; plot(Age_lag_C,auto_cov_c,'b');
%%% CORRELATION
Cr_cov = zeros (col,col-1,((2*row)-1)); Lag_cr = zeros (col,col-1, ((2*row)-1)); Max_cr = zeros (col,col); Lag_highest_corr = zeros (col,col); Cr_cov_C = zeros(col,((2*(row-3))-1)); Lag_cr_C = zeros(col,((2*(row-3))-1)); Lag_max_cr= zeros (col,col);
PC_whole = zeros(col,col); PC_window = zeros (col,col,(row-20)); data_20 = zeros (1,20); forcing_20 = zeros (1,20); Age_begin_window = Age(1:(length(Age)-20));
for i = 1 : col for j = 1 : col-1 % Cross-Covariance [Cr_cov(i,j,:), Lag_cr(i,j,:)] = xcov(DATA(:,i) ,DATA(:,j),'coeff');
[Max_cr(i,j),Index] = max(abs(Cr_cov(i,j,(row-10): (row+10)))); true_index = row-11+Index; Lag_highest_corr(i,j) = Lag_cr(i,j,true_index);
% Pearson Correlation Coeficcient of whole dataset PC_whole(i,j) = corr(DATA(:,i) ,DATA(:,j),'type','pearson');
% PCC with 20 point running window for k = 1 : (row-19) data_20 = DATA(k : (k+19),i); forcing_20= DATA(k : (k+19),j); PC_window(i,j,:) = corr(forcing_20,data_20,'type','pearson'); end end
% Dinocyst/ gram was not obtained for the last 3 samples
PC_whole(i,col) = corr(DATA(1:(row-3),i) ,DATA(1:(row-
3),col),'type','pearson');
% Cross-Covariance
[Cr_cov_C(i,:), Lag_cr_C(i,:)] = xcov(DATA(1:(row-3),i) ,DATA(1:(row-
3),col),'coeff');
% Lag for highest correlation in limited interval (10 points max) [Max_cr(i,col),Index]= max(abs(Cr_cov_C(i,(row-3)- 10: (row-3)+10))); true_index = row-3-11+Index; Lag_highest_corr(i,col)= Lag_cr_C(i,true_index); end
% Finishing the PC-matrix so it remains a symmetrical matrix % Probably a bit ontroversial to just coy past, but this is the most % convenient way to program PC_whole(col,:) = PC_whole(:,col);
%figure; %plot(squeeze(Lag_cr(1,8,:)),squeeze(Cr_cov(1,8,:))); %Lag_max_cr(1,8) %Lag_highest_corr(1,8)
%%% GENERATE AR1 DATA % Find the base of small peak (where boadens up); Manually => in Excel-file % Import the file sheetName='Auto-Corr'; [numbers, strings, raw] = xlsread('Matlab PCC.xlsx', sheetName); if ~isempty(numbers) newData1.data = numbers; end if ~isempty(strings) newData1.textdata = strings; end
if ~isempty(strings) && ~isempty(numbers) [strRows, strCols] = size(strings); [numRows, numCols] = size(numbers); likelyRow = size(raw,1) - numRows; % Break the data up into a new structure with one field per column. if strCols == numCols && likelyRow > 0 && strRows >= likelyRow newData1.colheaders = strings(likelyRow, :); end end
% Create new variables in the base workspace from those fields. vars = fieldnames(newData1); for i = 1:length(vars) assignin('base', vars{i}, newData1.(vars{i})); end
% Input parameters timeshift = data(12,:); % time-shift in datapoints COV = data(13,:);
Var = data(14,:); timelapse = 0.29; % timelapse during timeshift (in Ma since all Ages
are in Ma)
% Preallocation memory_factor = zeros(1,col-1); tau = zeros(1,col-1); WGN_var = zeros(1,col-1); WGN = zeros(row+10,col-1);
data_set = zeros(row,col-1,1000); P_corr = zeros(1000,col-1);
for i = 1 : col-1
% Memory factor memory_factor(i) = ((COV(i) / Var(i)) ^ (1/timeshift(i)));
% Determine the decay constant "tau" and use this to generate 1000 AR1 % data-sets tau(i) = timelapse / log (1 / memory_factor(i));
% Create White gaussian noice with mean: 0 and variance: WGN_var(i) = 1 - exp( (- 2* timelapse) / tau(i));
c = round( rand(1000,1) * (row-1))+1; % +1 so we don't get the value 0 row-1 = length dataset-1 % (else possible: nr > length dataset)
for j = 1 : 1000 % 1000 random datasets
WGN= sqrt(WGN_var(i)) * randn(row+10,1); % randn => random vector between -1 and 1 with mean=0 % Length of WGN: longer then data cause you want to get a certain
random % WGN out of it that's not linked to the data.
data_set(1,i,j) = DATA(c(j),i) ; % random value of dataset as first
value
for k = 2: row data_set(k,i,j) = memory_factor(i) * data_set(k-1,i,j) + WGN(k); end end end
% Idem dinocunts/gram memory_factor_C =(COV(col) / Var(col)) ^ (1/timeshift(col)); tau_C = timelapse / log (1 / memory_factor_C); WGN_var_C = 1 - exp( (- 2* timelapse) / tau_C); c_C = round( rand(1000,1) * (row-3-1))+1;
WGN_C = zeros(row-3+10); dino_gram = DATA (1:row-3,col);
data_set_C = zeros(row-3,1000);
for j = 1 : 1000 % 1000 random datasets
WGN_C = sqrt(WGN_var_C) * randn(row+10,1); % randn => random vector between -1 and 1 with mean=0 % Length of WGN: longer then data cause you want to get a certain
random % WGN out of it that's not linked to the data.
data_set_C(1,j) = dino_gram(c_C(j)) ; % random value of dataset as
first value
for k = 2: row-3 data_set_C(k,j) = memory_factor_C * data_set_C(k-1,j) + WGN_C(k); end end
% Pearson Correlation Coefficient Random data and all indexes/ ratios
% Preallocation
PC_synth = zeros(col,col,1000);
Quant_05 = zeros(col,col); % Quantiles to see the significance of the PCC Quant_10 = zeros(col,col); Quant_20 = zeros(col,col); Quant_30 = zeros(col,col); Quant_40 = zeros(col,col); Quant_50 = zeros(col,col); Quant_60 = zeros(col,col); Quant_70 = zeros(col,col); Quant_80 = zeros(col,col); Quant_90 = zeros(col,col); Quant_95 = zeros(col,col); Quant_99 = zeros(col,col); Quant_999 = zeros(col,col);
% Absolute value of PCC, since else function "quantile" does not function
for i = 1 : (col-1)
for j = i : (col-1)
for k = 1 : 1000 PC_synth(i,j,k) = abs(corr(data_set(:,i,k), DATA(:,j),
'type','pearson')); end
Quant_05(i,j) = quantile(PC_synth(i,j,:),0.05); Quant_10(i,j) = quantile(PC_synth(i,j,:),0.1); Quant_20(i,j) = quantile(PC_synth(i,j,:),0.5); Quant_30(i,j) = quantile(PC_synth(i,j,:),0.3);
Quant_40(i,j) = quantile(PC_synth(i,j,:),0.4); Quant_50(i,j) = quantile(PC_synth(i,j,:),0.5); Quant_60(i,j) = quantile(PC_synth(i,j,:),0.6); Quant_70(i,j) = quantile(PC_synth(i,j,:),0.7); Quant_80(i,j) = quantile(PC_synth(i,j,:),0.8); Quant_90(i,j) = quantile(PC_synth(i,j,:),0.9); Quant_95(i,j) = quantile(PC_synth(i,j,:),0.95); Quant_99(i,j) = quantile(PC_synth(i,j,:),0.99); Quant_999(i,j) = quantile(PC_synth(i,j,:),0.999); end end
PC_synth_C = zeros(col,1000); Quant_05_C = zeros(col,1); Quant_10_C = zeros(col,1); Quant_20_C = zeros(col,1); Quant_30_C = zeros(col,1); Quant_40_C = zeros(col,1); Quant_50_C = zeros(col,1); Quant_60_C = zeros(col,1); Quant_70_C = zeros(col,1); Quant_80_C = zeros(col,1); Quant_90_C = zeros(col,1); Quant_95_C = zeros(col,1); Quant_99_C = zeros(col,1); Quant_999_C= zeros(col,1);
for j = 1 : col
for k = 1 : 1000 PC_synth_C(j,k) = abs(corr(data_set_C(:,k), DATA((1:row-3),j),
'type','pearson')); end Quant_05_C(j) = quantile(PC_synth_C(j,:),0.05); Quant_10_C(j) = quantile(PC_synth_C(j,:),0.1); Quant_20_C(j) = quantile(PC_synth_C(j,:),0.2); Quant_30_C(j) = quantile(PC_synth_C(j,:),0.3); Quant_40_C(j) = quantile(PC_synth_C(j,:),0.4); Quant_50_C(j) = quantile(PC_synth_C(j,:),0.5); Quant_60_C(j) = quantile(PC_synth_C(j,:),0.6); Quant_70_C(j) = quantile(PC_synth_C(j,:),0.7); Quant_80_C(j) = quantile(PC_synth_C(j,:),0.8); Quant_90_C(j) = quantile(PC_synth_C(j,:),0.9); Quant_95_C(j) = quantile(PC_synth_C(j,:),0.95); Quant_99_C(j) = quantile(PC_synth_C(j,:),0.99); Quant_999_C(j)= quantile(PC_synth_C(j,:),0.999); end
%%% CHECK SIGNIFICANCE %Preallocation Significance = zeros(col,col);
for i =1:col for j= 1:col if Quant_999(i,j) == 0
Significance(i,j) = 0; elseif abs(PC_whole(i,j)) > Quant_999(i,j) Significance(i,j) = 1000; elseif abs(PC_whole(i,j)) > Quant_99(i,j) Significance(i,j) = 99; elseif abs(PC_whole(i,j)) > Quant_95(i,j) Significance(i,j) = 95; elseif abs(PC_whole(i,j)) > Quant_90(i,j) Significance(i,j) = 90; elseif abs(PC_whole(i,j)) > Quant_80(i,j) Significance(i,j) = 80; elseif abs(PC_whole(i,j)) > Quant_70(i,j) Significance(i,j) = 70; elseif abs(PC_whole(i,j)) > Quant_60(i,j) Significance(i,j) = 60; elseif abs(PC_whole(i,j)) > Quant_50(i,j) Significance(i,j) = 50; elseif abs(PC_whole(i,j)) > Quant_40(i,j) Significance(i,j) = 40; elseif abs(PC_whole(i,j)) > Quant_30(i,j) Significance(i,j) = 30; elseif abs(PC_whole(i,j)) > Quant_20(i,j) Significance(i,j) = 20; elseif abs(PC_whole(i,j)) > Quant_10(i,j) Significance(i,j) = 10; elseif abs(PC_whole(i,j)) > Quant_05(i,j) Significance(i,j) = 5; else Significance(i,j) = 0; end end end % Singificance of the dino/gram was derived manually
%%% END %%%
![18F]FLUORO-N,N-DIMETHYLAMINOETHANOL AS …lib.ugent.be/fulltxt/RUG01/001/790/044/RUG01-001790044_2012_0001... · universiteit gent faculteit farmaceutische wetenschappen vakgroep](https://static.fdocuments.in/doc/165x107/5af4e91e7f8b9a190c8da930/18ffluoro-nn-dimethylaminoethanol-as-libugentbefulltxtrug01001790044rug01-00179004420120001universiteit.jpg)
![Historische wetenschappen. Aanwinsten van UA — Periode …anet.ua.ac.be/awlijst/2015-05/UA/awh.pdf · Historische wetenschappen. Aanwinsten van UA — Periode 2015/05 [1]Historische](https://static.fdocuments.in/doc/165x107/5d521bd788c993a81d8b71c6/historische-wetenschappen-aanwinsten-van-ua-periode-anetuaacbeawlijst2015-05uaawhpdf.jpg)
